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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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.
Claim(s) 1-3, 11-12, and 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Saita et al, US Pub. 2022/0289056.
Regarding claims 1, 12, and 20, Saita et al disclose a charging control method for electric moving body and an electric moving body comprising: a battery consumption measurement module configured to measure a daily battery consumption of the battery (par. 0060-0063, departure SOC and return SOC are recorded and their difference is obtained as the day consumption amount); a distribution model generation module configured to generate a distribution model for the daily battery consumption by using the measured daily battery consumption (par. 0105-0108, most recent number of days are statistically processed and a cumulative distribution of daily data of SOC is calculated); and a recommendation module (par. 0117-0120, current SOC is compared with charging recommendation threshold and notification is issued) configured to provide a recommendation for charging of the battery based on the distribution model (par. 0107-0116’ cumulative distribution establishes the consumption/charging recommendation threshold) and a current state of charge (SOC) of the battery (par. 0117-0120, current remaining SOC is compared against the distribution threshold).
Regarding claim 2, wherein the battery consumption module is configured to measure the daily battery consumption of the battery on a daily basis (see par. 066-00680, first and second embodiments).
Regarding claim 3, wherein the battery consumption measurement module, the distribution model generation module, and the recommendation module are provided in a controller having at least one processor and memory (see par. 0055+).
Regarding claim 11, the prior art teaches a vehicle 10 (Fig. 1; par. 0036).
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.
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.
Claim(s) 4-10 and 13-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saita et al, US Pub. 2022/0289056, in view of Duan et al, US Pub. 2017/0106766. The teachings of Saita et al have been discussed above.
Saita et al fail to disclose the statistical distribution as the normal/Gaussian distribution defined by an average and standard deviation.
Duan et al disclose a system and a method for indicating battery age comprising: statistical processing of historical battery data using a normal distribution including determination of a mean and standard deviation and determination of a probability relative to a battery-value threshold (see Fig. 3; par. 37-49).
It would have been obvious for an ordinary artisan before the filling date of the claimed invention to implement Saita et al statistical battery consumption using the known normal distribution statistical technique taught by Duan et al in order to amount to the predictable use of a known statistical technique for its established purpose of characterizing historical battery data and calculating probabilities relative to a battery threshold. Therefore, it would have been an obvious extension as taught by the prior art.
Regarding claims 4 and 13, wherein the distribution model generation module generates a normal distribution model for the daily battery consumption by using an average and a standard deviation of the measured daily battery consumption (it would have been obvious to apply Duan et al known normal distribution technique to Saita et al historical daily battery consumption value to provide probabilistic characterization of the those values. Therefore, it would have been extension as taught by the prior art).
Regarding claims 5 and 14, wherein: the battery consumption measurement module clusters the measured daily battery consumption for each day type, the distribution model generation module generates the normal distribution model for the daily battery consumption of the corresponding day type by using the average and the standard deviation of the daily battery consumption clustered for each day type, and the recommendation module provides the recommendation for the battery charging based on a normal distribution model for a daily battery consumption of a day type of a next day, and the current SOC (it would have been obvious to apply Duan et al statical modeling technique separately to Saita et al respective day-type groups because Saita et al already separate the historical data according to the day category for the purpose of improving the relevance of the consumption estimate. Therefore, it would have been extension as taught by the prior art).
Regarding claims 6 and 15, wherein the day type includes a weekday, a weekend, a weekend holiday, and a weekday holiday (Saita et al further specify day type. Categorizing consumption information according to calendar based day categories, including individual days of the week and weekday/weekend grouping. Saita et al in view of Duan et al render the claim obvious).
Regarding claims 7 and 16, wherein the recommendation module calculates a complete battery consumption probability of the next day based on the normal distribution model and the current SOC, and recommends the battery charging when the complete battery consumption probability is equal to or more than a predetermined threshold (in view of Duan et al, it would have been obvious to perform Saita et al probability determination using normal distribution and to determine the probability that anticipated battery consumption would exceed the available battery energy represented by the current SOC).
Regarding claims 8 and 17, the claimed expression is the conventional probability density function defining such a normal distribution. Once the normal distribution model of Duan et al is applied to Saita et al historical consumption data, use of the conventional mathematical expression defining the distribution would have been obvious implementation of the selected statistical model. Therefore, Saita et al in view of Duan et al render the claim obvious.
Regarding claims 9 and 18, Saita et al in view of Duan et al would calculate the probability that Saita et al predicted consumption exceed current SOC by integrating the selected normal probability density function from the SOC threshold K through the upper limit. Such calculation is the conventional mathematical operation for determining the probability that a normally distributed variable exceed a selected threshold. The prior arts render the claim obvious.
Regarding claims 10 and 19, wherein when the measured daily battery consumption deviates from a predetermined range based on the average and the standard deviation of the normal distribution model of the corresponding day type, the battery consumption measurement module regards the daily battery consumption as abnormal data, and prevents the measured data from being used for generating the normal distribution model (it would have been obvious to define Saita et al outer range using mean and standard deviation supplied Duan et al technique because standard deviation based limits constitute a conventional method for identifying inconsistent with the modeled population. Such combination of identifying abnormal consumption dada based on statistical distribution and excluding the abnormal data from the data used for the resulting distribution model. Therefore, Saita et al as in view of Duan et al render the claim obvious.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Ayoola et al, US Patent No. 2023/0024900, disclose a system and method for real time distributed micro grid optimization using price signals.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL ST CYR whose telephone number is (571)272-2407. The examiner can normally be reached M to F 8:00-8:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pham Thomas can be reached at 571-272-3689. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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DANIEL ST CYR
Primary Examiner
Art Unit 2876
/DANIEL ST CYR/ Primary Examiner, Art Unit 2876
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