DETAILED ACTION
This action is in response to the original filing of 6-23-2023. Claims 1-19 are pending and have been considered below:
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 (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.
Claim(s) 1, 4-8, 10-11 and 14-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Quanz et al. (“Quanz” 20220138537 A1) in view of Zhou et al. (“Zhou” 20210089944 A1),
“Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting”, Nguyen et al. (“Nguyen”), pages 9117-9125, © 2021 and Avkhachev et al. (“Avkhachev” 20220351865 A1).
Claim 1: Quanz discloses a computer implemented method for providing multivariate time series forecasting of input data having multiple variables using deep learning models, the method comprising:
receiving a time series of a multivariate input dataset on a sliding window of time (Quanz: Paragraph 7 and 44; multivariate input and subsequent time points (window) );
providing the multivariate input dataset in a defined window to a machine learning model to predict future values of the dataset in a future time frame(Quanz: Figure 1, Paragraphs 44-46 (data provided to machine learning model for forecast) and 50 (future)),
the machine learning model being trained based on a data (Paragraphs 8-10 and 23) set wherein the data set having more data points in a past time frame than the future time frame being predicted, wherein predicting with the machine learning model further comprises: utilizing an autoencoder provided within the machine learning model to generate one or more autoencoder layers (Quanz: Figures 1 and 3, Paragraphs 15-17 and 50 (autoencoder));
Quanz may not explicitly disclose training on historical data
Zhou discloses forecasting functionality and further discloses training a model on historical data, further utilizing multiple data points for prediction (Paragraphs 12 and 19-20). Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to apply a known technique to a known device ready for improvement and incorporate historical training data in the system of Quanz. One would have been motivated to provide the functionality because the historical data provides data where patterns can be evaluated on factual data for more effective analysis.
Quanz also does not explicitly disclose analyse seasonality and co-variance information of the multivariate input dataset; implementing an autoregressor within the machine learning model to generate one or more autoregressor layers to analyse trend information of the multivariate input dataset;
Nguyen is provided because discloses time series forecasting and further provides functionality within the forecasting for analyzing seasonal and covariance data (Page 9119 (3), Column 2: Point Prediction and Page 9121 (5), Figure 1 and Column 1, Paragraphs 1-2; seasonal data and covariance matrix are utilized in the layers of the model).
Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to apply a known technique to a known device ready for improvement and incorporate different data in the system of Quanz. One would have been motivated to provide the additional data because it provides a capability to capture additional patterns for more effective analysis.
Further Quanz may not explicitly disclose implementing one or more layer mergers within the machine learning model to receive the information from the one or more autoregressor layers and the information from the one or more autoencoder layers; merging the one or more autoregressor layers and one or more autoencoder layers within the one or more layer mergers to form a set of merged layers; and, automatically generating a multivariate time series forecast using the machine learning model in the future time frame based on the set of merged layers.
Avkhachev discloses multivariate analysis (Paragraph 226), and further provides a system where an autoencoder and autoregressor are combined and used for prediction of future states (Paragraph 241 and 268-269).
Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to apply a known technique to a known device ready for improvement and incorporate the combined autoencoder and autoregressor in the system of Quanz. One would have been motivated to provide the functionality because the functionality can provide a compact set of latent variables for more effective analysis.
Claim 4: Quanz, Zhou, Nguyen and Avkhachev disclose a method of claim 1, wherein the autoencoder of the machine learning model further comprises an encoder and a decoder, the decoder to process seasonality and co-variance information received from one or more autoencoder layers and configured to flatten layers of data provided from the encoder into a same dimension as an expected output shape from the machine learning model so as to be merged with an output of the autoregressor (Nguyen: Page 9119 (3), Column 2: Point Prediction and Page 9121 (5), Figure 1 and Column 1, Paragraphs 1-2; seasonal data and covariance matrix are utilized in the layers of the model).
Claim 5: Quanz, Zhou, Nguyen and Avkhachev disclose a method of claim 4, wherein the autoencoder is configured with a convolutional neural network to provide multivariate time series forecasting by being applied in an iterative manner wherein initially receives a window of historical time series data in the multivariate input data set for encoding and decoding, while at subsequent iterations, the decoder is tuned to use its own output as an input for a subsequent time step thereby generating forecasted seasonality and covariance for future time steps (Quanz: Paragraphs 50-51; prediction samples taken, Page 9119 (3), Column 2: Point Prediction and Page 9121 (5), Figure 1 and Column 1, Paragraphs 1-2; seasonal data and covariance matrix are utilized in the layers of the model and Zhou: Paragraphs 12 and 19-20; historical data set).
Claim 6: Quanz, Zhou, Nguyen and Avkhachev disclose a method of claim 5, wherein the autoencoder in the machine learning model further comprises a bottleneck layer located between the encoder and the decoder, the bottleneck layer being a lower dimensional hidden layer having a least amount of neurons where the encoding is produced, and the encoder applying a dilated convolutional neural network (Avkhachev: Paragraph 241; bottleneck layer).
Claim 7: Quanz, Zhou, Nguyen and Avkhachev disclose a method of claim 1, wherein the autoencoder applies one of a dilated convolutional neural network (CNN), a long short term memory (LSTM) network, and other neural networks using convolutional layers (Quanz: Paragraph 50; convolutional NN).
Claim 8: Quanz, Zhou, Nguyen and Avkhachev disclose a method of claim 1, wherein the multivariate input dataset received at the machine learning model comprises one or more multivariate time series forecasts previously automatically output by the autoencoder of the machine learning model (Quanz: Figure 1 and Paragraph 46; forecast outputs).
Claims 10-11 are similar is scope to claim 1 and therefore rejected under the same rationale.
Computer program and computer readable medium (Quanz: Paragraph 26)
Apparatus (Quanz: Paragraphs 7 and 95)
Claim 14 is similar in scope to claim 4 and therefore rejected under the same rationale.
Claim 15 is similar in scope to claim 5 and therefore rejected under the same rationale.
Claim 16 is similar in scope to claim 6 and therefore rejected under the same rationale.
Claim 17 is similar in scope to claim 7 and therefore rejected under the same rationale.
Claim 18 is similar in scope to claim 8 and therefore rejected under the same rationale.
Claim(s) 2-3 and 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Quanz et al. (“Quanz” 20220138537 A1), Zhou et al. (“Zhou” 20210089944 A1), “Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting”, Nguyen et al. (“Nguyen”), pages 9117-9125, © 2021 and Avkhachev et al. (“Avkhachev” 20220351865 A1) in further view of Le Minh et al. (“Le Minh” 20250272535 A1).
Claim 2: Quanz, Zhou, Nguyen and Avkhachev disclose a method of claim 1 further comprising: stationarizing the multivariate input data as received and prior to providing the multivariate input dataset to the machine learning model.
Le Minh discloses multivariate time series data which is stationarized (Paragraph 21). Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to apply a known technique to a known device ready for improvement and incorporate the stationarizing in the system of Quanz. One would have been motivated to provide the functionality because the stationarizing allows for consistency across dataset time periods improving evaluation.
Claim 3: Quanz, Zhou, Nguyen and Avkhachev and Le Minh method of claim 2 wherein applying the sliding window to the multivariate input dataset of available historical time series data further comprises: designating a time portion of the time series of the multivariate input dataset as a training set for the machine learning model and another time portion of the multivariate input dataset as dropped data being ignored during a training phase of the machine learning model and a future time portion as a forecasted set for being generated by the machine learning model (Le Minh: Paragraph 20-22; resample time periods are determined and normalization can drop different data).
Claim 12 is similar in scope to claim 2 and therefore rejected under the same rationale.
Claim 13 is similar in scope to claim 3 and therefore rejected under the same rationale.
Claim(s) 9 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Quanz et al. (“Quanz” 20220138537 A1), Zhou et al. (“Zhou” 20210089944 A1), “Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting”, Nguyen et al. (“Nguyen”), pages 9117-9125, © 2021 and Avkhachev et al. (“Avkhachev” 20220351865 A1) in further view of Parsonnet et al. (“Parsonnet” 12229164 B1).
Claim 9: Quanz, Zhou, Nguyen and Avkhachev disclose a method of claim 1, however may not explicitly disclose herein the machine learning model is further configured to perform granger causality feature selection on received input data comprising the multivariate input dataset to predict an efficacy of utilizing a particular variable of the multivariate input dataset to predict a separate forecasted multivariate time series data with a change in time period.
Parsonnet discloses multivariate time series data where features are extracted to determine causality using techniques like Granger (Column 18, Lines 1-22). Therefore it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to apply a known technique to a known device ready for improvement and incorporate a Granger technique in the system of Quanz. One would have been motivated to provide the functionality because the technique effectively test time series data for future forecasting.
Claim 19 is similar in scope to claim 9 and therefore rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
20240202531 A1 [0090]
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)).
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/SHERROD L KEATON/Primary Examiner, Art Unit 2148
5-15-2026
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