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 .
Claim Status
Claims 1-7, 9, and 10 are pending and examined herein.
Claims 1-7, 9, and 10 are rejected.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Claims 1-7, 9, and 10 are granted the claim to the benefit of priority to Foreign application CN 202210624370.7 filed 02 June 2022. Thus, the effective filling date of claims 1-7, 9, and 10 is 02 June 2022.
Information Disclosure Statement
The information disclosure statement (IDS) was received on 15 May 2023. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Drawings
The drawings received 15 May 2023 are accepted.
Claim Interpretation
Claim 4 recites “wherein the performing, based on an individual to which the sample data belongs… and/or the determining a target data feature group corresponding to the processing target based on training process data of each machine learning model comprises…”, claim 6 recites “wherein the drawing a data distribution map of the target data feature based on sample data corresponding to the target data feature comprises… and/or the validation the target data feature based on the data distribution map of the target data feature comprises…”, and claim 7 recites “wherein the determining a data type of the target data feature comprises… and/or the drawing a data distribution map corresponding to the data type based on the sample data corresponding to the target data feature comprises…”. The BRI of “and/or” in the identified limitations encompasses an embodiment of the claims where the limitations are recited in combination and encompasses an embodiment of the claims where the limitations in each claim are recited in the alternative form.
Claim 6 recites “the validating the target data feature based on… comprises: in response to that the data distribution map of the target data feature does not conform to a distribution rule, removing the target data feature, or removing a target data feature group to which the target data feature belongs” which is interpreted as a contingent limitation. The MPEP states at 2111.04(II) “The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met.” The BRI of method claim 6 does not require the step of removing the target data feature or removing a target data feature group to which the target feature belongs because this limitation is contingent on the condition that the data distribution map of the target data feature does not conform to a distribution rule being met.
Claim Rejections - 35 USC § 112
112/b
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3 and 6 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 3 recites “the candidate data feature” in lines 3-4. There is insufficient antecedent basis for this limitation in the claim. The indefiniteness arises because the claim does not make clear what “the candidate data feature” is referring to. This rejection could be overcome by amendment of this limitation to “a candidate data feature”. For the sake of furthering examination, this limitation will be interpreted as “a candidate data feature”.
Claim 6 recites “drawing a data distribution map whose type corresponds to the data type based on the sample data corresponding to the target data feature” which renders the metes and bounds of the claim indefinite. The indefiniteness arises because it is unclear if “the sample data” is referring to the “sample data” in claim 1 or if “the sample data” is referring to the “sample data” in claim 5”. For the sake of furthering examination this limitation will be interpreted as referring to the “sample data” in claim 5.
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 1-7, 9, and 10 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.
(Step 1)
Claims 1-7 fall under the statutory category of a process and claims 9 and 10 fall under the statutory category of a machine.
(Step 2A Prong 1)
Under the BRI, the instant claims recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mental process”, such as procedures for evaluating, analyzing or organizing information, and forming judgement or an opinion. The instant claims further recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mathematical concept”, such as mathematical relationships and mathematical equations.
Independent claims 1, 9, and 10 recite mental processes of “determining a plurality of feature validation subsets based on data features in sample data”, “performing, based on an individual to which the sample data belongs, individual group partitioning on the sample data to obtain individual sample groups corresponding to different individuals”, and “performing cross-validation partitioning based on a plurality of individual sample groups, to determine a training dataset and a validation dataset that obtained through partitioning”, and “determining a target data feature group corresponding to the processing target based on training process data of each machine learning model”.
Independent claims 1, 9, and 10 recite mathematical concepts of “training a machine learning model of a processing target based on the training dataset and the validation dataset corresponding to each feature validation subset”.
Claim 2 recites mental processes of “wherein before the determining a plurality of feature validation subsets… determining association between each data feature in the sample data and the processing target”, “screening out a candidate data feature based on the association between…”, and “determining a plurality of feature validation subsets in the candidate data feature”. Claim 2 recites a mathematical concept of “wherein before the determining a plurality of feature validation subsets… determining association between each data feature in the sample and the processing target”. Claim 3 recites mental processes of “determining a plurality of feature validation subsets…”. Claim 4 recites a mental process of “partitioning at least one group of sample data… and performing the cross- validation partitioning…” and/or “sequencing and screening various machine learning models…”, and “determining a feature validation subset corresponding to a screened machine learning model as the target data feature group…”. Claim 4 recites a mathematical concept of “for any machine learning model, respectively determining a training indicator and a test indicator…”. Claim 5 recites mental processes of “wherein after determining the target data feature group… for any target data feature, drawing a data distribution map of the target data feature…” and “validating the target data feature based on the data distribution map…”. Claim 6 recites mental processes of “determining a data type of the target data feature and drawing a data distribution map…” and/or “in response to the data distribution map of the target data feature does not conform to a distribution rule, removing the target data feature, or removing a target data feature group to which the target data feature belongs”. Claim 7 recites mental processes of “performing deduplication on data values… in response to that each deduplicated data value is an integer… determining that the data type of the target data feature is a sub type, and in response to that each deduplicated data value is not an integer… determining that the data type of the target data feature is a numerical type” and/or “if the data type of the target data feature is the sub type, drawing, based on the sample data corresponding to the target data feature, a horizontal bar chart of the target data feature, and a box chart of… if the data type of the target data feature is the numerical type, drawing, based on the sample data corresponding to the target data feature, a histogram of the target data feature, and a scatter regression plot of…”.
The claims recite mental processes of organizing data and analyzing data of determining a plurality of feature validation subsets (which encompasses performing an analysis on data using criteria and organizing features into subsets based on this analysis), performing individual group partitioning (which encompasses organizing data based on criteria), and performing cross-validation partitioning based on a plurality of individual sample groups (which encompasses organizing data based on criteria), determining a target data feature group corresponding to the processing target based on training process data of each machine learning model (which encompasses analyzing the performance of the machine learning model when using each feature subset and making a determination on which subset met a performance criteria), screening out candidate features using a determined association (which encompasses screening candidate features based on criteria for the association), determining a plurality of subsets of features in candidate features (which encompasses selecting feature for the feature subsets from the candidate features), for any target data feature, drawing a data distribution map of the target data feature based on sample data corresponding to the target data feature (which encompasses plotting the values of the a particular target feature in the target feature group where the information of the values of the particular target feature are from the sample data), validating the target data feature based on the data distribution map of the target data feature (which encompasses analyzing the plotted distribution for values from a particular target feature to make a determination about feature validity), determine a data type (which encompasses using criteria to determine a data type) and drawing a distribution based on the data type (which encompass drawing particular data distribution maps such as a horizontal bar chart, a box chart, a histogram, and a scatter regression plot based on the data type), removing target features or target feature groups based on a criteria, performing deduplication (which encompasses removing data based on a criteria). These operations encompass performing steps of organizing data utilizing criteria and performing steps of analyzing data. The human mind is capable of organizing and analyzing data.
The MPEP states that “There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation” (see MPEP 2106.04(a)(2)(I)(C)). The claims recite mathematical concepts of mathematical calculations as training a machine learning model of a processing target based on the training dataset and the validation dataset corresponding to each feature validation subset (which encompasses training/fitting a linear regression model or other regression models see instant disclosure [0053] which provides “the machine learning model includes, but is not limited to a simple linear regression model, a ridge regression model, a lasso regression model…”), determining association between each data feature in the sample and the processing target (which encompass a mathematical calculation of calculating a univariate linear regression for a data feature and the processing target see instant disclosure [0073] and [0075]), for any machine learning model, respectively determining a training indicator and a test indicator based on training data and validation data in the training process data of the machine learning model wherein the training indicator and the test indicator respectively comprise a root-mean-square error and a goodness of fit (which encompasses calculating numerical values for these indicators utilizing mathematical formulas see instant disclosure [0057] and [0058]). Thus, claims 1-7, 9, and 10 recite abstract ideas.
(Step 2A Prong 2)
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). Integration into a practical application is evaluated by identifying whether there are any additional elements recited in the claim and evaluating those additional elements to determine whether they integrate the exception into a practical application.
The additional element in claim 9 of a generic computer (i.e., a processor and memory holding instructions which is in communication with the processor) and the additional element in claim 10 of a tangible computer readable storage medium storing computer instructions do not integrate the judicial exceptions into a practical application because this applying a generic computer and computer environment to the judicial exceptions without an improvement to computer technology (see MPEP 2106.04(d)(1)). These additional elements only interact with the judicial exceptions of feature screening in a manner by invoking the generic computer and generic computer environment as a tool to perform judicial exceptions of feature screening (see MPEP 2106.05(a)(I)).
Thus, the additional elements do not integrate the judicial exceptions into a practical application and claims 1-7, 9, and 10 are directed to the abstract idea.
(Step 2B)
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because:
The additional element in claim 9 of a generic computer (i.e., a processor and memory holding instructions which is in communication with the processor) and the additional element in claim 10 of a tangible computer readable storage medium storing computer instructions are conventional as shown by MPEP 2106.05(b) and MPEP 2106.05(d)(II).
Thus, the additional elements are not sufficient to amount to significantly more than the judicial exception because they are conventional.
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.
Claims 1-4, 9, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Perez-Riverol et al. (PLOS ONE 12(12): e0189875 2017) as evidence by Shroff et al. (2015 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, 2015, pp. 1-6) in view of Oner et al. (medRxiv (2020): 2020-04).
Claim 1 is directed to a feature screening method, comprising: determining a plurality of feature validation subsets based on data features in sample data
Perez-Riverol et al. shows a feature screening process includes a filtering step with univariate and/or multivariate approaches which are followed by a wrapper approach (Perez-Riverol et al. page 4 Fig. 1). Perez-Riverol et al. shows the wrapper process includes a search procedure and that the search procedures can be sequential forward selection (SFS) and sequential backward elimination (SBE) (Perez-Riverol et al. page 4 Fig. 1 and Supplemental Information page 3 Note 2: Figure 1.). Sequential forward selection (SFS) generates a plurality of feature subsets based on data features in sample data as evidence by Shroff et al. which shows that for sequential forward selection the performance of each feature is evaluated until all features are considered (it is interpreted that each feature is a separate subset of the data features in the sample data) (Shroff et al. page 3 left col. and page 3 Figure 3). Sequential backward elimination (SBE) generates a plurality of feature subsets based on data features in sample data as evidence by Shroff et al. which shows that for sequential backward elimination the process starts with the full set of features and a plurality of subsets are generated by removing one feature from the full set of features to generate multiple subsets where each subset has a different single feature removed (Shroff et al. page 3 left col. – right col. and page 3 Figure 4).
training a machine learning model of a processing target based on the training dataset and the validation dataset corresponding to each feature validation subset; and determining a target data feature group corresponding to the processing target based on training process data of each machine learning model.
Perez-Riverol et al. shows the wrapper method is used to find the optimal feature subsets, by iteratively selecting features based on classifier performance (Perez-Riverol et al. page 4 Fig. 1). Perez-Riverol et al. shows the wrapper approach utilizes cross-validation inside the wrapper feature selection process which is used to assess the results the learning analysis by defining a dataset as a test the model in the training phase (i.e., the validation dataset) (Perez-Riverol et al. page 6). Perez-Riverol et al. shows using a backward elimination approach (and can be a sequential forward selection and sequential backward elimination) in combination with machine learning models with cross-validation which is interpreted as training a machine learning model for a processing target based on the training dataset and the validation dataset corresponding to each feature validation subset where each feature subset generated in the sequential backward elimination (or sequential forward selection approach) is evaluated by the machine learning model using a cross-validation approach which trains the model on training datasets and “test” the model in the training phase to evaluate the performance of each feature subset (Perez-Riverol et al. page 6 second full paragraph – page 7 first full paragraph).
Perez-Riverol et al. as evidence by Shroff et al. does not show performing, based on an individual to which the sample data belongs, individual group partitioning on the sample data to obtain individual sample groups corresponding to different individuals, and performing cross-validation partitioning based on a plurality of individual sample groups, to determine a training dataset and a validation dataset that are obtained through partitioning
Like Perez-Riverol et al. as evidence by Shroff et al., Oner et al. shows a cross-validation approach in machine learning training using biological sample data derived from an individual. Oner et al. shows partitioning sample data using a patient-level segregation approach that groups biological sample data belonging to a particular patient to generate multiple groups of sample data where each group corresponds to a different patient in a plurality of patients (Oner et al. page 4 Figure 1 and pages 9-10 Supplementary Figure 2). Oner et al. further shows performing cross-validation based on a plurality of individual sample groups to determine a training dataset and a validation dataset by assigning groups of sample data where each group contains sample data derived from a particular patient to either a training dataset or a validation dataset which avoids data leakage (Oner et al. page 4 Figure 1 and pages 9-10 Supplementary Figure 2).
Claim 9 is directed to electronic device, wherein the electronic device comprises: at least one processor; and a memory in a communication connection with the at least one processor, wherein the memory stores a computer program that can be executed by the at least one processor to perform the process steps of claim 1. Claim 10 is directed to a tangible computer readable storage medium which stores instructions which when executed cause the processor to perform the process steps of claim 1.
Perez-Riverol et al. shows the process for feature selection utilizes a computer to implement the workflow (Perez-Riverol et al. page 11 Table 3). Further, the method steps are obvious over Perez-Riverol et al. in view of Oner et al. as described above.
Claim 2 is directed to wherein before the determining a plurality of feature validation subsets based on data features in sample data, the method further comprises: determining association between each data feature in the sample data and the processing target, and screening out a candidate data feature based on the association between the data feature and the processing target; and correspondingly, the determining a plurality of feature validation subsets based on data features in sample data comprises: determining a plurality of feature validation subsets in the candidate data feature.
Perez-Riverol et al. shows before performing the wrapper feature selection process with the searching process that generates a plurality of subsets, the process utilities a univariate correlation filter to screen out candidate data features based on their association to the processing target which removes all features that are not directly related to their class variable (Perez-Riverol et al. page 4 Fig. 1 and page 4 third full paragraph). Perez-Riverol et al. shows that the searching process, which is a sub-step of the wrapper feature selection process, is performed on features which have passed the filter with the wrapper process being implemented after the first filter stage (Perez-Riverol et al. page 4 Fig. 1 and page 6 second full paragraph).
Claim 3 is directed to wherein the determining a plurality of feature validation subsets based on data features in sample data comprises: determining a plurality of feature validation subsets in the data feature in the sample data or in a candidate data feature based on a quantity of features in the feature validation subsets.
Perez-Riverol et al. shows the wrapper process includes a search procedure and that the search procedures can be sequential forward selection (SFS) and sequential backward elimination (SBE) (Perez-Riverol et al. page 4 Fig. 1 and Supplemental Information page 3 Note 2: Figure 1). In sequential forward selection (SFS) the first iteration evaluates each feature separately (each feature is interpreted as a subset which is evaluated by the machine learning model during the wrapper feature selection process) and it is interpreted determining a plurality of feature validation subsets in the data feature in the sample data is based on a quantity of features in the feature validation subsets because the first step in sequential forward selection produces as many subsets as there are features because in the first step of this process each subset holds one feature.
Claim 4 is directed to wherein the performing, based on an individual to which the sample data belongs, individual group partitioning on the sample data to obtain individual sample groups corresponding to different individuals, and performing cross-validation partitioning based on a plurality of individual sample groups, to determine a training dataset and a validation dataset that are obtained through partitioning comprises: partitioning at least one group of sample data of a same individual to one individual group, to obtain individual sample groups corresponding to different individuals, and performing the cross- validation partitioning on the plurality of individual sample groups based on at least one preset cross validation rule, to determine the training dataset and the validation dataset that are obtained through partitioning.
Perez-Riverol et al. as evidence by Shroff et al. does not show partitioning at least one group of sample data of a same individual to one individual group, to obtain individual sample groups corresponding to different individuals, and performing the cross- validation partitioning on the plurality of individual sample groups based on at least one preset cross validation rule, to determine the training dataset and the validation dataset that are obtained through partitioning.
Oner et al. shows partitioning sample data into groups in a manner where the sample data from the same individual (patient) is grouped into one individual group to obtain multiple patient groupings where each group contains sample data corresponding to a patient (Oner et al. page 4 Figure 1 and pages 9-10 Supplementary Figure 2). Oner et al. further shows performing cross-validation based on a plurality of individual sample groups to determine a training dataset and a validation dataset by assigning groups of sample data where each group contains sample data derived from a particular patient to either a training dataset, a validation dataset, or test dataset which avoids data leakage (Oner et al. page 4 Figure 1 and pages 9-10 Supplementary Figure 2). It is interpreted that performing cross-validation in a manner where all sample data corresponding to a particular patient is placed either in the training dataset, validation dataset, or test dataset to avoid sample data from the same patient being present in multiple datasets of the training dataset, validation dataset, and test dataset is a preset rule of the cross-validation process.
and/or the determining a target data feature group corresponding to the processing target based on training process data of each machine learning model comprises: for any machine learning model, respectively determining a training indicator and a test indicator based on training data and validation data in the training process data of the machine learning model; sequencing and screening various machine learning models based on the training indicator and the test indicator of each machine learning model; and determining a feature validation subset corresponding to a screened machine learning model as the target data feature group of the processing target, wherein the training indicator and the test indicator respectively comprise a root-mean-square error and a goodness of fit.
The BRI of the claim does not require performing this step because it encompasses being recited in the alternative form. Thus, claim 4 is unpatentable over Perez-Riverol et al. as evidence by Shroff et al. in view of Oner et al. because the combination of references shows the steps of partitioning data and performing cross-validation in the manner set out in the alternate embodiment of the claim.
An invention would have been obvious to one or ordinary skill in the art if some motivation in the prior art would have led that person to modify reference teachings to arrive at the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to have modified the training process in the wrapper feature selection process which implements cross-validation of Perez-Riverol et al. as evidence by Shroff et al. to perform patient-level segregation on the sample data to group all sample data belonging to a particular patient into one group to produce multiple sample data groups each belonging to distinct patents and performing cross-validation on the patient groups in a manner by assigning groups of sample data to either a training dataset or a validation dataset of Oner et al. because this would allow for a wrapper feature selection process which segregates subject sample data and performs cross-validation on the segregated subject sample data in a manner which prevents data leakage that results in inflated model performance due to related data samples (i.e., sample data from the same individual) being used in both the training process and the validation process (Oner et al. page 3 para. 1, page 4 Figure 1, and pages 9-10 Supplementary Figure 2). One would have a reasonable expectation of success because Perez-Riverol et al. as evidence by Shroff et al. shows a wrapper feature selection process which includes training a machine learning model with a cross-validation process utilizing biological sample data derived from individuals while Oner et al. shows a process of segregating biological sample data based on individuals from which the sample data was derived and performing a cross-validation process which avoids data leakage by having related data (i.e., sample data from the same individual) partitioned into either the training dataset or the validation dataset.
Claims 5 is rejected under 35 U.S.C. 103 as being unpatentable over Perez-Riverol et al. as evidence by Shroff et al. in view of Oner et al. as applied to claim 1 above, and further in view of Jinwook et al. (Information visualization 4.2 (2005): 96-113).
Claim 5 is directed to wherein after determining the target data feature group, the method further comprises: for any target data feature, drawing a data distribution map of the target data feature based on sample data corresponding to the target data feature and validating the target data feature based on the data distribution map of the target data feature.
Perez-Riverol et al. as evidence by Shroff et al. in view of Oner et al. does not show wherein after determining the target data feature group, the method further comprises: for any target data feature, drawing a data distribution map of the target data feature based on sample data corresponding to the target data feature and validating the target data feature based on the data distribution map of the target data feature.
Like Perez-Riverol et al. as evidence by Shroff et al. in view of Oner et al., Jinwook et al. shows analyzing features in multidimensional data. Jinwook et al. shows drawing histograms and boxplots to graphically display the distribution of values for a variable (or feature) which can reveal scale and skewness of the data and outliers in the data (Jinwook et al. page 10 first full paragraph – second full paragraph and page 10 Figure 2). Jinwook et al. further shows using ranking criteria based on the distribution of values for a variable which is interpreted as validating a data feature based on the data distribution map of the data feature (Jinwook et al. page 11 fourth paragraph – page 12 fourth full paragraph). Jinwook et al. shows that a ranking criterion may be the number of potential outliers which is an important feature to identify noisy signals to be filtered (Jinwook et al. page 12 first full paragraph).
An invention would have been obvious to one or ordinary skill in the art if some motivation in the prior art would have led that person to combine reference teachings to arrive at the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filling date to have combined the feature selection process to identify a target feature group of Perez-Riverol et al. as evidence by Shroff et al. in view of Oner et al. with the process of generating feature value distributions in data and ranking the features based on the distributions Jinwook et al. because this would allow for a feature selection process to produce a target feature group which is further analyzed by a ranking criterion such as a number of potential outliers to identify noisy signals to be filtered (Jinwook et al. page 12 first full paragraph). One would have a reasonable expectation of success because Perez-Riverol et al. as evidence by Shroff et al. in view of Oner et al. shows performing feature selection on multidimensional datasets while Jinwook et al. shows ranking features from multidimensional data sets based on a ranking criteria and distribution of feature values from data.
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Perez-Riverol et al. as evidence by Shroff et al. in view of Oner et al. in view of Jinwook et al. as applied to claim 5 above, and further in view of Mao ("Data visualization in exploratory data analysis: An overview of methods and technologies." PhD diss., 2015).
Claim 6 is directed to wherein the drawing a data distribution map of the target data feature based on sample data corresponding to the target data feature comprises: determining a data type of the target data feature, and drawing a data distribution map whose type corresponds to the data type based on the sample data corresponding to the target data feature
Like Perez-Riverol et al. as evidence by Shroff et al. in view of Oner et al. in view of Jinwook et al., Mao shows generating and analyzing the data distribution of features in a dataset. Mao shows determining data types and generating a distribution map corresponding to the data type by utilizing horizontal bar charts for categorical data which may be represented as discrete values (i.e., not continuous values which is interpreted as a data type of subtype) and box charts when comparing multiple categorical variables (Mao page 11, page 17, and page 40). Mao further shows for continuous variables histograms and scatter regression plots are used (Mao page 29 – 30 and page 32 – 33).
and/or the validating the target data feature based on the data distribution map of the target data feature comprises: in response to that the data distribution map of the target data feature does not conform to a distribution rule, removing the target data feature, or removing a target data feature group to which the target data feature belongs.
The BRI of the claim does not require performing this step because it encompasses being recited in the alternative form. Thus, claim 6 is unpatentable over Perez-Riverol et al. as evidence by Shroff et al. in view of Oner et al. in view of Jinwook et al. in view of Mao because the combination of references shows determining a feature type and drawing a corresponding distribution based on the type of feature.
Claim 7 is directed to wherein the determining a data type of the target data feature comprises: performing deduplication on data values of the target data feature to obtain deduplicated data values; in response to that each deduplicated data value is an integer and a quantity of data values is less than or equal to a preset threshold, determining that the data type of the target data feature is a sub type; and in response to that each deduplicated data value is not an integer or the quantity of the data values is larger than or equal to the preset threshold, determining that the data type of the target data feature is a numerical type;
The BRI of the claim does not require performing this step because it encompasses being recited in the alternative form. Thus, claim 7 is unpatentable over Thus, claim 6 is unpatentable over Perez-Riverol et al. as evidence by Shroff et al. in view of Oner et al. in view of Jinwook et al. in view of Mao because the combination of references shows determining a feature type and drawing a corresponding distribution based on the type of feature.
and/or the drawing a data distribution map corresponding to the data type based on the sample data corresponding to the target data feature comprises: if the data type of the target data feature is the sub type, drawing, based on the sample data corresponding to the target data feature, a horizontal bar chart of the target data feature, and a box chart of the target data feature and the processing target; if the data type of the target data feature is the numerical type, drawing, based on the sample data corresponding to the target data feature, a histogram of the target data feature, and a scatter regression plot of the target data feature and the processing target.
Mao shows determining data types and generating a distribution map corresponding to the data type by utilizing horizontal bar charts for categorical data which may be represented as discrete values (i.e., not continuous values which is interpreted as a data type of subtype) and box charts when comparing multiple categorical variables (Mao page 11 and page 17). Mao further shows for continuous variables histograms and scatter regression plots are used (Mao page 29 – 30 and page 32 – 33).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to have substituted the distribution maps of in the process of performing exploratory data analysis with validation of Perez-Riverol et al. as evidence by Shroff et al. in view of Oner et al. in view of Jinwook et al. with the generating particular graphs based on the data type such as histograms and scatter plots for continuous variables and a horizontal bar chart and box charts for categorical variables (which are interpreted as being a sub type) of Mao because both processes utilize these distribution maps for exploratory data analysis for datasets.
Conclusion
No claims are allowed.
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/JONATHAN EDWARD HAYES/Examiner, Art Unit 1685
Prosecution Insights