DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 08/28/2026 has been entered.
Response to Amendment
Applicant’s “Response to Amendment and Reconsideration” filed on 08/28/2026 has been considered.
Applicant’s response by virtue of amendment to claim(s) 1-20 have NOT overcome the Examiner’s rejection under 35 USC § 101.
Claim(s) 1, 8, 15 are amended.
Claim(s) 1-20 are pending in this application and an action on the merits follows.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more and thus do not satisfy the criteria for subject matter eligibility.
Step 1
Claim(s) 1, 8, 15 fall(s) in two of the four statutory categories of invention.
Step 2A Prong One
The limitations of claims 1, 8, 15:
Claim 1: “
obtaining,
generating an adjacency matrix using the set of time series datasets and
applying a graphical Fourier transformation on the adjacency matrix using a Laplacian matrix and an inverse graph Fourier to obtain a graph Fourier transform;
applying to obtain output features;
performing
initiating an agent deployment of a plurality of agents
wherein the agent deployment comprises modifying a number of agents
Claim(s) 1, 8, 15 disclose(s) an abstract idea of ordering processing sequence forecasting, which falls under the “Certain Methods of Organizing Human Activities” and mathematical concepts.
More specifically, the claims limitations A-G listed above have concepts related to: receiving data (A), generating data and forecasting data using mathematical concepts (B-E), and initiating agent deployment including modifying a number of agents (F-G) based on the forecasting results of the future orders, and thus are considered commercial and fundamental economic practices and/or activities known in the retail and enterprise industry.
Under the broadest reasonable interpretation limitations B-E recite mathematical concepts used to forecast future orders sequences; and thus, are considered abstracts idea that falls under the category of mathematical concepts.
Claims 1-20 recite an abstract idea.
Step 2A Prong Two
The identified additional elements are:
Claims 1, 8, 15: “an agent deployment manager of an order processing system executing on a processor; “recurrent neural network (RNN)”; “sequential network”; “a fast Fourier transform network and a convolution layer”; “generative modeling”; “agents executing”, “agents are implemented as computing devices”; “computer-implemented services comprise at least: performing online chat services, tracking and managing inventory, initiating shipping of products, tracking orders, and providing transaction information associated with current, future, and past orders”;
Claim 8 “A non-transitory computer readable medium comprising computer readable program code, which when executed by a computer processor enables the computer processor to perform a method for managing order processing, the method comprising:”
Claim 15 “A system, comprising: a processor; and memory including instructions, which when executed by the processor, perform a method comprising:”
The claimed additional elements that perform limitation A is claimed at a high level of generality and are considered merely data received such as (see specification para. 42-44 historical orders data), and thus are considered nothing more than insignificant extra-solution activity; the additional elements B-E are claimed at a high level of generality and are considered nothing more than data being generated using existent tools and mathematical concepts (matrix, graphical Fourier transformation, generative modeling, a fast Fourier transform network and a convolution layer) without the recitation of technological improvement, and thus are considered generality linking the use of the judicial exception to a particular technological environment and/or field of use; the additional elements G-F are claimed at a high level of generality, and are considered nothing more than initiation of agents allocated based on the forecasting of future orders sequences result without the recitation of technological improvement, and thus are considered generality linking the use of the judicial exception to a particular technological environment and/or field of use. When view in combination, the additional elements merely describe how to generally “apply” the abstract idea in a generic or general-purpose computer, and generally linking the use of the judicial exception to a particular technological environment or field of use, and thus do not integrate the abstract idea into a practical application, and claim(s) 1, 8, and 15 are directed to the judicial exception.
Claims 1-20 are directed to an abstract idea.
Step 2B
Claim 1-20 are not including additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A Prong Two, to generally “apply” the abstract idea in a generic or general-purpose computer, and generally links the use of the judicial exception to a particular technological environment or field of use, and therefore, the claims do not recite significantly more than the judicial exception.
In addition, the courts have found computer functions claimed at high level of generality as not sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a) - i. Generating restaurant menus with functionally claimed features, Ameranth, 842 F.3d at 1245, 120USPQ2d at 1857, iv. Recording, transmitting, and archiving digital images by use of conventional or generic technology in a nascent but well-known environment, without any assertion that the invention reflects an inventive solution to any problem presented by combining a camera and a cellular telephone, TLICommunications, 823 F.3d at 611-12, 118 USPQ2dat 1747;), and well‐understood, routine, and conventional functions (see MPEP 2106.05(d) - Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 79-80, 101 USPQ2d 1969 (2012) (citing Parker v. Flook, 437 U.S. 584, 590, 198 USPQ 193, 199 (1978) (the additional elements were “well known” and, thus, did not amount to a patentable application of the mathematical formula)).), applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment - see MPEP 2106.05(e).
Even when claims 1-20 are viewed as a whole, nothing in the claims adds significantly more to the abstract idea.
Claims 1-20 are not patent eligible.
Allowable Subject Matter
Claims 1-20 are allowed over the prior art.
Luo et al. (US 20240242145 A1, hereinafter Luo) and XU et at. (US 20240029556 A1, hereinafter XU) are the closest prior art of the record.
Luo discloses an online concierge system 140 including a resource planning module 250. The resource planning module 250 includes a machine learning model such as generative adversarial networks. The machine learning model is trained with a time series model that predicts optimal staffing levels indirectly based on a sequence of performance metrics related to optimal staffing levels during the historic time periods, see Figure 2 para. 50, 54-59. The online concierge system 140 assigns 306 staff to shifts based on the predicted optimal staffing levels, see par. 71.
Luo discloses the staffing level prediction using a generative machine learning model; but does not disclose “generating an adjacency matrix using the set of time series datasets and; applying a graphical Fourier transformation on the adjacency matrix using a Laplacian matrix and an inverse graph Fourier to obtain a graph Fourier transform”;
XU discloses a prediction method using an adjacent matrix Aij derivate from traffic data flow, and a graph convolution that forms a Laplacian matrix from an adjacent matrix, calculates eigenvalue decomposition of the Laplacian, and perform a Fourier and Inverse Fourier transformation, see par. 34-37.
However, Luo in view of XU does not disclose the adjacent matrix is generated using a recurrent neural network (RNN); and applying a sequential network on the graph Fourier transform using a fast Fourier transform network and a convolution layer to obtain output features.
Therefore, the italic limitations in combination with other limitations clearly claimed in the independent claim(s) 1, 8, and 15 are novel and unobvious.
According claim(s) 1-20 are allowable over the prior art, however, a rejection under the 35 USC 101 should be overcome in order to have the current application allowable.
Response to Arguments
Applicant's arguments filed on 08/28/2026 have been fully considered but they are not persuasive.
Applicant argues Step 2A Prong One, see Remarks pages 12-13. Examiner respectfully disagrees. The generative modeling is considered an additional element; therefore, generating a forecasting sequence using output features (specification of para. 21) and decide the number of agents that are needed for the sequence of future orders and initiating agents deployment based on the forecast sequence are considered human activities that fall under “Certain Methods of Organizing Human Activities” known in the retail and enterprise industry. Further, forecasting sequence of future orders using output features (specification of para. 21) to manage the agents needed to be used to process the future orders are also considered mathematical concepts known in the retail and enterprise industry. Therefore, the claims recite an abstract idea.
Applicant argues Step 2A Prong Two, see Remarks pages 13-14. Examiner respectfully disagrees. Unlikely on Exempla 47, which recites a specific real-time remedial network action “identify malicious network packets and take remediation actions, including dropping suspicious packets and blocking traffic from suspicious source addresses in real time…realizes an improvement in network security by avoiding the delay involved in waiting on a network administrator to react to a network intrusion”, see Exempla 47, the current claimed invention merely recites initiation of computer agents based on forecast sequence, and does not recite how the computer services improve the computer functionality; mere allocating and initiating computer agents to preserve computer resource consumption, is not enough to overcome the 35 USC 101 rejection. In addition, In Enfish, the Supreme Court has found a specific improvement to the way the self-referential table functions when compared to conventional database structures; therefore improving conventional database with “more flexibility, faster search times, and smaller memory requirements”; this is not the case in the current claimed invention, the claimed mathematical concepts and machine learning technique do not improve the computer and/or computer functionality, or technical field itself, instead merely provide a forecasting sequence while generic computer agents are initiated based on the forecast sequence, and thus are not enough to be considered an improvement. Therefore, the claims are directed to the abstract idea, and thus do not integrate the exception into a practical application.
Applicant arguers Step 2B, see Remarks pages 16-17. Examiner respectfully disagrees. Examiner have considered each individual limitation, cited court cases, and saw the limitations as a whole. Therefore, the claims are directed to abstract idea.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VANESSA DELIGI whose telephone number is (571)272-0503. The examiner can normally be reached on Monday-Friday 07:30AM-5PM.
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/VANESSA DELIGI/Patent Examiner, Art Unit 3627
/FLORIAN M ZEENDER/ Supervisory Patent Examiner, Art Unit 3627
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