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 .
DETAILED ACTION
This Non-Final office is a response to the papers filed on 01/03/2024.
Claims 1-12 are pending.
Claim Rejections - 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2 and 7-8 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kumar et al. (Pub. No. 2019/0176639 A1).
Regarding claims 1 and 7, Kumar discloses:
A system for monitoring and optimizing battery performance for a site (see Fig. 1A-1B, see par [0001-0007], a vehicle system may include a battery whose end of life is predicted using statistical and experimental methods. Based on the nature of the battery (e.g., based on the chemical composition of the battery), a plurality of battery attributes….), the system (106) comprising:
one or more processors configured to (see Fig. 1A, CPU 106):
obtain sensor data captured by a plurality of sensors, the sensor data is related to a plurality of parameters associated with a battery (see Fig. 1A-1B, battery monitor sensors184, see par [0047-0048], The Battery Monitor Sensor (BMS) monitors and calculates the actual battery condition (State of charge (SOC), state of health (SOH) and state of function (SOF)). It consists of hardware and software. The hardware includes a single chip solution to measure battery voltage, battery current and temperature….);
an anomaly detection engine, communicatively coupled to the one or more processors, to predict, with aid of a first machine learning model (see par [0069-0070], Supervised learning may define “N best category labels” for a given set of features, given the associated probabilities of each category with associated confidence levels. Category labels used with supervised learning for battery end of life prediction …..), anomaly events based at least on the plurality of parameters from the plurality of sensors, wherein the first machine learning model has been trained on a plurality of training examples, wherein a training example of the plurality of training examples comprises (i) a plurality of historical parameters associated with the battery since installation, wherein the plurality of historical parameters comprises battery capacity (see par [0053-0058], the vehicle controller may define the thresholds based on battery history as well as statistical data collected from various sources such as fleet data, dealership vehicle data, warranty laboratory data, etc. The controller may be configured to use an algorithm that estimates the rate of convergence of the measured data to the defined thresholds, and uses the estimated rate in addition to a previous history of degradation behavior…., see par [0161]), and (ii) a label that indicates whether the battery experienced an anomaly event (see par [0133-135], an indicator on the dashboard may be activated at 1302. When a second intermediate threshold is passed at 1306, a text message may appear on the dashboard or an alternate multifunction display when the vehicle is started at 1308….);
a classification engine, communicatively coupled to the one or more processors, to classify the anomaly events as critical events or non-critical events (see par [0142-0143], the degradation of actuation and critical voltage levels on the power distribution network may cause the vehicle to leave its desired path and cause a safety hazard. For that reason, 12V SLI batteries may be classified as safety-critical components in autonomous vehicles….); and
a command engine, communicatively coupled to the one or more processors, to automatically issue one or more commands to an edge device when one or more of the anomaly events are classified as critical events, wherein the edge device is coupled with the battery and configured to perform the one or more commands (see par [0133-135], an indicator on the dashboard may be activated at 1302. When a second intermediate threshold is passed at 1306, a text message may appear on the dashboard or an alternate multifunction display when the vehicle is started at 1308…., see par [0145-0146]).
Regarding claims 2 and 8, Kumar discloses:
wherein the plurality of parameters associated with the battery comprises battery charge voltage, battery discharge voltage, currents, State of Charge (SOC), load, and temperature (see par [0047-0048] The Battery Monitor Sensor (BMS) monitors and calculates the actual battery condition (State of charge (SOC), state of health (SOH) and state of function (SOF)). It consists of hardware and software. The hardware includes a single chip solution to measure battery voltage, battery current and temperature….., see par [0055-0058]).
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 3, 5-6, 9, 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. (Pub. No. 2019/0176639 A1) further in view of Chang et al. (Pub. No. 20240094302 A1).
Regarding claims 3 and 9, Kumar fails to disclose:
Wherein the first machine learning model is trained using historical data associated with the site, wherein the historical data comprises charge and discharge pattern, load pattern of the battery, and environmental data.
Thus, Chang discloses:
Wherein the first machine learning model is trained using historical data associated with the site, wherein the historical data comprises charge and discharge pattern, load pattern of the battery, and environmental data (see par [0007], considering a previous use pattern of the rechargeable battery. In some examples, the previous use pattern comprises at least one of: a temperature of the rechargeable battery; storage conditions of the rechargeable battery; charging patterns of the rechargeable battery; discharge patterns of the rechargeable battery…, see par [0036-0037]).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to have modified a method and systems are provided for reliably providing a prognosis of the life-expectancy of a vehicle battery of Kumar to include historical data comprises charge and discharge pattern in order to use to monitor the battery (see Chang par [0040]).
Regarding claims 5 and 11, Chang discloses:
wherein the anomaly events comprise battery voltage fluctuations, current pattern variations, and overheating (see par [0088], a battery use pattern during discharge of the battery may be represented by values and sequence of current and/or voltage as they fluctuate over time during operation of a device powered by the battery…., see par [0082], environmental damage (e.g., due to high temperatures), and/or electrical damage (e.g., over-voltage). In some cases, defects may arise due to extreme temperatures….)
Regarding claims 6 and 12, Chang discloses:
wherein the one or more commands comprise charging the battery and forcing a discharge of the battery (see par [0009-0015], modifying a charging process used to charge the rechargeable battery; modifying a discharge process used in discharging the rechargeable battery….).
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 4 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. (Pub. No. 2019/0176639 A1) further in view of Shih et al. (Pub. No. 20190202414 A1).
Regarding claims 4 and 10, Kumar fails to disclose:
wherein the classification engine comprises a second machine learning model, and wherein the second machine learning model comprises K-Means cluster.
Thus Shih discloses:
wherein the classification engine comprises a second machine learning model, and wherein the second machine learning model comprises K-Means cluster (see par [103], the battery demand predictions generated by the disclosed technology can be used to divide the multiple sampling stations (or time intervals of the multiple sampling stations) into different clusters (e.g., by a K-means clustering algorithm). This clustering process is designed to identify a representative station….).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to have modified a method and systems are provided for reliably providing a prognosis of the life-expectancy of a vehicle battery of Kumar to include the second machine learning model comprises K-Means cluster in order to identify a representative station (e.g., a virtual/calculated station) for a group of sampling stations (see Shih par [0103]).
Conclusion
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/BRIAN NGO/ Primary Examiner, Art Unit 2851
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