A context information retrieving unit retrieves a document database with a characteristic vector of a question and thereby acquires as context information a text that a similarity level between the characteristic vector and a characteristic vector of the text satisfies a predetermined condition. A first prompt generating unit generates a first prompt that includes the question and the context information. An answer acquiring unit acquires as a first answer an answer corresponding to the first prompt using a large language model. A second prompt generating unit generates a second prompt that includes the context information and the first answer and causes to provide a confidence of the first answer. An answer verifying unit acquires as a second answer an answer corresponding to the second prompt using a large language model, and determines the confidence with the second answer.

A collaborative content generation system uses machine learning to generate a script and content depicting the performance of the script. A director may use the system to generate the script and optionally, may involve one or more collaborators who perform portions of the script. The system may use machine learning to generate or modify a script, a storyboard to visualize the story, a narrator (e.g., the narrator's voice), characters, music, sound effects, etc. A director may assign portions of the script to certain collaborators and select which of their recordings are interleaved into the final collaborative interleaved content series. The collaborators may independently perform their portions and provide clips of their performances to the system, which may then interleave the clips to produce the finalized content.

Methods and systems for retrieving relevant information in response to an input question. The method includes obtaining text content related to the input question and partitioning the content into one or more paragraphs based on predefined rules. The method further involves extracting one or more evidence spans that are relevant to the input question by inputting the text content and the question into a trained language model. A semantic search is then performed on both the paragraphs and the extracted evidence spans, ranking the candidate passages based on their relevance to the input question. Each candidate passage may comprise either a paragraph or an evidence span that addresses the question. The disclosed methods and systems improve the quality and relevance of retrieved information by combining heuristic-based content partitioning with machine learning-based evidence extraction.

An information processing apparatus includes at least one memory that stores instructions and at least one processor that executes the instructions. The at least one processor executes the instructions to: input a question sentence about a content of a targeted document to a language model generated by machine learning to generate an answer sentence to a question about the content of the document to cause the language model to generate a first answer sentence; and determine a similarity between a second answer sentence to the question sentence, the second answer sentence being generated by causing the language model to refer to at least a part of the document, and the first answer sentence.

A method for efficient identification of knowledge base data to support interactions in a contact center may include identifying a query associated with a contemporaneous interaction between a client and an agent of a contact center and performing a search of a knowledge base to determine a set of one or more relevant documents responsive to the identified query, including ranking a set of resultant documents from the knowledge base as a function of a vector-based similarity determination in which each resultant document and the identified query is represented with a corresponding embedding. Additionally, the method may include identifying, with a transformer-based artificial intelligence model, a subset of content within each relevant document as being most pertinent to the identified query and visually demarcating the identified subset of each relevant document from other content of the relevant document in a user interface as the interaction occurs.

An apparatus and method for generating resource output as a function of a query and multimodal data. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive multimodal data associated with a user profile, receive a first query of a plurality of queries, generate at least a first output of a plurality of outputs as a function of the first query and the multimodal data, wherein the plurality of outputs comprises a question data structure, wherein the question data structure comprises each output associated with a weight, generate a score associated with the at least a first output using system feedback, adjust at least the weight associated with the at least a first output, select a plurality of questions, and display the first question followed temporally by the second question.

An electronic device includes: a display; at least one processor, comprising processing circuitry; and a memory storing instructions, wherein at least one processor, individually and/or collectively, is configured to execute the instructions and to cause the electronic device to: based on obtaining a query for source data, extract a plurality of pieces of candidate data related to the query from the source data; select, from the extracted plurality of pieces of candidate data, input data based on contents of the plurality of pieces of candidate data; generate a response to the query by applying the query and the selected input data to a generative model; determine a partial response of the response derived from the selected input data of the response; and display, via the display, a visual representation indicating information regarding the selected input data in an area corresponding to the determined partial response.

A method of providing a response to a user based on a field-of-view and a gaze of the user is described. A head-wearable device is communicatively coupled to a non-transitory, computer-readable storage medium including executable instructions that, when executed by one or more processors cause the one or more processors to perform the method. The method includes, causing the one or more cameras of the head-wearable device to capture an image of a field-of-view of the user and causing an eye-tracking device of the head-wearable device to determine a gaze of the user. The method further includes, in response to a capture command, isolating a gaze area of the image from a remainder of the image based on the gaze of the user and identifying, using a machine-learning algorithm, an object in the gaze area. The method further includes generating a response, using another machine-learning algorithm, based on the object.

Methods and systems enabling multimodal inputs for interacting with a live digital object are provided. The system receives the live digital object which includes a digital artifact extracted from a digital model file through a model representation. The system receives a user's security level and determines the user's access permission and modification permission to access and modify the digital artifact. The system accesses a multimodal interface configured to receive a conversational input and a spatial input, outputs the digital artifact to the multimodal interface based on the access permission, receives from the multimodal interface a conversational input and a spatial input from the user, and generates a modified digital artifact from the digital artifact via the digital model representation, based on the modification permission and on the conversational or spatial input. The multimodal interface may include conventional interfaces (GUIs/APIs), conversational interfaces (text/voice), and spatial computing interfaces (VR/AR/MR/gestural).

A query is received from a client device. A subset of documents relevant to the query is determined in part by determining an optimal configuration for the query. The subset of documents is inputted in a context window for a response generator and the query is inputted as a prompt for a query response. The query response received from the response generator is outputted to the client device.