DETAILED ACTION
1. This is a Non-Final Office Action Correspondence in response to U.S. Application No. 19/203418 filed on May 09, 2025.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Double Patenting
3. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
4. Claims 1 – 20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1 – 20 of U.S. Patent No.12,332,904 (here as ‘Patent 904’). Although the conflicting claims are not identical, they are not patentably distinct from each other because both applications discuss relationships between variables.
This is an obviousness-type double patenting rejection because the conflicting claims have been patented.
U.S. Patent Application: 19/203418
U.S. Patent: 12,332,904
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As to claim 1 Patent 904 teaches a processor-implemented method of analysing a data set, wherein the data set includes data for a plurality of variables resulting from a single study, the method comprising:
uploading the data set by a user (Claim 1 Patent 904 discloses A processor-implemented method of analysing a data set, wherein the data set includes data for a plurality of variables resulting from a
selecting via a user interface configured to facilitate such selection, by the user, one or more user-selected variables from the plurality of variables in the data set based on what interests the user (Claim 1 Patent 904 discloses selecting via a user interface configured to facilitate such selection, by the user, one or more significant user-selected variables from the plurality of variables in the data set based on what interests the user and represents a priority & focus of a study, thereby ignoring remaining non-user-selected variables of the plurality of variables in the data set);
automatically selecting based on the type of the user-selected variable, by a processor,an appropriate statistical technique from a predefined library of statistical techniques for performing a correlation analysis for: (i) only measuring an interdependence of one or more user-selected variables associated with the data set and (ii) only assessing a magnitude of the relationship between the one or more user-selected variables (Claim 1 Patent 904 discloses automatically selecting based on the type of the user-selected variable, by a processor, an appropriate statistical technique from a predefined library of statistical techniques for performing a correlation analysis for: (i) only measuring an interdependence of one or more user-selected variables associated with the data set and not remaining non-user-selected variables and (ii) only assessing a magnitude of the relationship between the one or more user-selected variables and not remaining non- user-selected variables);
performing, by the processor, a correlation for generating one or more correlation results comprising at least one of:( a first set of user-selected variables related to the one or more user-selected variables, and (ii) a second set of user-selected variables related to the first set of user-selected variables, (Claim 1 Patent 904 discloses wherein performing the correlation comprises ranking the observations in an order of relevance by: classifying one or more variable observations associated with the data set based on at least one of: significance to the user, the variable type, the observation nature, and the observation result; generating a list of observations and insights based on the classification; running a relate process with two or more 1st, 2nd, and 3rd degree variables and collecting resulting observations);
wherein performing the correlation comprises ranking the observations by at least one of:(i) classifying one or more variable observations associated with the data set based on at least one of: significance to the user, the variable type, the observation nature, and the observation result (Claim 1 Patent 904 discloses performing, by the processor, a correlation for generating one or more correlation results comprising at least one of: a first set of user-selected variables related to the one or more significant user-selected variables, and a second set of user-selected variables related to the first set of user-selected variables, wherein the calculation results are determined independently of one another);
and generating a list of observations and insights based on the classification; or (ii) running a relate process with two or more 1st, 2nd, and 3rd degree variables and collecting resulting observations (Claim 1 Patent 904 discloses generating a list of observations and insights based on the classification; running a relate process with two or more 1st, 2nd, and 3rd degree variables and collecting resulting observations);
Patent 904 discloses and compiling the observations across the data set for the two or more 1st, 2nd, and 3rd degree variables and ranking the observations by assigning a score to each observation
and returning, by the processor, a list of key analysis, to the user, based on the one or more user-selected variables and the one or more correlation results related thereto, whereby the list of key analysis provides an insight to the user about the data set based only on the one or more user-selected variables (Claim 1 Patent 904 discloses).
The difference between claim 1 is the patent 904 is the Patent 904 contains this additional claim language:
and compiling the observations across the data set for the two or more 1st, 2nd, and 3rd degree variables and ranking the observations by assigning a score to each observation
As to claim 2 Patent 904 teaches wherein the list of key analysis describes at least one of: frequencies, correlation, regression, benchmark reports, and recommended clusters, and wherein the list of key analysis is in the form of at least one of: a summary table, a set of charts, data tables, and plain language explanation (Claim 2 Patent 904 discloses wherein the list of key analysis describes at least one of: frequencies, correlation, regression, benchmark reports, and recommended clusters, and wherein the list of key analysis is in the form of at least one of: a summary table, a set of charts, data tables, and plain language explanation).
Claim 2 and Patent 904 are considered the same.
As to claim 3 Patent 904 teaches wherein the interdependence of the user- selected variables is used as an indicator of whether one or more user-selected variables are related to the one or more significant user-selected variables (Claim 3 Patent 904 discloses wherein the interdependence of the user-selected variables is used as an indicator of whether one or more user-selected variables are related to the one or more significant user-selected variables).
Claim 3 and Patent 904 are considered the same.
As to claim 4 Patent 904 teaches further comprising automatically assigning, by the processor, individual weights to the first set of user-selected variables, the second set of user- selected variables and the one or more significant user-selected variables, observation type and analysis result based on at least the variable type (Claim 4 Patent 904 discloses further comprising automatically assigning, by the processor, individual weights to the first set of user-selected variables, the second set of user-selected variables and the one or more significant user-selected variables, observation type and analysis result based on at least the variable type).
Claim 4 and Patent 904 are considered the same.
As to claim 5 Patent 904 teaches wherein performing the correlation comprises ranking the observations in an order of relevance by ranking the list of observations based on a series of weights that are based on pre-defined factors (Claim 5 Patent 904 discloses wherein performing the correlation comprises ranking the observations in an order of relevance by: ranking the list of observations based on a series of weights that are based on pre-defined factors).
Claim 5 and Patent 904 are considered the same.
As to claim 6 Patent 904 teaches wherein a ranking score is given by the following equation:RankingScore = (Score / ValueRange) * FactorWeight; andwherein the RankingScore is assigned to each observation and the observations are ordered with the greatest RankingScore first (Claim 6 Patent 904 discloses wherein the ranking score is given by the following equation: Ranking_Score=(Score/Value_Range)*Factor_Weight; and Wherein the Ranking_Score is assigned to each observation and the observations are ordered with the greatest Ranking_Score first).
Claim 6 and Patent 904 are considered the same.
As to claim 7 Patent 904 teaches further comprising: selecting a task and a describe option, by the user, via the user interface configured to facilitate such selection; selecting a characterize data option by the user for characterizing the data, via the user interface configured to facilitate such selection (Claim 7 Patent 904 discloses further comprising: selecting a task and a describe option, by the user, via the user interface configured to facilitate such selection; selecting a characterize data option by the user for characterizing the data, via the user interface configured to facilitate such selection).
Claim 7 and Patent 904 are considered the same.
As to claim 8 Patent 904 teaches further comprising: automatically creating, by the processor, a list of meaningful observations that are equal to or higher than a predetermined score threshold, based on the list of key analysis (Claim 8 Patent 904 discloses automatically creating, by the processor, a list of meaningful observations that are equal to or higher than a predetermined score threshold, based on the list of key analysis).
Claim 8 and Patent 904 are considered the same.
As to claim 9 Patent 904 teaches wherein the correlation is performed based on a p-value to determine if a relationship exists between the user-selected variables and if an existing relationship is one of: statistically significant or not statistically significant (Claim 9 Patent 904 discloses wherein the correlation is performed based on a p-value to determine if a relationship exists between the user-selected variables and if an existing relationship is one of: statistically significant or not statistically significant).
Claim 9 and Patent 904 are considered the same.
As to claim 10 Patent 904 teaches wherein the observations are presented to the user in the order of importance, with the most important observations presented first and based on automated sorting via the processor (Claim 10 Patent 904 discloses wherein the observations are presented to the user in the order of importance, with the most important observations presented first and based on automated sorting via the processor).
Claim 10 and Patent 904 are considered the same.
As to claim 11 Patent 904 teaches a system for analysing a data set based on a ranking of observations, wherein the data set includes data for a plurality of variables resulting from a single study, the system comprising: a memory comprising one or more executable modules; and a processor configured to execute the one or more executable modules for analysing the data set based on the ranking of observations, the one or more executable modules comprising: (Claim 11 Patent 904 discloses a system for analysing a data set based on ranking of observations, wherein the data set includes data for a plurality of variables resulting from a study, the system comprising: a memory comprising one or more executable modules; and a processor configured to execute the one or more executable modules for analysing the data set based on ranking of observations, the one or more executable modules comprising);
a data module for receiving the data set and a selection of one or more significant user-selected variables from the plurality of variables via a user interface configured to facilitate such selection (Claim 11 Patent 904 discloses a data module for receiving the data set and a selection of one or more significant user-selected variables from the plurality of variables via a user interface configured to facilitate such selection, wherein the one or more user-selected variables in the data set corresponds to what interests the user and represents a priority & focus of a study, thereby ignoring remaining non-user-selected variables of the plurality of variables in the data set);
a selection module for automatically selecting an appropriate statistical technique from a predefined library of statistical techniques based on the type of the user- selected variable for: (i) only performing a correlation analysis for measuring an interdependence of one or more user-selected variables associated with the data set and (ii)only assessing a magnitude of the relationship between the one or more user-selected variables (Claim 11 Patent 904 discloses a selection module for automatically selecting an appropriate statistical technique from a predefined library of statistical techniques based on the type of the user-selected variable for: (i) only performing a correlation analysis for measuring an interdependence of one or more user-selected variables associated with the data set and not remaining non-user-selected variables and (ii) only assessing a magnitude of the relationship between the one or more user-selected variables and not remaining non-user-selected variables);
a correlation module for performing a correlation for generating one or more correlation results comprising at least one of: a first set of user-selected variables related to the one or more user-selected variables, and a second set of user-selected variables related to the first set of user-selected variables (Claim 11 Patent 904 discloses a correlation module for performing a correlation for generating one or more correlation results comprising at least one of: a first set of user-selected variables related to the one or more significant user-selected variables, and a second set of user-selected variables related to the first set of user-selected variables);
wherein performing the correlation comprises ranking the observations by at least one of: (i) classifying one or more variable observations associated with the data set based on at least one of: significance to the user, the variable type, the observation nature, and the observation result; and generating a list of observations and insights based on the classification; or (ii) running a relate process with two or more 1st, 2nd, and 3rd degree variables and collecting resulting observations (Claim 11 Patent 904 discloses a wherein performing the correlation comprises ranking the observations in an order of relevance by: classifying one or more variable observations associated with the data set based on at least one of: significance to the user, the variable type, the observation nature, and the observation result; generating a list of observations and insights based on the classification; running a relate process with two or more 1st, 2nd, and 3rd degree variables and collecting resulting observations);
and an analysis module for returning a list of key analysis and one or more analysis observations, to the user, based on the one or more user-selected variables and the one or more correlation results, whereby the list of key analysis provides a deep insight to the user around the data set based only on the one or more user-selected variables (Claim 11 Patent 904 discloses and an analysis module for returning a list of key analysis and one or more analysis observations, to the user, based on the one or more significant user-selected variables and the one or more correlation results, whereby the list of key analysis provides a deep insight to the user around the data set based only on the one or more user-selected variables).
The difference between claim 11 is the patent 904 is the Patent 904 contains this additional claim language:
wherein the one or more user-selected variables in the data set corresponds to what interests the user and represents a priority & focus of a study, thereby ignoring remaining non-user-selected variables of the plurality of variables in the data set.
As to claim 12 Patent 904 teaches wherein the list of key analysis describes at least one of: frequencies, correlation, regression, benchmark reports, and recommended clusters, and wherein the list of key analysisis in the form of at least one of: a summary table, a set of charts, data tables, and plain language explanation (Claim 12 Patent 904 discloses wherein the list of key analysis describes at least one of: frequencies, correlation, regression, benchmark reports, and recommended clusters, and wherein the list of key analysis is in the form of at least one of: a summary table, a set of charts, data tables, and plain language explanation).
Claim 12 and Patent 904 are considered the same.
As to claim 13 Patent 904 teaches wherein the interdependence of the user-selected variables is indicative of whether one or more user-selected variables are related to the one or more significant user-selected variables selected by the user (Claim 13 Patent 904 discloses wherein the interdependence of the user-selected variables is indicative of whether one or more user-selected variables are related to the one or more significant user-selected variables selected by the user).
Claim 13 and Patent 904 are considered the same.
As to claim 14 Patent 904 teaches further comprising a weight assignment module for assigning a plurality of weights to user-selected variables, observation type and analysis result, wherein the plurality of weights is based on at least the variable type and the results (Claim 14 Patent 904 discloses further comprising a weight assignment module for assigning a plurality of weights to user-selected variables, observation type and analysis result, wherein the plurality of weights is based on at least the variable type, and the results).
Claim 14 and Patent 904 are considered the same.
As to claim 15 Patent 904 teaches wherein performing the correlation comprises ranking the observations in an order of relevance by:ranking the list of observations based on a series of weights that are based on pre- defined factors (Claim 15 Patent 904 discloses wherein performing the correlation comprises ranking the observations in an order of relevance by: ranking the list of observations based on a series of weights that are based on pre-defined factors).
Claim 15 and Patent 904 are considered the same.
As to claim 16 Patent 904 teaches wherein a ranking score is given by the following equation:RankingScore = (Score / Value_Range) * Factor_Weight; andwherein the RankingScore is assigned to each observation and the observations are ordered with the greatest RankingScore first (Claim 16 Patent 904 discloses wherein the ranking score is given by the following equation: Ranking_Score=(Score/Value_Range)*Factor_Weight; and Wherein the Ranking_Score is assigned to each observation and the observations are ordered with the greatest Ranking_Score first).
Claim 16 and Patent 904 are considered the same.
As to claim 17 Patent 904 teaches wherein the analysis module is further configured to: create a list of meaningful observations based on the list of key analysis, wherein the analysis observations are in plain English language, and wherein the observations are presented to the user in the order of importance, with the most important observations presented first and based on sorting via the processor (Claim 17 Patent 904 discloses wherein the analysis module is further configured to: create a list of meaningful observations based on the list of key analysis, wherein the analysis observations are in plain English language and wherein the observations are presented to the user in the order of importance, with the most important observations presented first and based on sorting via the processor).
Claim 27 and Patent 904 are considered the same.
As to claim 18 Patent 904 teaches a processor-implemented method of performing correlation of one or more variables in a database, wherein the data set includes data for a plurality of variables resulting from a single study, the method comprising:
receiving an input from a user, the input comprising a selection via a user interface configured to facilitate such selection of one or more significant user-selected variables of interest to the user among the plurality of variables of the data set (Claim 18 Patent 904 discloses receiving an input from a user, the input comprising a selection via a user interface configured to facilitate such selection of one or more significant user-selected variables of interest to the user among the plurality of variables of the data set);
automatically selecting based on the type of the user-selected variable, by a processor, an appropriate statistical technique from a predefined library of statistical techniques for performing a correlation analysis for: (i) only measuring an interdependence of one or more user-selected variables associated with the data set and (ii) only assessing a magnitude of the relationship between the one or more user-selected variables (Claim 18 Patent 904 discloses automatically selecting based on the type of the user-selected variable, by a processor, an appropriate statistical technique from a predefined library of statistical techniques for performing a correlation analysis for: (i) only measuring an interdependence of one or more user-selected variables associated with the data set and not remaining non-user-selected variables and (ii) only assessing a magnitude of the relationship between the one or more user-selected variables and not remaining non-user-selected variables);
performing, by the processor, a correlation for generating one or more correlation results comprising at least one of: a first set of user-selected variables related to the one or more user-selected variables, and a second set of user-selected variables related to the first set of user-selected variables (Claim 18 Patent 904 discloses performing, by the processor, a correlation for generating one or more correlation results comprising at least one of: a first set of user-selected variables related to the one or more significant user-selected variables, and a second set of user-selected variables related to the first set of user-selected variables);
wherein performing the correlation comprises ranking the observations by at least one of: (i) classifying one or more variable observations associated with the data set based on at least one of: significance to the user, the variable type, the observation nature, and the observation result; and generating a list of observations and insights based on the classification; or (ii) running a relate process with two or more 1st, 2nd, and 3rd degree variables and collecting resulting observations (Claim 18 Patent 904 discloses wherein performing the correlation comprises ranking the observations in an order of relevance by: classifying one or more variable observations associated with the data set based on at least one of: significance to the user, the variable type, the observation nature, and the observation result; generating a list of observations and insights based on the classification; running a relate process with two or more 1st, 2nd, and 3rd degree variables and collecting resulting observations);
Patent 904 discloses generating a list of observations and insights based on the classification; running a relate process with two or more 1st, 2nd, and 3rd degree variables and collecting resulting observations; and compiling the observations across the data set for the two or more 1st, 2nd, and 3rd degree variables and ranking the observations by assigning a score to each observation; by ranking the observations in an order of relevance by: a) classifying one or more variable observations associated with the data set based on at least one of: significance to the user, the variable type, the observation nature, and the observation result; b) generating a list of observations and insights based on the classification; and c) ranking the list of observations based on a series of weights;
and returning, by the processor, a list of key analysis, to the user, based on the one or more user-selected variables and the one or more correlation results related thereto, whereby the list of key analysis provides an insight to the user about the data set based only on the one or more user-selected variables (Claim 18 Patent 904 discloses and returning, by the processor, a list of key analysis, to the user, based on the one or more significant user-selected variables and the one or more correlation results related thereto, whereby the list of key analysis provides an insight to the user about the data set based only on the one or more user-selected variables).
The difference between claim 18 is the patent 904 is the Patent 904 contains this additional claim language:
generating a list of observations and insights based on the classification; running a relate process with two or more 1st, 2nd, and 3rd degree variables and collecting resulting observations; and compiling the observations across the data set for the two or more 1st, 2nd, and 3rd degree variables and ranking the observations by assigning a score to each observation; by ranking the observations in an order of relevance by: a) classifying one or more variable observations associated with the data set based on at least one of: significance to the user, the variable type, the observation nature, and the observation result; b) generating a list of observations and insights based on the classification; and c) ranking the list of observations based on a series of weights;
As to claim 19 Patent 904 teaches wherein a ranking score is given by the following equation:RankingScore = (Score / Value_Range) * Factor_Weight; andwherein the RankingScore is assigned to each observation and the observations are ordered with the greatest RankingScore first ((Claim 18 Patent 904 discloses wherein the ranking score is given by the following equation: Ranking_Score=(Score/Value_Range)*Factor_Weight; and wherein the Ranking_Score is assigned to each observation and the observations are ordered with the greatest Ranking_Score first).
Claim 19 and Patent 904 are considered the same.
As to claim 20 Patent 904 teaches wherein the correlation is performed based on a p-value to determine if a relationship exists between the user-selected variables and if an existing relationship is one of: statistically significant or not statistically significant (Claim 20 Patent 904 discloses wherein the correlation is performed based on a p-value to determine if a relationship exists between the user-selected variables and if an existing relationship is one of: statistically significant or not statistically significant).
Claim 20 and Patent 904 are considered the same.
Claim Rejections - 35 USC § 103
5. 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.
6. 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.
7. Claim(s) 1-5, 7-15 and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatenable by Galloway et al. U.S. Patent Application Publication No. 2014/0046983 (herein as ‘Galloway’) and further in view of Moser et al. U.S. Patent No. 10,007,708 (herein as ‘Moser’).
As to claim 1 Galloway teaches a processor-implemented method of analysing a data set, wherein the data set includes data for a plurality of variables resulting from a single study, the method comprising:
uploading the data set by a user (Par. 0223 Galloway discloses a user selecting a dataset to be processed by an electronic processing device. Selecting the dataset is seen as uploading the dataset);
selecting via a user interface configured to facilitate such selection, by the user, one or more user-selected variables from the plurality of variables in the data set based on what interests the user (Par. 0226 Galloway discloses a user selecting variables of interest related to the dataset);
automatically selecting based on the type of the user-selected variable, by a processor, an appropriate statistical technique from a predefined library of statistical techniques for performing a correlation analysis for: (i) only measuring an interdependence of one or more user-selected variables associated with the data set and (ii) only assessing a magnitude of the relationship between the one or more user-selected variables (Par. 0228 Galloway discloses the system selecting between two different representations as a way to detail the relationship between the coefficient that are associated with the variables. One way of determining the relationship is using a simple list of coefficients for each of the variables. The other way of determining the variables is using a graphical network representation);
performing, by the processor, a correlation for generating one or more correlation results comprising at least one of: a first set of user-selected variables related to the one or more user-selected variables, and (ii) a second set of user-selected variables related to the first set of user-selected variables (Par. 0233 Galloway discloses determining which first variables cause effects on what second variables, and what first variable is related to what second variable).
Galloway does not teach but Moser teaches and wherein performing the correlation comprises ranking the observations by at least one of:(i) classifying one or more variable observations associated with the data set based on at least one of: significance to the user, the variable type, the observation nature, and the observation result; and generating a list of observations and insights based on the classification; or (ii) running a relate process with two or more 1st, 2nd, and 3rd degree variables and collecting resulting observations (Col. 2 Lines 15-25 Moser discloses visualization candidates are relationships between data within datasets. Col. 13 Lines 15-23 Moser discloses generating visualization candidates by comparing and developing data dimensions to M:N which is any number of dimensions to any number of dimensions);
and returning, by the processor, a list of key analysis, to the user, based on the one or more user-selected variables and the one or more correlation results related thereto, whereby the list of key analysis provides an insight to the user about the data set based only on the one or more user-selected variables (Col. 15 Lines 35-37 Moser discloses returning the visualization candidates to the user as suggestion. Col. 16 Lines 10-20 Moser discloses the visualization candidates are ranked and presented to the user).
Galloway and Moser are analogous art because they are in the same field of endeavor, data analysis. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the data analysis of Galloway to include the data analysis of Evans, to allow less frustration and wasted time in navigating or exploring data (Par. 0000 Moser).
As to claim 2 Galloway in combination with Moser teaches each and every limitation of claim 1.
In addition Galloway teaches wherein the list of key analysis describes at least one of: frequencies, correlation, regression, benchmark reports, and recommended clusters, and wherein the list of key analysis is in the form of at least one of: a summary table, a set of charts, data tables, and plain language explanation (Par. 0227 Galloway discloses the relationships are based upon the regression).
As to claim 3 Galloway in combination with Moser teaches each and every limitation of claim 1.
In addition Galloway teaches wherein the interdependence of the user- selected variables is used as an indicator of whether one or more user-selected variables are related to the one or more significant user-selected variables (Par. 0231 Galloway discloses visually identifying the degree of related between selected variables and the strength of the node connections).
As to claim 4 Galloway in combination with Moser teaches each and every limitation of claim 1.
In addition, Galloway teaches further comprising automatically assigning, by the processor, individual weights to the first set of user-selected variables, the second set of user- selected variables and the one or more significant user-selected variables, observation type and analysis result based on at least the variable type (Par. 0227 Galloway discloses assigning relationship coefficients between the selected variables and each other variable. The selected variables are seen as the first set of user selected variables, and the second set of user-selected variables, and each other variable is seen as the more significant user-selected variables).
As to claim 5 Galloway in combination with Moser teaches each and every limitation of claim 1.
In addition, Moser teaches wherein performing the correlation comprises ranking the observations in an order of relevance by ranking the list of observations based on a series of weights that are based on pre-defined factors (Col. 12 Lines 25-35 Moser discloses giving priority to ranking visualization candidates based on fields of data that has a high level of usage related to the other visualization candidates).
As to claim 7 Galloway in combination with Moser teaches each and every limitation of claim 1.
In addition, Galloway teaches further comprising: selecting a task and a describe option, by the user, via the user interface configured to facilitate such selection; selecting a characterize data option by the user for characterizing the data, via the user interface configured to facilitate such selection (Par. 0331 Galloway discloses allowing the user to define the parameters for displaying the representation).
As to claim 8 Galloway in combination with Moser teaches each and every limitation of claim 1.
In addition, Galloway teaches further comprising: automatically creating, by the processor, a list of meaningful observations that are equal to or higher than a predetermined score threshold, based on the list of key analysis (Par. 0323 Galloway discloses filtering out connections having relationship coefficients below a threshold).
As to claim 9 Galloway in combination with Moser teaches each and every limitation of claim 1.
In addition, Galloway teaches wherein the correlation is performed based on a p-value to determine if a relationship exists between the user-selected variables and if an existing relationship is one of: statistically significant or not statistically significant (Par. 0323 Galloway discloses filtering out connections having relationship coefficients below a threshold).
As to claim 10 Galloway in combination with Moser teaches each and every limitation of claim 1.
In addition, Moser teaches wherein the observations are presented to the user in the order of importance, with the most important observations presented first and based on automated sorting via the processor (Col. 14 Lines 42-45 Moser discloses the visualization candidates are based upon score in descending order, so the most important will be presented first).
As to claim 11 Galloway teaches a system for analysing a data set based on a ranking of observations, wherein the data set includes data for a plurality of variables resulting from a single study, the system comprising:
a memory comprising one or more executable modules (Par. 0253 Galloway discloses a memory);
and a processor configured to execute the one or more executable modules for analysing the data set based on the ranking of observations, (Par. 0253 Galloway discloses a procesor);
the one or more executable modules comprising: a data module for receiving the data set (Par. 0223 Galloway discloses a user selecting a dataset to be processed by an electronic processing device. Selecting the dataset is seen as uploading the dataset);
and a selection of one or more significant user-selected variables from the plurality of variables via a user interface configured to facilitate such selection (Par. 0223 Galloway discloses a user selecting a dataset to be processed by an electronic processing device. Selecting the dataset is seen as uploading the dataset);
a selection module for automatically selecting an appropriate statistical technique from a predefined library of statistical techniques based on the type of the user- selected variable for: (i) only performing a correlation analysis for measuring an interdependence of one or more user-selected variables associated with the data set and (ii)only assessing a magnitude of the relationship between the one or more user-selected variables (Par. 0228 Galloway discloses the system selecting between two different representations as a way to detail the relationship between the coefficient that are associated with the variables. One way of determining the relationship is using a simple list of coefficients for each of the variables. The other way of determining the variables is using a graphical network representation);
a correlation module for performing a correlation for generating one or more correlation results comprising at least one of: a first set of user-selected variables related to the one or more user-selected variables, and a second set of user-selected variables related to the first set of user-selected variables (Par. 0233 Galloway discloses determining which first variables cause effects on what second variables, and what first variable is related to what second variable);
Galloway does not teach but Moser teaches wherein performing the correlation comprises ranking the observations by at least one of: (i) classifying one or more variable observations associated with the data set based on at least one of: significance to the user, the variable type, the observation nature, and the observation result; and generating a list of observations and insights based on the classification; or (ii) running a relate process with two or more 1st, 2nd, and 3rd degree variables and collecting resulting observations (Col. 2 Lines 15-25 Moser discloses visualization candidates are relationships between data within datasets. Col. 13 Lines 15-23 Moser discloses generating visualization candidates by comparing and developing data dimensions to M:N which is any number of dimensions to any number of dimensions);
and an analysis module for returning a list of key analysis and one or more analysis observations, to the user, based on the one or more user-selected variables and the one or more correlation results, whereby the list of key analysis provides a deep insight to the user around the data set based only on the one or more user-selected variables (Col. 15 Lines 35-37 Moser discloses returning the visualization candidates to the user as suggestion. Col. 16 Lines 10-20 Moser discloses the visualization candidates are ranked and presented to the user).
Galloway and Moser are analogous art because they are in the same field of endeavor, data analysis. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the data analysis of Galloway to include the data analysis of Evans, to allow less frustration and wasted time in navigating or exploring data (Par. 0000 Moser).
As to claim 12 Galloway in combination with Moser teaches each and every limitation of claim 11.
In addition Galloway teaches wherein the list of key analysis describes at least one of: frequencies, correlation, regression, benchmark reports, and recommended clusters, and wherein the list of key analysisis in the form of at least one of: a summary table, a set of charts, data tables, and plain language explanation (Par. 0227 Galloway discloses the relationships are based upon the regression).
As to claim 13 Galloway in combination with Moser teaches each and every limitation of claim 11.
In addition Galloway teaches wherein the interdependence of the user-selected variables is indicative of whether one or more user-selected variables are related to the one or more significant user-selected variables selected by the user (Par. 0231 Galloway discloses visually identifying the degree of related between selected variables and the strength of the node connections).
As to claim 14 Galloway in combination with Moser teaches each and every limitation of claim 11.
In addition Galloway teaches further comprising a weight assignment module for assigning a plurality of weights to user-selected variables, observation type and analysis result, wherein the plurality of weights is based on at least the variable type and the results (Par. 0227 Galloway discloses assigning relationship coefficients between the selected variables and each other variable. The selected variables are seen as the first set of user selected variables, and the second set of user-selected variables, and each other variable is seen as the more significant user-selected variables).
As to claim 15 Galloway in combination with Moser teaches each and every limitation of claim 11.
In addition, Moser teaches wherein performing the correlation comprises ranking the observations in an order of relevance by: ranking the list of observations based on a series of weights that are based on pre- defined factors (Col. 12 Lines 25-35 Moser discloses giving priority to ranking visualization candidates based on fields of data that has a high level of usage related to the other visualization candidates).
As to claim 17 Galloway in combination with Moser teaches each and every limitation of claim 11.
In addition, Moser teaches wherein the analysis module is further configured to: create a list of meaningful observations based on the list of key analysis, wherein the analysis observations are in plain English language, and wherein the observations are presented to the user in the order of importance, with the most important observations presented first and based on sorting via the processor (Col. 14 Lines 42-45 Moser discloses the visualization candidates are based upon score in descending order, so the most important will be presented first).
As to claim 18 Galloway teaches a processor-implemented method of performing correlation of one or more variables in a database, wherein the data set includes data for a plurality of variables resulting from a single study, the method comprising:
receiving an input from a user (Par. 0223 Galloway discloses a user selecting a dataset to be processed by an electronic processing device. Selecting the dataset is seen as uploading the dataset);
the input comprising a selection via a user interface configured to facilitate such selection of one or more significant user-selected variables of interest to the user among the plurality of variables of the data set (Par. 0226 Galloway discloses a user selecting variables of interest related to the dataset);
automatically selecting based on the type of the user-selected variable, by a processor, an appropriate statistical technique from a predefined library of statistical techniques for performing a correlation analysis for: (i) only measuring an interdependence of one or more user-selected variables associated with the data set and (ii) only assessing a magnitude of the relationship between the one or more user-selected variables (Par. 0228 Galloway discloses the system selecting between two different representations as a way to detail the relationship between the coefficient that are associated with the variables. One way of determining the relationship is using a simple list of coefficients for each of the variables. The other way of determining the variables is using a graphical network representation);
performing, by the processor, a correlation for generating one or more correlation results comprising at least one of: a first set of user-selected variables related to the one or more user-selected variables, and a second set of user-selected variables related to the first set of user-selected variables (Par. 0233 Galloway discloses determining which first variables cause effects on what second variables, and what first variable is related to what second variable);
wherein performing the correlation comprises ranking the observations by at least one of: (i) classifying one or more variable observations associated with the data set based on at least one of: significance to the user, the variable type, the observation nature, and the observation result; and generating a list of observations and insights based on the classification; or (ii) running a relate process with two or more 1st, 2nd, and 3rd degree variables and collecting resulting observations (Col. 2 Lines 15-25 Moser discloses visualization candidates are relationships between data within datasets. Col. 13 Lines 15-23 Moser discloses generating visualization candidates by comparing and developing data dimensions to M:N which is any number of dimensions to any number of dimensions);
and returning, by the processor, a list of key analysis, to the user, based on the one or more user-selected variables and the one or more correlation results related thereto, whereby the list of key analysis provides an insight to the user about the data set based only on the one or more user-selected variables (Col. 15 Lines 35-37 Moser discloses returning the visualization candidates to the user as suggestion. Col. 16 Lines 10-20 Moser discloses the visualization candidates are ranked and presented to the user).
Galloway and Moser are analogous art because they are in the same field of endeavor, data analysis. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the data analysis of Galloway to include the data analysis of Evans, to allow less frustration and wasted time in navigating or exploring data (Par. 0000 Moser).
As to claim 20 Galloway in combination with Moser teaches each and every limitation of claim 18.
In addition, Galloway teaches wherein the correlation is performed based on a p-value to determine if a relationship exists between the user-selected variables and if an existing relationship is one of: statistically significant or not statistically significant (Par. 0323 Galloway discloses filtering out connections having relationship coefficients below a threshold).
Allowable Subject Matter
8. Claims 6, 16 and 19 does not contain any prior art rejection.
6. The processor-implemented method of claim 1, wherein a ranking score is given by the following equation:RankingScore = (Score / ValueRange) * FactorWeight; andwherein the RankingScore is assigned to each observation and the observations are ordered with the greatest RankingScore first.
16. The system of claim 11, wherein a ranking score is given by the following equation:RankingScore = (Score / Value_Range) * Factor_Weight; andwherein the RankingScore is assigned to each observation and the observations are ordered with the greatest RankingScore first.
19. The processor-implemented method of claim 18, wherein a ranking score is given by the following equation:RankingScore = (Score / Value_Range) * Factor_Weight; andwherein the RankingScore is assigned to each observation and the observations are ordered with the greatest RankingScore first.
Conclusion
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/J.A.M/ February 12, 2026Examiner, Art Unit 2159 /ANN J LO/Supervisory Patent Examiner, Art Unit 2159