DETAILED ACTION
1. Claims 1-12 are pending in the application.
Notice of Pre-AIA or AIA Status
2. 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 § 103
3. 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 (i.e., changing from AIA to pre-AIA ) 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.
4. Claim(s) 1-3 and 7-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pal et al (hereafter Pal)(US Pub. 20240012875) in view of Deng et al (hereafter Deng)(US Pub. 20220383109).
5. As to claim 1, Pal discloses a spatiotemporal data processing apparatus based on a graph neural controlled differential equation ([abstract, [0004], and [0081]) comprising:
a preprocessing unit configured to generate a continuous path for each node in time series data ([0038]-[0039], [0044], and [0056]).
Pal does not disclose a main processing unit configured to combine a graph convolution network (GCN) with a neural controlled differential equation (NCDE) for the generated path to perform integration processing on temporal information and spatial information,
wherein the main processing unit performs temporal processing and spatial processing on each node with two controlled differential equation (CDE) functions to calculate a last hidden vector and forecast an output layer.
However, Deng discloses a main processing unit configured to combine a graph convolution network (GCN) with a neural controlled differential equation (NCDE) for the generated path to perform integration processing on temporal information and spatial information ([0058] Neural controlled differential equations [0061] integral equations),
wherein the main processing unit performs temporal processing and spatial processing on each node with two controlled differential equation (CDE) functions to calculate a last hidden vector and forecast an output layer ([0058], [0066], [0070] and [0074] Even though latent ODE models may have continuous latent trajectories, the latent state may be decoded into observations at each time step independently. Neural controlled differential equations (CDEs) and rough differential equations (RDEs) may propagate a hidden state across time continuously using controlled differential equations driven by functions of time interpolated from observations on irregular time grids. While the above described example models can be applied to various inference tasks on irregular time series, these examples may not be a generative model of time series data).
6. Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention, to modify the teachings of Pal by incorporating the neural controlled differential equations and processing, as in Deng, for the benefit of improved flow-based decoding of a generic stochastic differential equation as a principled framework for continuous dynamics modeling from irregular time-series data.
7. As to claims 2 and 8, the combination of Pal and Deng discloses wherein the preprocessing unit performs an interpolation algorithm on each node to generate the continuous path (Deng [0037] and [0058]).
8. As to claims 3 and 9, the combination of Pal and Deng discloses wherein the preprocessing unit uses a natural cubic spline as the interpolation algorithm (Deng [0037] and [0058]).
9. As to claim 7, the claim is rejected for similar reasons as to claim 1 above.
Allowable Subject Matter
10. Claims 4-6 and 10-12 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
The claims recite at least wherein the main processing unit includes a first NCDE module configured to perform the temporal processing on the continuous path of each node to generate a hidden trajectory of the temporal information; and a second NCDE module configured to perform the spatial processing on the continuous path of each node to generate a hidden trajectory of the spatial information.
The prior art of record as in above, teaches the claimed limitations of the independent claims, however; the prior art of record does not teach or suggest at least the main processing unit includes a first NCDE module configured to perform the temporal processing on the continuous path of each node to generate a hidden trajectory of the temporal information; and a second NCDE module configured to perform the spatial processing on the continuous path of each node to generate a hidden trajectory of the spatial information.
Conclusion
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/MICHAEL D. YAARY/ Primary Examiner, Art Unit 2151
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