DETAILED ACTION
This Final Office Action is in response to the Request for Continued Examination, arguments, and amendments filed August 3, 2026.
Claims 1, 8, and 15 have been amended.
Claims 1-3, 5-10, 12-17, and 19-20 are currently pending and have been considered below.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on August 3, 2026 has been entered.
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, 5-10, 12-17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea without additional elements that are significantly more or transformative into a practical application.
Independent claims 1, 8, and 15 (as represented by claim 1) are directed towards, “A method comprising: receiving a document for analysis, the document being of a category of document type; applying, by the document management system, the first [model] trained on a plurality of past documents and configured to output a risk value that represents a likelihood that an aspect of the document may put a potential signer of the document at risk; receiving information from the potential signer identifying one or more jurisdictions associated with the document; applying, by the document management system, the second [model] trained on a set of rules associated with the jurisdiction and associated with the document type of the document to output a set of clauses in the document that are likely to differ from rules associated with the one or more identified jurisdictions associated with the document; generating a document summary comprising the risk value and the set of clauses, wherein the document summary comprises a metadata review section to present the non-visible document code; and transmitting to a device of the potential signer, the document summary for display that enables review of the risky clauses by the potential signer”. The claims are describing a risk analysis based on a document and jurisdiction information for a potential signer. The risk analysis is based on the clauses of the document with respect to the jurisdiction with falls within a legal interaction. The claims are describing a legal interaction that falls within the abstract idea grouping of certain method of organizing human activity.
Step 2(a)(II) considers the additional elements of the independent claim in terms of being transformative into a practical application. The additional elements of the independent claims are, “A non-transitory computer-readable storage medium storing executable instructions that, when executed by a hardware processor of a central networking system, cause the central networking system to perform steps comprising (claim 8), A system comprising a hardware processor and a non-transitory computer- readable storage medium storing executable instructions that, when executed by the hardware processor, cause the system to perform steps comprising (claim 15), by a document management system, wherein the document management system comprises a document analysis system that communicates with one or more user devices over a network, the document analysis system comprising a plug-in software component to provide document analysis functionality to a web browser; applying, by the document management system, a first machine learning model to the document, the first machine learning model trained on a plurality of past documents and configured to output a risk value that represents a likelihood that an aspect of the document may put a potential signer of the document at risk; applying, by the document management system, a second machine learning model to the document, the second machine learning model trained on a set of rules associated with the jurisdiction and associated with the document type of the document to output a set of clauses in the document that are likely to differ from rules associated with the one or more identified jurisdictions associated with the document; receiving metadata associated with the document for analysis, the metadata comprising non-visible document code, wherein the non-visible document code comprises source code of the document; applying the first machine learning model and the second machine learning model to the metadata; wherein the document summary comprises a metadata review section to present the non-visible document code by the document management system, to a device of the potential signer, display at an interface; and wherein the first machine learning model or the second machine learning model is periodically retrained based on new training data received for a certain model category”. The additional elements with respect to the computer and display aspects are within the originally filed specification [16-21 and 40-45] and are merely described as generic technology to implement the abstract idea. This further includes the plugin and network limitations that are merely providing the generic technology architecture to implement the abstract idea. The plugin and network elements are not directed towards technical improvements and are described as generic technology. In terms of the machine learning models, the specification describes the machine learning and training in paragraphs [17-28]. The specification merely states that the elements use trained machine learning models, but there is no specific specification description in terms of the specific models, techniques, or aspects describing the machine learning. The claims and specification merely describe the machine learning in terms of using off-the-shelf, high level analysis to implement the abstract idea. There is no specific algorithm or model beyond the title of a “machine learning model”. This further includes the training as the training is described and claimed as generic periodic training without specific elements towards techniques that are directed towards improving the technology, but rather utilizing a tool. Therefore, the machine learning and training additional elements are not directed towards aspects that are transformative into a practical application. The additional elements regarding the metadata are described in the originally filed specification [27-34]. The metadata is merely described in terms of document codes that are generic technology to implement the abstract idea. As such, the claims are not directed towards additional elements that are transformative into a practical application. Refer to MPEP 2106.05(f).
Step 2(b) considers the additional elements of the independent claim in terms of being significantly more than the identified abstract idea. The additional elements of the independent claims are, “A non-transitory computer-readable storage medium storing executable instructions that, when executed by a hardware processor of a central networking system, cause the central networking system to perform steps comprising (claim 8), A system comprising a hardware processor and a non-transitory computer- readable storage medium storing executable instructions that, when executed by the hardware processor, cause the system to perform steps comprising (claim 15), by a document management system, wherein the document management system comprises a document analysis system that communicates with one or more user devices over a network, the document analysis system comprising a plug-in software component to provide document analysis functionality to a web browser; applying, by the document management system, a first machine learning model to the document, the first machine learning model trained on a plurality of past documents and configured to output a risk value that represents a likelihood that an aspect of the document may put a potential signer of the document at risk; applying, by the document management system, a second machine learning model to the document, the second machine learning model trained on a set of rules associated with the jurisdiction and associated with the document type of the document to output a set of clauses in the document that are likely to differ from rules associated with the one or more identified jurisdictions associated with the document; receiving metadata associated with the document for analysis, the metadata comprising non-visible document code, wherein the non-visible document code comprises source code of the document; applying the first machine learning model and the second machine learning model to the metadata; wherein the document summary comprises a metadata review section to present the non-visible document code by the document management system, to a device of the potential signer, display at an interface; and wherein the first machine learning model or the second machine learning model is periodically retrained based on new training data received for a certain model category”. The additional elements with respect to the computer and display aspects are within the originally filed specification [16-21 and 40-45] and are merely described as generic technology to implement the abstract idea. This further includes the plugin and network limitations that are merely providing the generic technology architecture to implement the abstract idea. The plugin and network elements are not directed towards technical improvements and are described as generic technology. In terms of the machine learning models, the specification describes the machine learning and training in paragraphs [17-28]. The specification merely states that the elements use trained machine learning models, but there is no specific specification description in terms of the specific models, techniques, or aspects describing the machine learning. The claims and specification merely describe the machine learning in terms of using off-the-shelf, high level analysis to implement the abstract idea. There is no specific algorithm or model beyond the title of a “machine learning model”. This further includes the training as the training is described and claimed as generic periodic training without specific elements towards techniques that are directed towards improving the technology, but rather utilizing a tool. Therefore, the machine learning and training additional elements are not directed towards aspects that are transformative into a practical application. The additional elements regarding the metadata are described in the originally filed specification [27-34]. The metadata is merely described in terms of document codes that are generic technology to implement the abstract idea. As such, the claims are not directed towards additional elements that are significantly more than the identified abstract idea. Refer to MPEP 2106.05(f).
Dependent claims 2, 6, 7, 9, 13, 14, 16, and 20 are further describing additional elements further describing those identified above. The dependent claims are directed towards, “wherein the plurality of past documents used to train the first machine learning model are labeled with training data indicating clause types and document types”, “further comprising storing, for each of a set of jurisdictions, example documents that conform to the rules of the jurisdiction, for use in training the first machine learning model and the second machine learning model”, and “wherein the first machine learning model and the second machine learning model are further trained according to organizational rules set by an administrator of an organization associated with the potential signer”. The claims are further describing the additional elements regarding the machine learning elements including receiving metadata, training the model based on labeling documents and organizational rules, and storing jurisdiction rules that are used to train the models. The machine learning elements are described in the originally filed specification [17-28]. The specification merely states that the elements use trained machine learning models, but there is no specific specification description in terms of the specific models, techniques, or aspects describing the training. The claims and specification merely describe the training and machine learning in terms of using generic trained ML models to implement the abstract idea. As such, the claims are not directed towards additional elements that are significantly more or transformative into a practical application. Refer to MPEP 2106.05(f).
Dependent claims 3, 5, 10, 12, 17, and 19 are further describing additional elements beyond those identified above. The claims are directed towards, “wherein the set of rules associated with the jurisdiction is obtained using functions of an application programming interface (API) that obtain changes to rules associated with the jurisdiction” and “wherein the document is a HyperText Markup Language (HTML) document”. The API and HTML are described in the originally filed specification [19 and 25-27]. The specification merely describes the additional elements in terms of utilizing generic technology to implement the abstract idea. As such, the additional elements are not significantly more or transformative into a practical application. Refer to MPEP 2106.05(f).
The claimed invention is describing an abstract idea without additional elements that are transformative into a practical application or significantly more than the identified abstract idea. Therefore, claims 1-3, 5-10, 12-17, and 19-20 are rejected under 35 USC 101 for being directed towards non-eligible subject matter.
Response to Arguments
In response to the arguments filed August 3, 2026 on pages 8-23 regarding the 35 USC 101 rejection, specifically that the amended claim limitations and combination of elements are directed towards eligible subject matter.
Examiner respectfully disagrees.
Examiner notes that several arguments of the August 3, 2026 are the same arguments previously presented [March 23 and November 20, 2025]. As such, the response from May 1, 2026 has been provided below and new arguments will be addressed accordingly. The arguments are directed towards the claim limitations in terms of the consideration and analysis. The arguments allege first that the consideration does not provide analysis or recitation of claims that fall into the certain method of organizing human activity grouping. The arguments allege that the claims are not a legal or commercial activity, while explicitly stating that the claims are describing contract risk analysis. This is specifically stated on pages 13 of the November 20, 2025 arguments (and further cited/repeated on page 13 of the March 23, 2026 arguments) in terms of, “For example, the representative claim recites a process of analyzing documents using ML models and presenting a summary with non-visible document code as a technical operation, not a human activity like contract formation or legal review. The claim automates document analysis, which traditionally requires human legal expertise, and it does not describe commercial/legal interactions or managing human behavior”. The arguments allege that the claims are describing document analysis (in the specific instance of contract documents) to provide displayed reports based on ML analysis including non-visible document code. While the claims provide elements of ML analysis with a trained model and non-visible document code, and as will be discussed in terms of the 2(a)(II) and 2(b) arguments, the claims are merely applying the ML model techniques to the identified abstract idea and the document code is further describing the input elements into the ML model to implement the abstract idea. The consideration is that the specific limitations, “A method comprising: receiving a document for analysis, the document being of a category of document type; receiving information from the potential signer identifying one or more jurisdictions associated with the document; generating a document summary comprising the risk value and the set of clauses, wherein the document summary comprises a metadata review section to present the non-visible document code; and transmitting to a device of the potential signer, the document summary for display that enables review of the risky clauses by the potential signer” are directed towards a legal interaction for contract analysis that falls into the abstract idea grouping of certain method of organizing human activity.
The arguments continue with respect to the additional element consideration. The arguments discuss and allege that the additional elements of the claimed invention are directed towards a technical improvement. The alleged improvement is with respect to the claims providing two ML models and metadata/non-visible code received within the analysis to provide the document risk summary. The purported improvement is to provide enhanced computer functioning by increasing accuracy and completeness of risk assessment, however, that is not describing a technical improvement but rather is improving the abstract idea. While the arguments cite to Finjan v. Blue Coat and Bascom v. ATT, the specific instances in those decisions was providing a specific technical improvement. In Finjan, the claimed invention was directed towards, “a method of virus scanning that scans an application program, generates a security profile identifying any potentially suspicious code in the program, and links the security profile to the application program. 879 F.3d at 1303-04, 125 USPQ2d at 1285-86. The Federal Circuit noted that the recited virus screening was an abstract idea, and that merely performing virus screening on a computer does not render the claim eligible. 879 F.3d at 1304, 125 USPQ2d at 1286. The court then continued with its analysis under part one of the Alice/Mayo test by reviewing the patent’s specification, which described the claimed security profile as identifying both hostile and potentially hostile operations. The court noted that the security profile thus enables the invention to protect the user against both previously unknown viruses and "obfuscated code," as compared to traditional virus scanning, which only recognized the presence of previously-identified viruses. The security profile also enables more flexible virus filtering and greater user customization. 879 F.3d at 1304, 125 USPQ2d at 1286. The court identified these benefits as improving computer functionality, and verified that the claims recite additional elements (e.g., specific steps of using the security profile in a particular way) that reflect this improvement”. The claims in the pending application are not directed towards similar claim limitations. The ML modeling to provide document risk analysis is not describing or claiming a technical improvement. The use of off-the-shelf ML models to provide analysis to document risk is not providing a technical improvement. That is describing “mere instructions to apply it” with respect to the additional elements. This consideration further continues with respect to the argument that the visible and non-visible document elements enhance informed decision making with respect to the legal/contract analysis of the abstract idea. The arguments and claims are not providing a technical improvement nor is the description or arguments provided in terms of improving the analysis. The arguments allege that the non-visible document code improves accuracy without providing the specific detail or technical interaction that provides the improved accuracy. This further could fall within 2106.05(f) in terms of providing an idea of a solution that lacks the specific technical description to be considered a technical improvement.
With respect to Bascom, the eligibility was with respect to, “the court found that all of the additional elements in the claim recited generic computer network or Internet components, the elements in combination amounted to significantly more because of the non-conventional and non-generic arrangement that provided a technical improvement in the art. BASCOM Global Internet Servs. v. AT&T Mobility LLC, 827 F.3d 1341, 1350-51, 119 USPQ2d 1236, 1243-44 (2016)”. Within the pending application, the use of two machine learning models and non-visible metadata elements are not describing a technical improvement. There is no description or claim language that the machine learning models and/or non-visible code are providing a technical improvement but rather applying generic technology to implement the abstract idea.
Examiner notes that the specification and claims were both considered as well as the claims, individually and in combination. The specification describes the machine learning and training in paragraphs [17-28]. The specification merely states that the elements use trained machine learning models, but there is no specific specification description in terms of the specific models, techniques, or aspects describing the machine learning. The claims and specification merely describe the machine learning in terms of using off-the-shelf, high level analysis to implement the abstract idea. There is no specific algorithm or model beyond the title of a “machine learning model” and thus the additional elements are not directed towards aspects that are transformative into a practical application. The additional elements regarding the non-visible code/metadata are described in the originally filed specification [27-34]. The metadata is merely described in terms of document codes that are generic technology to implement the abstract idea. Further, the arguments are discussing the arguments in terms of well-understood, routine, and conventional, but the claims, as considered above, are with respect to 2106.05(f) in terms of being generic technology. The specification and claims are not describing elements that describe or claim the improvement nor, in response to the arguments regarding well-understood, routine, and conventional, are the claims and specification describing the ML model and non-visible code as beyond routine use of ML models and metadata data collection. There’s no description beyond merely stating a listing of techniques that could be used, but the claims are not directed towards those specific elements and the specification merely provides a listing of techniques that are generic technology provided under “apply it” {Examiner notes paragraph [20] in terms of the listed techniques}. As such, under Step 2(a)(II) consideration, the additional elements not directed towards a technical improvement, but rather generic technology to implement the abstract idea.
The arguments continue to allege a technical improvement with respect to the Kim Memo and the USPTO AI/ML guidance examples (specifically example 47) starting on page 16. The Kim Memo is directed towards the 35 USC 101 consideration with respect to Ex Parte Desjardins (hereafter Desjardins). The Desjardins decision was provided based on the improvement towards the machine learning model itself based on catastrophic forgetting technical improvements. Desjardins provides a technical improvement with specific written description to provide the technical improvement and was also recited in the claim to be directed towards the technical feature providing the improvement. The pending claims of the current application are merely using generic technology (non-visible document code, non-specific/generic ML models, and plugin, network, and other computer elements) to implement the abstract idea. There is no specific technical improvement towards the ML model itself, but rather utilizing the generic technology to implement the contract review. The arguments allege that the claims provide the technical features and written description towards a neural network (not claimed) that provides cloud-based AI (not claimed) with specific architecture interaction (not claimed). The mere allegation of providing the technical features and being directed towards a technical improvement is not commiserate with the claims as currently claimed. Further, the specification does not provide the specific technical improvement for the aspects as described in the arguments. As such, the claims are directed towards an abstract idea without additional elements that are transformative into a practical application.
In terms of the arguments regarding Step 2(b), Examiner notes that the consideration follows the response and consideration with respect to Step 2(a)(II) in that the additional elements are merely describing generic technology to implement the abstract idea. The specification and claims are not describing or claiming aspects of unconventional training or ML model application. Using multiple models is applying generic ML models to provide further analysis, but that is not described as a technical improvement or unconventional approach in terms of ML model analysis. The specification and claims are not describing elements that describe or claim the improvement nor, in response to the arguments regarding well-understood, routine, and conventional, are the claims and specification describing the ML model and non-visible code as beyond routine use of ML models and metadata data collection. There’s no description beyond merely stating a listing of techniques that could be used, but the claims are not directed towards those specific elements and the specification merely provides a listing of techniques that are generic technology provided under “apply it” {Examiner notes paragraph [20] in terms of the listed techniques}. In terms of the arguments citing Amdocs, the claimed invention was eligible with respect to, “A distributed network architecture operating in an unconventional fashion to reduce network congestion while generating networking accounting data records”. The claimed invention is not describing elements of specific unconventional techniques or modeling, but rather applying ML models to generate a contract risk to a user. Thus the claims and specification are not providing a technical improvement and the additional elements are merely generic technology to implement the identified abstract idea.
Examiner notes that new arguments are with respect to the machine learning training steps as amended. The training and machine learning elements are described in the originally filed specification [20-24 and 36-39]. The training is not described in terms of specific improvements to ML technology, but rather generic techniques merely implementing the abstract idea. This is further described in terms of periodic training but no discussion or description regarding an improved training technique. As such, the training is directed towards generic technology to implement the abstract idea. Therefore, the claims are not directed towards additional elements that are significantly more or transformative into a practical application.
Lacking any further arguments, claims 1-3, 5-10, 12-17, 19, and 20 are maintaining the 35 USC 101 rejection, as considered above in light of the amended claim limitations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Picard et al [2016/0132798] (SLA risk analysis);
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/ANDREW CHASE LAKHANI/Primary Examiner, Art Unit 3629
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