DETAILED ACTION
This Office Action is in response to Applicant's application filed on 19 March 2025. Currently, claims 1-20 are pending. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) submitted are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosures statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
Claims 1-20 are clearly drawn to at least one of the four categories of patent eligible subject matter recited in 35 U.S.C. 101 (methods and system). Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1, 12 and 18 recite the abstract idea of generating a list of tasks to be provided to a given assessor for selecting for completion of at least one thereof by receiving a request for the list of tasks from the given assessor and retrieving a plurality of tasks available for execution in the crowd-sourced platform responsive to the request and determining, for a given task of the plurality of tasks, a respective assessor interaction parameter, the respective assessor interaction parameter being indicative of a likelihood value of the given assessor selecting the given task, the assessor interaction parameter being determined based on at least one or more profile parameters associated with the given assessor and determining/obtaining for the given task, a respective accurate-completion parameter, the accurate-completion parameter being indicative of a likelihood value of the given assessor completing the given task correctly and ranking the plurality of tasks to generate a ranked plurality of tasks, the ranking being executed by optimizing a ranking quality parameter, the ranking quality parameter being determined based on a combination of: (i) a user-platform satisfaction parameter indicative of the given assessor being satisfied based on a position of the given task within a ranked list of the plurality of tasks, a higher user-platform satisfaction parameter being indicative of the position of the given task within the list being aligned with the at least one or more profile parameters of the given assessor, the user-platform satisfaction parameter being determined based on the respective assessor interaction parameter of the plurality of tasks and (ii) a requester-platform satisfaction parameter indicative of a likelihood of the given assessor correctly completing the given task, a higher requester- platform satisfaction parameter being indicative of the given assessor correctly completing the given task being positioned higher within the ranked list, the requester-platform satisfaction parameter being determined based on the respective accurate-completion parameter of the plurality of tasks and the optimizing including maximizing the value of the requester-platform satisfaction parameter while maintaining the value of the user-platform satisfaction parameter at a given predetermined level and selecting from the ranked plurality of tasks, a top N-number of tasks for inclusion thereof in the list of tasks. The claims are directed to selecting tasks to be included for a list used for crowdsourcing. Under prong 1 of Step 2A, these claims are considered abstract because the claims are certain methods of organizing human activity such as including commercial interactions such as business relations and managing personal behavior or relationships or interactions between people. The claims are a type of organizing human activity because the claims show organization (ranking) of human activity (tasks) that are used in a crowd-sourced platform (business) and the claims show managing personal behavior or interactions between people by determining likelihood of selecting an assessor based on various interactions by the respective assessors. Under prong 2 of Step 2A, the judicial exception is not integrated into a practical application because the claims (the judicial exception and any additional elements individually or in combination such as the given assessor being part of a crowd-sourced digital platform, digital tasks, the method being executable by a server hosting the crowd-sourced digital platform, the server comprising a processor configured to execute a Machine-Learning algorithm (MLA) and the system comprising at least one server, the at least one server hosting the crowd-sourced digital platform, the at least one server comprising a processor at least one processor and memory storing executable instructions which, when executed by the at least one processor, cause the system to perform steps) are not an improvement to a computer or a technology, the claims do not apply the judicial exception with a particular machine, the claims do not effect a transformation or reduction of a particular article to a different state or thing nor do the claims apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment such that the claims as a whole is more than a drafting effort designed to monopolize the exception. These limitations at best are merely implementing an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination such as the given assessor being part of a crowd-sourced digital platform, digital tasks, the method being executable by a server hosting the crowd-sourced digital platform, the server comprising a processor configured to execute a Machine-Learning algorithm (MLA) and the system comprising at least one server, the at least one server hosting the crowd-sourced digital platform, the at least one server comprising a processor at least one processor and memory storing executable instructions which, when executed by the at least one processor, cause the system to perform steps (as evidenced by para [0028]-[0034], [0050]-[0053], [0081]-[0082], [0187]-[0189] of applicant’s own specification) are well understood, routine and conventional in the field. Dependent claims 2-10, 13-17, 19-20 also do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements either individually or in combination are merely an extension of the abstract idea itself by further showing wherein the retrieving the plurality of tasks available for execution further comprises determining therein a subset of tasks, the determining including: generating a feature vector of the given accessor and generating a respective feature vector for each tasks of the plurality of tasks and selecting an N-number of digital tasks from the plurality of tasks for inclusion thereof in the subset of tasks, based on vector- proximity of the feature vector of the given accessor and respective feature vectors of the plurality of tasks and wherein the user-platform satisfaction parameter is an aggregate value of the assessor interaction parameters associated with the plurality of tasks and wherein the requester-platform satisfaction parameter is an aggregate value of the accurate-completion parameters associated with the plurality of tasks and wherein the respective assessor interaction parameter is indicative of whether the given assessor would click the given task or not and wherein the respective accurate-completion parameter is determined using control tasks and , wherein the respective accurate-completion parameter is determined based on a degree of consistency of an answer provided to the given task by the given assessor with other answers provided to the given task by other assessors of the crowd-sourced platform and wherein the given digital task is of a respective predetermined type, and the respective accurate-completion parameter is indicative of a set of skills of the given assessor in completing tasks of the respective predetermined type and ranking via a specific equation and wherein the optimizing includes applying one of a Stochastic Rank algorithm and a Yeti Rank Algorithm. Dependent claims 2-9, 11, 13-17, 19-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination such as digital tasks, the MLA, and wherein the MLA comprises an ensemble of CatBoost decision trees (as evidenced by para [0028]-[0034], [0050]-[0053], [0081]-[0082], [0187]-[0189] of applicant’s own specification) are well understood, routine and conventional in the field.
Allowable Subject Matter
Claims 1-20 would be allowable if rewritten or amended to overcome the rejections under 35 U.S.C. 101, set forth in this Office action.
Claims 1-20 are allowable over the prior art because the closest prior art, such as Lee et al. (US 2016/0210570 A1), does not teach in the context of such a crowdsourcing platform, determining, for a given digital task of the plurality of digital tasks, a respective assessor interaction parameter, the respective assessor interaction parameter being indicative of a likelihood value of the given assessor selecting the given digital task, the assessor interaction parameter being determined based on at least one or more profile parameters associated with the given assessor and the ranking quality parameter being determined based on a combination of: (i) a user-platform satisfaction parameter indicative of the given assessor being satisfied based on a position of the given digital task within a ranked list of the plurality of digital tasks, a higher user-platform satisfaction parameter being indicative of the position of the given digital task within the list being aligned with the at least one or more profile parameters of the given assessor, the user-platform satisfaction parameter being determined based on the respective assessor interaction parameter of the plurality of digital tasks; (ii) a requester-platform satisfaction parameter indicative of a likelihood of the given assessor correctly completing the given digital task, a higher requester- platform satisfaction parameter being indicative of the given assessor correctly completing the given digital task being positioned higher within the ranked list, the requester-platform satisfaction parameter being determined based on the respective accurate-completion parameter of the plurality of digital tasks; the optimizing including maximizing the value of the requester-platform satisfaction parameter while maintaining the value of the user-platform satisfaction parameter at a given predetermined level; and selecting, by the processor, from the ranked plurality of digital tasks, a top N-number of digital tasks for inclusion thereof in the list of digital tasks.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lee et al. (US 2016/0210570 A1), a method for recommending one or more first tasks to one or more workers by determining a first score for a count of transactions, a second score for each of one or more locations based on a result of one or more second tasks in each of the one or more locations, and a third score for each of one or more industries based on a result of the one or more second tasks in each of the one or more industries
Xu et al. (CN 113255966 A), a crowdsourcing task matching method considering preference and independent location, comprising the following steps: the crowdsourcing platform issues the worker information to all the task requesters; each requester submits a request to the platform; calculating the value of the task requester-crowdworker-place matching according to the type of the crowd scene and formalizing the value maximization problem of the crowdsourcing task matching model of the preference and the independent location and executing the crowdsourcing task matching mechanism considering the preference and the independent location and the crowdsourcing worker executes the distributed task at the distribution place and feeds back the result to the crowdsourcing platform
Prokhorenkova et al. "CatBoost: unbiased boosting with categorical features", a paper that presents the key algorithmic techniques behind CatBoost, a new gradient boosting toolkit and discusses two critical algorithmic advances introduced in CatBoost are the implementation of ordered boosting, a permutation-driven alternative to the classic algorithm, and an innovative algorithm for processing categorical features
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/SUJAY KONERU/
Primary Examiner, Art Unit 3624
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