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METHOD AND SYSTEM FOR GENERATING A RESPONSE STRUCTURE USING A CHATBOT USER INTERFACE

Absstract of: US20260147792A1

A system for generating a response structure using a chatbot user interface, wherein the system includes a display device, a memory; and at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive a query using a chatbot interface operating on the display device; generate an action protocol as a function of the query, including: generating a set of requested data constraints using a constraint machine-learning model; and mapping the set of requested data constraints to a range of database entries; generate a response structure as a function of the action protocol, wherein generating the response includes: retrieving requested data from a database; inputting the requested data and the query into a second large language model; and generating the response structure using the second large language model; and configure the display device to display the response structure using the chatbot interface.

INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM

Absstract of: US20260148105A1

A new and improved technology capable of further improving inference accuracy while securing security of a machine learning model by federated learning is proposed. Provided is an information processing apparatus including: a learning unit that learns an inference model by federated learning; and an acquisition unit that acquires, from a plurality of terminals, privacy-protected data obtained by executing privacy protection processing on local data obtained by each of the plurality of terminals, in which the learning unit is configured to: perform learning of the inference model on the basis of the privacy-protected data; and distribute information regarding the inference model including a hyperparameter of the inference model on the basis of a result of the learning to the plurality of terminals, the acquisition unit is configured to acquire, from the plurality of terminals, update information of the inference model obtained by performing the learning of the inference model using the local data as learning data, and the learning unit is configured to update the inference model using the update information.

SHARED MIXTURE OF EXPERTS

Absstract of: WO2026109962A1

A method, computer program product, and computer system for using a shared mixture of experts (MoE) architecture to implement inference with respect to a machine learning model. A gate mechanism receives, from N clients, N requests and an identification of N MoE models. N is at least 2. The gate mechanism selects, from N sets of experts, E experts to process the N requests. The N sets of experts collectively include at least one duplicative expert that is common to more than one set of the N sets of experts. Each duplicative expert has been deduplicated by being stored only once in the one or more graphic processing units (GPUs). The N requests are routed to the E experts. The E experts are executed to generate N respective responses to the N requests. Each response of the N responses is transmitted to the respective client of the N clients.

ADVANCED DATA AUGMENTATION METHOD FOR MEDICAL ARTIFICIAL INTELLIGENCE APPLICATIONS

Nº publicación: WO2026111703A1 28/05/2026

Applicant:

EGE UENIVERSITESI IDARI VE MALI ISLERDAIRE BSK [TR]
EGE \u00DCN\u0130VERS\u0130TES\u0130 \u0130DAR\u0130 VE MAL\u0130 \u0130\u015ELERDA\u0130RE B\u015EK.

Absstract of: WO2026111703A1

The invention relates to an advanced data augmentation method for medical artificial intelligence applications, which enables improving the generalizability and accuracy of machine learning models by overcoming the limited size and diversity of datasets that can be used in the diagnosis and treatment processes of diseases.