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 .
Status of Claims
This communication is a First Office Action Non-Final on Merits. Claims 1-20 are currently pending and have been considered below.
Priority
The present application, filed on 08/26/2024, claims priority to Provisional Application 63/633,391, filed on 04/12/2024.
Claim Objections
Claim 18 is objected to because of the following informality: Claim 18, line 1 recites “the method of claim 17” and it should be – the non-transitory computer-readable medium of claim 17 --. Appropriate correction is required. Claims 19-20 are objected to because of the following informality: Claims 19-20, line 1 recites “the method of claim 18” and it should be – the non-transitory computer-readable medium of claim 18 --. Appropriate correction is required. Examiners Note: Examiner is interpreting claims 18-20 as dependent of claim 17 non-transitory computer-readable medium for examining purposes.
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 a judicial exception without a practical application and significantly more.
Step 1: Identifying Statutory Categories
When considering subject matter eligibility under 35 U.S.C. § 101, it must be determined whether the claims are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (i.e., Step 1). In the instant case, claims 1-11 are directed to a method (i.e. a process). Claims 12-16 are directed to a system (i.e. a machine). Claims 17-20 are directed to a non-transitory computer readable medium (i.e. an article of manufacture). Thus, each of these claims fall within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A: Prong One: Abstract Ideas
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea. Independent claim 1, analogous to independent claims 12 and 17 recites: A method for attributing value to a plurality of members of a company, comprising: determining that each user of a plurality of users is associated with a respective member of a first group of the plurality of members of the company; retrieving, from each user associated with at least a respective member, first user data indicating a respective set of initial scores to be initially assigned to other members of the first group; assigning scores of each set of initial scores to respective members of the group; setting a group valuation for at least the group; assigning a respective preliminary first member valuation to each of at least one member of the group based at least on the group valuation; updating a respective preliminary first member valuation assigned to each member of the group by performing an iterative method comprising: determining a respective set of preliminary second member valuations assigned from each member in the group to other members in the group based at least on a respective preliminary first member valuation and a respective set of initial scores; producing a set of sums of valuations, wherein each sum of the set of sums of valuations is based on all preliminary second member valuations assigned to a respective member of the group; and updating the respective preliminary first member valuation assigned to each member of the group based on a respective sum of the set of sums of valuations; retrieving financial data associated with the company; retrieving stock data associated with the company; each first user input data to generate a first plurality of features; the respective preliminary first member valuation assigned to each member of the group to generate a second plurality of features; generating at least one feature vector based at least on the first plurality of features, the second plurality of features, the financial data associated with the company, and the stock data associated with the company; providing the at least one feature vector that is configured to generate final member valuations to be assigned to respective members of the first group of the company for generation of a recommendation that at least one member of the first group be replaced. The limitations as drafted, is a process that, under its broadest reasonable interpretation, falls under the abstract groupings of: Certain methods of organizing human activity (commercial or legal interactions (including 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). As the claims discuss attributing value to a plurality of members of a company, including retrieving financial data associated with the company; retrieving stock data associated with the company; and recommendation that at least one member of the first group be replaced, which is a clear business relations and one of certain methods of organizing human activity.
Mathematical concepts (mathematical relationships, mathematical formulas or equations and mathematical calculations (as independent claims recite for example: “set of initial scores to be initially assigned”; “assigning scores of each set of initial scores to respective members”; “a group valuation for at least the group”; “determining a respective set of preliminary second member valuations assigned from each member in the group to other members in the group based at least on a respective preliminary first member valuation and a respective set of initial scores”; “producing a set of sums of valuations”; “each sum of the set of sums of valuations is based on all preliminary second member valuations”; “updating the respective preliminary first member valuation assigned to each member of the group based on a respective sum of the set of sums of valuations”; “generating at least one feature vector”; “providing the at least one feature vector”.)) Further, dependent claims add additional limitations, for example: (claims 2, 13 and 18) each first user input data indicating a respective set of initial scores, the final member valuations assigned to respective members of the first group, the financial data associated with the company, and the stock data associated with the company; (claims 3, 14 and 19) retrieving the final member valuations assigned to respective members of the first group in response to a consensus among at least the plurality of user devices; (claims 4, 15 and 20) including a first clause to pay rewards to respective members of the first group based at least on the final member valuations assigned to respective members of the first group upon occurrence of a triggering event; (claims 5 and 16) determining that the triggering event occurred; in response to determining that the triggering event occurred, executing at least the first clause to pay rewards to respective members of the first group based at least on the final member valuations assigned to respective members of the first group; (claim 6) wherein determining that the triggering event occurred includes determining that a financial performance metric of the company meets a predetermined financial performance threshold; (claim 7) generate the final member valuations to be assigned to respective members of the first group for the generation of a recommendation that the at least one member of the first group be considered for at least one open job position at the company; (claim 8) retrieving, from each user associated with at least a respective member, second user input data indicating user textual feedback associated with other members of the first group; wherein the at least one feature vector is generated further based on at least each second user input data; (claim 9) retrieving, from each user associated with at least a respective member, user behavior data; wherein the at least one feature vector is generated further based on at least each user behavior data; (claim 10) retrieving, from each user associated with at least a respective member, user performance data; wherein the at least one feature vector is generated further based on at least each user performance data; (claim 11) retrieving, from each user associated with at least a respective member, second user input data indicating user textual feedback associated with other members of the first group; retrieving, from each user device associated with at least a respective member, user behavior data; retrieving, from each user device associated with at least a respective member, user performance data; each second user input data indicating user textual feedback to generate a third plurality of features; each user behavior data to generate a fourth plurality of features; each user performance data to generate a fifth plurality of features; generating at least one second feature vector based at least on the third plurality of features, the fourth plurality of features, and the fifth plurality of features; providing the at least one second feature vector that is configured to generate generative textual feedback for each member of the first group for the solicitation of additional user textual feedback, but these only serve to further limit the abstract idea. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of methods of organizing human activity and mathematical concepts, but for the recitation of generic computer components, the claims recite an abstract idea.
Step 2A: Prong Two
This judicial exception is not integrated into a practical application because the claims merely describe how to generally “apply” the abstract idea. In particular, the claims only recite the additional elements – (claim 1) user device(s); parsing data; machine learning model (claims 2, 13 and 18) recording on blockchain (claims 3, 14 and 19) blockchain (claims 4, 15 and 20) smart contract, blockchain, database (claim 5) smart contract (claim 12) memory, processor(s) (claim 17) non-transitory computer-readable medium. These additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Simply implementing the abstract idea on generic computer components is not a practical application of the abstract idea, as it adds the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). The limitations generally link the abstract idea to a particular technological environment or field of use (such as computing, machine learning or blockchain, see MPEP 2106.05(h)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and generally link the abstract idea to a particular technological environment or field of use. Furthermore, claims 1-20 have been fully analyzed to determine whether there are additional elements recited that amount to significantly more than the abstract idea. The limitations fail to include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Thus, nothing in the claim adds significantly more to the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. The claims are ineligible. Therefore, since there are no limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter.
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1, 6-12 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over JHAWAR et al. (AU 2017/268637 A1), hereinafter “Jhawar”, over Jorasch (US 2021/0373676), hereinafter “Jorasch”.
Regarding Claim 1, Jhawar teaches A method for attributing value to a plurality of members of a company, comprising: (Jhawar, Abstract; information regarding a plurality of groups of entities ... generate a data model relating to attributes of the plurality of groups of entities... a corresponding score of the set of scores);
determining that each user device of a plurality of user devices is associated with a respective member of a first group of the plurality of members of the company; (Jhawar, Figure 1 teaches client (user) device(s); See at least Jhawar, para 0002, teaches the plurality of client devices being selected based on the plurality of client devices being determined to be associated with the particular group of entities);
retrieving, from each user device associated with at least a respective member, first user input data indicating a respective set of ... to other members of the first group; (Jhawar, Abstract, first information to generate a data model relating to attributes of the plurality of groups of entities; Jhawar, para 0114, provide a user interface including a set of prompts to a first user (e.g., a first employee) to obtain information regarding a second user (e.g., a second employee));
assigning scores of each set of initial scores to respective members of the group; (Scores for employees is taught throughout Jhawar, see at least para 0186; Further, see for example Jhawar, Figure 7B, showing scores to members of the group for example CharlesD 3, 1 and 7);
setting a group valuation for at least the group; (See at least Jhawar, Figure 7B, Showing Team AAA123 Overall: 63/100 (Examiner notes group valuation));
assigning a respective preliminary first member valuation to each of at least one member of the group based at least on the group valuation; (See at least Jhawar, Figure 7B, showing group valuation of 63/100 and at least one member of the group, for example AdamB Execution 1, Influencing 4 and Relationships 5);
updating a respective preliminary first member valuation assigned to each member of the group by ...(Jhawar, para 0016, update the employee information; and update the team information);
updating the respective preliminary first member valuation assigned to each member of the group based on a respective sum of the set of sums of valuations; (Jhawar, para 0016, update the employee information; Jhawar, Abstract, based on a corresponding score of the set of scores. Examiner notes analytical data models is taught throughout Jhawar (see at least para 0119-0120), summing data is fundamental in data modeling);
parsing each first user input data to generate a first plurality of features; (Jhawar, para 0016. parse the results of the set of questionnaires to update the employee information (Examiner notes employee information is features));
parsing the respective preliminary first member valuation assigned to each member of the group to generate a second plurality of features; (Jhawar, para 0016. parse the results of the set of questionnaires to update the employee information; and update the team information based on updating the employee information (Examiner notes second information of features));
... for generation of a recommendation that at least one member of the first group be replaced (Jhawar, para 0001, teaches determine whether to promote the employee, provide a new leadership opportunity, alter a reward scheme for the employee, or take another employment action; Jhawar, para 0074, teaches replacement employees).
Yet, Jhawar does not appear to explicitly teach and in the same field of endeavor Jorasch teaches initial scores to be initially assigned (Jorasch, para 0106, teaches voting software. The voting software may facilitate voting, decision-making, or other joint or group action. Example votes (Examiner notes scores or valuations) may determine a plan of action at a company... Voting software may permit users or other participants to receive notification of votes, receive background information about decisions or actions they are voting on, cast their votes, and see the results of votes. Voting software may be capable of instituting various protocols, such as multiple rounds (Examiner notes iterative), win by the majority, win by the plurality, win by unanimous decision, anonymous voting, secure voting, differentially weighted votes, or any other voting protocol or format.) performing an iterative method comprising: determining a respective set of preliminary second member valuations assigned from each member in the group to other members in the group based at least on a respective preliminary first member valuation and a respective set of initial scores; producing a set of sums of valuations, wherein each sum of the set of sums of valuations is based on all preliminary second member valuations assigned to a respective member of the group; and (Jorasch, para 0106, teaches voting software may facilitate voting, decision-making, or other joint or group action. Example votes (Examiner notes scores or valuations) may determine a plan of action at a company... Voting software may be capable of instituting various protocols, such as multiple rounds (Examiner notes iterative method); Jorasch, para 0908, teaches voting or consensus-taking, to make decisions... facilitate voting or forming a consensus; para 0911, teaches aggregated (Examiner notes summing data) anonymized opinion of participants on a decision or topic.) retrieving financial data associated with the company; (Jorasch, para 0634, ‘financials’ portion; e.g. ‘project milestone slide’, ‘Q1 sales chart; Further, para 1134, Financial stats scroll for the company) retrieving stock data associated with the company; (Jorasch, para 0284, teaches stock price; para 0285, teaches stock quotes) generating at least one feature vector based at least on the first plurality of features, the second plurality of features, (Jorasch, para 0445, teaches once extracted, features then serve as an input to a K-nearest neighbor classification algorithm... the feature vector i.e., the “X” vector; Further see Jorasch, para 1219, “X” input (i.e., a vector of inputs) to a classification algorithm, or other AI algorithm, or other algorithm. Examiner notes vectors common are foundational in machine learning to process data) the financial data associated with the company, and the stock data associated with the company; (Jorasch, para 0634, ‘financials’ portion; e.g. ‘project milestone slide’, ‘Q1 sales chart; Further, Jorasch, para 1134, Financial stats scroll by for the company; Jorasch, para 0284, teaches stock price; para 0285, teaches stock quotes) providing the at least one feature vector to a machine learning model (Jorasch, para 0445, teaches once extracted, features then serve as an input to a K-nearest neighbor classification algorithm... the feature vector i.e., the “X” vector; Further see Jorasch, para 1219, “X” input (i.e., a vector of inputs) to a classification algorithm, or other AI algorithm, or other algorithm.) that is configured to generate final member valuations to be assigned to respective members of the first group of the company (Jorasch, para 0106, teaches votes may determine a plan of action at a company...Voting software may be capable of instituting various protocols, such as multiple rounds (Examiner notes iterative) .. see the results of votes (Examiner notes final votes or valuations/scores)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jhawar with initial scores to be initially assigned ... performing an iterative method comprising: determining a respective set of preliminary second member valuations assigned from each member in the group to other members in the group based at least on a respective preliminary first member valuation and a respective set of initial scores; producing a set of sums of valuations, wherein each sum of the set of sums of valuations is based on all preliminary second member valuations assigned to a respective member of the group; and retrieving financial data associated with the company; retrieving stock data associated with the company; generating at least one feature vector based at least on the first plurality of features, the second plurality of features, the financial data associated with the company, and the stock data associated with the company; providing the at least one feature vector to a machine learning model that is configured to generate final member valuations to be assigned to respective members of the first group of the company as taught by Jorasch with the motivation for decision-making, or other joint or group action to determine a plan of action at a company (Jorasch, para 0106). The Jhawar invention now incorporating the Jorasch invention, has all the limitations of claim 1.
Regarding Claim 6, Jhawar, now incorporating Jorasch, teaches The method of claim 5, wherein determining that the triggering event occurred includes determining that a financial performance metric of the company meets a predetermined financial performance threshold (Jhawar, para 0003, plurality of groups of entities satisfying a threshold level of performance; Jhawar, para 0120, set of employees associated with a threshold level of achievement (e.g., a threshold level of sales)).
Regarding Claim 7, Jhawar, now incorporating Jorasch, teaches The method of claim 1, wherein the machine learning model is configured to generate the final member valuations to be assigned to respective members of the first group for the generation of a recommendation that the at least one member of the first group be considered for at least one open job position at the company (Jhawar, para 0067, employee has satisfied a sales goal, and may recommend the employee for a reward for the employee; para 0172, rewards (e.g. such as recognition and promotion)).
Regarding Claim 8, Jhawar, now incorporating Jorasch, teaches The method of claim 1, further comprising: retrieving, from each user device associated with at least a respective member, second user input data indicating user textual feedback associated with other members of the first group; wherein the at least one feature vector is generated further based on at least each second user input data (Jhawar, para 0151, parse a result of the questionnaire using a machine learning technique to determine the employee information. For example, cloud platform may utilize a natural language processing technique to generate a particular employee priority from a text-based answer to a questionnaire prompt; Jorasch, para 0445, teaches once extracted, features serve as an input to a K-nearest neighbor classification algorithm... the feature vector i.e., the “X” vector; Further see Jorasch, para 1219, “X” input (i.e., a vector of inputs) to a classification algorithm, or other AI algorithm).
Regarding Claim 9, Jhawar, now incorporating Jorasch, teaches The method of claim 1, further comprising: retrieving, from each user device associated with at least a respective member, user behavior data; wherein the at least one feature vector is generated further based on at least each user behavior data (Jhawar, para 0049, monitoring utilization of the user interface or the user interface to detect a user interaction. Jorasch, para 0445, teaches once extracted, features then serve as an input to a K-nearest neighbor classification algorithm... the feature vector i.e., the “X” vector; Further see Jorasch, para 1219, “X” input (i.e., a vector of inputs) to a classification algorithm, or other AI algorithm.)
Regarding Claim 10, Jhawar, now incorporating Jorasch, teaches The method of claim 1, further comprising: retrieving, from each user device associated with at least a respective member, user performance data; wherein the at least one feature vector is generated further based on at least each user performance data (Jhawar teaches performance data throughout, see at least para 0003, 0047, 0057, para 0029, performance achievement of the employee; Jorasch, para 0445, teaches once extracted, features then serve as an input to a K-nearest neighbor classification algorithm... the feature vector i.e., the “X” vector; Further see Jorasch, para 1219, “X” input (i.e., a vector of inputs) to a classification algorithm, or other AI algorithm).
Regarding Claim 11, Jhawar, now incorporating Jorasch, teaches The method of claim 1, further comprising:
retrieving, from each user device associated with at least a respective member, second user input data indicating user textual feedback associated with other members of the first group; (Jhawar, para 0151, parse a result of the questionnaire using a machine learning technique ... For example, cloud platform may utilize a natural language processing technique to generate data from a text-based answer to a questionnaire prompt);
retrieving, from each user device associated with at least a respective member, user behavior data; (Jhawar, para 0049, monitoring utilization of the user interface or the user interface to detect a user interaction);
retrieving, from each user device associated with at least a respective member, user performance data; (Jhawar teaches performance data throughout, see at least para 0003, 0047, 0057, para 0029, performance of the employee);
parsing each second user input data indicating user textual feedback to generate a third plurality of features; parsing each user behavior data to generate a fourth plurality of features; parsing each user performance data to generate a fifth plurality of features; (Jhawar teaches parsing data throughout, see at least Jhawar, para 0016, parse the results of the set of questionnaires to update the employee information; para 00115, teaches parsing data for employee information (Examiner notes features, for example from a resume may be many identifying features including roles assigned, salary history, identifying information, etc.));
generating at least one second feature vector based at least on the third plurality of features, the fourth plurality of features, and the fifth plurality of features; (Jorasch, para 0445, teaches once extracted, features serve as an input to a K-nearest neighbor classification algorithm... the feature vector i.e., the “X” vector; Further see Jorasch, para 1219, “X” input (i.e., a vector of inputs) to a classification algorithm, or other AI algorithm. Examiner notes feature vectors are foundational in machine learning to process data);
providing the at least one second feature vector to a machine learning language model that is configured to generate generative textual feedback for each member of the first group for the solicitation of additional user textual feedback (Jorasch, para 0445, teaches feature vector i.e., the “X” vector; Further see Jorasch, para 1219, “X” input (i.e., a vector of inputs) to a classification algorithm, or other AI algorithm. Jhawar, para 0119, teaches a type of machine learning technique, such as a natural language processing technique to transcript document to generate an evaluation of employee performance).
Regarding Claims 12 and 17, the claims are an obvious variant to claim 1 above, and are therefore rejected on the same premise. Jhawar teaches a system, memory and one or more processors (claim 12) non-transitory computer-readable medium (claim 17) (See at least Jhawar, para 0105, describing the computing environment including memory and processor; Jhawar, para 0013, teaches a non-transitory computer-readable medium storing instructions).
Claims 2-5, 13-16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Jhawar and Jorasch, and further in view of (WO 2019/232789 A1), hereinafter “WO”.
Regarding Claim 2, Jhawar, now incorporating Jorasch, teaches The method of claim 1, further comprising: ..., each first user input data indicating a respective set of initial scores, the final member valuations assigned to respective members of the first group, the financial data associated with the company, and the stock data associated with the company (Scores for employees is taught throughout Jhawar, see at least para 0186; Jorasch, para 0106, teaches votes may determine a plan of action at a company...Voting software may be capable of instituting various protocols, such as multiple rounds (Examiner notes iterative) .. see the results of votes (Examiner notes final votes or valuations/scores); Jorasch, para 0634, ‘financials’ portion; e.g. ‘project milestone slide’, ‘Q1 sales chart; Further, Jorasch, para 1134, Financial stats scroll for the company; Jorasch, para 0284, teaches stock price; para 0285, teaches stock quotes).
Yet, Jhawar and Jorasch do not appear to explicitly teach and in the same field of endeavor WO teaches recording, on a blockchain (Blockchain is taught throughout WO, see at least page 2, teaching blockchain technology).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jhawar and Jorasch with recording, on a blockchain as taught by WO with the motivation for a novel consensus mechanism method based on voting for an alliance chain (WO, page 1). The Jhawar and Jorasch invention now incorporating the WO invention, has all the limitations of claim 2.
Regarding Claim 3, Jhawar, now incorporating Jorasch and WO, teaches The method of claim 2, further comprising: retrieving the final member valuations assigned to respective members of the first group from the blockchain in response to a consensus among at least the plurality of user devices (WO teaches blockchain technology throughout; Jorasch, para 0106, Voting software may be capable of instituting various protocols, such as multiple rounds (Examiner notes iterative) ... see the results of votes (Examiner notes final votes or valuations/scores); Jorasch, para 0908, teaches voting and consensus-taking; forming a consensus).
Regarding Claim 4, Jhawar, now incorporating Jorasch and WO, teaches The method of claim 2, further comprising: ... to pay rewards to respective members of the first group based at least on the final member valuations assigned to respective members of the first group upon occurrence of a triggering event (Jhawar, para 0067, employee has satisfied a sales goal, and may recommend the employee for a reward for the employee; Further, Jhawar, para 0172, rewards (e.g.., either monetary or non-monetary rewards, such as recognition, promotion, trophies, announcements, etc.).
Yet, Jhawar and Jorasch do not appear to explicitly teach and in the same field of endeavor WO teaches storing a smart contract on the blockchain or in a database, the smart contract including a first clause (Blockchain is taught throughout WO, see at least page 2, teaching blockchain technology; WO, page 13, teaches smart contracts are based on credible and immutable records and data on the blockchain, and can automatically execute rules and terms).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Jhawar and Jorasch with storing a smart contract on the blockchain or in a database, the smart contract including a first clause as taught by WO with the motivation for a novel consensus mechanism method based on voting for an alliance chain (WO, page 1).
Regarding Claim 5, Jhawar, now incorporating Jorasch and WO, teaches The method of claim 4, further comprising: determining that the triggering event occurred; in response to determining that the triggering event occurred, executing at least the first clause of the smart contract to pay rewards to respective members of the first group based at least on the final member valuations assigned to respective members of the first group (WO, page 13, teaches smart contracts are based on credible and immutable records and data on the blockchain, and can automatically execute rules and terms; Jhawar, para 0067, employee has satisfied a sales goal (Examiner notes triggering event); Jhawar, para 0172, rewards (e.g.., either monetary or non-monetary rewards, such as promotion trophies, announcements, etc.))
Regarding claims 13 and 18, the claims recite analogous limitations to claim 2 above, and is therefore rejected on the same premise.
Regarding claims 14 and 19, the claims recite analogous limitations to claim 3 above, and is therefore rejected on the same premise.
Regarding claims 15 and 20, the claims recite analogous limitations to claim 4 above, and is therefore rejected on the same premise.
Regarding claim 16, the claim recites analogous limitations to claim 5 above, and is therefore rejected on the same premise.
Additional Prior Art Consulted
The prior art made of record and not relied upon which is considered pertinent to applicant’s disclosure includes the following:
Bastian et al. US 10,395,191 B2 – recommending decision makers in an organization
Bradley US 2022/0350809 - A system, method, and platform for analyzing company data. Source data regarding one or more companies is captured utilizing a data platform. The source data captured based on criteria including at least equality, diversity, and inclusion associated with the one or more companies is analyzed to generate company data for each of the one or more companies. The one or more companies are scored based on the criteria. The one or more companies are ranked based on the criteria. The company information including at least the scores and ranking from the data platform is communicated to one or more designated parties.
El Majdoubi US 12,143,518 B1 – Systems and methods for secure networks
Frank et al. US 7,367,808 B1 – Employee retention system and associated methods
Henderson US 2022/0292543 - Henderson, para 0176, financial analytics embodiment including track budgeting and payroll management systems; retrieving stock data associated with the company; Henderson, para 0028, business inventorial stock keeping unit information.
Lebow et al US 2022/0222750 A1 – data communication platform
Ma US 2022/0051358 A1 – managing property using a blockchain
O’Malley (US 12,094,018 B1) – NLP, Prompts and collaborations of data, content and correlations in communications
NPL - Nesrine Ben Yahia; Jihen Hlel; Ricardo Colomo-Palacios, “From Big Data to Deep Data to Support People Analytics for Employee Attrition Prediction”, IEEE, Date of Publication: 20 April 2021, From Big Data to Deep Data to Support People Analytics for Employee Attrition Prediction | IEEE Journals & Magazine | IEEE Xplore -
Applicant is advised to review additional references supplied on the PTO-892 as to the state of the art of the invention.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REBECCA R NOVAK whose telephone number is (571)272-2524. The examiner can normally be reached Monday - Friday 8:30am - 5:00pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynda Jasmin can be reached on (571) 272-6782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/R.R.N./Examiner, Art Unit 3629/LYNDA JASMIN/Supervisory Patent Examiner, Art Unit 3629