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Analysis

1 / 1,495
1.20200272947Orchestrator for machine learning pipeline
US - 27.08.2020
Int.Class G06F 15/173
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
15Digital computers in general; Data processing equipment in general
16Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
163Interprocessor communication
173using an interconnection network, e.g. matrix, shuffle, pyramid, star or snowflake
 Appl.No 16284291 Applicant SAP SE Inventor Lukas Carullo

Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving an identification of a machine learning model, executing a machine learning pipeline comprising a plurality of services which train the machine learning model via at least one of an unsupervised learning process and a supervised learning process, the machine learning pipeline being controlled by an orchestration module that triggers ordered execution of the services, and storing the trained machine learning model output from the machine learning pipeline in a database associated with the machine learning pipeline.

2.20240428074OPTIMIZING METHOD OF SEMI-SUPERVISED LEARNING AND COMPUTING APPARATUS
US - 26.12.2024
Int.Class G06N 3/082
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
08Learning methods
082modifying the architecture, e.g. adding, deleting or silencing nodes or connections
 Appl.No 18446480 Applicant Wistron Corporation Inventor Jiun-In Guo

An optimizing method of semi-supervised learning and a computing apparatus are provided. In the method, a first predicted result of a labeled data set and a second predicted result of an unlabeled data set are respectively determined through a machine learning model. A pseudo-label threshold is determined according to a first confidence score of the first predicted result of a first sample of the labeled data set. The machine learning model is updated according to a compared result of the second predicted result of a second sample of the unlabeled data set and the pseudo-label threshold.

3.10990645System and methods for performing automatic data aggregation
US - 27.04.2021
Int.Class G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
08Learning methods
 Appl.No 16127764 Applicant Sophtron, Inc. Inventor Nanjuan Shi

Systems, apparatuses, and methods for automated data aggregation. In some embodiments, this is achieved by use of techniques such as natural language processing (NLP) and machine learning to enable the automation of data aggregation from websites without the use of pre-programmed scripts.

4.20230206137Orchestrator for machine learning pipeline
US - 29.06.2023
Int.Class G06F 15/173
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
15Digital computers in general; Data processing equipment in general
16Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
163Interprocessor communication
173using an interconnection network, e.g. matrix, shuffle, pyramid, star or snowflake
 Appl.No 18111839 Applicant SAP SE Inventor Lukas Carullo

Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving an identification of a machine learning model, executing a machine learning pipeline comprising a plurality of services which train the machine learning model via at least one of an unsupervised learning process and a supervised learning process, the machine learning pipeline being controlled by an orchestration module that triggers ordered execution of the services, and storing the trained machine learning model output from the machine learning pipeline in a database associated with the machine learning pipeline.

5.12112752Cohort 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.

6.20180341632Conversation utterance labeling
US - 29.11.2018
Int.Class G06F 17/24
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
17Digital computing or data processing equipment or methods, specially adapted for specific functions
20Handling natural language data
21Text processing
24Editing, e.g. insert/delete
 Appl.No 15603091 Applicant International Business Machines Corporation Inventor Rama Kalyani T. Akkiraju

A method, a computer program product, and an information handling system is provided for labeling unlabeled utterances given a taxonomy of labels utilizing topic word semi-supervised learning.

7.20130332481Predictive analysis by example
US - 12.12.2013
Int.Class G06F 7/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
7Methods or arrangements for processing data by operating upon the order or content of the data handled
 Appl.No 13908228 Applicant International Business Machines Corporation Inventor Alex T. Lau

An illustrative embodiment of a computer-implemented method for predictive analytic queries includes creating a user-defined predictive analytics query using a set of syntactic grammar that defines a correct syntax of the user-defined predictive analytics query including a created set of predictive analytics by-example vocabularies and a set of subject-specific by-example vocabularies forming a set of by-example vocabularies, wherein the set of syntactic grammar defines semantics of each syntactically correct predictive analytics query using the by-example vocabularies such that predictive analytics queries can be expressed with semantic precision using this constrained Natural Language Processing (cNLP) approach. The computer-implemented method further generates a predictive analytic model and runtime query, using the user-defined predictive analytics query, executes the runtime query using the predictive analytic model to create a result, and returns the result to the user.

8.4336413SYSTEM AND METHOD FOR CLASSIFICATION OF SENSITIVE DATA USING FEDERATED SEMI-SUPERVISED LEARNING
EP - 13.03.2024
Int.Class G06N 3/0895
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
08Learning methods
0895Weakly supervised learning, e.g. semi-supervised or self-supervised learning
 Appl.No 23192307 Applicant TATA CONSULTANCY SERVICES LTD Inventor MALAVIYA SHUBHAM MUKESHBHAI
This disclosure relates generally to system and method for classification of sensitive date using federated semi-supervised learning. Federated learning has emerged as a privacy-preserving technique to learn one or more machine learning (ML) models without requiring users to share their data. In federated learning, data distribution among clients is imbalanced resulting with limited data in some clients. The method includes extracting a training dataset from one or more data sources and preprocessing the training dataset into a machine readable form based on associated data type. Further, a federated semi-supervised learning model is iteratively trained based on a model contrastive and distillation learning to classify sensitive data from the unlabeled dataset. Then, sensitive data from a user query is received as input which are classified using the federated semi-supervised learning model.
9.20240086718SYSTEM AND METHOD FOR CLASSIFICATION OF SENSITIVE DATA USING FEDERATED SEMI-SUPERVISED LEARNING
US - 14.03.2024
Int.Class G06N 3/098
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
08Learning methods
098Distributed learning, e.g. federated learning
 Appl.No 18235504 Applicant Tata Consultancy Services Limited Inventor Shubham Mukeshbhai MALAVIYA

This disclosure relates generally to system and method for classification of sensitive date using federated semi-supervised learning. Federated learning has emerged as a privacy-preserving technique to learn one or more machine learning (ML) models without requiring users to share their data. In federated learning, data distribution among clients is imbalanced resulting with limited data in some clients. The method includes extracting a training dataset from one or more data sources and pre-processing the training dataset into a machine readable form based on associated data type. Further, a federated semi-supervised learning model is iteratively trained based on a model contrastive and distillation learning to classify sensitive data from the unlabeled dataset. Then, sensitive data from a user query is received as input which are classified using the federated semi-supervised learning model.

10.20250278645AUTONOMOUS RECOMMENDATION SYSTEMS USING MACHINE LEARNING
US - 04.09.2025
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 19067378 Applicant THE TORONTO-DOMINION BANK Inventor AZKA IFTIKHAR KHAN

An AI-driven recommendation system utilizes a machine learning model and a dynamically updated knowledge graph to generate personalized product recommendations. The system constructs a knowledge graph with nodes and edges representing relationships between users, prior product selections, and historical interactions. A supervised learning framework trains the machine learning model using labeled data from the knowledge graph to predict relevant products based on multi-dimensional constraints. A graphical user interface (GUI) presents dynamically adjusted interactive elements to capture user preferences. User responses are processed using natural language processing (NLP) to refine predictions and generate recommendations. The system continuously updates the knowledge graph with real-time user feedback and external data, retraining the machine learning model to enhance future recommendations. This adaptive approach enables personalized, context-aware recommendations that evolve based on user interactions and external influences.

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