Alerta

SYSTEMS AND METHODS TO PROCESS ELECTRONIC IMAGES FOR DETERMINING TREATMENT

Absstract of: US2025364120A1

A computer-implemented method for predicting a clinical outcome of a patient, the method comprising: obtaining a pathology image associated with the patient; processing the pathology image including: determining a salient region of the pathology image; and segmenting the pathology image into a plurality of tiles; providing the processed pathology image as an input to a machine learning model configured to annotate the pathology image; obtaining, as an output of the machine learning model, the annotated pathology image associated with the patient, wherein the annotated pathology image includes annotations for different classes of tissues including a tumor regression; and determining, based on the annotated pathology image, a score indicative of the clinical outcome of the patient.

Connecting Web Performance With User Experience

Absstract of: US2025362945A1

The disclosure relates to a computer-implemented method for simulating performance of a web application. The technical problem solved by the disclosure is to identify aspects of a web application that greatly affect user retention and to quantify user retention by modifying the identified aspects of the web application. This is solved by a collecting monitoring data associated with interactions between users and the web application; training a machine learning model with training data; generating virtual performance metrics for the web application; simulating a rate of users leaving the web application based on the virtual performance metrics; identifying at least one modified performance metric causing a change in user retention; and outputting a recommendation specific to the web application.

ARTIFICIAL INTELLIGENCE CHATBOTS THAT LEVERAGE MULTIPLE DATA SETS

Absstract of: US2025363313A1

Methods, systems, and apparatus, including computer-readable media, for artificial intelligence chatbots that leverage multiple data sets. In some implementations, a system receives a user prompt provided to an interactive application and accesses configuration data that indicates multiple data sets for answering user prompts. The system selects a data set from the multiple data sets based at least in part on the user prompt. The system sends a first request to one or more artificial intelligence and/or machine learning (AI/ML) models, and the system generates result data from the selected data set based on the data processing instructions generated by the AI/ML model. The system sends a second request to the AI/ML model and provides at least a portion of the result data to the AI/ML model. The system provides text that the one or more AI or machine learning models generated in response to the second request.

TECHNIQUES FOR GENERATING SYNTHETIC DATA

Absstract of: US2025363185A1

A system and method include generating synthetic data by generating a first set of hyperparameters for a first trained machine learning model and a second set of hyperparameters for a second trained machine learning model, generating a plurality of synthetic data vectors using the first and second trained machine learning models, computing an error function for the first and second set of hyperparameters using a third machine learning model, computing an objective function value, responsive to determining that the objective function value is not an optimal value, updating the first set of hyperparameters and the second set of hyperparameters or responsive to determining that the objective function value is an optimal value outputting the plurality of synthetic data vectors as a set of synthetic data.

METHOD AND SYSTEM FOR LEVERAGING LANGUAGE MODELS IN DESIGNING NO-CODE WORKFLOWS FOR MACHINE LEARNING WITHIN LOW-CODE/NO-CODE PLATFORMS

Absstract of: US2025363371A1

Here is natural language processing (NLP) for workflow development. A generative large language model (LLM) explains and modifies a workflow graph in an integrated development environment (IDE) that streamlines design, development, and deployment of machine learning (ML) workflows in a low-code/no-code (LC/NC) environment that is productive for users having a wide variety of engineering proficiency. A user is assisted in creating a sophisticated ML workflow through an intuitive and potentially no-code interface. This includes a variety of activities including the generation of code snippets, recommending best ML practices, automatically configuring workflow components, optimizing algorithmic parameters, and providing natural language explanations for each activity. The IDE generates a linguistic prompt that contains a definition of a workflow graph and natural language that specifies an interaction to apply to the workflow graph. The generative LLM accepts the linguistic prompt as input and inferentially generates a result of the interaction for the workflow graph.

CONNECTING WEB PERFORMANCE WITH USER EXPERIENCE

Absstract of: EP4654022A2

The disclosure relates to a computer-implemented method for simulating performance of a web application. The technical problem solved by the disclosure is to identify aspects of a web application that greatly affect user retention and to quantify user retention by modifying the identified aspects of the web application. This is solved by a collecting monitoring data associated with interactions between users and the web application; training a machine learning model with training data; generating virtual performance metrics for the web application; simulating a rate of users leaving the web application based on the virtual performance metrics; identifying at least one modified performance metric causing a change in user retention; and outputting a recommendation specific to the web application.

MEDIA DEVICE ON/OFF DETECTION USING RETURN PATH DATA

Absstract of: EP4654595A1

Example methods disclosed herein include accessing common homes data for a group of common homes, the common homes data including return path data and panel meter data. Disclosed example methods also include accessing common homes data for a group of common homes, the common homes data including first return path data and corresponding panel meter data associated with respective ones of the common homes, grouping the common homes data into view segments, classifying the view segments based on whether the return path data in respective ones of the view segments has matching panel meter data to determine labeled view segments, generating features from the labeled view segments, training a machine learning algorithm based on the features, and applying second return path data to the trained machine learning algorithm to determine whether a media device associated with the second return path data is on or off.

REGRESSION ANALYSIS METHOD, REGRESSION ANALYSIS SYSTEM, AND REGRESSION ANALYSIS PROGRAM

Nº publicación: EP4654095A1 26/11/2025

Applicant:

UNIV TOKYO [JP]
DAICEL CORP [JP]
The University of Tokyo,
Daicel Corporation

Absstract of: EP4654095A1

A more versatile technique is provided for constructing a regression model having a correspondence relationship with variation of an explanatory variable and variation of a response variable. A regression analysis method includes: retrieving, by a computer, training data from a storage device storing the training data, the training data being used as a response variable and an explanatory variable of a regression model; and performing, by the computer, machine learning by the regression model using the training data to minimize a cost function including a regularization term. The regularization term includes a first term that increases a cost more in an interval where a coefficient is positive than in an interval where the coefficient is negative, and a second term that increases the cost more in the interval where the coefficient is negative than in the interval where the coefficient is positive.