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 12/16/2025.
Claims 1-20 are pending. Claims 1, 15, and 18 are independent.
The rejection of claims 1-20 under 35 USC § 101 has been maintained 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The previous rejection of claims 1-20 under 35 USC § 103 have been withdrawn in view of the amendment.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 § 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 abstract idea without significantly more.
Step 1
According to the first part of the analysis, in the instant case, claims 1-14 are directed
towards a method, claims 15-17 are directed towards an apparatus, and 18-20 are directed towards an article comprising a non-transitory storage medium. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Step2A, Prong 1
Regarding Claims 1, 15, and 18 these claims recite
Determining a value of computing a result (This step for determining a value for performing a computation appear to be practically implementable in the human mind and is understood to be a recitation of a mental process.)
Determining a distribution of gains from computation of the result, 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. (This step for determining a distribution of gains appear to be practically implementable in the human mind and is understood to be a recitation of a mental process.)
Step 2A, Prong 2
Regarding Claims 1, 15, and 18 these claims recite
storing in an electronic memory a value of computing a result, the computing result to be determined based, at least in part on input values provided by multiple input value providing entities, wherein computing a result comprises training a machine learning and input values
comprise training parameters; (This limitation recites electronically storing a value from an abstract idea, which is not indicative of integration into a practical application. See MPEP 2106.05(f).) and
Electronically determining a distribution of gains from computation of the result between an/or among multiple parties including at least input value providing parties based at least in part on a calculated value of the input values and/or training parameters provided by the
multiple entities, 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;(This limitation recites electronically performing an abstract idea, which is not indicative of integration into a practical application. See MPEP 2106.05(f).)
Providing a reward between and/or among the multiple parties allocated based on the
electronically determined distribution of gains (Providing a reward is mere data gathering, an insignificant extra-solution activity).
Step 2B
Regarding Claims 1, 15, and 18 these claims recite
Electronically determining a value of computing a result, the computing result to be
determined based, at least in part on input values provided by multiple input value providing entities, wherein computing a result comprises training a machine learning and input values comprise training parameters; (This limitation recites electronically performing an abstract idea, the element taken alone or in combination fails to amount to significantly more that the judicial exception. MPEP 2106.05(f).) and
Electronically determining a distribution of gains from computation of the result between an/or among multiple parties including at least input value providing parties based at least in part on a calculated value of the input values and/or training parameters provided by the multiple parties; (This limitation recites electronically performing an abstract idea, the element taken alone or in combination fails to amount to significantly more that the judicial exception. MPEP 2106.05(f).)
distributing reward quantified as currency between and/or among the multiple parties allocated based on the stored distribution of gains (Providing a reward is mere data gathering, an insignificant extra-solution activity, the element taken alone or in combination fails to amount to significantly more that the judicial exception. See MPEP 2106.05(g)).
Step 2A, Prong 1 Dependent Claims
Regarding Claims 2 & 16
further comprising electronically sharing input values provided by the multiple input value providing entities at multiple levels of granularity to compute the result. (This limitation for sharing appear to be practically implementable in the human mind.)
Regarding claims 3 and 17
wherein the multiple entities comprise the input value providing entities, one or more model providing parties or one or more processor providing parties, or a combination thereof (This limitation appears to be practically implementable in the human mind and are understood to be a recitation of a mental process).
Regarding Claim 6
wherein electronically determining the value of computing the result further comprises
applying metrics including at least accuracy or reliability metrics. (Other than the recitation of generic computer equipment (electronically determining), this step appears to be practically implementable in the human mind and is understood to be a recitation of a mental process.)
Regarding Claims 7 &19
further comprising electronically determining the distribution of gains from computation of 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. (Other than the recitation of generic computer equipment (electronically determining), this step appears to be practically implementable in the human mind and is understood to be a recitation of a mental process.)
Regarding Claim 10
wherein input values provided by the one or more multiple input providing parties 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 (Other than the
recitation of generic computer equipment (electronically combining), this step appears to be practically implementable in the human mind and is understood to be a recitation of a mental process.)
Regarding Claim 11
wherein input values provided by the one or more multiple input providing parties 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 (Other than the recitation of generic computer equipment (electronically enabling), this step appears to be practically implementable in the human mind and is understood to be a recitation of a mental process.)
Regarding Claim 12
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 (Other than the recitation of generic computer equipment (electronically limiting), this step appears to be practically implementable in the human mind and is understood to be a recitation of a mental process.)
Regarding Claim 13
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 parties, 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 (This limitation appears to be practically implementable in the human mind and are understood to be a recitation of a mental process).
Regarding claim 14
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 model for computation of the result (This limitation appears to be practically implementable in the human mind and are understood to be a recitation of a mental process).
Step 2A, Prong 2 Dependent Claims
Regarding Claim 4
further comprising electronically maintaining input values provided by the multiple input value providing parties as signals and/or states in one or more physical devices expressing proprietary parameters and/or values in a common format (This limitation recites using devices to store input values, which is understood to be insignificant extra-solution activity. MPEP 2106.05(g).)
Regarding Claim 5
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 (This limitation recites using/applying a machine learning model as a tool to perform an abstract idea, which is not indicative of integration into a practical application. MPEP 2106.05(f).)
Regarding Claims 8 and 20
further comprising electronically transforming the input values provided by the multiple input value providing parties for storage in a shared common storage (This limitation recites using shared common storage to store input values, which is understood to be insignificant extra-solution activity. MPEP 2106.05(g).)
Regarding Claim 9
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 parties (This limitation recites using/applying homomorphic encryption, federated learning, masking, query restriction or usage tracking, or a combination thereof as a tool to perform an abstract idea, which is not indicative of integration into a practical application. MPEP 2106.05(f)).
Step 2B, Dependent Claims
Regarding Claim 4
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 (This limitation recites using devices to store input values, which is understood to be insignificant extra-solution activity, the element taken alone or in combination fails to amount to significantly more that the judicial exception. MPEP 2106.05(g).)
Regarding Claim 5
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 (This limitation recites using/applying a machine learning model as a tool to perform an abstract idea, the element taken alone or in combination fails to amount to significantly more that the judicial exception. MPEP 2106.05(f).)
Regarding Claims 8 and 20
further comprising electronically transforming the input values provided by the multiple input value providing parties for storage in a shared common storage (This limitation recites using shared common storage to store input values, which is understood to be insignificant extra-solution activity, the element taken alone or in combination fails to amount to significantly more that the judicial exception. MPEP 2106.05(g).)
Regarding Claim 9
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 parties (This limitation recites using/applying homomorphic encryption, federated learning, masking, query restriction or usage tracking, or a combination thereof as a tool to perform an abstract idea, the element taken alone or in combination fails to amount to significantly more that the judicial exception. MPEP 2106.05(f)).
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 Kain et al. (US2019/0304578).
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 (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 a calculated value of the input values and/or training parameters provided by the multiple entities (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 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).
Khan et al. does not explicitly disclose 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 Kain et al. substantially discloses 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 (Kain et al. para[0089], as value is derived from access to and discoveries from the database, that value flows back to the community and is deposited in their unique wallet or account.)
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the of Khan with the data valuation method of Kain et al. in order to calculate value of data based on benefits the data contributed to (Kain et al. para[0012])
In regards to claim 2, Khan as modified by Bahl et al. and Kain 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 Kain 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 Kain 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 Kain 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 Kain 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 Kain 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 Kain 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 Kain 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 Kain et al. substantially discloses the method of claim 8, wherein input values provided by the multiple 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 Kain 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 Kain 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 Kain 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 Kain 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 filed 12/16/2025 have been fully considered but they are not persuasive. In regards to claims 1, 15, and 18 applicant argues on page 7 that the amended claims now recite a process that may not be practically performed mentally. However the claims recite electronically storing values of computing a result and calculated distribution of gains, aside from electronically storing the values the claims still recited a process that could include mental calculation with pen and paper.
Applicant's arguments filed 12/16/2025 have been fully considered but they are not persuasive. Applicant argues on pg7 that because the office action, dated 10/01/2025, indicated the previous 103 rejection was withdrawn, the 103 rejection appearing in the action was in error.
However the office action dated 10/01/2025 was referring to the previous 103 rejection in the office action dated 1/29/2025 not the rejection of 10/01/2025 in view of which applicant has not responded to.
Conclusion
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
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/N.H/Examiner, Art Unit 2141
/TAN H TRAN/Primary Examiner, Art Unit 2141