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 .
Continuity/reexam data
Parent data
18704998 filed 04/26/2024 is a National Stage entry of PCT/EP2021/079976 , International Filing Date: 10/28/2021
Child data
None
Last updated by opsgusr on 04/26/2024 10:43:39
Foreign data
No foreign data information
(*) - Request to retrieve electronic copy of foreign priority from participating receiving offices.
1. Claims presented for examination: 1-12
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted on 04/26/2024. The submission is 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.
3. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1 (See MPEP 2106)
Claims 1-9 and 11-12 are directed to a method, a system and a tangible, non-transitory computer readable medium which belongs to a statutory class.
Step 2A, Prong One:
Claims recite “pre-processing (120) the multivariate time-series input data” which is a process that, under its broadest reasonable interpretation, covers performance of the limitation by Mental Process, but for the recitation of generic computer components. Nothing in the claim element precludes the steps from practically being performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mental process, 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.
Step 2A, Prong Two:
Claims recite processor and memory including instructions to process the instructions to process the method. These are generic computer components and program which use to perform abstract ideas.
“Receiving (110) multivariate time-series input data associated with the thermal system, wherein the multivariate time-series input data includes time-series temperature and one or more time-series variables” is the process of getting the data and provide to the system for retrieval or analysis which is a generic computer process.
“Processing (130) the pre-processed multivariate time-series input data in a machine learning architecture” is the process of executing data to produce result.
“Outputting (140), from the machine learning architecture, temperature predictions for the thermal system based on the processed multivariate time-series input data” is the process to display by providing data to the user.
The limitation is thus insignificant extra-solution activity. Limitations that the courts have found not to be enough to qualify as "significantly more” when recited in a claim with a judicial exception include: i. Adding the words "apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)). 2106.05(g)--Insignificant Extra-Solution Activity.
Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
As to claim 2, the limitation:
“Determining relationships between the time-series temperature and the one or more time-series variables” is a mental process.
As to claim 3, the limitation:
“Determining the relationships between the time- series temperature and the one or more time-series variables comprises performing a correlation test between the time-series temperature and the one or more time-series variables” is a mental process.
As to clam 4, the limitation:
“The thermal system comprises an energy storage system” is only further defined what the thermal system is and insignificantly to amount significantly more.
“The one or more time-series variables comprise a time-series variable representing a State-of-Charge of the energy storage system” is the one or more time-series is and in significantly is to amount significantly more.
As to claim 5, the limitation:
“The one or more time-series variables further comprise time-series ambient temperature of the energy storage system” is only further defined what one or more time-series variables are and insignificantly to amount significantly more.
“Obtaining temperature predictions for the thermal system comprises obtaining temperature predictions for the energy storage system in the thermal system” is the process of retrieve data from the processing system or stored in the database.
As to claim 6, the limitations:
“The thermal system further comprises an energy supply system” is only further defined what thermal system included which Is a energy supply system which is insignificantly to amount significantly more.
“The one or more time-series variables further comprising time-series power parameter of the energy supply system in the thermal system” only further defined what the one or more time-series variables are and significantly to amount significantly more.
“Obtaining temperature predictions for the thermal system comprises obtaining temperature predictions for the energy supply system in the thermal system” is the process of retrieving of temperature of energy supply system and insignificantly to amount significantly more.
As to clam 7, the limitation:
“Resampling the multivariate time-series input data to obtain resampled input data, the data points of which are spaced at equal time intervals” is the processed of selecting different data point in different time to product a result which is insignificantly to amount significant more.
“Normalizing a value of the multivariate time-series input data to a range [0, 1]” is the process selecting value in the range and insignificantly to amount significantly more.
“Transforming time-series format into supervised-learning format” is the process of transforming date into different format” is a converting format for one form in to different form to process in different system which is generic process of processing information which the two system in different language or format.
As to claim 8, the limitations:
“Pre-processing (120) the multivariate time- series input data further comprises splitting the multivariate time-series input data into training data (212, 222), validation data (214, 224) and testing data (216, 226)” is the process of partitioning data into different groups for further processing.
“Training phase, in which the training data is fed to an algorithm of the machine learning architecture” is a process of testing using the computer algorithm to determine whether the results are the same. This is compute-based algorithm.
“Validation phase, in which the validation data is fed to the algorithm of the machine learning architecture” is a checking the value using the computer process.
“Test phase, in which the test data is fed to the algorithm of the machine learning architecture” is the process trying the
As to claim 9, the limitation:
“Receiving (110) the multivariate time-series input data comprises receiving the multivariate time-series input data is received for an input period that is an integer multiple of an output period for which the temperature predictions are output” is a process of obtaining number from the mathematic algorithm.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
4. Claim(s) 1-3 and 11-12 is/are rejected under 35 U.S.C. 102(a)(2) as KO et al. (Pub. No. US 2021/0190362 A1).
As to claim 1. (Original) KO discloses a method (100) for predicting temperature of a thermal system (temperature prediction model) (paragraph 0013), using at least one processor (processor 260) (paragraph 0448), the method comprising:
receiving (110) multivariate time-series input data associated with the thermal system, wherein the multivariate time-series input data includes time-series temperature and one or more time-series variables (a relationship between the variables and temperature) (paragraph 0007) (temperature = temperature and one or more time-series = variables);
pre-processing (120) the multivariate time-series input data (the input unit 220 may acquire input data that is not processed. In this case, the processor 260 or the learning processor 240 may preprocesses the acquired data to generate training data that is capable of being inputted into the model learning or pre-processed input data) (paragraph 0413);
processing (130) the pre-processed multivariate time-series input data in a machine learning architecture (application program for learning time-series data using a recurrent neural network…) (paragraph 0057); and
outputting (140), from the machine learning architecture, temperature predictions for the thermal system based on the processed multivariate time-series input data (Fig. 10b show past control information (valve opening) 1030 and an output value (temperature) 1010 according to the control information at the specific place where the heating system is install using the RNN and predicts temperature change 1020 based on the result of learning and current control information) (paragraph 0245).
As to claim 2, (Original) KO discloses the method of claim 1, wherein processing (130) the pre-processed multivariate time-series input data in a machine learning architecture comprises determining relationships between the time-series temperature and the one or more time-series variables (a relationship between the variables and temperature) (paragraph 0007).
As to claim 3. (Original) KO discloses the method of claim 2, wherein determining the relationships between the time- series temperature and the one or more time-series variables comprises performing a correlation test between the time-series temperature and the one or more time-series variables (the temperature change pattern itself, which depends on the variable (the performance the air conditioner, the performance of the value, the information of the building…) (paragraph 0520).
As to claim 11. (Original) KO discloses a system (400) for predicting temperature of a thermal system, the system (400) comprising: a memory (410) (memories) (paragraph 0547); and at least one processor communicatively coupled to the memory (410) (processor 260) (paragraph 0448).
Claim 12 is rejected under the same reason as to claim 1, KO discloses a non-transitory computer-readable storage medium (memories) (paragraph 0547) containing instructions that when executed by a computer (processor 260) (paragraph 0448).
5. Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over KO et al. (Pub. No. US 2021/0190362 A1) in view OJI (Pub. No. US 2023/0268571 A1).
As to claim 4. (Currently Amended) KO discloses the method of claim 1 excepting for wherein the thermal system comprises an energy storage system, and wherein the one or more time-series variables comprise a time-series variable representing a State-of-Charge of the energy storage system.
However, OJI discloses wherein the thermal system comprises an energy storage system, and wherein the one or more time-series variables comprise a time-series variable representing a State-of-Charge of the energy storage system (when the SOC time-series data and the temperature time-series data of the power storage system are input…) (paragraph 0072).
Therefore, it would have been obvious been obvious to one ordinary skill in the art before the effective filing date of instant application to include wherein the thermal system comprises an energy storage system, and wherein the one or more time-series variables comprise a time-series variable representing a State-of-Charge of the energy storage system as disclosed by OJI in order to provide information of the storage system.
6. Claim(s) 5-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over KO et al. (Pub. No. US 2021/0190362 A1) in view of and further in view OJI (Pub. No. US 2023/0268571 A1) and further in view of UKUMORI (Pub. No. US 2021/0048482 A1).
As to claim 5. (Original) KO discloses the method of claim 4 wherein obtaining temperature predictions for the thermal system comprises obtaining temperature predictions for the energy storage system in the thermal system and excepting for wherein the one or more time-series variables further comprise time-series ambient temperature of the energy storage system.
However, UKOMORI discloses wherein the one or more time-series variables further comprise time-series ambient temperature of the energy storage system (since the predicted time-series data is data that is assumed in accordance with environmental states such as installation conditions of the energy storage device and ambient temperature, the learning model can learn charge-discharge behavior…) (paragraph 0032).
Therefore, it would have been obvious been obvious to one ordinary skill in the art before the effective filing date of instant application to include wherein the one or more time-series variables further comprise time-series ambient temperature of the energy storage system in order to provide information of the storage system disclosed by UKOMORI in order to provide data to the user.
As to claim 6, (Currently amended) KO discloses the method of claim 4, wherein the thermal system further comprises an energy supply system (power supply unit 250) (paragraph 0410), wherein the one or more time-series variables further comprises time-series power parameter of the energy supply system in the thermal system (the power supply unit may receive external power and internal power under the control of processor 260…) (paragraph 0428) excepting for wherein obtaining temperature predictions for the thermal system comprises obtaining temperature prediction for the energy supply system in the thermal system. However, UKOMORI discloses obtaining temperature predictions for the thermal system comprises obtaining temperature prediction for the energy supply system in the thermal system (since the predicted time-series data is data that is assumed in accordance with environmental states such as installation conditions of the energy storage device and ambient temperature, the learning model can learn charge-discharge behavior…) (paragraph 0032). Therefore, it would have been obvious been obvious to one ordinary skill in the art before the effective filing date of instant application to include obtaining temperature predictions for the thermal system comprises obtaining temperature prediction for the energy supply system in the thermal system disclosed by UKOMORI in order to provide data to the user.
6. Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over KO et al. (Pub. No. US 2021/0190362 A1) in view of Bandi et al. (Pub. No. US 2019/0394102 A1).
As to claim 7. (Currently Amended) KO discloses the method of claim 1 excepting for wherein pre-processing (120) the multivariate time-series input data comprises: resampling the multivariate time-series input data to obtain resampled input data, the data points of which are spaced at equal time intervals; normalizing a value of the multivariate time-series input data to a range [0, 1]; and transforming time-series format into supervised-learning format. However, Bandi discloses wherein pre-processing (120) the multivariate time-series input data comprises: resampling the multivariate time-series input data to obtain resampled input data, the data points of which are spaced at equal time intervals (time-series data is a sequence of data points indexed in time order, typically captured at equally-spaced time intervals…) (paragraph 0024): normalizing a value of the multivariate time-series input data to a range [0, 1] (such time-series are received, and for each time-series, a normalized fact quantity change is determined…) (paragraph 0006); and transforming time-series format into supervised-learning format (time-series data 118 may include operational and performance metrics for service 116. Alternatively, anomaly detection system 104 may be configured… to be converted to time-series data 118 and to convert…) (paragraph 0039). Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to include wherein pre-processing (120) the multivariate time-series input data comprises: resampling the multivariate time-series input data to obtain resampled input data, the data points of which are spaced at equal time intervals; normalizing a value of the multivariate time-series input data to a range [0, 1]; and transforming time-series format into supervised-learning format as disclosed by Bandi in order provide information to learning process.
7. Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over KO et al. (Pub. No. US 2021/0190362 A1) in view of Bandi et al. (Pub. No. US 2019/0394102 A1) and further in view of Liu et al. (Patent No. US 12,488,265 B2).
As to claim 8, KO discloses the method of claim 1 excepting for pre-processing (120) the multivariate time- series input data further comprises splitting the multivariate time-series input data into training data (212, 222), validation data (214, 224) and testing data (216, 226), and wherein the processing of the pre-processed multivariate time-series input data in the machine learning architecture further comprises: a training phase, in which the training data is fed to an algorithm of the machine learning architecture; a validation phase, in which the validation data is fed to the algorithm of the machine learning architecture; and a test phase, in which the test data is fed to the algorithm of the machine learning architecture. However, Liu discloses pre-processing (120) the multivariate time- series input data further comprises splitting the multivariate time-series input data into training data (212, 222) (the divides the training data into a training into a training set…) (col. 5, lines 15-22), validation data (validation set) (col. 5, line 21) (214, 224) and testing data (216, 226) (training data) (col. 5, line 21), and wherein the processing of the pre-processed multivariate time-series input data in the machine learning architecture further comprises: a training phase, in which the training data is fed to an algorithm of the machine learning architecture (; a validation phase, in which the validation data is fed to the algorithm of the machine learning architecture (the system then trains the inferential model to predict values of the time-series signals on the training set) (col. 5, lines 22-23); and a test phase, in which the test data is fed to the algorithm of the machine learning architecture (tests the inferential model to predict values of the time-series signal…) (col. 5, lines 24-25).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to modify teaching of KO to include splitting the multivariate time-series input data into training data (212, 222), validation data (214, 224) and testing data (216, 226), and wherein the processing of the pre-processed multivariate time-series input data in the machine learning architecture further comprises: a training phase, in which the training data is fed to an algorithm of the machine learning architecture; a validation phase, in which the validation data is fed to the algorithm of the machine learning architecture; and a test phase, in which the test data is fed to the algorithm of the machine learning architecture as disclosed by Liu in order to provide testing to provide predictive temperature.
8. Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over KO et al. (Pub. No. US 2021/0190362 A1) in view of Toril et al. (Pub. No. US 5,101,369).
As to claim 9. (Currently Amended) KO discloses the method of claim 1 wherein receiving (110) the multivariate time-series input data comprises receiving the multivariate time-series input data is received an input period excepting for that is an integer multiple of an output period for which the temperature predictions are output. However, Toril discloses that is an integer multiple of an output period for which the temperature predictions are output (a digital filter performs a predetermined arithmetic operation on input digital data inputted thereto in time series manner by the predetermined first sampling period to thereby generate output digital data corresponding to a second sampling frequency which is N (where N denotes an integer number) times larger than the predetermined first sampling frequency) (abstract). Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to include an integer multiple of an output period for which the temperature predictions are output as disclosed by Toril in order to provide testing data.
Allowable Subject Matter
9. Applicant(s) are advised that both objected claims are required to incorporate into all independence claims in order to allow the application.
As to claim 10, cited reference(s) KO, Torii, Liu, Bandi, UKUMORI AND OJI alone or in combination fail to teach or suggest “wherein the machine learning architecture (3000) comprises a Long Short-Term Memory (LSTM) model having an input layer (3000a), an output layer (3000e) and one or more hidden layers (3000b); wherein each of the layers of the LSTM model has a number of LSTM cells corresponding to time steps of the multivariate time-series input data; and wherein processing the pre-processed multivariate time-series input data in the machine learning architecture, in one of the LSTM cells at a time step t of one of the layers of the LSTM model, comprises the following processes: receiving a previous cell state parameter from an immediate previous LSTM cell at time step t-1 of the same layer; a previous hidden state parameter from the immediate previous LSTM cell at time step t-1 of the same layer; and a hidden state parameter from a previous corresponding LSTM cell at time step t of an immediate previous layer, wherein when the layer is the input layer, receiving an input of the pre-processed multivariate time-series input data at time step t; generating a cell state parameter for input to an immediate subsequent LSTM cell at time step t+1 of the same layer; and concatenating the previous hidden state parameter with the hidden state parameter for input to a subsequent corresponding LSTM cell at time step t of an immediate subsequent layer, and wherein when the layer is the input layer, concatenating the previous hidden state parameter with the input of the pre-processed multivariate time-series input data for input to the immediate subsequent LSTM cell at time step t+1 of the same layer” in conjunction with the language of preceding claim.
Conclusion
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to BAOQUOC N TO whose telephone number is (571)272-4041. The examiner can normally be reached Mon-Fri 9AM - 6PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Boris Gorney can be reached at 571-270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
BAOQUOC N. TO
Examiner
Art Unit 2154
/BAOQUOC N TO/Primary Examiner, Art Unit 2154
Prosecution Insights