388 results about "Chatbot" patented technology

Disclosed is a novel system and process for presenting a customized chatbot. The transaction in one example is a sales transactions. Information is retrieved that is associated with the user or customer. The information includes demographic information of the user or behavioral information of the first party, or a combination of both. A chatbot is presented on the system of the user as of a messaging window of a web page document of an entity selling a good or a service. The messaging window is customized to the end user based on the demographic information of the first party, or the behavioral information of the first party or a combination of both. The messaging window is customized to include one or more of an initial greeting, an appearance of the messaging window, and a picture of an agent.

Methods and computing systems are disclosed for a digital assistant platform that includes a cross-platform bot that can be used to bridge existing chat-bot platforms. The cross-platform bot would have its own single set of protocols and standards, such that a developer of a task-specific bot would only need to integrate with the cross-platform bot to enable its use across all the different chat-bot platforms. The cross-platform bot would enable the task-specific bots to coordinate to combine their functionality, while end users may continue using an existing chat-bot platform of their choice.

Disclosed is a novel system and process for launching a messaging window such as a chat bot, especially in a mobile platforms such a smart phones and tablets. The process begins with displaying a web page with webpage contents in a browser window with a primary top bar and a display window. In response to receiving user input to scroll the webpage contents of the browser window are scrolled in a horizontal direction, a vertical direction, or a combination thereof. Also, a secondary top bar, as a previously invisible graphical element within the webpage contents of the web page is now displayed in substantial position and with the substantially the same appearance to look like the primary the top bar. In response to receiving user input in the vicinity of the secondary top bar, the system triggers a presentation of a messaging window or chat window.

A method and system for iteratively searching large amounts of data in response to a user request by traversing a conversational scaffold and producing a document set in response to the request, producing category descriptors for the document set, transmitting the category descriptors to a chatterbot response composer for producing a chatterbot response, and providing the response to the user.

A system for coordinating multiple chatbots in a group conversation using natural dialog systems, including a creation unit that enables a user to create a group chat with chatbots; a response unit that allows the user to reply to any utterance extracted from a message received from a member of the group chat; a transmission unit that sends messages to every member of the group chat; a development unit that develops chatbots that understand natural language and interact in a group chat using natural dialogue; a network connection; a first database that stores a knowledge base extracted from all the utterances exchanged by members of the group chat; a second database that stores all interactions between the user and the group chat, and a third database that stores all interaction protocols used by the members of the group chat.

A system is disclosed for chatbot conversation construction, and includes user interface, coupled to a conversation construction and management platform, configured to provide entry of intents, associated responses and search terms, and to construct and store conversation controllers, each based at least in part on one or more of the intents, and build conversation flows, each based at least in part on one or more of the conversation controllers. An artificial intelligence (AI) connector platform couples to the conversation construction and management platform, and is configured to receive natural language (NL) search data from a chatbot channel, format the NL search data into an input protocol of a selectable NL processing engine and receive a response from the NLP engine and, based at least in part on one or more of the conversation flows, convert the NLP engine response to a personalized response, and send the personalized response to the chatbot channel.

A user-to-user communication system allows users to chat with each other by sending messages via a computer. The computer also includes a chatbot with which the users can chat; that chatbot generates response messages automatically. The computer keeps a history of messages sent by the users, infers users' interests from the content of the messages they send, matches users with similar interests, and presents the matched users as suitable chat partners. In inferring users' interests, the computer also considers message sent to the chatbot, so that a user can be matched with suitable partners even before the user has accumulated an extensive history of communication with other users.

A chatbot system and method with interactive chat log. A chatbot includes a processor, an interactive dialog interface and said knowledge database. The chatbot generates a chat log page including messages based on a chat session. Without leaving the chat log page, the chatbot uses the chat log page for updating chatbot messages and corresponding entries in a knowledge database.

The invention discloses a multi-round automatic chat and conversation method and system based on deep learning. The method comprises the following steps that: on the basis of the current input processing result of a user, using a deep learning model to mine the current input intention of the user; according to the current input intention of the user, the chat theme of a previous round of conversation and the current input processing result of the user, using the deep learning model to judge whether the current input intention of the user is consistent with the chat theme of the previous round of conversation, updating entity information according to a judgment result, then, obtaining response contents according to the current input intention of the user from a preset response database, and sending the response contents to the user. The method has the beneficial effects that the contents and the context scene information of the current round of conversation and the previous round of conversation can be simultaneously considered when a chatting robot carries out continuous multi-round conversations with the user, more accurate contents can be responded to the user, and answering quality is improved.

A system and method for matching a lender and a borrower based on color-coded financial ratios, and discussing the loan through an online meeting between the borrower and lender. The method creates an efficient environment for matching a borrower, such as a consumer, with at least one lender, such as a bank. The borrower and at least one lender interact on a network. The borrower submits loan related data to a database and algorithm for analyzing to generate a color-coded financial ratio that provides the lender with efficient tools to determine whether the potential borrower qualifies for the respective lending criteria. A chatbot answers queries from the borrower or lender. An interactive calendar allows the borrower and lender to corroborate on a meeting date and time, and an Internet operable image capturing device, such as webcam, allows for an online meeting between borrower and lender to discuss the loan.

The system comprises a delivery server and at least two robot servers. Wherein, the delivery server is used to receive the user's message and to deliver the user's message to the relevant robot server; the robot server is used to respond to the user's message and to automatically give a feedback reply. The invention also reveals a method thereof.

The system comprises: an instant communication server used for establishing the connection between the instant communication client side and the robot chat server; an instant communication client side used for sending the session message to the robot chat server via the instant communication connection; a robot chat server used for receiving the session message sent from the instant communication client side, finding the answer, and sending the answering message to the communication client side. The Invention also reveals a method thereof.

The invention provides a multi-role intelligent chatting method and system. The method comprises the following steps that information input by users and / or information subscribed by the users and / or information pushed to the users are / is acquired; semantics of the information are acquired through natural language processing, or the specific information is triggered according the preset condition; dialogue elements and at least two chatting robots participating in a dialogue are determined; a multi-role dialogue script is generated; dialogue lines are pushed to the chatting robots according to the dialogue script to achieve intelligent chatting. According to the method and system, through the mode that multiple chatting robots playing different roles have a group chat with the users, on one hand, the defect that in the current man-machine interaction process, the users need to continuously raise questions or a topic is overcome, and on the other hand, the topic is guided to be developed in a targeted mode through dialogue cooperation among the chatting robots according to the topic type and content, so that the interestingness, depth, breadth and knowledge transmission effect of information exchange are increased or enhanced. The invention further provides an information acquiring and information sharing method based on multi-role intelligent chatting. By means of role substation of the exclusive chatting robots of the users, the users who are not in the same time and space can communicate with one another just like they are face to face.

The present teaching relates to method system, and medium for generating knowledge for a chat bot. Training data are used to learn and generating knowledge and are received with at least some labeled training seeds and unlabeled conversation data. The training data are parsed and various linguistic elements are extracted therefrom. Such linguistic elements are then used to perform automated learning in accordance with at least one label used in labeling the training seeds. Based on the automated learning, at least one model associated with the at least one label is generated from the training data.

A user-aided learning chatbot system and method. The chatbot includes an interactive dialog interface for engaging in a chat session with a user. The user can enter knowledge entries during the chat session without losing context. The chatbot enters and stores the knowledge entry for interaction with subsequent users.

A context-aware chatbot method and system are provided. The context-aware chatbot method comprises receiving a user's voice; converting the user's voice to a question to be answered; determining a question type of the question to be answered; generating at least one answer to the question based on a context-aware neural conversation model; validating the answer generated by the context-aware neural conversation model; and delivering the answer validated to the user. The context-aware neural conversation model takes contextual information of the question into consideration, and decomposes the contextual information of the question into a plurality of high dimension vectors.

The invention discloses a chatting robot knowledge base and a construction method thereof. The knowledge base comprises a chatting word library and a chatting person attribute library, wherein the chatting word library consists of a plurality of chatting word records; each record comprises a chatting word, contextual information of the chatting word, a plurality of replying words of the chatting word and a limiting condition set of each replying word; the chatting person attribute library consists of a plurality of chatting person attribute records; each record contains static attribute and dynamic attribute of each chatting person. The invention also discloses the construction method of the chatting robot knowledge base. The information required by the knowledge base is acquired by using a data mining technology. The candidate chatting words, contextual information and replying words are respectively mined from mass artificial chatting data. The chatting person attributes are dynamically acquired from a chatting process. According to the chatting robot knowledge base and the construction method thereof disclosed by the invention, the defects that the chatting process is incoherent and the personalized reply cannot be given according to the chatting person attributes exiting in the current chatting robot can be overcome and the support can be provided for developing intelligent chatting robots in the specified fields.

Systems, devices, and methods of the present invention detect argumentation in text. In an example, an application executing on a computing device accesses text comprising fragments. The application creates a discourse tree from the text. The discourse tree includes nodes, each nonterminal node representing a rhetorical relationship between two of the fragments and each terminal node of the nodes of the discourse tree is associated with one of the fragments. The application matches each fragment that has a verb to a verb signature, thereby creating a communicative discourse tree. The application determines whether the communicative discourse tree represents text that includes argumentation by applying a classification model trained to detect argumentation to the communicative discourse tree.

An enterprise service bus (ESB) having a plurality of internet relay chat (IRC) clients and internet relay chat (IRC) ‘bots’ that provide ESB like routing and mediation functionality. The internet relay chat (IRC) framework provides messaging transport, and utilizes SOA specific tooling to turn IRC into an SOA framework.

According to an example embodiment of the present disclosure, there is provided a method and a device for cross-type conversation, an apparatus, and a computer-readable storage medium. The method includes receiving a message from a user and determining a reply type for the reply message based on the constructed map, wherein nodes in the map represent semantic content and edges in the map representsemantic associations between nodes. In addition, the method includes providing a reply to the message based on the reply type. According to an embodiment of the present disclosure, by using a prioriknowledge in a map to assist in determining a reply type, it is possible to reduce the dependence of a chat robot on training data, so that the chat robot can actively guide a cross-type conversationand maintain the logicality of the semantic content of the conversation.

The invention discloses a method for chatting with an online customer service representative, and belongs to the field of instant messaging (IM). IM connection between a customer service server and a client is established in advance; the client can make conversation with the customer service server in the manner that the client only communicate with an intelligent robot customer service platform, or priority is assigned to communication with the intelligent robot customer service platform, or priority is assigned to communication with a manual customer service platform; and free switching between the intelligent robot customer service platform and the manual customer service platform is realized. The problem that user experience is not good due to the fact that a chat robot and the manual online platform cannot be switched smoothly in the original customer service filed is solved.

The invention relates to an automatic generation method and system for replies of a chatting robot. The method comprises the steps that key words in a statement input by a user are extracted, searching is conducted with the key words as indexes, and the corresponding question-answer pair is found in a prior knowledge base; if the corresponding question-answer pair is found, the replay sentence inthe question-answer pair is taken as a reply statement to be input; if the corresponding question-answer pair is not found, the statement input by the user is input an answer generation module, and areplay statement is obtained and output. By means of the automatic generation method and system for the replies of the chatting robot, the conversations automatically generated by the robot can be closer to the conversations between real people, and the automatic generation method and system can be used in a development field.

Disclosed is a novel system and process to use information gathered by a chatbot system including explanations why a potential customer is discontinuing a transaction. The transaction in one example is a sales transaction. This information is associated with the potential customer. Using this information, the present invention enhances the message targeting by advertisers and remarketing system for the identified potential customer. The advertisers and remarketing systems use the information from the chatbot system to automatically select messaging and advertisements.

The invention discloses an individualized interactive method of a smart robot and an interactive system. The interactive method comprises steps of receiving dialogue interaction information input by a user, analyzing the dialogue interaction information; obtaining user property information, robot property information and environment information; inputting the analyzed result of the dialogue interaction information, the user property information, robot property information and environment information to a dialogue generating model to generate response information; outputting the response information in the form of multi modes. The interactive method can realize the individualized response of the smart robot, so that the response of the chatting robot is more individualized, and the user experience is improved.

An auto-reply electronic mail message with personalized content. Text is extracted from an inbound email message. The text is used for chatbot input messages. Chatbot output messages are generated. The chatbot output messages are composed. The composed messages are formed into a responsive outbound email communication.

The invention discloses a chatting robot system, which comprises at least a user (1), and a chatting robot (9) with a communication module (21), an AI server (3), a query server (4), a game server (5), and corresponding database; wherein, the user has a variety of dialog to the said robot by real time communication platform or SMS platform, and the chatting robot has some AI and powerful information service function.

Systems, devices, and methods of the present invention uses noisy-robust discourse trees to determine a rhetorical relationship between one or more sentences. In an example, a rhetoric classification application creates a noisy-robust communicative discourse tree. The application accesses a document that includes a first sentence, a second sentence, a third sentence, and a fourth sentence. The application identifies that syntactic parse trees cannot be generated for the first sentence and the second sentence. The application further creates a first communicative discourse tree from the second, third, and fourth sentences and a second communicative discourse tree from the first, third, and fourth sentences. The application aligns the first communicative discourse tree and the second communicative discourse tree and removes any elementary discourse units not corresponding to a relationship that is in common between the first and second communicative discourse trees.

Various embodiments each include at least one of systems, devices, methods, and software for chatbot order submission by group members for products to an entity that will provide the ordered products. The group may be just a single person or may be many people. One example embodiment that may be performed by a chatbot or an element of a chat host platform includes identifying an activity command received over a network from a participant within a text-based chat session. This method further includes executing a data processing activity associated with the identified activity command, the executing of the activity including generating and transmitting task data over the network to a data processing system of an entity that will perform a task based on the task data.

Disclosed herein are systems and methods of a digital therapy service to generate therapy regimen addressing a health condition, which may require the customer to perform various tasks and instruct devices to capture data related to the customer's therapy, including body metric measurements and information related to the number and quality of interactions between the user and aspects of the digital therapy service, sometimes referred to as “user-generated” inputs. The digital therapy service may calculate various metrics, such as scores and milestone determinations, to measure the customer's progress. The scores can be determined using dynamically generated and updated scoring models. Artificial intelligence chatbots may be used to deliver and capture information to and from customers during interactive sessions. Each customer may have a unique chatbot queue that contains the various chatbots that will be used to deliver particular aspects of the customer's therapy.