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
Rejections under 35 U.S.C. 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 menial processes without significantly more. Independent claims 1, 14, and 18 each recites generating, via the one or more processors, a first prompt based upon the at least one prompt criteria; inputting, via the one or more processors, the first prompt and a first document of the corpus of documents into the generative Al model to obtain a classification of the first document; updating, via the one or more processors, the at least one prompt criteria based on the classification of the first document and the review data; generating, via the one or more processors, a second prompt based upon the updated at least one prompt criteria. Generating a prompt and a second prompt are each generating data and mental processes accomplishable in the human mind or on paper, inputting the prompt and a first document into a model is inputting data into a process and a mental process accomplishable in the human mind or on paper, and updating a prompt is modifying data and a mental process accomplishable in the human mind or on paper. Examiner notes classifying a first document and a second document with a generative AI model is applying the model and is not significantly more than a mental process per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628). Each claim recites additional elements of obtaining, via one or more processors, at least one prompt criteria defining context for classifying a corpus of documents using the generative Al model; and obtaining, via the one or more processors, review data associated with the first document, and obtaining a prompt criteria and obtaining review data are input steps and insignificant extra-solution activity. Claim 14 recites one or more processors and claim 18 recites one or more processors and one or more non-transitory memories, which are each generic components of a computer system. Examiner notes paragraphs 0004, 0032, and 0034 discuss how deploying machine learning models during an eDiscovery process can be cumbersome and inefficient, such as if different attorneys deploy the models in different ways or of thousands of documents need to be used to train the classifier. The specification begins discussing improvements upon said drawbacks for eDiscovery of documents in paragraph 0033 in describing categories and/or criteria for said prompt. Prompt criteria is further discussed in paragraphs 0058-0077 and figures 2-4. The claim steps do not recite a particular improvement in any technology or function of a computer per MPEP 2106.04(d) and do not recite any unconventional steps in the invention per MPEP 2106.05(a). Therefore, the recited mental processes are not integrated into a practical application. Taking the claims as a whole, the input steps are recited broadly and amount to receiving data across a network per specification 0045 and figure 10, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II. The one or more processors and one or more non-transitory memories are each still generic components of a computer system. Thus the claims do not include additional elements that are sufficient to amount to significantly more than the recited mental processes.
Claims 2, 16, and 19 each recites wherein the review data comprises a comment associated with the first document, and review data is data and a mental process accomplishable in the human mind or on paper. Claim 3 recites obtaining, via the one or more processors, first review data comprising a first comment associated with the first document, which is recited broadly and amount to receiving data across a network per specification 0045 and figure 10, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II; obtaining, via the one or more processors, second review data comprising a second comment associated with the first document, which is recited broadly and amount to receiving data across a network per specification 0045 and figure 10, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II; and merging, via the one or more processors, the first review data with the second review data to create the review data, and merging data is recited broadly and is a mental process accomplishable in the human mind or on paper. Claims 4 and 15 each recites presenting, via the one or more processors, the first document via a document review user interface presented by a user device, wherein the document review user interface includes document review elements configured to enable a user of the user device to classify the first document, and presenting a document is recited broadly and amount to receiving data across a network per specification 0045 and figure 10, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II; and detecting, via the one or more processors and via the document review user interface: (i) an indication of whether the classification of the first document is correct, and/or (ii) a user- provided classification of the first document, and detecting that a classification is correct or detecting a classification is a evaluating and a mental process.
Claim 5 recites detecting comprises detecting, via the one or more processors and via the document review user interface, the indication, and wherein the indication indicates that the classification of the first document is not correct, and detecting that a classification is not correct is evaluating and a mental process; and updating the at least one prompt criteria comprises generating, via the one or more processors, a proposed update to the at least one prompt criteria by inputting, into the generative Al model, that the classification of the first document is not correct, and updating a prompt criteria is modifying data and a mental process accomplishable in the human mind or on paper. Claims 6, 17, ad 20 each recites generating, via the one or more processors, the first prompt by supplementing the at least one prompt criteria with additional context, and supplementing a criteria with additional text is adding data and a mental process accomplishable in the human mind or on paper. Claim 7 recites generating, via the one or more processors, a proposed update to the at least one prompt criteria via the generative Al model or via a second generative Al model, and applying an AI model and is not significantly more than a mental process per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628). Claim 8 recites presenting, via the one or more processors, the proposed update via a prompt criteria editor interface presented by a user device, and presenting data is recited broadly and amount to sending data across a network per specification 0045 and figure 10, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II; detecting, via the one or more processors, confirmation that the proposed update is acceptable via the prompt criteria editor interface, and detecting input is evaluating and a mental process; and in response to detecting the confirmation, updating, via the one or more processors, the at least one prompt criteria in accordance with the proposed update, and updating a criteria is modifying it and a mental process accomplishable in the human mind or on paper.
Claim 9 recites classifying, via the one or more processors, a plurality of training documents by inputting the second prompt into the generative Al model, and applying an AI model and is not significantly more than a mental process per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628); obtaining, via the one or more processors, review data associated with the plurality of training documents, and obtaining review data is recited broadly and amounts to receiving data across a network per specification 0045 and figure 10, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II; generating, via the one or more processors, one or more validation metrics based upon a comparison of the review data and the classifications of documents of the plurality of training documents, and generating metrics is recited broadly and is a mental process accomplishable in the human mind or on paper; and determining, via the one or more processors, that the second prompt is acceptable based on the one or more validation metrics, and determining a prompt is acceptable is evaluating and a mental process. Claim 10 recites obtaining, via the one or more processors, the review data comprising a comment associated with the first document, and obtaining review data is recited broadly and amounts to receiving data across a network per specification 0045 and figure 10, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II; storing, via the one or more processors, the comment associated with the first document in a memory, and storing data in a memory is routine and conventional per the list of such activities in MPEP 2106.05(d) part II; determining, via the one or more processors, that the comment associated with the first document is relevant to the second document, and determining a comment is relevant is evaluating and a mental process; and in response to determining that the comment associated with the first document is relevant to the second document: (i) retrieving, via the one or more processors, the comment associated with the first document from the memory, and (ii) presenting, via the one or more processors, both the second document and the comment associated with the first document via a document review user interface presented by a user device, and retrieving data from a memory is routine and conventional per the list of such activities in MPEP 2106.05(d) part II, and presenting retrieved data is recited broadly and amounts to receiving data across a network per specification 0045 and figure 10, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II.
Claim 11 recites wherein the at least one prompt criteria includes one or more categories of: case summary; relevance; and/or key documents, and prompt criteria is data and a mental process accomplishable in the human mind or on paper. Claim 12 recites wherein the classification includes classifying a document as one of: junk; responsive; not responsive; likely responsive; or likely not responsive, and a classification is data and a mental process accomplishable in the human mind or on paper. Claim 13 recites wherein the generative AI model is configured to output a confidence score that the classification of the first document is correct, and applying a generative AI model to output a score is applying the AI model which is not significantly more than a mental process per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628).
Rejections under 35 U.S.C. 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-8 and 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over Badr (US 20250124262) in view of Hall et al (US 20250371863), hereafter known as Hall.
With respect to claims 1, 14, and 18, Badr teaches:
obtaining, via one or more processors, at least one prompt criteria defining context for classifying a corpus of documents using the generative Al model (paragraphs 0062-0063 figure 3 steps 302, 304 obtain input from a user, parameters for a user as prompt criteria);
generating, via the one or more processors, a first prompt based upon the at least one prompt criteria (paragraph 0063 figure 3 step 304 generating prompt criteria from user parameters or input);
inputting, via the one or more processors, the first prompt and a first document of the corpus of documents into the generative Al model to obtain a classification of the first document (paragraph 0064 inputting the prompt and a first document into an AI model, paragraph 0107 AI model could be a classification model, determining a classification);
obtaining, via the one or more processors, review data associated with the first document (paragraphs 0067-0068 figure 3 step 310 generate feedback from the user on content item from model);
updating, via the one or more processors, the at least one prompt criteria based on the classification of the first document and the review data (paragraph 0071 figure 3 step 312 adjusting user parameters based on feedback); and
generating, via the one or more processors, a second prompt based upon the updated at least one prompt criteria (paragraph 0071 adjusted parameters can become another prompt).
Badr does not explicitly teach classifying, via the one or more processors, a second document by inputting the second prompt into the generative Al model.
Hall teaches this with an image classification system that uses a classification model (paragraph 0054, 0058 figure 1 step 114 submit an image (document) with a Prompt Set to a model, generates outputs including a classification label for said image) and iterates the use of a model on second or additional documents with a prompt that is iteratively updated from user feedback (paragraphs 0066-0067 figure 1 step 116 solicit feedback/reviews from users on output from classification model (per paragraph 0099 generative AI model) in step 114, step 118 add feedback/reviews to the Prompt Set, paragraphs 0073, 0075 iteration of steps 114-118 with the updated Prompt Set and a different image representing a particular condition highlighted during feedback/reviews).
It would have been obvious to have combined this function of submitting a second image with an updated prompt to a classification model in Hall with the techniques of generating and updating a prompt using user feedback in Badr to enhance the accuracy of the model in generating a classification of a content item.
With respect to claims 14 and 18, Badr teaches one or more processors (paragraphs 0104, 0115 figure 9A processor 112 on user device 102, processor 132 on server device 130 in computing system).
With respect to claim 18, Badr teaches one or more non-transitory memories (paragraphs 0104, 0115 figure 9A user device 102 has non-transitory memory 114, server 130 has non-transitory memory 134).
With respect to claims 2, 16, and 19, all the limitations in claims 1, 14, and 18 are addressed by Badr and Hall above. Hall also teaches wherein the review data comprises a comment associated with the first document (paragraph 0062 step 1 feedback solicited for classification for image includes free-text input).
With respect to claim 3, all the limitations in claim 1 are addressed by Badr and Hall above. Hall also teaches:
obtaining, via the one or more processors, first review data comprising a first comment associated with the first document (paragraph 0065 reviews include free-text input);
obtaining, via the one or more processors, second review data comprising a second comment associated with the first document (paragraph 0065 reviews solicited from multiple reviewers); and
merging, via the one or more processors, the first review data with the second review data to create the review data (paragraph 0065 aggregating reviews from multiple reviewers).
With respect to claims 4 and 15, all the limitations in claims 1 and 14 are addressed by Badr and Hall above. Hall also teaches:
presenting, via the one or more processors, the first document via a document review user interface presented by a user device, wherein the document review user interface includes document review elements configured to enable a user of the user device to classify the first document (paragraphs 0062-0063 user interface for review); and
detecting, via the one or more processors and via the document review user interface: (i) an indication of whether the classification of the first document is correct, and/or (ii) a user- provided classification of the first document (paragraph 0063 user add/edit label (classification) for image (ii)).
With respect to claim 5, all the limitations in claim 1 are addressed by Badr and Hall above. Hall also teaches:
detecting comprises detecting, via the one or more processors and via the document review user interface, the indication, and wherein the indication indicates that the classification of the first document is not correct (paragraphs 0063 detecting feedback for example to question “Is the diagnosis correct?”); and
updating the at least one prompt criteria comprises generating, via the one or more processors, a proposed update to the at least one prompt criteria by inputting, into the generative Al model, that the classification of the first document is not correct (paragraph 0063 inputting edit/clarification from reviewer).
With respect to claims 6, 17, and 20, all the limitations in claims 1, 14, and 18 are addressed by Badr and Hall above. Hall also teaches generating, via the one or more processors, the first prompt by supplementing the at least one prompt criteria with additional context (paragraph 0040 add contextual information to the prompt (Prompt Set)).
With respect top claim 7, all the limitations in claim 1 are addressed by Badr and Hall above. Hall also teaches generating, via the one or more processors, a proposed update to the at least one prompt criteria via the generative Al model or via a second generative Al model (paragraph 0070 modify prompt with diversity constraints via a monitoring algorithm (second model)).
With respect to claim 8, all the limitations in claims 1 and 7 are addressed by Badr and Hall above. Hall also teaches:
presenting, via the one or more processors, the proposed update via a prompt criteria editor interface presented by a user device (paragraph 0070 initially modifying prompt for reviewer);
detecting, via the one or more processors, confirmation that the proposed update is acceptable via the prompt criteria editor interface (paragraph 0070 update confirmed before constraints); and
in response to detecting the confirmation, updating, via the one or more processors, the at least one prompt criteria in accordance with the proposed update (paragraph 0070 updating with constraints in monitoring algorithm).
With respect to claim 11, all the limitations in claim 1 are addressed by Badr and Hall above. Hall also teaches wherein the at least one prompt criteria includes one or more categories of: case summary; relevance; and/or key documents (paragraph 0070, 0075 prompt criteria for update includes domain relevance).
With respect to claim 12, all the limitations in claim 1 are addressed by Badr and Hall above. Hall also teaches wherein the classification includes classifying a document as one of: junk; responsive; not responsive; likely responsive; or likely not responsive (paragraph 0110 images classified as relevant to a diagnosis (responsive to the diagnosis of supportive of it per specification 0172)).
With respect to claim 13, all the limitations in claim 1 are addressed by Badr and Hall above. Hall also teaches wherein the generative AI model is configured to output a confidence score that the classification of the first document is correct (paragraph 0061 classification model produces confidence score with output for image).
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRUCE M MOSER whose telephone number is (571)270-1718. The examiner can normally be reached M-F 9a-5p.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Boris Gorney can be reached at 571 270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BRUCE M MOSER/Primary Examiner, Art Unit 2154 12/10/25