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 .
This Office Action has been issued in response to Applicant’s Communication of application S/N 18/348,601 filed on 7,7, 2023. Claims 1 to 7 are currently pending with the application.
Priority
Acknowledgment is made of applicant's claim for foreign priority under 35 U.S.C. 119(a)-(d). The certified copy has been filed in parent Application No. JP2022-122055, filed on 07/29/20
22.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “an index value calculation module” and “ a parameter determination module” in claim 1-5, and “a prediction module update module” Therefore, U.S.C. 112(f) is invoked for claims 1 -5.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claims lack the necessary physical articles or objects to constitute a machine or a manufacture within the meaning of 35 USC 101
Claims 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Per Step 1, claim 1 is directed to an apparatus , claim 6 is directed to a method, and claim 7 is directed to a processing program, which are statutory categories of invention per Step 1. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application or are significantly more.
Step 2A, Prong One asks: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? See MPEP 2106.04 Part I. If a claim limitation, under its broadest reasonable interpretation, covers directed to a mathematical calculation. Therefore, they also would fall within the “Mathematical Concepts” groups of abstract ideas. See MPEP 2106.04(a) (2).
With respect to claims 1, 6, and 7, the limitation of “an index value calculation module that calculates values of preset indexes for each of adjustment parameter sets to calculate regression coefficients which are used to derive a predicted value from past data; and a parameter determination module that selects an appropriate adjustment parameter set based on the index values calculated for each of the adjustment parameter sets and index values of neighborhood adjustment parameter sets with respect to the interested each adjustment parameter set”.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or mathematical calculations but for the recitation of generic computer components, then it falls within the “Mental Processes” or “Mathematical Concepts” groupings of abstract ideas. All the limitations appear to also be directed to a mathematical calculation. Therefore, they also would fall within the “Mathematical Concepts” groups of abstract ideas. Accordingly, claims 1, 11, 20 recite multiple abstract ideas (Step 2A, Prong 1).
At step 2a, prong two, this judicial exception is not integrated into a practical application. Claims 1 and 6 recite an information processing apparatus, claim 7 recites an information processing program, however, this is recited as a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. The claim is not patent eligible.
With respect to claim 2, the claims disclose “wherein the parameter determination module selects the appropriate adjustment parameter set based on the index values of adjustment parameter sets in each of which a numerical value of one of elements in the adjustment parameter set is changed to adjacent one among candidates that are set as numerical values of the one element, those adjustment parameter sets being given as the neighborhood adjustment parameter sets. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, “determining” in the context of this claim encompasses the user mentally analyzing data. Similarly, the limitation of “determining”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, “determining” in the context of this claim encompasses the user mentally compiling skills. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
With respect to claim 3 , the claims are directed towards “wherein the index value calculation module calculates, as the index values, a root mean square error between a predicted value corresponding to a time point in the past data, the time point being a reference in calculation of the predicted value, and past actual data, a difference between the predicted value corresponding to the time point and a preset threshold, an anomaly detection success rate that is a ratio of one or more anomaly prediction sections each including the time point corresponding to the predicted value, for which anomaly prediction has succeeded, to all the anomaly prediction sections in the past data, and a normal section prediction success rate that is a ratio of the number of the time points corresponding to the predicted values, for which normal prediction has succeeded, to the total number of the time points in one or more normal sections of the past data.”. This claim does not recite further abstract ideas under step 2a prong one and instead recite additional elements. These elements do not integrate the abstract idea into a practical application because they do not impose a meaningful limit on the judicial exception and provide only insignificant extra solution activity.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or mathematical calculations but for the recitation of generic computer components, then it falls within the “Mental Processes” or “Mathematical Concepts” groupings of abstract ideas. All the limitations appear to also be directed to a mathematical calculation. Therefore, they also would fall within the “Mathematical Concepts” groups of abstract ideas.
With respect to claim 4 , the claims are directed towards “wherein the parameter determination module repeats a predetermined number of times a step of calculating, as the index values of the neighborhood adjustment parameter sets, index values for each of the adjustment parameter sets by setting a past predetermined reference time and by using data in a predetermined section with the past predetermined reference time being a reference, a step of changing the reference time, and a step of calculating index values for each of the adjustment parameter sets by using data in a predetermined section with the changed reference time being a reference, and selects the appropriate adjustment parameter set based on the calculated index values”. This claim does not recite further abstract ideas under step 2a prong one and instead recite additional elements. These elements do not integrate the abstract idea into a practical application because they do not impose a meaningful limit on the judicial exception and provide only insignificant extra solution activity.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or mathematical calculations but for the recitation of generic computer components, then it falls within the “Mental Processes” or “Mathematical Concepts” groupings of abstract ideas. All the limitations appear to also be directed to a mathematical calculation. Therefore, they also would fall within the “Mathematical Concepts” groups of abstract ideas
With respect to claim 5 , the claims are directed towards “wherein the index value calculation module calculates, as the index value, a root mean square error between the predicted value calculated by using data in the predetermined section with the reference time being a reference and past actual data, and the information processing apparatus further comprises a prediction model update module that updates the regression coefficients of a future prediction model in accordance with the adjustment parameter set selected by the parameter determination module.”. This claim does not recite further abstract ideas under step 2a prong one and instead recite additional elements. These elements do not integrate the abstract idea into a practical application because they do not impose a meaningful limit on the judicial exception and provide only insignificant extra solution activity.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or mathematical calculations but for the recitation of generic computer components, then it falls within the “Mental Processes” or “Mathematical Concepts” groupings of abstract ideas. All the limitations appear to also be directed to a mathematical calculation. Therefore, they also would fall within the “Mathematical Concepts” groups of abstract idea.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claim 1, 2, 4 and 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Kuwano et al. (JP2022103931A) Filed on Dec. 28, 2020 in view of Gardner et al. (US11,093,833) Patented on Aug. 17, 2021.
As per Claim 1, An information processing apparatus comprising: an index value calculation module that calculates values of preset indexes for each of adjustment parameter sets to calculate regression coefficients which are used to derive a predicted value from past data; (See page 2, Description, Para.11 to page3, para.3, describing the evaluation of an index using regression models, also see page.6 para 7, Further, the monitoring device 100 inputs the measurement data 1030 at the time of abnormality into the model. Next, when the monitoring device 100 outputs the value(outlier value) of the objective variable equal to or higher than the threshold value (when the abnormality is detected), the monitoring device 100 determines that the model can accurately detect the abnormality, and evaluates the model. Raise. This is because the measurement data 1030 at the time of abnormality is the data of the period during which the abnormality occurs in any of the equipment 150, and the model should detect the abnormality. Finally, it is desirable that the predicted value1040B of the objective variable output by the model is as close as possible to the measured value 1040A of the objective variable. And further see page.7 para.10 describing the use of past data in the evaluation engine; as taught Kuwano)
Kuwano fails to teach and a parameter determination module that selects an appropriate adjustment parameter set based on the index values calculated for each of the adjustment parameter sets and index values of neighborhood adjustment parameter sets with respect to the interested each adjustment parameter set.
On the other hand Gardner teaches and a parameter determination module that selects an appropriate adjustment parameter set based on the index values calculated for each of the adjustment parameter sets and index values of neighborhood adjustment parameter sets with respect to the interested each adjustment parameter set; ( See col.32 lines 55-col.33 line 5 , The first configuration list is divided into the successively nondominated Pareto front sets and Pareto points in a given front set can be ranked by crowding distance, where a hyperparameter configuration is associated with each Pareto point. Hyperparameter configurations with a higher crowding distance may be preferred. The crowding distance is a sum between nearest neighbors for each objective function value and is computed for each hyperparameter configuration in a Pareto front to quantify how close a given point is to its neighbors on the same Pareto front. “Nearest neighbors” refers to the two points on either side of a given point on the same Pareto front. If a point has a high crowding distance value, its neighbors are spread out and far away from each other. In this case, it is desirable to fill in these gaps on the Pareto front. As a result, points with a higher crowding distance are favored when determining which points to keep for further refinement in future iterations of the optimization process as taught by Gardner)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teachings of the cited references and modify the invention as taught by Kuwano, by including the teachings of Gardner relating to the multi objective/Pareto and neighbor-aware techniques to improve robustness and search efficiency (see column33, lines 35-40)
Claims 6 and 7 recite similar limitations as claim 1 and are rejected under the same rational.
As per Claim 2. The combination of Kuwano and Gardner further teaches the information processing apparatus according to Claim 1, wherein the parameter determination module selects the appropriate adjustment parameter set based on the index values of adjustment parameter sets in each of which a numerical value of one of elements in the adjustment parameter set is changed to adjacent one among candidates that are set as numerical values of the one element, those adjustment parameter sets being given as the neighborhood adjustment parameter sets. ( See col.32 lines 55-col.33 line 5 , The first configuration list is divided into the successively nondominated Pareto front sets and Pareto points in a given front set can be ranked by crowding distance, where a hyperparameter configuration is associated with each Pareto point. Hyperparameter configurations with a higher crowding distance may be preferred. The crowding distance is a sum between nearest neighbors for each objective function value and is computed for each hyperparameter configuration in a Pareto front to quantify how close a given point is to its neighbors on the same Pareto front. “Nearest neighbors” refers to the two points on either side of a given point on the same Pareto front. If a point has a high crowding distance value, its neighbors are spread out and far away from each other. In this case, it is desirable to fill in these gaps on the Pareto front. As a result, points with a higher crowding distance are favored when determining which points to keep for further refinement in future iterations of the optimization process, also see col.33,lines 57-67, In an operation 636, each GSS instance selects a hyperparameter configuration from the first Pareto front set such that each hyperparameter configuration includes a value for each hyperparameter to evaluate based on h′.sub.p,i=h.sub.p.i+Δ.sub.h.sub.p,ie.sub.i, i=1, . . . , n.sub.c, where h.sub.p is the hyperparameter configuration selected from the Pareto front, Δ.sub.h.sub.p is the step size of h.sub.p, and e is the current search direction of the associated GSS instance. Each GSS instance increments its internal pointer to point to a next column of the matrix I so that on a next iteration e is updated to point in a next search direction as taught by Gardner)
As per Claim 4 , The combination of Kuwano and Gardner further teaches The information processing apparatus according to Claim 1, wherein the parameter determination module repeats a predetermined number of times a step of calculating, as the index values of the neighborhood adjustment parameter sets, index values for each of the adjustment parameter sets by setting a past predetermined reference time and by using data in a predetermined section with the past predetermined reference time being a reference, ( See col.32 lines 55-col.33 line 5 , The first configuration list is divided into the successively nondominated Pareto front sets and Pareto points in a given front set can be ranked by crowding distance, where a hyperparameter configuration is associated with each Pareto point. Hyperparameter configurations with a higher crowding distance may be preferred. The crowding distance is a sum between nearest neighbors for each objective function value and is computed for each hyperparameter configuration in a Pareto front to quantify how close a given point is to its neighbors on the same Pareto front. “Nearest neighbors” refers to the two points on either side of a given point on the same Pareto front. If a point has a high crowding distance value, its neighbors are spread out and far away from each other. In this case, it is desirable to fill in these gaps on the Pareto front. As a result, points with a higher crowding distance are favored when determining which points to keep for further refinement in future iterations of the optimization process as taught by Gardner)
a step of changing the reference time, and a step of calculating index values for each of the adjustment parameter sets by using data in a predetermined section with the changed reference time being a reference, and selects the appropriate adjustment parameter set based on the calculated index values; ( See col.32 lines 55-col.33 line 5 , The first configuration list is divided into the successively nondominated Pareto front sets and Pareto points in a given front set can be ranked by crowding distance, where a hyperparameter configuration is associated with each Pareto point. Hyperparameter configurations with a higher crowding distance may be preferred. The crowding distance is a sum between nearest neighbors for each objective function value and is computed for each hyperparameter configuration in a Pareto front to quantify how close a given point is to its neighbors on the same Pareto front. “Nearest neighbors” refers to the two points on either side of a given point on the same Pareto front. If a point has a high crowding distance value, its neighbors are spread out and far away from each other. In this case, it is desirable to fill in these gaps on the Pareto front. As a result, points with a higher crowding distance are favored when determining which points to keep for further refinement in future iterations of the optimization process as taught by Gardner)
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Kuwano et al. (JP JP2022103931A) Filed on Dec. 28, 2020 in view of Gardner et al. (US11,093,833) Patented on Aug. 17, 2021, and further in view of Parthasarathy et al, (US 12,019,616) filed on Jan. 24, 2022 and further view of Schierz et al. (US 2021/0103580) Published on Apr. 8,2021
As per Claim 3, The combination of Kuwano and Gardner fails to teach The information processing apparatus according to Claim 2, wherein the index value calculation module calculates, as the index values, a root mean square error between a predicted value corresponding to a time point in the past data, the time point being a reference in calculation of the predicted value, and past actual data, a difference between the predicted value corresponding to the time point and a preset threshold, an anomaly detection success rate that is a ratio of one or more anomaly prediction sections each including the time point corresponding to the predicted value, for which anomaly prediction has succeeded, to all the anomaly prediction sections in the past data, and a normal section prediction success rate that is a ratio of the number of the time points corresponding to the predicted values, for which normal prediction has succeeded, to the total number of the time points in one or more normal sections of the past data.
On the other hand Parthasarathy teaches wherein the index value calculation module calculates, as the index values, a root mean square error between a predicted value corresponding to a time point in the past data, the time point being a reference in calculation of the predicted value, and past actual data, a difference between the predicted value corresponding to the time point and a preset threshold, (see col.8, lines 50-67, The method trains a forecast algorithm on a first portion, predicts a second portion, and “calculates a performance metric … based at least in part on a difference between (i) the second portion … and (ii) the prediction of the second portion.” It explicitly uses RMSE as an example performance metric (see col.11, lines 39-45, FIG. 6; FIG. 2, step 10; flow steps 812–816); furthermore see col.9, lines 1-8, describing the threshold preset value as taught by Parthasarathy)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teachings of the cited references and modify the invention as taught by Kuwano and Gardner, by including the teachings of Parthasarathy relating to the calculation of RMSE for anomaly detection to reduce forecast error (as taught by Parthasarathy, see col., lines 15-22)
However the combination of Kuwano, Gardner and Parthasarathy fails to teach an anomaly detection success rate that is a ratio of one or more anomaly prediction sections each including the time point corresponding to the predicted value, for which anomaly prediction has succeeded, to all the anomaly prediction sections in the past data, and a normal section prediction success rate that is a ratio of the number of the time points corresponding to the predicted values, for which normal prediction has succeeded, to the total number of the time points in one or more normal sections of the past data.
On the other hand Schierz teaches an anomaly detection success rate that is a ratio of one or more anomaly prediction sections each including the time point corresponding to the predicted value, for which anomaly prediction has succeeded, to all the anomaly prediction sections in the past data, (See para.293 describing the anomaly detection success rate calculation process; as taught by Schierz)
and a normal section prediction success rate that is a ratio of the number of the time points corresponding to the predicted values, for which normal prediction has succeeded, to the total number of the time points in one or more normal sections of the past data; (See para.293 describing the anomaly detection success rate calculation process which can be used to derive the normal success rate mathematically; as taught by Schierz)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teachings of the cited references and modify the invention as taught by Kuwano, Gardner and Parthasarathy, by including the teachings of Schierz relating to the calculation of anomaly detection success rate to improve anomalous detected data to be able to refine training (as taught by Schierz see para.17)
Claims 5 is rejected under 35 U.S.C. 103 as being unpatentable over Kuwano et al. (JP JP2022103931A) Filed on Dec. 28, 2020 in view of Gardner et al. (US11,093,833) Patented on Aug. 17, 2021, and further in view of Parthasarathy et al, (US 12,019,616) filed on Jan. 24, 2022.
As per Claim 5, The combination of Kuwano and Gardner fails to teach the information processing apparatus according to Claim 4, wherein the index value calculation module calculates, as the index value, a root mean square error between the predicted value calculated by using data in the predetermined section with the reference time being a reference and past actual data;
On the other hand Parthasarathy teaches wherein the index value calculation module calculates, as the index value, a root mean square error between the predicted value calculated by using data in the predetermined section with the reference time being a reference and past actual data; (see col.8, lines 50-67, The method trains a forecast algorithm on a first portion, predicts a second portion, and “calculates a performance metric … based at least in part on a difference between (i) the second portion … and (ii) the prediction of the second portion.” It explicitly uses RMSE as an example performance metric (see col.11, lines 39-45, FIG. 6; FIG. 2, step 10; flow steps 812–816); as taught by Parthasarathy)
and the information processing apparatus further comprises a prediction model update module that updates the regression coefficients of a future prediction model in accordance with the adjustment parameter set selected by the parameter determination module(See col.11, lines 30-36 The disclosure retrains a “forecast algorithm” on the first portion to generate a “trained forecast algorithm,” and then uses it to predict the second portion (FIGS. 5A–5C; steps 812–814). That retraining inherently updates model parameters (which, for regression models, are regression coefficients); as taught by Parthasarathy)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teachings of the cited references and modify the invention as taught by Kuwano and Gardner, by including the teachings of Parthasarathy relating to the calculation of RMSE for anomaly detection to reduce forecast error (as taught by Parthasarathy, see col., lines 15-22)
Conclusion
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/SHERIEF BADAWI/Supervisory Patent Examiner, Art Unit 2169