A system for generating a projection structure using a graphical user interface, wherein the system comprises: a display device configured to display a graphical user interface (GUI); a computing device comprising a memory; and a processor, wherein the memory contains instructions configuring the processor to: receive a plurality of datasets, wherein each dataset comprises a plurality of parameters; generate a first projection structure as a function of the datasets; display the first projection structure through the GUI; receive natural language query data corresponding to the first projection structure using a chatbot interface; generate response data as a function of the query data; map, as a function of a look-up table, each categorization to a database entry index; obtain response data from the database; adjust the parameters as a function of the response data; generate a second projection structure; and display it in the GUI.

An information processing method, by a processing unit of an information processing apparatus, includes: converting voice data into character string data; generating question data by extracting a first word from the character string data; extracting a second word from the character string data by inputting the character string data and the question data to a trained language learning model configured to output, when the character string data and the question data are input, a word corresponding to an answer to the question data from the character string data; and storing the voice data, the first word, and the second word in association with each other.

Text generation prompts may be determined based on an input document and a text generation prompt template. The text generation prompts may include text from the input document and questions related to the text. The text generation prompts may be sent to a remote text generation modeling system, which may respond with text generation prompt response messages including novel text portions generated by a text generation model. The text generation prompt response messages may be parsed to generate answers corresponding with the questions.

A system and method are disclosed for generating conversational content from human-readable documents. The method includes receiving a document comprising unstructured or semi-structured content and extracting linguistic and layout features using a language model and layout analysis techniques. The document is segmented into atomic content blocks representing discrete semantic units. For at least one content block, a natural language question is generated using a neural model, and a corresponding answer is extracted or synthesized. An optional rephrasing step modifies the surface form of the question or answer while preserving semantic meaning. Each question-answer pair is reviewed using automated or human-in-the-loop mechanisms for accuracy and alignment. Approved content is stored in a structured repository along with metadata supporting traceability and deployment. The system supports enterprise-scale generation of high-quality conversational data for downstream applications such as chatbots, virtual assistants, and retrieval-based AI systems.

According to one embodiment, an edge device includes a communication interface connectable to an edge server, a storage unit for storing a dialogue LLM trained to interact with a user for setting a function of the edge device and a database storing reference information related to functions of the edge device. A controller unit receives request text input indicating a user's desired process to be provided using the edge device, then inputs the request text input to a generative AI that is based on the dialogue LLM and can access the database to identify a function of the edge device that can provide the user's desired process. The controller unit outputs confirmation question text to confirm the identified function corresponds to the user's desired process, and then sets the edge device to execute the identified function if confirmed.

A target object management system includes a management server that manages a target object. The management server includes a situation acquisition unit, and the situation acquisition unit includes a use confirmation unit that acquires a position of a user. The situation acquisition unit acquires information on whether or not the position of the user is within a predetermined area, as a situation of the user. If the situation acquisition unit acquires information that the position of the user is within the predetermined area, the management server notifies the user of information on the target object via a terminal device, based on target object information stored in a storage unit.

An intelligent question answering method includes receiving an original question; determining an importance evaluation result of the original question based on the original question, where the importance evaluation result indicates an answering importance degree of the original question, and the importance evaluation result is determined based on at least one evaluation influencing factor; determining, based on the importance evaluation result, an answer corresponding to the original question in at least one candidate answer, where the at least one candidate answer has a different degree of detail; and outputting the answer corresponding to the original question. That is, importance of the original question is evaluated, and a degree of detail for answering the original question is determined based on the importance evaluation result.

An information processing apparatus includes, a setting unit that sets a condition for a user classified into a user group from input data; a selecting unit that selects a to-be-analyzed group based on a user attribute corresponding to each condition with respect to each the condition; a scoring unit that scores each user meeting the condition based on a behavior history of each user corresponding to each condition with respect to each the condition; an extractor that extracts a warm-prospect user having a higher score than other users in the to-be-analyzed group based on a result of scoring by the scoring unit; and a provision unit that selects a to-be-compared user, specifies information on a characteristic behavior of the warm-prospect user, transmits information on the specified characteristic behavior and attribute information indicating the attribute corresponding to the warm-prospect user to a terminal device, and provides the specified and attribute information.

Machine learning-based evaluation of recorded interactions is disclosed, including: obtaining an evaluation plan to correspond to a new question; retrieving a representative interaction based at least in part on the new question; using a reasoning and answer language model to evaluate the representative interaction against the new question based at least in part on the evaluation plan and to provide a preview evaluation result; outputting, at a user interface, the new question and the preview evaluation result of the representative interaction; receiving, via the user interface, user feedback to the preview evaluation result; updating the reasoning and answer language model based at least in part on the user feedback to the preview evaluation result; and storing a feedback data set including the user feedback.

A method and an apparatus for question-answering, a related device, and a computer program product are provided. An answering content corresponding to question information is generated via first large models in a configured large model set. A second large model is pre-configured, a consistency of the answering contents generated by the respective first large models is detected using reasoning capability of the second large model, and a determination result of whether each pair of the answering contents in the answering content set is consistent is obtained. If the determination result indicates that at least one pair of answering contents is consistent, the consistent answering contents are outputted as a final answer. An error may exist in a single large model, but if at least one pair of answering contents is consistent, an accuracy rate of the consistent answering contents can be improved.