DETAILED ACTION
This is the request for continued examination regarding application number 17/348,455 filed 09/16/2025 using claims filed 09/16/2025. Claims 2, 4, 6, 8 and 10 have been cancelled and claims 1, 3, 9, 11 and 14 have been amended. Claims 1, 3, 5, 7, 9, 11-17 have been examined and are pending.
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 .
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.
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 1, 3, 5, 7, 9, 11-17 rejected under 35 U.S.C. 101 as the claimed invention is directed to an abstract idea without significantly more.
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites clustering the plurality of inputs … based on a pre-defined parameter using a clustering technique to obtain grouped data using the domain knowledge, wherein the grouped data is in tabular format with a plurality of rows and a plurality of columns and the grouped data is associated with a dimension based on the plurality of rows and the plurality of column which, under the broadest reasonable interpretation, covers performance of the mind with or without a physical aid. The limitations encompass a user grouping information by similarities and making a table. See 2106.04.(a)(2).III.C.
The claim recites identifying at least one pattern in the grouped data based on the domain … using a pattern identification technique, wherein the at least one pattern is identified based on comprises a correlation factor and a predictability factor for each of the plurality of columns within the grouped data, which, under the broadest reasonable interpretation, covers performance of the mind. The limitations encompass a user identifying a pattern. See 2106.04.(a)(2).III.C.
The claim recites wherein the pattern identification technique comprises a similarity matching machine learning technique that comprises a clustering and a bayesian networks technique, wherein while clustering, for a given set of n data input, each input variable generates own cluster, and at next step, when pairs of clusters are identified to be similar, similar clusters are merged into a single cluster which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites wherein the correlation factor is associated with similarity between at least two columns from amongst the plurality of columns, the correlation factor is computed based on a correlation coefficient of the linear relationship between a variable x and a variable y, a values of x in a sample, a mean of values of x, a values of y in a sample, and is a mean of values of y which is an abstract idea (Mathematical Relationships (see MPEP 2106.04(a)(2)(I)(A)))).
The claim recites optimizing the grouped data … using an optimization technique based on the dimension to obtain an optimized grouped data, wherein the optimization technique comprises of a dimension reduction technique to optimize the plurality of rows and the plurality of columns and a principal component analysis (PCA) optimizes the plurality of rows and the plurality of columns in which variables in the columns or the rows are combined to remove redundancies, wherein the PCA creates artificial variables through a linear combination of original variables which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites identifying a plurality of predictable fields … from the optimized grouped data for the domain based on the at least one pattern, wherein the plurality of predictable fields comprises a set of predictable columns that are predictable and relevant to the domain which, under the broadest reasonable interpretation, covers performance of the mind. The limitations encompass a user identifying fields from a set of data. See 2106.04.(a)(2).III.C.
The claim recites providing a first recommendation … includes a machine learning technique for the plurality of predictable fields using a machine learning recommendation technique, wherein the machine learning technique is recommended based on a predictability score which, under the broadest reasonable interpretation, covers performance of the mind. The limitations encompass a user recommending a machine learning model based on identifying relevant fields data. See 2106.04.(a)(2).III.C.
The claim recites wherein the predictability score is expressed as an area under curve (AUC), and in the AUC, a value is equal to a probability that the machine learning technique ranks a randomly chosen positive example higher than a randomly chosen negative example which is an abstract idea (Mathematical Relationships (see MPEP 2106.04(a)(2)(I)(A)))).
The claim recites wherein the AUC of a false positive rate vs a true positive rate is represented as: False Positive Rate = False Positive / (True Negative + False Positive) True Positive Rate = True Positive / (False Negative + True Positive) which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites altering the plurality of predictable fields and the machine learning technique in accordance with the user feedback and providing the altered predictable fields and the machine learning technique as a second recommendation which, under the broadest reasonable interpretation, covers performance of the mind. The limitations encompass a user recommending a machine learning based on identifying relevant fields data and another set of data(user feedback). See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
processor-implemented (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
via a one or more hardware processors(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
receiving a plurality of inputs associated with an application from a plurality of sources…the plurality of inputs comprising a plurality of predictability data attribute received from an application interface, a plurality of metadata from received from an application database and a plurality of data attributes received from an application analytical source and a domain knowledge associated with a domain of the application received from a domain database(which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g)).
wherein the predictability data attribute includes values entered in a text box of a web page(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
wherein the application database provides features to create triggers to track when data gets created or modified along with timestamp information(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
wherein the plurality of data attributes includes a list of navigations performed by a user on the web page and text field entered by the user, a button clicked and an order of click(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
wherein the application analytical source includes details on a plurality of HTML tags associated with the navigations performed by the user on the web page(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
wherein the application is capable of recording user inputs and is a digitally enabled replica of an entity performing a digitally enabled task, wherein the application is one of a web interface, a thick client, a command line, a virtual reality, a voice interface, an augmented reality, and a haptic interface(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
wherein the pre-defined parameter is a time-stamp and a chronological order, and a time-stamp value is stored against each column among the plurality of columns, representing an order in which data entered as input(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
via the Input/Output Module (202) (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
obtaining user feedback for the first recommendation pertains to the plurality of predictable fields and the machine learning technique (which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) (b) and (j) do/does not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Further, additional element(s) (c) and (k) of obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362)
Additional elements (d) (e) (f) (g) (h) and (i) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) - (i) in the claim does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 3:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites wherein the clustering technique comprises a similarity matching technique and a sequencing technique which, under the broadest reasonable interpretation, covers performance of the mind. The limitations encompass a user assigning clusters based on time-stamps with chronological order and grouping by similarities. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include any additional elements, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 5:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites wherein the predictability factor is associated with a column's relevance to the domain which, under the broadest reasonable interpretation, covers performance of the mind. The limitations encompass a user identifying a pattern with respect to a source. See 2106.04.(a)(2).III.C.
The claim recites the predictability factor is computed that is represented as: using a causal Bayesian networks based on a joint probability function of three variables X,Y,Z that is represented as: Pr(X,Y,Z) = Pr(XIY,Z) Pr(YIZ)Pr(Z) which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
Claim 5 does not include any additional elements, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 7:
The rejection of claim 1 is incorporated and further claim recites further additional
elements/limitations:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites recommending at least a machine learning technique for the plurality of predictable fields from a machine learning database based on the predictability score which, under the broadest reasonable interpretation, covers performance of the mind. The limitations encompass a user recommending a machine learning technique. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
Claim 7 does not include any additional elements, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 9:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites cluster the plurality of inputs … based on a pre-defined parameter using a clustering technique to obtain grouped data using the domain knowledge, wherein the grouped data is in tabular format with a plurality of rows and a plurality of columns and the grouped data is associated with a dimension based on the plurality of rows and the plurality of column which, under the broadest reasonable interpretation, covers performance of the mind with or without a physical aid. The limitations encompass a user grouping information by similarities and making a table. See 2106.04.(a)(2).III.C.
The claim recites identify at least one pattern in the grouped data based on the domain … using a pattern identification technique, wherein the at least one pattern is identified based on comprises a correlation factor and a predictability factor for each of the plurality of columns within the grouped data, which, under the broadest reasonable interpretation, covers performance of the mind. The limitations encompass a user identifying a pattern. See 2106.04.(a)(2).III.C.
The claim recites wherein the pattern identification technique comprises a similarity matching machine learning technique that comprises a clustering and a bayesian networks technique, wherein while clustering, for a given set of n data input, each input variable generates own cluster, and at next step, when pairs of clusters are identified to be similar, similar clusters are merged into a single cluster which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites wherein the correlation factor is associated with similarity between at least two columns from amongst the plurality of columns, the correlation factor is computed as based on a correlation coefficient of the linear relationship between a variable x and a variable y, a values of x in a sample, a mean of values of x, a values of y in a sample, and is a mean of values of y which is an abstract idea (Mathematical Relationships (see MPEP 2106.04(a)(2)(I)(A)))).
The claim recites optimize the grouped data … using an optimization technique based on the dimension to obtain an optimized grouped data, wherein the optimization technique comprises of a dimension reduction technique to optimize the plurality of rows and the plurality of columns and a principal component analysis (PCA) optimizes the plurality of rows and the plurality of columns in which variables in the columns or the rows are combined to remove redundancies, wherein the PCA creates artificial variables through a linear combination of original variables which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites identify a plurality of predictable fields … from the optimized grouped data for the domain based on the at least one pattern, wherein the plurality of predictable fields comprises a set of predictable columns that are predictable and relevant to the domain which, under the broadest reasonable interpretation, covers performance of the mind. The limitations encompass a user identifying fields from a set of data. See 2106.04.(a)(2).III.C.
The claim recites provide a first recommendation … comprising a machine learning technique for the plurality of predictable fields using a machine learning recommendation technique, wherein the machine learning technique is recommended based on a predictability score which, under the broadest reasonable interpretation, covers performance of the mind. The limitations encompass a user recommending a machine learning based on identifying relevant fields data. See 2106.04.(a)(2).III.C.
The claim recites wherein the predictability score is expressed as an area under curve (AUC), and in the AUC, a value is equal to a probability that the machine learning technique ranks a randomly chosen positive example higher than a randomly chosen negative example which is an abstract idea (Mathematical Relationships (see MPEP 2106.04(a)(2)(I)(A)))).
The claim recites wherein the AUC of a false positive rate vs a true positive rate is represented as: False Positive Rate = False Positive / (True Negative + False Positive) True Positive Rate = True Positive / (False Negative + True Positive) which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites alter the plurality of predictable fields and the machine learning technique in accordance with the user feedback and provide the altered predictable fields and the machine learning technique as a second recommendation which, under the broadest reasonable interpretation, covers performance of the mind. The limitations encompass a user recommending a machine learning based on identifying relevant fields data and another set of data(user feedback). See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
The additional elements recited in Claim 9 do not integrate the abstract idea into a
practical application. Specifically Claim 9 lists the additional elements:
an input/output interface; one or more memories; and one or more hardware processors, the one or more memories coupled to the one or more hardware processors, (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
wherein the one or more hardware processors are configured to execute programmed instructions stored in the one or more memories to (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
receive a plurality of inputs associated with an application from a plurality of sources … the plurality of inputs comprising a plurality of predictability data attribute received from an application interface, a plurality of metadata from received from an application database and a plurality of data attributes received from an application analytical source and a domain knowledge associated with a domain of the application received from a domain database (which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g)).
wherein the predictability data attribute includes values entered in a text box of a web page(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
wherein the application database provides features to create triggers to track when data gets created or modified along with timestamp information(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
wherein the plurality of data attributes includes a list of navigations performed by a user on the web page and text field entered by the user, a button clicked and an order of click(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
wherein the application analytical source includes details on a plurality of HTML tags associated with the navigations performed by the user on the web page(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
wherein the application is capable of recording user inputs and is a digitally enabled replica of an entity performing a digitally enabled task, wherein the application is one of a web interface, a thick client, a command line, a virtual reality, a voice interface, an augmented reality, and a haptic interface(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
wherein the pre-defined parameter is a time-stamp and a chronological order, and a time-stamp value is stored against each column among the plurality of columns, representing an order in which data entered as input(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
via the Input/Output Module (202) (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
obtain user feedback for the first recommendation pertains to the plurality of predictable fields and the machine learning technique (which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g)).
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) (b) and (j) do/does not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Further, additional element(s) (c) and (k) of obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362)
Additional elements (d) (e) (f) (g) (h) and (i) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) - (i) in the claim does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 11:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites perform the clustering technique, pattern identification technique and wherein the clustering technique comprises a similarity matching technique and a sequencing technique which, under the broadest reasonable interpretation, covers performance of the mind with or without a physical aid. The limitations encompass a user grouping information by similarities. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
Claim 11 does not include any additional elements, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Claim 12, due to the substantially similar limitations as Claim 7, is rejected under the same 101 analysis.
Regarding Claim 13:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites wherein the predictability score is determined for each of the predictable fields from among the plurality of predictable fields based on a scoring technique and the machine learning techniques recommended are ranked and recommended based on the predictability score which, under the broadest reasonable interpretation, covers performance of the mind with or without a physical aid. The limitations encompass a user scoring each field and ranking the scorings. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
Claim 13 does not include any additional elements, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 14:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites clustering the plurality of inputs … based on a pre-defined parameter using a clustering technique to obtain grouped data using the domain knowledge, wherein the grouped data is in tabular format with a plurality of rows and a plurality of columns and the grouped data is associated with a dimension based on the plurality of rows and the plurality of column which, under the broadest reasonable interpretation, covers performance of the mind with or without a physical aid. The limitations encompass a user grouping information by similarities and making a table. See 2106.04.(a)(2).III.C.
The claim recites identifying at least one pattern in the grouped data based on the domain … using a pattern identification technique, wherein the at least one pattern is identified based on comprises a correlation factor and a predictability factor for each of the plurality of columns within the grouped data, which, under the broadest reasonable interpretation, covers performance of the mind. The limitations encompass a user identifying a pattern. See 2106.04.(a)(2).III.C.
The claim recites wherein the pattern identification technique comprises a similarity matching machine learning technique that comprises a clustering and a bayesian networks technique, wherein while clustering, for a given set of n data input, each input variable generates own cluster, and at next step, when pairs of clusters are identified to be similar, similar clusters are merged into a single cluster which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites wherein the correlation factor is associated with similarity between at least two columns from amongst the plurality of columns, the correlation factor is computed as based on a correlation coefficient of the linear relationship between a variable x and a variable y, a values of x in a sample, a mean of values of x, a values of y in a sample, and is a mean of values of y which is an abstract idea (Mathematical Relationships (see MPEP 2106.04(a)(2)(I)(A)))).
The claim recites optimizing the grouped data … using an optimization technique based on the dimension to obtain an optimized grouped data which, under the broadest reasonable interpretation, covers performance of the mind. The limitations encompass a user removing unwanted dimensions from a set of data. See 2106.04.(a)(2).III.C.
The claim recites identifying a plurality of predictable fields … from the optimized grouped data for the domain based on the at least one pattern, wherein the plurality of predictable fields comprises a set of predictable columns that are predictable and relevant to the domain which, under the broadest reasonable interpretation, covers performance of the mind. The limitations encompass a user identifying fields from a set of data. See 2106.04.(a)(2).III.C.
The claim recites providing a first recommendation … includes a machine learning technique for the plurality of predictable fields using a machine learning recommendation technique, wherein the machine learning technique is recommended based on a predictability score which, under the broadest reasonable interpretation, covers performance of the mind. The limitations encompass a user recommending a machine learning based on identifying relevant fields data. See 2106.04.(a)(2).III.C.
The claim recites wherein the predictability score is expressed as an area under curve (AUC), and in the AUC, a value is equal to a probability that the machine learning technique ranks a randomly chosen positive example higher than a randomly chosen negative example which is an abstract idea (Mathematical Relationships (see MPEP 2106.04(a)(2)(I)(A)))).
The claim recites wherein the AUC of a false positive rate vs a true positive rate is represented as: False Positive Rate = False Positive / (True Negative + False Positive) True Positive Rate = True Positive / (False Negative + True Positive) which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))).
The claim recites altering the plurality of predictable fields and the machine learning technique in accordance with the user feedback and providing the altered predictable fields and the machine learning technique as a second recommendation which, under the broadest reasonable interpretation, covers performance of the mind. The limitations encompass a user recommending a machine learning based on identifying relevant fields data and another set of data(user feedback). See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
The additional elements recited in Claim 14 do not integrate the abstract idea into a
practical application. Specifically Claim 14 lists the additional elements:
One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
the one or more hardware processors (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
receiving a plurality of inputs associated with an application from a plurality of sources … the plurality of inputs comprising a plurality of predictability data attribute received from an application interface, a plurality of metadata from received from an application database and a plurality of data attributes received from an application analytical source and a domain knowledge associated with a domain of the application received from a domain database (which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g)).
wherein the predictability data attribute includes values entered in a text box of a web page(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
wherein the application database provides features to create triggers to track when data gets created or modified along with timestamp information(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
wherein the plurality of data attributes includes a list of navigations performed by a user on the web page and text field entered by the user, a button clicked and an order of click(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
wherein the application analytical source includes details on a plurality of HTML tags associated with the navigations performed by the user on the web page(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
wherein the application is capable of recording user inputs and is a digitally enabled replica of an entity performing a digitally enabled task, wherein the application is one of a web interface, a thick client, a command line, a virtual reality, a voice interface, an augmented reality, and a haptic interface(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
wherein the pre-defined parameter is a time-stamp and a chronological order, and a time-stamp value is stored against each column among the plurality of columns, representing an order in which data entered as input(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h))
via the Input/Output Module (202) (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
obtaining user feedback for the first recommendation pertains to the plurality of predictable fields and the machine learning technique (which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g)).
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) (b) and (j) do/does not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
Further, additional element(s) (c) and (k) of obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362)
Additional elements (d) (e) (f) (g) (h) and (i) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) - (i) in the claim does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Claim 15, due to the substantially similar limitations as Claim 5, is rejected under the same 101 analysis.
Regarding Claim 16:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites wherein the predictability score is defined based on a Mean Squared Error that is an average of square of difference between original and predicted values expressed as Mean square error =
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which is an abstract idea (Mathematical Formulas or Equations (see MPEP 2106.04(a)(2)(I)(B)))).
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Claim 17, due to the substantially similar limitations as Claim 16, is rejected under the same 101 analysis.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 1, 3, 6, 7, 9, 11-14 and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Srinivasan et al.(US 20220237208 A1, henceforth known as Srinivasan) in view of Paul et al.(US 20220318323 A1, henceforth known as Paul) and further in view of Gelfenbeyn et al. (US 20170180499 A1, henceforth known as Gelfenbeyn), Liu et al.(“Bayesian hierarchical K-means clustering”, henceforth known as Liu) and Jiao et al.(“Performance measures in evaluating machine learning based bioinformatics predictors for classifications”, henceforth known as Jiao)
Regarding independent claim 1:
Srinivasan discloses receiving a plurality of inputs associated with an application from a plurality of sources(Srinivasan [0069], “Data sets may be acquired from input sources”), via a one or more hardware processors (Srinivasan [0007], “An example embodiment of the present disclosure pertains to a system for predictive analysis. The proposed system may include a processor”), the plurality of inputs comprising a plurality of predictability data attribute received from an application interface(Srinivasan [0034], “The data analyzer may then execute a first set of instructions on the encoded data set to obtain a transformed data set”), a plurality of metadata from received from an application database and a plurality of data attributes received from an application analytical source(Srinivasan, [0044], “The data lake 140 may be a central repository and may correspond to storing metadata information received from several devices and obtained from multiple entities”) and a domain knowledge associated with a domain of the application received from a domain database(Srinivasan, [0099], “Moreover, knowledge repositories and curated data may be other examples of the data source”)
Srinivasan discloses clustering the plurality of inputs, via the one or more hardware processors, based on a pre-defined parameter using a clustering technique to obtain grouped data using the domain knowledge(Srinivasan, [0073], “In the case of data imputation, depending on whether the imputation is because of time-series problem or ML model/prediction problem, an appropriate technique such as …clustering … can be implemented”) wherein the grouped data is in tabular format with a plurality of rows and a plurality of columns and the grouped data is associated with a dimension based on the plurality of rows and the plurality of columns(Srinivasan, [0062] “System 110 of the present disclosure can further include a model performance evaluator 216 that can be configured to maintain results and performance evaluation metrics obtained for each ML model.” and FIG. 2 showing that the Model Result Stack with arrows showing that tables can be used which is considered using tabular format with a plurality of rows and a plurality of columns) wherein the pre-defined parameter is a time-stamp and a chronological order, and a time-stamp value is stored against each column among the plurality of columns, representing an order in which data entered as input(Srinivasan, [0072], “In another example embodiment, data sets from the refined data zone 410 and real-time data sets, for example, from social media, web logs, and sensors may be ingested into data analyzer” where real-time data sets correspond to a pre-defined parameter is one of a time-stamp and a chronological order in which data entered as input as a real-time data corresponds to data as it relates to a time)
Srinivasan discloses identifying at least one pattern in the grouped data based on the domain, via the one or more hardware processors, using a pattern identification technique, wherein the at least one pattern(Srinivasan, [0042], “Data analyzer 150 may ….detect redundant occurrence …” where the ability for the data analyzer of Srinivasan to recognize these attributes/factors is considered identifying at least one pattern) is identified based on comprises a correlation factor(Srinivasan, [0042], “Data analyzer 150 may further be configured to detect redundant occurrence of the plurality of attributes in each of the one or more data tables, data sheets, and data matrices of the encoded data set, and eliminate the detected redundant plurality of attributes” where the non-eliminated attributes are considered attributes with a correlation factor) and a predictability factor(Srinivasan, [0047], “Data analyzer 150 can be configured to receive and facilitate tagging 202 of the ingested data sets” in which the data analyzer uses those tags for type(s) of ingested data and undertaking density analysis ([0048], “Data tagging may be useful in determining the type(s) of ingested data sets and in undertaking density analysis”) in which the tag considered a predictability factor as the density analysis tagging encompasses marking a position and predicting whether features align with the general pattern of its cluster including distance from a cluster core, clustering cohesion and feature distribution. With this understanding the tagging is considered a factor for prediction of density analysis) for each of the plurality of columns within the grouped data(Srinivasan, [0042], “… of the one or more data tables, data sheets, and data matrices” where of the plurality of attributes in each of the one or more data tables is consider for each of the plurality of columns within the grouped data).
Srinivasan discloses wherein the pattern identification technique comprises a similarity matching machine learning technique that comprises a clustering(Srinivasan ,[0073], “FIG. 5A illustrates how … depending on whether the imputation is because of time-series problem or ML model/prediction problem, an appropriate technique (such as … clustering) can be implemented”) and a bayesian networks technique(Srinivasan, [0079], “The model hyperparameter may be set using heuristics, and can be tuned for a given predictive modeling problem. Techniques that may be used to find out hyperparameters include, but are not limited to, any or a combination of Manual Search, Grid Search, Random Search, Bayesian Optimization, and the like” where Bayesian Optimization is considered a Bayesian network technique)
Srinivasan discloses wherein the correlation factor is associated with similarity between at least two columns from amongst the plurality of columns, the correlation factor is computed as based on a correlation coefficient of the linear relationship between a variable x and a variable y, a values of x in a sample, a mean of values of x, a values of y in a sample, and is a mean of values of y(Srinivasan, [0050], “…transformation of the data sets may be executed as a part of the automated feature selection, and may be carried out using techniques including, but not limited to... Pearson's correlation coefficient” where the Pearson's correlation coefficient quantifies the strength and direction of the linear relationship between two quantitative variables, x and y and is considered to computed as a linear relationship between a variable x and a variable y, a values of x in a sample, a mean of values of x, a values of y in a sample, and is a mean of values of y)
Srinivasan discloses optimizing the grouped data, via the one or more hardware processors, using an optimization technique based on the dimension to obtain an optimized grouped data(Srinivasan, [0050], “Moreover, dimension reduction and transformation of the data sets may be executed as a part of the automated feature selection”) wherein the optimization technique comprises of a dimension reduction technique to optimize the plurality of rows and the plurality of columns and a principal component analysis (PCA) optimizes the plurality of rows and the plurality of columns in which variables in the columns or the rows are combined to remove redundancies, wherein the PCA creates artificial variables through a linear combination of original variables(Srinivasan, [0050], “Data analyzer 150 can be configured to facilitate automated feature extraction 204 while solving problems related to redundant features .… dimension reduction and transformation of the data sets may be executed as a part of the automated feature selection, and may be carried out using techniques including…Principal Component Analysis (PCA)” where principle component analysis requires a matrix (corresponding to rows and columns) and creates artificial variables through linear combinations and using PCA for dimension reduction is considered a dimension reduction technique to optimize the rows and columns (See Also Srinivasan, [0051], “…dimensionality reduction may be defined as a process of reducing dimensionality of the feature space by obtaining a set of principal features....most of the features may be correlated, and hence may be determined as redundant and thereby may be eliminated, wherein such elimination may be performed using techniques including, but not limited to, PCA,”)
Srinivasan discloses identifying a plurality of predictable fields, via the one or more hardware processors, from the optimized grouped data for the domain based on the at least one pattern, wherein the plurality of predictable fields comprises a set of predictable columns that are predictable and relevant to the domain(Srinivasan [0034] “The data analyzer may then execute a first set of instructions on the encoded data set to obtain a transformed data set” and [0042] “Data analyzer may … eliminate the detected redundant plurality of attributes” where the non-eliminated attributes are considered predictable fields and a transformed data set is considered a set of predictable columns)
Srinivasan discloses providing a first recommendation via the Input/Output Module (202) includes a machine learning technique for the plurality of predictable fields via the one or more hardware processors, using a machine learning recommendation technique, wherein the machine learning technique is recommended (Srinivasan, [0059], “wherein the model selection block 212 may facilitate selection of an optimal ML model from a set of pre-stored/available ML models for processing the received data sets”)
Srinivasan discloses obtaining user feedback for the first recommendation pertains to the plurality of predictable fields and the machine learning technique(Srinivasan, [0067] “…the obtained results and performance metrics may be transmitted to users 314… Such users can utilize the live data sets and obtained results and performance metrics for further requirement…” and Figure 3, where 314 shows users who receive results and performance metrics to be analyzed and further requirements can be decided if needed) and altering the plurality of predictable fields and the machine learning technique in accordance with the user feedback(Srinivasan, [0067], “At block 316, a decision can be taken on whether the devices associated with the pre-processing of the data sets and performance of the predictive analysis need to be reset for a fresh data set in iteration” where the predictive analysis needing to be reset for a fresh data set in another iteration is considered altering a plurality of fields and machine learning technique) and providing the altered predictable fields and the machine learning technique as a second recommendation(Figure 3, where 316 shows the users deciding whether to reset and get a new machine learning model with different data and 302 showing a new model is selected)
Srinivasan does not teach:
wherein while clustering, for a given set of n data input, each input variable generates own cluster, and at next step, when pairs of clusters are identified to be similar, similar clusters are merged into a single cluster
wherein the machine learning technique is recommended wherein the predictability score is expressed as an area under curve (AUC)
and in the AUC a value is equal to a probability that the machine learning technique ranks a randomly chosen positive example higher than a randomly chosen negative example wherein the AUC of a false positive rate vs a true positive rate is represented as: False Positive Rate = False Positive / (True Negative + False Positive) True Positive Rate = True Positive / (False Negative + True Positive)
wherein the predictability data attribute includes values entered in a text box of a web page
wherein the application database provides features to create triggers to track when data gets created or modified along with timestamp information
wherein the plurality of data attributes includes a list of navigations performed by a user on the web page and text field entered by the user, a button clicked and an order of click
wherein the application analytical source includes details on a plurality of HTML tags associated with the navigations performed by the user on the web page
wherein the application is capable of recording user inputs and is a digitally enabled replica of an entity performing a digitally enabled task
wherein the application is one of a web interface, a thick client, a command line, a virtual reality, a voice interface, an augmented reality, and a haptic interface
Gelfenbeyn discloses wherein the application database provides features to create triggers to track when data gets created or modified(Gelfenbeyn, [0042], “…The dialog manager 230 may play many roles, which include discourse analysis, knowledge database query, and system action prediction based on the discourse context. In some embodiments, the dialog manager 230 may communicate with various computing, logic, or storage resources 240, which may include, for example, a triggering criteria database.) along with timestamp information(Gelfenbeyn, [0013], “In certain embodiments, the method may further comprise determining, by the processor, a current time associated with the user, and the identifying of the at least one triggering event may be based at least in part on determination that the current time relates to the predetermined time value.”)
Gelfenbeyn discloses wherein the application is capable of recording user inputs and is a digitally enabled replica of an entity performing a digitally enabled task(Gelfenbeyn, [0097], “The user may then select one of delivered push notifications or just provide a voice command, such as “Show my emails” or “Show details of scheduled events,” so as to get more information regarding desired items” where a device learning and completing a task without explicit actions required from a user is considered replicating and performing a digitally enabled task.)
Gelfenbeyn discloses wherein the application is one of a web interface, a thick client, a command line, a virtual reality, a voice interface, an augmented reality, and a haptic interface(Gelfenbeyn, [0003], “The users may also provide voice commands to the CIS so as to perform certain functions”)
References Srinivasan and Gelfenbeyn are analogous art because they are from the field of endeavor of using ML-driven systems for data-driven modeling using past user and system behavior.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Srinivasan and Gelfenbeyn before him or her, to modify the system of Srinivasan to include the database triggering of Gelfenbeyn as the suggestion/motivation for doing so would have been “The dialog manager 230 may employ multiple various approaches to generate output in response to the recognized input. Some approaches may include the use of statistical analysis, machine-learning algorithms (e.g., neural networks), heuristic analysis, and so forth” (Gelfenbeyn, [0042])
Paul discloses wherein the plurality of data attributes includes a list of navigations performed by a user on the web page and text field entered by the user, a button clicked and an order of click(Paul, [0039], “…session replay data may include one or more session events, arranged in order of occurrence, where a session event is a user interaction with a webpage zone or element. Such interactions may include, as examples and without limitation, keystrokes by which a user enters text in a text field, mouse clicks by which a user activates a button, mouse scroll events, navigation to or from a webpage, and the like.”)
Paul discloses wherein the application analytical source includes details on a plurality of HTML(Paul, [0029], “The tag 122 may be included within the source code of a webpage, such as the hypertext markup language (HTML) code underlying such a webpage, where such source code is hosted by the web server 130”) tags associated with the navigations performed by the user on the web page(Paul, [0028], “The tag 122 is a script, code element, or other, like, data feature, configured to collect activity relating to a client's interaction with web server 130 content through the app 121. The tag 122 may be included in one or more web server 130 data features accessed through the client device 120, providing for collection, via the tag, of various data features describing a user's interactions with web content” where tags configured to collect activity relating to a client's interaction with web content correspond to HTML tags associated with navigations performed by the user)
References Srinivasan-Gelfenbeyn and Paul are analogous art because they are from the field of endeavor of using ML-driven systems with webpages and HTML for identification of patterns.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Srinivasan-Gelfenbeyn and Paul before him or her, to modify the input of Srinivasan-Gelfenbeyn to include the navigational tag system of Paul as the suggestion/motivation for doing so would have been to interpret different parts of the webpage as Paul states “At S210, session replay data is imported or otherwise received. Session replay data is data describing one or more historical user journeys through a webpage or set of webpages...zoning information is extracted. Zoning information may be data, information, or the like, relevant to the contents and structure of a webpage. Zoning information may include, without limitation, descriptions of zone or element placements, relationships, contents, and the like, as well as any combination thereforth” (Paul, [0039]-[0040])
Liu does disclose wherein while clustering, for a given set of n data input, each input variable generates own cluster, and at next step, when pairs of clusters are identified to be similar, similar clusters are merged into a single cluster (Liu, Page 2, Paragraph 2, “agglomerative hierarchical clustering performs tree construction with a bottom-up merging manner, which starts with assigning each data point with a separate cluster and then recursively merges clusters that are similar according to predefined metric”)
References Srinivasan and Liu are analogous art because they are from the field of endeavor of using Bayesian statistics and clustering in prediction.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Srinivasan and Liu before him or her, to modify the clustering of Srinivasan to include the Bayesian hierarchical clustering of Liu to achieve a flexible clustering using probabilistic outcomes. The suggestion/motivation for doing so would have been “Accordingly, hierarchical clustering is flexible for exploratory data analysis due to its multiple levels of granularity” (Liu, Page 1, Paragraph 1) and “Specifically, based on the well-known K-means algorithm, an elegant hierarchical clustering model is developed which links hierarchical K-means clustering and Bayesian hierarchical model with principled probabilistic foundation”(Liu, Page 2, Paragraph 2)
Jiao does disclose wherein the machine learning technique is recommended based on a predictability score, wherein the predictability score is expressed as an area under curve (AUC)(Jiao, Page 327, Col. 1, Paragraph 1“When an ROC curve is plotted, we can use the area under the curve (AUC) to measure the performance of the predictor”) and in the AUC, a value is equal to a probability that the machine learning technique ranks a randomly chosen positive example higher than a randomly chosen negative example, (Jiao, Page 327, Col. 1, Paragraph 1“…the AUC of an ROC curve equals to the probability that a randomly selected positive sample gets higher scores than a randomly selected negative sample”)
Jiao does disclose wherein the AUC of a false positive rate vs a true positive rate is represented as: False Positive Rate = False Positive / (True Negative + False Positive) (Jiao, Page 326, Col. 2, Equation 26 where the FPR stands for False Positive rate, FP stands for False Positive and TN stands for True Negative)True Positive Rate = True Positive / (False Negative + True Positive) (Jiao, Page 325, Col. 1, Equation 14 where Sen(Sensitivity) is the True Positive Rate, FP stands for False Positive and TN stands for True Negative)
References Srinivasan and Jiao are analogous art because they are from the same field of measuring performances in evaluation of machine learning models.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Srinivasan and Jiao before him or her, to modify the machine learning evaluation of Srinivasan include the AUC of Jiao as the AUC is used to measure performance of predictors. The suggestion/motivation for doing so would have been Jiao, Page 327, Col. 1, Paragraph 1, “When an ROC curve is plotted, we can use the area under the curve (AUC) to measure the performance of the predictor” and Jiao, Page 327, Col. 2, Paragraph 1, “Although the AUC of a PR curve have no similar interpretations like Equation (27) in ROC, it can also be used to compare performances of different models”
Regarding Claim 3, the Srinivasan-Gelfenbeyn-Paul-Liu-Jiao combination teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated)
Srinivasan further teaches wherein the clustering technique comprises a similarity matching technique and a sequencing technique (Srinivasan, [0052], “…in order to make predictions and may use missing value techniques, such as, but not limited to, K-nearest neighbors” where K-nearest neighbors is considered a clustering technique that matches based on similarity and uses a sequence)
Regarding Claim 7, the Srinivasan-Gelfenbeyn-Paul-Liu-Jiao combination teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated)
Srinivasan further teaches wherein the machine learning recommendation technique comprises of recommending at least a machine learning technique for the plurality of predictable fields from a machine learning database based on the predictability score (Srinivasan [0045], “The model selector and evaluator 130 may include multiple ML models, and various sets of instructions that can be executed to select an optimal ML model for the ingested data set(s). Further, the selected ML model may be executed to conduct a predictive analysis on the transformed data set, wherein the execution may be performed based on predefined second set of instructions. Upon determining that the predictive analysis yields a positive response for the transformed data set, the model selector and evaluator 130 may validate the executed ML model” where the transformed data set is considered the plurality of predictable fields)
Regarding independent claim 9:
Srinivasan discloses an input/output interface; one or more memories; and one or more hardware processors, the one or more memories coupled to the one or more hardware processors, wherein the one or more hardware processors are configured to execute programmed instructions stored in the one or more memories to(Srinivasan, [0040], “Among other capabilities, processor 120 may fetch and execute computer-readable instructions in a memory operationally coupled with system 110 for performing tasks”)
Srinivasan discloses receiving a plurality of inputs associated with an application from a plurality of sources(Srinivasan, [0069], “Data sets may be acquired from input sources”), via a one or more hardware processors (Srinivasan [0007], “An example embodiment of the present disclosure pertains to a system for predictive analysis. The proposed system may include a processor”), the plurality of inputs comprising a plurality of predictability data attribute received from an application interface(Srinivasan [0034], “The data analyzer may then execute a first set of instructions on the encoded data set to obtain a transformed data set”), a plurality of metadata from received from an application database and a plurality of data attributes received from an application analytical source(Srinivasan, [0044], “The data lake 140 may be a central repository and may correspond to storing metadata information received from several devices and obtained from multiple entities”) and a domain knowledge associated with a domain of the application received from a domain database(Srinivasan, [0099], “Moreover, knowledge repositories and curated data may be other examples of the data source”)
Srinivasan discloses clustering the plurality of inputs, via the one or more hardware processors, based on a pre-defined parameter using a clustering technique to obtain grouped data using the domain knowledge(Srinivasan, [0073], “In the case of data imputation, depending on whether the imputation is because of time-series problem or ML model/prediction problem, an appropriate technique such as …clustering … can be implemented”) wherein the grouped data is in tabular format with a plurality of rows and a plurality of columns and the grouped data is associated with a dimension based on the plurality of rows and the plurality of columns(Srinivasan, [0062] “System 110 of the present disclosure can further include a model performance evaluator 216 that can be configured to maintain results and performance evaluation metrics obtained for each ML model.” and FIG. 2 showing that the Model Result Stack with arrows showing that tables can be used which is considered using tabular format with a plurality of rows and a plurality of columns) wherein the pre-defined parameter is a time-stamp and a chronological order, and a time-stamp value is stored against each column among the plurality of columns, representing an order in which data entered as input(Srinivasan, [0072], “In another example embodiment, data sets from the refined data zone 410 and real-time data sets, for example, from social media, web logs, and sensors may be ingested into data analyzer” where real-time data sets correspond to a pre-defined parameter is one of a time-stamp and a chronological order in which data entered as input as a real-time data corresponds to data as it relates to a time)
Srinivasan discloses identifying at least one pattern in the grouped data based on the domain, via the one or more hardware processors, using a pattern identification technique, wherein the at least one pattern(Srinivasan, [0042], “Data analyzer 150 may ….detect redundant occurrence …” where the ability for the data analyzer of Srinivasan to recognize these attributes/factors is considered identifying at least one pattern) is identified based on comprises a correlation factor(Srinivasan, [0042], “Data analyzer 150 may further be configured to detect redundant occurrence of the plurality of attributes in each of the one or more data tables, data sheets, and data matrices of the encoded data set, and eliminate the detected redundant plurality of attributes” where the non-eliminated attributes are considered attributes with a correlation factor) and a predictability factor(Srinivasan, [0047], “Data analyzer 150 can be configured to receive and facilitate tagging 202 of the ingested data sets” in which the data analyzer uses those tags for type(s) of ingested data and undertaking density analysis ([0048], “Data tagging may be useful in determining the type(s) of ingested data sets and in undertaking density analysis”) in which the tag considered a predictability factor as the density analysis tagging encompasses marking a position and predicting whether features align with the general pattern of its cluster including distance from a cluster core, clustering cohesion and feature distribution. With this understanding the tagging is considered a factor for prediction of density analysis) for each of the plurality of columns within the grouped data(Srinivasan, [0042], “… of the one or more data tables, data sheets, and data matrices” where of the plurality of attributes in each of the one or more data tables is consider for each of the plurality of columns within the grouped data).
Srinivasan discloses wherein the pattern identification technique comprises a similarity matching machine learning technique that comprises a clustering(Srinivasan ,[0073], “FIG. 5A illustrates how … depending on whether the imputation is because of time-series problem or ML model/prediction problem, an appropriate technique (such as … clustering) can be implemented”) and a bayesian networks technique(Srinivasan, [0079], “The model hyperparameter may be set using heuristics, and can be tuned for a given predictive modeling problem. Techniques that may be used to find out hyperparameters include, but are not limited to, any or a combination of Manual Search, Grid Search, Random Search, Bayesian Optimization, and the like” where Bayesian Optimization is considered a Bayesian network technique)
Srinivasan discloses wherein the correlation factor is associated with similarity between at least two columns from amongst the plurality of columns, the correlation factor is computed as based on a correlation coefficient of the linear relationship between a variable x and a variable y, a values of x in a sample, a mean of values of x, a values of y in a sample, and is a mean of values of y(Srinivasan, [0050], “…transformation of the data sets may be executed as a part of the automated feature selection, and may be carried out using techniques including, but not limited to... Pearson's correlation coefficient” where the Pearson's correlation coefficient quantifies the strength and direction of the linear relationship between two quantitative variables, x and y and is considered to computed as a linear relationship between a variable x and a variable y, a values of x in a sample, a mean of values of x, a values of y in a sample, and is a mean of values of y)
Srinivasan discloses optimizing the grouped data, via the one or more hardware processors, using an optimization technique based on the dimension to obtain an optimized grouped data(Srinivasan, [0050], “Moreover, dimension reduction and transformation of the data sets may be executed as a part of the automated feature selection”) wherein the optimization technique comprises of a dimension reduction technique to optimize the plurality of rows and the plurality of columns and a principal component analysis (PCA) optimizes the plurality of rows and the plurality of columns in which variables in the columns or the rows are combined to remove redundancies, wherein the PCA creates artificial variables through a linear combination of original variables(Srinivasan, [0050], “Data analyzer 150 can be configured to facilitate automated feature extraction 204 while solving problems related to redundant features .… dimension reduction and transformation of the data sets may be executed as a part of the automated feature selection, and may be carried out using techniques including…Principal Component Analysis (PCA)” where principle component analysis requires a matrix (corresponding to rows and columns) and creates artificial variables through linear combinations and using PCA for dimension reduction is considered a dimension reduction technique to optimize the rows and columns (See Also Srinivasan, [0051], “…dimensionality reduction may be defined as a process of reducing dimensionality of the feature space by obtaining a set of principal features....most of the features may be correlated, and hence may be determined as redundant and thereby may be eliminated, wherein such elimination may be performed using techniques including, but not limited to, PCA,”)
Srinivasan discloses identifying a plurality of predictable fields, via the one or more hardware processors, from the optimized grouped data for the domain based on the at least one pattern, wherein the plurality of predictable fields comprises a set of predictable columns that are predictable and relevant to the domain(Srinivasan [0034] “The data analyzer may then execute a first set of instructions on the encoded data set to obtain a transformed data set” and [0042] “Data analyzer may … eliminate the detected redundant plurality of attributes” where the non-eliminated attributes are considered predictable fields and a transformed data set is considered a set of predictable columns)
Srinivasan discloses providing a first recommendation via the Input/Output Module (202) includes a machine learning technique for the plurality of predictable fields via the one or more hardware processors, using a machine learning recommendation technique, wherein the machine learning technique is recommended (Srinivasan, [0059], “wherein the model selection block 212 may facilitate selection of an optimal ML model from a set of pre-stored/available ML models for processing the received data sets”)
Srinivasan discloses obtaining user feedback for the first recommendation pertains to the plurality of predictable fields and the machine learning technique(Srinivasan, [0067] “…the obtained results and performance metrics may be transmitted to users 314… Such users can utilize the live data sets and obtained results and performance metrics for further requirement…” and Figure 3, where 314 shows users who receive results and performance metrics to be analyzed and further requirements can be decided if needed) and altering the plurality of predictable fields and the machine learning technique in accordance with the user feedback(Srinivasan, [0067], “At block 316, a decision can be taken on whether the devices associated with the pre-processing of the data sets and performance of the predictive analysis need to be reset for a fresh data set in iteration” where the predictive analysis needing to be reset for a fresh data set in another iteration is considered altering a plurality of fields and machine learning technique) and providing the altered predictable fields and the machine learning technique as a second recommendation(Figure 3, where 316 shows the users deciding whether to reset and get a new machine learning model with different data and 302 showing a new model is selected)
Srinivasan does not teach:
wherein while clustering, for a given set of n data input, each input variable generates own cluster, and at next step, when pairs of clusters are identified to be similar, similar clusters are merged into a single cluster
wherein the machine learning technique is recommended wherein the predictability score is expressed as an area under curve (AUC)
and in the AUC a value is equal to a probability that the machine learning technique ranks a randomly chosen positive example higher than a randomly chosen negative example wherein the AUC of a false positive rate vs a true positive rate is represented as: False Positive Rate = False Positive / (True Negative + False Positive) True Positive Rate = True Positive / (False Negative + True Positive)
wherein the predictability data attribute includes values entered in a text box of a web page
wherein the application database provides features to create triggers to track when data gets created or modified along with timestamp information
wherein the plurality of data attributes includes a list of navigations performed by a user on the web page and text field entered by the user, a button clicked and an order of click
wherein the application analytical source includes details on a plurality of HTML tags associated with the navigations performed by the user on the web page
wherein the application is capable of recording user inputs and is a digitally enabled replica of an entity performing a digitally enabled task
wherein the application is one of a web interface, a thick client, a command line, a virtual reality, a voice interface, an augmented reality, and a haptic interface
Gelfenbeyn discloses wherein the application database provides features to create triggers to track when data gets created or modified(Gelfenbeyn, [0042], “…The dialog manager 230 may play many roles, which include discourse analysis, knowledge database query, and system action prediction based on the discourse context. In some embodiments, the dialog manager 230 may communicate with various computing, logic, or storage resources 240, which may include, for example, a triggering criteria database.) along with timestamp information(Gelfenbeyn, [0013], “In certain embodiments, the method may further comprise determining, by the processor, a current time associated with the user, and the identifying of the at least one triggering event may be based at least in part on determination that the current time relates to the predetermined time value.”)
Gelfenbeyn discloses wherein the application is capable of recording user inputs and is a digitally enabled replica of an entity performing a digitally enabled task(Gelfenbeyn, [0097], “The user may then select one of delivered push notifications or just provide a voice command, such as “Show my emails” or “Show details of scheduled events,” so as to get more information regarding desired items” where a device learning and completing a task without explicit actions required from a user is considered replicating and performing a digitally enabled task.)
Gelfenbeyn discloses wherein the application is one of a web interface, a thick client, a command line, a virtual reality, a voice interface, an augmented reality, and a haptic interface(Gelfenbeyn, [0003], “The users may also provide voice commands to the CIS so as to perform certain functions”)
References Srinivasan and Gelfenbeyn are analogous art because they are from the field of endeavor of using ML-driven systems for data-driven modeling using past user and system behavior.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Srinivasan and Gelfenbeyn before him or her, to modify the system of Srinivasan to include the database triggering of Gelfenbeyn as the suggestion/motivation for doing so would have been “The dialog manager 230 may employ multiple various approaches to generate output in response to the recognized input. Some approaches may include the use of statistical analysis, machine-learning algorithms (e.g., neural networks), heuristic analysis, and so forth” (Gelfenbeyn, [0042])
Paul discloses wherein the plurality of data attributes includes a list of navigations performed by a user on the web page and text field entered by the user, a button clicked and an order of click(Paul, [0039], “…session replay data may include one or more session events, arranged in order of occurrence, where a session event is a user interaction with a webpage zone or element. Such interactions may include, as examples and without limitation, keystrokes by which a user enters text in a text field, mouse clicks by which a user activates a button, mouse scroll events, navigation to or from a webpage, and the like.”)
Paul discloses wherein the application analytical source includes details on a plurality of HTML(Paul, [0029], “The tag 122 may be included within the source code of a webpage, such as the hypertext markup language (HTML) code underlying such a webpage, where such source code is hosted by the web server 130”) tags associated with the navigations performed by the user on the web page(Paul, [0028], “The tag 122 is a script, code element, or other, like, data feature, configured to collect activity relating to a client's interaction with web server 130 content through the app 121. The tag 122 may be included in one or more web server 130 data features accessed through the client device 120, providing for collection, via the tag, of various data features describing a user's interactions with web content” where tags configured to collect activity relating to a client's interaction with web content correspond to HTML tags associated with navigations performed by the user)
References Srinivasan-Gelfenbeyn and Paul are analogous art because they are from the field of endeavor of using ML-driven systems with webpages and HTML for identification of patterns.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Srinivasan-Gelfenbeyn and Paul before him or her, to modify the input of Srinivasan-Gelfenbeyn to include the navigational tag system of Paul as the suggestion/motivation for doing so would have been to interpret different parts of the webpage as Paul states “At S210, session replay data is imported or otherwise received. Session replay data is data describing one or more historical user journeys through a webpage or set of webpages...zoning information is extracted. Zoning information may be data, information, or the like, relevant to the contents and structure of a webpage. Zoning information may include, without limitation, descriptions of zone or element placements, relationships, contents, and the like, as well as any combination thereforth” (Paul, [0039]-[0040])
Liu does disclose wherein while clustering, for a given set of n data input, each input variable generates own cluster, and at next step, when pairs of clusters are identified to be similar, similar clusters are merged into a single cluster (Liu, Page 2, Paragraph 2, “agglomerative hierarchical clustering performs tree construction with a bottom-up merging manner, which starts with assigning each data point with a separate cluster and then recursively merges clusters that are similar according to predefined metric”)
References Srinivasan and Liu are analogous art because they are from the field of endeavor of using Bayesian statistics and clustering in prediction.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Srinivasan and Liu before him or her, to modify the clustering of Srinivasan to include the Bayesian hierarchical clustering of Liu to achieve a flexible clustering using probabilistic outcomes. The suggestion/motivation for doing so would have been “Accordingly, hierarchical clustering is flexible for exploratory data analysis due to its multiple levels of granularity” (Liu, Page 1, Paragraph 1) and “Specifically, based on the well-known K-means algorithm, an elegant hierarchical clustering model is developed which links hierarchical K-means clustering and Bayesian hierarchical model with principled probabilistic foundation”(Liu, Page 2, Paragraph 2)
Jiao does disclose wherein the machine learning technique is recommended based on a predictability score, wherein the predictability score is expressed as an area under curve (AUC)(Jiao, Page 327, Col. 1, Paragraph 1“When an ROC curve is plotted, we can use the area under the curve (AUC) to measure the performance of the predictor”) and in the AUC, a value is equal to a probability that the machine learning technique ranks a randomly chosen positive example higher than a randomly chosen negative example, (Jiao, Page 327, Col. 1, Paragraph 1“…the AUC of an ROC curve equals to the probability that a randomly selected positive sample gets higher scores than a randomly selected negative sample”)
Jiao does disclose wherein the AUC of a false positive rate vs a true positive rate is represented as: False Positive Rate = False Positive / (True Negative + False Positive) (Jiao, Page 326, Col. 2, Equation 26 where the FPR stands for False Positive rate, FP stands for False Positive and TN stands for True Negative)True Positive Rate = True Positive / (False Negative + True Positive) (Jiao, Page 325, Col. 1, Equation 14 where Sen(Sensitivity) is the True Positive Rate, FP stands for False Positive and TN stands for True Negative)
References Srinivasan and Jiao are analogous art because they are from the same field of measuring performances in evaluation of machine learning models.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Srinivasan and Jiao before him or her, to modify the machine learning evaluation of Srinivasan include the AUC of Jiao as the AUC is used to measure performance of predictors. The suggestion/motivation for doing so would have been Jiao, Page 327, Col. 1, Paragraph 1, “When an ROC curve is plotted, we can use the area under the curve (AUC) to measure the performance of the predictor” and Jiao, Page 327, Col. 2, Paragraph 1, “Although the AUC of a PR curve have no similar interpretations like Equation (27) in ROC, it can also be used to compare performances of different models”
Regarding Claim 11, the Srinivasan-Gelfenbeyn-Paul-Liu-Jiao combination teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated)
Srinivasan further teaches wherein the one or more hardware processors are configured by the instructions to perform the clustering technique, pattern identification technique and optimization technique wherein the clustering technique comprises a similarity matching technique and a sequencing technique (Srinivasan, [0052], “…in order to make predictions and may use missing value techniques, such as, but not limited to, K-nearest neighbors” where K-nearest neighbors is considered a clustering technique that matches based on similarity and uses a sequence)
Regarding claim 12, the Srinivasan-Gelfenbeyn-Paul-Liu-Jiao combination teaches the system of claim 9(and thus the rejection of claim 9 is incorporated) and due to the substantially similar limitations as claim 7 is rejected under the same prior art analysis as claim 7.
Regarding claim 13, the Srinivasan-Gelfenbeyn-Paul-Liu-Jiao combination teaches the method of claim 9(and thus the rejection of claim 9 is incorporated)
Srinivasan further teaches wherein the predictability score is determined for each of the predictable fields from among the plurality of predictable fields based on a scoring technique and the machine learning techniques recommended are ranked and recommended based on the predictability score (Srinivasan [0036], “The proposed system may also include a performance evaluator to assess/validate performance of a ML model for the transformed data set by any or a combination of regularization regression and/or bias-variance tradeoff techniques. Such performance can be assessed based on factors including, but not limited to, interpretability, simplicity, speed, and stability of the ML model for the transformed data set. Based the assessed performance, the chosen ML model may be validated.” where the transformed data set is considered the plurality of predictable fields)
Regarding independent claim 14:
Srinivasan discloses One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors(Srinivasan, [0040], “Among other capabilities, processor 120 may fetch and execute computer-readable instructions in a memory operationally coupled with system 110 for performing tasks”)
Srinivasan discloses receiving a plurality of inputs associated with an application from a plurality of sources(Srinivasan [0069], “Data sets may be acquired from input sources”), via a one or more hardware processors (Srinivasan [0007], “An example embodiment of the present disclosure pertains to a system for predictive analysis. The proposed system may include a processor”), the plurality of inputs comprising a plurality of predictability data attribute received from an application interface(Srinivasan [0034], “The data analyzer may then execute a first set of instructions on the encoded data set to obtain a transformed data set”), a plurality of metadata from received from an application database and a plurality of data attributes received from an application analytical source(Srinivasan, [0044], “The data lake 140 may be a central repository and may correspond to storing metadata information received from several devices and obtained from multiple entities”) and a domain knowledge associated with a domain of the application received from a domain database(Srinivasan, [0099], “Moreover, knowledge repositories and curated data may be other examples of the data source”)
Srinivasan discloses clustering the plurality of inputs, via the one or more hardware processors, based on a pre-defined parameter using a clustering technique to obtain grouped data using the domain knowledge(Srinivasan, [0073], “In the case of data imputation, depending on whether the imputation is because of time-series problem or ML model/prediction problem, an appropriate technique such as …clustering … can be implemented”) wherein the grouped data is in tabular format with a plurality of rows and a plurality of columns and the grouped data is associated with a dimension based on the plurality of rows and the plurality of columns(Srinivasan, [0062] “System 110 of the present disclosure can further include a model performance evaluator 216 that can be configured to maintain results and performance evaluation metrics obtained for each ML model.” and FIG. 2 showing that the Model Result Stack with arrows showing that tables can be used which is considered using tabular format with a plurality of rows and a plurality of columns) wherein the pre-defined parameter is a time-stamp and a chronological order, and a time-stamp value is stored against each column among the plurality of columns, representing an order in which data entered as input(Srinivasan, [0072], “In another example embodiment, data sets from the refined data zone 410 and real-time data sets, for example, from social media, web logs, and sensors may be ingested into data analyzer” where real-time data sets correspond to a pre-defined parameter is one of a time-stamp and a chronological order in which data entered as input as a real-time data corresponds to data as it relates to a time)
Srinivasan discloses identifying at least one pattern in the grouped data based on the domain, via the one or more hardware processors, using a pattern identification technique, wherein the at least one pattern(Srinivasan, [0042], “Data analyzer 150 may ….detect redundant occurrence …” where the ability for the data analyzer of Srinivasan to recognize these attributes/factors is considered identifying at least one pattern) is identified based on comprises a correlation factor(Srinivasan, [0042], “Data analyzer 150 may further be configured to detect redundant occurrence of the plurality of attributes in each of the one or more data tables, data sheets, and data matrices of the encoded data set, and eliminate the detected redundant plurality of attributes” where the non-eliminated attributes are considered attributes with a correlation factor) and a predictability factor(Srinivasan, [0047], “Data analyzer 150 can be configured to receive and facilitate tagging 202 of the ingested data sets” in which the data analyzer uses those tags for type(s) of ingested data and undertaking density analysis ([0048], “Data tagging may be useful in determining the type(s) of ingested data sets and in undertaking density analysis”) in which the tag considered a predictability factor as the density analysis tagging encompasses marking a position and predicting whether features align with the general pattern of its cluster including distance from a cluster core, clustering cohesion and feature distribution. With this understanding the tagging is considered a factor for prediction of density analysis) for each of the plurality of columns within the grouped data(Srinivasan, [0042], “… of the one or more data tables, data sheets, and data matrices” where of the plurality of attributes in each of the one or more data tables is consider for each of the plurality of columns within the grouped data).
Srinivasan discloses wherein the pattern identification technique comprises a similarity matching machine learning technique that comprises a clustering(Srinivasan ,[0073], “FIG. 5A illustrates how … depending on whether the imputation is because of time-series problem or ML model/prediction problem, an appropriate technique (such as … clustering) can be implemented”) and a bayesian networks technique(Srinivasan, [0079], “The model hyperparameter may be set using heuristics, and can be tuned for a given predictive modeling problem. Techniques that may be used to find out hyperparameters include, but are not limited to, any or a combination of Manual Search, Grid Search, Random Search, Bayesian Optimization, and the like” where Bayesian Optimization is considered a Bayesian network technique)
Srinivasan discloses wherein the correlation factor is associated with similarity between at least two columns from amongst the plurality of columns, the correlation factor is computed as based on a correlation coefficient of the linear relationship between a variable x and a variable y, a values of x in a sample, a mean of values of x, a values of y in a sample, and is a mean of values of y(Srinivasan, [0050], “…transformation of the data sets may be executed as a part of the automated feature selection, and may be carried out using techniques including, but not limited to... Pearson's correlation coefficient” where the Pearson's correlation coefficient quantifies the strength and direction of the linear relationship between two quantitative variables, x and y and is considered to computed as a linear relationship between a variable x and a variable y, a values of x in a sample, a mean of values of x, a values of y in a sample, and is a mean of values of y)
Srinivasan discloses optimizing the grouped data, via the one or more hardware processors, using an optimization technique based on the dimension to obtain an optimized grouped data(Srinivasan, [0050], “Moreover, dimension reduction and transformation of the data sets may be executed as a part of the automated feature selection”) wherein the optimization technique comprises of a dimension reduction technique to optimize the plurality of rows and the plurality of columns and a principal component analysis (PCA) optimizes the plurality of rows and the plurality of columns in which variables in the columns or the rows are combined to remove redundancies, wherein the PCA creates artificial variables through a linear combination of original variables(Srinivasan, [0050], “Data analyzer 150 can be configured to facilitate automated feature extraction 204 while solving problems related to redundant features .… dimension reduction and transformation of the data sets may be executed as a part of the automated feature selection, and may be carried out using techniques including…Principal Component Analysis (PCA)” where principle component analysis requires a matrix (corresponding to rows and columns) and creates artificial variables through linear combinations and using PCA for dimension reduction is considered a dimension reduction technique to optimize the rows and columns (See Also Srinivasan, [0051], “…dimensionality reduction may be defined as a process of reducing dimensionality of the feature space by obtaining a set of principal features....most of the features may be correlated, and hence may be determined as redundant and thereby may be eliminated, wherein such elimination may be performed using techniques including, but not limited to, PCA,”)
Srinivasan discloses identifying a plurality of predictable fields, via the one or more hardware processors, from the optimized grouped data for the domain based on the at least one pattern, wherein the plurality of predictable fields comprises a set of predictable columns that are predictable and relevant to the domain(Srinivasan [0034] “The data analyzer may then execute a first set of instructions on the encoded data set to obtain a transformed data set” and [0042] “Data analyzer may … eliminate the detected redundant plurality of attributes” where the non-eliminated attributes are considered predictable fields and a transformed data set is considered a set of predictable columns)
Srinivasan discloses providing a first recommendation via the Input/Output Module (202) includes a machine learning technique for the plurality of predictable fields via the one or more hardware processors, using a machine learning recommendation technique, wherein the machine learning technique is recommended (Srinivasan, [0059], “wherein the model selection block 212 may facilitate selection of an optimal ML model from a set of pre-stored/available ML models for processing the received data sets”)
Srinivasan discloses obtaining user feedback for the first recommendation pertains to the plurality of predictable fields and the machine learning technique(Srinivasan, [0067] “…the obtained results and performance metrics may be transmitted to users 314… Such users can utilize the live data sets and obtained results and performance metrics for further requirement…” and Figure 3, where 314 shows users who receive results and performance metrics to be analyzed and further requirements can be decided if needed) and altering the plurality of predictable fields and the machine learning technique in accordance with the user feedback(Srinivasan, [0067], “At block 316, a decision can be taken on whether the devices associated with the pre-processing of the data sets and performance of the predictive analysis need to be reset for a fresh data set in iteration” where the predictive analysis needing to be reset for a fresh data set in another iteration is considered altering a plurality of fields and machine learning technique) and providing the altered predictable fields and the machine learning technique as a second recommendation(Figure 3, where 316 shows the users deciding whether to reset and get a new machine learning model with different data and 302 showing a new model is selected)
Srinivasan does not teach:
wherein while clustering, for a given set of n data input, each input variable generates own cluster, and at next step, when pairs of clusters are identified to be similar, similar clusters are merged into a single cluster
wherein the machine learning technique is recommended wherein the predictability score is expressed as an area under curve (AUC)
and in the AUC a value is equal to a probability that the machine learning technique ranks a randomly chosen positive example higher than a randomly chosen negative example wherein the AUC of a false positive rate vs a true positive rate is represented as: False Positive Rate = False Positive / (True Negative + False Positive) True Positive Rate = True Positive / (False Negative + True Positive)
wherein the predictability data attribute includes values entered in a text box of a web page
wherein the application database provides features to create triggers to track when data gets created or modified along with timestamp information
wherein the plurality of data attributes includes a list of navigations performed by a user on the web page and text field entered by the user, a button clicked and an order of click
wherein the application analytical source includes details on a plurality of HTML tags associated with the navigations performed by the user on the web page
wherein the application is capable of recording user inputs and is a digitally enabled replica of an entity performing a digitally enabled task
wherein the application is one of a web interface, a thick client, a command line, a virtual reality, a voice interface, an augmented reality, and a haptic interface
Gelfenbeyn discloses wherein the application database provides features to create triggers to track when data gets created or modified(Gelfenbeyn, [0042], “…The dialog manager 230 may play many roles, which include discourse analysis, knowledge database query, and system action prediction based on the discourse context. In some embodiments, the dialog manager 230 may communicate with various computing, logic, or storage resources 240, which may include, for example, a triggering criteria database.) along with timestamp information(Gelfenbeyn, [0013], “In certain embodiments, the method may further comprise determining, by the processor, a current time associated with the user, and the identifying of the at least one triggering event may be based at least in part on determination that the current time relates to the predetermined time value.”)
Gelfenbeyn discloses wherein the application is capable of recording user inputs and is a digitally enabled replica of an entity performing a digitally enabled task(Gelfenbeyn, [0097], “The user may then select one of delivered push notifications or just provide a voice command, such as “Show my emails” or “Show details of scheduled events,” so as to get more information regarding desired items” where a device learning and completing a task without explicit actions required from a user is considered replicating and performing a digitally enabled task.)
Gelfenbeyn discloses wherein the application is one of a web interface, a thick client, a command line, a virtual reality, a voice interface, an augmented reality, and a haptic interface(Gelfenbeyn, [0003], “The users may also provide voice commands to the CIS so as to perform certain functions”)
References Srinivasan and Gelfenbeyn are analogous art because they are from the field of endeavor of using ML-driven systems for data-driven modeling using past user and system behavior.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Srinivasan and Gelfenbeyn before him or her, to modify the system of Srinivasan to include the database triggering of Gelfenbeyn as the suggestion/motivation for doing so would have been “The dialog manager 230 may employ multiple various approaches to generate output in response to the recognized input. Some approaches may include the use of statistical analysis, machine-learning algorithms (e.g., neural networks), heuristic analysis, and so forth” (Gelfenbeyn, [0042])
Paul discloses wherein the plurality of data attributes includes a list of navigations performed by a user on the web page and text field entered by the user, a button clicked and an order of click(Paul, [0039], “…session replay data may include one or more session events, arranged in order of occurrence, where a session event is a user interaction with a webpage zone or element. Such interactions may include, as examples and without limitation, keystrokes by which a user enters text in a text field, mouse clicks by which a user activates a button, mouse scroll events, navigation to or from a webpage, and the like.”)
Paul discloses wherein the application analytical source includes details on a plurality of HTML(Paul, [0029], “The tag 122 may be included within the source code of a webpage, such as the hypertext markup language (HTML) code underlying such a webpage, where such source code is hosted by the web server 130”) tags associated with the navigations performed by the user on the web page(Paul, [0028], “The tag 122 is a script, code element, or other, like, data feature, configured to collect activity relating to a client's interaction with web server 130 content through the app 121. The tag 122 may be included in one or more web server 130 data features accessed through the client device 120, providing for collection, via the tag, of various data features describing a user's interactions with web content” where tags configured to collect activity relating to a client's interaction with web content correspond to HTML tags associated with navigations performed by the user)
References Srinivasan-Gelfenbeyn and Paul are analogous art because they are from the field of endeavor of using ML-driven systems with webpages and HTML for identification of patterns.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Srinivasan-Gelfenbeyn and Paul before him or her, to modify the input of Srinivasan-Gelfenbeyn to include the navigational tag system of Paul as the suggestion/motivation for doing so would have been to interpret different parts of the webpage as Paul states “At S210, session replay data is imported or otherwise received. Session replay data is data describing one or more historical user journeys through a webpage or set of webpages...zoning information is extracted. Zoning information may be data, information, or the like, relevant to the contents and structure of a webpage. Zoning information may include, without limitation, descriptions of zone or element placements, relationships, contents, and the like, as well as any combination thereforth” (Paul, [0039]-[0040])
Liu does disclose wherein while clustering, for a given set of n data input, each input variable generates own cluster, and at next step, when pairs of clusters are identified to be similar, similar clusters are merged into a single cluster (Liu, Page 2, Paragraph 2, “agglomerative hierarchical clustering performs tree construction with a bottom-up merging manner, which starts with assigning each data point with a separate cluster and then recursively merges clusters that are similar according to predefined metric”)
References Srinivasan and Liu are analogous art because they are from the field of endeavor of using Bayesian statistics and clustering in prediction.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Srinivasan and Liu before him or her, to modify the clustering of Srinivasan to include the Bayesian hierarchical clustering of Liu to achieve a flexible clustering using probabilistic outcomes. The suggestion/motivation for doing so would have been “Accordingly, hierarchical clustering is flexible for exploratory data analysis due to its multiple levels of granularity” (Liu, Page 1, Paragraph 1) and “Specifically, based on the well-known K-means algorithm, an elegant hierarchical clustering model is developed which links hierarchical K-means clustering and Bayesian hierarchical model with principled probabilistic foundation”(Liu, Page 2, Paragraph 2)
Jiao does disclose wherein the machine learning technique is recommended based on a predictability score, wherein the predictability score is expressed as an area under curve (AUC)(Jiao, Page 327, Col. 1, Paragraph 1“When an ROC curve is plotted, we can use the area under the curve (AUC) to measure the performance of the predictor”) and in the AUC, a value is equal to a probability that the machine learning technique ranks a randomly chosen positive example higher than a randomly chosen negative example, (Jiao, Page 327, Col. 1, Paragraph 1“…the AUC of an ROC curve equals to the probability that a randomly selected positive sample gets higher scores than a randomly selected negative sample”)
Jiao does disclose wherein the AUC of a false positive rate vs a true positive rate is represented as: False Positive Rate = False Positive / (True Negative + False Positive) (Jiao, Page 326, Col. 2, Equation 26 where the FPR stands for False Positive rate, FP stands for False Positive and TN stands for True Negative)True Positive Rate = True Positive / (False Negative + True Positive) (Jiao, Page 325, Col. 1, Equation 14 where Sen(Sensitivity) is the True Positive Rate, FP stands for False Positive and TN stands for True Negative)
References Srinivasan and Jiao are analogous art because they are from the same field of measuring performances in evaluation of machine learning models.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Srinivasan and Jiao before him or her, to modify the machine learning evaluation of Srinivasan include the AUC of Jiao as the AUC is used to measure performance of predictors. The suggestion/motivation for doing so would have been Jiao, Page 327, Col. 1, Paragraph 1, “When an ROC curve is plotted, we can use the area under the curve (AUC) to measure the performance of the predictor” and Jiao, Page 327, Col. 2, Paragraph 1, “Although the AUC of a PR curve have no similar interpretations like Equation (27) in ROC, it can also be used to compare performances of different models”
Regarding Claim 16, the Srinivasan-Gelfenbeyn-Paul-Liu-Jiao combination teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated)
Srinivasan further teaches wherein the predictability score is defined based on a Mean Squared Error that is an average of square of difference between original and predicted values expressed as Mean square error
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(Srinivasan, [0072], “…The selected ML model may be validated for accuracy based on a user-defined universal criteria such as Root Mean Square Error (RMSE), mean absolute error (MAE), mean absolute percentage error (MARE), and confusion matrix.”)
Regarding Claim 17, The rejection of Claim 9 incorporated in Claim 17, and further, Claim 17 is rejected under the same rationale as set forth in the rejection of Claim 16.
Claim(s) 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Srinivasan et al.(US 20220237208 A1), henceforth known as Srinivasan and further in view of LUI et al. (“Bayesian hierarchical K-means clustering”), henceforth known as Liu and Cao et al.(“ Study of the Bayesian Networks”), henceforth known as Cao.
Regarding Claim 5, the Srinivasan-Liu- Jiao combination teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated)
The claim recites wherein the predictability factor is associated with a column's relevance to the domain and is a parameter that can be predicted using a machine learning technique (Srinivasan [0042], “Data analyzer 150 may further be configured to detect redundant occurrence of the plurality of attributes in each of the one or more data tables, data sheets, and data matrices of the encoded data set, and eliminate the detected redundant plurality of attributes” where the data analyzer that performs that tagging operates in various data tables is considered predictability factor is associated with a column's relevance as tables have columns and rows)
Srinivasan does not teach; however Cao does teach:
the predictability factor is computed using a causal bayesian networks based on a joint probability function of three variables X,Y,Z that is represented as: Pr(X,Y,Z) = Pr(X|Y,Z) Pr(Y|Z)Pr(Z)
Cao discloses the predictability factor is computed using a causal bayesian networks based on a joint probability function of three variables X,Y,Z that is represented as: Pr(X,Y,Z) = Pr(X|Y,Z) Pr(Y|Z)Pr(Z) (The formula Pr(X,Y,Z) = Pr(X|Y,Z)Pr(Y|Z)P(Z) represents the generic Bayesian conditional probability product rule (also called the chain rule) which Cao discloses in Cao, Page 172, Col. 2, Paragraphs 4-5, “we will talk about one of the most crucial elements of Bayesian network calculations, namely the chain rule... The chain rule provides a more compact representation of the joint probability P(U) = P(A,A,···,A.)” with Equation 1 showing the joint probability distribution P(U) is the product of all the conditional probabilities of each variable Ai conditioned on its parent variable in the network and Equation 2 showing how the joint probability of all variables in the network can be calculated by multiplying the conditional probabilities for each variable)
Regarding Claim 15, The rejection of Claim 9 incorporated in Claim 15, and further, Claim 15 is rejected under the same rationale as set forth in the rejection of Claim 5.
Response to Arguments
Applicant's arguments filed 09/16/2025 have been fully considered but they are not persuasive. A response to arguments can be found below.
103:
Applicant appears to argue further amended language that reciting use of a text box of a web page, database triggers, navigation by a user, button clicks, HTML tags, task replication and user inputs are not taught in prior art.
Examiner agrees and has added a prior art to Gelfenbeyn cite to the amended language.
Applicant appears to argue that the addition of the amended language specifying the use PCA to optimize the rows and columns by removing redundancies and creating artificial variables through linear combinations of original variables is not taught in prior art.
Examiner respectfully disagrees as Srinivasan discusses optimization through dimension reduction then states the dimension reduction and transformation of datasets may be executed using Principal Component Analysis. Further, Principal Component Analysis as a core principle uses a matrix (rows + columns) that use linear combinations of original values and creates artificial values. For support see below:
Srinivasan, [0050], “Data analyzer 150 can be configured to facilitate automated feature extraction 204 while solving problems related to redundant features .… dimension reduction and transformation of the data sets may be executed as a part of the automated feature selection, and may be carried out using techniques including…Principal Component Analysis (PCA)”
Srinivasan, [0051], “…dimensionality reduction may be defined as a process of reducing dimensionality of the feature space by obtaining a set of principal features....most of the features may be correlated, and hence may be determined as redundant and thereby may be eliminated, wherein such elimination may be performed using techniques including, but not limited to, PCA,”
101:
Applicant appears to argue on page 11-12 that an improvement in the computer as a result of the navigational flows of the data via capturing HTML tags and database triggers to record user inputs.
Examiner respectfully disagrees as the claims as presented do not result or highlight an improvement in neural networks, memory or hardware processors. Examiner notes MPEP 2106.05(a) which provides the requirements for how an improvement to the functioning of a computer or to any other technology or technical field is evaluated. Further claims that require a computer may still recite a mental process (please see MPEP 2106.04(a)(2).III.C). Applicant’s example does not explain how the additional elements reflect this improvement, or how the improvement is affected by any claimed additional elements.... at best applicants example describes in an improvement provided by the claimed abstract idea selecting fields to use.
Applicant appears to argue on page 13-14 that a storing a time-stamp value in a tabular format and dimension reduction using PCA to remove redundancies and combining rows and columns amounts to significantly more.
Examiner respectfully disagrees as examiner has identified a judicial exception and under step 2a and 2b examiner has identified the additional elements together and as a whole as corresponding to those limitations which are not indicative of practical application or significantly more as using principle component analysis with tabular data is a mental/abstract idea and a computer may still recite a mental process(please see MPEP 2106.04(a)(2).III.C)) and the stored time-stamp value against each column among the plurality of columns, representing an order in which data entered as input is merely describing the input data being received which specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/C.J.J./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122