DETAILED ACTION
Claim Status
This is first office action on the merits in response to the application filed on 6/29/2023.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-9 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 6/29/2023 and 3/24/2025 is(are) in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under the Step 1 of the Section 101 analysis, Claims 1-4 are drawn to a method which is within the four statutory categories (i.e., a process), and Claims 5-9 are drawn to a system which is within the four statutory categories (i.e. a machine).
Since the claims are directed toward statutory categories, it must be determined if the claims are directed towards a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea). Based on consideration of all of the relevant factors with respect to the claim as a whole, claims 1-9 are determined to be directed to an abstract idea. The rationale for this determination is explained below:
Regarding Claims 1, 5, and 7:
Claims 1, 5, and 7 are drawn to an abstract idea without significantly more. The claims recite “a compression processing step that performs predetermined compression processing on original data of the time-series data that is quantized, and that reduces the data amount to make saved data in a form that is able be saved in a predetermined storage unit; and a restoration processing step that performs restoration processing on the saved data, and that makes the saved data into study data in a form usable by predetermined calculation processing, wherein the compression processing step includes, a step of calculating a first statistical index regarding the original data, a step of excluding, from the original data, data corresponding to a predetermined first condition as a missing value, a step of performing the compression processing on the original data from which the missing value is excluded, and generating compressed data in which the data amount is reduced, and a step of storing the compressed data and the first statistical index as the saved data in the storage unit, and wherein the restoration processing step includes: a step of reading the saved data from the storage unit; a step of generating restored data in which predetermined processing of restoring the saved data is performed on the saved data; a step of calculating a second statistical index regarding the restored data; a step of specifying a position corresponding to data applicable to a predetermined second condition as an interpolation target part, from a missing part of the restored data containing a trace in which the missing value is excluded, and a step of calculating an interpolation value applied to the interpolation target part, compensating the interpolation target part with the interpolation value, and generating learning data in which the restored data is approximated to the original data, based on the first statistical index and the second statistical index.”
Under the Step 2A Prong One, the limitations, as underlined above, are processes that, under its broadest reasonable interpretation, cover Mathematical Concepts such as mathematical relationships, mathematical formulas or equations, or mathematical calculations. For example, but for the “compression processing”, “storage unit”, “restoration processing”, “calculation processing”, “compressed data”, and “learning” language, the underlined limitations in the context of this claim encompass the mathematical concepts. The series of steps belong to a typical mathematical relationships or mathematical calculations, because compressing and restoring of data by calculating or processing data or information such as statistical index or value.
Under the Step 2A Prong Two, this judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – “A time-series data processing method that reduces a data amount of a large amount of time-series data that varies continuously in time, the time-series data processing method comprising:”, “A time-series data processing device that reduces a data amount of a large amount of time-series data that varies continuously in time, the time-series data processing method comprising”, “A time-series data processing device that reduces a data amount of a large amount of time-series data that varies continuously in time, the time-series data processing method comprising”, “compression processing”, “storage unit”, “restoration processing”, “calculation processing”, “compressed data”, and “learning”. The additional elements are recited at a high-level of generality (i.e., performing generic functions of an interaction) such that it amounts no more than mere instructions to apply the exception using a generic computer component, merely implementing an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea. Additionally, regarding the specification and claims, there is no improvement in the functioning of a computer or an improvement to other technology or technical field present, there is no applying or using the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition present, there is no implementing the judicial exception with or using the judicial exception in conjunction with a particular machine or manufacture that is integral to the claim present, there is no effecting a transformation or reduction of a particular article to a different state or thing present, and there is no applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment present such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Accordingly, these additional elements, individually or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Under the Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements in the process amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Regarding Claims 2-4, 6, and 8-9:
Dependent claims 2, 6, and 8 only further elaborate the abstract idea and do not recite additional elements.
Dependent claims 3-4 and 9 include additional limitations, for example, “machine learning” and “learning” (Claim 3); “power storage device”, “vehicle”, and “machine learning” (Claim 4); and “machine learning”, “learning”, “power storage device”, “vehicle” (Claim 9), but none of these limitations are deemed significantly more than the abstract idea because, as stated above, they require no more than generic computer structures or signals to be executed, and do not recite any Improvements to the functioning of a computer, or Improvements to any other technology or technical field.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation or implementing the judicial exception on a generic computer.
Therefore, whether taken individually or as an ordered combination, claims 2-4, 6, and 8-9 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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.
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) 1-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yoffe (US 20230144333 A1) in view of Moustafa (WO 2020205597 A1).
Regarding Claims 1, 5, and 7, Yoffe teaches A time-series data processing method that reduces a data amount of a large amount of time-series data that varies continuously in time, the time-series data processing method comprising (Yoffe: Abstract; Paragraph(s) 0043, 0354): A time-series data processing device that reduces a data amount of a large amount of time-series data that varies continuously in time, the time-series data processing method comprising (Yoffe: Abstract; Paragraph(s) 0043, 0354) A time-series data processing device that reduces a data amount of a large amount of time-series data that varies continuously in time, the time-series data processing method comprising (Yoffe: Abstract; Paragraph(s) 0043, 0354)
a compression processing step that performs predetermined compression processing on original data of the time-series data that is quantized, and that reduces the data amount to make saved data in a form that is able be saved in a predetermined storage unit; and a restoration processing step that performs restoration processing on the saved data, and that makes the saved data into study data in a form usable by predetermined calculation processing (Yoffe: Abstract; Paragraph(s) 0252-0256, 0043, 0354 teach(es) radar device may be configured to compress radar values to be stored in memory and/or to decompress compressed radar values retrieved from memory, for example, according to a radar information compression scheme; the radar information compression scheme may be implemented to support a technical solution, which may utilize expected radar data statistical characteristics, e.g., the special characteristic and/or the nature of received signal statistics, for example, to compress the radar values corresponding to the radar processing pipe), wherein the compression processing step includes, a step of calculating a first statistical index regarding the original data (Yoffe: Paragraph(s) 0255 teach(es) the radar information compression scheme may be implemented to support a technical solution, which may utilize expected radar data statistical characteristics, e.g., the special characteristic and/or the nature of received signal statistics, for example, to compress the radar values corresponding to the radar processing pipe), a step of excluding, from the original data, data corresponding to a predetermined first condition as a missing value, a step of performing the compression processing on the original data from which the missing value is excluded, and generating compressed data in which the data amount is reduced (Yoffe: Paragraph(s) 0252, 0254, 0272, 0258 teach(es) radar device may be configured to compress radar values to be stored in memory according to a radar information compression scheme; the compress engine may be configured to decide for a sample, e.g., for each sample, if the sample may be considered as a “noise” sample, or as an “energy” sample, for example, based on a current Range Bin (RB) in process), and a step of storing the compressed data and the first statistical index as the saved data in the storage unit (Yoffe: Paragraph(s) 0252, 0289 teach(es) radar device may be configured to compress radar values to be stored in memory; processor may be configured utilize interface to access the memory, e.g., to store the compressed radar information in the memory), and wherein the restoration processing step includes: a step of reading the saved data from the storage unit; a step of generating restored data in which predetermined processing of restoring the saved data is performed on the saved data (Yoffe: Paragraph(s) 0427-0429, 0252, 0346, 0350 teach(es) to the statistical information corresponding to the compressed radar information may allow a decompressor, e.g., range decompressing 1018 (FIG. 10 ), to reconstruct the original signal; decompress compressed radar values retrieved from memory, for example, according to a radar information compression scheme); a step of calculating a second statistical index regarding the restored data (Yoffe: Paragraph(s) 0275, 0426-0428 teach(es) the radar information compression scheme may be configured to compress an AoA map, e.g., each AoA map, and data type within the AoA map, for example, using different statistical methods, for example, based on unique statistics of the AoA map; the statistical information corresponding to the compressed radar information may allow a decompressor, e.g., range decompressing 1018 (FIG. 10 ), to reconstruct the original signal); a step of specifying a position corresponding to data applicable to a predetermined second condition as an [interpolation] target part, from a missing part of the restored data containing a trace in which the missing value is excluded (Yoffe: Paragraph(s) 0459 teach(es) range-Doppler data compression scheme may include compressing an active list corresponding to the one or more active range-Doppler bins of the plurality of range-Doppler bins. For example, the active lists may describe positions of the active range-Doppler bins), and a step of calculating an [interpolation] value applied to the [interpolation] target part, compensating the [interpolation] target part with the [interpolation] value, and generating [learning] data in which the restored data is approximated to the original data, based on the first statistical index and the second statistical index (Yoffe: Paragraph(s) 0427-0429 teach(es) the statistical information corresponding to the compressed radar information may allow a decompressor, e.g., range decompressing, to reconstruct the original signal, e.g., vector x, for example, with some quantization error).
However, Yoffe does not explicitly teach interpolation and learning data.
Moustafa from same or similar field of endeavor teaches interpolation (Moustafa: Paragraph(s) 0760-0761, 0229-0230 teach(es) an inference phase for determining selective sampling and performed by the machine learning engine; the samples of the second sensor may be interpolated such that the time between samples for each sensor is the same), and learning data (Moustafa: Paragraph(s) 0186-0187 teach(es) parameters of a machine learning model may be adjusted during a training phase based on training data. A trained machine learning model may then be used during an inference phase to make predictions or decisions based on input data; any of the machine learning models may utilize supervised learning, semi-supervised learning, unsupervised learning, or reinforcement learning techniques).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Yoffe to incorporate the teachings of Moustafa for interpolation and learning data.
There is motivation to combine Moustafa into Yoffe because Moustafa’s teachings of interpolation and learning data would facilitate compressing/decompressing of time-series data (Moustafa: Paragraph(s) 0760-0761, 0229-0230, 0186-0187).
Regarding Claims 2, 6, and 8, the combination of Yoffe and Moustafa all the limitations of claims 1, 5, and 7 above; and Yoffe further teaches wherein of the original data, the data corresponding to the first condition is data equal to or lower than a lower limit threshold set as a lower limit value and equal to or higher than an upper limit threshold set as an upper limit value, wherein the data corresponding to the second condition is data in which data before and after the missing value in a time-series direction of the original data is data within a predetermined range including the lower limit threshold or a predetermined range including the upper limit threshold (Yoffe: Paragraph(s) 0266-0267 teach(es) the one or more operations for the range bin may include estimating an SNR level of radar samples in the range bin, normalizing the radar samples, and/or performing adaptive quantization, for example, according to the SNR level for the range bin), and wherein the interpolation value is extracted from a random number acquired based on a normal distribution function of the restored data (Yoffe: Paragraph(s) 0287-0288, 0291, 0502 teach(es) processor may be configured to generate the compressed radar information, for example, by quantizing a plurality of normalized values corresponding to the radar values in the plurality of data bins).
Regarding Claim 3, the combination of Yoffe and Moustafa all the limitations of claim 2 above; however the combination does not explicitly teach wherein the calculation processing is machine learning that performs learning based on a large amount of input data, and that performs estimation or determination based on a result of the learning, and wherein the learning data is the input data in the machine learning.
Moustafa further teaches wherein the calculation processing is machine learning that performs learning based on a large amount of input data, and that performs estimation or determination based on a result of the learning, and wherein the learning data is the input data in the machine learning (Moustafa: Paragraph(s) 0186, 0450, 0500 teach(es) parameters of a machine learning model may be adjusted during a training phase based on training data. A trained machine learning model may then be used during an inference phase to make predictions or decisions based on input data).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the combination of Yoffe and Moustafa to incorporate the teachings of Moustafa for wherein the calculation processing is machine learning that performs learning based on a large amount of input data, and that performs estimation or determination based on a result of the learning, and wherein the learning data is the input data in the machine learning.
There is motivation to combine Moustafa into the combination of Yoffe and Moustafa because Moustafa’s teachings of machine learning model would facilitate training of a machine learning model with training data (Moustafa: Paragraph(s) 0186, 0450, 0500).
Regarding Claim 4, the combination of Yoffe and Moustafa all the limitations of claim 3 above; however the combination does not explicitly teach wherein the original data is data regarding a power storage device mounted on a vehicle, and wherein the machine learning estimates a change over time of the power storage device.
Moustafa further teaches wherein the original data is data regarding a power storage device mounted on a vehicle, and wherein the machine learning estimates a change over time of the power storage device (Moustafa: Paragraph(s) 0182 teach(es) a system manager may also be provided, which monitors information collected by various sensors on the vehicle to detect issues relating to the performance of a vehicle's autonomous driving system. For instance, computational errors, sensor outages and issues, availability and quality of communication channels (e.g., provided through communication modules), vehicle system checks (e.g., issues relating to the motor, transmission, battery, cooling system, electrical system, tires, etc.), or other operational events may be detected by the system manager).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of the combination of Yoffe and Moustafa to incorporate the teachings of Moustafa for wherein the original data is data regarding a power storage device mounted on a vehicle, and wherein the machine learning estimates a change over time of the power storage device.
There is motivation to combine Moustafa into the combination of Yoffe and Moustafa because Moustafa’s teachings of vehicle system checks relating to the battery would facilitate monitoring information collected by various sensors on the vehicle (Moustafa: Paragraph(s) 0182).
Regarding Claim 9, the combination of Yoffe and Moustafa all the limitations of claim 8 above; and, as stated above with respect to Claims 3-4, the combination of Yoffe and Moustafa further teaches wherein the calculation processing is machine learning that performs learning based on a large amount of input data, and that performs estimation or determination based on a result of the learning, and wherein the learning data is the input data in the machine learning, wherein the original data is data regarding a power storage device mounted on a vehicle, and wherein the machine learning estimates a change over time of the power storage device (Moustafa: Paragraph(s) 0186, 0450, 0500, 0182).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cella (US 20230123322 A1) teaches Predictive Model Data Stream Prioritization, including compression, vehicle, threshold, and time-series data.
Castellano (US 20210082212 A1) including Electronic Device For Efficiently Saving Historic Data Of Ambient Sensors And Associated Method, including condition, reduction, compress, statistical, car, and threshold.
Kornmeier (US 20210271675 A1) including Systems And Methods For Enhancing Time Series Data Compression For Improved Data Storage, including compressed time series data and threshold.
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/CLAY C LEE/ Primary Examiner, Art Unit 3699