DETAILED ACTION
This Final Office Action is in response to the argument and amendment filed December 9, 2025.
Claims 1, 3, 4, 8, 10, 11, and 15 – 17 are amended.
Claims 2, 5-7, 9, 12, 13, 14, and 18-20 are originals.
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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture, or composition of matter? MPEP 2106.03.
Per Step 1, claims 1-7 is to a method (i.e., a process), claim 8-14 to a system (i.e., a machine), and claim 15-20 to a computer program product (i.e., a manufacture). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claim 1 and 10 is (claim 1 being representative):
A computer-implemented method for assessing terms and conditions (TnCs) of a legal agreement, the computer-implemented method comprising: training a cognitive system, with a set of labeled data that identified TnCs within sample of legal contracts; testing the trained cognitive system with unlabeled data; validating results of the trained cognitive system against expected results; tuning parameters of the cognitive system based on the validation results; identifying, using [[a]] the cognitive system, a block of text in the legal agreement, the block containing a TnC of a set of the TnCs in the legal agreement; assigning, by the cognitive system, a standardized significance value to the TnC by applying the TnC to a TnC model containing corresponding industry TnCs from industry standard documents; computing, by the cognitive system, a block currency for the TnC that indicates a significance of an impact of the TnC to a contract party; assessing, by the cognitive system, a personal impact of the TnC to the contract party based on the standardized significance value and the block currency; [[and]] determining whether there are financial terms in the legal agreement; in response to having determined that there are financial terms in the legal agreement, analyzing a financial impact of the financial terms that has an impact on the contract party; and alerting the contract party to the TnC, the financial impact and the personal impact corresponding to the TnC.
The abstract idea steps italicized above are those which could be performed mentally, including with pen and paper. The steps describe, at a high level, assessing, generating, assigning, and executing terms and conditions of a legal agreement. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, including assessing, generating, assigning, and executing terms and conditions of a legal agreement, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally, and alternatively, the abstract idea steps italicized above relate to rules and instructions pertaining to execution of terms and conditions of a legal agreement, which constitutes a process that, under its broadest reasonable interpretation, covers commercial activity. This is further supported by [0005] of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers commercial interactions, including contracts, legal obligations, advertising, marketing, sales activities or behaviors, and/or business relations, then it falls within the Certain Methods of Organizing Human Activity – Commercial or Legal Interactions grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f).
Claim 1 recites the following additional elements: implemented by a legal agreement,
a cognitive system, a block of text in the legal agreement, the block, the legal agreement, TnC model, and a block currency.
Claim 8 recites the following additional elements: Bus couple, legal agreement, memory medium, processor, a cognitive system, a block of text in the legal agreement, the block, TnC model, and a block currency.
Claim 15 recites the following additional elements: A computer readable storage device, legal agreement, a cognitive system, a block of text in the legal agreement, the block, TnC model, and a block currency.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen in [0055] of applicant’s specification as filed, for example.
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. Because the additional elements are merely instructions to apply the abstract idea to a generic computing system, they do not integrate the abstract idea into a practical application, when viewed in combination. See MPEP 2106.05(f).
Therefore, per Step 2A Prong Two, the additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two pertaining to MPEP 2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitate the tasks of the abstract idea, as described in MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
The analysis takes into consideration all dependent claims as well:
Dependent claims 2-4, 6-7, 9-11, 13-14, 16-17, and 19-20 contain additional steps that further narrow the abstract idea above.
Claim 2 and 9 recites the following additional elements: Legal agreement. Applicant has only described generic computing elements in their specification, as seen in {[0018]} of applicant’s specification as filed. This does not integrate the abstract idea into practical application and/or add significantly more. The claim is ineligible. Refer to MPEP 2106.05(F).
Claim 3, 7, 10, 14, 16 and 20 recites the following additional elements: Legal agreement, cognitive system and TnC model. Applicant has only described generic computing elements in their specification, as seen in {[0018]} of applicant’s specification as filed. This does not integrate the abstract idea into practical application and/or add significantly more. The claim is ineligible. Refer to MPEP 2106.05(F).
Claim 4, 11 and 17 recites the following additional elements: Cognitive system, block currency, legal agreements. Applicant has only described generic computing elements in their specification, as seen in {[0049]} of applicant’s specification as filed. This does not integrate the abstract idea into practical application and/or add significantly more. The claim is ineligible. Refer to MPEP 2106.05(F).
Claim 6, 13 and 19 recites the following additional elements: acyclic graph, Legal agreement. Applicant has only described generic computing elements in their specification, as seen in {[0055]} of applicant’s specification as filed. This does not integrate the abstract idea into practical application and/or add significantly more. The claim is ineligible. Refer to MPEP 2106.05(F).
Accordingly, claims 1-20 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
In conclusion the claims do not provide an inventive concept, because the claims do not recite additional elements or a combination of elements that amount to significantly more than the judicial exception of the claims. Therefore, whether taken individually or as an order combination, the claims are nonetheless rejected under 35 U.S.C. 101 as being directed to non - statutory subject matter.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1- 5, 7 - 12, 14-18 and 20 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over Wodetzki et al [US2018/0268506] hereafter Wodetzki.
As per claim 1, 8 and 15 (Similar scope and language);
Wodetzki discloses; See [0021 – 0022]
A computer-implemented method for assessing terms and conditions (TnCs) of a legal agreement, the computer-implemented method comprising:
training a cognitive system, with a set of labeled data that identified TnCs within sample of legal contracts;
{[0119] In another approach, the capture and creation of the data abstraction is performed by machines using a supervised learning technique. The machine (typically a software algorithm) is trained with examples of contract documents, contract clauses, contract sentences and/or contract phrases (the “contract corpus”), from which a set of rules is developed and refined using one or more heuristic or machine learning methods. Those methods may include one or more statistical natural language processing algorithms, neural network “deep learning” algorithms, and other machine learning algorithms.}
Wodetzki discloses
testing the trained cognitive system with unlabeled data;
{[0119] Non-public samples may also be sourced by agreement with private contracting parties, but these must be maintained and curated using strict privacy methods, ensuring that no human is able to discover private contract data via direct or indirect interaction with the corpus. In order to address the privacy need of the private-sourced corpus, two methods may be used. First, individual clauses/sentences are human-processed independently of their document context, ensuring the full meaning of those sentences/clauses is not disclosed. Second, each sentence clause is pre-processed via an anonymization gateway to obfuscate identifying information. In one method of anonymization, party name/alias information is substituted with a randomized pool of party names/aliases before presentation to a human reviewer.
[0120] In a hybrid approach, the system rules are optimized based on both human and machine capture, with human corrections to machine extraction feeding back into the machine learning algorithm and/or contract corpus. An important benefit of the machine learning approach is that it is not constrained by the learning limitations of human memory.……}
Wodetzki discloses
validating results of the trained cognitive system against expected results;
{[0126-0127] the clause training corpus 524 is amplified by legally re-classifying clauses from the clause training corpus 524 by passing them through ML/AI models to refine the clause classification. Clauses or sentences that remain unclassified from the document training corpus 526 are also classified with legal classifiers using ML/AI models and given confidence scores. Sentences that have high ambiguity due to external dependencies are joined to the dependent content/sentence to create sentence pairs (a type of clause) and resent the ML/AI to be classified with legal classifiers and given confidence scores. From the classification step, classified sentences/clauses with high confidence scores are passed directly to clause training corpus 524. Clauses with lower confidence scores are passed to an optional anonymizer and then presented to human experts to verify the classification.}
Wodetzki discloses
tuning parameters of the cognitive system based on the validation results;
{[0128] the clause training corpus 524 is further refined with direction classifiers by, first, taking legally and business classified clauses from the clause training corpus 524 and passing them through ML/AI models to refine clause classification models. Public source clauses are then selected for named legal entity recognition. Clauses are presented to human experts for “named entity” annotation and normalized “party role” annotation. Human expert annotations are fed into ML/AI models for training and development of named entity classifiers and party role tagging models. Clauses are then presented to human experts for “direction” annotation using normalized “party role” alias substitution.}
Wodetzki discloses
identifying, using [[a]] the cognitive system, a block of text in the legal agreement, the block containing a TnC of a set of the TnCs in the legal agreement;
assigning, by the cognitive system,
{[0021] In embodiments, the methods and systems include parsing the contract document into a collection of single sentences; with machine learning algorithm: 1) evaluating the sentences of the contract document to identify whether the sentences contain one or more core attributes pertaining to details of the contract, wherein the one or more core attributes may comprise one or more legal classifications, subject classifications, party directions, timing contingencies, conditionalities or contextual dependencies; and 2) evaluating the sentences of each contract document containing legal classifications to determine a type of the legal classification, wherein at least one of the types of legal classification is an obligation, a right, a representation, an act or deed, and a definition; assigning a confidence score to the evaluated sentences based upon the result of the machine learning algorithm; if the confidence score is below a predetermined value, using a human expert to review the evaluated sentences to determine whether the machine learning algorithm properly evaluated the sentences; and providing the result of the human expert's review of the evaluated sentences to a machine learning algorithm training corpus for use by the machine learning algorithm to evaluate future sentences of contract documents.}
Wodetzki discloses;
a standardized significance value to the TnC by applying the TnC to a TnC model containing corresponding industry TnCs from industry standard documents;
computing, by the cognitive system, a block currency for the TnC that indicates a significance of an impact of the TnC to a contract party;
assessing, by the cognitive system, a personal impact of the TnC to the contract party based on the standardized significance value and the block currency;
{[0027] Additionally, in embodiments, methods and systems for evaluating contract risk the include defining an object model containing a structural representation of the events and artefacts through which contracts are created, changed and brought to an end, the object model having at least three object types: contract objects, contract transaction objects and contract document objects; associating the contract objects with one or more contract transaction objects corresponding to one or more actions taken within the contract; associating contract document objects containing a corresponding contract document with one or more corresponding contract transaction objects, wherein the contract transaction object comprises contract data variables having contract data values; determining prevailing terms of the contract by evaluating all child contract transaction objects to build a single set of contract data variables and values for storage in the contract object; storing the prevailing terms in the contract object; evaluating the contract data variables and assigning a contract data risk value to one or more of the contract data values; evaluating the maximum risk for each contract data variable; and presenting a sum of the contract data risk values for the contract data risk variables and a sum of the maximum contract data risk values for each contract data variable.}
Wodetzki discloses;
[[and]] determining whether there are financial terms in the legal agreement;
{[0085] The contract details 300 outline particulars including actual words used in the contract title, whose paper (including, for example, “ours” standard, “ours” negotiated, counterparty paper, industry form standard and industry form negotiated), the contract type (including, for example, confidentiality/nondisclosure agreement, customer/sales, vendor/purchasing, reseller/channel partner, human resources/employment, corporate governance, and finance/trading) and a contract sub-type (including, for example, subtypes of a finance agreement such as custody agreement, fee agreement, International Swaps and Derivatives Association master agreement/credit support annex, loan/credit/facility agreement, investment management agreement, offering memorandum, master repurchase agreement, prime brokerage agreement, guarantee, side letter and the like).}
Wodetzki discloses;
in response to having determined that there are financial terms in the legal agreement, analyzing a financial impact of the financial terms that has an impact on the contract party; and
{[0135] A party can gain visibility into its contractual risk by scoring risk using an algorithm that analyzes risk factors to objectively measure the risk of each of a party's contracts, allowing the party to identify and manage issues before they can become problems. The present platform may include an implementation of a contractual risk model which measures the extent to which risk transfer is achieved or constrained (from a party-specific perspective) by the terms of any contract. In one implementation, one or more contractual terms/data points are declared to serve risk allocation purposes, and a maximum potential risk score is assigned to each such term, where a high score indicates an undesirable risk outcome (a likely risk increase) for the party whose contract portfolio is being assessed…… A normalized total contract risk score may now be evaluated for any one contract, for example, using an algorithm that scores contract risk as a percentage based on the instance score compared to the highest possible risk score.}
Wodetzki discloses;
alerting the contract party to the TnC, the financial impact and the personal impact corresponding to the TnC.
{[0027] determining prevailing terms of the contract by evaluating all child contract transaction objects to build a single set of contract data variables and values for storage in the contract object; storing the prevailing terms in the contract object; evaluating the contract data variables and assigning a contract data risk value to one or more of the contract data values; evaluating the maximum risk for each contract data variable; and presenting a sum of the contract data risk values for the contract data risk variables and a sum of the maximum contract data risk values for each contract data variable.}
As per claim 2 and 9 (Similar scope and language);
Wodetzki discloses;
The computer-implemented method of claim 1, the identifying further comprising detecting that the TnC is a new TnC that replaces a previous TnC in a previous version of the legal agreement.
{[0113] Referring now to FIG. 5, a Contract Object 100 is re-evaluated each time a Transaction Object 102, such as a create transaction, amend transaction or order transaction is added or changed. Transactions may be added or changed based upon re-evaluation of a Contract Document 104 or the addition of new Contract Documents 104, such as by addition of a new schedule, amendment, purchase order, change order, novation, or termination, etc. Every time there is a change at the transaction level, the platform re-evaluates the current active transactions to re-evaluate data for the Contract Object 100 from the currently active transactions to derive a single set of data that reflects those transactions for the point in time desired for the contract. For example, the Contract Object might be rolled up based upon the active transactions for the present point in time or the Contract Object 100 might be rolled up using the active transactions for previous or future point in time by only incorporating the transactions active at said given point in time.}
As per claim 3, 10 and 16 (Similar scope and language);
Wodetzki discloses;
The computer-implemented method of claim 1, further comprising:
searching a plurality of industry standard documents to discover industry TnCs within industry legal agreements; collating each industry TnC of the industry TnCs into a corresponding TnC group that contains corresponding industry TnCs that correspond to the industry TnCs;
{[0119] To build a large corpus, it is first seeded with examples sourced from public, open repositories such as those made available by government agencies. Non-public samples may also be sourced by agreement with private contracting parties, but these must be maintained and curated using strict privacy methods, ensuring that no human is able to discover private contract data via direct or indirect interaction with the corpus.
[0122] This task is performed by utilizing a contract AI developer module 502 and a contract AI platform 504. The contract AI developer module 502 comprises a rule development user interface 506 allowing a user to create rules for contract AI rule development using a human rule builder module 508. The contract AI developer module 502 further comprises a ML/AI rule builder module 510. The rule builder modules 508 and 510 process documents and clauses from contracts to create rule sets 518 that are categorized as universal contract module rule sets 514, industry specific rule sets 516, and customer specific rule sets 518, as described below. The rules are deployed to the contract AI platform 504 (described below). The contract AI platform 504 further provides a feedback training corpus 520 that further includes a contract document training corpus 526 and a contract clause training corpus 524.}
Wodetzki discloses;
determining, by the cognitive system, a TnC norm pertaining to the corresponding TnC group;
{[0119] In another approach, the capture and creation of the data abstraction is performed by machines using a supervised learning technique. The machine (typically a software algorithm) is trained with examples of contract documents, contract clauses, contract sentences and/or contract phrases (the “contract corpus”), from which a set of rules is developed and refined using one or more heuristic or machine learning methods. Those methods may include one or more statistical natural language processing algorithms, neural network “deep learning” algorithms, and other machine learning algorithms. The quality of a contract corpus for machine learning training purposes is enhanced by the size and diversity of examples in the corpus. To build a large corpus, it is first seeded with examples sourced from public, open repositories such as those made available by government agencies.
[0128] Referring to FIG. 12, in a fifth step, the clause training corpus 524 is further refined with direction classifiers by, first, taking legally and business classified clauses from the clause training corpus 524 and passing them through ML/AI models to refine clause classification models. Public source clauses are then selected for named legal entity recognition. Clauses are presented to human experts for “named entity” annotation and normalized “party role” annotation. Human expert annotations are fed into ML/AI models for training and development of named entity classifiers and party role tagging models. Clauses are then presented to human experts for “direction” annotation using normalized “party role” alias substitution. Relevant clauses are annotated with “direction” tags, including From [role], To [role], Mutual, etc. For example, an Obligation may be classified as “Mutual” or “From Supplier”, and a Right to Renew may be classified as “To Customer”. Directionally classified clauses are then passed to the clause training corpus 524 for use in training.}
Wodetzki discloses;
assigning, by the cognitive system, a significance value to each industry TnCs in the corresponding TnC group based on a deviation of the industry TnC from the TnC norm; and generating the TnC model from a set of industry TnC tuples that each include an industry TnC and the significance value assigned to the industry TnC.
{[0135] In one implementation, one or more contractual terms/data points are declared to serve risk allocation purposes, and a maximum potential risk score is assigned to each such term, where a high score indicates an undesirable risk outcome (a likely risk increase) for the party whose contract portfolio is being assessed. One or more instance scores may then be declared for one or more specific values assigned to the contractual terms/data points, within the range between zero and the maximum. A normalized total contract risk score may now be evaluated for any one contract, for example, using an algorithm that scores contract risk as a percentage based on the instance score compared to the highest possible risk score.}
As per claim 4, 11 and 17 (Similar scope and language);
Wodetzki discloses;
The computer-implemented method of claim 1, further comprising: searching, by the cognitive system, a plurality of prior legal agreements of the contract party to discover preferred TnCs within the prior legal agreements; and assigning, by the cognitive system, the block currency to the TnC based on a deviation of the TnC from a preferred TnC corresponding to the TnC.
{[0119] In another approach, the capture and creation of the data abstraction is performed by machines using a supervised learning technique. The machine (typically a software algorithm) is trained with examples of contract documents, contract clauses, contract sentences and/or contract phrases (the “contract corpus”), from which a set of rules is developed and refined using one or more heuristic or machine learning methods. Those methods may include one or more statistical natural language processing algorithms, neural network “deep learning” algorithms, and other machine learning algorithms. The quality of a contract corpus for machine learning training purposes is enhanced by the size and diversity of examples in the corpus. To build a large corpus, it is first seeded with examples sourced from public, open repositories such as those made available by government agencies. Non-public samples may also be sourced by agreement with private contracting parties, but these must be maintained and curated using strict privacy methods, ensuring that no human is able to discover private contract data via direct or indirect interaction with the corpus.
[0113] Referring now to FIG. 5, a Contract Object 100 is re-evaluated each time a Transaction Object 102, such as a create transaction, amend transaction or order transaction is added or changed. Transactions may be added or changed based upon re-evaluation of a Contract Document 104 or the addition of new Contract Documents 104, such as by addition of a new schedule, amendment, purchase order, change order, novation, or termination, etc. Every time there is a change at the transaction level, the platform re-evaluates the current active transactions to re-evaluate data for the Contract Object 100 from the currently active transactions to derive a single set of data that reflects those transactions for the point in time desired for the contract. For example, the Contract Object might be rolled up based upon the active transactions for the present point in time or the Contract Object 100 might be rolled up using the active transactions for previous or future point in time by only incorporating the transactions active at said given point in time.}
As per claim 5, 12 and 18 (Similar scope and language);
Wodetzki discloses;
The computer-implemented method of claim 1, wherein the personal impact is selected from a group, comprising: definite positive, positive from last occurrence, negative from last occurrence, definite negative, and not significant.
{[0135] The present platform may include an implementation of a contractual risk model which measures the extent to which risk transfer is achieved or constrained (from a party-specific perspective) by the terms of any contract. In one implementation, one or more contractual terms/data points are declared to serve risk allocation purposes, and a maximum potential risk score is assigned to each such term, where a high score indicates an undesirable risk outcome (a likely risk increase) for the party whose contract portfolio is being assessed. One or more instance scores may then be declared for one or more specific values assigned to the contractual terms/data points, within the range between zero and the maximum. A normalized total contract risk score may now be evaluated for any one contract, for example, using an algorithm that scores contract risk as a percentage based on the instance score compared to the highest possible risk score. An optional approach supports a bifurcation of the universal risk score (based on the views of a pool of experts) and a customer specific version of the risk score, under which the customer applies a secondary weighting to elements of the universal risk score based on the views of its own risk experts.}
As per claim 7, 14 and 20 (Similar scope and language);
Wodetzki discloses;
The computer-implemented method of claim 3, further comprising: incorporating the TnC into the TnC model; and re-baselining the significance value for each industry TnC in the TnC model based on the TnC.
{[0126] Referring to FIG. 10, in a third step, the clause training corpus 524 is amplified by legally re-classifying clauses from the clause training corpus 524 by passing them through ML/AI models to refine the clause classification. Clauses or sentences that remain unclassified from the document training corpus 526 are also classified with legal classifiers using ML/AI models and given confidence scores. Sentences that have high ambiguity due to external dependencies are joined to the dependent content/sentence to create sentence pairs (a type of clause) and resent the ML/AI to be classified with legal classifiers and given confidence scores. From the classification step, classified sentences/clauses with high confidence scores are passed directly to clause training corpus 524.
[0127] Referring to FIG. 11, in a fourth step, the clause training corpus 524 is refined by first taking legally classified clauses from the clause training corpus 524 and passing them through ML/AI models to refine clause classification models. Clauses with a specific legal classification are selected for additional annotation/training, e.g. “obligations”. The clauses then, optionally, are anonymized by obfuscating private data and presented to human experts for further sub-classification, e.g. “indemnification,” “payment,” etc. Next human classifications are fed into ML/AI models for training and development of new business classifier models. New clauses of the same legal classification are given “business” or “subject” classifications by the ML/AI models and presented for human review. Verified “business” classified clauses are passed to the clause training corpus 524 for use in training. Corrected “business” classified clauses are passed to the feedback training corpus 520 for use in training.}
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) 6, 13 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wodetzki et al, in view of Hunn et al [US2017/0287090] hereafter Hunn.
As per claim 6, 13 and 19 (Similar scope and language);
Wodetzki, does not disclose the following limitations. However, Hunn does disclose the
following limitations:
The computer-implemented method of claim 1, further comprising: including the TnC in a direct acyclic graph together with other TnCs of the set of TnCs in the legal agreement; determining the personal impact for each graphed TnC of the set of TnCs as a graph location of the graphed TnC within the direct acyclic graph is reached; aggregating the personal impact of each graphed TnC to measure a collective impact of the set of TnCs on the contract party; and
{[0104] The state transitioning system can include a data modeling system that takes the form of a Merkle tree or, preferably, a Directed Acyclic Graph (DAG) comprised of objects (nodes and edges/links) that replicate the structure of a legal contract (as shown in the example of FIG. 19). A node may represent any piece of data that pertains to the contract (e.g. an object of contract code, a state change, current state, data input to or output by a contract/clause to an external resource including a BDL, a transaction performed on an external system that pertains to the contract such as an asset transfer on a BDL or payment via API, etc.). Each node is preferably immutable, identified and content-addressed by a cryptographic hash of its contents. A DAG will have edges/links between the objects. When a programmable clause or programmable component performs an operation (e.g. outputs data to an external API) or changes state (e.g. the price decreases), the State Transitioning System is updated with this data/metadata. Preferably a new node object is added for each operation, update, event etc. Each change is preferably linked to nodes representing previous states as well as to atomic objects on which it depends for input (and metadata relating to the change/new event), thereby providing a complete immutable chronological record of the state of a data-driven contract as it changes over time. Contracting parties are therefore able to verify and ‘replay’ the cause of any changes to the state of the contract.}
Motivation: It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combined/modify/adjust the combination Wodetzki et al’s cognitive system to include Hunn et al’s TnC direct acyclic graph together with other TnCs of the set of TnCs in the legal agreement; determining the personal impact for each graphed TnC of the set of TnCs as a graph location of the graphed TnC within the direct acyclic graph is reached; aggregating the personal impact of each graphed TnC to measure a collective impact of the set of TnCs on the contract party since Wodetzki teaches accessing, assigning, training, testing and validating results in a cognitive system, determining financial terms in the legal agreement, See Wodetzki [0021, 0027, 0085, 0113, 0119, 0122, 0128 and 0135]. The combination would have been obvious because a person of ordinary skill in the art since the system and method provides identification and quantification of legal terms and conditions the inclusion of a data modeling system that takes the form of a Merkle tree or an Acyclic Graph to determine the personal and collective impacts of the terms and condition. See Hunn [0104].
Wodetzki discloses;
providing a recommendation as to whether the legal agreement is beneficial to the contract party based on the collective impact.
{[0059] Next, gaining visibility into the contracting outcomes reflected in a party's contract portfolio enables that party to protect its business from dangerous contracts by providing users with information about how to improve future contract drafting and negotiation processes and outcomes. A platform that combines automated contract analysis and automated contracting processes allows a party to easily create high quality, low risk documents through an intuitive, interview-driven platform. This lowers costs, reduces bottlenecks, and empowers business users. For example, insights gained through contract portfolio analysis can be used to “harvest” clauses from legacy contracts and automatically feed a clause library. Clauses can be classified and ranked for favorability and risk attributes, and made available to users drafting new agreements, supported by playbook guidance. Machine analysis of contractual outcomes can also be used to derive negotiation patterns, and to apply those patterns into templates and rule-sets for automated drafting. An example would be that analysis shows that all contracts of type A include a clause of type B. This inference is then used by the platform to propose a rule that all templates for new contracts of type A should include clause type B as a mandatory requirement.
[0149] Another implementation of the present platform allows for automated document assembly that accepts input for desired contractual terms and utilizes functional language to implement those terms according to desired risk scores measured against a normalized contract model. A recommendation can be further implemented which provides for comparison of an assembled document compared to the normalized contract model to determine addition terms and clauses that would be desirable to improve a contract's risk assessment versus the normalized contract model.}
Response to Arguments
In response to the argument filled December 09, 2025, regarding the 101 rejections, the Examiner Respectfully disagrees.
Applicant argues that the claims use judicial exception, in a manner that imposes a meaningful limit on the judicial exception, such that the claims are more than a drafting effort designed to monopolize the judicial exception. Applicant argues that the specification clearly supports a practical application.
The Examiner respectfully disagrees.
Examiner notes that the judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer.
The examiner notes that the system is directed to a mental process and Certain Methods of Organizing Human Activity – Commercial or Legal Interactions grouping of abstract ideas.
Examiner notes that the system is directed to a mental process. The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012) Mental processes [] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 19 3, 197 (1978).
Applicant argues that the cited specification Paragraphs 38-39 supports a practical application and the amended claims go beyond abstract ideas. Applicant argues that the claim “implementation within a computer system with specific components and functionalities transforms them into practical applications that go beyond mere mental activities. The technical aspects, computational requirements, and integration of machine learning techniques distinguish these limitations as practical implementations rather than abstract mental processes”.
The Examiner respectfully disagrees.
The Examiner notes that the cognitive system and machine learning techniques used to utilize, analyze a legal document, identify and access the impact of a TnC are generic tools. The Cognitive system and machine learning techniques are merely a generic technology with no technical improvement rather an improvement to the abstract idea using generic technology. See Applicant specification {[0006 - 0007] and [0041]}.
The Examiner maintains these claims recite an abstract idea.
Therefore, for the foregoing reasons the Examiner has maintained the 35 USC 101 rejection.
Regarding the prior art rejections 35 USC102/103, the Examiner respectfully disagrees.
Applicant argues that the prior art of record fails to teach the claims specifically that the amended claim limitations recite “ training a cognitive system, with a set of labeled data that identified TnCs within sample of legal contracts; testing the trained cognitive system with unlabeled data; validating results of the trained cognitive system against expected results;" and "determining whether there are financial terms in the legal agreement; in response to having determined that there are financial terms in the legal agreement, analyzing a financial impact of the financial terms that has an impact on the contract party”
The Examiner respectfully disagrees.
In response to the amended claims, the prior art of Wodetzki does disclose the training of the machine learning algorithm, testing to evaluate, assigning an evaluated score, identifying and validating predetermined values. See Wodetzki [0119, 0126 - 0128].
The Examiner notes that the prior art of Hunn et al, teaches the data modeling system that takes the form of a Merkle tree or an Acyclic Graph. See Hunn [0104].
In terms of the arguments Wodetzki and Hunn does teach specific limitations such as amended.
Based on the considered amendments cited, 35 USC 102/103 references have been utilized to teach the claimed invention (claim 1, 3, 4, 8, 10, 11, and 15 – 17). Lacking any further argument, claims 1-20 are maintaining the 35 USC 102/103 rejection, as considered above in light of the amended claim limitation above.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/VICTOR CHIGOZIRIM ESONU/
Examiner, Art Unit 3629
/SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629