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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on January 29, 2026 has been entered.
Claim Rejections - 35 USC § 103
Claim(s) 1-6, 9, 11, 12, 15, 16, 18, 19, is/are rejected under 35 U.S.C. 103 as being obvious by Fenech et al. (US 2022/0229116 A1) in view of Change et al. (US 2024/0094302 A1).
With regards to Claims 1, 15, and 18, Fenech teaches A system comprising:
one or more energy storage units (battery module or battery cell); and
a computing device comprising at least one processor and a memory storing instructions that, when executed by the at least one processor, cause the computing device to (Figure 1, 100, battery monitoring device with a calculation unit, Paragraph 53):
receive usage data of a battery located within the one or more energy storage units during a period of time (Paragraph 56);
input the usage data to a machine learning model (Paragraph 67, 70, 78) ;
generate, based on processing of the usage data by the machine learning model, a predicted temperature of the battery at the end of the time period (Paragraph 69);
receive, from a temperature sensor of the battery, a measured temperature of the battery at the end of the time period (Paragraph 69, “actual temperature”);
determine a difference between the predicted temperature and the measured temperature;
based on the determined difference, send an indication of a state of the battery; and
based on the state of the battery, configure usage of the battery (Paragraph 69, “determine the degradation of the battery”).
Fenech is silent with regards to based on the determined difference, detect an anomaly of the battery; and based on the detected anomaly, generate one or more control signals instructing one or more control components of the system to adjust the usage of the battery.
Chang teaches based on the determined difference, detect an anomaly of the battery; and based on the detected anomaly, generate one or more control signals instructing one or more control components of the system to adjust the usage of the battery (Paragraph 133, “ the control circuitry may be configured to implement any of the charge or discharge adjustments described herein to address defects or potential defects. In this regard, control circuitry adapts, adjusts and/or controls one or more characteristics of the charge or current applied to or injected into the battery (via controlling the operation of charging circuitry) so that the terminal voltage, the change in terminal voltage, or another battery parameter (in response to charge or current applied to or injected into the battery during a charging or recharging sequence/operation) is within a predetermined range and/or below a predetermined value.”)
It would have been obvious at the time of filing to include the limitations of based on the determined difference, detect an anomaly of the battery; and based on the detected anomaly, generate one or more control signals instructing one or more control components of the system to adjust the usage of the battery as taught by Chang in view of Fenech for the purpose of preventing dangerous battery conditions and preserving the life of the battery.
With regards to Claim 2, Fenech teaches wherein each energy storage unit of the one or more energy storage units comprises and enclosure including a plurality of batteries (Paragraph 53, “battery”).
With regards to Claim 3, Fenech teaches wherein the usage data of the battery comprises one or more of:
a starting state of charge of the battery for the time period (Figure 2, Paragraph 71, 74, 79, suggest that this process is continuous at any given time);
an ending state of charge of the battery for the time period (Figure 2, Paragraph 71, 74, 79 suggest that this process is continuous at any given time);
a sum of current squared of the battery for the time period (Paragraph 73);
a voltage pattern of the battery associated with the time period (Paragraph 56);
a dispatch pattern associated with the battery for the time period (dispatch pattern refers to the evaluation of a battery’s performance under various operating conditions, Paragraph 64-66) ;
a measured temperature of the battery at the start of the time period (Paragraph 64, 72);
an ambient temperature or humidity associated with the battery during the time period (Paragraph 64, 72);
one or more utilization parameters of a cooling system for the battery during the time period (Paragraph 74,75); or
a relative location of the battery within an enclosure of an energy storage unit including the battery (Paragrpah 61, battery monitoring device monitors any cell, which suggests knowing the location).
With regards to Claim 4, Fenech teaches wherein the machine learning model comprises one of a random forest model or neural network (Paragraph 70).
With regards to Claim 5, Fenech teaches wherein the instructions, when executed by the at least one processor, cause the computing device to:
receive particular usage data of the battery during period of time when the battery is manually deemed to exhibit normal behavior (Paragraph 65, 79 suggest that this process is continuous at any given time);
receive measured temperature data of the battery at the end of each of the periods of time (Paragraph 64); and
train the machine learning model using the particular usage data and the measurement temperature (Paragraph 67 and 68).
With regards to Claim 6, 16, 19 Fenech teaches wherein the instructions, when executed by the at least one processor, cause the computing device to:
for each particular batter of a plurality of battteries of the one or more energy storage units:
generate, using the machine learning mode, a predicted temperature of the particular battery at the end of the time period (Paragraph 78);
receive a measured temperature of the particular battery at the end of the time period (Paragraph 72, 79 suggests that this process is continuous at any given time); and
determine a temperature difference between the predicted temperature of the particular battery and the measured temperature of the particular battery (Paragraph 80-83).
With regards to Claim 9, Fenech teaches the computing device to:
determine, for the battery a difference between a predicted temperature and a measured temperature for each of the plurality of periods of time (Paragraph 69); and
determine, based on the difference for each of the plurality of periods of time, whether an anomaly (“degradation”) of the battery is detected (Paragraph 69).
With regards to Claim 11, Fenech teaches wherein the computing device if local to one more more energy units (Paragraph 53).
With regards to Claim 12, Fenech teaches the computing device to:
based on the determined difference satisfying a threshold, send an indication that an anomaly of the battery is detected (Paragraph 69, “indicator for degradation”).
With regards to Claim 14, Chang teaches generate one or more control signals instructing one or more control components of the system to adjust the usage of the battery by one or more of: suspending the usage of the battery, reducing the usage of the battery or modifying the usage pattern of the battery (Paragraph 133).
Claim(s) 7, 8, 10, 17, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fenech et al (US 2022/0229116 A1) in view of Tomar et al (US 2020/0280108 A1).
With regards to Claim 7, 17, 20 Fenech is silent with regards to a display of a user interface indicating the plurality of batteries, relative locations of the plurality of batteries, and the temperature difference for each of the plurality of batteries.
However Tomar teaches a display of a user interface indicating the plurality of batteries, relative locations of the plurality of batteries, and the temperature difference for each of the plurality of batteries (Paragraph 35, 46, 120 – computing system having a display).
It would have been obvious to one ordinary skilled in the art before the time the invention was filed to have a display of a user interface indicating the plurality of batteries, relative locations of the plurality of batteries, and the temperature difference for each of the plurality of batteries of Tomar into Fenech for the purposes of improving visualization of degrading systems.
With regards to Claim 8, Tomar teaches wherein the user interface indicates the relative locations of the plurality of batteries in a three-dimensional perspective, and indicates the temperature difference for each of the plurality of batteries using a color scale (Paragraph 120, 136).
Note: Although Tomar is silent with regards to a three-dimensional perspective and color scale of the display, absent a lack of criticality, this is an obvious modification of visual display indicating the temperature difference as cited in Tomar (Paragraph 120, 136 and Figure 8).
With regards to Claim 10, Tomar teaches wherein the computing device is associated with a cloud architecture (Paragraph 118).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fenech et al (US 2022/0229116 A1) in view of Ukumori (US 2021/0048482 A1).
With regards to Claim 12, Fenech is silent with regards to the computing device to:
input the usage data to a second machine learning model;
generate, based on processing of the usage data by the second machine learning model, a predicted voltage of the battery at the end of the time period;
receive, from a voltage sensor of the battery, a measured voltage of the batter at the end of the time period;
determine a voltage difference between the predicted voltage and the measured voltage;
send the indication that the anomaly of the battery is detected when the voltage difference satisfies a threshold.
Ukumori teaches the computing device to:
input the usage data to a second machine learning model (Paragraph 137-139);
generate, based on processing of the usage data by the second machine learning model, a predicted voltage of the battery at the end of the time period (Paragraph 143, 152);
receive, from a voltage sensor of the battery, a measured voltage of the batter at the end of the time period (Paragraph 152);
determine a voltage difference between the predicted voltage and the measured voltage (Paragraph 152); and
send the indication that the anomaly of the battery is detected when the voltage difference satisfies a threshold (Paragraph 153).
It would have been obvious to one ordinary skilled in the art before the time of the invention was to the computing device to: input the usage data to a second machine learning model; generate, based on processing of the usage data by the second machine learning model, a predicted voltage of the battery at the end of the time period; receive, from a voltage sensor of the battery, a measured voltage of the batter at the end of the time period; determine a voltage difference between the predicted voltage and the measured voltage; send the indication that the anomaly of the battery is detected when the voltage difference satisfies a threshold of Ukumori into Fenech for the purposes of improving the operation of the battery for an extended time.
With regards to Claim 14, Ukumori teaches the computing device to:
adjust, the usage of the battery by one or more of: suspending the usage of the battery, reducing the usage of the battery, or modifying a usage pattern of the battery (Paragraph 201, 202, 204).
Conclusion
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/SUJOY K KUNDU/Supervisory Patent Examiner, Art Unit 2471 April 22, 2026
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