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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 16 December 2025 has been entered.
Claims 1-20 are pending.
Response to Amendment
Applicant’s amendment filed 16 December 2025 is not sufficient to overcome the rejection under 35 U.S.C. 101 and 112 and introduces more 112 issues discussed below.
Response to Arguments
Applicant's arguments filed 16 December 2025 have been fully considered but they are not persuasive.
Regarding the rejection under 101, applicant argues at page 12 of the response:
“At Step 2A, Prong One, claim 1 is not directed to an abstract idea alone. The claim expressly requires analyzing the source data captured using a machine learning model executed by a processor of the data platform, where the model is trained to evaluate normalized workforce, governance, and organizational metrics. This language goes beyond generic analysis or evaluation and instead recites a specific computational technique, machine learning executed by a processor using normalized datasets, to generate company data. Such processing is inherently technological and is not a fundamental economic practice, mental process, or method of organizing human activity”.
In response, the examiner is not persuaded. Claim 1 as amended merely recites an intended use of the machine learning model of “to evaluate normalized workforce, governance, and organizational metrics” without any specific steps to show how the learning model is trained to obtain such result. In fact the sole occurrence of “normalize” in applicant’s specification appears to be related to the data platform 120 mentioned in paragraph 0076 reproduced below:
“The data platform 120 normalizes data monetization for the consumers 152 and sellers 150. Compensation performed by the data platform 120 may be performed utilizing known cryptocurrency and may fund the tokenization through traditional funding utilizing digital currencies or existing currencies”.
There is no specific computational technique involved as alleged by the applicant.
Applicant further argues at page 12 of the response, last paragraph:
“Even if the Examiner were to characterize the claim as involving an abstract idea, claim 1 satisfies Step 2A, Prong Two, because it integrates the alleged abstract idea into a practical application. The claim requires tokenizing the company data by generating one or more blockchain tokens to cryptographically encode the company data or a hash thereof, and storing those tokens in a blockchain-based digital ledger. The claim further requires linking the scores and rankings of each company to an index verifiably recorded on the blockchain, wherein the index is verifiable and resistant to modification after recordation. These limitations impose meaningful constraints on the claim and provide a concrete technological solution to the problem of unreliable, manipulable, and opaque company evaluation data by ensuring cryptographic integrity, immutability, and auditability. This is a classic example of integrating computation into a real-world technical environment to achieve a practical result”.
In response the examiner is not persuaded. Tokenizing company data and storing company data in a blockchain based ledger are recited at a high level of generality thus are mere insignificant extra solution activities because they do not provide an inventive concept, 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 linking the scores and rankings of each company to an index verifiable recorded on the blockchain and “wherein the index is verifiable and resistant to modification after recordation” merely describes the inherent nature of any data stored on blockchain.
Applicant argues at page 13 second paragraph of the response:
“Moreover, the claim improves the functioning of the computer and related technology itself. By cryptographically encoding company data or hashes thereof into blockchain tokens and recording an immutable index that is resistant to modification after recordation, the claims as currently constituted improve how computer systems store, verify, and communicate analytical results. This improvement is analogous to those found patent-eligible in cases such as Enfish, McRO, and DDR Holdings, where specific data structures, rules, or architectures produced technical benefits beyond abstract ideas”.
In response, the examiner is not persuaded. Encrypting company data into blockchain tokens and storing in an index to protect data recited at a high level of generality is mere well-understood, routine, conventional activity in the field of data encryption and safe storage.
Applicant presents no other specific argument regarding other claims. For all the reasons discussed above, the rejection of all pending claims under 101 is maintained.
Regarding the rejection under 35 U.S.C. 103, applicant argues ta page 14 second paragraph of the response:
“Claim 1 is not obvious over the cited references because none of Fombrun, Halloran, Williams, Smith, or Cella, alone or in any combination, teach or suggest the specific claimed method steps or their ordered technical interaction, particularly the execution of a trained machine learning model on normalized organizational data and the cryptographic encoding and immutable verification of resulting analytics”.
In response the examiner is not persuaded and maintains the machine learning model and cryptographic encoding recited at a high level of generality in the process of ranking companies regarding equality diversity and inclusion criteria do not integrate the abstract idea of scoring and ranking companies because they do not provide any inventive concept, 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.
Applicant argues at page 16 of the response:
“The Office Action's rejection relies on impermissible hindsight reconstruction by selectively extracting disparate features from unrelated references and combining them without any teaching, suggestion, or motivation to do so in the manner claimed. The cited art does not address the technical problem solved by the current claim set, namely, ensuring that analytically generated equality, diversity, and inclusion scores and rankings are computationally derived, cryptographically encoded, and rendered tamper-resistant after generation. The claimed method achieves this through the specific ordered steps recited in claim 1, which are absent from the cited references”.
In response the examiner points out 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 arguments regarding other claims. For all the reasons discussed above, the 103 rejection of all pending claims using the references of record is maintained.
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 following amended features recited in independent claims 1, 15:
machine learning model trained to evaluate normalized workforce and organizational metrics,
cryptographically encode the company data or a hash thereof,
the index is verifiable and resistant to modification after recordation
The specification as originally filed does not support the following amended feature recited in independent claim 13:
cryptographically encode the company data or a hash thereof.
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 following amended features recited in independent claims 1, 15:
machine learning model trained to evaluate normalized workforce and organizational metrics,
cryptographically encode the company data or a hash thereof,
the index is verifiable and resistant to modification after recordation
The specification as originally filed does not support the following amended feature recited in independent claim 13:
cryptographically encode the company data or a hash thereof.
Claim 1 “the blockchain” at line 9 up from last line lacks antecedent basis.
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 is trained to evaluate normalized workforce, governance, and organizational metrics”, “cryptographically encode the company data or a hash thereof” recited at a high level of generality is 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 § 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, the machine learning model is trained to evaluate normalized workforce, governance, and organizational metrics, 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 trained to evaluate normalized workforce, governance, and organizational metrics 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 blockchain" and “wherein the index is verifiable and resistant to modification after recordation” merely read on the fact that any record of a blockchain 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/Blaikie teaches 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 to cryptographically 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 blockchain" and “wherein the index is verifiable and resistant to modification after recordation” merely read on the fact that any record of a blockchain is verifiably recorded and resistant to modification after modification.
Fombrun/Halloran/Williams/Smith does not specifically show the amended features of: analyzing the source data capture using a machine learning model executed by a processor of the data platform; the machine learning model is trained to evaluate normalized workforce, governance, and organizational metrics,
However it is customary in the art to do so 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 ata 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/Williams/Smith in order to analyze captured data for the ledger and to train the machine learning model to evaluate normalized workforce, governance, and organizational metrics 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/Cella 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/Williams/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/Williams/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/Williams/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.
Newman et al (US 20200234216 A1) teach method, apparatus, and computer program product for digitally presenting statistically-relevant business insights into a set of business metrics for an organization. A computer system identifies a set of organizational characteristics from human resources data of employees of a plurality of organizations, and applies a selected inclusion criteria to the set of organizational characteristics to identify a set of candidate organizations. The computer system identifies a set of benchmark organizations from the set of candidate organizations, and creates a fixed panel of the benchmark organizations. The computer system applies the fixed panel to the human resources data of employees of a plurality of organizations to create an analysis dataset that consists of human resources data of employees of the benchmark organizations. The computer system generates a business insight into the set of business metrics of the organization based on the analysis dataset, and digitally presents the business insight.
BERKMAN et al (WO 2006047332 A2) teach methods (200) and systems (100) for modeling the performance of selected company metrics. Multiple, non-traditional sets of objective data along with mathematical analytical techniques are used to provide transparency and visibility into company performance relating to the particular metrics. Company inflection points and changes in strategy may be identified. The performance of a company and/or the performance of a selected industry or industry sector may be analyzed. The invention contemplates the use of various analytical techniques including, but not limited to, linear regression analysis, multivariate (nonlinear) regression analysis, time series analysis, smoothing methods, spectral analysis, neural networks, artificial intelligence and machine learning as well as a myriad of other analytical and predictive techniques.
Ohunakin, Folakemi, et al. "The effects of diversity management and inclusion on organisational outcomes: A case of multinational corporation." Business: Theory and Practice 20.3 (2019): 93-102.
Abstract. This article provides an empirical study on effects of diversity management and inclusion on organisational outcomes. The importance of diversity management and inclusion on organisation is of immense benefit especially in a Multinational Corporations, where diversity and inclusion are parts of their core values. However, in our context, which had been identified as the most diverse country in Africa, there is need to establish how the management and inclusion of these diverse workforce would benefit organisational activities, coupled with the fact that, there is dearth of research on these constructs in extant literature. This study investigated the effects of diversity management and inclusion on organisational outcomes (job satisfaction and job performance) among Shell Corporation employees. Pen and paper questionnaire of 384 copies were administered to the Lagos Branch employees of Shell Corporation. Cross-sectional research design was adopted. Confirmatory Factor Analysis (CFA), convergent and divergent validity, correlational analysis, and structural equation model were used for the analysis. The findings showed positive effect of diversity management and inclusion on employees’ job satisfaction and employees’ job performance. It implies that diversity management and inclusion have the potentials of assisting organisation in creating a climate in which employee will like to work harder with readiness to continue to work with the organisation.
Bakhshi T, Ghita B. Perspectives on Auditing and Regulatory Compliance in Blockchain Transactions. In Trust Models for Next-Generation Blockchain Ecosystems 2021 Apr 16 (pp. 37-65). Cham: Springer International Publishing.
Abstract The recent advent of blockchain technology is anticipated to revolutionize the operational processes of several industries including banking, finance, real estate, retail and benefit governmental as well as corporate information management structures. The underlying principles of information immutability, traceability and verifiability built in blockchain transactions may lead to greater adoption of distributed crypto-ledger applications in auditing automation, compliance monitoring and guaranteeing high assurance. The present chapter discusses the contemporary applications of blockchain technology in information auditing, exploring aspects such as data recording, accuracy, verification, transparency, and overall value of decentralised blockchain crypto ledger for auditors. Opportunities for timeliness, completeness, and re-conciliation in appraising regulatory compliance of organizations employing blockchain-based contractual frameworks are also investigated. The chapter reviews the existing and anticipated challenges blockchain applications pose to traditional regulatory compliance models and the inherent risks for businesses and stakeholders. We highlight the impact of operational concerns such as decentralised transactions, network complexity, transaction reversals, credential management, software quality and human resources. Finally, the chapter provides perspective on assurance complexities involved in transforming from proprietary to blockchain-based framework while adhering to IT control obligations dictated by three major auditing standards Sarbanes Oxley Act (SOX), Control Objectives for Information Technologies (COBIT) and ISO/IEC 27001.
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/UYEN T LE/Primary Examiner, Art Unit 2156 6 January 2026