Abstract: Systems and methods for pre-fetching results from large language models (LLMs) are provided. The method includes acquiring a context of an interaction between a user and an Artificial Intelligence (AI) character; predicting, based on the context, one or more anticipated words to be uttered by the user; generating, based on the one or more anticipated words, at least one query to an LLM; providing the at least one query to the LLM; generating, based on at least one response obtained from the LLM, an anticipated reply of the AI character model to the one or more anticipated words to be pronounced by the user; receiving one or more words uttered by the user; determining that a level of a discrepancy between the one or more words and the one or more anticipated words is below a predetermined threshold; and providing the anticipated reply to the user.

Abstract: Systems and methods for providing an interface for construction of an Artificial Intelligence (AI) character model are provided. An example method includes receiving a description of a character from a user via a client-side computing device; generating, based on the description, parameters of the AI character model corresponding to the character; and providing, via the client-side computing device, the interface enabling the user to modify at least one of the parameters of the AI character model. The description includes a guideline for an actor describing how to play the character, an age, an ethnicity, an appearance, a motivation, a habit, knowledge of the character, and a dialog of the character with a further character. Values of entry fields for modifying the parameters of the AI character model are determined based on the description.

Abstract: Systems and methods for observation-based training of an Artificial Intelligence (AI) character model are provided. An example method includes receiving log data including interactions of a user and a first AI character model, receiving internal parameters of a second AI character model including a first plurality of heuristic machine learning models and a second plurality of primary machine learning models, pre-processing the log data to obtain one or more data streams including behavioral characteristics of the user, running the one or more data streams through the first plurality of heuristic machine learning models to produce intermediate outputs, composing the intermediate outputs into templated formats, and providing the templated formats to the second plurality of primary machine learning models. The internal parameters of the second AI character model are adjusted based on the templated formats such that the second AI character model mimics the behavioral characteristics of the user.

Abstract: Systems and methods for retrieval of augmented parameters for an artificial intelligence (AI)-based character are provided. An example method includes receiving, from a user via a user interface, at least one keyword describing the AI-based character; retrieving, from at least one data source and based on the at least one keyword, the augmented parameters describing the AI-based character; and generating, based on the augmented parameters, an AI-based character model corresponding to the AI-based character. The at least one data source includes a database configured to store records associated with the AI-based character, an online search service, and a set of clusters associated with a type of a feature of the AI-based character and at least one hidden prompt corresponding to the type of the feature. The type of the feature includes one of the following: a voice, a dialog style, an emotional state, an age, and temperament.

Abstract: Systems and methods for observation-based training of an Artificial Intelligence (AI) character model are provided. An example method includes receiving log data including interactions of a user and a first AI character model, receiving internal parameters of a second AI character model including a first plurality of heuristic machine learning models and a second plurality of primary machine learning models, pre-processing the log data to obtain one or more data streams including behavioral characteristics of the user, running the one or more data streams through the first plurality of heuristic machine learning models to produce intermediate outputs, composing the intermediate outputs into templated formats, and providing the templated formats to the second plurality of primary machine learning models. The internal parameters of the second AI character model are adjusted based on the templated formats such that the second AI character model mimics the behavioral characteristics of the user.

Abstract: Systems and methods for observation-based training of an Artificial Intelligence (AI) character model are provided. An example method includes receiving log data including interactions of a first user and a second user and adjusting, based on the log data, parameters of the AI character model to cause the AI character model to mimic behavioral characteristics of the first user in follow-up conversations with further users.

Abstract: Systems and methods for retrieval of augmented parameters for an artificial intelligence (AI)-based character are provided. An example method includes receiving, from a user via a user interface, at least one keyword describing the AI-based character; retrieving, from at least one data source and based on the at least one keyword, the augmented parameters describing the AI-based character; and generating, based on the augmented parameters, an AI-based character model corresponding to the AI-based character. The at least one data source includes a database configured to store records associated with the AI-based character, an online search service, and a set of clusters associated with a type of a feature of the AI-based character and at least one hidden prompt corresponding to the type of the feature. The type of the feature includes one of the following: a voice, a dialog style, an emotional state, an age, and temperament.

Abstract: Systems and methods for providing an Artificial Intelligence (AI) character model with modifiable behavioral characteristics are provided. An example method includes determining that an event has occurred in a virtual environment associated with the AI character model; modifying, in response to the determination that the event has occurred and based on information associated with the event, parameters of the AI character model to obtain further parameters associated with behavioral characteristics of the AI character model; and causing the AI character model to interact with the user according to the further parameters. The virtual environment and the AI character model may be provided to a user via a client-side computing device.

Abstract: Systems and methods for conducting communications between a user and an Artificial Intelligence (AI) character model are provided. An example method includes determining a context of a dialog between the AI character model and the user, the context being determined based on a data stream received from a client-side computing device associated with the user; receiving a message of the user in the dialog; and generating, based on the context and the message, an input to a language model configured to predict a response to the message; providing the input to the language model to obtain the response; and transmitting the response to the client-side computing device, where the client-side computing device presents the response to the user. The input to the language model includes the message expanded by a keyword associated with the context. The context includes an intent of the user and an emotional state of the user.

Abstract: Systems and methods for observation-based training of an Artificial Intelligence (AI) character model are provided. An example method includes receiving log data including interactions of a first user and a second user and adjusting, based on the log data, parameters of the AI character model to cause the AI character model to mimic behavioral characteristics of the first user in follow-up conversations with further users.

Abstract: Systems and methods for providing interactions of an Artificial Intelligence (AI) character model with users are provided. An example method includes receiving a message from a user of a client-side computing device; retrieving, from a graph, information concerning relationships between the AI character model and the user; generating, based on the message and the information concerning relationships, an action associated with AI character model; and causing the AI character model to perform the action in a virtual environment provided to the user via the client-side computing device. The client-side computing device may be in communication with a computing platform. The graph may include a first node associated with the AI character model, a second node associated with the user, and an edge between the first node and the second node. The edge may be associated with the information concerning relationships between the AI character model and the user.

Abstract: Systems and methods for training an Artificial Intelligence (AI) character model are provided. An example method includes receiving, from a client-side computing device being in communication with a computing platform, input data to the AI character model; generating an action to be performed by the AI character model in response to the input data; transmitting the action to the client-side computing device, where the client-side computing device presents the action to a user; receiving, from the client-side computing device, a message including an indication that the action is incorrect; and, based on the message, modifying parameters of the AI character model such that the AI character model executes a corrected action in response to the input data.

Abstract: Systems and methods for providing an Artificial Intelligence (AI) character model with a goal-oriented behavior are provided. An example method includes assigning, to the AI character model, a goal to be achieved during an interaction of the AI character model and a user of a client-side computing device; receiving, from the client-side computing device, a message of the user; generating, based on the message, an input to a language model configured to predict a first response to the message; providing the input to the language model to obtain the first response; modifying, based on the goal, the first response to obtain a second response; and transmitting the second response to the client-side computing device. The client-side computing device presents the second response to the user. The second response is configured to prompt the user to provide a response to achieve the goal.

Abstract: Systems and methods for providing an interface for construction of an Artificial Intelligence (AI) character model are provided. An example method includes receiving a description of a character from a user via a client-side computing device; generating, based on the description, parameters of the AI character model corresponding to the character; and providing, via the client-side computing device, the interface enabling the user to modify at least one of the parameters of the AI character model. The description includes a guideline for an actor describing how to play the character, an age, an ethnicity, an appearance, a motivation, a habit, knowledge of the character, and a dialog of the character with a further character. Values of entry fields for modifying the parameters of the AI character model are determined based on the description.

Abstract: Provided are systems and methods for creating custom dialog system engines. The system comprises a dialog system interface installed on a first server or a user device and a platform deployed on a second server. The platform is configured to receive dialog system entities and intents associated with a developer profile and associate the dialog system entities with the dialog system intents to form a custom dialog system engine associated with the dialog system interface. The web platform receives a user request from the dialog system interface, activates the custom dialog system engine based on identification, and retrieves the dialog system entities and intents. The user request is processed by applying the dialog system entities and intents to generate a response to the user request. The response is sent to the dialog system interface.

Abstract: Provided are systems and methods for creating custom dialog system engines. The system comprises a dialog system interface installed on a first server or a user device and a platform deployed on a second server. The platform is configured to receive dialog system entities and intents associated with a developer profile and associate the dialog system entities with the dialog system intents to form a custom dialog system engine associated with the dialog system interface. The web platform receives a user request from the dialog system interface, activates the custom dialog system engine based on identification, and retrieves the dialog system entities and intents. The user request is processed by applying the dialog system entities and intents to generate a response to the user request. The response is sent to the dialog system interface.

Abstract: A method for push interaction with a mobile device using a chat interface includes establishing a chat protocol connection between a server and the mobile device, wherein the chat protocol format is used as a part of the push protocol; generating an event on the server; sending a push protocol command relating to the event from the server to the mobile device using the chat protocol connection; in the background, processing the chat protocol request that was packaged within the push protocol command; receiving the response from the mobile device through the chat protocol connection using the pull protocol mode; sending media contents from the server to the client using pull protocol mode; and displaying the media contents on the mobile device.

Abstract: Disclosed is a technology for providing information to users of a chat information system. The chat information system may receive an information request from a user. The information request may be processed to determine its subject area and forwarded to information sources or external communication services, such as social networks, forums, and the like. Answers received from the information sources and external communication services may be validated and selected based on user feedbacks and ratings. The selected answers may be then delivered to the users of the chat information system. After receiving the answers, the user may evaluate them to be further stored for future use by the chat information system.

Abstract: A method and graphical user interface intended to simplify interaction between a user and a chat information system running on a portable electronic device that includes a touch screen. User interfaces and methods include user input controls (i.e., a virtual keyboard or voice recognition devices). The system also includes output trays, means for viewing chat history, means for displaying the Internet content and means for displaying graphical elements and avatars.

Abstract: Disclosed is a technology for providing information to users of a chat information system. The chat information system may receive an information request from a user. The information request may be processed to determine its subject area and forwarded to information sources or external communication services, such as social networks, forums, and the like. Answers received from the information sources and external communication services may be validated and selected based on user feedbacks and ratings. The selected answers may be then delivered to the users of the chat information system. After receiving the answers, the user may evaluate them to be further stored for future use by the chat information system.