Patent US4290309A successfully refreshed from Google Patents.
Patent US8000928B2 successfully refreshed from Google Patents.
US8000928B2
Active / In Force
US
⏱️ 1.5 years remaining
Methods and apparatus for data analysis
Assignee: In Depth Test LLC
•
Filing: 2008-04-29
•
Granted: 2011-08-16
•
Expires: 2028-04-29
Abstract
Abstract A method and apparatus for data analysis according to various aspects of the present invention is configured to automatically select one or more outlier identification algorithms for identifying statistical outliers in test data for components.
Inventors
- 👤 Michael J. Scott
- 👤 Jacky Gorin
- 👤 Paul Buxton
- 👤 Eric Paul Tabor
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-09 20:11
CPC Classifications
G; G01; G01R; G01R31/00; G01R31/28; G01R31/317; G01R31/31707; G01R31/3181; G01R31/3183; G01R31/318314; G05; G05B; G05B23/00; G05B23/02; G05B23/0205; G05B23/0218; G05B23/0224; G05B23/0227; G05B23/0229; G05B23/0259; G05B23/0286; G05B23/0294; G06; G06F; G06F18/00; G06F18/20; G06F18/24; G06F18/243; G06F18/2433; G05B2219/00; G05B2219/10; G05B2219/11; G05B2219/1112; G05B2223/00; G05B2223/02
Claims Analysis
Total Claims: 48
Claim 1
Independent Claim
70 words
A test data analysis system for analyzing test data for a set of components, comprising: a memory for storing the test data; and an outlier identification system having access to the memory and configured to identify outliers in the test data according to a selected outlier identification algorithm, wherein the outlier identification system is configured to automatically select the selected outlier identification algorithm from a plurality of outlier identification algorithms.
Claim 2
Dependent Claim
70 words
A test data analysis system for analyzing test data for a set of components, comprising: a memory for storing the test data; and an outlier identification system having access to the memory and configured to identify outliers in the test data according to a selected outlier identification algorithm, wherein the outlier identification system is configured to automatically select the selected outlier identification algorithm from a plurality of outlier identification algorithms.
Claim 3
Dependent Claim
36 words
A test data analysis system according to claim 1 , wherein the outlier identification system is configured to select the selected outlier identification algorithm according to at least one descriptive statistic derived from the test data.
Claim 4
Dependent Claim
37 words
A test data analysis system according to claim 1 , wherein the outlier identification system is configured to forego automatically identifying the outliers in the test data if the test data is a selected data population type.
Claim 5
Dependent Claim
43 words
A test data analysis system according to claim 1 , wherein the outlier identification system comprises a pre-processing engine configured to identify the outliers in the test data using more of the plurality of outlier identification algorithms than the selected outlier identification algorithm.
Claim 6
Dependent Claim
28 words
A test data analysis system according to claim 1 , wherein the outlier identification system is configured to select the selected outlier identification algorithm according to user-defined rules.
Claim 7
Dependent Claim
36 words
A test data analysis system according to claim 1 , wherein the outlier identification system is configured to select the selected outlier identification algorithm according to a classification hierarchy based on characteristics of the test data.
Claim 8
Dependent Claim
46 words
A test data analysis system according to claim 1 , wherein the outlier identification system is configured to automatically identify outliers in the test data according to multiple outlier identification algorithms and discard results generated by outlier identification algorithms other than the selected outlier identification algorithm.
Claim 9
Dependent Claim
54 words
A test data analysis system according to claim 1 , wherein the outlier identification system comprises: a pre-processing engine configured to analyze the test data according to the plurality of outlier identification algorithms to generate pre-processing data; and a classification engine configured to select the selected outlier identification algorithm based on the pre-processing data.
Claim 10
Dependent Claim
86 words
A test data analysis system according to claim 1 , wherein the test data for the set of components includes test data from multiple tests for the components, and wherein the outlier identification system is configured to automatically identify outliers in the test data for each test according to a test-specific selected outlier identification algorithm selected for the test data for each test, wherein the test-specific selected outlier identification algorithm for each test is automatically selected for each test from the plurality of outlier identification algorithms.
Claim 11
Dependent Claim
25 words
A test data analysis system according to claim 1 , wherein the plurality of outlier identification algorithms is configured to he modified by a user.
Claim 12
Dependent Claim
37 words
A test data analysis system according to claim 1 , wherein the test data includes raw test data and filtered test data, wherein the filtered test data comprises the raw data without test data corresponding to failures.
Claim 13
Dependent Claim
61 words
A test data analysis system according to claim 1 , wherein the outlier identification system is configured to: forego automatically identifying the outliers in the test data according to the selected outlier identification algorithm if the test data is generated by a user-specified test; and identify outliers in the test data for the selected test using a user-specified outlier identification algorithm.
Claim 14
Dependent Claim
53 words
A test data analysis system according to claim 1 , wherein: the selected outlier identification compares the test data to an upper threshold and a lower threshold; the upper threshold and the lower threshold are defined according to different distances from a mean between an upper test limit and a lower test limit.
Claim 15
Dependent Claim
84 words
A test data analysis system according to claim 13 , wherein the outlier identification system is configured to forego dynamically adjusting the upper threshold and the lower threshold and assign selected values to the upper threshold and the lower threshold if a selected portion of the tests data is at least one of (a) within a selected range of the upper test limit or the lower test limit, and (b) beyond a selected range of the upper test limit or the lower test limit.
Claim 16
Dependent Claim
39 words
A test data analysis system according to claim 1 , wherein the outlier identification system is configured to: dynamically adjust a threshold according to the test data; and compare the test data to the threshold to identify the outliers.
Claim 17
Dependent Claim
47 words
A computer-implemented method for testing components, comprising: obtaining lest data for the components; automatically selecting an outlier identification algorithm from a plurality of outlier identification algorithms by the computer; and automatically identifying outliers in the test data according to the selected outlier identification algorithm by the computer.
Claim 18
Dependent Claim
47 words
A computer-implemented method for testing components, comprising: obtaining lest data for the components; automatically selecting an outlier identification algorithm from a plurality of outlier identification algorithms by the computer; and automatically identifying outliers in the test data according to the selected outlier identification algorithm by the computer.
Claim 19
Dependent Claim
32 words
A computer-implemented method according to claim 16 wherein selecting the outlier identification algorithm includes selecting the selected outlier identification algorithm according to at least one descriptive statistic derived from the test data.
Claim 20
Dependent Claim
29 words
A computer-implemented method according to claim 16 , further comprising foregoing automatically identifying the outliers in the test data if the test data is a selected data population type.
Claim 21
Dependent Claim
37 words
A computer-implemented method according to claim 16 , wherein selecting the outlier identification algorithm includes identifying the outliers in the test data using more of the plurality of outlier identification algorithms than the selected outlier identification algorithm.
Claim 22
Dependent Claim
25 words
A computer-implemented method according to claim selecting the 16 , wherein selecting the outlier identification algorithm includes selected outlier identification algorithm according to user-defined rules.
Claim 23
Dependent Claim
33 words
A computer-implemented method according to claim 16 , wherein selecting the outlier identification algorithm includes selecting the selected outlier identification algorithm according to a classification hierarchy based on characteristics of the test data.
Claim 24
Dependent Claim
43 words
A computer-implemented method according to claim 16 , wherein selecting the outlier identification algorithm includes automatically identifying outliers in the test data according to multiple outlier identification algorithms and discarding results generated by outlier identification algorithms other than the selected outlier identification algorithm.
Claim 25
Dependent Claim
43 words
A computer-implemented method according to claim 16 , wherein selecting the outlier identification algorithm includes: analyzing the test data according to the plurality of outlier identification algorithms to generate pre-processing data; and selecting the selected outlier identification algorithm based on the pre-processing data.
Claim 26
Dependent Claim
51 words
A computer-implemented method according to claim 16 , wherein the test data for the set of components includes tests data from multiple tests for the components, and wherein selecting the outlier identification algorithm includes selecting a test-specific selected outlier identification algorithm for each test from the plurality of outlier identification algorithms.
Claim 27
Dependent Claim
23 words
A computer-implemented method according to claim 16 , wherein the plurality of outlier identification algorithms is configured to be modified by a user.
Claim 28
Dependent Claim
35 words
A computer-implemented method according to claim 16 , wherein the test data includes raw test data and filtered test data, wherein the filtered test data comprises the raw data without test data corresponding to failures.
Claim 29
Dependent Claim
56 words
A computer-implemented method according to claim 16 , wherein automatically identifying outliers comprises: foregoing automatically identifying the outliers in the test data according to the selected outlier identification algorithm if the test data is generated by a user-specified test; and identifying outliers in the test data for the selected test using a user-specified outlier identification algorithm.
Claim 30
Dependent Claim
52 words
A computer-implemented method. according to claim 16 , wherein automatically identifying outliers comprises: comparing the test data to an upper threshold and a lower threshold., wherein the upper threshold and the lower threshold are defined according to different distances from a mean between an upper test limit and a lower test limit.
Claim 31
Dependent Claim
80 words
A computer-implemented method according to claim 28 , wherein automatically identifying outliers further comprises foregoing dynamically adjusting the upper threshold and the lower threshold and assigning selected values to the upper threshold and the lower threshold if a selected portion of the tests data is at least one of (a) within a selected range of the upper test limit or the lower test limit and (b) beyond a selected range of the upper test limit or the lower test limit.
Claim 32
Dependent Claim
34 words
A computer-implemented method according to claim 16 , wherein automatically identifying outliers comprises: dynamically adjusting a threshold according to the test data; and comparing the test data to the threshold to identify the outliers.
Claim 33
Dependent Claim
58 words
A non-transitory computer readable medium containing computer instructions stored therein for causing a computer processor to perform a method for analyzing test data comprising: obtaining test data for the components; automatically selecting an outlier identification algorithm from a plurality of outlier identification algorithms; and automatically identifying an outlier in the test data using the selected outlier identification algorithm.
Claim 34
Dependent Claim
58 words
A non-transitory computer readable medium containing computer instructions stored therein for causing a computer processor to perform a method for analyzing test data comprising: obtaining test data for the components; automatically selecting an outlier identification algorithm from a plurality of outlier identification algorithms; and automatically identifying an outlier in the test data using the selected outlier identification algorithm.
Claim 35
Dependent Claim
35 words
A non-transitory computer readable medium according to claim 31 , wherein selecting the outlier identification algorithm includes selecting the selected outlier identification algorithm according to at least one descriptive statistic derived from the test data.
Claim 36
Dependent Claim
31 words
A non-transitory computer readable medium according to claim 31 , further comprising foregoing automatically identifying the outliers in the test data if the test data is a selected data population type.
Claim 37
Dependent Claim
39 words
A non-transitory computer readable medium according to claim 31 , wherein selecting the outlier identification algorithm includes identifying the outliers in the test data using more of the plurality of outlier identification algorithms than the selected outlier identification algorithm.
Claim 38
Dependent Claim
27 words
A non-transitory computer readable medium according to claim 31 , wherein selecting the outlier identification algorithm includes selecting the selected outlier identification algorithm according to user-defined rules.
Claim 39
Dependent Claim
35 words
A non-transitory computer readable medium according to claim 31 , wherein selecting the outlier identification algorithm includes selecting the selected outlier identification algorithm according to a classification hierarchy based on characteristics of the test data.
Claim 40
Dependent Claim
45 words
A non-transitory computer readable medium according to claim 31 , wherein selecting the outlier identification algorithm includes automatically identifying outliers in the test data according to multiple outlier identification algorithms and discarding results generated by outlier identification algorithms other than the selected outlier identification algorithm.
Claim 41
Dependent Claim
45 words
A non-transitory computer readable medium according to claim 31 , wherein selecting the outlier identification algorithm includes: analyzing the test data according to the plurality of outlier identification algorithms to generate pre-processing data; and selecting the selected outlier identification algorithm based on the pre-processing data.
Claim 42
Dependent Claim
53 words
A non-transitory computer readable medium according to claim 31 , wherein the test data for the set of components includes tests data from multiple tests for the components, and wherein selecting the outlier identification algorithm includes selecting a test-specific selected outlier identification algorithm for each test from the plurality of outlier identification algorithms.
Claim 43
Dependent Claim
25 words
A non-transitory computer readable medium according to claim 31 , wherein the plurality of outlier identification algorithms is configured to be modified by a user.
Claim 44
Dependent Claim
37 words
A non-transitory computer readable medium according to claim 31 , wherein the test data includes raw test data and filtered test data, wherein the filtered test data comprises the raw data without test data corresponding to failures.
Claim 45
Dependent Claim
58 words
A non-transitory computer readable medium according to claim 31 , wherein automatically identifying outliers comprises: foregoing automatically identifying the outliers in the test data according to the selected outlier identification algorithm if the test data is generated by a user-specified test; and identifying outliers in the test data for the selected test using a user-specified outlier identification algorithm.
Claim 46
Dependent Claim
54 words
A non-transitory computer readable medium according to claim 31 , wherein automatically identifying outliers comprises: comparing the test data to an upper threshold and a lower threshold, wherein the upper threshold and the lower threshold are defined according to different distances from a mean between an upper test limit and a lower test limit.
Claim 47
Dependent Claim
82 words
A non-transitory computer readable medium according to claim 43 , wherein automatically identifying outliers further comprises foregoing dynamically adjusting the upper threshold and the lower threshold and assigning selected values to the upper threshold and the lower threshold if a selected portion of the tests data is at least one of (a) within a selected range of the upper test limit or the lower test limit and (b) beyond a selected range of the upper test limit or the lower test limit.
Claim 48
Dependent Claim
36 words
A non-transitory computer readable medium according to claim 35 , wherein automatically identifying outliers comprises: dynamically adjusting a threshold. according to the test data; and comparing the test data to the threshold to identity the outliers.
References Cited (Backward Citations)
US4109511A
Cited
US4989166A
Cited
US5694325A
Cited
US5495417A
Cited
US5130936A
Cited
US5287284A
Cited
US5240866A
Cited
US6338148B1
Cited
US5539652A
Cited
US5787190A
Cited
US5956251A
Cited
US5668745A
Cited
US5892949A
Cited
US5966527A
Cited
US5835891A
Cited
US6182022B1
Cited
US6516309B1
Cited
US6300772B1
Cited
US6279146B1
Cited
US20020054694A1
Cited
EP1067392A2
Cited
US6477685B1
Cited
US6366851B1
Cited
US6184048B1
Cited
US6442499B1
Cited
US20030014205A1
Cited
US20030144810A1
Cited
US7395170B2
Cited
US6787379B1
Cited
US6943042B2
Cited
US20030140287A1
Cited
US6807655B1
Cited
US6939727B1
Cited
USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 2014-08-15 | 2015-02-14 | 2015-08-16 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2018-08-15 | 2019-02-14 | 2019-08-16 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2022-08-15 | 2023-02-14 | 2023-08-16 | $7700.00 | Window Closed / Lapsed |