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 .
DETAILED ACTION
1. This Final Office action is in reply to the Applicant amendment filed on 21 November 2025.
2. Claims 1, 2, 6, 7, 10, 11, 15, 16, 19, 20 have been amended. Claims 3-5, 12-14 have been cancelled. Claims 21-24 are new and have been added.
3. Claims 1, 2, 6-11, 15-24 are currently pending and have been examined. The mis-labeled and docketed Information Disclosure Statement erroneously filed 21 August 2025 has been considered by the Examiner. A signed copy is enclosed with this Final Office action.
Response to Amendment
In the previous office action, Claims 1-20 were rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter (abstract idea). Applicants have not amended now Claims 1, 2, 6-11, 15-24 to provide statutory support and the rejection is maintained.
In the previous office action, Claims 1-20 were rejected under 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which Applicant regards as the invention. Applicants have amended/clarified now Claims 1, 2, 6-11, 15-24 and the rejection is withdrawn.
Response to Arguments
Applicant’s arguments filed 21 November 2025 have been fully considered but they are not persuasive. In the remarks regarding the 35 USC § 101 rejection for Claims 1, 2, 6-11, 15-24, Applicant argues that: (1) the claims are not directed to an abstract idea, and even if they were, they would amount to significantly more than the abstract idea. Examiner respectfully disagrees. Still commensurate to the two-part subject matter eligibility framework decision in the Federal court decision in Alice Corp. Pty. Ltd. V. CLS Bank International et al., (Alice), 2019 revised patent subject matter eligibility guidance (2019 PEG) and the October 2019 Update: Subject Matter Eligibility (“October 2019 Update), and the new “July 2024 Guidance Update on Patent Subject Matter Eligibility Examples, including on Artificial Intelligence”, and the Examiner details the maintained rejection under 35 U.S.C. 101 as now clarified with further explanation. Applicant argues that as amended, Applicant states: “…that each of the independent claims as a whole is not directed to an abstract idea, integrates the alleged abstract idea in to a practical application; The Claimed Invention Recites a Specific Improvement to Technology, not an Abstract Idea; The Claimed Invention is Similar to Enfish, McRO, CardioNet, and Diehr; The Claim Recites “Significantly More: Than any Alleged Abstract Idea (Alice Step 2; Step 2B ” (see Remarks/Arguments pages 16-). However the Examiner respectfully disagrees. The abstract idea limitations identified below in the maintained rejection (bolded emphasis) under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as the invention from representative Claim 1 as a computer-implemented method for skill-based contract assignment using AI, reinforcement learning and blockchain matchmaking utilizing broadly recited ranking, and contracting, implemented using data processing. The 2019 PEG explains that the abstract idea exception includes the following groupings of subject matter:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (i.e., equally distributed weighted percentage or adjustable weighted percentages to match an outcome state corresponding to the skill-based contract assignment; a forced-rank list of subject matter experts (SMEs) with rankings and contact information based on applying the weight; wherein the reinforcement learning model automatically adjusts weights). The application of "weighted percentages" to data, "training the machine learning model" and the "reinforcement learning model automatically adjusting weights" involve mathematical calculations and algorithms.
Certain methods of organizing human activity – fundamental economic principles or practices; commercial or legal interactions; business processes (including agreements in the form of contracts; legal obligations (i.e., receiving user input; from a user to assign a contract for completing the particular project or the program by selecting criteria determining data; establishing an agreement to select an SME from the forced-rank list); advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (setting the agreement into a contract on a blockchain upon receiving acceptance from the selected SME). The core of the claim involves creating a database of subject matter experts (SMEs), ranking them based on criteria, and establishing a contract. These are, at their core, methods of matchmaking/contracting, which are fundamentally commercial/human activities.
Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (i.e., receiving user input from the user, via the user interface, establishing an agreement to select an SME from the forced-rank list; setting the agreement into a contract on a blockchain upon receiving acceptance from the selected SME). The steps of "selecting criteria determining data" and "generating a forced-rank list" are actions that can be performed in the human mind or with pen-and-paper.
See MPEP § 2106.04(a) II C. Hence, the claims are ineligible under Step 2A Prong one. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. Regarding Applicants’ brief paragraph sentences on page 18 of the Argument/Remarks of the following Court decisions: The Claimed Invention is Similar to Enfish, McRO, CardioNet, and Diehr”, these court decisions are not analogous to Applicants’ abstract idea skill-based contract assignment using AI, reinforcement learning, and blockchain. Specifically, Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. Continuing with the analysis under Prong Two: Claims 1-20: With regard to this step of the analysis (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claims 1, 10, 19 recite additional elements directed to “one or more processors; memory storing computer readable instructions; user interface, a machine learning model, and the database via a communication interface” (e.g., see Applicants’ un-published Specification ¶’s 41-45, 82-86). Therefore, the claims contain computer components that are cited at a high level of generality and are merely invoked as a tool to perform the abstract idea. Simply implementing an abstract idea on a computer is not a practical application of the abstract idea. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components, and furthermore do not amount to an improvement to a computer or any other technology, and thus are ineligible. See MPEP § 2106.05(f) (h). Finally for Step 2B: As explained in MPEP § 2106.05, Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea nor recites additional elements that integrate the judicial exception into a practical application. The additional elements of “processor; memory; user interface; machine learning model”, etc. are generically-recited computer-related elements that amount to a mere instruction to “apply it” (the abstract idea) on the computer-related elements (see MPEP § 2106.05 (f) – Mere Instructions to Apply an Exception). These additional elements in the claims are recited at a high level of generality and are merely limiting the field of use of the judicial exception (see MPEP §2106.05 (h) – Field of Use and Technological Environment). There is no indication that the combination of elements improves the function of a computer or improves any other technology. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components, and furthermore do not amount to an improvement to a computer or any other technology, and thus are ineligible. In summary as indicated below through Steps 1-2B, the recitation of a computer (one or more processors) to perform the claim limitations amount to no more than mere instruction to apply the exception using generic computer components. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. For at least these reasons, the rejection is maintained.
Applicants submit that: (2) Mroczka (US 2021/0012288) in view of Mardikar et al. (Mardikar) (US 2023/0237491) does not teach or suggest in amended and representative Claim 1: “transmitting a verification or survey to the user interface to receive user verification data or survey data as to why the user selected this particular SME from the list; retraining the machine learning model on the received verification data or the survey data; and outputting a reinforcement learning model, wherein the reinforcement learning model automatically adjusts weights such that utilization and engagement with the user interface increases over time” [see Remarks pages 20-22]. With regard to argument (2), the Examiner respectfully disagrees. To avoid redundancy here in the Examiner’s response to arguments and explanation, the Examiner has provided in the below maintained rejection additional specific citations and clarification from Mroczka in view Mardikar for the new change in claim scope for at least representative independent Claim 1. It is noted that any citations to specific, pages, columns, paragraphs, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123. The Examiner has a duty and responsibility to the public and to Applicant to interpret the claims as broadly as reasonably possible during prosecution. In re Prater, 415 F.2d 1 393, 1404-05, 162 USPQ 541, 550-51 (CCPA 1969). For at least these reasons, the rejection is maintained.
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, 2, 6-11, 15-24 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) because the claimed invention is directed to a judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) without significantly more. The claims as a whole recite certain grouping of an abstract idea and are analyzed in the following step process:
Step 1: Claims 1, 2, 6-11, 15-24 are each focused to a statutory category of invention, namely “method; system; non-transitory computer readable medium” sets.
Step 2A: Prong One: Claims 1, 2, 6-11, 15-24 recite limitations that set forth the abstract ideas, namely, the claims as a whole recite the claimed invention is directed to an abstract idea without significantly more. The claims recite steps for:
“implementing a database that stores a set of known criteria data having either an equally distributed weighted percentage or adjustable weighted percentages to match an outcome state corresponding to the skill-based contract assignment;
establishing a communication link among a user interface, a machine learning model, and the database via a communication interface;
training the machine learning model on the set of known criteria data;
receiving a request, via the user interface, from a user to assign a contract for completing the particular project or the program by selecting criteria determining data;
applying, upon receiving the request, by the trained machine learning model, a weight to the selected criteria determining data;
generating, by the trained machine learning model, a forced-rank list of subject matter experts (SMEs) with rankings and contact information based on applying the weight;
transmitting the forced-rank list to the user interface;
receiving user input from the user, via the user interface, establishing an agreement to select an SME from the forced-rank list;
transmitting an electronic message to the selected SME to accept the agreement;
setting the agreement into a contract on a blockchain upon receiving acceptance from the selected SME, such that none of the data that has been utilized to generate the contract can be accessed without the user and the selected SME’s knowledge;
transmitting a verification or survey to the user interface to receive user verification data or survey data as to why the user selected this particular SME from the list;
retraining the machine learning model on the received verification data or the survey data; and
outputting a reinforcement learning model, wherein the reinforcement learning model automatically adjusts weights such that utilization and engagement with the user interface increases over time”
These abstract idea limitations identified (bolded emphasis) above under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as the invention from representative Claim 1 is a computer-implemented method for skill-based contract assignment using AI, reinforcement learning, and blockchain. The 2019 PEG explains that the abstract idea exception includes the following groupings of subject matter:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (i.e., equally distributed weighted percentage or adjustable weighted percentages to match an outcome state corresponding to the skill-based contract assignment; a forced-rank list of subject matter experts (SMEs) with rankings and contact information based on applying the weight; wherein the reinforcement learning model automatically adjusts weights). The application of "weighted percentages" to data, "training the machine learning model" and the "reinforcement learning model automatically adjusting weights" involve mathematical calculations and algorithms.
Certain methods of organizing human activity – fundamental economic principles or practices; commercial or legal interactions; business processes (including agreements in the form of contracts; legal obligations (i.e., receiving user input; from a user to assign a contract for completing the particular project or the program by selecting criteria determining data; establishing an agreement to select an SME from the forced-rank list); advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (setting the agreement into a contract on a blockchain upon receiving acceptance from the selected SME). The core of the claim involves creating a database of subject matter experts (SMEs), ranking them based on criteria, and establishing a contract. These are, at their core, methods of matchmaking/contracting, which are fundamentally commercial/human activities.
Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (i.e., receiving user input from the user, via the user interface, establishing an agreement to select an SME from the forced-rank list; setting the agreement into a contract on a blockchain upon receiving acceptance from the selected SME). The steps of "selecting criteria determining data" and "generating a forced-rank list" are actions that can be performed in the human mind or with pen-and-paper.
See MPEP § 2106.04(a) II C. Hence, the claims are ineligible under Step 2A Prong one. In conclusion for Step 2A, Prong One, the claim recites a judicial exception because it is directed to the abstract idea of matchmaking, ranking, and contracting, implemented using data processing. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception.
Prong Two: Claims 1, 2, 6-11, 15-24: With regard to this step of the analysis (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claims 1, 10, 19 recite additional elements directed to “one or more processors; memory storing computer readable instructions; user interface, a machine learning model, and the database via a communication interface” (e.g., see Applicants’ un-published Specification ¶’s 41-45, 82-86). Therefore, the claims contain computer components that are cited at a high level of generality and are merely invoked as a tool to perform the abstract idea. Simply implementing an abstract idea on a computer is not a practical application of the abstract idea. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components, and furthermore do not amount to an improvement to a computer or any other technology, and thus are ineligible. See MPEP § 2106.05(f) (h).
Step 2B: As explained in MPEP § 2106.05, Claims 1, 2, 6-11, 15-24 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea nor recites additional elements that integrate the judicial exception into a practical application. The additional elements of “processor; memory; user interface; machine learning model”, etc. are generically-recited computer-related elements that amount to a mere instruction to “apply it” (the abstract idea) on the computer-related elements (see MPEP § 2106.05 (f) – Mere Instructions to Apply an Exception). These additional elements in the claims are recited at a high level of generality and are merely limiting the field of use of the judicial exception (see MPEP §2106.05 (h) – Field of Use and Technological Environment). There is no indication that the combination of elements improves the function of a computer or improves any other technology. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components, and furthermore do not amount to an improvement to a computer or any other technology, and thus are ineligible.
The Examiner interprets that the steps of the claimed invention both individually and as an ordered combination result in Mere Instructions to Apply a Judicial Exception (see MPEP §2106.05 (f)). These claims recite only the idea of a solution or outcome with no restriction on how the result is accomplished and no description of the mechanism used for accomplishing the result. Here, the claims utilize a computer or other machinery (e.g., see Applicants’ un-published Specification ¶’s 41-45, 82-86) regarding using existing computer processors as well as program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored. “system 100” in its ordinary capacity for performing tasks (e.g., to receive, analyze, transmit and display data) and/or use computer components after the fact to an abstract idea (e.g., a fundamental economic practice and certain methods of organization human activities) and does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016)). Software implementations are accomplished with standard programming techniques with logic to perform connection steps, processing steps, comparison steps and decisions steps. These claims are directed to being a commonplace business method being applied on a general-purpose computer (see Alice Corp. Pty, Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357, 110 USPQ2d 1976, 1983 (2014)); Versata Dev. Group, Inc., v. SAP Am., Inc., 793 D.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) and require the use of software such as via a server to tailor information and provide it to the user on a generic computer. Based on all these, Examiner finds that when viewed either individually or in combination, these additional claim element(s) do not provide meaningful limitation(s) that raise to the high standards of eligibility to transform the abstract idea(s) into a patent eligible application of the abstract idea(s) such that the claim(s) amounts to significantly more than the abstract idea(s) itself. Accordingly, Claims 1, 2, 6-11, 15-24 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception (i.e. abstract idea exception) without significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 6-11, 15-24 are rejected under 35 U.S.C. 103 as being unpatentable over Mroczka (US 2021/0012288) in view of Mardikar et al. (Mardikar) (US 2023/0237491).
With regard to Claims 1, 10, 19, Mroczka teaches a method/system; non-transitory computer readable medium for enabling skill-based contract (contract; contractor; Will the project use salaried or contract personnel?) assignment for completing a particular project or a program (The set of desired SMEs is chosen based upon criteria retrieved from the SME profiles database 120, including technical level of skill relevant to the current project, salary requirements, experience, and availability to work on the project. The technical point of contact will communicate with the selected SMEs to negotiate an agreement for them to work on the project. Eventually the final team of SMEs will be formed, and the project development project continues; programs; program) processor; by utilizing one or more processors along with allocated memory; a processor; and a memory operatively connected to the processor via a communication interface, the memory storing computer readable instructions, when executed, causes the processor to (computer; system; engine 104) (see at least paragraphs 22-24, 32-36, 63, 109), the method/system/medium comprising:
implementing a database that stores a set of known criteria data (The set of desired SMEs is chosen based upon criteria retrieved from the SME profiles database 120, including technical level of skill relevant to the current project, salary requirements, experience, and availability to work on the project. The technical point of contact will communicate with the selected SMEs to negotiate an agreement for them to work on the project. Eventually the final team of SMEs will be formed, and the project development project continues) having either an equally distributed weighted percentage or adjustable weighted percentages to match an outcome state (The engine 104 will interface with the user interface/platform 102 that serves as the user interface, such as a web server, that allows interaction with the engine 104 by the various participants in the system such as end users, subject matter experts 106, and the like. The engine 104 includes a weighting/ranking module 114 that facilitates implementation of the fundamental prime measurements by the subject matter experts, and an artificial intelligence (AI) module 116 that uses higher order logic and reinforced learning in conjunction with the manual interactions of the subject matter experts 106) corresponding to the skill-based contract assignment (including technical level of skill relevant to the current project, salary requirements, experience, and availability to work on the project) (see at least paragraphs 23, 26, 36, 99);
establishing a communication link among a user interface, a machine learning model, and the database via a communication interface (The engine 104 includes a weighting/ranking module 114 that facilitates implementation of the fundamental prime measurements by the subject matter experts, and an artificial intelligence (AI) module 116 that uses higher order logic and reinforced learning in conjunction with the manual interactions of the subject matter experts 106) corresponding to the skill-based contract assignment (including technical level of skill relevant to the current project, salary requirements, experience, and availability to work on the project) (see at least paragraphs 23, 36, 99);
training the machine learning model (The artificial intelligence (AI) methodology employed by the AI module 116 implements reinforced learning and higher order logic (HOL) along with mathematical power sets) on the set of known criteria data (The set of desired SMEs is chosen based upon criteria retrieved from the SME profiles database 120, including technical level of skill relevant to the current project, salary requirements, experience, and availability to work on the project. The technical point of contact will communicate with the selected SMEs to negotiate an agreement for them to work on the project. Eventually the final team of SMEs will be formed, and the project development project continues) (see at least paragraphs 23, 26, 36, 99);
receiving a request, via the user interface, from a user to assign a contract for completing the particular project or the program by selecting criteria determining data (a method for generating an optimal set of parameters for a design project of a product. A team of subject matter experts is selected, each of the subject matter experts having expertise in at least one aspect of the design of the product. A product profile matrix is generated, wherein the product profile matrix is a power set of a plurality of fundamental prime measurements. Each of the fundamental prime measurements is itself a power set of a plurality of lower order fundamental measurement factors associated with an aspect of the product. To accomplish this, each of the subject matter experts (optionally with the assistance of artificial intelligence) performs the steps of (i) selecting, from a project database, a plurality of fundamental measurement factors, (ii) assigning a weight of importance to each of the plurality of fundamental measurement factors, (iii) assigning an evidence score to each of the plurality of fundamental measurement factors, (iv) generating a total point score as a function of the weight and evidence score for each of the fundamental measurement factors, (v) generating a strategic score for the associated fundamental prime measurement by averaging the total point scores for the fundamental measurement factors that comprise the fundamental prime measurement, (vi) generating a set of composite scores for the power set of fundamental prime measurements as a function of the strategic scores, and (vii) assigning a risk factor to each of the composite scores. If a risk factor falls below a predetermined level, then steps (ii)-(vii) are repeated throughout the product development process until the risk factor no longer falls below the predetermined level) (see at least paragraphs 9, 23, 26, 36, 99);
applying, upon receiving the request, by the trained machine learning model, a weight to the selected criteria determining data (Once the fundamental measurement factors have been decided on for the project, a weighting and scoring process for those FMFs is undertaken at step 326. For each of the fundamental measurement factors, the SME will assign a relative weight that reflects the importance of that particular fundamental measurement factor to the current project being analyzed. Since the weight of any given fundamental measurement factor will vary across various projects, its relative contribution to that project varies accordingly) (see at least paragraphs 9, 23-26, 36, 99);
generating, by the trained machine learning model, a forced-rank list of subject matter experts (SMEs) with rankings and contact information based on applying the weight (The engine 104 will interface with the user interface/platform 102 that serves as the user interface, such as a web server, that allows interaction with the engine 104 by the various participants in the system such as end users, subject matter experts 106, and the like. The engine 104 includes a weighting/ranking module 114 that facilitates implementation of the fundamental prime measurements by the subject matter experts, and an artificial intelligence (AI) module 116 that uses higher order logic and reinforced learning in conjunction with the manual interactions of the subject matter experts 106; ranking score; For each of the fundamental measurement factors, the SME will assign a relative weight that reflects the importance of that particular fundamental measurement factor to the current project being analyzed. Since the weight of any given fundamental measurement factor will vary across various projects, its relative contribution to that project varies accordingly) (see at least paragraphs 9, 23-26, 36, 99);
transmitting the forced-rank list to the user interface (The SME profiles database 120 tracks various data points for each SME 106 registered in the system. For example, in addition to the SME's name, title and contact information, the SME database 120 also tracks the compensation rate(s) for the SMEs, as well as a ranking score that reflects the desirability of the SME for subsequent work. This may be obtained through post-project evaluations from project administrators, other SMEs, etc. The SME database 120 may also indicate the current availability of an SME to work on a project, his education, experience and training levels, and a biographical statement that may be useful for future project selections) (see at least paragraphs 9, 23-26, 36, 99);
receiving user input from the user (The set of desired SMEs is chosen based upon criteria retrieved from the SME profiles database 120, including technical level of skill relevant to the current project, salary requirements, experience, and availability to work on the project), via the user interface, establishing an agreement (The technical point of contact will communicate with the selected SMEs to negotiate an agreement for them to work on the project. Eventually the final team of SMEs will be formed, and the project development project continues) to select an SME from the forced-rank list (The SME profiles database 120 tracks various data points for each SME 106 registered in the system. For example, in addition to the SME's name, title and contact information, the SME database 120 also tracks the compensation rate(s) for the SMEs, as well as a ranking score that reflects the desirability of the SME for subsequent work. This may be obtained through post-project evaluations from project administrators, other SMEs) (see at least paragraphs 23-26, 34-37);
transmitting an electronic message to the selected SME to accept the agreement (Once acceptable composite scores are attained, the process proceeds to step 330, where RFIs (requests for information) and RFPs (requests for proposal) may be disseminated as well known in the industry. In one embodiment, scores generated by the system may be shared with proposed vendors, wherein those vendors are able to match the scores to their own stored capabilities to provide for a more seamless interaction) (see at least paragraphs 36, 107);
setting the agreement into a contract upon receiving acceptance from the selected SME (The technical point of contact will communicate with the selected SMEs to negotiate an agreement for them to work on the project. Eventually the final team of SMEs will be formed, and the project development project continues), to ensure accuracy (Weighting factors and skewing factors may be adjusted as desired by the system designer in order to provide a meaningful range of scores that accurately reflect differences in the various factors and achieve a level of granularity and precision that is meaningful and robust. At step 328, the down-select stage is entered, where the composite scores are further analyzed to determine if they have met a certain level of acceptability. A break point range is defined against which the composite scores are compared to make this determination. In the preferred embodiment, the following break point range is utilized), and that none of the data that has been utilized to generate the contract can be accessed without the user and the selected SME’s knowledge (subject matter experts form what is referred to as the Innovation Expert Network (IEN). This is a membership organization that allows access by the project leader to select a team of SMEs to execute various tasks of the project. The IEN is a social network of professionals who provide expertise and knowledge in various technical and product areas. It provides a means to upload personal capabilities and interests to create a personal/professional profile for each expert. Both government and industry personnel can access the IEN to identify SMEs needed for various product development efforts) (see at least paragraphs 28, 33-37, 104);
transmitting a verification or survey (Once acceptable composite scores are attained, the process proceeds to step 330, where RFIs (requests for information) and RFPs (requests for proposal) may be disseminated as well known in the industry. In one embodiment, scores generated by the system may be shared with proposed vendors, wherein those vendors are able to match the scores to their own stored capabilities to provide for a more seamless interaction) to the user interface (The subject matter experts may be reviewed by using a web site as shown in FIG. 6. The web page of FIG. 6, which in this example is show being used for a project entitled “GAU-8/A Avenger Autocannon,” provides the technical point of contact the ability to view data related to all of the available SME's in the system) to receive user verification data or survey data (The subject matter experts may be reviewed by using a web site as shown in FIG. 6. The web page of FIG. 6, which in this example is show being used for a project entitled “GAU-8/A Avenger Autocannon,” provides the technical point of contact the ability to view data related to all of the available SME's in the system) as to why the user selected this particular SME (By selecting the button labelled “SME Search”, the database of SME's becomes available for searching. In the example of FIG. 6, a set of five SMEs have already been chosen, as listed in the column labelled “Current Team.” BY selecting any of the subject matter experts on the current team, e.g. Laura Wright, their bio, availability, and other pertinent information is accessible) from the list (see at least paragraphs 23, 35-37, 107).
retraining the machine learning model on the received verification data or the survey data (The engine 104 includes a weighting/ranking module 114 that facilitates implementation of the fundamental prime measurements by the subject matter experts, and an artificial intelligence (AI) module 116 that uses higher order logic and reinforced learning in conjunction with the manual interactions of the subject matter experts 106. These modules are software modules programmed to perform the functions that are described in detail further herein) (see at least paragraphs 23, 40, 105-107);
outputting a reinforcement learning model (a library of fundamental measurement factors is stored, for example in the project database shown in FIG. 1. Subject matter experts, acting individually and/or in concert with the artificial intelligence (AI) engine, will review the available fundamental measurement factors for a give type of project and develop a subset of those fundamental measurement factors that are deemed especially relevant to the current project. As shown in FIGS. 2A, 2B, and 2C, five of the most relevant fundamental measurement factors have been selected for his example for clarity of explanation; it is noted that dozens or even hundreds of such fundamental measurement factors may be utilized for a given project, as ascertained by the SMEs and/or AI engine. As the analysis of the available fundamental measurement factors, as well as their related scores sored from past projects, becomes more intricate, the system will rely more on the AI engine to cull out the most relevant FPMs for a given project), wherein the reinforcement learning model automatically adjusts weights such that utilization and engagement with the user interface increases over time (Since at least some of the FPMs require improved scores, the process loops back to step 312, where further solution brainstorming takes place or product development and testing may be required. The SMEs can analyze the FPMs that require improvement, and then implement revisions to the various processes at steps 314 and 316 that will cause the FPMs to increase, thus driving the scores in the desired direction. For example, at Stage 2, the scores have dramatically increased as can be seen in FIG. 5. The process in this example reiterates once more, resulting in the composite scores shown for Stage 3 (Final Product) in FIG. 5, all of which are in the low risk range. Optionally, the new iteration may re-enter the process flow at step 326 for the weighting and scoring stage) (see at least paragraphs 23, 40, 105-108)
Mroczka does not specifically teach on a blockchain; encryption. Mardikar teaches on a blockchain (The blockchain may comprise a ledger of interconnected blocks containing data. The blockchain tray provide enhanced security because each block may hold individual transactions and the results of any blockchain executables. Each block may link to the previous block and may include a timestamp); encryption (The various system 200 components (e.g., trust score provider 103, consumer device 113, merchant 109, etc.) may be in electronic communication with DLT network 201 and may run applications to interact with DLT network 201, transfer files over a network with other computing devices, perform crypto operations, and otherwise operate within system 200. For example, each system component may comprise a blockchain node configured to interact with DLT network 201. A blockchain address may be uniquely assigned to each system 200 component to function as a unique identifier for each respective system component; encryption) in analogous art of smart contracts for the purposes of: “the blockchain may implement smart contracts that enforce data workflows in a decentralized manner; The trust score provider may generate the asymmetric key pair using any suitable technique and asymmetric algorithm, such as, for example, RSA, DSA, elliptic curve cryptography, or the like. The trust score provider may encrypt and store the private key. The trust score provider may transmit the public key to the consumer device and/or the merchant, which may encrypt and store locally the public key. In various embodiments, the trust score provider may also encrypt and store locally the public key. In various embodiments, the trust score provider may write the digital identity entry to the digital identity management DLT network. In that regard, the public key may comprise a blockchain address” (see at least paragraphs 22, 48, 70-73).
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the dynamic trust score as taught by Mardikar in the system of Mroczka, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
With regard to Claims 2, 11, 20, teaches: selecting, by the user, the criteria determining data details giving the user an outcome of SMEs in an order of qualification to assist the user for completing the particular project or the program (see at least paragraphs 23-25, 35-37, 107).
With regard to Claims 6, 15, 21, teaches:
adjusting weights for an SME who has availability (see at least paragraphs 25, 102-104);
ranking said SME who has availability higher in the rank list compared to SMEs who do not have availability (see at least paragraphs 25, 102-104).
With regard to Claims 7, 16, 22, teaches:
adjusting weights for an SME who has been selected the most previously by the user or other users (see at least paragraphs 8, 30, 100-106);
ranking said SME who has been selected the most previously highest in the list compared to other SMEs (see at least paragraphs 8, 30, 100-106).
With regard to Claims 8, 17, 23, teaches:
applying weighting along a volume of output matching a determined sentiment such that 100% weighting is applied to an SME who has produced the most corresponding to the determined sentiment (see at least paragraphs 8, 30, 100-106);
retraining the machine learning model on the volume of output matching the determined sentiment (see at least paragraphs 8, 30, 100-106).
With regard to Claims 9, 18, 24, teaches:
applying weighting along the plurality of dimensions such that each one of the plurality of dimensions carries a percentage weight of the total 100% where the percentages at start are divided equally (see at least paragraphs 8, 30, 100-106);
retraining the machine learning model on the percentage weights assigned to the plurality of dimensions (see at least paragraphs 8, 30, 100-106).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Kapcar et al. (US 2021/0342527)
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS L MANSFIELD whose telephone number is (571)270-1904. The examiner can normally be reached M-Thurs, alt. Fri. (9-6).
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THOMAS L. MANSFIELD
Examiner
Art Unit 3623
/THOMAS L MANSFIELD/Primary Examiner, Art Unit 3624