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 .
Priority
Current application, US Application No. 18/637,221, is filed on 04/16/2024.
DETAILED ACTION
This office action is responsive to the application filed on 04/16/2024. Claims 1-20 are currently pending.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. As per claim 1, the limitation “recorded thereon” in “A non-transitory, computer-readable storage medium comprising instructions to increase accuracy of predicting a performance of a device recorded thereon” is ambiguous because it is not clear what is recorded on where.
For the sake of examination, the limitation is ignored because the ignoring the limitation does not impact the scope of the claim limitation.
As per claims 1, 8 and 14, the limitation “based on the reliability of each test among the multiple tests and the multiple test results, predicting the metric” is ambiguous because the specification discloses the prediction depends not only on the reliability of each test and the test results obtained from running the tests on the first/second history, but also the metric and categories obtained from input (see specification - the prediction module 120 can analyze the relationship in the context of the input 150, the first history 132, and the second history 134 and can provide the prediction 140 in the form of a natural language response to the input 150 [0018, Fig. 1]).
For the sake of examination, the limitation is interpreted as “based on the reliability of each test among the multiple tests, the multiple test results, the metric, the first category and the second category, predicting the metric”.
As per claims 2-7, 9-13 and 15-20, claims are also rejected because base claims 1, 8 and 14 are rejected.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to nonstatutory subject matter. The claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative claim 8 recites:
“A method comprising: (8.A)
obtaining an input indicating a metric to predict, a first category associated with the metric, and a second category associated with the metric; (8.B.1)
obtaining a first history of the metric associated with the first category and a second history of the metric associated with the second category; (8.B.2)
obtaining data indicating multiple assumptions (8.B.3.1) wherein the multiple assumptions include at least one of:
similarity of the first history of the metric to normal distribution, independence between the metric and the first category, homogeneity of variance associated with the first history of the metric, randomness associated with the first history of the metric, or a monotonic relationship between the first history of the metric and the first category; (8.B.3.2)
determining which of the multiple assumptions are satisfied by the first history of the metric and the second history of the metric to obtain multiple satisfied assumptions; (8.C)
obtaining multiple tests associated with the multiple satisfied assumptions; (8.D)
and increasing accuracy of predicting the metric (8.E) by:
performing the multiple tests on the first history of the metric and the second history of the metric to obtain multiple test results; (8.E.1)
based on the multiple test results, determining a reliability of each test among the multiple tests; (8.E.2)
and based on the reliability of each test among the multiple tests and the multiple test results, predicting the metric. (8.E.3)”
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (Process - Method).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exception. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations), and mental processes (concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion).
For example, highlighted limitations/steps (8.B.3.2)-(8.E.3) are treated by the Examiner as belonging to Mathematical Concept grouping or a combination of Mathematical Concept and Mental Processing groupings as the limitations include Mathematical calculation or show Mathematical Relationship with optional Mental observation.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
The above claims comprise the following additional elements: (Side Note: duplicated elements are not repeated)
In Claim 1: “A non-transitory, computer-readable storage medium comprising instructions to increase accuracy of predicting a performance of a device, wherein the instructions, when executed by at least one data processor of a system, cause the system to”, “obtain an input indicating a metric … a first category …a second category”, “obtain a first history of the performance associated with the first device and a second history of the performance associated with the second device”, “obtain data indicating multiple assumptions”;
In Claim 4: “obtain a natural language input”;
In Claim 8: “A method”;
In Claim 14: “A system”, “at least one hardware processor; and at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to”;
As per claim 1, the additional element in the preamble “A non-transitory, computer-readable storage medium comprising instructions to increase accuracy of predicting a performance of a device, wherein the instructions, when executed by at least one data processor of a system, cause the system to” is not a meaningful limitation because the limitation simply links a storage medium with an intended purpose or an abstract idea, i.e. increase accuracy of predicting a performance of a device.
The limitations/elements “A non-transitory, computer-readable storage medium comprising instructions” and “at least one data processor of a system” represent standard computer components and they are not particular in the art.
The limitations/steps “obtain an input indicating a metric … a first category …a second category”, “obtain a first history of the performance associated with the first device and a second history of the performance associated with the second device”, “obtain data indicating multiple assumptions” represent standard data collection steps in the art and only adds insignificant extra solution to the judicial exception.
As per claim 4, the limitation/step “obtain a natural language input” represents a standard input collection step in the art and only adds insignificant extra solution to the judicial exception.
As per claim 8, the additional element in the preamble “A method” is not qualified as
a meaningful limitation because the limitation even fails to link a methos to a particular operation or field of use.
As per claim 12, the additional element in the preamble “A system” is not qualified as
a meaningful limitation because the limitation even fails to link a methos to a particular operation or field of use. The limitations/elements “at least one hardware processor; and at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to” represent components of a general computer and they are not particular in the art.
In conclusion, the above additional elements considered individually and in combination with the other claim elements as a whole do not reflect an improvement to the computer technology or other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. No particular machine or real-world transformation are claimed. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
Under Step 2B analysis, the above claims fail to include additional elements that are sufficient to amount to significantly more than the judicial exception as shown in the prior art of record.
The limitations/elements listed as additional elements above are well understood, routine and conventional steps/elements in the art according to the prior art of record. (See Saxena, Cotroneo, Li ‘133, Li ‘465, Ropper and others in the list of prior art of record)
Claims 1-20, therefore, are not patent eligible.
Notes with regard to Prior Art
The prior arts made of record below are considered pertinent to applicant's disclosure and the claims.
Saxena (A. Saxena et al., "Metrics for evaluating performance of prognostic techniques," 2008 International Conference on Prognostics and Health Management, Denver, CO, USA, 2008, pp. 1-17, doi: 10.1109/PHM.2008.4711436) discloses improvement of predicting a performance metric using classification and algorithms that can be applied to historical data checking factors like reliability, validity, sensitivity (Performance evaluation allows comparing different schemes numerically and provides an objective way to measure how changes in training, equipment or prognostics models ‘algorithms’ affect the quality of predictions [pg. 2 left col par 3], provide a concise assessment of prediction performance evaluation methods in various domains. Specific relevant performance metrics have been listed in the next section [pg. 5 left col par. 2 – pg. 6 right col par. 2], prognostics metrics classification, prognostic metrics [pg. 6 right col par. 3 – pg. 12 table 3], use of a particular performance metric must be based on several factors like reliability, validity, sensitivity to small changes in errors, resistance to outliers, and how it relates to the health management that the prognostic information will trigger, survey indicates that there is no single metric that will capture all the complexities of an algorithm and that the selection of the forecasting method and the evaluation metric is always situation dependent [pg. 16 left col par. 2], ) Tests are conducted based on specific requirements to declare the goodness of the algorithms [pg. 1 right col par. 1], algorithms should be tested rigorously and evaluated on a variety of performance measures before they can be certified [pg. 2 left col par. 2], history data [pg. 4 left col par. 3], history data … may be utilized to make corresponding inferences, history data distribution [pg. 7 right col par. 1]).
Cotroneo (D. Cotroneo, R. Pietrantuono and S. Russo, "A learning-based method for combining testing techniques," 2013 35th International Conference on Software Engineering (ICSE), San Francisco, CA, USA, 2013, pp. 142-151, doi: 10.1109/ICSE.2013.6606560) discloses adaptive testing method using past experience (a method to combine testing techniques adaptively during the testing process. It intends to mitigate the sources of uncertainty of software testing processes, by learning from past experience and, at the same time, adapting the technique selection to the current testing session [abs])
Li (CN 116662133 A), hereinafter “Li ‘133” discloses (The invention improves the test efficiency and the reliability of the finally obtained preferable performance parameter configuration set [abs], for the target system, the performance of the corresponding system version that has been listed has always satisfied a certain requirement, so the corresponding main parameter configuration set and auxiliary parameter configuration set are historical preferred main parameter configuration set and historical preferred auxiliary parameter configuration set, These data can be correspondingly stored, so that when the performance of the system version needs to be optimized, the history data can be used as reference, and the optimization is performed on the basis of the history data, so as to improve the optimization efficiency [pg. 10 line 17-24], testing the performance of the target system [claim 10])
Li (CN 116578465 A), hereinafter “Li ‘465” discloses (a deep learning model performance detection system, method and prediction model generation method, determining a depth learning model to be tested, obtaining one or more operators of the depth learning model to be tested, aiming at each operator in the one or more operators, using the performance prediction model corresponding to the operator to perform performance index prediction, obtaining the performance index prediction value corresponding to the operator, wherein the performance prediction model is obtained by using the performance index data training of the corresponding operator, based on the performance index prediction value corresponding to each operator in the one or more operators, obtaining the comprehensive performance index prediction value of the deep learning model to be tested [abs].
Williams (US 20110246298 A1) discloses (maintaining anonymity of segment data from a third party provider while performing segment targeting via a demand side platform “DSP” [abs]).
Roper (WO 2025137657 A1) discloses digital tool selection and optimization model (Systems and methods for completing a digital task using an alternative digital tool [abs], methods and systems for the optimization and enhancement of digital workflows by providing alternative digital tools to perform user-requested digital tasks on digital models, thus better balancing cost, compute power, error, and overall digital workflow performance. Specifically, embodiments of the present invention are directed to alternative digital tool selection, a process that encompasses one or more of digital tool usage pattern and digital task history tracking, digital tool functionality and performance characteristic evaluation, mapping, and isomorphic analysis, alternative digital tool recommendation based on the digital task at hand and iterative user feedback, and token management to link selected alternative tools to execute and complete a requested digital task [pg. 6 par. 4-5], predict digital tools most suitable for a user’s specific requirements, based on user query, input model type file [ pg. 9 par. 1]).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DOUGLAS KAY, whose telephone number is (408) 918-7569. The examiner can normally be reached on M, Th & F 8-5, T 2-7, and W 8-1.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arleen M Vazquez can be reached on 571-272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DOUGLAS KAY/
Primary Examiner, Art Unit 2857
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