DETAILED ACTION Notice of Pre-AIA or AIA Status 1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 101 2. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 101 that form the basis for the rejections under this section made in this Office action: 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. 3. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Under the 2019 PEG (now been incorporated into MPEP 2106), the revised procedure for determining whether a claim is "directed to" a judicial exception requires a two-prong inquiry into whether the claim recites: (1) any judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human interactions such as a fundamental economic practice, or mental processes); and (2) additional elements that integrate the judicial exception into a practical application (see MPEP § 2106.05(a)-(c), (e)-(h)). Only if a claim (1) recites a judicial exception and (2) does not integrate that exception into a practical application, do we then look to whether the claim: (3) adds a specific limitation beyond the judicial exception that is not "well-understood, routine, conventional" in the field (see MPEP § 2106.0S(d)); or (4) simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. Claims 1-20 are directed to an abstract idea of stabilizing measured values against interfering influences associated with a switching component of power electronics. Specifically, representative claim 1 recites: A method, comprising: (S1) receiving via a communications network, by a remote computing system, agricultural information including prescribed operational settings for farming machines, crop variety information, and environmental information that includes weather condition factors and soil condition factors; (S2) recording, by the remote computing system, the agricultural information as first relational database elements in a relational database; (S3) determining, by the remote computing system, situational operational settings based on the recorded agricultural information; (S4) recording, by the remote computing system, the situational operational settings as second relational database elements in the relational database; (S5) linking, by the remote computing system, the second relational database elements to the first relational database elements according to inputs of the determinations of the situational operational settings; (S6) selecting, by the remote computing system, at least one setting of the recorded situational operational settings according to a database query based on or included in a request sent from a computer of a farming machine; and (S7) sending via the communications network, by the remote computing system, the at least one setting to the farming machine according to the request. The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”. The highlighted portion of the claim constitutes an abstract idea under the 2019 Revised Patent Subject Matter Eligibility Guidance and the additional elements are NOT sufficient to amount to significantly more than the judicial exceptions, as analyzed below: Step Analysis 1. Statutory Category ? Yes. Method 2A - Prong 1: Judicial Exception Recited? Yes. See the bolded portion listed above. Under the broadest reasonable interpretation (BRI), each of the limitations S2, S4, S5, and S6 encompasses a mental process, i.e. data recording, manipulation, evaluation and judgment, that can be performed in the human mind using mental steps/ critical thinking or by a human using a pen and paper . Note, the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. See CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). See also MPEP 2106.04(a)(2).III Under the BRI, the limitation S3 encompasses mathematical relationships, namely a series of calculations leading to one or more numerical results or answers, which also encompasses mental processes, i.e. data manipulation, evaluation and judgment, that can be performed in the human mind or by a human using a pen and paper . Although it does not spell out any particular equation or formula being used, the lack of specific equations for the series of calculations merely indicates that the claim would monopolize all possible math concepts in practicing the apparatus. The limitation of “ the remote computing system” is recited at a high level of generality. According to the MPEP 2106.04(a)(2), if a claim limitation, under its broadest reasonable interpretation, covers mental processes except for the mention of generic computer components performing computing activities via basic function of the computer, then the claim is likely considered to be directed to an ineligible abstract idea, as it essentially describes a mental process that could be performed by a human without the computer components adding any significant practical application beyond the abstract concept itself. Nothing in the bolded portion precludes the limitations S2-S6 from practically being performed in the mind and/or with the aid of pen/paper. Therefore, the bolded limitations fall within a combination of the mental process and the math concept groupings of abstract ideas under the 2019 PEG because they cover concepts performed in the human mind, including observation, evaluation, math calculations, judgment, and opinion. 2A - Prong 2: Integrated into a Practical Application? No. The claim recites the additional elements S1 and S7. Under the BRI, the limitation S1 is no more than mere instructions to apply the exception using a generic communications network and a computing system for gathering the data/information necessary for performing the abstract idea. The physical attributes/parameters of the data/information encompass m erely data characterization which can be viewed as generally linking the use of the judicial exception to the relevant technological environment or field of use. Under the BRI, the limitation S7 encompasses an insignificant post-solution activity as it merely outputs the results of the abstract idea , thus it does not amount to be meaningful to integrate the recited judicial exception into a practical application. See MPEP 2106.04(d) and 2106.05(g). In general, the claim as a whole does not meet any of the following criteria to integrate the abstract idea into a practical application: An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field; an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; an additional element effects a transformation or reduction of a particular article to a different state or thing; and an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Various considerations are used to determine whether the additional elements are sufficient to integrate the abstract idea into a practical application. However, in all of these respects, the claim fails to recite additional elements which might possibly integrate the claim into a particular practical application. Instead, based on the above considerations, the claim would tend to monopolize the algorithm across a wide range of applications. 2B: Claim provides an Inventive Concept? No. As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component or extra-solution activities which can be viewed as an attempt to link the use of the judicial exception to the relevant technological environment or field of use. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer and/or data characterization linking the use of the judicial exception to the relevant technological environment cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the additional elements discussed in Step 2A are re-evaluated and asserted to be extra-solution activities that are all well-understood, routine, conventional in the field (see discussion prior art as set forth in sections 4-5 below). The claim is therefore ineligible under 35 USC 101. The dependent claims 2-18 inherit attributes of the independent claim 1, but do not add anything which would render the claimed invention a patent eligible application of the abstract idea. These claims merely extend (or narrow) the abstract idea which do not amount for "significant more" because they merely add details to the algorithm which forms the abstract idea as discussed above. In particular, claims 2-6 recite the limitations of “the computing scheme” including an artificial neural network (claim 3), “deep learning” (claim 4), “a convolutional neural network” (claim 5) and “deep learning process includes a network of convolutional neural networks” (claim 6). However, in light of the USPTO’s July 17, 2024 Subject Matter Eligibility Examples (e.g., Examples 47-49), a computing scheme (e.g., prediction) using a machine learning model is considered an "abstract idea" if the claim focuses solely on the concept of performing the computing using a generic machine learning algorithm (e.g., deep learning, which may include a network of convolutional neural networks), without any specific technical improvements or applications that go beyond the basic idea of using a computer to analyze data and generate predictions; essentially, if the claim is too high-level and does not describe a concrete, inventive implementation of the machine learning process. In the instant case, it is deemed that the clamed “computing scheme” (including an ANN, CNN and/or deep learning process) simply applies a trained machine learning model to perform evaluations/judgments and generate the output. Pending claims 2-6 do not provide details of how the trained machine learning model operates to generate/predict the target situational operational settings . Rather, the combination of claims 2-6 only recites the outcome of the “computing scheme” which is used like a “Black Box AI” whose internal workings are a mystery to its users . As such, the subject matter of claims 2-6 are treated as part of the abstract idea identified for claim 1. Under the BRI, claims 7-16 recite additional limitations that are considered insignificant extra-solution activities or mere data characterization linking the use of the judicial exception to the relevant technological environment which neither integrate the judicial exception into a practical application nor provide any inventive concept (see discussion prior art as set forth in sections 4-5 below). Claims 17-18 recite the limitations of a graphical user interface. It has been held that merely displaying data with a generic graphical user interface is not a practical application because is it insignificant post solution activity as it merely displays the results of the abstract idea. See Electric Power Group, LLC v. Alstom S.A ., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) . Claims 19 and 20 are rejected under 35 USC 101 for the same reasons as for claims 1-18 set forth above. Claim Rejections - 35 USC § 102 4. 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 – (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; or (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 5. Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Davis (US 20210264252 A1). Regarding claims 1 and 19, Davis discloses a method and system for practicing the method (Fig. 1), comprising: receiving (para. 0037: “The geographical database may also be updated, as appropriate, utilizing data received from other network-connected data sources …”; see also para, 0045 and 0050 for data collection and database updating) via a communications network (22), by a remote computing system (12), agricultural information including prescribed operational settings for farming machines (para. 0036: “software solution can be a set of data parameters for entry into a program executed locally onboard a work vehicle included in the plurality of network-connected work vehicles 14, 16, 18, 20”), crop variety information, and environmental information that includes weather condition factors and soil condition factors (para. 0036-0037, 0045, 0050); recording (see discussion of PROCESS 72 in FIG. 2), by the remote computing system, the agricultural information as first relational database elements in a relational database (para. 0038: “Prior to this stage of the process occurring, however, the server end 12 modifies or updates the software solutions stored within the software solution database 58 on a repeated or iterative basis, as indicated in FIG. 2 by symbol 72”); determining (para. 0044), by the remote computing system (12), situational (i.e., “detects the occurrence of a pre-defined trigger event”, “in response to this determination, the controller architecture 32 may then seek a software solution specific to the newly-attached header type from the server end 12 by generating a corresponding software solution request transmitting to the server end 12 over the network 22”) operational settings (e.g., an optimized software algorithm or “optimal-fit software solution”) based on the recorded agricultural information (para. 0039-0040, 0044-0047); recording, by the remote computing system, the situational operational settings as second relational database elements in the relational database (para. 0048: “the server end 12 may avail the network-connected combine harvester 14 …”, by inherency, the determined “optimal-fit software solution” must be recorded at the server end 12 so that it can be transmitted to the relevant work machine); linking, by the remote computing system, the second relational database elements to the first relational database elements according to inputs of the determinations of the situational operational settings (para. 0036: “each software solution held within the database 58 is linked to or associated with a unique work vehicle task profile”; para. 0050: “The feedback data is then provided to the server end 12, which then modifies the software solution as appropriate (STEP 100), with the modified software solution stored in the software solution database 58 (STEP 102)”, i.e., the “optimal-fit software solution” must be linked to the first relational database elements thus the modified version can be stored in the software solution database 58); selecting, by the remote computing system, at least one setting of the recorded situational operational settings according to a database query based on or included in a request sent from a computer of a farming machine (para. 0037: “The server end 12 may then access the geographical database to recall the appropriate geographical information corresponding to a task location in building search criteria utilized to search the software solution database 58 for an optimal-fit software solution matching a software solution request issued by any given one of the network-connected work vehicles”; see also para. 0044-0047: “The server end 12 then determines whether an optimal-fit software solution matching the search criteria has been located in the software solution database 58 (STEP 86)”); and sending via the communications network, by the remote computing system, the at least one setting to the farming machine according to the request (para. 0048). Regarding claims 2 and 3, Davis discloses: wherein the determining the situational operational settings (i.e., the “optimal-fit software solution”) includes using the agricultural information or a derivative thereof as an input to a computing scheme (e.g., “neural network algorithms or other machine learning algorithms”; see para. 0020), wherein the determining the situational operational settings includes using the computing scheme to process the agricultural information or the derivative thereof, and wherein the determining the situational operational settings also includes using an output of the computing scheme as or to derive the situational operational settings, wherein the computing scheme includes an artificial neural network (ANN) (para. 0049: “the server end 12 may avail the network-connected combine harvester 14 of the optimal-fit neural network algorithm by executing the neural network algorithm at the server end 12 …”). Regarding claims 4-6, Davis discloses: wherein the ANN is part of a deep learning process that determines the situational operational settings or is a basis for the determination of the situational operational settings (para. 0020, 0049, 0089); wherein the deep learning process includes a convolutional neural network (para. 0051); wherein the deep learning process includes a network of convolutional neural networks (para. 0051). Regarding claim 7, Davis discloses: wherein the request includes information similar to parts of the agricultural information recorded in the relational database used to select the at least one setting sent to the farming machine (see discussion of STEP 80 in Fig. 2). Regarding claim 8, Davis discloses: the method of claim 1 comprising: receiving via the communications network, by the remote computing system, used operational settings from a plurality of farming machines as well as real-time operating information of the plurality of farming machines that is an outcome of the used operational settings (para. 0049-0051: see discussion of feedback data); recording in the relational database, by the remote computing system, the used operational settings and the real-time operating information to add to the first relational database elements (see discussion of STEPs 98/96/100/102 in Fig. 2); and further determining, by the remote computing system, the situational operational settings based on the used operational settings and the real-time operating information to enhance information provided by the second relational database elements (para. 0026: “the server end may also continually collect feedback data from the work machines regarding performance metrics and operator satisfaction levels associated with the software solutions. Such data can then be utilized to train or modify existing software solutions and to identify gaps in the software solution database desirably closed by developing additional software solutions”; para. 0038: “the software solutions stored with in the software solution database 58 may be updated (e.g., neural network algorithms or other algorithms may be iteratively trained) utilizing feedback data received from the other network-connected work vehicles and aggregated at the server end 12”; see also para. 0050). Regarding claim 9, Davis discloses: wherein the farming machine, receiving the at least one setting sent from the remote computing system, is one of the plurality of farming machines, wherein the at least one setting sent from the remote computing system causes at least one update in real-time operating information provided by the farming machine and eventually recorded in the relational database, and wherein the updated real-time operating information is used, by the computing system, to determine an updated version of the at least one setting (para. 0026, 0038, 0050). Regarding claim 10, Davis discloses: wherein the used operational settings include settings adjusted for different crop varieties, different geographic regions, different weather conditions, different soil conditions, or any combination thereof (para. 0024, 0026, 0036-0037, 0045, 0050). Regarding claim 11, Davis discloses: wherein the used operational settings include settings adjusted for different crop varieties, different geographic regions, different weather conditions, and different soil conditions (para. 0024, 0026, 0036-0037, 0045, 0050; see also discussion of the feedback data). Regarding claims 12-16, Davis discloses: wherein the used operational settings include settings adjusted for different crop varieties, wherein the used operational settings include settings adjusted for different geographic regions, wherein the used operational settings include settings adjusted for different weather conditions, wherein the used operational settings include settings adjusted for different soil conditions (para. 0024, 0026, 0036-0037, 0045, 0050; see also discussion of the feedback data); wherein the used operational settings include settings adjusted for a preference of the operator of the farming machine (para. 0037: “Operator preferences corresponding to a particular work vehicle may also be stored within this database in embodiments”; para. 0046: “the tolerance, confidence level, or sensitivity for deeming a software solution an optimal-fit will vary among embodiments and may be adjusted to operator preference, with such operator preferences included in the software solution request …”). Regarding claim 17, Davis discloses: the method of claim 16, comprising displaying, by a graphical user interface (GUI) rendered by the computer of the farming machine, a graphical indicator of an operational setting preferred and identified by the operator on a graphical output of a meter of the farming machine corresponding to the operational setting (para. 0013, 0033, 0039: “an operator may interact with a GUI elements generated on a screen of the display device 42 to navigate to a GUI screen including a software optimization or calibration option”; para. 0044: “The server end 12 may also transmit an acknowledgement signal to the controller architecture 32, which may then generate a visual indication of the display device 42 conveying to the operator that the software solution request has been received by the server end 12”). Regarding claim 18, Davis discloses: displaying, by a graphical user interface (GUI) rendered by the computer of the farming machine, a first graphical indicator (e.g., software solution requests, see para. 0022-0023, 0027) of an operational setting preferred and identified by the operator on a graphical output of a meter of the farming machine corresponding to the operational setting (para. 0013, 0033, 0039, 0044); and displaying, by the GUI, a second graphical indicator (e.g., responses from the server 12) of a situational setting of the situational operational settings on the graphical output of the meter of the farming machine corresponding to the operational setting (para. 0039-0040; see also Fig. 2 and related text). Regarding claim 20, Davis discloses a method, comprising: receiving, by a remote computing system, agricultural information including prescribed operational settings for farming machines, crop variety information, and environmental information that includes weather condition factors and soil condition factors (para. 0036-0037, 0045-0046, 0050); recording (see discussion of PROCESS 72 in FIG. 2), by the remote computing system, the agricultural information as first relational database elements in a relational database (para. 0038); determining, by the remote computing system, situational operational settings (e.g., an optimized software algorithm or “optimal-fit software solution”) based on the recorded agricultural information (para. 0039-0040, 0044-0047); recording, by the remote computing system, the situational operational settings as second relational database elements in the relational database (para. 0048: “the server end 12 may avail the network-connected combine harvester 14 …”, by inherency, the determined “optimal-fit software solution” must be recorded at the server end 12 so that it can be transmitted to the relevant work machine); selecting, by the remote computing system, at least one setting of the recorded situational operational settings according to a database query based on or included in a request sent from a computer of a farming machine (para. 0037, 0044-0047); and sending, by the remote computing system, the at least one setting to the farming machine according to the request (para. 0048; see also discussion for claims 1 and 19 above). Contact Information 6. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT XIUQIN SUN whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-2280 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT 9:30am-6:00pm . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, FILLIN "SPE Name?" \* MERGEFORMAT Shelby A. Turner can be reached on FILLIN "SPE Phone?" \* MERGEFORMAT (571) 272-6334 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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