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 § 103
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Guo et al [WO 2024073932 A1] in further view of Lee [KR 102884789 B1].
As for claim 1, Guo discloses a monitoring method implemented by an electronic supervision device connected to an energy production device (page 2, lines 5-6: The present invention relates to the technical field of fault detection based on electrical digital data, and in particular to a method, device and intelligent terminal for detecting faults in an energy storage system.), the method comprising:
determining, by a deviation cause classification model taking as input a history of deviations between a value representative of a prediction of an amount of energy produced by the energy production device and a value representative of an amount of energy actually produced by the energy production device, an occurrence of a failure in at least one component of said energy production device (page 4, lines 9-20: Compared with the scheme in the prior art that can only judge the fault of the energy storage system based on a single data indicator, a set of data to be detected in the present invention is composed of multiple data, specifically including the power information change data and equipment component image data of the energy storage system in the time period to be detected. In the present invention, the .sup.T2 statistic limit and the Q statistic limit are first calculated based on a set of fault-free data corresponding to the energy storage system, and the .sup.T2 statistic and Q statistic are calculated for the data to be detected. According to the size relationship between the corresponding statistics and the statistic limit, it can be analyzed and judged whether the data to be detected is fault data. When it is fault data, the above data to be detected is further detected by the trained fault detection model and the fault category is obtained. In the present invention, multiple information in the data to be detected (including power information change data and equipment component image data) can be combined for fault judgment and detection, which is conducive to improving the accuracy of fault detection. See also page 5, lines 15-24: In this embodiment, the above-mentioned power information change data includes voltage data, current data, temperature data and energy storage change data, and the above-mentioned equipment component image data includes the surface image data of solar cells and the surface image data of wind power equipment blades in the above-mentioned energy storage system. In this embodiment, the above-mentioned energy storage system is a new energy storage system based on wind power generation and solar power generation, so the main focus is on the surface image data of solar cells and wind power equipment, so as to facilitate the judgment of whether there are faults such as solar cell panel rupture and wind turbine blade cracks. In an application scenario, the above-mentioned power information change data may also include voltage difference data, energy storage density data, energy storage power data, energy storage efficiency data, etc., which are not specifically limited here.); and
generating output data according to which at least one component of said energy production device is defective (page 9, lines 12-30: The above step S500 specifically includes: inputting the above data to be detected into the above trained fault detection model, and outputting the fault category corresponding to the above data to be detected through the above trained fault detection model, wherein the above trained fault detection model classifies the power information change data in the above data to be detected based on the logistic regression method, and the above trained fault detection model classifies the equipment component image data in the above data to be detected based on VGG-Net. In this embodiment, the above two parts (including the logistic regression part and the VGG-Net part) are set in the same model and trained at the same time, so that the correlation between the two data can be fully utilized for training, thereby improving the accuracy of the trained fault detection model. In one application scenario, the above fault detection model may include two sub-models, which process power information change data and device component image data respectively. In another application scenario, two separately trained sub-models may also be used to form the above fault detection model, but this is not a specific limitation. In this way, fault classification is performed on the data with faults, and the time series is judged by using the logistic regression method to determine which type of fault it is. For the image data, VGG-net is used to classify the faults and determine which type of fault it is. Among them, the above fault categories can be pre-set according to actual needs. For example, the fault categories may include voltage instability, excessive current, solar panel breakage, wind blade breakage, damage to other specific components or equipment, etc., which are not specifically limited here.).
While Guo discloses providing an output, Guo does not explicitly disclose that an alert is generated. In an analogous art for electronic supervision of an energy production device, Lee discloses that it was known in the art to generate an alert according to the determination of a defective component in an energy production device (page 7, lines 20-23: The outlier detection model (140) monitors power plant operation data in real time, and if the restoration error exceeds a threshold, it determines it to be an anomaly and transmits a warning message to the monitoring system (150). The warning message includes detailed information such as the time, location, and data value at which the outlier was detected. and page 7, lines 25-27: The monitoring system (150) is a web-based application that provides users with real-time results from a power generation prediction model (130) and an outlier detection model (140). This system provides an intuitive user interface that allows users to grasp the operating status of a solar power plant at a glance.). Having each of the references on hand, it would have been obvious to the skilled artisan to modify Guo to include the alert generation and real-time messages taught by Lee, in order to yield the predictable results of a system and method wherein a user could easily and efficiently monitor the operation of an energy production device and provide a quick response to defective components.
Claim 2 is interpreted and rejected using the same reasoning as claim 1 above.
As for claim 3, both Guo and Lee disclose show that faults (outliers) are detected that indicate a defective component (see claim 1 above). As such, it would have been obvious to the skilled artisan that that history of deviations only include deviations greater than a threshold value.
As for claim 4, Guo discloses determining, by the deviation cause classification model, whether the deviations are caused by an evolution in environmental data of the energy production device, by a change of at least one sensor for measuring said environmental data, by a change of at least one component of the energy production device, or by a failure in at least one component of said energy production device (page 5, lines 15-24: In this embodiment, the above-mentioned power information change data includes voltage data, current data, temperature data and energy storage change data, and the above-mentioned equipment component image data includes the surface image data of solar cells and the surface image data of wind power equipment blades in the above-mentioned energy storage system. In this embodiment, the above-mentioned energy storage system is a new energy storage system based on wind power generation and solar power generation, so the main focus is on the surface image data of solar cells and wind power equipment, so as to facilitate the judgment of whether there are faults such as solar cell panel rupture and wind turbine blade cracks. In an application scenario, the above-mentioned power information change data may also include voltage difference data, energy storage density data, energy storage power data, energy storage efficiency data, etc., which are not specifically limited here. and page 9, lines 28-30: Among them, the above fault categories can be pre-set according to actual needs. For example, the fault categories may include voltage instability, excessive current, solar panel breakage, wind blade breakage, damage to other specific components or equipment, etc., which are not specifically limited here. Guo shows that deviations can be caused in various manners. Having these teachings on hand, it would have been obvious to the skilled artisan to try monitoring different types of data to determine deviations. The selection of desirable components and/or data to monitor is viewed as a matter of engineering preference that would be left to the artisan.).
Claim 5 is interpreted and rejected using the same reasoning as claim 1 above.
As for claim 6, Guo discloses producing an adaptation of the deviation cause classification model based on a re-training of the deviation cause classification model (The skilled artisan would have recognized that the historical data of both Guo and Lee are both updated to include new data as the system generates more data in real-time. This information inherently updates/re-trains the classification model. As such, both Guo and Lee read on the claimed limitations.).
Claim 7 is interpreted and rejected using the same reasoning as claims 1, 2, and 4 above.
Claims 8 and 9 are interpreted and rejected using the same reasoning as claim 1 above.
Claim 10 is interpreted and rejected using the same reasoning as claim 1, 2 and 5 above.
Claim 11 is interpreted and rejected using the same reasoning as claim 1 above.
Claims 12-15 are interpreted and rejected using the same reasoning as claims 2-5 above.
Claims 16-19 are interpreted and rejected using the same reasoning as claims 6-9 above.
Claim 20 is interpreted and rejected using the same reasoning as claim 1 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Each of the cited references discloses electronic supervision devices for monitoring an energy production device, as was known in the art at time of filing the instant application.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC M BLOUNT whose telephone number is (571)272-2973. The examiner can normally be reached M-F 9:00a - 5:30p.
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ERIC M. BLOUNT
Primary Examiner
Art Unit 2685
/Eric Blount/Primary Examiner, Art Unit 2685
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