Abstract: Embodiments disclosed herein generate a strategy insight report for a user's business, leveraging generative artificial intelligence—particularly large language models—and pre-stored data associated with the user. The large language models are used to capture subjective information associated with different insight areas, e.g., strength, weakness, opportunity, and threat (SWOT) of a SWOT model. The captured subjective information is augmented, supplemented, and/or modified by the pre-stored data to generate the strategy insight report. In contrast to conventional results and reports, the disclosed strategy insight report provides a current state of the user's business as well as next steps and recommendations.

Abstract: Implementations of the subject matter described in this disclosure may be used to process queries received from a user of an online resource and route the received queries to various agents that are determined to be the most suitable for performing one or more functions in response the user queries. For each of one or more received queries, an example method may determine a function corresponding to the one or more queries, select, for each function, at least one agent of a plurality of agents based on the one or more queries, send the one or more queries to a respective agent of the selected one or more agents, and receive, from a responding agent of the selected at least one agent, at least one of a document, a message, or a link representing a result of performing the function.

Abstract: Aspects of the present disclosure relate to an online resource that can initiate a conversation between a user and an automated assistant provided by the online resource. The online resource identifies a plurality of queries from the user during the conversation between the user and the automated assistant and determines a context for each query. The online resource selects an agent for each query based on its context, and then sends the queries to their respective selected agents to generate responses. The online resource combines the responses received from the selected agents to form an answer to the query, and then provides the answer to the user.

Abstract: An online resource is disclosed that can selectively add a new agent to a group of existing agents configured to generate responses to user queries. The online resource can compare a description of the new agent with one or more contexts associated with the new feature, and then add the new agent when the comparison indicates a minimum degree of similarity between the agent description and the one or more contexts associated with the new feature.

Abstract: An online resource receives a plurality of queries from a user, identifies a plurality of agents to which each query of the plurality of queries may be assigned, pairs each query with a corresponding agent of the plurality of agents based at least in part on a comparison of the respective query with agent descriptions associated with the plurality of agents, and transmits, via a communications interface, each query to its corresponding agent.

Abstract: Aspects of the present disclosure relate to an online resource that can initiate a conversation between a user and an automated assistant. The online resource receives from the user a query including a plurality of sub-queries, determines a context for each of the sub-queries, identifies a plurality of queries from the user during the conversation between the user and the automated assistant, and obtains responses to the sub-queries from a plurality of selected agents. The online resource determines a similarity score for each response by comparing the response with at least the context for the corresponding sub-query, summarizes the responses based at least in part on the similarity scores, and generates an answer to the query by combining the response summaries based at least in part on an alignment between the response summaries and their corresponding sub-queries.

Abstract: A method including receiving a context-specific query, specific to the user, from a user device of a user. The method also includes creating a computer-readable data structure for storing data. The computer-readable data structure is specific to the context-specific query. The method also includes determining a feature related to the context-specific query. The method also includes retrieving a user value for the feature. The user value is retrieved from a data repository storing user-specific data that includes the user value. The method also includes modifying the computer-readable data structure to generate a modified computer-readable data structure by adding the feature and the user value for the feature to the computer-readable data structure. The method also includes applying a generative model to the modified computer-readable data structure to generate an output. The output includes a context-specific answer to the context-specific query. The method also includes returning the output.

Abstract: Embodiments disclosed herein generate a strategy insight report for a user's business, leveraging generative artificial intelligence—particularly large language models—and pre-stored data associated with the user. The large language models are used to capture subjective information associated with different insight areas, e.g., strength, weakness, opportunity, and threat (SWOT) of a SWOT model. The captured subjective information is augmented, supplemented, and/or modified by the pre-stored data to generate the strategy insight report. In contrast to conventional results and reports, the disclosed strategy insight report provides a current state of the user's business as well as next steps and recommendations.

Abstract: A method, non-transitory computer readable medium, and an apparatus for automated estimation of repair data includes applying a first generated artificial intelligence model on a received vehicle damage image associated with an electronic claim to identify damaged component(s) on a vehicle without using any metadata. A heat map analysis is performed on the received actual vehicle damage image to identify a damage severity value associated with at least one of the identified damaged component(s). A second generated artificial intelligence model is applied on the received actual vehicle damage image and the damage severity value associated with the identified damaged component(s) to determine repair data and a repair-or-replace designation. The determined repair data and the determined repair-or-replace designation for at least one of the identified one or more damaged components is provided in response to the received actual vehicle damage image associated with the electronic claim.

Abstract: A processor may obtain historic clickstream data indicating a plurality of interactions with a user interface (UI) by a plurality of users. The processor may select at least one user for real-time monitoring by processing, using a machine learning (ML) model, the historic clickstream data and at least one user feature and predicting, from the processing, that the at least one user will utilize a UI resource. The processor may monitor ongoing clickstream data of the selected at least one user and configure the UI resource according to the ongoing clickstream data.

Abstract: Systems and methods are used to generate contact type predictions that route user customer service requests within a support platform. The contact type predictions are generated using a hybrid model that includes a deep learning component and a business logic component. The deep learning component may generate a multi-channel output based on text features and context features. The multi-channel output is modified based on one or more business rules to generate the contact type predictions.

Abstract: Embodiments disclosed herein provide language-agnostic routing prediction models. The routing prediction models input text queries in any language and generate a routing prediction for the text queries. For a language that may have sparse training text data, the models, which are machine learning models, are trained using a machine translation to a prevalent language (e.g., English) to the language having sparse training text data -with the original text corpus and the translated text corpus being an input to multi-language embedding layers. The trained machine learning model makes routing predictions for text queries for the language having sparse training text data.

Abstract: Systems and methods are used to generate contact type predictions that route user customer service requests within a support platform. The contact type predictions are generated using a hybrid model that includes a deep learning component and a business logic component. The deep learning component may generate a multi-channel output based on text features and context features. The multi-channel output is modified based on one or more business rules to generate the contact type predictions.

Abstract: Systems and methods for managing predictions for vehicle repair estimates are provided. A method includes providing one or more images of a damaged vehicle as input to a machine learning model, wherein the machine learning model has been trained with images of other damaged vehicles and corresponding vehicle operations, wherein each of the vehicle operations represents the repair or replacement of a vehicle component; receiving output of the machine learning model responsive to the input, wherein the output comprises a plurality of values each corresponding to one of a plurality of the vehicle operations; determining a confidence metric based on the values; making a comparison between the confidence metric and a confidence threshold value; and selecting the one of the plurality of the vehicle operations corresponding to the highest value as a predicted operation based on the comparison.

Abstract: Systems and methods are used to generate contact type predictions that route user customer service requests within a support platform. The contact type predictions are generated using a hybrid model that includes a deep learning component and a business logic component. The deep learning component may generate a multi-channel output based on text features and context features. The multi-channel output is modified based on one or more business rules to generate the contact type predictions.

Abstract: Systems and methods are used to generate contact type predictions that route user customer service requests within a support platform. The contact type predictions are generated using a hybrid model that includes a deep learning component and a business logic component. The deep learning component may generate a multi-channel output based on text features and context features. The multi-channel output is modified based on one or more business rules to generate the contact type predictions.

Abstract: Aspects of the present disclosure provide techniques for training a machine learning model. Embodiments include providing features of a plurality of content items as inputs to an embedding model and receiving embeddings of the plurality of content items as outputs from the embedding model. Embodiments include receiving a data set comprising features of a plurality of users associated with content items of the plurality of content items that correspond to the plurality of users. Embodiments include generating a training data set for a machine learning model, wherein the training data set comprises the features of the plurality of users associated with respective labels indicating which respective embeddings of the embeddings correspond to each respective user of the plurality of users. Embodiments include training the machine learning model, using the training data set, to output corresponding embeddings of relevant content items for users based on features of the users.

Abstract: Systems and methods are used to generate contact type predictions that route user customer service requests within a support platform. The contact type predictions are generated using a hybrid model that includes a deep learning component and a business logic component. The deep learning component may generate a multi-channel output based on text features and context features. The multi-channel output is modified based on one or more business rules to generate the contact type predictions.

Abstract: Systems and methods for managing predictions for vehicle repair estimates are provided. A method includes providing one or more images of a damaged vehicle as input to a machine learning model, wherein the machine learning model has been trained with images of other damaged vehicles and corresponding vehicle operations, wherein each of the vehicle operations represents the repair or replacement of a vehicle component; receiving output of the machine learning model responsive to the input, wherein the output comprises a plurality of values each corresponding to one of a plurality of the vehicle operations; determining a confidence metric based on the values; making a comparison between the confidence metric and a confidence threshold value; and selecting the one of the plurality of the vehicle operations corresponding to the highest value as a predicted operation based on the comparison.

Abstract: A method, non-transitory computer readable medium, and an apparatus for automated estimation of repair data includes applying a first generated artificial intelligence model on a received vehicle damage image associated with an electronic claim to identify damaged component(s) on a vehicle without using any metadata. A heat map analysis is performed on the received actual vehicle damage image to identify a damage severity value associated with at least one of the identified damaged component(s). A second generated artificial intelligence model is applied on the received actual vehicle damage image and the damage severity value associated with the identified damaged component(s) to determine repair data and a repair-or-replace designation. The determined repair data and the determined repair-or-replace designation for at least one of the identified one or more damaged components is provided in response to the received actual vehicle damage image associated with the electronic claim.