Prosecution Insights
Last updated: July 17, 2026
Application No. 18/638,061

FORECASTING ORDERS FOR DIVERSE SET OF TIME SERIES DATASETS AT GRANULAR LEVEL USING GRAPH STRUCTURE AND GENERATIVE MODELLING

Final Rejection §101
Filed
Apr 17, 2024
Examiner
DELIGI, VANESSA LIMA
Art Unit
3627
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Dell Products L.P.
OA Round
2 (Final)
56%
Grant Probability
Moderate
3-4
OA Rounds
8m
Est. Remaining
94%
With Interview

Examiner Intelligence

Grants 56% of resolved cases
56%
Career Allowance Rate
112 granted / 199 resolved
+4.3% vs TC avg
Strong +38% interview lift
Without
With
+38.0%
Interview Lift
resolved cases with interview
Typical timeline
2y 11m
Avg Prosecution
20 currently pending
Career history
218
Total Applications
across all art units

Statute-Specific Performance

§101
23.4%
-16.6% vs TC avg
§103
73.7%
+33.7% vs TC avg
§102
1.0%
-39.0% vs TC avg
§112
1.1%
-38.9% vs TC avg
Black line = Tech Center average estimate • Based on career data from 199 resolved cases

Office Action

§101
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 . Response to Amendment Applicant’s “Response to Amendment and Reconsideration” filed on 02/13/2026 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, 6-8, 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 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 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: “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”, “an agent deployment manager of an order processing system executing on a processor; 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 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 02/13/2026 have been fully considered but they are not persuasive. Applicant arguers Step 2A Prong One, see Remarks pages 10-12. Examiner respectfully disagrees. Forecasting future orders sequences (specification of para. 21) to decide the number of agents that are need for the orders and initiate the number of agents deployment are considered human activities that fall under “Certain Methods of Organizing Human Activities” with commercial and fundamental economic practices and/or activities concept, known in the enterprise industry. Further, generative modeling uses statistics to produce results, and therefore are considered mathematical concepts. Therefore, the claims recite to abstract idea. Applicant arguers Step 2A Prong Two, see Remarks pages 13-14. Examiner respectfully disagrees. In Enfish the Supreme Court has found a specific improvement to the way the self-referential table functions when compared to conventional database structures; this is not the case in the current claimed invention. The currented claimed invention is directed to initiate agents based on forecasting sequences (future orders sequences) using generative modeling, and using an existent technique is not the same as improving an existed technique or technical field, or the way a computers operate. Therefore, the claims are directed to abstract idea, and thus do not integrate the exception into a practical application. Applicant arguers Step 2B, see Remarks pages 13-14. 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 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 extension fee 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 date of this final action. 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. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Florian (Ryan) Zeender can be reached on (571) 272-6790. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center to authorized users only. Should you have questions about access to the USPTO patent electronic filing system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). /VANESSA DELIGI/Patent Examiner, Art Unit 3627 /FLORIAN M ZEENDER/Supervisory Patent Examiner, Art Unit 3627
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Prosecution Timeline

Apr 17, 2024
Application Filed
Nov 14, 2025
Non-Final Rejection mailed — §101
Jan 30, 2026
Interview Requested
Feb 13, 2026
Applicant Interview (Telephonic)
Feb 13, 2026
Response Filed
Feb 23, 2026
Examiner Interview Summary
May 28, 2026
Final Rejection mailed — §101 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

3-4
Expected OA Rounds
56%
Grant Probability
94%
With Interview (+38.0%)
2y 11m (~8m remaining)
Median Time to Grant
Moderate
PTA Risk
Based on 199 resolved cases by this examiner. Grant probability derived from career allowance rate.

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