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
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on April 9, 2026 has been entered.
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 § 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 model from a plurality of machine-learned models based on a type of the one or more electronic documents
(Claim 29) access the selected model to identify one or more portions of text within the one or more electronic documents corresponding to one or more obligations, the model using one or more portions of text corresponding to one or more historical obligations in a plurality of historical documents;
applying/access the selected model to the documents and using historical obligations in a plurality of historical documents, the model using positive data set including portions of text corresponding to historical legal obligations and a negative data set including portions of text that do not correspond to legal obligations
automatically identifying, using the selected model, 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 corresponding to the identified one or more portions of text.
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 an authorized human access to contracts for review and identifying obligations within the contract to include information representative of the obligations.
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 model from a plurality of machine-learned models based on a type of the one or more electronic documents
(Claim 29) access the selected model to identify one or more portions of text within the one or more electronic documents corresponding to one or more obligations, the model using one or more portions of text corresponding to one or more historical obligations in a plurality of historical documents;
applying/access the selected model to the documents and using historical obligations in a plurality of historical documents, the model using positive data set including portions of text corresponding to historical legal obligations and a negative data set including portions of text that do not correspond to legal obligations
automatically identifying, using the selected model, 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 corresponding to the identified one or more portions of text,
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 an authorized user accessing contracts to review and identify obligations within the contract to include information representative of the obligations. 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”, as well as training and retraining the machine learned 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 model from a plurality of machine-learned models based on a type of the one or more electronic documents
(Claim 29) access the selected model to identify one or more portions of text within the one or more electronic documents corresponding to one or more obligations, the model using one or more portions of text corresponding to one or more historical obligations in a plurality of historical documents;
applying/access the selected model to the documents and using historical obligations in a plurality of historical documents, the model using positive data set including portions of text corresponding to historical legal obligations and a negative data set including portions of text that do not correspond to legal obligations
automatically identifying, using the selected model, 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 corresponding to the identified one or more portions of text,
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 more 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 a positive training set including portions of text corresponding to historical legal obligations and a negative training set including portions of text that do not correspond to legal obligations
(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 selected machine-learned model to the one or more electronic documents, wherein the machine-learned model has been trained using portions of text corresponding to historical legal obligations and portions of text that do not correspond to legal obligations
(¶ 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; ¶ 27, 29, 31, 39, 49, 50, 51, 59, 60, 69, 70 wherein the clauses comprise positive and negative clauses as part of the training data, wherein the positive clauses include legal obligations (e.g., penalty details, laws, regulations, contractual liability, and the like) and the negative clauses include non-legal obligations (e.g., due dates, milestones, payment, and the like));
automatically identifying, using the selected machine-learned model, 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 corresponding to the identified one or more portions of text (¶ 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; ¶ 27, 29, 31, 39, 49, 50, 51, 59, 60, 69, 70 wherein the clauses comprise positive and negative clauses as part of the training data, wherein the positive clauses include legal obligations (e.g., penalty details, laws, regulations, contractual liability, and the like) and the negative clauses include non-legal obligations (e.g., due dates, milestones, payment, and the like)).
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 4/9/2026 have been fully considered but they are not persuasive.
Rejection under 35 USC 112(a)
The rejection under 35 USC 112(a) has been withdrawn due to amendments.
No rejection under 35 USC 112(b) was provided.
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 computer system.
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 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.
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 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.
Further, “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, as was found in Alice Corp v CLS Bank, the claims in Alice Corp v CLS Bank also required a computer that processed streams of data, but nonetheless were found to be abstract. There is no “inventive concept” in the claimed invention's use of a general-purpose computing devices to perform well-understood, routine, and conventional activities commonly used in the technical field. Although one may argue that the human mind is unable to process and recognize the electronic stream of data that is being received, transmitted, stored, and etc. by the computing device, the Examiner asserts that this is insufficient to overcoming the rejection under 35 USC 101 (see Content Extraction and Transmission LLC v Wells Fargo Bank, National Association and Cyberfone where the system uses categories to organize, store, and transmit information, which was considered by the courts to be an abstract idea). The claims in Alice Corp v CLS Bank also required a computer that processed streams of data, but nonetheless were found to be abstract. There is no “inventive concept” in the claimed invention's use of a general-purpose computing device to perform the activities that have been discussed above (Content Extraction and Transmission LLC v Wells Fargo Bank, National Association).
Rejection under 35 USC 102
The Examiner asserts that the applicant’s arguments are directed towards newly amended limitations and are, therefore, considered moot. However, the Examiner has responded to the newly submitted amendments, which the arguments are directed to, in the rejection above, thereby addressing the applicant’s arguments.
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.
Krishna (US Patent 11,755,973 B2); Katz et al. (US Patent 11,687,576 B1); Bonfante et al. (US PGPub 2024/0070794 A1) – which disclose the use of positive and negative training data for various uses, such as, but not limited to, document analysis
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GERARDO ARAQUE JR whose telephone number is (571)272-3747. The examiner can normally be reached Monday - Friday 8-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sarah Monfeldt can be reached at 571-270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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GERARDO ARAQUE JR
Primary Examiner
Art Unit 3629
/GERARDO ARAQUE JR/Primary Examiner, Art Unit 3629 6/4/2026
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