CTNF 18/318,212 CTNF 95014 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 07-30-03-h AIA Claim Interpretation 07-30-03 AIA The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. 07-30-05 The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. These limitations are “ one or more processing units to …” recited in claims 10-13, 16-17, and 20. In the above limitation, “unit” is considered to be a generic placeholder. Furthermore, the term “processing unit” in the current context is not considered to be understood by persons of ordinary skill in the art to have a sufficiently definite meaning as the name for structure in the instant context, different from more-specific terms such as “central processing unit.” Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. Support is found in paragraphs 27, 95, and 98, which teach a general-purpose computer that is programmed to perform the algorithm described in the specification. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 an abstract idea without significantly more. Independent Claims Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? Yes, the independent claims recite an abstract idea in the form of mental processes. A mental process is a process that “can be performed in the human mind, or by a human using a pen and paper” (MPEP § 2106.04(a)(2)(III), paragraph 1). Examples of mental processes include “observations, evaluations, judgments, and opinions” (MPEP § 2106.04(a)(2)(III), paragraph 2). Claim 1: determining, based at least on a first data set that includes a first number of data samples, one or more data subsets; [This step is recited at a high degree of generality, without any specific algorithm or methodology that distinguishes it from being a mental process that can be performed by observation, evaluation, judgment, and opinion.] determining, based at least on […], one or more validation scores associated with the one or more data subsets; [This step is recited at a high degree of generality, without any specific algorithm or methodology that distinguishes it from being a mental process that can be performed by observation, evaluation, judgment, and opinion.] determining, based at least on the one or more validation scores, a density function corresponding to an amount of data samples required to meet or exceed the one or more validation scores; [This step is recited at a high degree of generality, without any specific algorithm or methodology that distinguishes it from being a mental process that can be performed by observation, evaluation, judgment, and opinion. Note that the claim only requires “determining” a density function, and does not require computing it, much less in any specific form.] and determining, based at least on the density function, a second number of data samples to include in a second data set to update the one or more machine learning models. [This step is recited at a high degree of generality, without any specific algorithm or methodology that distinguishes it from being a mental process that can be performed by observation, evaluation, judgment, and opinion. Note that “to update the one or more machine learning models” does not require actually updating a machine learning model, but instead only recites that the second data set is configured to or suitable for updating the machine learning model.] Claim 10: determine, based at least on a first data set that includes a first number of data samples, one or more data subsets; [This step is recited at a high degree of generality, without any specific algorithm or methodology that distinguishes it from being a mental process that can be performed by observation, evaluation, judgment, and opinion.] determine, based at least on […], one or more validation scores associated with the one or more data subsets; [This step is recited at a high degree of generality, without any specific algorithm or methodology that distinguishes it from being a mental process that can be performed by observation, evaluation, judgment, and opinion.] determine, based at least on the one or more validation scores, at least: a second number of data samples to include in a second data set, the second data set to update the one or more machine learning models during a first stage; and a third number of data samples to include in a third data set, the third data set to update the one or more machine learning models during a second stage. [This step is recited at a high degree of generality, without any specific algorithm or methodology that distinguishes it from being a mental process that can be performed by observation, evaluation, judgment, and opinion. Similar to the limitation discussed above, “to update the one or more machine learning models” does not require actually updating a machine learning model, but instead only recites that the second data set is configured to or suitable for updating the machine learning model.] Claim 19: to determine, based at least on a density function associated with a data set, a number of data samples for updating one or more machine learning models, wherein the density function is determined based at least on updating the one or more machine learning models over one or more iterations using one or more data subsets. [This step is recited at a high degree of generality, without any specific algorithm or methodology that distinguishes it from being a mental process that can be performed by observation, evaluation, judgment, and opinion. Note that the claim only requires “determining” a density function, and does not require computing it, much less in any specific form. Furthermore, “for updating one or more machine learning models” does not require actually performing any machine learning process.] Therefore, the independent claims recite a judicial exception. Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No. The judicial exception recited in the above discussed claims is not integrated into a practical application. Independent claims 1, 10, and 19 recite the following additional elements, but these additional elements are not sufficient to integrate the judicial exception into a practical application: “ updating one or more machine learning models over one or more iterations using the one or more data subsets” (claims 1 and 10) [This element does no more than generally link the use of a judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)), namely the technological environment of machine learning models. Note that the context in which this limitation is recited only requires the mental processes to be “based on” the updating, and does not require actually performing the updating.] “ system comprising: one or more processing units ” (claim 10) and “ A processor comprising: one or more processing units ” (claim 19) [These elements constitute no more than mere instructions to apply the judicial exception using generic computer components (MPEP § 2106.04(d)(I)). These additional elements merely invoke the use of generic computer components as tools to perform the abstract idea, and do not place any limitations on the abstract idea other than the use of such generic computer components.] Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. The claims do not include additional elements that are sufficient for the claims to amount to significantly more than the judicial exception. Additional elements that are mere instructions to apply an exception or merely generally linking or generally linking the use of a judicial exception to a particular technological environment or field of use do not constitute significantly more than a judicial exception under MPEP § 2106.05(I)(A). Since the additional elements in the independent claims are all are mere instructions to apply an exception or are merely generally linking or generally linking the use of a judicial exception to a particular technological environment or field of use, they do not constitute significantly more than a judicial exception. Therefore, the additional elements identified in the Step 2A Prong Two analysis do not constitute significantly more than a judicial exception in the Step 2B analysis. Dependent Claims The remaining dependent claims being rejected do not recite additional elements, whether considered individually or in combination, that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. Claim 2: updating, using the second data set, the one or more machine learning models during a first stage; [This element is regarded as an additional element besides the abstract idea for purposes by Step 2A Prong Two, but constitutes no more than mere instructions to apply the judicial exception using generic computer functions (MPEP § 2106.04(d)(I)), namely the generic computer function of machine learning. These additional elements merely invoke the use of generic machine learning as a tool to apply an abstract idea. The Examiner notes that no improvement to the technology of machine learning is recited in this limitation because it only recites “using,” without any further details of how the data set is used.] determining, based at least on the density function, a third number of data samples to include in a third data set; [This step is recited at a high degree of generality, without any specific algorithm or methodology that distinguishes it from being a mental process that can be performed by observation, evaluation, judgment, and opinion.] updating, using the third data set, the one or more machine learning models during a second stage. [This element is regarded as an additional element besides the abstract idea for purposes by Step 2A Prong Two, but constitutes no more than mere instructions to apply the judicial exception using generic computer functions (MPEP § 2106.04(d)(I)), namely the generic computer function of machine learning. These additional elements merely invoke the use of generic machine learning as a tool to apply an abstract idea. The Examiner notes that no improvement to the technology of machine learning is recited in this limitation because it only recites “using,” without any further details of how the data set is used.] Claim 3: determining, […], a validation score associated with the one or more machine learning models; [This step is recited at a high degree of generality, without any specific algorithm or methodology that distinguishes it from being a mental process that can be performed by observation, evaluation, judgment, and opinion.] based at least on updating the one or more machine learning models using the second data set [This element does no more than generally link the use of a judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)), namely the technological environment of machine learning models. Note that the context in which this limitation is recited only requires the mental processes to be “based on” the updating, and does not require actually performing the updating.] and determining, based at least on the second number of data samples included in the second data set and the validation score, a fourth number of data samples to include in the third data set. [This step is recited at a high degree of generality, without any specific algorithm or methodology that distinguishes it from being a mental process that can be performed by observation, evaluation, judgment, and opinion.] Claim 4: determining, based at least on the first data set that includes the first number of data samples, one or more second data subsets; [This step a mental process that can be performed by observation, evaluation, judgment, and opinion.] determining, […], one or more second validation scores associated with the one or more second data subsets; [This step a mental process that can be performed by observation, evaluation, judgment, and opinion.] based at least on updating the one or more machine learning models over one or more second iterations using the one or more second data subsets [This element does no more than generally link the use of a judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)), namely the technological environment of machine learning models. Note that the context in which this limitation is recited only requires the mental processes to be “based on” the updating, and does not require actually performing the updating.] determining, based at least on the one or more second validation scores, a second density function, [This step a mental process that can be performed by observation, evaluation, judgment, and opinion.] wherein the determining the fourth number of data samples to include in the third data set is further based at least on the second density function. [This step a mental process that can be performed by observation, evaluation, judgment, and opinion.] Claim 5: wherein the determining the second number of data samples to include in the second data set is further based at least on one or more costs, the one or more costs associated with at least one of: [This step a mental process that can be performed by observation, evaluation, judgment, and opinion.] collecting the second number of data samples; or a risk that a validation performance for the one or more machine learning models is less than a target validation performance after a period of time elapses. [This limitation merely further defines the mental process recited above, and is considered to be part of the same mental process. This claim does not recite any non-abstract additional elements for purposes of Step 2A Prong Two and Step 2B analysis.] Claim 6: determining a target validation performance associated with the one or more machine learning models, [This step a mental process that can be performed by observation, evaluation, judgment, and opinion.] wherein the determining the second number of data samples to include in the second data set is further based at least on the target validation performance. [This step a mental process that can be performed by observation, evaluation, judgment, and opinion.] Claim 7: determining, based at least on the one or more validation scores and a target validation performance, one or more estimated number of data samples for updating the one or more machine learning models, wherein the determining the density function is based at least on the one or more estimated number of data samples. [These further limitations are also mental processes or further define the mental process recited in the parent claim. This claim does not recite any non-abstract additional elements for purposes of Step 2A Prong Two and Step 2B analysis.] Claim 8: the one or more data subsets comprises at least a first group of data subsets and a second group of data subsets; [This limitation merely further defines the mental process recited in the parent claim and is therefore considered to be part of the mental process of the parent claim.] the one or more validation scores comprises at least one or more first validation scores associated with the first group of data subsets and one or more second validation scores associated with the second group of data subsets; [This limitation merely further defines the mental process recited in the parent claim and is therefore considered to be part of the mental process of the parent claim.] and the determining the one or more estimated number of data samples for updating the one or more machine learning models comprises: determining, based at least on the one or more first validation scores and the target validation performance, a first estimated number of data samples for updating the one or more machine learning models; and determining, based at least on the one or more second validation scores and the target validation performance, a second estimated number of data samples for updating the one or more machine learning models, the one or more estimated number of data samples including at least the first estimated number of data samples and the second number of data samples. [These further limitations are mental processes. Here, “determining” is a mental process that can be performed by observation, evaluation, judgment, and opinion. This claim does not recite any non-abstract additional elements for purposes of Step 2A Prong Two and Step 2B analysis.] Claim 9: determining, based at least on updating the one or more machine learning models using the second data set, a validation score associated with the one or more machine learning models; [These further limitations are mental processes. Here, “determining” is a mental process that can be performed by observation, evaluation, judgment, and opinion.] one of: based at least the validation score being less than a target validation score, determining a third number of data samples to include in a third data set, the third data set for updating the one or more machine learning models; or based at least on the validation score being equal to or greater than the target validation score, determining that the updating of the one or more machine learning models is complete. [These further limitations are mental processes. Here, “determining” is a mental process that can be performed by observation, evaluation, judgment, and opinion. This claim does not recite any non-abstract additional elements for purposes of Step 2A Prong Two and Step 2B analysis.] Claim 11: determine, based at least on updating the one or more machine learning models using the second data set, a validation score associated with the one or more machine learning models; [These further limitations are mental processes. Here, “determine” is a mental process that can be performed by observation, evaluation, judgment, and opinion.] determine, based at least on the second number of data samples included in the second data set and the validation score, a fourth number of data samples to include in the third data set. [These further limitations are mental processes. Here, “determine” is a mental process that can be performed by observation, evaluation, judgment, and opinion. This claim does not recite any non-abstract additional elements for purposes of Step 2A Prong Two and Step 2B analysis.] Claim 12: determine, based at least on the one or more validation scores, a density function, [This step is recited at a high degree of generality, without any specific algorithm or methodology that distinguishes it from being a mental process that can be performed by observation, evaluation, judgment, and opinion. Note that the claim only requires “determining” a density function, and does not require computing it, much less in any specific form.] wherein the determination of the at least the second number of data samples to include in the second data set and the third number of data samples to include in the third data set is based at least on the density function . [These further limitations merely further define already-recited mental processes and are therefore considered to be part of the mental processes discussed above.] Claim 13 determine, based at least on the one or more validation scores and a target validation performance, one or more estimated number of data samples for updating the one or more machine learning models, [These further limitations are mental processes. Here, “determine” is a mental process that can be performed by observation, evaluation, judgment, and opinion.] wherein the determination of the density function is based at least on the one or more estimated number of data samples. [These further limitations merely further define already-recited mental processes and are therefore considered to be part of the mental processes discussed above.] Claim 14: the one or more data subsets comprise at least a first group of data subsets and a second group of data subsets; [These further limitations merely further define already-recited mental processes and are therefore considered to be part of the mental processes discussed above.] the one or more validation scores comprises at least one or more first validation scores associated with the first group of data subsets and one or more second validation scores associated with the second group of data subsets; [These further limitations merely further define already-recited mental processes and are therefore considered to be part of the mental processes discussed above.] the determination of the one or more estimated number of data samples for updating the one or more machine learning models comprises: determining, based at least on the one or more first validation scores and the target validation performance, a first estimated number of data samples for updating the one or more machine learning models; and determining, based at least on the one or more second validation scores and the target validation performance, a second estimated number of data samples for updating the one or more machine learning models, the one or more estimated number of data samples including the first estimated number of data samples and the second number of data samples. [These further limitations are mental processes. Here, “determining” is a mental process that can be performed by observation, evaluation, judgment, and opinion. This claim does not recite any non-abstract additional elements for purposes of Step 2A Prong Two and Step 2B analysis.] Claim 15: wherein the determination of the at least the second number of data samples to include in the second data set and the third number of data samples to include in the third data set is further based at least on one or more costs, [This step a mental process that can be performed by observation, evaluation, judgment, and opinion.] the one or more costs associated with at least one of: collecting the second number of data samples and the third number of data samples; or a risk that a validation performance for the one or more machine learning models is less than a target validation performance after a period of time elapses. [This limitation merely further defines the mental process recited above, and is considered to be part of the same mental process. This claim does not recite any non-abstract additional elements for purposes of Step 2A Prong Two and Step 2B analysis.] Claim 16: determine a target validation performance associated with the one or more machine learning models, [This step a mental process that can be performed by observation, evaluation, judgment, and opinion.] wherein the determination of the one or more validation scores is further based at least on the target validation performance. [These further limitations merely further define already-recited mental processes and are therefore considered to be part of the mental processes discussed above.] Claim 17: receive input data representative of at least one of a number of stages or a period of time associated with updating the one or more machine learning models, [This element constitutes “adding insignificant extra-solution activity to the judicial exception” (MPEP § 2106.05(g)) since it merely amounts to necessary data gathering or outputting, which identifies is identified in MPEP § 2106.05(g) as a form of extra-solution activity. For purposes of Step 2B analysis, this element is well-understood, routine, conventional activity because it is merely a limitation of “receiving or transmitting data over a network” or “storing and retrieving information in memory,” which MPEP § 2106.05(d)(II) identifies as an example of well‐understood, routine, and conventional computer functions.] wherein the determination of the at least the second number of data samples to include in the second data set and the third number of data samples to include in the third data set is further based at least on the at least one of the number of stages or the period of time. [These further limitations merely further define already-recited mental processes of the parent claim and are therefore considered to be part of the mental processes discussed above.] Claim 18: wherein the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing operations using a language model; a system for performing deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system for performing conversational AI operations; a system for generating synthetic data; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources. [These additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)). This element merely indicates a field of use or technological environment in which a judicial exception is applied, namely various types of systems.] Claim 20: This claim is given the same analysis as the one given for claim 18, above. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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 (i.e., changing from AIA to pre-AIA) 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. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-15 AIA Claim s 1-4, 6-14, and 16-20 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Kobayashi et al. (US 2018/0018586 A1) (“Kobayashi”) . As to claim 1 , Kobayashi teaches a method comprising: determining, based at least on a first data set that includes a first number of data samples, one or more data subsets; [[0068]: “The progressive sampling starts with a small sample size and uses progressively larger ones, and repeats machine learning until the prediction performance satisfies a predetermined condition. For example, the machine learning device 100 performs machine learning with the sample size s1 and evaluates the prediction performance of a learned model. If the prediction performance is not sufficient, the machine learning device 100 then performs machine learning with the sample size s2 and evaluates the prediction performance. …In like fashion, the machine learning device 100 performs machine learning with the sample size s3 and evaluates the prediction performance, and then performs machine learning with the sample size s4 and evaluates the prediction performance.” That is, selecting a sample size constitutes determining a data subset, since a sample in this context is a subset, and a sample selection that precedes another sample corresponds to “ one or more data subsets ” of the instant claim.] determining, based at least on updating one or more machine learning models over one or more iterations using the one or more data subsets, one or more validation scores associated with the one or more data subsets; [[0068]: “For example, the machine learning device 100 performs machine learning with the sample size s1 and evaluates the prediction performance of a learned model. If the prediction performance is not sufficient, the machine learning device 100 then performs machine learning with the sample size s2 and evaluates the prediction performance .” Note that “performs machine learning” as described here constitutes updating the model. [0062]: “the process of examining the prediction performance of the learned model may be referred to as ‘ validation’ .” [0043]: “The prediction performance is the model's ability to accurately predict the outcomes of an unknown instance, and may be referred to as the “degree of accuracy” of the model. Any prediction performance index may be used here, such as accuracy, precision, and the root mean square error (RMSE).” See also [0045].] determining, based at least on the one or more validation scores, a density function corresponding to an amount of data samples required to meet or exceed the one or more validation scores ; [[0121]: “Therefore, a condition for the machine learning device 100 to select the sample size s1 is that the sample size s1 satisfying t1*<Pstop×t2* meets the following inequality: s0<s1<s2.” Note that in this context, “s1” is used to represent the subsequent sample. This sample is based on Pstop, which is computed using a corresponding density function, as described in: [0126]: “Herewith, the discontinuation probability Pstop is calculated by the following equation (1) … where f(x) is the probability density function of the probability distribution 64.” Furthermore, in this context, when a subsequent sample size is selected, it attains for higher performance. See [0065]: “As illustrated by the curve 21, higher prediction performance is achieved with the sample size s2 than with the sample size s1…and higher prediction performance is achieved with the sample size s5 than with the sample size s.” Furthermore, the Pstop criterion evaluates whether performance improvement (exceeds) is expected. See [0099]: “The threshold of the improvement rate is denoted by R, which is related to a stopping condition of the machine learning.”] and determining, based at least on the density function, a second number of data samples to include in a second data set to update the one or more machine learning models. [In general, [0068] teaches determining subsequent sample sizes, e.g., “If the prediction performance is not sufficient, the machine learning device 100 then performs machine learning with the sample size s2 and evaluates the prediction performance. …In like fashion, the machine learning device 100 performs machine learning with the sample size s3 and evaluates the prediction performance, and then performs machine learning with the sample size s4 and evaluates the prediction performance.” In the context of the use of the Pstop calculation, the density function calculation is performed to determine that the evaluated condition does not result in stopping, and to select the next training dataset size. See [0047]: “The control unit 12 may determine a training dataset size with the maximum increase rate as the training dataset size 17a.”] As to claim 2 , Kobayashi teaches the method of claim 1, further comprising: updating, using the second data set, the one or more machine learning models during a first stage; [As noted above, Kobayashi, [0068] teaches that a subsequent sample selection is used to perform machine learning, which constitutes using the model using the second data set, e.g., “If the prediction performance is not sufficient, the machine learning device 100 then performs machine learning with the sample size s2 and evaluates the prediction performance. …In like fashion, the machine learning device 100 performs machine learning with the sample size s3 and evaluates the prediction performance, and then performs machine learning with the sample size s4 and evaluates the prediction performance.”] determining, based at least on the density function, a third number of data samples to include in a third data set; [This is taught by the part of Kobayashi quoted above, which refers to selecting subsequent sample sizes. See also Kobayashi, [0065]. Note that the selection of subsequent (e.g., third) sample size is based on the stopping condition described above, which is based on the density function used to compute Pstop.] and updating, using the third data set, the one or more machine learning models during a second stage. [This is taught by the part of Kobayashi quoted above, which refers to selecting subsequent sample sizes. E.g., in the context of [0068]: “In like fashion, the machine learning device 100 performs machine learning with the sample size s3 and evaluates the prediction performance, and then performs machine learning with the sample size s4 and evaluates the prediction performance.”] As to claim 3 , Kobayashi teaches the method of claim 2, further comprising: determining, based at least on updating the one or more machine learning models using the second data set, a validation score associated with the one or more machine learning models; [Kobayashi, [0068]: “If the prediction performance is not sufficient, the machine learning device 100 then performs machine learning with the sample size s2 and evaluates the prediction performance .” As noted above, the performance evaluation uses a validation score.] and determining, based at least on the second number of data samples included in the second data set and the validation score, a fourth number of data samples to include in the third data set. [Kobayashi, [0068]: “In like fashion, the machine learning device 100 performs machine learning with the sample size s3 and evaluates the prediction performance, and then performs machine learning with the sample size s4 and evaluates the prediction performance.”]” As to claim 4 , Kobayashi teaches the method of claim 3, further comprising: determining, based at least on the first data set that includes the first number of data samples, one or more second data subsets; [Kobayashi, [0068]: “The progressive sampling starts with a small sample size and uses progressively larger ones, and repeats machine learning until the prediction performance satisfies a predetermined condition. For example, the machine learning device 100 performs machine learning with the sample size s1 and evaluates the prediction performance of a learned model. If the prediction performance is not sufficient, the machine learning device 100 then performs machine learning with the sample size s2 and evaluates the prediction performance.” That is, the second sample size is based on the performance of the first sample size.] determining, based at least on updating the one or more machine learning models over one or more second iterations using the one or more second data subsets, one or more second validation scores associated with the one or more second data subsets; [Kobayashi, [0068]: “…If the prediction performance is not sufficient, the machine learning device 100 then performs machine learning with the sample size s2 and evaluates the prediction performance . …In like fashion, the machine learning device 100 performs machine learning with the sample size s3 and evaluates the prediction performance, and then performs machine learning with the sample size s4 and evaluates the prediction performance.”] and determining, based at least on the one or more second validation scores, a second density function, [This limitation is taught by this reference for the reasons given for the limitation of the “density function” in claim 1, since the process repeats until a stopping condition is met. See Kobayashi, [0068]: “The progressive sampling starts with a small sample size and uses progressively larger ones, and repeats machine learning until the prediction performance satisfies a predetermined condition.” Kobayashi, [0141]: “The learning control unit 135 repeats updating the improvement rates and selecting a machine learning algorithm until sufficiently low improvement rates are observed or the learning time exceeds the time limit.”] wherein the determining the fourth number of data samples to include in the third data set is further based at least on the second density function. [As noted above, the process, which in general includes determining a new number of data samples, repeats until a stopping condition is met.] As to claim 6 , Kobayashi teaches the method of claim 1, further comprising: determining a target validation performance associated with the one or more machine learning models, [Kobayashi, [0068]: “For example, the machine learning device 100 performs machine learning with the sample size s1 and evaluates the prediction performance of a learned model. If the prediction performance is not sufficient , the machine learning device 100 then performs machine learning with the sample size s2 and evaluates the prediction performance.” Kobayashi, [0069]: “As a procedure for examining the prediction performance of the learned model in each learning step (a validation technique), cross-validation or random subsampling validation may be used, for example.” Note that “sufficient” in this case constitutes a target validation performance, as the claim does not require any specific technique or metric.] wherein the determining the second number of data samples to include in the second data set is further based at least on the target validation performance. [Kobayashi, [0068]: “For example, the machine learning device 100 performs machine learning with the sample size s1 and evaluates the prediction performance of a learned model. If the prediction performance is not sufficient , the machine learning device 100 then performs machine learning with the sample size s2 and evaluates the prediction performance.”] As to claim 7 , Kobayashi teaches the method of claim 1, further comprising: determining, based at least on the one or more validation scores and a target validation performance, one or more estimated number of data samples for updating the one or more machine learning models, [Kobayashi, [0127]: “The above discontinuation probability Pstop changes with a change in the sample size s1. In the case of searching for the best suited sample size s1 by the method illustrated in FIG. 10, the machine learning device 100 repeats the simulation described in FIG. 11 using various sample sizes s1.”] wherein the determining the density function is based at least on the one or more estimated number of data samples. [Kobayashi, [0127]: “The above discontinuation probability Pstop changes with a change in the sample size s1. In the case of searching for the best suited sample size s1 by the method illustrated in FIG. 10, the machine learning device 100 repeats the simulation described in FIG. 11 using various sample sizes s1.”] As to claim 8 , Kobayashi teaches the method of claim 7, wherein: the one or more data subsets comprises at least a first group of data subsets and a second group of data subsets; [Kobayashi, [0069]: “In the cross-validation technique, the machine learning device 100 divides sampled data elements into K blocks (K is an integer equal to 2 or greater), and uses one block amongst the K blocks as a testing dataset and the other K−1 blocks as a training dataset.”] the one or more validation scores comprises at least one or more first validation scores associated with the first group of data subsets and one or more second validation scores associated with the second group of data subsets; [Kobayashi, [0069]: “The machine learning device 100 repeats model learning and evaluation of the prediction performance K times, each time using a different block as the testing dataset.”] and the determining the one or more estimated number of data samples for updating the one or more machine learning models comprises: determining, based at least on the one or more first validation scores and the target validation performance, a first estimated number of data samples for updating the one or more machine learning models; [Kobayashi, [0127]: “The above discontinuation probability Pstop changes with a change in the sample size s1. In the case of searching for the best suited sample size s1 by the method illustrated in FIG. 10, the machine learning device 100 repeats the simulation described in FIG. 11 using various sample sizes s1.” In regards to the limitation of “ based at least on the one or more first validation scores and the target validation performance ,” [0069] teaches that “As a result of one learning step, a model with the highest prediction performance amongst K models created and average prediction performance over the K rounds are obtained, for example.” That is, the average of the individual validation scores is used to determine whether the overall performance is “sufficient” (i.e., target validation performance).] and determining, based at least on the one or more second validation scores and the target validation performance, a second estimated number of data samples for updating the one or more machine learning models, the one or more estimated number of data samples including at least the first estimated number of data samples and the second number of data samples. [Kobayashi, [0127]: “The above discontinuation probability Pstop changes with a change in the sample size s1. In the case of searching for the best suited sample size s1 by the method illustrated in FIG. 10, the machine learning device 100 repeats the simulation described in FIG. 11 using various sample sizes s1.” That is, a subsequent round of determining the next sample size includes simulations of different sample sizes.] As to claim 9 , Kobayashi teaches the method of claim 1, further comprising: determining, based at least on updating the one or more machine learning models using the second data set, a validation score associated with the one or more machine learning models; [Kobayashi, [0068]: “If the prediction performance is not sufficient, the machine learning device 100 then performs machine learning with the sample size s2 and evaluates the prediction performance .” As noted above, the performance evaluation uses a validation score.] and one of: based at least the validation score being less than a target validation score, determining a third number of data samples to include in a third data set, the third data set for updating the one or more machine learning models; or based at least on the validation score being equal to or greater than the target validation score, determining that the updating of the one or more machine learning models is complete. [Kobayashi, [0068]: “In like fashion, the machine learning device 100 performs machine learning with the sample size s3 and evaluates the prediction performance, and then performs machine learning with the sample size s4 and evaluates the prediction performance.” That is, the first alternative is disclosed, noting that the instant claim recites an alternate expression.] As to claim 10 , Kobayashi teaches a system comprising: one or more processing units to: [[0041]: “The control unit 12 is, for example, a processor such as a central processing unit (CPU) or a digital signal processor (DSP). Note however that, the control unit 12 may include an electronic circuit designed for specific use, such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).”] determine, based at least on a first data set that includes a first number of data samples, one or more data subsets; [[0068]: “The progressive sampling starts with a small sample size and uses progressively larger ones, and repeats machine learning until the prediction performance satisfies a predetermined condition. For example, the machine learning device 100 performs machine learning with the sample size s1 and evaluates the prediction performance of a learned model. If the prediction performance is not sufficient, the machine learning device 100 then performs machine learning with the sample size s2 and evaluates the prediction performance. …In like fashion, the machine learning device 100 performs machine learning with the sample size s3 and evaluates the prediction performance, and then performs machine learning with the sample size s4 and evaluates the prediction performance.” That is, selecting a sample size constitutes determining a data subset, since a sample in this context is a subset, and a sample selection that precedes another sample corresponds to “ one or more data subsets ” of the instant claim.] determine, based at least on updating one or more machine learning models over one or more iterations using the one or more data subsets, one or more validation scores associated with the one or more data subsets; [[0068]: “For example, the machine learning device 100 performs machine learning with the sample size s1 and evaluates the prediction performance of a learned model. If the prediction performance is not sufficient, the machine learning device 100 then performs machine learning with the sample size s2 and evaluates the prediction performance .” Note that “performs machine learning” as described here constitutes updating the model. [0062]: “the process of examining the prediction performance of the learned model may be referred to as ‘ validation’ .” [0043]: “The prediction performance is the model's ability to accurately predict the outcomes of an unknown instance, and may be referred to as the “degree of accuracy” of the model. Any prediction performance index may be used here, such as accuracy, precision, and the root mean square error (RMSE).” See also [0045].] and determine, based at least on the one or more validation scores, at least: a second number of data samples to include in a second data set, the second data set to update the one or more machine learning models during a first stage; [In general, [0068] teaches determining subsequent sample sizes, e.g., “ If the prediction performance is not sufficient, the machine learning device 100 then performs machine learning with the sample size s2 and evaluates the prediction performance. …In like fashion, the machine learning device 100 performs machine learning with the sample size s3 and evaluates the prediction performance, and then performs machine learning with the sample size s4 and evaluates the prediction performance.” See also [0047]: “The control unit 12 may determine a training dataset size with the maximum increase rate as the training dataset size 17a.”] and a third number of data samples to include in a third data set, the third data set to update the one or more machine learning models during a second stage . [This is taught by the part of Kobayashi quoted above, which refers to selecting subsequent sample sizes. See also Kobayashi, [0065]. Furthermore, each sample selection is used to train a machine learning model, i.e., to update the one or more models, and each subsequent sample selection is based on the results of the previous machine learning process.] As to claim 11 , Kobayashi teaches the system of claim 10, wherein the one or more processing units are further to: determine, based at least on updating the one or more machine learning models using the second data set, a validation score associated with the one or more machine learning models; [Kobayashi, [0068]: “If the prediction performance is not sufficient, the machine learning device 100 then performs machine learning with the sample size s2 and evaluates the prediction performance .” As noted above, the performance evaluation uses a validation score.] and determine, based at least on the second number of data samples included in the second data set and the validation score, a fourth number of data samples to include in the third data set. [Kobayashi, [0068]: “In like fashion, the machine learning device 100 performs machine learning with the sample size s3 and evaluates the prediction performance, and then performs machine learning with the sample size s4 and evaluates the prediction performance.” As noted in the previous discussion, each subsequent sample size determination is based on the previous steps in the process.] As to claim 12 , Kobayashi teaches the system of claim 10, wherein the one or more processing units are further to: determine, based at least on the one or more validation scores, a density function, [[0121]: “Therefore, a condition for the machine learning device 100 to select the sample size s1 is that the sample size s1 satisfying t1*<Pstop×t2* meets the following inequality: s0<s1<s2.” Note that in this context, “s1” is used to represent the subsequent sample. This sample is based on Pstop, which is computed using a corresponding density function, as described in: [0126]: “Herewith, the discontinuation probability Pstop is calculated by the following equation (1) … where f(x) is the probability density function of the probability distribution 64.” Furthermore, in this context, when a subsequent sample size is selected, it attains for higher performance. See [0065]: “As illustrated by the curve 21, higher prediction performance is achieved with the sample size s2 than with the sample size s1…and higher prediction performance is achieved with the sample size s5 than with the sample size s.” Furthermore, the Pstop criterion evaluates whether performance improvement (exceeds) is expected. See [0099]: “The threshold of the improvement rate is denoted by R, which is related to a stopping condition of the machine learning.”] wherein the determination of the at least the second number of data samples to include in the second data set and the third number of data samples to include in the third data set is based at least on the density function. [In general, [0068] teaches determining subsequent sample sizes, e.g., “If the prediction performance is not sufficient, the machine learning device 100 then performs machine learning with the sample size s2 and evaluates the prediction performance. …In like fashion, the machine learning device 100 performs machine learning with the sample size s3 and evaluates the prediction performance, and then performs machine learning with the sample size s4 and evaluates the prediction performance.” In the context of the use of the Pstop calculation, the density function calculation is performed to determine that the evaluated condition does not result in stopping, and to select the next training dataset size. See [0047]: “The control unit 12 may determine a training dataset size with the maximum increase rate as the training dataset size 17a.”] As to claim 13 , Kobayashi teaches the system of claim 12, wherein the one or more processing units are further to: determine, based at least on the one or more validation scores and a target validation performance, one or more estimated number of data samples for updating the one or more machine learning models, [Kobayashi, [0127]: “The above discontinuation probability Pstop changes with a change in the sample size s1. In the case of searching for the best suited sample size s1 by the method illustrated in FIG. 10, the machine learning device 100 repeats the simulation described in FIG. 11 using various sample sizes s1.”] wherein the determination of the density function is based at least on the one or more estimated number of data samples. [Kobayashi, [0127]: “The above discontinuation probability Pstop changes with a change in the sample size s1. In the case of searching for the best suited sample size s1 by the method illustrated in FIG. 10, the machine learning device 100 repeats the simulation described in FIG. 11 using various sample sizes s1.”] As to claim 14 , Kobayashi teaches the system of claim 13, wherein: the one or more data subsets comprise at least a first group of data subsets and a second group of data subsets; [Kobayashi, [0069]: “In the cross-validation technique, the machine learning device 100 divides sampled data elements into K blocks (K is an integer equal to 2 or greater), and uses one block amongst the K blocks as a testing dataset and the other K−1 blocks as a training dataset.”] the one or more validation scores comprises at least one or more first validation scores associated with the first group of data subsets and one or more second validation scores associated with the second group of data subsets; [Kobayashi, [0069]: “The machine learning device 100 repeats model learning and evaluation of the prediction performance K times, each time using a different block as the testing dataset.”] and the determination of the one or more estimated number of data samples for updating the one or more machine learning models comprises: determining, based at least on the one or more first validation scores and the target validation performance, a first estimated number of data samples for updating the one or more machine learning models; [Kobayashi, [0127]: “The above discontinuation probability Pstop changes with a change in the sample size s1. In the case of searching for the best suited sample size s1 by the method illustrated in FIG. 10, the machine learning device 100 repeats the simulation described in FIG. 11 using various sample sizes s1.” In regards to the limitation of “ based at least on the one or more first validation scores and the target validation performance ,” [0069] teaches that “As a result of one learning step, a model with the highest prediction performance amongst K models created and average prediction performance over the K rounds are obtained, for example.” That is, the average of the individual validation scores is used to determine whether the overall performance is “sufficient” (i.e., target validation performance).] and determining, based at least on the one or more second validation scores and the target validation performance, a second estimated number of data samples for updating the one or more machine learning models, the one or more estimated number of data samples including the first estimated number of data samples and the second number of data samples. [Kobayashi, [0127]: “The above discontinuation probability Pstop changes with a change in the sample size s1. In the case of searching for the best suited sample size s1 by the method illustrated in FIG. 10, the machine learning device 100 repeats the simulation described in FIG. 11 using various sample sizes s1.” That is, a subsequent round of determining the next sample size includes simulations of different sample sizes.] As to claim 16 , Kobayashi teaches the system of claim 10, wherein the one or more processing units are further to: determine a target validation performance associated with the one or more machine learning models, [Kobayashi, [0099]: The threshold of the improvement rate is denoted by R, which is related to a stopping condition of the machine learning. For example, R is defined in advance as: R=0.001/3600.”] wherein the determination of the one or more validation scores is further based at least on the target validation performance. [As discussed above, the process in Kobayashi (which includes a determination of a validation score) continues when the stopping condition is not met. Therefore, the determination of a validation score in a subsequent iteration is based on not meeting the target performance previously.] As to claim 17 , Kobayashi teaches the system of claim 10, wherein the one or more processing units are further to: receive input data representative of at least one of a number of stages or a period of time associated with updating the one or more machine learning models, [Kobayashi, [0141]: “The learning control unit 135 repeats updating the improvement rates and selecting a machine learning algorithm until sufficiently low improvement rates are observed or the learning time exceeds the time limit .”] wherein the determination of the at least the second number of data samples to include in the second data set and the third number of data samples to include in the third data set is further based at least on the at least one of the number of stages or the period of time. [Kobayashi, [0141]: “The learning control unit 135 repeats updating the improvement rates and selecting a machine learning algorithm until sufficiently low improvement rates are observed or the learning time exceeds the time limit .”] As to claim 18 , Kobayashi teaches the system of claim 10, wherein the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing operations using a language model; a system for performing deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system for performing conversational AI operations; a system for generating synthetic data; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources. [Noting that the instant limitation is an alternative expression, the alternative of “system for performing simulation operations” is disclosed in Kobayashi, [0123]: “In this situation, the machine learning device 100 runs the following simulation ”; [0127]: “FIG. 10, the machine learning device 100 repeats the simulation described in FIG. 11 using various sample sizes s1.”] As to claim 19 , Kobayashi teaches a processor comprising: one or more processing units [[0041]: “The control unit 12 is, for example, a processor such as a central processing unit (CPU) or a digital signal processor (DSP). Note however that, the control unit 12 may include an electronic circuit designed for specific use, such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).”] to determine, based at least on a density function associated with a data set, [[0121]: “Therefore, a condition for the machine learning device 100 to select the sample size s1 is that the sample size s1 satisfying t1*<Pstop×t2* meets the following inequality: s0<s1<s2.” Note that in this context, “s1” is used to represent the subsequent sample. This sample is based on Pstop, which is computed using a corresponding density function, as described in: [0126]: “Herewith, the discontinuation probability Pstop is calculated by the following equation (1) … where f(x) is the probability density function of the probability distribution 64.” Furthermore, in this context, when a subsequent sample size is selected, it attains for higher performance. See [0065]: “As illustrated by the curve 21, higher prediction performance is achieved with the sample size s2 than with the sample size s1…and higher prediction performance is achieved with the sample size s5 than with the sample size s.” Furthermore, the Pstop criterion evaluates whether performance improvement (exceeds) is expected. See [0099]: “The threshold of the improvement rate is denoted by R, which is related to a stopping condition of the machine learning.”] a number of data samples for updating one or more machine learning models, [In general, [0068] teaches determining subsequent sample sizes, e.g., “If the prediction performance is not sufficient, the machine learning device 100 then performs machine learning with the sample size s2 and evaluates the prediction performance. …In like fashion, the machine learning device 100 performs machine learning with the sample size s3 and evaluates the prediction performance, and then performs machine learning with the sample size s4 and evaluates the prediction performance.” In the context of the use of the Pstop calculation, the density function calculation is performed to determine that the evaluated condition does not result in stopping, and to select the next training dataset size. See [0047]: “The control unit 12 may determine a training dataset size with the maximum increase rate as the training dataset size 17a.”] wherein the density function is determined based at least on updating the one or more machine learning models over one or more iterations using one or more data subsets. [[0068]: “For example, the machine learning device 100 performs machine learning with the sample size s1 and evaluates the prediction performance of a learned model. If the prediction performance is not sufficient, the machine learning device 100 then performs machine learning with the sample size s2 and evaluates the prediction performance .” Note that “performs machine learning” as described here constitutes updating the model, and that each subsequent iteration (including the selection of sample size under the criteria of “select the sample size s1 is that the sample size s1 satisfying t1*<Pstop×t2*” (as quoted above)) is based on the previous iterations. Here, “based on” does not require any specific relationship that distinguishes over the instant prior art.] As to claim 20 , Kobayashi teaches the processor of claim 19, wherein the processor is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system for performing operations using a language model; a system implemented using an edge device; a system implemented using a robot; a system for performing conversational AI operations; a system for generating synthetic data; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources. [Noting that the instant limitation is an alternative expression, the alternative of “system for performing simulation operations” is disclosed in Kobayashi, [0123]: “In this situation, the machine learning device 100 runs the following simulation ”; [0127]: “FIG. 10, the machine learning device 100 repeats the simulation described in FIG. 11 using various sample sizes s1.”] Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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 (i.e., changing from AIA to pre-AIA) 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. 07-20-aia AIA 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. 07-21-aia AIA Claim s 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Kobayashi in view of Sarkar et al., “Cost-Efficient Sampling for Performance Prediction of Configurable Systems (T),” 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE), Lincoln, NE, USA, 2015, pp. 342-352, doi: 10.1109/ASE.2015.45 (“Sarkar”) . As to claim 5 , Kobayashi teaches the method of claim 1, but does not teach the further limitations that the determining the second number of data samples to include in the second data set is further based at least “ one or more costs, the one or more costs associated with at least one of: collecting the second number of data samples; or a risk that a validation performance for the one or more machine learning models is less than a target validation performance after a period of time elapses. ” Sarkar teaches “ one or more costs, the one or more costs associated with at least one of: collecting the second number of data samples and the third number of data samples; or a risk that a validation performance for the one or more machine learning models is less than a target validation performance after a period of time elapses .” [§ IV, paragraph 2: “Therefore, given a training and testing set of size n each, we have the following cost function of n…, where 2n is the number of sample configurations in the training and testing sets…and R is a tuning parameter that controls the ratio of the cost incurred due to the prediction error to the cost of acquiring training samples .” That is, the cost function, which is based on earlier works (see § IV, first paragraph) is a representation of the cost of acquiring training samples and defines a “cost.” That is, the first alternative in the alternative expression in the instant claim limitation is taught. See also § V.A, paragraph 3, which also teaches “the cost of acquiring training samples.”] It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Kobayashi with the teachings of Sarkar by implementing the technique of using a cost function so as to arrive at the claimed invention. The motivation would have been to take into account sampling costs as a way of quantifying the performance of a sample (as suggested by Sarkar, § I, paragraph 3: “To quantify the goodness of a sample, we introduce a composite model of sampling cost, which considers the measurement effort and prediction accuracy simultaneously.”). As to claim 15 , Kobayashi teaches the system of claim 10, as set forth above, but does not teach the further limitations that the determination of the at least the second number of data samples to include in the second data set and the third number of data samples to include in the third data set is further based at least on “ one or more costs, the one or more costs associated with at least one of: collecting the second number of data samples and the third number of data samples; or a risk that a validation performance for the one or more machine learning models is less than a target validation performance after a period of time elapses .” Sarkar teaches “ one or more costs, the one or more costs associated with at least one of: collecting the second number of data samples and the third number of data samples; or a risk that a validation performance for the one or more machine learning models is less than a target validation performance after a period of time elapses .” [§ IV, paragraph 2: “Therefore, given a training and testing set of size n each, we have the following cost function of n…, where 2n is the number of sample configurations in the training and testing sets…and R is a tuning parameter that controls the ratio of the cost incurred due to the prediction error to the cost of acquiring training samples .” That is, the cost function, which is based on earlier works (see § IV, first paragraph) is a representation of the cost of acquiring training samples and defines a “cost.” That is, the first alternative in the alternative expression in the instant claim limitation is taught. See also § V.A, paragraph 3, which also teaches “the cost of acquiring training samples.”] It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Kobayashi with the teachings of Sarkar by implementing the technique of using a cost function so as to arrive at the claimed invention. The motivation would have been to take into account sampling costs as a way of quantifying the performance of a sample (as suggested by Sarkar, § I, paragraph 3: “To quantify the goodness of a sample, we introduce a composite model of sampling cost, which considers the measurement effort and prediction accuracy simultaneously.”) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The following document depicts the state of the art . Provost et al., “Efficient Progressive Sampling,” KDD-99, San Diego, CA (1999) teaches conventional techniques in progressive sampling. Any inquiry concerning this communication or earlier communications from the examiner should be directed to YAO DAVID HUANG whose telephone number is (571)270-1764. The examiner can normally be reached Monday - Friday 9:00 am - 5:30 pm. 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, Miranda Huang can be reached at (571) 270-7092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Y.D.H./Examiner, Art Unit 2124 /MIRANDA M HUANG/ Supervisory Patent Examiner, Art Unit 2124 Application/Control Number: 18/318,212 Page 2 Art Unit: 2124 Application/Control Number: 18/318,212 Page 3 Art Unit: 2124 Application/Control Number: 18/318,212 Page 4 Art Unit: 2124 Application/Control Number: 18/318,212 Page 5 Art Unit: 2124 Application/Control Number: 18/318,212 Page 6 Art Unit: 2124 Application/Control Number: 18/318,212 Page 7 Art Unit: 2124 Application/Control Number: 18/318,212 Page 8 Art Unit: 2124 Application/Control Number: 18/318,212 Page 9 Art Unit: 2124 Application/Control Number: 18/318,212 Page 10 Art Unit: 2124 Application/Control Number: 18/318,212 Page 11 Art Unit: 2124 Application/Control Number: 18/318,212 Page 12 Art Unit: 2124 Application/Control Number: 18/318,212 Page 13 Art Unit: 2124 Application/Control Number: 18/318,212 Page 14 Art Unit: 2124 Application/Control Number: 18/318,212 Page 15 Art Unit: 2124 Application/Control Number: 18/318,212 Page 16 Art Unit: 2124 Application/Control Number: 18/318,212 Page 17 Art Unit: 2124 Application/Control Number: 18/318,212 Page 18 Art Unit: 2124 Application/Control Number: 18/318,212 Page 19 Art Unit: 2124 Application/Control Number: 18/318,212 Page 20 Art Unit: 2124 Application/Control Number: 18/318,212 Page 21 Art Unit: 2124 Application/Control Number: 18/318,212 Page 22 Art Unit: 2124 Application/Control Number: 18/318,212 Page 23 Art Unit: 2124 Application/Control Number: 18/318,212 Page 24 Art Unit: 2124 Application/Control Number: 18/318,212 Page 25 Art Unit: 2124 Application/Control Number: 18/318,212 Page 26 Art Unit: 2124 Application/Control Number: 18/318,212 Page 27 Art Unit: 2124 Application/Control Number: 18/318,212 Page 28 Art Unit: 2124 Application/Control Number: 18/318,212 Page 29 Art Unit: 2124 Application/Control Number: 18/318,212 Page 30 Art Unit: 2124 Application/Control Number: 18/318,212 Page 31 Art Unit: 2124 Application/Control Number: 18/318,212 Page 32 Art Unit: 2124 Application/Control Number: 18/318,212 Page 33 Art Unit: 2124 Application/Control Number: 18/318,212 Page 34 Art Unit: 2124 Application/Control Number: 18/318,212 Page 35 Art Unit: 2124 Application/Control Number: 18/318,212 Page 36 Art Unit: 2124 Application/Control Number: 18/318,212 Page 37 Art Unit: 2124 Application/Control Number: 18/318,212 Page 38 Art Unit: 2124 Application/Control Number: 18/318,212 Page 39 Art Unit: 2124 Application/Control Number: 18/318,212 Page 40 Art Unit: 2124 Application/Control Number: 18/318,212 Page 41 Art Unit: 2124 Application/Control Number: 18/318,212 Page 42 Art Unit: 2124
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