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 .
DETAILED CORRESPONDENCE
Status of Claims
Claims 21, 29, 38 have been amended.
Claims 1 – 20 have been cancelled.
No claims have been added.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 21 – 40 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
In regards to claims 21, 29, 38, the Examiner asserts that the following is new matter:
“detecting, using the selected machine-learned model, one or more document attributes in the one or more electronic documents, the one or more document attributes include at least one attribute of the one or more electronic documents that corresponds to at least one obligation in one or more obligations and at least another attribute of the one or more electronic documents that does not correspond to at least one obligation in the one or more obligations”
(emphasis added)
Specifically, when referring to ¶ 38, 39 of the specification, there is no support for, “the one or more document attributes include … and at least another attribute of the one or more electronic documents that does not correspond to at least one obligation in the one or more obligations”. ¶ 38 only provides support that the attribute includes an attribute of the one or more documents that corresponds to at least one obligation in the one or more obligations, while ¶ 39 discloses,
“The training set 310 may be separated into a positive training set and a negative training set. The positive training set includes the portions of text in the historical contract documents 315 corresponding to the historical legal obligations 320, as well as their rankings. The negative training set includes portions of text or clauses in the historical contract documents 315 that do not correspond to historical legal obligations 320. The negative training set may include attributes of contracts that do not amount to legal obligations, such as a jurisdiction whose laws govern the contract, an expiration date of the contract, a monetary value of the contract, and so on.”
The Examiner asserts that ¶ 39 only discloses that the training data is separated into a positive training set and negative set, but makes no disclosure that the positive training set is directed to, “the one or more document attributes include at least one attribute of the one or more electronic documents that corresponds to at least one obligation in one or more obligations” nor that the negative training data set is directed to “the one or more document attributes include … and at least another attribute of the one or more electronic documents that does not correspond to at least one obligation in the one or more obligations”. ¶ 39 does not support that the negative training data set is tied back to the attributes of ¶ 36, 38, or the claimed invention and that it is included with “at least one attribute of the one or more electronic documents that corresponds to at least one obligation in one or more obligations”. ¶ 39 recites “The negative training set may include attributes of contracts that do not amount to legal obligations, such as a jurisdiction whose laws govern the contract, an expiration date of the contract, a monetary value of the contract, and so on” and not “the one or more document attributes include … and at least another attribute of the one or more electronic documents that does not correspond to at least one obligation in the one or more obligations”.
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 21 – 40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
receiving a request to access a document management system;
determining to grant access to the document management system based on a set of permission definitions;
receiving one or more documents, wherein the one or more documents comprise at least one signed document;
selecting a trained machine-learned model from a plurality of machine-learned models based on a type of the one or more electronic documents
applying/access the selected trained model to the documents and using historical obligations in a plurality of historical documents, the applying including
detecting one or more document attributes in the one or more electronic documents, the one or more document attributes include
at least one attribute of the one or more electronic documents that corresponds to at least one obligation in one or more obligations and
at least another attribute of the one or more electronic documents that does not correspond to at least one obligation in the one or more obligations
in response to the detecting, identifying using the one or more document attributes, one or more portions of text within the one or more documents corresponding to the one or more obligations; and
include information representative of the one or more obligations within the one or more documents identified.
The invention is directed towards the abstract idea of contract management, which corresponds to “Certain Methods of Organizing Human Activities” as it is directed towards steps that can be performed by humans or through the aid of pen and paper, e.g., providing a human access to contracts for review and identifying obligations within the contract.
The limitations of:
receiving a request to access a document management system;
determining to grant access to the document management system based on a set of permission definitions;
receiving one or more documents, wherein the one or more documents comprise at least one signed document;
selecting a trained machine-learned model from a plurality of machine-learned models based on a type of the one or more electronic documents
applying/access the selected trained model to the documents and using historical obligations in a plurality of historical documents, the applying including
detecting one or more document attributes in the one or more electronic documents, the one or more document attributes include
at least one attribute of the one or more electronic documents that corresponds to at least one obligation in one or more obligations and
at least another attribute of the one or more electronic documents that does not correspond to at least one obligation in the one or more obligations
in response to the detecting, identifying using the one or more document attributes, one or more portions of text within the one or more documents corresponding to the one or more obligations; and
include information representative of the one or more obligations within the one or more documents identified,
are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a generic processor executing computer code stored on a computer medium and machine-learned model. That is, other than reciting a generic processor executing computer code stored on a computer medium and machine-learned model nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the generic processor executing computer code stored on a computer medium and machine-learned model in the context of this claim encompasses a user access contracts to review and identify obligations within the contract. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a generic processor executing computer code stored on a computer medium and machine-learned 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 processor executing computer code stored on a computer medium and machine-learned model to communicate, store, and display information, as well as performing operations that a human can perform in their mind or using pen and paper, i.e. access contracts to review and identify obligations within the contract. The generic processor executing computer code stored on a computer medium and machine-learned model in the steps are recited at a high-level of generality (i.e., as a generic processor executing computer code stored on a computer medium and machine-learned model can perform the insignificant extra solution steps of communicating, storing, and displaying information (See MPEP 2106.05(g) while also reciting that the a generic processor executing computer code stored on a computer medium and machine-learned model 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 processor executing computer code stored on a computer medium and machine-learned model.
With regards to claims 21, 25, 26, 27, 28, 29, 33, 34, 36, 37, 38, although the claim recites “machine-learned model” and training and retraining a machine learning model, the claims and specification fail to provide sufficient disclosure regarding an improvement to how a machine-learned model can be trained or retrained, but simply recites a high-level generic recitation that a machine-learned model is being applied. 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, retraining, and applying a machine-learned model is simply application of a computer model, itself an abstract idea manifestation. Further, such training, retraining, and applying of a machine-learned model is no more than putting data into a black box machine learning operation. The nomination as being a machine-learned model is a functional label, devoid of technological implementation and application details. The specification 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 disclosed invention, as presented in the originally filed specification, is not directed towards the improvement of machine learning, but directed towards contract management, or, in this case, identifying information within a 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. The Examiner asserts that the claimed invention fails to recite any iterative process being performed on the machine learning algorithm/model in order to demonstrate that the machine learning algorithm/model is being improved upon, i.e. a demonstration that would support an improvement upon machine learning technology. 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). 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 processor executing computer code stored on a computer medium and machine-learned model to perform the steps of:
receiving a request to access a document management system;
determining to grant access to the document management system based on a set of permission definitions;
receiving one or more documents, wherein the one or more documents comprise at least one signed document;
selecting a trained machine-learned model from a plurality of machine-learned models based on a type of the one or more electronic documents
applying/access the selected trained model to the documents and using historical obligations in a plurality of historical documents, the applying including
detecting one or more document attributes in the one or more electronic documents, the one or more document attributes include
at least one attribute of the one or more electronic documents that corresponds to at least one obligation in one or more obligations and
at least another attribute of the one or more electronic documents that does not correspond to at least one obligation in the one or more obligations
in response to the detecting, identifying using the one or more document attributes, one or more portions of text within the one or more documents corresponding to the one or more obligations; and
include information representative of the one or more obligations within the one or more documents identified,
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 22, 23 are directed towards organizing information according to a rule, in this case, ranking obligations based on risk.
Claims 24, 35 are directed to descriptive subject matter.
Claims 27, 28 are directed towards the recitation of generic technology and applying it to the abstract idea, as was discussed above.
The remaining claims are similar in subject matter to what has already been discussed above.
In summary, the dependent claims are simply directed towards providing additional descriptive factors that are considered for identifying information in a contract. Accordingly, the claims are not patent eligible.
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 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)(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.
Claims 21 – 40 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Krishna et al. (US PGPub 2022/0261711 A1).
In regards to claims 21, 29, 38, Krishna discloses (Claim 21) a computer-implemented method, comprising; (Claim 29) a non-transitory computer-readable storage medium storing executable instructions that, when executed by at least one processor circuitry, cause the at least one processor circuitry to; (Claim 38) a document management system, comprising:
(Claim 38) at least one processor circuitry; and a non-transitory computer-readable storage medium storing executable instructions that, when executed, cause the at least one processor circuitry to (¶ 74, 75, 79, 80):
In regards to:
receiving a request to access a document management system by a client device;
determining to grant the client device access to the document management system based on a set of permission definitions associated with the client device
(¶ 33, 34, 35, 64 wherein the system implements access control mechanisms that regulate and/or limit access to contract-related data based on the roles associated with an end-user, thereby enabling portions of the contract and other information to be protected as necessary when a user is requesting access to the system, wherein end-users can include contract managers, legal team members, directors of delivery, portfolio and project leads, and solution architects, among others)
receiving, using at least one processor circuitry, one or more electronic documents from the client device, wherein the one or more electronic documents comprise at least one electronically signed document
(Claim 29) receive one or mor electronic documents from the client device, wherein the one or more electronic document comprise at least one electronically signed document
(¶ 36, 46, 49 wherein a plurality of electronic documents is received, stored, and managed by the system; ¶ 29, 38, 39, 51, 58 wherein the documents fed into the system include, at least, contracts that have been finalized and signed (third stage), which further allows the system to provide information on key clauses, predictions, recommendations, tasks that need to be performed, and generate alerts);
selecting, using the at least one processor circuitry, a trained machine-learned model from a plurality of machine-learned models based on a type of the one or more electronic documents (¶ 37 wherein a first model is selected if the electronic documents are newly drafted contracts and a second model is selected to calculate risk of contracts);
In regards to:
applying, using the at least one processor circuitry, the selected machine-learned model to the one or more electronic documents, the machine-learned model has been trained using one or more historical obligations in a plurality of historical electronic documents, the applying including automatically
(Claim 29) access the selected trained machine-learned model to identify one or more portions of text within the one or more electronic documents corresponding to one or more obligations, the machine-learned model has been trained using one or more portions of text corresponding to one or more historical obligations in a plurality of historical electronic documents
(Claim 29) apply the machine-learned model to the one or more electronic documents, applying the machine-learned model includes automatically
(¶ 37, 46, 49, 69, 72 wherein a machine learning model (ML) is trained using historical electronic documents and applied to the electronic documents, e.g., a first model is selected if the electronic documents are newly drafted contracts and a second model is selected to calculate risk of contracts);
In regards to:
detecting, using the selected machine-learned model, one or more document attributes in the one or more electronic documents, the one or more document attributes include
at least one attribute of the one or more electronic documents that corresponds to at least one obligation in one or more obligations, and
at least another attribute of the one or more electronic documents that does not correspond to at least one obligation in the one or more obligations
(In light of the rejection under 35 USC 112(a) and ¶ 36, 38, 39 of the applicant’s specification, ¶ 30, 31, 37, 46 the ML is trained and applied using attributes from historical documents and the document(s) that it is analyzing, wherein the detected attributes correspond to obligations that the current document and historical document have in common, e.g., the obligation is “warranty”, “due date”, “penalty” and the attribute is the type of warranty “evergreen warranty”, the actual due date (the value or actual date rather than the category, heading, field, or the like), or the penalty that applies for not fulfilling the particular obligation (i.e. “attribute of the one or more electronic documents that corresponds to at least one obligation in one or more obligations”, as well as attributes that do not correspond to obligations found in the historical documents that the ML has been trained on and is unique or only corresponds to the current document, e.g., security expectations, changes in management, ownership of tasks, scope of services, or compliance with certain laws or regulations with a provision for adequate compensation (i.e. “at least another attribute of the one or more electronic documents that does not correspond to at least one obligation in the one or more obligations”); see also ¶ 46 regarding positive and negative classification); and
in response to the detecting, identifying, using the one or more document attributes, one or more portions of text within the one or more electronic documents corresponding to the one or more obligations (¶ 30, 31, 37, 46, 59, 60, 70 wherein, using the machine learning model, clauses corresponding to obligations are identified within the electronic documents); and
modifying, using the at least one processor circuitry, an interface to include information representative of the one or more obligations within the one or more electronic documents identified by the machine-learned model (¶ 59, 60, 70 wherein alerts, messages, or the like are presented to a user regarding the obligations).
22. In regards to claims 22, 30, 39, Krishna discloses the method of claim 21 (the non-transitory computer-readable storage medium of claim 29; the system of claim 38), further comprising ranking each of the one or more obligations (Fig. 11; ¶ 27, 28, 29, 34, 39, 53, 57, 59, 60; Claim 3 wherein the system performs recurring scheduled alerts scan to identify upcoming delivery due dates, obligations, timelines, penalties, and etc. in documents, i.e. a document is scanned on a recurring basis to rank due dates and if a due date is identified as an upcoming due date it will be ranked higher than other due dates and a user will be notified of the upcoming due dates. In other words, due dates are ranked based on whether they are upcoming due dates and will bring attention to clauses that are more likely to increase costs to a party while also relieving some of the burden on a party via continuous vigilance regarding both critical and basic aspects of contract delivery. Finally, the system also takes into consideration risk associated with due dates, breaches, or penalties. The invention is directed towards a proactive alert system that runs a recurring scheduled time-bound alerts scan, identifying upcoming due delivery due dates, and milestones where key dates and corresponding contract text are sent to a risk delivery alert model to further determine whether dates are linked to one or more penalty types.).
In regards to claims 23, 31, 40, Krishna discloses the method of claim 22 (the non-transitory computer-readable storage medium of claim 30; the system of claim 39), wherein the modifying includes ordering each obligation in the one or more obligations based on the ranking, wherein the ranking is based on a level of risk associated with each obligation in the one or more obligations (Fig. 11; ¶ 27, 28, 29, 34, 39, 53, 57, 59, 60; Claim 3 wherein the system performs recurring scheduled alerts scan to identify upcoming delivery due dates, obligations, timelines, penalties, and etc. in documents, i.e. a document is scanned on a recurring basis to rank due dates and if a due date is identified as an upcoming due date it will be ranked higher than other due dates and a user will be notified of the upcoming due dates. In other words, due dates are ranked based on whether they are upcoming due dates and will bring attention to clauses that are more likely to increase costs to a party while also relieving some of the burden on a party via continuous vigilance regarding both critical and basic aspects of contract delivery. Finally, the system also takes into consideration risk associated with due dates, breaches, or penalties. The invention is directed towards a proactive alert system that runs a recurring scheduled time-bound alerts scan, identifying upcoming due delivery due dates, and milestones where key dates and corresponding contract text are sent to a risk delivery alert model to further determine whether dates are linked to one or more penalty types).
In regards to claims 24, 32, Krishna discloses the method of claim 23 (the non-transitory computer-readable storage medium of claim 31), wherein the level of risk is based on: the information representative of the one or more obligations, the information including a priority, a monetary value, a type of electronic document in the one or more electronic documents, an entity associated with each obligation in the one or more obligations, an input from a user interface, or any combinations thereof (Fig. 11; ¶ 27, 28, 29, 34, 39, 53, 57, 59, 60; Claim 3 wherein the system performs recurring scheduled alerts scan to identify upcoming delivery due dates, obligations, timelines, penalties, and etc. in documents, i.e. a document is scanned on a recurring basis to rank due dates and if a due date is identified as an upcoming due date it will be ranked higher than other due dates and a user will be notified of the upcoming due dates. In other words, due dates are ranked based on whether they are upcoming due dates and will bring attention to clauses that are more likely to increase costs to a party while also relieving some of the burden on a party via continuous vigilance regarding both critical and basic aspects of contract delivery. Finally, the system also takes into consideration risk associated with due dates, breaches, or penalties. The invention is directed towards a proactive alert system that runs a recurring scheduled time-bound alerts scan, identifying upcoming due delivery due dates, and milestones where key dates and corresponding contract text are sent to a risk delivery alert model to further determine whether dates are linked to one or more penalty types).
In regards to claims 25, 33, Krishna discloses the method of claim 21 (the non-transitory computer-readable storage medium of claim 29), wherein the machine-learned model is configured to be retrained based on an input received via the interface, wherein the input includes an identification of at least one obligation that the machine-learned model failed to identify or incorrectly identified (¶ 69; Claim 6 wherein feedback is provided regarding the accuracy of the model to retrain the model).
In regards to claims 26, 34, Krishna discloses the method of claim 25 (the non-transitory computer-readable storage medium of claim 33),
wherein the machine-learned model is configured to be retrained using the identified at least one obligation that the machine-learned model failed to identify or incorrectly identified
(¶ 69; Claim 6 wherein feedback is provided regarding the accuracy of the model to retrain the model).
In regards to claims 27, 36, Krishna discloses the method of claim 21 (the non-transitory computer-readable storage medium of claim 29), wherein the applying comprises
selecting the trained machine-learned model from a plurality of machine-learned models based on a type of the one or more electronic documents, a type of the one or more obligations, or any combinations thereof; and
applying the selected machine-learned model to the one or more electronic documents
(¶ 36, 39, 56, 66, 71 wherein one or more machine learning models can be selected and used; ¶ 37 wherein machine models are utilized to perform specific tasks on specific information, such as, but not limited to, using a first model to classify electronic documents and another model to calculate risk levels associated with clauses/obligations).
In regards to claims 28, 37, Krishna discloses the method of claim 21 (the non-transitory computer-readable storage medium of claim 29), wherein the machine-learned model is configured to be trained using a positive training, a negative training, or any combinations thereof;
wherein the positive training is based on the at least one attribute of the one or more electronic documents that corresponds to the at least one obligation; or
wherein the negative training is based on the at least another attribute of the one or more electronic documents that does not correspond to the at least one obligation
(¶ 45, 69; Claim 6 wherein feedback is provided regarding the accuracy of the model to retrain the model to ensure accuracy).
In regards to claim 35, Krishna discloses the non-transitory computer-readable storage medium of claim 33, wherein the input includes a manual modification to a description, a risk level, a due date, a party to the one or more obligations, or any combinations thereof (Fig. 14; ¶ 7, 27, 28, 30, 40 wherein the model is used to classify contract clauses which include contract type, obligations, due dates, penalties, breaches, risk, risk to parties, responsibilities, and etc.; ¶ 69; Claim 6 wherein feedback is provided regarding the accuracy of the model to retrain the model).
Response to Arguments
Applicant's arguments filed 9/30/2025 have been fully considered but they are not persuasive.
Rejection under 35 USC 112(a)
The rejection under 35 USC 112(a) has been maintained.
First, the Examiner asserts that the typographical error present in the prior non-final office action mailed on 6/18/2025 does not change the rejection or why it was provided since “another” is referring to the attributes that in the documents. Specifically, the detecting step explicitly recites “attributes” in the one or more documents and then recites that the “document attributes”. As a result, it is clear that recitation of “another” in the prior office action, despite missing the term “attribute”, is referring to “another attribute”.
Second, the basis of the rejection was and continues to be based on this position and continues to not be based on another document, but another attribute.
With that said, the rejection is maintained because, as stated in the rejection, there is no support for a document to include an attribute that correspond to obligation in the one or more obligations and another attribute that does not correspond to an obligation in the one or more obligations.
As stated in the rejection, ¶ 38 only provides support that the attribute includes an attribute of the one or more documents that corresponds to at least one obligation in the one or more obligations. Meanwhile, ¶ 39 only provides support that training data is separated into positive training and negative training. Additionally, ¶ 39 does not support that the negative training data set is tied back to the attributes of ¶ 36, 38, or the claimed invention and that it is included with “at least one attribute of the one or more electronic documents that corresponds to at least one obligation in one or more obligations”. ¶ 39 recites “The negative training set may include attributes of contracts that do not amount to legal obligations, such as a jurisdiction whose laws govern the contract, an expiration date of the contract, a monetary value of the contract, and so on” and not “the one or more document attributes include … and at least another attribute of the one or more electronic documents that does not correspond to at least one obligation in the one or more obligations”.
Rejection under 35 USC 101
The rejection under 35 USC 101 has been maintained.
The applicant argues that the claimed invention does not describe certain methods of organizing human activities because it is not directed towards, inter alia, a fundamental economic practice, commercial or legal interactions, or managing personal behavior or relationships or interactions between people.
However, the Examiner respectfully disagrees.
The claimed invention and specification explicitly recite that the invention is directed towards identifying legal obligations in contracts, which falls under commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations). Further still, the applicant explicitly argues in the Remarks received on 9/30/2025 on Page 18, ¶ 2, “In contrast, as previously stated, the current subject matter, as recited in claim 21 and discussed in the specification of the present invention, is directed to document management system that identifies certain content, e.g., legal obligations, included within a set of contract documents and populates a graphical user interface with information representative of the identified content, e.g., legal obligations, thereby providing a unified interface that a user can view the identified content, e.g., legal obligations.” As a result, the Examiner, again, asserts, that the claimed invention does, indeed, recite an abstract idea, as well as being admitted by the applicant, because it is directed towards commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations).
The applicant continues on to argue that the claimed invention recites additional elements and, therefore, the judicial exception is integrated into a practical application, namely, the claimed invention recites a trained machine learned model and processor circuitry.
However, the Examiner respectfully disagrees.
Other than reciting a generic processor executing computer code stored on a computer medium and machine-learned model nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the generic processor executing computer code stored on a computer medium and machine-learned model in the context of this claim encompasses a user accessing contracts to review and identify obligations within the contract and presenting their findings. The Examiner asserts that there is no improvement to technology, the computer, or machine learned model. The computer is nothing more than a generic computer that is simply being used to communicate, store, and make information available and the machine learning model is nothing more than a generic machine learned model that has already been previously trained and, eventually, retrained using generic techniques (See ¶ 40 of the applicant’s specification). Simply using different content to make up the training data is not an improvement to the training process nor an improvement to a machine learned model.
Additionally, selecting one model over another is not an improvement to technology, the generic computer, or generic machine learned model, but simply retrieving and applying generic technology, which is also a concept that can be applied to humans and further based on the abstract idea of collecting and comparing, i.e. collecting/storing two or more models and comparing the information that a model is configured to process with the information that is to be processed.
The applicant refers to Example 47, Claim 3, however, the example and its analysis is not applicable to the claimed invention as the claimed invention is not directed towards the monitoring of network data, identifying malicious data packets, and blocking malicious data packets, which are processes that are deeply rooted in technology. The claimed invention is not directed towards resolving an issue that arose in technology, improving technology, or deeply rooted in technology, but directed towards reciting generic technology at a high level of generality and applying it to the abstract idea. At no point is the claimed invention or the specification concerned with identifying an issue that arose in machine learning techniques and resolving the identified issue nor is the claimed invention deeply rooted in technology as it is directed towards the abstract idea of reviewing a document and extracting certain information from the document for presentation, e.g., writing it down, speaking it aloud, or the like, which can be performed by humans, e.g., having a user review a contract and explain another user’s legal obligations to the another user. Example 47, Claim 3 is directed towards, at least, utilizing the results of machine learning to improve technology and perform operations that are deeply rooted in technology, in this case, dropping malicious data packets in transit to block the malicious data packets from reaching their destination.
The Examiner asserts that the claimed invention is similar to Example 47, Claim 2, since, as discussed above, it is directed towards the recitation of generic technology at a high level of generality, e.g., generic computer and generic machine learning technology, and applying it to the abstract idea to, essentially, collect and compare information and, based on a rule, identify options and/or identify/extract information based on the comparison. As stated above, other than reciting a generic processor executing computer code stored on a computer medium and machine-learned model nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the generic processor executing computer code stored on a computer medium and machine-learned model in the context of this claim encompasses a user accessing contracts to review and identify obligations within the contract.
Further, the Examiner asserts that the claimed invention is not concerned with improving machine learning as the claimed invention is relying on machine-learned models, thereby demonstrating that the claimed invention is relying on established technology and not directed towards improving the technology or field of machine learning. Selecting a machine-learned model is not an improvement to the model and further encompasses human activities as a human can select an already trained machine-learning model. Moreover, the selection of pre-existing technology, which encompasses a human performed action, is not a demonstration that the claimed invention is deeply rooted in technology, which is further supported by the statements provided above. Simply reciting that a generic computing device (processor circuitry) is being used to make a selection is not a demonstration that the claimed invention is deeply rooted in technology, an improvement to technology, or resolving an issue that arose in the technology, but the recitation of generic technology at a high level of generality and applying it to the abstract idea, while also encompassing that the activity is still an activity that can be performed by a human as a human can use a computer and select an already trained model, e.g., using a generic computer mouse, touching a touchscreen, or etc.
Finally, “modifying” an interface, as recited in the claimed invention is not improving the interface, resolving an issue that arose in interfaces, or deeply rooted in interface technology as the limitation is directed towards describing that displayed (extra-solution activity) information is simply being updated in as much the same way that a human can rewrite information, erasing and write new information, and the like.
The claimed invention is simply reciting generic technology at a high level of generality and applying it to the abstract idea for the benefits that such technology provides, i.e. faster, more efficient, less prone to human error, and etc.
Finally, with regards to the applicant’s argument on Page 18, ¶ 1, no mention of the claimed invention reciting “Mathematical Concepts” was made in the office action. As a result, the argument is unpersuasive and moot as it does not apply to the rejection provided.
Rejection under 35 USC 102
The applicant argues that Krishna does not disclose the limitations 4 – 9 and, more specifically, argues that Krishna does not disclose “‘type of document’ nor does it select a specific machine-learned model to process it, nor does it distinguish between attributes that correspond to obligations (e.g., positive attribute) and that do not correspond to obligations (e.g., negative attributes) for the purposes of surfacing obligations.”
However, the Examiner respectfully disagrees.
First, in response to applicant's argument that the references fail to show certain features of applicant’s invention, it is noted that the features upon which applicant relies (i.e., “distinguish between attributes that correspond to obligations (e.g., positive attribute) and that do not correspond to obligations (e.g., negative attributes)”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Second, ¶ 37 of Krishna explicitly discloses that a first machine learning model is configured to classify newly drafted contracts and highlight potential risk factors, whereas a second machine learning model is configured to calculate risk levels with specific SLAs as another type of risk factor. In other words, the first model is only selected for document types that are newly drafted contracts and have not had potential risks highlighted, as well as contracts that are being drafted, whereas the second model is used to calculate risk levels with specific SLAs and only document types that have missing sections in a contract and have had risks highlighted.
More specifically, ¶ 30, 31, 37, 46 the ML is trained and applied using attributes from historical documents and the document(s) that it is analyzing, wherein the detected attributes correspond to obligations that the current document and historical document have in common, e.g., the obligation is “warranty”, “due date”, “penalty” and the attribute is the type of warranty “evergreen warranty”, the actual due date (the value or actual date rather than the category, heading, field, or the like), or the penalty that applies for not fulfilling the particular obligation (i.e. “attribute of the one or more electronic documents that corresponds to at least one obligation in one or more obligations”, as well as attributes that do not correspond to obligations found in the historical documents that the ML has been trained on and is unique or only corresponds to the current document, e.g., security expectations, changes in management, ownership of tasks, scope of services, or compliance with certain laws or regulations with a provision for adequate compensation (i.e. “at least another attribute of the one or more electronic documents that does not correspond to at least one obligation in the one or more obligations”) (see also ¶ 46 regarding positive and negative classification). To put it simply, each model is trained and applied to perform specific tasks for specific contract types and the second model cannot be used to perform the actions of the first model and vice-versa.
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.
Wodetzki et al. (WO 2018/170321 A1); Castallanos (US PGPub 2005/0182736 A1); McKeown et al. (US PGPub 2015/0032645 A1); Wodetzki et al. (US Patent 11,416,956 B2); Han et al. (US PGPub 2022/0004713 A1) – which are directed towards utilizing machine learning to review and analyze the contents of contracts and presenting its findings
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 10/7/2025