Abstract: A method of language generation includes, by a server, receiving from a user device a plurality of user preferences for natural-language outputs generated by a machine-learning language model based on user-provided natural-language text inputs and a natural-language text prompt provided by the user to a chat application operating on the user device, receiving a plurality of operator preferences for the natural-language outputs, generating a set of reconciled preferences according to a rules engine, modifying a system prompt for the machine-learning language model based on the set of reconciled preferences to generate a modified system prompt, providing the modified system prompt as an initial input to the machine-learning language model, and providing the natural-language text prompt as an input to the machine-learning language model to generate a natural-language text output.

Abstract: A method of automatic pre-prompt generation includes receiving, by a user device, an indication of at least one user preference, where the at least one user preference indicative of at least one first characteristic preferred by a user of natural-language outputs generated by a machine-learning language model. The method further includes, by a server, receiving a natural-language text prompt provided by the user, receiving the at least one user preference from the user device, modifying a system prompt based on the received at least one user preference and the at least one first operator preference, providing the modified system prompt as an initial input to the machine-learning language model, providing the natural-language text prompt as an input to the machine-learning language model to generate a natural-language text output after providing the modified system prompt, and transmitting the natural-language text output to the user device.

Abstract: A method of automated technical support includes receiving a natural-language text prompt provided by a user and including at least one technical query, providing a first system prompt to a primary general-purpose machine-learning language model, and providing the natural-language text prompt to the primary general-purpose machine-learning language model and each of a plurality of specialized machine-learning language models after providing the first system prompt.

Abstract: A method of hybrid technical support includes receiving, by a network-connected device, a first user prompt including at least one technical support query and generating, by a language model executed by the network-connected device, a first natural-language response to the first user prompt, the first natural-language response configured to elicit first additional information describing the at least one technical support query. The method further includes receiving, by the network-connected device, a second user prompt including the first additional information describing the at least one technical support query, generating a pre-summarization prompt based on the first user prompt and the second user prompt, generating a summarization of the pre-summarization prompt using a language summarization model executed by the network-connected device, and providing the summarization to a support technician device configured to be operated by a support technician.

Abstract: A method of automated technical support includes receiving a natural-language prompt from a user device and a user identifier corresponding to a user of the user device. The natural-language prompt includes a natural-language description of at least one technical problem. The method additionally includes querying a chat history database with a query including at least one of the user identifier, a representation of the natural-language response, and at least one keyword extracted from the natural-language prompt. At least one chat history segment from the chat history database is received in response to the query, and an augmented prompt is generated based on the natural-language prompt and the chat history segment. A natural-language technical support plan responsive to the at least one technical problem is then generated through execution of a language model based on the augmented prompt.

Abstract: A method of automatic pre-prompt generation includes receiving at least one user preference from a user device, modifying a system prompt for a machine-learning language model based on the received at least one user preference to generate a modified system prompt, providing the modified system prompt as an initial input to the machine-learning language model, receiving a natural-language text prompt provided by the user to a chat application on the user device, receiving a user identifier from the user device, querying a first database with the user identifier to retrieve first information, generating a representation of the first information and the natural-language prompt, querying a second database using the representation to retrieve second information, and generating a modified text prompt based on the natural-language prompt, the first information, and the second information.

Abstract: Personal and biometric information is obtained from an individual using a remote device and submitted to an application server. The application server transmits the personal or biometric information to an authenticated data repository that identifies the individual. The application server also submits the biometric information from the individual and biometric information from the authenticated data repository to a biometric comparison service for analysis. If the individual's submitted personal and biometric information matches the information in the authoritative data repository, the individual is issued a digital identification credential. An individual's digital identification credential can be authenticated at the time it is being used, and the user's identity can be verified in real-time if requested.

Abstract: A method of interacting with a large language model to elicit a semantic feature of interest from a document under review includes electronically inputting, in an application program interface of a chat application, a first prompt assigned to a user, the first prompt yielding a plurality of possible responses from the language model based on content of the document under review; generating an example set comprising text from example documents representative of each of the plurality of possible responses; and electronically inputting, before the first prompt in an application program interface of a chat application, a fabricated history of a conversation between the user and the language model. The fabricated history includes the example set and a plurality of possible responses assigned to the language model.

Abstract: A method of automated technical support includes receiving a natural-language prompt from a user and a user identifier corresponding to the user, the natural-language prompt including at least one technical support query, querying a first database with the user identifier to retrieve first information, generating a vector embedding representative of the first information and the natural-language prompt, querying a second database using the vector embedding to retrieve second information, and generating a natural-language response text based on the natural-language prompt, the first information, and the second information. The second database is a vector database comprising a plurality of vectors, each vector of the plurality of vectors representative of a text segment of a plurality of text segments, the second information comprises at least one text segment of the plurality of text segments, and the natural-language response text responsive to the at least one technical support query.

Abstract: A method includes receiving a natural-language prompt from a user and a user identifier corresponding to the user, querying a first database with the user identifier to retrieve first information, generating a vector embedding representative of the first information and the natural-language prompt, and querying a second database using the vector embedding to retrieve second information. The second database is a vector database comprising a plurality of vectors, each vector of the plurality of vectors representative of a text segment of a plurality of text segments, and the second information comprises at least one text segment of the plurality of text segment. The method further includes generating, by a language model executed by the processor, a natural-language response text responsive to the user query based on the natural-language prompt, the first information, and the second information.

Abstract: A method includes receiving, by a server and from a user device, a natural-language text prompt provided by the user to a chat application operating on the user device and at least one user preference for a user, the at least one user preference indicative of at least one preferred characteristic of natural-language outputs generated by a machine-learning language model based on user-provided natural-language text inputs. The method further includes, by the server, modifying a system prompt for the machine-learning language model with the received at least one user preference to generate a modified system prompt, providing the modified system prompt as an initial input to the machine-learning language model, providing the natural-language text prompt as an input to the machine-learning language model to generate a natural-language text output after providing the modified system prompt, and transmitting the natural-language text output to the user device.

Abstract: A method of hybrid technical support includes receiving, by a network-connected device, a first user prompt including at least one technical support query and generating, by a language model executed by the network-connected device, a first natural-language response to the first user prompt, the first natural-language response configured to elicit first additional information describing the at least one technical support query. The method further includes receiving, by the network-connected device, a second user prompt including the first additional information describing the at least one technical support query, generating a pre-summarization prompt based on the first user prompt and the second user prompt, generating a summarization of the pre-summarization prompt using a language summarization model executed by the network-connected device, and providing the summarization to a support technician device configured to be operated by a support technician.

Abstract: A method of generating a support summary includes extracting chat information including natural language data from a support chat with a user. Tokenized language is generated by performing feature extraction on this natural language data. This tokenized language is subjected to sentiment analysis to produce sentiment data reflecting sentiment of the support user during the support chat, and to semantic analysis to extract support-relevant features. A support summary made up of natural language text identifying a support issue and information germane to the support issue is then generated from the extracted chat information using a language model, at least in part from the extracted support-relevant features and the sentiment data.

Abstract: The present disclosure provides a method for secure verification of identities. The method includes obtaining personal identifying information from an individual using a remote device and transmitting the information to an application service. The application service accesses multiple disparate data sources via an API, including public entity data sources, third party data sources, and services sources. The API normalizes data received from the disparate data sources. The personal identifying information is verified against the normalized data. Biometric identifying information is obtained from the individual using the remote device and transmitted to the application service. The biometric identifying information is compared with normalized biometric data from the disparate data sources. Based on the comparison, a determination is made whether to issue a digital identification credential to the individual. An authentication result is transmitted to the remote device.

Abstract: Personal and biometric information is obtained from an individual using a remote device and submitted to an application server. The application server transmits the personal or biometric information to an authenticated data repository that identifies the individual. The application server also submits the biometric information from the individual and biometric information from the authenticated data repository to a biometric comparison service for analysis. If the individual's submitted personal and biometric information matches the information in the authoritative data repository, the individual is issued a digital identification credential. An individual's digital identification credential can be authenticated at the time it is being used, and the user's identity can be verified in real-time if requested.