DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/24/2024 is in compliance with the provisions of 37 CFR 1.97 and have been entered into the record. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 9-14), computer program product (claims 15-20), and system (claims 1-8) are directed to potentially eligible categories of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One, it is next noted that the claims recite an abstract idea by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “Mental Process” group; and by reciting mathematical relationships, mathematical formulas or equations, mathematical calculations which falls into the “Mathematical concepts” within the enumerated groupings of abstract ideas. The mere nominal recitation of a generic computer does not take the claim limitation out of mathematical concepts or the mental processes grouping. Thus, the claim recites a mental process for performing certain mathematical concepts.
A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.
The limitations reciting the abstract idea(s) (Mental process and Math), as set forth in exemplary claim 1, are: identify a plurality of possible control groups that are similar to a test group; receive, from a data source, first target variable information, associated with the test group, associated with at least a first time and a second time subsequent to the first time; receive, from the data source, second target variable information, associated with the plurality of possible control groups, associated with at least the first time and the second time; assemble a first control group, from a first random selection from the plurality of possible control groups, based on applying a nearest neighbor algorithm to the first and second target variable information associated with the first time; assemble a second control group, from a second random selection from the plurality of possible control groups, based on applying the nearest neighbor algorithm to the first and second target variable information associated with the first time; determine a target variable change by comparing the first target variable information, associated with the test group and with the second time, against a portion of the second target variable information, associated with the first control group and the second control group and with the second time; perform a first validation of the target variable change by comparing a distribution of the first target variable information, associated with the test group and with the first time, against a distribution of a portion of the second target variable information, associated with the first control group and the second control group and with the first time; perform a second validation of the target variable change by comparing a portion of the second target variable information, associated with the first control group and with the second time, against a portion of the second target variable information, associated with the second control group and with the second time; and output the target variable change in response to the first validation and the second validation. Independent claims 9 and 15 recite the method and CRM for performing the system of independent claim 1 without adding significantly more. Thus, the same rationale/analysis is applied.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements are directed to: one or more memories; and one or more processors, communicatively coupled to the one or more memories; A non-transitory computer-readable medium storing a set of instructions for identifying and validating control groups, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to…; (as recited in claims 1 and 15). However, these elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitation(s) is/are directed to: one or more memories; and one or more processors, communicatively coupled to the one or more memories; A non-transitory computer-readable medium storing a set of instructions for identifying and validating control groups, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to…; (as recited in claims 1 and 15) for implementing the claim steps/functions. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim.
The additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h). Even if the acquiring steps are considered as additional elements, these steps at most amount to insignificant extra-solution activity accomplished via receiving/transmitting data, which is not enough to amount to a practical application. See MPEP 2106.05(g).
In addition, Applicant’s Specification (paragraph [0068]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. See, e.g., Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself. Further, the courts have found the presentation of data to be a well-understood, routine, conventional activity, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 (see MPEP 2106.05(d)).
The dependent claims (2-8, 10-14, and 16-20) are directed to the same abstract idea as recited in the independent claims, and merely incorporate additional details that narrow the abstract idea via additional details of the abstract idea. For example claims 2-7 “receive, from an additional data source, first census information, associated with the test group; and receive, from the additional data source, second census information, associated with the plurality of possible control groups, wherein the plurality of possible control groups are identified using the first census information and the second census information; determine that a difference, between first census information associated with the test group and second census information associated with the plurality of possible control groups, satisfies a similarity threshold; performing standardization on the first and second target variable information; and performing winsorizing on the second target variable information; wherein the test group is associated with a census block group, and the plurality of possible control groups are associated with a plurality of additional census block groups; calculate a distance between a first trend line associated with the test group and a second trend line associated with the first control group or the second control group; wherein the one or more processors, to output the target variable change, are configured to: output a table including the target variable change”, without additional elements that integrate the abstract idea into a practical application and without additional elements that amount to significantly more to the claims. The remaining dependent claims (10-14 and 16-20) recite the CRM and system for performing the method of claims 2-8. Thus, the same rationale/analysis is applied. Thus, all dependent claims have been fully considered, however, these claims are similarly directed to the abstract idea itself, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 7, 9, 15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 10467572 (hereinafter “Bruce”) et al., in view of U.S. PGPub 20210264466 to (hereinafter “Aggarwal”) et al.
As per claim 1, Bruce teaches a system for identifying and validating control groups, the system comprising:
one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to: identify a plurality of possible control groups that are similar to a test group; 027-028: “The computer 100 transmits or otherwise provides historical data regarding entities 105 to a host entity 130. In this exemplary configuration, the host entity has a server 120 is coupled to the database 110, though the server 120 and the database 110 can be combined into a single device or each comprise multiple devices. The server 120 can be a computer system such as a desktop computer, workstation, or any other similar server side computing system that performs one or more service-side processes. The server 120 can have an interface unit for communicating information to and from the client's computer 100 over the network 140. In some embodiments, the server 120 may communicate with another server, such as a web server, that can more directly communicate over the network 140. The server 120 can use its processor to execute a computer program stored in memory that can access and analyze the data stored in the database 110. The database 110 can comprise one or more memory devices that store data and/or executable software that is used by the server 120 to perform processes consistent with certain aspects described herein. The database 110 may be located external to server 120 and accessible through the network 140 or other network, such as a dedicated back-end communication path. In one embodiment, the database 110 can be located at the client or another location, such as with server 120. The database 110 can be populated with records about the client's historical data for various locations, sales, promotions, pricing, personnel, and the like. The client computer 100 can communicate with the server 120 to request analysis and view results.” 062: “ In step 1730, the client computer or host server identifies the most similar potential control locations for each test location. Given a test location and a set of potential control locations, the system computer pairwise distances between each test location and every control location, and then the system chooses the best control locations based upon the closest locations, i.e., those with the lowest distance or highest similarity score…080: While using the test versus control methodology, it is often considered best practice to come up with a few different candidate control groups and then use the control group fit module to evaluate each for noise in the pre-period.”
receive, from a data source, first target variable information, associated with the test group, associated with at least a first time and a second time subsequent to the first time; receive, from the data source, second target variable information, associated with the plurality of possible control groups, associated with at least the first time and the second time;
068: “The system can fetch and aggregate pre-period and post-period data for each test and candidate control location for a metric of interest, such as lift. Lift can be calculated from a ratio of test versus control and pre-period to post-period performance. As shown in FIG. 11, a measure of how much the test location is outperforming the control location is called lift.”
determine a target variable change by comparing the first target variable information, associated with the test group and with the second time, against a portion of the second target variable information, associated with the first control group and the second control group and with the second time; 045-068: “Control strategy options (also referred to herein as control strategy settings) include a number of similar control sites for each test site, so a user has selected 10 or 20 similar control sites for each test site…The system can fetch and aggregate pre-period and post-period data for each test and candidate control location for a metric of interest, such as lift. Lift can be calculated from a ratio of test versus control and pre-period to post-period performance. As shown in FIG. 11, a measure of how much the test location is outperforming the control location is called lift.”
perform a first validation of the target variable change by comparing a distribution of the first target variable information, associated with the test group and with the first time, against a distribution of a portion of the second target variable information, associated with the first control group and the second control group and with the first time; 045: “Control strategy options (also referred to herein as control strategy settings) include a number of similar control sites for each test site, so a user has selected 10 or 20 similar control sites for each test site…078-079: A control group fit module examines the fit of the control strategies when modeling the pre-period of the test locations. It can provide a number of metrics to help the user understand if there is a mismatch between test and control locations. It can also provide a visual of a trend chart that can be used to determine whether this mismatch will continue into the post period. If there is an inherent bias between test and control locations that extends into the post period, the user would have to account for this before getting a proper read regarding whether the experiment was successful. The module works by computing the lift for every time period (for example, weeks for a weekly metric) in the pre-period. These lifts are plotted on the control group fit chart (whereby the x-axis represents these periods and the values on the y-axis represent lift) where “control pre” is the average metric value for a control location in the pre-period and “test pre” is the average metric value in the pre-period, and “control post” and “test post” are the metric value for control and test locations for a particular date in the pre-period, respectively. The resulting lift values are then readjusted so that the average is 0. Then one measure of goodness of fit is the standard deviation of the y values. This is called the “noise” in the control group fit. If test and control locations are well matched, one would expect a noise close to 0.”
perform a second validation of the target variable change by comparing a portion of the second target variable information, associated with the first control group and with the second time, against a portion of the second target variable information, associated with the second control group and with the second time; 054: “In step 1720, the client computer or host server randomly selects a plurality of test locations and event dates to create a plurality of null tests for a predetermined time frame based upon the randomly selected test locations…072: The output for a simulation ran for a particular control strategy and other analytic parameters such as pre-period duration and a particular metric category is a set of lifts and pre-period slope and noise measures for each randomly generated test. The “goodness” of a set of modeling parameters is measured by the standard deviation lift across a set of null tests. The average lift can also indicate any significant bias that the control strategy is introducing and can serve as a filter for such control strategies. The metrics of average pre-period noise and slope in general are not used as a deterministic measure of goodness, but to understand the domain of reasonable pre-period noise and slope values given the general behavior of the network.”
and output the target variable change in response to the first validation and the second validation;084-085: “These steps can be repeated for each null activity using a random draw of test sites and time frame. FIG. 14 shows a graphical user interface displaying a standard deviation and average lift of all null activities for each control strategy. In this example, this table has 1128 rows, because it built 564 control strategies for 2 different pre-period durations. Each row is driven by 200 lift numbers from 200 null activities (20 random draws of test sites and 10 time frames). It is then ranked by standard deviation of lift. One way to analyze the output of many simulations with different control strategies and pre-period durations is to rank order the simulations based upon the metric of “goodness,” standard deviation of lift, and filter based upon the metric that indicate bias, average lift. One can arrive at the best control strategies by examining the top control strategies of this ordered list.”
Bruce may not explicitly teach the following. However, Aggarwal teaches:
assemble a first control group, from a first random selection from the plurality of possible control groups, based on applying a nearest neighbor algorithm to the first and second target variable information associated with the first time; assemble a second control group, from a second random selection from the plurality of possible control groups, based on applying the nearest neighbor algorithm to the first and second target variable information associated with the first time; 0059: “The transaction service provider system 112 and/or modeling server 116 may receive transaction account data of a plurality of transaction accounts 110 in a first time period…0070: The synthetic control group 318 may include non-active transaction accounts, which may be randomly selected by a modeling server, such as with a k-d tree technique. The population size of a synthetic control group 318 of non-active transaction accounts may be set equal to twice the number of non-active transaction accounts in the exposed population 310 of transaction accounts. A modeling server may generate a feature set for a k-d tree technique, in step 314, using an exposed population 310 of transaction accounts. In step 316, the modeling server may run the k-d tree technique, selecting two nearest neighbors of non-active transaction accounts for every exposed transaction account. The sampling phase 302 yields a synthetic control group 318.”
Bruce and Aggarwal are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bruce with the aforementioned teachings from Aggarwal with a reasonable expectation of success, by adding steps that allow the software to apply algorithm(s) data with the motivation to more efficiently and accurately organize and analyze data [Aggarwal 0070].
As per claim 7, Bruce and Aggarwal teach all the limitations of claim 1:
In addition, Bruce teaches:
output a table including the target variable change; 084: “These steps can be repeated for each null activity using a random draw of test sites and time frame. FIG. 14 shows a graphical user interface displaying a standard deviation and average lift of all null activities for each control strategy. In this example, this table has 1128 rows, because it built 564 control strategies for 2 different pre-period durations. Each row is driven by 200 lift numbers from 200 null activities (20 random draws of test sites and 10 time frames). It is then ranked by standard deviation of lift.”
Claim 9 is the method for teaching the system of claim 1. Since the art teaches the method, the same art and rationale are applied. However, claim 9 adds “by a simulator” and “to a user device” which are both taught by Bruce in paragraphs 027-028.
Claim 15 is the CRM for teaching the system of claim 1. Since the art teaches the CRM, the same art and rationale are applied.
Claim 17 is the CRM for teaching the system of claim 7. Since the art teaches the method, the same art and rationale are applied.
Claims 2-3, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 10467572 (hereinafter “Bruce”) et al., in view of U.S. PGPub 20210264466 to (hereinafter “Aggarwal”) et al., in further view of U.S. PGPub 20140100945 (hereinafter “Kitts”) et al.
As per claim 2, Bruce and Aggarwal teach all the limitations of claim 1:
Bruce may not explicitly teach the following. However, Kitts teaches:
receive, from an additional data source, first census information, associated with the test group;0091: “A sixth factor for selecting treatment groups may be the census disparity from the United States (US) average (e.g., CensusDisparityFromUSAverage). The census disparity from the US average may be the mean absolute difference between the US population census demographic average and the demographic vector of a particular region. A lower value for the census disparity may be better since this may indicate that the area is not greatly different from the US average…0166: Zip-code-level demographics are publicly available from the US Census Bureau and these can be aggregated to the same level as the cable and broadcast systems.”
and receive, from the additional data source, second census information, associated with the plurality of possible control groups, wherein the plurality of possible control groups are identified using the first census information and the second census information; 0102-0103: “At block 230 of method 200, processing logic selects control groups. One or more control groups may be selected for and be paired with a particular treatment group. The control groups may be selected using a combination of criteria. A first criterion may be demographic similarity. Control groups may be selected if they have similar demographics to their counterpart treatment groups. In order to match the demographics of treatment groups, a paired t-test may be performed on the array of demographic readings for treatment groups and control groups. Table 2 illustrates exemplary control groups which are selected based on age, ethnicity and income levels of people in the areas selected for the control groups. Each row represents a control group and columns 2 through 8 indicate the percentage of difference between the control group and a corresponding treatment group. For example, Let's say that Eureka, Calif. (Eureka Calif.) is being considered as a possible treatment area. Eureka Calif. has a 1.47% difference in the number of males over the age of 15, as compared to the US Population.”
Bruce, Aggarwal, and Kitts are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bruce and Aggarwal with the aforementioned teachings from Kitts with a reasonable expectation of success, by adding steps that allow the software to receive data with the motivation to more efficiently and accurately organize and analyze data [Kitts 0102].
As per claim 3, Bruce and Aggarwal teach all the limitations of claim 1:
Bruce may not explicitly teach the following. However, Kitts teaches:
identify the plurality of possible control groups, are configured to: determine that a difference, between first census information associated with the test group and second census information associated with the plurality of possible control groups, satisfies a similarity threshold;0102-0109: “At block 230 of method 200, processing logic selects control groups. One or more control groups may be selected for and be paired with a particular treatment group. The control groups may be selected using a combination of criteria. A first criterion may be demographic similarity. Control groups may be selected if they have similar demographics to their counterpart treatment groups. In order to match the demographics of treatment groups, a paired t-test may be performed on the array of demographic readings for treatment groups and control groups… After obtaining the goodness value (e.g., the control score) for each control area, the areas with the top goodness values (e.g., the areas with the top 10 good values) may be selected as the control groups.”
Bruce, Aggarwal, and Kitts are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bruce and Aggarwal with the aforementioned teachings from Kitts with a reasonable expectation of success, by adding steps that allow the software to receive data with the motivation to more efficiently and accurately organize and analyze data [Kitts 0102].
As per claim 13, Bruce and Aggarwal teach all the limitations of claim 9.
Bruce may not explicitly teach the following. However, Kitts teaches:
wherein the test group is associated with a geographic area, and the plurality of possible control groups are associated with a plurality of additional geographic areas; 0057: The group may be a geographic cell or area. Different group granularities may include: designated market areas (DMAs), cable operator zones (e.g., an area serviced by a cable operator), 5-digit zip codes, 9-digit zip codes, street address, cities, states, counties, towns, etc. In one embodiment, the group granularities may be selected based on one or more conditions…0104: A second criterion used to select control groups may be spatial proximity to treatment groups and/or to other control groups. The control groups should preferably be geographically close to the treatment groups. For example, the control groups would preferably be neighboring DMAs or zones.”
Bruce, Aggarwal, and Kitts are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bruce and Aggarwal with the aforementioned teachings from Kitts with a reasonable expectation of success, by adding steps that allow the software to receive data with the motivation to more efficiently and accurately organize and analyze data [Kitts 0102].
Claim 20 is the CRM for teaching the method of claim 13. Since the art teaches the CRM, the same art and rationale are applied.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 10467572 (hereinafter “Bruce”) et al., in view of U.S. PGPub 20210264466 to (hereinafter “Aggarwal”) et al., in further view of U.S. PGPub 20230377044 (hereinafter “Wilcox”) et al.
As per claim 4, Bruce and Aggarwal teach all the limitations of claim 1:
Bruce and Aggarwal may not explicitly teach the following. However, Wilcox teaches:
performing standardization on the first and second target variable information; and performing winsorizing on the second target variable information; 0081-0086: “Next, at step 206, the metric data is normalized into a company score. To normalize scores for the metric, the system calculates a Z-score, or a standard score, for each raw data point. The Z-score measures of how many standard deviations a number is above or below the mean. Raw scores above the mean have positive Z-scores, while those below the mean have negative Z-scores… most data points will fall in the range of zero (negative three standard deviations) to 100 (plus three standard deviations). To combat outliers, the system winsorizes all scores at three standard deviations plus or minus, i.e., a minimum score of zero and a maximum score of 100. Winsorizing is the transformation of statistics by limiting extreme values in the statistical data to reduce the effect of possibly spurious outliers.”
Bruce, Aggarwal, and Wilcox are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bruce and Aggarwal with the aforementioned teachings from Wilcox with a reasonable expectation of success, by adding steps that allow the software to utilize math with the motivation to more efficiently and accurately organize and analyze data [Wilcox 0086].
Claims 5 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 10467572 (hereinafter “Bruce”) et al., in view of U.S. PGPub 20210264466 to (hereinafter “Aggarwal”) et al., in further view of U.S. PGPub 20170091795 (hereinafter “Mansour”) et al.
As per claim 5, Bruce and Aggarwal teach all the limitations of claim 1:
Bruce and Aggarwal may not explicitly teach the following. However, Mansour teaches:
wherein the test group is associated with a census block group, and the plurality of possible control groups are associated with a plurality of additional census block groups; 0015-0029: “In operation, the example CBG interface 104 selects one or more census block(s) of interest from the example census bureau service 124, and the example ACV interface 106 identifies ACV information in the example ACV data source 126 that is associated with each store within the selected CBG…The program 700 of FIG. 7 begins at block 702 where the example CBG interface 104 selects a CBG of interest and the example ACV interface 106 identifies ACV values for each store in the selected CBG (block 704). The example similarity index engine 108 calculates similarity index values for all pairs of stores (block 706), and the example CBG interface 104 determines whether one or more additional CBGs of interest are to be evaluated (block 708). If so, then control returns to block 702 to select an additional CBG of interest for evaluation. As described above, and as described in further detail below, the calculation of similarity index values for pairs of stores also assigns those stores to particular LTAs based on an index overlap. In some examples, evaluation of CBGs includes a focused geographic area of interest, such as a particular neighborhood, a particular city, or a particular county. In still other examples, evaluation of CBGs includes a relatively larger geographic area of interest, such as a particular metropolitan area, a particular state, a particular region of states, or a particular country.”
Bruce, Aggarwal, and Mansour are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bruce and Aggarwal with the aforementioned teachings from Mansour with a reasonable expectation of success, by adding steps that allow the software to utilize math with the motivation to more efficiently and accurately organize and analyze data [Mansour 0029].
Claim 19 is the CRM for teaching the system of claim 5. Since the art teaches the CRM, the same art and rationale are applied.
Claims 6, 8, 10, 14, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 10467572 (hereinafter “Bruce”) et al., in view of U.S. PGPub 20210264466 to (hereinafter “Aggarwal”) et al., in further view of U.S. Patent 10949752 (hereinafter “Burns”) et al.
As per claim 6, Bruce and Aggarwal teach all the limitations of claim 1.
Bruce and Aggarwal may not explicitly teach the following. However, Burns teaches:
calculate a distance between a first trend line associated with the test group and a second trend line associated with the first control group or the second control group; 029-046: “As shown in FIG. 2, the distance or deviation between a test location 210 and a control location 220 can be visualized by the area 200 between the financial metric curves of the test location 210 and the control location 220… If trend line were separated into its constituent components each associated with control locations 410, 420, 430, under a simplifying assumption that three control locations are selected for each test location, the resulting graph might resemble the graph shown in FIG. 4. In this exemplary embodiment, the control locations selected as part of this particular test site's control group are shown as having similar financial trends.”
Bruce, Aggarwal, and Burns are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bruce and Aggarwal with the aforementioned teachings from Burns with a reasonable expectation of success, by adding steps that allow the software to utilize math with the motivation to more efficiently and accurately organize and analyze data [Burns 046].
As per claim 8, Bruce and Aggarwal teach all the limitations of claim 1:
Bruce and Aggarwal may not explicitly teach the following. However, Burns teaches:
output instructions to display a user interface including a first trend line associated with the test group and a second trend line associated with the first control group or the second control group.029: “As shown in FIG. 2, the distance or deviation between a test location 210 and a control location 220 can be visualized by the area 200 between the financial metric curves of the test location 210 and the control location 220.”
Bruce, Aggarwal, and Burns are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bruce and Aggarwal with the aforementioned teachings from Burns with a reasonable expectation of success, by adding steps that allow the software to utilize math with the motivation to more efficiently and accurately organize and analyze data [Burns 046].
As per claim 10, Bruce and Aggarwal teach all the limitations of claim 9.
Bruce and Aggarwal may not explicitly teach the following. However, Burns teaches:
receiving, at the simulator device, an indication of the test group and an indication of the plurality of possible control groups; 018: “ In order to analyze a business initiative, inputs may be entered on a graphical user interface at the client computer 100 or host server 120. These inputs can assist in defining the algorithm or limit the scope of the calculations. The inputs can be entered manually on the graphical user interface and/or automatically selected and entered. Inputs can include, but are not limited to, one or more test locations, a control pool, matching criteria, a number of controls per test, and a maximum number of iterations. Inputs regarding a test location can include one or more locations where a test is going to be conducted. Input regarding a control pool can include a plurality of control locations that are potential candidates for matched control. Inputs for matching criteria can include a set of dimensions on which a test is compared to a control. A number of controls per test can be the size of a control cohort for each test location. A maximum number of iterations can be the maximum number of steps undertaken by the algorithm.”
Bruce, Aggarwal, and Burns are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bruce and Aggarwal with the aforementioned teachings from Burns with a reasonable expectation of success, by adding steps that allow the software to utilize math with the motivation to more efficiently and accurately organize and analyze data [Burns 046].
As per claim 14, Bruce and Aggarwal teach all the limitations of claim 9.
Bruce and Aggarwal may not explicitly teach the following. However, Burns teaches:
determining that a distance, between a first trend line associated with the first control group and a second trend line associated with the second control group, satisfies a validation threshold; 046-052: “If trend line were separated into its constituent components each associated with control locations 410, 420, 430, under a simplifying assumption that three control locations are selected for each test location, the resulting graph might resemble the graph shown in FIG. 4. In this exemplary embodiment, the control locations selected as part of this particular test site's control group are shown as having similar financial trends… In this example, if the A, B, and C cohort has a 4% deviation from the performance of the test location, then the A, B, and D cohort will replace this cohort if it has only a less than 4% deviation. But if the A, B, and D cohort has a higher than 4% deviation, then the system will attempt to replace one of the cohort of A, B, and D, selected at random, with another location from the control pool and see if the historical performance of the revised cohort more closely matches the test location. This process can be repeated until a certain level of performance is met or a certain number of iterations has occurred… The algorithm can automatically stop at a predetermined threshold and use the most closely matching cohort at that time.”
Bruce, Aggarwal, and Burns are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bruce and Aggarwal with the aforementioned teachings from Burns with a reasonable expectation of success, by adding steps that allow the software to utilize math with the motivation to more efficiently and accurately organize and analyze data [Burns 046].
Claim 16 is the CRM for teaching the system of claim 8. Since the art teaches the method, the same art and rationale are applied.
As per claim 18, Bruce and Aggarwal teach all the limitations of claim 15.
Bruce and Aggarwal may not explicitly teach the following. However, Burns teaches:
wherein the one or more instructions, that cause the device to perform the validation of the target variable change, cause the device to: determine that a difference measurement, between the distribution of the first target variable information and the distribution of the portion of the second target variable information, satisfies a validation threshold; 052: “The algorithm can automatically stop at a predetermined threshold and use the most closely matching cohort at that time. The predetermined threshold can be based upon a deviation from the performance of the test location. For example, the algorithm may stop once the deviation is less than 2% between the cohort performance and the test location performance over the pre-period. In an alternative, the predetermined threshold can be a number of iterations. Each time a location is replaced within a cohort and the combined performance does not improve, a counter can start, and that counter can increment for each replacement that does not improve performance. Once this counter reaches a predetermined amount, e.g., 100 or 200 iterations, then the algorithm will stop. Each time the replacement location improves the performance of the cohort, the counter will reset.”
Bruce, Aggarwal, and Burns are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bruce and Aggarwal with the aforementioned teachings from Burns with a reasonable expectation of success, by adding steps that allow the software to utilize math with the motivation to more efficiently and accurately organize and analyze data [Burns 046].
Claims 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 10467572 (hereinafter “Bruce”) et al., in view of U.S. PGPub 20210264466 to (hereinafter “Aggarwal”) et al., in further view of U.S. PGPub 20200294072 (hereinafter “Burton”) et al.
As per claim 11, Bruce and Aggarwal teach all the limitations of claim 9.
Bruce and Aggarwal may not explicitly teach the following. However, Burton teaches:
performing the first random selection, from the plurality of possible control groups, to generate a first random control sample; and selecting the first control group from the first random control sample using the nearest neighbor algorithm; 0060-0074: “For example, transaction service provider system 102 may determine a sample of control accounts from the combined plurality of accounts based on at least one random number (e.g., a random number, a plurality of random numbers, a plurality of random numbers within a range of random numbers, a plurality of random numbers within a plurality of ranges of random numbers, and/or the like)… In some non-limiting embodiments or aspects, transaction service provider system 102 may determine one or more control accounts of each cluster of control accounts… In such an example, transaction service provider system 102 may determine the one or more control accounts of each cluster of control accounts that correspond to the exposed account of the cluster using a K-nearest neighbor algorithm. In some non-limiting embodiments or aspects, transaction service provider system 102 may determine a propensity score for between each control account of the cluster of control accounts and the exposed account.”
Bruce, Aggarwal, and Burton are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bruce and Aggarwal with the aforementioned teachings from Burton with a reasonable expectation of success, by adding steps that allow the software to utilize math with the motivation to more efficiently and accurately organize and analyze data [Burton 0074].
As per claim 12, Bruce and Aggarwal teach all the limitations of claim 9.
Bruce and Aggarwal may not explicitly teach the following. However, Burton teaches:
performing the second random selection, from the plurality of possible control groups, to generate a second random control sample; and selecting the second control group from the second random control sample using the nearest neighbor algorithm; 0060-0074: “For example, transaction service provider system 102 may determine a sample of control accounts from the combined plurality of accounts based on at least one random number (e.g., a random number, a plurality of random numbers, a plurality of random numbers within a range of random numbers, a plurality of random numbers within a plurality of ranges of random numbers, and/or the like)… In some non-limiting embodiments or aspects, transaction service provider system 102 may determine one or more control accounts of each cluster of control accounts… In such an example, transaction service provider system 102 may determine the one or more control accounts of each cluster of control accounts that correspond to the exposed account of the cluster using a K-nearest neighbor algorithm. In some non-limiting embodiments or aspects, transaction service provider system 102 may determine a propensity score for between each control account of the cluster of control accounts and the exposed account.”
Bruce, Aggarwal, and Burton are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Bruce and Aggarwal with the aforementioned teachings from Burton with a reasonable expectation of success, by adding steps that allow the software to utilize math with the motivation to more efficiently and accurately organize and analyze data [Burton 0074].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Davidson; Gordon. MERCHANDISING COMMUNICATION AND STOCK-OUT CONDITION MONITORING SYSTEM, .U.S. PGPub 20180165626 The disclosure relates to merchandising communication systems and to systems and methods for monitoring conditions in various environments, particularly retail environments.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Arif Ullah, whose telephone number is (571) 270-0161. The examiner can normally be reached from Monday to Friday between 9 AM and 5:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Beth Boswell, can be reached at (571) 272-6737. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”)./Arif Ullah/Primary Examiner, Art Unit 3625
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