Through a workflow interface, a document management system enables a user to establish a document workflow. The document workflow establishes a set of document actions for the document management system to perform automatically in response to a request from the user. The user defines the document workflow by selecting from a set of workflow operations and specifying parameters for the selected workflow operation. The user additionally defines an approval workflow operation and a rejection workflow operation that designate document actions in the event that a document is approved or rejected, respectively. The document management system performs at least a portion of the document workflow in an order determined from the workflow interface.
A method, a system, and a computer program product for summarizing an electronic document. A structural representation of the document is generated and/or received. The structural representation specifies one or more elements of the document and one or more relationships between one or more elements of the document. A machine learning model is identified for the document. The model is applied, based on the structural representation, to one or more portions of the document to generate a hierarchical representation for the portions. A request to execute at least one processing task related to the document is received. At least one generated hierarchical representation of at least one portion of the document is sent to a generative artificial intelligence model to execute the processing task by generating a response to the request. The response is and presented on a graphical user interface of a user computing device.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 30/262 - Techniques de post-traitement, p. ex. correction des résultats de la reconnaissance utilisant l’analyse contextuelle, p. ex. le contexte lexical, syntaxique ou sémantique
G06V 30/413 - Classification de contenu, p. ex. de textes, de photographies ou de tableaux
G06V 30/414 - Extraction de la structure géométrique, p. ex. arborescenceDécoupage en blocs, p. ex. boîtes englobantes pour les éléments graphiques ou textuels
A method, a system, and a computer program product for connecting computing systems. A connection request to connect with one or more computing systems is received. Based on the connection request, at least one computing system in one or more computing systems is identified. The computing system is identified based on one or more tasks associated with the connection request and to be performed using the computing system. A connection interface for connection with the computing system is dynamically generated. The connection interface is generated using a connection data model associated with the computing system. Connection, using the connection interface to the computing system, is formed, and one or more tasks is executed using the connection interface.
A document management system sends a distinct copy of a set of documents to each of a plurality of recipients in a bulk sending process. Each copy is associated with one or more actions to be taken by a corresponding recipient. The document management system generates an interface that displays, for each copy of the set of documents, the recipient of the copy and a status of the copy. The document management system receives an identification of a subset of the copies of the set of documents. The document management system performs a bulk action on each of the subset of the copies without performing the bulk action on any remaining copies of the set of documents.
A method, a system, and a computer program product for determining renewal of agreement documents. A first machine learning model is used to generate one or more portions of a document. Each portion of the document is associated with a predetermined subject. A query is generated using the predetermined subject. The query and one or more definitions of one or more attributes are provided to a second machine learning model. At least one attribute and a value assigned to the attribute are received from the second machine learning model. The value is extracted from one or more portions and assigned to the attribute. A graphical user interface displaying the attribute and the value assigned to the attribute is generated.
G06F 16/30 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet de données textuelles non structurées
G06F 16/338 - Présentation des résultats des requêtes
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
G06F 40/166 - Édition, p. ex. insertion ou suppression
A document management system sends a distinct copy of a set of documents to each of a plurality of recipients in a bulk sending process. Each copy is associated with one or more actions to be taken by a corresponding recipient. The document management system generates an interface that displays, for each copy of the set of documents, the recipient of the copy and a status of the copy. The document management system receives an identification of a subset of the copies of the set of documents. The document management system performs a bulk action on each of the subset of the copies without performing the bulk action on any remaining copies of the set of documents.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Contract management services for business purposes; Computerized database management; Business data analysis services in the field of business contracts, agreements, and procurement activities; Data processing services in the field of business contracts, agreements, and procurement activities. Providing temporary use of online non-downloadable software for use in automating electronic agreement, contract, document and file generation; Providing temporary use of online non-downloadable software for use in creating, configuring, customizing, extracting data from, and generating electronic agreements, contracts, documents and files; Providing temporary use of online non-downloadable software for use in verification of signatory identity in business contracts; Providing temporary use of online non-downloadable software for use in summarizing, analyzing, and identifying risk in business contracts.
8.
MANAGING ENVIRONMENTAL, SOCIAL, AND GOVERNANCE (ESG) CLAUSES IN ELECTRONIC DOCUMENTS
A system may determine environmental, social, and governance (ESG) goal data for an ESG clause type and determine that a first clause of a first electronic document of a plurality of electronic documents and that a second clause of a second electronic document of the plurality of electronic documents correspond to the ESG clause type. The system may determine first ESG commitment data based on content of the first clause of the first electronic document and determine second ESG commitment data based on content of the second clause of the second electronic document. The system may generate ESG metric data based on the first ESG commitment data and the second ESG commitment data and generate dashboard data based on the ESG metric data. The system may output the dashboard data.
A centralized auditing system receives an audit block containing one or more audit files generated by an audit agent running in an audited device. The audit block additionally stores a first digital signature corresponding to a previous audit block, and a second digital signature generated based at least on the one or more audit files and the first digital signature. After receiving the audit block, the auditing system verifies the integrity of the received audit block based on the second digital signature stored in the audit block and/or the first digital signature corresponding to the previous audit block. In response to verifying the integrity of the received audit block, the auditing system adds the received audit block to an audit register. Moreover, the auditing system adds the one or more audit files included in the audit block to an audit database.
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuéesArchitectures de systèmes de bases de données distribuées à cet effet
H04L 9/00 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
10.
SOFTWARE APPLICATION EXECUTION USING CONTEXTUAL INFORMATION
Techniques are described for deploying software applications associated with document management. The techniques may include receiving a request, by a computing system and from a client device, to execute a software application for managing an electronic document, wherein the software application is configured to execute using at least a first widget and a second widget. The techniques may further include generating, based on the first widget comprising instructions to initiate an execution of the second widget, instructions for executing the software application at the client device, wherein the instructions are configured to cause the client device to generate contextual information for the first widget and wherein the second widget causes the client device to load the contextual information when executing the second widget. The techniques may further include outputting, by the computing system and to the client device, the instructions to execute the software application for managing the electronic document.
Techniques for artificial intelligence (AI) custom fields are described. A method comprises receiving a custom rule comprising a query request for information in an electronic document stored in a data repository, generating a vectorized rule from the custom rule, selecting a subset of vectorized portions from a set of vectorized portions of the electronic document stored in a vector database corresponding to the vectorized rule, sending the vectorized rule and the subset of vectorized portions to a machine learning (ML) model, receiving a query response to the query request from the ML model, the query response comprising a summary of information contained within one or more vectorized portions of the subset of vectorized portions or a comment on the information contained within the one or more vectorized portions. Other embodiments are described and claimed.
G06F 16/383 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
A method, a system, and a computer program product for federated document learning. A plurality of representations of a plurality of datasets from a plurality of client systems are received. Each representation corresponds to a dataset associated with a client system and is generated using a first machine learning (ML) model. A second ML model is applied to the plurality of representations to generate a combined representation of the plurality of datasets. Data from each dataset is not provided to the second ML model. The combined representation is filtered using one or more filtering parameters to generate a filtered representation. Using the second ML model, one or more model weights for training a third ML model in a plurality of third ML models are generated. Each third ML model is associated with a respective client system. The model weights are provided to the plurality of third ML models.
A method, a system, and a computer program product for federated document learning. A plurality of representations of a plurality of datasets from a plurality of client systems are received. Each representation corresponds to a dataset associated with a client system and is generated using a first machine learning (ML) model. A second ML model is applied to the plurality of representations to generate a combined representation of the plurality of datasets. Data from each dataset is not provided to the second ML model. The combined representation is filtered using one or more filtering parameters to generate a filtered representation. Using the second ML model, one or more model weights for training a third ML model in a plurality of third ML models are generated. Each third ML model is associated with a respective client system. The model weights are provided to the plurality of third ML models.
A method, a system, and a computer program product for content-based document access. A content of an electronic document is analyzed using a machine learning (ML) model. The ML model determines presence of a plurality sensitive data in the document. One or more document entity-based parameters are received. At least one sensitive data in the plurality of sensitive data is identified. At least one recipient computing device in a plurality of computing devices is prevented from receiving the document containing at least one sensitive data. The sensitive data is extracted from the document. The document is modified to redact the sensitive data and a modified electronic document is generated. The modified electronic document is transmitted to at least one recipient computing device.
A document management system trains a machine-learned model using a first training set of tagged documents to, when applied to a document, tag one or more portions of the document. The document management system applies the machine-learned model to a target document. One or more portions of the target document incorrectly tagged by the machine-learned model are identified. A feature vector representative of the target document is generated. Each entry of the feature vector is representative of a characteristic of the target document without including private information from the target document. The document management system queries a corpus of documents using the feature vector to identify a set of documents that correspond to the feature vector. A second training set of tagged documents is generated using the identified set of documents. The document management system retrains the machine-learned model using the second training set of tagged documents.
A method, a system, and a computer program product for generating a benchmarking dataset. A type of an electronic document is determined. Using the type of the electronic document, a first request to generate one or more labels for the electronic document is generated. Using a content of the electronic document, a second request to generate one or more labels for the electronic document is generated. The electronic document and the first and second requests are sent to a generative artificial intelligence (AI) model. The generative AI model generates one or more first labels for the electronic document based on the first request and one or more second labels for the electronic document based on the second request. Using one or more first and second labels, one or more labels for the electronic document are generated.
Techniques are disclosed for using a machine learning model to generate document templates from input templates. For example, a computing system receives an input document. A machine learning model of the computing system processes the input document to identify one or more text items corresponding to respective variable field types of the plurality of variable field types. The computing system creates, for each text item of the one or more text items, a variable field for the variable field type of the plurality of variable field types corresponding to the text item and creates, for each variable field of the one or more created variable fields, a mapping for the variable field to a corresponding data source. The computing system generates, based on the input document, a document template comprising the one or more created variable fields and the respective one or more mappings.
A method, a system, and a computer program product for generating a benchmarking dataset. One or more queries for generation of one or more summaries of one or more electronic documents are received. The queries are modified using one or more parameters associated with the electronic documents to generate modified queries. The electronic documents are sent to a generative artificial intelligence (AI) model. The generative AI model generates summaries of the electronic documents based on at least one of: the initial queries and the modified queries. One or more labels for the electronic documents are generated using the summaries.
G06F 16/383 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
G06F 40/40 - Traitement ou traduction du langage naturel
19.
MACHINE LEARNING BASED INSTANTIATION OF DOCUMENTS BASED ON TEMPLATES IN A DOCUMENT MANAGEMENT SYSTEM
A system, for example, a document management system stores documents and manages workflows associated with documents. The document management system allows discovery of templates based on explicit searches performed by users or automatic searches performed based on a context. The document management system allows generating new document templates based on selected versions of document component templates. The generated document template may be stored as a new version. The document management system instantiates documents based on templates by predicting values of variables used in the template based on various factors that describe the context in which the template is being used. The values used for instantiating variables may be generated using machine learning models that may be trained using historical data stored in the document management system.
Techniques are described for the use and implementation of web-based wallet authentication. An example method includes receiving, by processing circuitry, a request from a computing device for a document package. The document package may include an electronic document and specify a name of a signatory for the electronic document. In response to a determination that the request is associated with a user account, the processing circuitry may initiate a passkey challenge to the computing device using a public key associated with the user account. Responsive to successful completion of the passkey challenge, the processing circuitry may configure the computing device to accept a signature corresponding to the name of the signatory specified for the electronic document. The processing circuitry may implement a document management platform which coordinates with a third-party identification verifier to verify the identity of the signatory and provides password-less authentication for a known signatory.
G06Q 20/36 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des portefeuilles électroniques ou coffres-forts électroniques
G06Q 20/38 - Protocoles de paiementArchitectures, schémas ou protocoles de paiement leurs détails
21.
Inter-document search using knowledge-enriched vectors
A method, an apparatus, and a computer-readable storage medium for searching of electronic documents. A search query is encoded to form a search vector. The search query includes at least one search term and seeks information within a plurality of electronic documents. A knowledge graph structure is generated using at least one search term. The search term is associated with at least another term in a plurality of terms in the knowledge graph structure. The search query is modified using the other term to generate a modified search vector. A response to the search query is generated based on one or more responsive document vectors retrieved from the plurality of electronic documents and that are semantically similar to the modified search vector.
An online document system can allow users to participate in collaborative negotiation of documents stored with in the online document system. To facilitate negotiations between multiple entities (each potentially including multiple users with different roles in the negotiation), the online document system includes a permissions system that allows per-clause control over user's access to propose and approve changes to a negotiated document. Similarly, the user interfaces for viewing a negotiated document provided by the online document system to users can depend on the role of that user in editing the document and the current stage of the negotiation of the document. Finally, the online document system can track agreement between sides in a negotiation on a per-clause basis and can otherwise help facilitate the negotiation of the document.
A centralized auditing system receives an audit block containing one or more audit files generated by an audit agent running in an audited device. The audit block additionally stores a first digital signature corresponding to a previous audit block, and a second digital signature generated based at least on the one or more audit files and the first digital signature. After receiving the audit block, the auditing system verifies the integrity of the received audit block based on the second digital signature stored in the audit block and/or the first digital signature corresponding to the previous audit block. In response to verifying the integrity of the received audit block, the auditing system adds the received audit block to an audit register. Moreover, the auditing system adds the one or more audit files included in the audit block to an audit database.
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuéesArchitectures de systèmes de bases de données distribuées à cet effet
H04L 9/00 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
24.
EXECUTING DOCUMENT WORKFLOWS USING DOCUMENT WORKFLOW ORCHESTRATION RUNTIME
A system configures, executes, and monitors document workflows executing using workflow engines executing on cloud platforms. The system generates a platform independent document workflow specification that describes a document workflow configured for execution on any of a plurality of workflow engines, each workflow engine executing on a cloud platform. The system compiles the platform independent document workflow specification to generate a platform specific document workflow specification configured for execution on a target workflow engine executing on a target cloud platform. A document workflow orchestration runtime executes and monitors the execution of the platform specific document workflow specification.
A system determines first revision information based on a first plurality of changes and generates, based on the first revision information and content of an electronic document, first feedback information for the first persona. The system determines second revision information based on the second plurality of changes and generates, based on the second revision information and the content of the electronic document, second feedback information for the second persona. The system generates, based on the first feedback information for the first persona and the second feedback information for the second persona, a multi-agent report for the electronic document and outputs the multi-agent report.
A system generates a first agent for the first persona based on both a first plurality of changes and first source data. The system determines a first plurality of vector embeddings for the first agent based on the first source data and determines a second plurality of vector embeddings based on second source data. The system determines a similarity value based on the first plurality of vector embeddings and the second plurality of vector embeddings. Based on the similarity value, the system determines that the first source data is associated with a second persona different from the first persona. The system generates a second agent for the second persona based on the first source data.
G06F 16/335 - Filtrage basé sur des données supplémentaires, p. ex. sur des profils d’utilisateurs ou de groupes
G06F 16/383 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
27.
WORKFLOW GENERATOR FOR DOCUMENT MANAGEMENT SYSTEMS
A system determines definition information for an agent of a plurality of agents configured to perform one or more actions associated with an electronic document. The system determines, based on the definition information for the agent and user information for a user associated with the electronic document, an ordered list of agent assignments indicating a plurality of actions to be performed by the plurality of agents. Based on a determination that the ordered list of agent assignments indicates the agent, the system determines, based on the definition information for the agent, input data for the agent and cause the agent to generate output data based on the input data. The system generates an executed document based on the output data.
A system generates, based on a first agent for a first persona and content of an electronic document, first feedback information for the first persona. Based on a determination that the first feedback information for the first persona indicates a first value for an attribute of the electronic document that causes a conflict with second feedback information for a second persona and that a second value for the attribute of the electronic document has been selected to resolve the conflict, the system generates, based on the first agent for the first persona, the second value for the attribute, and the content of the electronic document, updated first feedback information for the first persona. The system generates, based on the updated first feedback information for the first persona, a multi-agent report for the electronic document and output the multi-agent report.
G06F 16/335 - Filtrage basé sur des données supplémentaires, p. ex. sur des profils d’utilisateurs ou de groupes
G06F 16/383 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
29.
EXECUTING DOCUMENT WORKFLOWS USING DOCUMENT WORKFLOW ORCHESTRATION RUNTIME
A system configures, executes, and monitors document workflows executing using workflow engines executing on cloud platforms. The system generates a platform independent document workflow specification that describes a document workflow configured for execution on any of a plurality of workflow engines, each workflow engine executing on a cloud platform. The system compiles the platform independent document workflow specification to generate a platform specific document workflow specification configured for execution on a target workflow engine executing on a target cloud platform. A document workflow orchestration runtime executes and monitors the execution of the platform specific document workflow specification.
A method, an apparatus, and a computer-readable storage medium for generating a document graph. A plurality of electronic documents is received. Each electronic document has a predetermined document type. A machine learning model is selected from the plurality of machine learning models based on the predetermined document type. The selected machine learning model is instructed to extract a plurality of document portions from each electronic document in the plurality of electronic documents in accordance with the predetermined document type. A relationship between two or more document portions is defined based on a content of each document portion, and the document portions are associated based on the relationship. A graph structure having a plurality of nodes is generated. Each node includes at least one document portion. Each node is connected to another node in accordance with the relationship between document portions included in the nodes. The graph structure is stored.
A system configures, executes, and monitors document workflows executing using workflow engines executing on cloud platforms. The system generates a platform independent document workflow specification that describes a document workflow configured for execution on any of a plurality of workflow engines, each workflow engine executing on a cloud platform. The system compiles the platform independent document workflow specification to generate a platform specific document workflow specification configured for execution on a target workflow engine executing on a target cloud platform. A document workflow orchestration runtime executes and monitors the execution of the platform specific document workflow specification.
Techniques are described for a system comprising processing circuitry and computer readable media comprising instructions that, when executed, cause the processing circuitry to determine, for a working document associated with an entity, a working attribute embedding that characterizes attributes for the working document. The instructions may further cause the processing circuitry to select, based on a plurality of compliance attribute embeddings associated with a plurality of historical documents and the working attribute embedding, a plurality of compliance documents from the plurality of historical documents. The instructions may further cause the processing circuitry to determine a compliance report based on a plurality of working document clauses of the working document and a plurality of compliance clauses of the plurality of compliance documents. The instructions may further cause the processing circuitry to output an indication of the compliance report.
A method, a system, and a computer program product for generating a benchmarking dataset. A type of an electronic document is determined. Using the type of the electronic document, a first request to generate one or more labels for the electronic document is generated. Using a content of the electronic document, a second request to generate one or more labels for the electronic document is generated. The electronic document and the first and second requests are sent to a generative artificial intelligence (AI) model. The generative AI model generates one or more first labels for the electronic document based on the first request and one or more second labels for the electronic document based on the second request. Using one or more first and second labels, one or more labels for the electronic document are generated.
A method, a system, and a computer program product for generating a benchmarking dataset. One or more queries for generation of one or more summaries of one or more electronic documents are received. The queries are modified using one or more parameters associated with the electronic documents to generate modified queries. The electronic documents are sent to a generative artificial intelligence (AI) model. The generative AI model generates summaries of the electronic documents based on at least one of: the initial queries and the modified queries. One or more labels for the electronic documents are generated using the summaries.
G06F 16/338 - Présentation des résultats des requêtes
G06F 16/383 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
G06F 40/40 - Traitement ou traduction du langage naturel
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Contract management services for business purposes; Computerized database management; Business data analysis services in the field of business contracts, agreements, and procurement activities; Data processing services in the field of business contracts, agreements, and procurement activities. Providing temporary use of online non-downloadable software for use in automating electronic agreement, contract, document and file generation; Providing temporary use of online non-downloadable software for use in creating, configuring, customizing, extracting data from, and generating electronic agreements, contracts, documents and files; Providing temporary use of online non-downloadable software for use in verification of signatory identity in business contracts; Providing temporary use of online non-downloadable software for use in summarizing, analyzing, and identifying risk in business contracts.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Contract management services for business purposes; computerized database management; business data analysis, namely, collecting, reporting, analyzing and integrating business data related to business contracts, agreements, and procurement activities; data processing services; collection and organization of data for others for business purposes Providing temporary use of online non-downloadable software for use in automating electronic agreement, contract, document and file generation; Providing temporary use of online non-downloadable software for use in creating, configuring, customizing, extracting data from, and generating electronic agreements, contracts, documents and files; Providing temporary use of online non-downloadable software for use in verification of signatory identity in business contracts; Providing temporary use of online non-downloadable software for use in summarizing, analyzing, and identifying risk in business contracts; Providing temporary use of online non-downloadable software for analyzing, identifying, reporting, and visualizing data and information in agreements, contracts, documents and files; Providing temporary use of online non-downloadable software for categorizing, indexing, searching, sorting, sharing, and transmitting agreements, contracts, data, documents, files, and information; Providing temporary use of online non-downloadable software using artificial intelligence for machine learning and deep learning
37.
Document management for an electronic document using a declarative expectation
A system for deploying a software application associated with document management includes processing circuitry and computer-readable storage media. The computer-readable storage media includes logic that, when executed, causes the processing circuitry to receive a request, from a client device, to execute a software application for managing an electronic document. The software application is configured to execute using at least a first widget and a second widget. The logic further causes the processing circuitry to generate instructions for executing the software application at the client device. The instructions are configured to cause the client device to generate contextual information for the first widget and to cause the client device to determine whether the contextual information satisfies a declarative expectation for the second widget. The logic further causes the processing circuitry to output, to the client device, the instructions to execute the software application for managing the electronic document.
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur
G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
G06F 16/248 - Présentation des résultats de requêtes
Techniques are described for a system document management comprising one or more processors having access to a memory. The system is configured to determine an attribute for an input document for execution by a signer. The system is also configured to generate a similarity score for each of a plurality of candidate documents using a machine learning model, wherein using the machine learning model comprises providing the attribute as an input to the machine learning model. The system is also configured to generate data for a graphical user interface comprising an indication of at least a subset of the candidate documents based on the similarity scores generated for each of the plurality of candidate documents. The system is configured to output, for display, the data to a user device.
G06F 16/383 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
G06V 30/19 - Reconnaissance utilisant des moyens électroniques
39.
IDENTIFICATION OF SENSITIVE INFORMATION IN DATASETS
A method, a system, and a computer program product for identifying sensitive data. A plurality of text portions associated with one or more data subjects is identified. A machine learning model is applied to the identified plurality of portions to extract one or more entities representative of one or more data subjects. The entities are grouped into one or more entity groups. Based on one or more entity groups, at least one data subject is identified for replacement or redaction in at least one text portion in the plurality of text portions.
A method, an apparatus, and a computer-readable storage medium for executing obligation management. One or more document portions are extracted from an electronic document using at least one machine learning model selected from a plurality of machine learning models based on at least one parameter associated with the electronic document. One or more entities are identified in one or more document portions of the electronic document. The entities are sent to a generative artificial intelligence (AI) model. The generative AI model is configured to generate one or more rules defining one or more obligations associated with one or more entities. One or more rules are executed to monitor compliance with one or more obligations by one or more entities.
A method, an apparatus, and a computer-readable storage medium for executing obligation management. One or more document portions are extracted from an electronic document using at least one machine learning model selected from a plurality of machine learning models based on at least one parameter associated with the electronic document. One or more entities are identified in one or more document portions of the electronic document. The entities are sent to a generative artificial intelligence (AI) model. The generative AI model is configured to generate one or more rules defining one or more obligations associated with one or more entities. One or more rules are executed to monitor compliance with one or more obligations by one or more entities.
A document management system uses an objective loss function to improve the performance of optical character recognition (OCR) processes on images of documents. The document management system performs OCR on a high resolution version of the image of the document, obtaining a first set of text representative of the text of the document. The document management system applies a machine-learned model on a low-resolution version of the image of the document, producing a denoised image that is of a higher resolution than that input into the machine-learned model. The document management system performs OCR on the denoised image, obtaining a second set of text representative of the text of the document. The document management system subsequently generates a feature vector from the comparison of the sets of text and retrains the machine-learned model with the generated feature vector.
An online document system provides a recommendation for one or more features within the online document system to an entity. The online document system accesses a set of feature training data to train a machine learning model. The set of feature training data may describe characteristics of entities associated with the online document system and historical activity associated with the entities' usage of the online document system's features. The machine learning model may be configured to identify a feature to recommend to an entity based on the entity's characteristics and history of using other features within the online document system. For example, data representing the entity's user accounts and use of an electronic signature feature is used by the machine learning model to identify a document authentication feature to recommend to the entity. The online document system may then provide the identified feature in a recommendation to the entity.
G06F 18/22 - Critères d'appariement, p. ex. mesures de proximité
G06F 18/21 - Conception ou mise en place de systèmes ou de techniquesExtraction de caractéristiques dans l'espace des caractéristiquesSéparation aveugle de sources
G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
G06N 5/025 - Extraction de règles à partir de données
A method, an apparatus, and a computer-readable storage medium for generating a document graph. A plurality of electronic documents is received. Each electronic document has a predetermined document type. A machine learning model is selected from the plurality of machine learning models based on the predetermined document type. The selected machine learning model is instructed to extract a plurality of document portions from each electronic document in the plurality of electronic documents in accordance with the predetermined document type. A relationship between two or more document portions is defined based on the content of each document portion, and the document portions are associated based on the relationship. A graph structure having a plurality of nodes is generated. Each node includes at least one document portion. Each node is connected to another node in accordance with the relationship between document portions included in the nodes. The graph structure is stored.
G06F 16/30 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet de données textuelles non structurées
Techniques are described for deploying software applications associated with document management. The techniques may include a system comprising processing circuitry implemented by one or more processors, the processing circuitry having access to a memory. The processing circuitry may be configured to determine, for a time window, a current version of a widget and a next version of the widget to implement a software application of the one or more software applications. The processing circuitry may further be configured to output instructions to execute the current version of the widget in response to receiving a first request for the software application. The processing circuitry may further be configured to output instructions to execute the next version of the widget based on a number of responses to previous requests that select the current version of the widget.
A document management system generates a document template within which a user can embed document tags. Each document tag represents a term of the document. The user selects a document tag and identifies a location for the document tag within the document template. The embedded document tag adheres to formatting rules of the document template. The document management system accesses a data value corresponding to the selected document tag and generates a modified document. The modified document includes the accessed data value at the identified location, and is subject to the formatting rules of the document template.
A document management system trains a machine learned model configured to facilitate contract generation. The document management system generates a training set for the machine learned model using user input. From a first set of agreement documents, the document management system identifies a first set of sentences relevant to clause terms input by the user. The document management system identifies, from a second set of agreement documents, a second set of sentences similar to the first set of sentences. In response to user feedback about the second set of sentences, the document management system updates the training set. The document management system retrains the machine learned model using the updated training set.
A method, a system, and a computer program product for generation of document rules. A structural arrangement of one or more portions of each electronic document in a plurality of electronic documents is determined using one or more machine learning models. One or more parameters associated with each electronic document in the plurality of electronic documents are identified. One or more document generation rules are generated based on one or more parameters and the structural arrangement of one or more portions. One or more document generation rules are generated for each type of electronic document in the plurality of electronic documents. One or more document generation rules are stored in a storage location.
A method, a system, and a computer program product for generation of templates for electronic documents. A first template is generated based on a plurality of electronic documents. The first template defines a first structural arrangement of one or more portions extracted from the electronic documents and includes one or more portions. A machine learning model determines the first structural arrangement. An update to at least one portion is received. A second template is generated based on the first template and the received update. The second template defines a second structural arrangement of the portions as determined based on the first structural arrangement and the received update. An object model representative of at least one of: the first template, the second template, and a difference between the first and second templates is stored.
A method, a system, and a computer program product for generation of templates for electronic documents. A plurality of electronic documents is sent to a generative artificial intelligence (AI) model to determine a structure and one or more portions of each electronic document. A type in a plurality of types is identified for each electronic document. One or more templates defining a structural arrangement of one or more portions of the electronic document for each type of electronic document are generated using a machine learning model based on the structure and one or more portions of each electronic document. At least one portion in one or more portions is associated with at least one template. The template and at least one portion associated with the template are stored in a storage location.
G06V 30/414 - Extraction de la structure géométrique, p. ex. arborescenceDécoupage en blocs, p. ex. boîtes englobantes pour les éléments graphiques ou textuels
A method, a system, and a computer program product for generation of document rules. A structural arrangement of one or more portions of each electronic document in a plurality of electronic documents is determined using one or more machine learning models. One or more parameters associated with each electronic document in the plurality of electronic documents are identified. One or more document generation rules are generated based on one or more parameters and the structural arrangement of one or more portions. One or more document generation rules are generated for each type of electronic document in the plurality of electronic documents. One or more document generation rules are stored in a storage location.
A document management system utilizes document models for querying, validating, and customizing electronic agreement documents. Document models can be a data structure including document attributes that represent an agreement document. Examples of document attributes include customizable data values, conditions related to the operation of the agreement document and satisfiable by certain values of the customized data values, and rendering parameters for the appearance of the electronic agreement document. The document management system enables a user to create custom document models for generating electronic agreement documents that can be queried, validated, and customized through querying, validating, and customizing the corresponding document models.
A system and a method are disclosed for generating recipient notification recommendations using a machine-learned model for a sending entity sending a set of documents to an acting entity and a subset of the set of documents to a receiving entity. The receiving user is subscribed to a notification service of a document management system to receive push notifications regarding statuses of inbound documents. The notifications for the receiving entity are generated based on recipient notification definition provided by the sending entity. The document management system trains a machine-learned model to generate recipient notification recommendations for the sending entity selecting event criteria that indicate when to generate the notifications and types of data to include in the notifications. The machine-learned model is trained based on data associated with historical notification definitions provided by historical sending entities.
A document management system utilizes document models for querying, validating, and customizing electronic agreement documents. Document models can be a data structure including document attributes that represent an agreement document. Examples of document attributes include customizable data values, conditions related to the operation of the agreement document and satisfiable by certain values of the customized data values, and rendering parameters for the appearance of the electronic agreement document. The document management system enables a user to create custom document models for generating electronic agreement documents that can be queried, validated, and customized through querying, validating, and customizing the corresponding document models.
A centralized document system generates a document package in response to a request by an originating entity. The document package includes at least one document for execution by a first receiving entity. The first receiving entity can specify a set of permissions for a second receiving entity to perform actions to documents within the package on behalf of the first receiving entity. Accordingly, the system may provide the document package to both the first and second receiving entities for the first receiving entity to execute the at least one document. Before providing the document to the second receiving entity, system may determine whether there is a sensitive document in the package and whether to delegate the document to the second entity. Accordingly, the system may prevent a sensitive document package from being provided to the second receiving entity for execution.
A system and method for managing an electronic contract for hybrid runtime execution, which includes a contract execution service that comprises an internal runtime environment and a set of integrations to external resources with at least one integration to an on-chain runtime, wherein the contract execution service comprises configuration to: establish the electronic contract, wherein the electronic contract comprises a set of executable programmable components and a set of natural language components; change the state of the electronic contract and generate a contract event; and in response to the contract event: extend execution of the electronic contract to an on-chain environment, when the contract event includes an associated on-chain integration; and extend execution of the electronic contract to an external application resource, when the contract event includes an associated external service integration.
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuéesArchitectures de systèmes de bases de données distribuées à cet effet
H04L 9/00 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité
57.
AUTOMATIC REVISIONS TO DOCUMENT CLAUSES BASED ON CLAUSE TYPE
A document management system can include an artificial intelligence-based document manager that can perform one or more predictive operations based on characteristics of a user, a document, a user account, or historical document activity. For instance, the document management system can apply a machine-learning model to determine how long an expiring agreement document is likely to take to renegotiate and can prompt a user to begin the renegotiation process in advance. The document management system can detect a change to language in a particular clause type and can prompt a user to update other documents that include the clause type to include the change. The document management system can determine a type of a document being worked on and can identify one or more actions that a corresponding user may want to take using a machine-learning model trained on similar documents and similar users.
A system predicts metadata attributes associated with documents using machine learning models. The document may represent an interaction between entities. The system trains machine learning models to predict scores indicating whether a token or a sequence of token of a document represents a metadata attribute. The metadata prediction is used to annotate the document and display to users. The system receives user feedback via the user interface and uses the user feedback to evaluate or retrain the model. The system generates training data by receiving a set of annotated documents and comparing the annotated documents against other documents to identify matching documents. The system determines when to execute the machine learning based metadata prediction based on steps of document workflow executed by the system.
G06F 16/383 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
Techniques are described for a system document management comprising one or more processors having access to a memory. The system is configured to open an electronic document that is to be electronically signed, the electronic document having an active component connected to an external data source. The system is further configured to, while the electronic document is open, execute the active component to retrieve data from the external data source. The system is further configured to insert the data retrieved from the external data source into a field of the electronic document. The system is further configured to output, for display, a graphical user interface that presents the content of the electronic document to a user device.
Techniques are described for the use and implementation of web-based wallet authentication. An example method includes receiving, by processing circuitry, a request from a computing device for a document package. The document package may include an electronic document and specify a name of a signatory for the electronic document. In response to a determination that the request is associated with a user account, the processing circuitry may initiate a passkey challenge to the computing device using a public key associated with the user account. Responsive to successful completion of the passkey challenge, the processing circuitry may configure the computing device to accept a signature corresponding to the name of the signatory specified for the electronic document. The processing circuitry may implement a document management platform which coordinates with a third-party identification verifier to verify the identity of the signatory and provides password-less authentication for a known signatory.
G06Q 20/00 - Architectures, schémas ou protocoles de paiement
G06Q 20/36 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des portefeuilles électroniques ou coffres-forts électroniques
G06Q 20/38 - Protocoles de paiementArchitectures, schémas ou protocoles de paiement leurs détails
Techniques are described for the use and implementation of web-based wallet authentication. An example method includes receiving, by processing circuitry, a request from a computing device for a document package. The document package may include an electronic document and specify a name of a signatory for the electronic document. In response to a determination that the request is associated with a user account, the processing circuitry may initiate a passkey challenge to the computing device using a public key associated with the user account. Responsive to successful completion of the passkey challenge, the processing circuitry may configure the computing device to accept a signature corresponding to the name of the signatory specified for the electronic document. The processing circuitry may implement a document management platform which coordinates with a third-party identification verifier to verify the identity of the signatory and provides password-less authentication for a known signatory.
G06Q 20/38 - Protocoles de paiementArchitectures, schémas ou protocoles de paiement leurs détails
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
A computing system remotely trains a public ensemble model of an artificial intelligence model management system. The system receives, by the model management system, an encrypted representation of a private data value from a client system. The encrypted representation includes annotation information provided by the client system. The system determines, using the encrypted representation and the annotation information, a data value cluster that corresponds to the private data value. Data value clusters are generated using encrypted representations of a private data values provided by client systems. The system obtains, based on the assigned data value cluster, an encrypted representation of a model. The model is trained remotely by the client system using the private data value. The system adds the encrypted representation of the model to the public ensemble model. The public ensemble model is generated using a plurality of encrypted representations of models remotely trained by the client systems.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Contract management services for business purposes; computerized database management; business data analysis, namely, collecting, reporting, analyzing and integrating business data related to business contracts, agreements, and procurement activities; data processing services; collection and organization of data for others for business purposes. Providing temporary use of non-downloadable software for use in automating electronic agreement, contract, document and file generation; Providing temporary use of non-downloadable software for use in creating, configuring, customizing, extracting data from, and generating electronic agreements, contracts, documents and files; Providing temporary use of non-downloadable software for use in verification of signatory identity in business contracts; Providing temporary use of non-downloadable software for use in summarizing, analyzing, and identifying risk in business contracts; Providing temporary use of non-downloadable software for analyzing, identifying, reporting, and visualizing data and information in agreements, contracts, documents and files; Providing temporary use of non-downloadable software for categorizing, indexing, searching, sorting, sharing, and transmitting agreements, contracts, data, documents, files, and information; Providing temporary use of non-downloadable software using artificial intelligence for machine learning and deep learning.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Contract management services for business purposes; Computerized database management; Business data analysis services in the field of business contracts, agreements, and procurement activities; Data processing services in the field of business contracts, agreements, and procurement activities. Providing temporary use of non-downloadable software for use in automating electronic agreement, contract, document and file generation; Providing temporary use of non-downloadable software for use in creating, configuring, customizing, extracting data from, and generating electronic agreements, contracts, documents and files; Providing temporary use of non-downloadable software for use in verification of signatory identity in business contracts; Providing temporary use of non-downloadable software for use in summarizing, analyzing, and identifying risk in business contracts.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Contract management services for business purposes; computerized database management; business data analysis, namely, collecting, reporting, analyzing and integrating business data related to business contracts, agreements, and procurement activities; data processing services; collection and organization of data for others for business purposes Providing temporary use of online non-downloadable software for use in automating electronic agreement, contract, document and file generation; Providing temporary use of online non-downloadable software for use in creating, configuring, customizing, extracting data from, and generating electronic agreements, contracts, documents and files; Providing temporary use of online non-downloadable software for use in verification of signatory identity in business contracts; Providing temporary use of online non-downloadable software for use in summarizing, analyzing, and identifying risk in business contracts; Providing temporary use of online non-downloadable software for analyzing, identifying, reporting, and visualizing data and information in agreements, contracts, documents and files; Providing temporary use of online non-downloadable software for categorizing, indexing, searching, sorting, sharing, and transmitting agreements, contracts, data, documents, files, and information; Providing temporary use of online non-downloadable software using artificial intelligence for machine learning and deep learning
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Contract management services for business purposes; Computerized database management; Business data analysis services in the field of business contracts, agreements, and procurement activities; Data processing services in the field of business contracts, agreements, and procurement activities Providing temporary use of online non-downloadable software for use in automating electronic agreement, contract, document and file generation; Providing temporary use of online non-downloadable software for use in creating, configuring, customizing, extracting data from, and generating electronic agreements, contracts, documents and files; Providing temporary use of online non-downloadable software for use in verification of signatory identity in business contracts; Providing temporary use of online non-downloadable software for use in summarizing, analyzing, and identifying risk in business contracts
67.
GUIDED FORM GENERATION IN A DOCUMENT MANAGEMENT SYSTEM
Systems and methods are disclosed herein for generating and modifying a workflow comprising a series of webpages based on an online document. A document management system accesses an online document selected by a user and classifies each field of the online document into one of a set of categories. For each category, the system generates a form webpage comprising questions corresponding to each field classified as the category and combines the generated webpages to create a workflow. The system may modify the workflow by generating and adding one or more additional form webpages based on one or more answers provided by an entity completing the webform page. In response to the entity completing the modified generated workflow, the system generates a completed document based on the online document and the answers provided by the entity.
A method, a system, and a computer program product for summarizing an electronic document. A structural representation of the document is generated and/or received. The structural representation specifies one or more elements of the document and one or more relationships between one or more elements of the document. A machine learning model is identified for the document. The model is applied, based on the structural representation, to one or more portions of the document to generate a hierarchical representation for the portions. A request to execute at least one processing task related to the document is received. At least one generated hierarchical representation of at least one portion of the document is sent to a generative artificial intelligence model to execute the processing task by generating a response to the request. The response is and presented on a graphical user interface of a user computing device.
A system configures, executes, and monitors document workflows executing using workflow engines executing on cloud platforms. The system generates a platform independent document workflow specification that describes a document workflow configured for execution on any of a plurality of workflow engines, each workflow engine executing on a cloud platform. The system compiles the platform independent document workflow specification to generate a platform specific document workflow specification configured for execution on a target workflow engine executing on a target cloud platform. A document workflow orchestration runtime executes and monitors the execution of the platform specific document workflow specification.
Techniques are described for deploying software applications associated with document management. The techniques may include retrieving, by a computing system and from a software repository, configuration information for a software application for managing an electronic document, the configuration information specifying a plurality of widgets for deploying the software application. The techniques may further include receiving, by the computing system and from a client device, a request to access the software application. The techniques may further include generating, by the computing system, a response to the request comprising an indication of the configuration information and instructions configured to cause the client device to request, from the software repository, the plurality of widgets specified by the configuration information. The techniques may further include outputting, by the computing system, the response to the client device to provide the software application at the client device.
G06F 8/38 - Création ou génération de code source pour la mise en œuvre d'interfaces utilisateur
G06F 8/71 - Gestion de versions Gestion de configuration
G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
G06F 16/958 - Organisation ou gestion de contenu de sites Web, p. ex. publication, conservation de pages ou liens automatiques
G06Q 30/02 - MarketingEstimation ou détermination des prixCollecte de fonds
Techniques are described for deploying software applications associated with document management. The techniques may include receiving a request, by a computing system and from a client device, to execute a software application for managing an electronic document, wherein the software application is configured to execute using at least a first widget and a second widget. The techniques may further include generating, based on the first widget comprising instructions to initiate an execution of the second widget, instructions for executing the software application at the client device, wherein the instructions are configured to cause the client device to generate contextual information for the first widget and wherein the second widget causes the client device to load the contextual information when executing the second widget. The techniques may further include outputting, by the computing system and to the client device, the instructions to execute the software application for managing the electronic document.
Techniques are described for a system document management comprising one or more processors having access to a memory. The system is configured to determine an attribute for an input document for execution by a signer. The system is also configured to generate a similarity score for each of a plurality of candidate documents using a machine learning model, wherein using the machine learning model comprises providing the attribute as an input to the machine learning model. The system is also configured to generate data for a graphical user interface comprising an indication of at least a subset of the candidate documents based on the similarity scores generated for each of the plurality of candidate documents. The system is configured to output, for display, the data to a user device.
G06F 16/383 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
G06V 30/19 - Reconnaissance utilisant des moyens électroniques
Techniques are described for a system document management comprising one or more processors having access to a memory. The system is configured to determine an attribute for an input document for execution by a signer. The system is also configured to generate a similarity score for each of a plurality of candidate documents using a machine learning model, wherein using the machine learning model comprises providing the attribute as an input to the machine learning model. The system is also configured to generate data for a graphical user interface comprising an indication of at least a subset of the candidate documents based on the similarity scores generated for each of the plurality of candidate documents. The system is configured to output, for display, the data to a user device.
Techniques for an artificial intelligence (AI) platform to search a document collection are described. Embodiments may use AI and machine learning techniques within a framework of an electronic document management system to perform semantic searching of an electronic document or a collection of electronic documents for certain types of information. The AI platform may summarize the information in a natural language representation of a human language. Other embodiments are described and claimed.
G06V 30/416 - Extraction de la structure logique, p. ex. chapitres, sections ou numéros de pageIdentification des éléments de document, p. ex. des auteurs
75.
ENFORCING APPLICATION PROGRAMMING INTERFACE LIMITS IN A DOCUMENT MANAGEMENT SYSTEM
A document management system processes application programming interface (API) requests received from entities. The document management system processes the API requests to perform operations such as modifying a document, executing a document, or sending a set of documents to another entity. The document management system enforces API limits on API requests received from entities and processed by the document management system. The document management system allows an entity to request a modification to an API limit to a target API limit and determines whether to approve the requested modification. The document management system determines whether to approve the requested API limits based on a comparison with other entities that are similar to the entity based on past API requests received from the other entities.
Techniques for an artificial intelligence (AI) platform to search a document collection are described. Embodiments may use AI and machine learning techniques within a framework of an electronic document management system to perform semantic searching of an electronic document or a collection of electronic documents for certain types of information. The AI platform may summarize the information in a natural language representation of a human language. Other embodiments are described and claimed.
Techniques for an artificial intelligence (AI) platform to search a document collection are described. Embodiments may use AI and machine learning techniques within a framework of an electronic document management system to perform semantic searching of an electronic document or a collection of electronic documents for certain types of information. The AI platform may summarize the information in a natural language representation of a human language. Other embodiments are described and claimed.
A document management system can include an artificial intelligence-based document manager that can perform one or more predictive operations based on characteristics of a user, a document, a user account, or historical document activity. For instance, the document management system can apply a machine-learning model to determine how long an expiring agreement document is likely to take to renegotiate and can prompt a user to begin the renegotiation process in advance. The document management system can detect a change to language in a particular clause type and can prompt a user to update other documents that include the clause type to include the change. The document management system can determine a type of a document being worked on and can identify one or more actions that a corresponding user may want to take using a machine-learning model trained on similar documents and similar users.
Through a workflow interface, a document management system enables a user to establish a document workflow. The document workflow establishes a set of document actions for the document management system to perform automatically in response to a request from the user. The user defines the document workflow by selecting from a set of workflow operations and specifying parameters for the selected workflow operation. The user additionally defines an approval workflow operation and a rejection workflow operation that designate document actions in the event that a document is approved or rejected, respectively. The document management system performs at least a portion of the document workflow in an order determined from the workflow interface.
An electronic document system can allow users to upload a document package containing multiple individual component documents. Each component document includes a subset of a plurality of pages that are included in the document package. The electronic document system identifies a page of each component document by applying a machine learning model to the document package. The electronic document system partitions the document package into the individual component documents based on the identified pages. For each individual component document, the electronic document system identifies a document topic corresponding to the component document by applying another machine learning model. The electronic document system modifies a user interface to display each component document and corresponding document topic.
G06V 10/22 - Prétraitement de l’image par la sélection d’une région spécifique contenant ou référençant une formeLocalisation ou traitement de régions spécifiques visant à guider la détection ou la reconnaissance
G06N 20/20 - Techniques d’ensemble en apprentissage automatique
G06V 30/416 - Extraction de la structure logique, p. ex. chapitres, sections ou numéros de pageIdentification des éléments de document, p. ex. des auteurs
81.
Generative artificial intelligence platform to manage smart documents
Techniques for an artificial intelligence (AI) platform to manage a document collection are described. Embodiments may use AI and machine learning techniques within a framework of an electronic document management system to manage and mine a collection of electronic documents for certain types of information. The information may be analyzed and used to generate insights for a defined entity. The insights may comprise deviations, modifications or changes made to an electronic document within the electronic document management system. Other embodiments are described and claimed.
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
82.
ARTIFICIAL INTELLIGENCE PLATFORM TO MANAGE A DOCUMENT COLLECTION
Techniques for an artificial intelligence (AI) platform to manage a document collection are described. Embodiments may use AI and machine learning techniques within a framework of an electronic document management system to manage and mine a collection of electronic documents for certain types of information. The information may be analyzed and used to generate insights for a defined entity. The insights may be used to generate a set of document rules for the entity. The set of document rules may be applicable to generating a new electronic document for the entity within the electronic document management system. Other embodiments are described and claimed.
Techniques are described for recognizing alternative representations of the same name. An example method includes transmitting, by a document management platform implemented by a computing system, a document package to a second computing device. The document package includes a document received from a first computing device and an indication of first name. The document management platform obtains an indication of a second name from an identity document provided by a user of the second computing device. The document management platform performs a name matching operation using a machine learning model to determine whether the first name and the second name are similar based on a similarity score generated by the machine learning model. Based on determining that the first name and the second name are similar, the document management platform grants the user of the second computing device access to the document.
G06F 18/2415 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur des modèles paramétriques ou probabilistes, p. ex. basées sur un rapport de vraisemblance ou un taux de faux positifs par rapport à un taux de faux négatifs
09 - Appareils et instruments scientifiques et électriques
35 - Publicité; Affaires commerciales
38 - Services de télécommunications
42 - Services scientifiques, technologiques et industriels, recherche et conception
45 - Services juridiques; services de sécurité; services personnels pour individus
Produits et services
Downloadable software for automating electronic agreement,
contract, document and file generation; downloadable
software for creating, configuring, customizing, and
generating electronic agreements, contracts, documents and
files; downloadable software for agreement, contract,
document and file templates; downloadable software for
transferring data and information into electronic
agreements, contracts, documents and files; downloadable
software for integrating data and information into
electronic agreements, contracts, documents and files;
downloadable software for creating electronic agreements,
contracts, documents, and files that automatically configure
based on user settings; downloadable software for guided
instructions on creating, configuring, customizing, and
generating electronic agreements, contracts, documents and
files; downloadable software for interactive instructions on
creating, configuring, customizing, and generating
electronic agreements, contracts, documents and files;
downloadable software for editing, redlining, and reviewing
electronic agreements, contracts, documents and files;
downloadable software for auditing electronic agreements,
contracts, documents and files; downloadable software for
tracking changes in electronic agreements, contracts,
documents and files; downloadable software for tracking
versions of electronic agreements, contracts, documents and
files; downloadable software for collaborating on the
creation, configuration, customization, and generation of
electronic agreements, contracts, documents and files;
downloadable software for indexing data, text, and
information in electronic agreements, contracts, documents
and files; downloadable software for agreement, contract,
document and file management; downloadable software for
sending, routing and transmitting electronic agreements,
contracts, data, documents, files, and information;
downloadable software for electronic and digital signatures;
downloadable software for capturing, managing, monitoring,
authenticating, and verifying electronic and digital
signatures; downloadable software for electronic signature
verification using technology to authenticate user identity;
downloadable software for identification, authentication,
and verification of users; downloadable software for
identification verification, namely, authentication of
personal identification information; downloadable software
for user authentication in e-commerce transactions by access
code, phone call, short message service (SMS) text message,
knowledge-based questions, and government identification;
downloadable software for updating agreements, contracts,
databases, records, and systems; downloadable software for
automating data warehousing; downloadable software for
collecting, facilitating, managing, processing, and
transferring electronic payments; downloadable software for
triggering actions upon user-defined events; downloadable
software for artificial intelligence, machine learning, and
deep learning; downloadable software for analyzing
agreements, contracts, data, documents, files, and
information; downloadable software for analyzing,
identifying, reporting, and visualizing data and information
in agreements, contracts, documents and files; downloadable
software for categorizing, indexing, searching, sorting,
sharing, and transmitting agreements, contracts, data,
documents, files, and information; downloadable software for
reporting data analytics; downloadable software for tracking
workflows, auditing information and data, and verifying
information and data; downloadable software for creating,
analyzing, monitoring, and managing a database of compliance
information and data; downloadable software for computer
security in the nature of providing authentication,
issuance, validation and revocation of digital certificates;
downloadable computer software for electronic data
verification and authentication; downloadable software for
generating, obtaining, managing, capturing, storing,
processing, and viewing electronic signatures and electronic
signature dates; downloadable software for electronic data
storage; downloadable software for a mobile notary;
downloadable software for creating, transmitting, and
storing electronic data, information, documents, and
messages; downloadable software for electronic business
transactions; downloadable software for digital document
exchange; downloadable software for digital and electronic
certificates and time and date stamping. Contract management services for business purposes;
computerized database management; business data analysis,
namely, collecting, reporting, analyzing and integrating
business data related to business contracts, agreements, and
procurement activities; data processing services; collection
and organization of data for others for business purposes. Electronic transmission, delivery, and reception of
agreements, contracts, data, documents, files, messages, and
information via telecommunication networks; encrypted
electronic transmission, delivery, and reception of
agreements, contracts, data, documents, files, messages, and
information via telecommunication networks; electronic
transmission, delivery, and reception of user authorization,
identification, and verification data and information via
telecommunication networks; electronic transmission of
electronic and digital signatures via telecommunication
networks; electronic transmission of payment and financial
data via telecommunication networks; signal and data
transmission for electronic commerce via telecommunication
systems and data communication system; providing access to
databases; providing access to agreements, contracts, data,
documents, files, messages, and information stored in
databases; electronic transmission, delivery, and reception
of database information via telecommunication networks;
electronic exchange of agreements, contracts, data,
documents, files, messages, and information stored in
databases via telecommunication networks. Providing temporary use of non-downloadable software for
automating electronic agreement, contract, document and file
generation; providing temporary use of non-downloadable
software for creating, configuring, customizing, and
generating electronic agreements, contracts, documents and
files; providing temporary use of non-downloadable software
for agreement, contract, document and file templates;
providing temporary use of non-downloadable software for
transferring data and information into electronic
agreements, contracts, documents and files; providing
temporary use of non-downloadable software for integrating
data and information into electronic agreements, contracts,
documents and files; providing temporary use of
non-downloadable software for creating electronic
agreements, contracts, documents, and files that
automatically configure based on user settings; providing
temporary use of non-downloadable software for guided
instructions on creating, configuring, customizing, and
generating electronic agreements, contracts, documents and
files; providing temporary use of non-downloadable software
for interactive instructions on creating, configuring,
customizing, and generating electronic agreements,
contracts, documents and files; providing temporary use of
non-downloadable software for editing, redlining, and
reviewing electronic agreements, contracts, documents and
files; providing temporary use of non-downloadable software
for auditing electronic agreements, contracts, documents and
files; providing temporary use of non-downloadable software
for tracking changes in electronic agreements, contracts,
documents and files; providing temporary use of
non-downloadable software for tracking versions of
electronic agreements, contracts, documents and files;
providing temporary use of non-downloadable software for
collaborating on the creation, configuration, customization,
and generation of electronic agreements, contracts,
documents and files; providing temporary use of
non-downloadable software for indexing data, text, and
information in electronic agreements, contracts, documents
and files; providing temporary use of non-downloadable
software for agreement, contract, document and file
management; providing temporary use of non-downloadable
software for sending, routing and transmitting electronic
agreements, contracts, data, documents, files, and
information; providing temporary use of non-downloadable
software for electronic and digital signatures; providing
temporary use of non-downloadable software for capturing,
managing, monitoring, authenticating, and verifying
electronic and digital signatures; electronic signature
verification services using technology to authenticate user
identity; providing temporary use of non-downloadable
software for identification, authentication, and
verification of users; identification verification services,
namely, providing authentication of personal identification
information; providing user authentication services in
e-commerce transactions by access code, phone call, short
message service (SMS) text message, knowledge-based
questions, and government identification; providing
temporary use of non-downloadable software for updating
agreements, contracts, databases, records, and systems;
providing temporary use of non-downloadable software to
automate data warehousing; providing temporary use of
non-downloadable software for collecting, facilitating,
managing, processing, and transferring electronic payments;
providing temporary use of non-downloadable software for
triggering actions upon user-defined events; providing
temporary use of non-downloadable software for artificial
intelligence, machine learning, and deep learning; providing
temporary use of non-downloadable software for analyzing
agreements, contracts, data, documents, files, and
information; providing temporary use of non-downloadable
software for analyzing, identifying, reporting, and
visualizing data and information in agreements, contracts,
documents and files; providing temporary use of
non-downloadable software for categorizing, indexing,
searching, sorting, sharing, and transmitting agreements,
contracts, data, documents, files, and information;
providing temporary use of non-downloadable software for
reporting data analytics; providing temporary use of
non-downloadable software for tracking workflows, auditing
information and data, and verifying information and data;
providing temporary use of non-downloadable software for
creating, analyzing, monitoring, and managing a database of
compliance information and data; computer security services
in the nature of providing authentication, issuance,
validation and revocation of digital certificates. Notary public services; providing electronic notarization
services via a global computer network for others to legally
notarize electronic agreements, contracts, documents or
files; legal information services; providing legal
information via an online interactive database; tracking and
monitoring legal requirements in the field of electronic and
digital signature technology.
85.
Systems and methods for distributed electronic signature documents
Systems and methods for distributed electronic signature documents. A method for distributed electronic signature documents includes creating a signing template that contains information about how a signable document is to be signed. A signable document is created to be distributed to a signer for signing. The signable document is electronically transmitted to the signer. A message is sent using an activatable control in the signing document to a web signing server to invoke the signing process. A browser window is opened on a signer's computer in order to execute the signing process.
09 - Appareils et instruments scientifiques et électriques
35 - Publicité; Affaires commerciales
38 - Services de télécommunications
42 - Services scientifiques, technologiques et industriels, recherche et conception
45 - Services juridiques; services de sécurité; services personnels pour individus
Produits et services
Downloadable software for automating electronic agreement,
contract, document and file generation; downloadable
software for creating, configuring, customizing, and
generating electronic agreements, contracts, documents and
files; downloadable software for agreement, contract,
document and file templates; downloadable software for
transferring data and information into electronic
agreements, contracts, documents and files; downloadable
software for integrating data and information into
electronic agreements, contracts, documents and files;
downloadable software for creating electronic agreements,
contracts, documents, and files that automatically configure
based on user settings; downloadable software for guided
instructions on creating, configuring, customizing, and
generating electronic agreements, contracts, documents and
files; downloadable software for interactive instructions on
creating, configuring, customizing, and generating
electronic agreements, contracts, documents and files;
downloadable software for editing, redlining, and reviewing
electronic agreements, contracts, documents and files;
downloadable software for auditing electronic agreements,
contracts, documents and files; downloadable software for
tracking changes in electronic agreements, contracts,
documents and files; downloadable software for tracking
versions of electronic agreements, contracts, documents and
files; downloadable software for collaborating on the
creation, configuration, customization, and generation of
electronic agreements, contracts, documents and files;
downloadable software for indexing data, text, and
information in electronic agreements, contracts, documents
and files; downloadable software for agreement, contract,
document and file management; downloadable software for
sending, routing and transmitting electronic agreements,
contracts, data, documents, files, and information;
downloadable software for electronic and digital signatures;
downloadable software for capturing, managing, monitoring,
authenticating, and verifying electronic and digital
signatures; downloadable software for electronic signature
verification using technology to authenticate user identity;
downloadable software for identification, authentication,
and verification of users; downloadable software for
identification verification, namely, authentication of
personal identification information; downloadable software
for user authentication in e-commerce transactions by access
code, phone call, short message service (SMS) text message,
knowledge-based questions, and government identification;
downloadable software for updating agreements, contracts,
databases, records, and systems; downloadable software for
automating data warehousing; downloadable software for
collecting, facilitating, managing, processing, and
transferring electronic payments; downloadable software for
triggering actions upon user-defined events; downloadable
software for artificial intelligence, machine learning, and
deep learning; downloadable software for analyzing
agreements, contracts, data, documents, files, and
information; downloadable software for analyzing,
identifying, reporting, and visualizing data and information
in agreements, contracts, documents and files; downloadable
software for categorizing, indexing, searching, sorting,
sharing, and transmitting agreements, contracts, data,
documents, files, and information; downloadable software for
reporting data analytics; downloadable software for tracking
workflows, auditing information and data, and verifying
information and data; downloadable software for creating,
analyzing, monitoring, and managing a database of compliance
information and data; downloadable software for computer
security in the nature of providing authentication,
issuance, validation and revocation of digital certificates;
downloadable computer software for electronic data
verification and authentication; downloadable software for
generating, obtaining, managing, capturing, storing,
processing, and viewing electronic signatures and electronic
signature dates; downloadable software for electronic data
storage; downloadable software for a mobile notary;
downloadable software for creating, transmitting, and
storing electronic data, information, documents, and
messages; downloadable software for electronic business
transactions; downloadable software for digital document
exchange; downloadable software for digital and electronic
certificates and time and date stamping. Contract management services for business purposes;
computerized database management; business data analysis,
namely, collecting, reporting, analyzing and integrating
business data related to business contracts, agreements, and
procurement activities; data processing services; collection
and organization of data for others for business purposes. Electronic transmission, delivery, and reception of
agreements, contracts, data, documents, files, messages, and
information via telecommunication networks; encrypted
electronic transmission, delivery, and reception of
agreements, contracts, data, documents, files, messages, and
information via telecommunication networks; electronic
transmission, delivery, and reception of user authorization,
identification, and verification data and information via
telecommunication networks; electronic transmission of
electronic and digital signatures via telecommunication
networks; electronic transmission of payment and financial
data via telecommunication networks; signal and data
transmission for electronic commerce via telecommunication
systems and data communication system; providing access to
databases; providing access to agreements, contracts, data,
documents, files, messages, and information stored in
databases; electronic transmission, delivery, and reception
of database information via telecommunication networks;
electronic exchange of agreements, contracts, data,
documents, files, messages, and information stored in
databases via telecommunication networks. Providing temporary use of non-downloadable software for
automating electronic agreement, contract, document and file
generation; providing temporary use of non-downloadable
software for creating, configuring, customizing, and
generating electronic agreements, contracts, documents and
files; providing temporary use of non-downloadable software
for agreement, contract, document and file templates;
providing temporary use of non-downloadable software for
transferring data and information into electronic
agreements, contracts, documents and files; providing
temporary use of non-downloadable software for integrating
data and information into electronic agreements, contracts,
documents and files; providing temporary use of
non-downloadable software for creating electronic
agreements, contracts, documents, and files that
automatically configure based on user settings; providing
temporary use of non-downloadable software for guided
instructions on creating, configuring, customizing, and
generating electronic agreements, contracts, documents and
files; providing temporary use of non-downloadable software
for interactive instructions on creating, configuring,
customizing, and generating electronic agreements,
contracts, documents and files; providing temporary use of
non-downloadable software editing, redlining, and reviewing
electronic agreements, contracts, documents and files;
providing temporary use of non-downloadable software for
auditing electronic agreements, contracts, documents and
files; providing temporary use of non-downloadable software
for tracking changes in electronic agreements, contracts,
documents and files; providing temporary use of
non-downloadable software for tracking versions of
electronic agreements, contracts, documents and files;
providing temporary use of non-downloadable software for
collaborating on the creation, configuration, customization,
and generation of electronic agreements, contracts,
documents and files; providing temporary use of
non-downloadable software for indexing data, text, and
information in electronic agreements, contracts, documents
and files; providing temporary use of non-downloadable
software for agreement, contract, document and file
management; providing temporary use of non-downloadable
software for sending, routing and transmitting electronic
agreements, contracts, data, documents, files, and
information; providing temporary use of non-downloadable
software for electronic and digital signatures; providing
temporary use of non-downloadable software for capturing,
managing, monitoring, authenticating, and verifying
electronic and digital signatures; electronic signature
verification services using technology to authenticate user
identity; providing temporary use of non-downloadable
software for identification, authentication, and
verification of users; identification verification services,
namely, providing authentication of personal identification
information; providing user authentication services in
e-commerce transactions by access code, phone call, short
message service (SMS) text message, knowledge-based
questions, and government identification; providing
temporary use of non-downloadable software for updating
agreements, contracts, databases, records, and systems;
providing temporary use of non-downloadable software to
automate data warehousing; providing temporary use of
non-downloadable software for collecting, facilitating,
managing, processing, and transferring electronic payments;
providing temporary use of non-downloadable software for
triggering actions upon user-defined events; providing
temporary use of non-downloadable software for artificial
intelligence, machine learning, and deep learning; providing
temporary use of non-downloadable software for analyzing
agreements, contracts, data, documents, files, and
information; providing temporary use of non-downloadable
software for analyzing, identifying, reporting, and
visualizing data and information in agreements, contracts,
documents and files; providing temporary use of
non-downloadable software for categorizing, indexing,
searching, sorting, sharing, and transmitting agreements,
contracts, data, documents, files, and information;
providing temporary use of non-downloadable software for
reporting data analytics; providing temporary use of
non-downloadable software for tracking workflows, auditing
information and data, and verifying information and data;
providing temporary use of non-downloadable software for
creating, analyzing, monitoring, and managing a database of
compliance information and data; computer security services
in the nature of providing authentication, issuance,
validation and revocation of digital certificates. Notary public services; providing electronic notarization
services via a global computer network for others to legally
notarize electronic agreements, contracts, documents or
files; legal information services; providing legal
information via an online interactive database; tracking and
monitoring legal requirements in the field of electronic and
digital signature technology.
A document management system uses machine learning to generate electronic documents. Based on user input into a workflow for generating an electronic document, the document management system determines fields that require definition in the electronic document. The document management system predicts values for a first set of fields and inputs signals for a second set of fields into a supervised machine learning model, which is configured to output predicted values for the second set of fields. A user provides feedback on the predicted values of the second set of fields. The document management system incorporates the user's feedback into the generated electronic document, using confirmed values for each of the fields.
A document management system trains a machine learned model configured to facilitate contract generation. The document management system generates a training set for the machine learned model using user input. From a first set of agreement documents, the document management system identifies a first set of sentences relevant to clause terms input by the user. The document management system identifies, from a second set of agreement documents, a second set of sentences similar to the first set of sentences. In response to user feedback about the second set of sentences, the document management system updates the training set. The document management system retrains the machine learned model using the updated training set.
A document management system trains a machine learned model configured to facilitate contract generation. The document management system generates a training set for the machine learned model using user input. From a first set of agreement documents, the document management system identifies a first set of sentences relevant to clause terms input by the user. The document management system identifies, from a second set of agreement documents, a second set of sentences similar to the first set of sentences. In response to user feedback about the second set of sentences, the document management system updates the training set. The document management system retrains the machine learned model using the updated training set.
A system predicts metadata attributes associated with documents using machine learning models. The document may represent an interaction between entities. The system trains machine learning models to predict scores indicating whether a token or a sequence of token of a document represents a metadata attribute. The metadata prediction is used to annotate the document and display to users. The system receives user feedback via the user interface and uses the user feedback to evaluate or retrain the model. The system generates training data by receiving a set of annotated documents and comparing the annotated documents against other documents to identify matching documents. The system determines when to execute the machine learning based metadata prediction based on steps of document workflow executed by the system.
G06F 16/38 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
G06F 16/383 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
91.
MACHINE LEARNING BASED PREDICTION OF DOCUMENT METADATA
A system predicts metadata attributes associated with documents using machine learning models. The document may represent an interaction between entities. The system trains machine learning models to predict scores indicating whether a token or a sequence of token of a document represents a metadata attribute. The metadata prediction is used to annotate the document and display to users. The system receives user feedback via the user interface and uses the user feedback to evaluate or retrain the model. The system generates training data by receiving a set of annotated documents and comparing the annotated documents against other documents to identify matching documents. The system determines when to execute the machine learning based metadata prediction based on steps of document workflow executed by the system.
G06F 16/30 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet de données textuelles non structurées
A system predicts metadata attributes associated with documents using machine learning models. The document may represent an interaction between entities. The system trains machine learning models to predict scores indicating whether a token or a sequence of token of a document represents a metadata attribute. The metadata prediction is used to annotate the document and display to users. The system receives user feedback via the user interface and uses the user feedback to evaluate or retrain the model. The system generates training data by receiving a set of annotated documents and comparing the annotated documents against other documents to identify matching documents. The system determines when to execute the machine learning based metadata prediction based on steps of document workflow executed by the system.
A system predicts metadata attributes associated with documents using machine learning models. The document may represent an interaction between entities. The system trains machine learning models to predict scores indicating whether a token or a sequence of token of a document represents a metadata attribute. The metadata prediction is used to annotate the document and display to users. The system receives user feedback via the user interface and uses the user feedback to evaluate or retrain the model. The system generates training data by receiving a set of annotated documents and comparing the annotated documents against other documents to identify matching documents. The system determines when to execute the machine learning based metadata prediction based on steps of document workflow executed by the system.
G06F 40/166 - Édition, p. ex. insertion ou suppression
G06F 16/383 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
G06F 40/284 - Analyse lexicale, p. ex. segmentation en unités ou cooccurrence
94.
Method for associating third party content with online document signing
Techniques for electronic signature process management are described. Some embodiments provide an electronic signature service (“ESS”) configured to associate third-party content with electronic signature documents by way of dynamic form fields. A dynamic form field is associated with a data store and an electronic signature document. The ESS may automatically populate the dynamic form field with data obtained from the associated data store. If a signer changes the data of the dynamic form field, the ESS may write back the changed data to the data store.
A system receives a request associated with a first account to delegate, to a second account, authority to send documents on behalf of the first account. The request identifies requirements that must be satisfied before the second account can send documents on behalf of the first account. Responsive to receiving a request to send a first document to a first entity from the second account and on behalf of the second account, the system sends the first document to the first entity. Responsive to receiving a request to send a second document to a second entity from the second account and on behalf of the first account, the system determines whether the request to send the second document satisfies the requirements. Responsive to the request satisfying the requirements, the system sends the second document to the second entity on behalf of the first account.
A system and method for computable contracts that includes a contract management system accessible by involved parties, managing a formation stage of a contract document by obtaining object components, assembling a contract object graph from the object components, and committing the contract object graph to post formation execution; and in an execution environment during a post-formation stage, executing the contract object graph where instances of execution include receiving a contract state update, and appending at least one update object component to the contract object graph in accordance with the contract state update. Variations of the system and method may apply peer-to-peer negotiation and execution, use a cryptographic directed acyclic contract object graph, and/or interface with distributed ledgers.
09 - Appareils et instruments scientifiques et électriques
35 - Publicité; Affaires commerciales
38 - Services de télécommunications
42 - Services scientifiques, technologiques et industriels, recherche et conception
45 - Services juridiques; services de sécurité; services personnels pour individus
Produits et services
(1) Downloadable software for automating electronic agreement, contract, document and legal digital file generation; downloadable software for creating, configuring, customizing, and generating electronic agreements, contracts, documents and legal digital files; downloadable software for agreement, contract, document and legal digital file templates; downloadable software for transferring legal data and information into electronic agreements, contracts, documents and legal digital files; downloadable software for integrating legal data and information into electronic agreements, contracts, documents and legal digital files; downloadable software for creating electronic agreements, contracts, documents, and legal digital files that automatically configure based on user settings; downloadable software for guided instructions on creating, configuring, customizing, and generating electronic agreements, contracts, documents and legal digital files; downloadable software for interactive instructions on creating, configuring, customizing, and generating electronic agreements, contracts, documents and legal digital files; downloadable software for editing, redlining, and reviewing electronic agreements, contracts, documents and legal digital files; downloadable software for auditing electronic agreements, contracts, documents and legal digital files; downloadable software for tracking changes in electronic agreements, contracts, documents and legal digital files; downloadable software for tracking versions of electronic agreements, contracts, documents and legal digital files; downloadable software for collaborating on the creation, configuration, customization, and generation of electronic agreements, contracts, documents and legal digital files; downloadable software for indexing text, and legal data and information in electronic agreements, contracts, documents and files; downloadable software for agreement, contract, document and digital file management; downloadable software for sending, routing and transmitting electronic agreements, contracts, documents, legal digital files, and legal data and information; downloadable software for generating, obtaining, managing, capturing, storing, processing, and viewing electronic and digital signatures; downloadable software for capturing, managing, monitoring, authenticating, and verifying electronic and digital signatures; downloadable software for electronic signature verification using technology to authenticate user identity; downloadable software for identification, authentication, and verification of users; downloadable software for identification verification, namely, authentication of personal identification information; downloadable software for user authentication in e-commerce transactions by access code, phone call, short message service (SMS) text message, knowledge based questions, and government identification; downloadable software for updating agreements, contracts, databases, and legal records; downloadable software for automating data warehousing; downloadable software for collecting, facilitating, managing, processing, and transferring electronic payments; downloadable software for triggering actions, namely, managing notifications, responses, and actions by and from users triggering actions based upon user-defined events all for facilitating signing of electronic documents; downloadable software for artificial intelligence, machine learning, and deep learning to create electronic documents for use as templates and for the co-editing and co-browsing of electronic documents for document management, for searching the contents of electronic documents for daily task management and for calendaring and sending reminders via email, for operating software for e-mailing documents and digital files and for maintaining and storing electronic documents on computer storage area network (SAN) servers, to provide user authentication for e-commerce transactions; downloadable software for analyzing agreements, contracts, documents, legal digital files, and legal data and information; downloadable software for analyzing, identifying, reporting, and visualizing legal data and information in agreements, contracts, documents and digital files; downloadable software for categorizing, indexing, searching, sorting, sharing, and transmitting agreements, contracts, documents, legal digital files, and legal data and information; downloadable software for reporting data analytics in the fields of electronic signature verification, online authentication of user's digital signatures in electronic commerce, digital signature verification and identity management platforms; downloadable software for tracking workflows, auditing information and data, and verifying information and data all in the fields of electronic signature verification, online authentication of user's digital signatures in electronic commerce, digital signature verification and identity management platforms; downloadable software for creating, analyzing, monitoring, and managing a database of compliance information and data; downloadable software for computer security in the nature of providing authentication, issuance, validation and revocation of digital certificates; downloadable computer software for electronic data verification and authentication of digital certificates and digital signatures; downloadable software for generating, obtaining, managing, capturing, storing, processing, and viewing electronic signatures and electronic signature dates; electronic data storage, namely, archiving business documents, personal documents, and corporate documents; downloadable software for sending electronic signature verification and enforcing multi-factor authentication for notaries to assign a legally binding electronic signature to a document via a global computer network for others; downloadable computer software and mobile application software interface software for completing contracts, opening accounts, completing employment forms, automating forms and form-field data, tracking documents in real-time and confirming receipt, routing and controlling document workflow and for downloading digitally-sealed documents for others to assign a legally binding electronic signature to a document for use by notaries via a global computer network; downloadable computer software and mobile application software interface software for configuring digital data security platform notifications for others to assign a legally binding electronic signature to a document for use by notaries via a global computer network; downloadable software for creating, transmitting, and storing electronic legal data, information, documents, and messages; downloadable software to perform electronic business transactions via a global computer network; downloadable software for digital document exchange; downloadable software for electronic data verification of digital and electronic certificates and time and date stamping. (1) Business management, namely, contract management services for business purposes; computerized database management; business data analysis, namely, collecting, reporting, analyzing and integrating business data related to business contracts, agreements, and procurement activities; data processing services all in the fields of electronic signature verification, online authentication of user's digital signatures in electronic commerce, digital signature verification and identity management platforms; collection and organization of legal data for others for business purposes.
(2) Electronic transmission, delivery, and reception of agreements, contracts, documents, legal digital files, messages, and legal data and information via telecommunication networks; encrypted electronic transmission, delivery, and reception of agreements, contracts, documents, legal digital files, messages, and legal data and information via telecommunication networks; electronic transmission, delivery, and reception of user authorization, identification, and verification of legal data and information via telecommunication networks; electronic transmission of electronic and digital signatures via telecommunication networks; electronic transmission of electronic payment data and financial data via telecommunication networks; transmission of digital legal information for electronic commerce via telecommunication networks; providing access to databases; providing access to agreements, contracts, documents, digital files, messages, and legal data and information stored in databases; electronic transmission, delivery, and reception of legal database information via telecommunication networks; Electronic exchange of agreements, contracts, documents, legal digital files, messages, and legal data and information stored in databases via telecommunication networks.
(3) Providing temporary use of non-downloadable software for automating electronic agreement, contract, document and legal digital file generation; providing temporary use of non-downloadable software for creating, configuring, customizing, and generating electronic agreements, contracts, documents and legal digital files; providing temporary use of non-downloadable software for agreement, contract, document and digital file templates; providing temporary use of non-downloadable software for transferring legal data and information into electronic agreements, contracts, documents and legal digital files; providing temporary use of non-downloadable software for integrating legal data and information into electronic agreements, contracts, documents and legal digital files; providing temporary use of non-downloadable software for creating electronic agreements, contracts, documents, and legal digital files that automatically configure based on user settings; providing temporary use of non-downloadable software for guided instructions on creating, configuring, customizing, and generating electronic agreements, contracts, documents and legal digital files; providing temporary use of non-downloadable software for interactive instructions on creating, configuring, customizing, and generating electronic agreements, contracts, documents and legal digital files; providing temporary use of non-downloadable software for editing, redlining, and reviewing electronic agreements, contracts, documents and legal digital files; providing temporary use of non-downloadable software for auditing electronic agreements, contracts, documents and legal digital files; providing temporary use of non-downloadable software for tracking changes in electronic agreements, contracts, documents and legal digital files; providing temporary use of non-downloadable software for tracking versions of electronic agreements, contracts, documents and legal digital files; providing temporary use of non-downloadable software for collaborating on the creation, configuration, customization, and generation of electronic agreements, contracts, documents and legal digital files; providing temporary use of non-downloadable software for indexing text, and legal data and information in electronic agreements, contracts, documents and legal digital files; providing temporary use of non-downloadable software for agreement, contract, document and digital file management; providing temporary use of non-downloadable software for sending, routing and transmitting electronic agreements, contracts, documents, legal digital files, legal data and information; providing temporary use of non-downloadable software for generating, obtaining, managing, capturing, storing, processing, and viewing electronic and digital signatures; providing temporary use of non-downloadable software for capturing, managing, monitoring, authenticating, and verifying electronic and digital signatures; electronic signature verification services using technology to authenticate user identity; providing temporary use of non-downloadable software for identification, authentication, and verification of users; identification verification services, namely, providing authentication of personal identification information; providing user authentication services in ecommerce transactions by access code, phone call, short message service (SMS) text message, knowledge-based questions, and government identification; providing temporary use of non-downloadable software for updating agreements, contracts, databases, and legal records; providing temporary use of non-downloadable software to automate data warehousing; providing temporary use of non-downloadable software for collecting, facilitating, managing, processing, and transferring electronic payments; Providing temporary use of non-downloadable software for triggering actions, namely, managing notifications, responses, and action by and from users based upon user-defined events all for facilitating signing of electronic documents; providing temporary use of non-downloadable software for artificial intelligence, machine learning, and deep learning to create electronic documents for use as templates and for the co-editing and co-browsing of electronic documents for document management, for searching the contents of electronic documents for daily task management and for calendaring and sending reminders via email, for operating software for e-mailing documents and digital files and for maintaining and storing electronic documents on computer storage area network (SAN) servers, to provide user authentication for e-commerce transactions; providing temporary use of non-downloadable software for analyzing agreements, contracts, documents, legal digital files and legal data and information; providing temporary use of non-downloadable software for analyzing, identifying, reporting, and visualizing legal data and information in agreements, contracts, documents and digital files; providing temporary use of non-downloadable software for categorizing, indexing, searching, sorting, sharing, and transmitting agreements, contracts, documents, legal digital files, and legal data and information; providing temporary use of non-downloadable software for reporting data analytics in the fields of electronic signature verification, online authentication of user's digital signatures in electronic commerce, digital signature verification and identity management platforms; providing temporary use of non-downloadable software for tracking workflows, auditing information and data, and verifying information and data all in the fields of electronic signature verification, online authentication of user's digital signatures in electronic commerce, digital signature verification and identity management platforms; providing temporary use of non-downloadable software for creating, analyzing, monitoring, and managing a database of compliance information and data; computer security services in the nature of providing authentication, issuance, validation and revocation of digital certificates.
(4) Notary public services; providing electronic notarization services via a global computer network for others to legally notarize electronic agreements, contracts, documents or files; legal information services; providing legal information via an online interactive database; tracking and monitoring legal requirements in the field of electronic and digital signature technology.
98.
DETECTING OBJECT BURN-IN ON DOCUMENTS IN A DOCUMENT MANAGEMENT SYSTEM
A document management system surfaces changes to a portable document format (PDF) document to a user. The document management system converts each page of the PDF document into images, segments those images, and processes each segment of those images using computer vision and/or natural language processing. The document management system compares segments from an original copy of the PDF document with segments from a modified copy of the PDF document to identify significant changes to the PDF document.
G06F 40/131 - Fragmentation de fichiers textes, p. ex. création de blocs de texte réutilisablesLiaison aux fragments, p. ex. par utilisation de XIncludeEspaces de nommage
G06F 21/64 - Protection de l’intégrité des données, p. ex. par sommes de contrôle, certificats ou signatures
G06F 40/194 - Calcul de la différence entre fichiers
A centralized auditing system receives an audit block containing one or more audit files generated by an audit agent running in an audited device. The audit block additionally stores a first digital signature corresponding to a previous audit block, and a second digital signature generated based at least on the one or more audit files and the first digital signature. After receiving the audit block, the auditing system verifies the integrity of the received audit block based on the second digital signature stored in the audit block and/or the first digital signature corresponding to the previous audit block. In response to verifying the integrity of the received audit block, the auditing system adds the received audit block to an audit register. Moreover, the auditing system adds the one or more audit files included in the audit block to an audit database.
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuéesArchitectures de systèmes de bases de données distribuées à cet effet
G06F 17/00 - Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des fonctions spécifiques
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
H04L 9/00 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité
100.
Detecting object burn-in on documents in a document management system
A document management system surfaces changes to a portable document format (PDF) document to a user. The document management system converts each page of the PDF document into images, segments those images, and processes each segment of those images using computer vision and/or natural language processing. The document management system compares segments from an original copy of the PDF document with segments from a modified copy of the PDF document to identify significant changes to the PDF document.
