# US 2021/0241154 A1 · Predictive methodology to identify potential unknown sweet spots > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0241154 A1 - Document ID: US20210241154A1 - Status: Published application - Kind: A1 - Application number: 16/781,310 - Filed: 2020-02-04 - Published: 2021-08-05 - Earliest claimed: 2020-02-04 - Assignees: Ford Global Technologies, LLC - Applicants: Ford Global Technologies, LLC - Inventors: Hossein Jacob Sadri; Steven Torey; Stephen Juszczyk; Lance David Marsac; Sueha Elmir Salame - Web page: https://patentgrep.com/patent/US20210241154A1 ## CPC - G01M 17/007: Wheeled or endless-tracked vehicles (G01M17/08 takes precedence) - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06F 16/9035: Filtering based on additional data, e.g. user or group profiles - G06N 20/00: Machine learning - G01M: TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract To identify the sweet spot for criteria attributes (i.e., variables) within a complex system, a data source having objectively acceptable values or ranges of values for the criteria attributes for each variant of the system is generated. The identification system used to analyze the complex system to identify the sweet spot receives measurement values from a testing system that measures values of the criteria attributes from variants of the complex system and provides the measurement values to the identification system. The criteria attributes may be prioritized for determination of the sweet spot, including a selection of a value or range of values that are to be used for the most highly prioritized criteria attribute. The prioritized criteria attributes and objectively acceptable values can be used to filter the measurement values to identify the sweet spot for the variants of the complex system. ## Claims ### Claim 1 A computer-implemented method, comprising: receiving, from a measurement system, measurement values for a subset of a plurality of criteria attributes for each of a subset of a plurality of variants of an object being tested to generate a plurality of measurement value sets, each measurement value set comprising the measurement values for each of the subset of criteria attributes for a variant of the plurality of variants of the object; prioritizing the subset of criteria attributes; accessing a data store comprising objective values for each of the plurality of criteria attributes for each of the plurality of variants of the object; and filtering the plurality of measurement value sets based on the objective values for each criteria attribute of the prioritized subset of criteria attributes using a sequential computing algorithm to identify a sweet spot for each criteria attribute in the subset of criteria attributes for each variant of the object. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein filtering the plurality of measurement value sets comprises: for each of the prioritized subset of criteria attributes: removing each measurement value set from the plurality of measurement value sets having a measurement value outside the objective values for the criteria attribute; comparing each measurement value for the criteria attribute from the remaining plurality of measurement value sets against each other measurement value for each other criteria attribute using the sequential computing algorithm to identify the sweet spot; and selecting, for the next criteria attribute, from the remaining plurality of measurement value sets, the identified sweet spot. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein the object is a vehicle. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, further comprising: receiving, from the measurement system, second measurement values for a second subset of the plurality of criteria attributes for the subset of the plurality of variants of the object being tested to generate a second plurality of measurement value sets; and filtering the plurality of measurement value sets and the second plurality of measurement value sets based at least in part on the objective values for each criteria attribute of the second subset of criteria attributes. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, further comprising: setting cross-functional variants for at least a portion of the plurality of criteria attributes in the data store. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein at least some of the objective values comprise a range of objective values. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein the object is a vehicle and wherein each variant is a sub-type of vehicle. ### Claim 8 A system, comprising: one or more processors; and a memory having stored thereon instructions that, when executed by the one or more processors, cause the one or more processors to: receive, from a measurement system, measurement values for a subset of a plurality of criteria attributes for each of a subset of a plurality of variants of an object being tested to generate a plurality of measurement value sets, each measurement value set comprising the measurement values for each of the subset of criteria attributes for a variant of the plurality of variants of the object; prioritize the subset of criteria attributes; access a data store comprising objective values for each of the plurality of criteria attributes for each of the plurality of variants of the object; and filter the plurality of measurement value sets based on the objective values for each criteria attribute of the prioritized subset of criteria attributes using a sequential computing algorithm to identify a sweet spot for each criteria attribute in the subset of criteria attributes for each variant of the object. ### Claim 9 (depends on 8) The system of claim 8, wherein the instructions for filtering the plurality of measurement value sets comprises further instructions that, when executed by the one or more processors, cause the one or more processors to: for each of the prioritized subset of criteria attributes: remove each measurement value set from the plurality of measurement value sets having a measurement value outside the objective values for the criteria attribute; compare each measurement value for the criteria attribute from the remaining plurality of measurement value sets against each other measurement value for each other criteria attribute using the sequential computing algorithm to identify the sweet spot; and select, for the next criteria attribute, from the remaining plurality of measurement value sets, the identified sweet spot. ### Claim 10 (depends on 8) The system of claim 8, wherein the object is a vehicle. ### Claim 11 (depends on 8) The system of claim 8, wherein the instructions comprise further instructions that, when executed by the one or more processors, cause the one or more processors to: receive, from the measurement system, second measurement values for a second subset of the plurality of criteria attributes for the subset of the plurality of variants of the object being tested to generate a second plurality of measurement value sets; and filter the plurality of measurement value sets and the second plurality of measurement value sets based at least in part on the objective values for each criteria attribute of the second subset of criteria attributes. ### Claim 12 (depends on 8) The system of claim 8, wherein the instructions comprise further instructions that, when executed by the one or more processors, cause the one or more processors to: set cross-functional variants for at least a portion of the plurality of criteria attributes in the data store. ### Claim 13 (depends on 8) The system of claim 8, wherein at least some of the objective values comprise a range of objective values. ### Claim 14 (depends on 8) The system of claim 8, wherein the object is a vehicle and wherein each variant is a sub-type of vehicle. ### Claim 15 A memory device having stored thereon instructions that, when executed by one or more processors, cause the one or more processors to: receive, from a measurement system, measurement values for a subset of a plurality of criteria attributes for each of a subset of a plurality of variants of an object being tested to generate a plurality of measurement value sets, each measurement value set comprising the measurement values for each of the subset of criteria attributes for a variant of the plurality of variants of the object; prioritize the subset of criteria attributes; access a data store comprising objective values for each of the plurality of criteria attributes for each of the plurality of variants of the object; and filter the plurality of measurement value sets based on the objective values for each criteria attribute of the prioritized subset of criteria attributes using a sequential computing algorithm to identify a sweet spot for each criteria attribute in the subset of criteria attributes for each variant of the object. ### Claim 16 (depends on 15) The memory device of claim 15, wherein the instructions for filtering the plurality of measurement value sets comprises further instructions that, when executed by the one or more processors, cause the one or more processors to: for each of the prioritized subset of criteria attributes: remove each measurement value set from the plurality of measurement value sets having a measurement value outside the objective values for the criteria attribute; compare each measurement value for the criteria attribute from the remaining plurality of measurement value sets against each other measurement value for each other criteria attribute using the sequential computing algorithm to identify the sweet spot; and select, for the next criteria attribute, from the remaining plurality of measurement value sets, the identified sweet spot. ### Claim 17 (depends on 15) The memory device of claim 15, wherein the object is a vehicle. ### Claim 18 (depends on 15) The memory device of claim 15, wherein the instructions comprise further instructions that, when executed by the one or more processors, cause the one or more processors to: receive, from the measurement system, second measurement values for a second subset of the plurality of criteria attributes for the subset of the plurality of variants of the object being tested to generate a second plurality of measurement value sets; and filter the plurality of measurement value sets and the second plurality of measurement value sets based at least in part on the objective values for each criteria attribute of the second subset of criteria attributes. ### Claim 19 (depends on 15) The memory device of claim 15, wherein the instructions comprise further instructions that, when executed by the one or more processors, cause the one or more processors to: set cross-functional variants for at least a portion of the plurality of criteria attributes in the data store. ### Claim 20 (depends on 15) The memory device of claim 15, wherein at least some of the objective values comprise a range of objective values.