Feedback
Settings

Settings

Goto Application

20,153 results
Offices all Languages en Stemming true Single Family Member false Include NPL false
RSS feed can only be generated if you have a WIPO account

Save query

A private query is only visible to you when you are logged-in and can not be used in RSS feeds

Query Tree

Refine Options

Offices
All
Specify the language of your search keywords
Stemming reduces inflected words to their stem or root form.
For example the words fishing, fished,fish, and fisher are reduced to the root word,fish,
so a search for fisher returns all the different variations
Returns only one member of a family of patents
Include Non-Patent literature in results

Full Query

AIfunctionalapplicationsControlMethods

Side-by-side view shortcuts

General
Go to Search input
CTRL + SHIFT +
Go to Results (selected record)
CTRL + SHIFT +
Go to Detail (selected tab)
CTRL + SHIFT +
Go to Next page
CTRL +
Go to Previous page
CTRL +
Results (First, do 'Go to Results')
Go to Next record / image
/
Go to Previous record / image
/
Scroll Up
Page Up
Scroll Down
Page Down
Scroll to Top
CTRL + Home
Scroll to Bottom
CTRL + End
Detail (First, do 'Go to Detail')
Go to Next tab
Go to Previous tab

Analysis

1 / 2,016
1.20200272112Failure mode analytics
US - 27.08.2020
Int.Class G05B 13/02
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
13Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
02electric
 Appl.No 16284369 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 a request to create a machine learning model for failure mode detection associated with an asset, retrieving historical notification data of the asset, generating an unsupervised machine learning model via unsupervised learning on the historical notification data, wherein the unsupervised learning comprises identifying failure topics from text included in the historical notification data and mapping the identified failure topics to a plurality of predefined failure modes for the asset, and storing the generated unsupervised machine learning model via a storage device.

2.20230168639Failure mode analytics
US - 01.06.2023
Int.Class G05B 13/02
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
13Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
02electric
 Appl.No 18096080 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 a request to create a machine learning model for failure mode detection associated with an asset, retrieving historical notification data of the asset, generating an unsupervised machine learning model via unsupervised learning on the historical notification data, wherein the unsupervised learning comprises identifying failure topics from text included in the historical notification data and mapping the identified failure topics to a plurality of predefined failure modes for the asset, and storing the generated unsupervised machine learning model via a storage device.

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.

4.20200026287Method of real time vehicle recognition with neuromorphic computing network for autonomous driving
US - 23.01.2020
Int.Class G05D 1/00
GPHYSICS
05CONTROLLING; REGULATING
DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
1Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
 Appl.No 16519814 Applicant HRL Laboratories, LLC Inventor Qin Jiang

Described is a system for online vehicle recognition in an autonomous driving environment. Using a learning network comprising an unsupervised learning component and a supervised learning component, images of moving vehicles extracted from videos captured in the autonomous driving environment are learned and classified. Vehicle feature data is extracted from input moving vehicle images. The extracted vehicle feature data is clustered into different vehicle classes using the unsupervised learning component. Vehicle class labels for the different vehicle classes are generated using the supervised learning component. Based on a vehicle class label for a moving vehicle in the autonomous driving environment, the system selects an action to be performed by the autonomous vehicle, and causes the selected action to be performed by the autonomous vehicle in the autonomous driving environment.

5.20190188743Allocation of service provider resources based on a capacity to provide the service
US - 20.06.2019
Int.Class G06Q 30/02
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
30Commerce
02Marketing; Price estimation or determination; Fundraising
 Appl.No 16193500 Applicant Capital One Services, LLC Inventor Jeremy Phillips

An example includes one or more devices may include one or more memories and one or more processors, communicatively coupled with at least one of the one or more memories, to identify a service that is provided within a region; identify a model that is associated with the service, the model having been trained based on consumer profile data, service provider data, and historical information; determine a current demand associated with the service in the region; predict, using the model and based on the current demand associated with the service, a future demand for the service during a time period; determine a current capacity to provide the service based on real-time service provider information associated with service providers that are providing the service in the region; and perform an action associated with the service based on the future demand for the service and the current capacity to provide the service.

6.3835894METHOD AND APPARATUS FOR CONFIGURING A MACHINE CONTROLLER
EP - 16.06.2021
Int.Class G05B 13/02
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
13Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
02electric
 Appl.No 19214433 Applicant SIEMENS AG Inventor GEIPEL MARKUS MICHAEL
For configuring a machine controller (CTL), the following steps are performed:a) a simulator (SIM) is interfaced for receiving machine control signals (CD), for simulating a physical reaction of the machine (MA), and for transmitting simulated measurement data (SSD) resulting from that reaction;b) for the machine controller (CTL) a plurality of different controller settings (CP1..., CPN) is generated;c) for a respective controller setting (CP1..., CPN):- an instance (CI1..., CIN) of the machine controller is generated,- the controller instance (CI1..., CIN) receives simulated measurement data (SSD) from the simulator (SIM), generates machine control signals (CD) depending on the simulated measurement data (SSD), and transmits the machine control signals (CD) to the simulator (SIM), and- a performance value (PV) is determined from the simulated measurement data (SSD);d) the performance values (PV) are compared, and an updated plurality of controller settings (CP1..., CPN) biased towards improved machine performance is determined;e) steps c) and d) are iterated with the updated plurality of controller settings (CP1..., CPN) while applying a reinforcement learning method to determine a controller setting (CPO) optimized with respect to the performance values (PV); andg) the machine controller (CTL) is configured with the optimized controller setting (CPO).
7.5448681Intelligent controller with neural network and reinforcement learning
US - 05.09.1995
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 07859328 Applicant National Semiconductor Corporation Inventor Khan Emdadur R.

A plant controller using reinforcement learning for controlling a plant includes action and critic networks with enhanced learning for generating a plant control signal. Learning is enhanced within the action network by using a neural network configured to operate according to unsupervised learning techniques based upon a Kohonen Feature Map. Learning is enhanced within the critic network by using a distance parameter which represents the difference between the actual and desired states of the quantitative performance, or output, of the plant when generating the reinforcement signal for the action network.

8.11900244Attention-based deep reinforcement learning for autonomous agents
US - 13.02.2024
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 16588789 Applicant Amazon Technologies, Inc. Inventor Sahika Genc

A data source configured to provide a representation of an environment of one or more agents is identified. Using a data set obtained from the data source, a neural network-based reinforcement learning model with one or more attention layers is trained. Importance indicators generated by the attention layers are used to identify actions to be initiated by an agent. A trained version of the model is stored.

9.3736646METHOD AND CONTROLLER FOR CONTROLLING A CHILLER PLANT FOR A BUILDING AND CHILLER PLANT
EP - 11.11.2020
Int.Class G05B 15/02
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
15Systems controlled by a computer
02electric
 Appl.No 19173598 Applicant SIEMENS SCHWEIZ AG Inventor SO KING FAI
According to the invention, environmental data (ED) of an environment of the building (BD) and cooling load demand data (CLD) are received as first training data, which are used for training a first machine learning model (NN1) to predict a cooling load demand from environmental data. Furthermore, control signals (CS) for the chiller plant (CP) and cooling power data (CPD) resulting from applying the control signals (CS) to the chiller plant (CP) are received as second training data. The second training data are used for training a second machine learning model (NN2) to predict a cooling power from control signals. Moreover, actual environmental data (ED) are received, from which a cooling load demand (CLDP) is predicted by the trained first machine learning model (NN1). Furthermore, candidate control signals (CCS) for the chiller plant (CP) are generated, and from the candidate control signals (CCS) a resulting cooling power (CPP) is predicted by the trained second machine learning model (NN2). From the candidate control signals (CCS), applicable control signals (ACS) are selected for which a predicted cooling power (CPP) fulfills the predicted cooling load demand (CLDP).
10.WO/2022/069498COMPUTER SYSTEM AND METHOD PROVIDING OPERATING INSTRUCTIONS FOR THERMAL CONTROL OF A BLAST FURNACE
WO - 07.04.2022
Int.Class G05B 13/02
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
13Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
02electric
 Appl.No PCT/EP2021/076710 Applicant PAUL WURTH S.A. Inventor SCHOCKAERT, Cédric
Computer system (100), computer-implemented method and computer program product are provided for training a reinforcement learning model (130) to provide operating instructions for thermal control of a blast furnace. A domain adaptation machine learning model (110) generates a first domain invariant dataset (22) from historical operating data (21) obtained as multivariate time series and reflecting thermal states of respective blast furnaces (BF1 to BFn) of multiple domains. A transient model (121) of a generic blast furnace process is used to generate artificial operating data (24a) as multivariate time series reflecting a thermal state of a generic blast furnace (BFg) for a particular thermal control action (26a). A generative deep learning network (122) generates a second domain invariant dataset (23a) by transferring the features learned from the historical operating data 21 to the artificial operating data (24a). The reinforcement learning model (130) determines (1400) a reward (131) for the particular thermal control action (26a) in view of a given objective function by processing the combined first and second domain invariant datasets (22, 23a). Dependent on the reward (131), the second domain invariant data set is regenerated based on modified parameters (123-2), and repeating the determining of the reward to learn optimized operating instructions for optimized thermal control actions to be applied for respective operating states of one or more blast furnaces.
# -