DETAILED ACTION
Case Status
This office action is in response to remarks and amendments of 18 February 2026. Claims 1-3, 5, 7-10, 12, 14-17 have been examined.
Claim Objections
Claims 7 and 14 are objected to because of the following informality: “the at least one document” should be re-written as --the at least one electronic document--. Appropriate correction is required.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
20220398397
Par. 29
Suggest rules to documents based on document metadata
20220366344
Pars. 36, 41
Machine learning models indicate degree of compliance of document sections/metadata and are trained using other documents, metadata and rules
20220164397
Pars. 34-36
AI/ML based analysis of metadata, content and features of documents to determine non-compliance, fraud and crime
20200133964
Pars. 38, 42
Determining rules for ML review of contractual document metadata for clause non-compliance
20190347429
Pars. 37, 72, 90
Using ML and document metadata to determine document sensitivity levels (rules) to determine document risk scores (compliance)
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 1-3, 5, 7-10, 12, 14-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-3, 5, 7-10, 12, 14-17 are directed to one of the eligible categories of subject matter.
With respect to independent claims 1, 8 and 15, the determining, processing, parsing limitations cover evaluating information, applying rules, and reaching a compliance determination, which are acts that can be performed manually and/or in the mind. The receiving, transmitting limitations are recited at a high level of generality and do not add meaningful limitations to the abstract idea because they merely gather and communicate the information used in the abstract idea; these limitations are directed to insignificant extra solution activities. The claims as a whole merely describe how to generally “apply” the exception in a computer environment using generic computer functions or components (such as the claimed data management system, vault, machine learning model, metadata, workflow state, apparatus, processor, memory, non-transitory computer program product). Even when viewed in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
With respect to dependent claims 2-3, 5, 7, 9-10, 12, 14, 16, 17 the determining, entering, generating, reviewing, to generate, trained cover performance of the limitations manually and/or in the mind (mental processes abstract idea). No additional elements are recited and so the claims do not provide a practical application and are not considered to be significantly more. The claims are not eligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 5, 7-10, 12, 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Cleveland et al., Pub. No.: US 20250238811 A1, hereinafter Cleveland, in view of Narayanan et al., Pub. No.: US 20240241901 A1, hereinafter Narayanan.
As per claim 1, Cleveland discloses A method for a data management system, the data management system comprising a vault for storing at least a plurality of electronic documents, where each of the electronic documents is associated with metadata, wherein the metadata comprises a set of properties (fig.’s 1, 3-6, 16-17), the method comprising: receiving by the data management system a selection of at least one electronic document (pars. 51, 56);
determining by the data management system a set of rules for the at least one electronic document, wherein the set of rules is indicated by at least one metadata property of the at least one electronic document or input by a user (pars. 52, 56, 138, 231, 242) and
Cleveland does not expressly disclose however Narayanan in the related field of endeavor of content management discloses wherein the set of rules is associated with a workflow state of the at least one electronic document (Narayanan see at least pars. 7, 30, 32, 34).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because Narayanan’s workflow state-based rule determination would have allowed Cleveland’s compliance checking system to automate ruleset selection at the correct or most appropriate stages of a document’s processing lifecycle and in a more relevant manner using workflow stage associated metadata. See Narayanan, pars. 29-30, 100-104.
transmitting said at least one electronic document, its associated metadata and the set of rules by the data management system to a machine learning model with a request to generate a prompt based on the set of rules and the associated metadata (Cleveland pars. 8, 75-77, 85, 95, 132 disclose generating and transmitting prompts to an LLM, the prompts based on, and sent along with, documents/content items, rulesets, user input, supplemental information etc. The prompt includes document metadata as seen in the metadata panel displayed during processing (fig. 16). See also, Narayanan as cited above as well as pars. 29-34 for a document management system that stores metadata associated with documents and uses metadata in document workflow processing);
processing said generated prompt by the machine learning model to review content of the at least one document based on the set of rules and a metadata of the at least one electronic document (see rejection of previous limitation; also, see Cleveland, pars. 85, 102, and 312 which includes “process the prompt using the compliance checker, wherein the compliance checker uses the set of machine learning models to verify compliance of the content item based at least in part on the compliance ruleset; receiving a compliance determination dataset from the compliance checker that indicates whether the mixed data type content item passes one or more criteria within the compliance ruleset”);
receiving from the machine learning model a result on the reviewing by the data management system (see rejection of above limitation); and
parsing by the data management system the result to determine whether the at least one electronic document complies with the set of rules or whether the at least one electronic document does not comply with the set of rules (see rejection of at least the processing limitation above; see also, Cleveland, pars. 103-104, 312).
As per claim 2, Cleveland in view of Narayanan discloses the method according to claim 1, wherein a metadata for the at least one electronic document defines an automated workflow, wherein the method further comprises determining a property value for a workflow state based on the result, and automatically entering the determined property value to the workflow state (see rejection of claim 1; note that Narayanan, as per pars. 29-30, 100-104, 111-112, discloses document metadata being used to control document workflows such as routing documents and requesting workflow actions and that ML based metadata prediction being auto triggered during workflow execution, and see Cleveland pars. 103, 247 for results that indicate compliance or non-compliance and the combination would use this compliance result to determine and enter a workflow state property value so that the document is handled/routed correctly; see rationale to combine as provided in the rejection of claim 1).
As per claim 3, Cleveland in view of Narayanan discloses the method according to claim 1, further comprising determining a metadata property and a corresponding value based on the result, and automatically entering the corresponding value to the determined metadata property (see rejection of claim 2, see Narayanan pars. 29-30, 100-104, 111-112 and note that claim 1 of Narayanan includes “annotating the document with one or more metadata attributes predicted using the one or more machine learning models”).
As per claim 5, Cleveland in view of Narayanan discloses The method according to claim 1, further comprising generating the set of rules based on a query received from a user via a user interface (see Cleveland pars. 74-76 for natural language request, pars. 227-230 for message portion 1004 and 1010, pars. 56, 242 for user selections, etc.).
As per claim 7, Cleveland in view of Narayanan discloses The method according to claim 1, further comprising reviewing content of the at least one document based on the set of rules and a metadata of another object being referred to by the set of rules (see Narayanan par. 59 which includes “the machine learning model is provided as input information describing other related documents, for example, information describing documents stored in the folder in which the document was uploaded. Accordingly, if the confidence in a particular prediction of a metadata attribute (e.g., the type of interaction, such as the contract type) as indicated by the output score value is below a threshold, the system may determine the metadata attribute based on metadata attributes of related documents.” See also, Cleveland pars. 75-77, 85.).
As per claim 16, Cleveland in view of Narayanan discloses The method of claim 1, wherein the transmitting the at least one electronic document, its associated metadata and the set of rules to the machine learning model with the request and the receiving from the machine learning model a result on the reviewing each comprise using an application programming interface to generate calls for transmitting and receiving data (Cleveland pars. 64, 67, 85-88, 284 Narayanan par. 36, 38, 78).
As per claim 17, Cleveland in view of Narayanan discloses the method of claim 1, wherein the machine learning model is not trained specifically for each of the set of rules (Cleveland pars. 7, 50, 85, 102, 293, 281, 282 disclose using LLM’s that are not trained specifically for each ruleset; see Narayanan as cited above).
As per claims 8-10, 12, 14-15, they are analogous to claims above and are therefore likewise rejected. See Cleveland pars. 251-274 for the apparatus and computer program product of claims 8 and 15.
Response to Arguments
Applicant's arguments filed 18 February 2026 have been considered.
With respect to the prior art rejection Cleveland and Narayanan have been applied in response to claim amendments.
With respect to the 35 USC 101 rejection, the remarks present the following on page 8:
PNG
media_image1.png
143
632
media_image1.png
Greyscale
Examiner respectfully disagrees. MPEP 2106.04(a)(2) III includes “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation” and “Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer.” The recited data management system, prompt, machine learning model merely automate the claimed document content review against rules and the compliance determination.
Page 8 of the remarks also includes:
PNG
media_image2.png
111
632
media_image2.png
Greyscale
Examiner respectfully disagrees. The improvement argued by Applicant is to the abstract idea itself and not an improvement to the computer. It is noted that the features upon which applicant relies (i.e., i.e., “business-critical metadata”, “various workflow stages”, “a combination” thereof, “fully automates the workflow changes by adding an extra layer of verification to address various deficiencies”, “different workflow states and their relationship to different verification results”, “continuously monitor changes”, “adjust workflow states accordingly”, etc.) are not recited in the rejected claims. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Pages 8-9 of the remarks also include:
PNG
media_image3.png
77
625
media_image3.png
Greyscale
PNG
media_image4.png
69
625
media_image4.png
Greyscale
Examiner respectfully disagrees. The recited data management system and machine learning model are generic computer components used to perform the abstract review process. The claimed result is a compliance determination which merely characterizes a document. It is not a transformation of a particular article to a different state or thing. Likewise, entering a value as per claims 2 and 3 corresponds to merely updating or changing information which is also not a transformation of a particular article to a different state or thing.
Page 9 of the remarks includes:
PNG
media_image5.png
49
633
media_image5.png
Greyscale
Examiner respectfully disagrees. The claims recite the computer components and machine learning operations functionally and at a high level. The claims do not specify any unconventional implement of the claimed components beyond using them to carry out rule-based document compliance review.
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 SYED HASAN whose telephone number is (571)270-5008. The examiner can normally be reached M-F 8am - 5 pm.
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.
/SYED H HASAN/Primary Examiner, Art Unit 2154
Prosecution Insights