DETAILED ACTION
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 .
This office action is in response to communication filed on 10/29/2024.
Claims 1-20 present for examination.
Information Disclosure Statement
It is hereby acknowledged that the following papers have been received and placed of record in the file:
Information Disclosure Statement(s) as received on 10/29/2024 is/are considered by the Examiner.
Claim Objections
Claims 7, 11, and 17 are objected to because of the following informalities:
Claim 7, line 1, “one of more of” should read “one or more of”;
Claim 11, line 2, “the one or more instructors” should read “the one or more training instructors”;
Claim 17, line 1, “one of more of” should read “one or more of”;
Appropriate correction is required.
Specification
The disclosure is objected to because of the following informalities:
Paragraph 17, line 11, “ais repeated” should read “is repeated”;
Appropriate correction is required.
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) accept first information associating training content with training content attributes, accept second information associating the AI system with training instructor profiles, generate the AI generated content in response to an AI content generation request from a user, provide the AI generated content, the first information, and the second information to the user, and generate an evaluation of the content according to a content evaluation profile having the first information and the second information and for providing the generated content and the evaluation to the user, which are directed to the abstract idea: “Mental Processes: concepts performed in the human mind (including an observation, evaluation, judgment, opinion)”. This is explained in detail below. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea.
The 2019 Revised Patent Subject Matter Eligibility Guidance (hereinafter “2019 PEG”) published in January 2019 requires a three step analysis to determine if the claims are directed to a judicial exception that is not “significantly more.” Step 1 asks whether the claims are directed to one of the four statutory categories of invention. Step 2A: Sets forth new procedure for Step 2A (called “revised Step 2A”) under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. Step 2B determines whether the claim recites additional elements that amount to significantly more than the judicial exception.
Claims 1-11 are directed to “A method”. Claims 12-20 are directed to “An apparatus”. Therefore, claims 1-20 fall under statutory categories of invention.
The claimed invention is directed to the abstract idea: Mental Processes: concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
Independent claims 1 and 12 recite features: accepting first information associating training content with training content attributes, accepting second information associating the AI system with training instructor profiles, generating the AI generated content in response to an AI content generation request from a user, and providing the AI generated content, the first information, and the second information to the user.
Independent claim 20 recites features: accepting first information associating a piece of training content with training content attributes according to a content classification profile, training instructor profiles having second information associating the AI system with training instructor attributes, generating the content in response to an AI content generation request from a user, and generating an evaluation of the content according to a content evaluation profile having the first information and the second information and providing the generated content and the evaluation to the user.
There is no technical detail in the limitations above the describe further the method of accepting first information associating training content with training content attributes, accepting second information associating the AI system with training instructor profiles, generating the AI generated content in response to an AI content generation request from a user, providing the AI generated content, the first information, and the second information to the user, and generating an evaluation of the content according to a content evaluation profile having the first information and the second information and providing the generated content and the evaluation to the user. These features are recited at a high level of generality which does not transform the abstract idea into a patentable invention. The limitation of ” accepting first information associating training content with training content attributes, accepting second information associating the AI system with training instructor profiles, generating the AI generated content in response to an AI content generation request from a user, and providing the AI generated content, the first information, and the second information to the user”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation by mental processes but for recitation of generic computer components. Accordingly, the claims recite abstract idea.
Further, the abstract idea is not integrated into a practical application. In particular, the claim only recites additional element – a processor and a memory to perform accepting, accepting, generating, and providing steps. The processor and memory in these steps is related at a high-level of generality (i.e., as a generic device performing a generic computer function of performing an action based on received input) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the processor and memory to perform the accepting, generating, and providing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Since all of the elements fail to provide an inventive concept when considered alone, and in combination the claimed invention is directed towards a judicial exception of an abstract idea and claims 1, 12, and 20 are not patent eligible.
Additionally, the dependent claims 2-11, and 13-19 have been considered and found to be directed towards the same abstract idea, mental processes, without significantly more as indicated above.
Regarding claims 2 and 13, the first information and the second information is signed by the AI system, which is abstract idea. The limitation is a process that, under its broadest reasonable interpretation, covers performance of the limitation by mental processes but for the recitation of generic computer components. Implementation on generic components is not a practical application, nor significantly more.
Regarding claims 3 and 14, generate third information, the third information comprising evaluated AI generated content including an AI generated content assessment and an AI system authentication of the AI generated content, and generating user authenticated AI generated content including the AI generated content, the third information, and a user score, which is abstract idea. The limitation is a process that, under its broadest reasonable interpretation, covers performance of the limitation by mental processes but for the recitation of generic computer components. Implementation on generic components is not a practical application, nor significantly more.
Regarding claim 4, the assessment includes an evaluation score and a trustworthiness score signed by the AI system, which is abstract idea. The limitation is a process that, under its broadest reasonable interpretation, covers performance of the limitation by mental processes but for the recitation of generic computer components. Implementation on generic components is not a practical application, nor significantly more.
Regarding claim 5, the user authenticated AI generated content is signed by the user, which is abstract idea. The limitation is a process that, under its broadest reasonable interpretation, covers performance of the limitation by mental processes but for the recitation of generic computer components. Implementation on generic components is not a practical application, nor significantly more.
Regarding claims 6 and 16, the user authenticated AI generated content is provided as a training input to the generation of second generated AI content, which is abstract idea. The limitation is a process that, under its broadest reasonable interpretation, covers performance of the limitation by mental processes but for the recitation of generic computer components. Implementation on generic components is not a practical application, nor significantly more.
Regarding claims 7 and 17, the first information comprises one of more of: a binary indication of whether the training content is digitally signed by the author of the training content; an author approval score, provided by the author of the training content, the author approval score indicating a measure of the approval of the training content by the author of the training content; a contributor allocation score, the contributor allocation score indicating a proportion of contributor content used in the generation of the training content; and an AI signature with an authenticated instructor profile, which is abstract idea. The limitation is a process that, under its broadest reasonable interpretation, covers performance of the limitation by mental processes but for the recitation of generic computer components. Implementation on generic components is not a practical application, nor significantly more.
Regarding claim 8, the first information includes a signature of an author of the training content, which is abstract idea. The limitation is a process that, under its broadest reasonable interpretation, covers performance of the limitation by mental processes but for the recitation of generic computer components. Implementation on generic components is not a practical application, nor significantly more.
Regarding claims 9 and 18, the contributor allocation score includes at least one of: human authored contributor content; approved AI authored contributor content; unapproved AI authored contributor content; and mixed approved AI, unapproved AI, and human authored contributor content, which is abstract idea. The limitation is a process that, under its broadest reasonable interpretation, covers performance of the limitation by mental processes but for the recitation of generic computer components. Implementation on generic components is not a practical application, nor significantly more.
Regarding claims 10 and 19, the training instructor profiles comprise aggregate training instructor profiles comprising at least one of: a distribution of the training of two or more of the training instructors; a distribution of experience or skill scores of two or more of the training instructors in a subject of the training content; a distribution of review scores of the training instructors, each review score based on an assessment by another of the trustworthiness of the generated content and approval of the generated content, which is abstract idea. The limitation is a process that, under its broadest reasonable interpretation, covers performance of the limitation by mental processes but for the recitation of generic computer components. Implementation on generic components is not a practical application, nor significantly more.
Regarding claim 11, training instructor profiles comprise individual profiles of the one or more instructors, which comprise one or more of: instructor certification information; an instructor experience score; an instructor skill score; instructor professional score in a subject of the generated content; and instructor review scores from another AI system user, which is abstract idea. The limitation is a process that, under its broadest reasonable interpretation, covers performance of the limitation by mental processes but for the recitation of generic computer components. Implementation on generic components is not a practical application, nor significantly more.
Regarding claim 15, the assessment includes a user evaluation score and a trustworthiness score, and user authenticated AI generated content is signed by the user, which is abstract idea. The limitation is a process that, under its broadest reasonable interpretation, covers performance of the limitation by mental processes but for the recitation of generic computer components. Implementation on generic components is not a practical application, nor significantly more.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a training input classification module for accepting and an AI content evaluation module for generating in claim 20.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 6-8, 10, 11, and 14-20 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.
Claim limitation “a training input classification module, for accepting first information” and “an AI content evaluation module, for generating an evaluation of the content” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed function. There is no disclosure of any particular structure, either explicitly or inherently, to perform the claimed functions. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which structure or structures perform(s) the claimed functions. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim 6 recites the limitation "the generation of second generated AI content" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 7, claim limitation recites “the training content” in line 2, which renders the claim vague and indefinite. It is unclear whether “the training content” is referring to “training content” in claim 1, line 2, or to “training content” in claim 1, line 4, or to a different/distinct training content.
Claim 7 recites the limitation "the author of the training content" in lines 2-3. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitation "the approval of the training content" in line 5. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitation "the generation of the training content" in line 8. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 8, claim limitation recites “the training content” in line 2, which renders the claim vague and indefinite. It is unclear whether “the training content” is referring to “training content” in claim 1, line 2, or to “training content” in claim 1, line 4, or to a different/distinct training content.
Claim 10 recites the limitation "the training of two or more of the training instructors" in line 3. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 10, claim limitation recites “the training content” in line 5, which renders the claim vague and indefinite. It is unclear whether “the training content” is referring to “training content” in claim 1, line 2, or to “training content” in claim 1, line 4, or to a different/distinct training content.
Claim 10 recites the limitation "the trustworthiness of the generated content" in line 7. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 10, claim limitation recites “the training instructors” in line 6, which renders the claim vague and indefinite. It is unclear whether “the training instructors” is referring to “one or more training instructors” in claim 1, line 2, or to “two or more of the training instructors” in claim 10, line 3, or to different/distinct training instructors.
Regarding claim 10, claim limitation recites “the generated content” in line 7, which renders the claim vague and indefinite. It is unclear whether “the generated content” is referring to “the AI generated content” in claim 1, line 6, or to a different/distinct generated content.
Regarding claim 11, claim limitation recites “the generated content” in line 6, which renders the claim vague and indefinite. It is unclear whether “the generated content” is referring to “the AI generated content” in claim 1, line 6, or to “training content” in claim 7, line 8, to a different/distinct generated content.
Claim 14 recites the limitation "the AI generated content" in lines 4-5. There is insufficient antecedent basis for this limitation in the claim.
Claim 14 recites the limitation "the AI generated content" in line 6. There is insufficient antecedent basis for this limitation in the claim.
Claim 16 recites the limitation "the generation of second generated AI content" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 17, claim limitation recites “the training content” in line 2, which renders the claim vague and indefinite. It is unclear whether “the training content” is referring to “training content” in claim 12, line 2, or to “training content” in claim 12, line 7, or to different/distinct training content.
Claim 17 recites the limitation "the author of the training content" in lines 2-3. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 17, claim limitation recites “the training content” in line 4, which renders the claim vague and indefinite. It is unclear whether “the training content” is referring to “training content” in claim 12, line 2, or to “training content” in claim 12, line 7, or to different/distinct training content.
Claim 17 recites the limitation "the approval of the training content" in line 5. There is insufficient antecedent basis for this limitation in the claim.
Claim 17 recites the limitation "the generation of the training content" in line 8. There is insufficient antecedent basis for this limitation in the claim.
Claim 19 recites the limitation "the training of two or more of the training instructors" in line 3. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 19, claim limitation recites “the training content” in line 5, which renders the claim vague and indefinite. It is unclear whether “the training content” is referring to “training content” in claim 12, line 2, or to “training content” in claim 12, line 7, or to different/distinct training content.
Claim 19 recites the limitation "the trustworthiness of the generated content" in line 7. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 20, claim limitation recites “the content” in line 10, which renders the claim vague and indefinite. It is unclear whether “the content” is referring to “content” in claim 20, line 1, or to “training content” in claim 20, line 2, or to “a piece of training content” in claim 20, line 5, or to a different/distinct content.
All dependent claims are rejected as having the same deficiencies as the claims they depend from.
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 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bendre et al. (US 2020/0005187 A1), hereinafter Bendre, in view of Moriya et al. (US 2021/0073676 A1), hereinafter Moriya.
Regarding claim 1, Bendre discloses
A method of generating AI generated content from an artificial intelligence (AI) system, trained by one or more training instructors using training content having one or more training content pieces, the method comprising:
accepting first information associating training content with training content attributes ([0011]: receive information indicating (i) training data that is associated with the computing system and that is to be used as basis for generating an ML model);
accepting second information associating the AI system with training instructor profiles ([0140]: assign an ML trainer process to serve that first ML training request at the first training time specified in the solution definition 630, so as to generate an ML model);
generating the AI generated content in response to an AI content generation request from a user ([0109]: a client device associated with a particular customer instance may submit a request for the network system to carry out a certain prediction and, once the network system generates an ML model and a corresponding ML prediction according to that request, the generated ML model and ML prediction may accessible only to client devices associated with the particular customer instance);
providing the AI generated content to the user ([0115]: transmit the ML prediction to the client device 600).
Bendre does not explicitly disclose
providing the first information and the second information to the user.
However, Moriya discloses
accepting first information associating training content with training content attributes ([0121]: the learning/evaluation setting table includes information indicative of which program and which dataset should be used to generate or evaluate a model when generating or evaluating the model using the learning/evaluation program registered in the model management system; & [0125]: the dataset identifier is an identifier of the dataset used in the learning/evaluation corresponding to this setting and, for example, may be a dataset name entered by the user on the model generation/evaluation screen or may be the dataset identifier of this dataset held by the dataset management table; & [0135]: the setting identifier is an identifier for identifying the information on what kind of program, what kind of dataset, and what kind of parameter was used to execute the learning/evaluation job);
accepting second information associating the AI system with training instructor profiles ([0063]: the application developer registers the indicator that the application developer focuses on in relation to the model used by the application software; the model evaluation result in the application software environment, and the dataset used by the application developer);
providing the first information and the second information to the user ([0157]: the user name may be displayed, for example, as the name of the developer who developed the model in the model information provided on the model details screen; & [0286]: for each row, information of one dataset is displayed).
It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate feature of Moriya to Bendre, because Bendre discloses using training data to generate machine learning model (abstract & [0006]) and Moriya further suggests display information associated with model ([0157]).
One of ordinary skill in the art would be motivated to utilize the teachings of Moriya in the Bendre system in order to provide insights.
Regarding claim 12, the limitations of claim 12 are rejected in the analysis of claim 1 above and this claim is rejected on that basis.
Claim(s) 2-8, and 13-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bendre in view of Moriya, and further in view of Dao et al. (US 2020/0219009 A1), hereinafter Dao.
Regarding claim 2, Bendre and Moriya disclose the method as described in claim 1. Bendre and Moriya do not explicitly disclose
the first information and the second information is signed by the AI system.
However, Dao discloses
the first information and the second information is signed by the AI system ([0097]: the training attestation checker 200 will approve that such signed training data are used in the ML model learner 130).
It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate feature of Dao to Bendre and Moriya, because Bendre and Moriya disclose using training data to generate machine learning model (Bendre: abstract & [0006]) and Dao further suggests training data are signed ([0097]).
One of ordinary skill in the art would be motivated to utilize the teachings of Dao in the Bendre and Moriya system in order to enhance security.
Regarding claim 3, Bendre, Moriya, and Dao disclose the method as described in claim 2. Bendre further discloses
generating third information, the third information comprising evaluated AI generated content including an AI generated content assessment and an AI system authentication of the AI generated content ([0069]: generate reports; & [0170]: the secure identifier may be a randomly generated bitstring, such as a security token cryptographically generated by the computing system); and
generating user authenticated AI generated content including the AI generated content ([0107]: generate ML prediction(s) on new data), the third information ([0069]: generate reports; & [0170]: the secure identifier may be a randomly generated bitstring, such as a security token cryptographically generated by the computing system), and a user score ([0115]: generate an ML model and may also provide information specifying a target variable to be predicted using the ML model; & [0116]: store any data obtained and/or generated by the enterprise network of the client device).
Regarding claim 4, Bendre, Moriya, and Dao disclose the method as described in claim 3. Bendre, Moriya, and Dao further disclose
the assessment includes an evaluation score and a trustworthiness score signed by the AI system (Dao: [0097]: the training attestation checker 200 will approve that such signed training data are used in the ML model learner 130). Therefore, the limitations of claim 4 are rejected in the analysis of claim 3 above, and the claim is rejected on that basis.
Regarding claim 5, Bendre, Moriya, and Dao disclose the method as described in claim 4. Bendre, Moriya, and Dao further disclose
the user authenticated AI generated content is signed by the user (Dao: [0095]: to prove reliability is carried out by adding a ML data attestation 220, which is preferably a digital signature for identifying the source of the data; & [0097]: the training attestation checker 200 will approve that such signed training data are used in the ML model learner 130). Therefore, the limitations of claim 5 are rejected in the analysis of claim 4 above, and the claim is rejected on that basis.
Regarding claim 6, Bendre, Moriya, and Dao disclose the method as described in claim 5. Bendre further discloses
the user authenticated AI generated content is provided as a training input to the generation of second generated AI content ([0134]: the input data may be received via the browser and the browser may responsively transmit the solution definition to the processor; & [0135]: the solution definition may include information according to which the network system could ultimately generate an ML model and an ML prediction).
Regarding claim 7, Bendre and Moriya disclose the method as described in claim 1. Bendre and Moriya do not explicitly disclose
the first information comprises one of more of:
a binary indication of whether the training content is digitally signed by the author of the training content;
an author approval score, provided by the author of the training content, the author approval score indicating a measure of the approval of the training content by the author of the training content;
a contributor allocation score, the contributor allocation score indicating a proportion of contributor content used in the generation of the training content; and
an AI signature with an authenticated instructor profile.
However, Dao discloses
the first information comprises one of more of:
a binary indication of whether the training content is digitally signed by the author of the training content ([0095]: to prove reliability is carried out by adding a ML data attestation 220, which is preferably a digital signature for identifying the source of the data; & [0097]: the training attestation checker 200 will approve that such signed training data are used in the ML model learner 130);
an author approval score, provided by the author of the training content, the author approval score indicating a measure of the approval of the training content by the author of the training content;
a contributor allocation score, the contributor allocation score indicating a proportion of contributor content used in the generation of the training content; and
an AI signature with an authenticated instructor profile.
It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate feature of Dao to Bendre and Moriya, because Bendre and Moriya disclose using training data to generate machine learning model (Bendre: abstract & [0006]) and Dao further suggests training data are signed ([0097]).
One of ordinary skill in the art would be motivated to utilize the teachings of Dao in the Bendre and Moriya system in order to enhance security.
Regarding claim 8, Bendre, Moriya, and Dao disclose the method as described in claim 7. Bendre, Moriya, and Dao further disclose
the first information includes a signature of an author of the training content (Dao: [0095]: to prove reliability is carried out by adding a ML data attestation 220, which is preferably a digital signature for identifying the source of the data). Therefore, the limitations of claim 8 are rejected in the analysis of claim 7 above, and the claim is rejected on that basis.
Regarding claim 13, the limitations of claim 13 are rejected in the analysis of claim 2 above and this claim is rejected on that basis.
Regarding claim 14, the limitations of claim 14 are rejected in the analysis of claim 3 above and this claim is rejected on that basis.
Regarding claim 15, the limitations of claim 15 are rejected in the analysis of claim 4 and 5 above and this claim is rejected on that basis.
Regarding claim 16, the limitations of claim 16 are rejected in the analysis of claim 6 above and this claim is rejected on that basis.
Regarding claim 17, the limitations of claim 17 are rejected in the analysis of claim 7 above and this claim is rejected on that basis.
Claim(s) 9-11 and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bendre in view of Moriya, in view of Dao, and further in view of Jha et al. (US 2021/0090251 A1), hereinafter Jha.
Regarding claim 9, Bendre, Moriya, and Dao disclose the method as described in claim 7. Bendre, Moriya, and Dao do not explicitly disclose
the contributor allocation score includes at least one of:
human authored contributor content;
approved AI authored contributor content;
unapproved AI authored contributor content; and
mixed approved AI, unapproved AI, and human authored contributor content.
However, Jha discloses
the contributor allocation score includes at least one of:
human authored contributor content ([0046]: the machine learning model 106 may be trained using only edited segmentations that are generated by users with a sufficiently high expertise score);
approved AI authored contributor content;
unapproved AI authored contributor content; and
mixed approved AI, unapproved AI, and human authored contributor content.
It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate feature of Jha to Bendre, Moriya, and Dao, because Bendre, Moriya, and Dao disclose using training data to generate machine learning model (Bendre: abstract & [0006]) and Jha further suggests training data are generated by users ([0046]).
One of ordinary skill in the art would be motivated to utilize the teachings of Jha in the Bendre, Moriya, and Dao system in order to improve quality of training data.
Regarding claim 10, Bendre, Moriya, and Dao disclose the method as described in claim 7. Bendre, Moriya, and Dao do not explicitly disclose
the training instructor profiles comprise aggregate training instructor profiles comprising at least one of:
a distribution of the training of two or more of the training instructors;
a distribution of experience or skill scores of two or more of the training instructors in a subject of the training content;
a distribution of review scores of the training instructors, each review score based on an assessment by another of the trustworthiness of the generated content and approval of the generated content.
However, Jha discloses
the training instructor profiles comprise aggregate training instructor profiles comprising at least one of:
a distribution of the training of two or more of the training instructors;
a distribution of experience or skill scores of two or more of the training instructors in a subject of the training content ([0046]: the machine learning model 106 may be trained using only edited segmentations that are generated by users with a sufficiently high expertise score);
a distribution of review scores of the training instructors, each review score based on an assessment by another of the trustworthiness of the generated content and approval of the generated content.
It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate feature of Jha to Bendre, Moriya, and Dao, because Bendre, Moriya, and Dao disclose using training data to generate machine learning model (Bendre: abstract & [0006]) and Jha further suggests training data are generated by users ([0046]).
One of ordinary skill in the art would be motivated to utilize the teachings of Jha in the Bendre, Moriya, and Dao system in order to improve quality of training data.
Regarding claim 11, Bendre, Moriya, Dao, and Jha disclose the method as described in claim 10. Bendre, Moriya, Dao, and Jha further disclose
training instructor profiles comprise individual profiles of the one or more instructors, which comprise one or more of:
instructor certification information;
an instructor experience score;
an instructor skill score;
instructor professional score in a subject of the generated content (Jha: [0018]: the expertise scores can be used to improve the quality of the training data used to train the segmentation system, e.g., by determining whether to include a segmentation generated by a user in the training data based on the expertise score of the user); and
instructor review scores from another AI system user.
Therefore, the limitations of claim 11 are rejected in the analysis of claim 10 above, and the claim is rejected on that basis.
Regarding claim 18, the limitations of claim 18 are rejected in the analysis of claim 9 above and this claim is rejected on that basis.
Regarding claim 19, the limitations of claim 19 are rejected in the analysis of claim 10 above and this claim is rejected on that basis.
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bendre in view of Moriya, and further in view of Schmidt et al. (US 2024/0338554 A1), hereinafter Schmidt.
Regarding claim 20, Bendre discloses
An apparatus for generating content from an artificial intelligence (AI) system, trained by one or more training instructors using training content having one or more training content pieces, comprising:
a training input classification module, for accepting first information associating a piece of training content with training content attributes according to a content classification profile ([0011]: receive information indicating (i) training data that is associated with the computing system and that is to be used as basis for generating an ML model);
training instructor profiles having second information associating the AI system with training instructor attributes ([0140]: assign an ML trainer process to serve that first ML training request at the first training time specified in the solution definition 630, so as to generate an ML model);
an AI system core, for generating the content in response to an AI content generation request from a user ([0109]: a client device associated with a particular customer instance may submit a request for the network system to carry out a certain prediction and, once the network system generates an ML model and a corresponding ML prediction according to that request, the generated ML model and ML prediction may accessible only to client devices associated with the particular customer instance);
providing the generated content to the user ([0115]: transmit the ML prediction to the client device 600).
Bendre does not explicitly disclose
providing the first information and the second information to the user.
However, Moriya discloses
providing the first information and the second information to the user ([0157]: the user name may be displayed, for example, as the name of the developer who developed the model in the model information provided on the model details screen; & [0286]: for each row, information of one dataset is displayed).
It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate feature of Moriya to Bendre, because Bendre discloses using training data to generate machine learning model (abstract & [0006]) and Moriya further suggests display information associated with model ([0157]).
One of ordinary skill in the art would be motivated to utilize the teachings of Moriya in the Bendre system in order to provide insights.
Bendre and Moriya do not explicitly disclose
an AI content evaluation module, for generating an evaluation of the content according to a content evaluation profile having the first information and the second information and for providing the evaluation to the user.
However, Schmidt discloses
an AI content evaluation module, for generating an evaluation of the content according to a content evaluation profile having the first information and the second information and for providing the evaluation to the user ([0190]: present an evaluation of a dataset to a user of a model development system).
It would have been obvious to a person with ordinary skill in the art before the effective filing date of the claimed invention to incorporate feature of Schmidt to Bendre, Moriya, and Dao, because Bendre, Moriya, and Dao disclose using training data to generate machine learning model (Bendre: abstract & [0006]) and Schmitdt further suggests present an evaluation of a dataset for model to a user ([0190]).
One of ordinary skill in the art would be motivated to utilize the teachings of Schmidt in the Bendre, Moriya, and Dao system in order to provide informative system to user.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Masubuchi et al. (US 2020/0160981 A1). The weighting function 244 can determine the quality of training data by a technical score of a creator by setting a weight score to the technical score ([0072]).
Hong et al. (US 2022/0329493 A1). Provide stored data to a model owner for model training ([0180]).
Yang et al. (US 2019/0370235 A1). Determine creation information of a model of target information according to the target information, send a training instruction to an external trainer, the training instruction is used to instruct the external trainer to train the data in the database according to the target information and the creation information of the model of the target information using a machine learning method, to obtain a first model of the target information ([0008]).
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Kaylee Huang
01/07/2026
/KAYLEE J HUANG/Primary Examiner, Art Unit 2447