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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
DETAILED ACTION
Claims status
Claims 1-23 are pending as the applicant filed on 07/15/2026.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1, Step 1 the claim is a process (or machine) (Yes),
Step 2A Prong One, does the claim recite an abstract idea? current claim related to an electronic system comprising: an internal resistance measurement circuit carried by the portable housing and configured to measure the internal resistance of the non-rechargeable battery, a processor carried by the portable housing and configured to collect and store battery performance data including the measured internal resistance and a corresponding operating parameter, a controller configured to collect the battery performance data and use machine learning to determine a State of Health (SoH) of the non-rechargeable battery appears is an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes.
Step 2A Prong Two, is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? the additional elements of an electronic device comprising a portable housing, communications circuitry carried by the portable housing,
a volatile memory carried by the portable housing and configured to store at least one encryption key, a non-rechargeable battery carried by the portable housing and coupled to the volatile memory, the non-rechargeable battery having an internal resistance are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application,
Step 2A Prong Two: NO.
Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? the additional element of a rechargeable battery removably coupled to the portable housing and configured to supply power to the volatile memory and with the non-rechargeable battery supplying power to the volatile memory otherwise appears to be field of use (See MPEP 2106.05(h) and MPEP 2106.05(f)) and/or merely amounts to insignificant extra-solution output of the results (see MPEP 2106.05(g)) and therefore fails to integrate the abstract idea into a practical application or amount to significantly more. Step 2B: No. claim 1 not eligible.
Claim 10, Step 1 the claim is a process (or machine) (Yes),
Step 2A Prong One, does the claim recite an abstract idea? current claim related to a controller for an electronic device comprising generate run to fail (RTF) training data for the non-rechargeable battery based upon different combinations of internal resistance measurements and RF operating parameters,
download the battery performance data from the RF device and perform machine learning pattern matching based upon the battery performance data and the RTF training data, and determine the State of Health (SoH) of the non-rechargeable battery based upon the machine learning pattern matching and the downloaded battery performance data appears is an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes.
Step 2A Prong Two, is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? the additional elements of a portable housing, communications circuitry carried by the portable housing, a volatile memory carried by the portable housing and configured to store at least one encryption key, a non-rechargeable battery carried by the portable housing and coupled to the volatile memory, the non-rechargeable battery having an internal resistance, an internal resistance measurement circuit carried by the portable housing and configured to measure the internal resistance of the non-rechargeable battery, a first processor carried by the portable housing and configured to collect and store battery performance data including the measured internal resistance and a corresponding operating parameter, and a rechargeable battery removably coupled to the portable housing and configured to supply power to the volatile memory and with the non-rechargeable battery supplying power to the volatile memory otherwise, the controller comprising are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application,
Step 2A Prong Two: NO.
Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? the additional element of a memory and a second processor cooperating with the memory to appears to be field of use (See MPEP 2106.05(h) and MPEP 2106.05(f)) and/or merely amounts to insignificant extra-solution output of the results (see MPEP 2106.05(g)) and therefore fails to integrate the abstract idea into a practical application or amount to significantly more. Step 2B: No. claim 10 not eligible.
Claim 17, Step 1 the claim is a process (or machine) (Yes),
Step 2A Prong One, does the claim recite an abstract idea? current claim related to a method of operating an electronic system comprising: a processor carried by the portable housing and configured to collect and store battery performance data including the measured internal resistance and a corresponding operating parameter, and a rechargeable battery removably coupled to the portable housing and configured to supply power to the volatile memory and with the non-rechargeable battery supplying power to the volatile memory otherwise; and using a controller to collect the battery performance data and determine a State of Health (SoH) of the non-rechargeable battery based upon machine learning appears is an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes.
Step 2A Prong Two, is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? the additional elements of operating an electronic device comprising a portable housing, communications circuitry carried by the portable housing, a volatile memory carried by the portable housing and configured to store at least one encryption key, a non-rechargeable battery carried by the portable housing and coupled to the volatile memory, the non-rechargeable battery having an internal resistance are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application,
Step 2A Prong Two: NO.
Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? the additional element of an internal resistance measurement circuit carried by the portable housing and configured to measure the internal resistance of the non-rechargeable battery appears to be field of use (See MPEP 2106.05(h) and MPEP 2106.05(f)) and/or merely amounts to insignificant extra-solution output of the results (see MPEP 2106.05(g)) and therefore fails to integrate the abstract idea into a practical application or amount to significantly more. Step 2B: No. claim 17 not eligible.
Claim 2 related to generate run to fail (RTF) training data for the non-rechargeable battery based upon different combinations of internal resistance measurements and operating parameters; and determine the SoH of the non-rechargeable battery based upon the machine learning pattern matching and the downloaded battery performance data, this appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 2 not eligible.
Claim 3 related to generate an alert when the SoH falls below a SoH threshold, this appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 3 not eligible.
Claim 4 related to the machine learning is performed by an Artificial Neural network (ANN), this appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 4 not eligible.
Claim 5 related to wherein the machine learning includes Dynamic Time Warping (DTW), this appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 5 not eligible.
Claim 6 related to wherein the operating parameter comprises at least one of frequency, voltage, current, and temperature, this appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 6 not eligible.
Claim 7 related to a Maintenance as a Service (MAAS) cloud computing controller, this appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 7 not eligible.
Claim 8 related to comprises a radio frequency (RF) transceiver; wherein the processor is configured to discontinue RF communications if the at least one encryption key is erased; and wherein the non-rechargeable battery is configured to supply power to the volatile memory when the rechargeable battery is uncoupled from the portable housing so that the at least one encryption key is not erased, this appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 8 not eligible.
Claim 9 related to wherein the internal resistance measurement circuit is configured to measure the internal resistance values based upon pulse impedance monitoring, this appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 9 not eligible.
Claim 11 related to generate an alert when the SoH falls below a SoH threshold, this appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 11 not eligible.
Claim 12 related to the machine learning is performed by an Artificial Neural network (ANN), this appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 12 not eligible.
Claim 13 related to wherein the machine learning includes Dynamic Time Warping (DTW), this appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 13 not eligible.
Claim 14 related to at least one of frequency, voltage, current, and temperature, this appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 14 not eligible.
Claim 15 related to a Maintenance as a Service (MAAS) cloud computing controller, this appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 15 not eligible.
Claim 16 related to a radio frequency (RF) transceiver; wherein the first processor is configured to discontinue RF communications if the at least one encryption key is erased; and wherein the non-rechargeable battery is configured to supply power to the volatile memory when the rechargeable battery is uncoupled from the portable housing so that the at least one encryption key is not erased, this appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 16 not eligible.
Claim 18 related to generating run to fail (RTF) training data for the non-rechargeable battery based upon different combinations of internal resistance measurements and operating parameters; and determining the SoH of the non-rechargeable battery based upon the machine learning pattern matching and the downloaded battery performance data, this appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 18 not eligible.
Claim 19 related to using the controller to generate an alert when the SoH falls below a SoH threshold, this appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 19 not eligible.
Claim 20 related to wherein the machine learning is performed by an Artificial Neural network (ANN), this appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 20 not eligible.
Claim 21 related to wherein the machine learning includes Dynamic Time Warping (DTW), this appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 21 not eligible.
Claim 22 related to at least one of frequency, voltage, current, and temperature, this appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 22 not eligible.
Claim 23 related to a Maintenance as a Service (MAAS) cloud computing controller, this appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 23 not eligible.
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.
Claim(s) 1-23 are rejected under 35 U.S.C. 103 as being unpatentable over Douthat et al. (US Patent Application Publication 2018/0316815 A1, Date Published: 2018-11-01) in view of Gesangwangji (CN 114872532 A, DATE PUBLISHED: 2022-08-09, CPC B 60 K 6/20) (note: both arts deal with battery measurement, try to solve the similar problem)
Regarding claim 1:
Douthat described an electronic system comprising: an electronic device comprising a portable housing (0094, fig. 8, portable device), communications circuitry carried by the portable housing (0094, fig. 8, portable device),
a volatile memory carried by the portable housing and configured to store at least one encryption key (0094, 0095, 0102, cryptographic keys in volatile media such as optical or magnetic disks and dynamic memory), a non-rechargeable battery carried by the portable housing and coupled to the volatile memory (0017, processor with non-rechargeable), the non-rechargeable battery having an internal resistance (0050, internal resistance),
an internal resistance measurement circuit carried by the portable housing and configured to measure the internal resistance of the non-rechargeable battery (0017, 0050, non-rechargeable battery with internal resistance),
a processor carried by the portable housing and configured to collect and store battery performance data including the measured internal resistance and a corresponding operating parameter (0050, main processor 105 determines condition of battery including internal resistance), and a rechargeable battery removably (0003, portable replaceable batteries) coupled to the portable housing and configured to supply power to the volatile memory and with the non-rechargeable battery supplying power to the volatile memory otherwise (0003, 0031, 0102, portable battery can be rechargeable or non- rechargeable or any combination to supply power for non-volatile and volatile); and a controller configured to collect the battery performance data and to determine a State of Health (SoH) of the non-rechargeable battery (0050, life of battery condition, nominal capacity, maximum continuous discharge current).
Douthat described does not described use machine learning.
Gesangwangji described use machine learning (page 17, machine learning), for the benefit of optimize performance of battery pack (page 22).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to modify Douthat to have the use machine learning taught by Gesangwangji for the benefit of optimize performance of battery pack.
Regarding claim 10:
Douthat described a controller for an electronic device comprising a portable housing (0094, fig. 8, portable device), communications circuitry carried by the portable housing, a volatile memory carried by the portable housing (0094, fig. 8, portable device) and configured to store at least one encryption key, a non-rechargeable battery carried by the portable housing and coupled to the volatile memory (0094, 0095, 0102, cryptographic keys in volatile media such as optical or magnetic disks and dynamic memory), the non-rechargeable battery having an internal resistance (0017, processor with non-rechargeable, 0050, internal resistance), an internal resistance measurement circuit carried by the portable housing and configured to measure the internal resistance of the non-rechargeable battery (0017, 0050, non-rechargeable battery with internal resistance), a first processor carried by the portable housing and configured to collect and store battery performance data including the measured internal resistance and a corresponding operating parameter (0050, main processor 105 determines condition of battery including internal resistance), and a rechargeable battery removably coupled to the portable housing 0003, portable replaceable batteries) and configured to supply power to the volatile memory and with the non-rechargeable battery (0094, 0095, 0102, cryptographic keys in volatile media such as optical or magnetic disks and dynamic memory) supplying power to the volatile memory otherwise (0017, 0050, 0102, non-rechargeable battery with internal resistance volatile media), the controller comprising:
a memory and a second processor cooperating with the memory to
generate run to fail (RTF) training data for the non-rechargeable battery based upon different combinations of internal resistance measurements and RF operating parameters (0102, any other system media use until error),
download the battery performance data from the RF device and perform machine learning pattern matching based upon the battery performance data and the RTF training data (0068, battery power discharge condition may issue warning), and determine the State of Health (SoH) of the non-rechargeable battery based upon the pattern matching and the downloaded battery performance data (0068, battery power discharge condition with threshold waring).
Douthat described does not described use machine learning.
Gesangwangji described use machine learning (page 17, machine learning), for the benefit of optimize performance of battery pack (page 22).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to modify Douthat to have the use machine learning taught by Gesangwangji for the benefit of optimize performance of battery pack.
Regarding claim 17:
Douthat described a method of operating an electronic system comprising:
operating an electronic device comprising a portable housing, communications circuitry carried by the portable housing (0094, fig. 8, portable device), a volatile memory carried by the portable housing and configured to store at least one encryption key (0094, 0095, 0102, cryptographic keys in volatile media such as optical or magnetic disks and dynamic memory), a non-rechargeable battery carried by the portable housing and coupled to the volatile memory (0017, processor with non-rechargeable, 0094, 0095, 0102, cryptographic keys in volatile media such as optical or magnetic disks and dynamic memory), the non-rechargeable battery (0017, processor with non-rechargeable) having an internal resistance (0050, internal resistance),
an internal resistance measurement circuit carried by the portable housing and configured to measure the internal resistance of the non-rechargeable battery (0017, 0050, processor with non-rechargeable internal resistance),
a processor carried by the portable housing and configured to collect and store battery performance data including the measured internal resistance (0050, life of battery condition, nominal capacity, maximum continuous discharge current, processor with non-rechargeable internal resistance) and a corresponding operating parameter, and a rechargeable battery removably coupled to the portable housing and configured to supply power to the volatile memory and with the non-rechargeable battery supplying power to the volatile memory otherwise (0003, 0031, 0102, portable battery can be rechargeable or non- rechargeable or any combination to supply power for non-volatile and volatile); and using a controller to collect the battery performance data and determine a State of Health (SoH) of the non-rechargeable battery (0050, life of battery condition, nominal capacity, maximum continuous discharge current).
Douthat described does not described use machine learning.
Gesangwangji described use machine learning (page 17, machine learning), for the benefit of optimize performance of battery pack (page 22).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to modify Douthat to have the use machine learning taught by Gesangwangji for the benefit of optimize performance of battery pack.
Regarding claim 2, 18, Douthat further described generate run to fail (RTF) training data for the non-rechargeable battery based upon different combinations of internal resistance measurements and operating parameters (0017, 0088, based on threshold on non-rechargeable battery power); and determine the SoH of the non-rechargeable battery based upon pattern matching and the downloaded battery performance data (0050, life of battery condition, nominal capacity, maximum continuous discharge current).
Douthat described does not described use machine learning.
Gesangwangji described use machine learning (page 17, machine learning), for the benefit of optimize performance of battery pack (page 22).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to modify Douthat to have the use machine learning taught by Gesangwangji for the benefit of optimize performance of battery pack.
Regarding claim 3, 11, 19, Douthat further described wherein the controller is configured to generate an alert when the SoH falls below a SoH threshold (0068, warning depend on battery threshold).
Regarding claim 4, 12, 20, Douthat further described except the machine learning is performed by an Artificial Neural network (ANN).
Douthat described does not described use machine learning artificial intelligence network.
Gesangwangji described use machine learning artificial intelligence network (page 17, page 50, machine learning, artificial intelligence), for the benefit of optimize performance of battery pack (page 22).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to modify Douthat to have the use machine learning artificial intelligence network taught by Gesangwangji for the benefit of optimize performance of battery pack.
Regarding claim 5, 13, 21, Douthat further described wherein the machine learning includes Dynamic Time Warping (DTW) (0031, 0102 compare to similar property like battery defect dynamic memory data).
Regarding claim 6,14, 22, Douthat further described at least one of frequency (0089, frequency measurement units), voltage, current, and temperature.
Regarding claim 7, 15, 23, Douthat further described the above except a Maintenance as a Service (MAAS) cloud computing controller.
Gesangwangji described a Maintenance as a Service (MAAS) cloud computing controller (page 39, 50, Maintenance as a Service in cloud), for the benefit of optimize performance of battery pack (page 22).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to modify Douthat to have the Maintenance as a Service (MAAS) cloud computing controller taught by Gesangwangji for the benefit of optimize performance of battery pack.
Regarding claim 8, 16, Douthat further described a radio frequency (RF) transceiver; wherein the processor is configured to discontinue RF communications (0027, radio-frequency identification (RFID)) if the at least one encryption key is erased; and wherein the non-rechargeable battery is configured to supply power to the volatile memory when the rechargeable battery is uncoupled from the portable housing so that the at least one encryption key is not erased (0017, processor with non-rechargeable, 0025, can use NFC sometimes modify only no encrypt).
Regarding claim 9, Douthat further described to measure the internal resistance values based upon pulse impedance monitoring (0050, internal resistance, or some combination thereof).
Response to Arguments
4. Applicant's arguments filed 07/15/2026 have been fully considered, the argument as follow:
A. Applicant recite claims and argues in the lengthy arguments is patentable because the claim limitation (remarks page 10-14) and that improved the art because of machine leaning.
B. the applicant argued because different in the art the combination the examiner use impermissible hindsight reconstruction (remarks page 14-17)
As regards to A.
Appears the applicant ignored the 101 full analysis where the computer/memory etc are in the analysis. Repeat of the full 101 analysis here seems not needed (please see above 101 rejection), the applicant rely on a generic computer memory to perform a task in the claim context appears not persuasive. In USPTO 101 example 47: a live stock management using computer analysis is not patenable because is a generic computer/memory, even the claims here are not identical the present claims idea are similar.
The mere generic presentation of collected and analyzed data in Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016).
MPEP 2106.05(a)(I), particularly FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095 (Fed. Cir. 2016) (accelerating a process of analyzing audit log data is not an improvement when the increased speed comes solely from the capabilities of a general-purpose computer) and Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055 (Fed. Cir. 2017) (using a generic computer to automate a process of applying to finance a purchase is not an improvement to the computer’s functionality). Accordingly, the claim as a whole does not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception (Step 2A: YES).
As regards to B.
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
In this case both arts deal with battery measurement (Douthat, 0031, 0050-0051, Gesangwangji, page 20-24), try to solve the similar problem. Both art are pertinent art related because they try to solve similar problem.
In view of the above analysis, the examiner respectfully disagreed with the applicant argument, applicant's arguments filed 07/15/2026 have been fully considered but they are not persuasive.
THIS ACTION IS MADE FINAL. 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.
Contact information
5. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tung Lau whose telephone number is (571)272-2274, email is Tungs.lau@uspto.gov. The examiner can normally be reached on Tuesday-Friday 7:00 AM-5:00 PM EST.
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, TURNER SHELBY, can be reached on 571-272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TUNG S LAU/Primary Examiner, Art Unit 2857
Technology Center 2800
July 24, 2026
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