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 application received on 2/12/25. Claims 1-20 are pending in the application.
Claims 1-10 and 20 are rejected under 35 U.S.C. 112.
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 Rajpara (US 2022/0327173).
Claims 2-9 and 12-19 are rejected under 35 U.S.C. 103 as being unpatentable over Rajpara, and further in view of Labutov (US 2024/0386042).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Rajpara, and further in view of Will et al. (US 11,557,381).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10 and 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 1 recites the limitation "the prompt criteria" in the “generating… a prompt” limitation. There is insufficient antecedent basis for this limitation in the claim.
Claim 20 recites the limitation " the generative Al model" in the “generating… a prompt” limitation. There is insufficient antecedent basis for this limitation in the claim.
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” and “generating, via the one or more processors, a 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, “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 “by a processor” 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. Claims 11 and 20 recite a processor to execute the operations 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 generic computer components. Additionally, the claim recites “obtaining, via one or more processors, an initial set of documents associated with an inquiry.” 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 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 one or more processors, 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 “storing the first content”, the courts have found limitations directed towards storing to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts") and “storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
With respect to “presenting, on a display device, results”, 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 “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. 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 “by a processor” 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 that is mere data gathering 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 and 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over Rajpara (US 2022/0327173).
With respect to claim 1, Rajpara 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 (Rajpara, pa 0100, At block 1202, a set of documents associated with data discovery are received. The set of documents may be associated with an issue, such as privilege or a cause of action.);
generating, via 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 (Rajpara, pa 0101, At block 1204, for each document in a subset of the set of documents, an indication of relevancy or non-relevancy of the document for an issue is received);
generating, via the one or more processors, a prompt for input to the generative Al model based on the prompt criteria (Rajpara, Fig. 6a-b & pa 0075, FIG. 6A illustrates an example user interface 600 that enables a user to modify a machine-learning model configuration. Examiner note: user can define parameters of prompt criteria to reference during model processing); 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 (Rajpara, Fig. 7 & pa 0084, FIG. 7 illustrates an example user interface 700 that includes an overview window 705 once the machine learning model outputs predictions & pa 0103, At block 1208, for each document in the set of documents, output by a machine-learning model, a prediction probability of relevancy to an issue associated with the data discovery, and a ranking of the set of documents based on the prediction probability of relevancy. Examiner note: documents have been classified according to user configured settings).
Rajpara doesn't expressly discuss generating, via the one or more processors, a prompt for input to the generative Al model based on the prompt criteria.
Rajpara teaches classifier models that are created with specific criteria to classify documents. Therefore, providing a prompt to classify documents that is based on the defined tags and configurations 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 because it provides the classification of documents according to the user criteria.
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 Rajpara, and further in view of Labutov (US 2024/0386042).
With respect to claim 2, Rajpara teaches the computer-implemented method of claim 1, as discussed above. Rajpara 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 Rajpara with the teachings of Labutov because it provides an efficient way to classify and review documents (Labutov, pa 0024).
With respect to claim 3, Rajpara in view of Labutov 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 (Rajpara, pa 0051, The processing module 202 may receive a set of documents related to data discovery issues. For example, the data discovery issues may relate to compliance, backup data, organizational data, electronic discovery issues, etc. Electronic discovery issues are associated with discovery in a legal proceeding, such as a litigation, a government investigation, a Freedom of Information Act request, etc. The set of documents may include forwarded emails, attached documents, a filed pleading, etc. For example, the set of documents may be associated with a lawsuit that includes both a contract cause of action and a tort cause of action.).
With respect to claim 4, Rajpara in view of Labutov teaches the computer-implemented method of claim 1, as discussed above. Rajpara 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 Rajpara with the teachings of Labutov because it allows the machine learning models to be improved (Labutov, pa 0053).
With respect to claim 5, Rajpara in view of 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 prompt criteria (Rajpara, Fig. 6a-b) and wherein the 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 prompt criteria by inputting the prompt and the classification performance of the 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 Rajpara with the teachings of Labutov because it allows the machine learning models to be improved (Labutov, pa 0053).
With respect to claim 6, Rajpara in view of 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, Rajpara in view of Labutov teaches the computer-implemented method of claim 6, further comprising: comparing, via the one or more processors, the classification performance of the 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, Rajpara in view of 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 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, Rajpara in view of Labutov teaches the computer-implemented method of claim 7, wherein evaluating classification performance of the updated 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 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 Rajpara, and further in view of Will et al. (US 11,557,381).
With respect to claim 10, Rajpara teaches the computer-implemented method of claim 1, as discussed above. Rajpara doesn't expressly discuss 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.
Will teaches 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 (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 with the teachings of Will because it can identify potential problems in the given data (Will, Col. 2 Li. 35-42).
Conclusion
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.
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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.
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/BRITTANY N ALLEN/Primary Examiner, Art Unit 2169