DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to communications: Amendment filed on 7/16/2026.
Claims 1-20 are pending. Claims 1, 15, and 18 are independent.
The rejection of claims 1-20 under 35 USC § 101 has been withdrawn in view of the amendment.
The previous rejection of claims 1-20 under 35 USC § 103 have been withdrawn in view of the amendment.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khan (US2021/0279202) in view of Bahl et al. (US2021/0019194) and Zhan et al. (“A learning-based Incentive Mechanism for Federated Learning”).
In regards to claim 1, Khan substantially discloses a method comprising:
determined based, at least in part on input values provided by multiple input value providing entities (Khan para[0037], calculates value of input values), wherein computing a result comprises training a machine learning model and input values comprise training parameters and/or training data (Khan [0049], multiple parties pledge their data assets to generate a machine learning model),
and
storing in the electronic memory a distribution of gains from computation of the result between and/or among multiple entities including at least input value providing entities based at least in part on the perceived contribution(Khan fig. 2 para[0043], distribute profits to entities who provided data, use of data is tracked on data management block chain); and
distributing a reward quantified as currency between and/or among the multiple parties allocated based on the stored distribution of gains (Khan fig. 2 para[0043] ln 14-17, data security tokens used to distribute profits) [under writer].
Khan et al. does not explicitly disclose storing in an electronic memory a value of computing a result;
However Bahl et al. substantially discloses storing in an electronic memory a value of computing a result (Bahl et al. para[0022] ln6-15, determines values (cost and reward) of computing a result).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the monetization control of Khan with the multi-cloud orchestration of Bahl et al. in order to manage and use resources across multiple networks (Bahl et al. para[0002] ln8-14).
Khan et al. does not explicitly disclose wherein the input values are maintained at computing resources controlled by respective input value providing entities, and wherein training the machine learning model comprises deploying a central model-training agent to the computing resources controlled by the respective input value providing entities to obtain updates to the machine learning model based, at least in part, on the input values, and sending the updates to the machine learning model to a central server;
determining, for each of a plurality of the multiple input value providing entities, a perceived contribution of respective input values to achieve a quantified evaluation of the machine learning model;
the distribution of gains reflecting a determined degree of improvement in training the machine learning model associated with the input values for at least one of the multiple entities providing the input values.
However Zhan et al. substantially discloses wherein the input values are maintained at computing resources controlled by respective input value providing entities, and wherein training the machine learning model comprises deploying a central model-training agent to the computing resources controlled by the respective input value providing entities to obtain updates to the machine learning model based, at least in part, on the input values, and sending the updates to the machine learning model to a central server (Zhan et al. pg6361 section III.A para1, edge nodes (input value providing entities) collaboratively train a global model stored on a central parameter server);
determining, for each of a plurality of the multiple input value providing entities, a perceived contribution of respective input values to achieve a quantified evaluation of the machine learning model (Zhan pg6361 section I.2, the parameter server can accurately evaluate their (input value providing entities/participant) contributions to the training accuracy);
the distribution of gains reflecting a determined degree of improvement in training the machine learning model associated with the input values for at least one of the multiple entities providing the input values (Zhan et al. pg6365 section V.B.5-6, distributes reward to from central parameter server to edge nodes)
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the monetization control of Khan with the incentive mechanism of Zhan et al. in order to reward participants according to their contributions (Zhan et al. pg6360 section I para3)
In regards to claim 2, Khan as modified by Bahl et al. and Zhan et al. substantially discloses the method of claim 1, and further comprising electronically sharing input values provided by the multiple input value providing entities at multiple levels of granularity to compute the result (Khan fig. 1A para[0036] ln3-9, shares input values created by multiple parties at multiple levels of enhancement and derivation).
In regards to claim 3, Khan as modified by Bahl et al. and Zhan et al. substantially discloses the method of claim 1, wherein the multiple entities comprise the multiple input value providing entities, one or more model providing parties or one or more processor providing parties, or a combination thereof (Khan para[0036] ln9-13, multiple parties include those providing data and ML/AI models).
In regards to claim 4, Khan as modified by Bahl et al. and Zhan et al. substantially discloses the method of claim 1, and further comprising electronically maintaining input values provided by the multiple input value providing entities as signals and/or states in one or more physical devices expressing proprietary parameters and/or values in a common format (Khan para[0045] ln11-17, user data maintained in encrypted database).
In regards to claim 5, Khan as modified by Bahl et al. and Zhan et al. substantially discloses the method of claim 1, and further comprising electronically iterating a machine-learning model to complete a compute task and/or prediction result based, at least in part, on the input values (Khan para[0049] ln1-7, data assets used to create machine learning models).
In regards to claim 6, Khan as modified by Bahl et al. and Zhan et al. substantially discloses the method of claim 1, wherein the value of computing the result is further determined based, at least in part, on applying metrics including at least accuracy or reliability metrics (Bahl et al. [0069] ln1-23, cost can also refer to other metrics such as reliability and accuracy of results).
It would have obvious to one of ordinary skill in the art before the filing date of the invention to have combined the of Khan with the multi-cloud orchestration of Bahl et al. in order to manage and use resources across multiple networks (Bahl et al. para[0002] ln8-14).
In regards to claim 7, Khan as modified by Bahl et al. and Zhan et al. substantially discloses the method of claim 1, and further comprising electronically determining the distribution of gains from computation of the value of computing the result value based, at least in part, on a determination of a perceived contribution of proprietary parameters and/or values by individual ones of the multiple input value providing parties in determination of the computed result (Khan para[0044] ln1-4, determines payment to relevant parties when data is used).
In regards to claim 8, Khan as modified by Bahl et al. and Zhan et al. substantially discloses the method of claim 1, and further comprising electronically transforming the input values provided by the multiple input value providing entities for storage in a shared common storage (Khan para[0035] ln4-8, parties have joint ownership of derived assets).
In regards to claim 9, Khan as modified by Bahl et al. and Zhan et al. substantially discloses the method of claim 8, and further comprising electronically applying homomorphic encryption, federated learning, masking, query restriction or usage tracking, or a combination thereof, to input values provided by one or more of the multiple input value providing entities (Khan para[0043] ln11-17, implements asset tracking).
In regards to claim 10, Khan as modified by Bahl et al. and Zhan et al. substantially discloses the method of claim 8, wherein input values provided by the multiple value input providing entities are provided at different input value granularities, and further comprising electronically combining input values provided by the one or multiple input providing entities at different input value granularities by electronically rewarding input providing parties that improve granularity of contributed proprietary parameters and/or values, or electronically compensating a model providing party to combine input values provided at different granularities (Khan para[0035] ln3-9, provides compensation to owners of aggregated or derived datasets).
In regards to claim 11, Khan as modified by Bahl et al. and Zhan et al. substantially discloses the method of claim 8, wherein input values provided by the multiple input value providing entities are provided at different input value granularities, and further comprising electronically enabling sharing of proprietary parameters and/or secrets at a raw granularity, partially aggregated proprietary parameters and/or secrets or proprietary parameters and/or secrets generated by a sub-coalition of input providing parties, or a combination thereof (Khan para[0036] ln1-6, includes multiple iterations of derived and aggregated data).
In regards to claim 12, Khan as modified by Bahl et al. and Zhan et al. substantially discloses the method of claim 1, and further comprising electronically limiting use of at least a portion of input values provided by at least one of the input value providing parties for computation of particular tasks (Khan para[0046] ln7-10, User controls who can access data with encryption key).
In regards to claim 13, Khan as modified by Bahl et al. and Zhan et al. substantially discloses the method of claim 1, and wherein at least one of the multiple entities comprises a task owner that controls and/or owns a particular task and/or prediction to be computed based, at least in part, on the input values provide by the multiple input value providing entities, and wherein the task owner publishes one or more attributes of the particular task and/or prediction to include a goal, success criteria, a bounty, identification of functions/processes sought for computation of the particular task and/or prediction, a time scale for computation of the particular task and/or prediction, geography, reputation, identification of particular proprietary parameters and/or secret sought for use in computing the particular task and/or prediction, identification of particular models sought for use in computing the particular task and/or prediction or particular schema sought for use in computing the particular task and/or prediction, or a combination thereof (Bahl et al. para[0013] ln16-23, task (application) owner provides SLA defining performance, reliability, and pricing for application).
It would have obvious to one of ordinary skill in the art before the filing date of the invention to have combined the of Khan with the multi-cloud orchestration of Bahl et al. in order to manage and use resources across multiple networks (Bahl et al. para[0002] ln8-14).
In regards to claim 14, Khan as modified by Bahl et al. and Zhan et al. substantially discloses the method of claim 1, and wherein at least one of the multiple entities comprises a model owner that controls and/or owns a particular model that may be used for computation of the result based, at least in part, on the input values, and wherein the model owner publishes one or more performance expectations, one or more accuracy expectations, one or more reliability expectations, one or more cost expectations, one or more open source features or an expected reward, or a combination thereof, associated with use of the particular model for computation of the result (Khan para[0048] ln4-9, model owner uses machine learning model to optimize data monetization (expected reward)).
Claims 15-17 recite substantially similar limitations to claims 1-3. Thus claims 15-17 are rejected along the same rationale as claims 1-3.
Claims 18-20 recite substantially similar limitations to claim 1, and 7-8. Thus claims 18-20 are rejected along the same rationale as claims 1 and 7-8.
Response to Arguments
Applicant’s arguments, see pg2 ln10-22, filed 7/16/2026, with respect to Claims 1-20 have been fully considered and are persuasive. The 101 rejection of 4/7/26 has been withdrawn.
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the arguments do not apply the current rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kang et al.(“Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory”) teaches providing incentive mechanisms for participating in federated learning.
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/N.H/Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
