DETAILED ACTION Remarks This non-final office action is in response to the application filled on 08 / 28 / 20 23 . Claims 1- 15 are pending and examined below. 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 PCT/US2021/023161 was filled on 03/19/2021 . Information Disclosure Statement As of date of this action, IDS filled has been annotated and considered . 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. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. Claim(s) 1-7 and 9 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. Regarding claim 1 , which recites “the workload” line 3 is not clear since the claim recites “plurality of workloads” previously. It is not clear whether performance is collected for all workloads or one workload or something else. Dependent c laim(s) 2-7 is/are also rejected because they do not resolve their parent deficiencies. Regarding claim 2 , which recites “ a workload” line 3 is not clear since workload is mentioned previously on claim 1. It is not clear whether both workloads are same or different. Regarding claim 9 , which recites “ better than ” line 4 is not clear since the claim does not clearly recite the metes and bounds of the desired patent protection. Claim Rejections - 35 USC § 103 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 -11, 14 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over IEEE paper, 20 1 5; title “Learning-based analytical cross-platform performance prediction” by (“ Zheng ”) , and further in view of US 2020/01 18039 (“ Kocberber ”). Regarding claim 1 (and similarly claim 8) , as best understood in view of indefiniteness rejection explained above, Zheng discloses a method comprising: for each of a plurality of workloads ( see page 53, where “Each workload inside the training set should individually be a good representative of the programs encountered during the later prediction phase.” ) , collecting first time-series execution performance information during execution of the workload on a first hardware platform ( see at least page 52, where “processor hardware” ) ; for each workload, collecting second time-series execution performance information during execution of the workload on a second hardware platform ( see at least page 52, where “Being able to predict performance of software running on a target processor”; see also page 52, where “Consider the simple scenario of a program A that takes t seconds to finish its execution on a particular machine.” ) ; and training an encoder-decoder machine learning model ( see at least fig 1, predictive model ) that outputs predicted performance ( see page 53, where “Once the predictive model is constructed, we can use this model to make a prediction of the performance of a program running on the target given counter measurements efficiently obtained on the host.” ) on the second hardware platform relative to known performance on the first hardware platform ( see at least fig 1, shows cycle accurate simulator as a first platform run app parallel with host machine. see also page 53, where “During the training phase, a large amount of sample programs (which we denote as the "training set") are collected and executed on the host.” ) , the encoder-decoder machine learning model trained from the first and second time-series execution performance information for each workload ( see at least page 52, where “Consider the simple scenario of a program A that takes t seconds to finish its execution on a particular machine.” ) . Zheng does not disclose an encoder-decoder machine learning model . However, Kocberber discloses a method comprising an encoder-decoder machine learning model (see at least [0035] and [0170]) . Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Zheng to incorporate the teachings of Kocberber by including the above feature for predicting performance by establishing a machine learning model. Regarding claim 2 , as best understood in view of indefiniteness rejection explained above, Zheng further discloses a method comprising: using the trained encoder-decoder machine learning model to predict performance of a workload on the second hardware platform relative to the known performance on the first hardware platform, by inputting into the encoder- decoder machine learning model time-series execution performance information that was collected during execution of the workload on the first hardware platform ( see at least fig 2 and fig 3 ) . Rejection relied on Kocberber for machine learning model. Regarding claim 3 , Zheng discloses a method wherein the model outputs one or multiple of: estimated time-series execution performance information of the workload on the second hardware platform ( see at least page 52, where “seconds”; see also fig 2 ) ; a numeric ratio of a predicted execution time of the workload on the second hardware platform to a known execution time of the workload on the first hardware platform; an estimated distribution of a ratio of the predicted execution time of the workload on the second hardware platform to the known execution time of the workload on the first hardware platform. Regarding claim 4 , Zheng further discloses a method comprising: executing each workload on the first hardware platform, wherein the first time-series execution performance information is collected while each workload is executing on the first hardware platform ( see at least page 5 4 , where “We perform profiling of the training set on two different host machines configurations: an Intel Core i7 920 processor with 24 GB of memory, and an AMD Phenom II X6 1055T processor with 8GB of memory.” ) ; and executing each workload on the second hardware platform, wherein the second time-series execution performance information is collected while each workload is executing on the second hardware platform ( see at least page 52, where “We can then expect A to run longer on a less powerful machine.” ) . Regarding claim 5 , Zheng further discloses a method wherein the first and second time-series execution performance information are each collected at identical fixed time intervals ( see at least page 52, where “Simulation-based approaches, such as cycle-accurate instruction set simulators (ISSs) are widely used in obtaining accurate estimates of program performance on a given target.”; cycle is interpreted as fixed time interval ) . Regarding claim 6 (and similarly claim 14) , Zheng further discloses a method wherein for each workload, the first and second time-series execution performance information each comprise values of hardware and software statistics, metrics, counter, and/or traces over time as the workload is executed ( see at least page 54, where “Figure 2 shows the 14 hardware performance events that we collect on the Intel host machine,”; see also page 54, where “ hardware performance counter ”; see also page 52, where “collect target-specific traces or execution statistics, such as instruction counts, memory traces, or branch statistics.”) . Regarding claim 7 (and similarly claim 15) , Zheng further discloses a method wherein the encoder-decoder machine learning model is trained and subsequently used to predict performance on the second hardware platform relative to the known performance on the first hardware platform without using any identifying information of any application code run during execution of any workload or any identifying information of any user data of any workload ( see at least page 53, where “During the training phase, a large amount of sample programs (which we denote as the "training set") are collected and executed on the host.”; no need any identifying information when running sample programs ) . Regarding claim 9, as best understood in view of indefiniteness rejection explained above, Zheng further discloses a system comprising: executing the workload on the second hardware platform if the predicted performance of the workload on the second hardware platform is better than known performance of the workload on the first hardware platform ( see at least fig 8 and fig 9) ; and executing the workload on the first hardware platform if the predicted performance of the workload on the second hardware platform is worse than the known performance of the workload on the first hardware platform ( see at least fig 8 and fig 9) . Regarding claim 10, Zheng further discloses a system wherein the predicted performance of the workload is used to assess whether to procure the second hardware platform for executing the workload ( see at least page 52, where “Being able to predict performance of software running on a target processor that does not yet physically exist is a necessary component to enable such co-development of software and hardware.”) . Regarding claim 11, Zheng further discloses a system wherein receiving and outputting the predicted performance of the workload on the second hardware platform relative to the first hardware platform comprises: receiving and outputting estimated time-series execution performance information of the workload on the second hardware platform (see at least fig 2 and fig 3) . Claim(s) 12 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over IEEE paper, 2025; title “Learning-based analytical cross-platform performance prediction” by (“Zheng”) , and in view of US 2020/0118039 (“Kocberber ”) , as applied to claim 8 above, and further in view of IEEE paper, 2011; title “On performance modeling and prediction in support of scientific workflow optimization” by (“Wu”) . Regarding claim 12, Zheng further discloses a system wherein receiving and outputting the predicted performance of the workload on the second hardware platform relative to the first hardware platform comprises: receiving and outputting a numeric ratio of a predicted execution time of the workload on the second hardware platform to a known execution time of the workload on the first hardware platform ( see at least page 52 and 53 ) . Zheng does not disclose numeric ratio . However, discloses Wu discloses a system wherein numeric ratio of execution time is determined (see at least page 166, where “To improve the performance of the RCP algorithm, we estimate the effective processing (EP) power of the machine to be the ratio of the estimated number of CPU cycles required for a given module and the predicted module execution time on the machine using the proposed performance modeling and prediction method”). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Zheng in view of Kocberber to incorporate the teachings of Wu by including the above feature for determining more accurate execution time of each individual module by different machines . Regarding claim 13, Zheng further discloses a system wherein receiving and outputting the predicted performance of the workload on the second hardware platform relative to the first hardware platform comprises: receiving and outputting an estimated distribution of a ratio of a predicted execution time of the workload on the second hardware platform to a known execution time of the workload on the first hardware platform ( see at least page 52 and 53 ) . Zheng does not disclose distribution of a ratio . However, discloses Wu discloses a system wherein distribution of a ratio of execution time is determined (see at least page 164 and 166). Same motivation of claim 12 applies. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT SOHANA TANJU KHAYER whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (408)918-7597 . The examiner can normally be reached on FILLIN "Work Schedule?" \* MERGEFORMAT Monday - Thursday, 7 am-5.30 pm, PT . 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, FILLIN "SPE Name?" \* MERGEFORMAT Abby Lin can be reached on FILLIN "SPE Phone?" \* MERGEFORMAT 571-270-3976 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair . Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /SOHANA TANJU KHAYER/ Primary Examiner, Art Unit 3657