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 .
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 1-4 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) converting time series data to an image data.
The limitation of “encoding each univariate time series data into a binary image, combining the binary image with a multi-channel image, performing convolution on the multi-channel image; and performing task using data obtained by the convolution”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind. Furthermore, language, “encoding”, “combining”, and “performing” in the context of this claim encompasses the user manually converting the univariate time series data into a binary image through calculations, manually combining the binary image with a multi-channel image via drawing or mathematically, and manually performing the calculation of convolution. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
RE claims 2-4, claims 2-4 are further rejected under 35 U.S.C. 101 due to their dependency of claim 1. The claim language fails to further remedy the situation.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/24/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings were received on 02/18/2024. These drawings are acceptable.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by WANG et al. (“Encoding Time Series as Images for Visual Inspection and Classification Using Tiled Convolutional Neural Networks”).
RE claim 1, Wang teaches a framework to encode time series data as different types of images [Abstract[. Wang teaches an encoding method of converting time series data to an image, the encoding method comprising:
(a)
encoding each univariate time series data into a binary image;
Wang teaches encoding time series data as images to allow machines to “visually” recognize and classify the time series [0001]. Wang uses two frames works for encoding time series as images. The first type of image is a Gramian Angular Field (GAF), in which the time series is represented in a polar coordinate system instead of the typical Cartesian coordinates [0006]. The second framework is the Markov Transition Field (MTF). The main idea is to build the Markov matrix of quantile bins after discretization and encode the dynamic transition probability in a quasi-Gramian matrix [0006]. The polar coordinate based representation is a novel way to understand time series [0008]. The map functions of GAF and MTF will each produce only one image with fixed S and Q for each given time series X [0026].
(b)
combining the binary image with a multi-channel image;
GAF encodes static information while MTF depicts information about dynamics [0027]. The GAF and MTF images of the same size can be combined to construct a double-channel image [0027].
(c)
performing convolution on the multi-channel image; and
Wang further teaches using tiled convolutional neural networks with GAF and MTF images [0016-0020].
(d)
performing task using data obtained by the convolution.
Wang further teaches classifying GAF and MTF using Tiled CNNs [0025].
RE claim 2, Wang teaches wherein, in the encoding of each univariate time series data into the binary image, multiple pieces of univariate time series data are encoded into the binary image by using a time axis and a time series value axis respectively as an x-axis and a y-axis.
Wang teaches encoding time series data as images to allow machines to “visually” recognize and classify the time series [0001]. Wang uses two frames works for encoding time series as images. The first type of image is a Gramian Angular Field (GAF), in which the time series is represented in a polar coordinate system instead of the typical Cartesian coordinates [0006]. The second framework is the Markov Transition Field (MTF). The main idea is to build the Markov matrix of quantile bins after discretization and encode the dynamic transition probability in a quasi-Gramian matrix [0006]. The polar coordinate based representation is a novel way to understand time series [0008]. The map functions of GAF and MTF will each produce only one image with fixed S and Q for each given time series X [0026].
RE claim 3, Wang teaches wherein, in the combining of the binary image with the multi-channel image, multiple binary images encoded in the encoding of each univariate time series into the binary image are concatenated to one multi-channel image.
The rationale of claim 1 provides teachings of combining of the binary image with the multi-channel image [0001, 0006, 0026]. GAF encodes static information while MTF depicts information about dynamics [0027]. The GAF and MTF images of the same size can be combined to construct a double-channel image (said multi-channel image) [0027].
Allowable Subject Matter
Claim 4 is 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 along with overcoming the 35 U.S.C. 101 rejection above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHELLE L SAMS:
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(571) 272-7661
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michelle.sams@uspto.gov
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/MICHELLE L SAMS/
Primary Examiner, Art Unit 2611
31 October 2025