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Analysis

1 / 2,979
1.20220180975METHODS AND SYSTEMS FOR DETERMINING GENE EXPRESSION PROFILES AND CELL IDENTITIES FROM MULTI-OMIC IMAGING DATA
US - 09.06.2022
Int.Class G16B 40/30
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
40ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
30Unsupervised data analysis
 Appl.No 17553691 Applicant The Broad Institute, Inc. Inventor Aviv Regev

The present disclosure relates to systems and method of determining transcriptomic profile from omics imaging data. The systems and methods train machine learning methods with intrinsic and extrinsic features of a cell and/or tissue to define transcriptomic profiles of the cell and/or tissue. Applicants utilize a convolutional autoencoder to define cell subtypes from images of the cells.

2.12112752Cohort determination in natural language processing
US - 08.10.2024
Int.Class G10L 15/22
GPHYSICS
10MUSICAL INSTRUMENTS; ACOUSTICS
LSPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
15Speech recognition
22Procedures used during a speech recognition process, e.g. man-machine dialog
 Appl.No 17688279 Applicant Amazon Technologies, Inc. Inventor Rahul Gupta

Devices and techniques are generally described for cohort determination in natural language processing. In various examples, a first natural language input to a natural language processing system may be determined. The first natural language input may be associated with a first account identifier. A first machine learning model may determine first data representing one or more words of the first natural language input. A second machine learning model may determine second data representing one or more acoustic characteristics of the first natural language input. Third data may be determined, the third data including a predicted performance for processing the first natural language input by the natural language processing system. The third data may be determined based on the first data representation and the second data representation.

3.20250053832Systems, Methods and Apparatus for Machine Learning Predictive Analytics
US - 13.02.2025
Int.Class G06N 5/022
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
5Computing arrangements using knowledge-based models
02Knowledge representation; Symbolic representation
022Knowledge engineering; Knowledge acquisition
 Appl.No 18448966 Applicant Ryan Newsome Inventor Ryan Newsome

Systems, methods and apparatus of machine-learning-predictive-analytics, the method performed by a predictive analytic control computer and including receiving from a second computer a training-profile data that describes one or more contributions of resources that are associated and identified with particular entities, receiving from a third computer a training-profile data that are associated and identified with the particular entities, that does not describe one or more contributions of resources the training-profile data, and that includes data that is that is received from additional computers that host websites and applications that focus on communication, community-based input, interaction, content-sharing and collaboration that describe a first set of features and representations of issues of interest of the particular entities, generating a machine-learning-predictive-analytic model by a machine learning-predictive-analytic trainer in reference to the training-profile data, generating predictions from the machine-learning-predictive-analytic model and from a second set of features and representations of issues of interest.

4.20260087477Artificially Intelligent System and Method for Automatic Generation and Transmission of Digital Receipts
US - 26.03.2026
Int.Class G06Q 20/20
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
QINFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
20Payment architectures, schemes or protocols
08Payment architectures
20Point-of-sale network systems
 Appl.No 19316483 Applicant KBI Investment & Management AG Inventor Richard Kellerer

Systems and methods leverage a multi-stage artificial-intelligence pipeline to convert raw point-of-sale data into bank-grade digital receipts. A convolutional-OCR front end extracts line-item text, which a bidirectional-LSTM classifier normalises and categorises in real time, learning continuously from user feedback. A graph-based anomaly detector flags suspicious spend patterns, while a recommender sub-engine delivers personalised rewards and sustainability insights by fusing purchase context with external carbon-intensity data. The enriched receipt is cryptographically hashed, streamed through an encrypted gateway, and auto-matched to the corresponding payment entry inside the banking core. By driving extraction, classification, enrichment and integrity checks entirely through AI, the system eliminates manual mapping and enables immediate, tamper-evident reconciliation across heterogeneous merchants and payment rails.

5.WO/2026/062606ARTIFICIALLY INTELLIGENT SYSTEM AND METHOD FOR AUTOMATIC GENERATION AND TRANSMISSION OF DIGITAL RECEIPTS
WO - 26.03.2026
Int.Class G06Q 20/40
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
QINFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
20Payment architectures, schemes or protocols
38Payment protocols; Details thereof
40Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check of credit lines or negative lists
 Appl.No PCT/IB2025/059512 Applicant KBI INVEST & MANAGEMENT AG Inventor KELLERER, Richard
Systems and methods leverage a multi-stage artificial-intelligence pipeline to convert raw point-of-sale data into bank-grade digital receipts. A convolutional-OCR front end extracts line-item text, which a bidirectional-LSTM classifier normalises and categorises in real time, learning continuously from user feedback. A graph-based anomaly detector flags suspicious spend patterns, while a recommender sub-engine delivers personalised rewards and sustainability insights by fusing purchase context with external carbon-intensity data. The enriched receipt is cryptographically hashed, streamed through an encrypted gateway, and auto-matched to the corresponding payment entry inside the banking core. By driving extraction, classification, enrichment and integrity checks entirely through AI, the system eliminates manual mapping and enables immediate, tamper-evident reconciliation across heterogeneous merchants and payment rails.
6.20230135553AI-managed additive manufacturing for value chain networks
US - 04.05.2023
Int.Class G05B 17/02
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
17Systems involving the use of models or simulators of said systems
02electric
 Appl.No 17942061 Applicant Strong Force VCN Portfolio 2019, LLC Inventor Charles Howard Cella

A distributed manufacturing network information technology system includes a cloud-based additive manufacturing management platform with a user interface, connectivity facilities, data storage facilities, and monitoring facilities. The distributed manufacturing network information technology system includes a set of applications for enabling the additive manufacturing management platform to manage a set of distributed manufacturing network entities. The distributed manufacturing network information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the distributed manufacturing network entities to optimize manufacturing and value chain workflows.

7.20220308943System and AI pattern model for actionable alerts for events within a ChatOps platform
US - 29.09.2022
Int.Class G06F 9/54
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
9Arrangements for program control, e.g. control units
06using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
46Multiprogramming arrangements
54Interprogram communication
 Appl.No 17210853 Applicant KYNDRYL, INC. Inventor Raghuram Srinivasan

In an approach for building a machine learning model that predicts the appropriate action to resolve a malfunction or system error, a processor receives an alert that a malfunction or a system error has occurred. A processor creates a workspace on a ChatOps platform integrated with a chatbot and one or more tools. A processor inputs data relating to the alert in a natural language format. A processor processes the data using a natural language processing algorithm. Responsive to determining a pre-set threshold for outputting the appropriate action is not met, a processor establishes a conversation between two or more support service agents in the workspace. A processor monitors the conversation using the natural language processing algorithm. A processor analyzes a transcript of the conversation using text analytics or pattern matching. A processor creates and trains a machine learning model to predict the appropriate action in future iterations.

8.20200311520Training machine learning model
US - 01.10.2020
Int.Class G06T 7/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
 Appl.No 16369135 Applicant International Business Machines Corporation Inventor Shiwan Zhao

Techniques are provided for training machine learning model. According to one aspect, a training data is received by one or more processing units. The machine learning model is trained based on the training data, wherein the training comprises: optimizing the machine learning model based on stochastic gradient descent (SGD) by adding a dynamic noise to a gradient of a model parameter of the machine learning model calculated by the SGD.

9.WO/2018/017467DISTRIBUTED MACHINE LEARNING SYSTEMS, APPARATUS, AND METHODS
WO - 25.01.2018
Int.Class G06N 99/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
99Subject matter not provided for in other groups of this subclass
 Appl.No PCT/US2017/042356 Applicant NANTOMICS, LLC Inventor SZETO, Christopher
A distributed, online machine learning system is presented. Contemplated systems include many private data servers, each having local private data. Researchers can request that relevant private data servers train implementations of machine learning algorithms on their local private data without requiring de-identification of the private data or without exposing the private data to unauthorized computing systems. The private data servers also generate synthetic or proxy data according to the data distributions of the actual data. The servers then use the proxy data to train proxy models. When the proxy models are sufficiently similar to the trained actual models, the proxy data, proxy model parameters, or other learned knowledge can be transmitted to one or more non-private computing devices. The learned knowledge from many private data servers can then be aggregated into one or more trained global models without exposing private data.
10.2017300259Distributed machine learning systems, apparatus, and methods
AU - 25.01.2018
Int.Class G06N 99/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
99Subject matter not provided for in other groups of this subclass
 Appl.No 2017300259 Applicant Nant Holdings IP, LLC Inventor Benz, Stephen Charles
A distributed, online machine learning system is presented. Contemplated systems include many private data servers, each having local private data. Researchers can request that relevant private data servers train implementations of machine learning algorithms on their local private data without requiring de-identification of the private data or without exposing the private data to unauthorized computing systems. The private data servers also generate synthetic or proxy data according to the data distributions of the actual data. The servers then use the proxy data to train proxy models. When the proxy models are sufficiently similar to the trained actual models, the proxy data, proxy model parameters, or other learned knowledge can be transmitted to one or more non-private computing devices. The learned knowledge from many private data servers can then be aggregated into one or more trained global models without exposing private data.
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