DETAILED ACTION
This action is in response to the filing 06/27/2024. Claims 1-16 are pending and have been fully examined.
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 .
Status of the Claims
Claims 1-5, 11-13, and 15-16 are rejected under 35 U.S.C. 102.
Claims 6, 10, and 14 are rejected under 35 U.S.C. 103.
Claims 7-9 contain allowable subject matter but are objected to as being dependent upon a rejected base claim.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5, 11-13, and 15-16 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being unpatentable over Shibuya et al. (JP2012089057A, furnished by Examiner).
Regarding Claim 1, and similarly the method and program of Claims 15 and 16 respectively, Shibuya teaches,
An information processing apparatus comprising a presentation control unit that generates presentation information (see at least figs. 9A and 9B showing the display; see [0050] describing that figs. 9A and 9B are displaying anomaly identification information) corresponding to
a combination of a detection result obtained by a first detection model that detects an abnormality in time-series data by using auxiliary data related to variation in the time-series data (the display shows GUIs related to statistical anomaly identification, including anomaly judgement results ("detection result obtained by a first detection model") [0050]; where the 'judgement result' is created by rule-based anomaly identification unit (104) ("first detection model") [0027]; where (104) performs anomaly detection corresponding to abnormality identification rules ("auxiliary data") [0027]; where abnormality identification rules are generated by a rule extraction unit (108) and abnormality measure is used to generate abnormality identification rules [0025-0026] where anomaly measure is identified via variability ("variance") of time-series sensor data [0037])
and a detection result obtained by a second detection model that detects an abnormality in the time-series data without using the auxiliary data (the display shows GUIs related to statistical anomaly identification, including anomaly measurements ("result obtained by a second detection model") [0050]; where anomaly measurements are generated by statistical anomaly identification unit (106) ("second detection model") [0032-0033]).
Regarding Claim 2, Shibuya teaches,
The information processing apparatus according to claim 1, wherein the presentation control unit further generates visualized information obtained by visualizing the time- series data (time-series data is displayed [0050; figs. 9A-9B]).
Regarding Claim 3, Shibuya teaches,
The information processing apparatus according to claim 2, wherein the visualized information includes a value of the time-series data in which an abnormality is detected by at least one of the first detection model and the second detection model (anomaly measurements (of (106), the second detection model) and judgment results (of (104), the first detection model) are displayed as time-series data [0050]).
Regarding Claim 4, Shibuya teaches,
The information processing apparatus according to claim 1, wherein the presentation control unit generates the presentation information regarding an abnormality of the time-series data (a GUI related to rule-testing may further be displayed, including results from both models and relevant data ("presentation information") [0083]).
Regarding Claim 5, Shibuya teaches,
The information processing apparatus according to claim 4, wherein the presentation control unit generates, as the presentation information, advice on a cause of an abnormality of the time-series data (the relevant data displayed in the GUI related to rule testing includes "sensor signal display window 1703 [which] allows users to check why an abnormality was detected …" ("advice on a cause of an abnormality") [0085]).
Regarding Claim 11, Shibuya teaches,
The information processing apparatus according to claim 1, wherein the first detection model is an inference model acquired in advance by learning using the time-series data and the auxiliary data (unit (104) ("the first detection model") receives rules extracted by unit (108) on the basis of time-series data (102) ("in advance") to detect or predict anomalies according to the rules (an "inference model") [0020]).
Regarding Claim 12, Shibuya teaches,
The information processing apparatus according to claim 11, wherein the auxiliary data indicates a discrete value (the anomaly identification rules generated by rule extraction unit (108) ("auxiliary data") are extracted via a decision tree [0026]; the paths of a decision tree require discrete choices, therefore the rules ("auxiliary data") is discrete).
Regarding Claim 13, Shibuya teaches,
The information processing apparatus according to claim 11, further comprising a monitoring unit that detects an abnormality in the time-series data by using the first detection model and the second detection model (see Fig. 1, item (102) ("time-series data") being fed into the group ("monitoring unit") of items (104-108), comprising the "first detection model" (104) and the "second detection model" (106)).
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 6, 10, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Shibuya in view of Callot et al. (U.S. Patent No. 12265446).
Regarding Claim 6, Shibuya does not appear to disclose and Callot teaches,
The information processing apparatus according to claim 5, wherein the first detection model acquires a prediction value corresponding to the time-series data on a basis of the auxiliary data, and detects an abnormality of the time- series data by comparing the time-series data with the prediction value (received metric data is compared to a range of values generated by a predicted probability distribution to identify whether the metric value for that given time is anomalous [Col. 2, lines 44-48]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the anomaly identification unit of Shibuya to include anomaly detection via comparison with predicted data as disclosed by Callot. The resulting combination allows for a reduced probability of detecting false positive anomalies [Col. 4, lines 23-24].
Regarding Claim 10, Shibuya does not appear to disclose and Callot teaches,
The information processing apparatus according to claim 5, wherein, in a case where an abnormality has been detected by the first detection model, the presentation control unit generates the advice of urging a user to check other data different from the time-series data and the auxiliary data (when an analytics service identifies an anomaly, the analytic service may suggest further that metrics are investigated, even when those metrics are not used to result in the detection of the anomaly [Col. 16, lines 21-25]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the anomaly display of Shibuya to further include displaying relevant metrics for further consideration as disclosed by Callot. The resulting combination of now directing the user to unconsidered metrics for detecting the anomaly may aid the user to explain the increase in divergence between considered metrics that actually caused the anomaly identification [Callot; Col. 16, lines 17-45].
Regarding Claim 14, while Shibuya discloses performing training to generate the anomaly measures, further used to generate rules for the "first detection model," with all groups of [sensor] data [0034], Shibuya does not appear to disclose performing explicit re-training on new data. Callot teaches,
The information processing apparatus according to claim 13, wherein, In a case where the time-series data is newly input, the monitoring unit performs re-learning of the first detection model by using the time-series data and the auxiliary data (where a probabilistic forecasting model employed to analyze time-series metrics and is employed as a factor of anomaly detection (the "first detection model") [Col. 3, line 57- Col. 4, line 28]; the probabilistic model may be retrained as new data becomes available [Col. 19, lines 46-50]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the rules-based anomaly detection of Shibuya to incorporate retraining the model as disclosed by Callot. The resulting combination allows for a more robust, highly trained model that is relevant to updated data as input data may drift over time, therefore improving the model's classification ability.
Allowable Subject Matter
Claims 7-9 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 following is the Examiner’s statement of reasons for indicating allowable subject matter:
Regarding Claim 7, Callot discloses: metrics used to derive the indication of anomaly are presented, including conditions found via predictive techniques to identify data [Col. 4, lines 29-49]. The prior art of record does not disclose, without the use of impermissible hindsight reasoning, wherein the first detection model outputs information indicating a factor by which the prediction value is acquired, and the presentation control unit generates the presentation information including the information indicating the factor [emphasis added to particular subject matter that is distinguishing from prior art]. Claims 8-9 are dependent upon the allowable subject matter of Claim 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kawabata et al. (JP2008140100, furnished by Examiner) discloses where abnormal data is compared to predicted data to verify an anomaly relative mere spike-like noise [0014].
Bledsoe et al. (U.S. Patent No. 10387900) discloses comparing estimated time-series data with observed sampled values to determine the accuracy of the predictive model [Col. 9, lines 1-5].
Zaker (U.S. PGPub No. 20220207326) discloses presenting via a display: an indication of an anomaly, predicted data by the system including predicted time-series data, and one or more features contributing to the anomaly and the amount of each contribution [0045]. Zaker does not disclose that the aforementioned features are particular features of the forecasted data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AUDREY E WHITESELL whose telephone number is (703)756-4767. The examiner can normally be reached 8:30am - 5:00pm MST.
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, Bryce Bonzo can be reached at 5712723655. 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.
/A.E.W./Examiner, Art Unit 2113 /BRYCE P BONZO/Supervisory Patent Examiner, Art Unit 2113
Prosecution Insights