CTNF 18/109,283 CTNF 94006 DETAILED ACTION The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This action is responsive to the Application filed on 02/14/2023. Claims 1-20 are pending in the case. Claims 1, 8, and 15 are independent claims. Claim Rejections - 35 U.S.C. § 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. Step 1: Claims 1-7 are directed towards the statutory category of an article of manufacture. Claims 8-14 are directed towards the statutory category of a process. Claims 15-20 are directed towards the statutory category of a machine. With respect to claim 1: 2A Prong 1 : This claim is directed to a judicial exception. … that includes shape information of a heat sink that serves as an explanatory variable and heat distribution information of the heat sink that serves as an objective variable (mental process). 2A Prong 2 : This judicial exception is not integrated into a practical application. Additional elements: a non-transitory computer-readable recording medium storing a machine learning program for causing a computer to execute a process, the process comprising (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)); obtaining training data (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g)); and executing, based on the training data, machine learning of a machine learning model according to a loss function that includes an expression that constrains a temperature relationship of a plurality of positions in the heat sink (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f) – high level machine learning). 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: a non-transitory computer-readable recording medium storing a machine learning program for causing a computer to execute a process, the process comprising (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)); obtaining training data (MPEP 2106.05(d) indicates that merely “storing and retrieving information in memory” and/or "receiving or transmitting data over a network" are well‐understood, routine, conventional functions when they are claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer); and executing, based on the training data, machine learning of a machine learning model according to a loss function that includes an expression that constrains a temperature relationship of a plurality of positions in the heat sink (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f) – high level machine learning). With respect to claim 2: 2A Prong 1 : This claim is directed to a judicial exception. obtains the training data that further includes, as the explanatory variable, fin spacing of the heat sink calculated by using the shape information of the heat sink (mental process). 2A Prong 2 : This judicial exception is not integrated into a practical application. Additional elements: executes the machine learning based on the training data that includes the fin spacing and the shape information of the heat sink as the explanatory variable and the heat distribution information of the heat sink as the objective variable (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f) – high level machine learning). 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: executes the machine learning based on the training data that includes the fin spacing and the shape information of the heat sink as the explanatory variable and the heat distribution information of the heat sink as the objective variable (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f) – high level machine learning). With respect to claim 3: 2A Prong 1 : This claim is directed to a judicial exception. the expression that constrains the temperature relationship includes a physics-based loss function which is a function that describes heat distribution in the heat sink in line with physics (mental process and/or mathematical concept). 2A Prong 2 : This judicial exception is not integrated into a practical application. 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to claim 4: 2A Prong 1 : This claim is directed to a judicial exception. the physics-based loss function includes a relational expression between enveloping volume and thermal resistance of the heat sink (mental process and/or mathematical concept). 2A Prong 2 : This judicial exception is not integrated into a practical application. 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to claim 5: 2A Prong 1 : This claim is directed to a judicial exception. the physics-based loss function returns a value according to a magnitude relationship of temperatures at the plurality of positions in the heat sink at different distances from a heat source (mental process and/or mathematical concept). 2A Prong 2 : This judicial exception is not integrated into a practical application. 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to claim 6: 2A Prong 1 : This claim is directed to a judicial exception. wherein the plurality of positions include a first position and a second position closer to the heat source than the first position (mental process); and wherein, when a temperature at the first position is lower than a temperature at the second position, the physics-based loss function returns a value smaller than a value when the temperature at the first position is higher than the temperature at the second position (mental process). 2A Prong 2 : This judicial exception is not integrated into a practical application. 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to claim 7: 2A Prong 1 : This claim is directed to a judicial exception. the physics-based loss function includes a rectified linear unit (ReLU) function that uses a difference between the temperature at the first position and the temperature at the second position as an argument (mathematical concept and/or mental process). 2A Prong 2 : This judicial exception is not integrated into a practical application. 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The remaining claims 8-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more for at least the same reasons as those given above with respect to claims 1-7 with only the addition of generic computer components under step 2A prong 1. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper but for the recitation of a generic computer component. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the "Mental Process" grouping of abstract ideas. A person would readily be able to perform this process either mentally or with the assistance of pen and paper. See MPEP § 2106.04(a)(2). Limitations that merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). These additional elements do not integrate the judicial exception into a practical application under step 2A prong 2. Refer to MPEP §2106.04(d). Moreover, the limitations are merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). These additional elements do not recite any additional elements/limitations that amount to significantly more. Accordingly, the claimed invention recites an abstract idea without significantly more. Claim Rejections - 35 U.S.C. § 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 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 of this title, 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. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant are advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim s 1, 8, and 15 are rejected under 35 U.S.C. § 103 as being unpatentable over Zhao et al. (Zhao, Xiaoyu, Zhiqiang Gong, Jun Zhang, Wen Yao, and Xiaoqian Chen. "A surrogate model with data augmentation and deep transfer learning for temperature field prediction of heat source layout." Structural and Multidisciplinary Optimization 64, no. 4 (2021): 2287-2306, hereinafter Zhao) in view of Chen et al. (Chen, Xianqi, Xiaoyu Zhao, Zhiqiang Gong, Jun Zhang, Weien Zhou, Xiaoqian Chen, and Wen Yao. "A deep neural network surrogate modeling benchmark for temperature field prediction of heat source layout." Science China Physics, Mechanics & Astronomy 64, no. 11 (2021): 1, hereinafter Chen) . As to independent claims 1, 8, and 15 , Zhao teaches a non-transitory computer-readable recording medium storing a machine learning program for causing a computer to execute a process, the process comprising: obtaining training data that includes shape information of a heat sink that serves as an explanatory variable… ( Page 2290, "For simplicity, the shape of each heat source is assumed as square with the side length of 0.01m and the intensity φ0 is uniformly set to 10,000W/m2". Page 2288, "A novel data augmentation method is proposed to improve temperature field prediction precision of DNN with a small sample. The data augmentation method, which utilizes pairwise information, enlarges the number of training samples and further improves the training efficiency. (2) Deep transfer learning is introduced for the DNN training to take advantage of information between different HSL-TFP tasks, which adds adaptability of DNN surrogate in different situations" ); and executing, based on the training data, machine learning of a machine learning model according to a loss function that includes an expression that constrains a temperature relationship of a plurality of positions in the heat sink ( Page 2292, "As general regression problems, the L1 loss function is adopted for the training" ). Zhao does not appear to expressly teach heat distribution information of the heat sink that serves as an objective variable. Chen teaches heat distribution information of the heat sink that serves as an objective variable ( Page 4, "where φ(x, y) denotes the intensity distribution function affected by the HSL X, and k is the thermal conductivity of the domain which is set to 1 in this paper" ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the surrogate model temperature field prediction of Zhao to include the surrogate modeling for temperature field prediction of Chen to alleviate computational complexity (see Chen at abstract) . 07-21-aia AIA Claim s 2, 3, 5, 6, 9, 10, 12, 13, 16, 17, 19, and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Zhao in view of Chen and Hennigh et al. (Hennigh, Oliver, Susheela Narasimhan, Mohammad Amin Nabian, Akshay Subramaniam, Kaustubh Tangsali, Zhiwei Fang, Max Rietmann, Wonmin Byeon, and Sanjay Choudhry. "NVIDIA SimNet™: An AI-accelerated multi-physics simulation framework." In International conference on computational science , pp. 447-461. Cham: Springer International Publishing, 2021, hereinafter Hennigh) . As to dependent claims 2, 9, and 16 , the respective rejections of claim 1, 8, and 15 are incorporated. Zhao does not appear to expressly teach the process obtains the training data that further includes, as the explanatory variable, fin spacing of the heat sink calculated by using the shape information of the heat sink, and executes the machine learning based on the training data that includes the fin spacing and the shape information of the heat sink as the explanatory variable and the heat distribution information of the heat sink as the objective variable. Hennigh teaches the process obtains the training data that further includes, as the explanatory variable, fin spacing of the heat sink calculated by using the shape information of the heat sink, and executes the machine learning based on the training data that includes the fin spacing and the shape information of the heat sink as the explanatory variable and the heat distribution information of the heat sink as the objective variable ( Page 9, "we train a conjugate heat transfer problem over the Nvidia’s NVSwitch heat sink whose fin geometry are variable, as shown in Figure 10 (nine geometry variables in total). Following the training, we perform a design optimization to find out the most optimal fin configuration" ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the surrogate model temperature field prediction of Zhao to include the AI-accelerated multi-physics simulation of Hennigh to enable design optimization of a heat sink (see Hennigh at page 2). As to dependent claims 3, 10, and 17 , the respective rejections of claim 1, 8, and 15 are incorporated. Zhao does not appear to expressly teach the expression that constrains the temperature relationship includes a physics-based loss function which is a function that describes heat distribution in the heat sink in line with physics. Hennigh teaches the expression that constrains the temperature relationship includes a physics-based loss function which is a function that describes heat distribution in the heat sink in line with physics ( Page 12, "we construct a neural network model with a hybrid data and physics-driven loss function" ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the surrogate model temperature field prediction of Zhao to include the AI-accelerated multi-physics simulation of Hennigh to enable design optimization of a heat sink (see Hennigh at page 2). As to dependent claims 5, 12, and 19 , Hennigh further teaches the physics-based loss function returns a value according to a magnitude relationship of temperatures at the plurality of positions in the heat sink at different distances from a heat source ( Page 8, "a conjugate heat transfer problem to simulate flow over the heat sink placed inside a channel" ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the surrogate model temperature field prediction of Zhao to include the AI-accelerated multi-physics simulation of Hennigh to enable design optimization of a heat sink (see Hennigh at page 2). As to dependent claims 6, 13, and 20 , Hennigh further teaches wherein the plurality of positions include a first position and a second position closer to the heat source than the first position, and wherein, when a temperature at the first position is lower than a temperature at the second position, the physics-based loss function returns a value smaller than a value when the temperature at the first position is higher than the temperature at the second position ( Page 8, "a conjugate heat transfer problem to simulate flow over the heat sink placed inside a channel" ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the surrogate model temperature field prediction of Zhao to include the AI-accelerated multi-physics simulation of Hennigh to enable design optimization of a heat sink (see Hennigh at page 2) . 07-21-aia AIA Claim s 4, 11, and 18 are rejected under 35 U.S.C. § 103 as being unpatentable over Zhao in view of Chen and Lee et al. (Lee, Seri. “HOW TO SELECT A HEAT SINK.” (1996), hereinafter Lee) . As to dependent claims 4, 11, and 18 , the respective rejections of claim 3, 10, and 17 are incorporated. Zhao does not appear to expressly teach the physics-based loss function includes a relational expression between enveloping volume and thermal resistance of the heat sink.. Lee teaches the physics-based loss function includes a relational expression between enveloping volume and thermal resistance of the heat sink ( Page 6, "The volume of a heat sink for a given low condition can be obtained by dividing the volumetric thermal resistance by the required thermal resistance" ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the surrogate model temperature field prediction of Zhao to include the heat sink selection criteria of Lee to allow more heat to be dissipated and/or lower the device operating temperature (see Lee at page 2) . 07-21-aia AIA Claim s 7 and 14 are rejected under 35 U.S.C. § 103 as being unpatentable over Zhao in view of Chen and Chuttar et al. (Chuttar, Aditya, and Debjyoti Banerjee. "Machine learning (ML) based thermal management for cooling of electronics chips by utilizing thermal energy storage (TES) in packaging that leverages phase change materials (PCM)." Electronics 10, no. 22 (2021): 2785, hereinafter Chuttar) . As to dependent claims 7 and 14 the respective rejections of claim 6 and 13 are incorporated. Zhao does not appear to expressly teach the physics-based loss function includes a rectified linear unit (ReLU) function that uses a difference between the temperature at the first position and the temperature at the second position as an argument. Chuttar teaches the physics-based loss function includes a rectified linear unit (ReLU) function that uses a difference between the temperature at the first position and the temperature at the second position as an argument ( Page 11, "The Rectified Linear Unit (ReLU) activation function is used in the network nodes" ). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the surrogate model temperature field prediction of Zhao to include the ML based thermal management of Chuttar to facilitate better thermal management strategies and paradigms to address higher cooling loads (see Chuttar at page 2). Citation of Pertinent Prior Art 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Suzuki (U.S. Pat. App. Pub. No. 2022/0343042) teaches an information processing device includes a memory and one or more processors coupled to the memory. The memory stores therein time-series data including one or more variables. The one or more processors are configured to: calculate one or more time differential values of the one or more variables; calculate one or more differences representing variation of the one or more variables from an initial value; estimate a coefficient of a linear regression equation by machine learning in which the time differential values and the differences are used as learning data; and output the linear regression equation . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action . It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck , 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson , 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)). Any inquiry concerning this communication or earlier communications from the examiner should be directed to Casey R. Garner whose telephone number is 571-272-2467. The examiner can normally be reached Monday to Friday, 8am to 5pm, Eastern Time. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached on 571-270-3428. 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 Patent Center and the Private Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from Patent Center or Private PAIR. Status information for unpublished applications is available through Patent Center and Private PAIR to authorized users only. Should you have questions about access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). 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) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form. /Casey R. Garner/Primary Examiner, Art Unit 2123 Application/Control Number: 18/109,283 Page 2 Art Unit: 2123 Application/Control Number: 18/109,283 Page 3 Art Unit: 2123 Application/Control Number: 18/109,283 Page 4 Art Unit: 2123 Application/Control Number: 18/109,283 Page 5 Art Unit: 2123 Application/Control Number: 18/109,283 Page 6 Art Unit: 2123 Application/Control Number: 18/109,283 Page 7 Art Unit: 2123 Application/Control Number: 18/109,283 Page 8 Art Unit: 2123 Application/Control Number: 18/109,283 Page 9 Art Unit: 2123 Application/Control Number: 18/109,283 Page 10 Art Unit: 2123 Application/Control Number: 18/109,283 Page 11 Art Unit: 2123 Application/Control Number: 18/109,283 Page 12 Art Unit: 2123 Application/Control Number: 18/109,283 Page 13 Art Unit: 2123 Application/Control Number: 18/109,283 Page 14 Art Unit: 2123 Application/Control Number: 18/109,283 Page 15 Art Unit: 2123 Application/Control Number: 18/109,283 Page 16 Art Unit: 2123 Application/Control Number: 18/109,283 Page 17 Art Unit: 2123
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