System and Method Providing Specific Insights into Data-Confidentiality-Preserving Applications in Case of Deviations

§101 §102 §103 §112

Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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. DETAILED ACTION The following NON-FINAL Office action is in response to application 19344758 filed 09/30/2025. Status of Claims Claims 1-14 are currently pending and have been rejected as follows. Priority Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file. IDS The information disclosure statement filed on 09/30/2025 complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 and is considered by the Examiner. Claim Interpretation with respect to 35 U.S.C. 112(f) / 112, sixth paragraph The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, 6th paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are in: Claims 1,13,14 as follows: - wherein the company data are secured by first IT security means and are associated with a process related to a first company; [bolded emphasis added] - wherein the calculation module is secured by second IT security means and is associated with a second company; [bolded emphasis added] Claim 6 as follows: - wherein at least part of the additional company data is not included in the company data and is secured by third IT security means, [bolded emphasis added] and - wherein making the additional company data accessible for the calculation module comprises transferring the at least part of the additional company data to the calculation module, and/or enabling the calculation module to access the at least part of the additional company data secured by the third IT security means. [bolded emphasis added]. Claim 9. The method according to claim 6, wherein at least two IT security means of the first IT security means, the second IT security means and the third IT security means are different from each other, or wherein the first IT security means, the second IT security means and the third IT security means are the same IT security means. [bolded emphasis added] Claim 10. The method according to claim 6, the second IT security means and the third IT security means comprises at least one of: … proving the state of the company data and/or the additional company data by means of zero-knowledge proofs; [bolded emphasis added] Accordingly, it appears the claim invokes 35 USC 112(f). Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Claim Rejections - 35 USC § 112 - based on 112 (f) interpretation - 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. Claims 1-14 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), ¶1, as failing to comply with the written description requirement. The claims contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 1,13,14 as follows: - wherein the company data are secured by first IT security means and are associated with a process related to a first company; [bolded emphasis added] - wherein the calculation module is secured by second IT security means and is associated with a second company; [bolded emphasis added] Claim 6 as follows: - wherein at least part of the additional company data is not included in the company data and is secured by third IT security means, [bolded emphasis added] and - wherein making the additional company data accessible for the calculation module comprises transferring the at least part of the additional company data to the calculation module, and/or enabling the calculation module to access the at least part of the additional company data secured by the third IT security means. [bolded emphasis added]. Claim 9. The method according to claim 6, wherein at least two IT security means of the first IT security means, the second IT security means and the third IT security means are different from each other, or wherein the first IT security means, the second IT security means and the third IT security means are the same IT security means. [bolded emphasis added] Claim 10. The method according to claim 6, the second IT security means and the third IT security means comprises at least one of: … proving the state of the company data and/or the additional company data by means of zero-knowledge proofs; [bolded emphasis added] * Original Specification ¶ [0062] merely exemplifies that “IT security means may comprise encryption, digital signatures, message authentication codes, anonymization and obfuscation”. * Original Specification doesn’t provide adequate respective structure for each of the three distinct three means to perform the respective “secured” function (Claims 1,6,9,13,14) * Original Specification also doesn’t provide adequate respective structure for each of the means of zero-knowledge proofs to perform the proving the state of the company data and/or the additional company data. (Claim 10) * Original Specification also does not clearly link each of the respective “secured” and “providing” functions to each of the respective structure for the claimed “means” such by an algorithm. * Original Specification thus does not appear to demonstrate that Applicant has made an invention that achieve the claimed “secured” and “providing” functions function by the respective structure because the invention is not described with sufficient detail such that one or ordinary skills in the art can reasonably conclude that inventor had possession of the claimed invention. Examiner recommends, as an example only that Applicant amend, said claim to no longer invoke 35 USC 112 (f), such as by replacing the expression “IT security means” and “means of zero-knowledge proofs” with expression “IT security and “ Claims 2-12 are dependent claims rejected based on rejected parent Claim 1. Clarification and/or correction is/are required. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Claim Rejections - 35 USC § 112 - based on 112 (f) interpretation – 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. Claims 1-14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), ¶2, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. Claims 1,13,14 as follows: - wherein the company data are secured by first IT security means and are associated with a process related to a first company; [bolded emphasis added] - wherein the calculation module is secured by second IT security means and is associated with a second company; [bolded emphasis added] Claim 6 as follows: - wherein at least part of the additional company data is not included in the company data and is secured by third IT security means, [bolded emphasis added] and - wherein making the additional company data accessible for the calculation module comprises transferring the at least part of the additional company data to the calculation module, and/or enabling the calculation module to access the at least part of the additional company data secured by the third IT security means. [bolded emphasis added]. Claim 9. The method according to claim 6, wherein at least two IT security means of the first IT security means, the second IT security means and the third IT security means are different from each other, or wherein the first IT security means, the second IT security means and the third IT security means are the same IT security means. [bolded emphasis added] Claim 10. The method according to claim 6, the second IT security means and the third IT security means comprises at least one of: … proving the state of the company data and/or the additional company data by means of zero-knowledge proofs; [bolded emphasis added] * Original Specification ¶ [0062] merely exemplifies that “IT security means may comprise encryption, digital signatures, message authentication codes, anonymization and obfuscation”. * Original Specification doesn’t provide adequate respective structure for each of the three distinct three means to perform the respective “secured” function (Claims 1,6,9,13,14) * Original Specification also doesn’t provide adequate respective structure for each of the means of zero-knowledge proofs to perform the proving the state of the company data and/or the additional company data. (Claim 10) * Original Specification also does not clearly link each of the respective “secured” and “providing” functions to each of the respective structure for the claimed “means” such by an algorithm. Thus, the above identified claims are rendered vague and indefinite. Examiner recommends, as an example only that Applicant amend, said claim to no longer invoke 35 USC 112 (f), such as by replacing the expression “IT security means” and “means of zero-knowledge proofs” with expression “IT security and “ Claims 2-12 are dependent claims rejected based on rejected parent Claim 1. Clarification and/or correction is/are required. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Claim Rejections - 35 USC § 112 - aside from 112(f) interpretation - 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. Claims 1-14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), ¶2, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. Claims 1,3,5-8,10-14 repeatedly recite the expression “and/or” rendering said claims vague and indefinite because it is unclear if the subject matter following the expression “and/or” is required, as introduced by conjunction “and” or is optional as introduced by conjunction “or”. Claims 2-12 are dependent rejected based on rejected parent independent Claim 1. Claims 1,3,5-8,10-14 are recommended to be amended to recite among others “” Claim 11 recites: “The method according to claim 1, wherein one or more predetermined a posteriori methods are associated with respective one or more predetermined KPI thresholds, and wherein the method further comprises using one or more predetermined a posteriori methods based on determined corresponding one or more predetermined KPI thresholds; and/or wherein one or more predetermined a priori methods are associated with respective one or more predetermined KPI thresholds, and wherein the method further comprises using one or more predetermined a priori methods based on determined corresponding one or more predetermined KPI thresholds”. [bolded emphasis added]. Claim 11 is rendered vague and indefinite because it is unclear if “one or more predetermined KPI thresholds” and “one or more predetermined a posteriori methods” as subsequently recited in said claim relates back to “one or more predetermined KPI thresholds” and “one or more predetermined a posteriori methods” as antecedently recited ins aid claim. Claim 11 is recommended to be amended to recite: wherein one or more predetermined a posteriori methods are associated with respective one or more predetermined KPI thresholds, and wherein the method further comprises using the one or more predetermined a posteriori methods based on determined ones of the one or more predetermined KPI thresholds; and/or wherein the one or more predetermined a priori methods are associated with respective ones of the one or more predetermined KPI thresholds, and wherein the method further comprises using the one or more predetermined a priori methods based on determined corresponding ones of the one or more predetermined KPI thresholds. Clarifications and/or corrections are required. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Claim Rejections - 35 USC § 101 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. Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the claim(s) is/are directed to signal per se (MPEP 2106.03 I) Claim 14 is independent reciting among others “A computer program product comprising instructions”, said computer product read in light of the Original Specification ¶ [0105] 2nd sentence as “a computer-readable medium”, with Original Specification ¶ [0102] 2nd sentence clarifying that “The computer-readable medium may be transitory”. Examiner tests such findings per MPEP 2106.03 I and Step 1 of the flowchart at MPEP 2106 III, and submits that the term “computer product, under broadest reasonable interpretation, as read in light of the Original Specification ¶ [0105] 2nd sentence and ¶ [0102] 2nd sentence, covers transitory forms or signals per se. Specifically, the computer product claim 14 as read in light of the Original Specification does not exclude signals per se under broadest reasonable interpretation. See USPTO “Evaluating Subject Matter Eligibility Under 35 USC 101 August 2012 Update” Slides 13,14 it is not acceptable to just add physical or tangible-Nuitjen’s ineligible signals were physical and tangible. Therefore, Claim 14 is rejected under 101 as directed to a non-statutory subject matter. Claim 14 is recommended, as example only, to be amended to recite, among others: A computer program product comprising a non-transitory computer readable storage medium with instructions stored thereupon, which, when executed by a computing system etc. Correction is required. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Claim Rejections - 35 USC § 101 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 1-14 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, here abstract idea) without significantly more. The claim(s) recite(s) describe or set forth the fundamental or commercial principles and associated practices of: “providing insights into confidential”, thus abstract, business relationships, recited here as “company data” (preamble of independent Claims 1,13,14) “associated with” “a first company” (independent Claims 1,13,14) and, with security considerations “associated with a second company” (independent Claims 1,13,14). Such fundamental or commercial principles and business relationships are restricted to equally abstract best business practices as in “at least part of the additional company data is not included in the company data” (dependent Claim 6), or more relaxed, as in “additional company data is included in the company data” “making additional company data accessible” (dependent Claim 4), to allow “access the at least part of the additional company data included in the company data”, “transferring the at least part of the additional company data”, “access the at least part of the additional company data included in the company data” (dependent Claim 5), “process the company data and/or the additional company data, and to obtain values for additional KPIs from the processing” (dependent Claim 8), “receiving the company data and/or the additional company data at the platform” (dependent Claim 12). Also, when tested per MPEP 2106.04(a)(2) II, 1st bullet point, such “providing insights into confidential company data” (independent Claims 1,13,14 preamble), is intended “in case of” [risk or] “data deviations in an industrial plant context” as an example of abstract, best business practice summarized at preamble of each independent Claims 1,13,14, then detailed in the body of Claims 1-14 as: “determining data deviations by determining whether a predetermined KPI threshold is violated by the obtained” [key performance indicators] “KPI values”, followed by mitigation of the aforesaid risk by “providing action recommendations for data deviation reduction”, “perform additional processing related to the company data and to provide additional information related to the company data as compared with the first processing state” (independent Claims 1,13,14) “determining whether the predetermined KPI threshold is violated based on determining whether an embedded predetermined KPI threshold is violated” (dependent Claim 2), “perform the switching and/or the applying when it is determined that the predetermined KPI threshold is violated” (dependent Claim 3). As one non-limiting example the Examiner points to MPEP 2106.04(a)(2) II A (i) which cites Alice Corp. v. CLS Bank,573 U.S. 208, 218, 110 USPQ2d 1976, 1982 (2014) to state that mitigating settlement risk is an example of a fundamental practice or principle that falls within the abstract Certain Methods of Organizing Human Activities grouping. Specifically, in Alice supra, a supervisory institution, instructed other respective institutions to exchange only those records of the respective parties for which the parties’ updated shadow records indicated sufficient resources to satisfy their mutual obligations. Here, the “providing insights into confidential company data in case of data deviations” summarized by the preamble of each of independent Claims 1,13,14, and further detailed throughout Claims 1-14, is not meaningfully different than confidential processing of shadow accounts data in Alice supra. Also, MPEP 2106.04(a)(2) II A at ¶2 is clear that the term fundamental is not used in the sense of being old or well-known, but rather as a building block of modern economy. Here, the “company data” and its associated “processing state and obtaining key performance indicator (KPI)” and the ensuing “determining data deviations by determining whether a predetermined KPI threshold is violated by the obtained KPI values” (independent Claims 1,13,14) represent such ineligible building blocks of economy, no matter whether or not the “action recommendations for data deviation reduction” are provid[ed] after the fact, that is; “a-posteriori” to the “determining of data deviations” (independent Claims 1,13,14), or “remote attestation” or proof from one business entity to another (i.e. prover to verifier) without revealing more information beyond what is needed (i.e. validation), that is; “zero-knowledge proof”, akin to the fundamental principle or practice of need-to-know basis, which is performed before the fact, that is; “a-priori”. Next, Examiner points to MPEP 2106.04 I 2. ¶3 also cites RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) to stress that “Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract”. It then follows that here1, the abstract encoding, set forth as: “embedding one or more predetermined KPI thresholds”, “encrypting the company data and/or the additional company data”; “using homomorphic encryption to perform computations on encrypted company data and/or additional company data without decrypting it” (dependent Claim 10), does merely narrow the aforementioned abstract exception without rendering the claims less abstract and eligible. In a similar vein, the addition of mathematical concepts expressed in words, tested per MPEP 2106.04(a)(2) I A, set forth the abstract mathematical concepts grouping of MPEP 2106.04(a)(2) These are set forth here by: “determining whether the predetermined KPI threshold is violated based on determining whether an embedded predetermined KPI threshold is violated” (dependent Claim 2), “determining data deviations by determining whether a predetermined KPI threshold is violated by the obtained KPI values; and based on a result of the determining: using an a-posteriori” [or after the fact] “method comprising providing action recommendations for data deviation reduction” “and/or switching” “into a second processing state, wherein the second processing state enables” “to perform additional processing related to the company data and to provide additional information related to the company data as compared with the first processing state; and/or using an a-priori” [or before the fact] “method comprising applying a zero-knowledge proof method and/or a remote attestation method related to the company data” (independent Claims 1,13,14), “perform the switching and/or the applying when it is determined that the predetermined KPI threshold is violated” (dependent Claim 3), “perform additional calculation methods; and processing the company data and/or the additional company data by using one or more of the additional calculation methods” (dependent Claim 7), “wherein one or more predetermined a posteriori methods are associated with respective one or more predetermined KPI thresholds, and wherein the method further comprises using one or more predetermined a posteriori methods based on determined corresponding one or more predetermined KPI thresholds; and/or wherein one or more predetermined a priori methods are associated with respective one or more predetermined KPI thresholds, and wherein the method further comprises using one or more predetermined a priori methods based on determined corresponding one or more predetermined KPI thresholds” (dependent Claim 11). Specifically, when tested per MPEP 2106.04(a)(2) I A iv., these recitations are not meaningfully different than the abstract generating first and second data by taking existing information, manipulating the data using mathematical functions, such as through mathematical correlations. Thus, the Examiner submits that there is a preponderance of legal evidence showing that the claims’ character as a whole, recites, describes or at least sets forth the abstract exception. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- This judicial exception is not integrated into a practical application because per Step 2A prong two, because the individual or combination of the additional, computer-based elements such as the instruct[ed] “processor” (Claim 13) and “system” (Claim 14) and associated “calculation module” (Claims 1-8, 10,12-14), “confidential machine learning model” (independent Claims 1,13,14), “first” and “second” “security” “means” (Independent Claims 1,13,14, dependent Claim 10), “third security means” (dependent Claim 6), “at least two IT security means of the first IT security means, the second IT security means and the third IT security” (dependent Claim 9), “platform” (dependent Claim 12) are/is found to apply the abstract exception, as tested per MPEP 2106.05(f), and/or narrow it to a technological environment or field of use, as tested per MPEP 2106.05(h), neither of which integrates said abstract exception into a practical application. Specifically, here, under MPEP 2106.05(f)(2) ¶1, the additional computer-based elements represent mere invocation of machinery to perform economic or other tasks to receive, store, or transmit data, as recited throughout Claims 1-14. Such invocation of machinery for economic or other tasks to receive, store, or transmit data does not integrate the aforementioned abstract exception into a practical application. Also, they can be argued as application on a computer of a business method of “providing insights into confidential company data in case of data deviations in an industrial plant context” (independent Claims 1,13,14 preamble) and associated algorithm [here “confidential machine learning model” at independent Claims 1,13,14 “calculation module” at claims 1-8,10,12-14, and associated “a-posterior” and/or “a-priori” considerations], as tested per MPEP 2106.05(f)(2)(i). The same application on a computer, is recited as “processing company data by a calculation module in a first processing state” and “the second processing state enables the calculation module to perform additional processing related to the company data and to provide additional information related to the company data as compared with the first processing state” at independent Claims 1,13,14, “wherein switching the calculation module into the second processing state comprises making additional company data accessible for the calculation module” at dependent Claim 4, “wherein switching of the calculation module into the second processing state comprises enabling the calculation module to perform additional calculation methods” at dependent Claim 7. Such “first” and “second” “processing” state[s], correspond along with “company data” to audit log data. Relevant to this is MPEP 2106.05(f)(2) iii, which cites FairWarning to show that such monitoring of audit log data (akin here to “processing state”, “company data” etc.) at Claims 1, 4,7-8,13,14 executed on a computer, does not integrate the abstract exception into a practical application. Looking closer at FairWarning as cited by MPEP 2106.05(f)(2), the Examiner finds that the claims in Fairwarning were argued to provide a system that accessed, compiled and combined disparate information sources that made it possible to generate a full picture of activity, identity, frequency of activity, and the like in a computer environment. Yet, the Federal Circuit ruled that: “The mere combination of data sources, however, does not make the claims patent eligible”. “As we have explained, "merely selecting information, by content or source, for collection, analysis, and [announcement] does nothing significant to differentiate a process from ordinary mental processes, whose implicit exclusion from § 101 undergirds the information-based category of abstract ideas" citing Elec. Power 830 F3d 1350,[2016 BL 247416] 2016 WL 4073318 at *4. It then follows that here, capabilities of the computer components to perform the analogous receiving or accessing, and subsequent processing or compilation and additional processing combination of disparate information sources for “providing action recommendations” [or picture] for data deviation reduction” or “zero-knowledge proof method and/or a remote attestation method related to the company data” (independent Claims 1,13,14) would similar not save the claims from patent ineligibility. Also, MPEP 2106.05(f)(2) v. finds requiring use of computer components, to tailor information and provide it to the user on a computer, does not integrate the abstract exception into a practical application. It follows that here, recitation of “a platform” as in “providing the calculation module on a platform; and receiving the company data and/or the additional company data at the platform” at dependent Claim 12, would also not integrate the abstract exception into a practical application. Finally, the level of automation recited throughout Claims 1-14, including the use of generally recited “security means” when tested per MPEP 2106.05 (h)(vi), can also be argued as example of technological environment upon which the combination of collecting information, analyzing it, and displaying certain results of the collection and analysis is being performed. Yet, MPEP 2106.05(h) vi. finds that such narrowing of the abstract idea to a technological environment does not integrate the abstract exception into a practical application. Here, such technological environment is represented by “first” and “second” “security” “means” (independent Claims 1,13,14, dependent Claim 10), “third security means” (dependent Claim 6), “at least two IT security means of the first IT security means, the second IT security means and the third IT security” (dependent Claim 9), as read in light of Original Specification ¶ [0062] 1st sentence as mere examples of: encryption, digital signatures, message authentication codes, anonymization and obfuscation. In a similar manner, the preponderantly recited, “IT security means” when tested per MPEP 2106.05(f)(3), can also be argued as an example of generality of application of the judicial exception, given the high level of generality of expression “IT security means”. The same rationales apply to: “providing the calculation module, the company data and/or the additional company data in a trusted execution environment (TEE); encrypting the company data and/or the additional company data; protecting the company data and/or the additional company data using distributed ledger technology; using homomorphic encryption to perform computations on encrypted company data and/or additional company data without decrypting it; proving the state of the company data and/or the additional company data by means of zero-knowledge proofs; anonymizing the company data and/or the additional company data; obfuscating the company data and/or the additional company data; authenticating the company data and/or the additional company data; and enforcing access control on the company data and/or the additional company data via attribute-based, role-based, and/or context-based authorization” at dependent Claim 10. Thus Examiner provided a preponderance of legal evidence showing that the additional computer-based elements above do/does not integrate the abstract idea into a practical application. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because Examiner follows MPEP 2106.05(d) II guidelines and carries over the above findings tested per MPEP 2106.05 (f) and/or (h) to submit that as shown above, the additional computer-based elements, merely apply the already recited abstract idea [MPEP 2106.05(f)] and/or narrow the abstract exception to a field of use or technological environment [MPEP 2106.05(h)]. For these same reasons, said computer-based additional elements also do not provide significantly more than the abstract idea itself, in light of MPEP 2106.05(f) and/or (h) as sufficient option(s) for evidence without having to rely on the well understood routine and conventional test of MPEP 2106.05(d). Based on such legal evidence conferred by the MPEP 2106.05(f),(h) tests above, the Examiner submits that the additional computer-based elements do not provide significantly more without having to rely on the well understood routine and conventional test of MPEP 2106.05(d). Yet, assuming arguendo that further evidence would be required to demonstrate conventionality of the additional, computer-based elements, the Examiner would further point to MPEP 2106.05(d) to demonstrate that said additional elements remain well-understood, routine, conventional. In such case, Examiner would rely on case law and Specification as follows: Per MPEP 2106.05(d) II: (i) receiving or transmitting data, including utilizing an intermediary, (iii). electronic recordkeeping, (ii). performing repetitive calculations. These are set forth as: “second processing state enables the calculation module to perform additional processing related to the company data and to provide additional information related to the company data as compared with the first processing state” at independent Claims 1,13,14; “making additional company data accessible for the calculation module, wherein the additional company data are associated with the process related to the first company; and processing the additional company data by the calculation module” at dependent Claim 4, “wherein at least part of the additional company data is included in the company data and the making the additional company data accessible for the calculation module comprises transferring the at least part of the additional company data to the calculation module, and/or enabling the calculation module to access the at least part of the additional company data included in the company data”, at dependent Claim 5; “wherein at least part of the additional company data is not included in the company data and is secured by third IT security means, and wherein making the additional company data accessible for the calculation module comprises transferring the at least part of the additional company data to the calculation module, and/or enabling the calculation module to access the at least part of the additional company data secured by the third IT security means” at dependent Claim 6; “wherein switching of the calculation module into the second processing state comprises enabling the calculation module to perform additional calculation methods; and processing the company data and/or the additional company data by using one or more of the additional calculation methods” at dependent Claim 7; “wherein enabling of the calculation module to perform the additional calculation methods comprises enabling the calculation module to process the company data and/or the additional company data, and to obtain values for additional KPIs from the processing” at dependent Claim 8. Per MPEP 2106.05(d)(I)(2) Examiner points to Applicant’s own Specification as follows: - Original Specification ¶ [0062] 1st sentence, reciting at high level of generality: “In the context of the present disclosure, examples for IT security means may comprise encryption, digital signatures, message authentication codes, anonymization and obfuscation” - Original Specification ¶ [0060]: “Any unit, module, circuitry or methodology described herein may be implemented using hardware, software, and/or firmware configured to perform any of the operations described herein. Hardware may comprise one or more processor cores, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip systems (SOCs), complex programmable logic devices (CPLDs), etc. Software may be embodied as a software package, code, instructions, instruction sets and/or data recorded on at least one transitory or non-transitory computer readable storage medium. Firmware may be embodied as code, instructions or instruction sets and/or data hard-coded in memory devices (e.g., non-volatile memory devices)”. - Original Specification ¶ [0103] reciting at high level of generality: “The computing system can typically be a processor, for example a processor that is part of a computer”. All of this preponderance of factual and/or legal evidence demonstrates that the additional computer-based elements fail to provide anything significantly more than what was already identified as the abstract exception. Therefore Claims 1-14, although directed to statutory categories (here “method” or process at Claims 1-12, “apparatus” or machine at Claim 13, and or “computer program product” or article of manufacture at Claim 142) they still recite, or at least set forth the abstract idea (Step 2A prong one), with their additional, computer-based elements not integrating the abstract idea into a practical application (Step 2A prong two) or providing significantly more than abstract idea (Step 2B). Accordingly, Claims 1-14 are ineligible. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Claim Rejections - 35 USC § 102 The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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. Claims 1,2 and 11-14 are rejected under 35 U.S.C. 102(a)(1) based upon a public use or sale or other public availability of the invention as disclosed by: Kind et al US 20220284446 A1 by Applicant Siemens AG hereinafter Kind. As per, Claims 1,13,14 Kind teaches “A method for providing insights into confidential company data in case of data deviations in an industrial plant context, the method comprising:” / “A data processing apparatus for providing insights into confidential company data in case of data deviations in an industrial plant context, the data processing apparatus comprising a processor being configured to carry out a method for providing insights into confidential company data in case of data deviations in an industrial plant context, the method comprising:” / “A computer program product comprising instructions which, when executed by a computing system, enable and/or cause the computing system to perform a method for providing insights into confidential company data in case of data deviations in an industrial plant context, comprising: instructions for” (Kind ¶ [0001] invention relates to a method and system comprising subject participants, at least one verifier participant and participant of a communication network exchanging information about environmental, social and corporate governance (ESG) data of a product related entity. Kind ¶ [0016] 4th sentence: the computing component may comprise a central processing unit (CPU) and a memory operatively connected to the CPU. Kind ¶ [0095] there is in interest of the participants contributing to the overall ESG information, not be forced to openly share all information that would be needed for traditional verification of ESG information within the ecosystem. Information, such as the identity of suppliers and logistics providers, or the countries of sourced material, components and parts, or the serial and batch numbers of components and parts, or the exact carbon off-setting scheme, has to be kept confidential between two direct business partners only. For instance, a buyer of a product must not learn about the actual identity of tier 1, tier 2, tier 3, ... suppliers of a product. Kind ¶ [0065] the first cryptographic signature mechanism is based on zero-knowledge proof. The usage of zero-knowledge proof mechanisms allows a certifier to issue an ESG credential without necessarily providing the ESG data as such or completely or in detail. For example, only attributes of the ESG data, like “value lower than threshold value” or “not manufactured in specific countries” or similar, are provided. These attributes however are verifiably provided, so that an integrity of the attributes and an authenticity of the certifier are guaranteed. Kind ¶ [0067] When using zero-knowledge proof mechanisms to attest certain presented attributes or condition statements about ESG information, such as value larger than, or value smaller than, includes, and/or excludes, the authenticity is, proven by usage of a secret of the subject participant. When a subject requests an ESG credential from an issuer it also include with that request a secret. The certifier also signs that secret. This signed secret is part of the credential that the subject gets from the certifier. When creating a proof out of a credential by the subject, this signed secret is also included. The verifier can verify this secret using the public keys that are on the registry and receives confirmation that the proof is coming from the sender, i.e., the subject participant, and the proof is based on the ESG credential issued by the one who created the credential definition ID. The credential definition ID is created when the certifier registers the credential definition for the subject in the decentralized registry. ¶ [0073] the second cryptographic signature mechanism is based on a zero-knowledge proof. A zero-knowledge proof mechanism can also advantageously be used by the subject participant. In use cases, where the certifier participant provides ESG credentials that disclose detailed ESG data, the subject participant can decide how much information to provide to the verifier participant. For example, only the information necessary to provide evidence that certain standard requirements are fulfilled, can be provided. The zero-knowledge proof is based on cryptographic key pairs, i.e., the public and the corresponding private key of the subject. The usage of a zero-knowledge signature mechanism by the subject participant assigns integrity of the ESG data provided by the subject participant and the authenticity of the subject participant. Moreover, depending on the data disclosed in the zero-knowledge proof, the authenticity of the certifier and the integrity of the ESG credential on which the ESG data provided is based, is determined by the zero-knowledge proof) - “processing company data by a calculation module in a first processing state and obtaining key performance indicator (KPI) values from the processing”; (Kind ¶ [0100] 1st sentence: Within a computing and data management system M′ of the manufacturer M, a data platform 40 is installed to collect 70 local ESG information, to verify ESG credentials received, from suppliers S1, Sn, from utility provider U, and generate a recursive ESG credential comprising for example attributes of own ESG data, such as remaining below threshold GHG [greenhouse gases] value, and attributes of external ESG data, such as again remaining below a different threshold GHG level. For example at ¶ [0096] 3rd sentence: An external section ESGe comprises external information on all upstream Scope 3 input in the form of pointers to other recursive ESG credentials ESG1 with PCF or CO2 or CO2-e values and information how to calculate the total PCF based on these other ESG credentials ESG1, such as the number of units defined in a bill-of-material. Similarly, Kind ¶ [0039] The recursive ESG information, includes PCF [product carbon footprint] or CO2 or CO2-e related values. Here, the local information has metadata information such as the carbon emission standard(s) being used, the method of data entry (manual, automatic), date or calculation, geo location, or company personnel ¶ [0041] 4th sentence: The external section of the recursive ESG credential comprises pointers to other proofs providing information about PCF- or CO2- or CO2-e-values and information how to calculate the total PCF based on those other proofs, such as the number of units define in a bill-of-material). - “wherein the company data are secured by first IT security means and are associated with a process related to a first company”; (Kind ¶ [0013]…product related entity… is defined in an ESG credential definition that is generated by the certifier participant. This credential definition is dependent on the attributes, that will be certified by the certifier participant. If the product related entity is…a manufacturers production site, then ESG data or part of the ESG data that is in the responsibility of the manufacturer for this production site is to be certified. The credential definition will describe all the metadata about that data, i.e., defines the schema of the future issued credential. The credential definition is created by the certifier participant, for example, based on a request for credential by the subject participant, where the subject participant defines the data or attributes to be certified. Kind ¶ [0015] A decentralized registry…manage at least the public key of the at least one certifier participant. For example, for each certifier participant, a corresponding public key is stored in the decentralized registry. The decentralized registry…is a distributed database, such as a database on the basis of distributed ledger technology, in particular a blockchain database. The decentralized registry is a trusted registry in the system. The main function of the decentralized registry is to store and provide public keys of the certifier participants in a trustworthy manner. The decentralized register is preferably accessible by the ecosystem participants only. The decentralized registry manages the public keys for example by assigning respective public addresses or pseudonyms or identifiers to the respective public keys and storing this assignment. Kind ¶ [0017] The decentralized registry…moreover stores the credential definitions … registered by the certifier participants. The certifier participants define the schema for ESG credentials they issue for subject participants, so that the verifier can use the ESG credential definition accessible via the nodes of the decentralized database to process the certified data. Kind ¶ [0019] The nodes in the decentralized registry network may be servers or terminal devices that have the function of processing registry data. For example, each node in a blockchain network performs functions such as validating transaction data, appending transaction data into a current block, committing a block, linking a new block to an existing block. ¶ [0020] For example, a manufacturer as subject participant provides a proof to a customer of a manufactured product as verifier participant. The customer verifies the proof to obtain trustworthy information about the ESG data. The customer therefore preferably extracts from the proof information about a certifier participant. The customer derives that the proof is based on ESG credential issued by a specific certifier participant and requests the public key of this certifier participant in the decentralized registry. With the public key, the proof can be verified, so that the customer obtains a trustworthy statement concerning ESG data of the product offered by the manufacturer. This includes a trustworthy statement about the authenticity of the certificate issuer, i.e., the certifier participant, as well as about an integrity of ESG data or a part of the ESG data or an attribute of the ESG data. Therefore, the customer receives trustworthy info about ESG data of the product of the manufacturer. ¶ [0023] The decentralized registry preferably only maps pseudonyms or public addresses of subject participants with the corresponding public keys of subject participants. A certifier participant, either registers its own public key with a mapping to its real identity as certifier or also uses a pseudonym. In embodiments, where a subject participant also acts as certifier participant and itself creates an ESG credential, the public key in the decentralized register is preferably mapped to a pseudonym. ¶ [0025] ESG credential in certain embodiments is further signed by the subject participant providing the proof. In addition to the public key of the certifier, also the public key of the subject participant is therefore requested by the verifier participant and looked up, for example, in the decentralized registry or is provided through other communication channels. ¶ [0026] Preferably, the subject participant uses a public address, or a pseudonym only and, moreover, only uses the pseudonym for the communication with that specific verifier participant in the signature mechanism to allow confidentiality for future verification processes. If, a further participant in the supply chain, e.g., a different customer or a different buyer of a further tier will verify the proof in the future, then from the proof and the signature mechanism used, there is no information extractable about the identity of the subject participant and due to renewal of the pseudonym for every proof, there is moreover no trace extractable about subject-verifier- or subject-certifier-relations. ¶ [0028] The certified ESG information might be used for shareholder, regulatory, or NGO reporting or for audits, tax declaration, sourcing simulation or supply chain engineering to gain higher resiliency or lower total product or service cost. Kind ¶ [0030] … a subject participant, who wants to provide a proof derived from an ESG credential, in the first place provides ESG information relating to its own ESG impact, for example, its own PCF impact or data about material used in the production process or an own production facility or location or present employment conditions etc. Moreover, information about ESG data from other subject participants in the supply chain are preferably included in the proof. Kind ¶ [0061] In accordance with an embodiment a 1st cryptographic signature mechanism for assigning the integrity is based on a first cryptographic signature generated with a private key of the at least one certifier participant and a first cryptographic verification mechanism is based on verifying the first cryptographic signature with the public key of the at least one certifier participant. A first cryptographic signature mechanism is, for example, chosen by the certifier. This allows a flexibility in integrating certifier participants into the network. The signature mechanism can be chosen according to computing power or available crypto primitives. For example, the first cryptographic signature mechanism is fixed for all certifier participants within the ecosystem. Certifier participant then have to fulfill the given criteria for applying crypto mechanisms. Kind ¶ [0072] In accordance with another embodiment, the at least one subject participant derives the proof from the ESG credential by using a second cryptographic signature mechanism and a private key of the at least one subject participant. The subject participant can provide the ESG credential supplemented with the subject's own signature to assign an integrity and authenticity of the proof. In other embodiments, a secure communication between subject and verifier is used which dispenses with the second cryptographic signature mechanism) - “wherein the calculation module is secured by second IT security means and is associated with a second company”; (Kind ¶ [0100] Within a computing and data management system M′ of manufacturer M, a data platform 40 is installed to…verify ESG credentials…from suppliers S1,n… and generate recursive ESG credential… via a secure communication protocol. The data received from the manufacturer M to generate the ESG credential is received via a communication path within the ecosystem linking direct business partner, for example, via a secure communication protocol. The manufacturer M gets the data, in particular public keys, needed to perform the verification steps, from the decentralized registry, for which all participants at least have read access. The ecosystem is encompassed by a communication system linking the business partners and in addition linking regulatory instances A, NGO at least to key subject participants like manufacturers or OEMs. The ecosystem is further encompassed by a decentralized registry network, e.g., a blockchain network, which is primarily used to manage the identities and pseudonyms of the network participants and their corresponding public keys as well as to guarantee a uniform structure and a workability of the credentials used in the ecosystem. Kind ¶ [0103] Fig.5 is a flowchart of a computer-implemented method for providing at least one cryptographical proof derived from an environmental, social and governance ESG credential by at least one subject participant M of a communication network exchanging info about ESG data of a product related entity, where the network comprises a plurality of subject participants M, S1, S2, at least one verifier participant C and at least one certifier participant T, T0. Kind ¶ [0104] The method comprises generating the at least one cryptographical proof by the at least one subject participant M, as indicated in step 510. In accordance with the invention, the at least one cryptographical proof comprises assigning an authenticity of the at least one certifier participant T, T0 providing the ESG credential. Kind ¶ [0105] Next, the ESG credential is received by the at least one subject participant M from the at least one certifier participant T, T0, as indicated in step 520. In accordance with the invention, a public key of the at least one certifier participant T, T0 is derivable by the at least one verifier participant C from the proof via a decentralized registry R and is utilized by the at least one verifier participant C to verify the authenticity, and the proof further comprises assigning an integrity of the ESG data, parts of the ESG data or an attribute of the ESG data. Kind ¶ [0106] Next, the public key of the at least one certifier participant T, T0 is utilized by the verifier participant C to verify the integrity, as indicated in step 530. ¶ [0107] Although the present invention has been described in accordance with preferred embodiments, it is obvious for the person skilled in the art that modifications are possible in all embodiments). - “determining data deviations by determining whether a predetermined KPI threshold is violated by the obtained KPI values”; (Kind ¶ [0094] 1st-2nd sentences: … verify that the product through its life cycle fulfills certain environmental or social requirements/criteria. This could be… that the total product carbon footprint is below a given value) “and” “based on a result of the determining”: =“using an a-posteriori method comprising providing action recommendations for data deviation reduction based on a confidential machine learning model and/or switching the calculation module into a second processing state, wherein the second processing state enables the calculation module to perform additional processing related to the company data and to provide additional information related to the company data as compared with the first processing state; and/or = “using an a-priori method comprising applying a zero-knowledge proof method” (Kind ¶ [0044] 1st, 3rd sentences: the embodiments with a chain of proofs derived from ESG credentials and recursive ESG credentials… an erroneous transfer of emissions by a supplier to a recipient to reduce its own emissions to meet its pledged effort and a subsequent claiming of same reductions by the recipient to meet again its pledged effort is also prevented. Specifically at Kind ¶ [0065] 2nd-3rd sentences: usage of zero-knowledge proof mechanisms allows a certifier to issue ESG credential without necessarily providing the ESG data as such or completely or in detail. For example, only attributes of the ESG data, like “value lower than threshold value” or “not manufactured in specific countries” or similar, are provided. Kind ¶ [0066] For generating a zero-knowledge proof, a private key of the certifier is used in a zero-proof-signature mechanism, and for verifying, the corresponding public key is used. For use cases, where the ESG credential is forwarded by the subject participant as the proof, the usage of a zero-knowledge signature mechanism for generating the ESG credential enables to hold back [or a-priori] certain detailed information about the ESG data, e.g. details about PCF values, which do not have to be disclosed to the verifier to fulfill the criteria of standards applied. Kind ¶ [0067] When using zero-knowledge proof mechanisms to attest certain presented attributes or condition statements about ESG information, such as value larger than, or value smaller than, includes, and/or excludes, the authenticity is, for example, proven by usage of a secret of the subject participant. When a subject requests an ESG credential from an issuer it also include with that request a secret. The certifier also signs that secret. This signed secret is part of the credential that the subject gets from the certifier. When creating a proof out of a credential by the subject, this signed secret is also included. The verifier can verify this secret using the public keys that are on the registry and receives confirmation that the proof is coming from the sender, i.e., the subject participant, and the proof is based on the ESG credential issued by the one who created the credential definition ID. The credential definition ID is created when the certifier registers the credential definition for the subject in the decentralized registry. Kind ¶ [0073] In accordance with an embodiment, the second cryptographic signature mechanism is based on a zero-knowledge proof. A zero-knowledge proof mechanism can also advantageously be used by the subject participant. In use cases, where the certifier participant provides ESG credentials that disclose detailed ESG data, the subject participant can decide how much information to provide to the verifier participant. For example, only the information necessary to provide evidence that certain standard requirements are fulfilled, can be provided. The zero-knowledge proof is based on cryptographic key pairs, i.e., the public and the corresponding private key of the subject. The usage of a zero-knowledge signature mechanism by the subject participant assigns integrity of the ESG data provided by the subject participant and the authenticity of the subject participant. Moreover, depending on the data disclosed in the zero-knowledge proof, the authenticity of the certifier and the integrity of the ESG credential on which the ESG data provided is based, is determined by the zero-knowledge proof) “and/or a remote attestation method related to the company data” (Kind Figs. 1,2,4, ¶ [0051] providing at least one cryptographical proof derived from an ESG credential by at least one subject participant of a communication network exchanging info about ESG data of a product related entity, the network comprising several subject participants, at least one verifier participant and at least one certifier participant. Specifically, per ¶ [0019] 1st sentence: the nodes in the decentralized registry network may be servers or terminal devices that have the function of processing registry data. ¶ [0091] 2nd sentence: the verifier rely on the integrity of any ESG information extractable from the proof, which originates [or a-priori] from a further participant of the supply chain as the trusted certifier T0 also acted as verifier regarding proofs derived from further ESG credentials of further subject participants in the supply chain, ¶ [0099] 2nd sentence: external inspection 100 by trusted ecosystem participant, e.g. certifier T responsible for certifying a plurality of participants, is performed. ¶ [0037] 4th sentence: the verifier participant can use the information in the recursive proof, more specific the external section of the ESG credential comprised in the proof). Claim 2. Kind teaches all the limitations in claim 1 above. Kind further teaches: - “embedding one or more predetermined KPI thresholds into the calculation module”; (Kind ¶ [0035] 2nd-3rd sentences: the recursive ESG credential has a section for local ESG information as well as one for external ESG information. Entries in the external section contain, information about how to calculate or weight or normalize input [or embedding] of the further proof. Kind ¶ [0096] 3rd sentence: external section ESGe comprises external information on all upstream Scope 3 input [or embedding] in the form of pointers to other recursive ESG credentials ESG1 with PCF or CO2 or CO2-e values and information how to calculate the total PCF based on these other ESG credentials ESG1… For example, at ¶ [0065] 3rd sentence: attributes of the ESG data, like “value lower than threshold value” … Similarly, ¶ [0094] 2nd sentence: … the total product carbon footprint is below a given value. Similarly, ¶ [0100] Within a computing and data management system M′ of the manufacturer M, a data platform 40 is installed [or embedded] to collect 70 local ESG information, to verify ESG credentials received, for example, … ESG data, … remaining below a threshold GHG value, and attributes of external ESG data, such as again remaining below a different threshold GHG level) “and” - “determining whether the predetermined KPI threshold is violated based on determining whether an embedded predetermined KPI threshold is violated”. (Kind ¶ [0065] 3rd sentence: … value lower than threshold value … Similarly, ¶ [0094] 1st - 2nd sentence: in Fig.2, ESG info is be shared…so…customer C… verify that the product through its life cycle fulfills [or does not violate] certain environmental … requirements/criteria. This could be, for example … the total product carbon footprint is below a given value. Similarly, ¶ [0100] Within a computing and data management system M′ of the manufacturer M, a data platform 40 is installed to collect 70 local ESG information, to verify ESG credentials received, from suppliers S1, Sn, from the utility provider U, and to generate a recursive ESG credential comprising for example attributes of own ESG data, such as remaining below a threshold GHG value, and attributes of external ESG data, such as again remaining below a different threshold GHG level). Claim 11. Kind teaches all the limitans in claim 1 above. Kind further teaches: wherein one or more predetermined a posteriori methods are associated with respective one or more predetermined KPI thresholds, and wherein the method further comprises using one or more predetermined a posteriori methods based on determined corresponding one or more predetermined KPI thresholds; and/or wherein one or more predetermined a priori methods are associated with respective one or more predetermined KPI thresholds, and wherein the method further comprises using one or more predetermined a priori methods based on determined corresponding one or more predetermined KPI thresholds. (Kind ¶ [0044] 1st, 3rd sentences: … the disclosed embodiments of the system with a chain of proofs derived from ESG credentials and recursive ESG credentials… an erroneous transfer of emissions by a supplier to a recipient to reduce its own emissions to meet its pledged effort and a subsequent claiming of those same reductions by the recipient to meet again its pledged effort is also prevented. Specifically, at ¶ [0065] 2nd-3rd sentences: usage of zero-knowledge proof mechanisms allows a certifier to issue ESG credential without necessarily providing the ESG data as such or completely or in detail. For example, only attributes of the ESG data, like “value lower than threshold value” … or similar, are provided. ¶ [0066] For generating a zero-knowledge proof, a private key of the certifier is used in a zero-proof-signature mechanism, and for verifying, the corresponding public key is used. For use cases, where the ESG credential is forwarded by the subject participant as the proof, the usage of a zero-knowledge signature mechanism for generating the ESG credential enables to hold back [or a-priori] certain detailed information about the ESG data, e.g. details about PCF values, which do not have to be disclosed to the verifier to fulfill the criteria of standards applied. ¶ [0067] When using zero-knowledge proof mechanisms to attest certain presented attributes or condition statements about ESG information, such as value larger than, or value smaller than, includes, and/or excludes, the authenticity is, for example, proven by usage of a secret of the subject participant. When a subject requests an ESG credential from an issuer it also include with that request a secret. The certifier also signs that secret. This signed secret is part of the credential that the subject gets from the certifier. When creating a proof out of a credential by the subject, this signed secret is also included. The verifier can verify this secret using the public keys that are on the registry and receives confirmation that the proof is coming from the sender, i.e., the subject participant, and the proof is based on the ESG credential issued by the one who created the credential definition ID. The credential definition ID is created when the certifier registers the credential definition for the subject in the decentralized registry. ¶ [0073] In accordance with an embodiment, the second cryptographic signature mechanism is based on a zero-knowledge proof. A zero-knowledge proof mechanism can also advantageously be used by the subject participant. In use cases, where the certifier participant provides ESG credentials that disclose detailed ESG data, the subject participant can decide how much information to provide to the verifier participant. For example, only the information necessary to provide evidence that certain standard requirements are fulfilled, can be provided. The zero-knowledge proof is based on cryptographic key pairs, i.e., the public and the corresponding private key of the subject. The usage of a zero-knowledge signature mechanism by the subject participant assigns integrity of the ESG data provided by the subject participant and the authenticity of the subject participant. Moreover, depending on the data disclosed in the zero-knowledge proof, the authenticity of the certifier and the integrity of the ESG credential on which the ESG data provided is based, is determined by the zero-knowledge proof. ¶ [0094] As illustrated in Fig. 2, ESG information is be shared within the ecosystem so that the customer C can verify that the product through its life cycle fulfills certain environmental or social requirements/criteria. This could be, for example, that its production does not involve any child or forced labor, that the product does not contain material categorized as conflict mineral or that the total product carbon footprint is below a given value. Such information may be used, besides for customers, for shareholder, regulatory and NGO reporting as well as audits, tax or customs declaration, sourcing simulations and supply chain engineering to gain higher resiliency or lower total product cost. ¶ [0100] 1st sentence: Within a computing and data management system M′ of the manufacturer M, a data platform 40 is installed to collect 70 local ESG information, to verify ESG credentials received, for example, from suppliers S1, Sn, from the utility provider U, and to generate a recursive ESG credential comprising for example attributes of own ESG data, such as remaining below a threshold GHG value, and attributes of external ESG data, such as again remaining below a different threshold GHG level). Claim 12. Kind teaches all the limitans in claim 1 above. Kind further teaches: - “providing the calculation module on a platform”; (Kind ¶ [0100] 1st sentence: a data platform 40 is installed to collect 70 local ESG information, to verify ESG credentials received, for example, from suppliers S1, Sn, from the utility provider U, and to generate a recursive ESG credential comprising for example attributes of own ESG data, such as remaining below a threshold GHG value, and attributes of external ESG data, such as again remaining below a different threshold GHG level) “and” - “receiving the company data and/or the additional company data at the platform”. (Kind ¶ [0100] 1st-3rd sentences: data platform 40 is installed to collect 70 local ESG info… data received from manufacturer M to generate the ESG credential is received via communication path within the ecosystem linking direct business partner, via a secure communication protocol. The manufacturer M gets the data, in particular public keys, needed to perform the verification steps, from the decentralized registry, for which all participants at least have read access). ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Rejections under 35 § U.S.C. 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 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. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant 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 as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. The factual inquiries 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 nonobviousness. Claims 3-5 and 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Kind as applied to claim 1 above and in view of Daniel Bennett US 20240031437 A1 hereinafter Bennett. As per, Claim 3. Kind teaches all the limitations in claim 1 above. Kind does not explicitly recite: “further comprising” - “triggering the calculation module to perform the switching and/or the applying when it is determined that the predetermined KPI threshold is violated” as claimed. Bennett in analogous providing insights on deviations teaches or suggests: - “triggering the calculation module to perform the switching and/or the applying when it is determined that the predetermined KPI threshold is violated” (Bennett ¶ [0151] 2nd-3rd sentence: the process in Fig.10 is example of one implementation for step 604 in Fig.6. The process changes, by the computer system, the collection of the additional emission data from the sensor devices over the network based on the comparison between the emission data and the set of thresholds changing the emission data collected from the sensor devices in response to the emission data crossing of at least one of the set of thresholds (step 1000). ¶ [0093] This change in the collection of additional emission data 240 can take a number of different forms. For example, the collection of additional emission data 240 can be performed at a higher frequency [or detail] as compared to the frequency used for collecting emission data 224. In another example, the collection of additional emission data 240 can be collecting a larger [or more detailed] amount of emission data of sensor data 222 in a sampling of emission data 224. For example, ¶[0152] 1st-2nd sentences: with reference to Fig.11, an illustration of a flowchart of a process for collecting additional emission data at higher frequency is depicted in accordance with an illustrative embodiment. The process in Fig.11 is an example of one implementation for step 604 in Fig.6. Similarly, ¶ [0153] 1st-2nd sentences: Fig.12 , an illustration of a flowchart of a process for collecting a larger amount of additional emission data is depicted in accordance with an illustrative embodiment. The process illustrated in Fig.12 is an example of one implementation for step 604 in Fig.6. ¶ [0154] 1st-2nd sentences: turning to Fig.13, an illustration of a flowchart of a process for collecting additional emission data from a second number of sensor devices is depicted in accordance with an illustrative embodiment. The process illustrated in Fig.13 is an example of one implementation for step 604 in Fig.6. Then at Bennett ¶ [0097] data acquisition manager 210 can analyze and store emission data 224 and additional emission data 240 in historical database 246. Emission data 224 and additional emission data 240 can be processed by data acquisition manager 210 to form time series emission data 248 that is stored in historical database 246. ¶ [0099] 1st sentence: In this illustrative example, the processing of emission data 224 and additional emission data 240 can be performed by data acquisition manager 210 using machine learning model 250). It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Kind’s “method” to have included Bennett’s teachings or suggestions in order to have overcome a technical problem with acquiring emission data in manner increases at least one of the useability or value of the emission data (Bennett ¶ [0006] 2nd sentence in view of MPEP 2143 G) or prompted as incentivized by sustainability marker forces (Bennett ¶ [0002] in view of MPEP 2143 F).The predictability of such modification would have been corroborated by the broad level of skill of one of ordinary skills in the art as demonstrated by Kind ¶ [0107]- ¶ [0108] in view of Bennett ¶ [0205]. Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor dealing with providing insights on deviations. In such combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Kind in view of Bennett, the to be combined elements would have fitted together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the combination results would have been predictable (MPEP 2143 A). Claim 4 Kind teaches all the limitations in claim 1 above. Kind does not explicitly recite: “wherein switching the calculation module into the second processing state comprises making additional company data accessible for the calculation module, wherein the additional company data are associated with the process related to the first company; and processing the additional company data by the calculation module” as claimed. Bennett in analogous providing additional insights on deviations teaches or suggests: “wherein switching the calculation module into the second processing state comprises making additional company data accessible for the calculation module, wherein the additional company data are associated with the process related to the first company” (Bennett ¶ [0151] 2nd-3rd sentence: the process in Fig.10 is an example of one implementation for step 604 in Fig.6. The process changes, by the computer system, the collection of the additional emission data from the sensor devices over the network based on the comparison between the emission data and the set of thresholds changing the emission data collected from the sensor devices in response to the emission data crossing of at least one of the set of thresholds (step 1000). ¶ [0093] This change in the collection of additional emission data 240 can take a number of different forms. For example, the collection of additional emission data 240 can be performed at a higher frequency [or detail] as compared to the frequency used for collecting emission data 224. In another example, the collection of additional emission data 240 can be collecting a larger [or more detailed] amount of emission data of sensor data 222 in a sampling of emission data 224.For example, ¶ [0152] 1st-2nd sentences: With reference to Fig.11, an illustration of a flowchart of a process for collecting additional emission data at higher frequency is depicted in accordance with an illustrative embodiment. The process in Fig.11 is an example of one implementation for step 604 in Fig.6. Similarly, ¶ [0153] 1st-2nd sentences: Fig.12 , an illustration of a flowchart of a process for collecting a larger amount of additional emission data is depicted in accordance with an illustrative embodiment. The process illustrated in Fig.12 is an example of one implementation for step 604 in Fig.6. ¶ [0154] 1st-2nd sentences: turning to Fig.13, an illustration of a flowchart of a process for collecting additional emission data from a second number of sensor devices is depicted in accordance with an illustrative embodiment. The process illustrated in Fig.13 is an example of one implementation for step 604 in Fig.6); “and processing the additional company data by the calculation module” (Bennett ¶ [0097] data acquisition manager 210 can analyze and store emission data 224 and additional emission data 240 in historical database 246. Emission data 224 and additional emission data 240 can be processed by data acquisition manager 210 to form time series emission data 248 that is stored in historical database 246. ¶ [0099] 1st sentence: In this illustrative example, the processing of emission data 224 and additional emission data 240 can be performed by data acquisition manager 210 using machine learning model 250). Rationale to have modified/combined Kind with/and Bennett are above and reincorporated. Claim 5 Kind / Bennett teaches all the limitations in claim 4 above. Kind does not explicitly recite: “wherein at least part of the additional company data is included in the company data and the making the additional company data accessible for the calculation module comprises transferring the at least part of the additional company data to the calculation module, and/or enabling the calculation module to access the at least part of the additional company data included in the company data” as claimed. Nevertheless, Bennett in analogous providing additional insights on deviations teach/suggest: “wherein at least part of the additional company data is included in the company data” (Bennett ¶ [0097] data acquisition manager 210 analyze and store emission data 224 and additional emission data 240 in historical database 246) “and the making the additional company data accessible for the calculation module comprises” - “transferring the at least part of the additional company data to the calculation module”, (Bennett ¶ [0083] 2nd sentence: data acquisition manager 210 actively collects sensor data 222 from selected ones of sensor devices 216 by sending requests for sensor data 222 to the selected ones of sensor devices 216. ¶ [0085] 1st-2nd sentence noting a different example where: data processing system 229 send sensor data 222 to data acquisition manager 210 for collection and processing. As result, sensor data 222 collected by data acquisition manager 210 occurs indirectly through receiving sensor data 222 from data processing system 229 rather than directly from sensor devices 216. ¶ [0086] this type of data collection and transmission from data processing system 229 to data acquisition manager 210 can be in real time in which sensor data 222 is received and transmitted as quickly as possible without any intentional delay. Bennett ¶ [0146] With reference to Fig.6, an illustration of a flowchart of a process for monitoring emission data transmitted over a network is depicted in accordance with an illustrative embodiment. The process illustrated in Fig. 6 can be implemented using data acquisition manager 140 in server computer 104 in FIG. 1 or data acquisition manager 210 in computer system 208 in Fig.2. ¶ [0168] 1st-3rd sentences: Turning to Fig.22 , an illustration of a flowchart of a process for changing collection of additional emission data is depicted in accordance with an illustrative embodiment. The process illustrated in Fig.22 is an example of one implementation for step 2104 in Fig.21. The process changes, by the computer system, a collection of additional emission data transmitted over the network based on the comparison (step 2200). ¶ [0065] 2nd sentence, noting a different example where Additional sensor devices that are not depicted in this example can be present in client devices 110 that send emission data to data acquisition manager 140) “and/or” - “enabling the calculation module to access the at least part of the additional company data included in the company data”. (Bennett ¶ [0097] 2nd sentence: Emission data 224 and additional emission data 240 can be processed by data acquisition manager 210 to form time series emission data 248 stored in historical database 246. ¶ [0099] 1st sentence: the processing of emission data 224 and additional emission data 240 can be performed by data acquisition manager 210 using machine learning model 250. ¶ [0114] With reference next to Fig.4, an illustration of dataflow for processing emission data using a machine learning model is depicted where machine learning model 250 can be used by data acquisition manager 210 to process input 400 in the form of sensor data 222 and additional sensor data 237. ¶ [0115] For example, machine learning model 250 can correlate measurements 300 for emission parameters 302 in the emission data 224 with locations 304 and timestamps 306 in metadata 226. Machine learning model 250 can also correlate measurements 300 for emission parameters 302 in additional emission data 240 with locations 304 and timestamps 306 in additional metadata 239. The correlations performed for emission data 224 and additional emission data 240 form time series emission data 248. ¶ [0117] Additional processing performed by machine learning model 250 in addition to correlating measurements 300 with locations 304 and timestamps 306. For example, machine learning model 250 can also correlate time series emission data 248 with physical infrastructures 204. Locations 304 in time series emission data 248 can be correlated to physical infrastructure locations 402 in input 400. Physical infrastructure locations 402 are locations for physical infrastructures 204. Bennett ¶ 0121] In an illustrative example, machine learning model 250 can be trained to perform at least one of correlations or filtering using input 400. As depicted, data acquisition manager 210 can create training dataset 410 for use in training machine learning model 250. Bennett ¶ 0123] 1st sentence: time series emission data 248 can be used to provide further training of machine learning model 250. Bennett ¶ [0134] 2nd sentence: machine learning model 504 can be used to correlate ground truth from sensors with other data to build historical time series emission data 517). Rationale to have modified/combined Kind with/and Bennett are above and reincorporated. Claim 7. Kind teaches all the limitations in claim 1 above. Kind does not explicitly recite: “wherein switching of the calculation module into the second processing state comprises enabling the calculation module to perform additional calculation methods; and processing the company data and/or the additional company data by using one or more of the additional calculation methods” as claimed. However, Bennett in analogous providing additional insights on deviations teaches or suggests: “wherein switching of the calculation module into the second processing state comprises enabling the calculation module to perform additional calculation methods” (Bennett ¶ [0100] The machine learning model can learn using various types of machine learning algorithms. The machine learning algorithms include at least one of a supervised learning, and unsupervised learning, a feature learning, a sparse dictionary learning, an anomaly detection, a reinforcement learning, a recommendation learning, or other types of learning algorithms. Examples of machine learning models include an artificial neural network, a decision tree, a support vector machine, a regression machine learning model, a classification machine learning model, a random forest learning model, a Bayesian network, a genetic algorithm, and other types of models. These machine learning models can be trained using data and process additional data to provide a desired output. ¶ 0144] The illustration of data flow diagram 500 is an example of one implementation for dataflow using an emission data collection system. This illustration is not meant to limit the manner in which other data flows can be performed. For example, the dataflow only shows a single sensor device. Other illustrative examples can employ hundreds, thousands, or hundreds of thousands of sensor devices. Further, in another illustrative example one or more machine learning models can be present in addition to machine learning model 504. Data acquisition manager 502 can use these additional machine learning models to perform different types of analysis or correlations. ¶ [0164] Turning to Fig.19, an illustration of a flowchart of a process for supplementing time series emission data with third party time series emission data is depicted in accordance with an illustrative embodiment. The step in this figure is an example of an additional step that can be used within the steps in the process in Fig.6. The process supplements, by the machine learning model in the computer system, the time series emission data for a location in the historical database with third party time series emission data in response to insufficient time series emission data being present for the location (step 1900). The process terminates thereafter); “and processing the company data and/or the additional company data by using one or more of the additional calculation methods” (Bennett ¶ [0056] 5th sentence: data acquisition manager 140 can be operated by independent organization, company generating emissions, or some other entity. ¶ [0115] For example, machine learning model 250 correlate measurements 300 for emission parameters 302 in emission data 224 with locations 304 and timestamps 306 in metadata 226. Machine learning model 250 also correlate measurements 300 for emission parameters 302 in additional emission data 240 with locations 304 and timestamps 306 in additional metadata 239. The correlations performed for emission data 224 and additional emission data 240 form time series emission data 248. ¶ [0117] Additional processing can be performed by machine learning model 250 in addition to correlating measurements 300 with locations 304 and timestamps 306. For example, machine learning model 250 can also correlate time series emission data 248 with physical infrastructures 204. Locations 304 in time series emission data 248 can be correlated to physical infrastructure locations 402 in input 400. Physical infrastructure locations 402 are locations for physical infrastructures 204. ¶ [0121] In this illustrative example, machine learning model 250 can be trained to perform at least one of correlations or filtering using input 400. As depicted, data acquisition manager 210 can create training dataset 410 for use in training machine learning model 250. ¶ [0137] Further, machine learning model 504 can generate localized emission data 518. Localized emission data 518 is emission data that is correlated to physical infrastructures such as known plants, refineries, or other facets of interest such as weather. This correlation can be performed by identifying localized emission data 518 being generated by locations within some distance of the physical infrastructure. The distance can also depend on weather conditions such as wind, temperature, humidity, and other types of conditions. ¶ [0138] Additionally, machine learning model 504 can generate interpolated emission data 520. In this example, interpolated emission data 520 is emission data that is generated to fill in gaps in the emission data generated by sensors. Interpolated emission data 520 can be generated using satellite imagery for areas not covered by sensor devices. This sensor data can be obtained from various sources such as macro satellite imagery of methane, carbon dioxide (CO2), other emissions or pollutants. ¶ [0164] 1st-3rd sentences: Turning to Fig.19, an illustration of a flowchart of a process for supplementing time series emission data with third party time series emission data is depicted in accordance with an illustrative embodiment. The step in this figure is an example of an additional step that can be used within the steps in the process in Fig.6. The process supplements, by the machine learning model in the computer system, the time series emission data for a location in the historical database with third party time series emission data in response to insufficient time series emission data being present for the location (step 1900)) Rationale to have modified/combined Kind with/and Bennett are above and reincorporated. Claim 8. Kind / Bennett teaches all the limitations in claim 7 above. Kind does not: “wherein enabling of the calculation module to perform the additional calculation methods comprises enabling the calculation module to process the company data and/or the additional company data, and to obtain values for additional KPIs from the processing”. Bennett in analogous providing additional insights on deviations teaches or suggests: “wherein enabling of the calculation module to perform the additional calculation methods comprises enabling the calculation module to process the company data and/or the additional company data” (Bennett ¶ [0147] 3rd sentence: The process changes, by the computer system, a collection of additional emission data from the sensor devices over the network based on the comparison between the emission data and the set of thresholds (step 604).¶ [0151] 2nd - 3rd sentence: The process illustrated in Fig.10 is an example of one implementation for step 604 in Fig.6. The process changes, by the computer system, the collection of the additional emission data from the sensor devices over the network based on the comparison between the emission data and the set of thresholds changing the emission data collected from the sensor devices in response to the emission data crossing of at least one of the set of thresholds (step 1000) ), “and to obtain values for additional KPIs from the processing” (Bennett ¶ [0138] machine learning model 504 generate interpolated emission data 520. In this example, interpolated emission data 520 is emission data generated to fill in gaps in emission data generated by sensors. Interpolated emission data 520 can be generated using satellite imagery for areas not covered by sensor devices. This sensor data can be obtained from various sources such as macro satellite imagery of methane, carbon dioxide (CO2), other emissions or pollutants. ¶ [0152] 1st-2nd sentences: With reference to Fig.11, an illustration of a flowchart of a process for collecting additional emission data at a higher frequency is depicted in accordance with an illustrative embodiment. The process illustrated in Fig.11 is one implementation for step 604 in Fig.6. ¶ [0153] 1st-2nd sentences: Fig.12, an illustration of a flowchart of a process for collecting a larger amount of additional emission data is depicted in accordance with an illustrative embodiment. The process in Fig.12 is example of one implementation for step 604 in Fig.6. ¶ [0154] 1st-2nd sentences: Turning to Fig.13, an illustration of a flowchart of a process for collecting additional emission data from a second number of sensor devices is depicted in accordance with an illustrative embodiment. The process illustrated in Fig.13 is an example of one implementation for step 604 in Fig.6. ¶ [0160] The process begins by correlating, by a machine learning model in the computer system, the emission data and the additional emission data with locations and timestamps to form time series emission data (step 1500). The process saves, by the computer system, the time series emission data in a historical database (step 1502). The process terminates thereafter. ¶ [0161] Turning to Fig.16 , an illustration of a flowchart of a process for correlating time series emission data with physical infrastructures is depicted in accordance with an illustrative embodiment. The step in this figure is an example of an additional step that can be used within the steps in the process in Fig.6 . The process correlates, by the machine learning model in the computer system, the time series emission data with physical infrastructures (step 1600). The process terminates thereafter. ¶ [0162] With reference to Fig.17, an illustration of a flowchart of a process for correlating time series emission data with weather data is depicted in accordance with an illustrative embodiment. The step in this figure is an example of an additional step that can be used within the steps in the process in Fig.6 . The process correlates, by the machine learning model in the computer system, the time series emission data with weather data (step 1700). The process terminates thereafter. ¶ [0164] Turning to Fig.19 , an illustration of a flowchart of a process for supplementing time series emission data with third party time series emission data is depicted in accordance with an illustrative embodiment. The step in this figure is an example of an additional step that can be used within the steps in the process in Fig.6. The process supplements, by the machine learning model in the computer system, the time series emission data for a location in the historical database with third party time series emission data in response to insufficient time series emission data being present for the location (step 1900). The process terminates thereafter). Rationale to have modified/combined Kind with/and Bennett are above and reincorporated. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Claims 6, 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over: Kind / Bennett as applied to claim 4 above, and in further view of Freier et al, US 20230289911 A1 hereinafter Freier. As per, Claim 6 Kind / Bennett teaches all the limitations in claim 4 above. Kind/Bennett not “wherein at least part of the additional company data is not included in the company data and is secured by third IT security means, and wherein making the additional company data accessible for the calculation module comprises transferring the at least part of the additional company data to the calculation module, and/or enabling the calculation module to access the at least part of the additional company data secured by the third IT security means”. Freier however in analogous art of providing insights teaches/suggests “wherein at least part of the additional company data is not included in the company data” (Freier ¶ [0020] 1st, 3rd sentences:, a company may find it challenging to provide carbon emissions reporting information because of lack of data associated with an upstream activity and a product of the upstream activity, both associated with a supplier… In particular, a company may have no way to systematically gather information upstream supplier to request the necessary upstream-supplier carbon emissions information to support generating carbon emissions reporting data. Indeed, per ¶ [0042] 2nd sentence: suppliers may be understandably reluctant to share production details and data with other manufacturers, such as the manufacturer of product 102) “and is secured by third IT security means” (Freier [0042] 4th-5th sentences solves this by carbon emissions management system 100 allowing suppliers to input data directly to the carbon emissions management system 100. For example, the first tier subassemblies 104 a-104 n manufacturers can submit carbon emissions data directly to the carbon emissions management system 100, bypassing the manufacturer of product 102. The carbon emissions data submitted by the suppliers is encrypted, anonymized, and secured in the infrastructure of carbon emissions management system 100 [as exemplary third IT security means], which also facilitates safe data exchange), “and wherein making the additional company data accessible for the calculation module comprises transferring the at least part of the additional company data to the calculation module,” (Freier ¶ [0042] 5th sentence: tier subassemblies 104 a-n manufacturers submit carbon emissions data directly to carbon emissions management system 100, bypassing the manufacturer of product 102. ¶ [0043] 3rd sentence: The carbon emissions data analytics interfaces configuration engine 110A receives carbon emissions interface data and may also provide simulation interface data and ecosystem interface data, as part of the carbon emissions management system 100 calculations. ¶ [0059] The carbon emissions data analytics interfaces configuration engine 110A operates to provide carbon emissions interface data 112 and simulation interface data and ecosystem carbon emissions data, discussed further below. The carbon emissions factor engine generates activity-carbon emissions data which is used to generate the carbon emissions interface data 112. The simulation computation and machine learning engine 140 can be used to generate the simulation interface data 114 associated with assessing the effect of different abatement levers, or mitigation efforts) “and/or enabling the calculation module to access the at least part of the additional company data secured by the third IT security means” (Freier ¶ [0042] 2nd sentence: suppliers may be understandably reluctant to share production details and data with other manufacturers, such as the manufacturer of product 102. To solve this Freier ¶ [0042] 4th-5th sentences the carbon emissions management system 100 allows suppliers to input data directly to the carbon emissions management system 100. For example, the first-tier subassemblies 104 a-104 n manufacturers can submit carbon emissions data directly to the carbon emissions management system 100, bypassing the manufacturer of product 102. The carbon emissions data submitted by the suppliers is encrypted, anonymized, and secured in the infrastructure of carbon emissions management system 100 [as exemplary third IT security means], which also facilitates safe data exchange. Then, at Freier ¶ [0043] 3rd sentence: The carbon emissions data analytics interfaces configuration engine 110A receives carbon emissions interface data and may also provide simulation interface data and ecosystem interface data, as part of the carbon emissions management system 100 calculations). It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Kind/Bennett’s method to have included Freier’s teaching or suggestion in order to have provided a more comprehensive carbon emissions management system, an alternative basis for performing carbon emissions management operations, can improve computing operations and interfaces for carbon emissions management (Freier ¶ [0003] 3rd sentence, ¶ [0019] in view of MPEP 2143 G and/or F). The predictability of such modification would have been corroborated by the broad level of skill of one of ordinary skills in the art as demonstrated by Kind ¶ [0107]-[0108] in view of Bennett ¶ [0205], in further view of Freier ¶ [0092]. Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar providing insights field of endeavor. In such combination each element merely would have performed the same analytical and insight related function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Kind / Bennett in further view of Freier, the to be combined elements would have fitted together, like puzzle pieces in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A). Claim 9. Kind/Bennett/Freier teaches all the limitations in claim 6 above. Furthermore, Kind teaches wherein at least two IT security means of the first IT security means, the second IT security means and the third IT security means are different from each other (Kind ¶ [0061] 1st, 4th-5th sentences: a first cryptographic signature mechanism for assigning the integrity is based on 1st cryptographic signature … and 1st cryptographic verification mechanism is based on verifying the 1st cryptographic signature with the public key of the at least one certifier participant. The signature mechanism can be chosen according to computing power or available crypto primitives. For example, the 1st cryptographic signature mechanism is fixed for all certifier participants within the ecosystem. Certifier participant then have to fulfill the given criteria for applying crypto mechanisms. ¶ [0072] the at least one subject participant derives the proof from the ESG credential by using a 2nd cryptographic signature mechanism and a private key of the subject participant. The subject participant can provide the ESG credential supplemented with the subject's own signature to assign an integrity and authenticity of the proof. In other embodiments, a secure communication between subject and verifier is used which dispenses with the second cryptographic signature mechanism), “or wherein the first IT security means, the second IT security means and the third IT security means are the same IT security means”. Claim 10. Kind/Bennett/Freier teaches all the limitations in claim 6 above. Furthermore, Kind teaches or suggests: “the second IT security means and the third IT security means comprises at least one of: providing the calculation module, the company data and/or the additional company data in a trusted execution environment (TEE); encrypting the company data and/or the additional company data; protecting the company data and/or the additional company data using distributed ledger technology; using homomorphic encryption to perform computations on encrypted company data and/or additional company data without decrypting it; proving the state of the company data and/or the additional company data by means of zero-knowledge proofs; (Kind ¶ [0061] 1st, 4th-5th sentences and ¶ [0072] teaches different security means associated with different business entities. ¶ [0065], the first cryptographic signature mechanism is based on a zero-knowledge proof. The usage of zero-knowledge proof mechanisms allows a certifier to issue an ESG credential without necessarily providing the ESG data as such or completely or in detail. For example, only attributes of the ESG data, like “value lower than threshold value” or “not manufactured in specific countries” or similar, are provided. These attributes however are verifiably provided, so that an integrity of the attributes and an authenticity of the certifier are guaranteed. ¶ [0066] For generating a zero-knowledge proof, a private key of the certifier is used in a zero-proof-signature mechanism, and for verifying, the corresponding public key is used. For use cases, where the ESG credential is forwarded by the subject participant as the proof, the usage of a zero-knowledge signature mechanism for generating the ESG credential enables to hold back certain detailed information about the ESG data, e.g. details about PCF values, which do not have to be disclosed to the verifier to fulfill the criteria of standards applied. ¶ [0073] In accordance with an embodiment, the second cryptographic signature mechanism is based on a zero-knowledge proof. A zero-knowledge proof mechanism can also advantageously be used by the subject participant. In use cases, where the certifier participant provides ESG credentials that disclose detailed ESG data, the subject participant can decide how much information to provide to the verifier participant. For example, only the information necessary to provide evidence that certain standard requirements are fulfilled, can be provided. The zero-knowledge proof is based on cryptographic key pairs, i.e., the public and the corresponding private key of the subject. The usage of a zero-knowledge signature mechanism by the subject participant assigns integrity of the ESG data provided by the subject participant and the authenticity of the subject participant. Moreover, depending on the data disclosed in the zero-knowledge proof, the authenticity of the certifier and the integrity of the ESG credential on which the ESG data provided is based, is determined by the zero-knowledge proof). anonymizing the company data and/or the additional company data; (Kind mid-¶ [0063] hen using group signatures, the actual certifier can be kept anonymous and the ESG credential is still trustworthy) obfuscating the company data and/or the additional company data; authenticating the company data and/or the additional company data (Kind ¶ [0008]: In accordance with the invention, the proof is configured to assign an authenticity of the at least one certifier participant providing the ESG credential … Kind ¶ [0020] authenticate the certificate issuer Kind ¶ [0043] authenticate the certifier and authenticate the subject participant); and enforcing access control on the company data and/or the additional company data via attribute-based, role-based, and/or context-based authorization”. Freier in analogous art of providing insights also teaches/suggests: providing the calculation module, the company data and/or the additional company data in a trusted execution environment (TEE); encrypting the company data and/or the additional company data; (Freier ¶ [0042] 4th-5th: the carbon emissions management system 100 allows suppliers to input data directly to the carbon emissions management system 100. For example, the first tier subassemblies 104 a-104 n manufacturers can submit carbon emissions data directly to the carbon emissions management system 100, bypassing the manufacturer of product 102. The carbon emissions data submitted by the suppliers is encrypted … and secured in the infrastructure of carbon emissions management system 100, which also facilitates safe data exchange) protecting the company data and/or the additional company data using distributed ledger technology; using homomorphic encryption to perform computations on encrypted company data and/or additional company data without decrypting it; proving the state of the company data and/or the additional company data by means of zero-knowledge proofs; anonymizing the company data and/or the additional company data; (Freier ¶ [0042] 4th-5th: the carbon emissions management system 100 allows suppliers to input data directly to the carbon emissions management system 100. For example, the first tier subassemblies 104 a-104 n manufacturers can submit carbon emissions data directly to the carbon emissions management system 100, bypassing the manufacturer of product 102. The carbon emissions data submitted by the suppliers is… anonymized, and secured in the infrastructure of carbon emissions management system 100, which also facilitates safe data exchange) obfuscating the company data and/or the additional company data; authenticating the company data and/or the additional company data; and enforcing access control on the company data and/or the additional company data via attribute-based, role-based, and/or context-based authorization”. Rationales to have modified/combined Kind/Bennett/Freier were presented above. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Conclusion Following is made of record and considered pertinent to Applicant’s disclosure: - Coppolino et al, Combining intel sgx and homomorphic encryption for cloud industrial control systems, IEEE Transactions on Computers, 70 no5 p711-p24, May 18 2020 - EP 3514742 A1 teaching In manufacturing data analysis such problem has until now been solved with two approaches. The first approach is an "a-priori" one and the second approach is "a-posteriori" one. According to the first "a-priori" approach, the link to the missing contexts in the MOM data warehouse is performed at a product level. This approach is typically used in case of a general purpose data warehouse design, where the data models of all relevant MOM data sources are known in advance. Hence the calculation of the extensions is done, within the system, in a built-in manner, where the user might even be unaware of it. a) providing a data extensor module for processing the data received from the data source in order to add, upon need, a context identifier linked to the source performance parameter, whereby the added context identifier is present in the basic set but it is not present in the source set; b) by the data extensor module, processing data received from the data source to obtain augmented data, where the augmented data are exposed in an extended data model compatible with the basic data model; and, c) collecting the augmented data into the data-warehouse - EP 4579505 A1 teaching devices and methods for compliance of cybersecurity requirements of hardware and software components: verification process may point to a-priori verification of the corresponding digital entity 120 and/or the product. A-priori verification may include that the processor may perform a verification process for the digital entity 120 before the intended use of the digital entity 120 and/or the product. The verification process may include any operation that the processor performs to determine a verification state of the digital entity, such as to access to the container of the digital entity, determination of the result or the verification status as described herein. The processor may perform a-posteriori verification which may include a monitoring of the container of the digital entity 120. In that case, the processor may monitor the digital entity 120 and perform the verification process continually or in predefined time intervals, which may include a periodic monitoring. The monitoring may be based on the intended use of the digital entity 120 and/or the product in the real-world applications. Furthermore, the verification process may also be relevant to both a-priori and a-posteriori types of verification of the digital entity 120 and/or the product. In that case, a mixture of the both approaches may be used to ensure the compliance of the digital entity (120 and/or the product with the cybersecurity requirements. - US 20100131316 A1 Carbon management for sourcing and logistics - US 20110004925 A1 Data processing with a posteriori or a priori authentication - US 20250117807 A1 Zero Knowledge Attestations for Carbon Footprint Metrics - US 20250258951 A1 Systems and methods for zero-knowledge proof (zkp) modeling - US 20090018884 A1 Method and system for tracking and reporting emissions Any inquiry concerning this communication or earlier communications from the examiner should be directed to OCTAVIAN ROTARU whose telephone number is (571)270-7950. The examiner can normally be reached on 571.270.7950 from 9AM to 6PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PATRICIA H MUNSON, can be reached at telephone number (571)270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form. /OCTAVIAN ROTARU/ Primary Examiner, Art Unit 3624 A October 8th, 2026 1 MPEP 2106.04(a): “examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible”. 2 Assuming correction as per above