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 .
Priority
Application 17/875,202, filed on 07/27/2022, claims foreign priority to CHINA 202210653059.5, filed on 06/09/2022.
Response to Amendment
This office action is in response to Amendments submitted on 11/12/2025 wherein claims 1-3, 5-10, 12-17, and 19-20 are pending and ready for examination. Claims 4, 11, and 18 have been canceled.
Claim Objections
Claims 16-20 objected to because of the following informalities:
Regarding claims 16-20: Claims 16-20 depend from claim 15 and should have the same preamble. Examiner respectfully suggests the preamble of claims 16-20 include “non-transitory” before “computer-readable medium.” Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-3, 5-10, 12-17, and 19-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding Independent claims 1, 8, and 15: The recited “training a machine learning model to generate a group of estimated latencies in the group of data persistence operations, using the group of records and historical records of the system” (claim 1 line 6-8, claim 8 line 9-11, claim 15 line 8-10) is not described or recited in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention.
With respect to the claimed parts, the examiner was unable to find adequate structure (or material or acts) for performing the recited function and therefor fails the description required in 35 USC 112, first paragraph (see MPEP 2181). The written specification states “In some embodiments computing device 110 may use a trained predictor to generate an estimated latency for a data persistence operation of system 120. For example, computing device 110 may train and verify a machine learning model based on historical records of system 120 or a system similar to system 120, and use a machine model that has been verified to be of sufficient quality as a predictor. In some embodiments, computing device 110 may use the group of records as a training set for training a model” (written specification, ¶ 0023) disclosing training using historical records and a separate type of training using a group of records. However, the specification, written specification, figures, and original claims do not support the combination of training using both a group of records and historical records.
Regarding claims 2-3, 5-7, 9-10, 12-14, 16-17, and 19-20: Claims 2-3, 5-7, 9-10, 12-14, 16-17, and 19-20 are rejected under 35 U.S.C. 112(a) as they depend from the parent claims.
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 1-3, 5-10, 12-17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite an abstract idea as discussed below. This abstract idea is not integrated into a practical application for the reasons discussed below. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons discussed below.
Step 1 of the 2019 Guidance requires the examiner to determine if the claims are to one of the statutory categories of invention. Applied to the present application, the claims belong to one of the statutory classes of a process or product as a computer implemented method or a computer system/product.
Step 2A of the 2019 Guidance is divided into two Prongs. Prong 1 requires the examiner to determine if the claims recite an abstract idea, and further requires that the abstract idea belong to one of three enumerated groupings: mathematical concepts, mental processes, and certain methods of organizing human activity.
Claim 1 is copied below, with the limitations belonging to an abstract idea being underlined.
A method for processing a system latency, comprising:
obtaining a group of records in a system for a group of data persistence operations of a particular type, wherein each record in the group of records comprises a group of metrics for a group of states of the system within a predetermined period when each data persistence operation occurs;
training a machine learning model to generate a group of estimated latencies in the group of data persistence operations using the group of records and historical records of the system;
constructing a Shapley (SHAP) interpreter of the machine learning model for the group of records and the group of estimated latencies generated by the machine learning model;
using the SHAP interpreter to determine SHAP values that reflect corresponding contributions of each state in the group of states to latencies of the group of data persistence operations;
identifying a plurality of states, from the group of states, that increase the latencies of the group of data persistence operations based on the SHAP values that reflect the corresponding contributions; and
determining a suggested action that reduces the latencies of the group of data persistence operations based on a mapping relationship between a group of metrics for the plurality of states.
Claim 8 is copied below, with the limitations belonging to an abstract idea being underlined.
An electronic device, comprising:
a processor; and
a memory coupled to the processor having instructions stored therein which, when executed by the processor, cause the processor to perform actions, the actions comprising: obtaining a group of records in a system for a group of data persistence operations of a particular type, wherein each record in the group of records comprises a group of metrics for a group of states of the system within a predetermined period when each data persistence operation occurs;
training a machine learning model to generate a group of estimated latencies of the group of data persistence operations using the group of records and historical records of the system;
constructing a Shapley (SHAP) interpreter of the machine learning model for the group of records and the group of estimated latencies generated by the machine learning model;
using the SHAP interpreter to determine SHAP values that reflect corresponding contributions of each state in the group of states to latencies of the group of data persistence operations;
identifying a plurality of states, from the group of states, that increase the latencies of the group of data persistence operations based on the SHAP values that reflect the corresponding contributions; and
determining a suggested action that reduces the latencies of the group of data persistence operations based on a mapping relationship between a group of metrics for the plurality of states.
Claim 15 is copied below, with the limitations belonging to an abstract idea being underlined.
A non-transitory computer-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform actions, the actions comprising:
obtaining a group of records in a system for a group of data persistence operations of a particular type, wherein each record in the group of records comprises a group of metrics for a group of states of the system within a predetermined period when each data persistence operation occurs;
training a machine learning model to generate a group of estimated latencies in the group of data persistence operations using the group of records and historical records of the system;
constructing a Shapley (SHAP) interpreter of the machine learning model for the group of records and the group of estimated latencies generated by the machine learning model;
using the SHAP interpreter to determine SHAP values that reflect corresponding contributions of each state in the group of states to latencies of the group of data persistence operations;
identifying a plurality of states, from the group of states that increase the latencies of the group of data persistence operations based on the SHAP values that reflect the corresponding contributions; and
determining a suggested action that reduces the latencies of the group of data persistence operations based on a mapping relationship between a group of metrics for the plurality of states.
The limitations underlined can be considered to describe a series of mental and/or mathematical concepts where “determining” and “identifying” may include an observation, evaluation, judgement, and/or opinion which are concepts performed in the human mind or may include a series of calculations leading to one or more numerical results or answers, obtained by a sequence of mathematical operations on numbers. The lack of a specific equation in the claim merely points out that the claim would monopolize all possible appropriate equations/two-group significance tests for accomplishing this purpose in all possible systems.
These steps recited by the claim therefore amount to a series of mental and/or mathematical steps, making these limitations amount to an abstract idea.
In summary, the underlined steps in the claim above therefore recite an abstract idea at Prong 1 of the 101 analysis.
The additional elements in the claim have been left in normal font. The additional concept of “obtaining a group of records” equates to extra-solution data activity and does not qualify as “significantly more” (See MPEP 2106.05(g)).
Regarding the limitations in relation to the computer, computer product, or computer system does not offer a meaningful limitation beyond generally linking the use of the method to a computer (see ALICE CORP. v. CLS BANK INT’L 573 U. S. 208 (2014)). Using the broadest reasonable interpretation, “constructing a SHAP interpreter” involves generating, by a general purpose computer, an algorithm designed to run on a generic computer and therefore does not recite a particular machine applying or being used by the abstract idea. Moreover, using the broadest reasonable interpretation “using the SHAP interpreter” involves a general purpose computer applying an algorithm designed to run on a generic computer and, again, does not recite a particular machine applying or being used by the abstract idea.
Additionally, using the broadest reasonable interpretation, “training a machine learning model” requires specific mathematical calculations and therefore encompasses mathematical concepts (see 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, example 47 claim 2). These steps recited by the claim therefore amount to a series of mathematical steps, making these limitations amount to an abstract idea.
The claims do not integrate the abstract idea into a practical application. Various considerations are used to determine whether the additional elements are sufficient to integrate the abstract idea into a practical application. The claim does not recite a particular machine applying or being used by the abstract idea. The claim does not effect a real-world transformation or reduction of any particular article to a different state or thing. (Manipulating data from one form to another or obtaining a mathematical answer using input data does not qualify as a transformation in the sense of Prong 2.)
The claim does not contain additional elements which describe the functioning of a computer, or which describe a particular technology or technical field, being improved by the use of the abstract idea. (This is understood in the sense of the claimed invention from Diamond v Diehr, in which the claim as a whole recited a complete rubber-curing process including a rubber-molding press, a timer, a temperature sensor adjacent the mold cavity, and the steps of closing and opening the press, in which the recited use of a mathematical calculation served to improve that particular technology by providing a better estimate of the time when curing was complete. Here, the claim does not recite carrying out any comparable particular technological process.) In all of these respects, the claim fails to recite additional elements which might possibly integrate the claim into a particular practical application. Instead, based on the above considerations, the claim would tend to monopolize the abstract idea itself, rather than integrate the abstract idea into a practical application.
Step 2b of the 2019 Guidance requires the examiner to determine whether the additional elements cause the claim to amount to significantly more than the abstract idea itself. The considerations for this particular claim are essentially the same as the considerations for Prong 2 of Step 2a, and the same analysis leads to the conclusion that the claim does not amount to significantly more than the abstract idea.
Therefore, claims 1, 8, and 15 are rejected under 35 U.S.C. 101 as directed to an abstract idea without significantly more.
Dependent claims 2-7, 9-14, and 16-20 are similarly ineligible. The dependent claims merely add limitations which further detail the abstract idea with limitations such as “generating,” “determining,” “adjusting” and “excluding” constitute extra solution activity. These do not help to integrate the claim into a practical application or make it significant more than the abstract idea (which is recited in slightly more detail, but not in enough detail to be considered to narrow the claim to a particular practical application itself). Considering all the limitations individually and in combination, the claimed additional elements do not show any inventive concept to applying algorithms such as improving the performance of a computer or any technology, and do not meaningfully limit the performance of the application.
Response to Arguments
Applicant’s arguments (remarks), filed 11/12/2025, have been fully considered.
Regarding Claim Rejections – 35 USC § 101 page 10-13 of Applicant remarks, Applicant argues “As noted on pages 2-3 of Memorandum dated August 4, 2025 on Evaluating SME of claims under 35 U.S.C. 101 ("Memorandum dated August 4, 2025"), "[t]he mental process grouping is not without limits. Examiners are reminded not to expand this grouping in a manner that encompasses claim limitations that cannot practically be performed in the human mind. The MPEP and the AI-SME Update provide examples of claim limitations that cannot be practically performed in the human mind. Claim limitations that encompass Al in a way that cannot be practically performed in the human mind do not fall within this grouping... Consider for example, the published USPTO examples 39, which illustrates claim limitations that merely involve an abstract idea, and 47, which shows limitations that recite an abstract idea. The claim limitation "training the neural network in a first stage using the first training set" of example 39 does not recite a judicial exception. Even though "training the neural network" involves a broad array of techniques and/or activities that may involve or rely upon mathematical concepts, the limitation does not set forth or describe any mathematical relationships, calculations, formulas, or equations using words or mathematical symbols..." (emphasis added).
Here, amended claim 1 (and similarly claims 8 and 15), for example, recites "training a machine learning model to generate a group of estimated latencies in the group of data persistence operations using the group of records and historical records of the system; constructing a Shapley (SHAP) interpreter of the machine learning model for the group of records and the group of estimated latencies generated by the machine learning model; using the SHAP interpreter to determine SHAP values that reflect corresponding contributions of each state in the group of states to latencies of the group of data persistence operations".
Thus, similar to Example 39 provided by the Memorandum dated August 4, 2025, while the training of the machine learning model to generate a group of estimated latencies (as recited in claims 1, 8 and 15) may involve a broad array of techniques and/or activities that may involve or rely upon mathematical concepts, those limitations do not set forth or describe any mathematical relationships, calculations, formulas, or equations using words or mathematical symbols” (remarks page 11).
Examiner respectfully disagrees. Using the broadest reasonable interpretation, “training a machine learning model” using “a group of records and historical records of a system” is an algorithm that requires specific mathematical calculations and therefore encompasses mathematical concepts (see 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, example 47 claim 2). These steps recited by the claim therefore amount to a series of mathematical steps, making these limitations amount to an abstract idea.
Applicant argues “As noted in the MPEP, one way to determine integration into a practical application is when the claimed invention improves the functioning of a computer or improves another technology or technical field. To evaluate an improvement to a computer or technical field, the specification must set forth an improvement in technology and the claim itself must reflect the disclosed improvement. See MPEP §2106.04(d)(1) and §2106.05(a).
Here, the claimed invention recites a combination of additional elements that include "training a machine learning model to generate a group of estimated latencies in the group of data persistence operations using the group of records and historical records of the system", "constructing a Shapley (SHAP) interpreter of the machine learning model for the group of records and the group of estimated latencies generated by the machine learning model", "using the SHAP interpreter to determine SHAP values that reflect corresponding contributions of each state in the group of states to latencies of the group of data persistence operations", "identifying a plurality of states, from the group of states, that increase the latencies of the group of data persistence operations based on the SHAP values that reflect the corresponding contributions" and "determining a suggested action that reduces the latencies of the group of data persistence operations based on a mapping relationship between a group of metrics for the plurality of states"” (remarks, page 12).
Examiner respectfully disagrees. With respect to the “training” and the “Shapley (SHAP) interpreter” these are mathematical algorithms applied by a generic computer and therefore not significantly more or a practical application. The limitation "determining a suggested action that reduces the latencies of the group of data persistence operations based on a mapping relationship between a group of metrics for the plurality of states" does not claim the “suggested action” is actively used to reduce latencies but claims the “suggested action” is only determined. This limitation describes an intended use and is not a specific practical application.
Regarding Claim Rejections – 35 USC § 103 page 13-16 of Applicant’s remarks, based on Applicant’s arguments and changes made to the claims, the 35 USC § 103 rejections have been withdrawn.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Nawab et al., U.S. 2023/0334365 A1, teaches reducing a dataset in cache memory in order to reduce latencies.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Denise R Karavias whose telephone number is (469)295-9152. The examiner can normally be reached 7:00 - 3:00 M-F.
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 at 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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/DENISE R KARAVIAS/Examiner, Art Unit 2857
/MICHAEL J DALBO/Primary Examiner, Art Unit 2857