DETAILED ACTION
This Final Office Action is in response to the arguments filed May 27, 2025.
Claims 1-3, 5-10, 12-17, 19, and 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 .
Response to Arguments
In response to the arguments filed January 13 and entered May 27, 2025 on pages 8-18 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.
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 mathematical concept abstract idea, however, Examiner notes that the Non-Final Office Action does not recite or consider the claims to be under the mathematical concept abstract idea grouping. This further holds consideration in terms of the arguments regarding the mental process grouping, as the Examiner has not considered the claims to be under the mental process grouping in response to the argument allegations of the claims not reciting a mental process.
The arguments further continue in terms of the consideration for certain method of organizing human activity. 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 13-14 of the May 27, 2025 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. While the claims provide elements of ML analysis with a trained model, 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. 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 on pages 15-18 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.
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 and 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 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.
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.
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.
In response to the arguments filed May27, 2025 on pages 18-24 regarding the 35 USC 103 rejection, specifically that the claim limitations are not taught by the cited prior art.
Examiner respectfully disagrees.
The arguments allege the combination of prior art elements are not teaching the claimed invention, specifically the non-visible document code/metadata and displaying the information based on the contract analysis. The arguments allege that the prior art does not teach the specific element of non-visible document code, however, there is no specific support in terms of the differing interpretations. The originally filed specification provides support in terms of metadata or document codes and non-visible document codes with respect to paragraph [27-34]. Based on the specification, the prior art teaches the metadata text according to the support provided in the specification. Further, the arguments are not providing specification support in light of the prior art citations. There is merely an allegation that the prior art does not teach the claim element. While the arguments discuss “non-visible document code” that is merely describing the metadata and not providing specific interpretive support that the collected metadata. The interpretation is that Gupta provides metadata including frequency and other contract elements, and those aspects are non-visible according to Gupta Fig 3 (cited and considered based on paragraph [22] within the Non-Final Office Action). Further, the arguments allege that the combination of prior art elements does not teach displaying the metadata in a document summary. The cited prior art Hunn provides a visualization display that includes metadata which within the combination provides the non-visible code (specifically within Gupta). The further arguments regarding the display and window are directed towards elements that are not claimed, in terms of specificity and functionality. The claims are limited to, “generating, by the document management system, 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, by the document management system, to a device of the potential signer, the document summary for display at an interface that enables review of the risky clauses by the potential signer”. Functionally, the display provides metadata and what the metadata includes is within the combination of elements. Further, what is displayed is non-functional descriptive material. The functional aspect is providing a display review area for metadata (which the prior art provides) and the presentation of what is within the section is non-functional. Examiner notes that the prior art teaches the section and elements of the specific data, but wanted to note that the arguments are alleging and arguing features which are not specifically recited in the claims.
Lacking any further arguments, claims 1-3, 5-10, 12-17, 19, and 20 are maintaining the 35 USC 103 rejection, as considered above in light of the amended claim limitations.
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; 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”. 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, 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; applying the first machine learning model and the second machine learning model to the metadata; 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 by the document management system, to a device of the potential signer, display at an interface”. 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. 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” and thus the 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, 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; applying the first machine learning model and the second machine learning model to the metadata; 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 by the document management system, to a device of the potential signer, display at an interface”. 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. 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” and thus the 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.
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.
Claim(s) 1-3, 6-10, 13-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wodetzki et al [2018/0268506], hereafter Wodetzki, in view of Broudou et al [2016/0321582], hereafter Broudou, further in view of Gupta [2020/0226510], and Hunn et al [2018/0315141], hereafter Hunn.
Regarding claim 1, Wodetzki discloses a method comprising: receiving, by a document management system, a document for analysis, the document being of a category of document type (Paragraphs [66-72 and 134-139]; Wodetzki discloses a contract document that is provided for analysis.);
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 (Paragraphs [119-122 and 134-139]; Wodetzki discloses a contractual risk based on machine learning rules for the contract clauses. An alert or other notification is sent based on the risk score and clause [148-150].);
Wodetzki discloses a contract risk system that provides ML algorithms and rule sets for contract clauses, however, Wodetzki does not specifically teach a user (potential signer) identifying jurisdictions and training
Broudou teaches receiving, by the document management system, information from the potential signer identifying one or more jurisdictions associated with the document; 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 (Paragraphs [89-95, 112, and 128]; Broudou teaches a similar contract report and risk system that specifically teaches jurisdiction input and providing a machine learning model to determine contract clause risk. Further, Wodetzki teaches [134-138] country risk within the contract risk assessment. The second model trained is taught within Broudou providing the jurisdiction rules and Wodetzki provides [122] the structure of the ML/AI models that are utilized to reevaluate the contract clauses and terms based on three rules (universal, industry, and customer specific). Broudou provides the specific jurisdiction rules as the second ML rule within the rules of Wodetzki.);
transmitting, by the document management system, to a device of the potential signer, the document summary for display at an interface that enables review of the risky clauses by the potential signer (Fig 7A and paragraphs [202]; Broudou teaches a report based on the contract risk.).
Wodetzki discloses a contract clause risk assessment system that provides ML/AI rules based on contract terms including country rules, however, Wodetzki does not teach the specific jurisdiction input and ML rules.
Broudou teaches a similar contract risk assessment system that specifically provides jurisdiction inputs and ML rules for jurisdiction that are trained.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to include in the contract clause risk assessment system that provides ML/AI rules based on contract terms including country rules of Wodetzki the ability to include a similar contract risk assessment system that specifically provides jurisdiction inputs and ML rules for jurisdiction that are trained as taught by Broudou since the claimed invention is merely a combination of prior art elements and in the combination each element would have performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination were predictable.
The combination teaches the above-enclosed limitations with respect to a contract clause and risk assessment system based on input data and outputting based on the analysis, however, the combination does not specifically teach receiving metadata comprising non-visible document codes;
Gupta teaches receiving metadata associated with the document for analysis, the metadata comprising non-visible document code (Paragraphs [22 and 29-31]; Gupta teaches a similar contract risk analysis system that specifically provides inputs into an ML based on metadata associated with the document. The metadata is described as attributes of the clause includes service name, frequency, termination, etc that is interpreted as non-visible document code. Within the combination, Wodetzki discloses [Fig 4A and paragraphs 91-106] document data including metadata elements and Gupta provides the specific structural aspects of metadata input into a similar contract risk assessment ML model.);
Within the combination, Broudou teaches applying the first machine learning model and the second machine learning model to the metadata (Paragraphs [109-112 and 123-128]; Broudou teaches the metadata analysis including compliance with respect to the input data and providing the report based on the compliance check. Further, within the combination, Wodetzki teaches [90-104 and 121-123] metadata analysis by scanning a document within the ML/AI rule set and providing the risk assessment to the user [149-150]. Based on the combination, Gupta provides metadata contract clause analysis and Wodetzki and Broudou teach applying input data to the first and second models.);
The combination teaches a contract clause risk assessment system that provides ML/AI rules based on contract terms and other input values, however, Wodetzki does not teach the specific input in terms of non-visible document code metadata.
Gupta teaches a similar contract risk assessment system that specifically provides metadata based on non-visible elements.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to include in the contract clause risk assessment system that provides ML/AI rules based on contract terms and other input values of the combination the ability to include a similar contract risk assessment system that specifically provides metadata based on non-visible elements as taught by Gupta since the claimed invention is merely a combination of prior art elements and in the combination each element would have performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination were predictable.
The combination teaches the above-enclosed limitations with respect to a contract clause and risk assessment system based on input data and outputting based on the analysis, however, the combination does not specifically teach generating a document summary including metadata information;
Hunn teaches generate, by the document management system, 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 (Paragraphs [122-124]; Hunn teaches a similar contract system that specifically provides a visualization display to provide contract details and other information including metadata. Within the combination, Gupta teaches that the metadata is described as attributes of the clause includes service name, frequency, termination, etc {interpreted as non-visible document code}. Further, Wodetzki discloses [Fig 4A and paragraphs 91-106] document data including metadata elements and Gupta provides the specific structural aspects of metadata input into a similar contract risk assessment ML model. The combination is that Hunn teaches the specific structure of a visualization and display that presents metadata information.
Examiner notes that the claim limitation “wherein the document summary further comprises metadata review section to present the non-visible code” is describing non-functional descriptive material. The document summary is providing an output function in terms of displaying information, however, what is displayed merely describes non-functional information. The document summary is providing a message or meaning to a human reader independent of the supporting product. As such, what the document summary consists of is merely non-functional descriptive material. Refer to MPEP 2111.05.).
The combination teaches a contract clause risk assessment system including metadata within the contract that provides contract display aspects, however, the combination does not teach the specific visualization providing the metadata information.
Hunn teaches a similar contract system that specifically provides a visualization including metadata.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to include in the contract clause risk assessment system including metadata within the contract that provides contract display aspects of the combination the ability to include a similar contract system that specifically provides a visualization including metadata as taught by Hunn since the claimed invention is merely a combination of prior art elements and in the combination each element would have performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination were predictable.
Regarding claim 2, the combination teaches the above-enclosed limitations of the method of claim 1,
Wodetzki further discloses 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 (Fig 7 and paragraphs [122-12-127 and 142-149]; Wodetzki discloses past documents within the training corpus that provides classification for the documents clauses and types within the ML/AI models.).
Regarding claim 3, the combination teaches the above-enclosed limitations of the method of claim 1,
Broudou further teaches 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 (Paragraphs [181 and 187]; Broudou teaches that the jurisdiction rules include updates within the legislation and other elements for compliance including an API to monitor and receive the information.).
Wodetzki discloses a contract clause risk assessment system that provides ML/AI rules based on contract terms including country rules, however, Wodetzki does not teach the specific jurisdiction input and ML rules.
Broudou teaches a similar contract risk assessment system that specifically provides jurisdiction inputs and ML rules for jurisdiction that are including updates within an API to monitor.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to include in the contract clause risk assessment system that provides ML/AI rules based on contract terms including country rules of Wodetzki the ability to include a similar contract risk assessment system that specifically provides jurisdiction inputs and ML rules for jurisdiction that are including updates within an API to monitor as taught by Broudou since the claimed invention is merely a combination of prior art elements and in the combination each element would have performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination were predictable.
Regarding claim 6, the combination teaches the above-enclosed limitations of the method of claim 1,
Broudou further teaches 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 (Paragraphs [120-121]; Broudou teaches learning documents based on jurisdictional legislation and rules to train (further described paragraphs [179-191]).).
Wodetzki discloses a contract clause risk assessment system that provides ML/AI rules based on contract terms including country rules, however, Wodetzki does not teach the specific jurisdiction input and ML rules.
Broudou teaches a similar contract risk assessment system that specifically provides jurisdiction inputs and ML rules that are for training the model.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to include in the contract clause risk assessment system that provides ML/AI rules based on contract terms including country rules of Wodetzki the ability to include a similar contract risk assessment system that specifically provides jurisdiction inputs and ML rules for jurisdiction that are for training the model as taught by Broudou since the claimed invention is merely a combination of prior art elements and in the combination each element would have performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination were predictable.
Regarding claim 7, the combination teaches the above-enclosed limitations of the method of claim 1,
Wodetzki further teaches 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 (Fig 7 and paragraphs [119-122 and 127-131]; Wodetzki discloses training rules including customer specific rule sets (interpreted as organization) to train the ML/AI models.).
Regarding claim 8, Wodetzki discloses 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 (Paragraphs [160-169]; Wodetzki discloses the computer and system elements to implement the contract clause analysis.):
receive, by a document management system, a document for analysis, the document being of a category of document type (Paragraphs [66-72 and 134-139]; Wodetzki discloses a contract document that is provided for analysis.);
apply, 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 (Paragraphs [119-122 and 134-139]; Wodetzki discloses a contractual risk based on machine learning rules for the contract clauses. An alert or other notification is sent based on the risk score and clause [148-150].);
Wodetzki discloses a contract risk system that provides ML algorithms and rule sets for contract clauses, however, Wodetzki does not specifically teach a user (potential signer) identifying jurisdictions and training
Broudou teaches receive, by the document management system, information from the potential signer identifying one or more jurisdictions associated with the document; apply, 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 (Paragraphs [89-95, 112, and 128]; Broudou teaches a similar contract report and risk system that specifically teaches jurisdiction input and providing a machine learning model to determine contract clause risk. Further, Wodetzki teaches [134-138] country risk within the contract risk assessment. The second model trained is taught within Broudou providing the jurisdiction rules and Wodetzki provides [122] the structure of the ML/AI models that are utilized to reevaluate the contract clauses and terms based on three rules (universal, industry, and customer specific). Broudou provides the specific jurisdiction rules as the second ML rule within the rules of Wodetzki.);
transmit, by the document management system, to a device of the potential signer, the document summary for display at an interface that enables review of the risky clauses by the potential signer (Fig 7A and paragraphs [202]; Broudou teaches a report based on the contract risk. Further, within the combination, Wodetzki discloses [149-150] an assessment to the user that displays the risk and contract clauses. Wodetzki further discloses [139 and 150] an alert based on the analyzed information, input, and ML/AI output.).
Wodetzki discloses a contract clause risk assessment system that provides ML/AI rules based on contract terms including country rules, however, Wodetzki does not teach the specific jurisdiction input and ML rules.
Broudou teaches a similar contract risk assessment system that specifically provides jurisdiction inputs and ML rules for jurisdiction that are trained.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to include in the contract clause risk assessment system that provides ML/AI rules based on contract terms including country rules of Wodetzki the ability to include a similar contract risk assessment system that specifically provides jurisdiction inputs and ML rules for jurisdiction that are trained as taught by Broudou since the claimed invention is merely a combination of prior art elements and in the combination each element would have performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination were predictable.
The combination teaches the above-enclosed limitations with respect to a contract clause and risk assessment system based on input data and outputting based on the analysis, however, the combination does not specifically teach receiving metadata comprising non-visible document codes;
Gupta teaches receive metadata associated with the document for analysis, the metadata comprising non-visible document code (Paragraphs [22 and 29-31]; Gupta teaches a similar contract risk analysis system that specifically provides inputs into an ML based on metadata associated with the document. The metadata is described as attributes of the clause includes service name, frequency, termination, etc that is interpreted as non-visible document code. Within the combination, Wodetzki discloses [Fig 4A and paragraphs 91-106] document data including metadata elements and Gupta provides the specific structural aspects of metadata input into a similar contract risk assessment ML model.);
Within the combination, Broudou teaches apply the first machine learning model and the second machine learning model to the metadata (Paragraphs [109-112 and 123-128]; Broudou teaches the metadata analysis including compliance with respect to the input data and providing the report based on the compliance check. Further, within the combination, Wodetzki teaches [90-104 and 121-123] metadata analysis by scanning a document within the ML/AI rule set and providing the risk assessment to the user [149-150]. Based on the combination, Gupta provides metadata contract clause analysis and Wodetzki and Broudou teach applying input data to the first and second models.);
The combination teaches a contract clause risk assessment system that provides ML/AI rules based on contract terms and other input values, however, Wodetzki does not teach the specific input in terms of non-visible document code metadata.
Gupta teaches a similar contract risk assessment system that specifically provides metadata based on non-visible elements.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to include in the contract clause risk assessment system that provides ML/AI rules based on contract terms and other input values of the combination the ability to include a similar contract risk assessment system that specifically provides metadata based on non-visible elements as taught by Gupta since the claimed invention is merely a combination of prior art elements and in the combination each element would have performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination were predictable.
The combination teaches the above-enclosed limitations with respect to a contract clause and risk assessment system based on input data and outputting based on the analysis, however, the combination does not specifically teach generating a document summary including metadata information;
Hunn teaches generate, by the document management system, 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 (Paragraphs [122-124]; Hunn teaches a similar contract system that specifically provides a visualization display to provide contract details and other information including metadata. Within the combination, Gupta teaches that the metadata is described as attributes of the clause includes service name, frequency, termination, etc {interpreted as non-visible document code}. Further, Wodetzki discloses [Fig 4A and paragraphs 91-106] document data including metadata elements and Gupta provides the specific structural aspects of metadata input into a similar contract risk assessment ML model. The combination is that Hunn teaches the specific structure of a visualization and display that presents metadata information.
Examiner notes that the claim limitation “wherein the document summary further comprises metadata review section to present the non-visible code” is describing non-functional descriptive material. The document summary is providing an output function in terms of displaying information, however, what is displayed merely describes non-functional information. The document summary is providing a message or meaning to a human reader independent of the supporting product. As such, what the document summary consists of is merely non-functional descriptive material. Refer to MPEP 2111.05.).
The combination teaches a contract clause risk assessment system including metadata within the contract that provides contract display aspects, however, the combination does not teach the specific visualization providing the metadata information.
Hunn teaches a similar contract system that specifically provides a visualization including metadata.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to include in the contract clause risk assessment system including metadata within the contract that provides contract display aspects of the combination the ability to include a similar contract system that specifically provides a visualization including metadata as taught by Hunn since the claimed invention is merely a combination of prior art elements and in the combination each element would have performed the same function as it did separately and one of ordinary skill in the art would have recognized the results of the combination were predictable.
Regarding claim 9, the combination teaches the above-enclosed limitations of the non-transitory computer-readable storage medium of claim 8,
Wodetzki further disc