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 .
Status of Claims
No claims have been amended.
No claims have been cancelled.
No claims have been added.
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 an abstract idea without significantly more. The claims recite:
generating a database of contract text portions, each contract text portion comprising a portion of text within one or more historical contract documents;
generating data, the data comprising, for each of a plurality of historical contract documents, 1) for each of a plurality of initial text portions within the historical contract document, a corresponding completed text portion within the historical contract document that includes the initial text portion, and 2) characteristics representative of the historical contract document and entities associated with the historical contract document;
[the data and process further including]
receiving one or more completed text portions within the plurality of historical contract documents and one or more initial text portions within the plurality of historical contract documents;
determining one or more relationships between the one or more completed text portions and the one or more initial text portions;
the determined one or more relationships and one or more characteristics representative of content of the plurality of historical contract documents and one or more entities associated with the plurality of historical contract documents, to predict one or more candidate text portion suggestions to follow the one or more initial text portions; and
determines a set of text portion suggestions related to an initial text portion received in a creation of a contract document and based on characteristics of the contract document;
determines a likelihood that each text portion suggestion in the set of text portion suggestions will be selected as a completed text portion for the received initial text portion;
ranks the set of text portion suggestions based on the determined likelihood; and
generates a set of top-ranked text portion suggestions based on ranking of the set of text portion suggestions
receiving a feedback information on an accuracy or relevance of the set of top-ranked text portion suggestions;
in response to receiving the feedback information,
modifying the set of top-ranked text portion suggestions
based on the modifying, the data with the feedback information; and
updating with the updated set of data
The invention is directed towards the abstract idea of contract drafting, which corresponds to “Certain Methods of Organizing Human Activities” as it is directed towards steps that can be performed by humans with the aid of pen and paper, e.g., having a user search through pre-existing information, matching their particular scenario with the stored information, retrieving and writing in those portions from the stored information into their particular scenario, and drafting a contract that includes the terms/conditions/etc., which further includes those selected matched portions, as well as updating information based on feedback provided by a user.
The limitations of:
generating a database of contract text portions, each contract text portion comprising a portion of text within one or more historical contract documents;
generating data, the data comprising, for each of a plurality of historical contract documents, 1) for each of a plurality of initial text portions within the historical contract document, a corresponding completed text portion within the historical contract document that includes the initial text portion, and 2) characteristics representative of the historical contract document and entities associated with the historical contract document;
[the data and process further including]
receiving one or more completed text portions within the plurality of historical contract documents and one or more initial text portions within the plurality of historical contract documents;
determining one or more relationships between the one or more completed text portions and the one or more initial text portions;
the determined one or more relationships and one or more characteristics representative of content of the plurality of historical contract documents and one or more entities associated with the plurality of historical contract documents, to predict one or more candidate text portion suggestions to follow the one or more initial text portions; and
determines a set of text portion suggestions related to an initial text portion received in a creation of a contract document and based on characteristics of the contract document;
determines a likelihood that each text portion suggestion in the set of text portion suggestions will be selected as a completed text portion for the received initial text portion;
ranks the set of text portion suggestions based on the determined likelihood; and
generates a set of top-ranked text portion suggestions based on ranking of the set of text portion suggestions
receiving a feedback information on an accuracy or relevance of the set of top-ranked text portion suggestions;
in response to receiving the feedback information,
modifying the set of top-ranked text portion suggestions
based on the modifying, the data with the feedback information; and
updating with the updated set of data,
are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a generic document management system (which the Examiner asserts is equivalent to a generic computer), database, and machine learning model. That is, other than reciting a generic system, database, and machine learning model nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the generic system, database, and machine learning model in the context of this claim encompasses a user can searching through previously stored information, such as, but not limited to, previous contracts, clauses, and the like, matching the stored information with what they desire to be included in their contract and utilize the matched information as a template or to fill in missing information in their contract that best fits with their scenario, and drafting a contract, as well as updating information based on feedback provided by a user. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a generic system, database, and machine learning model, then it falls within the “Certain Methods of Organizing Human Activities” groupings of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – a generic system and database to communicate and store information, as well as performing operations that a human can perform in their mind or using pen and paper, i.e. collecting and comparing information, selecting desired portions based on the results of the comparison, and incorporating/writing the selected portions into a contract, as well as updating information based on feedback provided by a user. The generic system, database, and machine learning model in the steps are recited at a high-level of generality (i.e., as a generic system and database can perform the insignificant extra solution steps of communicating and storing information (See MPEP 2106.05(g) while also reciting that the a generic system and database are merely being applied to perform the steps that can be performed by humans or using pen and paper (See MPEP 2106.05(f)) such that it amounts no more than mere instructions to apply the exception using a generic system, database, and machine learning model.
Although the claims recite training and applying a machine learning model,” the claims fail to provide sufficient disclosure regarding an improvement to how a machine learning model can be trained, but simply recites a high-level generic recitation that a machine learning algorithm is being trained and applied. There is insufficient evidence from the claimed invention to indicate that the use of the machine learning model involves anything other than the generic application of a known technique in its normal, routine, and ordinary capacity or that the claimed invention purports to improve the functioning of the computer itself or the machine learning algorithm. None of the limitations reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field, applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Even training and applying a machine learning model is simply application of a computer model, itself an abstract idea manifestation. Further, such training and applying of a model is no more than putting data into a black box machine learning operation. The nomination as being a trained machine learning model is a functional label, devoid of technological implementation and application details. The claimed invention does not contend it invented any of these activities, or the creation and use of such machine learning models. In short, each step does no more than require a generic computer to perform generic computer functions. As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. InvestPic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018).
The Examiner asserts that the scope of the claimed invention, as presented, is not directed towards the improvement of machine learning, but directed towards drafting a contract based on collecting and comparing information and, based on a rule(s), identify options, i.e. incorporating the results of the comparison into the contract. The specification’s disclosure on machine learning is nothing more than a high general explanation of generic technology and applying it to the abstract idea. This is further supported by ¶ 35 of the applicant’s specification, wherein the applicant explicitly recites that the training/retraining process is based on generic training/retraining techniques and not directed towards improving the techniques. Referring to MPEP § 2106.05(f), the training and re-training are merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP § 2106.05(f). Accordingly, the Examiner asserts that in light of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the claimed invention is analogous to Example 47, Claim 2.
Further, the combination of these elements is nothing more than a generic computing system with machine learning model(s). Because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP § 2106.05(f), they do not integrate the abstract idea into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a generic system, database, and machine learning model to perform the steps of:
generating a database of contract text portions, each contract text portion comprising a portion of text within one or more historical contract documents;
generating data, the data comprising, for each of a plurality of historical contract documents, 1) for each of a plurality of initial text portions within the historical contract document, a corresponding completed text portion within the historical contract document that includes the initial text portion, and 2) characteristics representative of the historical contract document and entities associated with the historical contract document;
[the data and process further including]
receiving one or more completed text portions within the plurality of historical contract documents and one or more initial text portions within the plurality of historical contract documents;
determining one or more relationships between the one or more completed text portions and the one or more initial text portions;
the determined one or more relationships and one or more characteristics representative of content of the plurality of historical contract documents and one or more entities associated with the plurality of historical contract documents, to predict one or more candidate text portion suggestions to follow the one or more initial text portions; and
determines a set of text portion suggestions related to an initial text portion received in a creation of a contract document and based on characteristics of the contract document;
determines a likelihood that each text portion suggestion in the set of text portion suggestions will be selected as a completed text portion for the received initial text portion;
ranks the set of text portion suggestions based on the determined likelihood; and
generates a set of top-ranked text portion suggestions based on ranking of the set of text portion suggestions
receiving a feedback information on an accuracy or relevance of the set of top-ranked text portion suggestions;
in response to receiving the feedback information,
modifying the set of top-ranked text portion suggestions
based on the modifying, the data with the feedback information; and
updating with the updated set of data.
amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Additionally:
Claims 2, 3, 5 are directed towards descriptive subject matter, in this case, describing what the characteristics are intended to be.
Claims 4, 6 are directed collecting and comparing information and, based on a rule(s), identify options.
Claim 7 is directed to human activities, in this case, risk assessment based on the review of the previously stored information, updating a risk assessment, and the recitation of generic technology and applying it to the abstract idea (as was discussed above).
Claims 8, 9 are directed towards the recitation of generic technology and description of stored information.
The remaining claims are similar to the subject matter already discussed above.
In summary, the dependent claims are simply directed towards providing additional descriptive factors that are considered for drafting/creating a contract. Accordingly, the claims are not patent eligible.
Claim Rejections - 35 USC § 103
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.
Claims 1 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Iori et al. (US Patent 10540373 B1) in view of Kursun (US PGPub 20210176276 A1).
In regards to claims 1, 10, 19 Iori discloses (Claim 1) a method comprising; (Claim 10) a non-transitory computer-readable storage medium storing executable instructions that, when executed by a hardware processor, cause the hardware processor; (Claim 19) a document management system, comprising:
In regards to:
(Claim 19) hardware processor; and
(Claim 19) a non-transitory computer-readable storage medium storing executable instructions that, when executed, cause the hardware processor to perform operations comprising
(Fig. 1, 4):
generating (Claim 1: , by a document management system,) a database of contract text portions, each contract text portion comprising a portion of text within one or more historical contract documents (Col. 2 – 3 Lines 50 – 10; Col. 4 Lines 42 – 57; Col. 5 – 6 Lines 58 – 18 wherein a database of contract clauses is generated to store portions of texts within historical contract documents);
generating (Claim 1: , by a document management system,) […] data, the […] data comprising, for each of a plurality of historical contract documents, 1) for each of a plurality of initial text portions within the historical contract document, a corresponding completed text portion within the historical contract document that includes the initial text portion, and 2) characteristics representative of the historical contract document and entities associated with the historical contract document (Col. 2 – 3 Lines 50 – 10; Col. 4 Lines 42 – 57; Col. 5 Lines 9 – 30; Col. 5 – 6 Lines 58 – 18 wherein a database of contract clauses is generated to store portions of texts within historical contract documents that include initial (initial text portion; see ¶ 26 of applicant’s specification which defines “initial text portion as being, “may be, for example, a word, a sentence, or a paragraph corresponding to a contract”) and modified text portions; search terms (initial text portion); entities associated with the historical contract document; history associated with the clause (e.g., revision history); dates associated with the submission, update, receipt of comments on, and/or use of the clause, and/or other suitable information for identifying the clause; initial version and the revised version enabling the user to efficiently identify the changes that were implemented; and a historical record associated with the use of a clause enabling the user to review the historical record associated with a clause);
In regards to:
[…], […] the document management system, […] using the […] set of data, the [utilization] including:
receiving one or more completed text portions within the plurality of historical contract documents and one or more initial text portions within the plurality of historical contract documents;
determining one or more relationships between the one or more completed text portions and the one or more initial text portions;
[utilizing the system based on] the determined one or more relationships and one or more characteristics representative of content of the plurality of historical contract documents and one or more entities associated with the plurality of historical contract documents, to predict one or more candidate text portion suggestions to follow the one or more initial text portions; and
[…]
(Col. 2 – 3 Lines 50 – 10; Col. 4 Lines 42 – 57; Col. 5 Lines 9 – 30; Col. 5 – 6 Lines 58 – 18 wherein a database of contract clauses is generated to store portions of texts within historical contract documents that include initial text portions (see ¶ 26 of applicant’s specification which defines “initial text portion” as being, “may be, for example, a word, a sentence, or a paragraph corresponding to a contract”) and modified text portions; search terms (also initial text portion); entities associated with the historical contract document; history associated with the clause (e.g., revision history); dates associated with the submission, update, receipt of comments on, and/or use of the clause, and/or other suitable information for identifying the clause; initial version and the revised version enabling the user to efficiently identify the changes that were implemented; and a historical record associated with the use of a clause enabling the user to review the historical record associated with a clause
Col. 3 Lines 37 – 52, 58 – 63; Col. 4 Lines 42 – 57; Col. 5 Lines 9 – 30; Col. 5 – 6 Lines 58 – 18; Col. 7 Lines 5 – 18 wherein the clauses are rated and ranked and presented to user with their corresponding rating, as well as the system maintaining a historical record of the use of the clause in particular agreements for a user to review, revisions that may have been made to a clause, measure of success in previously negotiated events, and any other relevant information that a user can use to determine whether to employ the clause in a current event, e.g., preparation of a document, a negotiation, or etc. (i.e. predict candidate text portion suggestions to follow the initial text portion));
wherein the [system]
determines a set of text portion suggestions related to an initial text portion received in a creation of a contract document and based on characteristics of the contract document;
determines a likelihood that each text portion suggestion in the set of text portion suggestions will be selected as a completed text portion for the received initial text portion;
ranks the set of text portion suggestions based on the determined likelihood; and
generates a set of top-ranked text portion suggestions based on ranking of the set of text portion suggestions
(Col. 3 Lines 37 – 52, 58 – 63; Col. 4 Lines 42 – 57; Col. 5 Lines 9 – 30; Col. 5 – 6 Lines 58 – 18; Col. 7 Lines 5 – 18 wherein the clauses are rated and ranked and presented to user with their corresponding rating, as well as the system maintaining a historical record of the use of the clause in particular agreements for a user to review, revisions that may have been made to a clause, measure of success in previously negotiated events, and any other relevant information that a user can use to determine whether to employ the clause in a current event, e.g., preparation of a document, a negotiation, or etc.
The claim recites that the system using data that comprises, at least, initial text portions within historical contract documents, corresponding completed text portion within the historical contract document, and characteristics representative of the historical contract document and entities associated with the historical contract document. In other words, using claim 1 as the exemplary claim, limitation 2 already discloses "relationships between data in the […] set data" and because having the system use the data already results in disclosing "based on".);
receiving (Claim 1: , by a document management system,) a feedback information on an accuracy or relevance of the set of top-ranked text portion suggestions generated by the [system];
in response to receiving the feedback information,
modifying (Claim 1: , by a document management system,) the set of top-ranked text portion suggestions
updating, by the document management system, based on the modifying, the […] data with the feedback information; and
[updating] (Claim 1: , by a document management system,) the [system] with the updated […] data […]
(Col. 2 Lines 6 – 17; Col. 4 – 5 Lines 58 – 8; Col. 6 Lines 43 – 64; Col. 7 Lines 5 – 18 wherein the system receives feedback from users to update the clause library and clause ratings, which are used as recommendations to provide to users).
Iori discloses a system and method of utilizing a clause library manager to collect and manage a plurality of contract clauses, each clause having, at least, rating information, to assist a user with generating a contract. Iori discloses that the clause library manager is configured to store and utilize, at least, rating information to determine which of the plurality of clauses have been previously employed with a measure of success, such as, but not limited to, user provided feedback (Col. 2 Lines 6 – 24; Col. 4 – 5 Lines 58 – 8; Col. 6 Lines 43 – 64) and, in turn, present top-ranking clauses to a user for use in a contract that is being generated. Although Iori discloses a system with a clause library manager configured to store and use this information to assist with contract drafting, Iori fails to explicitly disclose whether it would have been obvious to take this step further by employing machine learning in contract drafting and analysis, as well as the improving the results of the model based on users providing feedback on the results.
To be more specific, Iori fails to explicitly disclose:
generating (Claim 1: , by a document management system,) a training set of data, the training set of data comprising, for each of a plurality of historical contract documents, 1) for each of a plurality of initial text portions within the historical contract document, a corresponding completed text portion within the historical contract document that includes the initial text portion, and 2) characteristics representative of the historical contract document and entities associated with the historical contract document;
training, by the document management system, a machine learning model using the training set of data, the training including:
training the machined learned model based on the determined one or more relationships and one or more characteristics representative of content of the plurality of historical contract documents and one or more entities associated with the plurality of historical contract documents, to predict one or more candidate text portion suggestions to follow the one or more initial text portions; and
generating a trained machine learned model;
wherein the trained machine learned model
receiving (Claim 1: , by a document management system,) a feedback information on an accuracy or relevance of the set of top-ranked text portion suggestions generated by the trained machine learned model;
updating, by the document management system, based on the modifying, the (Claim 1: , by a document management system,) the training set of data with the feedback information; and
retraining (Claim 1: , by a document management system,) the trained machine learned model with the updated training set of data.
However, Kursun, which is also directed towards utilizing and incorporating contract templates and historical information for generating a custom contract, further teaches that contract generation can be performed using machine learning. Kursun teaches that training data, such as, but not limited to, historical information, contract templates, user input, and etc., can be inputted into a machine learning model to assist a user with the customization and finalization of a contract. Kursun further teaches that the system can be retrained based on, at least, input received from users and will continue to retrain itself in an iterative manner based on, at least, input received from users until a threshold has been satisfied and resulting in termination of the process. That is to say, the machine learning model outputs a template and receives feedback from users and, based on the feedback, the machine learning model will retrain itself until it is able to output a template that satisfies the users.
One of ordinary skill in the art looking upon the teachings of Kursun would have found that the advantage of using machine learning in contract generation is that it provides a user(s) with the peace of mind that there would be little to no negative issues with the contract, such as, but not limited to, potential exposure or misappropriation level, an exposure appetite for the users involved, and generating the appropriate contract template. Kursun teaches that machine learning can be trained on recent tactics or strategies so that it can learn from these events and apply them to future contract generation.
The Examiner asserts that Kursun has not been provided to explicitly teach the specific type of contract information, relationships, and the like as Iori has been provided to disclose these aspects of the claimed invention, as was discussed above. The sole difference between Iori and the claimed invention is that Iori does not explicitly whether it would have been obvious to one of ordinary skill in the art of contract drafting and analysis to utilize machine learning. However, one of ordinary skill in the art looking upon the teachings of Kursun would have, indeed, found it obvious and beneficial that machine learning can be implemented into the system and method of Iori for the benefits that this advancement in technology provides, i.e. faster, more efficient, and etc. Both Iori and Kursun provide a computer-based system and method to analyze information found within historical contracts to assist a user with analyzing and drafting a current contract and one of ordinary skill in the art would have been motivated to look to and incorporate the teachings of Kursun as this would improve upon the less technological and/or manual system and method of Iori.
The Examiner further refers to and incorporates the response provided in the Non-Final Office Action mailed on January 16, 2025, which stated:
“As was emphasized in the rejection, Kursun was provided to teach whether it would have been obvious to improve the system of lori, which discloses the use of a clause library manager configured to store and use the information to assist with contract drafting, by further having it employ machine learning in contract drafting, as well as improving the results of the model based on users providing some type of feedback on the results, as well as using user provided feedback to update the system to identify the most appropriate information to be provided based on the relationships, labels, and etc. (Col. 2 Lines 6 – 24; Col. 4 – 5 Lines 58 – 8; Col. 6 Lines 43 – 64). Kursun teaches that the feedback provided to the system is used to establish that the machine learning model's output should be improved upon and the feedback is used as the basis for determining how/what information the model should now be trained on, i.e. retraining using the feedback. The Examiner asserts that Kursun has not been provided to teach the entirety of the limitations, but to simply teach whether it would have been obvious to take this step further by employing machine learning in contract drafting, as well as the improving the results of the model based on users providing feedback on the results, as Iori teaches [the] rest of the limitations which would exclude the use of machine learning, training set data, updating the training data, and training/retraining of the machine learning model, as this provides the advantage of learning from events and applying it to future contract generation while providing a user(s) with the peace of mind that there would be little to no negative issues with the contract, such as, but not limited to, potential exposure or misappropriation level, an exposure appetite for the users involved, and generating the appropriate contract template, as well as be trained on recent tactics or strategies so that it can learn from these events and apply them to future contract generation.”
(For support see: Fig. 12; ¶ 33, 34, 37, 40, 60, 62, 74, 75, 77, 78, 79, 80, 90, 97, 98)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate into the system and method that is configured to generating a contract using provided information as disclosed by Iori, as was discussed above, with the ability to utilize machine learning to assist with contract generation, as taught by Kursun, as this would provide provides a user(s) with the peace of mind that there would be little to no negative issues with the contract, such as, but not limited to, potential exposure or misappropriation level, an exposure appetite for the users involved, and generating the appropriate contract template, as well as be trained on recent tactics or strategies so that it can learn from these events and apply them to future contract generation.
Further, one of ordinary skill in the art of contract drafting and analysis based on historical information would have found it obvious to update the non-machine learning based system of Iori using modern data processing techniques, i.e. machine learning, as taught in Kursun, in order to gain the commonly understood benefits of such adaptation, such as, but not limited to, efficiency, speed, and the like.
Accommodating the prior arts more manual and antiquated process with modern electronics, in this case, training and retraining machine learning to assist with analyzing historical contracts, relationships associated with the contract, and drafting/recommending content, especially since, Kursun teaches that utilizing such technology in this technical field would have been obvious. As stated in Leapfrog, “applying modern electronics to older mechanical devices has been commonplace in recent years.”
In regards to claims 2, 11, the combination of Iori and Kursun discloses the method of claim 1 (the non-transitory computer-readable storage medium of claim 10), wherein the characteristics of the contract document comprise at least one of:
a type of the contract document;
one or more parties to the contract document;
characteristics of an entity associated with the contract document; and
characteristics of a user associated with the contract document
(Col. 2 – 3 Lines 50 – 10; Col. 5 Lines 9 – 30 wherein the characteristic of a document can be, at least, an agreement type, i.e. type of contract document
Additionally, the Examiner refers to and incorporates MPEP § 2111.04 and 2111.05 as what the characteristic is intended to be is directed towards descriptive subject matter describing an intended result. The Examiner asserts that the data identifying the characteristic as “a type of the contract document; one or more parties to the contract document; characteristics of an entity associated with the contract document; and characteristics of a user associated with the contract document” are labels for the characteristic and add little, if anything, to the claimed invention and thus does not serve to distinguish over the prior art. Any differences related merely to the meaning and information conveyed through labels (i.e., the type of the characteristics) which does not explicitly alter or impact the steps of the claimed invention does not patentably distinguish the claimed invention from the prior art in terms of patentability.).
In regards to claims 3, 12, the combination of Iori and Kursun discloses the method of claim 2 (the non-transitory computer-readable storage medium of claim 11), wherein the characteristics of the entity associated with the contract document comprise at least one of:
a legal type of the entity;
an industry associated with the entity; and
a jurisdiction associated with the entity
(Col. 2 – 3 Lines 50 – 10; Col. 5 Lines 9 – 30 wherein the characteristic of an entity can be, at least, industry associated with the entity
Additionally, the Examiner refers to and incorporates MPEP § 2111.04 and 2111.05 as what the characteristic is intended to be is directed towards descriptive subject matter describing an intended result. The Examiner asserts that the data identifying the characteristic as “a type of the contract document; one or more parties to the contract document; characteristics of an entity associated with the contract document; and characteristics of a user associated with the contract document” are labels for the characteristic and add little, if anything, to the claimed invention and thus does not serve to distinguish over the prior art. Any differences related merely to the meaning and information conveyed through labels (i.e., the type of the characteristics) which does not explicitly alter or impact the steps of the claimed invention does not patentably distinguish the claimed invention from the prior art in terms of patentability.).
In regards to claims 4, 13, the combination of Iori and Kursun discloses the method of claim 1 (the non-transitory computer-readable storage medium of claim 10), wherein the likelihood that each text portion suggestion will be selected is further based on a type of the initial text portion received in the creation of the contract document (Col. 2 – 3 Lines 50 – 10; Col. 4 Lines 42 – 57; Col. 5 – 6 Lines 9 – 42 wherein the system utilizes provided information (initial text portion), such as, but not limited to, search terms, keywords, party name, name of an agreement, clause type, agreement type, and the like, corresponding to contract that is wished to be generated and compares it against a plurality of clauses to determine which of the plurality clauses can be identified and presented to a user as candidate clauses for inclusion in the user’s contract).
In regards to claims 5, 14, the combination of Iori and Kursun discloses the method of claim 4 (the non-transitory computer-readable storage medium of claim 13), wherein the type of the initial text portion comprises at least one of a word, a phrase, a sentence, a clause, a paragraph, and a heading (Col. 2 – 3 Lines 50 – 10; Col. 4 Lines 42 – 57; Col. 5 Lines 9 – 30; Col. 5 – 6 Lines 58 – 18 wherein a database of contract clauses is generated to store portions of texts within historical contract documents that include initial (initial text portion; see ¶ 26 of applicant’s specification which defines “initial text portion as being, “may be, for example, a word, a sentence, or a paragraph corresponding to a contract”) and modified text portions, search terms (initial text portion), and entities associated with the historical contract document; . 2 – 3 Lines 50 – 10; Col. 4 Lines 42 – 57; Col. 5 – 6 Lines 9 – 42 wherein the system utilizes provided information (initial text portion), such as, but not limited to, search terms, keywords, party name, name of an agreement, clause type, agreement type, and the like, corresponding to contract that is wished to be generated and compares it against a plurality of clauses to determine which of the plurality clauses can be identified and presented to a user as candidate clauses for inclusion in the user’s contract).
In regards to claims 6, 15, the combination of Iori and Kursun discloses the method of claim 1 (the non-transitory computer-readable storage medium of claim 10), wherein the likelihood that each text portion suggestion will be selected as the completed text portion for the initial text portion received in the creation of a contract document is further based on feedback (Col. 6 – 7 Lines 43 – 4 wherein user feedback (from the creator/other users) is received to determine whether to include a clause into the contract. Alternatively, Col. 2 – 3 Lines 50 – 10; Col. 5 Lines 9 – 30; Col. 5 – 6 Lines 58 – 42 wherein the system utilizes the clause profile to identify and present top-ranked clauses a user, wherein these candidate clauses can be reviewed by the user for selection and inclusion into a document, i.e. the document is modified to include the selected clause, wherein the selection is the feedback.).
In regards to claims 7, 16, 20, the combination of Iori and Kursun discloses the method of claim 1 (the non-transitory computer-readable storage medium of claim 10, wherein the instructions cause the hardware processor to; the document management system of claim 19 wherein the hardware processor is further configured to), further comprising:
receiving ((Claim 7), by the document management system,) a target initial text portion (Claims 16, 20: from a creator) of a target contract document
(Col. 3 Lines 37 – 52; Col. 4 Lines 42 – 57; Col. 5 Lines 9 – 30 wherein the clauses are rated and ranked and presented to user with their corresponding rating, as well as the system maintaining a historical record of the use of the clause in particular agreements for a user to review, revisions that may have been made to a clause, measure of success in previously negotiated events, and any other relevant information that a user can use to determine whether to employ the clause in a current event, e.g., preparation of a document, a negotiation, or etc.);
searching ((Claim 7), by the document management system,) the database of contract text portions to identify a candidate set of text portion suggestions relevant to the target initial text portion (Col. 2 – 3 Lines 50 – 10; Col. 4 Lines 42 – 57; Col. 5 – 6 Lines 9 – 18 wherein the database of clauses can be searched to identify clauses relevant to the current event);
In regards to:
applying ((Claim 7), by the document management system,) the re-trained machine learning model to the candidate set of text portion suggestions and to characteristics of the target contract document to identify a set of top-ranked text portion suggestions; and
modifying ((Claim 7), by the document management system,) a contract creation interface to include the identified set of top-ranked text portion suggestions such that, in response to a selection of a top-ranked text portion suggestion (Claims 16, 20: by a creator of the target contract document), the target contract document is modified to include text of the selected text portion suggestion
(Iori – Col. 2 – 3 Lines 50 – 10; Col. 5 Lines 9 – 30; Col. 5 – 6 Lines 58 – 42 wherein the system utilizes the clause profile to identify and present top-ranked clauses a user, wherein these candidate clauses can be reviewed by the user for selection and inclusion into a document, i.e. the document is modified to include the selected clause;
Kursun – ¶ 33, 34, 37, 40, 60, 62, 74, 75, 77, 78, 79, 80, 90, 97, 98 wherein the contract generation can be performed using machine learning. Kursun teaches that training data, such as, but not limited to, historical information, contract templates, user input, and etc., can be inputted into a machine learning model to assist a user with the customization and finalization of a contract. Kursun further teaches that the system can be retrained based on, at least, input received from users and will continue to retrain itself in an iterative manner based on, at least, input received from users until a threshold has been satisfied and resulting in termination of the process. Kursun teaches that machine learning can be trained on recent tactics or strategies so that it can learn from these events and apply them to future contract generation.;
As discussed above, although Iori discloses a system with a clause library manager configured to store and use this information to assist with contract drafting, Iori fails to explicitly disclose whether it would have been obvious to take this step further by employing machine learning in contract drafting and analysis, as well as the improving the results of the model based on users providing feedback on the results. However, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate into the system and method that is configured to generating a contract using provided information as disclosed by Iori, as was discussed above, with the ability to utilize machine learning to assist with contract generation, as taught by Kursun, as this would provide provides a user(s) with the peace of mind that there would be little to no negative issues with the contract, such as, but not limited to, potential exposure or misappropriation level, an exposure appetite for the users involved, and generating the appropriate contract template, as well as be trained on recent tactics or strategies so that it can learn from these events and apply them to future contract generation.
Further, one of ordinary skill in the art of contract drafting and analysis based on historical information would have found it obvious to update the non-machine learning based system of Iori using modern data processing techniques, i.e. machine learning, as taught in Kursun, in order to gain the commonly understood benefits of such adaptation, such as, but not limited to, efficiency, speed, and the like.
Accommodating the prior arts more manual and antiquated process with modern electronics, in this case, training and retraining machine learning to assist with analyzing historical contracts, relationships associated with the contract, and drafting/recommending content, especially since, Kursun teaches that utilizing such technology in this technical field would have been obvious. As stated in Leapfrog, “applying modern electronics to older mechanical devices has been commonplace in recent years.”)
In regards to:
determining ((Claim 7), by the document management system,) a level of risk associated with each of the text portion suggestions in the identified set of top-ranked text portion suggestions; and
responsive to determining (Claims 16, 20: a determination) that the level of risk is above a threshold, modifying ((Claim 7), by the document management system,) the contract creation interface to include the level of risk
(Col. 3 Lines 37 – 63; Col. 4 Lines 42 – 57; Col. 4 – 5 Lines 58 – 8; Col. 5 Lines 9 – 30 wherein a level of risk is associated with each clause and is presented to a user for consideration of whether to select the clause for inclusion into their contract).
In regards to claims 8, 17, the combination of Iori and Kursun discloses the method of claim 1 (the non-transitory computer-readable storage medium of claim 10), wherein the database of contract text portions corresponds to a user of the document management system (Col. 2 Lines 40 – 49; Col. 6 Lines 19 – 42; Col. 6 – 7 Lines 43 – 4 wherein the database of contract text portions corresponds to a user of the document management system).
In regards to claims 9, 18, the combination of Iori and Kursun discloses the method of claim 1 (the non-transitory computer-readable storage medium of claim 10), wherein the database of contract text portions corresponds to an entity associated with the document management system (Col. 2 Lines 40 – 49; Col. 5 Lines 9 – 30; Col. 6 Lines 19 – 42; Col. 6 – 7 Lines 43 – 4 wherein the database of contract text portions corresponds to a user of the document management system).
Response to Arguments
Applicant's arguments filed 3/9/2026 have been fully considered but they are not persuasive.
Rejection under 35 USC 101
The rejection under 35 USC 101 has been maintained.
The Examiner asserts that the applicant’s arguments regarding the claimed invention improving upon machine learning by reciting “details of how” the training/retraining are performed is unpersuasive.
Specifically, the Examiner asserts that the claimed invention’s recitation of how the model is being trained/retrained is not directed towards the technology and techniques for improving machine learning, but describing what is included in the training data. The amended limitations are directed towards describing that data that is to be used for training/retraining the machine learning model and not directed towards technological techniques to improve machine learning training/retraining. The claimed invention is directed towards what the applicant believes, in their mind, is the “best” data. This is further supported by ¶ 35 of the applicant’s specification, wherein the applicant explicitly recites that the training/retraining process is based on generic training/retraining techniques and not directed towards improving the techniques. As a result, the Examiner asserts that the claimed/disclosed invention is reciting generic technology at a high level of generality and applying it to the abstract idea.
The applicant’s reference to “a method for training a neural network for facial detection…” does not apply to the claimed invention because, as stated above, the claimed invention is directed towards describing the data that the applicant believes, in their mind, is the “best” data to use for training/retraining the machine learning model and, as supported by ¶ 35 of the applicant’s specification, utilizing generic training/retraining techniques, rather than improving upon training/retraining techniques or resolving an issue that arose in the training/retraining of machine learning. Contrary to the claimed invention, the example that the applicant is relying on (Page 15, last paragraph) is directed towards identifying an issue that arose in the deeply rooted technology of facial detection and resolving those issues and/or improving upon the technology of facial detection. The claimed invention is not directed towards providing how machine learning training/retraining can be improved upon or identifying and resolving an issue that arose in the training/retraining of machine learning, but describing what information should be used and, again, relying on generic training/retraining techniques, as supported by ¶ 35 of the applicant’s specification.
The claimed invention is directed towards the recitation of generic technology and applying it to the abstract idea of contract drafting, as stated in ¶ 1 of the applicant’s specification, which recites, “The disclosure relates to the field of document management, and specifically to contract generation in document management systems,” as well as, (Claim 1) “determines a set of text portion suggestions related to an initial text portion received in a creation of a contract document and based on characteristics of the contract document; determines a likelihood that each text portion suggestion in the set of text portion suggestions will be selected as a completed text portion for the received initial text portion; … generates a set of top-ranked text portion suggestions based on ranking of the set of text portion suggestions”. The claimed invention is directed towards reciting generic machine learning to assist a user with identifying text that should be included for the creation of a contract. The claimed invention recites activities that can be performed by a human in their mind and/or with the aid of pen and paper, e.g., having a user search through pre-existing information, matching their particular scenario with the stored information, retrieving and writing in those portions from the stored information into their particular scenario, and drafting a contract that includes the terms/conditions/etc., which further includes those selected matched portions, as well as updating information based on feedback provided by a user. The claimed invention recites the collection and comparison of information, as well as the organization of information, and, based on a rule(s), identify options, in this case, ranking text that should be included in a contract.
Referring to MPEP § 2106.05(f), the training and re-training are merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP § 2106.05(f). Accordingly, the Examiner asserts that in light of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the claimed invention is analogous to Example 47, Claim 2.
Further, the combination of these elements is nothing more than a generic computing system with machine learning model(s). Because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP § 2106.05(f), they do not integrate the abstract idea into a practical application.
The claimed invention does not provide technological improvements to improve upon the accuracy of machine learning output, but describing the information that they believe, in their mind, is the “best” data to utilize and that they believe are “more accurate” predictions while still relying on generic machine learning training/retraining techniques to provide those predictions, as supported by ¶ 35 of the applicant’s specification.
Rejection under 35 USC 103
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
As was emphasized in the rejection, Kursun was provided to teach whether it would have been obvious to improve the system of lori, which discloses the use of a clause library manager configured to store and use the information to assist with contract drafting, by further having it employ machine learning in contract drafting, as well as improving the results of the model based on users providing some type of feedback on the results, as well as using user provided feedback to update the system to identify the most appropriate information to be provided based on the relationships, labels, and etc. (Col. 2 Lines 6 – 24; Col. 4 – 5 Lines 58 – 8; Col. 6 Lines 43 – 64). Kursun teaches that the feedback provided to the system is used to establish that the machine learning model's output should be improved upon and the feedback is used as the basis for determining how/what information the model should now be trained on, i.e. retraining using the feedback. The Examiner asserts that Kursun has not been provided to teach the entirety of the limitations, but to simply teach whether it would have been obvious to take this step further by employing machine learning in contract drafting, as well as the improving the results of the model based on users providing feedback on the results, as Iori teaches these rest of the limitations which would exclude the use of machine learning, training set data, updating the training data, and training/retraining of the machine learning model, as this provides the advantage of learning from events and applying it to future contract generation while providing a user(s) with the peace of mind that there would be little to no negative issues with the contract, such as, but not limited to, potential exposure or misappropriation level, an exposure appetite for the users involved, and generating the appropriate contract template, as well as be trained on recent tactics or strategies so that it can learn from these events and apply them to future contract generation.”
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate into the system and method that is configured to generating a contract using provided information as disclosed by Iori, as was discussed above, with the ability to utilize machine learning to assist with contract generation, as taught by Kursun, as this would provide provides a user(s) with the peace of mind that there would be little to no negative issues with the contract, such as, but not limited to, potential exposure or misappropriation level, an exposure appetite for the users involved, and generating the appropriate contract template, as well as be trained on recent tactics or strategies so that it can learn from these events and apply them to future contract generation.
Further, one of ordinary skill in the art of contract drafting and analysis based on historical information would have found it obvious to update the non-machine learning based system of Iori using modern data processing techniques, i.e. machine learning, as taught in Kursun, in order to gain the commonly understood benefits of such adaptation, such as, but not limited to, efficiency, speed, and the like.
Accommodating the prior arts more manual and antiquated process with modern electronics, in this case, training and retraining machine learning to assist with analyzing historical contracts, relationships associated with the contract, and drafting/recommending content, especially since, Kursun teaches that utilizing such technology in this technical field would have been obvious. As stated in Leapfrog, “applying modern electronics to older mechanical devices has been commonplace in recent years.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the attached PTO-892 Notice of References Cited:
Denninghoff (US Patent 12,561,392 B2); Izzat (WO 2025/243070 A1) – which disclose systems to assist a user with drafting a contract by providing the user with snippets, clauses, language, or the like to include in a contract
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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GERARDO ARAQUE JR
Primary Examiner
Art Unit 3629
/GERARDO ARAQUE JR/Primary Examiner, Art Unit 3629 3/17/2026