Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
2. The Amendment filed December 30, 2025 has been entered. Claims 1-20 are pending and are rejected for the reasons set forth below.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention recites and is directed to a judicial exception to patentability (i.e., a law of nature, a natural phenomenon, or an abstract idea) and does not include an inventive concept that is “significantly more” than the judicial exception under the January 2019 and October 2019 patentable subject matter eligibility guidance (2019 PEG) analysis which follows.
Step 1
5. Under the 2019 PEG step 1 analysis, it must first be determined whether the claims are directed to one of the four statutory categories of invention (i.e., process, machine, manufacture, or composition of matter). Applying step 1 of the analysis for patentable subject matter to the claims, it is determined that the claims are directed to the statutory category of a process (claims 16-19), a machine (claims 1-15) and a manufacture (claim 20); where the machine and the manufacture are substantially directed to the subject matter of the process. (See e.g., MPEP §2106.03). Therefore, we proceed to step 2A, Prong 1.
Step 2A, Prong 1
6. Under the 2019 PEG step 2A, Prong 1 analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories of patent ineligible subject matter (i.e., organizing human activity, mathematical concepts, and mental processes) that amount to a judicial exception to patentability.
Claim 1 recites the abstract idea of:
assign, [[on at least one database]], a label to data associated with a plurality of products, the label representing one of transaction record data, supply chain data, supply chain entity data, or supply chain agreement data
create, [[using the training engine]], a first model training dataset by retrieving and parsing the labeled data [[from the at least one database]], wherein the first model training dataset comprises transaction trends of the plurality of products;
develop, [[using the trained first machine learning model]], a set of rules to classify the plurality of products having the transaction trends as suitable for supply chain insurance;
output, [[using the trained first machine learning model]] and the set of rules, a classification of suitability of a first product of the plurality of products for the supply chain insurance based on the corresponding transaction trends;
create, [[using the training engine]], a second model training dataset by retrieving and parsing, [[from the at least one database]], the labeled data associated with the first product, wherein the second model training dataset comprises terms of supply chain agreements for the first product;
output, [[using the trained second machine learning model]], one or more terms of a supply chain insurance contract for the first product, wherein the one or more terms comprise at least one of a volume term of the supply chain insurance contract or costs associated with the volume term;
collect, [[using the interactive user platform]], user interaction data associated with interactions on [[the user platform]], the user interaction data including a user input that indicates whether the one or more terms of the supply chain insurance contract are accepted;
receive, [[from at least one external computing device associated with at least one of a plurality of supply chain entities]], bid data for the supply chain insurance contract, wherein the bid data includes a proposed term of the supply chain insurance contract that is different from at least one of the one or more terms for the supply chain insurance contract outputted by [[the second machine learning model]];
update, using the collected user interaction data and the received bid data, at least one of the first model training dataset or second model training dataset.
Here, the recited abstract idea falls within one or more of the three enumerated 2019 PEG categories of patent ineligible subject matter, to wit: certain methods of organizing human activity, which includes fundamental economic practices or principles and/or commercial interactions (e.g., insurance - here, generating a supply chain insurance contract).
Step 2A, Prong 2
7. Under the 2019 PEG step 2A, Prong 2 analysis, the identified abstract idea to which claim 1 is directed does not include limitations or additional elements that integrate the abstract idea into a practical application.
Besides reciting the abstract idea, the limitations of claim 1 also recite generic computer components (e.g., a computer system for training machine learning models comprising at least one processor comprising a machine learning engine that comprises a training engine and a plurality of trainable modules, and a memory in communication with the at least one processor, the memory comprising computer-executable instructions; at least one database; a first machine learning model implemented by the market module; a second machine learning model implemented by the contract term module; an interactive user platform displayed on a user interface of a computing device; and at least one external computing device associated with at least one of a plurality of supply chain entities). In particular, the recited features of the abstract idea are merely being applied on a computer or computing device or via software programming that is simply being used as a tool (“apply it”) to implement the abstract idea. (See e.g., MPEP §2106.05(f)).
Claim 1 also recites the following limitations:
train, using the first model training dataset and event data, a first machine learning model implemented by a market module of the plurality of trainable modules;
train, using the second model training dataset, a second machine learning model implemented by a contract term module of the plurality of trainable modules; and
continuously retrain, using at least one of the updated first model training dataset or updated second model training dataset, at least one of the trained first machine learning model or the trained second machine learning model to refine at least one of the trained first machine learning model or the trained second machine learning model.
These limitations recite processes for training a market module and a contract term module based on training data sets. However, the claims do not provide significant technical detail regarding how these modules are trained and/or how the modules function to produce the desired output. Therefore, these limitations amount to no more than simply applying generic machine-learning models to implement the abstract idea on a computer.
Therefore, these additional elements are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components. In other words, the additional elements are simply used as tools to perform the abstract idea.
Claim 1 also includes the following limitation:
generate an interactive user platform to display on a user interface of a computing device associated with a supply chain entity, wherein the interactive user platform receives user input and displays (i) the classification suitability of the first product outputted by the first machine learning model and (ii) the one or more terms of the supply chain insurance contract for the first product outputted by the second machine learning model.
This limitation merely states that the system generates/displays a user platform via a user interface. However, the claim does not provide significant technical detail regarding how the user interface is structured, or how the information is displayed to the user. Therefore, this limitation amounts to no more than merely outputting/displaying data, which is a form of insignificant extra-solution activity (See MPEP 2016.05(g): OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015)).
Thus, claim 1 does not include any limitations or additional elements that integrate the abstract idea into a practical application. As a result, claim 1 is directed to an abstract idea.
Step 2B
8. Under the 2019 PEG step 2B analysis, the additional elements of claim 1 are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea. (i.e., an innovative concept). Here, the recited additional elements (e.g., a computer system for training machine learning models comprising at least one processor comprising a machine learning engine that comprises a training engine and a plurality of trainable modules, and a memory in communication with the at least one processor, the memory comprising computer-executable instructions; at least one database; a first machine learning model implemented by the market module; a second machine learning model implemented by the contract term module; and an interactive user platform displayed on a user interface of a computing device; and at least one external computing device associated with at least one of a plurality of supply chain entities), do not amount to an innovative concept since, as stated above in the Step 2A, Prong 2 analysis, the claims are simply using the additional elements as a tool to carry out the abstract idea (i.e., “apply it”) on a computer or computing device and/or via software programming (See e.g., MPEP §2106.05(f)). The additional elements are specified at a high level of generality such that they are being used in the claims to simply implement the abstract idea and are not themselves being technologically improved (See e.g., MPEP §2106.05 I.A.); (See also e.g., applicant’s Specification at least Paragraphs x).
Additionally, the following limitation identified above as insignificant extra-solution activity (mere data outputting) has been reevaluated under Step 2B:
generate an interactive user platform to display on a user interface of a computing device associated with a supply chain entity, wherein the interactive user platform receives user input and displays (i) the classification suitability of the first product outputted by the first machine learning model and (ii) the one or more terms of the supply chain insurance contract for the first product outputted by the second machine learning model.
As stated in MPEP 2106.05(d), a factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity (Berkheimer v. HP, Inc., 881 F.3d 1360, 1368 (Fed. Cir. 2018)). In view of this requirement set forth by Berkheimer, this limitation does not integrate the abstract idea into a practical application, or amount to significantly more than the abstract idea, because the courts have found the concept of merely outputting/displaying data to be well-understood, routine, and conventional activity (See MPEP 2106.05(d): OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015)).
Thus, claim 1 does not recite any additional elements that amount to “significantly more” than the abstract idea.
Additional Independent Claims
9. Independent claims 16 and 20 are similarly rejected under 35 U.S.C. 101 for the reasons described below:
Claim 16 recites limitations that are substantially similar to those recited in claim 1. However, the primary difference between claims 16 and 1 is that claim 16 is drafted as a method rather than as a system. Similarly, as described above regarding claim 1, claim 16 recites generic computer components (e.g., a computing device including a processor including a machine learning engine that includes a training engine and a plurality of trainable modules, at least one database, a machine learning engine, a first machine learning model implemented by a market module, a second machine learning model implemented by a contract term module, an interactive user platform generated on a user interface of a computing device, and at least one external computing device associated with at least one of a plurality of supply chain entities) that are simply being used as a tool (“apply it”) to implement the abstract idea. Therefore, since the same analysis should be used for claims 1 and 16, claim 16 is not patent eligible (See Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2354 (2014)).
Claim 20 recites limitations that are substantially similar to those recited in claim 1. However, the primary difference between claims 20 and 1 is that claim 20 is drafted as a computer-readable storage medium rather than as a system. Similarly, as described above regarding claim 1, claim 20 recites generic computer components (e.g., a non-transitory computer-readable storage medium that includes computer-executable instructions, a processor including a machine learning engine that includes a training engine and a plurality of trainable modules implemented by the machine learning engine, a computing device, at least one database, a machine learning engine, a first machine learning model implemented by the market module, a second machine learning model implemented by the contract term module, an interactive user platform displayed on a user interface of a computing device, and at least one external computing device associated with at least one of a plurality of supply chain entities) that are simply being used as a tool (“apply it”) to implement the abstract idea. Therefore, since the same analysis should be used for claims 1 and 20, claim 20 is not patent eligible (See Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2354 (2014)).
Dependent Claims
10. Dependent claims 2-15 and 17-19 are also rejected under 35 U.S.C. 101 for the reasons described below:
Claim 2 recites limitations similar to those recited in independent claim 1. This claim describes a process for training a “volume term module” based on a training dataset comprising volume terms of supply chain agreements and information associated with inventory shortages of the first product. The volume term module is then applied to determine one or more volume terms of the supply chain insurance contract. Similarly, as described above regarding claim 1, this claim does not provide significant technical detail regarding how the module is trained and/or how the module is implemented to provide the desired output. Therefore, these limitations amount to no more than simply applying a generic machine learning model to implement the abstract idea on a computer.
Claim 3 recites limitations similar to those recited in independent claim 1. This claim describes a process for training a “pricing term module” based on a training dataset comprising pricing terms of supply chain agreements and information associated with historical costs of the first product. The pricing term module is then applied to determine a pricing term of the supply chain insurance contract. Similarly, as described above regarding claim 1, this claim does not provide significant technical detail regarding how the module is trained and/or how the module is implemented to provide the desired output. Therefore, these limitations amount to no more than simply applying a generic machine learning model to implement the abstract idea on a computer.
Claims 4 and 5 provides further definition to the “third machine learning model” recited in claim 3. Specifically, claims 4 and 5 simply state that the third machine learning model is trained based on models of economic costs to supply chain entities. However, simply describing the type of data that is used to train the model does not provide an indication of an improvement to any technology or technological field. Rather, this amounts to no more than merely applying a generic training process using a specific type of data.
Claims 6 and 17 simply refine the abstract idea because they recite a process step (e.g., generating a supply chain contract that includes the terms outputted by the contract term module) that falls under the category of organizing human activity, namely generating a supply chain insurance contract, as described above regarding claim 1. Additionally, claims 6 and 17 recite a limitation for displaying the contract via the user platform. However, as described above regarding claim 1, this amounts to no more than merely outputting/displaying data, which is a form of insignificant extra-solution activity.
Claims 7 and 18 simply state that the first and/or second machine learning model are “retrained” using information regarding the acceptance of the insurance contract. Similarly, as described above regarding claim 1, this claim does not provide significant technical detail regarding how the module is trained and/or how the module is implemented to provide the desired output. Therefore, these limitations amount to no more than simply applying a generic machine learning model to implement the abstract idea on a computer.
Claims 8 and 19 simply state that the second machine learning model is “retrained” using information regarding the differences between the proposed term and the one or more terms outputted by the contract module. Similarly, as described above regarding claim 1, this claim does not provide significant technical detail regarding how the module is trained and/or how the module is implemented to provide the desired output. Therefore, these limitations amount to no more than simply applying a generic machine learning model to implement the abstract idea on a computer
Claim 9 simply further refines the abstract idea because it recites a process step (e.g., receiving user input to modify an existing contract, and generating an updated contract according to the proposed modifications) that falls under the category of organizing human activity, namely generating a supply chain insurance contract, as described above regarding claim 1.
Claim 10 simply states that the first and/or second machine learning model are “retrained” based on the user input described in claim 9. Similarly, as described above regarding claim 1, this claim does not provide significant technical detail regarding how the module is trained and/or how the module is implemented to provide the desired output. Therefore, these limitations amount to no more than simply applying a generic machine learning model to implement the abstract idea on a computer.
Claim 11 recites limitations similar to those recited in independent claim 1. This claim describes a process for training the market module based on a training dataset comprising data regarding the historical demand for a plurality of products. Similarly, as described above regarding claim 1, this claim does not provide significant technical detail regarding how the module is trained and/or how the module is implemented to provide the desired output. Therefore, these limitations amount to no more than simply applying a generic machine learning model to implement the abstract idea on a computer.
Claim 12 recites limitations similar to those recited in independent claim 1. This claim describes a process for training a demand module based on a dataset, and generating a number of supply chain insurance contracts based on the output of the model. Similarly, as described above regarding claim 1, this claim does not provide significant technical detail regarding how the module is trained and/or how the module is implemented to provide the desired output. Therefore, these limitations amount to no more than simply applying a generic machine learning model to implement the abstract idea on a computer. Additionally, claim 12 recites a limitation for displaying the contracts via the user platform. However, as described above regarding claim 1, this amounts to no more than merely outputting/displaying data, which is a form of insignificant extra-solution activity.
Claim 13 recites limitations similar to those recited in independent claim 1. This claim describes a process for training a risk module based on a dataset of insurance events, and generating a number of supply chain insurance contracts based on the output of the model. Similarly, as described above regarding claim 1, this claim does not provide significant technical detail regarding how the module is trained and/or how the module is implemented to provide the desired output. Therefore, these limitations amount to no more than simply applying a generic machine learning model to implement the abstract idea on a computer. Additionally, claim 13 recites a limitation for displaying the contracts via the user platform. However, as described above regarding claim 1, this amounts to no more than merely outputting/displaying data, which is a form of insignificant extra-solution activity.
Claim 14 provides further definition to the plurality supply chain insurance contracts recited in claim 12. Specifically, claim 14 simply states that the number of contracts generated is less than the volume of demand determined by the demand module. However, simply defining the number of contracts generated does not provide an indication of an improvement to any technology or technological field. Rather, this amounts to no more than merely applying a restriction to the number of contracts that may be generated by the system.
Claim 15 recites limitations similar to those recited in independent claim 1. This claim describes a process for training a pooling module based on a dataset comprising information associated with procurement logistics of a product, and generating a pooled contract comprising the terms determined by contract term module. Similarly, as described above regarding claim 1, this claim does not provide significant technical detail regarding how the module is trained and/or how the module is implemented to provide the desired output. Therefore, these limitations amount to no more than simply applying a generic machine learning model to implement the abstract idea on a computer. Additionally, claim 15 recites a limitation for displaying the contracts via the user platform. However, as described above regarding claim 1, this amounts to no more than merely outputting/displaying data, which is a form of insignificant extra-solution activity.
Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Response to Arguments
11. Applicant’s arguments filed December 30, 2025 have been fully considered.
Arguments Regarding 35 U.S.C. 101
12. Applicant’s arguments (Amendment, Pgs. 14-18) concerning the prior rejection of the claims under 35 USC §101, including supposed deficiencies in the rejection, are not persuasive for the following reasons. Under the prior and current 101 analysis under 2019 PEG, the amended claims recite and are directed to a patent ineligible abstract idea, without something significantly more, for the reasons given above after consideration of the claimed features and elements. The abstract idea has been restated herein in line with the 2019 PEG guidance and the amended claims. Applicant is directed to the above full Alice/Mayo analysis in the 101 rejection.
Additionally, on page 15 of their remarks, the applicant argues, “To the contrary to the allegations in the Office Action, the present claims do not recite certain methods of organizing human activity… Although the claims may involve supply chain insurance contracts, the claims do not recite insurance contract processing, but instead recite technical operations for training machine learning models to generate accurate, predictive outputs.” The examiner respectfully disagrees. While the examiner acknowledges that the claims recite limitations regarding the training of machine learning models, the claims also recite limitations which fall under the category of organizing human activity. For example, the claims recite limitations for assigning labels to supply chain-related data, creating datasets comprising transaction trends and terms of supply chain agreements, developing rules to classify products, and receiving/storing user input indicating whether an entity is agreeable to one or more terms of a supply chain insurance contract. Such limitations fall under the category of fundamental economic practices (e.g., insurance) and/or commercial interactions (e.g., agreements in the form of contracts) (See MPEP 2106.04(a)). As described in the 101 rejection above, the claims simply apply generic machine learning technology to facilitate the creation, presentation, and storage of data corresponding to the supply chain insurance contracts.
Additionally, on pages 15 and 16 of their remarks, the applicant argues, “Similar to claim 2 of Example 39, the pending claims recite "training, using a first model training dataset, a first machine learning model" and "training, using a second model training dataset, a second machine learning model." These limitations encompass a range of implementation techniques but do not define mathematical formulas, equations, or symbolic computations. In addition, it is clear that these limitations do not recite any methods of organizing human activity or mental processes. Further, a person of ordinary skill in the art would understand the claims, in light of the specification, as reciting machine learning models trained to identify patterns in data and generate accurate predictive outputs associated with values, such as supply chain insurance parameters. The claims therefore recite upstream machine-learning functionality consistent with the eligible claims of Example 39.” The examiner respectfully disagrees. Specifically, the examiner disagrees that the analysis of Example 39 is applicable to the claims of the instant application. The examiner notes that the August 4, 2025 USPTO memorandum, as it relates to Example 39, clarifies that claim limitations that may involve mathematical concepts do not necessarily recite a mathematical calculation. Therefore, the limitations recited in Example 39 regarding the training of machine learning models do not recite a mathematical calculation simply because machine learning techniques involve mathematical calculations. The examiner has not taken the position that the training/retraining of such machine learning models, as currently recited in the claims, is itself an abstract idea, or that the claims recite mathematical calculations. Rather, as discussed in the 101 rejection above, the claims recite limitations that fall under the category of organizing human activity, and the machine learning model is simply used as a tool to facilitate the abstract idea. The examiner agrees that the limitations for training/retraining the machine learning models, when considered individually, do not recite certain methods of organizing human activity. However, the claims recite limitations outside of training/retraining the machine learning models that do fall under the category of organizing human activity. When considered as additional elements, the application of machine learning in the claims does not integrate the abstract idea into a practical application for the reasons described in the 101 rejection above.
Additionally, on page 17 of their remarks, the applicant argues, “The pending claims include additional elements that enable the claimed system to address the technical problems of these known computer platforms. In particular, the claimed system addresses these technical deficiencies by reciting technical details of a system that improves machine learning models through iterative training by updating model training datasets and using user interaction data collected via a custom-generated user platform that presents model outputs on a computing device. This architecture overcomes limitations of conventional systems, including inaccurate market prediction, ineffective presentation of machine-generated market intelligence, and insufficient training data for refining machine learning models.” The examiner respectfully disagrees. Specifically, the examiner disagrees that the claims recite an improvement to any technology or technological field. As described above, the claims do not provide any indication of an improvement to machine learning technology. While there may be benefits to applying machine learning technology to the processes recited in the claims, the claims do not provide any indication that the machine learning models and training methods are anything other than generic machine learning technologies and techniques. Therefore, the application of such models and techniques amounts to no more than merely applying generic machine learning technology to implement the abstract idea on a computer.
Additionally, on page 18 of their remarks the applicant argues, “In the instant application, the pending claims clearly recite more than well-understood, routine, or conventional activities at least with respect to improving machine learning models through iterative training by implementing a combination of dynamically updated training datasets, user interaction-driven retraining, and a custom-generated user platform that operate in a non-generic manner to enhance machine learning outputs. In other words, the present claims include a combination of limitations that operate in a non-conventional and non- generic way to refining machine learning models. (Emphasis added.) The fact that there is no art cited against the pending claims strengthens the conclusion that the recited limitations are not well understood, routine, and conventional.” The examiner respectfully disagrees. Specifically, the examiner notes that whether a prior art rejection is applied to a claim is not a consideration under 35 U.S.C. 101 (See MPEP 2106.04(I): “The Supreme Court’s decisions make it clear that judicial exceptions need not be old or long-prevalent, and that even newly discovered or novel judicial exceptions are still exceptions”). Simply applying generic machine learning and other computer-based technology to facilitate an abstract idea does not amount to significantly more than the abstract idea.
Therefore, for these reasons and the reasons given above, the rejection of these claims under 35 U.S.C. §101 is maintained.
Citation of Pertinent Prior Art
13. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kiebler (U.S. Pre-Grant Publication No. 20230289914): Describes systems for providing data analytics to support intuitive, risk-based underwriting for product contamination insurance coverage. Using the insights provided by the platform, for example, insurers may establish an affordable, risk-based pricing model. The pricing model, for example, may reward companies having risk-mitigated supply chains with lower premiums.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM D NEWLON whose telephone number is (571)272-4407. The examiner can normally be reached Mon - Fri 8:30 - 4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Gart can be reached at (571) 272-3955. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WILLIAM D NEWLON/Examiner, Art Unit 3696
/MATTHEW S GART/Supervisory Patent Examiner, Art Unit 3696