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 .
Response to Amendment
Applicant’s amendment filed 01/05/2026 has been entered. Claims 1-12 and 15-22 remain pending.
Response to Arguments
Applicant’s arguments, see Pages 8-9, filed 01/05/2026, with respect to the rejection(s) of claim(s) 1, 10, and 17 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of previously disclosed prior art Lee (KR20120038112A) in view of previously disclosed prior art Segelmark (US20240256977) and newly discovered prior art Hua (CN110532600A).
Applicant argues on Page 8 that Lee does not teach or suggest a method for preventing battery failure as it teaches a response after overheating occurs, without proactive identification of actions to avert failure and that Lee discloses the output of an alarm information when an overheated secondary battery cell exists.
Examiner respectfully disagrees. Previously disclosed prior art Lee details in [0052] that it is desirable to configure it so that it is possible to distinguish between secondary batteries that are overheated to the extent that immediate actions is required and secondary batteries that are overheated to the extent that caution or preliminary action is required. As previously disclosed prior art Lee is detailing different action levels dependent on the extent that overheating is occurring in the battery means that battery failure has not occurred and that the battery is still functional if only caution or preliminary action is required. Furthermore, Lee details in [0012] that potential follow-up measures that would be preliminary actions include the operation of cooling air condition systems. A battery that only needs operation of the cooling air condition system as thus would not be a battery that has failed as it is still operational.
Applicant’s arguments, see Pages 9-11, filed 01/05/2026, with respect to the rejection(s) of claim(s) 2-3, 11-12, and 18-19 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of previously disclosed prior art Lee (KR20120038112A) in view of previously disclosed prior art Segelmark (US20240256977), newly discovered prior art Hua (CN110532600A) and newly discovered prior art Huck (US20230132883).
Applicant argues on Page 9 that the Segelmark’s neural network outputs estimates for different image settings and that the loss function is not a real time prediction of future pixel state values based on a plurality of previous and sequential pixel states. Applicant argues on Page 10 with respect to Claims 3, 12, and 19 that Segelmark fails to teach or suggest determining “battery predictive actions” that are forward-looking and explicitly “based on the one or more predicted pixel state values” as claimed and Segelmark’s teachings are limited to immediate fault assessments and generic recommendations for current overheating in non-battery contexts, not predictive actions derived from prognostic pixel state predictions. Applicant details that Segelmark describes determining whether overheating is occurring based on current thermal characteristics and generating recommended course of action like repair/replace and that this is confined to analyzing present image data for faults without any teaching of predictive actions as Applicant’s Claim 2, as amended, which requires time-series prediction using previous and sequential pixel states. Applicant argues the claim requires actions that are predictive with respect to time, and tied to forward-looking pixel state values as described in applicant’s specification in [0050].
Examiner agrees that previously disclosed prior art Segelmark is silent with regards to utilizing previous and sequential pixel states. Newly discovered prior art Huck (US20230132883) teaches in [0052]-[0055] and [0069] the utilization of a temporal sequence of thermal images of a component with machine learning to perform a prediction.
Examiner notes that claims 3, 12, and 19 are dependent on amended Claims 2, 11, and 18. As detailed above, newly discovered prior art Huck teaches the utilization of temporal sequence of thermal images of a component to perform a prediction. Furthermore, previously disclosed prior art Segelmark details predicted recommended actions in [0103], which would correspond to a battery predictive actions. As the predicted action would thus be from the predicted pixel states as taught by Lee in view of Segelmark, Hua, and Huck, the claim limitations are fully taught by the combination. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
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.
Claims 1, 6-10, 15-17 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Lee (KR20120038112A) in view of Segelmark (US20240256977) and Hua (CN110532600A)
In regards to Claims 1 and 17, Lee teaches “receiving, by a data acquisition system, at least one thermal image corresponding to at least one battery of at least one vehicle, the at least one thermal image including pixel data (monitoring and managing a secondary battery – [0001]; secondary batteries are widely used in electric or hybrid vehicles – [0002]; image module performs function of generating an infrared/thermal image of a battery – [0032]; mechanism through image processing to determine whether a battery is overheated and to perform subsequent processing – [0033]; each pixel constitutes the infrared image, i.e. thermal image, has color information including Y, Cb, and Cr and the color information included in the image information has different values dependent on the heat of the object – [0035]);
converting, by the data acquisition system, the at least one thermal image into one or more parameters, the one or more parameters corresponding to one or more pixels of the pixel data (each pixel constitutes the infrared image has color information including Y, Cb, and Cr – [0035]; infrared image has different color, especially brightness information, depending on the temperature of the object and thus, by applying this principle, color information corresponding to each temperature is stored – [0038]; six types of color information are illustrated as an example, and it is possible to configure the size of the brightness or luminance information of the color according to each temperature as data – [0039]; from data processing, the brightness information of a pixel has a data value of 0 to 255, and a larger number means a brighter bright, and a brighter brightness means a subject with higher heat – [0044]);
transmitting, by the data acquisition system, the one or more parameters to a condition monitoring system (infrared image information is input from the image module and the control unit reads out the reference color information stored in the storage unit and compares the color information of the infrared image with the reference color information to determine whether the battery is overheated – [0040]; Figures 1 and 2 detail the control unit connected to the cameras 110);
evaluating, by the condition monitoring system and in advance of the battery failure, the one or more parameters based on one or more conditions to determine that at least one of the one or more parameters meet at least one of the one or more conditions (temperatures below 25C are judged as normal and temperatures above 25C are judged as overheating, i.e. condition – [0039]; control unit reads out the reference color information stored in the storage unit and compares the color information of the infrared image with the reference color information to determine whether the battery is overheated – [0040]; “In addition, the control unit of the present invention outputs alarm information when there is an overheated secondary battery cell, and it is preferable to configure it so that differential alarm information is output according to the degree of overheating” – [0020]; “is desirable to configure it so that it is possible to distinguish between secondary batteries that are overheated to the extent that immediate action is required and secondary batteries that are overheated to the extent that caution or preliminary action is required” – [0052]; as the system has different degrees of overheating in the evaluation, this would mean that the evaluation is done in before the failure of the battery); and
outputting, by the condition monitoring system and in advance of the battery failure, the one or more battery maintenance actions to one or more user interfaces of a user device (“Therefore, batteries used as energy storage sources assembled into large structures require monitoring of heat generation, and it can be said that follow-up measures based on the monitoring results, such as the operation of cooling air conditioning systems” – [0012]; “It is desirable to configure it so that it is possible to distinguish between secondary batteries that are overheated to the extent that immediate action is required and secondary batteries that are overheated to the extent that caution or preliminary action is required” – [0052]; by checking the interface screen, administrators can accurately determine which secondary battery cells require replacement or repair – [0053]; “In this case, as previously examined, the degree of overheating can be recognized in stages by utilizing the difference from the standard color information. Therefore, the control unit (130) of the present invention can be configured to output alarm information, etc. as described above, but to output differential alarm information according to the degree of overheating, thereby inducing an administrator, etc., to take differential follow-up or preliminary measures” – [0055]).”
Lee is silent with regards to the language of “based on the evaluating, determining, by the condition monitoring system and in advance of the battery failure, one or more battery maintenance actions to prevent the battery failure based on the at least one of the one or more parameters that meets the at least one of the one or more conditions.”
Segelmark teaches “based on the evaluating, determining, by the condition monitoring system and in advance of the battery failure, one or more battery maintenance actions to prevent the battery failure based on the at least one of the one or more parameters that meets the at least one of the one or more conditions (infrared imaging system may determine image settings using trained machine learning models and includes the functionality of recommending actions – [0017]; trained machine learning model performs target recognition based on temperature profile to identify targets of interesting including overheating components, and provides recommended actions based on the assessment – [0087]; recommended action can include repair [i.e. action to prevent the battery failure] or replace – [0103]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lee to incorporate the teaching of Segelmark to determine recommendation actions based on the analysis. This is an improvement that yields predictable results in the analysis of the temperature by thermal imaging of a battery to determine overheating of the battery.
Lee in view of Segelmark are silent with regards to the language of “predict a future temperature or health of the at least one battery.”
Hua teaches “predict a future temperature or health of the at least one battery (“This invention also relates to a power battery thermal management method, which corresponds to the aforementioned power battery thermal management system and can be understood as a practical application method of the aforementioned system. This method involves establishing a digital battery thermal management module in the cloud that matches the physical battery thermal management module, and creating a digital twin system based on the data and information of both. The system calculates and analyzes the battery system temperature distribution in real time in the cloud, obtaining the current highest temperature and temperature inconsistencies within the system, predicting its future development trend, and formulating a reasonable thermal management control strategy to manage the inconsistencies in the battery pack temperature distribution, control the highest and lowest temperatures, and predict future temperature trends” – [0038]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lee in view of Segelmark to incorporate the teaching of Hua to utilize the information to predict the future temperature trend of the battery. By predicting the future temperature trend of the battery this is an improvement that yields predictable results to the thermal management of the battery system.
In regards to Claim 6 and 15, Lee in view of Segelmark and Hua discloses the claimed invention as detailed above. Lee is silent with regards to the language of “wherein the condition monitoring system includes at least one of: a vehicle condition monitoring function module, vehicle configuration data, or one or more fault models.”
Segelmark further teaches “wherein the condition monitoring system includes at least one of: a vehicle condition monitoring function module, vehicle configuration data, or one or more fault models (infrared imaging system utilizes trained machine learning model that performs fault classifications and recommendations – [0017]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lee in view of Segelmark and Hua to incorporate the further teaching of Segelmark to utilizing machine learning models for fault classification. This is an improvement that yields predictable results in the analysis of the temperature by thermal imaging of a battery to determine overheating of the battery.
In regards to Claim 7, Lee in view of Segelmark and Hua discloses the claimed invention as detailed above. Lee further teaches “wherein the one or more parameters includes one or more temperature parameters (each pixel constitutes the infrared image has color information including Y, Cb, and Cr – [0035]; infrared image has different color, especially brightness information, depending on the temperature of the object and thus, by applying this principle, color information corresponding to each temperature is stored – [0038]).”
In regards to Claim 8 and 16, Lee in view of Segelmark and Hua discloses the claimed invention as detailed above. Lee further teaches “wherein the one or more battery maintenance actions include a battery condition or an indication of whether the at least one battery should be replaced (the brightness is shown in a different form so it can be distinguished between red, orange, yellow to enable situational judgment and corresponding action at different levels including danger, warning, and caution, and it is configured that the batteries that are overheated to the extent that immediate action is required and secondary batteries that are overheated to the extent that caution or precautionary action is required – [0052]; replacement or repair of batteries – [0053]).”
In regards to Claim 9, Lee in view of Segelmark and Hua discloses the claimed invention as detailed above. Lee further teaches “wherein the one or more conditions are defined based on at least one of: one or more pre-defined fault models or one or more pre-defined boundary conditions (temperatures below 25C are judged as normal and temperatures above 25C, i.e. pre-defined boundary conditions, are judged as overheating, i.e. condition – [0039]).”
In regards to Claim 10, Lee teaches “ a memory having processor-readable instructions stored therein (storage unit 120 – [0031]); and
one or more processors configured to access the memory and execute the processor-readable instructions, which when executed by the one or more processors configures the one or more processors to perform a plurality of functions (control unit, i.e. processor, connected to the storage unit 120 – [0031]), including functions for:
receiving, by a data acquisition system, at least one thermal image corresponding to at least one battery of at least one vehicle, the at least one thermal image including pixel data (monitoring and managing a secondary battery – [0001]; secondary batteries are widely used in electric or hybrid vehicles – [0002]; image module performs function of generating an infrared/thermal image of a battery – [0032]; mechanism through image processing to determine whether a battery is overheated and to perform subsequent processing – [0033]; each pixel constitutes the infrared image, i.e. thermal image, has color information including Y, Cb, and Cr and the color information included in the image information has different values dependent on the heat of the object – [0035]);
converting, by the data acquisition system, the at least one thermal image into one or more parameters, wherein the one or more parameters correspond to one or more pixels of the pixel data (each pixel constitutes the infrared image has color information including Y, Cb, and Cr – [0035]; infrared image has different color, especially brightness information, depending on the temperature of the object and thus, by applying this principle, color information corresponding to each temperature is stored – [0038]; six types of color information are illustrated as an example, and it is possible to configure the size of the brightness or luminance information of the color according to each temperature as data – [0039]; from data processing, the brightness information of a pixel has a data value of 0 to 255, and a larger number means a brighter bright, and a brighter brightness means a subject with higher heat – [0044]);
transmitting, by the data acquisition system, the one or more parameters to a condition monitoring system (infrared image information is input from the image module and the control unit reads out the reference color information stored in the storage unit and compares the color information of the infrared image with the reference color information to determine whether the battery is overheated – [0040]; Figures 1 and 2 detail the control unit connected to the cameras 110);
evaluating, by the condition monitoring system and in advance of the battery failure, the one or more parameters based on one or more conditions to determine that at least one of the one or more parameters meet at least one of the one or more conditions (temperatures below 25C are judged as normal and temperatures above 25C are judged as overheating, i.e. condition – [0039]; control unit reads out the reference color information stored in the storage unit and compares the color information of the infrared image with the reference color information to determine whether the battery is overheated – [0040]; “In addition, the control unit of the present invention outputs alarm information when there is an overheated secondary battery cell, and it is preferable to configure it so that differential alarm information is output according to the degree of overheating” – [0020]; “is desirable to configure it so that it is possible to distinguish between secondary batteries that are overheated to the extent that immediate action is required and secondary batteries that are overheated to the extent that caution or preliminary action is required” – [0052]; as the system has different degrees of overheating in the evaluation, this would mean that the evaluation is done in before the failure of the battery); and
outputting, by the condition monitoring system and in advance of the battery failure, the one or more battery maintenance actions to one or more user interfaces of a user device (“Therefore, batteries used as energy storage sources assembled into large structures require monitoring of heat generation, and it can be said that follow-up measures based on the monitoring results, such as the operation of cooling air conditioning systems” – [0012]; “It is desirable to configure it so that it is possible to distinguish between secondary batteries that are overheated to the extent that immediate action is required and secondary batteries that are overheated to the extent that caution or preliminary action is required” – [0052]; by checking the interface screen, administrators can accurately determine which secondary battery cells require replacement or repair – [0053]; “In this case, as previously examined, the degree of overheating can be recognized in stages by utilizing the difference from the standard color information. Therefore, the control unit (130) of the present invention can be configured to output alarm information, etc. as described above, but to output differential alarm information according to the degree of overheating, thereby inducing an administrator, etc., to take differential follow-up or preliminary measures” – [0055]).”
Lee is silent with regards to the language of “based on the evaluating, determining, by the condition monitoring system and in advance of the battery failure, one or more battery maintenance actions to prevent the battery failure based on the at least one of the one or more parameters that meets the at least one of the one or more conditions.”
Segelmark teaches “based on the evaluating, determining, by the condition monitoring system and in advance of the battery failure, one or more battery maintenance actions to prevent the battery failure based on the at least one of the one or more parameters that meets the at least one of the one or more conditions (infrared imaging system may determine image settings using trained machine learning models and includes the functionality of recommending actions – [0017]; trained machine learning model performs target recognition based on temperature profile to identify targets of interesting including overheating components, and provides recommended actions based on the assessment – [0087]; recommended action can include repair [i.e. action to prevent the battery failure] or replace – [0103]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lee to incorporate the teaching of Segelmark to determine recommendation actions based on the analysis. This is an improvement that yields predictable results in the analysis of the temperature by thermal imaging of a battery to determine overheating of the battery.
Lee in view of Segelmark are silent with regards to the language of “predict a future temperature or health of the at least one battery.”
Hua teaches “predict a future temperature or health of the at least one battery (“This invention also relates to a power battery thermal management method, which corresponds to the aforementioned power battery thermal management system and can be understood as a practical application method of the aforementioned system. This method involves establishing a digital battery thermal management module in the cloud that matches the physical battery thermal management module, and creating a digital twin system based on the data and information of both. The system calculates and analyzes the battery system temperature distribution in real time in the cloud, obtaining the current highest temperature and temperature inconsistencies within the system, predicting its future development trend, and formulating a reasonable thermal management control strategy to manage the inconsistencies in the battery pack temperature distribution, control the highest and lowest temperatures, and predict future temperature trends” – [0038]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lee in view of Segelmark to incorporate the teaching of Hua to utilize the information to predict the future temperature trend of the battery. By predicting the future temperature trend of the battery this is an improvement that yields predictable results to the thermal management of the battery system.
In regards to Claim 22, Lee in view of Segelmark and Hua discloses the claimed invention as detailed above. Lee further teaches “receiving, by the data acquisition system, a plurality of thermal images corresponding to the at least one battery of the at least one vehicle, the plurality of thermal images including the pixel data (monitoring and managing a secondary battery – [0001]; secondary batteries are widely used in electric or hybrid vehicles – [0002]; image module provided in multiple units to generate infrared images [i.e. plurality of thermal images] of the secondary battery cells [i.e. at least one battery] - [0018]; image module performs function of generating an infrared/thermal image of a battery – [0032]; mechanism through image processing to determine whether a battery is overheated and to perform subsequent processing – [0033]; each pixel constitutes the infrared image, i.e. thermal image, has color information including Y, Cb, and Cr and the color information included in the image information has different values dependent on the heat of the object – [0035]); and converting, by the data acquisition system, the plurality of thermal images into the one or more parameters, the one or more parameters corresponding to the one or more pixels of the pixel data (each pixel constitutes the infrared image has color information including Y, Cb, and Cr – [0035]; infrared image has different color, especially brightness information, depending on the temperature of the object and thus, by applying this principle, color information corresponding to each temperature is stored – [0038]; six types of color information are illustrated as an example, and it is possible to configure the size of the brightness or luminance information of the color according to each temperature as data – [0039]; from data processing, the brightness information of a pixel has a data value of 0 to 255, and a larger number means a brighter bright, and a brighter brightness means a subject with higher heat – [0044]), wherein at least one of the plurality of thermal images is a thermal image of the at least one vehicle taken from a different angle than another one of the plurality of thermal images (the battery is divided into multiple sections and the image module is configured to be provided in multiple units to generate infrared images of the battery cells of the corresponding sections [i.e. different angles] – [0018])”
Claims 2-3, 11-12, 18-19, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Segelmark and Hua as applied to claims 1, 10, and 17 above, and further in view of Huck (US20230132883)
In regards to Claims 2, 11, and 18, Lee in view of Segelmark and Hua discloses the claimed invention as detailed above. Lee is silent with regards to the language of “transmitting, by the data acquisition system, the one or more parameters to a prognostic analytics system; predicting, by the prognostic analytics system, one or more predicted pixel state values utilizing a machine-learning algorithm based on the one or more parameters and previous pixel states; and storing, by the prognostic analytics system, the one or more predicted pixel state values in one or more data stores.”
Segelmark further teaches “transmitting, by the data acquisition system, the one or more parameters to a prognostic analytics system (neural network, i.e. prognostic analytics system, has input from the thermal imaging camera – Figure 6); predicting, by the prognostic analytics system, one or more predicted pixel state values utilizing a machine-learning algorithm based on the one or more parameters and one or more previous pixel states (neural network output estimates/predictions for different image settings, the neural network determines measurement locations in the image data at which to provide temperature data and determines that spot measurements are to be used to generate the temperature data, where the image data includes the thermal imaging with affected pixel values – [0096]; neural network trained with a loss function provided by L(x,y)- |T(x,y)-P(x,y)|, where a predicted value P is a spot measurement location band the target value is a spot measurement location, [i.e. the neural network is trained with the previous pixel states] – [0097]; T(x, y) is a target temperature value of a pixel at coordinate (x, y) – [0100]); and storing, by the prognostic analytics system, the one or more predicted pixel state values in one or more data stores (memory devices configured to store data and information including the infrared image data – [0030]; image data stored in training dataset – [0092]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lee in view of Segelmark and Hua to incorporate the further teaching of Segelmark to perform analysis with a neural network. This is an improvement that yields predictable results in the analysis of the temperature by thermal imaging of a battery to determine overheating of the battery.
Lee in view of Segelmark and Hua are silent with regards to the language of “predicting, by the prognostic analytics system, one or more predicted pixel state values utilizing a machine-learning algorithm based on the one or more parameters and a plurality of previous and sequential pixel states.”
Huck teaches “predicting, by the prognostic analytics system, one or more predicted pixel state values utilizing a machine-learning algorithm based on the one or more parameters and a plurality of previous and sequential pixel states (“The infrared thermal camera 3 B is further capable of supplying a temporal sequence of digital thermal images reflecting this response over the acquisition period. In a preferred manner, the infrared thermal camera 3 B makes it possible to acquire a digital video comprising digital images” – [0052]; “With reference to FIG. 3 , each digital image IMj, j=1, . . . , 4 supplied by the infrared camera corresponds to an acquisition instant tj defined over the acquisition period, J designating an integer greater than 1. The instants tj are for example spaced apart uniformly over the acquisition period.” – [0053]; “As mentioned previously, each digital image IMj(ZUi) of the aeronautical component 2 reflects the thermal response of the unit zone ZUi of the aeronautical component 2 at the acquisition instant tj, further to the excitation (here pulsed) applied by means of the excitation source(s) 3 A. Each digital image is obtained with a same viewpoint. It comprises a plurality of pixels corresponding to a spatial sampling of the unit zone ZUi, in other words, each pixel is associated with a point of the unit zone ZUi. With reference to FIG. 3 , each pixel is associated on the image IMj(ZUi) with an amplitude at the acquisition instant tj, this amplitude here being a determined increasing function of the surface temperature of the component” – [0055]; “According to the invention, with reference to FIG. 5 A, each image of characteristics IMC of a unit zone ZUi is divided/carved/partitioned into a plurality of prediction micro-zones MZPm (ZUi) comprising a plurality of pixels. The division is made from a carving model DEC-MOD configured to form prediction micro-zones MZPm(ZUi) which are relevant. In this example, the prediction micro-zones MZPm(ZUi) have the same form but it goes without saying that they could be different. Each prediction micro-zone MZPm(ZUi) comprises a plurality of pixels. Each pixel of an image of characteristics IMC only belongs to a single prediction micro-zone MZPm(ZUi). The fact of using micro-zones larger than a pixel makes it possible to learn the distribution of the local variability by exploiting the hypothesis of locally homogeneous data” – [0069])”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lee in view of Segelmark and Hua to incorporate the teaching of Huck to utilize a temporal sequence of thermal images and the pixels therein with machine learning to perform a prediction. By utilizing a temporal sequence of thermal images this is an improvement that yields predictable results in the non-destruction inspection of components.
In regards to Claims 3, 12, and 19, Lee in view of Segelmark, Hua, and Huck discloses the claimed invention as detailed above. Lee further teaches “outputting, by the condition monitoring system, the one or more battery predictive actions to the one or more user interfaces of the user device (by checking the interface screen, administrators can accurately determine which secondary battery cells require replacement or repair – [0053]).”
Lee is silent with the language of “determining, by the prognostic analytics system, one or more battery predictive actions based on the one or more predicted pixel state values.”
Segelmark further teaches “determining, by the prognostic analytics system, one or more battery predictive actions based on the one or more predicted pixel state values (image analysis determine based on the thermal characteristics whether overheating is occurring and the thermal characteristics used to identify faults and generate recommended course of action – [0074]; recommended action can include repair or replace – [0103]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lee in view of Segelmark, Hua, and Huck to incorporate the further teaching of Segelmark to determine recommendation actions based on the analysis. This is an improvement that yields predictable results in the analysis of the temperature by thermal imaging of a battery to determine overheating of the battery.
In regards to Claim 21, Lee in view of Segelmark, Hua, and Huck discloses the claimed invention as detailed above. Lee further teaches “the one or more predicted pixel state values indicate that the at least one battery is close to overheating (“Meanwhile, a battery management method using an infrared image according to the present invention for achieving the objective of another aspect of the present invention may be configured to include: an image generation step in which an image module generates an infrared image of a battery composed of a plurality of secondary battery cells; and an overheating determination step in which the color information of the infrared image and a reference color information for determining whether the secondary battery cell is overheated are compared with each other to determine whether the secondary battery cell is overheated” – [0023]; “In this case, as previously examined, the degree of overheating can be recognized in stages by utilizing the difference from the standard color information. Therefore, the control unit (130) of the present invention can be configured to output alarm information, etc. as described above, but to output differential alarm information according to the degree of overheating, thereby inducing an administrator, etc., to take differential follow-up or preliminary measures” – [0055])”
Claims 4-5, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Segelmark and Hua as applied to claims 1, 10, and 17 above, and further in view of Yang (CN115063418A).
In regards to Claims 4 and 20, Lee in view of Segelmark and Hua discloses the claimed invention as detailed above. Lee in view of Segelmark and Hua is silent with regards to the language of “wherein the at least one battery is a lithium battery.”
Yang teaches “wherein the at least one battery is a lithium battery (lithium-ion batteries – [n0002]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lee in view of Segelmark and Hua to incorporate the teaching of Yang to utilize lithium ion batteries. Lithium ion batteries are well known and understood in the art, and therefor yield predictable results when monitoring the heat generation of said batteries.
In regards to Claims 5, Lee in view of Segelmark and Hua discloses the claimed invention as detailed above. Lee in view of Segelmark and Hua is silent with regards to the language of “capturing, by the data acquisition system, the at least one thermal image during a charge cycle of the at least one battery or a discharge cycle of the at least one battery.”
Yang teaches “capturing, by the data acquisition system, the at least one thermal image during a charge cycle of the at least one battery or a discharge cycle of the at least one battery (data collection occurring during charging and discharging cycles of the batteries with infrared images of the battery collected – [n0017]).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lee in view of Segelmark and Hua to incorporate the teaching of Yang to perform the thermal imaging during the charging and discharging of the battery to reveal issues. By monitoring the battery during charging and discharging, this yields predictable results in the field of determining battery operating conditions.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YOSSEF KORANG-BEHESHTI whose telephone number is (571)272-3291. The examiner can normally be reached Monday - Friday 10:00 am - 6:30 pm.
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, Catherine Rastovski can be reached at (571) 270-0349. 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.
/YOSSEF KORANG-BEHESHTI/Examiner, Art Unit 2857
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