The present disclosure provides techniques for fast record matching using machine learning. One example method includes receiving a request indicating one or more attributes, identifying, from a plurality of records using a first machine learning model, a set of records, wherein each record of the set of records indicates the one or more attributes, computing, for each record of the set of records using a second machine learning model, a first relevance score for the record, computing, for each record of the set of records using a third machine learning model, a second relevance score for the record, and identifying, based on the first relevance score for each record of the set of records and the second relevance score for each record of the set of records, a given record of the set of records best matching the request.
A method for generating supplemental content for an explanation for a particular result determined by a software application includes receiving data indicative of a user selecting a first modality of a plurality of different modalities for supplementing the explanation. In response to receiving the data, the method includes providing inputs to a generative artificial intelligence model. The inputs include data indicative of the explanation and data indicative of a first natural language prompt associated with the first modality. The method includes receiving an output from the generative artificial intelligence model. The output includes supplemental content for the explanation. The method includes displaying the supplemental content for viewing via a user interface.
A first large language model (LLM) instance may be instructed to request data while being prevented from performing calculations using the data. A second LLM instance may be instructed to provide a response to the request for data based on a known complete data set. The response may be translated into a machine-readable response in a format configured for processing by a calculation engine. The calculation engine may process the machine-readable response, thereby generating a calculation engine output. A mismatch between the calculation engine output and a known result obtained using the known complete data set may be identified, and the instruction to the first LLM may be modified in response.
Systems and methods are disclosed for detecting hallucinations in large language models (LLMs). An example method includes receiving a first prompt for submission to the first LLM, generating, using the first LLM, a plurality of semantically equivalent prompts to the first prompt, generating, using the first LLM, a first response to the first prompt and a plurality of second responses to the plurality of semantically equivalent prompts, generating, using a second LLM, a plurality of third responses to the semantically equivalent prompts, generating a semantic consistency score for the first response based at least in part on the first prompt, the plurality of semantically equivalent prompts, the plurality of second responses, and the plurality of third responses, and determining whether or not the first response is an accurate response to the first prompt based at least in part on the semantic consistency score.
Certain aspects of the disclosure provide systems and methods for detecting hallucinations in machine learning models. A method generally includes generating a potential answer from an initial prompt received from a user. The method generally includes interrogating the machine learning model with a verification prompt formulated to elicit a positive or negative response from the machine learning model based on the potential answer and initial prompt. A negative response by the neural network model to the verification prompt is indicative of the potential answer being a hallucination. A positive response by the neural network model to the verification prompt is indicative of the potential answer being free from a hallucination. The method generally includes outputting to the user the potential answer as a final answer upon receiving a positive response to the verification prompt.
Systems and methods for training an encoder-decoder model are disclosed. An example method includes receiving, over a communications network, a plurality of extraction model outputs from a corresponding plurality of extraction models, each extraction model output received from a corresponding extraction model and each extraction model output including a respective plurality of key-value pairs corresponding to extracted text from one or more training documents, receiving, over the communications network, character recognition data corresponding to the one or more training documents, receiving, over the communications network, ground truth key-value data corresponding to the one or more training documents, and training the encoder-decoder model based at least in part on the plurality of extraction model outputs, the character recognition data, and the ground truth key-value data, wherein the trained encoder-decoder model is configured to generate key-value pairs for subsequent outputs of the plurality of extraction models.
G06V 30/19 - Reconnaissance utilisant des moyens électroniques
G06V 30/416 - Extraction de la structure logique, p. ex. chapitres, sections ou numéros de pageIdentification des éléments de document, p. ex. des auteurs
Certain aspects of the present disclosure provide techniques for selecting between a model output of a machine learning (ML) model and a generic output. A method generally includes processing user-specific data with the ML model to generate the model output and a model predicted score associated with the model output; calculating a Shapley Additive Explanations (SHAP) score based on the model output, the model predicted score, and the user-specific data; and providing the model output or the generic output as output from the ML model based on the SHAP score.
Aspects of the present disclosure provide techniques for providing a graphical user interface for customizable application navigation. Embodiments include displaying a list of pages associated with a software application in a navigation customization screen and receiving selections of two or more pages of the pages as bookmarks. Embodiments include receiving drag and drop input via the navigation customization screen that changes an ordering of the two or more pages within the list of the plurality of pages and receiving a search query comprising a text string. Embodiments include moving one or more pages matching the search query to a top of the list of the pages within the navigation customization screen and displaying an indication in the navigation customization screen that one of the two or more pages also matches the search query without changing the ordering of the two or more pages within the list of the pages.
G06F 16/955 - Recherche dans le Web utilisant des identifiants d’information, p. ex. des localisateurs uniformisés de ressources [uniform resource locators - URL]
G06F 3/04817 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] fondées sur des propriétés spécifiques de l’objet d’interaction affiché ou sur un environnement basé sur les métaphores, p. ex. interaction avec des éléments du bureau telles les fenêtres ou les icônes, ou avec l’aide d’un curseur changeant de comportement ou d’aspect utilisant des icônes
G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p. ex. des menus
A method for automatically populating a document being prepared via a software application based on extracted data from one or more of a plurality of different source documents may include displaying a graphical user interface associated with the software application and include a first area configured to display data associated with the document and a second area displaying a queue including at least a first graphical object descriptive of a first source document of the plurality of source documents. The method includes automatically populating one or more data fields of the document that are displayed within the first area of the graphical user interface with the extracted data from the first source document. In response to the automatically populating, the method includes automatically updating the second area of the graphical user interface to reflect the data fields have been auto populated with the extracted data from the first source document.
G06F 3/0481 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] fondées sur des propriétés spécifiques de l’objet d’interaction affiché ou sur un environnement basé sur les métaphores, p. ex. interaction avec des éléments du bureau telles les fenêtres ou les icônes, ou avec l’aide d’un curseur changeant de comportement ou d’aspect
Certain aspects of the disclosure provide a method for detecting data collection errors by processing error data with a plurality of regression models to generate a plurality of predicted error rates over a plurality of time intervals. The method includes determining an error mode by applying a set of policy rules optimized for determining the error mode to the plurality of predicted error rates.
09 - Appareils et instruments scientifiques et électriques
35 - Publicité; Affaires commerciales
36 - Services financiers, assurances et affaires immobilières
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Computer software; downloadable computer software for use in personal and business finance, financial planning and management, financial and business transaction processing, accounting, bookkeeping, tax preparation, planning, and filing; payment processing software; downloadable computer software for creating, customizing, and managing invoices, recording payments, and issuing receipts; downloadable computer software for electronic invoicing and payment processing; downloadable computer software for facilitating payments; downloadable computer software for processing electronic transactions; downloadable computer software for electronic funds transfer; downloadable computer software that enables communication between business and financial professionals and their clients; downloadable computer software to automate creation of invoices; downloadable computer software to create, customize, print, export, and e-mail purchase orders, invoices, receipts, documents, and reports; downloadable software for sending, receiving and recording monetary transactions; downloadable software for use in organizing, servicing and tracking sales, collections, and receivables data; downloadable software that provides financial data for use by small businesses; downloadable computer software for controlling access to financial and business information, data, and documents; downloadable computer software to track sales, expenses, and payments; downloadable computer software for enabling consumers to make, and merchants to accept, payments using cryptocurrency, digital currency, electronic cash, and virtual currency; downloadable computer software for managing online bank accounts; downloadable computer software for tracking income, expenses, sales, and profitability by business location, department, type of business, or other user set field; downloadable computer software for use in transaction processing, accounting, customer relationship management, inventory management, business operations and operations management; downloadable computer software that enables users to capture, upload, download, store, organize, view, create, edit, encrypt, send and share documents, information, data, images, photographs, and electronic messages; downloadable computer software to import contacts and business data from other electronic services and software. Business invoicing services; online accounting and bookkeeping services; online bill payment services. Banking services; online banking services; bank account management services; bill payment services; electronic bill presentment for others; electronic commerce payment services; electronic money transfer services; electronic payment processing services; electronic payment services; electronic processing and transmission of bill payment data for others; financial management services via global computer networks; financial services; financial transaction services; payment processing services; providing electronic cash, credit card, and debit card transaction services via computer and communication networks; provision of financial information; transaction processing services for consumers and businesses. Providing non-downloadable computer software; Providing non-downloadable financial management software; Providing non-downloadable software for personal and business finance, accounting, bookkeeping, financial and business transaction processing management, financial and business transaction management, tax preparation, tax planning, and tax filing, business process management, and financial planning; Providing non-downloadable payment processing software; Providing non-downloadable payment software; Providing non-downloadable electronic payment processing software; Providing non-downloadable computer software for facilitating payments; Providing non-downloadable software for creating, customizing, and managing invoices, recording payments, and issuing receipts; Providing non-downloadable software for electronic funds transfer; Providing non-downloadable software for electronic invoicing and payment processing; Providing non-downloadable software to automate creation of invoices; Providing non-downloadable software to create, customize, print, export, and e-mail purchase orders, invoices, receipts, documents, and reports; Providing non-downloadable software for executing, processing, and recording financial transactions; Providing non-downloadable software for sending, receiving and recording monetary transactions; non-downloadable computer software that enables communication between business and financial professionals and their clients; Providing non-downloadable business management software; Providing non-downloadable computer software for enabling consumers to make, and merchants to accept, payments using cryptocurrency, digital currency, electronic cash, and virtual currency; Providing non-downloadable computer software for managing online bank accounts; Providing non-downloadable software for controlling access to financial and business information, data, and documents; Providing non-downloadable software for tracking income, expenses, sales, and profitability by business location, department, type of business, or other user set fields; Providing non-downloadable software for use in organizing, servicing and tracking sales, collections, and receivables data; Providing non-downloadable software for use in transaction processing, accounting, customer relationship management, inventory management, and operations management; Providing non-downloadable software that enables users to capture, upload, download, store, organize, view, create, edit, encrypt, send and share documents, information, data, images, photographs, and electronic messages; Providing non-downloadable software that provides financial data for use by small businesses; Providing non-downloadable software to import contacts and business data from other electronic services and software; Providing non-downloadable software to track sales, expenses, and payments; software as a service (SaaS) featuring cloud computing capabilities for accounting, bookkeeping, financial and business transaction processing management, financial and business transaction management, tax preparation and tax planning, business process management, and financial planning; software as a service (SaaS) services.
12.
LARGE LANGUAGE MODEL AND DETERMINISTIC CALCULATOR SYSTEMS AND METHODS
At least one large language model (LLM) may be instructed to request data from a user while being prevented from performing calculations using the data. A user-generated response to the request for data, including at least a portion of the data, may be received. The user-generated response may be translated into a machine-readable response in a format configured for processing by a calculation engine. The calculation engine may process the machine-readable response, thereby generating a calculation engine output.
A method for detecting fraudulent financial transactions in information technology networks involves obtaining a multitude of features associated with a financial transaction conducted over an information technology network by an unknown transaction party. The multitude of features includes clickstream data obtained from the unknown transaction party. The clickstream data is associated with data of the financial transaction being entered by the unknown transaction party. The method further involves obtaining a first fraud indicator using a machine learning classifier operating on the multitude of features, obtaining a second fraud indicator using a rule-based classifier operating on the multitude of features, obtaining a fraud prediction for the financial transaction, using the first fraud indicator and the second fraud indicator, and taking an action, in response to the fraud prediction.
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
G06F 18/243 - Techniques de classification relatives au nombre de classes
G06F 21/52 - Contrôle des utilisateurs, des programmes ou des dispositifs de préservation de l’intégrité des plates-formes, p. ex. des processeurs, des micrologiciels ou des systèmes d’exploitation au stade de l’exécution du programme, p. ex. intégrité de la pile, débordement de tampon ou prévention d'effacement involontaire de données
At least one processor may receive a query response generated by a query machine learning (ML) model, wherein the query response is generated in response to a query from a client device. The at least one processor may generate an evaluated likelihood of the query response being found in a training data set comprising known valid data, wherein the generating is performed using an evaluation ML model. The at least one processor may determine that the evaluated likelihood indicates the query response is likely to include valid data. In response to the determining, the at least one processor may return the query response to the client device.
A method includes obtaining matches between target records in a target dataset and a reference records in a reference dataset, each match of the matches comprising a corresponding confidence level of the match, categorizing the target records into review level categories according to the corresponding confidence level, and presenting a graphical user interface (GUI). The GUI includes a first section for a first review level category showing a first subset of the target records assigned to the first review level category, the first subset comprising target records related, in the GUI, to at least one matching reference record. The GUI includes a second section for a second review level category, wherein the second section shows a second subset of the target records assigned to the second review level category, the second subset comprising target records related, in the GUI, to at least one matching reference record.
Systems and methods for enriching raw user text with a database to identify relevant context, wherein generated prompts provide responses to user queries is provided. A method includes receiving a query, wherein the query comprises the raw text, creating a first embedding based on the query, retrieving a plurality of other embeddings, wherein the plurality of other embeddings are complementary to the first embedding, creating a contextual prompt including context from at least one of the plurality of other embeddings, processing the contextual prompt using a trained machine learning model, thereby generating a response to the query, and causing the response to be displayed by a display device.
Certain aspects of the disclosure provide a method of providing an interactive user support interface, the method comprising receiving a communication with a support request for an application. The method further comprising determining, based on the communication, an account associated with the application and determining, based on the account, that the user is an active session in to the application. The method further comprising determining support content responsive to the support request and causing the support content to be displayed within the application based on the determination that the user is an active session in to the application.
A method classifies feedback from transcripts. The method includes receiving an utterance from a transcript from a communication session and processing the utterance with a classifier model to identify a topic label for the utterance. The classifier model is trained to identify topic labels for training utterances. The topic labels correspond to topics of clusters of the training utterances. The training utterances are selected using attention values for the training utterances and clustered using encoder values for the utterances. The method further includes routing the communication session using the topic label for the utterance.
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 40/289 - Analyse syntagmatique, p. ex. techniques d’états finis ou regroupement
22.
Methods and systems for implementing large language models and smart caching with zero shot
A Large Language Model (LLM) for classifying documents by identifying indicators within the documents. A smart caching mechanism stores document classifications and associated indicators output from the LLM. The database contains document details, classifications, and associated indicators. A classification module classifies a new document by analyzing it for indicators, checking the cache for a match, and querying the database for the indicators if no match is found. The module applies a majority vote based on the classifications associated with the indicators.
G06F 12/0875 - Adressage d’un niveau de mémoire dans lequel l’accès aux données ou aux blocs de données désirés nécessite des moyens d’adressage associatif, p. ex. mémoires cache avec mémoire cache dédiée, p. ex. instruction ou pile
Certain aspects of the present disclosure provide techniques and systems for automatically detecting, tracking, and processing certain information content, based on voice input from a user. A voice enabled content tracking system receives natural language content corresponding to audio input from a user. A determination is made as to whether the natural language content includes a first type of information, based on evaluating the natural language content with a first machine learning model. In response to determining the natural language content comprises the first type of information, a temporal association of the first type of information is determined, based on evaluating the natural language content with a second machine learning model, and a message including an indication of the temporal association of the first type of information is transmitted to the user.
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
A transaction model of a general model generates a target transaction vector for a target transaction record. The general model also generates account vectors for accounts. A match score is generated between the account vectors and the transaction vector. The general model selects a first account identifier of an account using the match score. The transaction model also generates historical transaction vectors for historical transaction records. Further, a comparison score is generated between the historical transaction vectors and the target transaction vector. A second account identifier of an historical transaction is selected according to the comparison score. One of the first account identifier and the second account identifier is selected as the account identifier for the transaction record, and the transaction record is stored with the account identifier.
The present disclosure provides techniques for schema-based machine learning model monitoring. One example method includes receiving input data to and output data related to a machine learning model, wherein the input data and the output data conform to a data schema, retrieving, based on the data schema, a set of fields associated with the input data and the output data, performing statistical analysis for the machine learning model based on the set of fields retrieved, and predicting one or more attributes of the machine learning model based on the statistical analysis, wherein the one or more attributes of the machine learning model indicate a result of monitoring of the machine learning model, explainability information related to the machine learning model, or health of the machine learning model.
Systems and methods for matching received product information with stored product information. Incoming product information has multiple attributes, which may be fuzzy matched with corresponding attributes of stored product information to generate corresponding fuzzy matching scores. Each of the fuzzy matching scores is associated with a weighting factor, which is used to indicate a contribution of the corresponding fuzzy matched attribute to a match between the entire product information. A matching coefficient is initialized and progressively updated by using the weighted fuzzy matching scores. When a desired number of fuzzy matchings between the corresponding attributes is reached and the matching coefficient is finalized, the matching coefficient is compared to a threshold. If the matching coefficient is above the threshold, a recommendation is generated indicating a match between the received product information and the stored product information.
A method including receiving a user input from a user device. The method also includes generating test inputs including the user input and modified inputs. The user input is processed with a rephrasing model to form the modified inputs. The method also includes executing a test model to generate test outputs, including an original test output and modified test outputs, from processing the test inputs. The method also includes generating similarity scores by performing similarity comparisons among the test outputs. The method also includes determining a model confidence from the similarity scores. The method also includes routing the user input responsive to the model confidence satisfying or failing to satisfy a confidence threshold.
Certain aspects of the disclosure provide a method for training a machine learning model to predict text containing sensitive information. The method includes extracting one or more features from a historical data set. The method further includes anonymizing the historical data set, including determining, for each feature of the extracted one or more features, tokens containing personally identifiable information (sensitive information); assigning a category placeholder to each of the tokens containing sensitive information; and generating a new data set where each token containing sensitive information is replaced with the assigned category placeholder. The method further includes determining a probability associated with each token containing sensitive information; and training a generalized model to predict anonymized text given the one or more features.
A computer-implemented method includes receiving data comprising a plurality of application programming interface (API) requests from a plurality of client devices. The method includes generating a plurality of API sessions based on the data, wherein each of the API sessions is associated with a corresponding client device of the plurality of client devices and includes a sequence of API requests originating from the corresponding client device. The method includes determining one or more API sessions of the plurality of API sessions generated based on the data are abnormal. Finally, the method includes performing one or more actions based on determining the one or more API sessions are abnormal.
Certain aspects of the present disclosure provide techniques for executing a function in a software application through a conversational user interface based on a knowledge graph associated with the function. An example method generally includes receiving a request to execute a function in a software application through a conversational user interface. A graph definition of the function is retrieved from a knowledge engine. Input is iteratively requested through the conversational user interface for each parameter of the parameters identified in the graph definition of the function based on a traversal of the graph definition of the function. Based on a completeness graph associated with the function, it is determined that the requested inputs corresponding to the parameters identified in the graph definition of the function have been provided through the conversational user interface. The function is executed using the requested inputs as parameters for executing the function.
G06F 16/901 - IndexationStructures de données à cet effetStructures de stockage
G06N 5/02 - Représentation de la connaissanceReprésentation symbolique
G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
31.
Contextual bandit for multiple machine learning models for content delivery
A processor may receive user information for a request payload from an external device and data describing a plurality of user interface (UI) elements configured to be presented in a UI of the external device. The processor may select a machine learning (ML) model from a plurality of ML models using a contextual bandit ML model that is trained based on the user information. The processor determines at least one recommended user interface (UI) element with a selected ML model, based on the user information and the data describing the plurality of UI elements. The at least one recommended UI element may be presented in the UI of the external device. The processor may receive event data indicating a user interaction with the at least one recommended UI element in the UI of the external device. The contextual bandit ML model may be re-trained based on the event data.
G06Q 30/02 - MarketingEstimation ou détermination des prixCollecte de fonds
G06N 3/006 - Vie artificielle, c.-à-d. agencements informatiques simulant la vie fondés sur des formes de vie individuelles ou collectives simulées et virtuelles, p. ex. simulations sociales ou optimisation par essaims particulaires [PSO]
G06N 3/082 - Méthodes d'apprentissage modifiant l’architecture, p. ex. par ajout, suppression ou mise sous silence de nœuds ou de connexions
G06N 5/04 - Modèles d’inférence ou de raisonnement
Certain aspects of the disclosure pertain to predicting a candidate entity match for a transaction with a machine learning model. A description of a transaction comprising encoded transaction data associated with an organization is received as input. In response, at least one machine learning model can be invoked to infer a transaction embedding based on the description, a first score that captures similarity between the transaction embedding entity embeddings associated with a global list of entities and organizations, a second score that captures a probability of interaction between the first organization and the entities based on organization and entity embeddings that capture profile data associated with the organization and the entities, and at least one candidate entity based on the first score and the second score. Finally, the inferred candidate entity can be output for use by an automated data entry or other process or system.
A method including identifying first and second training data having first and second subsets of click-through information for a dataset. Identifying includes associating the first and second subsets first and second applications executing on first and second domains having divergent first and second ontologically defined groupings of entities. The method also includes storing, as first and second vector data structures, the first and second training data. The method also includes training, on the first and second vector data structures, first and second ARIMA machine learning models. The first trained ARIMA machine learning model is trained on the first domain and the second trained ARIMA machine learning model is trained on the second domain. The method also includes deploying the first and second trained ARIMA machine learning models.
09 - Appareils et instruments scientifiques et électriques
41 - Éducation, divertissements, activités sportives et culturelles
Produits et services
(1) Downloadable electronic newsletters delivered via email in the fields of accounting, bookkeeping, personal and business finance, financial and business transaction management, tax preparation and tax planning, business process management, and financial planning (1) Providing online newsletters via email in the fields of accounting, bookkeeping, personal and business finance, financial and business transaction management, tax preparation and tax planning, business process management, and financial planning
36.
Display device with graphical user interface having a strategy card
The present disclosure provides techniques for friendship-based automated recommender system. One example method includes receiving electronic record data indicating interactions between a plurality of users and a plurality of providers, constructing a bipartite graph based on the interactions, identifying, for each user of the plurality of users, a set of other users in the plurality of users, adding to the bipartite graph, for each user of the plurality of users, intra-user edges between the user and the set of other users, computing, for each respective intra-user edge of the intra-user edges, a weight of the respective intra-user edge, computing, for each respective user of the plurality of users, a popularity score, computing, for each respective provider of the plurality of providers, a reputation score, and training a recommender system using the reputation scores of the plurality of providers, wherein the recommender system is used to automatically determine a provider recommendation.
In a pipeline, data events generated by a producer application are temporally grouped by using a group identification tag. For each data event, data points are generated and uploaded to a storage and cache at each point of production and consumption. The storage allows a matching of data events between the production point and the consumption point, thereby ensuring that streaming parity is maintained. In cases of mismatch, the cache allows for detecting missing data events, i.e., identifying data events that were generated by an upstream producer application, but not consumed by a downstream consumer. While being agnostic to the transformations applied by the various applications in the pipeline, the embodiments disclosed herein keep track of the output data events and input data events and precisely identify the missing data events.
Aspects of the present disclosure provide techniques for active multifidelity machine learning. Embodiments include selecting, based on one or more criteria, a first subset of unlabeled training data for manual review and a second subset of unlabeled training data for providing to a pre-trained machine learning model for automated labeling. Embodiments include receiving manual label data for the first subset of unlabeled training data. Embodiments include providing inputs to the pre-trained machine learning model based on a subset of the manual label data and the second subset of training data. Embodiments include receiving, as outputs from the pre-trained machine learning model, automated label data for the second subset of unlabeled training data. Embodiments include generating a training data set for a target machine learning model based on the set of unlabeled training data, the manual label data, and the automated label data.
A method for operating a stateful advice system includes determining, based on configuration data, whether a selected mode for the stateful advice system corresponds to a first mode in which a domain model is directly modified to implement one or more of a plurality of different strategies or a second mode in which the domain model is indirectly modified via a model schema of the domain model. The method includes providing a prompt to a generative artificial intelligence (AI) model configured to determine applicability of each of the plurality of different strategies to the domain model. The prompt includes the domain model when the selected mode corresponds to the first mode or the model schema when the selected mode corresponds to the second mode. The method includes receiving one or more recommendations from the generative AI model.
A method for generating stateful advice includes obtaining configuration data associated with configuring a stateful advice system to provide stateful advice within a domain. The method includes obtaining data indicative of an attribute for a strategy that is associated with the domain. The method includes providing the configuration data and the data indicative of the attribute to a generative artificial intelligence (AI) model configured to automatically author content for an advice artifact for the strategy. The content includes a series of prompts associated with providing stateful advice regarding the strategy. The method includes determining the strategy is applicable to a domain model associated with the domain based, at least in part, on the content automatically authored by the generative AI model. The method includes generating a recommendation to apply the strategy to the domain model in response to determining the strategy is applicable to the domain model.
Certain aspects of the disclosure provide a method of constructing a report incorporating data stored in a plurality of database tables. The method generally includes receiving, via an interactive user interface (UI), a selection of a first database table, generating a visualization of data associated with the first database table organized in rows and columns, wherein each column includes data for a data field in the first database table, displaying, via the interactive UI, shared data fields associated with other related database tables and shared among all of the other database tables, receiving, via the interactive UI, a selection of a first shared data field, and displaying, via the interactive UI, data for the first shared data field from all the other database tables in a first new column added to the visualization, wherein each row in the first new column includes data from one of the other database table.
Certain aspects of the disclosure provide a method for transferring an achievement token, comprising: receiving a request to transfer an achievement token to a user; querying a smart contract to obtain a requirement associated with the achievement token; verifying, via a blockchain, the user completed the requirement, including retrieving user evidence associated with the requirement from the blockchain; and storing user evidence with a transaction history associated with the transfer of the achievement token to the user; and transferring, via the blockchain, the achievement token to the user.
A method includes receiving a user input including natural language text. The method also includes generating modified inputs from the user input. The method also includes executing a machine learning model on the modified inputs to generate model outputs. The method also includes sampling the model outputs using a statistical sampling strategy to generate sampled model outputs. The method also includes clustering the sampled model outputs into clusters. The method also includes generating a confidence metric of the clusters. The method also includes routing, automatically in a computing system, the user input based on whether the confidence metric satisfies a threshold value.
41 - Éducation, divertissements, activités sportives et culturelles
Produits et services
matching lenders with borrowers in the fields of consumer lending and commercial lending; providing information in the nature of business and marketing advice, news, and opinions for professionals in the fields of accounting, finance, financial planning, small business management, tax preparation, tax filing and tax planning; Accounting and bookkeeping services; association and membership services, namely promoting the interests of, and providing business referral, marketing, and business management services to member professionals in the fields of accounting, finance, financial planning, small business management, tax preparation, tax filing, and tax planning; business information and accounting advisory services; business management in the field of bookkeeping and accounting; member benefits program, namely, customer loyalty services for commercial, promotional and/or advertising purposes that provides a variety of amenities to member accounting professionals, computer consultants, tax professionals, and business consultants educational services, namely, conducting classes, seminars, conferences, workshops and webcasts in the fields of computer software, computer hardware, software as a service (SaaS) technology, cloud computing, and mobile computing, and distributing course materials in connection therewith; providing training of accountants, bookkeepers, and business managers for certification in the fields of accounting, business and computer software; educational services, namely, conducting classes, seminars, conferences, workshops and webcasts in the fields of accounting, tax, finance, business, and productivity and distributing course materials in connection therewith; Providing training and educational services in the fields of accounting, tax, finance, business, and computer software; educational services, namely, conducting classes, seminars, conferences, workshops and webcasts in the fields of business development and business management, and distributing course materials in connection therewith
46.
TRUST-AWARE MULTI-VIEW STACKING BASED RISK ASSESSMENT
A method and system are provided for generating a combined prediction using an ensemble machine learning system. The prediction may be used in risk assessment for payroll processing. A data point is received as input to a multitude of trained models. Each model is trained from a respective data subset of a disparate data. A model prediction this generated by each of a multitude of machine learning models. For each respective trained model, a trust score is generated based on a data sparseness metric of the data point and a feature importance vector of the respective model. The model predictions and trust scores are received as input to a meta-model that was trained from the trust score and the model prediction of the multitude of trained models over the respective data subset of the disparate data. A combined prediction is generated using the trained meta-model.
Certain aspects of the disclosure pertain to inferring a candidate entity associated with a transaction with a machine learning model. An organization identifier and description associated with a transaction can be received as input. In response, an entity embedding, comprising a vector for each entity of an organization based on the organization identifier, can be retrieved from storage. A machine learning model can be invoked with the entity embedding and description. The machine learning model can be trained to infer a transaction embedding from the description and compute a similarity score between the transaction embedding and each vector of the entity embedding. A candidate entity with a similarity score satisfying a threshold can be identified and returned. The candidate entity with the highest similarity score can be identified in certain aspects.
Customizable template-based user interfaces for digital CRM tools. For instance, the CRM tools are pre-loaded with customizable templates, which are rendered as interactive user interfaces that allow customization of the templates based on user needs. This customization therefore allows a user to rapidly, visually, and easily define a customer engagement pipeline and customer segments without any specialized programming knowledge. The user interfaces may dynamically show the flow of customers between different phases and provide recommended actions to further drive customer engagement. Additionally, the CRM tools are integrated with other on-line platforms such as social media sites and e-commerce sites.
G06Q 30/01 - Services de relation avec la clientèle
G06Q 50/00 - Technologies de l’information et de la communication [TIC] spécialement adaptées à la mise en œuvre des procédés d’affaires d’un secteur particulier d’activité économique, p. ex. aux services d’utilité publique ou au tourisme
49.
Automated user experience orchestration using natural language based machine learning techniques
Certain aspects of the present disclosure provide techniques for orchestrating a user experience using natural language input. A user experience is orchestrated within an ecosystem of features for which a plurality of corresponding tokens is defined. Natural language describing a desired user experience result is received by a user experience orchestrator. A sequence of tokens corresponding to operations belonging to an ecosystem of features which produce a correct result for the natural language input can be identified by a trained large language model and executed by the user experience orchestrator using a token operator. The output operations determined by the model to produce or be likely to produce the correct result based on the natural language input can be disambiguated, confirmed, and/or executed.
A system and method that leverage a hybrid bi-directional user experience system that bi-directionally transfers an application session between a first application and a migrated application based on the availability of application features in the migrated application stack.
A method includes receiving a set of execution paths for a directed acyclic graph. The directed acyclic graph includes multiple nodes and multiple edges. The nodes include sets of executable code. The edges represent an operational relationship between at least two nodes. The execution paths include a subset of the nodes connected by a sequence of edges. The method further includes setting a current training level to a maximum training level. The method further includes constructing a transition probability set for the current training level and adding the transition probability set to a transition probability dictionary. The method further includes storing the transition probability dictionary as a final transition probability dictionary.
A system is configured to train a machine learning tree network using path based features, such as leaf nodes or connections between nodes. A first machine learning tree network model, for example, may be trained using a first set of training data, and used to generate predictions for a second set of training data. The path based features are determined from the first machine learning tree network model when generating the predictions for the second set of training data. The path based features may then be used to train a second machine learning tree network model, e.g., using logistic regression.
Augmented Denoising Diffusion Implicit Models (“DDIMs”) using a latent trajectory optimization process can be used for image generation and manipulation using text input and one or more source images to create an output image. Noise bias and textual bias inherent in the model representing the image and text input is corrected by correcting trajectories previously determined by the model at each step of a diffusion inversion process by iterating multiple starts the trajectories to find determine augmented trajectories that minimizes loss at each step. The trajectories can be used to determine an augmented noise vector, enabling use of an augmented DDIM and resulting in more accurate, stable, and responsive text-based image manipulation.
This disclosure relates to systems and methods for providing user content on a social network. In some aspects, the social network receives, over a communications network from a first computing device associated with a first user of the social network, a transmission including a post to be published on the social network. The social network detects, in the post, goods or services sought or inquired about by the first user, and determines a proximity of the first user. The social network identifies one or more other users of the social network located within a geographical area or the proximity associated with the first user, and presents the post only to the one or more identified users of the social network.
H04L 51/222 - Surveillance ou traitement des messages en utilisant des informations de localisation géographique, p. ex. des messages transmis ou reçus à proximité d'un certain lieu ou d'une certaine zone
H04L 51/52 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel pour la prise en charge des services des réseaux sociaux
A system and method implementing K-anonymity processing of a data record to protect sensitive information, while still revealing useful information. The system and method performing K-anonymity processing of categories in the data record, and choosing to mask the data of the category that produces the highest anonymity score. The system and method repeats the process until a K-value of the data record is achieved.
A method for controlling software agent workers for migrating data between databases. The method monitors resource utilization of at least one of the databases during the migration of the data and compares the monitored resource utilization to a desired resource utilization range. When the comparison indicates that the monitored resource utilization is less than the desired resource utilization range, the method additively deploys additional software agent workers. When the comparison indicates that the monitored resource utilization is greater than the desired resource utilization range, the method multiplicatively removes software agent workers.
Certain aspects of the disclosure provide a method of benchmarking a target entity. The method generally includes, for each respective entity characteristic in a set of entity characteristics and starting with a first entity characteristic: determining a wide distribution comprising dissimilarity metric values associated with the respective entity characteristic and measured between the target entity and each entity in a current set of entities; determining subsets of entities within the current set of entities; for each respective subset: determining a narrow distribution comprising dissimilarity metric values associated with the respective entity characteristic and measured between the target entity and each entity in the respective subset; resetting the current set of entities to include only a subset of entities in the subsets of entities having a highest benchmark score; and determining benchmark data for the target entity based on the current set of entities.
Aspects of the present disclosure provide techniques for image-based document search. Embodiments include receiving an image of a document and providing the image of the document as input to a machine learning model, where the machine learning model generates separate embeddings of a plurality of patches of the image of the document and the machine learning model generates an embedding of the image of the document based on the separate embeddings of the plurality of patches. Embodiments include determining a compact embedding of the image of the document based on applying a dimensionality reduction technique to the embedding of the image of the document generated by the machine learning model. Embodiments include performing a search for relevant documents based on the compact embedding of the image of the document. Embodiments include performing one or more actions based on one or more relevant documents identified through the search.
G06V 30/418 - Appariement de documents, p. ex. d’images de documents
G06F 16/532 - Formulation de requêtes, p. ex. de requêtes graphiques
G06V 10/762 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant le regroupement, p. ex. de visages similaires sur les réseaux sociaux
G06V 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
G06V 10/776 - ValidationÉvaluation des performances
G06V 30/413 - Classification de contenu, p. ex. de textes, de photographies ou de tableaux
59.
Bi-directional federation link for seamless cross-identity SSO
A federation link is used to facilitate bi-directional identity federation between software applications. The federation link is created to include user and account identity information for software applications having respective authentication providers. The federation link is created by one of the software applications and shared, for example, with the authentication provider of the other software application. The federation link can be utilized by both software applications to facilitate automated user authentication when navigating in either direction between the software applications.
A method including transcribing, automatically, an ongoing stream of voice data into text phrases. The method also includes receiving an indication of a selected text phrase in the text phrases. The method also includes converting the selected text phrase to a selected phrase vector. The method also includes generating a subsequent text phrase, after the selected text phrase, from the ongoing stream of voice data, and adding the subsequent text phrase to the text phrases. The method also includes converting the subsequent text phrase to a subsequent phrase vector. The method also includes generating a similarity confidence score from the selected phrase vector and the subsequent phrase vector, using a machine learning model. The method also includes highlighting, responsive to the similarity confidence score exceeding a threshold value, the subsequent text phrase in the text phrases.
G10L 15/06 - Création de gabarits de référenceEntraînement des systèmes de reconnaissance de la parole, p. ex. adaptation aux caractéristiques de la voix du locuteur
G10L 15/18 - Classement ou recherche de la parole utilisant une modélisation du langage naturel
G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
G10L 15/30 - Reconnaissance distribuée, p. ex. dans les systèmes client-serveur, pour les applications en téléphonie mobile ou réseaux
A sequence of data entry screens are configured to collect the data from a user. The method and system receive data entered by a user into a data entry screen. The method and system then determine metrics of the collected data, and ranks the collected data and the data entry screens based on the determined metrics. The ranking is then used to display the next best screen in the sequence for collecting data.
Certain aspects of the disclosure provide a method for transacting on a blockchain with an amount of user token for a transfer; an amount of user token for funding the transfer; and an amount of system token for paying a cost of the transfer, wherein the amount of user token for funding the transfer is determined to be equivalent to the amount of system token for paying the cost of the transfer; executing the transfer of user token; and reimbursing the amount of system token paid for the cost of the transfer.
G06Q 20/02 - Architectures, schémas ou protocoles de paiement impliquant un tiers neutre, p. ex. une autorité de certification, un notaire ou un tiers de confiance
63.
Detection of abnormal application programming interface (API) sessions including a sequence of API requests
A computer-implemented method includes receiving data comprising a plurality of application programming interface (API) requests from a plurality of client devices. The method includes generating a plurality of API sessions based on the data, wherein each of the API sessions is associated with a corresponding client device of the plurality of client devices and includes a sequence of API requests originating from the corresponding client device. The method includes comparing each of the plurality of API sessions to one or more of a plurality of different patterns indicative of permissible API sessions determined based on training data. The method includes determining one or more API sessions of the plurality of API sessions generated based on the data are abnormal based, at least in part, on the comparing. Finally, the method includes performing one or more actions based on determining the one or more API sessions are abnormal.
Certain aspects of the present disclosure provide techniques for detecting data errors. A method generally includes training each of a plurality of models on a plurality of training data sets to generate a set of trained models, determining a plurality of subsets of trained models from the set of trained models, for each respective subset: determining a plurality of ensemble outputs for the respective subset based on a plurality of validation data sets; and determining at least one evaluation metric for the respective subset based on the plurality of ensemble outputs; and determining an ensemble model as a subset of trained models having a best evaluation metric among a plurality of evaluation metrics associated with the plurality of subsets, wherein each subset comprises a different selection of models from the set of trained model than each other subset of trained models in the plurality of subsets of trained models.
A method implements brand engine for extracting and presenting brand data with user interfaces. The method includes receiving a blueprint with a set of structure blocks extracted from a selected content. A structure block of the set of structure blocks includes a set of style parameter requests for a section of the selected content. The method further includes processing the set of structure blocks with a first set of smart blocks to generate a set of scores. A smart block of the first set of smart blocks includes brand data with style parameter selections. The method further includes selecting a second set of smart blocks, for the set of structure blocks, from the first set of smart blocks, using the set of scores. The method further includes presenting the second set of smart blocks with the brand data.
The present disclosure provides techniques for hybrid pagination for retrieving data. One example method includes receiving, at a server, a first application programming interface (API) request indicating a first offset and a first limit, retrieving, by the server, a given page comprising a list of items based on the first offset and the first limit in response to the first API request, creating, by the server, an entry in a cache based on the first API request, wherein the entry comprises cursor information indicating a last item in the list of items in the given page, receiving, by the server, a second API request indicating a second offset and a second limit for a subsequent page of the given page, generating, by the server, a cache key based on the second API request, retrieving, by the server, the entry from the cache based on the generated cache key, and retrieving, by the server, based on the last item in the list of items in the given page indicated in the entry and the second limit, the subsequent page in response to the second API request.
This disclosure relates to restricting access in a social network. The social network stores profile information for each of a plurality of users of the social network in a database. The social network receives, from a first user of the social network, a request to invite a second user to establish a connection with the first user. The social network transmits, to the first user, one or more questions pertaining to the profile information of the second user. The social network receives, from the first user, one or more answers responsive to the one or more questions. The social network determines whether each of the answers is correct based on the stored profile information of the second user. The social network transmits, to the second user, an invitation to establish the connection with the first user when at least a number of the answers are correct.
G06Q 50/00 - Technologies de l’information et de la communication [TIC] spécialement adaptées à la mise en œuvre des procédés d’affaires d’un secteur particulier d’activité économique, p. ex. aux services d’utilité publique ou au tourisme
Certain aspects of the disclosure provide a method for transferring an achievement token, comprising: receiving a request to transfer an achievement token to a user; querying a smart contract to obtain a requirement associated with the achievement token; verifying, via a blockchain, the user completed the requirement, including retrieving user evidence associated with the requirement from the blockchain; and storing user evidence with a transaction history associated with the transfer of the achievement token to the user; and transferring, via the blockchain, the achievement token to the user.
A method in a client backoff filter. The method includes receiving, from a server, a backoff data packet having backoff metadata. The method also includes saving the backoff metadata in a cache local to the client backoff filter. The method also includes receiving a subsequent request for service. The method also includes checking whether an attribute of the subsequent request for service matches the backoff metadata in the cache. The method also includes performing, responsive to checking, an action including at least one of the group including: blocking, responsive to the attribute matching the backoff metadata in the cache, transmission of the subsequent request for service to a server, and transmitting, responsive to the attribute failing to match the backoff metadata in the cache, the subsequent request for service to the server.
Certain aspects of the present disclosure provide techniques for detecting data entry errors. A method generally includes receiving a string value as user input for a data field, selecting a plurality of reference values previously entered into the data field within a time period, processing, with an embedding model configured to classify an input string value as a valid or invalid entry, the string value and the reference values and thereby generating a first vector as output, computing one or more statistics for the reference values and the string value, creating a second vector based on the one or more statistics, generating a concatenated vector by concatenating the first vector and the second vector, processing, with a classifier model configured to classify the string value as valid or invalid, the concatenated vector and thereby generating a classification output, and taking action based on the classification output.
Certain aspects of the disclosure provide a method, comprising: processing input data with an ensemble of nonlinear machine learning models; generating a sparse high-dimensional embedding based on one or more leaf nodes of each nonlinear machine learning model in the ensemble of nonlinear machine learning models; projecting the high-dimensional embedding into a lower-dimensional embedding, wherein the lower-dimensional embedding is less sparse than the high-dimensional embedding; processing the lower-dimensional embedding with a linear machine learning model to generate a binary class prediction; determining a confidence for the binary class prediction; and outputting: the binary class prediction if the confidence is greater than or equal to a threshold; or a flipped binary class prediction if the confidence is lower than the threshold.
Systems and methods for training machine learning models are disclosed. An example method includes receiving historical event timing data including event data for a first portion including events from a first time period, and a second portion comprising events from a second time period not including the first time period, predicting, based on the first portion of the historical event timing data, a first plurality of predicted events, the first plurality of predicted events corresponding to the second time period, determining a first subset of predicted events to be accurate predictions based at least in part on comparing the first plurality of predicted events to the historical events occurring within the second time period, generating training data based at least in part on the first subset of the first plurality of predicted events, and training the machine learning model based at least in part on the training data.
Aspects of the present disclosure provide techniques for automated health scoring through meta-learning. Embodiments include retrieving text data related to an entity that was provided by a user and providing one or more first inputs to a first machine learning model based on a subset of the text data. Embodiments include determining, based on an output from the first machine learning model, whether the text data includes an address. Embodiments include determining that the text data includes a name and determining, based on the address and the name, that one or more text results from one or more data sources relate to the entity. Embodiments include providing one or more second inputs to a second machine learning model based on the one or more text results and determining, based on an output from the second machine learning model, a health score for the entity.
G06Q 30/02 - MarketingEstimation ou détermination des prixCollecte de fonds
G06N 5/022 - Ingénierie de la connaissanceAcquisition de la connaissance
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G06Q 30/0282 - Notation ou évaluation d’opérateurs commerciaux ou de produits
G06Q 30/06 - Transactions d’achat, de vente ou de crédit-bail
G16H 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le calcul des indices de santéTIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour l’évaluation des risques pour la santé d’une personne
09 - Appareils et instruments scientifiques et électriques
35 - Publicité; Affaires commerciales
37 - Services de construction; extraction minière; installation et réparation
38 - Services de télécommunications
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(1) Downloadable computer software for marketing automation; Downloadable computer software for creating, deploying and managing information technology (IT) infrastructure for use in networks, computer systems, data centers, cloud computing and data storage (1) Marketing services; market research
(2) Installation and maintenance of computer hardware; IT infrastructure consulting services relating to installation and maintenance of computer systems
(3) Electronic transmission of messages for marketing purposes
(4) Providing temporary use of online non-downloadable software for use in the field of marketing automation; providing computer software for marketing automation and marketing campaign management, and technical support related to same; Software as a service platforms featuring technology that enables users to automate and track marketing and advertising campaigns and measure the results of marketing and advertising campaigns; Technical consultation in the fields of computer hardware, software and infrastructure of computer software; computer software installation and maintenance, setup, and configuration services; computer software and systems integration services; Scientific and technological services in the field of artificial intelligence and artificial intelligence software development for others, and research and design relating thereto; Computer systems security services, namely, password reset, removal, multi-factor authentication, and protection; encoding of valuable documents with identification information; fraud protection services, namely, computer security services in the nature of threat analysis for protecting data; Providing temporary use of non-downloadable software for use in sharing data with others; data hosting services; enabling data hosting services, namely, hosting software programs for use in managing, organizing, and sharing data on a computer server on a global computer network and on internal computer networks; enabling data management, namely, technical support services, namely, troubleshooting computer database problems by telephone and via a global computer; technical support services, namely, troubleshooting of problems with web sites and online services; technical support services, namely, troubleshooting in the nature of diagnosing computer hardware, software, electronic data storage and security, and electronic database problem
09 - Appareils et instruments scientifiques et électriques
35 - Publicité; Affaires commerciales
38 - Services de télécommunications
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable computer software for marketing automation; Downloadable computer software for creating, deploying and managing information technology (IT) infrastructure for use in networks, computer systems, data centers, cloud computing and data storage. Marketing services; market research. Electronic transmission of messages for marketing purposes. Providing temporary use of online non-downloadable software for use in the field of marketing automation; providing computer software for marketing automation and marketing campaign management, and technical support related to same; Software as a service platforms featuring technology that enables users to automate and track marketing and advertising campaigns and measure the results of marketing and advertising campaigns; Technical consultation in the fields of computer hardware, software and infrastructure of computer software; computer software installation and maintenance, setup, and configuration services; computer software and systems integration services; Scientific and technological services in the field of artificial intelligence and artificial intelligence software development for others, and research and design relating thereto; Computer systems security services, namely, password reset, removal, multi-factor authentication, and protection; encoding of valuable documents with identification information; fraud protection services, namely, computer security services in the nature of threat analysis for protecting data; Providing temporary use of non-downloadable software for use in sharing data with others; data hosting services; enabling data hosting services, namely, hosting software programs for use in managing, organizing, and sharing data on a computer server on a global computer network and on internal computer networks; enabling data management, namely, technical support services, namely, troubleshooting computer database problems by telephone and via a global computer; technical support services, namely, troubleshooting of problems with web sites and online services; technical support services, namely, troubleshooting in the nature of diagnosing computer hardware, software, electronic data storage and security, and electronic database.
The present disclosure provides techniques for data retrieval using machine learning. One example method includes receiving a plurality of training episodes associated with different environments, wherein each training episode of the plurality of training episodes includes a sequence of states, computing, based on the plurality of training episodes, total counts of a plurality of values in the states, initializing, for each state of the sequence of states in each training episode of the plurality of training episodes, a reward based on the total counts of the plurality of values, and training a reinforcement learning agent using the rewards.
An access graph is constructed based on access policy data from user accounts, data lake buckets, and/or access policy statements from any other location. Access logs are analyzed to determine actual access to the data tables. For a given user role, an initial set of data tables that are actually accessed is generated forming the baseline of data tables for which access privileges are to be maintained. User roles that are similar to the given user role are identified and additional data tables accessed by the similar user roles are added to the initial set of data tables to generate a final set of data tables. Access privileges to the final set of data tables are maintained for the given user role, while access privileges to the remaining data tables may be revoked.
Aspects of the present disclosure relate to watermarks and watermarking techniques for data streaming pipelines. Time stamp and offset timeline data is shared by computing instances along the pipeline to enable improved watermarking of the data stream through the pipeline. The improved watermarks enable better determination of completeness for the data stream and improve materialization of the results. The watermarking techniques can include periodically publishing watermark data by processing units of a vertex, fetching a merged watermark for a vertex by a vertex, and/or watching a data storage for the watermark data for events. Consensus algorithms can be used to maintain consensus among vertices for the watermark data.
A transcript of an audio conversation between multiple users (e.g., two users) is generated. The transcript is displayed in real time within a VR environment as the conversation takes place. A virtual selection tool is displayed within the VR environment to allow for a selection of different portions of the transcript. In addition, a virtual keyboard and or virtual panels with characters may be displayed and the virtual selection tool may be used to make selections from these displays as well. These selections are used to generate new text. The new text may form part of a user's notes of the conversation or an entry for a text field within the VR environment.
G06T 17/00 - Modélisation tridimensionnelle [3D] pour infographie
G06F 3/04815 - Interaction s’effectuant dans un environnement basé sur des métaphores ou des objets avec un affichage tridimensionnel, p. ex. modification du point de vue de l’utilisateur par rapport à l’environnement ou l’objet
G06F 3/04842 - Sélection des objets affichés ou des éléments de texte affichés
G06F 3/04886 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] utilisant des caractéristiques spécifiques fournies par le périphérique d’entrée, p. ex. des fonctions commandées par la rotation d’une souris à deux capteurs, ou par la nature du périphérique d’entrée, p. ex. des gestes en fonction de la pression exercée enregistrée par une tablette numérique utilisant un écran tactile ou une tablette numérique, p. ex. entrée de commandes par des tracés gestuels par partition en zones à commande indépendante de la surface d’affichage de l’écran tactile ou de la tablette numérique, p. ex. claviers virtuels ou menus
G10L 15/26 - Systèmes de synthèse de texte à partir de la parole
81.
FINITE RANK DEEP KERNEL LEARNING WITH LINEAR COMPUTATIONAL COMPLEXITY
Certain aspects of the present disclosure provide techniques for performing finite rank deep kernel learning. In one example, a method for performing finite rank deep kernel learning includes receiving a training dataset; forming a set of embeddings by subjecting the training dataset to a deep neural network; forming, from the set of embeddings, a plurality of dot kernels; linearly combining the plurality of dot kernels to form a composite kernel for a Gaussian process; receiving live data from an application; and predicting a plurality of values and a plurality of uncertainties associated with the plurality of values simultaneously using the composite kernel.
A method including receiving an input and embedding the input into a first data structure that defines first relationships among images and texts. The method also includes comparing the first data structure to an index including a second data structure that defines second relationships among pre-determined texts and pre-determined images. The method also includes returning a subset of images from the pre-determined images. The subset includes those images in the pre-determined images for which matches exist between the first relationships and the second relationships.
G06F 16/583 - 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 16/951 - IndexationTechniques d’exploration du Web
G06F 40/40 - Traitement ou traduction du langage naturel
83.
MACHINE LEARNING MODEL ARCHITECTURE FOR COMBINING NETWORK DATA AND SEQUENTIAL DATA
A method including building a graph data structure storing network data from a relational data structure that stores sequential data describing object identifiers and relationships between the object identifiers. The method also includes generating, from the sequential data, a features matrix for the object identifiers. The method also includes building a machine learning model layer including a long short-term memory neural network (LSTM) programmed to take, as input, the features matrix and to generate, as output, a prediction vector. The method also includes building machine learning model layers including graph convolutional neural network (GCN) layers. The machine learning model layers is programmed to take, as input, the graph data structure and the prediction vector, and generate, as output, a future prediction regarding the sequential data. The method also includes combining, into a machine learning model ensemble, the machine learning model layer and the machine learning model layers.
Aspects of the present disclosure relate to data-backed compensation estimation based on disparate electronic data sources. User data, Client data, External data, and Legal standard data are used to identify “Like Me” entities and to determine degrees of reasonableness of compensation for “Like Me” entities. User data includes various information about the user and the company from which the user is being compensated. Client data includes data about other entities which may be like the user. External data includes data which may factor in adjusting the value or reasonableness of a compensation amount. Legal standards may determine that a compensation amount is or is not reasonable for an entity. Estimates for reasonable compensation may be based on the “Like Me” entities in consideration of external factors and legal standards relevant to reasonableness of the compensation for that entity.
Systems and methods or determining tax recommendations for a taxpayer by using a tax calculation graph to identify tax variables that a taxpayer can control and modify, including a recommendation engine configured to analyze a tax calculation graph which is calculated using tax data of the taxpayer. An identified tax variable can be analyzed by determining nodes of the graph affecting a value of the identified tax variable, providing a user interface enabling at least one modification to the nodes, and determining an effect on the identified tax variable due to the at least one modification.
Certain aspects of the disclosure provide a method for managing users, the method comprising: obtaining a first set of visitor identification records; identifying a subset of outlier visitor identification records within the first set of visitor identification records; creating a second set of visitor identification records including all visitor identification records from the first set of visitor identification records other than the subset of outlier visitor identification records; creating a third set of visitor identification records by applying a first filtering scheme to the second set of visitor identification records; creating a fourth set of visitor identification records by applying a second filtering scheme to the second set of visitor identification records; generating one or more stitched visitor IDs by stitching visitor identification records in the fourth set of visitor identification records; and determining a stitching accuracy probability for each of the one or more stitched visitor IDs.
A method including detecting, in a written electronic communication, an input sentence satisfying a readability metric threshold. The method also includes transforming, by a sentence transformer model, the input sentence to output suggested sentences. The method also includes evaluating the suggested sentences along a set of acceptability criteria. The method also includes determining, based on the evaluating, that the set of acceptability criteria is satisfied. The method also includes modifying, based on determining that the set of acceptability criteria is satisfied, the written electronic communication with the suggested sentences to obtain a modified written electronic communication. The method also includes returning the modified written electronic communication.
A computing system generates a plurality of training data sets for generating the NLP model. The computing system trains a teacher network to extract and classify tokens from a document. The training includes a pre-training stage where the teacher network is trained to classify generic data in the plurality of training data sets and a fine-tuning stage where the teacher network is trained to classify targeted data in the plurality of training data sets. The computing system trains a student network to extract and classify tokens from a document by distilling knowledge learned by the teacher network during the fine-tuning stage from the teacher network to the student network. The computing system outputs the NLP model based on the training. The computing system causes the NLP model to be deployed in a remote computing environment.
A method is provided for authenticating a user. A request to access a resource is received from a user agent. A cookie associated with the request is identified. The cookie includes a first subset of data that was previously used to authenticate the user. The cookie is validated based on the first subset of the data. Responsive to validating the cookie, a second subset of the data is retrieved from server-side storage. A risk decision is generated based on the first subset and the second subset. When the risk decision meets a threshold, the user is authenticated without presenting an authentication challenge, and access to the resources permitted.
A method implements efficient real time serving of ensemble models. The method includes receiving an input and processing the input with an abridged model to generate a set of component scores and an abridged score. The method further includes processing the set of component scores with a deviation threshold to select one of the abridged score and an ensemble score as an output and presenting the output.
Methods and computer systems are provided for generating synthetic data. A real vector is generated representing real data. Using a classification model, a first output vector that represents a first class is generated from the real vector. the real vector is mutated to generate a counterfactual vector. using the classification model, the second output vector that represents a second class is generated from the counterfactual vector. the counterfactual vector is then mutated to generate a synthetic vector. Using the classification model, a third output vector that corresponds to the first class is generated from the synthetic vector, synthetic data is generated from the synthetic vector.
09 - Appareils et instruments scientifiques et électriques
37 - Services de construction; extraction minière; installation et réparation
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Marketing services; market research Electronic transmission of messages for marketing purposes (Based on Use in Commerce) Downloadable computer software for marketing automation; (Based on Intent To Use) Downloadable computer software for creating, deploying and managing information technology (IT) infrastructure for use in networks, computer systems, data centers, cloud computing and data storage Installation and maintenance of computer hardware; IT infrastructure consulting services relating to installation and maintenance of computer systems (Based on Use in Commerce) Providing temporary use of online non-downloadable software for marketing automation; Providing temporary use of online non-downloadable computer software for marketing automation and marketing campaign management, and technical support related to same; Software as a service platforms featuring software that enables users to automate and track marketing and advertising campaigns and measure the results of marketing and advertising campaigns; (Based on Intent To Use) Technical consultation in the technology fields of computer hardware, software and infrastructure of computer software; computer software installation and maintenance, setup, and configuration services; computer software and systems integration services; Scientific and technological services, namely research, design, and development in the field of artificial intelligence and artificial intelligence software; industrial analysis and research services in the field of computer systems; Computer systems security services, namely, password reset, removal, multi-factor authentication, and protection; computer programing services, namely, encoding of valuable documents with identification information; fraud protection services, namely, computer security services in the nature of threat analysis for protecting data; Providing temporary use of non-downloadable software for use in sharing data with others; data hosting services; enabling data hosting services, namely, hosting software programs for use in managing, organizing, and sharing data on a computer server on a global computer network and on internal computer networks; enabling data management, namely, technical support services, namely, troubleshooting computer database problems by telephone and via a global computer; technical support services, namely, troubleshooting of web and database application problems with web sites and online services; technical support services, namely, troubleshooting in the nature of diagnosing computer hardware, software, electronic data storage and security, and electronic database problem
09 - Appareils et instruments scientifiques et électriques
41 - Éducation, divertissements, activités sportives et culturelles
Produits et services
(Based on Use in Commerce) Conducting trade shows and trade fairs in the fields of accounting, tax, financial management, personal finance, productivity, business development, business management, economics, leadership, email marketing, marketing automation, technology, artificial intelligence, computer software, computer hardware, software as a service (SaaS) technology, cloud computing, and mobile computing; Conducting trade shows and trade fairs in the fields of software engineering, software interoperability, and open source software; (Based on Intent To Use) Conducting online trade shows and trade fairs via webcasts, all in the fields of accounting, tax, financial management, personal finance, productivity, business development, business management, economics, leadership, email marketing, marketing automation, technology, artificial intelligence, computer software, computer hardware, software as a service (SaaS) technology, cloud computing, and mobile computing; arranging and conducting special events for business purposes; Conducting online trade shows and trade fairs via webcasts, all in the fields of software engineering, software interoperability, and open source software Downloadable computer software, namely, an application allowing conference attendees to view event and conference schedules, explore conference sessions, obtain conference speaker information and network with other conference attendees via a mobile device; Downloadable computer software, namely, an application allowing conference attendees to sign up for event and conference schedules via a mobile device (Based on Use in Commerce) Arranging and conducting educational conferences in the field of accounting, tax, financial management, personal and business finance, productivity, business development, personal development, business management and organization, client management, economics, personnel and human resources, leadership, communications and marketing, email marketing, marketing automation, technology, artificial intelligence, computer software, computer hardware, software as a service (SaaS) technology, cloud computing, and mobile computing; educational services, namely, conducting classes, seminars, conferences, and workshops in the fields of accounting, tax, financial management, personal finance, productivity, business development, business management, economics, leadership, email marketing, marketing automation, technology, artificial intelligence, computer software, computer hardware, software as a service (SaaS) technology, cloud computing, and mobile computing, and distributing course materials in connection therewith; continuing education services, namely, providing continuing professional education seminars in the field of accounting; Educational services, namely, conducting classes, seminars, conferences and workshops in the fields of software engineering, software interoperability, and open source software; (Based on Intent To Use) Educational services, namely, conducting webcasts in the fields of accounting, tax, financial management, personal finance, productivity, business development, business management, economics, leadership, email marketing, marketing automation, technology, artificial intelligence, computer software, computer hardware, software as a service (SaaS) technology, cloud computing, and mobile computing, and distributing course materials in connection therewith; Educational services, namely, conducting webcasts in the fields of software engineering, software interoperability, and open source software
09 - Appareils et instruments scientifiques et électriques
35 - Publicité; Affaires commerciales
41 - Éducation, divertissements, activités sportives et culturelles
Produits et services
(1) Downloadable computer software, namely, an application allowing conference attendees to view event and conference schedules, explore conference sessions, obtain conference speaker information and network with other conference attendees via a mobile device (1) Conducting trade shows and trade fairs in the fields of accounting, tax, financial management, personal finance, productivity, business development, business management, economics, leadership, email marketing, marketing automation, technology, artificial intelligence, computer software, computer hardware, software as a service (SaaS) technology, cloud computing, and mobile computing; conducting online trade shows and trade fairs via webcasts, all in the fields of accounting, tax, financial management, personal finance, productivity, business development, business management, economics, leadership, email marketing, marketing automation, technology, artificial intelligence, computer software, computer hardware, software as a service (SaaS) technology, cloud computing, and mobile computing; arranging and conducting special events for business purposes
(2) Arranging and conducting educational conferences; educational services, namely, conducting classes, seminars, conferences, workshops and webcasts in the fields of accounting, tax, financial management, personal finance, productivity, business development, business management, economics, leadership, email marketing, marketing automation, technology, artificial intelligence, computer software, computer hardware, software as a service (SaaS) technology, cloud computing, and mobile computing, and distributing course materials in connection therewith; continuing education services, namely, providing continuing professional education seminars in the field of accounting; entertainment in the nature of live musical performances; organizing cultural and arts events
Systems and methods are disclosed for model based document image enhancement. Instead of requiring paired dirty and clean images for training a model to clean document images (which may cause privacy concerns), two models are trained on the unpaired images such that only the dirty images are accessed or only the clean images are accessed at one time. One model is a first implicit model to translate the dirty images from a source space to a latent space, and the other model is a second implicit model to translate the images from the latent space to clean images in a target space. The second implicit model is trained based on translating electronic document images in the target space to the latent space. In some implementations, the implicit models are diffusion models, such as denoising diffusion implicit models based on solving ordinary differential equations.
G06V 10/70 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique
Systems and methods for inferring recommendations and experiences for anonymous users of an online website are disclosed. Anonymous users of the online website are assigned anonymous user identifiers, and the browsing activity of the anonymous users is converted into features and aggregated over time. The anonymous users' interactions are monitored and used to generate labels that are combined with the feature dataset to produce a training dataset which is used to train a machine learning model. The browsing activity of an anonymous user may be converted into features and aggregated over time and fed into the trained machine learning model from which personalized experiences and recommendations may be generated and provided to the anonymous user.
A method including receiving a selected domain from a set of domains. The method also includes selecting, based on the selected domain, a selected machine learning model from among a set of machine learning models. Each of the machine learning models is configured to receive, as input, a dataset of past time-dependent data and generate, as output, a corresponding predicted quality measure for each of a number of time periods. The selected machine learning model is trained using training data generated for an entity corresponding to the domain. The method also includes executing the selected machine learning model on the dataset to generate predicted quality measures for the time periods. The method also includes generating, using the predicted quality measures, a schedule for executing a computer process. The method also includes presenting the schedule.
A method and system assists train a classifier model with a machine learning process. The method and system trains the classifier with a labeled training set and with an unlabeled training set. The method and system trains the classifier model to correctly classify data items that fall within a distribution of the labeled training set. The method and system trains the classifier to indicate a lack of confidence in classification for data items that do not fall within the distribution of the labeled training set.
Disambiguity in large language models (LLMs) includes receiving an original query in a user interface, generating an ambiguity query from the original query, and sending, via an application programming interface (API) of an LLM, the ambiguity query to the LLM. The ambiguity query includes the original query and training the LLM to recognize ambiguities. The method further includes receiving, via the API and responsive to the ambiguity query, a binary response and detecting, based at least in part on the binary response, the original query as ambiguous. Disambiguity may include detecting an ambiguity location in the original query using perturbed queries and the LLM.
The present disclosure provides techniques for recommending vendors using machine learning models. One example method includes generating a set of features based on a sequence of recurring transactions associated with a user, a payee, and a transaction amount, predicting simultaneously, using a machine learning model based on the set of features, that the sequence of recurring transactions will continue with a subsequent transaction and a time window within which the subsequent transaction of the sequence will occur, receiving electronic transaction data indicative of a transaction associated with the user, the payee, and the transaction amount, indicating that the transaction is the subsequent transaction in the sequence of recurring transactions based on the transaction and the prediction, and automatically creating one or more future recurring transactions based on the indication.
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
