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 .
Remark
There is a very long and well-established history of predictive tools for outputting the state batteries using characteristics such as voltage, current and temperature, including based on AI.
The earliest that this examiner knows of is Kozlowski et al. (US 2006/0284617) which was filed in 2005. That could be applied in the present application, but there is very abundant prior art available since then.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 6-9 and 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Chemali et al. (US 11,171,498).
Re claim 1:
Chemali et al. teaches:
A series of sensors coming from a battery feed into a neural network – based state-of-charge estimator.
See figure 1 of Chemali et al. below:
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Chemali et al. teaches at column 1, line 59 to column 2, line 44 (with emboldening by the examiner):
“In one aspect, in general, an approach to control or monitoring of battery operation makes use of a recurrent neural network (RNN), which receives one or more battery attributes for a Lithium ion (Li-ion) battery, and determines, based on the received one or more battery attributes, a state-of-charge (SOC) estimate for the Li-ion battery.
In another aspect, in general, a method for monitoring battery operation includes receiving by a recurrent neural network (RNN) a time series of values one or more battery attributes for a battery. Based on the received time series, the RNN determines a state estimate for the battery.
Aspects can include one or more of the following.
The battery comprises a rechargable battery
Receiving the time series of values of the attributes includes receiving a time series spanning multiple charging and discharging cycles.
Receiving time time series of the values the one or more battery attributes comprises receiving values of one or more of a battery voltage, a battery current, and a battery temperature.
The battery comprises a Lithium-ion (Li-ion) battery.
The state estimate for the battery comprises a time series of values of state estimates corresponding to the time series of the values of the one or more battery attributes.
The state estimate comprises a state-of-charge (SOC) estimate.
The state estimate for the battery comprises data representative of one or more of battery capacity, and battery resistance.
Operation of at least one of the battery or a system powered by the battery is controlled based on the determined state estimate for the battery.
The RNN is periodically configured according to one or more of age of the battery, and a long-term condition of the battery.
The recurrent neural network includes a memory cell configured to be selectively updated according to inputs to the recurrent neural network.
The recurrent neural network comprises a Long Short-Term Memory cell.
In another aspect, in general, a system for monitoring battery operation includes at least one sensor for measuring one or more battery attributes of at least one battery. A recurrent neural network (RNN) system is configured according to stored numerical parameters to receive a time series of values one or more battery attributes for a battery from the at least one sensor, and determining, based on the received time series, a state estimate for the battery. The RNN comprises a memory cell configured to be selectively updated according to inputs to the recurrent neural network. The one or more battery attributes may include a battery voltage, a battery current, and a battery temperature.”
Figure 12 of Chemali et al. is shown below. Note that it is equivalent to figure 4 of the instant invention, showing the close commonality.
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Regarding the first stage or level, the second stage of level and the third stage or level, this is the general characteristic of deep learning neural networks. They have exactly the format that is claimed, involving an input layer (in this case for things like voltage, current and temperature) and then multiple layers in the middle and an output layer (in this case for state of charge).
Re claim 2:
Chemali et al. teaches (column 2): “…a system for monitoring battery operation includes at least one sensor for measuring one or more battery attributes of at least one battery. A recurrent neural network (RNN) system is configured according to stored numerical parameters to receive a time series of values one or more battery attributes for a battery from the at least one sensor, and determining, based on the received time series, a state estimate for the battery. The RNN comprises a memory cell configured to be selectively updated according to inputs to the recurrent neural network. The one or more battery attributes may include a battery voltage, a battery current, and a battery temperature.”
Re claim 3: This is the essential structure of a neural network.
Re claim 6: Adding Gaussian noise is a standard technique for improving neural networks.
Re claim 7-9: The use of multiple data sets is almost always a sought-after goal to build the best neural networks.
In general, the rule is the more diverse training data the better. In this case, that means multiple datasets with a variety of battery types.
Re claims 12-15: Chemali et al. would cover all aspects of the battery’s charge including within the voltage flat zone.
Allowable Subject Matter
Claims 4, 5, 10 and 11 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The prior art fails to teach or fairly suggest the method of claim 1, wherein the first-level neural network includes a denoising autoencoder model, the second-level neural network includes a temporal convolution model, and the third-level neural network includes an attention model.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL A HESS whose telephone number is (571)272-2392. The examiner can normally be reached Monday through Friday, from 9 AM to 5 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Thomas K. Pham 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 A HESS/Primary Examiner, Art Unit 2876
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