DETAILED ACTION
Non-Final Rejection
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/11/2026 has been entered.
Response to Amendment
Applicant’s amendments, filed 05/11/2026 to claims are accepted. In this amendment, claims 1 and 12 have been amended. Regarding Claims 14-16: added.
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 1-13 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Each of claims1-16 falls within one of the four statutory categories. See MPEP § 2106.03. Each of claim 1-11 and 14-16 falls within category of machine, i.e., a “concrete thing, consisting of parts, or of certain devices and combination of devices.” Digitech, 758 F.3d at 1348–49, 111 USPQ2d at 1719 (quoting Burr v. Duryee, 68 U.S. 531, 570, 17 L. Ed. 650, 657 (1863)); For example, each of claim 12 fall within category of process and claim 13 is directed to a “A computer program product” and therefore falls within category of manufacture.1
Regarding Claims 1-11 and 14-16
Step 2A – Prong 1
Exemplary claim 1 is directed to an abstract idea of identifying failure types of a technical system.
The abstract idea is set forth or described by the following italicized limitations:
1. An assistance apparatus for automatically identifying failure types of a technical system analyzing monitored time series of more than one different sensor data each sensor data representing a different parameter of the technical system, comprising;
at least one processor configured to:
determine for each sensor data a set of specific temporal courses of first time series of the sensor data which are obtained previous in time with respect to the monitored time series of the technical system and assign a symbolic representation to each of the different specific temporal courses;
provide at least one failure pattern, each failure pattern representing one failure type out of several failure types of the technical system and each failure pattern consisting of a failure-type-specific combination of specific temporal courses of the first time series of at least a subset of sensor data in the same segment of time, wherein each specific temporal course is represented by the respective symbolic representation;
obtain more than one monitored time series of more than one of said sensor data of the technical system, each of the time series divided into a sequence of time segments, wherein each time segment covers only one specific temporal course of the sensor data values and a current time segment is closed only if a change in the temporal course of the sensor data has been detected, and automatically assign to each time segment one of the symbolic representations a symbolic representation according to the temporal course of the sensor data in the time segment;
-calculate a similarity measure providing a probability for the set of symbolic representations of a selected time interval of the obtained more than one monitored time series of sensor data and all failure patterns
-determine a ranking of the failure patterns depending on decreasing values of the calculated similarity measure; and
-output the ranking via a user interface, wherein the user interface is configured as a graphical user interface and the symbolic representations of a selected time interval and the symbolic representations of the failure patterns are visualized according to the determined ranking for the selected time segment at the graphical user interface.
The italicized limitations above represent a combination of mathematical concept (i.e., a process that can be performed by mathematical relationships or rules or idea) and mental step (i.e., a process that can be performed by can be performed mentally and/or with pen and paper or a mental judgment) . Therefore, the italicized limitations fall within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 Revised Patent Subject Matter Eligibility Guidance.
For example, the limitations “determine for each sensor data a set of specific temporal courses [..]; calculate a similarity measure [..]” is mathematical concept (i.e., a process that can be performed by mathematical relationships or rules or idea), see 2106.04(a)(2).
For example, the limitations “determine a ranking of the failure patterns [..]” are mental step (i.e., a process that can be performed by can be performed mentally and/or with pen and paper or a mental judgment), see 2106.04(a)(2). Limitations are considered together as a single abstract idea for further analysis. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)).
Step 2A – Prong 2
Claims 1 does not include additional elements (when considered individually, as an ordered combination, and/or within the claim as a whole) that are sufficient to integrate the abstract idea into a practical application.
For example, first additional first element is “ provide at least one failure pattern, each failure pattern representing one failure type out of several failure types of the technical system and each failure pattern consisting of a failure-type-specific combination of specific temporal courses of the first time series of at least a subset of sensor data in the same segment of time, wherein each specific temporal course is represented by the respective symbolic representation;
obtain more than one monitored time series of more than one of said sensor data of the technical system, each of the time series divided into a sequence of time segments, wherein each time segment covers only one specific temporal course of the sensor data values and a current time segment is closed only if a change in the temporal course of the sensor data has been detected, and automatically assign to each time segment one of the symbolic representations a symbolic representation according to the temporal course of the sensor data in the time segment;” to be performed, at least in-part, these additional elements appear to only add insignificant extra-solution activity (e.g., data gathering) and only generally link the abstract idea to a particular field. Therefore, this element individually or as a whole does not provide a practical application. See MPEP 2106.05(g).
For example, 2nd additional first element is “An assistance apparatus for automatically; at least one processor; graphical user interface ”. This element amounts to mere use of a generic device with computer components, which is well understood routine and conventional (see background of current discloser and IDS and PTO 892) and this element individually does not provide a practical application. In view of the above, the “additional element” individually or combine does not provide a practical application of the abstract idea. see MPEP 2106.05(d).
The 3rd additional element of “output the ranking via a user interface, wherein the user interface is configured as a graphical user interface and the symbolic representations of a selected time interval and the symbolic representations of the failure patterns are visualized according to the determined ranking for the selected time segment at the graphical user interface” to be performed, at least in-part, these additional elements appear to only add insignificant extra-solution activity (e.g., data gathering) and only generally link the abstract idea to a particular field. Therefore, this element individually or as a whole does not provide a practical application. See MPEP 2106.05(g).
In view of the above, the three “additional elements” individually do not provide a practical application of the abstract idea. Furthermore, the “additional elements” in combination amount to a plurality of generic DUT data collection system with computer component with software, where such computers and software amount to mere instructions to implement the abstract idea on a computer(s) and/or mere use of a generic computer component(s) as a tool to perform the abstract idea. Therefore, these elements in combination do not provide a practical application. The combination of additional elements does no more than generally link the use of the abstract idea to a particular technological environment, and for this additional reason, the combination of additional elements does not provide a practical application of the abstract idea.
.
Step 2B
Claims1 does not include additional elements, when considered individually and as an ordered combination, that are sufficient to amount to significantly more than the abstract idea. For example, the limitation of Claim 1 contains additional elements that are, i.e. An assistance apparatus; at least one processor; graphical user interface”, generic devices, which are well understood, routine and conventional (see background of current discloser and IDS and PTO 892) and MPEP 2106.05(d))The reasons for reaching this conclusion are substantially the same as the reasons given above in § Step 2A – Prong 2. For brevity only, those reasons are not repeated in this section. See MPEP §§ 2106.05(g) and MPEP §§2106.05(II).
.
Dependent Claims 2-9 and 14-16
Dependent claims 2-11 fail to cure this deficiency of independent claim 1 (set forth above) and are rejected accordingly. Particularly, claims 2-11 recite limitations that represent (in addition to the limitations already noted above) either the abstract idea or an additional element that is merely extra-solution activity, mere use of instructions and/or generic computer component(s) as a tool to implement the abstract idea, and/or merely limits the abstract idea to a particular technological environment.
For Examples, claim 2-11 and 14: a combination of mathematical concept (i.e., a process that can be performed by mathematical relationships or rules or idea) and mental step (i.e., a process that can be performed by can be performed mentally and/or with pen and paper or a mental judgment).
For Examples, claim 15-16: to be performed, at least in-part, these additional elements appear to only add insignificant extra-solution activity (e.g., data gathering) and only generally link the abstract idea to a particular field. Therefore, this element individually or as a whole does not provide a practical application. See MPEP 2106.05(g).
Regarding Claims 12-13
Claims 12-13 contains language similar to claims 1-11 as discussed in the preceding paragraphs, and for reasons similar to those discussed above, claims 12-13 are also rejected under 35 U.S.C. § 101(abstract idea).
Examiner Notes
Regarding claims 1-16, There is no prior art rejection over claims, however there is 101 rejections, Closet prior arts fail to teach the limitations, specifically claim 1 (“calculate a similarity measure providing a probability for the set of symbolic representations of a selected time interval of the obtained more than one monitored time series of sensor data and all failure patterns ; determine a ranking of the failure patterns depending on decreasing values of the calculated similarity measure”),.
Response to Argument
Applicant’s arguments with respect 101 rejection, specially claim 1, the applicant did not agree with it., see pages 7-12.
The Applicant argus that “amended limitans recites a specific technical framework and patent eligible like examples 40”.
In response, the Examiner respectfully disagree because the amended limitations represent details of abstract concepts and data gathering steps only. Not like, example 40, where the claim recites the combination of additional elements of collecting at least one of network delay, packet loss, or jitter relating to the network traffic passing through the network appliance, which further directed to practical filed of application as network traffic passing through the network appliance.
Current claim invention still similar to ELECTRIC POWER GROUP, LLC v. ALSTOM S.A., where Court cites “Two of our decisions that rejected § 101 challenges are materially different from this case. The claims at issue here do not require an arguably inventive device or technique for displaying information, unlike the claims at issue in DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1257 (Fed. Cir. 2014) (at JMOL stage finding inventive concept in modification of conventional mechanics behind website display to produce dual-source integrated hybrid display). Nor do the claims here require an arguably inventive distribution of functionality within a network, thus distinguishing the claims at issue from those in Bascom, 2016 WL 3514158, at *6 (at pleading stage finding sufficient inventive concept in “the installation of a filtering tool at a specific location, remote from the endusers, with customizable filtering features specific to each end user”). The claims in this case specify what information in the power-grid field it is desirable to gather, analyze, and display, including in “real time”; but they do not include any requirement for performing the claimed functions of gathering, analyzing, and displaying in real time by use of anything but entirely conventional, generic technology. The claims therefore do not state an arguably inventive concept in the realm of application of the information- based abstract ideas.” And a an post solution activity well known in the particular industry. In summery, the current claim invention only recite the idea of a solution or outcome “outputting an analysis result” and do not include any details about how the “outputting a analysis result” is accomplished. See MPEP 2106.05(f) As such 101 rejection is maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
a) Calmon et al. (US 12/159,237) disclose a methods and apparatus are provided for real-time anomaly detection over sets of time-series data. One method comprises: obtaining a state-space representation of a plurality of states and transitions between said states based on sets of historical time-series data; obtaining an anomaly detection model trained using a supervised learning technique, wherein the anomaly detection model associates sequences of states in the state-space representation with annotated anomalies in the sets of historical time-series data and assigns a probability to said sequences of states; and, for incoming real-time time-series data, determining a likelihood of a current state belonging to a plurality of possible states in the state-space representation; and determining a probability of incurring said annotated anomalies based on a plurality of likely current state sequences that satisfy a predefined likelihood criteria. Anomalous behavior is optionally distinguished from previously unknown behavior based on a predefined likelihood threshold.
b) Cantwell (US 12,131,269) disclose a method includes receiving historical time-series data and generating training data comprising a plurality of randomized data points associated with the historical time-series data. The historical time-series data was generated by one or more sensors during one or more processes. The method further includes training a logistic regression classifier based on the training data to generate a trained logistic regression classifier. The trained logistic regression classifier is associated with a logistic regression that indicates a location of a transition pattern from a first data point to a second data point. The transition pattern reflects about a reflection point located on the transition pattern. The trained logistic regression classifier is capable of indicating a probability that new time-series data generated during a new execution of the one or more processes matches the historical time-series data.
c) Beck et al. (US 2023/0196638) disclose identifying the condition monitoring of other types of engines, for estimating quality in process industries, for anomaly detection in industrial assets, for forecasting of failures, for trend deviations, for cyber security or the like.
d) Kola. Et al. (US 2022/0180205) disclose a temporal relationship between data points associated with the multivariate time series data when representing the data points of the multivariate time series data in an embedded space. The method may further include, in response to receiving the multivariate time series data, applying the optimized generative deep learning neural network to the input, and representing and displaying the multivariate time series data in the embedded space such that the temporal relationship between the data points from the input is captured by and presented in the representation, wherein the representation comprises an embedding of the multivariate time series data in the embedded space.
e) Phan et al. (US 2021/0066141) disclose Anomaly detection and remedial recommendation techniques for improving the quality and yield of microelectronic products are provided. In one aspect, a method for quality and yield improvement via anomaly detection includes: collecting time series sensor data during individual steps of a semiconductor manufacturing process; calculating anomaly scores for each of the individual steps using a predictive model; and implementing changes to the semiconductor manufacturing process based on the anomaly scores. A system for quality and yield improvement via anomaly detection is also provided.
F) Pelloin et al. (US 2020/0183946) disclose A reference pattern corresponding to a model time series having no anomalies, as well as a reference threshold, may be determined and stored. Runtime time series data may then be obtained and time aligned with the reference pattern. Deviations between the runtime time series and the reference pattern may be identified as anomalies if they exceed the reference threshold. Identified anomalies may be displayed in a display device.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD K ISLAM whose telephone number is (571)270-0328. The examiner can normally be reached M-F 9:00 a.m. - 5:00 p.m..
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/MOHAMMAD K ISLAM/Primary Examiner, Art Unit 2857
1 Applicant’s specification defines “a computer program product” as “(non-transitory computer readable storage medium having instructions, which when executed by a processor, perform actions)” ([0044] of current application PgPub).
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