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:
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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:
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736
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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:
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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:
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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:
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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:
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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:
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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:
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710
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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:
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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:
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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:
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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:
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630
802
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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:
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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.
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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.
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/SHERROD L KEATON/Primary Examiner, Art Unit 2148
1-23-2026