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 § 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 1-26 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. Claims 1 and 14 both recite “find[ing] an impact on performance of a device from changing settings of preprocessor engines applied to benchmark applications being executed by the device ”. This recites a result achieved without identifying how it is achieved. §2163.03(V). Claims 2-13 and 15-26 are rejected only for this defect they inherit through their dependence on claims 1 and 14. 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 1-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the judicial exception of abstract ideas without significantly more. The claim(s) recite(s) abstract ideas as indicated by in-line comments below . This judicial exception is not integrated into a practical application for reasons also indicated by in-line comments below . The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception for reasons also indicated by in-line comments below . 1. A method, comprising: finding an impact on performance of a device from changing settings of preprocessor engines applied to benchmark applications being executed by the device (does not integrate into a practical application because generally linking the use of the judicial exception to a particular technological environment or field of use; not significantly more because generally linking the use of the judicial exception to a particular technological environment or field of use) ; defining groups of the preprocessor engines responsively to the impact on the performance of the device from changing the settings of the preprocessor engines (abstract; mental processes; observation, evaluation, judgment, or opinion) ; and providing different preprocessor engine configurations based on the settings to be applied to the preprocessor engines such that for each one of the defined groups a respective setting is to be applied equally to the preprocessor engines of the one group, thereby reducing a number of the preprocessor engine configurations available for selection by a machine learning agent (abstract; mental processes; observation, evaluation, judgment, or opinion) . 2. The method according to claim 1, wherein the preprocessor engines are prefetcher engines, the settings are aggressiveness levels, and the preprocessor engine configurations are prefetcher engine configurations (merely further details of ineligible subject matter) . 3. The method according to claim 1, further comprising: executing the benchmark applications while changing the settings of the preprocessor engines (abstract; software per se ) ; and measuring the performance of the device during the execution of the benchmark applications, wherein the finding includes finding the impact on performance based on the measured performance of the device (does not integrate into a practical application because insignificant extra-solution activity; not significantly more because insignificant extra-solution activity) . 4. The method according to claim 1, wherein: a number of the preprocessor engines is equal to X; a number of the settings is equal to Y; a number of the defined groups is equal to Z; and the number of the preprocessor engine configurations is reduced from Y X to Y Z (merely further details of ineligible subject matter) . 5. The method according to claim 1, further comprising computing statistical measures based on vectors for corresponding ones of the prefetchers engines describing the impact on the performance of the device of changing settings of the preprocessor engines applied to benchmark applications being executed by the device, wherein the defining includes defining the groups based on the computed statistical measures (abstract; mathematical concepts; mathematical calculations) . 6. The method according to claim 5, wherein: the computing includes: computing a measure of dispersion for each of the vectors (abstract; mathematical concepts; mathematical calculations) ; and computing measures of similarity between pairs of the vectors (abstract; mathematical concepts; mathematical calculations) ; and the defining includes defining the groups based on the computed measure of dispersion of at least some of the vectors and the computed measures of similarity between at least some of the pairs of vectors (abstract; mental processes; observation, evaluation, judgment, or opinion) . 7. The method according to claim 6, wherein the measure of dispersion is a variance or a standard deviation (abstract; mathematical concepts; mathematical relationships) . 8. The method according to claim 6, wherein each of the measures of similarity is a cosine similarity or correlation (abstract; mathematical concepts; mathematical relationships) . 9. The method according to claim 6, further comprising selecting pivotal members of the groups such that a different one of the preprocessor engines is selected as a pivotal member of each of the groups based on the measure of dispersion of corresponding ones of the vectors (abstract; mental processes; observation, evaluation, judgment, or opinion) . 10. The method according to claim 9, wherein the selecting includes selecting the pivotal members based on highest measures of deviation of corresponding ones of the vectors (abstract; mental processes; observation, evaluation, judgment, or opinion) . 11. The method according to claim 9, wherein the selecting includes selecting the pivotal members based on the measure of dispersion of corresponding ones of the vectors while minimizing the measures of similarity between the corresponding ones of the vectors of the pivotal members (abstract; mental processes; observation, evaluation, judgment, or opinion) . 12. The method according to claim 9, further comprising, for each one of the groups, adding other ones of the preprocessor engines to the one group based on the measures of similarity of ones of the vectors with the vector of the pivotal member of the one group (abstract; mental processes; observation, evaluation, judgment, or opinion) . 13. The method according to claim 9, further comprising, for each one of the groups, adding at least one of the preprocessor engines to the one group based on at least one of the measures of similarity of at least one of the vectors with at least one of the vectors of at least one existing member of the one group (abstract; mental processes; observation, evaluation, judgment, or opinion) . 14. A system, comprising: a processor (does not integrate into a practical application because generic computer performing generic computer functions; not significantly more because generic computer performing generic computer functions) to: find an impact on performance of a device from changing settings of preprocessor engines applied to benchmark applications being executed by the device (does not integrate into a practical application because generally linking the use of the judicial exception to a particular technological environment or field of use; not significantly more because generally linking the use of the judicial exception to a particular technological environment or field of use) ; define groups of the preprocessor engines responsively to the impact on the performance of the device from changing the settings of the preprocessor engines (abstract; mental processes; observation, evaluation, judgment, or opinion) ; and provide different preprocessor engine configurations based on the settings to be applied to the preprocessor engines such that for each one of the defined groups a respective setting is to be applied equally to the preprocessor engines of the one group, thereby reducing a number of the preprocessor engine configurations available for selection by a machine learning agent (abstract; mental processes; observation, evaluation, judgment, or opinion) ; and a memory to store data used by the processor (does not integrate into a practical application because insignificant extra-solution activity; not significantly more because insignificant extra-solution activity) . 15. The system according to claim 14, wherein the preprocessor engines are prefetcher engines, the settings are aggressiveness levels, and the preprocessor engine configurations are prefetcher engine configurations (merely further details of ineligible subject matter) . 16. The system according to claim 14, further comprising the preprocessor engines, wherein the processor is to: execute the benchmark applications while changing the settings of the preprocessor engines (abstract; software per se ) ; measure the performance of the device during the execution of the benchmark applications (does not integrate into a practical application because insignificant extra-solution activity; not significantly more because insignificant extra-solution activity) ; and find the impact on performance based on the measured performance of the device (abstract; mental processes; observation, evaluation, judgment, or opinion) . 17. The system according to claim 14, wherein: a number of the preprocessor engines is equal to X; a number of the settings is equal to Y; a number of the defined groups is equal to Z; and the number of the preprocessor engine configurations is reduced from Y X to Y Z (merely further details of ineligible subject matter) . Regarding claims 18-26, see the foregoing rejections of claims 5-13, respectively. 27. A system, comprising: preprocessor engines (does not integrate into a practical application because generally linking the use of the judicial exception to a particular technological environment or field of use; not significantly more because generally linking the use of the judicial exception to a particular technological environment or field of use) ; and a processor (does not integrate into a practical application because generic computer performing generic computer functions; not significantly more because generic computer performing generic computer functions) to: execute a software application (abstract; software per se ) ; execute a machine learning agent to select from different preprocessor engine configurations to control the preprocessor engines, the different preprocessor engine configurations being based on settings to be applied to the preprocessor engines such that groups of the preprocessor engines are defined and for each one of the defined groups a respective setting is to be applied equally to the preprocessor engines of the one group (abstract; mental processes; observation, evaluation, judgment, or opinion) ; and control the preprocessor engines according to the different preprocessor engine configurations selected by the machine learning agent during execution of the software application (does not integrate into a practical application because insignificant extra-solution activity; not significantly more because insignificant extra-solution activity) . 28. The system according to claim 27, wherein the settings are aggressiveness levels, the preprocessor engine configurations are prefetcher engine configurations (merely further details of ineligible subject matter) , and the preprocessor engines are prefetcher engines to: predict next memory access addresses of a memory from which to load data to a cache during execution of the software application (abstract; mental processes; observation, evaluation, judgment, or opinion) ; and load the data from the predicted next memory access addresses to the cache during execution of the software application (does not integrate into a practical application because insignificant extra-solution activity; not significantly more because insignificant extra-solution activity) . Regarding claim 29, see the foregoing rejection of claim 27, for all limitations except the following: executing a software application (abstract; software per se ) ; ... (limitations similar to those of claim 27) . Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Mannor et al. (12,499,048), Szapiro et al (2025/0181968), Rosen et al. (12,579,070), Rosen et al. (2025/0181411), Rosen et al. (12,450,171), and Rosen et al. (12,450,172) are each cited for disclosing inventions similar to those of the current application, and for appearing to share its assignee and have the same or overlapping inventive entities. However, none of these references support any rejections. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT GEOFFREY T EVANS whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-2369 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT M-F, 9 AM - 5:30 PM . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. 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