DETAILED ACTION
This office action is in response to the request for continuation filed on March 2, 2026 in application 18/426,678.
Claims 1-4, 6-12, 14-20 are presented for examination. Claims 1, 4, 18-19 are amended. Claim 5 and 13 are cancelled.
IDS submitted on March 6, 2024 and July 7, 2025 was acknowledged.
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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-4, 6-12, 14-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 U.S.C. § 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 1-4, 6-12, 14-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claim 1: (similarly claims 18 and 19)
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine (system).
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04.
Yes, the limitation “to determine which regulations and standards are applicable to the device under test to be tested based on the user input, to generate a set of rules describing the application of the applicable regulations and standards to the device under test that is to be tested, wherein the generated set of rules comprises at least one of links or a map between the applicable regulations and standards and the compliance tests to be performed on the device under test to be tested, to generate test data based on the generated set of rules, wherein the test data comprises information on compliance tests to be performed on the device under test in view of the applicable regulations and standards, and to generate visualization data based on the test data, wherein the visualization data comprises information on the test data” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
MPEP § 2106.04(a)(2)(III)(A), limitation that cannot practically be performed in the mind: 1. calculating an absolute position of a GPS receiver, 2. detecting suspicious activity using network monitors and analyzing packets, 3. Specific data encryption, 4. Rendering a halftone image that requires manipulation of computer data structures.
MPEP § 2106.04(a)(2)(III)(B) humans performing mental process (ex. List making, screening messages,
MPEP § 2106.04(a)(2)(III)(C), requires a computer but still recites a mental process: 1. Perform on generic computer, 2. Perform in a computer environment, 3. Using a computer as a tool
MPEP § 2106.04(a)(2)(III)(D), both product and process claims recite mental process (ex., interface for extracting and processing information, searching, detecting fraud, receive and redistributed message, verifying, real-time performance monitoring, determining a price)
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “a user interface, a machine-learning circuit, and a database, wherein the database comprises compliance testing data, wherein the compliance testing data comprises information on regulations and standards for performing compliance tests on different devices under test, wherein the user interface is configured to receive a user input, wherein the user input comprises text and/or speech relating to the device under test to be tested, wherein the machine-learning circuit is configured to determine and to generate, and wherein the user interface is configured to display the visualization data comprising the information on the test data ” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “a user interface, a machine-learning circuit, and a database, wherein the database comprises compliance testing data, wherein the compliance testing data comprises information on regulations and standards for performing compliance tests on different devices under test, wherein the user interface is configured to receive a user input, wherein the user input comprises text and/or speech relating to the device under test to be tested, wherein the machine-learning circuit is configured to determine and to generate, and wherein the user interface is configured to display the visualization data comprising the information on the test data” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore, the additional element is directed to receiving or transmitting data over a network / performing repetitive calculations / electronic recordkeeping / storing and retrieving information in memory / electronically scanning or extracting data from a physical document, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
As to claim 2-4, 6-12, 14-17, 20
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine (system).
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04.
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 6-12, 14-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pingle et al. (US 2018/0277235) in further view of G Rao et al. (US 2021/0342742).
In regard to claim 1, Pingle et al. teach a compliance testing system for testing an electronic device under test, the compliance testing system comprising a user interface, a machine-learning circuit, a visualization circuit and a database,
wherein the database comprises compliance testing data, wherein the compliance testing data comprises information on regulations and standards for performing compliance tests on different devices under test (feature identifier data, fig. 2, 220, para. 42),
wherein the user interface is configured to receive a user input, wherein the user input comprises text and/or speech relating to the device under test to be tested (user interface, fig. 1, 136),
wherein the machine-learning circuit comprises an artificial neural network (machine learning circuit may use one or more machine learning techniques (neural network, Bayesian statistics, decision tree, linear classification, random forests, etc.), para. 39) and is configured to determine which regulations and standards are applicable to the device under test to be tested based on the user input (information and/or data associated with semiconductor platform features, information and/or data associated with the test blocks, fig. 2, para. 41-42, extracted using machine learning, para. 43),
wherein the machine-learning circuit is configured to generate a set of rules describing the application of the applicable regulations and standards to the device under test that is to be tested (information and/or data associated with the test blocks, fig. 2, para. 41-42), wherein the generated set of rules comprises at least one of links or a map between the applicable regulations and standards and the compliance tests to be performed on the device under test to be tested (a plurality of features 120, each logically associated with a number of test blocks 122, para. 76, logical association is formed between the test block and the semiconductor platform features that use the test block, para. 79, fig. 8, 806),
wherein the machine-learning circuit is configured to generate test data based on the generated set of rules, wherein the test data comprises information on compliance tests to be performed on the device under test in view of the applicable regulations and standards (test report, fig. 2, 230, 232, para. 45),
wherein the visualization circuit is configured to generate visualization data based on the test data, wherein the visualization data comprises information on the test data (the semiconductor platform test circuitry may generate test results that are reported to the system user, para. 74, fig. 7, 704); and
wherein the user interface is configured to display the visualization data comprising the information on the test data (provide test results and/or the comparison results to a system operator or user via one or more user interfaces, such as a display device, para. 40),
Pingle et al. teach of output devices (e.g., haptic feedback or similar) (para. 60) but Pingle et al. does not explicitly teach wherein the user interface is configured to receive feedback data, wherein the feedback data comprises information on errors in the set of rules and/or in the test data, and wherein the machine learning circuit is configured to adjust its operational parameters by reinforcement learning in order to minimize the errors.
G Rao et al. teach of providing validation data or feedback for categorization produced by the scoring systems (para. 25) and the threshold adjustment engine can be trained to adjust the various values in response to the feedback (para. 36). The automatic adjuster includes ML components that is trained via supervised training on historical data to automatically adjust one or more of the priorities of the criteria, the severity of the associated rulesets and the lower and upper thrush threshold values based on the feedback (para. 47).
It would have been obvious to modify the system of Pingle et al. by adding G Rao et al. artificial intelligence-based testing and scoring. A person of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make the modification because it would aid in providing feedback into ML components for automatically adjusting criteria, rulesets and threshold (para. 47).
In regard to claim 2, Pingle et al. teach the compliance testing system of claim 1, wherein the test data comprises information on individual test steps and/or test sequences to be performed on the device under test in view of the applicable regulations and standards (determine the validity of each test method, para. 19).
In regard to claim 3, Pingle et al. teach the compliance testing system of claim 1, wherein the test data comprises a list of the applicable regulations and standards (assesses each test method to determine whether the test blocks fulfill conditions precedent, condition subsequent, and/or test block compatibility, para. 19).
In regard to claim 4, Pingle et al. teach the compliance testing system of claim 1, wherein the machine learning circuit (system may include machine learning circuitry, para. 27) comprises an analysis sub-circuit configured to select the compliance tests (the semiconductor platform test circuitry includes logic to select and assemble test blocks of one or more test methods, para. 44), wherein the test data comprises machine-readable instructions for at least one test instrument performing the compliance tests (assesses each test method to determine whether the test blocks fulfill conditions precedent, condition subsequent, and/or test block compatibility, para. 19), and wherein the machine-readable instructions are configured to control the at least one test instrument to automatically perform the compliance tests selected by the analysis sub-circuit (autonomous generation of a large number of test methods may generate a large number of test results that are reported to the system users or are logged, para. 74).
In regard to claim 6, Pingle et al. teach the compliance testing system of claim 1, wherein the machine-learning circuit is configured to determine whether the user input is sufficient in order to generate the set of rules and/or in order to determine which regulations and standards are applicable to the device under test to be tested (all or a portion of the information and/or data associated with test blocks may be manually entered by a system user via the UDL generator, the semiconductor platform test circuitry may collect, gather, or extract all or a portion of the information and/or data associated with the test blocks using machine learning circuitry, para. 43).
In regard to claim 7, Pingle et al. teach the compliance testing system of claim 6, wherein the machine-learning circuit is configured to generate a user query if it is determined that the user input is insufficient, wherein the user query generated queries information on data missing in the user input (the validity of a particular test method may be based on any measurable or detectable parameter, including but not limited to: condition precedent (e.g., if test block 122B is dependent on prior execution of test block 122A, does test block 122A appear in the test method prior to test block 122B, para. 95).
In regard to claim 8, Pingle et al. teach the compliance testing system of claim 7, wherein the user query is formulated in natural language (user may enter a test step in a natural language, para. 32).
In regard to claim 9, Pingle et al. teach the compliance testing system of claim 1, wherein the machine-learning circuit is configured to determine whether the compliance testing data is sufficient in order to generate the set of rules (machine-learning circuitry is trained using training data sets, the training data sets may include positive training data sets and the logically associated valid test methods and may include negative training data sets that include invalid test methods, para. 81).
In regard to claim 10, Pingle et al. teach the compliance testing system of claim 9, wherein the machine-learning circuit is configured to generate a user message if it is determined that the compliance testing data is insufficient in order to generate the set of rules, wherein the user message comprises information on data missing in the compliance testing data (platform test circuitry confirms the validity of each of the test methods and test methods found to violate one or more dependency rules are discarded and not executed, para. 95).
In regard to claim 11, Pingle et al. teach the compliance testing system of claim 10, wherein the user message comprises information on an authority responsible for the relevant regulations and standards (the semiconductor platform test circuitry may provide the test result and/or the comparison results to a system operator or user via one or more user interfaces, para. 40).
In regard to claim 12, Pingle et al. teach the compliance testing system of claim 11, wherein the user message comprises a pre-formulated query to the authority (a PASS or FAIL test reporting, para. 45).
In regard to claim 14, Pingle et al. teach the compliance testing system of claim 1, wherein the compliance testing system further comprises at least one test instrument, wherein the at least one test instrument is configured to determine whether the at least one test instrument is capable of performing the compliance tests to be performed on the device under test based on the test data (the semiconductor platform test circuitry generates new test method and determines whether each of the generated test methods violate any dependency rules, para. 46, report whether the performance of the tested semiconductor platform feature falls within acceptable limits, para. 45).
In regard to claim 15, Pingle et al. teach the compliance testing system of claim 14, wherein the machine-learning circuit is configured to generate a user query if it is determined that the at least one test instrument is not capable of performing the tests, wherein the user query comprises information on necessary adaptations of a test setup and/or necessary changes to the compliance tests to be performed (result may contain incompatible test blocks, para. 93, the semiconductor platform test circuitry combines the test blocks selected at 1016 into a test method and may generates additional test methods by altering the sequence of the test blocks (i.e., generating test block permutations, para. 94).
In regard to claim 16, Pingle et al. teach the compliance testing system of claim 1, wherein the compliance testing system further comprises at least one test instrument, wherein the at least one test instrument is configured to perform the compliance tests to be performed on the device under test, thereby obtaining measurement data, and wherein the machine-learning circuit is configured to generate a test report based on the measurement data (the semiconductor platform test circuitry may receive information and/or data collected using one or more communicably coupled sensors, para. 42, data collected by the semiconductor platform test circuitry, para. 75).
In regard to claim 17, Pingle et al. teach the compliance testing system of claim 16, wherein the test report is formulated in natural language (PASS or FAIL test reporting indicating whether the performance of the tested semiconductor platform feature falls within acceptable limits, para. 45).
In regard to claim 18, Pingle et al. teach a compliance testing method of testing an electronic device under test, the compliance testing method comprising:
receiving, by a user interface (user interface, fig. 1, 136), a user input, wherein the user input comprises text and/or speech relating to the device under test to be tested (feature identifier data, fig. 2, 220, para. 42);
determining, by a machine-learning circuit, which regulations and standards are applicable to the device under test to be tested based on the user input and based on compliance testing data, wherein the machine-learning circuit comprises an artificial neural network (machine learning circuit may use one or more machine learning techniques (neural network, Bayesian statistics, decision tree, linear classification, random forests, etc.), para. 39), and wherein the compliance testing data comprises information on regulations and standards for performing compliance tests on different devices under test (information and/or data associated with semiconductor platform features, information and/or data associated with the test blocks, fig. 2, para. 41-42, extracted using machine learning, para. 43);
generating, by the machine-learning circuit, a set of rules describing the application of the applicable regulations and standards to the device under test that is to be tested (information and/or data associated with the test blocks, fig. 2, para. 41-42), wherein the generated set of rules comprises at least one of links or a map between the applicable regulations and standards and the compliance tests to be performed on the device under test to be tested (a plurality of features 120, each logically associated with a number of test blocks 122, para. 76, logical association is formed between the test block and the semiconductor platform features that use the test block, para. 79, fig. 8, 806);
generating, by the machine-learning circuit, test data based on the generated set of rules, wherein the test data comprises information on compliance tests to be performed on the device under test in view of the applicable regulations and standards (test report, fig. 2, 230, 232, para. 45);
generating, by a visualization circuit, visualization data based on the test data, wherein the visualization data comprises information on the test data (the semiconductor platform test circuitry may generate test results that are reported to the system user, para. 74, fig. 7, 704); and
displaying, by the user interface, the visualization data comprising the information on the test data (provide test results and/or the comparison results to a system operator or user via one or more user interfaces, such as a display device, para. 40).
Pingle et al. teach of output devices (e.g., haptic feedback or similar) (para. 60) but Pingle et al. does not explicitly teach receiving, by the user interface, feedback data comprises information on errors in the set of rules and/or in the test data, and adjusting the machine learning circuit’s operational parameters by reinforcement learning in order to minimize the errors.
G Rao et al. teach of providing validation data or feedback for categorization produced by the scoring systems (para. 25) and the threshold adjustment engine can be trained to adjust the various values in response to the feedback (para. 36). The automatic adjuster includes ML components that is trained via supervised training on historical data to automatically adjust one or more of the priorities of the criteria, the severity of the associated rulesets and the lower and upper thrush threshold values based on the feedback (para. 47).
Refer to claim 1 for motivational statement.
In regard to claim 19, Pingle et al. teach a compliance testing system for testing an electronic device under test, the compliance testing system comprising a user interface, a machine learning circuit, a visualization circuit, and a database,
wherein the database comprises compliance testing data, wherein the compliance testing data comprises information on regulations and standards for performing compliance tests on different devices under test (data related to dependency rules 112, one or more data structures 114, one or more instruction sets 116 or a combination thereof, para. 35),
wherein the user interface is configured to receive a user input (user interface, fig. 1, 136), wherein the user input comprises text and/or speech relating to the device under test to be tested (feature identifier data, fig. 2, 220, para. 42), wherein the user input if formulated in natural language (user may enter a test step in a natural language, para. 32),
wherein the machine-learning circuit comprises an artificial neural network (machine learning circuit may use one or more machine learning techniques (neural network, Bayesian statistics, decision tree, linear classification, random forests, etc.), para. 39) and is configured to determine which regulations and standards are applicable to the device under test to be tested based on the user input, wherein the machine learning circuit is configured to extract key words and/or key phases from the user input (extracted using machine learning, para. 43),
wherein the machine learning circuit is configured to match the extracted key words and/or key phrases to the database to determine which regulations and standards are applicable to the device under test to be tested (machine learning circuitry may use one or more machine learning techniques to extract test blocks 122, test block 122 dependencies, semiconductor platform feature 120/test block 122 logical associations and similar information and/or data from the number of training data sets 142, para. 39),
wherein the machine learning circuit is configured to generate a set of rules describing the application of the applicable regulations and standards to the device under test that is to be tested, wherein the generated set of rules comprises at least one of links or a map between the applicable regulations and standards and the compliance tests to be performed on the device under test to be tested (a plurality of features 120, each logically associated with a number of test blocks 122, para. 76, logical association is formed between the test block and the semiconductor platform features that use the test block, para. 79, fig. 8, 806),
wherein the machine learning circuit is configured to generate test data based on the generated set of rules, wherein the test data comprises information on compliance tests to be performed on the device under test in view of the applicable regulations and standards (information and/or data associated with semiconductor platform features, information and/or data associated with the test blocks, fig. 2, para. 41-42, extracted using machine learning, para. 43),
wherein the visualization circuit is configured to generate visualization data based on the test data, wherein the visualization data comprises information on the test data (the semiconductor platform test circuitry may generate test results that are reported to the system user, para. 74, fig. 7, 704), and
wherein the user interface is configured to display the visualization data comprising the information on the test data (provide test results and/or the comparison results to a system operator or user via one or more user interfaces, such as a display device, para. 40).
Pingle et al. teach of output devices (e.g., haptic feedback or similar) (para. 60) but Pingle et al. does not explicitly teach wherein the user interface is configured to receive feedback data, wherein the feedback data comprises information on errors in the set of rules and/or in the test data, and wherein the machine learning circuit is configured to adjust its operational parameters by reinforcement learning in order to minimize the errors.
G Rao et al. teach of providing validation data or feedback for categorization produced by the scoring systems (para. 25) and the threshold adjustment engine can be trained to adjust the various values in response to the feedback (para. 36). The automatic adjuster includes ML components that is trained via supervised training on historical data to automatically adjust one or more of the priorities of the criteria, the severity of the associated rulesets and the lower and upper thrush threshold values based on the feedback (para. 47).
Refer to claim 1 for motivational statement.
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Claim 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pingle et al. (US 2018/0277235) in further view of G Rao et al. (US 2021/0342742) in view of Maury et al. (US 2022/0019204).
In regard to claim 20, Pingle et al. and G Rao et al. does not explicitly teach the compliance testing system of claim 11, wherein the authority is an institution that is responsible for regulations and standards governing different aspects of the performance of the device under test to be tested in a certain country.
Maury et al. teach of a compliance testing taking into consideration the requirements by the country or province of manufactured, assembled and sold (para. 33).
It would have been obvious to modify the system of Pingle et al. and G Rao et al. by adding Maury et al. intelligent data object model. A person of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make the modification because it would compliance testing for different areas (para. 33).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO 892.
Lui et al. (US 2018/0275667) (Uber) feedback and deep neural network for machine learning
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Brown et al. (US 6,745,146) compliance testing
Kube et al. (US 2016/0188433) testing and mitigation
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Carranza et al. (US 2019/0318366) resolving compliance issue with ML
Baker et al. (US 2025/0077374) service validation and compliance with desired specifications
Ahuja et al. (US 2024/0232693) compliance evaluation via machine learning model
Kuris et al. (US 2008/0157959) compliance with current regulations testing
Kursun et al. (US 2019/0311277) compliance checks based on regulations and internal policies
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/Loan L.T. Truong/Primary Examiner, Art Unit 2114 HYPERLINK "mailto:Loan.truong@uspto.gov" Loan.truong@uspto.gov
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