CTNF 18/690,089 CTNF 80493 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1: Is the Claim to a Process, Machine, Manufacture or Composition of Matter? Claim 1 recites a system, and Claim 7 recites a method. Thus, the claims are to a machine and a method, which are among the statutory categories of invention. Step 2A: Prong One: Does the Claim Recite an Abstract Idea? Independent claim 1 recites: A battery malfunctioning behavior detection system comprising: a field data calculation unit configured to receive real-time measurement values from a battery in operation and calculate and output first field data from the real-time measurement values [the examiner finds that the foregoing underlined element recites mathematical concepts, and also a mental process because they can be performed by a human using pen and paper]; a field data pre-processing unit configured to extract and output second field data for predicting a cell voltage from the first field data [the examiner finds that the foregoing underlined element recites mathematical concepts, and also a mental process because they can be performed by a human using pen and paper]; an artificial intelligence neural network unit configured to receive an output of the field data pre-processing unit to predict the cell voltage and output a prediction value of the cell voltage [the examiner finds that the foregoing underlined element recites mathematical concepts, and also a mental process because they can be performed by a human using pen and paper]; and a battery malfunctioning behavior detection unit configured to compare the prediction value output by the artificial intelligence neural network unit with the first field data and determine that it is a malfunctioning behavior when a deviation between the prediction value and the first field data is greater than or equal to a predetermined range [the examiner finds that the foregoing underlined element recites mathematical concepts, and also a mental process because they can be performed by a human using pen and paper]. Step 2A: Prong Two: Does the Claim Recite Additional Elements That Integrate The Abstract Idea Into a Practical Application? The elements that are not underlined above are the additional elements (i.e., the field data calculation unit, the field data pre-processing unit, the artificial intelligence neural network unit, and the battery malfunctioning behavior detection unit, and “receive real-time measurement values from a battery in operation”). The examiner submits that each of the following additional elements does no more than generally link the use of the abstract idea to a particular technological environment or field of use because they are merely an incidental or token addition to the claim that does not alter or affect how the process steps of the abstract idea are performed. The receiving step recites mere gathering of data for use in the abstract idea, and the field data calculation unit, the field data pre-processing unit, the artificial intelligence neural network unit, and the battery malfunctioning behavior detection unit are merely generic computer hardware for performing the abstract idea. Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For example, there is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Step 2B: Does the Claim Recite Additional Elements That Amount to Significantly More Than the Abstract Idea? The examiner submits that the additional elements identified in Step 2A do not amount to significantly more than the abstract idea for the same reasons discussed above with respect to the conclusion that the additional elements do not integrate the abstract idea into a practical application. The additional elements identified in Step 2A are not unconventional or otherwise more than what is well-understood, routine, conventional activity in the field; and simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP § 2106.05(d). Independent Claim 7 recites the same steps as Claim 1, and is also not patent eligible. Dependent Claims 2-6 and 8-13 are also not patent eligible. Claims 2-6 and 8-11 merely recite further details of the mathematical concepts/mental process, and Claims 12-13 merely recite an insignificant outputting of a result of the abstract idea. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1-2 and 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kwon et al (U.S. Pub. No. 2022/0052389, hereinafter “Kwon”) in view of Ukumori (U.S. Pub. No. 2021/0048482, hereinafter “Ukumori”) . Regarding Claim 1, Kwon teaches a battery malfunctioning behavior detection system (Fig. 6) comprising: a field data calculation unit (Fig. 6, battery data collection unit 620 and memory unit 610) configured to receive real-time measurement values from a battery in operation and output first field data from the real-time measurement values (Fig. 1, S110; paragraphs [0080]-[0081]); a field data pre-processing unit (battery defect prediction unit 630) configured to extract and output second field data for predicting a cell voltage from the first field data (paragraph [0083]; Fig. 1, S120 and S130); an artificial intelligence neural network unit (battery defect prediction unit 630) configured to receive an output of the field data pre-processing unit to predict the cell voltage and output a prediction value of the cell voltage (paragraph [0083]; Fig. 1, S140 and Fig. 2, S210 and S220); and a battery malfunctioning behavior detection unit (battery defect prediction unit 630) configured to compare the prediction value output by the artificial intelligence neural network unit with the first field data and determine that it is a malfunctioning behavior when a deviation between the prediction value and the first field data is greater than or equal to a predetermined range (paragraph [0084]; Fig. 2, S230, S232). Kwon does not specifically teach that the field data calculation unit is configured to receive real-time measurement values from a battery in operation and calculate and output first field data from the real-time measurement values (emphasis added). However, Ukumori teaches, that the field data calculation unit is configured to receive real-time measurement values from a battery in operation and calculate and output first field data from the real-time measurement values (Fig. 20, first calculation unit 231; paragraph [0178]). It would have been obvious to one skilled in the art before the effective filing date of the invention to calculate measured voltage differences, as is taught in Ukumori, and use the calculated measured voltage differences for determining battery abnormalities, as is performed in Ukumori and Kwon, because such calculated measured voltage differences may indicate that the battery is abnormal (see Ukumori, paragraph [0196]). Regarding Claim 2, Kwon in view of Ukumori teaches everything that is claimed above with respect to Claim 1. Kwon further teaches wherein the field data pre-processing unit is configured to additionally extract and output third field data for predicting cell temperature (Fig. 1, S120 and S130; paragraphs [0041]-[0044], temperature), wherein the artificial intelligence neural network unit is configured to receive the output of the field data pre-processing unit, additionally predict the cell temperature, and output a prediction value of the cell temperature (Fig. 1, S140 and Fig. 2, S210 and S220), and wherein the battery malfunctioning behavior detection unit is configured to determine that it is battery malfunctioning behavior when the cell voltage prediction value output by the artificial intelligence neural network unit is compared with a cell voltage value of the first field data and a deviation between a predicted cell voltage value and the cell voltage value is greater than or equal to a predetermined range (Fig. 2, S230, S232), or when a cell temperature prediction value output by the artificial intelligence neural network unit is compared with a cell temperature value of the first field data and a deviation between the predicted cell temperature prediction value and the cell temperature value is greater than or equal to a predetermined range (Fig. 2, S230, S232). Regarding Claim 7, Kwon teaches a battery malfunctioning behavior detection method (Figs. 1-2) comprising: a field data calculation process of measuring real-time battery state information data from a battery in operation (Fig. 1, S110; paragraphs [0080]-[0081]); a field data pre-processing process including a field data pre-processing process for cell voltage prediction of extracting data for cell voltage prediction from calculated field data (paragraph [0083]; Fig. 1, S120 and S130); a real-time prediction process including a cell voltage prediction process of inputting data for predicting cell voltage into a cell voltage prediction model and calculating a cell voltage prediction value of the next cycle (paragraph [0083]; Fig. 1, S140 and Fig. 2, S210 and S220); and a battery malfunctioning behavior detection process including a cell voltage malfunctioning behavior detection process of comparing the cell voltage prediction value with a cell voltage value of the field data and generating a cell voltage malfunctioning behavior detection signal when a deviation is greater than or equal to a predetermined range (paragraph [0084]; Fig. 2, S230, S232). Kwon does not specifically teach a field data calculation process of measuring and calculating real-time battery state information data from a battery in operation (emphasis added). However, Ukumori teaches measuring and calculating real-time battery state information data from a battery in operation (Fig. 20, first calculation unit 231; paragraph [0178]). It would have been obvious to one skilled in the art before the effective filing date of the invention to calculate measured voltage differences, as is taught in Ukumori, and use the calculated measured voltage differences for determining battery abnormalities, as is performed in Ukumori and Kwon, because such calculated measured voltage differences may indicate that the battery is abnormal (see Ukumori, paragraph [0196]). Regarding Claim 8, Kwon in view of Ukumori teaches everything that is claimed above with respect to Claim 7. Kwon further teaches wherein the field data pre-processing process further comprises a field data pre-processing process for cell temperature prediction of extracting data for predicting cell temperature from the calculated field data, (Fig. 1, S120 and S130; paragraphs [0041]-[0044], temperature) wherein the real-time prediction process further comprises a cell temperature prediction process of inputting data for predicting cell temperature into a cell temperature prediction model and calculating a cell temperature prediction value of the next cycle (Fig. 1, S140 and Fig. 2, S210 and S220), and wherein the battery malfunctioning behavior detection process further comprises a cell temperature malfunctioning behavior detection process of comparing the cell temperature prediction value with a cell temperature value of the field data and generating a cell temperature malfunctioning behavior detection signal when a deviation between the cell temperature prediction value and the cell temperature value is greater than or equal to a predetermined range (Fig. 2, S230, S232) . 07-21-aia AIA Claim (s) 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kwon in view of Ukumori and Damgaard (U.S. Pub. No. 2021/0151811) . Regarding Claim 12, Kwon in view of Ukumori teaches everything that is claimed above with respect to Claim 7. Kwon does not specifically teach further comprising an alarm generation process of outputting an alarm generation signal or outputting a control signal for blocking a battery charging or discharging operation (optional due to “or”), when a malfunctioning behavior is detected. However, Kwon does teach detecting malfunctioning behavior in a battery (paragraph [0084]; Fig. 2, S230, S232). Further, Damgaard teaches generating alarms based on problems detected in batteries (see paragraphs [0024], [0043], and [0053]). It would have been obvious to one skilled in the art before the effective filing date of the invention to include the alarms of Damgaard in the battery defect detection system of Kwon, in order to alarm faults in the battery system in an early state to allow predictive maintenance (see Damgaard, paragraphs [0024]). Regarding Claim 13, Kwon in view of Ukumori teaches everything that is claimed above with respect to Claim 1. Kwon does not specifically teach further comprising a control unit configured to output an alarm generation signal or output a control signal for blocking a battery charging or discharging operation (optional due to “or”), when a malfunctioning behavior is detected. However, Kwon does teach a control unit configured to detecting malfunctioning behavior in a battery (paragraph [0084]; Fig. 2, S230, S232; Fig. 6, battery defect prediction unit 630). Further, Damgaard teaches generating alarms based on problems detected in batteries (see paragraphs [0024], [0043], and [0053]). It would have been obvious to one skilled in the art before the effective filing date of the invention to include the alarms of Damgaard in the battery defect detection system of Kwon, in order to alarm faults in the battery system in an early state to allow predictive maintenance (see Damgaard, paragraphs [0024]). Allowable Subject Matter Although there are no prior art rejections for Claims 3-6 and 9-11, the Examiner cannot comment on their allowability until the rejections under 35 U.S.C 101 are satisfactorily addressed. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to CYNTHIA L DAVIS whose telephone number is (571)272-1599. The examiner can normally be reached Monday-Friday, 7am to 3pm. 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, Shelby A Turner can be reached at (571)272-6334. 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. /CYNTHIA L DAVIS/Examiner, Art Unit 2857 /SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857 Application/Control Number: 18/690,089 Page 2 Art Unit: 2857 Application/Control Number: 18/690,089 Page 3 Art Unit: 2857 Application/Control Number: 18/690,089 Page 4 Art Unit: 2857 Application/Control Number: 18/690,089 Page 5 Art Unit: 2857 Application/Control Number: 18/690,089 Page 6 Art Unit: 2857 Application/Control Number: 18/690,089 Page 7 Art Unit: 2857 Application/Control Number: 18/690,089 Page 8 Art Unit: 2857 Application/Control Number: 18/690,089 Page 9 Art Unit: 2857 Application/Control Number: 18/690,089 Page 10 Art Unit: 2857 Application/Control Number: 18/690,089 Page 11 Art Unit: 2857
Prosecution Insights