Abstract: A device may receive a command associated with identifying a merchant for a virtual card swap procedure wherein the virtual card swap procedure is to replace a credit card of a user with a virtual card corresponding to the credit card. The device may identify the merchant for the virtual card swap procedure based on the command. The device may obtain the virtual card for the user. The device may determine a virtual card swap procedure template for the merchant. The device may perform the virtual card swap procedure based on the virtual card swap procedure template.

Abstract: Systems and methods of adaptive context partitioning in a retrieval-augmented generation system, including receiving a query from a user; forming a combined context by retrieving relevant documents from a document database based on the query, determining characteristics of the combined context, monitoring current system resources including processor availability and memory utilization, dynamically determining a partition size based on the current system resources and the one or more characteristics of the combined context, partitioning the combined context into context partitions according to the partition size, generating intermediate analysis results by processing each context partition using a mapper prompt large language models (LLMs), generating a final response by processing the intermediate analysis results using a reducer prompt through the LLMs, and transmitting the final response to the user.

Abstract: In a method to decode signals, a computing device decodes spectral coefficients of a current frame are grouped into a plurality of sub-bands. The computing device classifies a sub-band as a bit allocation unsaturated sub-band based on an average quantity of allocated bits per spectral coefficient of a sub-band of the plurality of sub-bands and a threshold. The computing device obtains a noise filling gain based on an envelope of the sub-band, and obtains a reconstructed spectral coefficient of the sub-band by performing noise filling based on the noise filling gain. The computing device then obtains a frequency domain audio signal based on spectral coefficients in the sub-band obtained by decoding and the reconstructed spectral coefficient.

Abstract: The present disclosure relates to systems and methods for creating a copilot. The copilot uses plugins to provide additional features and functionalities to the copilot. The systems and methods use a large language model (LLM) pipeline to generate a knowledge resource used by the plugins and/or an LLM in the copilot to answer queries from a user.

Abstract: Certain aspects of the present disclosure provide techniques for generating an answer using a reasoning system. In examples, a query related to at least one input signal and contextual information associated with the at least one input signal can be obtained. Using a machine learning model, one or more reasoning paths based on the contextual information and the query can be generated, where each of the one or more reasoning paths includes a sequence of respective one or more sub-queries and respective one or more sub-query answers corresponding to the respective one or more sub-queries. An answer to the query based on at least one of the one or more reasoning paths can be generated and provided as an output.

Abstract: A system and method for automatically evaluating computer generated content may include: calculating a plurality of metrics for an input text, where the plurality of metrics may include one or more perplexity scores describing a prediction of the input text by a large language model (LLM); determining, based on one or more of the calculated metrics, whether to accept or reject the input text; and performing an exchange of data between remotely connected computer devices based on the determining to accept or reject the text. In some embodiments, calculating of metrics and determining whether to accept or reject the input text may be performed without relying on any information received subsequent to the initial receiving of the input text. Some embodiments may perform automated computerized actions such as, e.g., deploy or discard an update to the LLM based on the determining whether to accept or reject the input text.

Abstract: A system can identify respective computer function terms utilized by respective vendor equipment of computer function vendors; tag the respective computer function terms in a latent space based on natural language processing, to produce respective clusters; associate respective metatags with the respective clusters based on respective semantic meanings; receive a request to perform a computer function, wherein the request comprises a generic term; identify hardware with which to perform the computer function based on the respective metatags; identify equipment of a computer function vendor, of the vendor equipment of the respective computer function vendors, which corresponds to the hardware; based on the respective metatags and respective clusters, identify a computer function term of the respective computer function terms used by the equipment of the computer function vendor, wherein the computer function term corresponds to the generic term; and carry out the request using the hardware with the computer fu

Abstract: Systems and methods to group terms based on context to facilitate determining intent of a command are disclosed. Exemplary implementations to train a model: obtain a set of writings within a particular knowledge domain; obtain a vector generation model that generates vectors for individual instances of the terms in the set of writings; generate a first set of vectors that represent the instances of a first term and other vectors that represent instances of the other terms of the set of writings; train the vector generation model to group the vectors of a similar context in a space of a vector space; obtain a transcript include a new term generated from user audio dictation; generate a new vector that represent the instance of the new term; obtain the space; compare the new vector with the space; utilize the new term as the first term.

Abstract: Systems and methods for screening and scoring outputs generated by artificial intelligence language models, including receiving a generated output generated by an artificial intelligence language model in response to a user query, analyzing the output using detection engines, each detection engine evaluating different error dimensions, classifying each detected error into a severity level, generating color-coded visual indicators that correspond to a highest severity level among the one or more detected errors, generating remediation guidance configured to remediate at least one detected error of the one or more detected errors, and presenting screening results including the color-coded visual indicator a list of the one or more detected errors with their respective severity levels and locations, and the remediation guidance.

Abstract: Systems and methods for dynamic knowledge integration in LLM systems including receiving multimodal input data comprising text, image, audio, video, and/or code data, extracting information by processing the multimodal input data through a document processor, storing the extracted information in a dynamic knowledge base, receiving a user query at a query processor, identifying knowledge domains related to the user query using domain-specific agents, retrieving real-time information from the dynamic knowledge base responsive to the identified knowledge domains, integrating the real-time information into the processing of an LLM by a dynamic knowledge integrator, and generating a response using the LLM with the real-time information.

Abstract: Aspects of the present disclosure relate to systems and methods for detecting emerging events. In various examples, a method for detecting emerging events includes obtaining communication data associated with communication between multiple sources, segmenting communication data into multiple segments, determining whether a data segment belongs to a familiar topic or none, and generating a notification when a familiar topic is mentioned for more or less than a mention prediction. Additionally, or alternatively, a notification may be generated when an unfamiliar topic emerges from a set of unfamiliar data segments if an associated segment count exceeds a critical mass threshold. To determine whether a data segment belongs to a familiar topic, the data segment may be transformed into a feature vector and mapped onto a feature space, where a distance-based similarity score may be determined.

Abstract: A system and method of identifying occurrence of a semantic variation of a phrase in a passage by at least one processor may include calculating a phrase embedding vector, representing a semantic meaning of the phrase; extracting, from a textual representation of the passage, at least one hierarchical set of nested sequences of words; for each sequence, calculating a corresponding sequence embedding vector, representing a semantic meaning of the sequence; for one or more sequence embedding vectors, calculating a corresponding vector similarity value, representing similarity of the sequence embedding vectors to the phrase embedding vector, identifying a sequence corresponding to a maximal vector similarity value of the one or more vector similarity values; and determining the identified sequence as a semantic variation of the phrase, based on the maximal vector similarity value.

Abstract: A system and method for enhancing performance of h-LLMs including providing specialized h-LLMs specialized for different tasks and trained on a training dataset specific to the specific task to be performed by the specialized h-LLM, generating synthetic data by processing input prompts through the h-LLMs to produce synthetic outputs, feeding the synthetic data back to the specialize h-LLMs through a feedback loop process implemented in a cloud container environment, and performing model refinement on the specialized h-LLMs by retraining at least one specialized h-LLM using the synthetic data.

Abstract: A method for generating targeted advertisements LLM systems including receiving a user query, identifying categories of information by analyzing the user query using modeling techniques, generating derived queries from the user query, generating query responses by processing the user query and the derived queries through h-LLMs, determining advertisement content based on the categories of information and a user intention or a user attitude, generating targeted advertisements responsive to the advertisement content, and creating an advertisement-enhanced response by integrating the advertisements with the query responses.

Abstract: Systems and methods for intelligent processing of requests in an artificial intelligence system, including receiving an incoming request at an AI broker, evaluating the incoming request to determine routing characteristics, selecting selected specialized language models from a plurality of specialized language models based on the routing characteristics, computing performance metrics for the one or more selected specialized language models, routing the incoming request to the selected specialized language models based on the performance metrics, and generating a final result by receiving and processing results from the selected specialized language models.

Abstract: A method for improving responses to large language model (LLM) prompts including receiving at an input broker a request input from a user including an LLM prompt, deriving a search query from the LLM prompt, searching a plurality of documents using the search query to identify a first subset of documents and a second subset of documents, generating a first answer using a first context-specific LLM, where the first context-specific LLM uses the first subset of documents as the context and the LLM prompt as the prompt, generating a second answer using a second context-specific LLM, where the second context-specific LLM uses the second subset of documents as the context and the LLM prompt as the prompt, providing each of the first answer and the second answer to an output broker, determining a primary result at the output broker, and transmitting the primary result to the user.

Abstract: An information processing apparatus outputs answer information corresponding to inquiry information that is input. The information processing apparatus includes a memory and circuitry. The memory is configured to store a plurality of databases each having at least a first field and a second field. The circuitry is configured to: perform morphological analysis on the inquiry information, to divide the inquiry information into morphemes; perform a first matching process based on the morphemes and the first field of each of the plurality of databases, to determine whether to adopt the database as an extraction source from which the answer information is to be extracted; and perform a second matching process based on the morphemes and the first field of the database, which is determined to be adopted as the extraction source, to output, as the answer information, data in the second field corresponding to data in the first field.

Abstract: The present disclosure provides a system for semantic analysis and automatic solution of a mathematical application problem.

Abstract: Systems and methods of processing domain-specific content in a generative AI system including receiving a prompt, tokenizing the prompt, identifying an identified domain of the tokenized prompt identifying domain-specific functions within the identified domain, generating domain-specific sub-functions from the domain-specific functions according to a hierarchical mapping, generating H-Tokens, each encapsulating one of a domain-specific function or a domain-specific sub-function and relationships domain-specific functions and the domain-specific sub-functions, implementing the of H-Tokens, assembling a response using the implemented of H-Tokens, and transmitting the response to the user.

Abstract: Systems and methods are directed to minimizing hallucinations in a generated summary. A summary generation system embodied within a server triggers a large language model (LLM) to generate an initial summary for a subject. Based on the initial summary, the server prompts the LLM to generate a list of factual questions about the initial summary. The server then triggers the LLM to answer the list of factual questions without knowledge of the initial summary and using internal knowledge of the LLM. Questions from the list of factual questions that received a positive answer are identified. Based on the questions, the server prompts the LLM to generate a refined summary from the initial summary. The server then generates a user interface that presents the refined summary. Approval of the refined summary triggers generation of a publication using the refined summary.