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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on are in compliance with the provisions of 37 CFR 1.97 and have been entered into the record. Accordingly, the information disclosure statements are being considered by the examiner.
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 non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 1-7), computer program product (claims 8-13), and system (claims 14-20) are directed to potentially eligible categories of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong, it is next noted that the claims recite an abstract idea by reciting mathematical relationships, mathematical formulas or equations, mathematical calculations which falls into the “Mathematical concepts”” within the enumerated groupings of abstract ideas. The mere nominal recitation of a generic computer does not take the claim limitation out of mathematical concepts grouping.
The limitations reciting the abstract idea (mathematical concepts), as set forth in exemplary claim 1, are: acquire a reference data group including an objective variable and an explanatory variable, and target data including an objective variable and an explanatory variable; calculate an outlier score indicating a degree of deviation of the explanatory variable of the target data from a first distribution relating to the explanatory variable of the reference data group; calculate a relationship weight representing strength of a relationship between the objective variable and the explanatory variable of the reference data group; calculate a property weight representing a degree of match between values of the objective variable and the explanatory variable of the target data, and a second distribution relating to the objective variable and the explanatory variable of the reference data group; and calculate a cause score of the explanatory variable for a change in the objective variable of the target data based on the outlier score, the relationship weight, and the property weight. Independent claims 19 and 20 recite the CRM and method for performing the apparatus of independent claim 1 without adding significantly more. Thus, the same rationale/analysis is applied.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements are directed to: A non-transitory computer readable storage medium including computer executable instructions, wherein the instructions, when executed by a processor, cause the processor to… (as recited in claim 20). However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitation(s) is/are directed to: A non-transitory computer readable storage medium including computer executable instructions, wherein the instructions, when executed by a processor, cause the processor to… (as recited in claim 20) for implementing the claim steps/functions. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim.
The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h). Even if the acquiring steps are considered as additional elements, these steps at most amount to insignificant extra-solution activity accomplished via receiving/transmitting data, which is not enough to amount to a practical application. See MPEP 2106.05(g).
In addition, Applicant’s Specification (paragraph [0140]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. See, e.g., Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself. Further, the courts have found the presentation of data to be a well-understood, routine, conventional activity, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 (see MPEP 2106.05(d)).
The dependent claims (2-18) are directed to the same abstract idea as recited in the independent claims, and merely incorporate additional details that narrow the abstract idea via additional details of the abstract idea. For example claims 2-18 “the reference data group includes two or more pieces of reference data including one or more objective variables and two or more explanatory variables, the target data includes one or more objective variables and two or more explanatory variables, and the processing circuit is configured to calculate the cause score for each combination of one objective variable and one explanatory variable in the target data; to calculate the cause score by multiplying the outlier score, the relationship weight, and the property weight by each other; to generate visualized data that visualizes and represents information including an index including at least the cause score among the outlier score, the relationship weight, the property weight, and the cause score, and the objective variable and the explanatory variable of the target data corresponding to the cause score; to generate the visualized data including a display mode according to the cause score; wherein the target data includes one or more objective variables and two or more explanatory variables, and the processing circuit is configured to generate the visualized data including a display mode in which the two or more explanatory variables are ranked according to the cause score; wherein the display mode is a mode of hiding or suppressing display of an explanatory variable ranked lower than a predetermined rank among the ranked explanatory variables; wherein the processing circuit is configured to generate the visualized data including a display mode for highlighting an index included in the information and deviating from an allowable range; wherein the processing circuit is configured to generate the visualized data including a display mode for highlighting the property weight to prompt to check an unknown abnormality in a case where the outlier score and the relationship weight among the indices are within the allowable range and the property weight deviates from the allowable range and is small; wherein the processing circuit is configured to calculate the degree of match as the property weight based on a residual between a theoretical value of the objective variable of the reference data group calculated by an estimation model for the second distribution and an actual measured value of the objective variable of the target data; wherein the estimation model is a linear regression model or a nonlinear regression model; wherein the outlier score is a Z score, a score based on a Hotelling’s T2 method, or a score based on kernel density estimation; wherein the relationship weight is an absolute value of a correlation coefficient, a correlation coefficient, a maximum information coefficient, or a cosine similarity; further comprising an operation unit configured to receive a user’s operation, wherein the processing circuit is configured to update the visualized data in accordance with the received operation; wherein the processing circuit is configured to update the visualized data to a display mode in which a part of the information is displayed or hidden; wherein the part of the information includes at least one of tabular data relating to the index and scatter diagram data relating to the second distribution and the target data; wherein the processing circuit is configured to update the visualized data to a display mode in which an arrangement order of the index is changed; wherein the processing circuit is configured to change the arrangement order of the index according to any one of descending order or ascending order of the cause score, descending order or ascending order of the outlier score, descending order or ascending order of the relationship weight, and descending order or ascending order of the property weight” without additional elements that integrate the abstract idea into a practical application and without additional elements that amount to
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea itself.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20220215289 (hereinafter “Mopur”) et al., in view of U.S. PGPub 20220215296 to (hereinafter “Chen”) et al.
As per claim 1, Mopur teaches a cause analyzing apparatus comprising a processing circuit configured to:
acquire a reference data group including an objective variable and an explanatory variable, and target data including an objective variable and an explanatory variable; 0016: “At first, deep learning networks, for example, a Convolutional Neural Network, may be used for training a Machine Learning (ML) model with image training data for classification. Further, an autoencoder is trained at a device hosting a training environment i.e. a cloud server, using the image training data used to train the ML model, without any anomalies. The autoencoder is trained till it is able to reconstruct expected output with minimum losses i.e. reconstruction errors. The Autoencoder output data comprising stabilized error (loss) values after training within the watermarks is called Baseline data and is used as reference. The baseline data is used as a reference for drift analysis in a device hosting a production environment i.e. at edge devices. The baseline data can be continuously refined based on stabilized error values generated in the production environment…claim 1: providing a plurality of images to an autoencoder and a earning (ML) model, each pre-trained on similar training data, wherein the autoencoder reconstructs the plurality of images and the ML model classifies the plurality of images into one or more categories; capturing reconstruction errors that occur during reconstruction of the plurality of images; clustering data points representing the reconstruction errors using affinity propagation”Examiner note: The image training data which the autoencoder and ML model are pre trained match to the claimed reference data group containing historical/bassline data with objective variables (classification output) and explanatory variables (image features). The plurality of digital images in the system during production matches with target data from the claims, which also includes objective variables (current classification output) and explanatory variables (current image features).
calculate an outlier score indicating a degree of deviation of the explanatory variable of the target data from a first distribution relating to the explanatory variable of the reference data group; 0018: “Data points representing the reconstruction errors are clustered using affinity propagation. For the clustering operation, the data points are typically supplied in batches of a predefined tunable size. Affinity propagation performs the clustering operation based on a preference value that indicates likelihood of a data point to represent a cluster of data points. An important characteristic of current disclosure includes dynamically setting the preference value by applying linear regression on the data points, so that clustering operation performs efficiently to produce an optimum number of clusters…claim 1: capturing reconstruction errors that occur during reconstruction of the plurality of images; clustering data points representing the reconstruction errors using affinity propagation; determining outliers from clusters of the data points based on one or more of maximum distance from one or more densely populated clusters, count of values of the data points, and comparison of the values with predefined watermarks in baseline data.” Examiner note: The art teaches reconstruction errors which quantifies deviation between target data (production images), and what is expected based on reference data (training data used to train autoencoder). The outliers based on max distance from densely populated clusters and comparison with predefined watermarks in baseline data inherently calculates a score indicating degree of deviation from the reference data, which matches ‘outlier score’ from the claim.
calculate a property weight representing a degree of match between values of the objective variable and the explanatory variable of the target data, and a second distribution relating to the objective variable and the explanatory variable of the reference data group; 0018-0038: “dynamically setting the preference value by applying linear regression on the data points, so that clustering operation performs efficiently to produce an optimum number of clusters. Upon formation of clusters of the data points using affinity propagation, outliers are determined based on one or more factors, such as maximum distance from one or more densely populated clusters, count of values of the data points, and comparison of the values with predefined watermarks in baseline data. Further, data drift is identified based on changes in densities of the clusters, over a predefined period of time. The changes in densities of the clusters are determined through histogram analysis and/or auto-correlation across cluster densities… At first, linear regression is performed on a batch of data points, and a parameter indicating goodness of fit (r_scor) is determined. This parameter indicates whether the data points examined through linear regression are having a high affinity or a scatter…claim 6: wherein a preference value used by the affinity propagation for determining similarity between the data points is dynamically set by applying linear regression on the data points.” Examiner note: The art teaches linear regression to determine similarity, while also using the association of classification output (objective variables) with the data characteristics (explanatory variables), teaches calculating a degree match between the target data’s objective/explanatory variables and the distribution established by the reference data.; thus, matching with property weight.
Mopur may not explicitly teach the following. However, Chen teaches:
calculate a relationship weight representing strength of a relationship between the objective variable and the explanatory variable of the reference data group; 0100: “the server may determine the weight values of the combined features by using the following method: determining a positive sample statistic corresponding to each feature value of the combined feature and a negative sample statistic corresponding to the feature value of the combined feature; and obtaining the weight values corresponding to the combined features based on the positive sample statistics and the negative sample statistics corresponding to the feature values…claims 1 and 8: determining weight values corresponding to the combined features based on the feature values of the combined features in the corresponding feature value sets; constructing weight value sets corresponding to the feature combinations based on the weight values of the combined features; and respectively determining effectivenesses of the feature combinations based on the weight value sets of the feature combinations, the effectiveness being used for predicting an accuracy of performing content recommendation according to features obtained based on the corresponding feature combination…. wherein the obtaining the weight values corresponding to the combined features based on the positive sample statistics and the negative sample statistics corresponding to the feature values comprises: obtaining the weight values corresponding to the combined features based on the positive sample statistics and the negative sample statistics corresponding to the feature values by using the following formula: wF,j=logNj+Nj-; wherein w.sub.F,j is the weight value of the combined feature; F is the feature combination; j is the feature value corresponding to the combined feature in the feature combination F; N.sub.j.sup.+ is the positive sample statistic corresponding to the feature value j; and N.sub.j.sup.− is the negative sample statistic corresponding to the feature value j.”Note: Wight calculations based on positive and negative sample statistics represent the strength of relationship between the feature (explanatory variables) and the outcome (objective variables) in the reference data. The log ration of positive to negative shows how strongly a feature value is associated with the positive versus the negative outcomes, which corresponds to the claimed relationship weight.
and calculate a cause score of the explanatory variable for a change in the objective variable of the target data based on the outlier score, the relationship weight, and the property weight; 0142-0145: “ the effectiveness of the feature combination is used for predicting an accuracy of performing content recommendation according to features obtained based on the corresponding feature combination… the server may determine the effectiveness of the feature combination by using the following method: weighting weight values of all combined features in the weight value set, to obtain a score corresponding to each of the feature combinations; and respectively determining the effectivenesses of the feature combinations based on the scores of the feature combinations…claim 1 and 12: constructing weight value sets corresponding to the feature combinations based on the weight values of the combined features; and respectively determining effectivenesses of the feature combinations based on the weight value sets of the feature combinations, the effectiveness being used for predicting an accuracy of performing content recommendation according to features obtained based on the corresponding feature combination… wherein the respectively determining effectivenesses of the feature combinations based on the weight value sets of the feature combinations comprises: for each of the weight value sets, weighting weight values of all combined features in the weight value set, to obtain a score corresponding to each of the feature combinations; and respectively determining the effectivenesses of the feature combinations based on the scores of the feature combinations.”Note: The art teaches combining multiple weight values to obtain a finale effectiveness score for features, which matches with outlier score, relationship weight, and property weight to calculate a cause score.
Mopur and Chen are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Mopur with the aforementioned teachings from Chen with a reasonable expectation of success, by adding steps that allow the software to calculate data with the motivation to more efficiently and accurately organize and analyze information [Chen 0145].
As per claim 2, Mopur and Chen teach all the limitations of claim 1.
In addition, Chen teaches:
wherein the reference data group includes two or more pieces of reference data including one or more objective variables and two or more explanatory variables, the target data includes one or more objective variables and two or more explanatory variables, and the processing circuit is configured to calculate the cause score for each combination of one objective variable and one explanatory variable in the target data; claims 1- 2: constructing a feature combination set comprising a plurality of feature combinations, the feature combination being a combination of original features of to-be-recommended content; … for each of the combined features, determining a positive sample statistic corresponding to each feature value of the combined feature and a negative sample statistic corresponding to the feature value of the combined feature; and obtaining the weight values corresponding to the combined features based on the positive sample statistics and the negative sample statistics corresponding to the feature values…0133:
Mopur and Chen are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Mopur with the aforementioned teachings from Chen with a reasonable expectation of success, by adding steps that allow the software to calculate data with the motivation to more efficiently and accurately organize and analyze information [Chen 0145].
As per claim 3, Mopur and Chen teach all the limitations of claim 1.
In addition, Chen teaches:
wherein the processing circuit is configured to calculate the cause score by multiplying the outlier score, the relationship weight, and the property weight by each other; claim 10: “wherein the positive sample represents sample data corresponding to clicked content among a plurality of pieces of displayed to-be-recommended content during display of the to-be-recommended content; and the negative sample represents sample data corresponding to unclicked content among the plurality of pieces of displayed to-be-recommended content during display of the to-be-recommended content.”
Mopur and Chen are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Mopur with the aforementioned teachings from Chen with a reasonable expectation of success, by adding steps that allow the software to calculate data with the motivation to more efficiently and accurately organize and analyze information [Chen 0145].
As per claim 4, Mopur and Chen teach all the limitations of claim 1.
In addition, Chen teaches:
wherein the processing circuit is further configured to generate visualized data that visualizes and represents information including an index including at least the cause score among the outlier score, the relationship weight, the property weight, and the cause score, and the objective variable and the explanatory variable of the target data corresponding to the cause score;claim 12: “wherein the respectively determining effectivenesses of the feature combinations based on the weight value sets of the feature combinations comprises: for each of the weight value sets, weighting weight values of all combined features in the weight value set, to obtain a score corresponding to each of the feature combinations; and respectively determining the effectivenesses of the feature combinations based on the scores of the feature combinations.”
Mopur and Chen are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Mopur with the aforementioned teachings from Chen with a reasonable expectation of success, by adding steps that allow the software to calculate data with the motivation to more efficiently and accurately organize and analyze information [Chen 0145].
As per claim 5, Mopur and Chen teach all the limitations of claim 4.
In addition, Chen teaches:
wherein the processing circuit is configured to generate the visualized data including a display mode according to the cause score;claim 12: “wherein the respectively determining effectivenesses of the feature combinations based on the weight value sets of the feature combinations comprises: for each of the weight value sets, weighting weight values of all combined features in the weight value set, to obtain a score corresponding to each of the feature combinations; and respectively determining the effectivenesses of the feature combinations based on the scores of the feature combinations.”
Mopur and Chen are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Mopur with the aforementioned teachings from Chen with a reasonable expectation of success, by adding steps that allow the software to calculate data with the motivation to more efficiently and accurately organize and analyze information [Chen 0145].
As per claim 6, Mopur and Chen teach all the limitations of claim 5.
In addition, Chen teaches:
wherein the target data includes one or more objective variables and two or more explanatory variables, and the processing circuit is configured to generate the visualized data including a display mode in which the two or more explanatory variables are ranked according to the cause score;claim 12: “wherein the respectively determining effectivenesses of the feature combinations based on the weight value sets of the feature combinations comprises: for each of the weight value sets, weighting weight values of all combined features in the weight value set, to obtain a score corresponding to each of the feature combinations; and respectively determining the effectivenesses of the feature combinations based on the scores of the feature combinations…0149: the target quantity may be preset, and the target quantity of feature combinations ranking in the top are used as the target feature combinations. For example, the target quantity is set to 30, and top 30 feature combinations in the ranking are determined as the target feature combinations. Certainly, an effectiveness threshold may also be preset. The effectivenesses of the feature combinations are compared with the effectiveness threshold, and feature combinations whose effectivenesses reach the effectiveness threshold are used as the target feature combinations.”
Mopur and Chen are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Mopur with the aforementioned teachings from Chen with a reasonable expectation of success, by adding steps that allow the software to calculate data with the motivation to more efficiently and accurately organize and analyze information [Chen 0145].
As per claim 7, Mopur and Chen teach all the limitations of claim 6.
In addition, Chen teaches:
wherein the display mode is a mode of hiding or suppressing display of an explanatory variable ranked lower than a predetermined rank among the ranked explanatory variables; claim 12: “wherein the respectively determining effectivenesses of the feature combinations based on the weight value sets of the feature combinations comprises: for each of the weight value sets, weighting weight values of all combined features in the weight value set, to obtain a score corresponding to each of the feature combinations; and respectively determining the effectivenesses of the feature combinations based on the scores of the feature combinations.”
Mopur and Chen are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Mopur with the aforementioned teachings from Chen with a reasonable expectation of success, by adding steps that allow the software to calculate data with the motivation to more efficiently and accurately organize and analyze information [Chen 0145].
As per claim 8, Mopur and Chen teach all the limitations of claim 4.
In addition, Mopur teaches:
wherein the processing circuit is configured to generate the visualized data including a display mode for highlighting an index included in the information and deviating from an allowable range; claim 3: “wherein the autoencoder is pre-trained till it is able to reconstruct images with error values present within predefined tunable thresholds.”
As per claim 9, Mopur and Chen teach all the limitations of claim 8.
In addition, Chen teaches:
wherein the processing circuit is configured to generate the visualized data including a display mode for highlighting the property weight to prompt to check an unknown abnormality in a case where the outlier score and the relationship weight among the indices are within the allowable range and the property weight deviates from the allowable range and is small; claim 13: “wherein the respectively determining the effectivenesses of the feature combinations based on the scores of the feature combinations comprises: comparing the scores of the feature combinations with a target score, to obtain comparison results respectively corresponding to the feature combinations; and determining the effectivenesses corresponding to the feature combinations based on the comparison results…0145: The scores of the feature combinations are compared with the corresponding target score to obtain comparison results, so that the effectivenesses corresponding to the feature combinations can be determined based on the comparison results. It can be learned that, by applying the foregoing embodiments, the effectiveness of each feature combination is calculated, so that a target feature combination can be selected according to the effectivenesses of the feature combinations.”
Mopur and Chen are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Mopur with the aforementioned teachings from Chen with a reasonable expectation of success, by adding steps that allow the software to calculate data with the motivation to more efficiently and accurately organize and analyze information [Chen 0145].
As per claim 10, Mopur and Chen teach all the limitations of claim 1.
In addition, Mopur teaches:
wherein the processing circuit is configured to calculate the degree of match as the property weight based on a residual between a theoretical value of the objective variable of the reference data group calculated by an estimation model for the second distribution and an actual measured value of the objective variable of the target data; claim 1: capturing reconstruction errors that occur during reconstruction of the plurality of images; clustering data points representing the reconstruction errors using affinity propagation; determining outliers from clusters of the data points based on one or more of maximum distance from one or more densely populated clusters, count of values of the data points, and comparison of the values with predefined watermarks in baseline data; determining data drift based on changes in densities of the clusters, over a predefined period of time; and associating classification output of the ML model with the outliers and the data drift, thereby producing data that is useable for refinement of the ML model…0027: The autoencoder 208 may be implemented using an unsupervised artificial neural network. The autoencoder 208 is pre-trained on the cloud server 106 to efficiently compress and encode image data and then reconstruct the image data back from its compressed and encoded representation. The image data is reconstructed such that it is as close as possible to the image data provided to the autoencoder 208. During the process of compression, encoding, and reconstruction, the autoencoder 208 learns to compress the image data into fewer dimensions, wherein encoded representation of the image data is present in a latent space.”
As per claim 11, Mopur and Chen teach all the limitations of claim 10.
In addition, Mopur teaches:
wherein the estimation model is a linear regression model or a nonlinear regression model; claim 6: “wherein a preference value used by the affinity propagation for determining similarity between the data points is dynamically set by applying linear regression on the data points.”
As per claim 12, Mopur and Chen teach all the limitations of claim 1.
In addition, Mopur teaches:
wherein the outlier score is a Z score, a score based on a Hotelling’s T2 method, or a score based on kernel density estimation; claim 12: “ determining outliers from clusters of the data points based on one or more of maximum distance from one or more densely populated clusters, count of values of the data points, and comparison of the values with predefined watermarks in baseline data; determining data drift based on changes in densities of the clusters, over a predefined period of time; and associating classification output of the ML model with the outliers and the data drift, thereby producing data that is useable for refinement of the ML model.”
As per claim 13, Mopur and Chen teach all the limitations of claim 1.
In addition, Mopur teaches:
wherein the relationship weight is an absolute value of a correlation coefficient, a correlation coefficient, a maximum information coefficient, or a cosine similarity; claim 8: “wherein the obtaining the weight values corresponding to the combined features based on the positive sample statistics and the negative sample statistics corresponding to the feature values comprises: obtaining the weight values corresponding to the combined features based on the positive sample statistics and the negative sample statistics corresponding to the feature values by using the following formula: wF,j=logNj+Nj-; wherein w.sub.F,j is the weight value of the combined feature; F is the feature combination; j is the feature value corresponding to the combined feature in the feature combination F; N.sub.j.sup.+ is the positive sample statistic corresponding to the feature value j; and N.sub.j.sup.− is the negative sample statistic corresponding to the feature value j.”
As per claim 14, Mopur and Chen teach all the limitations of claim 4.
In addition, Chen teaches:
further comprising an operation unit configured to receive a user’s operation, wherein the processing circuit is configured to update the visualized data in accordance with the received operation; 0054-0058: “The terminal 100 may present the target recommendation content in a graphical interface 110 (such as a graphical interface 110-1 of the terminal 100-1 or a graphical interface 110-2 of the terminal 100-2) after receiving the target recommendation content… The user interface 230 includes one or more output apparatuses 231 that enable presentation of media content, including one or more speakers and/or one or more visualization display screens. The user interface 230 further includes one or more input apparatuses 232, including user interface components helping a user input, such as a keyboard, a mouse, a microphone, a touch display screen, a camera, and other input buttons and controls.”
Mopur and Chen are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Mopur with the aforementioned teachings from Chen with a reasonable expectation of success, by adding steps that allow the software to calculate data with the motivation to more efficiently and accurately organize and analyze information [Chen 0145].
As per claim 15, Mopur and Chen teach all the limitations of claim 14.
In addition, Mopur teaches:
wherein the processing circuit is configured to update the visualized data to a display mode in which a part of the information is displayed or hidden; 0149: “the target quantity may be preset, and the target quantity of feature combinations ranking in the top are used as the target feature combinations. For example, the target quantity is set to 30, and top 30 feature combinations in the ranking are determined as the target feature combinations. Certainly, an effectiveness threshold may also be preset. The effectivenesses of the feature combinations are compared with the effectiveness threshold, and feature combinations whose effectivenesses reach the effectiveness threshold are used as the target feature combinations…claim 12: for each of the weight value sets, weighting weight values of all combined features in the weight value set, to obtain a score corresponding to each of the feature combinations; and respectively determining the effectivenesses of the feature combinations based on the scores of the feature combinations.”
As per claim 16, Mopur and Chen teach all the limitations of claim 15.
In addition, Mopur teaches:
wherein the part of the information includes at least one of tabular data relating to the index and scatter diagram data relating to the second distribution and the target data; 0035: “ FIG. 3A illustrates clusters 302 and 304 prepared from reconstruction errors corresponding to a first batch of images. Cluster 302 could be seen as a densely populated cluster including several data points. Cluster 304 could be identified as an outlier cluster for being present far from the densely populated cluster i.e. cluster 302. FIG. 3B illustrates a sample representation of clusters prepared through affinity propagation, using the optimal preference value. All the data points in a first cluster could be seen to be linked with exemplar 306. Similarly, in a second cluster, a single data point could be seen connected with exemplar 308.”
As per claim 17, Mopur and Chen teach all the limitations of claim 14.
In addition, Chen teaches:
wherein the processing circuit is configured to update the visualized data to a display mode in which an arrangement order of the index is changed; Chen 0147: “the feature effectiveness assessment method further includes: selecting a target quantity of feature combinations from the feature combination set as target feature combinations based on ranking of the effectivenesses of the feature combinations after the effectivenesses of the feature combinations are determined; and performing feature combination on the original features based on the target feature combinations to obtain target combined features, so as to perform content recommendation based on the target combined features…claim 12: wherein the respectively determining effectivenesses of the feature combinations based on the weight value sets of the feature combinations comprises: for each of the weight value sets, weighting weight values of all combined features in the weight value set, to obtain a score corresponding to each of the feature combinations; and respectively determining the effectivenesses of the feature combinations based on the scores of the feature combinations.”
Mopur and Chen are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Mopur with the aforementioned teachings from Chen with a reasonable expectation of success, by adding steps that allow the software to calculate data with the motivation to more efficiently and accurately organize and analyze information [Chen 0145].
As per claim 18, Mopur and Chen teach all the limitations of claim 17.
In addition, Chen teaches:
wherein the processing circuit is configured to change the arrangement order of the index according to any one of descending order or ascending order of the cause score, descending order or ascending order of the outlier score, descending order or ascending order of the relationship weight, and descending order or ascending order of the property weight; Chen 0147: “the feature effectiveness assessment method further includes: selecting a target quantity of feature combinations from the feature combination set as target feature combinations based on ranking of the effectivenesses of the feature combinations after the effectivenesses of the feature combinations are determined; and performing feature combination on the original features based on the target feature combinations to obtain target combined features, so as to perform content recommendation based on the target combined features…claim 12: wherein the respectively determining effectivenesses of the feature combinations based on the weight value sets of the feature combinations comprises: for each of the weight value sets, weighting weight values of all combined features in the weight value set, to obtain a score corresponding to each of the feature combinations; and respectively determining the effectivenesses of the feature combinations based on the scores of the feature combinations.”
Mopur and Chen are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Mopur with the aforementioned teachings from Chen with a reasonable expectation of success, by adding steps that allow the software to calculate data with the motivation to more efficiently and accurately organize and analyze information [Chen 0145].
Claims 19 and 20 teach the method and CRM for the apparatus of claim 1. Since Mopur and Chen teach the method and CRM, the same logic and rejection applies.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Ranjan; Nitin. SYSTEMS AND METHODS FOR IMPROVING RESOURCE UTILIZATION, .U.S. PGPub 20190087762 An effective consumption management system may significantly reduce an enterprise's environmental footprint, as well as operating costs.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Arif Ullah, whose telephone number is (571) 270-0161. The examiner can normally be reached from Monday to Friday between 9 AM and 5:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Beth Boswell, can be reached at (571) 272-6737. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”)./Arif Ullah/Primary Examiner, Art Unit 3625
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