DETAILED ACTION
This Non Final Office Action is in response to the Request for Continued Examination, arguments, and amendments filed September 30, 2025.
This Non-Final Office Action is being sent to restart the time to reply based on the filed Suspension under 37 CFR 1.17. The suspension was filed September 30, 2025 for a period of 3 months and this Non-Final Office Action is being sent in response to the suspension period being completed.
Claims 1, 3, 7, 14, and 19 have been amended.
Claims 1-8, 10-12, and 14-20 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 September 30, 2025 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 1-8, 10-12, and 14-20 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 transformed into a practical application.
In terms of step 1, claims 1-8, 10-12, and 14-20 are directed towards one of the four categories of statutory subject matter.
In terms of step 2(a)(1), In Independent claims 1, 14, and 19 are directed towards (as represented by claim 1), “A method, performed by a computing device, for enabling real-time authenticated collaboration between remote parties, the method comprising: receiving a draft digital contract (DDC) from a remote party; dividing the DDC into a plurality of draft clauses; selecting a reference digital contract (RDC) to which to compare the DDC; for each draft clause: determining a reference clause of (the selected RDC) that is most similar to that draft clause; calculating a risk score associated with adopting that draft clause in place of that reference clause; sending to a user for display: that draft clause; that reference clause; and that calculated risk score; and receiving from a user whether to accept, reject, or modify that draft clause; in response to detecting that at least one draft clause of the plurality of draft clauses has been accepted by the user, recording acceptance of that draft clause”. The claims are describing a legal process in terms of contract risk assessments based on reference (interpreted as template/boilerplate) legal clauses and providing the accepted draft clause based on user selection. Claims describing a legal interaction fall into the abstract idea grouping of certain method of organizing human activity.
The claims further describe aspects of receiving information (legal draft clauses), high level analyzing the information (providing a risk score), and displaying the results (displaying the score and providing a user response to accept and store). A person is capable to mentally opine and judge the risk score of a draft legal clause based on reference clauses to determine to accept/reject/modify the clause and provide aspects completing the draft clause based on acceptance. As such, the claims are also directed towards an abstract idea under the mental process grouping.
Step 2(a)(II) considers the additional elements in terms of being transformative into a practical application. The additional elements of the independent claims are, “a system comprising: a first client computing device; a second client computing device; a computer network; and a server computing device communicatively coupled to the first client computing device and the second client computing device via the computer network, the server computing device being configured to {claim 14}, a computer program product comprising a non-transitory computer-readable storage medium storing instructions for enabling real-time authenticated collaboration between remote parties, which, when executed by a computing device, causes the computing device to {claim 19}, receiving a draft digital contract (DDC) from a remote party operating a remote device, the remote device being remote from the computing device; sending to a user device for display on a display device of the user device; receiving an instruction from a user operating the user device whether to accept, reject, or modify that draft clause, the user device being remote from the computing device and the remote device, in response to detecting that at least one draft clause of the plurality of draft clauses has been accepted by the user, recording acceptance of that draft clause in a blockchain; in response to a requesting device requesting an acceptance history of the DDC from the computing device, querying the blockchain service for acceptances of draft clauses of the DDC, the requesting device being remote from the computing device and the remote device; receiving cryptographically authenticated timestamped acceptance records of draft clauses of the DDC from the blockchain service; and causing details of the received cryptographically authenticated timestamped acceptance records of draft clauses of the DDC to be displayed to the requesting device”. The computer elements (computing device, storage medium, display device, etc) are described in the originally filed specification pages 3-5 (II. System Architecture) and 17-20 (X. Graphical User Interface). The computer elements are merely describing generic technological elements to implement the abstract idea. The further additional elements in terms of the blockchain are described in the originally filed specification pgs. 8-10 (VI. Blockchain-Based Activity Logs). The blockchain is not describing a technical improvement towards the blockchain technology, but rather utilizing blockchain as a generic computer element to store information. The cryptographically authenticated timestamps are described alongside the blockchain structure (identified and considered above) in pages 8-10 (VI Blockchain-Based Activity Logs). The cryptographically authenticated timestamps are merely describing generic technology in terms of blockchain elements. The cryptographically authenticated timestamps are not describing an improved technology, but rather utilizing generic blockchain technology to implement the abstract idea. The additional elements are not describing a technical improvement, but rather using generic technology to implement the abstract idea. As such, the claim limitations 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 in terms of being significantly more than the identified abstract ideas. The additional elements of the independent claims are, “a system comprising: a first client computing device; a second client computing device; a computer network; and a server computing device communicatively coupled to the first client computing device and the second client computing device via the computer network, the server computing device being configured to {claim 14}, a computer program product comprising a non-transitory computer-readable storage medium storing instructions for enabling real-time authenticated collaboration between remote parties, which, when executed by a computing device, causes the computing device to {claim 19}, receiving a draft digital contract (DDC) from a remote party operating a remote device, the remote device being remote from the computing device; sending to a user device for display on a display device of the user device; receiving an instruction from a user operating the user device whether to accept, reject, or modify that draft clause, the user device being remote from the computing device and the remote device, in response to detecting that at least one draft clause of the plurality of draft clauses has been accepted by the user, recording acceptance of that draft clause in a blockchain; in response to a requesting device requesting an acceptance history of the DDC from the computing device, querying the blockchain service for acceptances of draft clauses of the DDC, the requesting device being remote from the computing device and the remote device; receiving cryptographically authenticated timestamped acceptance records of draft clauses of the DDC from the blockchain service; and causing details of the received cryptographically authenticated timestamped acceptance records of draft clauses of the DDC to be displayed to the requesting device”. The computer elements (computing device, storage medium, display device, etc) are described in the originally filed specification pages 3-5 (II. System Architecture) and 17-20 (X. Graphical User Interface). The computer elements are merely describing generic technological elements to implement the abstract idea. The further additional elements in terms of the blockchain are described in the originally filed specification pgs. 8-10 (VI. Blockchain-Based Activity Logs). The blockchain is not describing a technical improvement towards the blockchain technology, but rather utilizing blockchain as a generic computer element to store information. The cryptographically authenticated timestamps are described alongside the blockchain structure (identified and considered above) in pages 8-10 (VI Blockchain-Based Activity Logs). The cryptographically authenticated timestamps are merely describing generic technology in terms of blockchain elements. The cryptographically authenticated timestamps are not describing an improved technology, but rather utilizing generic blockchain technology to implement the abstract idea. The additional elements are not describing a technical improvement, but rather using generic technology to implement the abstract idea. As such, the claim limitations are not directed towards additional elements that are significantly more than the identified abstract ideas. Refer to MPEP 2106.05(f).
Dependent claims 2, 7, 8, 10, 11, 15, 16, 17, and 18 are further describing the abstract idea and based on the additional elements considered above. The claims are directed towards, “wherein the method further comprises, in response to at least one other draft clause of the plurality of draft clauses being rejected or modified by the user, notifying the remote party that the user has rejected or modified the at least one other draft clause”, “wherein selecting the RDC to which to compare the DDC includes selecting the RDC from a plurality of template digital contracts stored on the computing device”, “wherein selecting the RDC to which to compare the DDC includes selecting a prior draft of the DDC as the RDC”, “wherein the method further comprises: in response to all clauses being accepted by the user, prompting the user to sign the DDC; and in response to detecting that the user has signed the DDC, recording complete acceptance of the DDC by the user in the blockchain”, “wherein the method further comprises: in response to the user signing the DDC, prompting the remote party to sign the DDC; and in response to detecting that the remote party has signed the DDC, recording ratification of the DDC in the blockchain”, “wherein the server computing device is further configured to, in response to at least one other draft clause of the plurality of draft clauses being rejected or modified by the user, notify the remote party at the second client computing device that the user has rejected or modified the at least one other draft clause”, “wherein: the server computing device is further configured to, in response to the at least one other draft clause being rejected or modified by the user: determine a new reference clause of the DDC that is most similar to the at least one other draft clause; and calculate a new risk score associated with adopting the at least one other draft clause in place of the new reference clause; notifying the remote party at the second client computing device that the user has rejected or modified the at least one other draft clause includes causing to be displayed, on another display device of the second client computing device: the at least one other draft clause; the new reference clause; and the new risk score; and the server computing device is further configured to, in response to detecting that the at least one other draft clause of the plurality of draft clauses has been accepted by the remote party, recording acceptance of the at least one other draft clause in the blockchain”, “wherein the server computing device is further configured to: in response to detecting that the at least one other draft clause of the plurality of draft clauses has been accepted by the remote party, prompt the remote party to sign the DDC as modified by the at least one other draft clause; and in response to detecting that the remote party has signed the DDC as modified by the at least one other draft clause, record complete acceptance of the DDC, as modified by the at least one other draft clause, by the remote party in the blockchain”, and “wherein the server computing device is further configured to: in response to the remote party signing the DDC as modified by the at least one other draft clause, prompting the user to sign the DDC as modified by the at least one other draft clause; and 25 in response to detecting that the user has signed the DDC, as modified by the at least one other draft clause, recording ratification of the DDC, as modified by the at least one other draft clause, in the blockchain”. The claims are further describing the legal process in terms of signing, ratifying, modifying, and accepting/denying the clauses. The claims provide aspects of the legal interaction from draft, modification, denial, acceptance, and signing/ratifying the legal clauses. Further consideration is given to claim 16 that merely describes the process of legal interactions based on the reference clause and risk score based on new reference clauses. The dependent claims are further describing the legal interaction in terms of the processing of draft clauses to ratification that fall under the abstract idea grouping of certain method of organizing human activity. The additional elements of the claims are not significantly more or transformative into a practical application. Refer to MPEP 2106.05(f).
Dependent claims 3 is further describing additional elements beyond those identified above. The dependent claim is directed towards, “wherein dividing the DDC into a plurality of draft clauses includes parsing the DDC into a plurality of nested data structures representing sections and clauses”. The claim is describing the nested data structure for the section and clauses. The nested structures are described in pages 11-16 (VII Document Hierarchy and IX Environment and Method). The nested clauses and sections are merely describing data structures in terms of generic technology to provide document objects parsed down. There is no specific technical improvement towards the nested structures, but rather using generic technology to implement the abstract idea. Refer to MPEP 2106.05(f).
Dependent claims 4-6 and 20 further describe additional elements beyond those identified above. The dependent claims are directed towards, “wherein determining the reference clause of the reference digital contract (RDC) that is most similar to that draft clause includes: converting that draft clause into a vector; calculating a cosine similarity for each pairing of the vector created for that draft clause with a respective vector of each reference clause of a plurality of reference clauses of the RDC; and selecting as the reference clause, the reference clause of the plurality of reference clauses of the RDC whose vector has a lowest calculated cosine similarity with respect to that draft clause”, “wherein calculating the risk score associated with adopting that draft clause in place of that reference clause is based on a combination of the cosine similarity calculated between that draft clause and that reference clause as well as on a semantic meaning or semantic context of that draft clause within the DDC in comparison to a semantic meaning or semantic context of that reference clause within the RDC”, “wherein the method further comprises, for each draft clause: selecting as additional reference clauses for that draft clause, a predetermined number of other reference clauses of the plurality of reference clauses of the RDC whose respective vectors have next lowest calculated cosine similarities with respect to that draft clause; calculating additional risk scores respectively associated with adopting that draft clause in place of the additional reference clauses; and causing to be displayed, on the display device, the calculated additional risk scores”, and “herein determining the reference clause of the reference digital contract (RDC) that is most similar to that draft clause includes: converting that draft clause into a vector; calculating a cosine similarity for each pairing of the vector created for that draft clause with a respective vector of each reference clause of a plurality of reference clauses of the RDC; and 26 selecting as the reference clause, the reference clause of the plurality of reference clauses of the RDC whose vector has a lowest calculated cosine similarity with respect to that draft clause”. The dependent claims further describe aspects in terms of cosine similarity with respect to reference clause, draft clause, and semantic context.
Dependent claims 12 is further describing additional elements beyond those identified above. The dependent claim is directed towards, “wherein detecting that at least one draft clause of the plurality of draft clauses has been accepted by the user is performed by a listening service executing on the computing device, the listening service being configured to communicate with the blockchain service”. The listening service is described alongside the blockchain structure (identified and considered above) in pages 8-10 (VI Blockchain-Based Activity Logs). The listening service is merely describing generic technology in terms of blockchain updates or other message brokers. The listening device is not describing an improved technology, but rather utilizing generic blockchain technology to implement the abstract idea. The additional elements are not transformative into a practical application or significantly more than the identified abstract idea. Refer to MPEP 2106.05(f).
The claimed invention is describing an abstract idea without additional elements that are significantly more or transformative into a practical application. Therefore, claims 1-8, 10-12, and 14-20 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) 1-8, 10-12, and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wodetzki et al [2018/0268506], hereafter Wodetzki, in view of Hunn [2018/0005186], further in view of Zhou et al [2021/0366065], hereafter Zhou.
Regarding claim 1, Wodetzki discloses a method, performed by a computing device, for enabling real-time authenticated collaboration between remote parties, the method comprising: receiving a draft digital contract (DDC) from a remote party operating a remote device, the remote device being remote from the computing device; dividing the DDC into a plurality of draft clauses (Paragraphs [132-134 and 140-143]; Wodetzki discloses receiving a contract document and providing AI/ML classification based on the document text clauses. Wodetzki provides that the analysis is determined based on a contract clause that is compared to clause types based on the training corpus [94-101]. Further, Wodetzki discloses [150] remote devices for the system computer elements.);
calculating a risk score associated with adopting that draft clause in place of that reference clause; sending to a user device for display on a display device of the user device: that draft clause; that reference clause; and that calculated risk score (Fig 20A-20D and paragraphs [134-140 and 147-150]; Wodetzki discloses determining and displaying a risk score, the clause, and reference elements.); and
Wodetzki discloses a contract assessment system that provides risk scores based on classification using boilerplate clauses to display alerts and other notifications based on the clause, however, Wodetzki does not specifically teach receiving instruction to accept, deny, or modify the clause based on the score;
Hunn teaches receiving an instruction from a user operating the user device whether to accept, reject, or modify that draft clause, the user device being remote from the computing device and the remote device (Fig 19 and paragraphs [199-200]; Hunn teaches a similar contract system that specifically provides user instructions to accept, modify, or deny a commit (interpreted as a clause such as the contract objects and amendments within Wodetzki). Wodetzki teaches the contract objects/clauses that are scored and presented and Hunn teaches the specific aspect of receiving user input to accept, deny, or modify the entered contract amendments. Further, Wodetzki teaches the remote user interface to provide contract elements [141-143].);
in response to detecting that at least one draft clause of the plurality of draft clauses has been accepted by the user, recording acceptance of that draft clause in a blockchain (Fig 2 and paragraphs [218-221 and 244-247]; Hunn teaches the contract system that updates a master contract within a blockchain in terms of accepting the proposed amendments. Within the combination, Wodetzki teaches [150-157] a blockchain timeline for contract events and trigger transactions which, within the combination, is Hunn acceptance or other inputs regarding contract amendments.);
in response to a requesting device requesting an acceptance history of the DDC from the computing device, querying the blockchain service for acceptances of draft clauses of the DDC, the requesting device being remote from the computing device and the remote device; receiving cryptographically authenticated timestamped acceptance records of draft clauses of the DDC from the blockchain service; and causing details of the received cryptographically authenticated timestamped acceptance records of draft clauses of the DDC to be displayed to the requesting device (Paragraphs [173, 207-215, and 226-227]; Hunn teaches updating the blockchain with UUID identifiers and timestamps to store the updated objects within the blockchain. Within the combination, Wodetzki teaches [150-153] cryptographic timestamps based on blocks within the blockchain to trigger the distributed ledger transactions and smart contract.).
Wodetzki discloses a contract clause analysis system based on boilerplate clause and risk assessments, however, Wodetzki does not specifically teach receiving user instruction in terms of accept, reject, or modify the draft clause.
Hunn teaches a similar contract system that specifically provides user input in terms of user submitted clause amendments for contracts that can be accepted, modified, or rejected.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the contract clause analysis system based on boilerplate clause and risk assessments of Wodetzki includes a similar contract system that specifically provides user input in terms of user submitted clause amendments for contracts that can be accepted, modified, or rejected 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 as predictable.
The combination teaches the above-enclosed limitations of the contract analysis system, however, the combination does not specifically teach selecting the clause for comparison;
Zhou teaches selecting a reference digital contract (RDC) to which to compare the DDC; for each draft clause: determining a reference clause of a reference digital contract (the selected RDC) that is most similar to that draft clause (Paragraphs [66-74]; Zhou teaches similarity scores/thresholds for contract clauses and a selection of matching features based on the set of similar contracts to generate contract terms and analysis. Within the combination, Wodetzki teaches [142-148] that the ML/AI system provides selected clauses that are analyzed for the input contract clause/analysis. The combination is that Zhou provides similarity scores and selections of the reference contract in a specific aspect that is utilized in the contract analysis within Wodetzki.);
The combination teaches the above-enclosed limitations of the contract analysis and similar document system using ML/AI analysis of contract terms to a boilerplate/normalized document, however, the combination does not specifically teach document selection and similarity scoring for the contract analysis.
Zhou teaches a similar contract system that specifically provides similar contract documents based on similarity scores and analysis.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the contract analysis and similar document system using ML/AI analysis of contract terms to a boilerplate/normalized document of the combination to include a similar contract system that specifically provides similar contract documents based on similarity scores and analysis as taught by Zhou 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 as predictable.
Regarding claim 2, the combination teaches the method of claim 1
Hunn further teaches wherein the method further comprises, in response to at least one other draft clause of the plurality of draft clauses being rejected or modified by the user, notifying the remote party that the user has rejected or modified the at least one other draft clause (Paragraphs [200-201, 209-211, and 217-219]; Hunn teaches notifications based on clause modifications and state updates within the blockchain, specifically for clause modifications and object changes.).
Regarding claim 3, the combination teaches the above-enclosed limitations of the method of claim 1
Hunn teaches wherein dividing the DDC into a plurality of draft clauses includes parsing the DDC into a plurality of nested data structures representing sections and clauses (Fig 13, 14 and paragraphs [66-68 and 119-123]; Hunn teaches providing the contract structure based on section and clause objects.).
Regarding claim 4, the combination teaches the above-enclosed limitations of the method of claim 1, however, the combination does not specifically teach vector and cosine similarity in terms of the reference clause comparison to the draft clause;
Zhou teaches wherein determining the reference clause of the reference digital contract (RDC) that is most similar to that draft clause includes: converting that draft clause into a vector; calculating a cosine similarity for each pairing of the vector created for that draft clause with a respective vector of each reference clause of a plurality of reference clauses of the RDC; and selecting as the reference clause, the reference clause of the plurality of reference clauses of the RDC whose vector has a lowest calculated cosine similarity with respect to that draft clause (Paragraphs [53-56 and 59-62]; Zhou teaches a similar contract analysis system that provides similar contract reference documents that specifically provides cosine similarity analysis based on vector document tagging. Within the combination, Wodetzki teaches natural language processing to determine similar documents within a trained document corpus and Zhou teaches the specific analysis being cosine similarity in terms of similar contract elements. In terms of the lowest similarity, Zhou specifically teaches [96-102] that the score can be based on the lowest error within the ML model for the similar contract documents. Within the combination, Wodetzki teaches the aspects of the per clause elements of the risk score {Fig 19 and paragraphs [142-150]}.).
The combination teaches the above-enclosed limitations of the contract analysis and similar document system using natural language processing, however, the combination does not specifically teach cosine similarity and vectorized documents.
Zhou teaches a similar contract system that specifically provides similar contract documents based on vector elements and cosine similarity.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the contract analysis and similar document system using natural language processing of the combination to include a similar contract system that specifically provides similar contract documents based on vector elements and cosine similarity as taught by Zhou 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 as predictable.
Regarding claim 5, the combination teaches the above-enclosed limitations of the method of claim 4;
Zhou teaches wherein calculating the risk score associated with adopting that draft clause in place of that reference clause is based on a combination of the cosine similarity calculated between that draft clause and that reference clause as well as on a semantic meaning or semantic context of that draft clause within the DDC in comparison to a semantic meaning or semantic context of that reference clause within the RDC (Paragraphs [173-179]; Zhou teaches proposing clauses and terms based on the cosine similarity. With respect to the meaning and context, Wodetzki teaches the classification model based on the content and tagging classification for the contract elements and Zhou teaches the semantic analysis based on the cosine similarity.).
The combination teaches the above-enclosed limitations of the contract analysis and similar document system using natural language processing, however, the combination does not specifically teach cosine similarity and vectorized documents.
Zhou teaches a similar contract system that specifically provides similar contract documents based on vector elements and cosine similarity.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the contract analysis and similar document system using natural language processing of the combination to include a similar contract system that specifically provides similar contract documents based on vector elements and cosine similarity as taught by Zhou 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 as predictable.
Regarding claim 6, the combination teaches the above-enclosed limitations of the method of claim 4
Zhou further teaches wherein the method further comprises, for each draft clause: selecting as additional reference clauses for that draft clause, a predetermined number of other reference clauses of the plurality of reference clauses of the RDC whose respective vectors have next lowest calculated cosine similarities with respect to that draft clause; calculating additional risk scores respectively associated with adopting that draft clause in place of the additional reference clauses; and causing to be displayed, on the display device, the calculated additional risk scores (Paragraphs [173-179]; Zhou teaches proposing reference clauses to adopt within the contract document based on the similarity score. Within the combination, Wodetzki teaches the context, content, and classification for the similar document corpus and Zhou teaches the specific proposed based on similarity scores. With respect to the risk scores, Wodetzki teaches the risk scores for the classification model for the amended document elements and Zhou provides similarity scores for the clause elements.).
The combination teaches the above-enclosed limitations of the contract analysis and similar document system using natural language processing, however, the combination does not specifically teach cosine similarity and vectorized documents.
Zhou teaches a similar contract system that specifically provides similar contract documents based on vector elements and cosine similarity.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the contract analysis and similar document system using natural language processing of the combination to include a similar contract system that specifically provides similar contract documents based on vector elements and cosine similarity as taught by Zhou 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 as predictable.
Regarding claim 7, the combination teaches the above-enclosed limitations of the method of claim 1
Wodetzki further teaches wherein selecting the RDC to which to compare the DDC includes selecting the RDC from a plurality of template digital contracts stored on the computing device (Fig 8, 10, 11, and paragraphs [119-123]; Wodetzki teaches an AI platform interface to provide contract rules and the ML/AI analysis based on the contract corpus of documents. Within the combination, Zhou teaches the selection of the comparison clause and Wodetzki teaches providing the corpus/templates within the contract analysis system.).
Regarding claim 8, the combination teaches the above-enclosed limitations of the method of claim 1
Hunn teaches wherein selecting the RDC to which to compare the DDC includes selecting a prior draft of the DDC as the RDC (Paragraphs [212-215]; Hunn teaches reverting the status updates to previous draft states of the contract amendments. Within the combination, Zhou teaches the selection of the contract clause and Hunn teaches the prior draft as a reference document for analysis.).
Regarding claim 10, the combination teaches the above-enclosed limitations of the method of claim 1
Hunn further teaches wherein the method further comprises: in response to all clauses being accepted by the user, prompting the user to sign the DDC; and in response to detecting that the user has signed the DDC, recording complete acceptance of the DDC by the user in the blockchain (Paragraphs [209-215 and 218-220]; Hunn teaches the signature and storage aspect based on completed contract document to be stored in the blockchain. Further, Wodetzki teaches [113-115 and 153-158] the blockchain verification and storage for the valid contract on a blockchain.).
Regarding claim 11, the combination teaches the above-enclosed limitations of the method of claim 10
Hunn teaches wherein the method further comprises: in response to the user signing the DDC, prompting the remote party to sign the DDC; and in response to detecting that the remote party has signed the DDC, recording ratification of the DDC in the blockchain (Paragraphs [209-215 and 218-220]; Hunn teaches the signature and storage aspect based on completed contract document to be stored in the blockchain. Further, Wodetzki teaches [113-115 and 153-158] the blockchain verification and storage for the valid contract on a blockchain.).
Regarding claim 12, the combination teaches the above-enclosed limitations of the method of claim 1;
Hunn further teaches wherein detecting that at least one draft clause of the plurality of draft clauses has been accepted by the user is performed by a listening service executing on the computing device, the listening service being configured to communicate with the blockchain service (Paragraphs [259]; Hunn teaches a message queue (interpreted as listening service) to provide state updates and objects to the event log.).
Regarding claim 14, Wodetzki discloses a system for enabling real-time authenticated collaboration between remote parties, the system comprising: a first client computing device; a second client computing device remote from the first client computing device; a computer network; and a server computing device communicatively coupled to the first client computing device and the second client computing device via the computer network, the server computing device being remote from both the first client computing device and the second client computing device, the server computing device being configured to (Paragraphs [139-141, 149-150, and 162-174]; Wodetzki teaches the structural aspects of the computer system to implement the contract clause analysis. Wodetzki further teaches the notifications and risk score being displayed to a remote user device including the clause and other contract variable information.):
receive a draft digital contract (DDC) from a remote party at the second client computing device; divide the DDC into a plurality of draft clauses (Paragraphs [132-134 and 140-143]; Wodetzki discloses receiving a contract document and providing AI/ML classification based on the document text clauses. Wodetzki provides that the analysis is determined based on a contract clause that is compared to clause types based on the training corpus [94-101].);
calculate a risk score associated with adopting that draft clause in place of that reference clause; send to the first client computing device for display on a display device of the first client computing device: that draft clause; that reference clause; and that calculated risk score (Fig 20A-20D and paragraphs [134-140 and 147-149]; Wodetzki discloses determining and displaying a risk score, the clause, and reference elements.); and
Wodetzki discloses a contract assessment system that provides risk scores based on classification using boilerplate clauses to display alerts and other notifications based on the clause, however, Wodetzki does not specifically teach receiving instruction to accept, deny, or modify the clause based on the score;
Hunn teaches receive an instruction from a user at the first client computing device whether to accept, reject, or modify that draft clause (Fig 19 and paragraphs [199-200]; Hunn teaches a similar contract system that specifically provides user instructions to accept, modify, or deny a commit (interpreted as a clause such as the contract objects and amendments within Wodetzki). Wodetzki teaches the contract objects/clauses that are scored and presented and Hunn teaches the specific aspect of receiving user input to accept, deny, or modify the entered contract amendments.);
in response to detecting that at least one draft clause of the plurality of draft clauses has been accepted by the user, record acceptance of that draft clause in a blockchain (Fig 2 and paragraphs [218-221 and 244-247]; Hunn teaches the contract system that updates a master contract within a blockchain in terms of accepting the proposed amendments. Within the combination, Wodetzki teaches [151-157] a blockchain timeline for contract events and trigger transactions which, within the combination, is Hunn acceptance or other inputs regarding contract amendments.);
in response to a requesting device requesting an acceptance history of the DDC from the server computing device, query the blockchain service for acceptances of draft clauses of the DDC, the requesting device being remote from the server computing device and the second client computing device; receive cryptographically authenticated timestamped acceptance records of draft clauses of the DDC from the blockchain service; and cause details of the received cryptographically authenticated timestamped acceptance records of draft clauses of the DDC to be displayed to the requesting device (Paragraphs [173, 207-215, and 226-227]; Hunn teaches updating the blockchain with UUID identifiers and timestamps to store the updated objects within the blockchain. Within the combination, Wodetzki teaches [150-153] cryptographic timestamps based on blocks within the blockchain to trigger the distributed ledger transactions and smart contract.).
Wodetzki discloses a contract clause analysis system based on boilerplate clause and risk assessments, however, Wodetzki does not specifically teach receiving user instruction in terms of accept, reject, or modify the draft clause.
Hunn teaches a similar contract system that specifically provides user input in terms of user submitted clause amendments for contracts that can be accepted, modified, or rejected.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the contract clause analysis system based on boilerplate clause and risk assessments of Wodetzki includes a similar contract system that specifically provides user input in terms of user submitted clause amendments for contracts that can be accepted, modified, or rejected 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 as predictable.
The combination teaches the above-enclosed limitations of the contract analysis system, however, the combination does not specifically teach selecting the clause for comparison;
Zhou teaches select a reference digital contract (RDC) to which to compare the DDC; for each draft clause: determine a reference clause of the selected RDC that is most similar to that draft clause (Paragraphs [66-74]; Zhou teaches similarity scores/thresholds for contract clauses and a selection of matching features based on the set of similar contracts to generate contract terms and analysis. Within the combination, Wodetzki teaches [142-148] that the ML/AI system provides selected clauses that are analyzed for the input contract clause/analysis. The combination is that Zhou provides similarity scores and selections of the reference contract in a specific aspect that is utilized in the contract analysis within Wodetzki.);
The combination teaches the above-enclosed limitations of the contract analysis and similar document system using ML/AI analysis of contract terms to a boilerplate/normalized document, however, the combination does not specifically teach document selection and similarity scoring for the contract analysis.
Zhou teaches a similar contract system that specifically provides similar contract documents based on similarity scores and analysis.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the contract analysis and similar document system using ML/AI analysis of contract terms to a boilerplate/normalized document of the combination to include a similar contract system that specifically provides similar contract documents based on similarity scores and analysis as taught by Zhou 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 as predictable.
Regarding claim 15, the combination teaches the above-enclosed limitations of the system of claim 14;
Hunn further teaches wherein the server computing device is further configured to, in response to at least one other draft clause of the plurality of draft clauses being rejected or modified by the user, notify the remote party at the second client computing device that the user has rejected or modified the at least one other draft clause (Paragraphs [200-201, 209-211, and 217-219]; Hunn teaches notifications based on clause modifications and state updates within the blockchain, specifically for clause modifications and object changes.).
Regarding claim 16, the combination teaches the above-enclosed limitations of the system of claim 15
Wodetzki discloses wherein: the server computing device is further configured to, in response to the at least one other draft clause being rejected or modified by the user: determine a new reference clause of the DDC that is most similar to the at least one other draft clause; and calculate a new risk score associated with adopting the at least one other draft clause in place of the new reference clause (Paragraphs [132-134 and 140-143]; Wodetzki discloses receiving a contract document and providing AI/ML classification based on the document text clauses. Wodetzki provides that the analysis is determined based on a contract clause that is compared to clause types based on the training corpus [94-101]. The new clause is in terms of a repeating of the risk score for each contract clause object within the Wodetzki analysis system.);
causing to be displayed, on another display device of the second client computing device: the at least one other draft clause; the new reference clause; and the new risk score Fig 20A-20D and paragraphs [134-140 and 147-150; Wodetzki discloses determining and displaying a risk score, the clause, and reference elements.); and
notifying the remote party at the second client computing device that the user has rejected or modified the at least one other draft clause includes (Fig 19 and paragraphs [199-200]; Hunn teaches a similar contract system that specifically provides user instructions to accept, modify, or deny a commit (interpreted as a clause such as the contract objects and amendments within Wodetzki). Wodetzki teaches the contract objects/clauses that are scored and presented and Hunn teaches the specific aspect of receiving user input to accept, deny, or modify the entered contract amendments.);
the server computing device is further configured to, in response to detecting that the at least one other draft clause of the plurality of draft clauses has been accepted by the remote party, recording acceptance of the at least one other draft clause in the blockchain (Fig 2 and paragraphs [218-221 and 244-247]; Hunn teaches the contract system that updates a master contract within a blockchain in terms of accepting the proposed amendments. Within the combination, Wodetzki teaches [151-157] a blockchain timeline for contract events and trigger transactions which, within the combination, is Hunn acceptance or other inputs regarding contract amendments.).
Regarding claim 17, the combination teaches the above-enclosed limitations of the system of claim 16
Hunn further teaches wherein the server computing device is further configured to: in response to detecting that the at least one other draft clause of the plurality of draft clauses has been accepted by the remote party, prompt the remote party to sign the DDC as modified by the at least one other draft clause; and in response to detecting that the remote party has signed the DDC as modified by the at least one other draft clause, record complete acceptance of the DDC, as modified by the at least one other draft clause, by the remote party in the blockchain (Paragraphs [209-215 and 218-220]; Hunn teaches the signature and storage aspect based on completed contract document to be stored in the blockchain. Further, Wodetzki teaches [113-115 and 153-158] the blockchain verification and storage for the valid contract on a blockchain.).
Regarding claim 18, the combination teaches the above-enclosed limitations of the system of claim 17
Hunn further teaches wherein the server computing device is further configured to: in response to the remote party signing the DDC as modified by the at least one other draft clause, prompting the user to sign the DDC as modified by the at least one other draft clause; and in response to detecting that the user has signed the DDC, as modified by the at least one other draft clause, recording ratification of the DDC, as modified by the at least one other draft clause, in the blockchain (Paragraphs [209-215 and 218-220]; Hunn teaches the signature and storage aspect based on completed contract document to be stored in the blockchain. Further, Wodetzki teaches [113-115 and 153-158] the blockchain verification and storage for the valid contract on a blockchain.).
Regarding claim 19, Wodetzki discloses a computer program product comprising a non-transitory computer-readable storage medium storing instructions for enabling real-time authenticated collaboration between remote parties, which, when executed by a computing device, causes the computing device to (Paragraphs [139-141, 149-150, and 162-174]; Wodetzki teaches the structural aspects of the computer system to implement the contract clause analysis. Wodetzki further teaches the notifications and risk score being displayed to a remote user device including the clause and other contract variable information.):
receive a draft digital contract (DDC) from a remote party operating a remote device, the remote device being remote from the computing device; divide the DDC into a plurality of draft clauses (Paragraphs [132-134, 140-143, and 150]; Wodetzki discloses receiving a contract document and providing AI/ML classification based on the document text clauses. Wodetzki provides that the analysis is determined based on a contract clause that is compared to clause types based on the training corpus [94-101]. Further, Wodetzki teaches the remote user interface to provide contract elements [141-143 and 150].);
calculate a risk score associated with adopting that draft clause in place of that reference clause; send to a user device for display on a display device of the user device: that draft clause; that reference clause; and that calculated risk score (Fig 20A-20D and paragraphs [134-140 and 147-150]; Wodetzki discloses determining and displaying a risk score, the clause, and reference elements.); and
Wodetzki discloses a contract assessment system that provides risk scores based on classification using boilerplate clauses to display alerts and other notifications based on the clause, however, Wodetzki does not specifically teach receiving instruction to accept, deny, or modify the clause based on the score;
Hunn teaches receive an instruction from a user operating the user device whether to accept, reject, or modify that draft clause, the user device being remote from the computing device and the remote device (Fig 19 and paragraphs [199-200]; Hunn teaches a similar contract system that specifically provides user instructions to accept, modify, or deny a commit (interpreted as a clause such as the contract objects and amendments within Wodetzki). Wodetzki teaches the contract objects/clauses that are scored and presented and Hunn teaches the specific aspect of receiving user input to accept, deny, or modify the entered contract amendments. Further, Wodetzki teaches the remote user interface to provide contract elements [141-143].);
in response to detecting that at least one draft clause of the plurality of draft clauses has been accepted by the user, record acceptance of that draft clause in a blockchain (Fig 2 and paragraphs [218-221 and 244-247]; Hunn teaches the contract system that updates a master contract within a blockchain in terms of accepting the proposed amendments. Within the combination, Wodetzki teaches [151-157] a blockchain timeline for contract events and trigger transactions which, within the combination, is Hunn acceptance or other inputs regarding contract amendments.);
in response to a requesting device requesting an acceptance history of the DDC from the computing device, query the blockchain service for acceptances of draft clauses of the DDC, the requesting device being remote from the computing device and the remote device; receive cryptographically authenticated timestamped acceptance records of draft clauses of the DDC from the blockchain service; and cause details of the received cryptographically authenticated timestamped acceptance records of draft clauses of the DDC to be displayed to the requesting device (Paragraphs [173, 207-215, and 226-227]; Hunn teaches updating the blockchain with UUID identifiers and timestamps to store the updated objects within the blockchain. Within the combination, Wodetzki teaches [151-153] cryptographic timestamps based on blocks within the blockchain to trigger the distributed ledger transactions and smart contract.).
Wodetzki discloses a contract clause analysis system based on boilerplate clause and risk assessments, however, Wodetzki does not specifically teach receiving user instruction in terms of accept, reject, or modify the draft clause.
Hunn teaches a similar contract system that specifically provides user input in terms of user submitted clause amendments for contracts that can be accepted, modified, or rejected.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the contract clause analysis system based on boilerplate clause and risk assessments of Wodetzki includes a similar contract system that specifically provides user input in terms of user submitted clause amendments for contracts that can be accepted, modified, or rejected 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 as predictable.
Zhou teaches select a reference digital contract (RDC) to which to compare the DDC; for each draft clause: determine a reference clause of the selected RDC that is most similar to that draft clause (Paragraphs [66-74]; Zhou teaches similarity scores/thresholds for contract clauses and a selection of matching features based on the set of similar contracts to generate contract terms and analysis. Within the combination, Wodetzki teaches [142-148] that the ML/AI system provides selected clauses that are analyzed for the input contract clause/analysis. The combination is that Zhou provides similarity scores and selections of the reference contract in a specific aspect that is utilized in the contract analysis within Wodetzki.);
The combination teaches the above-enclosed limitations of the contract analysis and similar document system using ML/AI analysis of contract terms to a boilerplate/normalized document, however, the combination does not specifically teach document selection and similarity scoring for the contract analysis.
Zhou teaches a similar contract system that specifically provides similar contract documents based on similarity scores and analysis.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the contract analysis and similar document system using ML/AI analysis of contract terms to a boilerplate/normalized document of the combination to include a similar contract system that specifically provides similar contract documents based on similarity scores and analysis as taught by Zhou 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 as predictable.
Regarding claim 20, the combination teaches the above-enclosed limitations of the computer program product of claim 19, however, the combination does not specifically teach clause conversion into a vector and calculating a cosine similarity;
Zhou teaches wherein determining the reference clause of the reference digital contract (RDC) that is most similar to that draft clause includes: converting that draft clause into a vector; calculating a cosine similarity for each pairing of the vector created for that draft clause with a respective vector of each reference clause of a plurality of reference clauses of the RDC; and selecting as the reference clause, the reference clause of the plurality of reference clauses of the RDC whose vector has a lowest calculated cosine similarity with respect to that draft clause (Paragraphs [53-56 and 59-62]; Zhou teaches a similar contract analysis system that provides similar contract reference documents that specifically provides cosine similarity analysis based on vector document tagging. Within the combination, Wodetzki teaches natural language processing to determine similar documents within a trained document corpus and Zhou teaches the specific analysis being cosine similarity in terms of similar contract elements. In terms of the lowest similarity, Zhou specifically teaches [96-102] that the score can be based on the lowest error within the ML model for the similar contract documents.).
The combination teaches the above-enclosed limitations of the contract analysis and similar document system using natural language processing, however, the combination does not specifically teach cosine similarity and vectorized documents.
Zhou teaches a similar contract system that specifically provides similar contract documents based on vector elements and cosine similarity.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention for the contract analysis and similar document system using natural language processing of the combination to include a similar contract system that specifically provides similar contract documents based on vector elements and cosine similarity as taught by Zhou 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 as predictable.
Response to Arguments
In response to the arguments filed September 30, 2025 on pages 9-10 regarding the 35 USC 101 rejection, specifically that the amended claim limitations are directed towards eligible subject matter.
Examiner respectfully disagrees.
The amended claim limitations are with respect to the independent claims providing user devices operating at remote locations across the network and obtaining cryptographically authenticated timestamps to provide records of the contract clause. The described and discussed elements in the arguments were considered as additional elements in terms of the blockchain, cryptographic, and user device elements. Based on the considered elements (cited above) the blockchain and other technological claim limitations were found to be generic technology to implement the abstract idea. Examiner notes that the abstract idea is with respect to a contract legal service. The contract process is being validated and record-kept using cryptographic time stamps and the user devices are providing generic computers to implement the contract risk scoring abstract idea identified. Further, the cryptographic timestamps and blockchain are providing elements of the abstract idea in terms of providing record keeping within a technological environment (blockchain). The claim limitations were considered as additional elements and, based on the consideration, the claims are not transformative into a practical application or significantly more than the identified abstract idea. Refer to MPEP 2106.05(f). The arguments further discuss that a pen allows a lawyer to draft and sign a legal document, however, a pen is not directed towards an abstract idea. The claims are not directed towards a pen. The claims are describing a process of evaluating a contract/legal document with respect to risk using a reference document to the draft document. Assessing risk to a contract based on a reference contract is a legal process that is directed towards an abstract idea under the certain method of organizing human activity grouping. Further, the claims describe a process that a person could mentally opine and judge. A person (e.g. a lawyer) would be able to compare a draft contract to a template/reference contract and assess the risks and other aspects based on the clauses of the contracts. Thus, the claims are further directed towards an abstract idea under the mental process grouping. In terms of the arguments conflating the step 1 and step 2(a)(1), there is no such conflation. The claims were considered, individually and in combination, and found to be ineligible for being directed towards an abstract idea and the additional elements are not significantly more or transformative into a practical application. As such, amended claims 1, 14, and 19 are maintaining the 35 USC 101 rejection.
Lacking any further arguments, claims 1-8, 10-12, and 14-20 are maintaining the 35 USC 101 rejection, as considered above in light of the amended claim limitations.
In response to the arguments filed September 30, 2025 on pages 10-13 regarding the 35 USC 103 rejection, specifically that the amended claim limitations are not taught by the cited prior art.
Examiner respectfully disagrees.
The amended claim limitations are with respect that the reference document is selected and provided in comparison as the reference clause for the draft digital contract. The arguments allege that Wodetzki does not specifically teach that a reference clause is not selected. Based on further consideration, Zhou is relied upon to teach the amended claim limitations. Zhou teaches [66-74] a similarity scores/thresholds for contract clauses and a selection of matching features based on the set of similar contracts to generate contract terms and analysis. Within the combination, Wodetzki teaches [142-148] that the ML/AI system provides selected clauses that are analyzed for the input contract clause/analysis. The combination is that Zhou provides similarity scores and selections of the reference contract in a specific aspect that is utilized in the contract analysis within Wodetzki. As such, on a per clauses basis, Wodetzki teaches the functional aspects of a clause risk score based on comparisons for the reference clause within the training corpus and Zhou teaches the selection of the reference elements for comparison to the draft contract. As such, the amended claims 1, 14, and 19 are maintaining the 35 USC 103 rejection, as considered above in light of the amended claim limitations.
All rejections made towards the dependent claims are maintained due to the lack of a reply by the applicant in regards to distinctly and specifically point out the supposed errors in the Examiner’s action in the prior Office Action (37 CFR 1.111). The Examiner asserts that the applicant only argues that the dependent claims should be allowable because the independent claims are unobvious and patentable over Wodetzki in view of Hunn, and, where appropriate, in further view of Zhou.
Lacking any further arguments, claims 1-8, 10-12, and 14-20 are maintaining the 35 USC 103 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:
Krishna et al [2022/0261711] (contract guidance and recommendations);
Slattery et al [2022/0414153] (contract classification and clause clustering);
Jain et al [10,162,850] (clause risk score and workflow);
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW CHASE LAKHANI whose telephone number is (571)272-5687. The examiner can normally be reached M-F 730am - 5pm (EST).
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/ANDREW CHASE LAKHANI/Primary Examiner, Art Unit 3629