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: 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 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: 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: The present teaching relates to a method and system for selecting content. Upon receiving a request with an indication of a first piece of content for selecting one or more pieces of second content to be presented together with the first piece of content, a plurality of pieces of candidate second content are identified. At least one sentiment feature associated with the first piece of content is determined and the one or more pieces of second content are selected from the plurality of pieces of candidate second content based on the at least one sentiment feature of the first piece of content so that the one or more pieces of second content are emotionally compatible with the first piece of content. The one or more pieces of second content are sent in response to the request.

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 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: The present teaching relates to a method and system for selecting content. Upon receiving a request with an indication of a first piece of content for selecting one or more pieces of second content to be presented together with the first piece of content, a plurality of pieces of candidate second content are identified. At least one sentiment feature associated with the first piece of content is determined and the one or more pieces of second content are selected from the plurality of pieces of candidate second content based on the at least one sentiment feature of the first piece of content so that the one or more pieces of second content are emotionally compatible with the first piece of content. The one or more pieces of second content are sent in response to the request.