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: 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 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: 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: 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.