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 .
Claims 1-20 are pending.
Applicant’s amendment filed 9 April 2026 is not sufficient to overcome the previous rejections under 35 U.S.C. 101, 112 and introduces more issues of 112 discussed below.
Response to Arguments
Applicant's arguments filed 9 April 2026 have been fully considered but they are not persuasive.
Regarding the rejection under 35 U.S.C.101, applicant argues at page 12 of the response:
“The Office Action's characterization treats the claims at an improper level of abstraction by ignoring the specific technical limitations. Under MPEP § 2106.04(a), the USPTO must evaluate the claim as a whole when determining whether a claim is directed to an abstract idea. See also Enfish, LLC V. Microsoft Corp., 822 F.3d 1327, 1337 (Fed. Cir. 2016) (claims directed to "a specific improvement to the way computers operate" are not abstract). The amended claims describe a specific technical architecture, a data platform executing a trained ML model on EDI-specific data types and using blockchain tokenization to create a tamper-resistant audit trail, that constitutes a specific technical improvement over generic methods of evaluating companies”.
In response the examiner is not persuaded. Processing data using a machine learning model is recited at a high level of generality. As written no specific technical limitations show how the recited model is trained. The specification merely repeats the claim language without further details. Furthermore the amended claim 1 no longer include the term “trained”.
Applicant argues at page 13 of the response:
“The Office Action dismissed the blockchain tokenization as "insignificant extra- solution activity" recited at "a high level of generality." Applicant respectfully disagrees. The claims do not simply recite "store data on a blockchain." The claims specifically require: generating one or more blockchain tokens from company data; encoding the company data or a hash thereof in those tokens; storing the tokens in a blockchain- based digital ledger; linking the scores and rankings to an index verifiably recorded on that ledger; and communicating the tokens to designated parties. This specific combination of steps tied to an EDI evaluation workflow represents a practical application with concrete technological effects, namely, creating an immutable, independently verifiable record of company EDI performance data. This directly parallels the practical application analysis in DDR Holdings, LLC V. Hotels.com, L.P., 773 F.3d 1245 (Fed. Cir. 2014), where the court found claims patent-eligible because they address a challenge specifically arising in a technological environment”.
In response the examiner is not persuaded.
Encoding data or a hash in one or more blockchain token, storing the token in a digital ledger is mere routine to secure data in a tamper-resistant format. Ranking companies Equity Diversity Inclusion (EDI) even when using a machine learning model without any specifics on the machine learning model amounts to a mere abstract idea implemented with computerized steps.
Applicant argues at page 14 of the response:
“The claims include significantly more than the judicial exception under Step 2B. The additional elements in the claims collectively amount to significantly more than the judicial exception. The combination of: (1) a trained machine learning model that specifically processes EDI-type data (workforce composition, governance structure, and organizational diversity data); (2) automated scoring using output values generated by the ML model; (3) tokenization of EDI company data into blockchain tokens; and (4) a verifiable, tamper-resistant blockchain index, goes well beyond what was "well- understood, routine, and conventional" at the time of filing. The Office Action has not met the burden of citing actual evidence that this particular combination of features was routinely performed in the field. See MPEP § 2106.07(a) (examiner must provide actual evidence or take official notice when asserting a combination is well-understood, routine, and conventional). The integration of a trained ML model specifically processing EDI-type data with blockchain-based tokenization and immutable index recording in a company evaluation workflow was not conventional or well-understood as a combination at the filing date of April 29, 2021”.
In response the examiner points out applicant seems to argue the claims as amended. However the now claimed “workforce composition data”, “governance structure data” are not even discussed in the specification thus are mere company data in general used in training a machine learning model. The examiner points out data used to train a machine learning model of course has to be appropriate for each type of model to learn. “EDI-type data” is not specifically defined in any manner in the specification as originally filed. In fact Equity Diversity Inclusion merely appear twice in the entire specification, each type only as “e.g.” not a defined “EDI-type data”. The pertinent paragraphs are partially reproduced below:
[0010]: Token based compensation for company data allows for the direct control and monetization of company data (e.g., hiring practices, equity, diversity, and inclusion.
[0074]: “the sellers 150 may represent any number of fund managers, monitoring groups (e.g., diversity, equity, inclusion, LGBTQ+, etc.),
Even if applicant intend for the letter E to mean “equality”, there is no discussion of what “EDI-type data” is in the entire specification as originally filed.
Applicant presents no further arguments. For all the reasons discussed above, the rejection of all pending claims under 35 U.S.C. 101 is maintained.
Regarding the rejection under 35 U.S.C. 112, applicant’s amendment filed 9 April 2026 is not sufficient to overcome the previous rejection of claim 12 and introduces more 112 issues in claims 1, 13, 15.
Applicant seems to overlook the 112 issue of “cryptographically encode the company data or a hash thereof” of independent claim 13 while amending similar features in independent claims 1 and 15.
The amendment also introduces new 112 issues discussed in this Final Office action below.
Regarding the rejection under 35 U.S.C. 103 of all pending claims, applicant argues the claims as amended. However the amended claim language of “workforce composition data”, “governance structure data”, organization diversity data” are not supported by the specification as originally filed, thus are interpreted as any data related to a company including the claimed data.
Applicant argues at page 15 last paragraph to page 16 1st paragraph of the response:
“There does not appear to be any motivation to combine Fombrun, Halloran, and Blaikie. An obviousness rejection requires not only that the prior art disclose each claimed element, but also that there be an articulated reason with some rational underpinning to combine known elements to arrive at the claimed invention. KSR Int'l Co. V. Teleflex Inc., 550 U.S. 398, 418 (2007). The Office Action's stated motivation - that it is "customary in the art to use artificial intelligence and machine learning models in data analysis" - is impermissibly conclusory. The fact that ML models are used generally in data analysis does not provide motivation to train a specific ML model on EDI organizational data and integrate it with blockchain-based tokenization of EDI company scores. Fombrun is an academic survey of corporate reputation indices, Halloran relates to social accountability standards, and Blaikie is directed to different systems. There is no teaching, suggestion, or motivation in any of these references, individually or in combination, to build the specific EDI-focused machine learning and blockchain tokenization system claimed here”.
In response the examiner is not persuaded. Applicant seems to ignore the capabilities of one of ordinary skills in the art. One of ordinary skills in the art supposedly knows something about the art and knows how to apply the principles taught in the references alone or in combination to achieve desired results. The examiner maintains motivations were fully provided and repeated in this Final Office action below.
Furthermore as amended, the claims do not require “train a specific ML model on EDI organizational data and integrate it with blockchain-based tokenization of EDI company scores” nor “to build the specific EDI-focused machine learning and blockchain tokenization system” as argued by the applicant.
The examiner points out applicant quote of the previous rejection of claims 1 over the combination of Fombrun/Halloran/Blaikie is incomplete. The examiner did cite Blaikie paragraph [0089] as teaching the use of a machine learning model and artificial intelligence in data analysis is customary and did provide motivations to combine Blaikie with the Fombrun and Halloran references in the previous Office action.
Regarding the second set of rejection of claims 1-20 over the combination of five references Fombrun/Halloran/Williams/Smith/Cella, applicant argues at page 16 last paragraph of the response:
“Applicant respectfully traverses this rejection for the same reasons articulated above regarding the first ground of rejection, and for the following additional reasons. Cella Does Not Cure the Deficiencies of the Other References for the ML Model”.
In response the examiner is not persuaded and maintains the claims are obvious in light of the combined references as discussed in the previous Office action.
Applicant argues at page 18 first paragraph of the response:
“The Office Action includes impermissible hindsight reconstruction from five non- analogous references. The combination of five references, an academic survey of reputation ratings (Fombrun), a social accountability standard framework (Halloran), a company valuation/scoring system (Williams), a commercial document blockchain platform (Smith), and an industrial loT Al/ledger system (Cella), drawn from entirely different technical fields and addressing fundamentally different problems, reflects impermissible hindsight. There is no principled technical reason why a person of ordinary skill would look to an industrial loT blockchain system (Cella) and a UCC-filing distributed ledger (Smith) to address the problem of creating an ML-based EDI company scoring and blockchain tokenization system”.
In response the examiner is not persuaded. Applicant again seems to ignore the capabilities of one of ordinary skills in the art. One of ordinary skills in the art supposedly knows something about the art and knows how to apply the principles taught in the references alone or in combination to achieve desired results.
Furthermore In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Applicant presents no other specific arguments. For all the reasons discussed above, the rejection of all pending claims is maintained using the references of record in both sets of rejection under 35 U.S.C. 103.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Analysis of eligibility subject matter of claim 1:
Step 1: claim 1 recites a "method for..."; the claim recites a series of steps and therefore is a process.
Step 2A Prong One: claim 1 recites the limitations: analyzing the source data captured based on criteria including at least equality, diversity, and inclusion associated with the one or more companies to generate company data for each of the one or more companies; scoring each of the plurality of companies... ; ranking the one or more companies based on the criteria;
Nothing in the claim element precludes the steps from practically being performed by a human mind or with pen and paper. The criteria of equality, diversity, and inclusion associated with the company are mere generic attributes of any company. Thus the claim limitations fall within the mental process grouping of abstract ideas (concepts performed in the human mind including collection, analysis, scoring, ranking and communicating with other entities). Note automatically scoring and automatically ranking are no more than mere instructions to apply the exception using a generic computer component.
Step 2A Prong Two: the judicial exception is not integrated into a practical application. The claim recites the additional elements:
capturing source data regarding one or more companies utilizing a data platform;
providing feedback to the plurality of companies including the ranking and the scores to implement changes.
linking the scores and rankings of each of the plurality of companies to an index verifiably recorded on the blockchain;
communicating the company data including at least the scores and ranking from the data platform to one or more designated parties.
However, the additional elements amount to mere insignificant extra solution activity (MPEP 2106.05(g). Accordingly, the claim as a whole does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Note any data capture, feedback, index and communication has to utilize a data platform.
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claimed "tokenizing the company data in one or more blockchain tokens stored in a digital ledger of the data platform", "using a machine learning model executed by the data platform", "the machine learning model processes workforce composition data, governance structure data and organizational diversity data”, “generating one or more blockchain tokens wherein the one or more tokens encode the company data or a hash thereof”, recited at a high level of generality are simply utilizing a machine learning model and a blockchain as a tool to automate and perform an abstract idea thus do not provide an inventive concept because they do not improve any technology or technical field, do not apply the judicial exception with or by use of a particular machine, do not add specific limitation other than what is well-understood, routine, conventional activity in the field, do not add unconventional steps that confine the claim to a particular useful application, do not include other meaningful limitations beyond linking the use of the judicial exception to a particular technological environment. The additional elements of tokenizing and storing are recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner. (See MPEP 2106.05(d)(II) (iv).
Thus claim 1 is ineligible.
Claim 2 merely further describes the data source, considered insignificant extra solution activity (MPEP 2106.05(g).
Claim 3 merely adds storing the company data, considered insignificant extra solution activity (MPEP 2106.05(g).
Claim 4 merely specifies how data is accessed, considered insignificant extra solution activity (MPEP 2106.05(g).
Claim 5 merely adds grouping data into a data asset, performing transactions on the data platform and verifying of the transactions, considered insignificant extra solution activity (MPEP 2106.05(g).
Claim 6 merely adds an index, considered insignificant extra solution activity (MPEP 2106.05(g).
Claim 7 merely adds providing feedback to the company, considered insignificant extra solution activity (MPEP 2106.05(g)..
Claim 8 merely further describe the feedback, considered insignificant extra solution activity (MPEP 2106.05(g)..
Claim 9 merely adds searching the company based on the company data, considered insignificant extra solution activity (MPEP 2106.05(g).
Claim 10 merely adds an automatic transaction in response to company data, considered insignificant extra solution activity (MPEP 2106.05(g).
Claim 11 merely specifies when communicating is performed, considered insignificant extra solution activity (MPEP 2106.05(g).
Claim 12 merely describes the data platform, considered insignificant extra solution activity (MPEP 2106.05(g).
Claims 13, 14 merely correspond to a system of a data platform and a plurality of electronic devices for performing the method of claims 1, 3-4.
Claim 15 essentially corresponds to a server and databases for performing the operations recited in claim 1.
Claim 16 recites limitations similar to claim 10.
Claim 17 merely adds a blockchain and ledger recited at ahigh level of generality thus considered insignificant extra solution activity (MPEP 2106.05(g).
Claim 18 recites limitations similar to claim 8.
Claim 19 recites limitations similar to claim 5.
Claim 20 recites limitations similar to claim 4.
Thus, although claims 13, 15 describe components that facilitate operations recited in claim 1, they are mere generic computer components that do not constitute additional elements sufficient to amount to significantly more than the judicial exception.
The dependent claims in the same manner do not integrate the judicial exception into a practical application and do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
For all the reasons discussed above all the pending claims are ineligible.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph 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 the first paragraph of pre-AIA 35 U.S.C. 112:
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 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) 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 applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The specification as originally filed does not support the amended features of:
“the machine learning model processes workforce composition data, governance structure data, and organizational diversity data” now recited in claims 1, 13, 15.
The specification as originally filed does not support the “cryptographically encode the company data or a hash thereof” recited in claim 13.
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 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The specification as originally filed does not support the amended features of:
“the machine learning model processes workforce composition data, governance structure data, and organizational diversity data” now recited in claims 1, 13, 15.
The specification as originally filed does not support the “cryptographically encode the company data or a hash thereof” recited in claim 13.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fombrun, Charlesu. "List of lists: A compilation of international corporate reputation ratings." Corporate reputation review 10 (2007): 144-153., of record, in view of Halloran, Jr. et al (US 20060100897) of record, further in view of Blaikie III et al (US 202190192852) of record.
Regarding claim 1, Fombrun teaches a method for analyzing company data from a plurality of companies, comprising:
capturing source data regarding the plurality of companies utilizing a data platform (see at least the abstract);
analyzing the source data captured based on criteria associated with each of the plurality of companies to generate company data for each of the plurality of companies (see at least Introduction page 144 right column last paragraph).
The difference is Fombrun does not specifically show the captured source data is from one or more public data sources and private data sources and Fombrun does not specify the criteria including at least equality, diversity, and inclusion;
however it is well known in the art to include such sources and criteria as shown by Halloran (see at least 0096 free trade, equal conditions for competition, fair and equitable treatment for all participants, Halloran 0005: provide implementation procedures to further the goal of measuring the level of social responsibility in a business and providing a vehicle for comparing that level to other businesses or to a standard). Note the private data sources are met by the enterprise data, the public sources are met by the standard to which enterprise data is compared;).
it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include such data sources and criteria while implementing the company analysis of Fombrun depending on required types of company analysis;
Fombrun/Halloran further teaches: automatically scoring each of the plurality of companies based on the criteria to generate scores (see at least Fombrun page 146);
automatically ranking each of the plurality of companies based on the criteria to generate rankings (see at least Fombrun page 146);
providing feedback to the plurality of companies including the ranking and the scores through the data platform to enable data driven changes (see at least Halloran 0066 obtain feedback, 0709 suggestions for managing improvements); and
communicating the one or more blockchain tokens associated with the company data including at least the scores and rankings from the data platform to one or more designated parties (see at least Fombrun pages 147-.153),
Fombrun/Halloran does not specifically show:
analyzing the source data capture using a machine learning model executed by a processor of the data platform, wherein the machine learning model processes workforce composition data, governance structure data, and organizational diversity data associated with the plurality of companies,
wherein the company data provides an index for the scores and rankings of the plurality of companies.
However, it is customary in the art to create an index to catalog data sets as shown by Blaikie (see at least 0039) and to use artificial intelligence and machine learning models in data analysis (see Blaikie 0089). Since the method of Fombrun/Halloran scores and ranks companies, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the company data to provide an index for the scores/ranks in order to categorize company data and to use a machine learning model to process company data including the claimed workforce composition, governance structure, and organizational diversity in order to analyze captured company data regarding its human resources,
Blaikie further teaches: linking the scores and rankings of each of the plurality of companies to an index (see at least 0039). The claimed "verifiably recorded on the digital ledger" and "wherein the index is verifiable and resistant to modification after recordation" merely read on the fact that any record of a digital ledger is verifiably recorded and resistant to modification after modification.
it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include such features while implementing the method of Fombrun/Halloran in order to securely store the index for subsequent verification;
Blaikie further teaches: tokenizing the company data in one or more blockchain tokens utilizing the data platform, the one or more blockchain tokens are stored in a digital ledger of the data platform to cryptographically encode the company data or a hash thereof (see at least Blaikie 0082: in one embodiment, the data platform 123 may implement a blockchain ledger, manager, or technology. 0083: The blockchain is utilized as a way to store and communicate the data 126, transactions 128, and legal actions 129. The blockchain may utilized one or more distinct ledgers for different entities, services providers, types of data, users, or so forth.).
Note Halloran clearly suggests company data include equality diversity and inclusion associated with each of the plurality of companies (see at least Halloran 0096 free trade, equal conditions for competition, fair and equitable treatment for all participants). Halloran further teaches the importance of securing gathered company data for authorized access (see at least 0010: issues touched upon by the SAIP are highly sensitive to organizational management).
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the claimed tokenizing company data including equality diversity and inclusion associated with each of the plurality of companies, storing the tokens in blockchain ledger and communication the blockchain tokens as taught by Blaikie while implementing the method of Fombrun/Halloran in order to keep and communicate sensitive company data in a secure environment.
Regarding claim 2, Fombrun/Halloran/Blaikie further teaches or suggests the method of claim 1, wherein the source data is captured from a plurality of public resources and private resources (see at least Halloran 0005: provide implementation procedures to further the goal of measuring the level of social responsibility in a business and providing a vehicle for comparing that level to other businesses or to a standard). Note the private resources are met by the enterprise data, the public resources are met by the standard to which enterprise data is compared.
Regarding claim 3, Fombrun/Halloran/Blaikie further teaches storing the company data in a secure storage for access by authorized parties (see at least Blaikie 0004).
Regarding claim 4, Fombrun/Halloran/Blaikie teaches the method of claim 1, wherein the company data is accessible through the one or more blockchain or a non-fungible token (see at least Blaikie 0083: the blockchain is utilized as a way to store and communicate the data 126, transactions 128, and legal actions 129. The blockchain may utilized one or more distinct ledgers for different entities, services providers, types of data, users, or so forth.).
Regarding claim 5, Fombrun/Halloran/Blaikie teaches or suggests the method of claim 1, further comprising grouping the company data into a data asset associated with the one or more blockchain tokens (see at least Halloran 0686-0688);
associating the data asset with a data platform including one or more servers and databases (see at least Halloran 0158 record keeping infrastructure);
receiving transaction information for the one or more blockchain tokens (see at least Halloran 0689);
performing one or more transactions for the one or more blockchain tokens based on the transaction information, wherein the one or more transactions are performed utilizing the data platform (see at least Halloran 0690 distribute copies to members); and
providing verification of the transaction for the one or more blockchain tokens (see at least Halloran 0040-0041).
Regarding claim 6, Fombrun/Halloran/Blaikie teaches or suggests the method of claim 1, further comprising:
creating an index of at least a portion of the plurality of companies based on the company data, the scoring, and the ranking (see at least Halloran 0065 matrix of categories of criteria).
Regarding claim 7, Fombrun/Halloran/Blaikie teaches or suggests the method of claim 1, further comprising: providing feedback to the plurality companies to enhance equality, diversity, and inclusion in response to the scoring and the ranking (see at least Halloran 0066! obtain feedback).
Regarding claim 8, Fombrun/Halloran/Blaikieteaches or suggests the method of claim 7, wherein the feedback includes at least suggestions for hiring and promotions within the plurality of companies (see at least Halloran 0709 suggestions for managing improvements).
Regarding claim 9, Fombrun/Halloran/Blaikie teaches or suggests the method of claim 1, further comprising: searching the plurality of companies in the data platform based on the company data (see at least Halloran 0543 find international business partners).
Regarding claim 10, Fombrun/Halloran/Blaikie teaches or suggests the method of claim 1, further comprising: automatically performing a transaction in response to the company data or changes to the company data (see at least Halloran 0717 Fix-As- Fail).
Regarding claim 11, Fombrun/Halloran/Blaikie teaches or suggests the method of claim 1, wherein the communicating is performed in response to generating the company data or changes in the company data (see at least Halloran 0739 shift in culture).
Regarding claim 12, Fombrun/Halloran/Blaikie teaches or suggests the method of claim 1, wherein the data platform is a trading platform for performing the one or more transactions, wherein the data platform communicates with a plurality of devices executing a mobile application in communication with the data platform (see at least Halloran 0054 multilateral trade).
Claims 13-14 essentially recite limitations similar to claims 6, 3, 4 in form of systems thus are rejected for the same reasons discussed in claims 6, 3, 4 above.
Claims 15, 16, 18, 19, 20 correspond to data platforms of server and databases for method claims 6, 10, 7, 5, 4 respectively thus are rejected for the same reasons discussed in claims 6, 10, 7, 5, 4 above.
Regarding claim 17, Fombrun/Halloran/Blaikie teaches the data platform of claim 16, wherein the transaction is performed utilizing blockchain, and wherein the verification is recorded in a blockchain ledger associated with the plurality of databases (see at least Blaikie 0082: In one embodiment, the data platform 120 may implement a blockchain ledger, manager, or technology. 0083: The blockchain is utilized as a way to store and communicate the data 126, transactions 128, and legal actions 129. The blockchain may utilized one or more distinct ledgers for different entities, services providers, types of data, users, or so forth.).
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Fombrun, Charlesu. "List of lists: A compilation of international corporate reputation ratings." Corporate reputation review 10 (2007): 144-153., of record, in view of Halloran, Jr. et al (US 20060100897) of record, in view of Williams (US 20080300960) of record, in view of Smith et al (US 10476847) of record, further in view of Cella et al (US 20200348662 A1) of record.
Regarding claim 1, Fombrun teaches a method for analyzing company data from a plurality of companies, comprising:
capturing source data regarding the plurality of companies utilizing a data platform (see at least the abstract);
analyzing the source data captured based on criteria associated with each of the plurality of companies to generate company data for each of the plurality of companies (see at least Introduction page 144 right column last paragraph).
The difference is Fombrun does not specifically show the captured source data is from one or more public data sources and private data sources and Fombrun does not specify the criteria including at least equality, diversity, and inclusion;
however it is well known in the art to include such criteria and data sources as shown by Halloran (see at least 0096 free trade, equal conditions for competition, fair and equitable treatment for all participants, Halloran 0005: provide implementation procedures to further the goal of measuring the level of social responsibility in a business and providing a vehicle for comparing that level to other businesses or to a standard). Note the private data sources are met by the enterprise data, the public sources are met by the standard to which enterprise data is compared;).
it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include such data sources and criteria while implementing the company analysis of Fombrun depending on required types of company analysis;
Fombrun/Halloran further teaches:
automatically scoring each of the plurality of companies based on the criteria to generate scores (see at least Fombrun page 146);
automatically ranking each of the plurality of companies based on the criteria to generate rankings (see at least Fombrun page 146);
providing feedback to the plurality of companies including the ranking and the scores through the data platform to enable data driven changes (see at least Halloran 0066 obtain feedback, 0709 suggestions for managing improvements); and
communicating the one or more blockchain tokens associated with the company data including at least the scores and rankings from the data platform to one or more designated parties (see at least Fombrun pages 147-.153),
Fombrun/Halloran does not specifically show:
linking the scores and rankings of each of the plurality of companies to an index,
wherein the company data provides the index for the scores and rankings of the plurality of companies,
However it is customary in the art to provide scores index as shown by Williams (see at least 0224). it would have been obvious to one of ordinary skill in the art to include such features taught by Williams while implementing the method of Fombrun/Halloran in order to categorize company data to assist searching;
Fombrun/Halloran/Williams does not specifically show:
tokenizing the company data in one or more blockchain tokens utilizing the data platform, wherein the one or more blockchain tokens encode the company data or a hash thereof, the one or more blockchain tokens are stored in a digital ledger of the data platform that is blockchain-based;
However it is customary in the art to tokenize company data, store them in a digital ledger and communicate them as shown by Smith (see at least the abstract, col.9 lines 27-47). Note the tokenized company data reads on any company documents and/or communication on a distributed ledger or blockchain platform of Smith.
Note Halloran clearly suggests company data include equality diversity and inclusion associated with each of the plurality of companies (see at least Halloran 0096 free trade, equal conditions for competition, fair and equitable treatment for all participants). Halloran further teaches the importance of securing gathered company data for authorized access (see at least 0010: issues touched upon by the SAIP are highly sensitive to organizational management).
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the claimed tokenizing company data including equality diversity and inclusion associated with each of the plurality of companies, storing the tokens in blockchain ledger and communicating the blockchain tokens as taught by Smith while implementing the method of Fombrun/Halloran in order to store and communicate sensitive company data in a secure environment. Note the claimed index "verifiably recorded on the digital ledger" and "wherein the index is verifiable and resistant to modification after recordation" merely read on the fact that any record of a digital ledger is verifiably recorded and resistant to modification after modification.
Fombrun/Halloran/Williams/Smith does not specifically show the claimed: analyzing the source data capture using a machine learning model executed by a processor of the data platform; the machine learning model processes workforce composition data, governance structure data and organizational diversity data,
However it is customary in the art to analyze data using a machine learning model as shown by Cella (see at least 0039: the AI model operates on sensor data from an industrial environment; for an industrial loT distributed ledger, including a distributed edger supporting the tracking of transactions executed in an automated data marketplace for industrial loT data; for a network-sensitive collector).
it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include a machine learning model as taught by Cella while implementing the method of Fombrun/Halloran/Wlliams/Smith in order to analyze captured data for the ledger and to use the machine learning model to process captured data from each company including workforce composition data, governance structure data, organizational diversity data in order to analyze captured company data regarding its human resources.
Regarding claim 2, Fombrun/Halloran/Williams/Smith/Cella further teaches or suggests the method of claim 1, wherein the source data is captured from a plurality of public resources and private resources (see at least Halloran 0005: provide implementation procedures to further the goal of measuring the level of social responsibility in a business and providing a vehicle for comparing that level to other businesses or to a standard). Note the private resources are met by the enterprise data, the public resources are met by the standard to which enterprise data is compared.
Regarding claim 3, Fombrun/Halloran/Williams/Smith/Cela further teaches storing the company data in a secure storage for access by authorized parties (see at least Smith col.9 lines 21-24: For example, in some embodiments, the system can utilize one or more embodiments for private sharing of data on a trustless DLT platform as described herein.).
Regarding claim 4, Fombrun/Halloran/Williams/Smith/Cella teaches the method of claim 1, wherein the company data is accessible through the one or more blockchain or a non-fungible token (see at least Smith col.9 lines 33-40: As a non-limiting example, in some embodiments, implementation of one or more specific smart contracts and/or private data sharing technologies on a DLT platform can provide frameworks and/or solutions to generate a smart UCC platform that facilitates submission and tracking of Uniform Commercial Code (UCC) filings on a distributed ledger or blockchain platform as further described herein.).
Regarding claim 5, Fombrun/Halloran/Williams/Smith/Cella teaches or suggests the method of claim 1, further comprising grouping the company data into a data asset associated with the one or more blockchain tokens (see at least Halloran 0686-0688);
associating the data asset with a data platform including one or more servers and databases (see at least Halloran 0158 record keeping infrastructure);
receiving transaction information for the one or more blockchain tokens (see at least Halloran 0689); performing one or more transactions for the one or more blockchain tokens based on the transaction information, wherein the one or more transactions are performed utilizing the data platform (see at least Halloran 0690 distribute copies to members); and
providing verification of the transaction for the one or more blockchain tokens (see at least Halloran 0040-0041).
Regarding claim 6, Fombrun/Halloran/Williams/Smith/Cella teaches or suggests the method of claim 1, further comprising: creating an index of at least a portion of the one or more companies based on the company data, the scoring, and the ranking (see at least Halloran 0065 matrix of categories of criteria).
Regarding claim 7, Fombrun/Halloran/Williams/Smith/Cella teaches or suggests the method of claim 1, further comprising: providing feedback to the one or more companies to enhance equality, diversity, and inclusion in response to the scoring and the ranking (see at least Halloran 0066 obtain feedback).
Regarding claim 8, Fombrun/Halloran/Williams/Smith/Cella teaches or suggests the method of claim 7, wherein the feedback includes at least suggestions for hiring and promotions within the one or more companies (see at least Halloran 0709 suggestions for managing improvements).
Regarding claim 9, Fombrun/Halloran/Williams/Smith/Cella teaches or suggests the method of claim 1, further comprising: searching the one or more companies in the data platform based on the company data (see at least Halloran 0548 find international business partners).
Regarding claim 10, Fombrun/Halloran/Villiams/Smith/Cella teaches or suggests the method of claim 1, further comprising: automatically performing a transaction in response to the company data or changes to the company data (see at least Halloran 0717 Fix-As- Fail).
Regarding claim 11, Fombrun/Halloran/Villiams/Smith/Cella teaches or suggests the method of claim 1, wherein the communicating is performed in response to generating the company data or changes in the company data (see at least Halloran 0739 shift in culture).
Regarding claim 12, Fombrun/Halloran/Williams/Smith/Cella teaches or suggests the method of claim 1, wherein the data platform is a trading platform for performing the one or more transactions, wherein the data platform communicates with a plurality of devices executing a mobile application in communication with the data platform (see at least Halloran 0054 multilateral trade).
Claims 13-14 essentially recite limitations similar to claims 6, 3, 4 in form of systems thus are rejected for the same reasons discussed in claims 6, 3, 4 above.
Claims 15, 16, 18, 19, 20 correspond to data platforms of server and databases for method claims 6, 10, 7, 5, 4 respectively thus are rejected for the same reasons discussed in claims 6, 10, 7, 5, 4 above.
Regarding claim 17, Fombrun/Halloran/Villiams/Smith/Cella teaches the data platform of claim 16, wherein the transaction is performed utilizing blockchain, and wherein the verification is recorded in a blockchain ledger associated with the plurality of databases (see at least Smith col.9 lines 40- 47: Further, in some embodiments, implementation of one or more specific smart contracts and/or private data sharing technologies on a DLT platform can provide frameworks and/or solutions to generate a smart company platform for controlling, managing, and/or communicating company documents and/or communications on a distributed ledger or blockchain platform as further described herein., see also col.29 lines 26-28: In some embodiments, each individual/entity has a unique identifier on the blockchain or DLT network called a network identity. In some embodiments, actions can be taken by each individual/entity by publishing messages to the blockchain or DLT using their network identity. In some embodiments, a network identity should meet the following criteria: (1) ability to verify the authenticity of a message that is published to the blockchain or DLT using network identity; and (2) ability to share information privately between a group of network identities. In some embodiments, network identities can be permissioned via the smart contracts to act in their predefined capacities based on their role(s), such as a shareholder for example, within the company.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zheng et al (CN 107066599 A) teach obtaining company information. An interpreter is provided with an analyzer. An enterprise data is established for a specific enterprise. An enterprise company description is classified. A business entity base is established. A part of speech is marked to identify label rule matching technology. An enterprise information structure of a paragraph is analyzed. Entity relationship is extracted for keyword optimizing similarity ranking through a word vector model and an inverted index. Mark enterprise of enterprise information is returned according to a search keyword.
Cole (AU-2013100087-A4) teaches a method and system implement a functional model (11) for measuring corporate performance based on principles of corporate governance and utilise a hierarchical data structure (27) having a plurality of tiers (29) ranging from a base level tier (29a) to a final level tier. The base level tier (29a) has members made up of a plurality of elements (31), each element defining an attribute of a corresponding element grouping (35) being one of a plurality of element groupings of a higher level tier than the elements (31). Each element grouping (35) is a member of a higher order further element grouping (37), which is one of a plurality of further element groupings of a higher level tier than the preceding element grouping.
April et al (US-20110015958-A1) teach Systems, devices, and methods are provided for workforce planning models. Technologies are described to manage human capital decisions. Decision making models and related tools are described that support the development and implementation of workforce strategies, programs and policies. In one model, resources may be allocated to specific practices (policies, programs, initiatives, organizational culture) used to attract and retain valued employees. Resources may be increased or decreased until the optimal allocation of resources is found that is most likely to enable the achievement of specific goals (e.g., attraction, retention, readiness, and representation).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
/UYEN T LE/Primary Examiner, Art Unit 2156 23 June 2026
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