DETAILED ACTION
Notice of 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 action is in reply to the preliminary amendment filed on July 9, 2025.
Claims 3–13 have been amended and are hereby entered.
Claims 1–13 are currently pending and have been examined.
Response to Amendment
The amendment filed July 9, 2025 has been entered. Claims 1–13 remain pending in the application.
Information Disclosure Statement
The Information Disclosure Statement filed on July 13, 2026 has been considered. An initialed copy of the Form 1449 is enclosed herewith.
Claim Rejections - 35 USC § 101
The following is a quotation of 35 U.S.C. 101:
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 1–13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
First of all, claims must be directed to one or more of the following statutory categories: a process, a machine, a manufacture, or a composition of matter. Claims 1–12 are directed to a machine (“A system”), and claim 13 is directed to a process (“A method”). Thus, claims 1–13 satisfy Step One because they are all within one of the four statutory categories of eligible subject matter.
Claims 1–13, however, are directed to an abstract idea without significantly more. For claim 1, the specific limitations that recite an abstract idea are:
An attribution/revenue sharing system . . . for expert curation of source materials . . ., comprising . . . tracks participation of plurality of individuals wherein any or all of the plurality of individuals are incentivized to contribute to the curation of training data.
The claims, therefore, recite an attribution and revenue sharing system for data contribution, which is the abstract idea of certain methods of organizing human activity because they recite a commercial interaction. The claims also recite tracking and incentivizing contribution of data, which is the abstract idea of mental processes because it involves observations and evaluations that can be performed by the human mind.
The judicial exception recited above is not integrated into a practical application. The additional elements of the claims are various generic technologies and computer components to implement this abstract idea (“system”, “artificial intelligence (Al)”, and “computer processor”). These additional elements are not integrated into a practical application because the invention merely applies the abstract idea to generic computer technology, using the computer to track contributions and provide attribution and revenue sharing. Claim 1 does introduce a more specific technology, artificial intelligence, but this is merely recited as an implementation that the data is being applied to. The claims as a whole are only directed to the abstract ideas recited above, and the claims then recite that the data from these abstract ideas is merely applied to the artificial intelligence technology. Because the invention is using the computer simply as a tool to perform the abstract idea on, the judicial exception is not integrated into a practical application.
Finally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, the additional elements in combination are at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic components. Because merely “applying” the exception using generic computer components cannot provide an inventive concept, the additional elements do not recite significantly more than the judicial exception. Thus, claim 1 is not patent eligible.
Dependent claims 2–12 have been given the full two part analysis, analyzing the additional limitations both individually and in combination. The dependent claims, when analyzed individually and in combination, are also held to be patent ineligible under 35 U.S.C. 101.
For claims 2, 3, 9, and 11, the additional recited limitations of these claims merely further narrow the abstract idea discussed above. These dependent claims only narrow the attribution and revenue sharing recited in claim 1 by further specifying how they are rewarded—“based on individual contributions”, “to individuals that provide content”, “based on membership”, and “contingent on the correction of errors”.
For claims 4–6 and 12, the additional recited limitations of these claims merely further narrow the abstract idea discussed above. These dependent claims only narrow the attribution and revenue sharing recited in claim 1 by further specifying the contributions—“individuals that provide content”, “weighted based on . . . citations and attributions”, “weighted based on . . . user interaction”, and “metadata is collected”.
For claims 7, 8, and 10, the additional recited limitations of these claims merely further narrow the abstract idea discussed above. These dependent claims only narrow the attribution and revenue sharing recited in claim 1 by further specifying the information considered—“track number of times . . . considered”, “denominators selected from a group”, and “provide an explanation . . . of the factors considered”. The limitations of these claims fail to integrate the abstract idea into a practical application because these claims do not introduce additional elements other than the generic components discussed above (“artificial intelligence”). These dependent claims, therefore, also amount to merely using a computer, in its ordinary capacity, as a tool to perform the abstract idea. Finally, the additional recited limitations of these dependent claims fail to establish that the claims provide an inventive concept because claims that merely use a computer, in its ordinary capacity, as a tool to perform the abstract idea cannot provide an inventive concept.
For claim 13, the additional recited limitations of this claim merely further narrow the abstract idea discussed above. This dependent claim only narrows the attribution and revenue sharing system recited in claim 1 by further specifying that it is performed by a method—“method . . . using a system of claim 1”. The limitations of this claim fail to integrate the abstract idea into a practical application because this claim does not introduce additional elements other than the generic components discussed above (“artificial intelligence (AI)”). This dependent claim, therefore, also amounts to merely using a computer, in its ordinary capacity, as a tool to perform the abstract idea. Finally, the additional recited limitations of this dependent claim fails to establish that the claim provides an inventive concept because claims that merely use a computer, in its ordinary capacity, as a tool to perform the abstract idea cannot provide an inventive concept.
Claim Rejections - 35 USC § 102
In the event that the determination of the status of the application as subject to 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1–9 and 13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Keski-Valkama, U.S. Patent App. No. 2020/0311757 (“Keski-Valkama”).
For claim 1, Keski-Valkama teaches:
An attribution/revenue sharing system for use with a system for expert curation of source materials for an artificial intelligence (Al) system, comprising a computer processor that tracks participation of plurality of individuals wherein any or all of the plurality of individuals are incentivized to contribute to the curation of training data (¶ 29–30, 38: incentive/reward for data contributed to machine learning model).
For claim 2, Keski-Valkama teaches all the limitations of claim 1 above and further teaches:
The system of claim 1, wherein the revenue sharing system awards attributions or compensation to any or all of the plurality of individuals proportionally based on individual contributions (¶ 32, 58: individual data contributors rewarded based on contributions).
For claim 3, Keski-Valkama teaches all the limitations of claim 1 above and further teaches:
The system of claim 1, wherein the revenue sharing system awards attributions or compensation to individuals that provide content evaluated by the system for expert curation (¶ 58: individuals rewarded based on impact of contribution to training machine learning model).
For claim 4, Keski-Valkama teaches all the limitations of claim 3 above and further teaches:
The system of claim 3, wherein the individuals that provide content comprise authors, publishers, researchers, universities, intellectual property (IP) owners, end users, or members of the curation system (¶ 64, 65, 33: data contributors are various users of the system).
For claim 5, Keski-Valkama teaches all the limitations of claim 1 above and further teaches:
The system of claim 1, wherein the individual contributions are weighted based on number of citations and attributions to each individual contribution (¶ 57–58: contributions weighted based on which data was most impactful and significant).
For claim 6, Keski-Valkama teaches all the limitations of claim 1 above and further teaches:
The system of claim 1, wherein the individual contributions are weighted based on a determination of aggregate user interaction with the artificial intelligence system (¶ 57–58: total reward amount weighted based on amount of contributions).
For claim 7, Keski-Valkama teaches all the limitations of claim 1 above and further teaches:
The system of claim 1, wherein the attribution/revenue sharing system includes a counter configured to track the number of times any individual contribution is considered by the artificial intelligence system (¶ 30, 33: incentives based on number and amount of useful data used by the machine learning).
For claim 8, Keski-Valkama teaches all the limitations of claim 1 above and further teaches:
The system of claim 1, wherein the attribution/revenue sharing system considers one or more denominators selected from a group consisting of: profit, EBITDA, and top-line revenue (¶ 34: consideration of data that is meaningful for generating revenue).
For claim 9, Keski-Valkama teaches all the limitations of claim 1 above and further teaches:
The system of claim 1, wherein the individuals of the plurality of individuals are organized into participant tiers and the attribution/revenue sharing system assigns royalty rates based on membership to the participant tiers (¶ 60: reward output to contributors based on their respective relative relevance).
For claim 13, Keski-Valkama teaches all the limitations of claim 1 above and further teaches:
A method of incentivizing the curation of source material for an artificial intelligence (Al) system, comprising awarding attribution or compensation to a plurality of individuals using a system of claim 1 (¶ 29–30, 38: incentive/reward for data contributed to machine learning model).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for determining obviousness under 35 U.S.C. 103 are summarized as follows:
(1) Determining the scope and contents of the prior art.
(2) Ascertaining the differences between the prior art and the claims at issue.
(3) Resolving the level of ordinary skill in the pertinent art.
(4) Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 10–12 are rejected under 35 U.S.C. 103 as being unpatentable over Keski-Valkama, U.S. Patent App. No. 2020/0311757 (“Keski-Valkama”) in view of Maughan et al., U.S. Patent App. No. 2017/0372232 (“Maughan”).
For claim 10, Keski-Valkama teaches all the limitations of claim 1 above. Keski-Valkama does not teach: wherein the attribution/revenue sharing system includes a system of feedback configured to provide an explanation to individuals of the factors considered in determining awarded compensation.
Maughan, however, teaches:
The system of claim 1, wherein the attribution/revenue sharing system includes a system of feedback configured to provide an explanation to individuals of the factors considered in determining awarded compensation (¶ 107: results displayed to user; ¶ 60: feedback provided).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the machine learning in Keski-Valkama by adding the feedback from Maughan. One of ordinary skill in the art would have been motivated to make this modification for the purpose of improving evaluation of datasets in machine learning modeling—a benefit explicitly disclosed by Maughan (¶ 3: data predictions may contain mistakes, and users may benefit from evaluations of datasets; ¶ 4: invention provides system for modifying training data to take corrective actions).
For claim 11, Keski-Valkama teaches all the limitations of claim 1 above. Keski-Valkama does not teach: wherein attribution or compensation to any individual is at least partially contingent on the correction of errors.
Maughan, however, teaches:
The system of claim 1, wherein attribution or compensation to any individual is at least partially contingent on the correction of errors (¶ 41: compensation based on correcting errors).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the machine learning in Keski-Valkama by adding the error corrections from Maughan. One of ordinary skill in the art would have been motivated to make this modification for the purpose of improving evaluation of datasets in machine learning modeling—a benefit explicitly disclosed by Maughan (¶ 3: data predictions may contain mistakes, and users may benefit from evaluations of datasets; ¶ 4: invention provides system for modifying training data to take corrective actions).
For claim 12, Keski-Valkama teaches all the limitations of claim 1 above. Keski-Valkama does not teach: wherein metadata is collected on each contribution and included in the source materials.
Maughan, however, teaches:
The system of claim 1, wherein metadata is collected on each contribution and included in the source materials (¶ 65–66: metadata maintained).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the machine learning in Keski-Valkama by adding the metadata from Maughan. One of ordinary skill in the art would have been motivated to make this modification for the purpose of improving evaluation of datasets in machine learning modeling—a benefit explicitly disclosed by Maughan (¶ 3: data predictions may contain mistakes, and users may benefit from evaluations of datasets; ¶ 4: invention provides system for modifying training data to take corrective actions).
Prior Art Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure. Those prior art references are as follows:
Oh, WIPO Patent App. Pub. No. WO 2023/113091 A1, discloses distributing revenue related to artificial neural network learning data.
Conclusion
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/DIVESH PATEL/Examiner, Art Unit 3696
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