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 .
Remarks
This action is in response to the amendments received on 5/22/26. Claims 1-20 are pending in the application.
Claims 1-20 are rejected under 35 U.S.C. 101.
Claims 1, 11, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over O’Kelly et al. (US 2025/0005300) and further in view of Yang et al. (US 12,579,179).
Claims 2-9 and 12-19 are rejected under 35 U.S.C. 103 as being unpatentable over O’Kelly in view of Yang, and further in view of Labutov (US 2024/0386042).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over O’Kelly in view of Yang, and further in view of Will et al. (US 11,557,381).
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 a judicial exception without significantly more.
Step 2A, Prong One asks: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? See MPEP 2106.04 Part I. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. See MPEP 2106.04(a).
With respect to claims 1, 11, and 20, the limitation of “generating, via one or more processors, initial prompt criteria,” “generating, via the one or more processors, an initial prompt,” and “generating… an explanation”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “via the one or more processors,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “via the one or more processors” language, “generating” in the context of this claim encompasses the user thinking about data. Similarly, the limitation of “classifying, via the one or more processors, a sample of documents from a corpus of documents”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the “via the one or more processors” language, “classifying” in the context of this claim encompasses the user analyzing data. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
At step 2a, prong two, this judicial exception is not integrated into a practical application. The claims recite one or more processors and generative AI models, however, this is recited as a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer components. The claim recites “obtaining, via the one or more processors, modified prompt criteria” and “presenting, via the one or more processors, the explanation for the classification.” These elements do not integrate the abstract idea into a practical application because they do not impose a meaningful limit on the judicial exception and provide only insignificant extra solution activity in conjunction with the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
With respect to “obtaining… an initial set of documents associated with an inquiry”, the courts have found limitations directed towards data gathering to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
With respect to “presenting… the explanation for the classification”, the courts have found limitations directed towards storing to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93.
Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. The claim is not patent eligible.
With respect to claims 2 and 12, the limitations are directed towards “inputting” data into an AI model. These elements do not integrate the abstract idea into a practical application because they do not impose a meaningful limit on the judicial exception and provide only insignificant extra solution activity that is mere data gathering in conjunction with the abstract idea.
The courts have found limitations directed towards data gathering to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
With respect to claims 4 and 14, the limitation of “generating, via the one or more processors, a modified prompt based on the modified prompt criteria; and generating, via the one or more processors, an updated classification”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “via one or more processors,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “via one or more processors” language, “generating” in the context of this claim encompasses the user thinking about data. Similarly, the limitation of “evaluating, via the one or more processors, classification performance of the prompt based on ground truth data associated with the sample of documents;”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the “via one or more processors” language, “evaluating” in the context of this claim encompasses the user analyzing data. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
At step 2a, prong two, this judicial exception is not integrated into a practical application. The claim recites “obtaining, via the one or more processors, modified prompt criteria.” These elements do not integrate the abstract idea into a practical application because they do not impose a meaningful limit on the judicial exception and provide only insignificant extra solution activity in conjunction with the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
With respect to “obtaining, via the one or more processors, modified prompt criteria”, the courts have found limitations directed towards data gathering to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
With respect to claims 5 and 15, the limitation of “generating, via the one or more processors, one or more modified component fields” and “generating, via the one or more processors, the modified prompt criteria”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “by a processor,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “by a processor” language, “generating” in the context of this claim encompasses the user thinking about data.
With respect to claims 6 and 16, the limitation of “evaluating, via the one or more processors, classification performance of the modified prompt” and “based on the evaluation, approving, via the one or more processors, the modified prompt or the initial prompt”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “by a processor,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “by a processor” language, “evaluating” and “approving” in the context of this claim encompasses the user analyzing data. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
With respect to claims 7 and 17, the limitation of “comparing, via the one or more processors, the classification performance of the prompt”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “by a processor,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “by a processor” language, “comparing” in the context of this claim encompasses the user analyzing data. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
With respect to claims 8, 9, 18, and 19, the limitation of “determining, via the one or more processors, that classification performance of the modified prompt with respect to the issue has improved” and “determining, via the one or more processors, that classification performance of the modified prompt with respect to the relevancy requirement has not degraded”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “by a processor,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “by a processor” language, “determining” in the context of this claim encompasses the user thinking about data. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
With respect to claim 10, the limitation of “analyzing, via the generative Al model, the prompt criteria to determine that no contradiction exists between the relevancy requirement and the description of the issue; and in response to determining that a contradiction exists, generating, via the one or more processors, an alert”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “by a processor,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “by a processor” language, “analyzing” and “generating” in the context of this claim encompasses the user analyzing data. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 11, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over O’Kelly et al. (US 2025/0005300) and further in view of Yang et al. (US 12,579,179).
With respect to claim 1, O’Kelly teaches a computer-implemented method for using a generative artificial intelligence (Al) model to classify documents, the method comprising:
obtaining, via one or more processors, an initial set of documents associated with an inquiry (O’Kelly, pa 0084, Input text is determined at 406. In some embodiments, the input text may be determined by applying one or more text processing, search, or other operations based on the request received from the client machine. For example, the input text may be determined at least in part by retrieving one or more documents identified in or included with the request received from the client machine.);
generating, via the one or more processors, initial prompt criteria by inputting the initial set of documents to a first generative Al model, wherein the initial prompt criteria defines at least (i) a relevancy requirement for the inquiry and (ii) a description of an issue (O’Kelly, pa 0058, the text generation model 276 may be a large language model. & pa 0086, determining input text may involve processing responses received from a text generation modeling system. & pa 0090, the one or more text response messages include one or more novel text portions generated by a text generation model);
generating, via the one or more processors, an initial prompt for input to a second generative Al model based on the initial prompt criteria (O’Kelly, pa 0088, At 410, one or more prompts based on the prompt templates are determined. In some embodiments, a prompt may be determined by supplementing and/or modifying a prompt template based on the input text.); and
classifying, via the one or more processors, a sample of documents from a corpus of documents based upon classification instructions included in the initial prompt by inputting the sample of documents and the initial prompt to a second generative Al model (O’Kelly, pa 0342, A text portion type associated with the text portion is determined at 1406. A machine learning model is determined at 1408 based on the text portion type. In some embodiments, the text portion type may be determined based on the application of a classification model. For instance, a machine learning model may be configured to).
O’Kelly doesn't expressly discuss classifying, via the one or more processors, a sample of documents from a corpus of documents based upon classification instructions included in the initial prompt by inputting the sample of documents and the initial prompt to a second generative Al model generating, via the one or more processors and for each document in the sample of documents, an explanation for the classification, the explanation for the classification generated by the second generative AI model; and presenting, via the one or more processors, the explanation for the classification..
Yang teaches classifying, via the one or more processors, a sample of documents from a corpus of documents based upon classification instructions included in the initial prompt by inputting the sample of documents and the initial prompt to a second generative Al model (Yang, Col. 21 Li. 1-5, at step/operation 504, the document analysis computing entity 106 assigns, using a generative machine learning model, a plurality of model-assigned categorical identifiers to the plurality of text segment data objects.)
generating, via the one or more processors and for each document in the sample of documents, an explanation for the classification, the explanation for the classification generated by the second generative AI model (Yang, Col. 23 Li. 33-37, at step/operation 508, the document analysis computing entity 106 generates, using a verifier machine learning model, one or more evidence predictions on respective ones of the one or more evidence text portions. & Col. 25 Li. 3-6, at step/operation 804, the document analysis computing entity 106 generates a plurality of evidence scores for respective ones of the plurality of categorical identifier-evidence text portion pairs. Col. 25 Li. 33-41, at step/operation 806, the document analysis computing entity 106 determines whether one or more model-assigned categorical identifiers are correctly assigned to the plurality of text segment data objects based on the plurality of evidence scores. The one or more model assigned categorical identifiers may comprise categorical identifiers that are assigned to a plurality of text segment data objects by the generative machine learning model that determined the plurality of evidence text portions); and
presenting, via the one or more processors, the explanation for the classification (Yang, Col. 25 Li. 64-67, the one or more evidence text portions may be provided along with a classification generated for the document data object based on the one or more evidence predictions.).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified O’Kelly with the teachings of Yang because being able to explain a machine learning model's decision process may be useful in correcting classifications derived from potentially biased information (Yang, Col. 1 Li. 44-46).
With respect to claims 11 and 20, the limitations are essentially the same as claim 1, and are rejected for the same reasons.
Claims 2-9 and 12-19 are rejected under 35 U.S.C. 103 as being unpatentable over O’Kelly in view of Yang and further in view of Labutov (US 2024/0386042).
With respect to claim 2, O’Kelly in view of Yang teaches the computer-implemented method of claim 1, as discussed above. O’Kelly in view of Yang doesn't expressly discuss wherein generating the initial prompt criteria further comprises: inputting, via the one or more processors, an indication of a review protocol associated with the inquiry and the initial set of documents to the first generative Al model.
Labutov teaches wherein generating the initial prompt criteria further comprises: inputting, via the one or more processors, an indication of a review protocol associated with the inquiry and the initial set of documents to the first generative Al model (Labutov, pa 0040, At S220, text of a document review protocol is parsed. The parsing may be performed in order to identify and understand the definitions of concepts defined in the document review protocol. To this end, in an embodiment, the text is parsed in order to extract one or more descriptions of concepts to be tagged. In other words, the parsing is performed to identify structures describing how documents are to be tagged.).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified O’Kelly in view of Yang with the teachings of Labutov because it provides an efficient way to classify and review documents (Labutov, pa 0024).
With respect to claim 3, O’Kelly in view of Yang teaches the computer-implemented method of claim 1, wherein the initial set of documents includes one or more of a complaint, a request for production, key documents, and one or more background documents (O’Kelly, pa 0085, determining input text may involve executing a search query. For example, a search of a database, set of documents, or other data source may be executed base at least in part on one or more search parameters determined based on a request received from a client machine. For instance, the request may identify one or more search terms and a set of documents to be searched using the one or more search terms.).
With respect to claim 4, O’Kelly in view of Yang teaches the computer-implemented method of claim 1, as discussed above. O’Kelly in view of Yang doesn't expressly discuss evaluating, via the one or more processors, classification performance of the prompt based on ground truth data associated with the sample of documents; obtaining, via the one or more processors, modified prompt criteria including one or more of (i) a modified relevancy requirement or (ii) a modified description of the issue; generating, via the one or more processors, a modified prompt based on the modified prompt criteria; and generating, via the one or more processors, an updated classification of the sample of documents by inputting the sample of documents and the modified prompt to the second generative Al model.
Labutov teaches evaluating, via the one or more processors, classification performance of the prompt based on ground truth data associated with the sample of documents; obtaining, via the one or more processors, modified prompt criteria including one or more of (i) a modified relevancy requirement or (ii) a modified description of the issue (Labutov, pa 0053, At S260, the classifier machine learning models are updated and improved based on feedback data…. The feedback data may include, but is not limited to, feedback tags (e.g., tags provided as user inputs indicating a "correct" tag to be compared to the outputs of the classifiers), feedback modifications to the document review protocol ( e.g., a new version of the document review protocol or portion thereof), both, and the like.);
generating, via the one or more processors, a modified prompt based on the modified prompt criteria; and generating, via the one or more processors, an updated classification of the sample of documents by inputting the sample of documents and the modified prompt to the second generative Al model (Labutov, pa 0053, At S260, the classifier machine learning models are updated and improved based on feedback data. In an embodiment, each classifier may be updated continuously, iteratively, or otherwise until one or more performance metrics meet respective performance thresholds.).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified O’Kelly in view of Yang with the teachings of Labutov because it allows the machine learning models to be improved (Labutov, pa 0053).
With respect to claim 5, O’Kelly in view of Yang and Labutov teaches the computer-implemented method of claim 4, wherein the relevancy requirement and the description of the issue are associated with respective component fields of the initial prompt criteria (O’Kelly, pa 0197, a prompt template may include a set of instructions for causing a large language model to identify values for the one or more data fields based on the one or more clauses.) and wherein the computer-implemented method further comprises:
generating, via the one or more processors, one or more modified component fields corresponding to one or more component fields of the initial prompt criteria by inputting the initial prompt and the classification performance of the initial prompt to a third generative Al model, wherein at least one of the one or more modified component fields is associated with the relevancy requirement or the description of the issue (Labutov, pa 0026, The machine learning models may be continuously updated and improved for each category or issue based on additional modifications to the review protocol or explicit example tags applied to documents, e.g., tags provided via user inputs. This process may be iterated until performance of the model meets one or more performance targets (e.g., targets defined based on one or more performance metrics meeting respective thresholds).); and
generating, via the one or more processors, the modified prompt criteria based on the one or more modified component fields (Labutov, pa 0055, When the feedback data includes feedback modifications to the document review protocol, updating the classifier machine learning models may further include determining second new tags for the documents using concepts to be tagged extracted from a modified version of the document review protocol).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified O’Kelly in view of Yang with the teachings of Labutov because it allows the machine learning models to be improved (Labutov, pa 0053).
With respect to claim 6, O’Kelly in view of Yang and Labutov teaches the computer-implemented method of claim 4, further comprising: evaluating, via the one or more processors, classification performance of the modified prompt at classifying documents with respect to the relevancy requirement and the description of the issue (Labutov, pa 0053, At S260, the classifier machine learning models are updated and improved based on feedback data. In an embodiment, each classifier may be updated continuously, iteratively, or otherwise until one or more performance metrics meet respective performance thresholds.); and based on the evaluation, approving, via the one or more processors, the modified prompt or the initial prompt to classify additional documents in the corpus of documents (Labutov, pa 0056, The review may include the user manually reviewing each such document and providing a set of user inputs including manually created labels for the documents. The documents may be re-tagged based on the review, for example by re-tagging the documents with the labels provided by the user).
With respect to claim 7, O’Kelly in view of Yang and Labutov teaches the computer-implemented method of claim 6, further comprising: comparing, via the one or more processors, the classification performance of the initial prompt to classification performance of the modified prompt (Labutov, pa 0053, At S260, the classifier machine learning models are updated and improved based on feedback data. In an embodiment, each classifier may be updated continuously, iteratively, or otherwise until one or more performance metrics meet respective performance thresholds.).
With respect to claim 8, O’Kelly in view of Yang and Labutov teaches the computer-implemented method of claim 7, wherein evaluating classification performance of the modified prompt includes: determining, via the one or more processors, that classification performance of the modified prompt with respect to the issue has improved over the classification performance of the initial prompt with respect to the issue (Labutov, pa 0053, At S260, the classifier machine learning models are updated and improved based on feedback data. In an embodiment, each classifier may be updated continuously, iteratively, or otherwise until one or more performance metrics meet respective performance thresholds.).
With respect to claim 9, O’Kelly in view of Yang and Labutov teaches the computer-implemented method of claim 7, wherein evaluating classification performance of the modified prompt includes:
determining, via the one or more processors, that classification performance of the modified prompt with respect to the relevancy requirement has not degraded over the classification performance of the initial prompt with respect to the relevancy requirement (Labutov, pa 0053, At S260, the classifier machine learning models are updated and improved based on feedback data. In an embodiment, each classifier may be updated continuously, iteratively, or otherwise until one or more performance metrics meet respective performance thresholds).
With respect to claims 12-19, the limitations are essentially the same as claims 2-9, and are rejected for the same reasons.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over O’Kelly in view of Yang, and further in view of Will et al. (US 11,557,381).
With respect to claim 10, O’Kelly in view of Yang teaches the computer-implemented method of claim 1, as discussed above. O’Kelly in view of Yang doesn't expressly discuss analyzing, via the first generative Al model, the initial prompt criteria to determine that no contradiction exists between the relevancy requirement and the description of the issue; and in response to determining that a contradiction exists, generating, via the one or more processors, an alert.
Will teaches analyzing, via the first generative Al model, the initial prompt criteria to determine that no contradiction exists between the relevancy requirement and the description of the issue; and in response to determining that a contradiction exists, generating, via the one or more processors, an alert (Will, Col. 6 Li. 3-6, Method 200 begins at 210, where the trial editing server receives information of a first clinical trial, wherein the information includes a plurality of criteria for the first clinical trial and a title of the first clinical trial. & Li. 44- Col. 7 Li. 3, At 250, the trial editing server prompts the user to verify criteria whose confidence values fell below the confidence threshold, as determined at 240… At 260, the trial editing server presents the user with an explanation of the request to verify criteria of 250. … If the low confidence value was based on a contradiction of the criteria, the contradictory criteria may be presented to the user with an explanation of the logical conflict.).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified O’Kelly in view of Yang with the teachings of Will because it can identify potential problems in the given data (Will, Col. 2 Li. 35-42).
Response to Arguments
35 U.S.C. 112
With regard to claims 1-10 and 20, the amendments to the claims have overcome the 35 U.S.C. 112 rejection. The Examiner withdraws the 35 U.S.C. 112 rejection to claims 1-10 and 20.
35 U.S.C. 101
Applicant argues that under Step 2A Prong Two, amended claim 1 integrates any alleged abstract idea into a practical application by “generating, via one or more processors, initial prompt criteria by inputting the initial set of documents to a first generative Al model,” “classifying, via the one or more processors, a sample of documents from a corpus of documents by inputting the sample of documents and the prompt to a second generative Al model,” and “presenting, via the one or more processors, the explanation for the classification” to provide an explanation as to why the classifier applied a particular label to a document. The Examiner respectfully disagrees. Prong Two asks does the claim recite additional elements that integrate the judicial exception into a practical application? Here, “generating, via one or more processors, initial prompt criteria by inputting the initial set of documents to a first generative Al model” and “classifying, via the one or more processors, a sample of documents from a corpus of documents by inputting the sample of documents and the prompt to a second generative Al model,” are abstract ideas and “presenting, via the one or more processors, the explanation for the classification” is a recited additional element to the judicial exception. This additional element merely provides only insignificant extra solution activity in conjunction with the abstract idea. See MPEP 2106.05(g). Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. The courts have found limitations directed towards outputting to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93.
Applicant argues that the limitations “generating, via one or more processors, initial prompt criteria by inputting the initial set of documents to a first generative Al model,” “classifying, via the one or more processors, a sample of documents from a corpus of documents by inputting the sample of documents and the prompt to a second generative Al model,” and “presenting, via the one or more processors, the explanation for the classification” are similar to the instruction in the Ex Parte Desjardins Memo to provide an improvement in technology because they provide a particular technique to achieve a desired outcome. The Examiner respectfully disagrees. Ex Part Desjardins described claims that improved training of machine learning models. The present claims merely utilize generative AI models to perform tasks that can be done mentally. The limitations do not improve the training of a machine learning model or improve the computer performance. Instead, the computer is merely used as a tool to perform an existing process. See Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016).
35 U.S.C. 103
Applicant seems to argue a newly amended limitation. Applicant’s amendment has rendered the previous rejection moot. Upon further consideration of the amendment, a new grounds of rejection is made in view of O’Kelly et al. (US 2025/0005300) and Yang et al. (US 12,579,179).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRITTANY N ALLEN whose telephone number is (571)270-3566. The examiner can normally be reached M-F 9 am - 5:00 pm EST.
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, Sherief Badawi can be reached at 571-272-9782. 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.
/BRITTANY N ALLEN/Primary Examiner, Art Unit 2169
Prosecution Insights