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 .
Application # 19/122,424 was filed on 4/18/2025.
Claims 1-6 are subject to examination.
An IDS filed on 9/8/2025 has been fully considered and entered by the Examiner.
Claim Rejections - 35 USC § 101
Claims 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) 1, 45, 6 recite(s) calculating an average and a variance of a traffic data and calculating Z score representing a deviation from an average of traffic data wherein both of the limitations fall under mathematical calculations wherein one can calculate using formulas/equations and acquired data; acquiring past traffic data is falls under gathering step wherein it is just collecting/acquiring traffic and it can also be considered pre-solution activity. This judicial exception is not integrated into a practical application because acquiring past traffic by using acquisition unit which comprises processors to acquire past traffic and store the data covers performance of the limitation in mind but for recitation of generic computer components of processors. That is other than reciting “by acquisition unit, calculation unit, analysis unit”, nothing in the claim element precludes the step from practically being performed in human mind. The processors and the units in the steps are recited at a high-level of generality such that amounts to no more than mere instructions to apply the exceptions using a generic computer device/processor . The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because processors, and units perform the acquiring, calculating steps. The processors and the units in the steps are recited at a high-level of generality such that amounts to no more than mere instructions to apply the exceptions using a generic computer device/processor and does not provide the inventive concept. The claims are not patent eligible.
With respect to dependent claims 2-4, they do not include additional elements that are sufficient to an amount to a significantly more than the judicial exception because claims recite using processor to performing the calculating step. The processors and the units in the steps are recited at a high-level of generality such that amounts to no more than mere instructions to apply the exceptions using a generic computer device/processor . The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because processors, and units perform the acquiring, calculating steps. The processors and the units in the steps are recited at a high-level of generality such that amounts to no more than mere instructions to apply the exceptions using a generic computer device/processor and does not provide the inventive concept.
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) 1-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over St. Louis et al. U.S. Publication # 2018/0307797 (hereinafter St. Louis) in view of Sasaki et al. U.S. Patent Publication # 2024/0280598 (hereinafter Sasaki)
With respect to claim 1, St. Louis teaches an abnormality detection device (Paragraph 66) comprising:
-an acquisition unit comprising one or more processors, configured to acquire past traffic data and store the past traffic data in a buffer region of a storage unit (i.e. obtaining handpiece data which is stored in the data logging equipment and cloud storage. Furthermore, obtaining previous handpiece usage history data from the data logging equipment) (Paragraph 67, 55);
-a calculation unit, comprising one or more processors, configured to calculate an average and a variance of traffic data in the buffer region by applying the traffic data to a predetermined recurrence formula (i.e. calculating variance and average from heat generating rates wherein the temperature data is executed by the processor through series of thermal equations that calculate and obtain heat generating rates. This data is comparing with the heat generates locally with the history of previously calculated results)(Paragraph 67); and
St. Louis does not teach an analysis unit, comprising one or more processors, configured to calculate a Z score representing a deviation from an average of traffic data as a processing target by using the average and the variance that have been calculated, and analyze whether the traffic data is abnormal by using the Z score.
Sasaki teaches abnormality detection device (Fig. 1 element 1 and sensor) an analysis unit, comprising one or more processors, configured to calculate a Z score representing a deviation from an average of traffic data as a processing target by using the average and the variance that have been calculated (i.e. Z score is calculated by dividing the deviation from the average value of basic data by the standard deviation. Furthermore, if the magnitude of the average values of the Z scores is larger than the first determination value (i.e. variance), then the sensor for the diagnosis is presumed abnormal)(Paragraph 45), and analyze whether the traffic data is abnormal by using the Z score (i.e. Furthermore, if the magnitude of the average values of the Z scores is larger than the first determination value (i.e. variance), then the sensor for the diagnosis is presumed abnormal) (Paragraph 45). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement Sasaki’s teaching in St. Louis’s teaching to come up with calculating a Z-score representing a deviation from average traffic data. The motivation for doing so would be to compare and come to conclusion if the sensor for diagnosis is presumed to be normal or abnormal based on Z score, thereby appropriate action can take place.
With respect to claim 2, St. Louis and Sasaki teaches the method abnormality detection device according to claim 1, but St. Louis further teaches wherein, when calculating an average and a variance of traffic data acquired next, the calculation unit is configured to further use the average and the variance further calculated (Paragraph 67, 55)
With respect to claim 3, St. Louis and Sasaki teaches the method abnormality detection device according to claim 1, but St. Louis further teaches wherein the calculation unit is configured to apply traffic data to the recurrence formula that has been weighted such that a contribution degree of traffic data is smaller as the traffic data is further past data (Paragraph 67, 55, 57, 59-60)
With respect to claim 4, St. Louis and Sasaki teaches the method abnormality detection device according to claim 1, but Sasaki further teaches wherein the analysis unit is configured to determine the traffic data as the processing target is abnormal when the Z score that has been calculated is larger than a predetermined threshold (Paragraph 45, 47)
With respect to claim 5, St. Louis teaches an abnormality detection method performed by an abnormality detection device (Paragraph 66),the abnormality detection device including a storage unit (i.e. cloud computer),the abnormality detection method comprising: acquiring past traffic data and store the past traffic data in a buffer region of a storage unit (i.e. obtaining handpiece data which is stored in the data logging equipment and cloud storage. Furthermore, obtaining previous handpiece usage history data from the data logging equipment) (Paragraph 67, 55);
-calculating an average and a variance of traffic data in the buffer region by applying the traffic data to a predetermined recurrence formula (i.e. calculating variance and average from heat generating rates wherein the temperature data is executed by the processor through series of thermal equations that calculate and obtain heat generating rates. This data is comparing with the heat generates locally with the history of previously calculated results)(Paragraph 67); and
St. Louis does not teach calculating a Z score representing a deviation from an average of traffic data as a processing target by using the average and the variance that have been calculated, and analyze whether the traffic data is abnormal by using the Z score.
Sasaki teaches abnormality detection device (Fig. 1 element 1 and sensor) calculating a Z score representing a deviation from an average of traffic data as a processing target by using the average and the variance that have been calculated (i.e. Z score is calculated by dividing the deviation from the average value of basic data by the standard deviation. Furthermore, if the magnitude of the average values of the Z scores is larger than the first determination value (i.e. variance), then the sensor for the diagnosis is presumed abnormal)(Paragraph 45), and analyze whether the traffic data is abnormal by using the Z score (i.e. Furthermore, if the magnitude of the average values of the Z scores is larger than the first determination value (i.e. variance), then the sensor for the diagnosis is presumed abnormal) (Paragraph 45). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement Sasaki’s teaching in St. Louis’s teaching to come up with calculating a Z-score representing a deviation from average traffic data. The motivation for doing so would be to compare and come to conclusion if the sensor for diagnosis is presumed to be normal or abnormal based on Z score, thereby appropriate action can take place.
With respect to claim 6, it teaches same limitation as claim 5, therefore rejected under same basis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
A). Sun et al. U.S. Patent Publication # 2021/0136099.
B). Neznal et al. U.S. Patent Publication # 2020/0204571 which teaches about malware detection in network traffic time series using neural network.
C). Tasaki et al. U.S. Patent Publication # 2008/0219544.
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DHAIRYA A. PATEL
Primary Examiner
Art Unit 2453
/DHAIRYA A PATEL/Primary Examiner, Art Unit 2453
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