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 .
This action is in response to amendments filed January 5th, 2026. The status of the claims is as follows. Claims 1-12 and 15 are amended. Claims 5-7 and 13-14 have been canceled. Claims 1-15 are currently pending.
Claim Objections
Claim 1 is objected to because of the following informalities: “the data acquisition node I and the data acquisition node j” should read clearer on its relationship to the aforementioned “two data acquisition nodes”, one possible remediation reading “the first data acquisition node I and the second data acquisition node j”. Appropriate correction is required.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 8-12 are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al. (“Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting” [2018], hereinafter “Yu”) in view of Jiang (CN113656461A) in view of Zhang et al. (CN114169649A, hereinafter “Zhang”).
Regarding Claim 1,
Yu discloses A method for predicting spatio-temporal perception information based on a graph neural network, comprising: constructing a perception data monitoring network, and acquiring original perception data through data acquisition nodes in the perception data monitoring network; (Yu [Section 4.1 Paragraph 3]; “PeMSD7 was collected from Caltrans Performance Measurement System (PeMS) in real-time by over 39, 000 sensor stations, deployed across the major metropolitan areas of California state highway system [Chen et al., 2001]. The dataset is also aggregated into 5-minute interval from 30-second data samples” wherein the PeMSD7 dataset derived from a network of 39000 sensors reads on a sensing data monitoring network comprising data acquisition nodes)
pre-processing and converting the original perception data into spatio- temporal graph perception data; (Yu [Figure 1];
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Yu [Section 2.1]; “Traffic forecast is a typical time-series prediction problem, i.e. predicting the most likely traffic measurements (e.g. speed or traffic flow) in the next H time steps given the previous M traffic observations as
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where vt ∈ R n is an observation vector of n road segments at time step t, each element of which records historical observation for a single road segment. In this work, we define the traffic network on a graph and focus on structured traffic time series. The observation vt is not independent but linked by pairwise connection in graph. Therefore, the data point vt can be regarded as a graph signal that is defined on an undirected graph (or directed one) G with weights wij as shown in Figure 1. At the t-th time step, in graph Gt = (Vt, E, W), Vt is a finite set of vertices, corresponding to the observations from n monitor stations in a traffic network; E is a set of edges, indicating the connectedness between stations; while W ∈ R n×n denotes the weighted adjacency matrix of Gt.” wherein a traffic network is defined on a graph and comprises observation vectors which are connected pairs within the graph. Data points vt are used as graph signals and are defined as undirected graph G with a vector wij, thus reading on pre-processing the original sensing data and conversion into spatio-temporal graph sensing data)
constructing a graph neural network model, and training parameters of the graph neural network model by using the spatio-temporal graph perception data; (Yu [Figure 2];
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Yu [Section 4.3 Paragraph 3]; “STGCN Model For BJER4 and PeMSD7(M/L), the channels of three layers in ST-Conv block are 64, 16, 64 respectively. Both the graph convolution kernel size K and temporal convolution kernel size Kt are set to 3 in the model STGCN(Cheb) with the Chebyshev polynomials approximation, while the K is set to 1 in the model STGCN(1st) with the 1st-order approximation. We train our models by minimizing the mean square error using RMSprop for 50 epochs with batch size as 50. The initial learning rate is 10−3 with a decay rate of 0.7 after every 5 epochs.” wherein the preprocessed PeMSD7 dataset comprising spatio-temporal graph perception data is used for training parameters of the graph neural network model)
a predicted value output by the graph neural network model (Yu [Figure 4];
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wherein pre-processing the original perception data comprises performing time slicing on the original perception data and processing the original perception data into a matrix sequence in chronological sequence, (Yu [Figure 1];
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wherein elements in the matrix sequence correspond to the data acquisition node, arrangement positions of the elements in the matrix sequence correspond to spatial feature information of the data acquisition nodes, values of the elements in the matrix sequence correspond to temporal feature information of the data acquisition node at a current time, and the matrix sequence constitutes the spatio-temporal graph perception data. (Yu [Figure 1];
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wherein the matrix sequence constitutes a spatio-temporal graph: Gt - (Vi,E".W), where Vt represents the data acquisition nodes at time t, and features of the data acquisition nodes represent feature information of perception data recorded by the data acquisition nodes; E represents a set of edges, and each edge in the set of edges represents a relationship between two data acquisition nodes; and W represents an adjacency matrix that records a weight of each edge in the set of edges (Yu [Section 2.1 Paragraph 2]; “Therefore, the data point vt can be regarded as a graph signal that is defined on an undirected graph (or directed one) G with weights wij as shown in Figure 1. At the t-th time step, in graph Gt = (Vt, E, W), Vt is a finite set of vertices, corresponding to the observations from n monitor stations in a traffic network; E is a set of edges, indicating the connectedness between stations; while W ∈ R n×n denotes the weighted adjacency matrix of Gt.”)
wherein the adjacency matrix is: Wy=
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… di; represents the distance between the data acquisition node i and the data acquisition node j, a and a are used to adjust the distribution and sparsity of the adjacency matrix W (Yu [Section 4.2 Paragraph 3];
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Yu fails to explicitly disclose but Jiang discloses sending early warning information in response to a predicted value … exceeding a preset threshold (Jiang [Page 5 Paragraph 14]; “the measuring point data of each device can be monitored through a process control method, so that an abnormal detection result for the device involved in the industrial production process can be obtained. For example, when the station data with the deviation larger than the preset deviation threshold value from the normal data is detected in the station data sequence of the equipment, the equipment can be considered to be abnormally operated.”
Jiang [Page 7 Last Paragraph]; “Besides detecting whether the prediction result has an abnormality by using a data-driven method (such as a process control algorithm), the data of each device contained in the prediction result can be checked whether the data of the device conforms to the working mechanism among the associated devices by using the device working mechanism. By utilizing the prediction result of the prediction model, the risk assessment can be carried out on the industrial production process, namely, the real-time early warning is carried out according to the time sequence characteristic analysis of the data and the prediction result.” wherein the abnormality detection, dependent on a preset threshold being exceeded, results in early warning)
It would have been obvious to modify Yu’s method of training a graph neural network to predict spatio-temporal perception information to be modified to incorporate Jiang’s method of sending early warnings when Yu’s graph neural network prediction exceeds some abnormal threshold. One would have been motivated to do so because “more information of the space-time correlation … can be obtained through the data intelligent model, so that the subsequent equipment maintenance and process optimization are facilitated” (Jiang [Page 8 Paragraph 1]).
Yu/Jiang does not explicitly disclose but Zhang discloses wherein, I(i,j) represents the degree of correlation between respective locations of the data acquisition node i and the data acquisition node j within a value range of 0-1, (Zhang [Page 7 “Background” Section, Line 9]; “weights represent correlations between sensors”
Zhang [Page 20 Line 5];
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wherein the beta weight coefficient representative of correlation between the sensors of respectively located data acquisition devices is read as a degree of correlation between respective locations of data acquisition nodes including at least a first “I” and second “j” node; wherein the attribute of adjacency matrices being bounded from 0 to 1 and the summation of the alpha and beta coefficient parts of the equation thus inherently read on the weights naturally being bounded within a value range of 0-1)
It would have been obvious to modify Yu/Jiang’s method of training a graph neural network to predict spatio-temporal perception information to be modified to incorporate Zhang’s correlation coefficient as a dynamic weight applied upon Yu/Jiang’s adjacency matrix. One would have been motivated to do so because “existing research mainly uses the distance between the sensors to construct a static graph with fixed weight, and ignores the fact that the correlation between the sensors changes along with the change of time … the present embodiment proposes a new graph generation method to generate different dynamic correlation graphs for different time periods, thereby helping the GCN to effectively model the dynamic correlation between the sensors” (Zhang [Page 18 Line 6]).
Regarding Claim 2,
The combination of Yu/Jiang/Zhang teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). The combination already discloses wherein constructing the perception data monitoring network comprises constructing a real perception data monitoring network or a virtual perception data monitoring network (Yu [Section 4.1 Paragraph 3]; “PeMSD7 was collected from Caltrans Performance Measurement System (PeMS) in real-time by over 39, 000 sensor stations, deployed across the major metropolitan areas of California state highway system [Chen et al., 2001]. The dataset is also aggregated into 5-minute interval from 30-second data samples” wherein the PeMSD7 dataset derived from a network of 39000 real-time sensors deployed across the city in sensor stations reads on a construction of a real perception data monitoring network)
Regarding Claim 3,
The combination of Yu/Jiang/Zhang teaches the method of Claim 2 (and thus the rejection of Claim 2 is incorporated). The combination already discloses wherein the in the real perception data monitoring network comprise a data monitor and a communication network module, the data acquisition nodes are arranged in a matrix. (Yu [Figure 3];
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Yu [Section 4.1 Paragraph 3]; “PeMSD7 was collected from Caltrans Performance Measurement System (PeMS) in real-time by over 39, 000 sensor stations, deployed across the major metropolitan areas of California state highway system [Chen et al., 2001]. The dataset is also aggregated into 5-minute interval from 30-second data samples” wherein the 39000 sensors each able to communicate monitored geographic and traffic observation data to an aggregate dataset reads on data acquisition nodes composed at least in part of a data monitor and a communication network module; wherein each of the sensors arranged at specific locations in the sensor network map of Figure 3 reads on data acquisition nodes arranged at preset distances in a matrix for the current region)
Regarding Claim 4,
The combination of Yu/Jiang/Zhang teaches the method of Claim 2 (and thus the rejection of Claim 2 is incorporated). The combination already discloses wherein constructing the virtual perception data monitoring network comprises performing coordinate grid division on a monitoring region to obtain a plurality of grid regions (Yu [Figure 3];
Yu [Figure 3];
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wherein the monitoring region of Metropolitan California being divided into smaller grids appropriate for different Districts of California reads on performed coordinate grid division on the monitoring region to obtain a plurality of grid regions)
virtualizing virtual data acquisition nodes corresponding to the grid regions (Yu [Figure 3];
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wherein the graphical representation of the communicated sensor information as dots corresponding to the District 7 grid region reads on virtualization of virtual data acquisition nodes corresponding to the smaller grid regions)
mapping historical perception data into the grid regions according to a location of occurrence of the historical perception data, and regarding the historical perception data as original perception data recorded by the virtual data acquisition nodes. (Yu [Figure 3];
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Yu [Section 4.1 Paragraph 3]; “PeMSD7 was collected from Caltrans Performance Measurement System (PeMS) in real-time by over 39, 000 sensor stations, deployed across the major metropolitan areas of California state highway system [Chen et al., 2001]. The dataset is also aggregated into 5-minute interval from 30-second data samples” wherein the aggregation of a plurality of sensor stations, each sensor stations interpreted as “other data sources” by which their historical perception data is recorded corresponding to their sensor station location of occurrence, reads on mapping of the historical perception data; wherein the aggregated data comprising integrated historical perception data being used as original perception data input into the model reads on regarding the historical perception data as original perception data)
Regarding Claim 8,
The combination of Yu/Jiang/Zhang teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). The combination already discloses wherein the graph neural network model comprises an input layer, a spatio-temporal graph convolution module and an output layer, wherein the spatio- temporal graph convolution module is composed of two first temporal domain convolution modules and a spatial domain convolution module therebetween, and the output layer is composed of a second temporal domain convolution module and a fully connected layer (Yu [Figure 2];
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Regarding Claim 9,
The combination of Yu/Jiang/Zhang teaches the method of Claim 8 (and thus the rejection of Claim 8 is incorporated). The combination already discloses wherein the two first temporal domain convolution modules are configured to: divide an input into two paths, perform a sigmod operation on one path among the two paths and an addition operation with a residual on the other path among the two paths after one-dimensional convolution, and output the results of the two paths through a Hadamard product; (Yu [Section 3.3 Paragraph 2];
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wherein the two first temporal domain convolution modules are configured to perform the one-dimensional convolution on input spatio-temporal graph data
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along a time dimension, where M represents a total of M times in the spatio-temporal graph, n represents a number of the data acquisition nodes in the monitoring region, and Ci represents a dimension at which the data acquisition nodes record features; (Yu [Section 3.3 Paragraph 2];
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a size of a temporal domain convolution kernel is
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, a number of the temporal domain convolution kernels is 2Co, and the spatio-temporal graph is
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(Yu [Section 3.3 Paragraph 2];
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Regarding Claim 10,
The combination of Yu/Jiang/Zhang teaches the method of Claim 8 (and thus the rejection of Claim 8 is incorporated). The combination already discloses wherein the spatial domain convolution module is configured to: perform a graph convolution operation on an input to obtain spatial feature information and output the spatial feature information; (
Yu [Section 3.4 Paragraph 2];
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wherein the input and output being both 3-D tensors of spatial and temporal feature in formation reads on inputting and outputting of spatial feature information through a graph convolution operation)
wherein graph data corresponding to each time on the spatio-temporal graph output by the first temporal domain convolution modules is input
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and subjected to graph convolution according to the Chebyshev approximation;
(Yu [Section 3.2];
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a size of each spatial convolution kernel selected during the graph convolution is
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, where K=2, and a number of the convolution kernels is Ci, and an output is
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(Yu [Section 3.2];
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Yu [Section 3.4 Paragraph 2];
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wherein the output of the spatial-temporal block constituting the spatial domain convolution module reads as the output of the spatial domain convolution)
Regarding Claim 11,
The combination of Yu/Jiang/Zhang teaches the method of Claim 8 (and thus the rejection of Claim 8 is incorporated). The combination already discloses wherein each time spatio- temporal graph perception data pass through one of the two first temporal domain convolution modules, a time dimension is reduced by (Kt-1)
Regarding Claim 12,
The combination of Yu/Jiang/Zhang teaches the method of Claim 8 (and thus the rejection of Claim 8 is incorporated). The combination already discloses
wherein a size of each convolution kernel of the second temporal domain convolution module is
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, a number of convolution kernels is Co;
(Yu [Section 3.4 Paragraph 2];
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an output after the second temporal domain convolution module is
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, the fully connected layer is
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where the parameters are
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, and a predicted value
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is output by the output layer; (Yu [Section 3.4 Paragraph 2];
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wherein a loss function is a distance measure between the predicted value v and a real value
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; (Yu [Equation 9];
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Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Yu et al. (“Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting” [2018], hereinafter “Yu”) in view of Jiang (CN113656461A) in view of Zhang et al. (CN114169649A, hereinafter “Zhang”) in view of Nagalapatti et al. (US20230177385A1, hereinafter “Nagalapatti”).
Regarding Claim 15,
Yu/Jiang/Zhang discloses the method according to claim 1 (Yu [Section 4.1 Paragraph 3]; “PeMSD7 was collected from Caltrans Performance Measurement System (PeMS) in real-time by over 39, 000 sensor stations, deployed across the major metropolitan areas of California state highway system [Chen et al., 2001]. The dataset is also aggregated into 5-minute interval from 30-second data samples” wherein the PeMSD7 dataset derived from a network of 39000 sensors reads on a sensing data monitoring network comprising data acquisition nodes
Yu [Figure 1];
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Yu [Section 2.1]; “Traffic forecast is a typical time-series prediction problem, i.e. predicting the most likely traffic measurements (e.g. speed or traffic flow) in the next H time steps given the previous M traffic observations as
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where vt ∈ R n is an observation vector of n road segments at time step t, each element of which records historical observation for a single road segment. In this work, we define the traffic network on a graph and focus on structured traffic time series. The observation vt is not independent but linked by pairwise connection in graph. Therefore, the data point vt can be regarded as a graph signal that is defined on an undirected graph (or directed one) G with weights wij as shown in Figure 1. At the t-th time step, in graph Gt = (Vt, E, W), Vt is a finite set of vertices, corresponding to the observations from n monitor stations in a traffic network; E is a set of edges, indicating the connectedness between stations; while W ∈ R n×n denotes the weighted adjacency matrix of Gt.” wherein a traffic network is defined on a graph and comprises observation vectors which are connected pairs within the graph. Data points vt are used as graph signals and are defined as undirected graph G with a vector wij, thus reading on pre-processing the original sensing data and conversion into spatio-temporal graph sensing data
Yu [Figure 2];
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Yu [Section 4.3 Paragraph 3]; “STGCN Model For BJER4 and PeMSD7(M/L), the channels of three layers in ST-Conv block are 64, 16, 64 respectively. Both the graph convolution kernel size K and temporal convolution kernel size Kt are set to 3 in the model STGCN(Cheb) with the Chebyshev polynomials approximation, while the K is set to 1 in the model STGCN(1st) with the 1st-order approximation. We train our models by minimizing the mean square error using RMSprop for 50 epochs with batch size as 50. The initial learning rate is 10−3 with a decay rate of 0.7 after every 5 epochs.” wherein the preprocessed PeMSD7 dataset comprising spatio-temporal graph perception data is used for training parameters of the graph neural network model Yu [Figure 4];
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which discloses outputting a predicted value from inputted spatio-temporal graph perception data
Jiang [Page 5 Paragraph 14]; “the measuring point data of each device can be monitored through a process control method, so that an abnormal detection result for the device involved in the industrial production process can be obtained. For example, when the station data with the deviation larger than the preset deviation threshold value from the normal data is detected in the station data sequence of the equipment, the equipment can be considered to be abnormally operated.”
Jiang [Page 7 Last Paragraph]; “Besides detecting whether the prediction result has an abnormality by using a data-driven method (such as a process control algorithm), the data of each device contained in the prediction result can be checked whether the data of the device conforms to the working mechanism among the associated devices by using the device working mechanism. By utilizing the prediction result of the prediction model, the risk assessment can be carried out on the industrial production process, namely, the real-time early warning is carried out according to the time sequence characteristic analysis of the data and the prediction result.” wherein the abnormality detection, dependent on a preset threshold being exceeded, results in early warning)
Yu/Jiang/Zhang fails to explicitly disclose but Nagalapatti discloses A computer program product comprising a non-transitory computer readable medium having instructions recorded thereon, the instructions when executed by a computer implementing the method … (Nagalapatti [0041];
“As used herein, including the claims, a “server” includes a physical data processing system (for example, system 612 as shown in FIG. 6) running a server program. It will be understood that such a physical server may or may not include a display and keyboard.
An exemplary embodiment may include a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out exemplary embodiments of the present disclosure”
Nagalapatti [0035]; “An exemplary embodiment or elements thereof can be implemented in the form of an apparatus including a memory and at least one processor that is coupled to the memory and configured to perform exemplary method steps.” which discloses a processor to execute software to implement the method)
It would have been obvious for Yu/Jiang/Zhang’s method of training a graph neural network to predict spatio-temporal perception information to be performed using Nagalapatti’s software program which implements the method on a coupled processor. One would have been motivated to do so “to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks” (Nagalapatti [0047]).
Response to Arguments
The Examiner acknowledges the Applicant’s amendments to Claims 1-12 and 15.
Applicant’s arguments filed January 5th, 2026, traversing the rejection of claims 3-7 and 9-13 under 35 U.S.C. § 112 have been fully considered, and are fully persuasive.
Applicant’s arguments filed January 5th, 2026, traversing the rejection of claims 1-15 under 35 U.S.C. § 101 have been fully considered, and are fully persuasive.
Applicant’s arguments filed January 5th, 2026, traversing the rejection of claims 1-15 under 35 U.S.C. § 103 have been fully considered and are persuasive, necessitating new grounds of rejection under a second non-final rejection. However, applicant’s arguments are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
“Managing Spatial Graph Dependencies in Large Volumes of Traffic Data for Travel-Time Prediction” [2016] which discloses adjacency graph computation in-part through related Pearson correlation-coefficients.
“Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework” [2016] which discloses spatio-temporal feature information in graph-based network models being learned
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN J KIM whose telephone number is (571)272-0523.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matt El can be reached on (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JONATHAN J KIM/Examiner, Art Unit 2141 /MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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