Provided is a process including: writing, with a computing system, a first plurality of classes using object-oriented modelling of modelling methods; writing, with the computing system, a second plurality of classes using object-oriented modelling of governance; scanning, with the computing system, a set of libraries collectively containing both modelling object classes among the first plurality of classes and governance classes among the second plurality of classes to determine class definition information; using, with the computing system, at least some of the class definition information to produce object manipulation functions, wherein the object manipulation functions allow a governance system to access methods and attributes of classes among first plurality of classes or the second plurality of classes to manipulate objects of at least some of the modelling object classes; and using at least some of the class definition information to effectuate access to the object manipulation functions.
G06F 16/25 - Systèmes d’intégration ou d’interfaçage impliquant les systèmes de gestion de bases de données
G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
G06F 18/21 - Conception ou mise en place de systèmes ou de techniquesExtraction de caractéristiques dans l'espace des caractéristiquesSéparation aveugle de sources
G06F 18/243 - Techniques de classification relatives au nombre de classes
G06F 30/20 - Optimisation, vérification ou simulation de l’objet conçu
G06N 5/04 - Modèles d’inférence ou de raisonnement
G06N 20/20 - Techniques d’ensemble en apprentissage automatique
G06Q 10/067 - Modélisation d’entreprise ou d’organisation
G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
G06Q 10/0637 - Gestion ou analyse stratégiques, p. ex. définition d’un objectif ou d’une cible pour une organisationPlanification des actions en fonction des objectifsAnalyse ou évaluation de l’efficacité des objectifs
G06Q 10/0639 - Analyse des performances des employésAnalyse des performances des opérations d’une entreprise ou d’une organisation
G06Q 30/016 - Fourniture d’une assistance aux clients, p. ex. pour assister un client dans un lieu commercial ou par un service d’assistance après-vente
G06Q 30/0202 - Prédictions ou prévisions du marché pour les activités commerciales
Provided is a process including: writing modelling-object classes using object-oriented modelling of the modelling methods, the modelling-object classes being members of a set of class libraries; writing quality-management classes using object-oriented modelling of quality management, the quality-management classes being members of the set of class libraries; scanning modelling-object classes in the set of class libraries to determine modelling-object class definition information; scanning quality-management classes in the set of class libraries to determine quality-management class definition information; using the modelling-object class definition information and the quality-management class definition information to produce object manipulation functions that allow a quality management system to access methods and attributes of modelling-object classes to manipulate objects of the modelling-object classes; and using the modelling-object class definition information and the quality-management class definition information to produce access to the object manipulation functions.
G06N 5/04 - Modèles d’inférence ou de raisonnement
G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
G06Q 10/0637 - Gestion ou analyse stratégiques, p. ex. définition d’un objectif ou d’une cible pour une organisationPlanification des actions en fonction des objectifsAnalyse ou évaluation de l’efficacité des objectifs
G06Q 10/0639 - Analyse des performances des employésAnalyse des performances des opérations d’une entreprise ou d’une organisation
G06Q 10/067 - Modélisation d’entreprise ou d’organisation
G06Q 30/016 - Fourniture d’une assistance aux clients, p. ex. pour assister un client dans un lieu commercial ou par un service d’assistance après-vente
G06Q 30/0202 - Prédictions ou prévisions du marché pour les activités commerciales
G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
G06F 18/21 - Conception ou mise en place de systèmes ou de techniquesExtraction de caractéristiques dans l'espace des caractéristiquesSéparation aveugle de sources
G06F 18/243 - Techniques de classification relatives au nombre de classes
Provided is a process including: obtaining, for a plurality of entities, entity logs, wherein: the entity logs comprise events involving the entities, a first subset of the events are actions by the entities, at least some of the actions by the entities are targeted actions, and the events are labeled according to an ontology of events having a plurality of event types; training, with one or more processors, based on the entity logs, a predictive machine learning model to predict whether an entity characterized by a set of inputs to the model will engage in a targeted action in a given duration of time in the future; and storing the trained predictive machine learning model in memory.
Provided is a process, including: obtaining a first training dataset of subject-entity records; training a first machine-learning model on the first training dataset; forming virtual subject-entity records by appending members of a set of candidate action sequences to time-series of at least some of the subject-entity records; forming a second training dataset by labeling the virtual subject-entity records with predictions of the first machine-learning model; and training a second machine-learning model on the second training dataset.
In some implementations, an event timeline that includes one or more interactions between a customer and a supplier may be determined. A starting value may be assigned to individual events in the event timeline. A sub-sequence comprising a portion of the event timeline that includes at least one reference event may be selected. A classifier may be used to determine a previous relative value for a previous event that occurred before the reference event and to determine a next relative value for a next event that occurred after the reference event until all events in the event timeline have been processed. The events in the event timeline may be traversed and a monetized value index assigned to individual events in the event timeline.
G06F 18/2321 - Techniques non hiérarchiques en utilisant les statistiques ou l'optimisation des fonctions, p. ex. modélisation des fonctions de densité de probabilité
G06F 18/2415 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur des modèles paramétriques ou probabilistes, p. ex. basées sur un rapport de vraisemblance ou un taux de faux positifs par rapport à un taux de faux négatifs
G06N 7/01 - Modèles graphiques probabilistes, p. ex. réseaux probabilistes
G06N 5/025 - Extraction de règles à partir de données
G06F 8/35 - Création ou génération de code source fondée sur un modèle
Provided is a process, including: obtaining a first training dataset, training a first machine-learning model on the first training dataset, obtaining a set of candidate question sequences, forming virtual subject-entity records, forming a second training dataset, training a second machine-learning model, and storing the adjusted parameters of the second machine-learning model in memory.
Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.
G06F 15/16 - Associations de plusieurs calculateurs numériques comportant chacun au moins une unité arithmétique, une unité programme et un registre, p. ex. pour le traitement simultané de plusieurs programmes
G06Q 10/063 - Recherche, analyse ou gestion opérationnelles
G06F 21/62 - Protection de l’accès à des données via une plate-forme, p. ex. par clés ou règles de contrôle de l’accès
Provided is process, including: obtaining interaction-event records; determining, based on at least some of the interaction-event records, sets of event-risk scores, wherein: at least some respective event-risk scores are indicative of an effective of a respective risk ascribed by a first entity to a respective aspect of a second entity; and at least some respective event-risk scores are based on both: respective contributions of respective corresponding events to a subsequent event, and a risk ascribed to a subsequent event; and storing the sets of event-risk scores in memory.
Provided is a process that affords out-of-band authentication for confirmation of physical access or when a device utilized for out-of-band authentication lacks connectivity to a network. An asymmetric cryptographic key-pair is established, a first device obtaining a key operable to decrypt data. A remote server obtaining a key operable to encrypt data and associating that key with an identifier of an identity or account associated with a user. An access attempt from the second device is received in association with the identifier of the identity associated with the user. A notification including data encrypted by the encryption key is generated by the remote server and transmitted to the second device. The first device obtains the notification data from the second device and decrypts the data to determine a notification response which is returned to the remote server for verification to permit or deny the access attempt of the second device.
Provided is a process, including: obtaining a first training dataset, training a first machine-learning model on the first training dataset, obtaining a set of candidate question sequences, forming virtual subject-entity records, forming a second training dataset, training a second machine-learning model, and storing the adjusted parameters of the second machine-learning model in memory.
Disclosed herein are methods, systems, and processes for distributed logging for securing non-repudiable transactions. Credentials, request information, response information, and action items generated and received by a requesting computing system and a responding computing system, and transmitted between the requesting computing system and the responding computing system are separately recorded and stored in a requestor log maintained by the requesting computing system and in a responder log maintained by the responding computing system.
G06F 21/00 - Dispositions de sécurité pour protéger les calculateurs, leurs composants, les programmes ou les données contre une activité non autorisée
14.
AUDITABLE SECURE REVERSE ENGINEERING PROOF MACHINE LEARNING PIPELINE AND METHODS
Provided is a process including: searching code of a machine-learning pipeline to find a first and a second object code sequences performing similar tasks; modifying the code of the machine learning pipeline by inserting a third object code sequence into the code of the machine learning pipeline, the third code sequence being operable to pass control to the first object code sequence; inserting a branch at the end of the first code sequence, the branch being operable to: pass control, upon detection of a first predefined condition, to an instruction following the first object code sequence, and to pass control, upon detection of a second predefined condition, to an instruction following the third object code sequence; and wherein the third code sequence is executed in place of the second object sequence without affecting completion of the tasks.
G06F 21/75 - Protection de composants spécifiques internes ou périphériques, où la protection d'un composant mène à la protection de tout le calculateur pour assurer la sécurité du calcul ou du traitement de l’information par inhibition de l’analyse de circuit ou du fonctionnement, p. ex. pour empêcher l'ingénierie inverse
Provided is a process including: receiving a data token to be passed from a first node to a second node; retrieving machine learning model attributes from a collection of one or more of the sub-models of a federated machine-learning model; determining based on the machine learning model attributes, that the data token is learning relevant to members of the collection of one or more of the sub-models and, in response, adding the data toke to a training set to be used by at least some members of the collection of one or more of the sub-models; determining a collection of data tokens to transmit from the second node to a third node of the set of nodes participating in a federated machine-learning model; and transmitting the collection of data tokens.
G06N 5/02 - Représentation de la connaissanceReprésentation symbolique
G06F 16/28 - Bases de données caractérisées par leurs modèles, p. ex. des modèles relationnels ou objet
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuéesArchitectures de systèmes de bases de données distribuées à cet effet
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
H04L 29/06 - Commande de la communication; Traitement de la communication caractérisés par un protocole
In some implementations, an event timeline that includes one or more interactions between a customer and a supplier may be determined. A starting value may be assigned to individual events in the event timeline. A sub-sequence comprising a portion of the event timeline that includes at least one reference event may be selected. A classifier may be used to determine a previous relative value for a previous event that occurred before the reference event and to determine a next relative value for a next event that occurred after the reference event until all events in the event timeline have been processed. The events in the event timeline may be traversed and a monetized value index assigned to individual events in the event timeline.
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G06Q 30/02 - MarketingEstimation ou détermination des prixCollecte de fonds
In some implementations, a computing device determines an event timeline that comprises one or more finance-related events associated with a person. A production classifier may be used to determine (i) an individual contribution of each event in the event timeline to a financial capacity of the person and (ii) a first decision regarding whether to extend credit to the person. A bias monitoring classifier may, based on the event timeline, determine a second decision whether to extend credit to the person. The bias monitoring classifier may be trained using pseudo-unbiased data. If a difference between the first decision and the second decision satisfies a threshold, the production classifier may be modified to reduce bias in decisions made by the production classifier.
G06F 18/2321 - Techniques non hiérarchiques en utilisant les statistiques ou l'optimisation des fonctions, p. ex. modélisation des fonctions de densité de probabilité
G06F 18/2415 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur des modèles paramétriques ou probabilistes, p. ex. basées sur un rapport de vraisemblance ou un taux de faux positifs par rapport à un taux de faux négatifs
G06N 5/025 - Extraction de règles à partir de données
G06N 7/01 - Modèles graphiques probabilistes, p. ex. réseaux probabilistes
G06Q 10/067 - Modélisation d’entreprise ou d’organisation
G06Q 30/0201 - Modélisation du marchéAnalyse du marchéCollecte de données du marché
G06F 8/35 - Création ou génération de code source fondée sur un modèle
18.
PREDICTING THE EFFECTIVENESS OF A MARKETING CAMPAIGN PRIOR TO DEPLOYMENT
In some implementations, a computing device may determine, from multiple data sources, multiple event timelines, with each event timeline associated with a customer. Each event in an event timeline represents an interaction between the customer and a vendor of goods and/or services. For N (N>1) marketing campaigns, N augmented timelines may be created for each timeline by augmenting each event timeline with the individual marketing campaigns. Thus, for M (M>1) customers, M×N augmented event timelines may be created. A trained machine learning model may perform an analysis of each augmented event timeline to predict results of executing each marketing campaign. The results may include total predicted revenue and total predicted cost resulting from executing each marketing campaign. A particular marketing campaign from the N marketing campaigns may be selected and execution of one or more marketing events may be initiated.
Provided is a process of modeling methods organized in racks of a machine learning pipeline to facilitate optimization of performance using modelling methods for implementation of machine learning design in an object-oriented modeling (OOM) framework, the process including: writing classes using object-oriented modelling of optimization methods, modelling methods, and modelling racks; writing parameters and hyper-parameters of the modeling methods as attributes as the modeling methods; scanning modelling racks classes to determine first class definition information; selecting a collection of rack and selecting modeling method objects; scanning modelling method classes to determine second class definition information; assigning racks and locations within the racks to modeling method objects; and invoking the class definition information to produce object manipulation functions that allow access the methods and attributes of at least some of the modeling method objects, the manipulation functions being configured to effectuate writing locations within racks and attributes of racks.
Provided is a process including: writing modelling-object classes using object-oriented modelling of the modelling methods, the modelling-object classes being members of a set of class libraries; writing quality-management classes using object-oriented modelling of quality management, the quality-management classes being members of the set of class libraries; scanning modelling-object classes in the set of class libraries to determine modelling-object class definition information; scanning quality-management classes in the set of class libraries to determine quality-management class definition information; using the modelling-object class definition information and the quality-management class definition information to produce object manipulation functions that allow a quality management system to access methods and attributes of modelling-object classes to manipulate objects of the modelling-object classes; and using the modelling-object class definition information and the quality-management class definition information to produce access to the object manipulation functions.
G06N 5/04 - Modèles d’inférence ou de raisonnement
G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
G06Q 10/0637 - Gestion ou analyse stratégiques, p. ex. définition d’un objectif ou d’une cible pour une organisationPlanification des actions en fonction des objectifsAnalyse ou évaluation de l’efficacité des objectifs
G06Q 10/0639 - Analyse des performances des employésAnalyse des performances des opérations d’une entreprise ou d’une organisation
G06Q 10/067 - Modélisation d’entreprise ou d’organisation
G06Q 30/016 - Fourniture d’une assistance aux clients, p. ex. pour assister un client dans un lieu commercial ou par un service d’assistance après-vente
Provided is a process including: writing, with a computing system, a first plurality of classes using object-oriented modelling of modelling methods; writing, with the computing system, a second plurality of classes using object-oriented modelling of governance; scanning, with the computing system, a set of libraries collectively containing both modelling object classes among the first plurality of classes and governance classes among the second plurality of classes to determine class definition information; using, with the computing system, at least some of the class definition information to produce object manipulation functions, wherein the object manipulation functions allow a governance system to access methods and attributes of classes among first plurality of classes or the second plurality of classes to manipulate objects of at least some of the modelling object classes; and using at least some of the class definition information to effectuate access to the object manipulation functions.
G06N 5/04 - Modèles d’inférence ou de raisonnement
G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
G06Q 10/0637 - Gestion ou analyse stratégiques, p. ex. définition d’un objectif ou d’une cible pour une organisationPlanification des actions en fonction des objectifsAnalyse ou évaluation de l’efficacité des objectifs
G06Q 10/0639 - Analyse des performances des employésAnalyse des performances des opérations d’une entreprise ou d’une organisation
G06Q 10/067 - Modélisation d’entreprise ou d’organisation
G06Q 30/016 - Fourniture d’une assistance aux clients, p. ex. pour assister un client dans un lieu commercial ou par un service d’assistance après-vente
G06Q 30/0202 - Prédictions ou prévisions du marché pour les activités commerciales
G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
G06F 18/21 - Conception ou mise en place de systèmes ou de techniquesExtraction de caractéristiques dans l'espace des caractéristiquesSéparation aveugle de sources
G06F 18/243 - Techniques de classification relatives au nombre de classes
Provided is a process of modeling methods organized in racks of a machine learning pipeline to facilitate optimization of performance using modelling methods for implementation of machine learning design in an object-oriented modeling (OOM) framework, the process including: writing classes using object-oriented modelling of optimization methods, modelling methods, and modelling racks; writing parameters and hyper-parameters of the modeling methods as attributes as the modeling methods; scanning modelling racks classes to determine first class definition information; selecting a collection of rack and selecting modeling method objects; scanning modelling method classes to determine second class definition information; assigning racks and locations within the racks to modeling method objects; and invoking the class definition information to produce object manipulation functions that allow access the methods and attributes of at least some of the modeling method objects, the manipulation functions being configured to effectuate writing locations within racks and attributes of racks.
G06N 5/04 - Modèles d’inférence ou de raisonnement
G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
G06Q 10/0637 - Gestion ou analyse stratégiques, p. ex. définition d’un objectif ou d’une cible pour une organisationPlanification des actions en fonction des objectifsAnalyse ou évaluation de l’efficacité des objectifs
G06Q 10/0639 - Analyse des performances des employésAnalyse des performances des opérations d’une entreprise ou d’une organisation
G06Q 10/067 - Modélisation d’entreprise ou d’organisation
G06Q 30/016 - Fourniture d’une assistance aux clients, p. ex. pour assister un client dans un lieu commercial ou par un service d’assistance après-vente
Provided is a process including: writing classes using object-oriented modelling of modeling topics; scanning the classes to determine class definition information; receiving from a subscribing modeling object a request for a subscription to a given modeling topic in a given modeling topic class, the subscription request including a modeling topic filter to select the given modeling topic from a plurality of modeling topics described by the given modeling topic class; registering a modeling topic accessor associated with the subscribing modeling object and a modeling topic mutator associated with the subscribing modeling object; processing, through the modeling topic filter a modeling topic that is accessed through an accessor and is described by the modeling topic class, the modeling topic being received from a modeling publisher object; notifying the subscribing object of the received modeling topic through a registered modeling topic listener; and mutating the received modeling topic.
G06N 5/04 - Modèles d’inférence ou de raisonnement
G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
G06Q 10/0637 - Gestion ou analyse stratégiques, p. ex. définition d’un objectif ou d’une cible pour une organisationPlanification des actions en fonction des objectifsAnalyse ou évaluation de l’efficacité des objectifs
G06Q 10/0639 - Analyse des performances des employésAnalyse des performances des opérations d’une entreprise ou d’une organisation
G06Q 10/067 - Modélisation d’entreprise ou d’organisation
G06Q 30/016 - Fourniture d’une assistance aux clients, p. ex. pour assister un client dans un lieu commercial ou par un service d’assistance après-vente
Provided is a process including: obtaining, for a plurality of entities, entity logs, wherein: the entity logs comprise events involving the entities, a first subset of the events are actions by the entities, at least some of the actions by the entities are targeted actions, and the events are labeled according to an ontology of events having a plurality of event types; training, with one or more processors, based on the entity logs, a predictive machine learning model to predict whether an entity characterized by a set of inputs to the model will engage in a targeted action in a given geographic locale in the future; and storing the training the trained predictive machine learning model in memory.
Provided is a process including: obtaining, for a plurality of entities, entity logs, wherein: the entity logs comprise events involving the entities, a first subset of the events are actions by the entities, at least some of the actions by the entities are targeted actions, and the events are labeled according to an ontology of events having a plurality of event types; training, with one or more processors, based on the entity logs, a predictive machine learning model to predict whether an entity characterized by a set of inputs to the model will engage in a targeted action in a given duration of time in the future; and storing the trained predictive machine learning model in memory.
Provided is process, including: obtaining interaction-event records; determining, based on at least some of the interaction-event records, sets of event-risk scores, wherein: at least some respective event-risk scores are indicative of an effective of a respective risk ascribed by a first entity to a respective aspect of a second entity; and at least some respective event-risk scores are based on both: respective contributions of respective corresponding events to a subsequent event, and a risk ascribed to a subsequent event; and storing the sets of event-risk scores in memory.
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G06Q 40/02 - Opérations bancaires, p. ex. calcul d'intérêts ou tenue de compte
Provided is a process including: obtaining, for a plurality of entities, entity logs, wherein: the entity logs comprise events involving the entities, a first subset of the events are actions by the entities, at least some of the actions by the entities are targeted actions, and the events are labeled according to an ontology of events having a plurality of event types; training, with one or more processors, based on the entity logs, a predictive machine learning model to predict whether an entity characterized by a set of inputs to the model will engage in a targeted action in a given geographic locale in the future; and storing the training the trained predictive machine learning model in memory.
Provided is a process, including: obtaining a first training dataset, training a first machine-learning model on the first training dataset, obtaining a set of candidate question sequences, forming virtual subject-entity records, forming a second training dataset, training a second machine-learning model, and storing the adjusted parameters of the second machine-learning model in memory.
Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.
G06F 21/57 - Certification ou préservation de plates-formes informatiques fiables, p. ex. démarrages ou arrêts sécurisés, suivis de version, contrôles de logiciel système, mises à jour sécurisées ou évaluation de vulnérabilité
G06N 5/04 - Modèles d’inférence ou de raisonnement
H04L 67/10 - Protocoles dans lesquels une application est distribuée parmi les nœuds du réseau
30.
MULTI-STAGE MACHINE-LEARNING MODELS TO CONTROL PATH-DEPENDENT PROCESSES
Provided is a process, including: obtaining a first training dataset of subject-entity records; training a first machine-learning model on the first training dataset; forming virtual subject-entity records by appending members of a set of candidate action sequences to time-series of at least some of the subject-entity records; forming a second training dataset by labeling the virtual subject-entity records with predictions of the first machine-learning model; and training a second machine-learning model on the second training dataset.
Provided is a process, including: obtaining a first training dataset of subject-entity records; training a first machine-learning model on the first training dataset; forming virtual subject-entity records by appending members of a set of candidate action sequences to time-series of at least some of the subject-entity records; forming a second training dataset by labeling the virtual subject-entity records with predictions of the first machine-learning model; and training a second machine-learning model on the second training dataset.
Disclosed herein are methods, systems, and processes for distributed logging for securing non-repudiable transactions. Credentials, request information, response information, and action items generated and received by a requesting computing system and a responding computing system, and transmitted between the requesting computing system and the responding computing system are separately recorded and stored in a requestor log maintained by the requesting computing system and in a responder log maintained by the responding computing system.
G06F 21/00 - Dispositions de sécurité pour protéger les calculateurs, leurs composants, les programmes ou les données contre une activité non autorisée
H04L 29/06 - Commande de la communication; Traitement de la communication caractérisés par un protocole
G06F 21/62 - Protection de l’accès à des données via une plate-forme, p. ex. par clés ou règles de contrôle de l’accès
Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.
G06F 15/16 - Associations de plusieurs calculateurs numériques comportant chacun au moins une unité arithmétique, une unité programme et un registre, p. ex. pour le traitement simultané de plusieurs programmes
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G06F 21/62 - Protection de l’accès à des données via une plate-forme, p. ex. par clés ou règles de contrôle de l’accès
Provided is process, including: obtaining interaction-event records; determining, based on at least some of the interaction-event records, sets of event-risk scores, wherein: at least some respective event-risk scores are indicative of an effective of a respective risk ascribed by a first entity to a respective aspect of a second entity; and at least some respective event-risk scores are based on both: respective contributions of respective corresponding events to a subsequent event, and a risk ascribed to a subsequent event; and storing the sets of event-risk scores in memory.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Software as a service (SaaS) services featuring software for improving business processes, sales, and service by using machine learning, natural language processing, data integration, data manipulation, data analysis, and development of predictive systems; computer software consulting; customized software development and deployment services, namely, developing and installing customized software and machine learning models for others; project management services in the field of software design and implementation; consulting services in the field of artificial intelligence and data management software; consulting services in the field of artificial intelligence, software and data management software, namely, consulting with others regarding how artificial intelligence software and data management software may be used to analyze business intelligence, analyze business risk, predict consumer and market trends, and to prepare reports on said subjects that can be disseminated via electronic communication networks
36.
Distributed logging for securing non-repudiable multi-party transactions
Disclosed herein are methods, systems, and processes for distributed logging for securing non-repudiable transactions. Credentials, request information, response information, and action items generated and received by a requesting computing system and a responding computing system, and transmitted between the requesting computing system and the responding computing system are separately recorded and stored in a requestor log maintained by the requesting computing system and in a responder log maintained by the responding computing system.
G06F 21/00 - Dispositions de sécurité pour protéger les calculateurs, leurs composants, les programmes ou les données contre une activité non autorisée
H04L 29/06 - Commande de la communication; Traitement de la communication caractérisés par un protocole
G06F 21/62 - Protection de l’accès à des données via une plate-forme, p. ex. par clés ou règles de contrôle de l’accès
Provided is process, including: obtaining interaction-event records; determining, based on at least some of the interaction-event records, sets of event-risk scores, wherein: at least some respective event-risk scores are indicative of an effective of a respective risk ascribed by a first entity to a respective aspect of a second entity; and at least some respective event-risk scores are based on both: respective contributions of respective corresponding events to a subsequent event, and a risk ascribed to a subsequent event; and storing the sets of event-risk scores in memory.
Provided is a process, including: obtaining a first training dataset of subject-entity records; training a first machine-learning model on the first training dataset; forming virtual subject-entity records by appending members of a set of candidate action sequences to time-series of at least some of the subject-entity records; forming a second training dataset by labeling the virtual subject-entity records with predictions of the first machine-learning model; and training a second machine-learning model on the second training dataset.
G06F 15/18 - dans lesquels un programme est modifié en fonction de l'expérience acquise par le calculateur lui-même au cours d'un cycle complet; Machines capables de s'instruire (systèmes de commande adaptatifs G05B 13/00;intelligence artificielle G06N)
09 - Appareils et instruments scientifiques et électriques
36 - Services financiers, assurances et affaires immobilières
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(1) Computer software for improving business processes by using machine learning, natural language processing, data integration, data manipulation, data analysis, and predictive systems in the field of artificial intelligence and data management (1) Financial analysis consultation services; Financial forecasting; Financial analysis and research services; Financial analysis and preparation of reports relating thereto; Business credit reporting services; Credit consultation; Credit counseling services; Credit inquiry and consultation; Credit rating services; Evaluation of credit scores and credit worthiness of companies and individuals
(2) Software as a service (SAAS) in the field of artificial intelligence based software products for pattern discovery, recognition, classification, segmentation and visualization of all sectors of business data for use in improving business decisions and sales; computer software consulting; customized software development and deployment services, namely, developing and installing customized software for others; project management services in the field of software design and implementation; consulting services in the field of artificial intelligence and data management software; consulting services in the field of artificial intelligence software and data management software, namely, consulting with others regarding how artificial intelligence software and data management software may be used to analyze business intelligence, analyze business risk, predict consumer and market trends
09 - Appareils et instruments scientifiques et électriques
36 - Services financiers, assurances et affaires immobilières
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(1) Computer software for improving commercial and personal banking processes, sales, and customer service by enabling machine learning, natural language processing, data integration, ontology development, data manipulation, data analysis and aggregation, risk analysis, credit analysis, and regulatory compliance (1) Providing financial risk assessment services to financial institutions to assess actual and potential business, products, services, regulatory compliance, and risk management; business credit reporting services; Evaluation of credit scores and credit worthiness of companies and individuals
(2) SaaS featuring software for improving banking processes, products, services, regulatory compliance, risk management by using machine language, natural language processing, data integration, data manipulation, and data analysis; computer software consulting; customized software development and deployment services, namely, developing and installing customized software for others; consulting services in the field of artificial intelligence and data management software; creating searchable databases of information and ontologies of knowledge applied to banking product, services, and banking; consulting services in the field of artificial intelligence software and data management software, namely, consulting with others regarding how artificial intelligence software and data management software may be used to analyze product, service, and process offerings in commercial and personal banking, software as a service (SaaS) provider in the field of preparing and exporting reports about artificial intelligence that can be disseminated via electronic communication networks
09 - Appareils et instruments scientifiques et électriques
36 - Services financiers, assurances et affaires immobilières
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(1) Computer software for improving commercial and personal banking processes, sales, and customer service by enabling machine learning, natural language processing, data integration, ontology development, data manipulation, data analysis and aggregation, risk analysis, credit analysis, and regulatory compliance (1) Providing financial risk assessment services to financial institutions to assess actual and potential business, products, services, regulatory compliance, and risk management; business credit reporting services; Evaluation of credit scores and credit worthiness of companies and individuals
(2) SaaS featuring software for improving banking processes, products, services, regulatory compliance, risk management by using machine language, natural language processing, data integration, data manipulation, and data analysis; computer software consulting; customized software development and deployment services, namely, developing and installing customized software for others; consulting services in the field of artificial intelligence and data management software; creating searchable databases of information and ontologies of knowledge applied to banking product, services, and banking; consulting services in the field of artificial intelligence software and data management software, namely, consulting with others regarding how artificial intelligence software and data management software may be used to analyze product, service, and process offerings in commercial and personal banking, software as a service (SaaS) provider in the field of preparing and exporting reports about artificial intelligence that can be disseminated via electronic communication networks
09 - Appareils et instruments scientifiques et électriques
36 - Services financiers, assurances et affaires immobilières
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(1) Computer software for improving commercial and personal banking processes, sales, and customer service by enabling machine learning, natural language processing, data integration, ontology development, data manipulation, data analysis and aggregation, risk analysis, credit analysis, and regulatory compliance (1) Providing financial risk assessment services to financial institutions to assess actual and potential business, products, services, regulatory compliance, and risk management; business credit reporting services; Evaluation of credit scores and credit worthiness of companies and individuals
(2) SaaS featuring software for improving banking processes, products, services, regulatory compliance, risk management by using machine language, natural language processing, data integration, data manipulation, and data analysis; computer software consulting; customized software development and deployment services, namely, developing and installing customized software for others; consulting services in the field of artificial intelligence and data management software; creating searchable databases of information and ontologies of knowledge applied to banking product, services, and banking; consulting services in the field of artificial intelligence software and data management software, namely, consulting with others regarding how artificial intelligence software and data management software may be used to analyze product, service, and process offerings in commercial and personal banking, software as a service (SaaS) provider in the field of preparing and exporting reports about artificial intelligence that can be disseminated via electronic communication networks
43.
Business artificial intelligence management engine
In some implementations, an event timeline that includes one or more interactions between a customer and a supplier may be determined. A starting value may be assigned to individual events in the event timeline. A sub-sequence comprising a portion of the event timeline that includes at least one reference event may be selected. A classifier may be used to determine a previous relative value for a previous event that occurred before the reference event and to determine a next relative value for a next event that occurred after the reference event until all events in the event timeline have been processed. The events in the event timeline may be traversed and a monetized value index assigned to individual events in the event timeline.
G06Q 30/02 - MarketingEstimation ou détermination des prixCollecte de fonds
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
In some implementations, a computing device determines an event timeline that comprises one or more finance-related events associated with a person. A production classifier may be used to determine (i) an individual contribution of each event in the event timeline to a financial capacity of the person and (ii) a first decision regarding whether to extend credit to the person. A bias monitoring classifier may, based on the event timeline, determine a second decision whether to extend credit to the person. The bias monitoring classifier may be trained using pseudo-unbiased data. If a difference between the first decision and the second decision satisfies a threshold, the production classifier may be modified to reduce bias in decisions made by the production classifier.
G06N 5/02 - Représentation de la connaissanceReprésentation symbolique
G06Q 30/02 - MarketingEstimation ou détermination des prixCollecte de fonds
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
SaaS featuring software improving business processes by using machine language, natural language processing, data integration, data manipulation, data analysis, and predictive systems; computer software consulting; customized software development and deployment services, namely, developing and installing customized software for others; project management services in the field of software design and implementation; consulting services in the field of artificial intelligence and data management software; consulting services in the field of artificial intelligence software and data management software, namely, consulting with others regarding how artificial intelligence software and data management software may be used to analyze business intelligence, analyze business risk, predict consumer and market trends, and to prepare reports on said subjects that can be disseminated via electronic communication networks
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Computer software for improving business processes by using machine learning, natural language processing, data integration, data manipulation, data analysis, and predictive systems SaaS featuring software improving business processes by using machine language, natural language processing, data integration, data manipulation, data analysis, and predictive systems; computer software consulting; customized software development and deployment services, namely, developing and installing customized software for others; project management services in the field of software design and implementation; consulting services in the field of artificial intelligence and data management software; consulting services in the field of artificial intelligence software and data management software, namely, consulting with others regarding how artificial intelligence software and data management software may be used to analyze business intelligence, analyze business risk, predict consumer and market trends, and to prepare reports on said subjects that can be disseminated via electronic communication networks
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Computer software for improving business processes, sales, and service by using machine learning, natural language processing, data integration, data manipulation, data analysis, and development of predictive systems SaaS featuring software for improving business processes, sales, and service by using machine learning, natural language processing, data integration, data manipulation, data analysis, and development of predictive systems; computer software consulting; customized software development and deployment services, namely, developing and installing customized software and machine learning models for others; project management services in the field of software design and implementation; consulting services in the field of artificial intelligence and data management software; consulting services in the field of artificial intelligence software and data management software, namely, consulting with others regarding how artificial intelligence software and data management software may be used to analyze business intelligence, analyze business risk, predict consumer and market trends, and to prepare reports on said subjects that can be disseminated via electronic communication networks
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Computer software for improving business processes, sales, and customer service by enabling machine learning, natural language processing, data integration, data manipulation, data analysis, and development of predictive systems SaaS featuring software for improving business processes, sales, and customer service by enabling machine learning, natural language processing, data integration, data manipulation, data analysis, and development of predictive systems; computer software consulting; customized software development and deployment services; project management services in the field of software design and implementation; consulting services in the field of artificial intelligence and data management software
09 - Appareils et instruments scientifiques et électriques
Produits et services
Computer application software for mobile phones, portable media players, handheld computers, namely, software for customer service; Computer software for customer service
