Abstract: A device may receive a request to identify items that satisfy parameters of the request. The device may identify a plurality of items that satisfy the parameters. The device may generate a plurality of explanation sets. An explanation set of the plurality of explanation sets may relate to an item of the plurality of items. The explanation set may include at least one of: a positive explanation indicating that the item is positively associated with a first characteristic that relates to a first preference of a user, or a negative explanation indicating that the item is negatively associated with a second characteristic that relates to a second preference of the user. The device may select an item from the plurality of items based on the plurality of explanation sets. The device may provide information that includes an explanation set of the item selected.

Abstract: A system for providing data security and protection using distributed ledgers to store validated data in a global knowledge graph for fraud detection is disclosed. The system may comprise a data access interface, a processor, and an output interface. The data access interface may receive data associated with an individual from a data source. The processor may convert the data into knowledge graph data by: extracting entities and relations from the data; and translating the data into knowledge graph triples to generate the knowledge graph data. The processor may validate the knowledge graph data using a cryptographic validation, to provide secured contents to update a global knowledge graph to determine a fraudulent activity level associated with the individual based on the updated global knowledge graph. The output interface may transmit a report associated with the fraudulent activity level to a report requestor at a computing device.

Abstract: According to an example, collaborative healthcare may include retrieving healthcare data for a patient from at least one data source, and generating a plurality of distinct healthcare programs for the patient based on the healthcare data. Collaborative healthcare may further include reconciling the plurality of distinct healthcare programs to generate a universal patient healthcare plan for the patient. The universal patient healthcare plan may include a universal view of the overall healthcare for the patient and healthcare provider-specific views for the patient. The reconciling may include detecting conflicts for predetermined components of the healthcare programs, and in response to the detection of the conflicts, eliminating errors related to the detected conflicts.

Abstract: A device may receive a request to identify items that satisfy parameters of the request. The device may identify a plurality of items that satisfy the parameters. The device may generate a plurality of explanation sets. An explanation set of the plurality of explanation sets may relate to an item of the plurality of items. The explanation set may include at least one of: a positive explanation indicating that the item is positively associated with a first characteristic that relates to a first preference of a user, or a negative explanation indicating that the item is negatively associated with a second characteristic that relates to a second preference of the user. The device may select an item from the plurality of items based on the plurality of explanation sets. The device may provide information that includes an explanation set of the item selected.

Abstract: A device may obtain, for a set of transactions, a set of transaction values associated with a particular industry. The device may determine one or more sample statistical distributions for a probabilistic transaction model by using one or more machine learning techniques. The one or more sample statistical distributions may be similar to one or more actual statistical distributions that are associated with the set of transaction values. The device may generate simulated transaction information using the probabilistic transaction model. The device may perform one or more actions after generating the simulated transaction information.

Abstract: A system includes one or more devices to receive a first set of data. The system transmits the first set of data to a first data store for storage. The first data store is of a first data format type. The system transmits the first set of data to a node of a plurality of nodes. The node to perform an action on the first set of data and a second set of data to generate a third set of data. The node to transmit the third set of data to a third data store. The third data store being of a third data format type that is different than the first data format type and the second data format type. The system transmits the third set of data from the third data store to the first data store for reporting.

Abstract: An example method may include receiving unstructured data; identifying a first plurality of entities in the unstructured data; identifying first relationships associated with the first plurality of entities using an entity relation model; receiving structured data; identifying a second plurality of entities in the structured data; identifying second relationships associated with the second plurality of entities using the entity relation model; generating a knowledge graph that is representative of the first plurality of entities, wherein the first relationships are associated with the first plurality of entities and the second relationships are associated with the second plurality of entities; determining a probability that a first node from the knowledge graph and a second node from the knowledge graph corresponds to a same entity; and performing an action associated with the probability that the first node and the second node corresponds to the same entity.

Abstract: A system for providing data security and protection using distributed ledgers to store validated data in a global knowledge graph for fraud detection is disclosed. The system may comprise a data access interface, a processor, and an output interface. The data access interface may receive data associated with an individual from a data source. The processor may convert the data into knowledge graph data by: extracting entities from the data; extracting relations from the data; and translating the data into knowledge graph triples to generate the knowledge graph data. The processor may validate the knowledge graph data using a cryptographic validation, such as blockchain or distributed ledgers, on the knowledge graph data to provide secured contents to update a global knowledge graph. The processor may then determine a fraudulent activity level associated with the individual based on the updated global knowledge graph.

Abstract: A platform may obtain, for a first set of entities involved in a money laundering investigation, target entity information for a target entity and related entity information for a set of related entities. The platform may analyze the target entity information and the related entity information to identify money laundering candidates. The platform may determine one or more relationships indicating a degree of similarity between the target entity and the one or more related entities. The platform may generate a graph data structure that associates the target entity and the one or more related entities using the one or more relationships. The platform may determine a score for the target entity and one or more scores for the one or more related entities. The platform may provide a recommendation indicating whether the target entity and/or the one or more related entities are likely to be engaging in money laundering.

Abstract: Implementations are directed to providing an exemplar electronic document (EED) with actions including receiving input that is at least partially representative of a subject, receiving a plurality of stored subjects, each including data representative of a respective stored subject, and provided in a knowledge graph, processing the input based on semantic comparison between the input and each of the stored subjects to provide a set of semantic differences, each semantic difference representing the input and a respective stored subject, processing a profile in view of each of a plurality of other profiles to provide a set of semantic correlations, each semantic correlation representing the profile and a respective other profile, and providing the EED based on the sets of semantic differences and semantic correlations, the EED including at least a portion of the input, and respective portions of each of a plurality of stored subjects based on respective scores.

Abstract: A system includes one or more devices to receive a first set of data. The system transmits the first set of data to a first data store for storage. The first data store is of a first data format type. The system transmits the first set of data to a node of a plurality of nodes. The node to perform an action on the first set of data and a second set of data to generate a third set of data. The node to transmit the third set of data to a third data store. The third data store being of a third data format type that is different than the first data format type and the second data format type. The system transmits the third set of data from the third data store to the first data store for reporting.

Abstract: A device may obtain, for a set of transactions, a set of transaction values associated with a particular industry. The device may determine one or more sample statistical distributions for a probabilistic transaction model by using one or more machine learning techniques. The one or more sample statistical distributions may be similar to one or more actual statistical distributions that are associated with the set of transaction values. The device may generate simulated transaction information using the probabilistic transaction model. The device may perform one or more actions after generating the simulated transaction information.

Abstract: A platform may obtain, for a first set of entities involved in a money laundering investigation, target entity information for a target entity and related entity information for a set of related entities. The platform may analyze the target entity information and the related entity information to identify money laundering candidates. The platform may determine one or more relationships indicating a degree of similarity between the target entity and the one or more related entities. The platform may generate a graph data structure that associates the target entity and the one or more related entities using the one or more relationships. The platform may determine a score for the target entity and one or more scores for the one or more related entities. The platform may provide a recommendation indicating whether the target entity and/or the one or more related entities are likely to be engaging in money laundering.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for promoting healthy behavior. The methods, systems, and apparatus include an action of receiving device capability information for multiple devices. The device capability information from each device describes capabilities of the device. Additional actions include receiving an indication of a health goal of a user and receiving health information of the user sensed by a first device of the multiple devices. Further actions include generating a health related instruction for a second device of the multiple devices based on the health information of the user sensed by the first device of the multiple devices, the health goal of the user, and the device capability information of the second device. An additional action includes providing the health related instruction to the second device.

Abstract: Implementations are directed to providing an explanatory electronic document with actions including providing a target subject profile based on user input and one or more ontologies, the target subject profile including associations describing a subject at respective degrees of specificity, providing a set of peer user profiles using semantic user profile association between the user profile and each peer user profile in a superset of peer user profiles, retrieving one or more peer subject profiles, each peer subject profile being associated with a peer user profile in the set of peer user profiles, and including associations describing a past subject experienced by a peer user, filtering at least one association from a peer subject profile based on data provided in a knowledge graph, and providing at least one explanatory text string associated with the subject based on at least one remaining association in the peer subject profile.

Abstract: Implementations are directed to providing an explanatory electronic document with actions including providing a target subject profile based on user input and one or more ontologies, the target subject profile including associations describing a subject at respective degrees of specificity, providing a set of peer user profiles using semantic user profile association between the user profile and each peer user profile in a superset of peer user profiles, retrieving one or more peer subject profiles, each peer subject profile being associated with a peer user profile in the set of peer user profiles, and including associations describing a past subject experienced by a peer user, filtering at least one association from a peer subject profile based on data provided in a knowledge graph, and providing at least one explanatory text string associated with the subject based on at least one remaining association in the peer subject profile.

Abstract: An interactive virtual care system may include a user sensory module to acquire multi-modal user data related to user movement. A data analysis module may compare the multi-modal user data to predetermined historical user data and/or statistical norm data for users to identify an anomaly in the user movement.

Abstract: Implementations are directed to providing an exemplar electronic document (EED) with actions including receiving input that is at least partially representative of a subject, receiving a plurality of stored subjects, each including data representative of a respective stored subject, and provided in a knowledge graph, processing the input based on semantic comparison between the input and each of the stored subjects to provide a set of semantic differences, each semantic difference representing the input and a respective stored subject, processing a profile in view of each of a plurality of other profiles to provide a set of semantic correlations, each semantic correlation representing the profile and a respective other profile, and providing the EED based on the sets of semantic differences and semantic correlations, the EED including at least a portion of the input, and respective portions of each of a plurality of stored subjects based on respective scores.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for promoting healthy behavior. The methods, systems, and apparatus include an action of receiving device capability information for multiple devices. The device capability information from each device describes capabilities of the device. Additional actions include receiving an indication of a health goal of a user and receiving health information of the user sensed by a first device of the multiple devices. Further actions include generating a health related instruction for a second device of the multiple devices based on the health information of the user sensed by the first device of the multiple devices, the health goal of the user, and the device capability information of the second device. An additional action includes providing the health related instruction to the second device.

Abstract: An interactive virtual care system may include a user sensory module to acquire multi-modal user data related to user movement. A data analysis module may compare the multi-modal user data to predetermined historical user data and/or statistical norm data for users to identify an anomaly in the user movement.