DETAILED ACTION
This Final Office Action is in response to the arguments and amendments filed April 20, 2026.
Claims 1, 5, 8, 12, 15, 19, 21, and 23 have been amended.
Claims 2, 3, 6, 7, 13, 14, 20, 22, and 24 have been cancelled.
Claims 25-28 are newly added.
Claims 1, 4-5, 8-12, 15-19, 21, 23, and 25-28 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 .
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, 4-5, 8-12, 15-19, 21, 23, and 25-28 are rejected under 35 U.S.C. 101 because
In terms of Step 1, claims 1, 4-5, 8-12, 15-19, 21, 23, and 25-28 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, wherein the transaction agreement request includes a plurality of order details; 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; providing, a multi-party interactive environment, wherein the chatbot enables real time communication between the user and one or more providers and displays offers and counteroffers to the user; and negotiating, in the interactive user interface, the revised transaction agreement between the user and the 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. This consideration further includes the aspect of the chatbot as the chatbot is further interacting with and facilitating the contract negotiation providing offers and counteroffers. As such, the claims are describing a commercial interaction and business relation, as well as an interaction between people. 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}; training, 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; providing, by the machine learning component, a multi-party interactive environment through a chatbot within 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. In terms of the chatbot, the element is described in the originally filed specification [77 and 84]. The chatbot is not described in terms of being a technical improvement, but rather a tool to implement the abstract idea (a tool to interface the negotiation and business relation, as well as the interaction between people). The chatbot and interactive interface are not directed towards a technical improvement, but rather 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 idea. 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}; training, 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; providing, by the machine learning component, a multi-party interactive environment through a chatbot within 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. In terms of the chatbot, the element is described in the originally filed specification [77 and 84]. The chatbot is not described in terms of being a technical improvement, but rather a tool to implement the abstract idea (a tool to interface the negotiation and business relation, as well as the interaction between people). The chatbot and interactive interface are not directed towards a technical improvement, but rather 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 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 claim 5, 12, and 19 are further describing the abstract idea and not directed towards additional elements beyond those identified above. The claims are directed towards, “further including: identifying one or more transaction agreement fulfillment options for performing the transaction agreement request from the user; and the entity; and selecting a transaction agreement fulfillment option for performing the transaction agreement request from the user by the entity having a least amount of constraints and transaction agreement fulfillment requirements for fulfilling the transaction agreement request”. The claims are further describing the contract negotiation based on transaction requests and fulfillment based on having the least amount of constraints. The claims are not directed towards additional elements that are transformative into a practical application or significantly more than the identified abstract idea.
Dependent claims 9 and 16 are further describing the abstract idea. The claims are directed towards, “wherein the executable instructions when executed cause the system to query 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 10 and 17 are further describing the identified abstract ideas. The claims are directed towards, “wherein the executable instructions when executed cause the system to generate and monitor 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 21, 23, and 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, wherein the intelligent supply chain enhancing service suggests offers and counteroffers to the user which are accepted or rejected by the user in the interactive user interface”, “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”, 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.
Claims 26-28 are directed towards additional elements beyond those identified above. The claims are directed towards, “wherein the machine learning component is retrained using the reinforcement learning based on at least observations, rewards, and actions between the user and the one or more providers”, “wherein the observations include a set of orders and associated parameters from the one or more providers, the actions include offer decisions by the user within the interactive user interface, and the rewards include revenue and cost outcomes associated with the actions of the user”, and “wherein the machine learning component learns for the retraining based on at least learning a supply chain state, one or more acceptance or rejections of historical transaction agreements and revised transaction agreement requests, actions of the user within the interactive user interface, and one or more policies based on a value function, wherein the value functions is a prediction of a future total award”. The additional elements are with respect to the machine learning elements for training and retraining. 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 and retraining in the claims are describing observations and outcomes, as well as historical agreements and other transaction policy elements. The training is merely generic technology to implement the abstract idea. The training is not describing an improvement to training machine learning models but rather utilizing generic training techniques to implement the ML modeling to implement the abstract idea. The additional elements are not directed towards a technical improvement and thus are 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, 4-5, 8-12, 15-19, 21, 23, and 25-28 are rejected under 35 USC 101 for being directed towards non-eligible subject matter.
Response to Arguments
In response to the arguments filed April 20, 2026 on pages 8-11 regarding the 35 USC 101 rejection, specifically that the claimed invention is directed towards eligible subject matter.
Examiner respectfully disagrees.
The arguments are with respect to the Ex parte Desjardins memo and that the claimed invention provides a technical improvement by providing machine learning and negotiation efficiencies. In terms of the arguments regarding the supply chain elements and negotiation, those aspects are considered as the abstract idea. The claims are describing a contract negotiation and agreement between a user and provider. The claims further describe additional elements of chatbot, machine learning, and feedback for the machine learning (feedback loop and training). These elements were considered based on the originally filed specification and determined to be generic technology to implement the abstract idea. With respect to Desjardins, the additional elements of the specific training was an improvement to machine learning training as a technical solution. The pending application and claims merely provide training as generic techniques to implement the contract negotiation. There is no description that provides the technical improvement to machine learning itself, but rather tools to implement. As such, the additional elements are not transformative into a practical application or significantly more than the identified abstract idea. Refer to MPEP 2106.05(f). Lacking any further arguments, claims 1, 8, and 15 are maintaining the 35 USC 101 rejection, as considered above in light of the amended claim language.
Lacking any further arguments, claims 1, 4-5, 8-12, 15-19, 21, 23, and 25-28 are maintaining the 35 USC 101 rejection, as considered above in light of the amended and new added claim language.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Vuvich et al [2020/0111187] (chatbot negotiation);
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 ANDREW CHASE LAKHANI whose telephone number is (571)272-5687. The examiner can normally be reached M-F 730am - 5pm (EST).
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/ANDREW CHASE LAKHANI/Primary Examiner, Art Unit 3629
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