At least some embodiments of the present disclosure are directed to systems and methods for providing interactive instructions for a medical device. In some embodiments, a method includes receiving information associated with the medical device, identifying device parameters of the medical device, causing to display a representation of the medical device associated with the device parameters of the medical device, receiving a user query related to the representation of the medical device, identifying a language of the user query, generating a device prompt based at least in part on the user query and the device parameters, and generating a query response in the identified language by applying a machine learning model, and causing to deliver the query response in the identified language.

A system determines scores for evaluating users for assessment. The system receives a plurality of responses from a user based in interactions based on a simulated meeting with the user. The system stores a plurality of expected responses. For each response received from the user, the system determines a plurality of raw metrics. Each raw metric evaluates the user based on the response by comparing the response received from the user with the expected responses stored in the database. The system determines a plurality of scores, each score i determined as a weighted aggregate of a set of raw metrics, each score evaluating the user. The system configures a second user interface for presenting the plurality of scores. The second user interface displays associations between a particular score and portions of response determined to be relevant for determining at least a raw metric considered for evaluating the score.

A system performs assessment of users based on a simulated meeting. The system configures a user interface for performing interactions with the user and causes the user interface to display via a device associated with the user. The system stores a plurality of media objects storing text data, video data, or audio data. The system retrieves an execution plan for a simulated interaction and performs interactions with the user using one or more channels. Some of the interactions comprise sending, via a channel, information describing a case study according to the execution plan, and receiving via the channel, a response from the user. The system captures information describing delivery of the response by the user via the channel while receiving the response. The system determines a set of metrics based on responses and uses them to evaluate the user. The system takes actions based on the metrics.

An intelligent communication secretary system, applicable to being connected to a first communication system and including an intelligent interaction unit and a database unit. The intelligent interaction unit includes an intelligent interaction module and an interaction outline generation module. The database unit includes a knowledge storage module and a historical interaction storage module. The intelligent interaction module can interact with a user by using the first communication system. The knowledge storage module stores interaction knowledge for the intelligent interaction module to interact with the user and generate a first interaction content. The historical interaction storage module stores a historical interaction content. The interaction outline generation module analyzes the historical interaction content and generates interaction outline data.

Systems and methods are presented herein for generating representative models and assigning members of a task-facilitation service to representatives based on corresponding representative models. The task-facilitation service can transmit a set of queries that when received cause a computing device to generate a set of responses. The task-facilitation service may generate a feature vector that corresponds to the set of responses. The feature vector may be used to generate a representative model that corresponds to a user of the computing device. The representative model may be usable to establish communications one or more members of the task-facilitation service. The task-facilitation service may determine a correspondence between the representative model and one or more user models that correspond to the one or more members. The task-facilitation service may receive a selection of a particular user model and facilitate a communication to a client device associated with the particular user model.

Systems and methods for a context aware Artificial Intelligence (AI) assistant for troubleshooting network issues includes operating an Artificial Intelligence (AI) agent system that includes an agent core connected to memory, one or more tools, and a planner; receiving a request from a user; and generating, via the AI agent, an answer to the request using a plurality of inputs related to user experience of one or more users associated with a tenant of a cloud-based system.

Techniques are provided for producing an answer to a question regarding a domain. A natural-language textual sequence representing the question is received. From a knowledge graph associated with the domain, first and second textual passages are received using rankings corresponding to the natural-language textual sequence, a first textual summary is received summarizing textual information in a first vicinity of the first textual passage, and a second textual summary summarizing textual information in a vicinity of the second textual passage is received. An answer to the question is obtained using a language model by encoding a first intermediate output based on the natural-language textual sequence, the first textual passage, and the first textual summary, encoding a second intermediate output based on the natural language textual sequence, the second textual passage, and the second textual summary, and decoding a concatenation of the first and second intermediate outputs. An output is provided.

An AI-driven system is disclosed for generating a dynamic and interactive thought tree to assist users in resolving complex questions. The system includes a memory storing instructions and one or more processors configured to execute those instructions. Upon receiving an origin question from the user through a user interface, the system prompts an AI engine to decompose the question into a hierarchical structure of system-generated sub-questions. These are displayed in a branching tree format, categorized as intermediary or ultimate sub-questions, depending on whether they lead to further inquiry. The system captures user interactions with the sub-questions and uses them to re-prompt the AI engine, dynamically updating the thought tree in real time. This iterative process continues until the user submits a final input. Based on the completed interaction, the system generates a preliminary output in the form of a response to the original question, offering structured, AI-guided reasoning.

Some implementations of the disclosure provide a computer-implemented method including operations of receiving a user question by an orchestration agent, where generating a response to a user question includes generation of programming code, executing, by a sub-large language model (LLM), an instruction to generate the programming code and performing, by the sub-LLM, a validation process including determining whether the programming code generated by the sub-LLM includes a syntax error. When the validation process indicates the programming code does not include the syntax error, the method includes operations of invoking a logic module configured to execute the programming code, wherein the logic module is provided the programming code generated by the sub-LLM and executes the programming code and generating, by the orchestration agent, a graphical user interface that displays the response to the user question that includes or is based on results of execution of the programming code.

A plant recognition method and related devices. The plant recognition method includes: obtaining a plant image and question text about recognizing the plant in the plant image; inputting the plant image and the question text to a plant recognition model, the plant recognition model includes a first visual model and a multimodal large language model, the first visual model is configured to receive the plant image to extract first image features of the plant image, the multimodal large language model is configured to receive the first image features and the question text to recognize the plant in the plant image, the plant recognition model is trained with multimodal data, the multimodal data includes plant images, questions about recognizing plants in the plant images, and answers to the questions; and outputting answer text provided by the plant recognition model about recognizing the plant in the plant image.