Provided is a smart city service system based on an Internet of Things large model. The system includes a smart city user platform and a smart city service platform, the smart city service platform includes a chatbot, and the chatbot is configured to: obtain a query sent by a user through a user interface; determine, based on the query, initial service information of the query from a service information database through semantic search; determine, based on the query and modal information of the initial service information, a target service model from a user service model library; generate a response to the query based on the query, the initial service information, and an expression evaluation value through the target service model; and send the response to the smart city user platform, and output the response through the user interface.

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.

A question-answering service generation method includes displaying a functional code encapsulation component set in an edition interaction page of a question-answering service edition application, and in response to a component selection operation on the functional code encapsulation component set, displaying, in a question-answering service edition page included in the edition interaction page, a trigger component, a question-answering strategy component, and a language question-answering model component that are selected from the functional code encapsulation component set through the component selection operation, and a processing flow connection relationship among the trigger component, the question-answering strategy component, and the language question-answering model component, and generating, in response to a question-answering service confirmation operation triggered in the edition interaction page, a question-answering service based on the processing flow connection relationship.

A system and method combine programmatic control and a guided and constrained Artificial Intelligence (AI) engine to generate personalized video responses for a user. Receiving user inputs to identify a historical figure and a question directed to the corresponding historical figure. Information about the historical figure, including image, voice identifier, and biographical profile, is retrieved. Under programmatic logic control, an AI engine analyzes a question and generates an excuse for any delay in responding, else generate a dialogue response. The dialogue response is integrated with relevant information to generate a response having an audio and visual component with the voice identifier and dialogue response, and the image of the selected historical figure. A prompt is generated to guide and constrain the AI engine to create a video response that integrating audio and visual components to simulate the historical figure delivering the dialogue response and providing to an online learning platform.

Systems, methods, and devices that relate to generation of responses to query sets are disclosed. In one example aspect, the system uses multiple models to retrieve data relevant to queries and appropriate for the requesting user and to output the data in a certain style. In particular, the system uses a first model to determine a type of user submitting a query, a second model to retrieve data relevant to the query, subject to constraints based on the type of user, and a third model to formulate a response to the query, based on the retrieved data, using a consistent style. In some implementations, another model enables certain users to validate the outputs from one or more of the first, second, and third models. The system can output a compilation of the responses to the queries in a particular manner, style, or presentation that is consistent across all outputs.

An object of the present disclosure is to improve a technique of presenting a portfolio of asset management according to a subject. An information processing apparatus includes a question selection unit that selects a question to be presented next from among unpresented questions included in a question group based on an answer to a question selected from the question group for determining a portfolio of asset management and presented to a subject, and a presentation control unit that presents, to the subject, a portfolio according to an answer of the subject to each of the selected and presented questions. According to this information processing apparatus, it is possible to support the decision making of the subject for asset management.

Systems and methods for automated named entity recognition (NER) using artificial intelligence models are disclosed. In some examples, a contextualized word embedding is generated for each of a plurality of words. Further, for each contextualized word embedding, example contextualized word embeddings are received. Each of the example contextualized word embeddings are associated with a corresponding digital textual example. A similarity value is generated between each contextualized word embedding and each of the corresponding example contextualized word embeddings. Based on the similarity values, one or more of the contextualized word embeddings are determined. An input prompt is generated that includes a command, the plurality of words, and the digital textual example associated with each of the determined contextualized word embeddings. The input prompt is then inputted to a generative artificial intelligence model to receive a response that associates at least one of the plurality of words with an entity type.

An information processing apparatus includes a display configured to display a screen, an acceptance unit configured to accept a natural language from a user, a transmission unit configured to transmit information about the screen displayed when the acceptance unit accepts the natural language and a prompt using the accepted natural language, the information being transmitted being based on the accepted natural language, and a reception unit configured to receive an output from a language model, the output being based on the transmitted information about the screen and the transmitted prompt. The display is caused to display the received output from the language model, the output indicating an operation method for the information processing apparatus that is displayed on the screen displayed by the display after the acceptance unit accepts the natural language.

An information processing apparatus generates a query text that instructs, in natural language, extraction of a first state transition that satisfies a predetermined condition regarding meanings of states before and after a transition from a data set indicating a series of transitions of states of a plurality of events. Next, the information processing apparatus inputs the query text to a dialogue system that performs a dialogue in natural language using a language model. Then, the information processing apparatus determines that, for each event, information based on whether the first state transition indicated in a response text output by the dialogue system is included in a series of transitions of states of the event is set as a feature of the event.

A method for controlling an artificial intelligence (AI) deice can include configuring a dialogue agent with a predetermined personality profile including a plurality of defined personality traits to generate a configured dialogue agent, receiving, by the configured dialogue agent, a user input prompt, in response to receiving the user input, performing a multi-step internal reflection process that includes generating a first internal response based on the input prompt and the predetermined personality profile and generating a second internal response by refining the first internal response to reinforce alignment with the plurality of defined personality traits, and generating a response for the user input prompt based on the second internal response.