DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) the abstract ideas of a mathematical algorithm for determining scores for electronic device tests based on tests results (i.e., generation of Cpk values through mathematical equations) and a mental activity algorithm for constructing an appropriate test set. A combination of abstract ideas is an abstract idea [See MPEP 2106.05(I) – "Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"].
This judicial exception is not integrated into a practical application because no use of the test set is recited that would amount to an improvement to either testing or any improvement to the electronic devices subject to testing.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because receiving the test results is necessary to implement the algorithm and amounts to routine data gathering. The recitations of Claim 5 amount to mere field-of-use limitations with regards to the performance of the abstract idea. The recitation of a machine-learned model, model training, and the greedy algorithm amount to the recitation of general-purpose computer components/steps for implementing the abstract idea through use of a general-purpose computer and do not serve to amount to significantly more than the recitation of the abstract idea itself (see Alice Corp. v. CLS Bank International, 573 U.S. 208 (2014)).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-4 and 6-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al., Fine-grained Adaptive Testing Based on Quality Prediction, ACM Trans. Des. Autom. Electron. Syst. 25, 5, Article 38, July 2020 [hereinafter “Liu”] and Li et al., A Simulation Study on Some Search Algorithms for Regression Test Case Prioritization, IEEE, 2010 [hereinafter “Li”].
Regarding Claim 1, Liu discloses a method for identifying test parameters for testing a manufactured electronic device [Abstract – “The ever-increasing complexity of integrated circuits inevitably leads to high test cost. Adaptive testing provides an effective solution for test-cost reduction; this testing framework selects the important test items for each set of chips. … To incorporate fabrication variations and random defects in the testing framework, we propose a fine-grained adaptive testing method based on machine learning.”], the method comprising:
receiving test parameter data comprising base results of base types of tests for the manufactured electronic device [Section 5.2 – “Chips in Group 1 are predicted to be high-quality chips. For this set, we use a test-selection method based on statistical learning. As part of fine-grained adaptive testing, a complete set of FT tests is applied to the sample chips, and these test results are collected. … To evaluate the capability of each individual test item to detect failed chips, the Cpk metric was proposed in Reference [4]. … To select only the most-important test items, a threshold for Cpk is required. … In the proposed fine-grained adaptive testing, a threshold of 2 is adopted, which means that we select only the test items with a Cpk value less than 2. For the chips in Group 1, we utilize only the selected set of FT tests. Based on the outcomes of the selected test items, the pass/fail result of each chip for the FT stage is obtained.”].
Liu fails to disclose receiving new parameter data comprising a new result of a new type of test for the manufactured electronic device, the new type of test not a member of the base types of tests; and generating a correlation value between the new parameter data and the test parameter data.
However, Li discloses the use of a greedy machine learning algorithm in evaluating the effectiveness of candidate test sets [Page 72, first column – “Test case prioritization is an approach aiming at increasing the rate of faults detection during the testing phase, by reordering test case execution. Test case prioritization makes regression testing effective because it provides a way to detect faults as early as possible and reserve more time to fix the bugs found.”Page 73, first column – “The principle of all Greedy Algorithms is to select the most promising element at the current moment. This algorithm maintains two sets of test cases, one is the chosen set, the other is the candidate set. Its strategy is to repeatedly select one test case which satisfies the most test requirements from the candidate set, regardless of the requirement's current state (satisfied or not). If there are several test cases satisfying the same number of the requirements, one of them is randomly picked. Under the circumstances of test case prioritization, this process terminates when all the test requirements are satisfied.”]. It would have been obvious to use such a strategy in the context of Liu (using Liu’s Cpk values for the evaluation as “correlation values”) in order to evaluate the relative effectiveness of new candidate tests because doing so would have provided a manner of improving the overall testing effectiveness.
The combination would further disclose comparing the correlation value with a threshold value; determining that the correlation value meets or exceeds the threshold value; and constructing a final test type set, the final test type set representing a subset of the union between the base types of tests and the new type of test [Section 5.2 of Liu – “In the proposed fine-grained adaptive testing, a threshold of 2 is adopted, which means that we select only the test items with a Cpk value less than 2. For the chips in Group 1, we utilize only the selected set of FT tests. Based on the outcomes of the selected test items, the pass/fail result of each chip for the FT stage is obtained.” All tests, including new candidate tests, below the Cpk threshold being included in the final set (the final set being the union of all such tests of the base type and new type).].
Regarding Claim 2, the combination would disclose generating a process capability index, wherein the generation of the correlation value is based on the process capability index value [Using Liu’s Section 5.2 Cpk values for the evaluation as “correlation values” in evaluating the relative effectiveness of new candidate tests per Li.].
Regarding Claim 3, the combination would disclose that the process capability index is a critical process capability (CpK) score [Using Liu’s Section 5.2 Cpk values for the evaluation as “correlation values” in evaluating the relative effectiveness of new candidate tests per Li.].
Regarding Claim 4, the combination would disclose generating a second process capability index value, wherein: the second process capability index is generated by updating the CpK score using the new parameter data; and the generation of the correlation value is further based at least in part on the second process capability index value [Using Liu’s Section 5.3 “improved Cpk” values for the evaluation as “correlation values” in evaluating the relative effectiveness of new candidate tests per Li.].
Regarding Claim 6, the combination would disclose that the comparing of the correlation value with the threshold value is performed by a machine-learned model [Section 5 of Liu – “For the chips in Group 1 (i.e., the high-QI chips), a test-selection method based on statistical learning is used; we utilize Process Capability Index (Cpk) as a metric for this purpose (Section 5.2).”Using Liu’s Section 5.2 Cpk values for the evaluation as “correlation values” in evaluating the relative effectiveness of new candidate tests per Li.].
Regarding Claim 7, the combination would disclose that the machine learned model is trained at least in part using a greedy algorithm [Using the learning for test selection of Liu through use of the greedy algorithms of Li].
Regarding Claim 8, the combination would disclose generating a first process capability index and a second process capability index [The Cpk values of Liu for existing and new candidate tests], wherein:
the first process capability index is based on a CpK score [The Cpk values of Liu for existing tests];
the second process capability index is generated by updating the CpK score using the new parameter data [Using Liu’s Section 5.3 “improved Cpk” values for the evaluation as “correlation values” in evaluating the relative effectiveness of new candidate tests per Li.]; and
the machine-learned model [Using the learning for test selection of Liu through use of the greedy algorithms of Li] takes as inputs:
the test parameter data [Section 5.2 of Liu – “Chips in Group 1 are predicted to be high-quality chips. For this set, we use a test-selection method based on statistical learning. As part of fine-grained adaptive testing, a complete set of FT tests is applied to the sample chips, and these test results are collected.”];
the new parameter data [Section 5.2 of Liu, as applied for new candidate tests – “Chips in Group 1 are predicted to be high-quality chips. For this set, we use a test-selection method based on statistical learning. As part of fine-grained adaptive testing, a complete set of FT tests is applied to the sample chips, and these test results are collected.”];
the first process capability index [The Cpk values of Liu for existing tests]; and
the second process capability index [Using Liu’s Section 5.3 “improved Cpk” values for the evaluation as “correlation values” in evaluating the relative effectiveness of new candidate tests per Li.].
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al., Fine-grained Adaptive Testing Based on Quality Prediction, ACM Trans. Des. Autom. Electron. Syst. 25, 5, Article 38, July 2020 [hereinafter “Liu”]; Li et al., A Simulation Study on Some Search Algorithms for Regression Test Case Prioritization, IEEE, 2010 [hereinafter “Li”]; and Reykhert (US 20210193977 A1).
Regarding Claim 5, Liu fails to disclose that the manufactured electronic device is one of: a smartphone; a computer; a smartwatch; true-wireless earbuds; a tablet; smart glasses; hearing aids; AR goggles; or a smart helmet.
However, Reykhert discloses such fields of use [Paragraph [0017]]. It would have been obvious to develop test sets for such types of electronic devices in order to appropriately perform quality control.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Darfeuille et al., Production Test of an RF Receiver Chain Based on ATM Combining RF BIST and Machine Learning Algorithm, IEEE, 2011
Liang et al., Identifying the Optimal Subsets of Test Items through Adaptive Test for Cost Reduction of ICs, MDPI, 2021
Siddiqui et al., A Novel System to Increase Yield of Manufacturing Test of an RF Transceiver through Application of Machine Learning, MDPI, 1.8.2023
US 20220163951 A1 – MANUFACTURING A PRODUCT USING CAUSAL MODELS
US 20220399081 A1 – MACHINE LEARNING METHOD AND APPARATUS USING STEPS FEATURE SELECTION BASED ON GENETIC ALGORITHM
US 20100161276 A1 – System And Methods For Parametric Test Time Reduction
US 20040006447 A1 – Methods And Apparatus For Test Process Enhancement
US 20230214568 A1 – DETECTION METHOD, SYSTEM, ELECTRONIC EQUIPMENT, AND STORAGE MEDIUM OF PRODUCT TEST DATA
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/KYLE R QUIGLEY/Primary Examiner, Art Unit 2857
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