CTFR 17/553,747 CTFR 88453 DETAILED ACTION 12-151 AIA 26-51 12-51 Status of Claims 07-03-aia AIA 15-10-aia The present application, filed on or after 3/16/2013, is being examined under the first inventor to file provisions of the AIA. This action is in reply to the Remarks and Amendments filed 03/20/2026. Claims 1, 4, 8, 11, 15, 19 have been amended. 12-151-10 AIA 12-51-10 Claim s 7, 14 have been canceled. Claims 1-6, 8-13, 15-20 have been examined and are pending. (AIA) Examiner Note 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. 07-20-02-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 at the time any inventions covered therein were effectively filed absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned at the time a later invention was effectively filed 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. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claims 15-20 are rejected under 35 U.S.C. 112(b) or (for pre-AIA) 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor, a joint inventor, or (for pre-AIA) the applicant regards as the invention. Independent claim 15 recites , “…performing by the trained LSTM-based autoenoder neural network…” However, this limitation lacks an antecedent basis. The term “the” is a definite article. As such, this term must refer to a definite previous recitation of the noun which it modifies. However, no previous recitation of “trained LSTM-based autoencoder neural network” has been provided. No previous recitation of training such neural network has been referenced. Although claim 15 has been amended to recite in part the following: “…generating a long short-term memory (LSTM)-based autoencoder neural network…” it does not appear that generation of an LSTM-based autoencoder neural network is intended to convey the same idea as “training” such neural network. Indeed, the basic structure of a model is vastly different than training such a model which is vastly different than having a trained model which connotes the idea that all of the generic model parameters are specified based on whatever method of training was used to train the generic model. For each of these reasons, the claim is held to be indefinite. For the purpose of compact prosecution, Examiner interprets claim 15 commensurate with claims 1 and 8 which do not recite a step of “generating” but instead recite a step of “training” a long short-term memory (LSTM)-based autoencoder neural network. Dependent claims 16-20 inherit the deficiencies of their parent claim and are also rejected under 35 U.S.C. 112(b) or (for pre-AIA) 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor, a joint inventor, or (for pre-AIA) the applicant regards as the invention. Claim Rejections - 35 USC § 112 07-30-01 AIA The following is a quotation of 35 U.S.C. 112(a): (a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), first paragraph: The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same and shall set forth the best mode contemplated by the inventor of carrying out his invention. 07-31-01 Claims 1-6, 8-13, 15-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA), first paragraph, as failing to comply with the written description requirement. The claims contain subject matter which is not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor(s), at the time the application was filed, had possession of the claimed invention. Independent claims 1, 8 recite limitations directed towards the following: [claims 1] “A computer-implemented method for generating news headlines, the computer-implemented method comprising: …training a long short-term memory (LSTM)-based autoencoder neural network on the retrieved climate data for the specified geographic region of interest, wherein the training of the LSTM-based autoencoder neural network comprises minimizing a cross-entropy loss function and a task-specific regressor loss function; performing, by the trained LSTM-based autoencoder neural network, an impact analysis on a supply chain for the first industry of interest in the specified geographic region of interest based on the retrieved climate data, the supply chain dependencies, and the retrieved carbon emissions data, wherein the performing of the impact analysis comprises identifying news facts associated with the supply chain;…” [claims 8] “A computer program product comprising: one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions causing a processor to execute operations comprising: …training a long short-term memory (LSTM)-based autoencoder neural network on the retrieved climate data for the specified geographic region of interest, wherein the training of the LSTM-based autoencoder neural network comprises minimizing a cross-entropy loss function and a task-specific regressor loss function; performing, by the trained LSTM-based autoencoder neural network, an impact analysis on a supply chain for the first industry of interest in the specified geographic region of interest based on the retrieved climate data, the supply chain dependencies, and the retrieved carbon emissions data, wherein the performing of the impact analysis comprises identifying news facts associated with the supply chain;…” These limitations recite functions, e.g. “training” an LSTM-based autoencoder neural network on climate data and then “performing” an an impact analysis, by the trained LSTM-based autoencoder neural network, on a supply chain for an industry in a specified geographic region. However, the recited LSTM-based autoencoder neural network has not been claimed to be trained to perform an impact analysis for an industry nor is it clear what LSTM structure is necessary to achieve the functionality ascribed to the “trained LSTM-based autoencoder neural network”. The Specification, does not provide sufficient written description support for the recited functions to achieve the recited desired results; i.e. the original disclosure (original claims, drawings, and specification) does not show that the Applicant possessed a computer program product (as claimed) comprising program instructions capable of training some generic LSTM-based autoencoder neural network on simply retrieved climate data such that it is capable, once trained, to perform an impact analysis on a supply chain for an industry as now claimed. Similarly, claim 15 recites the following: [claims 15] “A news generation computer server, comprising:… a computer program product comprising program instructions collectively stored on the one or more computer readable storage media, the program instructions causing the processor to execute operations comprising: …generating a long short-term memory (LSTM)-based autoencoder neural network using the specified geographic region of interest, the first industry, the retrieved climate data, the carbon emissions data, and the supply chain dependencies,… performing, by the trained LSTM-based autoencoder neural network, an impact analysis on a supply chain for the first industry in the specified geographic region of interest based on the retrieved climate data, the supply chain dependencies, and the retrieved carbon emissions data, wherein the performing of the impact analysis comprises identifying news facts associated with the supply chain;” The Specification, does not provide sufficient written description support for the recited functions to achieve the recited desired results; i.e. the original disclosure (original claims, drawings, and specification) does not show that the Applicant possessed program instructions capable of causing the processor to execute operations comprising: … generating a long short-term memory (LSTM)-based autoencoder neural network using the specified geographic region of interest, the first industry, the retrieved climate data, the carbon emissions data, and the supply chain dependencies,…”. Furthermore, the original disclosure does not provide sufficient written description support showing evidence the applicant was in possession of instruction, such that when executed, is capable of training some generic LSTM-based autoencoder neural network such that once trained, it may perform an impact analysis on a supply chain for an industry as now claimed. Although LSTM-based autoencoder neural networks are known at the time of filing, the applicant is claiming possession of a very specific model capable of providing the recited results but the applicant has not shown he is in possession of such a specific model with the recited capability. Instead, the claims and original disclosure present a wish for a specific LSTM-based autoencoder neural network capable of the functionality ascribed to applicant’s now claimed LSTM-based autoencoder neural network; applicant has a desired capability but does not describe in sufficient detail any particular algorithm necessary to achieve the claimed functionality as presently recited in the claims.. The Specification is devoid of any description of a specific algorithm of any kind for either “training” or “generating” the specific LSTM-based autoencoder neural network capable of the functionality recited in the claims. This deficiency further indicates that the Specification describes only "a mere wish or plan for" generating a machine learning model and only "a mere wish or plan for" performing the analysis as recited. See Eli Lilly, 119 F .3d at 1566 (citation omitted). As such, the broadly recited limitation "merely recite[s] a description of the problem to be solved," and leaves to future inventors to "complete an unfinished invention." See Ariad, 598 F.3d at 1353. In this case, without the Specification describing any particular algorithm to achieve the claimed functions, one of ordinary skill in the art would not have reasonably concluded that the inventors invented the claimed invention or that they possessed the claimed subject matter at the time of filing of the application. See Vasudevan, 782 F.3d at 683; see also Regents , 119 F.3d at 1566; § 112 Guidance at 61. To satisfy the written description requirement of 35 U.S.C. § 112, first paragraph, the Specification must reasonably convey to an artisan of ordinary skill that Applicant had possession of the claimed invention at the time the application was filed. Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 682 (Fed. Cir. 2015) (citing Ariad Pharm., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1351 (Fed. Cir. 2010) (en banc)). Functional claim language that merely describes an intended result and fails to support the scope of the claimed invention is insufficient to show possession, even when the claim recitations are found word-for-word in the Specification. Vasudevan, 782 F.3d at 682 ("[t]he written description requirement is not met if the specification merely describes a 'desired result"') (citing Ariad , 598 F.3d at 1349); Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 968 (Fed. Cir. 2002) ("[t]he appearance of mere indistinct words in a specification or a claim, even an original claim, does not necessarily satisfy" the written description requirement). The Specification must explain, for example, how Applicant intended to achieve the claimed function to satisfy the written description requirement. Vasudevan, 782 F.3d at 683. While "[t]here is no rigid requirement that the disclosure contain 'either examples or an actual reduction to practice,"' due to the written description requirement, the Specification must set forth "an adequate description that 'in a definite way identifies the claimed invention' in sufficient detail such that a person of ordinary skill would understand that the inventor had made the invention at the time of filing." Allergan, Inc. v. Sandoz Inc., 796 F.3d 1293, 1308 (Fed. Cir. 2015) (citing Ariad, 598 F.3d at 1352); see also Examining Computer-Implemented Functional Claim Limitations for Compliance with 35 US.C. 112, 84 F.R. 57, 61- 62 (January 7, 2019) ("112 Guidance"). Here, the Specification does not sufficiently support the recited functions and or their use to achieve the recited result in the aforementioned limitations; the Specification at [0034] merely asserts an "engine 110" [i.e. undisclosed software executed by a process] can perform this function, as follows: "...The auto-generating news headline engine 110 builds various AI/ML prediction models, explainable models, and uncertainty-aware models for a given task for a sub-portion of the supply chain. The auto-generating news headline engine 110 identifies all possible dependencies such as climate predictions, carbon emission metrics, prediction models, etc. for each of the identified stages in the supply chain of user interest." A similar finding is made regarding the disclosure found in applicant’s Specification at [0038]; therein, applicant merely generally and very briefly mentions his generic deep learning model may be “long short-term memory” architecture as per Figs. 7A and 7B. However, these Figures are the most generic and do not support a specific LSTM-based autoencoder neural network capable of performing the specific functionality now recited in the claims. The Specification simply does not reasonably convey to an artisan of ordinary skill that Applicant had possession of the invention, as now claimed, at the time the application was filed. Under these circumstances, the Examiner has determined the Claims and Specification merely state a wish for a desired method and system; i.e. the recited limitations in question are wishes for the functionality being claimed but Applicant does not demonstrate that he has possession any actual method, algorithm, or technique for actually accomplishing the functionality now claimed. The Applicant merely provides a general description of the problem to be solved and an outline of a research plan that invites others to explore the contours of the broadly claimed subject matter. Thus, the Examiner rejects claims 1, 8, and 15 under 35 U.S.C. § 112(a) for a lack of written description support. Dependent claims 1-6, 9-13, and 16-20 inherit the deficiencies of their parent claim and are also rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. 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 1-6, 8-13, 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (i.e. a judicial exception) without significantly more. Per step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed towards a process, machine, or manufacture. Per step 2A Prong One , the claims recite specific limitations which fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG, as follows: Per Independent claims 1, 8, 15: “[claim 15] generating a long short-term memory (LSTM)-based autoencoder neural network using the specified geographic region of interest, the industry, the retrieved climate data, the carbon emissions data, and the supply chain dependencies…; [claim 1, 8, 15] performing, by the trained LSTM-based autoencoder neural network, an impact analysis on a supply chain for the first industry in the specified geographic region of interest based on the retrieved climate data, the determined supply chain dependencies, and the carbon emissions data; predicting, by the machine learning model, a supply chain performance for the first industry based on the impact analysis; automatically generating a news headline describing the predicted supply chain performance executing a troubleshooting operation for the supply chain for the first industry based on the displaying of the combined automatically generated news headline, wherein the troubleshooting operation includes automatically modifying one or more operations of the supply chain As noted supra , these limitations fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG. Specifically, these limitations fall within the group Certain Methods Of Organizing Human Activity (e.g. fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). That is, these steps, as drafted, are a business decisions to generate, via an undisclosed method, a machine learning model which is then supposed to be used to perform complicated supply chain analysis and perform predictions regarding impact to supply chain and performance prediction of a supply chain for a particular industry of interest, thus falling into Certain Methods of Organizing Human Activity. At this exceptionally high level of generality, there is no technical problem being solved and no technical solution to solve a technical problem (i.e. the desire to create and then use a concept such a machine learning models to achieve business analysis is not a technical solution to a technical problem at this high level of generality). Furthermore, the mere nominal recitation of a generic computer components to implement the abstract idea does not take the claim limitation out of the enumerated grouping. Thus, the claims recite an abstract idea. Per step 2A Prong 2 , the Examiner finds that the judicial exception is not integrated into a practical application. Although there are additional elements, other than those noted supra , recited in the claims, none of these additional element(s) or a combination of elements as recited in the claims apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception. As drafted, the claims as a whole merely describe how to generally “apply” the aforementioned concepts and link them to a field of use (i.e. in this case supply chain analysis through use of applying the concept of LSTM modeling) or serve as insignificant extra-solution activity. The claimed computer components are recited at a high level of generality and are merely invoked as tools to implement the idea but are not technical in nature. Simply implementing the abstract idea on or with generic computer components is not a practical application of the abstract idea. These additional limitations are all elements and features not specifically pointed out as encompassing the abstract idea. However, these elements and features not encompassing the abstract idea do not present a technical solution to a technical problem; i.e. Applicant’s invention is not a technique nor technical solution for “retrieving” or “receiving” input data such as climate data and carbon emissions data nor a method of storing information in such a database nor displaying data nor has applicant invented any particular LSTM-based model. The additional elements do not recite a specific manner of performing any of the steps core to the already identified abstract idea. Instead, these features merely serve to generally “apply” the aforementioned concepts within a computing environment, or link them to a field of use (business data analysis of supply chains) or are insignificant extra-solution activity (e.g. data-gathering, such as receiving and retrieving data, generic combining of gathered news headline information, and data-transfer such as generic display of news information to a generic device via a generic electronic user interface – none of which applicant has invented from a hardware or protocol standpoint) to the already identified abstract idea and do not integrate the abstract idea into a practical application thereof. Per Step 2B , the Examiner does not find that the claims provide an inventive concept, i.e. , the claims do not recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception recited in the claim. As discussed with respect to Step 2A Prong Two, the additional elements in the independent claims were considered as merely serving to generally “apply” the aforementioned concepts via generically described computer components (e.g. by one or more processors, etc…) or “link” them to a field of use (i.e. supply chain analysis), or as insignificant extra-solution activity (e.g. data gathering, transfer, and/or display). For the same reason these elements are not sufficient to provide an inventive concept; i.e. the same analysis applies here in 2B. Mere instructions to apply an exception using a generic computer component and conventional data gathering cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. So, upon revaluating here in step 2B, these elements are determined to amount to no more than mere instructions to apply the exception using generic computer components and/or gather and transmit data which is well-understood, routine, conventional activity in the field; i.e. note the Symantec, TLI, and OIP Techs Court decisions cited in MPEP 2106.05(d)(ll) indicate that mere receipt or transmission of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, alone and in combination, these elements do not integrate the abstract idea into a practical application, as found supra , nor provide an inventive concept, and thus the claims are not patent eligible. As for the dependent claims, the dependent claims do recite a combination of additional elements. However, these claims as a whole, considered either independently or in combination with the parent claims, do not integrate the identified abstract idea into a practical application thereof nor do they provide an inventive concept. For example, dependent claims 2, 9, 16 recite the following: “ further comprising broadcasting the combined automatically generated news headline through an online network..” However, transfer of data, i.e. broadcasting, is not applicant’s invention. Applicant has not invented any particular technique of broadcasting. Instead, at this level of generality, this step is insignificant extra-solution activity and not significantly more than the already recited abstract idea. Therefore, the Examiner does not find that these additional claim limitations integrate the abstract idea into a practical application nor provide an inventive concept. Instead, these limitations, as a whole and in combination with the already recited claim elements of the parent claims, are not significantly more than the already identified abstract idea. A similar finding is found for the remaining dependent claims. For these reasons, the claims are not found to include additional elements that are sufficient to amount to significantly more than the judicial exception and are therefore patent ineligible. Please see the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019 (found at http://www.uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials). Claim Rejections - 35 USC § 103 (AIA) 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. 07-23-aia AIA The factual inquiries set forth in Graham v. John Deere Co. , 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or non-obviousness. Claims 1-20 are rejected under 35 U.S.C. 103 as obvious over Taylor et al. (U.S. 2022/0124963 A1; hereinafter, "Taylor”), in view of Lomada et al. (U.S. 2020/0107072 A1; hereinafter, "Lomada”), further in view of Kwok et al. (U.S. 2022/0215332 A1; hereinafter, "Kwok”) and Alfonseca et al. (US 20150006512 A1; hereinafter, “Alfonseca”). Claims 1, 8, 15: (Currently amended) Pertaining to claims 1, 8, 15 exemplified in the limitations of method claim 1, Taylor as shown teaches the following: A computer-implemented method for generating news headlines, the computer-implemented method comprising: receiving, from a user, user input parameters, wherein the user input parameters include a specified geographic region of interest (Taylor, see at least [0054]-[0086], teaching: “Data Inputs” include “Boundary/Geolocation” [specified geographic region of interest]) and a first industry of interest (Taylor, see citations noted supra , including at least [0054]-[0086], teaching inputs include: “sources” and “…inputs including localized inherent soil and climate data such as soil sample baselines 105, and farmer-produced data 107…”; Furthermore, Per [0156]: “user device 710” which provides the input data “…may be located at a food-producing farm [a type of industry], a livestock farm [another type of industry], etc…” and per [0047]: “…These capabilities will enable us to track correlations between farming methods [industry types], inherent soil characteristics, and soil health, and thus form the basis for soil health improvement…”; Therefore, the difference between the teachings of Taylor and the limitation in question is only that Taylor, although he explicitly teaches he receives data input parameters including “Boundary/Geolocation” [specified geographic region of interest] and he receives “farmer-produced data 107” from “a food-producing farm [a type of industry], a livestock farm [another type of industry], etc…”, and he teaches his objective is to “enable us to track correlations between farming methods [industry types], inherent soil characteristics, and soil health, and thus form the basis for soil health improvement…”; he may not explicitly teach receiving data input of a type of “an industry of interest”. However, because Taylor is aware of the type of industry from which he is receiving data, e.g. his “food-producing farm” [a type of industry] and “livestock farm” [another type of industry], and these are both characteristics of “farmer-produced data”, and his objective is to correlate such data, coming from different types of farms using different “farming methods”, Taylor therefore is found to supply motivation to distinguish between types of farms [industries of interest] and their methods at least within the greater category of “farming” and therefore this type of industry [industry of interest] would be an obvious input to also receive by which to enable Taylor’s knowledge of which type of farm he is receiving his farmer-produced data 107 – i.e. whether the data 107 is from “food-producing farm” [a type of industry] or a “livestock farm” [another type of industry] to enable the correlation which he teaches is an aspect of his objective. Therefore, the Examiner finds that it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have also received as “Inputs” Taylor’s farming method or type of farm from which he receives his data, e.g. a “food-producing farm” [a type of industry] or “livestock farm” [another type of industry] as either a piece of “farmer-produced data 107” or as an additional input as a means by which to distinguish the industry to which other data from such farms is to be related because per MPEP 2143(I) (B) Simple substitution of one known element for another to obtain predictable results is obvious and/or because per MPEP 2143(I) (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference teachings to arrive at the claimed invention is obvious. The motivation may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. Id. at 1366, 80 USPQ2d at 1649.) ; retrieving climate data for the specified geographic region of interest (Taylor, see at least [0054]-[0086], e.g. “Inputs” include “Climate”, e.g. at his Geolocation” [specified geographic region of interest]; it would be obvious that Taylor’s climate data is for Taylor’s geolocation of interest and not some random location or region. Therefore, whether explicitly stated, it nonetheless would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have provided the input “climate” for the specific geolocation [specified geographic region of interest] because per MPEP 2143(I) (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference teachings to arrive at the claimed invention is obvious. The motivation may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. Id. at 1366, 80 USPQ2d at 1649.) ; retrieving carbon emissions data for the specified geographic region of interest (Taylor, see citations noted supra , and at least [0044] again in view [0054]-[0086], e.g. “Inputs” also include “Flux tower emissions” [carbon emissions for specified geographic region of interest]; where again per [0044]: “…According to certain embodiments, sensors on farm equipment can upload farmer [i.e. farming is the industry] supplied data 107 as part of flux tower data 106…”) ; determining supply chain dependencies for the first industry of interest in the specified geographic region of interest (Taylor, see at least Fig. 6 and [0149], e.g.: “…initial suppliers 601 [supply chain dependencies], tier 3 suppliers 602, tier 2 suppliers 603, and tier 1 suppliers 604. Similarly, various downstream supply chain entities [more supply chain dependencies] may also access the distributed data model 620, such as tier 1 customers 605, tier 2 customers 606, and ultimately, end users 607…”; i.e. Taylor is aware of supply chain dependencies for each farm and therefore must have determined the supply chain dependencies for at least a farm, which is known to Taylor to be within a particular industry, e.g. “food-producing farm” [a type of industry] or “livestock farm” [another type of industry], and therefore any supply chain dependency for a farm within an industry is also a supply chain dependency for the industry although there may be other dependencies for the industry.) ; Although Taylor teaches the above limitations, and Taylor, teaches training his AI, e.g. deep neural net, on retrieved climate data for the specified geographic region of interest, e.g. per citations noted supra in view of at least [0037], e.g. teaching: “…Soil Health Data Fabric 102 is implemented via a full-stack AI marketplace solution powered by a decentralized protocol via decentralized platform allowing AIs to cooperate and coordinate at scale. For example, AI solutions for implementation of the Soil Health Data Fabric 102 may be created using an advanced palette of technologies including deep neural net architectures,… Outcomes 114 may include assessments of healthy environment 113, soil and plant health 112, GHG emissions 111…” and per [0196]: “…training an AI algorithm using the integrated data measurements to determine most accurate local soil quality estimates; executing the AI algorithm, via a machine learning toolbox...”; which in view of Taylor’s disclosure as a whole appear to render obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention that Taylor trains his AI and deep neural net on the inputs he has already taught he receives which includes climate data for geographic locations of interest as well as related emissions, i.e. to enable his disclosure that his “Outcomes may include assessments of healthy environment 113, soil and plant health 112, GHG emissions 111, etc…”, he may not explicitly teach that his AI solution which may include a deep neural net is an LSTM-based autoencoder neural net. However, regarding this feature, Taylor in view of Lomada teaches the following: training long short-term memory (LSTM)-based autoencoder neural network 1 on the retrieved climate data for the specified geographic region of interest, wherein the training of the LSTM-based autoencoder neural network comprises minimizing a cross-entropy loss function and a task-specific regressor loss function (Lomada, see at least [0043], teaching, e.g.: “…As mentioned above, in some embodiments, the LSTM autoencoder model includes a loss layer that includes has a loss function or loss model to train the LSTM autoencoder model. As used herein, the term “loss function” or “loss model” refers to a function or set of algorithms that determine training error loss. In some embodiments, a machine-learning algorithm can repetitively train to minimize total overall loss. For example, the loss function determines an amount of error loss with respect to training data by analyzing the output of the LSTM autoencoder model (e.g., the decoder) with the ground truth provided by the training data (e.g., a corresponding user trait sequence provided as input to the encoder). The loss function then provides feedback, via back propagation, to one or more networks, layers, units, cells, matrices, biases, and/or parameters of the LSTM autoencoder model for tuning/fine-tuning (e.g., depending on the learning rate). Examples of loss functions include a softmax classifier function (with or without cross-entropy loss [cross-entropy loss function]), a hinge loss function, and a least squares loss function [a task-specific regressor loss function]…”) Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Lomada which is applicable to a known base device/method of Taylor to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the technique of Lomada to the device/method of Taylor in order to implement Taylor’s AI solution which may include a deep neural net as an LSTM-based autoencoder neural net such that Taylor also trains such a long short-term memory (LSTM)-based autoencoder neural network on his retrieved climate data for the specified geographic region of interest, wherein the training of the LSTM-based autoencoder neural network also comprises minimizing a cross-entropy loss function and a task-specific regressor loss function because Lomada is pertinent to Taylor’s AI solution and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious. Although Taylor/Lomada teaches the above limitations including an LSTM-based autoencoder neural network, and as shown above Taylor teaches he retrieves climate data, the supply chain dependencies, and the carbon emissions data, and he teaches, e.g. per [0149], various supply chain entities may access the distributed data model 620, such as “the initial suppliers 601, tier 3 suppliers 602, tier 2 suppliers 603, and tier 1 suppliers 604” as well as “tier 1 customers 605, tier 2 customers 606, and ultimately, end users 607”, he may not explicitly teach all the nuances as recited below regarding supply chain impact analysis although his methods/systems are found to gather all data necessary to enable such analysis. Nonetheless, the combination Taylor/Lomada in view of Kwok (directed towards predicting impacts to a supply chain by analyzing current events) teaches the following: performing, by the trained LSTM-based autoencoder neural network , an impact analysis on a supply chain for the first industry of interest in the specified geographic region of interest based on the retrieved […data] wherein the performing of the impact analysis comprises identifying news facts associated with the supply chain (Kwok, see at least Abstract, [0019], [0028], and [0037] regarding: “Predicting Impacts to a Supply Chain by Analyzing Current Events [news facts]” and also at least [0047]-[0052], teaching: a first machine learning model may take as input a geographic location as well information regarding weather, such as a flood, and determine for a merchant category code [an industry], an impact on supply chain related to merchants associated with this merchant category code; e.g.: per [0047]-[0052]: “…At step 318, a first machine learning model may be used to determine a first small business merchant that will be impacted by the event… The determination may be based on a first industry of the one or more industries matching a first merchant category… The first machine learning model may use additional inputs, such as the geographic location… The first machine learning model may also use inputs, such as a time period while the flood may impact local lumber supply in the next six months, a regulation change may impact the supply of goods starting in the second quarter of 2022…”) ; predicting, by the trained LSTM-based autoencoder neural network, a supply chain performance for the industry based on the impact analysis (Kwok, see citations noted supra , again at least per [0047]-[0052], e.g.: “…the first machine learning model may determine the relevant small business merchants that may be impacted by the events/news. For example, among the 30 MCCs impacted by the flood in New Jersey, the first machine learning model may determine a first small business builder located within 20 miles of the flooding area and the builder may have recurrent purchases of lumber that may be impacted by the flood. The first machine learning model may determine a second small business customer, a local school, who has recently been granted a construction loan for a school expansion project that may be impacted by the lumber shortage…”) ; automatically generating a news headline describing the predicted supply chain performance, wherein the automatically generated news headline includes an underlying basis for the predicted supply chain performance (Kwok, see citations noted supra , further in view of at least [0052]: “…At step 322, an alert [news headline] may be sent to the first small business merchant on a second device indicating a possible disruption [predicted supply chain performance] in one or more supply chains, including one or more shortages of one or more products [underlying basis]…”) ; combining the automatically generated news headline of the first industry of interest with a news headline of a second industry to generate a combined automatically generated news headline, wherein the combined automatically generated news headline is generated based on overlapping supply chain points between the first industry and the second industry, and the second industry is a neighboring entity to the first industry in the supply chain; (Kwok, see citations noted supra , again per at least [0052] in view of at least [0057]; e.g. per [0052]: “an alert [news headline] may be sent to the first small business merchant on a second device indicating a possible disruption [predicted supply chain performance] in one or more [e.g. in first and second] supply chains, including one or more shortages of one or more products [underlying basis]…”; therefore, the alert [automatically generated news headline] may be a combined alert regarding multiple, i.e. one or more, supply chains. Furthermore, per at least [0057]: “…the third machine learning model may determine that shortages of lumber may lead to shortages of other goods, such as hardwood flooring… The merchant prediction servers may send alerts [combined news headline] to the small business builder for the predicted shortages of the lumber [lumber industry] and hardwood flooring [flooring industry]…”; Examiner notes that lumber and flooring overlap as flooring industry receives wood from the lumber industry and therefore they have overlapping supply chain points and as a person of ordinary skill in the art readily recognizes the flooring industry may directly receive the wood from the lumber industry and be a neighbor either in geolocation or in supply chain contact, e.g. a direct supplier. Furthermore, whether explicitly stated by Kwok, Examiner finds that Kwok’s teachings provide motivation to combine Kwok’s news alerts as opposed to sending two separate news alerts and therefore it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have combined Kwok’s automatically generated news alert regarding a first industry, e.g. lumber, with his news alert regarding a second industry, e.g. flooring, to minimize message traffic being sent to merchant’s interested in receiving such combined news alerts and because per MPEP 2143(I) (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference teachings to arrive at the claimed invention is obvious. The motivation may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. Id. at 1366, 80 USPQ2d at 1649.) ; Therefore, the Examiner understands that the limitations in question are merely applying known techniques of Kwok (directed towards particular techniques of performing an impact analysis on a supply chains including sending merchants combined news alerts regarding impacted industries as well as recommended actions and also automatically executing operations which troubleshoot the identified impacts such as modifying the credit limit of merchants affected by an impact to particular industries) which are applicable to a known base device/method of Taylor/Lomada (already directed towards to systems and methods for implementing data modeling for supply chain entities such as retrieving input data including climate data, supply chain dependencies, and carbon emissions data, for the purpose of modeling impact, e.g. via a trained LSTM-based autoencoder neural net, on various farming industries due to changes in the aforementioned variables such as climate and emissions which may impact these industries) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the techniques of Kwok to the device/method of Taylor/Lomad in order to implement the limitations in question including performing the analysis techniques of Kwok using the type of data gathered by Taylor and Lomada and because Kwok is pertinent to the problems addressed by Taylor and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious. Although Taylor/Lomada/Kwok teaches the aforementioned features regarding combined news facts, and teaches determining an impact to a supply chain and sending merchants an alert regarding impact to multiple different industries, etc… Kwok may not explicitly teach generation of a knowledge graph representing these news facts nor that her alert is based on such graph even though she does send alerts based on supply chain impact/disruption and recommendations, e.g. to stockpile goods, etc…. However, regarding such features, the combination of Taylor/Lomada/Kwok in view of Alfeaches the following: generating a knowledge graph comprising a hierarchical representation of the news facts of the combined automatically generated news headline (Alfonseca, see at least Fig. 1 and [0038], e.g.: “…The news system 116 is a computing system capable of aggregating news and processing news collections, automatically learning equivalent syntactic patterns, and automatically generating headlines and updating a knowledge graph using the syntactic patterns. Further, it should be understood that the headlines generated, training performed, and the knowledge graph management performed by the news system 116 may be done in real-time (e.g., upon user request), may be processed for news collections as they are aggregated by the search engine 118, may be processed at regular time intervals (e.g., minute(s), hour(s), days(s), end of the day, etc.), in other applicable fashions. In some instances, the news system 116 may provide users with the ability to search for relevant news documents and receive news summaries containing the relevant headlines and news collections about the news objects the users are interested in. In the depicted implementation, the news system 116 includes a search engine 118, a headline generation engine 120, a knowledge graph management engine 122, a knowledge graph 124a, and a news portal 125); displaying , based on the generating of the knowledge graph, the combined automatically generated news headline to the user through an electronic user interface (Alfonseca, see citations noted supra, e.g. [0038]: “…the news system 116 may provide users with the ability to search for relevant news documents and receive news summaries containing the relevant headlines and news collections about the news objects the users are interested in. In the depicted implementation, the news system 116 includes a search engine 118, a headline generation engine 120, a knowledge graph management engine 122, a knowledge graph 124a, and a news portal 125…”) executing a troubleshooting operation for the supply chain for the first industry of interest based on the displaying of the combined automatically generated news headline, wherein the troubleshooting operation includes sourcing supply chain goods from alternate suppliers that are not affected by the underlying basis (Kwok, see citations noted supra , including at least [0050]-[0052], e.g.: “…For example, among the 30 MCCs impacted by the flood in New Jersey, the first machine learning model may determine a first small business builder located within 20 miles of the flooding area and the builder may have recurrent purchases of lumber that may be …an alert may be sent to the first small business merchant on a second device indicating a possible disruption in one or more supply chains, including one or more shortages of one or more products. Merchant prediction servers may send an alert to a small business merchant device to recommend stockpiling the one or more products, or to stockpile a larger quantity of goods than in its regular purchases. Merchant prediction servers may also automatically increase the credit limits [a type of troubleshooting operation] to facilitate the recommended purchases… increase the current credit limit based on a prediction that the small business builder may be stockpiling the one or more products based on the alert…”; in view of these teachings, the Examiner finds there is motivation to also make a purchase [i.e. source] supply chain goods, such as from an alternate supplier not affected by flooding, rather than simply provide increased credit earmarked to make such purchase, e.g. as a mechanism to ensure funds are being spent on necessary supplies/goods so the bank may hedge or control their exposure to various risks. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have also executed a troubleshooting operation for the supply chain for the first industry of interest based on the displaying of the combined automatically generated news headline, wherein the troubleshooting operation includes sourcing supply chain goods from alternate suppliers that are not affected by the underlying basis , e.g. a flood, because per MPEP 2143(I) (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference teachings to arrive at the claimed invention is obvious. The motivation may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. Id. at 1366, 80 USPQ2d at 1649.) Therefore, the Examiner understands that the limitations in question are merely applying known techniques of Alfonseca and Kwok (directed towards techniques regarding aggregating news and processing news collections, automatically learning equivalent syntactic patterns, and automatically generating headlines and updating a knowledge graph using the syntactic patterns as well as displaying aggregated headlines based on such knowledge graph, e.g. related news, and then based on such news predicting impacts on supply chain and recommending troubleshooting such as sourcing goods from alternate suppliers not affected by an impact to a supply chain) which are applicable to a known base device/method of Taylor/Lomada/Kwok (directed towards system/method by which to predict supply chain impact based on climate, e.g. weather, and other current events and generate news headlines regarding such impact) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the techniques of Alfonseca and Kwok to the device/method of Taylor/Lomada in order to perform the limitations in question because Alfonseca is pertinent to the news generation and aggregation already being performed by Taylor/Lomada/Kwok and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious. Claims 2, 9, 16: (previously presented) Taylor/Lomada/Kwok/Alfonseca teaches the limitations upon which these claims depend. Furthermore, as shown, Taylor in view of Kwok teaches the following: …further comprising broadcasting the combined automatically generated news headline through an online network (Kwok, see at least citations noted supra , including Fig. 1, the news alerts are sent over the network to merchant devices.) . Claim 17: (previously presented) Taylor/Lomada/Kwok/Alfonseca teaches the limitations upon which these claims depend including sending an automatically generated news alert to merchants and the alert may include indications of supply chain issues and recommended courses of action such as purchasing more products, etc…. Furthermore, as shown, Taylor in view of Kwok teaches the following: …wherein the operations further comprise measuring a public reaction to the broadcasted combined automatically generated news headline through the online network (Kwok, see citations noted supra , including at least [0029] noting: “…small business merchants may change the spending behavior [a measured public reaction] in response to the alert [automatically generated news headline]…”) . Claims 3, 10, 18: (previously presented) Taylor/Lomada/Kwok/Alfonseca teaches the limitations upon which these claims depend. Furthermore, as shown, Taylor/Kwok teaches the following: …further comprising automatically initiating the troubleshooting operation for the supply chain for the first industry in a case where the measurement of the public reaction is negative for the combined automatically generated news headline (Kwok, see citations noted supra , including at least [0029] noting: “…small business merchants may change the spending behavior [a measured public reaction] in response to the alert [automatically generated news headline]…”; and see [0052]-[0057]: “…Merchant prediction servers may also automatically increase [automatically initiating a troubleshooting operation for the supply chain industry] the credit limits to facilitate the recommended purchases…”; i.e. Kwok’s teachings at least suggest that Kwok’s action of automatically initiating a credit limit increase may be in response to an action which he notes at least at [0029] – i.e. small business merchants may change the spending behavior [a measured public reaction] in response to the alert [automatically generated news headline]. Therefore, whether explicitly stated in a single embodiment, there is motivation for a person of ordinary skill in the art in view of the totality of Kwok’s teachings to try initiating Kwok’s credit limit increase [troubleshooting operation for the supply chain industry] in response to a measurement of small business merchants changing their spending behavior [a measured public reaction] in response to the alert [automatically generated news headline], e.g. if merchant’s don’t have enough credit to purchase needed goods for a short period during a time when short supply has driven prices higher, then it makes sense to increase a credit limit for the merchant. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have performed the limitation in question for the reasons noted because per MPEP 2143(I) (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference teachings to arrive at the claimed invention is obvious. The motivation may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. Id. at 1366, 80 USPQ2d at 1649.) Claims 4, 11: (currently amended) Taylor/Lomada/Kwok/Alfonseca teaches the limitations upon which these claims depend. Furthermore, as shown, Taylor teaches the following: …further comprising training the LSTM-based autoencoder neural network using the specified geographic region of interest, the first industry of interest, the retrieved climate data, the carbon emissions data, and the supply chain dependencies (Taylor, see at citations noted supra in view of at least [0196]: “…training an AI algorithm using the integrated data measurements [i.e. the aforementioned inputs] to determine most accurate local soil quality estimates; executing the AI algorithm, via a machine learning toolbox interfaced with an ecosystem soil dynamics engine, to output a plurality of soil quality estimate data models based on the data measurements, in which the plurality of local soil quality estimate data models are based on pre-determined soil quality indicators; training the local soil quality estimate data models by comparing the local soil quality estimate data models to the plurality of soil samples; refining the local soil quality estimate data models via feedback processing to generate a regional soil quality estimate data model, in which the feedback processing applies transfer learning and feedback using additional data and comparison to the plurality of soil samples ; scaling the local and regional soil quality estimate data models; and outputting soil quality estimates for a target region based on extended satellite imagery….”) . Claims 5, 12, 19: (currently amended) Taylor/Lomada/Kwok/Alfonseca teaches the limitations upon which these claims depend. Furthermore, as shown, Taylor in view of Kwok teaches the following: …further comprising attaching an indicator to the combined automatically generated news headline, the indicator provides a positive or negative indication of the combined automatically generated news headline (Kwok, see citations noted supra , e.g. again per at least [0052]-[0057], e.g.: “…Merchant prediction servers may send an alert to a small business merchant device to recommend stockpiling [a negative indication] the one or more products, or to stockpile a larger quantity of goods than in its regular purchases…”) Claims 6, 13, 20: (previously presented)) Taylor/Lomada/Kwok/Alfonseca teaches the limitations upon which these claims depend. Furthermore, as shown, Taylor teaches the following: …further comprising: receiving personalization parameters from the user wherein the personalization parameters include information that is used to generate news of potentially disruptive events; (Taylor, see at least [0046] teaching e.g.: “…The producer app is a lightweight interface that allows farmers, ranchers, landowners to contribute information 107 [ personalization parameters including information that is used to generate news of potentially disruptive events ] directly to the smart farm fabric.…”; see also [0071] and [0156] system may also receive user device location [another personalization parameter]; location is another piece of information that is used to generate news of a potentially disruptive event personalized to the location of the user, such as news of weather at such location; note Taylor also teaches at [0060] Satellite Data 104 (myriad of wavelengths/types, LIDAR)) and Although Taylor teaches the above features, he may not explicitly teach the below nuance. However, as shown Taylor in view of Kwok teaches the following: performing the impact analysis based on the news of the potentially disruptive events (Kwok, again per at least Title, abstract, and citations as already noted supra , e.g. Fig. and [0008] and [0019]-[0023], Kwok teaches using machine learning models to predict impact [perform impact analysis] based on analysis of current events which may be gleaned from news sources [news of potentially disruptive events], such as hurricanes at a location, etc…; note per [0019]-[0023]: “…News scraping system 120 may collect, parse, and/or store events, such as news, weather, and traffic related to a geographic location. The geographic location may be associated with a location of a small business merchants or a supplier that may cause disruption to a supply chain… Merchant prediction servers 130 may retrieve the news data from the corpus database… Merchant prediction servers 130 may determine one or more industries associated with the geographic area that will be impacted by the news, based on one or more keywords. For example, merchant prediction servers 130 may detect a news spike reporting on a wildfire in Colorado, and determine the industries that may be impacted by the news based on topic keywords, such as "fire," "forest," "lun1ber," and/or "construction.") Therefore, the Examiner understands that the limitations in question are merely applying known techniques of Kwok (directed towards particular techniques of performing, by machine learning models, an impact analysis on a supply chain) which are applicable to a known base device/method of Taylor (already directed towards to systems and methods for implementing data modeling for supply chain entities such as modeling impact on various farming industries of changes in various variables such as climate and emissions which impact these industries) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the techniques of Kwok to the device/method of Taylor in order to implement the limitations in question because Kwok is pertinent to the problems addressed by Taylor and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious. Response to Arguments Applicant canceled claims 7 and 14 and amended claims 1, 4, 8, 11, 15, 19 on 03/20/2026. Applicant's arguments (hereinafter “Remarks”) also filed 03/20/2026, have been fully considered but are moot in view of the new grounds of rejection necessitated by applicant’s amendments. Note the new 101, 112(b) and 112(a), and 103 rejections with Taylor in view of Lomada, Kwok, and Alfonseca. Conclusion Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J SITTNER whose telephone number is (571)270-3984. The examiner can normally be reached M-F; ~9:30-6:30. 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, Waseem Ashraf can be reached on (571) 270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Michael J Sittner/ Primary Examiner, Art Unit 3621 Application/Control Number: 17/553,747 Page 2 Art Unit: 3621 Application/Control Number: 17/553,747 Page 4 Art Unit: 3621 Application/Control Number: 17/553,747 Page 5 Art Unit: 3621 Application/Control Number: 17/553,747 Page 6 Art Unit: 3621 Application/Control Number: 17/553,747 Page 7 Art Unit: 3621 Application/Control Number: 17/553,747 Page 8 Art Unit: 3621 Application/Control Number: 17/553,747 Page 9 Art Unit: 3621 Application/Control Number: 17/553,747 Page 10 Art Unit: 3621 Application/Control Number: 17/553,747 Page 11 Art Unit: 3621 Application/Control Number: 17/553,747 Page 12 Art Unit: 3621 Application/Control Number: 17/553,747 Page 13 Art Unit: 3621 Application/Control Number: 17/553,747 Page 14 Art Unit: 3621 Application/Control Number: 17/553,747 Page 15 Art Unit: 3621 Application/Control Number: 17/553,747 Page 16 Art Unit: 3621 Application/Control Number: 17/553,747 Page 17 Art Unit: 3621 Application/Control Number: 17/553,747 Page 18 Art Unit: 3621 Application/Control Number: 17/553,747 Page 19 Art Unit: 3621 Application/Control Number: 17/553,747 Page 20 Art Unit: 3621 Application/Control Number: 17/553,747 Page 21 Art Unit: 3621 Application/Control Number: 17/553,747 Page 22 Art Unit: 3621 Application/Control Number: 17/553,747 Page 23 Art Unit: 3621 Application/Control Number: 17/553,747 Page 24 Art Unit: 3621 Application/Control Number: 17/553,747 Page 25 Art Unit: 3621 Application/Control Number: 17/553,747 Page 26 Art Unit: 3621 Application/Control Number: 17/553,747 Page 27 Art Unit: 3621 Application/Control Number: 17/553,747 Page 28 Art Unit: 3621 1 Specification [0038]: “…The input may be used by a machine learning/deep learning model to predict 425 supply chain conditions and impact… The deep learning model is trained 445 by minimizing the cross-entropy loss function and the task-specific regressor loss function (MSE). An illustrative embodiment of an autoencoder used to automatically generate a news headline from climate data for a region is shown in Figures 7 A and 7B. While long short-term memory architecture is shown for the architecture, it will be understood that other types of architecture may be used…” ; applicant’s original disclosure only mentions LSTM at this one point in the Specification and Figs. 7A and 7B are completely generic and devoid of any particular “model” algorithm and appears to use the terms autoencoder, LSTM, and loss function generically to encompass all possible known concepts for autoencoders using the idea of LSTM and the known cross-entropy loss function. As applicant has not invented any particular “LSTM” nor any particular autoencoder using LSTM, and the cross-entropy loss function is not applicant’s invention, these features are understood to be general references to the concepts themselves.
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