Abstract: A method, computer program product, and computing system for: receiving a request from a user to use grounding material in a generative AI system; establishing a network connection with trusted-source material to allow access to the trusted-source material; processing the grounding material to confirm the integrity of the grounding material; and allowing the grounding material to be utilized in the generative AI system if the integrity of the grounding material is confirmed.

Abstract: A method measures domain-meaningful edits between a generated text result and an edited text result. The generated text result is generated by a generative artificial intelligence model, and the edited text result is an edited version of the generated text result. Entities are extracted from the two text results using one or more name-entity algorithms, and each entity is linked to domain concepts in a domain-specific ontology. One or more edited areas are identified as one or more corresponding deltas between the two text results. One or more of the edits is determined to be domain-meaningful based on linkings of the entities to the domain concepts in the domain-specific ontology. A weight is assigned to each domain-meaningful edit based on the domain concepts in the domain-specific ontology. Aggregated edits are scored by aggregating the weights for each domain-meaningful edit to generate a meaningful change score.

Abstract: A corrupter may receive first output data of a designated domain from the large language model. The corrupter may synthesize qualified corrupt data for training the safeguard model configured to detect errors in second output of the large language model by: identifying a mapping of a first entity of the first output data to a first concept in an ontology corresponding to the designated domain, and generating the qualified corrupt data by replacing the first entity in the first output data with a second entity, wherein the second entity is mapped to a second concept of the ontology that complies with predefined corruption rule relative to the first concept of the ontology.

Abstract: Example solutions for augmenting text inputs for analyzing clinical documents include: identifying a clinical named entity within text content of a clinical input document; adding an anchor tag to the text content, the anchor tag including an entity ID and a clinical attribute associated with the clinical named entity, thereby generating an enhanced input document; and submitting a first query prompt and the enhanced input document to a generative artificial intelligence (GAI) model, the first query prompt including task text and anchoring markup text, the task text includes instructions of a task to be performed by the GAI model on the enhanced input document, the anchoring markup text includes a template of the anchor tag and instruction to add a reference anchor tag to output generated by the GAI model, where the reference anchor tag is to include the entity ID and the clinical attribute of the anchor tag.

Abstract: An exemplary implementation verifies an answer generated by a generative artificial intelligence model. The answer is responsive to an augmented query. The augmented query comprises a query submitted by a user and a request to answer the query using evidence determined to be relevant to the query. The answer is verified to be relevant to both the query and the evidence, facts are extracted from the evidence, and claims are extracted from the answer. Claim-to-fact entailment scores corresponding to each of the respective claim-to-fact pairings are determined. The scores are determined by an agreement analyzer comprising a natural language inference model. The answer is verified based on a check of the scores. A verified answer, a qualified answer, and/or a failure message is communicated to the user based on the verification.

Abstract: A method, computer program product, and computing system for: receiving a request from a user to use grounding material in a generative AI system; establishing a network connection with trusted-source material to allow access to the trusted-source material; processing the grounding material to confirm the integrity of the grounding material; and allowing the grounding material to be utilized in the generative AI system if the integrity of the grounding material is confirmed.

Abstract: A method, computer program product, and computing system for processing a raw content portion to identify personal information, thus defining identified personal information; confirming the efficacy of the identified personal information; replacing the identified personal information within the raw content portion with one or more surrogates, thus defining a deidentified content portion; and performing a federated learning operation on the deidentified content portion to generate training data for a machine learning model.

Abstract: Context-aware content filtering adapted for a knowledge domain is provided. In certain examples, a classification confidence score by a classifier indicates a level of confidence that a prompt from a user is associated with the knowledge domain. The classification confidence score is compared with a threshold. When the score is below a threshold, a violation notice is provided to the user without submitting the prompt to a generative artificial intelligence (GAI) model. When the classification confidence score is above the threshold, the prompt is further processed to determine, according to rules, whether the prompt should be submitted to the GAI model. In various examples, the rules are applied to contextual information, safety score information, and intent information derived from the prompt.

Abstract: A computer-implemented method, computer program product and computing system for: enabling a generative AI system to effectuate an analysis protocol; enabling a user to utilize the generative AI system during an interactive session concerning the analysis protocol; receiving content during the interactive session, thus defining received content; and determining whether the received content is relevant with respect to the analysis protocol.

Abstract: A computer-implemented method, computer program product and computing system for: enabling a generative AI system to effectuate an analysis protocol; defining a visual representation of the analysis protocol; associating various portions of the analysis protocol with various portions of the visual representation; enabling a user to utilize the generative AI system during an interactive session concerning the analysis protocol; and identifying an associated portion of the visual representation based, at least in part, upon a portion of the analysis protocol that is the current topic of the interactive session.

Abstract: An online service may maintain or create data for a user, and a user may be allowed to exert control over how the data are used. In one example, there may be several categories of data, and the user may be able to specify who may use the data, and the purpose for which the data may be used. Additionally, a user may be able to see how many of his “friends” (or other contacts) have extended trust to a particular entity, which may aid the user in making a decision about whether to extend trust to that entity. User interfaces may be provided to allow users to specify how their data are to be used.

Abstract: A method, computer program product, and computing system for: receiving a request from a user to use grounding material in a generative AI system; establishing a network connection with trusted-source material to allow access to the trusted-source material; processing the grounding material to confirm the integrity of the grounding material; and allowing the grounding material to be utilized in the generative AI system if the integrity of the grounding material is confirmed.

Abstract: A method, computer program product, and computing system for: accessing a first siloed dataset concerning one or more medical notes; processing the first siloed dataset to identify clinical items within the one or more medical notes, thus defining a first tagged clinical item set; selecting a first set of templates for the first siloed dataset based, at least in part, upon the first tagged clinical item set within the first siloed dataset; defining a first instruct dataset for the first siloed dataset based, at least in part upon the first set of templates and first tagged clinical item set; and training a generative model using the first instruct dataset, thus defining a generative model trained for the first siloed dataset.

Abstract: A method, computer program product, and computing system for processing target content generated by processing source content using a generative artificial intelligence (AI) model, where the generative AI model performs a task using the source content to generate the target content. An ontological concept is extracted from the source content using a natural language processing (NLP) engine. An ontological concept is extracted from the target content using the NLP engine. An ontological concept comparison score is generated by comparing the ontological concept from the source content and the ontological concept from the target content based upon, at least in part, the task performed using the source content to generate the target content. An issue is identified in the target content based upon, the ontological concept comparison score and the task performed using the source content to generate the target content.

Abstract: A method, computer program product, and computing system for identifying a token associated with data previously processed by a data processing service. In response to identifying the token, a subset of processing functionality not performed by the data processing service previously is determined. The data is processed with the subset of processing functionality not performed by the data processing service previously.

Abstract: A method, computer program product, and computing system for processing a raw content portion to identify personal information, thus defining identified personal information; confirming the efficacy of the identified personal information; replacing the identified personal information within the raw content portion with one or more surrogates, thus defining a deidentified content portion; and performing a federated learning operation on the deidentified content portion to generate training data for a machine learning model.

Abstract: Technology is disclosed for improving user privacy and providing user control over user-activity data collected from personal computing devices (i.e., user devices). User devices may be configured to operate in a private mode that enables a user to control, for example, which aspects of user-activity data are provided to applications and services running on their user device; to obscure or modify aspects of user-activity data so that certain applications and services, which may require this information to operate, may still function, but that the obscured information provided to these applications and services preserves user privacy or no longer may be used to identify the user; or to remove evidence of user-activity data created, monitored, reported, or otherwise collected by or on the user device while the user is operating their user device in the private mode setting.

Abstract: Technology is disclosed for improving user privacy and providing user control over user-activity data collected from personal computing devices (i.e., user devices). User devices may be configured to operate in a private mode that enables a user to control, for example, which aspects of user-activity data are provided to applications and services running on their user device; to obscure or modify aspects of user-activity data so that certain applications and services, which may require this information to operate, may still function, but that the obscured information provided to these applications and services preserves user privacy or no longer may be used to identify the user; or to remove evidence of user-activity data created, monitored, reported, or otherwise collected by or on the user device while the user is operating their user device in the private mode setting.

Abstract: An online service may maintain or create data for a user, and a user may be allowed to exert control over how the data are used. In one example, there may be several categories of data, and the user may be able to specify who may use the data, and the purpose for which the data may be used. Additionally, a user may be able to see how many of his “friends” (or other contacts) have extended trust to a particular entity, which may aid the user in making a decision about whether to extend trust to that entity. User interfaces may be provided to allow users to specify how their data are to be used.

Abstract: Technology is disclosed for improving user privacy and providing user control over user-activity data collected from personal computing devices (i.e., user devices). User devices may be configured to operate in a private mode that enables a user to control, for example, which aspects of user-activity data are provided to applications and services running on their user device; to obscure or modify aspects of user-activity data so that certain applications and services, which may require this information to operate, may still function, but that the obscured information provided to these applications and services preserves user privacy or no longer may be used to identify the user; or to remove evidence of user-activity data created, monitored, reported, or otherwise collected by or on the user device while the user is operating their user device in the private mode setting.