Abstract: An exemplary system for constructing data structures that can perform inferential reasoning to answer input queries may receive input data, extract and cluster entities in the input data into topic clusters, and for a first topic cluster construct a data structure comprising a plurality of nodes, wherein nodes of the data structure respectively represent a topic entity extracted from the input data and grouped into the first topic cluster, and wherein a first node of the data structure is associated with a second node of the data structure based on the first node and the second node respectively representing a first topic entity and a second topic entity associated in the input data with a common one of the one or more identified linguistic modalities. An exemplary system comprising the data structure may receive an input query and generate a response to the input query using the data structure.

Abstract: A method for providing a hybrid AI and human electronic communication interface includes receiving a first electronic transmission comprising a user query from a user device. The method further includes automatically generating, by processing the user query by a set of AI models, an automatic response to the user query. The method further includes electronically transmitting the automatic response to the user device. The method further includes receiving, from the user device, a second electronic transmission comprising a user input in response to the automatic response. The method further includes determining, based at least in part on processing the user input, that a set of criteria is met; and in accordance with the determination that the set of criteria are met, automatically instantiating an electronic communication connection between the user device and a second user device.

Abstract: A system for automatically generating responses to user queries includes one or more processors, operable when executing instructions to: receive a query from a user, determine a first set of similarity scores between the query and a plurality of stored queries stored in a data store, and determine whether a first similarity score of the first set of similarity scores meets a first threshold. In accordance with a determination that the first similarity score meets the first threshold, the processors are further operable to obtain a set of case information for the current case; retrieve case information for at least one precedent case; determine a second set of similarity scores between the current case and the at least one precedent case; determine whether a second similarity score of the second set of similarity scores meets a second threshold; and generate a response to the query.

Abstract: Provided is a method for generating a personalized content suggestion in response to a query from a user. The method may be performed by a system comprising one or more processors. The method may include receiving the query from the user, generating one or more user parameters associated with the user, determining a predicted content topic of the query, and generating the content suggestion based on the predicted content topic and the one or more user parameters.

Abstract: Provided is a method for generating a knowledge graph for a topic. First, a set of documents may be received. A user may then provide an indication of exemplary entities associated with a topic of interest. Then, using one or more artificial intelligence models, a plurality of textual entities may be extracted from the documents. A quality level of each extracted textual entity may be determined. Each quality level may indicate a degree of similarity between a textual entity and each exemplary entity of the one or more exemplary entities. Next, a plurality of high-quality textual entities may be identified and categorized according to one or more sub-topics associated with the topic. Connection information indicating relationships between the sub-topics may be determined. Finally, a knowledge graph for the topic that represents the sub-topics in the documents and the relationships between said sub-topics may be generated.

Abstract: A system for generating and inferencing using enterprise knowledge graphs is provided. The system receives first input data related to one or more entities from one or more data sources. The system extracts a first set of data components from the first input data and determines, based upon the extracted data components, a second set of data components. The system identifies one or more relationships between the first set of data components and the second set of data components and generate a knowledge graph comprising a plurality of nodes. A first node of the knowledge graph can represent a first respective data component of the first set of data components and a second node of the knowledge graph can represent a second respective data component of the second set of data components. The first node can be associated with the second node based on an identified relationship between the nodes.

Abstract: Provided herein are methods and systems for identifying a related data values in a plurality of datasets. The method can include extracting data from the plurality of datasets, identifying potential linking categories, and determining a validity of the potential linking categories. One or more linking categories can be selected from the potential linking categories based on the validity, and related data values can be identified between the plurality of datasets based on the selected linking categories. Also provided herein are methods and systems for classifying data values as reconciled or non-reconciled. The method can include identifying a plurality of related data values in a plurality of datasets. For each of the plurality of related data values, a similarity score and confidence score can be determined, and the plurality of related data values can be classified as reconciled or non-reconciled based on the similarity score and/or the confidence score.

Abstract: Provided herein are methods and systems for determining a roll forward amount. The method can include identifying a starting balance, extracting reconciled data values from a database, each reconciled data value having a confidence score that indicates a confidence level that a reconciled data value of a first dataset is the same as a corresponding reconciled data value of a second dataset. The method can include identifying a first subset of the reconciled data values that are a first type and a second subset of the reconciled data values that are a second type, and determining a roll forward amount based on the starting balance, the first subset, and the second subset.

Abstract: Provided herein are systems and methods for decentralized privacy preserving audits comprising receiving by a first local system, from an orchestrator system, instructions to perform a local audit; executing, by the first local system, in response to receiving the instructions to perform the local audit, a first local audit model epoch, wherein executing the first local audit model epoch comprises: analyzing information from one or more data sources from a local data set; receiving first input information from a second local system through a privacy preserving communication framework in response to requesting the first input information; determining whether information from the one or more data sources from the local data set is reconciled based on the first input information; and in accordance with a determination that the information from the one or more data sources from the local data set is not reconciled, executing a second local audit model epoch.

Abstract: An exemplary system for constructing data structures that can perform inferential reasoning to answer input queries may receive input data, extract and cluster entities in the input data into topic clusters, and for a first topic cluster construct a data structure comprising a plurality of nodes, wherein nodes of the data structure respectively represent a topic entity extracted from the input data and grouped into the first topic cluster, and wherein a first node of the data structure is associated with a second node of the data structure based on the first node and the second node respectively representing a first topic entity and a second topic entity associated in the input data with a common one of the one or more identified linguistic modalities. An exemplary system comprising the data structure may receive an input query and generate a response to the input query using the data structure.

Abstract: An entity trust index relating to an entity may indicate a trustworthiness level of the entity. To generate an entity trust index, entity information may be received from one or more information sources. Using the entity information and one or more machine learning models, a performance index, an innovation index, and a sustainability index for the entity may be generated. The entity trust index may be generated based on the performance index, the innovation index, and the sustainability index. One or more actions for the entity that will modify the entity trust index may then be determined.