Prosecution Insights
Last updated: April 19, 2026
Application No. 17/873,190

TRAFFIC FLOW FORECASTING METHOD BASED ON DEEP GRAPH GAUSSIAN PROCESSES

Non-Final OA §101§103§112§Other
Filed
Jul 26, 2022
Examiner
KEATON, SHERROD L
Art Unit
2148
Tech Center
2100 — Computer Architecture & Software
Assignee
Huzhou University
OA Round
1 (Non-Final)
52%
Grant Probability
Moderate
1-2
OA Rounds
4y 6m
To Grant
88%
With Interview

Examiner Intelligence

Grants 52% of resolved cases
52%
Career Allow Rate
295 granted / 563 resolved
-2.6% vs TC avg
Strong +36% interview lift
Without
With
+36.1%
Interview Lift
resolved cases with interview
Typical timeline
4y 6m
Avg Prosecution
32 currently pending
Career history
595
Total Applications
across all art units

Statute-Specific Performance

§101
14.9%
-25.1% vs TC avg
§103
62.0%
+22.0% vs TC avg
§102
11.1%
-28.9% vs TC avg
§112
8.0%
-32.0% vs TC avg
Black line = Tech Center average estimate • Based on career data from 563 resolved cases

Office Action

§101 §103 §112 §Other
DETAILED ACTION This action is in response to the original filing of 7-26-2022. Claims 1-14 are pending and have been considered below: 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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1: Claims 1-14 represent method type claims. Therefore claims 1-14 are directed to either a process, machine, manufacture or composition of matter. Regarding claim 1: 2A Prong 1: constructing a deep graph Gaussian process method integrating a Gaussian process and a depth structure from the aggregation Gaussian process, the temporal convolutional Gaussian process and the Gaussian process with a linear kernel function, inputting a data sample to be forecasted into the deep graph Gaussian process method, extracting the spatial dependency by the aggregation Gaussian process in step S1, then obtaining the spatiotemporal features by the convolution function in step S2, and inputting the spatiotemporal features into the Gaussian process with the linear kernel function to obtain a forecasted result. As drafted, under the broadest reasonable interpretation, the claim covers mental processes (concepts performed in the human mind including an observation, evaluation, judgment, opinion-a user graph out a temporal gaussian output). 2A Prong 2: This judicial exception is not integrated into a practical application. Additional elements: with respect to the dynamics existing in a spatial dependency, using an attention kernel function to describe a dynamic dependency among vertices on a topological graph, and using the attention kernel function as a covariance function in an aggregation Gaussian process to extract dynamic spatial features; S2, obtaining a temporal convolutional Gaussian process from weights at different times and a convolution function obeying Gaussian processes, and obtaining temporal features in traffic data by combining the aggregation Gaussian process; (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)) 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: with respect to the dynamics existing in a spatial dependency, using an attention kernel function to describe a dynamic dependency among vertices on a topological graph, and using the attention kernel function as a covariance function in an aggregation Gaussian process to extract dynamic spatial features; S2, obtaining a temporal convolutional Gaussian process from weights at different times and a convolution function obeying Gaussian processes, and obtaining temporal features in traffic data by combining the aggregation Gaussian process; (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)) Regarding claim 2: PNG media_image1.png 438 744 media_image1.png Greyscale PNG media_image2.png 350 732 media_image2.png Greyscale As drafted, under the broadest reasonable interpretation, the claim covers mathematical concepts (concepts defined as mathematical relationships, mathematical formulas or equations, or mathematical calculations). 2A Prong 2: This judicial exception is not integrated into a practical application. No additional elements: 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. No additional elements: Regarding claim 3: PNG media_image3.png 230 736 media_image3.png Greyscale As drafted, under the broadest reasonable interpretation, the claim covers mathematical concepts (concepts defined as mathematical relationships, mathematical formulas or equations, or mathematical calculations). 2A Prong 2: This judicial exception is not integrated into a practical application. No additional elements: 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. No additional elements: Regarding claim 4: PNG media_image4.png 436 718 media_image4.png Greyscale PNG media_image5.png 392 744 media_image5.png Greyscale As drafted, under the broadest reasonable interpretation, the claim covers mathematical concepts (concepts defined as mathematical relationships, mathematical formulas or equations, or mathematical calculations). 2A Prong 2: This judicial exception is not integrated into a practical application. No additional elements: 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. No additional elements: Regarding claim 5: PNG media_image6.png 560 584 media_image6.png Greyscale PNG media_image7.png 116 618 media_image7.png Greyscale As drafted, under the broadest reasonable interpretation, the claim covers mathematical concepts (concepts defined as mathematical relationships, mathematical formulas or equations, or mathematical calculations). 2A Prong 2: This judicial exception is not integrated into a practical application. No additional elements: 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. No additional elements: Regarding claim 6: PNG media_image8.png 614 818 media_image8.png Greyscale As drafted, under the broadest reasonable interpretation, the claim covers mathematical concepts (concepts defined as mathematical relationships, mathematical formulas or equations, or mathematical calculations). 2A Prong 2: This judicial exception is not integrated into a practical application. No additional elements: 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. No additional elements: Regarding claim 7: PNG media_image9.png 424 804 media_image9.png Greyscale PNG media_image10.png 406 788 media_image10.png Greyscale As drafted, under the broadest reasonable interpretation, the claim covers mathematical concepts (concepts defined as mathematical relationships, mathematical formulas or equations, or mathematical calculations). 2A Prong 2: This judicial exception is not integrated into a practical application. No additional elements: 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. No additional elements: Regarding claim 8: PNG media_image11.png 612 794 media_image11.png Greyscale As drafted, under the broadest reasonable interpretation, the claim covers mathematical concepts (concepts defined as mathematical relationships, mathematical formulas or equations, or mathematical calculations). 2A Prong 2: This judicial exception is not integrated into a practical application. No additional elements: 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. No additional elements: Regarding claim 9: PNG media_image12.png 128 710 media_image12.png Greyscale PNG media_image13.png 484 710 media_image13.png Greyscale As drafted, under the broadest reasonable interpretation, the claim covers mathematical concepts (concepts defined as mathematical relationships, mathematical formulas or equations, or mathematical calculations). 2A Prong 2: This judicial exception is not integrated into a practical application. No additional elements: 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. No additional elements: Regarding claim 10: PNG media_image14.png 526 640 media_image14.png Greyscale PNG media_image15.png 224 638 media_image15.png Greyscale As drafted, under the broadest reasonable interpretation, the claim covers mathematical concepts (concepts defined as mathematical relationships, mathematical formulas or equations, or mathematical calculations). 2A Prong 2: This judicial exception is not integrated into a practical application. No additional elements: 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. No additional elements: Regarding claim 11: PNG media_image16.png 612 784 media_image16.png Greyscale As drafted, under the broadest reasonable interpretation, the claim covers mathematical concepts (concepts defined as mathematical relationships, mathematical formulas or equations, or mathematical calculations). 2A Prong 2: This judicial exception is not integrated into a practical application. No additional elements: 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. No additional elements: Regarding claim 12: PNG media_image17.png 224 634 media_image17.png Greyscale PNG media_image18.png 454 648 media_image18.png Greyscale As drafted, under the broadest reasonable interpretation, the claim covers mathematical concepts (concepts defined as mathematical relationships, mathematical formulas or equations, or mathematical calculations). 2A Prong 2: This judicial exception is not integrated into a practical application. No additional elements: 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. No additional elements: Regarding claim 13: PNG media_image19.png 630 802 media_image19.png Greyscale As drafted, under the broadest reasonable interpretation, the claim covers mathematical concepts (concepts defined as mathematical relationships, mathematical formulas or equations, or mathematical calculations). 2A Prong 2: This judicial exception is not integrated into a practical application. No additional elements: 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. No additional elements: Regarding claim 14: PNG media_image20.png 632 724 media_image20.png Greyscale As drafted, under the broadest reasonable interpretation, the claim covers mathematical concepts (concepts defined as mathematical relationships, mathematical formulas or equations, or mathematical calculations). 2A Prong 2: This judicial exception is not integrated into a practical application. No additional elements: 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. No additional elements: Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. Claim 3 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 3 describes a limitation with i.e. (in example), it is not clear if the formula is being explicitly claimed. Claim Objections Claims 2-14 are rejected under 101, however would be allowable over prior art if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Claim Rejections - 35 USC § 103 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. Claim 1 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Self-Attention Graph Pooling” Lee et al. “Lee” Pages 1-10, 2019 in view of “A Hybrid Short-term traffic flow forecasting method based on EMDW-LSSVM” Wang et al. “Wang”, Pages 1-6, 12-24-2020, “Gaussian Processes for Traffic Speed predicted at different aggregated levels”, “Comert” Pages 1-13, 11-24-2020, “Convolutional Gaussian Process” Wilk et al. “Wilk” Pages 1-18, 9-6-2017 and “Short-Term Traffic Flow Prediction with Linear Conditional Gaussian Bayesian Network” Zhu et al. “Zhu” Pages 1-16, 3-2-2016. Claim 1: Lee discloses a traffic flow forecasting method based on deep graph Gaussian processes, comprising the following steps: S1, with respect to the dynamics existing in a spatial dependency, using an attention to describe a dynamic dependency among vertices on a topological graph (Figure 1 and Page 3, Section 3.1 attention mask for topological graph edges); Lee may not explicitly disclose a kernel function and using the attention kernel function as a covariance function in an aggregation Gaussian process to extract dynamic spatial features; Wang is disclosed because it provides a traffic flow forecasting which utilizes a kernel function with Gaussian process (Page 3, Column 2 (Introducing kernel function)-Page 4, Column 1). Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve similar devices in the same way and utilize the kernel functionality with Gaussian processes for the model in Lee. One would have been motivated to provide the functionality as an improvement to model optimization and accuracy. Lee also may not explicitly disclose S2, obtaining a temporal convolutional Gaussian process from weights at different times and a convolution function obeying Gaussian processes, and obtaining temporal features in traffic data by combining the aggregation Gaussian process; Hence, Comert and Wilk are incorporated. Comert is disclosed because it provides a Gaussian process for traffic data with time series modeling (Page 1, Introduction Paragraph 2; Gaussian process). Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve similar devices in the same way and utilize Gaussian processes for the model in Lee. One would have been motivated to provide the functionality as an improvement to model optimization and accuracy. Further, Wilk’s is disclosed because it provides a convolutional gaussian process well suited for imaging in order to obtain optimal weighting (Page 1, abstract). Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve similar devices in the same way and utilize the convolutional Gaussian processes for the model in Lee. One would have been motivated to provide the functionality for imaging analysis in order to improve performance. Wilk further discloses S3, constructing a deep graph Gaussian process method integrating a Gaussian process and a depth structure from the aggregation Gaussian process, the convolutional Gaussian process and the Gaussian process with a linear kernel function, inputting a data sample to be forecasted into the deep graph Gaussian process method, by the aggregation Gaussian process (Page 1; abstract and Introduction; gaussian process with deep architecture utilizing a kernel functionality); Lee as modified by Wilk may not explicitly disclose features below. Therefore Zhu is provided to disclose the temporal feature, extracting the spatial dependency, nor S1, then obtaining the spatiotemporal features by the convolution function in step S2 (Zhu: Page 3, Lines 8-19; spatial and temporal information), and inputting the spatiotemporal features into the Gaussian process with the linear kernel function to obtain a forecasted result (Zhu: abstract and Page 9, Lines 10-22; features provide in process for prediction). Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve similar devices in the same way and utilize the spatial and temporal information in Zhu. One would have been motivated to provide the functionality as an improvement to model prediction accuracy (Zhu: abstract). Conclusion The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure: “Learning Traffic as Images: A deep convolutional neural network for large-scale transportation network speed prediction” Ma et al. “Ma” Pages 1-16, 4-10-2017 Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action. It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)). In the interests of compact prosecution, Applicant is invited to contact the examiner via electronic media pursuant to USPTO policy outlined MPEP § 502.03. All electronic communication must be authorized in writing. Applicant may wish to file an Internet Communications Authorization Form PTO/SB/439. Applicant may wish to request an interview using the Interview Practice website: http://www.uspto.gov/patent/laws-and-regulations/interview-practice. Applicant is reminded Internet e-mail may not be used for communication for matters under 35 U.S.C. § 132 or which otherwise require a signature. A reply to an Office action may NOT be communicated by Applicant to the USPTO via Internet e-mail. If such a reply is submitted by Applicant via Internet e-mail, a paper copy will be placed in the appropriate patent application file with an indication that the reply is NOT ENTERED. See MPEP § 502.03(II). Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHERROD KEATON whose telephone number is 571-270-1697. The examiner can normally be reached 9:30am to 5:00pm. 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 MICHELLE BECHTOLD can be reached at 571-431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /SHERROD L KEATON/Primary Examiner, Art Unit 2148 1-23-2026
Read full office action

Prosecution Timeline

Jul 26, 2022
Application Filed
Jan 24, 2026
Non-Final Rejection — §101, §103, §112 (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

1-2
Expected OA Rounds
52%
Grant Probability
88%
With Interview (+36.1%)
4y 6m
Median Time to Grant
Low
PTA Risk
Based on 563 resolved cases by this examiner. Grant probability derived from career allow rate.

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