100 results about "Persona" patented technology

This invention relates to a system and method that enhances user interaction and marketing effectiveness through advanced data integration and personalized algorithms. The system and method collects detailed user data through body scanning and questionnaires, creating comprehensive profiles using machine learning. These profiles facilitate precise matching of influencers with businesses based on physical attributes, audience demographics, and content preferences. The system generates AI-driven marketing personas tailored to specific demographics and enables live shopping integration with social platforms. A novel double-blind review mechanism ensures transparent feedback between businesses and influencers. The platform supports group profiles that aggregate multiple users' data, enabling family-based shopping with privacy controls and preference weighting. The system continuously refines its algorithms using campaign performance data and engagement metrics, optimizing future matches and marketing strategies. This innovative approach significantly improves marketing ROI, user engagement, and personalization for digital social interaction and targeted marketing.

In an example embodiment, a system is provided having multiple software assistants act as an interface to one or more LLMs. These assistants share contextual information about an ongoing shared conversation, but otherwise direct their respective LLM(s) to generate content based on the assistants' individual personas. The result is that a single conversation can include generated content from one or more LLMs based on multiple different personas.

A system includes a hardware processor configured to execute a machine learning (ML) model training pipeline to train an ML model using data relevant to a world of a digital persona to provide a dialogue model, generate, using the dialogue model, first conversational outputs, train the dialogue model, based on the first conversational outputs, to avoid hallucinations and / or undesirable expressions to provide a guardrailed dialogue model, generate, using the guardrailed dialogue model, second conversational outputs, train the guardrailed dialogue model, based on the second conversational outputs and persona data identifying interaction characteristics of the digital persona to provide a persona-specific model, generate, using the persona-specific model, a response to a scripted question, determine a quality score for the response, and further train the persona-specific model or validate the persona-specific model for human interaction, depending upon whether the quality score fails to satisfy or satisfies a quality criterion.

A device for creating digital persona, which includes a data collection module responsible for collecting personality data of a target object, a personality training module utilizing a large language model and the personality data to train and generate a virtual personality model with personality characteristics of the target object, thereby generating a virtual personality consistent with the personality characteristics of the target object, an appearance video generation module and voice generation module respectively utilizing face replacement and voice cloning technologies to extract the pictures and sounds from the personality data to generate a virtual personality model with the appearance and voice characteristics of the target object, a lip synchronization module, utilizing lip synchronization technology to ensure that the mouth shape and voice of the digital persona are synchronized, and an interactive module, providing an interactive interface that allows users to interact with the virtual personality and receive responses from it.

Implementations relate to dynamically adapting a given assistant output based on a given persona, from among a plurality of disparate personas, assigned to an automated assistant. In some implementations, the given assistant output can be generated and subsequently adapted based on the given persona assigned to the automated assistant. In other implementations, the given assistant output can be generated specific to the given persona and without having to subsequently adapt the given assistant output to the given persona. Notably, the given assistant output can include a stream of textual content to be synthesized for audible presentation to the user, and a stream of visual cues utilized in controlling a display of a client device and / or in controlling a visualized representation of the automated assistant. Various implementations utilize large language models (LLMs), or output previously generated utilizing LLMs, to reflect the given persona in the given assistant output.

Certain aspects provide a computer-implemented method for evaluating social media marketing campaigns using artificial intelligence (AI). The method comprises using a large language model (LLM) to generate a plurality of AI personas. Each AI persona represents a different segment of a target audience of a marketing campaign. The method uses a transformer model, a decision tree-based model, and a natural language processing (NLP) model to predict a response, a rating, and a feedback to the marketing campaign for each AI persona that represents a different segment of the target audience. The predicted responses, ratings, and feedback for the AI personas that represent different segments of the target audience are aggregated to form an evaluation of the marketing campaign for each segment of the target audience. The method sends the evaluation of the marketing campaign to a user.

The present disclosure discloses systems and methods for managing behaviour-based personalized services The system (102) obtains multi-modal data associated with user (116) from data sources. The system (102) determines user emotions and behavioural attributes associated with user (116) based on multi-modal data using artificial-intelligence model. The system (102) further generates user persona model based on user emotions and behavioural attributes. Further, the system (102) computes behavioural score for user based on user persona model. The system (102) predicts probable interactions of the user with applications based on behavioural score using generative artificial intelligence (Gen-AI) models. The system(102) determines o modifications to be made to service configurations of the applications (108A-N) by simulating the behavioural score and the predicted future interactions onto virtual applications. The system (102) modifies the service configurations based on the modifications. Furthermore, the system (102) outputs behavioural score as a token to the applications (108A- N).

An embodiment may receive observed data from a monitored system. The embodiment may receive a request to generate a contextual report based on the observed data. The embodiment may identify a relevant persona corresponding to the request based on a series of linguistic cues. The embodiment may identify a list of suitable keywords based on the identified relevant persona. The embodiment may generate a persona-based contextual report for the observed data based on the identified relevant persona and the list of suitable keywords.

Disclosed herein is a service provider system and method to generate a persona token for a user, store the persona token at a restricted access database, and return a persona token identifier to the user. The persona token is used to generate persona insights, that can be provided to an artificial intelligence / machine learning algorithm along with an inquiry. The algorithm may identify goods or services based on the persona insights. Also disclosed herein are system and methods to attribute a user clicking on a product link, retaining and using the product information to link item level details to purchases, and making a purchase on the linked e-commerce / merchant website such that the originating website knows that a purchase ultimately resulted from the clicked ad.

Systems and methods for persona and entitlement handling in a firmware framework. In some embodiments, an Information Handling System (IHS) may include a controller, where the controller comprises firmware that, upon execution by a processing core, causes the processing core to instantiate an orchestrator; and a plurality of devices coupled to the controller, where each device comprises firmware that, upon execution by a corresponding processing core, causes the corresponding processing core to instantiate a node as part of a firmware framework, and where the orchestrator is configured to translate an Operating System (OS) setting into one or more node settings.

Systems and methods for migrating users and modifying workspace definitions of persona groups are described. In some embodiments, a memory storage device may have program instructions stored thereon that, upon execution by a workspace orchestration service, cause the service to: distribute a first workspace definition based upon a first persona group associated with a user to a client device operated by the user, where the user IHS is configured to instantiate a first workspace based upon the first workspace definition; and in response to a change in a productivity or security metric of the first workspace, determine whether to: (a) migrate the user to an existing second persona group, (b) create a third persona group and migrate the user to the third persona group, (c) modify the first persona group and migrate the user to the modified first persona group, or (d) identify a potential security threat.

A method for generating automated discourse data using computer-generated synthetic personas is disclosed. The method is implemented at a synthetic discourse data service executing as an API-based application within a distributed network of computers. The method includes receiving discourse input data via an API endpoint from a client device, generating an embedding vector representation of the discourse input data using a GPU-accelerated embedding generation model, and identifying candidate computer-simulated personas from a vector database. A subset of personas is dynamically selected based on a computed persona relevance score derived from vector distance calculations. A discourse activation value is computed for each selected persona, determining its engagement in a simulated discourse session. A batch processing framework instantiates an engagement subset of personas, which participate in a computer-simulated discourse session, generating synthetic discourse data stored in memory. The synthetic discourse data is selectively transmitted to a client device via an API response.

A method for generating automated discourse data using computer-generated synthetic personas is disclosed. The method is implemented at a synthetic discourse data service executing as an API-based application within a distributed network of computers. The method includes receiving discourse input data via an API endpoint from a client device, generating an embedding vector representation of the discourse input data using a GPU-accelerated embedding generation model, and identifying candidate computer-simulated personas from a vector database. A subset of personas is dynamically selected based on a computed persona relevance score derived from vector distance calculations. A discourse activation value is computed for each selected persona, determining its engagement in a simulated discourse session. A batch processing framework instantiates an engagement subset of personas, which participate in a computer-simulated discourse session, generating synthetic discourse data stored in memory. The synthetic discourse data is selectively transmitted to a client device via an API response.

This application is directed to systems and methods for information recommendation. In some embodiments, a disclosed method includes obtaining historic interaction data associated with past interactions of a plurality of users with a plurality of information items; generating a first set of item types involved in the past interactions jointly with an anchor item type; generating a second set of item types semantically associated with the anchor item type, e.g., for a user class; combining the first set of item types and the second set of item types to generate a list of target item types; generating a list of recommended information items based on the list of target item types; and in response to a first user's interaction with the anchor item type, enabling display of at least a subset of information items in the list of recommended information items on an electronic device associated with the first user.

Persona based modeling systems and techniques are described. In one or more examples, a user interface is displayed having a plurality of representations of a plurality of personas, each persona of the plurality of personas modeling one or more user characteristics. An input is received via the user interface as selecting a first persona and in response digital content is output via the user interface. The digital content is selected from a plurality of digital content based on the first persona. An input is then received selecting an option via the user interface to add the first persona to a second persona. A combined persona is generated based on the first persona and the second persona.

A computer-implemented method for generating a descriptive persona text for one or more users of a content recommendation system comprising: obtaining user data and / or associated content metadata for the one or more users, wherein the user data and / or associated content metadata is based on user activity of the one or more users; generating the descriptive persona text for the one or more users based on the user data and / or associated content metadata, wherein the generated descriptive persona text comprises at least a persona title and / or a persona description.

A system generates, based on a first agent for a first persona and content of an electronic document, first feedback information for the first persona. Based on a determination that the first feedback information for the first persona indicates a first value for an attribute of the electronic document that causes a conflict with second feedback information for a second persona and that a second value for the attribute of the electronic document has been selected to resolve the conflict, the system generates, based on the first agent for the first persona, the second value for the attribute, and the content of the electronic document, updated first feedback information for the first persona. The system generates, based on the updated first feedback information for the first persona, a multi-agent report for the electronic document and output the multi-agent report.

To easily increase variations of attributes of a dialogue partner and to support dialogue in accordance with the attributes of the dialogue partner.SOLUTION: A dialogue support system of one aspect of the present disclosure includes: a receiving unit for receiving persona information indicating a persona that characterizes a dialogue partner of a user; a persona generation unit for generating a persona generation prompt indicating features of the persona by using a large-scale language model on the basis of the persona information received by the receiving unit; and a dialogue control unit for controlling an avatar corresponding to the persona on the basis of the persona generation prompt generated by the persona generation unit and controlling dialogue between the avatar and the user.SELECTED DRAWING: Figure 1

Methods and systems are provided for operating a persona-driven intelligence platform. Responsive to receiving inputs and / or instructions corresponding to a digital task, data records are retrieved from a database. The data records are clustered to generate a plurality of clusters of data records, based on a first set of attributes. A corresponding second set of attributes is obtained for each of the plurality of clusters, and a corresponding personality score vector is generated for each of the plurality of clusters, based on the second set of attributes. A dynamic persona corresponding to at least one of the plurality of clusters is generated by one or more large language model (LLM) agents and stored in a persona library. The disclosed methods and systems may enable improved real-time management of audience-driven digital workflows by automating unique persona generation, for enabling the optimization of digital tasks and data workflows to maximize relevancy.

Systems and methods are provided for data intake of data from an ingestion service indicating a user's online or offline behavior with respect to a domain. A domain persona system may receive a request to access an ingestion service. The domain persona system may then access the ingestion service to obtain the data and identify a subset of the data relevant to the user's online or offline behavior with respect to the domain. The domain persona system may further generate insights with respect to the identified subset and output at least one of the identified subset and the generated insights to a system that updates the domain persona.

This disclosure relates generally to method and system to generate persona-based commentary data for machine learning model card document. Existing techniques on model card are designed mainly for personas and understanding section of the model card document requires a certain level of expertise in machine learning. The method of the present disclosure receives a model card document comprising a plurality of sections and the model card document corresponds to a persona. Each section of the model card document obtains a metadata for the persona. The data curator machine learning model automatically generates a persona-based report trajectory for a plurality of sections of the metadata and a plurality of schema rules to generate a prompt template. The commentary generator ML model generates one or more commentary data for each section associated with the prompt template corresponding to the persona.

A dialogue support system according to one aspect of the present disclosure includes: a receiver configured to receive persona information indicating a persona that characterizes a dialogue partner of a user, a persona generator configured to generate a persona generation prompt indicating characteristics of the persona based on the persona information received by the receiver using a large language model, and a dialogue controller configured to control an avatar corresponding to the persona based on the persona generation prompt generated by the persona generator, and to control a dialogue between the avatar and the user.

A method for creating a training playbook to train a potential target of social engineering against an attack by an adversarial actor includes generating a training playbook based on a plurality of conversational patterns from a respective plurality of dialogs between non-human synthetic persona with the adversarial actor. A given conversational pattern of the plurality of conversational patterns comprises one or more dialog interaction elements utilized by the adversarial actor during a respective dialog of the plurality of dialogs. The method also includes configuring a dialog model for a second synthetic persona to engage in a training dialog with a target trainee to train the target trainee to detect, avoid and / or mitigate a future attack by the adversarial actor. The dialog model is configured to emulate the future attack by the adversarial actor by utilizing the training playbook. The method additionally includes outputting the training playbook.

Provided in the embodiments of the present disclosure are a role-playing dialogue data generation method and system, and a computer program product. The role-playing dialogue data generation method in the present disclosure comprises: inputting multiple segments of persona dialogue data into a persona generation model to respectively obtain a plurality of persona description datasets output by the persona generation model, wherein each of the multiple segments of persona dialogue data separately comprises one or more turns of dialogue data between different personas, and each of the plurality of persona description datasets separately comprises persona description data for one persona participating in the dialogue; building a persona repository on the basis of the plurality of persona description datasets; and selecting different personas from the persona repository, and, on the basis of the persona description datasets respectively corresponding to the different personas, generating dialogue data between the different personas, thereby obtaining role-playing dialogue data.

Some aspects relate to technologies for generating persona simulators and employing the persona simulators for interaction simulation. The persona simulators can comprise user persona simulators that act as end users during simulated interactions and agent persona simulators that act as agent users during simulated interactions. To generate a persona simulator, interaction data for an end user or an agent user is accessed. The interaction data can be based on, for instance, transcript(s) from interaction(s) involving the end user or agent user. Parameter scores for different interaction parameters are generated from the interaction data, and in some cases, the parameter scores are used to determine an index score. The scores define the persona of the end user or agent user and are used to provide an input to a generative model to provide a persona simulator. The persona simulator is then used to generate text for messages during a simulated interaction.

An electronic device and method for providing recommended content on the basis of a persona profile are provided. A method by which an electronic device provides recommended content on the basis of a persona profile comprises: an operation of setting a plurality of persona profiles including a first persona profile corresponding to a first application and a second persona profile corresponding to a second application; an operation of receiving a request for input of content related to the first application; an operation of identifying the first persona profile among the plurality of persona profiles in response to the request; an operation of obtaining recommended content outputted from an artificial intelligence model on the basis of the first persona profile, wherein the recommended content outputted from the artificial intelligence model is related to the content for which the input is requested; and an operation of providing the obtained recommended content through a user interface of the first application.

To provide information that is useful for evaluating content. [Solution] The information processing device comprises a content selection unit, a comment collection unit, a persona generation unit, and an information output unit. The content selection unit selects target content from among multiple contents published on the internet. The comment collection unit collects one or more comments posted by one or more viewers for the selected target content. The persona generation unit generates one or more personas representing the one or more viewers who posted the one or more target comments based on the collected one or more target comments, and obtains one or more persona information, which is information indicating the one or more generated personas. The information output unit outputs the obtained one or more persona information.

A method includes obtaining multiple raw dark web posts. The multiple raw dark web posts are preprocessed to obtain a tokenized dark web post set. A prompt is constructed for a threat analyzer large language model (LLM). The prompt includes at least a first tokenized dark web post a set of threat personas, each threat persona including a category and a definition, and an instruction to categorize the first tokenized dark web post with a threat persona and generate a first explanation corresponding to the categorization of the first tokenized dark web post. The prompt is processed by the threat analyzer LLM, to obtain a first annotated post and a corresponding first threat persona category. The first annotated post includes a post summary corresponding to the first tokenized dark web post, and the first explanation. The first explanation is based on a first threat persona definition.

Methods and a system for personalizing self-checkout terminals based on customer loyalty information includes receiving customer preferences during registration, generating themed personas with specific color schemes, graphics, and animations relevant to the customer's registered interests, and dynamically applying personalized interface themes when customers are identified at self-checkout terminals. The system maintains cached personas tagged with attributes like interests, religious preferences, and dietary restrictions, enabling sub-second personalization by matching customer preferences to pre-generated themes. The system avoids showing inappropriate content by filtering themes based on offensive content, collects user feedback on displayed themes through a rating mechanism, and employs time-delay factors to prevent theme repetition. New personas and corresponding themes are periodically generated to maintain customer engagement while keeping processing costs manageable.

Generative artificial intelligence (AI) is leveraged to generate content corresponding to a persona. Context comprising a persona and a prompt is received from a user. The context is provided to a generative AI model. Based on the context, content at least partially derived from one or more files in a dataset is received from the generative AI model. The content corresponding to the context is provided to the user.