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 .
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted on 08/02/2022 was filed in compliance with the provisions of 37 CFR 1.97 and 1.98. Accordingly, the information disclosure statement is being considered by the examiner.
Status of Claims
3. Claims 1-20 are pending in this application.
Oath/Declaration
The receipt of Oath/Declaration is acknowledged.
Drawings
5. The receipt of Drawings is acknowledged.
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 17-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Regarding claim 17, “A computer-readable storage medium" (See line 1) fails to specify the term “non-transitory” prior to the “computer-readable storage medium”, therefore, the definition does not exclude the possibility of a signal as being one type of “computer-readable storage medium”. A " computer-readable storage medium” is defined in the Applicant’s Specification at paragraphs [0098], “Although an exemplary embodiment of at least one of a system, method, and non-transitory computer readable medium has been illustrated in the accompanied drawings and described in the foregoing detailed description, it will be understood that the application is not limited to the embodiments disclosed, but is capable of numerous rearrangements, modifications, and substitutions as set forth and defined by the following claims. For example, the capabilities of the system of the various figures can be performed by one or more of the modules or components described herein or in a distributed architecture and may include a transmitter, receiver or pair of both. For example, all or part of the functionality performed by the individual modules, may be performed by one or more of these modules. Further, the functionality described herein may be performed at various times and in relation to various events, internal or external to the modules or components. Also, the information sent between various modules can be sent between the modules via at least one of: a data network, the Internet, a voice network, an Internet Protocol network, a wireless device, a wired device and/or via plurality of protocols. Also, the messages sent or received by any of the modules may be sent or received directly and/or via one or more of the other modules.” Although the Applicant’s Specification states that the invention can be non-transitory it also states “it will be understood that the application is not limited to the embodiments disclosed, but is capable of numerous rearrangements, modifications, and substitutions as set forth and defined by the following claims”; therefore, the definition, does not exclude the possibility of a signal as one type of medium. The broadest reasonable interpretation of a claim drawn to “a computer-readable storage medium” includes both forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer-readable media. See Subject Matter Eligibility of Computer Readable Medium, Jan. 26, 2010. Since, the Applicant’s specification and claim(s) does not limit “computer-readable storage medium” to only non-transitory embodiments. Hence, “a computer-readable storage medium” in claim 17 is broad enough to cover both transitory and non-transitory embodiments. As a result, the claim is not eligible subject matter. It is recommended to amend the claim by adding the limitation/term "non-transitory" prior to "computer-readable storage medium" in order for the claim to cover only statutory embodiments.
Allowable Subject Matter
Claim(s) 1-16 are allowed.
Claims 17-20 are objected to but would be allowable if rewritten to overcome the 35 USC § 101 Rejection directed to non-statutory subject matter.
10. The following is an examiner’s statement of reasons for allowance:
Regarding Independent Claim 1, the prior art(s) searched and of record neither anticipates nor makes obvious nor discloses or suggests the claimed limitations of the claimed subject matter of independent claim 1 as follows:
“a memory configured to store a hierarchical time-series data set; and
a processor configured to
initially train a first time-series forecasting model based on a lower level of time- series data in the hierarchical data set;
train a second time-series teaching forecasting model based on an upper level of time-series data from the hierarchical data set which includes an additional level of aggregation with respect to the lower level of time-series data;
optimize one or more parameters of the initially trained first time-series forecasting model based on predicted outputs from the trained second time-series forecasting model in comparison to predicted outputs from the initially trained first time-series forecasting model; and
store the optimized first time-series forecasting model in the memory.”, in addition to all the limitations as required by the independent claim 1.
In the primary prior art cited but not relied upon of Sen et al. (US PG. Pub. 2022/0383145 A1) teaches in Fig. 5, Sect. [0008], a system for forecasting a time series using a model. The system includes data processing hardware and memory hardware in communication with the data processing hardware. The memory hardware stores instructions that when executed on the data processing hardware cause the data processing hardware to perform operations. The operations include obtaining a set of hierarchical time series, each time series in the set of hierarchical time series including a plurality of time series data values. The operations further include determining, using the set of hierarchical time series, a basis regularization of the set of hierarchical time series. The operations include determining, using the set of hierarchical time series, an embedding regularization of the set of hierarchical time series. The operations further include training a model using the set of hierarchical time series and a loss function based on the basis regularization and the embedding regularization. The operations include forecasting, using the trained model and one of the time series in the set of hierarchical time series, an expected time series data value in the one of the time series.
In the secondary prior art cited but not relied upon of Cetintas (US. Pub. 2022/0129790 A1) discloses in Abstract a method, system, medium, and implementations for machine learning. Upon receiving input data associated with a time series, hidden representations associated with the time series in a feature space are obtained and used to generate a query vector in a query space. Such generated query vector is then used to query relevant historic information related to the time series. The query vector and the relevant historic information are aggregated to generate at least one queried vector, which is aggregated with the hidden representations to generate enriched hidden representations that enhance the expressiveness of the hidden representations.
In particular, the closest Cited reference of Sen fails to disclose and would not have rendered obvious the claimed subject matter of independent claim 1. Also, secondary cited reference of Cetintas does not remedy the deficiencies required by claim 1 as follows:
“a memory configured to store a hierarchical time-series data set; and
a processor configured to
initially train a first time-series forecasting model based on a lower level of time- series data in the hierarchical data set;
train a second time-series teaching forecasting model based on an upper level of time-series data from the hierarchical data set which includes an additional level of aggregation with respect to the lower level of time-series data;
optimize one or more parameters of the initially trained first time-series forecasting model based on predicted outputs from the trained second time-series forecasting model in comparison to predicted outputs from the initially trained first time-series forecasting model; and
store the optimized first time-series forecasting model in the memory.”, since both Sen and Cetintas fail to a data set training device for training a first time-series forecasting model based on a lower level of time- series data in a stored hierarchical data set and trained second time-series teaching forecasting model based on an upper level of time-series data from the stored hierarchical data set which includes an additional level of aggregation with respect to the lower level of time-series data as suggested by the claim.
11. Therefore, whether taken individually or in combination therof, the prior arts searched, cited and of record to include Sen and Cetintas fails to explicitly teach the claimed limitation(s) as required by independent claims 1, 9 and 17.
12. Independent claims 9 and 17 are essentially the same as Independent Claim 1 and refers to “A method” and “A computer-readable storage medium” of Claim 1; and is therefore allowed for the same reasons as applied to Claim 1 above.
13. It follows that claims 2-8 and 10-16 are then inherently allowable for depending on an allowable base claim.
14. Any comments considered necessary by Applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance."
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DARRYL V DOTTIN whose telephone number is (571)270-5471. The examiner can normally be reached M-F 9am-5pm.
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/DARRYL V DOTTIN/
Primary Examiner, Art Unit 2681
/DARRYL V DOTTIN/
Primary Examiner, Art Unit 2681