628 results about "Chatbot" patented technology

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.

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.

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.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for maintaining and restoring context for artificial intelligence chatbots. In some implementations, a system receives a user prompt from a user through a chatbot interface, and the system provides a chatbot response to the user prompt through the chatbot interface. The system provides a control that is associated with the chatbot response on the chatbot interface and is selectable by the user to cause the chatbot response to be saved. In response to user selection with the control, the system saves the chatbot response and corresponding metadata that includes context information used by the one or more AI / ML models to generate the chatbot response. The chatbot interface is configured to display the saved chatbot response a and answer a subsequent user prompt using the context information in the metadata corresponding to the saved chatbot response.

Systems and methods are described for obtaining accurate responses from large language models (LLMs) and chatbots, including for question and answering, exposition, and summarization. These systems and methods accomplish these objectives via use of noun phrase avoiding processes such as a noun phrase collision detection process, a query splitting process, and a topical splitting process as well as by use of formatted facts, formatted fact model correction interfaces (FF MCIs), bounded-scope deterministic (BSD) neural networks, processes and methods, and intelligent storage and retrieval (ISAR) systems and methods. These systems and methods avoid and bypass noun phrase collisions and correct for errors caused by noun phrase collisions so that hallucinations are eliminated from LLM responses.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for creating and distributing customized artificial intelligence chatbots. In some implementations, a system provides an interface for creating or editing an interactive application configured to provide responses generated using one or more artificial intelligence (AI) or machine learning models. The system receives customization data through the interface, where the customization data indicates customizations specified by a user to customize the interactive application. The system stores one or more records specifying configuration settings representing the customizations for the interactive application. The system provides one or more users access to the interactive application with the customizations.

Techniques for operating a chatbot system for enterprise-level conversational agents are disclosed. These techniques are performed by an application or cloud service executing on one or more computing devices. An enterprise system can deploy conversational agents onto user devices to run as chat interfaces for logging analytics question-answering. One example application or cloud service may be a multi-model chat mechanism configured to support these chat interfaces with backend functionality. In response to an incoming question, the chat mechanism first consolidates the question with any conversation history and then, classifies the user's question as either a question regarding unstructured document data, a question regarding structured log data, or a hybrid question. Based on the classification, the chat mechanism can generate a proper large language model (LLM) response.

Implementations of the present disclosure provide receiving, by a conversational commerce optimizer system, a user input from a user, the user input being provided to a chatbot of an e-commerce platform, providing a refined query based on the user input, selecting, by the conversational commerce optimizer system, a foundation model that is to be queried using the refined query, determining a set of search results from a database that stores data representative of products available through the e-commerce platform, prompting the foundation model using a prompt that is generated based on the refined query and the set of search results, the foundation model providing an output responsive to the prompt, and providing a response to the user through the user interface of the chatbot, the response being generated based on the output.

Methods, systems, and apparatus, including computer-readable media, for managing interactions with multiple artificial intelligence chatbots. In some implementations, a text input from a user is received. The system identifies multiple chatbots that the user is authorized to access, and the system selects a subset of the multiple chatbots based on the text input from the user. The system provides the text input from the user to each of the chatbots in the subset to generate a response to the text input from each of the chatbots in the subset. The system provides an output response to the text input from the user for presentation at the user device, where the response is based on one or more of the responses generated the chatbots in the subset.

Techniques are described for performing automated operations related to generating and providing housing-related information, such as to automatically respond to free-form natural language query requests for housing-related information of various types received by a chatbot by using a combination of automated tools to generate and provide responsive housing-related information. In at least some situations, the described techniques generates responses using a trained large language model that maintains context over an interaction session with a user involving multiple user queries and corresponding responses, and ensuring accurate response information by restricting the generation of the response information in particular ways and by identifying and providing citations to authoritative sources used to generate the response information.

A computing system includes at least one processing circuit having at least one processor and at least one memory device. The processing circuit performs operations including: providing a chat interface to allow a plurality of travelers to initiate a chat session in which the plurality of travelers collaborate to plan a trip; receiving, via the chat session, input from the plurality of travelers related to the trip; and automatically interacting, via a chatbot, with the plurality of travelers within the chat session to plan the trip by: processing, using a machine learning model, a content of the chat session to generate one or more recommendations for the trip, wherein processing the content of the chat session includes generating the one or more recommendations using the input from the plurality of travelers; and providing the generated one or more recommendations to the plurality of travelers within the chat session.

There are provided systems and methods for a fine-tuning system for large language models trained for open-ended domain-specific tasks. An online transaction processor or other service provider may provide computing services and platforms to entities, which may include chatbots, information retrieval systems, question-and-answer systems, and the like. To provide better LLM training and fine-tuning, which may improve LLM performance in answering users' questions in an automated manner, the service provider may implement a fine-tuning system that may utilize automated annotations of training data, such as query and response pairs. An LLM may be prompted to determine an annotation to such pairs, and the annotations may be used to label the training data. A fine-tuning system and operations may then be implemented to fine-tune the LLMs using different processes including question-answering, retrieval augmented generation, or a continuous fine-tuning based on a size of the training data.

Methods, systems, and apparatus, including computer-readable media, for artificial intelligence chatbots using external knowledge assets. In some implementations, a system stores a knowledge base that comprises one or more knowledge items for an organization. The system receives a user prompt for a chatbot, and generates a chatbot response to the user prompt using one or more artificial intelligence and / or machine learning (AI / ML) chatbots. The chatbot response to the user prompt is generated at least in part based on the one or more AI / ML models processing the one or more knowledge items from the knowledge base. The system provides the chatbot response for presentation.

Apparatuses, systems, and techniques to cause weights assigned to properties of chatbot answers to be dynamically adjusted. In at least one embodiment, one or more neural networks are used to characterize one or more chatbot queries and cause weight values assigned to properties of answers to the one or more chatbot queries to be dynamically adjusted, based, at least in part, on the characterization of the one or more chatbot queries.

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.

A method for developing a chatbot for mental health using generative Artificial Intelligence (genAI) and a system for recognition and recommendation are disclosed. The method comprises: designing a conversation flow, creating a flowchart that outlines the logical steps; defining guiding questions and the Depression, Anxiety, and Stress Scale (DASS) examination; fine-tuning large language models with the technical prompt engineering; collecting and labeling data for each model mental health issues detection: ill-being detection model and keyword recognition model; building pipeline and training Artificial Intelligent (AI) model for ill-being detection model and keyword recognition model with a Bidirectional Encoder Representations from Transformers (BERT) model or a pre-train model; collecting and processing mental health support resources such as Cognitive Behavioral Therapy (CBT) exercises, informative articles, inspiring movies, and effective coping strategies, and psychologists; developing mental health support resources system; and developing and integrating speech-to-text and text-to-speech models into the system.

Aspects of the disclosed technology include computer-implemented systems and methods for conversational recommendation systems, such as conversational chatbots that are configured to process user queries and generate responses. A recommendation system can receive a user query, provide a recommendation response, and solicit feedback from a user in a target domain. The system can display a first set of items and receive inputs indicative of preferences relative to the first set of items. The system can generate preference embeddings in an embedding space of the target domain based at least in part on the preferences and compare the preference embeddings with item embeddings in the embedding space. The system can select content items based at least in part on a distance between the preference embeddings and the item embeddings in the target embedding space and generate data for displaying the selected content items via the user interface.

As cybersecurity alerts are detected and logged as formatted descriptors across an organization, a text converter converts the formatted descriptors into natural language descriptors by extracting and inserting metadata fields into natural language templates corresponding to user personas for the organization. In response to an alert-based query from a user, a persona classifier predicts a persona of the user from historical chat logs and retrieves natural language descriptors for alerts related to the user and predicted persona. A prompt generator receives the retrieved natural language descriptors and generates a prompt that instructs a large language model (LLM) to respond to the user with data from the natural language descriptors. Once prompted, the LLM establishes a conversation with the user via an interface for alert resolution.

In various examples, systems and methods are disclosed relating to displaying images in chatbot / NPC / virtual agent / digital avatar / etc. responses. A system can identify text corresponding to an image in an electronic document and can store a representation of the text in association with an identifier of the image. The system can receive an input prompt for a machine-learning model. The system can generate a response to the input prompt using the machine-learning model. The response can include the image responsive to identifying the representation of the text using a searching function and an output of the machine-learning model.

Methods and systems for interacting with users via a chatbot. A natural language query is received and processed by submitting a search query to a search engine. The search engine identifies relevant information including textual information and images for formulating a response. The identified information and query are submitted to a Large Language Model which generates a response displayed via the chatbot. The response may include textual information and relevant images. The system can extract text from images of documents and convert textual information into numerical vector representations for processing. Selectable options based on clustered relevant information can be provided to users for query refinement when appropriate. The chatbot interface enables natural language interactions while leveraging search capabilities and Artificial Intelligence to provide informative and helpful responses with both text and visual elements.

A system and method for improving computer functionality by retrieving answers / responses to questions / input from a cache such as those used with chatbots and generative AI systems. Disclosed is a multi-layered caching strategy that focuses on the relevance of a cache hit by improving the quality of the answer. The approach demonstrates that response latency is significantly reduced when using caching and how a caching strategy could be applied in various layers of increasing relevance for a simple Question-and-Answer system with the possibility of extending to more complex generative AI interactions.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for persistent learning for artificial intelligence chatbots. In some implementations, a system stores data indicating criteria for initiating learning in a chatbot system, where the criteria indicate conditions that trigger storage of a learned item to persist across sessions or conversations. The system receives a user prompt and generates a chatbot response. The system provides the chatbot response, and detects that the one or more criteria are satisfied, for example, based on a subsequent user prompt or other user feedback after the chatbot response is provided. In response to detecting that the one or more criteria are satisfied, the system adds a learned item to data storage, and the system is configured to use the learned item to generate chatbot responses in other sessions or conversations.

An end-side-cloud three-in-one active chat robot system for high-emotional merchants comprises an end side, a local edge server and a cloud end, and the end side is used for collecting multi-modal data of a user and performing local lightweight real-time processing and response execution; the local edge server is used for receiving and fusing the multi-modal features and the context information from the end side, and carrying out sentiment calculation, dialogue management and active trigger decision making with medium complexity; the cloud end is used for operating a super-large-scale model and providing global knowledge management, long-term user portrait storage and model training optimization; and the end side, the local edge server and the cloud end carry out cooperative communication through an encrypted channel to form a distributed intelligent processing architecture. According to the method, global optimization is realized by integrating end-side lightweight sensing, edge multi-modal fusion and cloud long-term memory. A composite finite state machine (FSM) active questioning mechanism is combined with sentiment calculation, and is different from traditional rule type triggering. Off-line and on-line fusion scheduling and multi-agent role playing are combined to be applied to a high-emotional-quotient interaction scene, and the simulation is improved. Multi-modal emotion perception circulation is introduced, and the problem of single text emotion misjudgment is solved.

A retrieval-augmented generation method for a large language model receives a user query and generates a vector embedding of the user query. The vector embedding of the user query is stored in an embedding vectors space. Also stored in the embedding vectors space are embeddings of documents retrieved from a corpus of information / A retrieval-based artificial intelligence (Al) model identifies which documents are relevant to the user query according to a similarity measure applied to the vector embedding of the user query and the vector embeddings of the documents. The generative Al model receives the user query and the documents identified as relevant to the user query and generates new content, based on the user query and the documents identified as relevant to the user query.

Aspects of the disclosure relate to using machine learning methods for chatbot selection. A computing platform may train a plurality of machine learning models, each corresponding to a chatbot. The computing platform may train an additional machine learning model to route queries to the plurality of machine learning models based on contents of the queries. The computing platform may receive a query, and may analyze the query using the additional machine learning model. The computing platform may route, based on the query analysis, the query to the plurality of machine learning models. The computing platform may generate, using the plurality of machine learning models, a response to the query. The computing platform may send the response to the query and one or more commands directing a client device to display the response to the query, which may cause the client device to display the response to the query.

Example embodiments of the present disclosure relate to a visual media-based multimodal chatbot. According to example embodiments, a method for operating a multimodal chatbot may include receiving a user input via a chatbot interface. The user input may include at least one of: a text, an audio, a first image, and a first video. The method may further include obtaining a visual media associated with the user input. The visual media may include at least one of: a second image, a second video, and an avatar associated with a person. The method may further include outputting the visual media via the chatbot interface.

Methods, systems, and apparatus, including computer-readable media, for enhancing artificial intelligence chatbots with search functionality. In some implementations, a system stores a search index for data sets, where the search index describes data objects of the data sets and values for the data objects in the data sets. The system receives a user prompt to a chatbot and searches for data objects and values that are relevant to the user prompt, including using the search index to search for data objects and values of one or more data sets that the chatbot is configured to access. The system uses one or more results obtained using the search index to generate a chatbot response to the user prompt, including providing the one or more results to an artificial intelligence and / or machine learning (AI / ML) model. The system provides the chatbot response as a response to the user prompt.

In general, techniques are described that enable a computing system to execute an artificial intelligence model for data security analysis. A computing system that includes a memory and processing circuitry may be configured to implement the techniques. The memory may store a query from an end user regarding security services provided by the data platform. The processing circuitry may parse the query to identify one or more intents, and process the one or more intents to retrieve data for formulating a natural language response to the query. The processing circuitry may also process, using a large language model, the intents and the data to generate the natural language response, and output the natural language response to a user interface for display to the end user.