US 11,385,633 B2Granted

Model reduction and training efficiency in computer-based reasoning and artificial intelligence systems

AssigneeDiveplane Corporation

InventorsChristopher James Hazard, Michael Resnick, Ravisutha Sakrepatna Srinivasamurthy, David R. Cheeseman, Ju Hyun Kim, Yamac Alican Isik

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Abstract

Techniques are provided herein for creating well-balanced computer-based reasoning systems and using those to control systems. The techniques include receiving a request to determine whether to use one or more particular data elements, features, cases, etc. in a computer-based reasoning model (e.g., as data elements, cases or features are being added, or as part of pruning existing features or cases). Conviction measures are determined and inclusivity conditions are tested. The result of comparing the conviction measure can be used to determine whether to include or exclude the feature, case, etc. in the model and/or whether there are anomalies in the model. A controllable system may then be controlled using the computer-based reasoning model. Examples controllable systems include self-driving cars, image labeling systems, manufacturing and assembly controls, federated systems, smart voice controls, automated control of experiments, energy transfer systems, health care systems, cybersecurity systems, and the like.

Claims20

Dependent claims

A method comprising:

receiving a request to determine whether to one or more particular data elements in a computer-based reasoning model meet inclusivity conditions;
determining one or more conviction scores for the one or more particular data elements, wherein:
determining for each of the one or more particular data elements, one or more conviction scores, wherein determining one or more conviction scores for the one or more particular data elements comprises determining a familiarity conviction score for the one or more particular data elements and determining a distance contribution score for the one or more particular data elements;
wherein:
the familiarity conviction score is a measure of how much the one or more particular data elements distort a model, and
the distance contribution score is a locally weighted expected value of the distance from one point to its nearest neighbors;
determining whether the one or more conviction scores meet one or more inclusivity conditions;
in response to determining that the one or more conviction scores meet the one or more inclusivity conditions:
including the one or more particular data elements in the computer-based reasoning model when the inclusivity conditions comprise an inclusion condition;
excluding the one or more particular data elements in the computer-based reasoning model when the inclusivity conditions comprise an exclusion condition;
causing, with a control system, control of a controllable system with the computer-based reasoning model,
wherein determining whether the one or more conviction scores meet the inclusivity conditions comprises determining that the one or more particular data elements meet the inclusion condition when the distance contribution score is beyond a first threshold and the familiarity conviction score is beyond a second threshold,
wherein the method is performed on one or more computing devices.

10 more elements

A system for executing instructions, wherein said instructions are instructions which, when executed by one or more computing devices, cause performance of a process including:

receiving a request to determine whether to one or more particular data elements in a computer-based reasoning model meet inclusivity conditions;
determining one or more conviction scores for the one or more particular data elements, wherein:
determining for each of the one or more particular data elements, one or more conviction scores, wherein determining one or more conviction scores for the one or more particular data elements comprises determining a familiarity conviction score for the one or more particular data elements and determining a distance contribution score for the one or more particular data elements;
wherein:
the familiarity conviction score is a measure of how much the one or more particular data elements distort a model, and
the distance contribution score is a locally weighted expected value of the distance from one point to its nearest neighbors;
determining whether the one or more conviction scores meet one or more inclusivity conditions;
in response to determining that the one or more conviction scores meet the one or more inclusivity conditions:
including the one or more particular data elements in the computer-based reasoning model when the inclusivity conditions comprise an inclusion condition;
excluding the one or more particular data elements in the computer-based reasoning model when the inclusivity conditions comprise an exclusion condition;
causing, with a control system, control of a controllable system with the computer-based reasoning model,
wherein determining whether the one or more conviction scores meet the inclusivity conditions comprises determining that the one or more particular data elements meet the inclusion condition when the distance contribution score is beyond a first threshold and the familiarity conviction score is beyond a second threshold,
wherein the method is performed on one or more computing devices.

10 more elements

A non-transitory computer readable medium storing instructions which, when executed by one or more computing devices, cause the one or more computing devices to perform a process of:

receiving a request to determine whether to one or more particular data elements in a computer-based reasoning model meet inclusivity conditions;
determining one or more conviction scores for the one or more particular data elements, wherein:
determining for each of the one or more particular data elements, one or more conviction scores, wherein determining one or more conviction scores for the one or more particular data elements comprises determining a familiarity conviction score for the one or more particular data elements and determining a distance contribution score for the one or more particular data elements;
wherein:
the familiarity conviction score is a measure of how much the one or more particular data elements distort a model, and
the distance contribution score is a locally weighted expected value of the distance from one point to its nearest neighbors;
determining whether the one or more conviction scores meet one or more inclusivity conditions;
in response to determining that the one or more conviction scores meet the one or more inclusivity conditions:
including the one or more particular data elements in the computer-based reasoning model when the inclusivity conditions comprise an inclusion condition;
excluding the one or more particular data elements in the computer-based reasoning model when the inclusivity conditions comprise an exclusion condition;
causing, with a control system, control of a controllable system with the computer-based reasoning model,
wherein determining whether the one or more conviction scores meet the inclusivity conditions comprises determining that the one or more particular data elements meet the inclusion condition when the distance contribution score is beyond a first threshold and the familiarity conviction score is beyond a second threshold,
wherein the method is performed on one or more computing devices.

10 more elements

Dependencies are inferred from the claim text.

Facts

Application: 16/992,876Filed 13 Aug 2020 · continuation
Priority: 9 Apr 2018
Published: US 2021/0064018 A14 Mar 2021
Granted: 12 Jul 2022
Term ends: Not estimatedThe source records don’t support an estimate.

Classification14 codes

G05B 23/0281
Fault isolation and identification, e.g. classify fault; estimate cause or root of failure: quantitative, e.g. mathematical distance; Clustering;…
G06N 3/006
Artificial life, i.e. computing arrangements simulating life: based on simulated virtual individual or collective life forms, e.g. social…
G06F 18/214
Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation: generating training…
G06F 18/22
Analysing: matching criteria, e.g. proximity measures

Family

Application15/948,805Parent of this continuation-in-part · filed 9 Apr 2018
Application16/220,986Parent of this continuation-in-part · filed 14 Dec 2018
Application16/376,509Parent of this continuation-in-part · filed 5 Apr 2019
Application63/038,335Us provisional application · filed 12 Jun 2020
Application16/992,842Parent of this continuation · filed 13 Aug 2020
US 2021/0064018 A1Pre-grant publication · published 4 Mar 2021
US 10,817,750 B2Parent of this continuation-in-part
Application16/992,842Parent of this continuation-in-part
US 10,816,980 B2Parent of this continuation-in-part
US 11,262,742 B2Related patent
US 2019/0310592 A1Published application
US 2019/0310634 A1Published application
US 2019/0311220 A1Published application
US 2020/0371512 A1Published application
US 11,385,633 B2This patent

From the US filing history. Foreign family members aren’t listed.