DETAILED ACTION
This Non-Final Office Action is in response to the arguments and Request for Continued Examination filed December 16, 2025.
Claims 1-5, 8-12, 15-19, and 21-25 are currently pending and have been considered below.
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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 December 16, 2025 has been entered.
Response to Arguments
In response to the arguments filed December 16, 2025 regarding the 35 USC 101 rejection, specifically that the claimed invention is directed towards eligible subject matter.
Examiner respectfully disagrees.
The arguments allege that the claims providing AI within a transaction agreement between multi-party negotiations is directed towards a technical improvement. In terms of the consideration, the claims are directed towards receiving transaction agreement information, generating a revised agreement, and negotiating the revised agreement. The claims are directed towards an abstract idea under the certain method of organizing human activity grouping. In terms of the additional elements, the claims are directed towards training a machine learning component to collect feedback and retrain the ML. The machine learning training and retraining is described in the originally filed specification [19-25]. The specification merely describes techniques and high level ML elements to implement the abstract idea. The training steps are describing the input elements that are used to train and the ML is a generic model to provide the analysis to the identified abstract idea. As such, the training and ML are generic technology to implement the abstract idea. Therefore, the additional elements are not transformative into a practical application nor are they significantly more than the identified abstract idea. The dependent claims were considered individually and as whole/combination. The claimed invention is directed towards an abstract idea without additional elements that are significantly more or transformative into a practical application. As such, claims 1-5, 8-12, 15-19, and 21-25 are maintaining the 35 USC 101 rejection.
Lacking any further arguments, claims 1-5, 8-12, 15-19, and 21-25 are maintaining the 35 USC 101 rejection, as considered with respect to the response arguments and request for further consideration.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5, 8-12, 15-19, and 21-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea without additional elements that are significantly more or transformative into a practical application.
In terms of Step 1, claims 1-5, 8-12, 15-19, and 21-25 are directed towards one of four categories of statutory subject matter.
In terms of Step 2(a)(1), independent claims 1, 8, and 15 are directed towards (as represented by claim 1), “a method for providing intelligent supply chain optimization, comprising: receiving a transaction agreement request from a user; and generating a revised transaction agreement request based on one or more user profiles, a multi-party entity feedback loop, one or more constraints relating to the transaction agreement request, and a transaction agreement fulfillment requirements of the entity; negotiate, in an interactive user interface, the revised transaction agreement between the user and one or more providers of the multi-party entity feedback loop”. The claims are describing the creation of a transaction agreement based on negotiating partner feedback, fulfillment requirements, and restrictions. As such, the claims are describing a commercial interaction and business relation. Therefore, the claims are directed towards an abstract idea under the certain method of organizing human activity grouping.
Step 2(a)(II) considers the additional elements in terms of being transformative into a practical application. The additional elements of the independent claims are, “by a processor {claim 1}, A system for providing intelligent supply chain optimization, comprising: one or more computers with executable instructions that when executed cause the system to {claim 8}, and A computer program product for providing intelligent supply chain optimization in a computing environment, the computer program product comprising: one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instruction comprising: program instructions to {claim 15}; train, a machine learning component, to learn and collect feedback data relating to a supply chain state, one or more acceptance or rejections of historical transaction agreements and revised transaction agreement requests, behaviors of users, and one or more policies based on a value function; in an interactive user interface, using a machine learning operation of the machine learning component, wherein the machine learning component is retrained using reinforcement learning”. The additional elements are described in the originally filed specification figure 1 and paragraphs [46-55]. The additional elements are merely described as generic technology to implement the abstract idea. The computer elements are not describing a technical improvement. In terms of the machine learning, the originally filed specification describes the ML and training steps in paragraphs [19-25]. The specification merely describes techniques and high level ML elements to implement the abstract idea. The training steps are describing the input elements that are used to train and the ML is a generic model to provide the analysis to the identified abstract idea. As such, the training and ML are generic technology to implement the abstract idea. Therefore, the additional elements are not transformative into a practical application. Refer to MPEP 2106.05(f).
Step 2(b) considers the additional elements in terms of being significantly more than the identified abstract ideas. The additional elements of the independent claims are, “by a processor {claim 1}, A system for providing intelligent supply chain optimization, comprising: one or more computers with executable instructions that when executed cause the system to {claim 8}, and A computer program product for providing intelligent supply chain optimization in a computing environment, the computer program product comprising: one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instruction comprising: program instructions to {claim 15} train, a machine learning component, to learn and collect feedback data relating to a supply chain state, one or more acceptance or rejections of historical transaction agreements and revised transaction agreement requests, behaviors of users, and one or more policies based on a value function; in an interactive user interface, using a machine learning operation of the machine learning component, wherein the machine learning component is retrained using reinforcement learning”. The additional elements are described in the originally filed specification figure 1 and paragraphs [46-55]. The additional elements are merely described as generic technology to implement the abstract idea. The computer elements are not describing a technical improvement. In terms of the machine learning, the originally filed specification describes the ML and training steps in paragraphs [19-25]. The specification merely describes techniques and high level ML elements to implement the abstract idea. The training steps are describing the input elements that are used to train and the ML is a generic model to provide the analysis to the identified abstract idea. As such, the training and ML are generic technology to implement the abstract idea. Therefore, the additional elements are not significantly more than the identified abstract idea. Refer to MPEP 2106.05(f).
Dependent claims 2, 9, and 16 are further describing the abstract idea. The claims are directed towards (as represented by claim 2), “further including querying a supply chain state to identify a cost for servicing the transaction agreement request”. The claims are further describing the commercial aspect in terms of providing a query to identify cost for servicing the agreement request (i.e. contract). The claims are also describing the mental process as a person with pen and paper can mentally opine and consider the cost to a contract. As such, the claims are further describing the identified abstract ideas and are not directed towards additional elements that are significantly more or transformative into a practical application.
Dependent claims 3, 10, and 17 are further describing the identified abstract ideas. The claims are directed towards (as represented by claim 3), “further including generating and monitoring the one or more user profiles”. The claims are further describing providing user profiles that are generated and monitored. The claims are further describing a collection of information through the mental process consideration and providing elements of the contract under the commercial activity consideration. The claims, as considered with respect to the independent claims, are providing elements of the contract negotiation that falls within the identified abstract ideas. As such, the claims are further describing the identified abstract ideas and are not directed towards additional elements that are significantly more or transformative into a practical application.
Dependent claims 4, 11, and 18 are further describing the identified abstract ideas. The claims are directed towards (as represented by claim 4), “further including identifying the one or more marginal transaction agreement fulfillment requirements for performing the transaction agreement request”. The claims are further describing the commercial activity in terms of identifying transactional requirements. The claims also fall into the mental process as a person with pen and paper can provide and revise a contract based on marginal transaction requirements presented in the negotiation. As such, the claims are further describing the identified abstract ideas and are not directed towards additional elements that are significantly more or transformative into a practical application.
Dependent claims 21 and 23-25 are further describing the abstract idea and are not directed towards additional elements beyond those identified above. The claims are directed towards, “wherein the processor is internal to an intelligent supply chain enhancing service, the intelligent supply chain enhancing service being comprised of an interactive supply chain component, a transaction agreement generator component, a monitoring component, the machine learning component, and a feedback component”, “further comprising: offering, in the interactive user interface, one or more time slots to the user associated with the one or more providers, wherein each of the one or more time slots includes an estimated cost of shipment determined by the machine learning component”, “wherein the transaction agreement request from a user includes a plurality of order details”, and “further comprising: querying, by an interactive orchestrator component, a generation component based on the plurality of order details received in the transaction agreement request from the user; assessing, by a logistics component, a current logistics state based on the plurality of order details received and the one or more user profiles of the multi-party entity feedback loop; and providing, in the interactive user interface, one or more updated counteroffers to the user based on the assessment of the current logistics state”. The claims are further describing the commercial activity in terms of the negotiation and offer/counteroffer for the order details. As such, the claims are further describing the abstract idea identified above. The claims are further directed towards additional elements considered above. The claims are not directed towards additional elements that are significantly more or transformative into a practical application.
Claim 22 is directed towards additional elements beyond those identified above. The claim is directed towards, “wherein the intelligent supply chain enhancing service provides a multi-party interactive environment to the user and the one or more providers through a chatbot of the interactive user interface”. The additional element beyond those identified above is the chatbot within the interface. The chatbot is described in the originally filed specification [77 and 84]. The chatbot is merely generic technology to implement the abstract idea. The chatbot is not directed towards a technical improvement and thus is not significantly more or transformative into a practical application. Refer to MPEP 2106.05(f).
The claims are describing an abstract idea without additional elements that are significantly more or transformative into a practical application. Therefore, claims 1-5, 8-12, 15-19, and 21-25 are rejected under 35 USC 101 for being directed towards non-eligible subject matter.
Conclusion
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/ANDREW CHASE LAKHANI/Primary Examiner, Art Unit 3629