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 .
Status of the claims
The amendment received on November 5, 2025 has been acknowledged and entered. Claims 1, 4, and 11 are amended. Thus, claims 1 and 3-12 are currently pending.
Response to Arguments
Applicant’s amendments filed November 5, 2025 with respect to the rejection under 35 U.S.C. 101 have been fully considered but they are not persuasive.
On the page 11 of the Remarks, Applicant alleges that “[I]mportantly, the October 2019 PEG Update does not make any distinction with regard to the type of technology improved (Id.). It is believed, therefore, that under this clarified USPTO guidance for analyzing subject matter eligibility under 35 U.S.C.101, that the claims of the instant application integrate any abstract idea upon which they might touch into a practical application, namely water plant management. Accordingly, Claims 1 and 3-12 are patent eligible under 35 U.S.C. 101.”
Examiner respectfully disagrees. The above advantages relate to abstract idea limitations which are not considered. The Improvements in the abstract idea are not qualified as improvements indicating a practical application. The pending claims are not patent eligible since a claim for a new abstract idea is still an abstract idea (see MPEP 2106.05(a).I) and an improvement in the abstract idea itself is not an improvement in technology (see MPEP 2106.05(a).II : Examples that the courts have indicated may not be sufficient to show an improvement to technology include: iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48).
Applicant’s arguments filed on November 5, 2025 with respect to claims 1 and 3-12 under 35 U.S.C. 103 have been considered but are moot because the new ground of rejection.
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 and 3-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
An operational know-how estimation device for a water supply and sewage plant comprising:
a data acquirer configured to acquire target time-series data being time-series data of measured values regarding a state of the water supply and sewage plant and control values regarding operation manipulation, the measured values including at least one of water level or reaction tank phosphorus concentration, the control values including at least one of water supply quantity or drug injection amount;
a classification information attacher configured to extract maximum and minimum values from sections of the target time-series data of the measured values, cluster a distribution of extracted maximum and minimum values to determine boundaries of attribute values and attach as the first classification information, a magnitude attribute based on the determined boundaries, and perform pattern classification on the time-series data of the control values to classify sections of the control values and to attach second classification information to the sections of the control values;
a know-how estimator configured to calculate a conditional probability that the first classification information and the second classification information are simultaneously established for corresponding sections of the time-series data, and estimate operational know-how representing the control values to be set for the measured values based on the conditional probability; and
a display controller configured to create display information that supports state understanding or operation manipulation of the water supply and sewage plant based on the operational know-how and cause a display to display the display information, the display information including at least one of an operation guidance know-how diagram or an abnormality sign monitoring screen.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements.”
Step 1: under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (machine).
Step 2A, Prong One: under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the groupings of subject matter when recited as such in a claim limitation that falls into the grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations and mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, limitation of “a classification information attacher configured to extract maximum and minimum values from sections of the target time-series data of the measured values, cluster a distribution of extracted maximum and minimum values to determine boundaries of attribute values and attach as the first classification information, a magnitude attribute based on the determined boundaries (see paras. [0037], [0040]-[0041]),” and “a know-how estimator configured to calculate a conditional probability that the first classification information and the second classification information are simultaneously established for corresponding sections of the time-series data, and estimate operational know-how representing the control values to be set for the measured values based on the conditional probability (see paras, [0024]-[0027], [0029]: calculates the conditional probability),” as drafted, are a mathematical calculations.
Similar limitations comprise the abstract ideas of Claims 4 and 11.
Step 2A, Prong Two: under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception. This judicial exception is not integrated into a practical application. Therefore, none of the additional elements indicate a practical application.
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
Step 2B:
The above claims comprise the following additional elements:
In Claim 1: an operational know-how estimation device for a water supply and swage plant (preamble); a data acquirer configured to acquire target time-series data being time-series data of measured values regarding a state of the water supply and sewage plant and control values regarding operation manipulation, the measured values including at least one of water level or reaction tank phosphorus concentration, the control values including at least one of water supply quantity or drug injection amount; a display controller configured to create display information that supports state understanding or operation manipulation of the water supply and sewage plant based on the operational know-how and cause a display to display the display information, the display information including at least one of an operation guidance know-how diagram or an abnormality sign monitoring screen;
In Claim 4: an operational know-how estimation device for a water supply and swage plant (preamble); a data acquirer configured to acquire target time-series data being time-series data of measured values regarding a state of the water supply and sewage plant and control values regarding operation manipulation, the measured values including at least one of water level or reaction tank phosphorus concentration, the control values including at least one of water supply quantity or drug injection amount; a display controller configured to create display information that supports state understanding or operation manipulation of the water supply and sewage plant based on the operational know-how and cause a display to display the display information, the display information including at least one of an operation guidance know-how diagram or an abnormality sign monitoring screen; and
In Claim 11: an operational know-how estimation method for a water supply and swage plant (preamble); a data acquirer configured to acquire target time-series data being time-series data of measured values regarding a state of the water supply and sewage plant and control values regarding operation manipulation, the measured values including at least one of water level or reaction tank phosphorus concentration, the control values including at least one of water supply quantity or drug injection amount; creating display information that supports state understanding or operation manipulation of the water supply and sewage plant based on the operational know-how, the display information including at least one of an operation guidance know-how diagram or an abnormality sign monitoring screen; and causing a display to display the display information.
The additional element such as the operational know-how estimation device and method for a water supply and sewage plant are recited at a high-level of generality (MPEP 2106.05(d)). Further, note that the additional element such as a data acquirer configured to acquire target time-series data being time-series data of measured values regarding a state of the water supply and sewage plant and control values is insignificant (gathering data) extra solution activity (see MPEP 2106.05(g)). Furthermore, the additional element such as a display controller configured to create display information that supports state understanding or operation manipulation of the water supply and sewage plant based on the operational know-how and cause a display to display the display information, the display information including at least one of an operation guidance know-how diagram or an abnormality sign monitoring screen is also not meaningful and represent insignificant (post-activity) extra-solution activity (see MPEP 2106.05(g)).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these additional elements/steps are well-understood, routine, and conventional in the relevant based on the prior art of record (Takayuki, Mizutani, Obara, Makoto, Yamaguchi (JP2012230553A), Furukawa (JPH11244890A), Namba (WO 2014/141837A1)). For example, Takayuki and Obara teach operational know-how estimation device and method for a water supply and sewage plant (page 7, lines 14-15 of Takayuki; page 14, lines 4-5 and page 14, lines 31-40 of Obara). Further, Takayuki, Obara, and Makoto teach a data acquirer configured to acquire target time-series data being time-series data of measured values regarding a state of a plant and control values regarding operation manipulation (page 7, lines 12-13 of Takayuki; page 14, liens 54 of Obara; page 18, lines 17-19 of Makoto). Further, Obara and Frukawa teach the measured values including at least one of water level or reaction tank phosphorus concentration (page 3, lines 57-58, page 19, lines 4-5, and page 12, line 38 of Obara; page 14, lines 20-21 and page 5, line 41 of Frukawa). Further, Takayuki and Yamaguchi teach a display controller configured to create display information that supports state understanding or operation manipulation of the plant based on the operational know-how and cause a display to display the display information (page 9, lines 23-27 of Mizutani; page 12, lines 37-45 of Takayuki). Furthermore, Namba and Yamaguchi teach the display information including at least one of an operation guidance know-how diagram or an abnormality sign monitoring screen (page 8, lines 41-43 and page 9, line 22 of Namba; page 3, lines 8-18 and page 4, lines 3-5 of Yamaguchi).
The independent claim, therefore, are not patent eligible.
Claim 3
The additional limitation of “a pattern of the water supply and sewage pattern classification is a magnitude attribute representing understanding of an operator of the water supply and sewage plant with respect to a size of a value of the target time-series data” is mathematical calculations (para. [0037]-[0038]).
Claim 5
The additional limitation of “the event determiner is configured to detect the event occurrence period based on a change pattern of an extreme value in the time-series data of the measured value” is mental process based on mathematical calculations (see para. [0059]).
Claim 6
The additional limitation of “the event determiner is configured to detect the event occurrence period based on a time interval of an extreme value in the time-series data of the measured values” is mental process based on mathematical calculations (see para. [0059]).
Claim 7
The additional limitation of “an inputter configured to receive input from a user” is insignificant extra-solution activity (see MPEP 2106.05(g)).
The additional limitation of “a know-how re-estimator configured to re-estimate the operational know-how, wherein the know-how estimator is configured to determine that the time-series data of the measured values is response operation unknow n data when the time-series data of the control values in the event occurrence period determined from the time-series data of the measured values does not exist” is mathematical calculations (see para. [0045]).
The additional limitation of “the inputter is configured to receive the time-series data of the control values in the event occurrence period in the response operation unknown data as response operation data from the user” is insignificant extra-solution activity (see MPEP 2106.05(g)).
The additional limitation of “the know-how re-estimator is configured to re-estimate the operational know- how based on the response operation unknown data and the response operation data” is mathematical calculations (see para. [0079]).
Claim 8
The additional element of “an inputter configured to receive input from a user” is insignificant extra-solution activity.
The additional element of “the know-how estimator is configured to determine that the time-series data of the measured values and the time-series data of the control values as operation basis lacking data when a mutual information amount between the time-series data of the measured values and the time-series data of the control values is equal to or less than a predetermined threshold value” is mental process based on mathematical concepts (see para. [0089]).
The additional element of “the inputter is configured to receive the time-series data of the control values in the event occurrence period in the response operation unknown data as response operation data from the user” is insignificant extra-solution activity (see MPEP 2106.05(g)).
The additional element of “the inputter is configured to receive attribute information regarding a period of the operation basis lacking data as operation basis data from the user” is insignificant extra-solution activity (see MPEP 2106.05(g)).
The additional element of “the know-how re-estimator is configured to re-estimate the operational know-how based on the operation basis lacking data and the operation basis data” is mental process based on mathematical calculations (see para. [0079]).
Claim 9
The additional element of “the classification information attacher is configured to attach the first and second classification information to respective sections of the target time-series data according to an attribute attachment constraint which is a constraint regarding a section length of the target time-series data to which one piece of classification information is attached” is insignificant extra-solution activity (see MPEP 2106.05(g)).
Claim 10
The additional element of “an event detection sensitivity recorder configured to record event detection sensitivity that defines a degree of matching of a feature amount in the time-series data of the measured values for detecting the event occurrence period in the event determiner, and the event determiner is configured to detect the event occurrence period based on the event detection sensitivity” is insignificant extra-solution activity (see MPEP 2106.05(g)).
Claim 12
The additional element of “when a plurality of pieces of first and second classification information are estimated in a section of the target time-series data, which is restricted that one piece of classification information is supposed to be attached thereto by the attribute attachment constraint, the classification information attacher is configured to attach the first and second classification information that integrates the plurality of the pieces of classification information to the section of the target time-series data” is insignificant extra-solution activity (see MPEP 2106.05(g)).
Claim Rejections - 35 USC § 103
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.
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 1, 4, 6, 8, and 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Takayuki (JP 2018063656 A, “hereinafter referred to as “Takayuki”) (cited in IDS dated April 19, 2022) in view of Mizutani et al. (JP 5075009 B2, “hereinafter referred to as “Mizutani”) further in view of Obara et al. (JP 2004188268 A, “hereinafter referred to as “Obara”) further in view of Namba et al. (WO 2014/141837 A1, “hereinafter referred to as “Namba”).
Regarding claim 1, Takayuki teaches an operational know-how estimation device for a wager supply and sewage plant (page 5, lines 2-3: monitoring and control systems related to water treatment facilities such as water and sewage plants have been replaced with remote monitoring systems; page 7, lines 14-15: the control determination model 1033 is a model for predicting a control parameter that maximizes the KPI value of water treatment control from the operation history of each facility of the water treatment facility) comprising:
a data acquirer configured to acquire target time-series data being time-series data of measured values regarding a state of a plant and control values regarding operation manipulation (page 7, lines 12-13: in FIG. 3, in the observation data, data obtained from various sensors provided in each facility of the water treatment facility is accumulated in time series as observation values. in the observation data, data obtained from various sensors provided in each facility of the water treatment facility is accumulated in time series as observation values. In FIG. 3, the time and the observed value are associated with each other, but actually, the value of the control parameter when the observed value is obtained is associated with these pieces of information);
a know-how estimator configured to calculate a conditional probability that the first classification information and the second classification information are simultaneously established for corresponding sections of the time-series data (page 7, lines 10-12: in the observation data, data obtained from various sensors provided in each facility of the water treatment facility is accumulated in time series as observation values) each classification information (page 7, lines 27-30: the most weighted control parameter is output, and the KPI value when controlled by the control parameter is set as the predicted KPI value. FIG. 4D shows an example in which five control parameters are input, two most weighted control parameters are output, and a predicted KPI value is output when each control parameter is used. The number can be arbitrarily determined, note that the above feature of “two most weighted control parameters are output” and “the predicted KPI value“ in page 7, lines 27-30 reads on “ first classification information and second classification information; page 7, lines 31-32: the selection probability model 1034 is a model for obtaining the selection probability of the control parameter that is most easily selected by the skilled person from the operation history of each facility in the water treatment facility; page 7, lines 33-35: FIG. 5D shows a model generated using the correlation table. As shown in FIGS. 5A to 5C, the correlation table stores the weight value for the control parameter for each layer constituting the model, note that the above feature of “correlation table in page 7, lines 33-35” and “a model for obtaining the selection probability of the control parameter” in page 7, lines 31-32 reads on “calculate a conditional probability that the measured values and the control values are simultaneously established for each classification information”), and
estimate operational know-how representing the control values to be set for the measured values (page 7, lines 12-13: observed value; page 7, lines 14-15: the control determination model 1033 is a model for predicting a control parameter that maximizes the KPI value of water treatment control from the operation history of each facility of the water treatment facility, note that “observed value” in page 7, lines 12-13 and “operation history” in page 7, lines 14-15 reads on “measured values”) based on the conditional probability (page 7, lines 31-32: the selection probability model 1034 is a model for obtaining the selection probability of the control parameter that is most easily selected by the skilled person from the operation history of each facility in the water treatment facility, note that “selection probability of the control parameter” reads on “conditional probability”) and
a display controller configured to create display information that supports state understanding or operation manipulation of the water supply and sewage plant (page 5, lines 2-3: monitoring and control systems related to water treatment facilities such as water and sewage plants have been replaced with remote monitoring systems) based on the operational know-how and cause a display to display the display information (page 12, lines 37-45: control display program 1029 and display unit 104).
Takayuki does not specifically teaches a classification information attacher configured to extract maximum and minimum values from sections of the target time-series data of the measured values, cluster a distribution of extracted maximum and minimum values to determine boundaries of attribute values and attach as the first classification information, a magnitude attribute based on the determined boundaries, and perform pattern classification on the time-series data of the control values to classify sections of the control values and to attach second classification information to the sections of the control values.
However, Mizutani teaches a classification information attacher configured to extract maximum and minimum values from sections of the target time-series data of the measured values, cluster a distribution of extracted maximum and minimum values (Fig. 12) to determine boundaries of attribute values and attach as the first classification information( Fig. 12, 21 and 22), a magnitude attribute based on the determined boundaries (Fig. 12, 21 and 22; page 10, lines 26-27: subtracts the minimum value from the maximum value of the numerical data and divides the value by the number of evaluation categories), and perform pattern classification on the time-series data of the control values to classify sections of the control values and to attach second classification information (Fig. 12, 23) to the sections of the control values (Fig. 12, 23; page 10, lines 27-29: as shown in FIG. 12, the average value of the clusters is calculated, the clusters are sorted in the ascending order of the average values, two clusters having large average values are selected, and the average of the median values for the two clusters is calculated. There is a method that uses values as boundary values).
Takauiki and Mizutani are both considered to be analogous to the claimed invention because they are in the same filed of classifying patterns of a certain period and analyzing similarities for a large amount of time-series data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the classification information attacher such as is described in Mizutani into Takauiki, in order to obtain a similarity analysis evaluation system that can arbitrarily classify based on the extracted feature quantity and realize the fitness evaluation based on the attribute of the time series data and the classification result (Mizutani, page 2, lines 30-31).
Takauiki and Mizutani do not specifically teach the measured values including at least one of water level or reaction tank phosphorus concentration, the control values including at least one of water supply quantity or drug injection amount.
However, Obara teaches the measured values including at least one of water level or reaction tank phosphorus concentration (page 3, lines 57-58: there is a The present invention relates to a water quality monitoring control device for monitoring the quality of a predetermined target water quality of sewage treated in a sewage treatment system; page 19, lines 4-5: a phosphoric acid phosphorus concentration meter 37 are provided), the control values including at least one of water supply quantity or drug injection amount (page 12, line 38: Since the inflow flow meter 30 and the ORP meter 4 are sensors that measure a physical quantity without using a reagent).
Takauiki and Obara are both considered to be analogous to the claimed invention because they are in the same filed of a water quality monitoring and control device for controlling a sewage treatment plant. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the measured values such as is described in Obara into Takauiki, in order to provide a water quality monitoring control device for monitoring the quality of a predetermined target water quality of sewage treated in a sewage treatment system having one or more biological reaction tanks for purifying sewage (Obara, page 3, lines 58-59).
Takauiki, Mizutani, and Obara do not specifically teach the display information including at least one of an operation guidance know-how diagram or an abnormality sign monitoring screen.
However, Namba teaches the display information including at least one of an operation guidance know-how diagram (page 8, lines 41-43: the extraction part 12 may be provided with the guidance addition part 123 as shown in FIG. The guidance adding unit 123 issues an instruction to add guidance to a position corresponding to the current state quantity in the matrix display displayed on the display unit 1 ) or an abnormality sign monitoring screen (page 9, line 22: the abnormality determination unit 125 determines that the situation is abnormal and notifies the operator that the situation is abnormal).
Takauiki and Namba are both considered to be analogous to the claimed invention because they are in the same filed of control of water and sewage facilities. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the display information such as is described in Namba into Takauiki, in order to support operators by extracting operation know-how from the history of operation data of skilled operators and visualizing and displaying the extracted operation know-how to operators in the monitoring and control system of water and sewage facilities (Namba, page 4, lines 17-19).
Regarding claim 4, it is an apparatus (estimation device) type claim and has similar limitations as of claim 1 above. Therefore, it is rejected under the same rational as of claim 1 above. The additional element of an event determiner configured to detect an event occurrence period used by the operator of the water supply and sewage plant (page 3, lines 57-58: there is a The present invention relates to a water quality monitoring control device for monitoring the quality of a predetermined target water quality of sewage treated in a sewage treatment system) to determine operation manipulation from the time-series data of the measured values (page 13, lines 1-4: In FIG. 16, it can be seen that the detection result by the weather sensor is “Sunny” indicating clear, and the detection results by the other sensors are displayed in the current state part 1041 in a graph format in time series. These detection results can be obtained from the observation data 1032. It can also be seen that the user selects the third recommended parameter set and predicted KPI value from the top among the plurality of recommended parameter sets and predicted KPI values, note that the above feature of ““Sunny” indicating clear” and “selects the third recommended parameter set” in page 13, lines 1-4 reads on “an event determiner configured to detect an event occurrence period used by the operator of the plant to determine operation manipulation from the time-series data of the measured values”),
wherein the know-how estimator is configured to calculate a conditional probability that the measured values and the control values are simultaneously held for each piece of classification information with the event occurrence period and other periods being distinguished (page 7, lines 33-35: FIG. 5D shows a model generated using the correlation table. As shown in FIGS. 5A to 5C, the correlation table stores the weight value for the control parameter for each layer constituting the model; page 7, lines 31-32: the selection probability model 1034 is a model for obtaining the selection probability of the control parameter that is most easily selected by the skilled person from the operation history of each facility in the water treatment facility, note that the above feature of “correlation table in page 7, lines 33-35” and “a model for obtaining the selection probability of the control parameter” in page 7, lines 31-32 reads on “calculate a conditional probability that the measured values and the control values are simultaneously established for each classification information”)” are taught by Takayuki and clustering algorithm.
Regarding claim 6, Takayuki in view of Mizutani, Obara, and Namba teaches all the limitation of claim 4, in addition, Takayuki teaches that the event determiner is configured to detect the event occurrence period based on a time interval of an extreme value in the time-series data of the measured values (page 3, lines 19-21: the control parameter determination program 1023 reads the observation data 1032 having the same date and time as the date and time when the control parameter having the maximum evaluation value is stored in the control log 1036).
Regarding claim 10, Takayuki in view of Makoto teaches all the limitation of claim 4, in addition, Takayuki teaches, further comprising:
an event detection sensitivity recorder (page 9, line 11: a control parameter stored in the control parameter log 1035) reconfigured to record event detection sensitivity that defines a degree of matching of a feature amount in the time-series data of the measured values for detecting the event occurrence period in the event determiner (page 13, lines 1-4: In FIG. 16, it can be seen that the detection result by the weather sensor is “Sunny” indicating clear, and the detection results by the other sensors are displayed in the current state part 1041 in a graph format in time series. These detection results can be obtained from the
observation data 1032. It can also be seen that the user selects the third recommended parameter set and predicted KPI value from the top
among the plurality of recommended parameter sets and predicted KPI values, note that the above feature of “control parameter log,” “Sunny” indicating clear” and “recommended parameter set” in page 13, lines 1-4 reads on “record event detection sensitivity that defines a degree of matching of a feature amount in the time-series data of the measured values for detecting the event occurrence period in the event determiner”), and
the event determiner is configured to detect the event occurrence period based on the event detection sensitivity (page 13, lines 1-4: In FIG. 16, it can be seen that the detection result by the weather sensor is “Sunny” indicating clear, and the detection results by the other sensors are displayed in the current state part 1041 in a graph format in time series. These detection results can be obtained from the observation data 1032. It can also be seen that the user selects the third recommended parameter set and predicted KPI value from the top
among the plurality of recommended parameter sets and predicted KPI values, note that the above feature of “Sunny” indicating clear” and “recommended parameter set” in page 13, lines 1-4 reads on “detect the event occurrence period based on the event detection sensitivity”).
Regarding claim 11, it is a method type claim and has similar limitations as of a part of claim 1 above. Therefore, it is rejected under the same rational as of claim 1 above.
Claims 3, 5, 7, 9, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Takayuki (JP 2018063656 A, “hereinafter referred to as “Takayuki”) (cited in IDS dated April 19, 2022) in view of Mizutani further in view of Obara further in view of Namba further in view of Makoto et al et al. (JP 2013175108 A, “hereinafter referred to as “Makoto”) (cited in IDS dated April 19, 2022).
Regarding claim 3, Takayuki in view of Mizutani, Obara, and Namba teaches all the limitation of claim 1. Takayuki, Mizutani, Obara, and Namba do not specifically teach that pattern of the pattern classification is a magnitude attribute representing understanding of an operator of the plant with respect to a size of a value of the target time-series data.
However, Makoto teaches pattern of the pattern classification is a magnitude attribute (page 8, lines 9-11: small or large cluster) representing understanding of an operator of the plant with respect to a size of a value of the target time-series data (page 8, lines 6-8: the “scatter diagram” is a set of time series data x (t) and y (t) at a certain time mapped as a point <x (t), y (t)> on a two-dimensional graph. When there is a relationship between signals as shown in FIG. 5, a set of time series data on the scatter diagram can be classified into two clusters, a cluster 701 and a cluster 702. Hereinafter, FIG. 7 will be described; page 8, lines 9-11: in FIG. 7, small clusters 703 and 704 and large clusters 701 and 702 are shown as clusters. Small clusters 703 and 704 are called local clusters (also referred to as local time series clusters), and large clusters 701 and 702 are called global clusters. The global cluster 701 corresponds to a set of x and y values when an efficient fuel is used, note that the above feature of “ the “scatter diagram” is a set of time series data x (t) and y (t) at a certain time mapped as a point <x (t), y (t)> on a two-dimensional graph” in page 8, lines 6-8 and “small cluster (local) and large cluster (global) presenting an efficient fuel” in page 8, lines 9-11 reads on “a pattern of the pattern classification is a magnitude attribute representing understanding of an operator of the plant with respect to a size of a value of the target time-series data“).
Takauiki and Makoto are both considered to be analogous to the claimed invention because they are in the same filed of detecting a sign of abnormality such as a failure of a device constituting a plant or performance deterioration. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the a pattern of the pattern classification such as is described in Makoto into Takauiki, in order to improve the accuracy of abnormality detection of an apparatus for detecting a sign of abnormality such as a failure of a facility or equipment constituting a plant or performance deterioration by utilizing time series data accumulated by an instrumentation system (Makoto, page 5, lines 29-31).
Regarding claim 5, Takayuki in view of Mizutani, Obara, and Namba teaches all the limitation of claim 4. Takayuki, Mizutani, Obara, and Namba do not specifically teach that the event determiner is configured to detect the event occurrence period based on a change pattern of an extreme value in the time-series data of the measured values.
However, Makoto teaches that the event determiner is configured to detect the event occurrence period based on a change pattern of an extreme value in the time-series data of the measured values (page 8, lines 9-11: in FIG. 7, small clusters 703 and 704 and large clusters 701 and 702 are shown as clusters. Small clusters 703 and 704 are called local clusters (also referred to as local time series clusters), and large clusters 701 and 702 are called global clusters. The global cluster 701 corresponds to a set of x and y values when an efficient fuel is used. The global cluster 702 corresponds to a set of x and y values when using inefficient fuel, note that the above feature of “global cluster 701 and 702” reads on “detect the event occurrence period based on a change pattern of an extreme value in the time-series data of the measured values”).
Takauiki and Makoto are both considered to be analogous to the claimed invention because they are in the same filed of detecting a sign of abnormality such as a failure of a device constituting a plant or performance deterioration. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the event determiner such as is described in Makoto into Takauiki, in order to improve the accuracy of abnormality detection of an apparatus for detecting a sign of abnormality such as a failure of a facility or equipment constituting a plant or performance deterioration by utilizing time series data accumulated by an instrumentation system (Makoto, page 5, lines 29-31).
Regarding claim 7, Takayuki in view of Mizutani, Obara, and Namba teaches all the limitation of claim 4, in addition, Takayuki teaches that, further comprising:
an inputter configured to receive input from a user (page 7, lines 1-3: the user can select a control parameter value from predetermined candidates. In the example of FIG. 2B, the user can set the upper and lower limit values. Any value can be set within the range);
the inputter is configured to receive the time-series data of the control values in the event occurrence period in the response operation data as response operation data from the user (page 7, lines 12-13: in FIG. 3, in the observation data, data obtained from various sensors provided in each facility of the water treatment facility is accumulated in time series as observation values. in the observation data, data obtained from various sensors provided in each facility of the water treatment facility is accumulated in time series as observation values. In FIG. 3, the time and the observed value are associated with each other, but actually, the value of the control parameter when the observed value is obtained is associated with these pieces of information).
Takayuki, Mizutani, Obara, and Namba do not specifically teaches unknown data, a know-how re-estimator configured to re-estimate the operational know-how, wherein the know-how estimator is configured to determine that the time-series data of the measured values is response operation unknow n data when the time-series data of the control values in the event occurrence period determined from the time-series data of the measured values does not exist, and the know-how re-estimator is configured to re-estimate the operational know- how based on the response operation unknown data and the response operation data.
However, Makoto teaches unknown data (page 15, lines 18-20: a non-linear relationship that is unknown), a know-how re-estimator configured to re-estimate the operational know-how, wherein the know-how estimator is configured to determine that the time-series data of the measured values (page 8, lines 6-8: The “scatter diagram” is a set of time series data x (t) and y (t) at a certain time mapped as a point <x (t), y (t)> on a two-dimensional graph. When there is a relationship between signals as shown in FIG. 5, a set of time series data on the scatter diagram can be classified into two clusters, a cluster 701 and a cluster 702. Hereinafter, FIG. 7 will be described) is response operation unknow n data when the time-series data of the control values in the event occurrence period determined from the time-series data of the measured values does not exist (page 15, lines 18-20: the processing for obtaining this global relational expression connects locally expressed relationships even when the relationship between signals is a non-linear relationship that is unknown in advance. It also has the effect that a global relational expression can be obtained, note that “a non-linear relationship that is unknown” in page 15, lines 18-20 reads on “unknown data” in which the response operation is unknown); and
the know-how re-estimator is configured to re-estimate the operational know- how based on the response operation unknown data and the response operation data (page 15, lines 18-20: the processing for obtaining this global relational expression connects locally expressed relationships even when the relationship between signals is a non-linear relationship that is unknown in advance. It also has the effect that a global relational expression can be obtained, note that “a non-linear relationship that is unknown” in page 15, lines 18-20 reads on “unknown data” in which the response operation is unknown).
Takauiki and Makoto are both considered to be analogous to the claimed invention because they are in the same filed of detecting a sign of abnormality such as a failure of a device constituting a plant or performance deterioration. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the re-estimator such as is described in Makoto into Takauiki, in order to improve the accuracy of abnormality detection of an apparatus for detecting a sign of abnormality such as a failure of a facility or equipment constituting a plant or performance deterioration by utilizing time series data accumulated by an instrumentation system (Makoto, page 5, lines 29-31).
Regarding claim 8, Takayuki in view of Mizutani, Obara, Namba, and Makoto teaches all the limitation of claim 7, in addition, Takayuki teaches that the know-how estimator is configured to determine that the time-series data of the measured values and the time-series data of the control values as operation basis lacking data when a mutual information amount between the time-series data of the measured values and the time-series data of the control values is equal to or less than a predetermined threshold value (page 3, lines 19-21: the control parameter determination program 1023 reads the observation data 1032 having the same date and time as the date and time when the control parameter having the maximum evaluation value is stored in the control log 1036, note that “maximum evaluation value is stored in the control log 1036” reads on “the control values as operation basis lacking data when a mutual information amount between the time-series data of the measured values and the time-series data of the control values is equal to or less than a predetermined threshold value”),
the inputter is configured to receive attribute information regarding a period of the operation basis lacking data as operation basis data from the user (page 3, lines 19-21: see above; page 8, lines 41-44: a predetermined abnormal condition such as a typhoon (storm), and determines that the information indicating the abnormal condition is included. In such a case, it is more likely to control the facilities of the water treatment facility with higher accuracy by using the control parameters selected by the skilled person; page 7, lines 1-3: the user can
select a control parameter value from predetermined candidates. In the example of FIG. 2B, the user can set the upper and lower limit values. Any value can be set within the range, notes, that above feature in page 8, lines 41-44 and “any value can be set within the range” in page 7, lines 1-3 reads on “receive attribute information regarding a period of the operation basis lacking data as operation basis data from the user”), and
the know-how re-estimator is configured to re-estimate the operational know-how based on the operation basis lacking data and the operation basis data (page 9, lines 25-26: The selection probability calculation program 1024 estimates the control parameter using the observation data 1032 corresponding to the time included in the extracted record as an input (step S902); page 13, lines 1-4: It can also be seen that the user selects the third recommended parameter set and predicted KPI value from the top among the plurality of recommended parameter sets, note that the above feature of “estimates the control parameter” and “extracted record” in page 9, lines 25-26 and “recommended parameter set” in page 13, lines 1-4 reads on “re-estimate the operational know-how based on the operation basis lacking data and the operation basis data”).
Regarding claim 9, Takayuki in view of Mizutani, Obara, Namba, and Makoto teaches all the limitation of claim 3. Takayuki in view of Mizutani, Obara, and Namba do not specifically teach that the classification information attacher is configured to attach the classification information to the section of the target time-series data according to an attribute attachment constraint which is a constraint regarding a section length of the target time-series data to which one piece of classification information is attached.
However, Makoto teaches the classification information attacher is configured to attach the first and second classification information to respective sections of the target time-series data according to an attribute attachment constraint which is a constraint regarding a section length of the target time-series data to which one piece of classification information is attached (page 8, lines 6-8: The “scatter diagram” is a set of time series data x (t) and y (t) at a certain time mapped as a point <x (t), y (t)> on a two-dimensional graph. When there is a relationship between signals as shown in FIG. 5, a set of time series data on the scatter diagram can be classified into two clusters, a cluster 701 and a cluster 702. Hereinafter, FIG. 7 will be described, note that a cluster 701 and a cluster 702 reads on “one piece of classification information,” respectively).
Takauiki and Makoto are both considered to be analogous to the claimed invention because they are in the same filed of detecting a sign of abnormality such as a failure of a device constituting a plant or performance deterioration. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the classification information attacher such as is described in Makoto into Takauiki, in order to improve the accuracy of abnormality detection of an apparatus for detecting a sign of abnormality such as a failure of a facility or equipment constituting a plant or performance deterioration by utilizing time series data accumulated by an instrumentation system (Makoto, page 5, lines 29-31).
Regarding claim 12, Takayuki in view of Mizutani, Obara, Namba, and Makoto teaches all the limitation of claim 9. Takayuki in view of Mizutani, Obara, and Namba do not specifically teach that when a plurality of pieces