An information processing device includes a controller, a ride detection device configured to detect a ride of a user, a storage device configured to store action data of the user, an output device configured to output question data for requesting an answer from the user, and an input device configured to receive an input from the user. The controller outputs, from the output device, output data including at least a question regarding an action of the user taken before riding in a vehicle in accordance with positional information of the vehicle when detecting the ride of the user based on a signal acquired from the ride detection device, acquires an answer to the question from the user as input data via the input device, and associates the input data with the positional information of the vehicle or a POI to store the associated data in a storage device.

Novel tools and techniques are provided for implementing call center-based automated interface between call center agents and network orchestration systems, in some cases, via application programming interfaces (“APIs”). In various embodiments, an automated services platform may collect, from data sources, data associated with services provided by a service provider. The collected data may be autonomously analyzed to identify any issues with provisioning a service(s) to a customer. If so, one or more automation actions may be autonomously identified to address the determined issue(s). The automated services platform may autonomously send instructions to automation bots to perform the identified one or more automation actions, and may generate and present a UI on a user terminal operated by a user, the UI displaying information regarding at least one of the at least one issue, the affected service(s), the identified one or more automation actions, or the automation bots, and/or the like.

A method including generating, by a state engine from data describing behaviors of users in an environment external to the state engine, an executable process. An agent executes the executable process by determining, from the data describing the behaviors of the users, a problem of at least some of the users, and selects, based on the problem, a chosen action to alter the problem. At a first time, a first electronic communication describing the chosen action to the at least some of the users is transmitted. Ongoing data describing ongoing behaviors of the users is monitored. A reward is generated based on the ongoing data to change a parameter of the agent. The parameter of the agent is changed to generate a modified agent. The modified agent executes the executable process to select a modified action. At a second time, a second electronic communication describing the modified action is transmitted.

Techniques for generating conversational responses for a conversational user interface are disclosed. In one embodiment, a method is disclosed comprising obtaining user input from a user via a conversational user interface, using the user input to obtain a user emotion and a user intent, obtaining candidate probabilities for a fragment of a response to the user input using the obtained user emotion, the obtained user intent and the user input, generating the response to the user input using the candidate probabilities obtained for the fragment to select a candidate for the fragment of the response, and communicating the response to the user via the conversational user interface.

A computer retrieves data from a database. The computer retrieves a Machine Learning (ML) model trained to generate database queries. The computer applies the ML model to generate a primary database query based, at least in part, on a user inquiry available to the computer. The computer retrieves the primary database query, an initial set of data from a database available to the computer. The computer, in response to retrieving the initial set of data, receives feedback assessing the initial set of data. The computer, in response to receiving the feedback, applies a Natural Language Processing (NLP) model to identify query adjustment content within the feedback. The computer revises the primary database query based, at least in part, on the model adjustment content, to generate a secondary database query. The computer retrieves using the secondary database query, a secondary set of data from the database.

The present invention relates to an AI-based voice ordering system and a method therefor and provides a voice ordering method and system, the voice ordering method comprising: a first step of an ordering smart terminal standing by for voice data reception; a second step of the ordering smart terminal analyzing whether an input signal has been received by an input unit corresponding to a microphone activation button; and a third step of, if the analysis result indicates that an input signal has not been received, returning to the first step and, conversely, if an input signal has been received, the ordering smart terminal receiving a voice signal from a microphone, converting the voice signal into voice data of a preset format, and then transmitting the converted voice data to a voice ordering server via a host terminal connected to a network, so that analysis of text data is performed.

A system for generating wrap-up information is capable of learning how interactions are transformed into contact notes and outcome codes using natural language processing and can generate the contact notes and outcome codes for new incoming interactions by applying prediction models trained on interaction data, contact notes and outcome codes. The system for generating wrap-up information receives interaction data, including interaction audio data, interaction transcripts, associated contact notes and associated outcome codes. The interaction transcripts are generated from the previous interactions between agents and customers. The contact notes and outcome codes are generated by agents during the associated previous interactions. The system processes and uses the interaction data to train prediction models to analyze interaction audio data and interaction transcripts and predict appropriate contact notes and outcome codes for the interaction. Once trained the prediction model(s) can generate appropriate contact notes and outcome codes for new interactions.

A non-transitory computer-readable recording medium may storing instructions that, when executed by a processor, cause the processor to set a communication session for a conference in which a plurality of users participates through a server, transmit, to the server, an identifier of an avatar to be represented on a virtual space for the conference and coordinate information of the avatar in the virtual space, receive, from the server, resources of neighboring avatars selected based on the coordinate information, transmit, to the server, motion data of the avatar through the communication session, receive, from the server, motion data of the neighboring avatars through the communication session, and represent the neighboring avatars on the virtual space based on the resources of the neighboring avatars and the motion data of the neighboring avatars.

A system and method for generating a response to a user query. The method encompasses receiving, at a transceiver unit [102], the user query. The method thereafter leads to identifying, by an encoder unit [104], a user context associated with the user query based on one or more pre-stored datasets. Further the method encompasses predicting, by a prediction unit [106], one or more parameters corresponding the user query based on at least one of one or more offline-policies and one or more online-policies. The method thereafter comprises generating, by a decoder unit [108], the response to the user query based at least on the user context associated with the user query and the one or more parameters corresponding to the user query.

Embodiments of the present disclosure provide for identification and output of improved predicted operational support data object(s). Embodiments utilize particular data sets and data model implementations to identify and select predicted operational support data object(s) determined as associated with the highest confidence to assist in resolving a particular malfunction affecting a networked device in a dynamic home communications network. Such embodiments enable resolution of the malfunction, utilizing the predicted operational support data object(s), with improved success rates and without requiring performance of additional and/or alternative support processes.