DETAILED ACTION
Status of Claims
The present application, filed on or after 3/16/2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to the Application and claims filed 01/11/2024 and preliminary amendments also filed 01/11/2024; not to be confused with translation of PCT application, including original claims, submitted 3/27/2024.
Claims 7-10 have been preliminarily amended and claims 11-20 are newly added.
Claims 1-20 have been examined and are pending.
Information Disclosure Statement (IDS)
Acknowledgement is hereby made of receipt of Information Disclosure Statements filed by applicant on 01/11/2024 and 09/24/2025.
(AIA ) Examiner Note
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were effectively filed absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned at the time a later invention was effectively filed in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (i.e. a judicial exception) without significantly more.
Per step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed towards a process, machine, or manufacture.
Per step 2A Prong One, the claims recite specific limitations which fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG, as follows:
Per Independent claims 1, 8, 9, 10:
determining at least one data filtering rule based on the preliminary analysis data
comparing the target metering abnormality data with preconfigured abnormal case data and determining at least one target case data from the preconfigured abnormal case data; and
performing multidimensional cluster analysis on the target metering abnormality data to obtain an aggregation level of the target metering abnormality data in a plurality of data dimensions and
analyzing a cause of metering abnormality based on the at least one target case data and the aggregation level.
As noted supra, these limitations fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG. Specifically, these limitations fall within the group Mathematical Concepts (e.g. mathematical relationships; mathematical formulas or equations; mathematical calculations); Mental Processes (concepts performed in the human mind including an observation, evaluation, judgment, opinion); and/or Certain Methods Of Organizing Human Activity (e.g. fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
That is, the steps are in general directed towards business analysis of equipment operations data to mitigate risk (e.g. mitigate abnormalities such as equipment failure, etc...) thus falling within at least Certain Methods Of Organizing Human Activity, as further shown below.
The determining a “data filtering rule” step, as drafted, is nothing more than a business decisions regarding what data a business wishes to evaluate and this “filtering rule” appears to be arbitrary and left to the decision of a business manager, such as whom may be given charge of maintaining equipment. Thus, this step appears to fall within either Certain Methods Of Organizing Human Activity or Mental Processes or both.
Regarding the "comparing" steps, such comparison may be considered a Mental Process as it can be performed in the human mind, or by a generic computer performing a generic function, when recited at this high level of generality. Furthermore, the decision to compare without any particular method for making the comparison and no particular relationship being provided between such desired comparison and the subsequent desired determination of a “target case data” when recited at this high-level of generality is nothing more than a business decision to select data based upon an arbitrary comparison thus also falling in Certain Methods Of Organizing Human Activity.
Regarding the performing a multidimensional cluster analysis step, this is found to be nothing more than a business decision to use a generic mathematical concept for the purpose of performing a general class of mathematical calculations useful in data analysis (e.g. K-average clustering, or any of a number of other multidimensional clustering techniques). Thus, this step appears to fall within Mathematical Concepts or Certain Methods Of Organizing Human Activity or both.
Finally, the step of “analyzing a cause of metering abnormality…” is either a Mental Process, because a person may make such an evaluation [analysis] of this sort either in their mind or via use of generic computer equipment performing a generic analysis function, or this feature falls within Certain Methods Of Organizing Human Activity, because this generic analysis is done for business purposes, e.g. to ascertain efficiency or abnormality of equipment operation for example. As no particular technique for implementing this analysis is recited, but instead the feature is only recited at this very high level of generality, it can be considered nothing more than an abstraction.
Furthermore, the mere nominal recitation of a generic computing equipment does not take the claim limitation out of the enumerated groupings. There being no technical problem being solved and no technical solution offered or recited for a technical problem, the Examiner finds the claims recite an abstract idea or combination thereof.
Per step 2A Prong 2, the Examiner finds that the judicial exception is not integrated into a practical application. Although there are additional elements, other than those noted supra, recited in the claims, none of these additional element(s) or a combination of elements as recited in the claims apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception. As drafted, the claims as a whole merely describe how to generally “apply” the aforementioned concepts or, link them to a field of use (i.e. in this case business decision to analyze equipment operation data) or, serve as insignificant extra-solution activity (e.g. data-gathering and collection). The claimed computer components are recited at a high level of generality and are merely invoked as tools to implement the idea but are not technical in nature. Simply implementing the abstract idea on or with generic computer components is not a practical application of the abstract idea.
These additional limitations are, as exemplified in the features of method claim 1, as follows: “1. “A metering abnormality analysis method, comprising: acquiring preliminary analysis data analyzed by a source-end system and… filtering monitoring data of the source-end system based on the at least one data filtering rule to obtain target metering abnormality data…”
However, these elements do not present a technical solution to a technical problem; i.e. Applicant’s invention is not a technique nor technical solution for “acquiring” data (i.e. data-gathering) nor is it “filtering” data nor is it any of the generic computer components used to implement the abstract idea. Instead, these features are nothing more than mere instructions to apply the exception using generic computer components (i.e. a server) or insignificant pre-solution or insignificant extra-solution activity steps which are used to gather the set of data of interest to the business upon which the abstract idea is intended to operate. The additional elements do not recite a specific manner of performing any of the steps core to the already identified abstract idea.
Per Step 2B, the Examiner does not find that the claims provide an inventive concept, i.e., the claims do not recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception recited in the claim. As discussed with respect to Step 2A Prong Two, the additional elements in the independent claims were considered as merely instructions to apply the exception using generic computer components (i.e. a server) or insignificant pre-solution or extra-solution activity. For the same reason these elements are not sufficient to provide an inventive concept; i.e. the same analysis applies here in 2B. Mere instructions to apply an exception using a generic computer component and conventional data gathering cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. So, upon revaluating here in step 2B, these elements are determined to amount to no more than mere instructions to apply the exception using generic computer components (i.e. a server) and/or gather and transmit data which is well-understood, routine, conventional activity in the field; i.e. note the Symantec, TLI, and OIP Techs Court decisions cited in MPEP 2106.05(d)(ll) indicate that mere receipt or transmission of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
Accordingly, alone and in combination, these elements do not integrate the abstract idea into a practical application, as found supra, nor provide an inventive concept, and thus the claims are not patent eligible.
As for the dependent claims, the dependent claims do recite a combination of additional elements. However, these claims as a whole, considered either independently or in combination with the parent claims, do not integrate the identified abstract idea into a practical application thereof nor do they provide an inventive concept.
For example, dependent claims 7, 16-20 recite the following: “performing risk grading on the metering abnormality according to a preset classification and grading strategy based on the cause of the metering abnormality to obtain a metering abnormality risk level; in response to the metering abnormality risk level exceeding a risk level threshold, generating risk warning information by using a language model; and sending the risk warning information to a maintenance end through a preset risk warning interface.” However, these ideas are only recited at a high-level of generality with no technical solution being recited and no technical solution being solved. Instead, this is a business decision to implement an arbitrary “risk grading” on potential abnormalities and issue a generic warning if the risk is determined to be above, e.g. outside, an arbitrary threshold established by the business. The “use” of a “language model” cannot be considered inventive at this high level of generality and indeed applicant’s specification is devoid of any technical improvement to the field of language models. Instead, this is use of known tools in a capacity for which they were designed – e.g. transmit computer signals into human language. Therefore, these features are not found to be significantly more than the already recited abstract idea.
Therefore, the Examiner does not find that these additional claim limitations integrate the abstract idea into a practical application nor provide an inventive concept. Instead, these limitations, as a whole and in combination with the already recited claim elements of the parent claims, are not significantly more than the already identified abstract idea. A similar finding is found for the remaining dependent claims.
For these reasons, the claims are not found to include additional elements that are sufficient to amount to significantly more than the judicial exception and therefore the claims are not found to be patent eligible.
Please see the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019 (found at http://www.uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials).
Claim Rejections - 35 USC § 103 (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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1-5, 7-14, 16-20 are rejected under 35 U.S.C. 103 as obvious over Hiruta et al. (US 2018/0011480 A1; hereinafter, "Hiruta") in view of Maruyama (US 2020/0333777 A1; hereinafter, "Maruyama").
Claims 1, 8, 9, 10
Pertaining to claims 1, 8, 9, 10 exemplified in the limitations of method claim 1, Hiruta as shown teaches the following:
Claims 1, 8, 9, 10:
A metering abnormality analysis method, comprising:
acquiring preliminary analysis data analyzed by a source-end system (Hiruta, see at least Figs. 3-4, 7, and [0048]-[0053], teaching, e.g.: “…the operation mode data creation unit 24 reads [acquiring] data [preliminary analysis data] acquired in the past for the selected measurement data from the sensor data storage unit 31 [source-end system], and displays a time-series data of the measurement data and a time-series change in the amplitude and the frequency thereof to the display device 42 (step S11). Here, the amplitude means a value obtained from an envelope curve of the sensor data and the frequency means a frequency obtained by frequency analysis (frequency conversion) of the sensor data sorted in a predetermined time.…”
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and determining at least one data filtering rule based on the preliminary analysis data (Hiruta, see citations noted supra, e.g. Fig. 7 regarding at least “boxes 521 for inputting the operation mode ID [a data filtering rule] of the operation mode”, in view of at least [0053], teaching, e.g.: “…For example, as illustrated in FIG. 7, in the operation mode definition screen 50, when the broken-line rectangle 51 that defines the range of the operation mode is input, a definition window 52 of the operation mode ID is also displayed in the operation mode definition screen 50. Input boxes 521 for inputting the operation mode ID [a data filtering rule] of the operation mode… and the like are displayed in the definition window 52…”);
filtering monitoring data of the source-end system based on the at least one data filtering rule to obtain target metering abnormality data (Hiruta, see citations noted supra, including [0061]-[0064], e.g.: “…As described above, the processing illustrated in FIG. 8 is [a process] of searching for whether or not the sensor data 311 [monitoring data] successively acquired at a predetermined time cycle matches any of the definition data of each operation mode [filtering rule] stored in the operation mode data storage unit 32…”; applicant’s “target metering abnormality data” reads on the time-series sensor data which Hiruta determines is a match to “definition data” corresponding to a stipulated “operation mode” [filtering rule].)
[…] and
determining at least one target case data from the preconfigured abnormal case data (Hiruta, see at least Fig. 9, and at least [0067]-[0069] and [0080]-[0083], e.g.: “…In the example of FIG. 9, the abnormality diagnosis unit 23 selects [determines] and reads one row [at least one target case data] among rows including "1" in the column of the "operation mode ID" of the abnormality mode data 331 [from the preconfigured case data]. For example, data (data in which the abnormality mode is "scratch in inner ring of bearing" and the diagnosis procedure ID is "1") on the first row is read…”
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e.g.: “…the "operation mode ID" [filtering rule] is information for indicating the operation mode in which the "abnormality mode" can occur…”; Hiruta’s sensor data which matches a particular operation mode [filtering rule] therefore has an “operation mode ID”. This data is compared with data of Fig. 9, which is “a configuration of abnormality mode data 331” [preconfigured abnormal case data] to determine possible corresponding abnormality modes, e.g. “Abnormality Mode: Scratch In Inner Ring of Bearing” [target case data] is determined from a preconfigured set of possible abnormality modes, based on the identified operation mode ID “1”, e.g. as provided per Fig. 9. Note per [0069]: “Moreover, in the embodiment, the abnormality mode data 331 described above is data prepared in advance [i.e. it is preconfigured abnormal case data] based on experience and results of the abnormality diagnosis in the past performed by the expert familiar with the action or operation of the machine 1…”;see also at least [0081]-[0082]);
performing multidimensional cluster analysis on the target metering abnormality data to obtain an aggregation level of the target metering abnormality data in a plurality of data dimensions (Hiruta, see citations noted supra, e.g. Figs. 10-11, e.g. S46-S48 of Fig. 11 in view of at least [0073]-[0077] teaching, e.g.: “…In the example of FIG. 10, as the "algorithm", it is indicated that cluster analysis of "K average method" [a multidimensional cluster analysis] is used and is stored in "cluster information [aggregation level data of the target metering abnormality data in a plurality of dimensions] in Datafile0" used in the cluster analysis as attached information… Moreover, in the cluster analysis, n pieces of the measurement data [target metering abnormality data] designated in the "sensor" are acquired for each predetermined time so that an n-dimensional [multidimensional] vector space is assumed in which the n pieces of the measurement data are components. In the n-dimensional vector space, cluster information [aggregation level information] is created using the measurement data having n components of each time acquired in the past. That is, the measurement data having the n components at each time is divided into each cluster in the n-dimensional vector space. In the embodiment, the cluster information [aggregation level data of the target metering abnormality data in a plurality of dimensions] (for example, Datafile0) is created by the operation mode of the machine 1…”) and analyzing a cause of metering abnormality based on the at least one target case data and the aggregation level (Hiruta, see citations noted supra, further in view of e.g. [0081]-[0085] regarding: “…diagnosing [analyzing] the presence [a cause] or absence of the abnormality mode that is "scratch in inner ring of bearing" [the at least one target case data]…”; the diagnosis is based on the cluster information [the aggregation level data] created via “the cluster analysis of the “K average method””.)
Although Hiruta teaches the above limitations and teaches, e.g. again per at least Fig. 9 and [0069]: “…the abnormality mode data 331 [abnormal case data] described above is data prepared in advance [is preconfigured abnormal case data] based on experience and results of the abnormality diagnosis in the past performed by the expert familiar with the action or operation of the machine 1…”; and Hiruta teaches, also as shown supra, per [0061]-[0069] and at least [0080]-[0083], that he compares his determined “operation mode”, of his acquired time-series sensor data [target metering abnormality data] which matches definition data corresponding to a stipulated operation mode, with the abnormality mode data 331 [abnormal case data] for the purpose of selecting at least one set of data related to known “abnormality modes” [abnormal cases], e.g. the abnormality mode “Scratch In Inner Ring Of Bearing” and Hiruta discusses techniques of diagnosis, such as k-means clustering. Hiruta, may not explicitly teach that he directly compares his acquired time-series sensor data [target metering abnormality data], which matches definition data corresponding to a stipulated operation mode, with his abnormality mode data 331 [abnormal case data] or the underlying time-series definition data for the abnormality mode data. Therefore, the difference between the limitation in question and the teachings of the prior art is that Hiruta per the noted citations discloses a type of classification, i.e. his operation mode, of his acquired time-series sensor data [target metering abnormality data] to represent this acquired time series sensor data in making his comparison with his abnormality mode data 331 [abnormal case data], which itself is a compact representation of many different sets, i.e. cases, of abnormal data. However, Examiner finds that direct comparison of the underlying time series data with data indicating abnormal cases is explicitly taught, as shown below by Hiruta in view Maruyama:
comparing the target metering abnormality data with preconfigured abnormal case data (Maruyama, see at least Figs. 4-5 showing a tabular and graphical comparison of target sensor [metering] data which has potential abnormalities [i.e. target metering abnormality data] with predicted upper and lower thresholds corresponding to abnormal cases [preconfigured abnormal case data]; see also at least [0125]-[0139], e.g.: “…FIG. 5 is a diagram illustrating an example of information output by the abnormality detection method according to the first embodiment. The example of FIG. 5 plots results of 20 runs executed in a day in the semiconductor manufacturing apparatus 4. Part (A) of FIG. 5 illustrates the summary values in the respective runs and the upper and the lower limit thresholds set based on the predictive value. The upper and the lower limit thresholds are set based on a predetermined confidence interval of the predictive value, approximately 95% in this example. In the example of FIG. 5, the predictive value is calculated in the first predictive value generator 204 using a Kalman filter…”
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Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Maruyama (directed towards a technique of comparing time series data [target metering abnormality data] with predicted data for upper and lower thresholds of abnormal cases [preconfigured abnormal case data]) which is applicable to a known base device/method of Hiruta (already directed towards detecting and determining abnormalities in time series data) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the technique of Maruyama to the device/method of Hiruta in order to perform the limitation in question because Hiruta and Maruyama are analogous art in the same field of endeavor (at least G05B23 e.g. /0208) and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claims 2, 11:
Hiruta/Maruyama teaches the limitations upon which these claims depend. Furthermore, Hiruta teaches the following:
…wherein the preliminary analysis data comprises at least one key indicator deviation item (Hiruta, see at least Fig. 14, depicting deviations in time-series data from expected reference data; applicant’s “key indicator deviation item” reads on the data abnormalities, i.e. deviations from expected, in Hiruta’s time series data; this is the data which is being analyzed.); and determining the at least one data filtering rule based on the preliminary analysis data comprises: acquiring a mapping relationship between a key indicator and indicator association information and determining target indicator association information corresponding to the at least one key indicator deviation item based on the mapping relationship (Hiruta, see citations noted supra, including again at least [0067]: “…the "operation mode ID" [data filtering rule] is information for indicating [mapping relationship] the operation mode [indicator association information] in which the "abnormality mode" can occur…”); and acquiring a time period in which the at least one key indicator deviation item is generated and determining the at least one data filtering rule based on the time period and the target indicator association information (Hiruta, see again [0067] and at least Figs. 7, 9, and 13, e.g. #74 of Fig. 13 is a time at which abnormality is indicated to occur and the box #75 represents the time period [time period] for analysis in which the abnormality. The "operation mode ID" [data filtering rule] is based on the “operation mode” [indicator association information] in which the "abnormality mode" can occur. In addition, the "diagnosis procedure ID" is information for identifying the diagnosis procedure information for detecting the "abnormality mode"…”
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Claims 3, 12:
Hiruta/Maruyama teaches the limitations upon which these claims depend, and Hiruta teaches pre-processing of data, e.g. per [0072]: “…Examples of the "pre-processing" include filtering processing for noise removal, moving average processing, and the like…”. Hiruta may not explicitly teach the following pre-processing nuance as recited below. However, regarding this feature, Hiruta in view of Maruyama, teaches the following:
…after filtering the monitoring data of the source-end system based on the at least one data filtering rule to obtain the target metering abnormality data, further comprising at least one of the following: performing data logic verification processing on the target metering abnormality data and deleting target metering abnormality data that does not conform to data logic in the target metering abnormality data; or[,] performing data consistency verification processing on the target metering abnormality data and deleting target metering abnormality data that does not conform to data consistency in the target metering abnormality data (Maruyama, see at least Figs. 2, 5, and [0093] in view of [0102], e.g.: “…The abnormality score calculator 206 sets a predetermined confidence interval (for example, 95%) [data logic verification] of the predictive value as the threshold. The abnormality score calculator 206 may set predetermined probability of distribution acquired by trimming the calculated abnormality score to remove [deleting] the outliers [target metering abnormality data that does not conform to data logic] as the abnormality determination line, that is, the threshold. As another example, the abnormality score calculator 206 may determine abnormality and normality in an unsupervised state by machine learning using a support vector machine or the like, to set the threshold. The detection unit 208 (described later) detects whether abnormality exists in accordance with whether the summary value falls within the set threshold.)
Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Maruyama (directed towards a technique of pre-processing data to be used in determining abnormalities in sensor data involving removal [deleting] of outlier data [target metering abnormality data that does not conform to data logic] as an abnormality determination line, that is, a threshold for data validity.) which is applicable to a known base device/method of Hiruta (also directed towards system/method of abnormality detection making use of pre-processing of sensor data) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the technique of Maruyama to the device/method of Hiruta in order to perform the pre-processing data steps of Maruyama because Maruyama is pertinent to the objective of Hiruta and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claims 4, 13:
Hiruta/Maruyama teaches the limitations upon which these claims depend. Furthermore, Hiruta teaches the following:
…acquiring case attribute information of the abnormal case data, matching the case attribute information with to-be-matched attribute information of the target metering abnormality data to determine target attribute information from the to-be-matched attribute information (Hiruta, see citations noted supra, e.g. Fig. 9 and associated disclosure:
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); and performing similarity calculation based on an attribute value corresponding to the target attribute information and an attribute value corresponding to the case attribute information and determining abnormal case data corresponding to case attribute information whose similarity is greater than a threshold as the at least one target case data (Hiruta, see citations noted supra, including Fig. 10 and associated disclosure such as [0077]: “…In the cluster analysis, the "abnormality degree" is defined as a Euclidean distance between a position represented by the measurement data of each time and a center of the cluster closest [similarity calculation] to the position in then-dimensional vector space… in a case where the abnormality degree is 3 or more and 3 seconds or more [greater than a threshold], it is regarded as the abnormality of the machine 1…”
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Claims 5, 14:
Hiruta/Maruyama teaches the limitations upon which these claims depend. Furthermore, Hiruta teaches the following:
…wherein performing the multidimensional cluster analysis on the target metering abnormality data to obtain the aggregation level of the target metering abnormality data in the plurality of data dimensions comprises: acquiring an attribute value corresponding to attribute information in the target metering abnormality data (Hiruta, see citations noted supra, e.g. Fig. 7 amplitude and frequency [attributes] of measured current in the sensor data); performing cluster analysis on the attribute value by using a cluster analysis model to obtain an aggregation level corresponding to the attribute information in each of the plurality of data dimensions (Hiruta, see citations noted supra, e.g. per at least [0075]-[0077] and [0083] cluster analysis, e.g. via “K average method” is performed); and merging the aggregation level corresponding to the attribute information in the each of the plurality of data dimensions to obtain the aggregation level in the plurality of data dimensions (Hiruta, see citations noted supra, again e.g. per [0075]-[0077]: “…in the cluster analysis, n pieces [aggregation level corresponding to the attribute information] of the measurement data designated in the "sensor" are acquired for each predetermined time so that an n-dimensional vector space [aggregation of the attribute information] is assumed in which the n pieces of the measurement data are components [i.e. a level comprised of the aggregate of the attribute data]. In the n-dimensional vector space, cluster information is created using the measurement data having n components of each time acquired in the past. That is, the measurement data having the n components at each time is divided into each cluster in then-dimensional vector space…”).
Claims 7, 16, 17, 18, 19, 20:
Hiruta/Maruyama teaches the limitations upon which these claims depend. Furthermore, Hiruta in view of Maruyama teaches the following:
… further comprising: performing risk grading on the metering abnormality according to a preset classification and grading strategy based on the cause of the metering abnormality to obtain a metering abnormality risk level (Maruyama, see citations noted supra, including also at least Fig. 2(b) and [0091]-[0096], e.g.: “…The abnormality score calculator 206 calculates an abnormality score [risk grading] serving as an index of presence/absence of abnormality… The abnormality score is an element obtained by scoring the possibility of occurrence of abnormality at each point in time of the [manufacturing] apparatus 4 based on the predictive value [preset classification]…”); in response to the metering abnormality risk level exceeding a risk level threshold, generating risk warning information by using a language model (Maruyama, see citations noted supra, including also at least [0107]-[0110]: “…When the detection unit 208 determines that one of the abnormality score and the change score has exceeded the threshold, the detection unit 208 notifies the warning unit 209 thereof… The warning unit 209 transmits a warning to the remote server 3 through the communication unit 10 [language model],…”; applicant’s entire specification appears to be devoid of any particular “language model” and therefore this reads on Hiruta’s “communication unit” as some language is required to be used to execute “communication” even if it is a computer language.); and sending the risk warning information to a maintenance end through a preset risk warning interface (Maruyama, see citations noted supra, e.g. again at least [0107]-[0110], e.g.: “…[0110] The warning unit 209 [warning interface] transmits a warning [risk warning information] to the remote server 3 [a maintenance end] through the communication unit 10, in accordance with notification from the detection unit 208. The warning unit 209 transmits warnings distinguishing the case of notifying the first level abnormality, the case of notifying the second level abnormality, and the case of notifying the third level abnormality from each other…”)
Therefore, the Examiner understands that the limitation in question is merely applying known techniques of Maruyama (directed towards techniques of using an abnormality score calculator to calculate an abnormality score [risk grading] serving as an index of presence/absence of abnormality and sending a warning to a server/user interface when such abnormality score surpasses a preset threshold) which is applicable to a known base device/method of Hiruta (already directed towards system/method of abnormality detection and alerts) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the technique of Maruyama to the device/method of Hiruta in order to perform these steps of Maruyama because Maruyama is pertinent to the objective of Hiruta and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claims 6, 15 are rejected under 35 U.S.C. 103 as obvious over Hiruta in view of Maruyama, and Official Notice.
Claims 6, 15:
Hiruta/Maruyama teaches the limitations upon which these claims depend. Furthermore, Hiruta in view of Official Notice teaches the following:
… further comprising:
sorting an aggregation level in at least one of the plurality of data dimensions in a descending order and acquiring a to-be-analyzed attribute value having a highest aggregation level in each of the at least one of the plurality of data dimensions (Hiruta, see citations noted supra, including again at least [0073], teaching: “K average method” is used together with “main component analysis” as algorithms for abnormality detection. Examiner notes that another term for Hiruta’s “main component analysis” is “principle component analysis” which is a technique understood by a person of ordinary skill in the art to require sorting – i.e. Examiner takes Official Notice of the following facts: It was old and well-known before the effective filing date of the claimed invention that after finding eigenvectors (principal components) and their corresponding eigenvalues (representations of variability) from the covariance matrix, the PCA technique requires sorting the eigenvalues in descending order, then selecting the top k eigenvectors [to-be-analyzed attribute values having a highest aggregation level] to form a new, lower-dimensional feature space, effectively reducing complexity while retaining most data variance. Furthermore, because PCA reduces high-dimensional data into fewer, more meaningful components the K-average or K-Means clustering is applied, as noted by Hiruta per at least [0073]-[0083], to these reduced components to find groups (clusters) in the data. Hiruta’s use of PCA handles complex feature reduction, while his use of K-average method finds the clusters of these components [aggregation levels], based on the PCA scores, which simplifies the complex datasets.)
Therefore, in view of these teachings, the Examiner finds that a person of ordinary skill in the art would understand the aforementioned implications of Hiruta’s teachings and, as part of implementing Hiruta’s “main component analysis”, i.e. implementing PCA “principle component analysis”, perform a step of “sorting” the principal components [aggregation levels], in at least one of a plurality of noted n-dimensions to acquire a sorted list of to-be-analyzed components corresponding to highest variability components [highest aggregation level] because per MPEP 2143(I) (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention is obvious. The motivation to combine may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. Id. at 1366, 80 USPQ2d at 1649.);
Furthermore, although Hiruta teaches the aforementioned features, he may not explicitly delve into the minutia of implementing a control variate technique as recited below. However, regarding this feature, Examiner finds that Hiruta in view of Official Notice teaches the following:
acquiring a source-end system backtracking rule associated with the to-be-analyzed attribute value, wherein the source-end system backtracking rule is used to represent a method for performing source-end system abnormality backtracking on the to-be-analyzed attribute value by using a control variate method; and determining whether the to-be-analyzed attribute value is a cause of the metering abnormality based on the source-end system backtracking rule (Examiner takes Official Notice of the following facts: Control Variate technique1 was an old and well-known technique to a person of ordinary skill in the art before the effective filing date of the claimed invention and this technique, being a type of variance reduction technique, exploits information about errors in estimates of known quantities to reduce the error of an estimate of an unknown quantity and fulfills all the steps recited supra of applicant’s limitations; e.g. see "Variance Reduction" by Botev and Ridder as noted below.) deleting a to-be-analyzed attribute value not corresponding to the cause of the metering abnormality (Examiner finds it to be within the level of skill of a person of ordinary skill in the art to recognize that attributes which are found to not be significantly important need not be further analyzed and therefore may be deleted from any pending analysis. For example, as a well-known saying goes, “what does that [an attribute value not corresponding to the cause of the market price of tea in China] have to do with the price of tea in China?” Which implies that whatever variable or attribute that is inconsequential need not be considered further. Therefore, it would have been obvious to a person of ordinary skill in the art to have removed or deleted from their analysis any attribute value found to not significantly correspond to the cause of Hiruta’s metering abnormality so as to not waste time and resources on inconsequential issues/attributes.)
Therefore, in view of these findings and because Hiruta is interested in estimating the error in sensor data [i.e. applicant’s unknown quantity] and Hiruta has information about errors in estimates of known quantities [e.g. his abnormality mode data] it would have been obvious to a person of ordinary skill in the art before the effective filing date, to have recognized the applicability of this Control Variate technique to the data analysis system/method of Hiruta to determine whether a to-be-analyzed attribute value is a cause of the metering abnormality based on a source-end system backtracking rule and if found to not significantly correspond to the cause of metering abnormality, remove or delete such attribute from further analysis because per MPEP 2143(I) (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference teachings to arrive at the claimed invention is obvious. The motivation may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. Id. at 1366, 80 USPQ2d at 1649.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J SITTNER whose telephone number is (571)270-3984. The examiner can normally be reached M-F; ~9:30-6:30. 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, Waseem Ashraf can be reached on (571) 270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael J Sittner/
Primary Examiner, Art Unit 3621
1 Botev, Z.; Ridder, A. (2017). "Variance Reduction". Wiley StatsRef: Statistics Reference Online. pp. 1–6. doi:10.1002/9781118445112.stat07975. hdl:1959.4/unsworks_50616. ISBN 9781118445112.