20 results about "Human agent" patented technology

Systems and methods are provided to assess a communication conducted over a network. The systems monitor the communication and submit a survey to the customer and agent. The agent may be a human agent or an artificially intelligent (AI) agent. The survey is selected or generated, such as in response to communication content, and presented to the customer and agent. If the scoring is significantly different, an automated agent initiates a remediation action, such as to coach a human agent or to provide feedback or a corrective prompt to an AI agent.

The present invention provides a robust and effective solution to an entity or an organization by enabling them to implement a system for automatic switching between visual responses, audio responses and textual responses in an omni-channel single view experience. Particularly, the system and method may empower a user to choose between any mode of interaction, the modes being provision of a visual interaction, audio interaction or a textual based interaction and a combination thereof based on a machine learning architecture and also provide seamless human agent handover. Thus, the system and method of the present disclosure may be beneficial for both entities and users.

A human and a first AI agent are connected in a conversation which may be spoken (via telephone call) or via text (such as in a mobile or other messaging application). Keywords or phrases identified from the conversation are then compared to entries in an intent database to identify (a) an intent of the human indicating why they joined the conversation. (b) a necessity for involving a second agent in the conversation; and / or (c) based on the intent, which of several other agents are an appropriate match for the second agent. The second agent is then gracefully involved in the conversation by ensuring the human is continuously engaged until the second agent is available.

User interfaces for identifying communication sessions requiring human supervision are disclosed. According to an aspect, a system includes a communication session manager configured to define a supervising human agent. The communication session manager is configured to identify a virtual entity communication sessions assigned to the supervising human agent, thus defining a plurality of monitored virtual entity communication sessions. Further, the communication session manager is configured to render a user interface that presents the monitored virtual entity communication sessions. The communication session manager is also configured to receive selection, by the supervising human agent, of one of the plurality of monitored virtual entity communication sessions, thus defining a selected virtual entity communication session. Further, the communication session manager is configured to involve the supervising human agent in the selected virtual entity communication session.

A system is provided.SOLUTION: A system comprising: means for receiving a query; means for analyzing the received query to identify query content; means for accessing an in-house system to obtain relevant information based on the identified query content; means for generating an answer to the query using a generative AI model based on the obtained information; means for evaluating the reliability of the generated answer; means for determining whether the answer is appropriate based on the evaluation results and escalating to a human representative if not appropriate; means for sending a final answer to the user; and means for storing the query and answer in a log.SELECTED DRAWING: Figure 1

This application provides an AI-powered call transfer method, apparatus, electronic device, and medium, comprising: responding to a detected call transfer to human agent, extracting session parameters from the current AI interaction process and querying full metadata parameters associated with the currently interacting user; fusing the session parameters and full metadata parameters to obtain a complete transfer parameter package; generating a transfer work order based on the transfer parameter package using a large language model; and sending the generated transfer work order to the target human agent's port. This invention achieves seamless integration from AI interaction to human service, significantly improves the integrity of context transmission, greatly reduces the number of times users repeatedly describe their problems, and effectively improves customer satisfaction and service efficiency.

A method for generating an optimal set of parameters for a design project of a product, in which subject matter experts, working with an artificial intelligence module, select a number of fundamental measurement factors that are related to a group of fundamental prime measurements, all of which in the aggregate comprise a product profile matrix. The fundamental measurement factors are weighted and scored so that the resulting matrix reflects the various aspect of the proposed design. Through an iterative process, the fundamental measurement factors are modified until the product profile matrix provides a set of satisfactory scores that yields an acceptably low risk in proceeding with the selected design.

Contact centers that handle real-time interactions with customers commonly utilize automated agents to handle one set of work items while human agents handle the work items that are more complicated, rare, or otherwise unable to be successfully resolved by an automated agent. Customers may erroneously select a human agent when an automated agent would suffice and, similarly, may be forced to wade through automated options when a human agent is required. Systems and methods disclosed herein provide for the determination of a reason, or a prioritized list of reasons, for a call and present resolution options to a specific customer for that specific reason(s). As a result, the resources of the contact center are better utilized and the customer experience improved.

The application discloses a kind of human in loop multi-agent microservice system AgentInn design and construction method, including human in loop control module, multi-agent cooperation module, dynamic capability expansion module and task arrangement module.Human in loop control module realizes the control, participation and observation of human to agent by starting confirmation, running intervention, abnormal recovery, human agent registration, cooperation routing and experience feedback;Multi-agent cooperation module realizes cooperation arrangement and load balancing by cooperation space, role load balancing and agent discovery;Dynamic capability expansion module supports on-demand creation and hierarchical exposure of tool service and intelligent service;Task arrangement module manages progress using Cycle-Sprint-Phase three-layer time abstraction, and realizes task flow by combining flow decision and context transmission.The application solves the problems of lack of human participation in multi-agent cooperation, high complexity of cooperation arrangement and limited capability expansion, and realizes multi-agent microservice cooperation.

Disclosed are systems and methods for enabling collaborative AI-human voice interactions in an outbound call environment. An AI voice engine synthesizes real-time speech responses, optionally using a voice model representing a specific human agent. A sentiment analysis engine monitors user speech to classify emotional state, and an emotion modulation engine dynamically adjusts tone, pitch, and prosody of AI output based on inferred sentiment or operator commands. An operator console allows live human oversight, including editing AI-generated text, selecting emotional tone presets, or overriding responses entirely. The system supports real-time disclosure of AI identity and records call metadata for compliance. Training modules log operator interventions, sentiment patterns, and interaction outcomes to improve future AI behavior, enabling a scalable voice platform that balances automation with human empathy.

The application relates to the technical field of natural language processing, in particular to an intelligent assistance method and system suitable for call center human agents, which comprises the following steps: obtaining target dialogue text; taking the segmented words after removing stop words in the target dialogue text as target segmented words; obtaining all co-occurrence words of each target segmented word according to the stop word features and emotional intensity of the sentence where the target segmented word is located; obtaining the associated words of each target segmented word according to the co-occurrence frequency of each target segmented word and its co-occurrence words and the appearance frequency of each co-occurrence word; dividing all target segmented words into multiple entity sets according to the similarity of associated words and the similarity of pinyin between target segmented words, then obtaining the entity vocabulary of each entity set, and finally replacing all target segmented words with the entity vocabulary corresponding to the entity set where the target segmented word is located. The application accurately obtains the co-occurrence words of each target segmented word, thereby accurately obtaining the entity vocabulary corresponding to each target segmented word, and improving the error correction accuracy of the dialogue text.

A human and a first AI agent are connected in a conversation which may be spoken (via telephone call) or via text (such as in a mobile or other messaging application). Keywords or phrases identified from the conversation are then compared to entries in an intent database to identify (a) an intent of the human indicating why they joined the conversation. (b) a necessity for involving a second agent in the conversation; and / or (c) based on the intent, which of several other agents are an appropriate match for the second agent. The second agent is then gracefully involved in the conversation by ensuring the human is continuously engaged until the second agent is available.

This invention relates to a knowledge graph-based intelligent customer service question-and-answer precision recommendation and processing method, belonging to the field of electronic digital data technology. The method includes: constructing a conversation prediction knowledge graph based on the historical conversation counts from multiple channels; predicting the future conversation counts from multiple channels based on the conversation prediction knowledge graph and the current conversation counts from multiple channels; setting the number of robot agents and human agents based on the future conversation counts from multiple channels; accessing conversations from multiple channels, using robot agents to perform semantic understanding of user questions in the conversation, constructing a user demand profile, and determining whether to assign a human agent; if so, assigning the optimal human agent based on the status data of the human agent; if not, generating answers based on the user demand profile, conducting a dialogue, and generating recommended questions based on a question association graph. The question association graph records the relationships between multiple questions, which has the advantage of improving the quality and efficiency of customer service.

A system includes a processing circuit configured to receive a first input from a user device. The processing circuit is further configured to determine a first task that the processing circuit is authorized to autonomously perform based on the first input. The processing circuit is further configured to autonomously perform the first task based on determining the processing circuit is authorized to autonomously perform the first task. The processing circuit is further configured to receive a second input from the user device. The processing circuit is further configured to determine a second task that the processing circuit is unauthorized to autonomously perform based on the second input. The processing circuit is further configured to provide the second input to a human agent device associated with a human agent based on determining the processing circuit is unauthorized to autonomously perform the second task.

The invention belongs to the technical field of customer service seat service, particularly relates to a novel customer service queuing method based on joint participation of AI and manpower, and aims to solve the problems that the existing AI can only process simple problems and complex problems and needs to transfer human seats, and the number of AI seats can be adjusted in real time based on system load (such as elastic expansion of cloud resources). In order to solve the problems that in the prior art, the number of human seats is relatively fixed, the following scheme is proposed and comprises the following steps: S1, preparing a queuing system and receiving a request; s2, classifying incoming requests, and identifying problem complexity; s3, distributing the requests to queues with different priorities according to a classification result, and determining a routing strategy; s4, processing the request according to the strategy; s5, the system state is monitored in real time, and the number of the AI seats is adjusted in real time, the waiting time can be shortened during peak load (dynamic AI capacity expansion), and the cost can be reduced during low peak load (dynamic capacity reduction).

An automated interactive voice dialogue system using a human-in-the-loop design may enable human-supported interventions, where the automated system still conducts most of the interaction but enables a human agent to assume control of the dialogue and assist, if deemed necessary, so that the user may continue the interaction with little interruption or frustration. In some examples, the user of the dialogue system of this disclosure may not realize that there was a problem, and that the interaction is being or has switched from an automated dialogue system to a human. In some examples, the automated dialogue system of this disclosure may also automatically switch back to machine interaction when the human agent has resolved the situation.

A robot system for executing at least one behavior in an interaction with at least one human agent in an environment comprising a robot configured to execute the at least one behavior, at least one sensor configured to obtain information on the environment, and a processing means configured to detect the at least one human agent based on the obtained information. The system comprises an interface for accessing a common-sense inference engine in the form of a large language model. The processing means is configured to predict a tree of behaviors of a human-robot interaction for performing the task by prompting the large language model a plurality of times and to assign cost to each behavior of the robot in the predicted tree of behaviors. The assigned costs include behavior cost for the robot and target achievement satisfaction cost of the at least one human agent in reaction to the behavior of the robot. In addition, the processing means selects a sequence of behaviors from the predicted tree of behaviors based on the assigned cost and controls at least one actuator of the robot based on the selected sequence of behaviors.

This invention relates to the field of digital human intelligent interaction technology, specifically disclosing a digital human interaction method and system that supports direct switching between robots and human agents. The invention receives user input and generates robot responses, identifies user intent based on interaction information, determines whether to trigger human service and performs permission verification, extracts user-robot dialogue information, analyzes user request characteristics and obtains agent status data, achieves precise matching and connection between users and optimal agents through an intelligent scheduling model, and then integrates robot-human dialogue data to extract multi-dimensional interaction features and quantitatively evaluate service efficiency. This invention can improve the efficiency of robot-human collaboration, enhance service efficiency and user experience, reduce ineffective interactions and resource waste, and improve problem-solving rates and user satisfaction.

Implementations relate to processing multi-turn dialogs each showing (1) dialog turns that correspond to user input(s) providing user intent(s) and associated parameter(s), and (2) dialog turns that correspond to input(s) from a virtual assistant (or a human agent / responder) that are responsive to the user input(s). A multi-turn dialog (e.g., a pre-processed variation thereof) can be processed, using a generative model, to generate one or more one-shot queries summarizing the user input(s) of the multi-turn dialog. Whether the generated one-shot queries accurately reflect the user intent(s) and / or the associated parameters can be verified, and only verified one-shot queries are selected to form part of a dataset. The dataset can be used, for example, for training machine learning model(s) for handling a single, complex user query and / or for validating machine learning model(s).

A question is posed by a user of a user device as part of an online chat session with an online system. An intake artificial intelligence (AI) agent interacts with the user via the online chat session in one or more rounds of messaging to gather information that may be used by a human agent to respond to the question. At some point, the online system may identify in an output of the intake AI agent an indication that there is sufficient context regarding the question to transfer the question to the human agent. The online system provides session information (e.g., the question and gathered context) to a user device associated with the human agent. The human agent may use the session information to develop a response to the question that may be provided to the user.