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Analysis

1 / 1,541
1.4134879METHOD AND SYSTEM FOR TRAINING A MACHINE LEARNING MODEL
EP - 15.02.2023
Int.Class G06N 5/02
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
5Computing arrangements using knowledge-based models
02Knowledge representation; Symbolic representation
 Appl.No 21190562 Applicant SIEMENS AG Inventor HILDEBRANDT MARCEL
An initially trained machine learning model is used by an active learning module to generate candidate triples, which are fed into an expert system for verification. As a result, the expert system outputs novel facts that are used for retraining the machine learning model. This approach consolidates expert systems with machine learning through iterations of an active learning loop, by bringing the two paradigms together, which is in general difficult because training of a neural network (machine learning) requires differentiable functions and rules (used by expert systems) tend not to be differentiable. The method and system provide a data augmentation strategy where the expert system acts as an oracle and outputs the novel facts, which provide labels for the candidate triples. The novel facts provide critical information from the oracle that is injected into the machine learning model at the retraining stage, thus allowing to increase its generalization performance. Furthermore, deployment effort and maintenance costs are reduced, since in industrial AI applications mentioned above, only the machine learning model needs to be deployed instead of deploying a machine learning model as well as an expert system. According to an embodiment, the active learning module identifies candidate triples, for which, if the truth value was known, it would maximally boost the performance of the machine learning model. In other words, the active learning module chooses candidate triples representing unknown facts that lie close to the decision boundary of the machine learning model. In this way, the novel facts are sampled efficiently from the expert system such that the generalization performance of the machine learning model is maximized.
2.20210397895INTELLIGENT LEARNING SYSTEM WITH NOISY LABEL DATA
US - 23.12.2021
Int.Class G06K 9/62
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
KGRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for recognising patterns
62Methods or arrangements for pattern recognition using electronic means
 Appl.No 16946465 Applicant INTERNATIONAL BUSINESS MACHINES CORPORATION Inventor Yang SUN

Various embodiments are provided for providing machine learning with noisy label data in a computing environment using one or more processors in a computing system. A label corruption probability of noisy labels may be estimated for selected data from a dataset using temporal inconsistency in a machine model prediction during a training operation in a neural network.

3.20140188462System and method for analyzing ambiguities in language for natural language processing
US - 03.07.2014
Int.Class G06F 17/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
17Digital computing or data processing equipment or methods, specially adapted for specific functions
 Appl.No 14201974 Applicant Zadeh Lotfi A. Inventor Zadeh Lotfi A.

Specification covers new algorithms, methods, and systems for artificial intelligence, soft computing, and deep learning/recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, facial, OCR (text), background, relationship, position, pattern, and object), large number of images (“Big Data”) analytics, machine learning, training schemes, crowd-sourcing (using experts or humans), feature space, clustering, classification, similarity measures, optimization, search engine, ranking, question-answering system, soft (fuzzy or unsharp) boundaries/impreciseness/ambiguities/fuzziness in language, Natural Language Processing (NLP), Computing-with-Words (CWW), parsing, machine translation, sound and speech recognition, video search and analysis (e.g. tracking), image annotation, geometrical abstraction, image correction, semantic web, context analysis, data reliability (e.g., using Z-number (e.g., “About 45 minutes; Very sure”)), rules engine, control system, autonomous vehicle, self-diagnosis and self-repair robots, system diagnosis, medical diagnosis, biomedicine, data mining, event prediction, financial forecasting, economics, risk assessment, e-mail management, database management, indexing and join operation, memory management, and data compression.

4.WO/2024/054286MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING (NLP)-BASED SYSTEM FOR SYSTEM-ON-CHIP (SOC) TROUBLESHOOTING
WO - 14.03.2024
Int.Class G06F 30/33
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
30Computer-aided design
30Circuit design
32Circuit design at the digital level
33Design verification, e.g. functional simulation or model checking
 Appl.No PCT/US2023/026170 Applicant QUALCOMM INCORPORATED Inventor CAKIR, Murat
A method for processor-implemented method includes receiving an integrated circuit (IC) troubleshooting query for an IC (816). The IC troubleshooting query (816) is received from a user. The method also includes performing natural language processing and machine learning to cluster the IC troubleshooting query into one of a number of semantically similar troubleshooting categories. The method further includes retrieving resolution data from an expert system library (812), based on a mapping between categories of user solutions and a topic of the IC troubleshooting query. The method also includes generating a recommendation in response to the IC troubleshooting query, based on the resolution data (818). The method outputs the recommendation to the user.
5.20230046653METHOD AND SYSTEM FOR TRAINING A MACHINE LEARNING MODEL
US - 16.02.2023
Int.Class G06N 20/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
 Appl.No 17879178 Applicant Siemens Aktiengesellschaft Inventor Mitchell Joblin

An initially trained machine learning model is used by an active learning module to generate candidate triples, which are fed into an expert system for verification. As a result, the expert system outputs novel facts that are used for retraining the machine learning model. This approach consolidates expert systems with machine learning through iterations of an active learning loop, by bringing the two paradigms together, which is in general difficult because training of a neural network (machine learning) requires differentiable functions and rules (used by expert systems) tend not to be differentiable. The method and system provide a data augmentation strategy where the expert system acts as an oracle and outputs the novel facts, which provide labels for the candidate triples. The novel facts provide critical information from the oracle that is injected into the machine learning model at the retraining stage, thus allowing to increase its generalization performance.

6.4163833DEEP NEURAL NETWORK MODEL DESIGN ENHANCED BY REAL-TIME PROXY EVALUATION FEEDBACK
EP - 12.04.2023
Int.Class G06N 3/04
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
04Architecture, e.g. interconnection topology
 Appl.No 22186944 Applicant INTEL CORP Inventor CUMMINGS DANIEL J
The present disclosure is related to artificial intelligence (AI), machine learning (ML), and Neural Architecture Search (NAS) technologies, and in particular, to Deep Neural Network (DNN) model engineering techniques that use proxy evaluation feedback. The DNN model engineering techniques discussed herein provide near real-time feedback on model performance via low-cost proxy scores without requiring continual training and/or validation cycles, iterations, epochs, etc. In conjunction with the proxy-based scoring, semi-supervised learning mechanisms are used to map proxy scores to various model performance metrics. Other embodiments may be described and/or claimed.
7.10990645System 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.

8.WO/2019/113122SYSTEMS AND METHODS FOR IMPROVED MACHINE LEARNING FOR CONVERSATIONS
WO - 13.06.2019
Int.Class G06F 15/18
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
15Digital computers in general; Data processing equipment in general
18in which a program is changed according to experience gained by the computer itself during a complete run; Learning machines
 Appl.No PCT/US2018/063928 Applicant CONVERSICA, INC. Inventor TERRY, George, Alexis
Systems and methods for improvements in AI model learning and updating are provided. The model updating may reuse existing business conversations as the training data set. Features within the dataset may be defined and extracted. Models may be selected and parameters for the models defined. Within a distributed computing setting the parameters may be optimized, and the models deployed. The training data may be augmented over time to improve the models. Deep learning models may be employed to improve system accuracy, as can active learning techniques. The models developed and updated may be employed by a response system generally, or may function to enable specific types of AI systems. One such a system may be an AI assistant that is designed to take use cases and objectives, and execute tasks until the objectives are met. Another system capable of leveraging the models includes an automated question answering system utilizing approved answers. Yet another system for utilizing these various classification models is an intent based classification system for action determination. Lastly, it should be noted that any of the above systems may be further enhanced by enabling multiple language analysis.
9.12125146Multimodal 3D deep learning fusion system and method for reducing the need of 3D training dataset of 3D object tracking for enterprise digital twin mixed reality
US - 22.10.2024
Int.Class G06T 19/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
19Manipulating 3D models or images for computer graphics
 Appl.No 17575091 Applicant GridRaster, Inc. Inventor Yiyong Tan

A mixed reality (MR) system and method performs three dimensional (3D) tracking using 3D deep neural network structures in which multimodal fusion and simplified machine learning to only cluster label distribution (output of 3D deep neural network trained by generic 3D benchmark dataset) is used to reduce the training data requirements of to directly train a 3D deep neural network structures for non-generic user case. In one embodiment, multiple 3D deep neural network structures, such as PointCNN, 3D-Bonet, RandLA, etc., may be trained by different generic 3D benchmark datasets, such as ScanNet, ShapeNet, S3DIS, inadequate 3D training dataset, etc.

10.20050149459Automatic creation of neuro-fuzzy expert system from online anlytical processing (OLAP) tools
US - 07.07.2005
Int.Class G06F 17/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
17Digital computing or data processing equipment or methods, specially adapted for specific functions
 Appl.No 11020542 Applicant Dintecom, Inc. Inventor Kofman Gene I.

A method for automatic generation of a Neuro-Fuzzy Expert System (Fuzzy Logic Expert System implemented as a Neural Network) from data. The method comprising a Data Interface allowing description of location, type, and structure of the Data. The Interface also allows designation of input attributes and output attributes in the Data Structure; automatic Neuro-Fuzzy Expert System generation driven by the Data; Training of the Expert System's Neural Network on the Data and the presentation of results which include new knowledge embedded in the parameters and structure of the trained Neuro-Fuzzy Expert System to a user.

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