1245 results about "Chatbot" patented technology

An example operation may include one or more of receiving an input from a user during a conversation that includes a plurality of prompts between the user and a chatbot within a chat window of a software application, converting text content within the received input into a vector, executing a large language model (LLM) on the vector and a database of vectorized responses to identify a vectorized response to output from among the plurality of vectorized responses within the database, converting the vectorized response into a text response, and displaying the text response output by the chatbot within the chat window of the software application.

An example operation may include one or more of storing a database of payment card data, conversing with a user via a chatbot within a chat window of a software application, wherein the conversing comprises receiving a query from the user about a payment card during a chat session between the user and the chatbot, executing a large language model (LLM) on the query about the payment card and the database of payment card data to generate a chatbot response, and displaying the generated chatbot response via the chatbot within the chat window of the software application during the chat session.

A Generative Artificial Intelligence (Gen. AI) based chatbot apparatus implements classical AI models and Generative AI models to answer user queries with results retrieved from relational databases and unstructured knowledge bases. When a user query is received, it is determined if the intent of the user query can be determined with a confidence greater than a configured confidence limit. If yes, a structured query mapped to the intent is employed to answer the user query. If the intent determination confidence is less than the configured confidence limit then Gen. AI-based techniques using a plurality of LLMs are used to respond to the query. A Gen. AI switch is also implemented to switch between the plurality of LLMs to answer user queries with greater accuracy.

Network infrastructure for user-specific generative intelligence. Providing user-specific context to a generically trained LLM introduces a variety of complications (privacy, resource utilization, training costs, etc.). Various aspects of the present disclosure provide novel user-specific data structures, privacy and access control, layers of data, and session management, within a network infrastructure for generative intelligence. For example, user-specific embedding vectors may be used to provide user context to a generically trained foundation model. In some variants, edge devices capture multiple modalities of user context (images, audio; not just text). Privacy and access control mechanisms also allow a user to control information that is captured and sent to the foundation model. Session management further decouples a user's conversational state from the foundation model's session state. These concepts and others may be used to emulate e.g., a chatbot based virtual assistant that responds based on user context.

The present disclosure relates to computer-implemented methods, software, and systems for transferring contexts from a generic chatbot to a domain-specific chatbot. In one example, a registration at a first conversational interface is received that identifies at least one endpoint associated with a first entity that is separate from the first conversational interface and is associated with a domain-specific conversational interface. The first conversational interface can interact with a user through a natural language search interaction. The interface can identify at least one response associated with one of the at least one endpoints associated with the first entity, and can provide the response and an interactive response associated with initiating a transfer of the interaction from the first conversational interface to a domain-specific conversational interface of the first entity. Once accepted, the interaction is transferred to the first entity and its domain-specific conversational interface.

An example operation may include one or more of training a large language model (LLM) to learn credit card data via execution of the LLM on content from one or more credit card documents, executing the LLM to generate a sequence of prompts which are output to a user via a chatbot within a chat window of a software application, receiving responses to the sequence of prompts for the user via the chat window of the software application, and retraining the LLM model to further learn credit card data via execution of the LLM on a combination of the sequence of prompts and the received responses.

Disclosed herein are systems and methods for graph-based dynamic information retrieval and synthesis, particularly for enhancing the response generation of chatbot applications using large language models (LLMs). The methods involve receiving a user query, generating a query embedding, and accessing a knowledge graph comprising nodes representing documents with associated document embeddings. A set of documents is retrieved based on similarity scores, and a distance matrix is constructed to calculate shortest-path distances between nodes. Documents are clustered to identify a largest cluster from which content is retrieved to formulate a response. The disclosed embodiments improve upon retrieval augmented generation (RAG) by leveraging the hierarchical relationships of a knowledge graph and node interconnectedness to provide contextually coherent information and mitigate the inclusion of information from irrelevant or superficially related documents in LLM responses.

An example operation may include one or more of receiving context of a user during an inquiry of a feature via a software application, executing a waiting period via the software application, during the waiting period, selecting an animation to display via the software application based on the context of the user and the feature inquiry wherein the animation provides contextual data associated with the feature, wherein the contextual data is based on a determined need of the user, displaying the animation via the software application during the waiting period, and determining if the user has accepted the feature via the software application. At least one portion of the example operation: integrates with an artificial intelligence (AI) chatbot, interacts with the AI chatbot, is performed by the AI chatbot, and / or is associated with an AI model.

An example operation may include one or more of executing a waiting period of time within a software application being accessed by a user, executing an animation via the software application during the waiting period, determining a result of the software application being accessed by the user, determining additional time to add to the waiting period of time based on the result, via the software application, executing the waiting period with the additional time, augmenting the animation based on the additional time to add to the waiting period, via the software application, and executing the augmented animation via the software application during the additional time. At least one portion of the example operation: integrates with an artificial intelligence (AI) chatbot, interacts with the AI chatbot, is performed by the AI chatbot, and / or is associated with an AI model.

An example operation may include one or more of registering a user for a first feature by a software application, wherein the registering of the user comprises receiving information about the user, determining that the received information does not meet a first condition related to a first feature, determining that the received information meets a second condition related to a second feature, generating an offer to the user the second feature based on the information, detecting acceptance of the second feature, by the software application, and enabling the second feature by the software application for the user in response to the acceptance. At least one portion of the example operation: integrates with an artificial intelligence (AI) chatbot, interacts with the AI chatbot, is performed by the AI chatbot, and / or is associated with an AI model.

Network infrastructure for user-specific generative intelligence. Providing user-specific context to a generically trained LLM introduces a variety of complications (privacy, resource utilization, training costs, etc.). Various aspects of the present disclosure provide novel user-specific data structures, privacy and access control, layers of data, and session management, within a network infrastructure for generative intelligence. For example, user-specific embedding vectors may be used to provide user context to a generically trained foundation model. In some variants, edge devices capture multiple modalities of user context (images, audio; not just text). Privacy and access control mechanisms also allow a user to control information that is captured and sent to the foundation model. Session management further decouples a user's conversational state from the foundation model's session state. These concepts and others may be used to emulate e.g., a chatbot based virtual assistant that responds based on user context.

A system and method for facilitating natural language conversations between customers and vendors for product purchases is provided. The system ingests product catalogs from vendors, normalizes the data, and provides a conversational interface for customers to query the catalogs. In some examples, a chatbot system receives a natural language query from a customer about a product in a chat interface, identifies vendors offering that product by searching uploaded product catalogs, determines available inventory for the product by querying the catalogs, and generates a natural language response to the customer using the vendor and inventory information. The system can extract product details from the query, search based on those details, rank and recommend vendors and products, update user profiles, offer purchase incentives, and complete transactions within the conversation. The system handles the conversational and technical aspects to enable natural dialog between businesses and customers regarding products.

This disclosure improves computer functionality by enabling various hierarchies of chatbot application programs operative based on data structures containing unstructured texts. Therefore, such hierarchies enable some chatbot application programs to manage other chatbot application programs, which improves virtual assistance, reduces programming efforts, customizes output by user types, and enhances process management.

A stateful chatbot system leverages generative AI to provide an interface by which users can retrieve information from backend IoT databases of a security provider via natural language queries. Upon receiving a natural language query that corresponds to a request for information from the database, the chatbot generates a corresponding database query having a format compatible with the database. The chatbot comprises a generative model adapted to generate database queries based on natural language queries via prompt engineering using natural language and database query pairs. The chatbot queries the database with the generated database query, retrieves results comprising data / metadata that satisfy the query, and generates a summary of the results, both of which it presents as a response to the user's query. The chatbot also has access to a vulnerability database from which it can obtain information about known vulnerabilities documented therein to respond to user queries.

Techniques for triggering a transfer of a chat conversation with a user from a chatbot to a human agent based on detection of transfer criteria are disclosed. The chatbot uses natural language processing and a generative model to collect and organize information from the chat conversation to present to the human agent in a report when the chat conversation is transferred to the human agent. The chat conversation is transferred to the human agent by presenting the report and a graphical chat interface to the human agent. The graphical chat interface displays messages from chat conversation between the human agent and the user and displays messages from chat conversations between the human agent and multiple other users. Transferring the chat conversation from the chatbot to the human agent includes presenting interface elements to the human agent for receiving user input from the human agent for transmission to the user.

Techniques are disclosed for enabling creators to create multimodal chatbots that are based on or simulate / model characters. The characters may be from audiovisual (AV) media such as films and TV shows or real people. The application leverages a combination of Visual Interpretation AI, Retrieval-Augmented Generation (RAG), Low-Rank Adaptation (LoRA), and function calling to provide rich, interactive experiences. The inferencing performed by an instant multimodal chatbot utilizes base weights, character weights, relationship weights, experience weights as we as environmental inputs. An instant chatbot uses a number of AI models including a video to text model, an image to text model, a sensory to text model, a large language model, a text to video model, a text to image model and a text to voice model. A user can interact with the chatbot in a variety of ways including text, audio and video.

In one implementation, a computer-implemented method involves receiving a user message corresponding to a query or a statement to AI chatbot, performing preprocessing operations resulting in generation of initial context of the user message by extracting text of the user message, metadata of the user message, and a conversation identifier, obtaining historical context pertaining to the user message from a plurality of storage mechanisms provided in differing formats including a knowledge graph, a vector database comprised of vector embeddings, and a database comprising text summaries of prior conversations between the user and the AI chatbot, generating a prompt for a LLM that instructs the LLM to generate a response to the user message that is based on and consistent with the user message, the initial content, and the historical context, and providing a final response to the user that is corresponds to an LLM-generated response.

A hosting system to facilitate connecting to multiple cloud providers may comprise a first cloud exchange; a second cloud exchange; one or more cloud point-of-presence (PoPs) communicatively coupled to the first and second cloud exchanges; a software-defined network (“SDN”) automation engine; a data collection layer; an artificial intelligence (AI) layer comprising one or more AI modeling engines configured to deploy one or more AI models; and a non-transitory computer readable medium comprising computer program instructions that when executed by a processor cause the system to generate a real-time conversational agent (a “chat-bot”), display the chat-bot on a user device, receive input comprising network specification parameters via the chat-bot, convert the input into one or more commands, generate a customized, on-demand SDN that complies with the network specification parameters, capture and model user data and network data, and automatically initiate one or more actions responsive to the modeling.

An unstructured data query-response pair generation system (generation system) populates a knowledge base of query-response pairs for queries of natural language content in unstructured data by prompting a first large language model (LLM) text extracted from the unstructured data. An unstructured data chatbot (chatbot) leverages the knowledge base by augmenting prompts to a second LLM responding to user queries for natural language content in the unstructured data with query-response pairs having queries that are semantically similar to the user queries. The knowledge base and LLMs are updated based on user feedback correcting responses, continually improving quality of the generation system and chatbot.

Systems and methods disclosed herein relate to fine-tuning machine learning (ML) chatbots for an enterprise. The systems and methods may use ML chatbots and / or generative ML to generate social media content for a user associated with an enterprise. The systems and methods may fine-tune a base ML model, and use the fine-tuned ML model for the ML chatbot. A user profile may indicate user attributes, and a fine-tuned ML model may be loaded for the ML chatbot based upon an identified user profile.

A method of evaluating performance of a chatbot includes identifying a test scenario including a request and an expected outcome, initiating an automated conversation between a first machine learning model and the chatbot based on the test scenario, storing a recording of the automated conversation, providing the recording of the automated conversation and the test scenario to a second machine learning model, and receiving an evaluation of the automated conversation from the second machine learning model based on the recording of the automated conversation and the expected outcome.

Disclosed are a method and apparatus for automatically analyzing a malicious event log that is an endpoint threat event, by applying an artificial intelligence (AI)-based chat bot engine. An endpoint automatic analysis method comprises processing a log through grouping according to processes for efficient analysis of an event log, generating a sentence for a process behavior through a neuro-linguistic programming technique for AI analysis, generating data of malicious behaviors that actually operate to use the same as training data for AI model training, realizing a transformer algorithm-based learning system to learn the training data, identifying, in real time, a grouped event log through a BERT and LSTM algorithm-based event maliciousness automatic analyzer to analyze an actual malicious behavior, and detecting and automatically determining a threat event when a malicious behavior target is equal to or greater than a certain similarity.

An AI chatbot responds to the intent of a customer question by triggering an automatic workflow appropriate for the intention of the question. An information retrieval pipeline may be initiated to response to question corresponding to an information request. A Large Language Model may be initiated to generate a workflow to respond to other types of questions. The Large Language Model is provided with policies, tools and prompts to implement workflows. An evaluation engine evaluates factualness and helpfulness of responses to information questions and workflow intent accuracy and workflow appropriateness. Overall conversation resolution verification ay also be performed.

An example operation may include one or more of executing an interaction with an account device about a first topic of interest and a chatbot within a chat element, wherein the interaction comprises an exchange of content between the account device and the chatbot within the chat element, receiving an interaction data from the account device within the chat element; determining that the interaction data is a second topic of interest based on an execution of an artificial intelligence model on the interaction and the first topic of interest, generating a chatbot response to the interaction data based on the execution of the artificial intelligence model on a state of the interaction prior to receipt of the interaction data, and outputting the chatbot response within the chat element.

Herein disclosed is receiving predetermined content, receiving a request to transform the predetermined content into a derivative work, receiving a requested theme for the derivative work, using generative artificial intelligence to create the derivative work generated as a function of the predetermined content and the requested theme, determining if the generated derivative work is approved, in response to determining the generated derivative work is approved, applying a digital watermark to the approved derivative work, configuring an authorization server to govern use of the approved derivative work based on the digital watermark and providing user access to the authorized derivative work. The requested theme may be determined using a Large Language Model (LLM) and a chatbot interview. The generative artificial intelligence may comprise a diffusion model. The content may comprise music.

Apparatuses, systems and methods are disclosed for providing property improvement recommendations. The method comprises: (1) receiving, by one or more processors from a user, a first prompt associated with a property; (2) generating, by the one or more processors, a second prompt based upon the first prompt, data associated with the property and one or more property metrics associated with the property; (3) transmitting, by the one or more processors to a chatbot, the second prompt to generate a response, the response comprising a recommended action associated with the property; and / or (4) presenting, by the one or more processors, the response to the user.

The invention discloses a role-playing chatting robot implementation method based on a human background big language model, and the method comprises the steps: collecting the text data of a large amount of literature works, extracting role dialogues, actions and abstracts through a big model, and extracting role setting information at the same time; extracting a double-person multi-round dialogue from the role dialogue, and matching with role setting to form a preliminary data set; converting the preliminary data set into a Json format, and constructing a structured multi-round dialogue data set; a LoRA method and a DPO method are adopted to carry out fine tuning on the large language model; building a chat robot based on the fine-tuned large language model, and integrating role information, background stories, historical dialogue abstracts and a small amount of example dialogues to form a dynamic cue word framework; an intelligent chat framework is constructed for a chat robot and comprises a time planning module, an active pushing module and a delayed chat module. By utilizing the method and the system, the dialogue quality, the role personalization, the role consistency, the interaction continuity and the user experience can be effectively improved.

A server for handling interaction in a live streaming platform, comprising one or a plurality of processors, wherein the one or plurality of processors execute a machine-readable instruction to perform: receiving a character data corresponding to a character; feeding the character data into a machine learning model to generate a virtual chatbot in a live streaming room; setting the virtual chatbot in the live streaming room; wherein the virtual chatbot is configured to provide assistance to a livestreamer or viewers in the live streaming room. According to the present disclosure, the AI chatbot with a character may increase the interaction and fun in the live streaming room. Moreover, the monitoring and handling of the atmosphere in the live streaming may also be partially or entirely entrusted to the AI chatbot. Therefore, the user experience may be improved.

A method for a chat bot includes receiving multiple instruction sets corresponding to multiple goals, each instruction set defining a prompt to a large language model (LLM) to achieve a corresponding goal. The method further includes receiving a chat history from a user, comprising multiple messages, each message being one of a user-authored message or a chat bot-authored message, and further comprising metadata associated with said chat history. The method further includes identifying a particular goal of said user from the multiple goals, selecting a particular instruction set corresponding to said particular goal, and defining a particular prompt to the LLM. The method further includes executing said particular instruction set by providing said particular prompt to said LLM and providing a reply to said user with a result of executing said particular instruction set.

There are provided systems and methods for automated chatbots that detect privacy data sharing and leakage by other automated chatbot systems. A service provider and other chatbot services, including an electronic transaction processor, may provide self-service channels for assistance through chatbot and other automated computing processes. However, these chatbots may be regulated by compliance with privacy protection requirements, and therefore use of the chatbots and / or user data when responding to users via chatbots in chat sessions may be noncompliant and / or attacked by malicious users to compromise privacy protected data. In order to facilitate detection of leaks and shares by chatbots, an AI for a privacy protection chatbot may be trained on regulations for chatbot use and / or data security and privacy protection. The chatbot may then interact with other chatbots and question the other chatbots using the trained AI model to detect whether privacy protected data is exposed.