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 .
DETAILED ACTION
Status of the Application
The following is a Non-Final Office Action in response to communication received on 5/16/2025. Claims 1-20 are pending in this office action. This is the first action on the merits. As of the date of this communication, there has been no Information Disclosure Statement (IDS) filed on behalf of this case.
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 an abstract idea without significantly more.
The claim(s) recite(s) the idea of providing compensation distributions based on an entities contribution to the development of a product.
The idea of providing compensation distributions based on an entities contribution to the development of a product recites observations, evaluations, judgements, and opinions that can be performed in the human mind or with pen and paper aid accordingly the claims recite a mental process (see MPEP 2106.04(a)).
Further the claims recite business relations as the claims recite a relationship between an entity’s who’s product is being used and an entity using the product. Business relations are certain methods of organizing human activity and hence the claims recite an abstract idea (see MPEP 2106.04(a)).
Mental process and certain methods of organizing human activities are in the groupings of enumerated abstracts ideas, and hence the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the claims merely recite limitations that are not indicative of integration into a practical application in that the claims merely recite:
(1) Adding 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 (see MPEP 2106.05(f)) and (2) Generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).
Specifically as recited in the claims:
Examiner notes that the Examiner has underlined and bolded additional elements. Limitations not bolded and underlined are considered a part of the abstract idea.
1. A method for determining contribution to a generated product being generated by a generative artificial intelligence (Al) model, comprising:
receiving a plurality of assets that contributed to or intended to be used for the training of the Al model, wherein each asset of the plurality of assets is attributed to at least one owner that owns its copyrights;
analyzing said plurality of assets to extract from each asset meta features and to generate, for each asset of the plurality of assets, an asset data set that comprises owners data indicative of the owner of the asset and the meta features;
processing the plurality of asset data sets to determine a contribution distribution data indicative of the contribution distribution of assets owners to the training of the model, wherein the contribution distribution data comprises a contribution score for each assets owner;
outputting contribution distribution output data that comprises said contribution distribution data.
2. The method of claim 1, comprising filtering the received plurality of assets, said filtering comprises excluding assets that do not qualify to train the model.
3. The method of claim 2, wherein said filtering further comprises identifying a first asset that is identical or has a degree of similarity higher than a defined threshold to a second asset and excluding the second asset.
4. The method of claim 1, wherein said plurality of assets are graphical assets and the generated product is a graphical product.
5. The method of claim 4, wherein said meta features comprises tag of the graphical asset, caption associated with the graphical asset, style of the graphical asset, objects in the graphical asset, or any combination thereof;
wherein the plurality of graphical assets comprises images, drawings, photos, or any combination thereof.
6. The method of claim 1, comprising determining the contribution of one or more assets owners to a specific generated product asset by the Al model in response to a guidance prompt, said determining comprises extracting generated product meta features,
identifying matching assets from the plurality of assets that has a degree of correlation above a selected threshold of one or more of their asset meta features with one or more of the generated product meta features, defining for the matching assets, based on the degree of correlation, a specific contribution score;
wherein said contribution distribution output data further comprises said specific contribution score attributed to an asset owner.
7. The method of claim 6, wherein said plurality of assets are graphical assets and the generated product is a graphical product, and wherein the generated product meta features comprise tag of the generated graphical asset, caption associated with the graphical asset, style of the graphical asset, objects in the graphical asset, or any combination thereof;
wherein said determining further comprises calculating the similarity between a caption attributed to an asset and the guidance prompt for said identifying;
wherein said determining further comprises calculating the similarity between a tag attributed to an asset and a contextual analysis of the guidance prompt for said identifying.
8. The method of claim 7, wherein said determining further comprises calculating the similarity between a tag attributed to an asset and a graphical analysis of an image guidance prompt for said identifying; wherein said selected threshold is defined to obtain a selected limited number of matching assets with a correlation degree that satisfies a certain condition.
9. The method of claim 1, wherein said outputting is triggered in response to a generated product by the AI model.
10. The method of claim 9, wherein determining distribution parameters for attribution of value associated with the generated product by the AI model based on the contribution score.
11. The method of claim 1, wherein the contribution score is determined according to at least one of the following parameters: quantity of the assets, the age of each of the assets, community score indicative of an evaluation of users of the value of the asset.
12. A system for determining contribution to a generated product being generated by a generative artificial intelligence (AI) model, comprising:
at least one processing circuitry configured for:
receiving a plurality of assets that contributed to or intended to be used for the training of the AI model, wherein each asset of the plurality of assets is attributed to at least one owner that owns its copyrights;
analyzing said plurality of assets to extract from each asset meta features and to generate, for each asset of the plurality of assets, an asset data set that comprises owners data indicative of the owner of the asset and the meta features;
processing the plurality of asset data sets to determine a contribution distribution data indicative of the contribution distribution of assets owners to the training of the model, wherein the contribution distribution data comprises a contribution score for each assets owner; and for outputting contribution distribution output data that comprises said contribution distribution data.
13. The system of claim 12, wherein said plurality of assets are graphical assets and the generated product is a graphical product; wherein said meta features comprises tag of the graphical asset, caption associated with the graphical asset, style of the graphical asset, objects in the graphical asset, or any combination thereof; wherein the plurality of graphical assets comprises images, drawings, photos, or any combination thereof.
14. The system of claim 12, wherein the processing circuitry is further configured for determining the contribution of one or more assets owners to a specific generated product asset by the AI model in response to a guidance prompt, said determining comprises extracting generated product meta features, identifying matching assets from the plurality of assets that has a degree of correlation above a selected threshold of one or more of their asset meta features with one or more of the generated product meta features, defining for the matching assets, based on the degree of correlation, a specific contribution score; wherein said contribution distribution output data further comprises said specific contribution score attributed to an asset owner.
15. The system of claim 14, wherein said plurality of assets are graphical assets and the generated product is a graphical product, and wherein the generated product meta features comprise tag of the generated graphical asset, caption associated with the graphical asset, style of the graphical asset, objects in the graphical asset, or any combination thereof.
16. The system of claim 14, wherein said determining further comprises calculating the similarity between a caption attributed to an asset and the guidance prompt for said identifying;
Wherein said determining further comprises calculating the similarity between a tag attributed to an asset and a contextual analysis of the guidance prompt for said identifying;
wherein said determining further comprises calculating the similarity between a tag attributed to an asset and a graphical analysis of an image guidance prompt for said identifying.
17. The system of claim 14, wherein said selected threshold is defined to obtain a selected limited number of matching assets with a correlation degree that satisfies a certain condition.
18. The system of claim 12, wherein said outputting is triggered in response to a generated product by the AI model; wherein said outputting comprises determining distribution parameters for attribution of value associated with the generated product by the AI model based on the contribution score.
19. The system of claim 12, wherein the contribution score is determined according to at least one of the following parameters: quantity of the assets, the age of each of the assets, community score indicative of an evaluation of users of the value of the asset.
20. The system of claim 12, wherein the at least one processing circuitry is further configured for calculating a relative contribution parameter indicative of the relative contribution to the training of the model between assets protected by copyrights and assets not protected by copyrights; wherein the distribution output data comprises said relative contribution parameter.
As per claim 1, the claims recite mental process and certain methods of organizing human activities steps of receiving a plurality of assets to be used in a setup of a model (e.g. set of rules) where the assets are contributed to an owner for an copyright, analyzing the information to extract meta features for owners of the asset, processing the asset to determine contribution distribution of the assets used to make the model, and outputting a contribution distribution as broadly recited in the claims. This is part of the abstract idea. The additional element that the set of rules (or model) is a generative AI model and the model is set up by “training” merely results in apply it.
Here the additional elements merely invoke computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more.
Further additional element that the set of rules (or model) is a generative AI model and the model is set up by “training” merely results in generally linking it to the field of computers (the AI technological environment or field of use).
As per claim 2, the claims recite mental process and certain methods of organizing human activities steps of filtering out assets that do not qualify as being used to establish or create the model (set of rules), as broadly recited in the claims. This is part of the abstract idea. The additional element that this model is trained has been addressed above under claim 1 as mere apply or generally linking it to the field of computers.
As per claim 3, the claims recite mental process and certain methods of organizing human activities steps of filtering assets that is identical or has a degree of similarity higher than a threshold to an asset. This is part of the abstract idea. There are no additional elements beyond those previously discussed above.
As per claim 4, the claims recite mental process and certain methods of organizing human activities steps merely describing the assets used in generating the set of rules (model) are graphical, as broadly recited in the claims. This is part of the abstract idea. There are no additional elements beyond those previously discussed above in the claim.
As per claim 5, the claims recite mental process and certain methods of organizing human activities steps of merely describing the assets used in generating the set of rules (model) are either images, drawings, or photos, and the type of meta data of the assets includes tag, style, captions, or objects of the graphical assets, as broadly recited in the claims. This is part of the abstract idea. There are no additional elements beyond those previously discussed above.
As per claim 6, the claims recite mental process steps and certain methods of organizing human activities steps of determining contribution in response to information used to generate the product (prompt), which includes extracting product features, identifying matching assets from the plurality of assets that have a degree of correlation above a threshold, defining for the matching assets based on correlation a score, and wherein the contribution distribution comprises the score attributed to the owner, as broadly recited in the claims. This is part of the abstract idea. The additional element the model is “AI” has been addressed above as apply it or generally linking it to the field of computers (see claim 1).
As per claim 7, the claims recite mental process steps and certain methods of organizing human activities steps of describing a generated product includes meta data including tag, style, captions, or objects of the asset, determining a distribution based on similarity between a caption and request (prompt), and determining a distribution based on a tag and the contextual information of a request (prompt). This is part of the abstract idea. There are no additional elements beyond those previously discussed above.
As per claim 8, the claims recite mental process steps and certain methods of organizing human activities steps of merely determining a similarity between a tag of a generated product and the information used to generate the product (prompt), where a threshold is used to define a selected limited number of assets matching a correlation degree. This is part of the abstract idea. There are no additional elements beyond those previously discussed above.
As per claim 9, the claims recite mental process steps and certain methods of organizing human activities steps of the output of the distribution (compensation) is based on a product generated by a model, as broadly recited in the claims. This is part of the abstract idea. The additional element that the set of rules (model) is AI results in merely apply it or generally linking it to the field of computers, as discussed above in claim 1.
As per claim 10, the claims recite mental process steps and certain methods of organizing human activity of determining the distribution parameters value associated with the product generated by the set of rules (model) based on the contribution score, e.g. how much you get in royalties/compensation is based on how much you contributed to the final product. This is part of the abstract idea. The additional element that the set of rules (model) is AI results in merely apply it or generally linking it to the field of computers, as discussed above in claim 1.
As per claim 11, the claims recite mental process steps and certain methods of organizing human activity steps of the value of contributing product that contributes to the contribution score is based on parameters like quality, age, and community score. This is part of the abstract idea. There are no additional elements beyond those discussed above.
As per claim 12, the claims recite mental process steps and certain methods of organizing human activities of receiving a plurality of assets to be used in a setup of a model (e.g. a set of rules) where the assets are contributed to an owner for an copyright, analyzing the information to extract meta features for owners of the asset, processing the asset to determine contribution distribution of the assets used to make the model, and outputting a contribution distribution as broadly recited in the claims. This is part of the abstract idea.
The additional elements that the set of rules (or model) is a generative AI model and the model is set up by “training” and these mental process or human activity steps are instead being performed by “processing circuitry” merely results in apply it. Here the additional elements merely invoke computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more.
Further the additional elements that the set of rules (or model) is a generative AI model and the model is set up by “training” and these mental process or human activity steps are instead being performed by “processing circuitry” merely results in generally linking it to the field of computers (the AI technological environment or field of use).
As per claim 13, the claims recite mental process steps and certain methods organizing human activity steps of merely describing the assets used in generating the set of rules (model) are either images, drawings, or photos, and the type of meta data includes tag, style, captions, or objects of the graphical objects, as broadly recited in the claims. This is part of the abstract idea. There are no additional elements beyond those previously discussed above.
As per claim 14, the claims recite mental process steps and certain methods organizing human activity steps of determining contribution in response to information used to generate the product ( prompt), which includes extracting product features, identifying matching assets from the plurality of assets that have a degree of correlation above a threshold, defining for the matching assets based on correlation a score, and wherein the contribution distribution comprises the score attributed to the owner, as broadly recited in the claims. This is part of the abstract idea. These additional elements that these mental process and human activity steps are being performed instead by “processing circuity” and the model or set of rules is AI merely results in apply it or generally linking it to the field of computers as discussed above in claim 12
As per claim 15, the claims recite mental process steps and certain methods of organizing human activities of merely describing the assets used in generating the set of rules (model) are graphical, and the generated products are graphical include tags, captions, styles, and options, as broadly recited in the claims. This is part of the abstract idea. There are no additional elements beyond those previously discussed above.
As per claim 16, the claims recite mental process steps and certain methods of organizing human activity steps of merely determining a similarity between a caption attributed to an asset and the request information (prompt), determining a similarity between a tag of a generated product and contextual information of the request to generate the product (prompt), and determining similarity between a tag of a generated product and graphical analysis of a request used to generate the product (prompt). This is part of the abstract idea. There are no additional elements beyond those previously discussed above.
As per claim 17, the claims recite mental process steps and certain methods of organizing human activity of a threshold is defined to obtain a selected limited number of matching assets with a correlation degree that satisfies a certain condition. There are no additional elements beyond those previously discussed above.
As per claim 18, the claims recite mental process steps and certain methods of organizing human activities steps of determining the distribution parameters value associated with the product generated by the set of rules (model) based on the contribution score, e.g. how much you get in royalties/compensation is based on how much you contributed to the final product. This is part of the abstract idea. The additional element that the set of rules (model) is “AI” results in merely apply it or generally linking it to the field of computers, as discussed above in claim 1.
As per claim 19, the claims recite mental process steps and certain methods of organizing human activities steps of the value of contributing product that contributes to the contribution score includes parameters like quality, age, and community score. This is part of the abstract idea. There are no additional elements beyond those discussed above.
As per claim 20, the claims recite mental process steps and certain methods of organizing human activity steps of calculating a contribution between assets protected by copy right and those that are not. This is part of the abstract idea. There are no additional elements beyond those discussed above.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims merely recite limitations that are not indicative of an inventive concept (“significantly more”) in that the claims merely recite:
(1) Adding 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 (see MPEP 2106.05(f)) and (2) Generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), as detailed above with respect to the practical application step.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per claim 1, Applicant recites receiving a plurality of assets to or intended to be used for the training of the AI model. There is insufficient antecedent basis for this limitation in the claim, the training, as the limitation of training was not previously recited in the claim. For the purposes of this examination, the Examiner will interpret the claim as follows: receiving a plurality of assets to or intended to be used for
Further claim 12 recites substantially the same subject matter and is rejected under the same grounds. Further claims 2-11 based on their dependency on claim 1 and claims 13-20 based on their dependency on claim 12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per claim 7, Applicant recites comprise tag of the generated graphical asset. There is insufficient antecedent basis for the limitation, the generated graphical asset, as the limitation of the graphical asset being generated is not previously recited in the claim or the claim from which it depends. For purposes of this examination, the Examiner will interpret the claim as follows (based on other limitations in the claims): comprise tag of the
Further claim 8 that depends from claim 7 is rejected based on its dependency.
Further claim 15 that recites substantially the same subject matter as above is rejected under the same grounds as above.
As per claim 11, Applicant recites wherein the contribution score is determined according to at least one of the following parameters: quantity of the assets, the age of each of the assets, community score indicative of an evaluation of users of the value of the asset. There is insufficient antecedent basis for the limitations, (1) the age and (2) the value of the asset, as the limitations are not previously recited in the claim or the claim from which they depend. Further there appears to be an ”or” missing between the terms “assets” and “community” as the list of alternatives is defined as “at least one of.” For the purposes of this examination, the Examiner will interpret the claim as follows: wherein the contribution score is determined according to at least one of the following parameters: quantity of the assets, an age of each of the assets, or community score indicative of an evaluation of users of a value of the asset.
Further claim 19 recites substantially the same subject matter and is rejected under the same grounds.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kuhn et al. (United States Patent Application Publication Number: US 2024/0419720) further in view of Wang et al. An Economic Solution to Copyright Challenges of Generative AI dated April 22, 2024.
As per claim 1, Khun teaches A method for determining contribution to a generated product being generated by a generative artificial intelligence (Al) model, comprising: (see paragraphs 0024, Examiner’s note: method of determining an output embedding produced by artificial intelligence).
receiving a plurality of assets that contributed to or intended to be used for the training of the Al model, wherein each asset of the plurality of assets is attributed to at least one owner that owns it; analyzing said plurality of assets to extract from each asset meta features and to generate, for each asset of the plurality of assets, an asset data set that comprises owners data indicative of the owner of the asset and the meta features; processing the plurality of asset data sets to determine a contribution distribution data indicative of the contribution distribution of assets owners to the training of the model, wherein the contribution distribution data comprises a contribution score for each assets owner; outputting contribution distribution output data that comprises said contribution distribution data (see paragraphs 0003, 0018, and 0030-0032, Examiner’s note: teaches creating a work through generative AI. The original creators of the work used to train the AI and output the created work are compensated based on the percentage of their work was used to generate the image, where the compensation may be monetary or otherwise like a statement regarding influence).
Kuhn does not expressly teach an author of a work owning its copyrights
However, Wang et al. which is in the art of economic solution to copyright challenges of generative AI (see abstract) teaches an author of a work owning its copyrights (see abstract and page 1 introduction, Examiner’s note: teaches copy right owners information being used in training data for generative artificial intelligence and then the copy right owners receiving fair compensation for their information).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Kuhn with the aforementioned teachings from Wang et al. with the motivation of providing a known element that when users create work it is often subject to copyrights (see Wang et al. abstract), when providing compensation for information created by creators like mages is known (see Wang et al. paragraph 0032).
As per claim 2, Khun teaches
comprising filtering the received plurality of assets, said filtering comprises excluding assets that do not qualify to train the model (see paragraphs 0031-0032 and 0037, Examiner’s note: this limitation is very broad in the claims and be assets that are below a threshold, e.g. dissimilar (as in paragraphs 0031-0032) or editing content used to train the AI (see paragraph 0037)).
As per claim 3, Khun teaches
wherein said filtering further comprises identifying a first asset that is identical or has a degree of similarity higher than a defined threshold to a second asset and excluding the second asset (see paragraphs 0031-0032 and 0037-0038, Examiner’s note: can change or adjust creator influence (see paragraphs 0037-0038), where the creators are returned based on level of similarity (see paragraphs 0031-0032)).
As per claim 4, Khun teaches
wherein said plurality of assets are graphical assets and the generated product is a graphical product (see paragraphs 0028 and 0033, Examiner’s note: generative AI may be trained using images of a particular person to create new images of that particular person).
As per claim 5, Khun teaches
wherein said meta features comprises tag of the graphical asset, caption associated with the graphical asset, style of the graphical asset, objects in the graphical asset, or any combination thereof; (see paragraph 0028, Examiner’s note: training data may include for example artwork. Categories of main colors, high level content, content details, and style, which could be interpreted as the above, tag, caption, style, and objects, it is noted only one is required by the claims based on the recited combination thereof).
wherein the plurality of graphical assets comprises images, drawings, photos, or any combination thereof. (see paragraphs 0028 and 0055, Examiner’s note: creator content includes images, photographs (See paragraph 0028), can include drawings (see paragraph 0055). it is noted only one is required by the claims based on the recited combination thereof).
As per claim 6, Khun teaches
comprising determining the contribution of one or more assets owners to a specific generated product asset by the Al model in response to a guidance prompt, (see paragraphs 0029 and 0058, Examiner’s note: teaches creating content based on prompts).
said determining comprises extracting generated product meta features, identifying matching assets from the plurality of assets that has a degree of correlation above a selected threshold of one or more of their asset meta features with one or more of the generated product meta features, defining for the matching assets, based on the degree of correlation, a specific contribution score; wherein said contribution distribution output data further comprises said specific contribution score attributed to an asset owner (see paragraphs 0031-0032, Examiner’s note: teaches determining a degree of similarity and then outputting compensation based on things like monetary compensation).
As per claim 7, Khun teaches
wherein said plurality of assets are graphical assets (see paragraph 0028, Examiner’s note: training data may include for example artwork).,
and the generated product is a graphical product, and wherein the generated product meta features comprise tag of the generated graphical asset, caption associated with the graphical asset, style of the graphical asset, objects in the graphical asset, or any combination thereof; (see paragraph 0034, Examiner’s note: output like digital image, where this may include style information, creator style, component of the output like man, woman, or animal. It is noted only one is required by the claims based on the recited “any combination thereof”, however, this section teaches, tag, caption, style, and objects as broadly recited in the claims).
wherein said determining further comprises calculating the similarity between a caption attributed to an asset and the guidance prompt for said identifying; wherein said determining further comprises calculating the similarity between a tag attributed to an asset and a contextual analysis of the guidance prompt for said identifying (see paragraphs 0031, 0053, and 0058, Examiner’s note: users can change information output or similarities based on prompts, therefore changing the information calculated based on degree of similarity).
As per claim 8, Khun teaches
Wherein said determining further comprises calculating the similarity between a tag attributed to an asset and a graphical analysis of an image guidance prompt for said identifying; (see paragraphs 0031 , 0053, and 0058, Examiner’s note: users can change information output based on prompts, therefore changing the information calculated based on degree of similarity. It is noted here that this is considered image guidance prompt as the system is requesting an image as broadly recited here in the claims).
wherein said selected threshold is defined to obtain a selected limited number of matching assets with a correlation degree that satisfies a certain condition (see paragraph 0032, Examiner’s note: teaches using the top X contributors where this may be the top five, top 8, top 10, etc.).
As per claim 9, Khun teaches
wherein said outputting is triggered in response to a generated product by the AI model (see paragraph 0032, Examiner’s note: here teaches compensation based on the output).
As per claim 10, Khun teaches
wherein determining distribution parameters for attribution of value associated with the generated product by the AI model based on the contribution score. (see paragraph 0032, Examiner’s note: here teaches compensation based on the contribution score).
As per claim 11, Khun teaches
wherein the contribution score is determined according to at least one of the following parameters: quantity of the assets, the age of each of the assets, community score indicative of an evaluation of users of the value of the asset (see paragraph 0032, Examiner’s note: percentage contribution would read on quantity of the assets, it is noted only one is required in the claims based on the cited “at least one of”).
As per claim 12, Khun teaches A system for determining contribution to a generated product being generated by a generative artificial intelligence (AI) model, comprising: (see paragraphs 0025, Examiner’s note: teaches a server determining an output embedding produced by artificial intelligence).
at least one processing circuitry configured for: (see paragraphs 0025 and 0093, Examiner’s note: teaches a server determining an output embedding produced by artificial intelligence (see paragraph 0025) and paragraph 0093 teaches software running on a computer to perform the functions).
receiving a plurality of assets that contributed to or intended to be used for the training of the AI model, wherein each asset of the plurality of assets is attributed to at least one owner that owns it; analyzing said plurality of assets to extract from each asset meta features and to generate, for each asset of the plurality of assets, an asset data set that comprises owners data indicative of the owner of the asset and the meta features; processing the plurality of asset data sets to determine a contribution distribution data indicative of the contribution distribution of assets owners to the training of the model, wherein the contribution distribution data comprises a contribution score for each assets owner; and for outputting contribution distribution output data that comprises said contribution distribution data. (see paragraphs 0003, 0018, and 0030-0032, Examiner’s note: teaches creating a work through AI. The original creators of the work used to train the AI and output the created work are compensated based on the percentage of their work used to generate the image, where the compensation may be monetary or otherwise like a statement regarding influence).
Kuhn does not expressly teach an author creating a work owning its copyrights
However, Wang et al. which is in the art of economic solution to copyright challenges of generative AI (see abstract) teaches an author creating a work owning its copyrights (see abstract and page 1 introduction, Examiner’s note: teaches copy right owners information being used in training data for generative artificial intelligence and then the copy right owners receiving fair compensation for their information).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Kuhn with the aforementioned teachings from Wang et al. with the motivation of providing a known element that when users create work it is often subject to copyrights (see Wang et al. abstract), when providing compensation for information created by creators like mages is known (see Wang et al. paragraph 0032).
As per claim 13, Khun teaches
wherein said plurality of assets are graphical assets(see paragraph 0028, Examiner’s note: training data may include for example artwork).,
and the generated product is a graphical product; (see paragraph 0034, Examiner’s note: output like digital image).
wherein said meta features comprises tag of the graphical asset, caption associated with the graphical asset, style of the graphical asset, objects in the graphical asset, or any combination thereof; (see paragraph 0028, Examiner’s note: training data may include for example artwork. Categories of main colors, high level content, content details, and style, which could be interpreted as the above, tag, caption, style, and objects, it is noted only one is required by the claims based on the recited combination thereof).
wherein the plurality of graphical assets comprises images, drawings, photos, or any combination thereof. (see paragraph 0028, Examiner’s note: creator content includes images, photographs (See paragraph 0028), can include drawings (see paragraph 0055). it is noted only one is required by the claims based on the recited combination thereof).
As per claim 14, Khun teaches
wherein the processing circuitry is further configured for determining the contribution of one or more assets owners to a specific generated product asset by the AI model in response to a guidance prompt, said determining comprises (see paragraphs 0029 and 0058, Examiner’s note: teaches creating content based on prompts).
extracting generated product meta features, identifying matching assets from the plurality of assets that has a degree of correlation above a selected threshold of one or more of their asset meta features with one or more of the generated product meta features, defining for the matching assets, based on the degree of correlation, a specific contribution score; wherein said contribution distribution output data further comprises said specific contribution score attributed to an asset owner. (see paragraphs 0031-0032, Examiner’s note: teaches determining a degree of similarity and then outputting compensation based on this like monetary compensation).
As per claim 15, Khun teaches
wherein said plurality of assets are graphical assets (see paragraph 0028, Examiner’s note: training data may include for example artwork).,
and the generated product is a graphical product, (see paragraph 0034, Examiner’s note: output like digital image).
and wherein the generated product meta features comprise tag of the generated graphical asset, caption associated with the graphical asset, style of the graphical asset, objects in the graphical asset, or any combination thereof. (see paragraph 0034, Examiner’s note: output like digital image, where this may include style information, creator style, component of the output like man, woman, or animal. It is noted only one is required by the claims based on the recited “any combination thereof”, however, this section teaches, tag, caption, style, and objects as broadly recited in the claims).
As per claim 16, Khun teaches
wherein said determining further comprises calculating the similarity between a caption attributed to an asset and the guidance prompt for said identifying; wherein said determining further comprises calculating the similarity between a tag attributed to an asset and a contextual analysis of the guidance prompt for said identifying; wherein said determining further comprises calculating the similarity between a tag attributed to an asset and a graphical analysis of an image guidance prompt for said identifying. (see paragraphs 0031, 0053, and 0058, Examiner’s note: users can change information output or similarities based on prompts, therefore changing the information calculated based on degree of similarity).
As per claim 17, Khun teaches
wherein said selected threshold is defined to obtain a selected limited number of matching assets with a correlation degree that satisfies a certain condition. (see paragraph 0032, Examiner’s note: teaches using the top X contributors where this may be the top five, top 8, top 10, etc.).
As per claim 18, Khun teaches
wherein said outputting is triggered in response to a generated product by the AI model; wherein said outputting comprises determining distribution parameters for attribution of value associated with the generated product by the AI model based on the contribution score. (see paragraph 0032, Examiner’s note: percentage contribution for the contribution percentage to the created product).
As per claim 19, Khun teaches
wherein the contribution score is determined according to at least one of the following parameters: quantity of the assets, the age of each of the assets, community score indicative of an evaluation of users of the value of the asset. (see paragraph 0032, Examiner’s note: percentage contribution which is interpreted to read on the broad recitation of “quantity of the assets”, it is noted only one is required in the claims based on the cited “at least one of”).
As per claim 20, Khun teaches
wherein the at least one processing circuitry is further configured for calculating a relative contribution parameter indicative of the relative contribution to the training of the model between assets protected by first parameter (like a specific user who owns the content) and assets not protected by the first parameter (like a specific user who owns the content); wherein the distribution output data comprises said relative contribution parameter. (see paragraph 0032, Examiner’s note: percentage contribution for the contribution percentage to the created product).
Khun does not expressly teach assets protected by copyrights or more specifically an author creating a product owning copyrights.
However, Wang et al. which is in the art of economic solution to copyright challenges of generative AI (see abstract) teach assets protected by copyrights or more specifically an author creating a product owning copyrights. (see abstract and page 1 introduction, Examiner’s note: teaches copy right owners information being used in training data for generative artificial intelligence and then the copy right owners receiving fair compensation for their information).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Kuhn with the aforementioned teachings from Wang et al. with the motivation of providing a known element that when users create work it is often subject to copyrights (see Wang et al. abstract), when providing compensation for information created by creators like images is known (see Wang et al. paragraph 0032).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Deng Computational Copyright: Towards a Royality Model for AI Music Generation Platforms dated 12/11/2023 teaches providing royalties for copyrighted information used in AI generation of information (see abstract and page 1)
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/KIERSTEN V SUMMERS/Primary Examiner, Art Unit 3626