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 .
Notice to Applicant
The following is a Final Office action to Application Serial Number 18/229,462, filed on August 2, 2023. In response to Examiner’s Office Action of April 30, 2025, Applicant, on July 22, 2025, amended claims 1, 3-7, 9, 11, and 19-20; and cancelled claims 5 and 19. Claims 1-4, 6-18 and 20-28 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are acknowledged.
Regarding the 35. U.S.C. § 101 rejection, Applicant’s arguments have been
considered and is insufficient to overcome the rejection.
The 35 U.S.C. § 103 rejections are hereby amended pursuant to applicants’ amendments. Updated 35 U.S.C. § 103 rejections have been applied to amended claims. Please refer to the § 103 rejection for further explanation and rationale.
Response to Arguments
Applicant’s arguments filed July 22, 2025 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed July 22, 2025.
On Pg. 7-10 of the Remarks, regarding 35 U.S.C. § 101 rejections, Applicant states the claim language is both an improvement to the computerized technology in a highly specific way to the nature of human emotions and influences, and is a practical application of an underlying abstraction. This practical application is no less powerful simply by the involvement of an underlying abstraction, assumed arguendo, and rather emphasizes the purpose of the practical application standard within Step 2A, prong two. In response, Examiner respectfully disagrees. Examiner finds the present claims improve an existing business process of communication analysis and there are currently no functional advancements to any technology or technological field, in order for the claim elements to be considered significantly more than the abstract idea itself. Utilizing generic computer structure and technology to determine are all, both individually and in combination, generic computer functions such as receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); 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); electronic recordkeeping, Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log) and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 (See MPEP 2106.05(d)(II). Applicant has not identified any meaningful limitations that would alter this analysis.
On Pg. 11-12 of the Remarks, regarding 35 U.S.C. § 103 rejections. Applicant states Frazer alone of in view of Zoldi (or others) fails to disclose or achieve at least incremental product traffic values as previously recited, and on that same basis cannot disclose or achieve those values as further defined in the amended claim. In response, The examiner respectfully disagrees: The claim recites: "product traffic”. Under the broadest reasonable interpretation, product traffic can equate to customer sales, transactions, etc. Frazer in view of Zoldi disclose the changes in customer transaction as disclosed in Par. 48-49“ Recurring transactions help establish purchase behavior patterns. It is noteworthy that ranking table is kept fixed in size, and membership and ranking keeps changing over time as new transactions are taken into account and the users' favorite purchased items change in frequency and recency.”; Par. 40-“ The dynamic profiling technology mathematically compresses large amounts of historical transactional data to facilitate real-time transactional analytics. The user profile contains a variety of variables such as the frequency of purchase of an SKU by the user, time elapsed since last purchase, quantity purchased, price paid,…. Please see the 103 analysis below for additional detail.
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 1-4, 6-18 and 20-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-4, 6-18 and 20-28 are directed to consumer communication.
Claim 1 recites a method for consumer communication, and Claim 15 recites a system for consumer communication, which include obtaining actual product traffic data concerning consumer products, applying a mixed model to determine an incremental product traffic value for each consumer product based on the actual product traffic data, receiving the incremental product traffic values, and predicting impending incremental product traffic based on the incremental product traffic values, determining a ranking of each consumer product according to an assigned department based on the impending incremental product traffic, and outputting a ranked list of the consumer products based on the ranking to address the impending incremental product traffic, wherein each incremental product traffic value comprises a difference between actual product transactions and an estimated baseline of transactions for each consumer product assuming exclusion from the ranked list.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Methods of Organizing Human Activity” – managing interactions. The recitation of “system”, “processor”, and “memory”, provide nothing in the claim elements to preclude the step from being “Methods of Organizing Human Activity”- managing interactions. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “system”, “processor”, and “memory”, is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). Furthermore, the claim 1and claim 15 recite using one or more machine learning techniques. The specification discloses the machine learning analysis at a high-level of generality, providing examples of different techniques that may be applied. The general use of machine learning does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in consumer analysis.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “computing system”, “processor”, and “memory”, is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 2-14, and 16-28 recite wherein outputting the ranked list includes displaying a list of ranked consumer products; wherein outputting the ranked list includes displaying a front-page newsletter comprising a design arrangement based on the ranking; wherein the mixed model is a generalized linear model; wherein the estimated baseline of transactions for each consumer product is determined based on family group; wherein family group comprises a grouping of similar consumer product items sharing a brand and designated price point; wherein each family group is assigned to a department selected from the group comprising: meat, delicatessen, general merchandizing, produce, and frozen foods; wherein the estimated baseline of transactions for each consumer product is determined based on seasonality; wherein the estimated baseline of transactions for each consumer product is determined based on seasonality and family group, if promoted during the applicable time period, and based on the effect of family group on seasonality; wherein the estimated baseline comprises a simulated number of transactions for each consumer product assuming that the corresponding consumer product is excluded from a front-page newsletter comprising a design arrangement based on the ranking; further comprising cross-correlating the consumer products of the ranked list; wherein cross-correlating the consumer products of the ranked list includes determining a correlation coefficient between ranked consumer products; wherein cross-correlating the consumer products of the ranked list includes indicating one or more ranked consumer products for exclusion from the ranked list based on the correlation coefficients; and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claims 1 and 15. Regarding Claims, 26, and the additional elements of “processor” and “memory”- it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
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 1-4, 6-7 and 9-18 and 20-21 and 23-28 are rejected under 35 U.S.C. 103 as being unpatentable over Frazer et al., US Publication No. 20100049538A1, [hereinafter Frazer], in view of Zoldi et al., US Publication No. 20180197200A1, [hereinafter Zoldi].
Regarding Claim 1,
Frazer teaches
A method of consumer communication, the method comprising: obtaining actual product traffic data concerning consumer products (Frazer Par. 49-“FIG. 4 shows a more detailed embodiment of a system 400 for selecting a next best action, according to an example embodiment. The system 400 shows a hardware portion of the system 300. It should be understood that various portions of hardware will execute software or firmware. The system 400 will initially be described briefly and the process used in the various modules will be set forth in further detail. The system 400 includes a data warehouse 410 that includes data and information promotion history 411, customer attributes 412, product hierarchy 413, and purchase data 414. The purchase data includes data related to the actual purchase of goods, whether over the internet or at a point of sale device within a retail store. The client warehouse data 410 also include content attributes 415.”; Par. 27; Fig. 16),
applying a mixed model to determine an incremental product traffic value for each consumer product based on the actual product traffic data (Frazer Par. 236-240“Technique 1 below describes how the insight/relationship determination module 320 creates market basket context instances, Bn, given: A customer's transaction history: x(n); The last update date (for incremental updates): tlast (which is 0 for the first update); The window width parameter ω (number of days)”; Par. 86-90-“Transaction data are a mixture of two types of interspersed customer purchases”)
receiving the incremental product traffic values by a machine learning engine as inputs (Frazer Par. 75-76-“The Insight/Relationship Determination Module 320 Framework-Traditional modeling frameworks in statistical pattern recognition and machine learning, such as classification and regression, seek optimal causal or correlation based mapping from a set of input features to one or more target values. The systems (input-output) approach suits a large number of decision analytics problems, such as fraud prediction and credit scoring. The transactional data in these domains is typically collected in, or converted to, a structured format with fixed number of observed and/or derived input features from which to choose. There are a number of data and modeling domains,”)
and predicting, as an output of the machine learning model, impending incremental product traffic based on the incremental product traffic values (Frazer Par. 332-337-“The Insight/Relationship Determination Module 320 Suite of Applications- The insight/relationship determination module 320 includes a general framework that allows formulation and solution of a number of different problems in retail. For example, it may be used to solve problems as varied as: (i) customer segmentation using pair-wise similarity relationships between customers, (ii) creating product bundles or consistent item-sets using pair-wise consistency between products purchased in market basket context, or (iii) predicting the time and product of the next possible purchase of a customer using pair-wise consistency between products purchased in a purchase sequence context.”)
product traffic value … assuming exclusion from the ranked list(Frazer Par. 10; Par. 56-57; Par. 143-146-“ Data Pre-processing—In this stage, the raw transaction data are (a) filtered and (b) customized for the next stage. Filtering cleans the data by removing the data elements (customers, transactions, line-items, and products) that are to be excluded from the analysis. Customization creates different slices of the filtered transaction data that may be analyzed separately and whose results may be compared for further insight generation, e.g. differences between two customer segments. This stage results in one or more clean, customized data slices on which further analyses may be done. Details of the Data Pre-processing stage are provided below”; Par. 158-“ New products are added, customers change over time, new customers get added to the market place and purchase trends change over time. To cope up with these dynamics of the modern day retail market, one needs a system that can quickly assimilate the newly generated transaction data and adapt its models accordingly. The insight/relationship determination module 320 is very adaptive as it can update its graph structures quickly to reflect any changes in the transaction data)
Frazer teaches customer analysis and the feature is expounded upon by Zoldi:
determining a ranking of each consumer product according to an assigned department based on the impending incremental product traffic (Zoldi Par. 48-49“ Recurring transactions help establish purchase behavior patterns. It is noteworthy that ranking table is kept fixed in size, and membership and ranking keeps changing over time as new transactions are taken into account and the users' favorite purchased items change in frequency and recency.”; Par. 40-“ The dynamic profiling technology mathematically compresses large amounts of historical transactional data to facilitate real-time transactional analytics. The user profile contains a variety of variables such as the frequency of purchase of an SKU by the user, time elapsed since last purchase, quantity purchased, price paid, and recursively updated variables such as the ratio of a SKU's purchase in the recent past as compared to more distant past. Recursive variables represent the transaction history concisely using velocities, averages, and ratios of purchase variables. The system computes the updated value of the variable solely using information related to the current transaction and the time since the last transaction without any reference to any other historical information. The variable can be regarded as weighted averages of certain characteristics that get updated by each transaction recursively. Since variables are recursively updated with incremental transactions, they eliminate the overhead associated with storing and accessing large volumes of historical transactions and makes real time scoring possible.”),
and outputting a ranked list of the consumer products based on the ranking to address the impending incremental product traffic, wherein each incremental product traffic value comprises a difference between actual product transactions and an estimated baseline of transactions for each consumer product …. (Zoldi Par. 47-54- Recurring transactions help establish purchase behavior patterns. It is noteworthy that ranking table is kept fixed in size, and membership and ranking keeps changing over time as new transactions are taken into account and the users' favorite purchased items change in frequency and recency. … Upon each new item data received for a consumer the respective Number table is looked up to determine whether it is frequently occurring for that user utilizing the rank of the number from the Ranking table.”; Par. 40-“ The user profile contains a variety of variables such as the frequency of purchase of an SKU by the user, time elapsed since last purchase, quantity purchased, price paid, and recursively updated variables such as the ratio of a SKU's purchase in the recent past as compared to more distant past. Recursive variables represent the transaction history concisely using velocities, averages, and ratios of purchase variables. The system computes the updated value of the variable solely using information related to the current transaction and the time since the last transaction without any reference to any other historical information. The variable can be regarded as weighted averages of certain characteristics that get updated by each transaction recursively. Since variables are recursively updated with incremental transactions, they eliminate the overhead associated with storing and accessing large volumes of historical transactions and makes real time scoring possible. FIG. 3 illustrates an example of a profile creation based on input transaction data where a user's purchase history is captured by a decayed frequency metric.)
Frazer and Zoldi are directed to consumer behavior analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Frazer, as taught by Zoldi, by utilizing ranking analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Frazer with the motivation of real time decisioning, … to transform the model based predictions into actions (Zoldi Par. 29).
Regarding Claim 2 and Claim 16,
Frazer teaches customer analysis and the feature is expounded upon by Zoldi:
wherein outputting the ranked list includes displaying a list of ranked consumer products. (Zoldi Par. 47-54- Recurring transactions help establish purchase behavior patterns. It is noteworthy that ranking table is kept fixed in size, and membership and ranking keeps changing over time as new transactions are taken into account and the users' favorite purchased items change in frequency and recency. ; Par. 117- “To provide for interaction with a user, one or more aspects or features of the subject matter described herein can be implemented on a computer having a display device, such as for example a cathode ray tube (CRT), a liquid crystal display (LCD) or a light emitting diode (LED) monitor for displaying information to the user and a keyboard and a pointing device, such as for example a mouse or a trackball, by which the user may provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well.”)
Frazer and Zoldi are directed to consumer behavior analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Frazer, as taught by Zoldi, by utilizing ranking analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Frazer with the motivation of real time decisioning, … to transform the model based predictions into actions (Zoldi Par. 29).
Regarding Claim 3 and Claim 17,
Frazer in view of Zoldi teach wherein outputting the ranked list… based on the ranking stated above.
… includes displaying a front-page newsletter comprising a design arrangement (Frazer Par. 663-“ This matrix is stored in a set of files, with one output file corresponding to one input line item transaction file. The columns of the output file are the propensity of a customer to buy each of the target products (one column per product), and a column of the customer id. Each row is a single customer found in the corresponding input line item transaction file”),
Regarding Claim 4 and Claim 18,
wherein the mixed model is a generalized linear model (Frazer Par. 236-240“Technique 1 below describes how the insight/relationship determination module 320 creates market basket context instances, Bn, given: A customer's transaction history: x(n); The last update date (for incremental updates): tlast (which is 0 for the first update); The window width parameter ω (number of days)”; Par. 294-298-“Statistical Measures of Consistency;Pearson's Correlation Coefficient; Correlation coefficient quantifies the degree of linear dependence between two variables which are binary in our case indicating the presence or absence of two products.”)
Regarding Claim 5 and Claim 19,
wherein each incremental product traffic value comprises a difference between actual product transactions and an estimated baseline of transactions for each consumer product assuming exclusion from the ranked list (Frazer Par. 143-146-“ Data Pre-processing—In this stage, the raw transaction data are (a) filtered and (b) customized for the next stage. Filtering cleans the data by removing the data elements (customers, transactions, line-items, and products) that are to be excluded from the analysis. Customization creates different slices of the filtered transaction data that may be analyzed separately and whose results may be compared for further insight generation, e.g. differences between two customer segments. This stage results in one or more clean, customized data slices on which further analyses may be done. Details of the Data Pre-processing stage are provided below”)
Regarding Claim 6 and Claim 20,
wherein the estimated baseline of transactions for each consumer product is determined based on family group. (Frazer Par. 109-113-“ similar to a product space, a typical customer space exhibits the following four characteristics: Large—A customer base might have hundreds of thousands to millions of customers.; Heterogeneous—Customers are from various demographics, regions, life styles/stages.; Dynamic—Customers are changing over time as they go through different life stages.; Multi-Resolution—Customers may be organized by household, various segmentations.”; Par. 143-146)
Regarding Claim 7 and Claim 21,
wherein family group comprises a grouping of similar consumer product items sharing a brand and designated price point. (Frazer Par. 56-“ The scorecards take into account previous transaction information (in the form of recency and frequency attributes), as well as seasonal information. This information is often very rich and predictive of future behavior. Other potential inputs are customer demographics, behavior summary features, marketing variables, pricing information, economic and competitor data, etc.)”; Par. 109-113-“ similar to a product space, a typical customer space exhibits the following four characteristics: Large—A customer base might have hundreds of thousands to millions of customers.; Heterogeneous—Customers are from various demographics, regions, life styles/stages.; Dynamic—Customers are changing over time as they go through different life stages.; Multi-Resolution—Customers may be organized by household, various segmentations.”; Par. 143-146)
Regarding Claim 9 and Claim 23,
wherein the estimated baseline of transactions for each consumer product is determined based on seasonality. (Frazer Par. 10-“ Many retailers retain data related to these transactions, which is sometimes referred to as transaction data. Transaction data includes all data related to a transaction including, for example, promotions, price changes, product features, store features, seasonal factors and customer loyalty data that may affect the transaction.”)
Regarding Claim 10 and Claim 24,
wherein the estimated baseline of transactions for each consumer product is determined based on seasonality and family group, if promoted during the applicable time period, and based on the effect of family group on seasonality. (Frazer Par. 10-“ Many retailers retain data related to these transactions, which is sometimes referred to as transaction data. Transaction data includes all data related to a transaction including, for example, promotions, price changes, product features, store features, seasonal factors and customer loyalty data that may affect the transaction.”; Par. 43)
Regarding Claim 11 and Claim 25,
Frazer discloses
…assuming that the corresponding consumer product is excluded from a front-page newsletter comprising a design arrangement based on the ranking(Frazer Par. 145-146 -“ Data Pre-processing—In this stage, the raw transaction data are (a) filtered and (b) customized for the next stage. Filtering cleans the data by removing the data elements (customers, transactions, line-items, and products) that are to be excluded from the analysis. Customization creates different slices of the filtered transaction data that may be analyzed separately and whose results may be compared for further insight generation, e.g. differences between two customer segments. This stage results in one or more clean, customized data slices on which further analyses may be done. Details of the Data Pre-processing stage are provided below.”; Par. 210-214; Par. 663; Par. 47; Par. 615 )
Frazer teaches customer analysis and the feature is expounded upon by Zoldi:
wherein the estimated baseline comprises a simulated number of transactions for each consumer product …. (Zoldi Par. 70-72- To address implicit feedback data, like item purchases, the approach has the option of modeling the matrix of purchase values and treats the data as a combination of binary preferences and confidence values. The purchase values are then related to the level of confidence in observed user preferences, rather than explicit ratings given to items. The user purchase matrix, P is created based on the user profile data….The binarized purchase matrix, P is factorized into a user feature matrix, X and an item feature matrix, Y.)
Frazer and Zoldi are directed to consumer behavior analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Frazer, as taught by Zoldi, by utilizing ranking analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Frazer with the motivation of real time decisioning, … to transform the model based predictions into actions (Zoldi Par. 29).
Regarding Claim 12 and Claim 26,
Frazer teaches customer analysis and the feature is expounded upon by Zoldi:
further comprising cross-correlating the consumer products of the ranked list. (Zoldi Par. 47-54; Par. 53- Then, Number table and Frequency table are updated as follows: If the current number is not in the Frequency table, then least-frequent number (determined by the Ranking table) is replaced with the current number if the least frequent number's frequency (based on the Frequency table) is less than a threshold δ, where …Other techniques to determine the threshold δ can be used, including use of adaptive thresholds based on match rates and recycling rates associated with the Number table. The frequency of the current number is initialized to be α. If the current number is already in the Number table, then its frequency is increased by λ. Finally, the Ranking table is updated accordingly to reflect any changes to the ranking of numbers in the Number Table based on the update..”)
Frazer and Zoldi are directed to consumer behavior analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Frazer, as taught by Zoldi, by utilizing ranking analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Frazer with the motivation of real time decisioning, … to transform the model based predictions into actions (Zoldi Par. 29).
Regarding Claim 13 and Claim 27,
Frazer in view of Zoldi teach wherein cross-correlating the consumer products of the ranked list… stated above
…includes determining a correlation coefficient between ranked consumer products.. (Frazer Par. 295-298- Pearson's Correlation Coefficient Correlation coefficient quantifies the degree of linear dependence between two variables which are binary in our case indicating the presence or absence of two products.”)
Regarding Claim 14 and Claim 28,
Frazer in view of Zoldi teach wherein cross-correlating the consumer products of the ranked list… stated above
… includes indicating one or more ranked consumer products for exclusion from the ranked list based on the correlation coefficients. (Frazer Par. 213-215-“ Rules based on line item properties may be defined to include or exclude certain line items in the analyses. Transaction Filter—Entire transactions may be filtered out of the analyses based on transaction level properties. For example, one may be interested only in analyzing data from last three years or transactions containing at least three or more products, or the like. Rules based on transaction properties may be used to include or exclude certain transactions from the analysis.; Customer Filter—Finally, transaction data from a particular customer may be included or excluded from the analysis. For example, the retailer may want to exclude customers who did not buy anything in the last six months or who are in the bottom 30% by value. Rules based on customer properties may be defined to include or exclude certain customers from the analysis. Par. 295-298- Pearson's Correlation Coefficient Correlation coefficient quantifies the degree of linear dependence between two variables which are binary in our case indicating the presence or absence of two products.”)
Regarding Claim 15,
Frazer teaches
A consumer communication system comprising: at least one processor configured to execute instructions stored on memory to: obtain actual product traffic data concerning consumer products, (Frazer Par. 49-“FIG. 4 shows a more detailed embodiment of a system 400 for selecting a next best action, according to an example embodiment. The system 400 shows a hardware portion of the system 300. It should be understood that various portions of hardware will execute software or firmware. The system 400 will initially be described briefly and the process used in the various modules will be set forth in further detail. The system 400 includes a data warehouse 410 that includes data and information promotion history 411, customer attributes 412, product hierarchy 413, and purchase data 414. The purchase data includes data related to the actual purchase of goods, whether over the internet or at a point of sale device within a retail store. The client warehouse data 410 also include content attributes 415.”; Par. 27; Fig. 16; Par. 680),
apply a mixed model to determine an incremental product traffic value for each consumer product based on the actual product traffic data (Frazer Par. 236-240“Technique 1 below describes how the insight/relationship determination module 320 creates market basket context instances, Bn, given: A customer's transaction history: x(n); The last update date (for incremental updates): tlast (which is 0 for the first update); The window width parameter ω (number of days)”; Par. 86-90-“Transaction data are a mixture of two types of interspersed customer purchases”)
receive the incremental product traffic values by a machine learning engine (Frazer Par. 75-76-“The Insight/Relationship Determination Module 320 Framework-Traditional modeling frameworks in statistical pattern recognition and machine learning, such as classification and regression, seek optimal causal or correlation based mapping from a set of input features to one or more target values. The systems (input-output) approach suits a large number of decision analytics problems, such as fraud prediction and credit scoring. The transactional data in these domains is typically collected in, or converted to, a structured format with fixed number of observed and/or derived input features from which to choose. There are a number of data and modeling domains,”)
and predicting impending incremental product traffic based on the incremental product traffic values (Frazer Par. 332-337-“The Insight/Relationship Determination Module 320 Suite of Applications- The insight/relationship determination module 320 includes a general framework that allows formulation and solution of a number of different problems in retail. For example, it may be used to solve problems as varied as: (i) customer segmentation using pair-wise similarity relationships between customers, (ii) creating product bundles or consistent item-sets using pair-wise consistency between products purchased in market basket context, or (iii) predicting the time and product of the next possible purchase of a customer using pair-wise consistency between products purchased in a purchase sequence context.”)
product traffic value … exclusion from the ranked list(Frazer Par. 10; Par. 56-57; Par. 143-146-“ Data Pre-processing—In this stage, the raw transaction data are (a) filtered and (b) customized for the next stage. Filtering cleans the data by removing the data elements (customers, transactions, line-items, and products) that are to be excluded from the analysis. Customization creates different slices of the filtered transaction data that may be analyzed separately and whose results may be compared for further insight generation, e.g. differences between two customer segments. This stage results in one or more clean, customized data slices on which further analyses may be done. Details of the Data Pre-processing stage are provided below”; Par. 158-“ New products are added, customers change over time, new customers get added to the market place and purchase trends change over time. To cope up with these dynamics of the modern day retail market, one needs a system that can quickly assimilate the newly generated transaction data and adapt its models accordingly. The insight/relationship determination module 320 is very adaptive as it can update its graph structures quickly to reflect any changes in the transaction data)
Frazer teaches customer analysis and the feature is expounded upon by Zoldi:
determine a ranking of each consumer product according to an assigned department based on the impending incremental product traffic (Zoldi Par. 48-49“ Recurring transactions help establish purchase behavior patterns. It is noteworthy that ranking table is kept fixed in size, and membership and ranking keeps changing over time as new transactions are taken into account and the users' favorite purchased items change in frequency and recency.”; Par. 40-“ The dynamic profiling technology mathematically compresses large amounts of historical transactional data to facilitate real-time transactional analytics. The user profile contains a variety of variables such as the frequency of purchase of an SKU by the user, time elapsed since last purchase, quantity purchased, price paid, and recursively updated variables such as the ratio of a SKU's purchase in the recent past as compared to more distant past. Recursive variables represent the transaction history concisely using velocities, averages, and ratios of purchase variables. The system computes the updated value of the variable solely using information related to the current transaction and the time since the last transaction without any reference to any other historical information. The variable can be regarded as weighted averages of certain characteristics that get updated by each transaction recursively. Since variables are recursively updated with incremental transactions, they eliminate the overhead associated with storing and accessing large volumes of historical transactions and makes real time scoring possible.”),
and output a ranked list of the consumer products based on the ranking to address the impending incremental product traffic, wherein each incremental product traffic value comprises a difference between actual product transactions and an estimated baseline of transactions for each consumer product assuming ... (Zoldi Par. 47-54- Recurring transactions help establish purchase behavior patterns. It is noteworthy that ranking table is kept fixed in size, and membership and ranking keeps changing over time as new transactions are taken into account and the users' favorite purchased items change in frequency and recency. … Upon each new item data received for a consumer the respective Number table is looked up to determine whether it is frequently occurring for that user utilizing the rank of the number from the Ranking table.”; Par. 40-“ The user profile contains a variety of variables such as the frequency of purchase of an SKU by the user, time elapsed since last purchase, quantity purchased, price paid, and recursively updated variables such as the ratio of a SKU's purchase in the recent past as compared to more distant past. Recursive variables represent the transaction history concisely using velocities, averages, and ratios of purchase variables. The system computes the updated value of the variable solely using information related to the current transaction and the time since the last transaction without any reference to any other historical information. The variable can be regarded as weighted averages of certain characteristics that get updated by each transaction recursively. Since variables are recursively updated with incremental transactions, they eliminate the overhead associated with storing and accessing large volumes of historical transactions and makes real time scoring possible. FIG. 3 illustrates an example of a profile creation based on input transaction data where a user's purchase history is captured by a decayed frequency metric.)
Frazer and Zoldi are directed to consumer behavior analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Frazer, as taught by Zoldi, by utilizing ranking analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Frazer with the motivation of real time decisioning, … to transform the model based predictions into actions (Zoldi Par. 29).
Claims 8 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Frazer et al., US Publication No. 20100049538A1, [hereinafter Frazer], in view of Zoldi et al., US Publication No. 20180197200A1, [hereinafter Zoldi] and in further view of Ouimet, US Publication No. 20130325656A1, [hereinafter Ouimet].
Regarding Claim 8 and Claim 22,
Frazer in view of Zoldi teach The method of claim 7, wherein each family group is assigned to a department selected from the group… and The system of claim 21, wherein each family group is assigned to a department selected from the group…:
Frazer in view of Zoldi fail to teach the following feature taught by Ouimet:
the group comprising: meat, delicatessen, general merchandizing, produce, and frozen foods. (Ouimet Abstract; Par. 122- Retailers 46-50 each have a physical layout of the premise with aisles, shelves, end caps, walls, floor displays, dairy cases, wine and spirit cases, frozen cases, meat counters, deli counters, bakery area, fresh produce area, prepared foods counters, and check-out displays. While the specific location of each food area within any given store may differ between retailers, each retailer offers similar products arranged in a logical layout, e.g., dairy products are stocked in the same general area, frozen foods are stocked in the same general area, and so on. FIG. 26 shows webpage 350 with a virtual layout of one or more retailers with virtual aisles or cases for each category of food product. The virtual dairy case presents all dairy product families, i.e., DP1-DP6, for the retailer. The virtual breakfast cereal aisle presents all breakfast cereal product families, i.e., BC1-BC6, for the retailer. The virtual canned soup aisle presents all canned soup product families, i.e., CS1-CS6, for the retailer. The virtual bakery goods area presents all bakery goods product families, i.e., BG1-BG6, for the retailer. The virtual fresh produce area presents all fresh produce product families, i.e., FP1-FP6, for the retailer. The virtual frozen vegetable case presents all frozen vegetable product families, i.e., FV1-FV6, for the retailer. Consumer 42 can select products from the virtual layout by clicking on box 352. The selected products are displayed for each food category.”)
Frazer and Zoldi are directed to consumer behavior analysis. Ouimet improves upon the data collection analysis of consumer products. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data collection of Frazer in view of Zoldi, as taught by Ouimet, by utilizing ranking analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Frazer with the motivation of providing a list that can be optimized based on the product information and weighted preferences for the product attributes for the product families. (Ouimet Abstract).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Publication No. 20160253710A1 to Publicover et al.- Abstract-“ Targeted Content solutions can be provided using a variety of techniques. Targeted Content can be provided in place of generic advertisements on a first device or on personal computing devices. Targeted Content can be presented during, or in place of, generic advertisements in Content (e.g., television content, streaming content, etc.). Targeted Content can be provided in individual and/or group environments. In a group environment, Users and/or Devices can be grouped into a shared advertising group and Targeted Content can be selected based on Profiles of one or more members of the group. Feedback can be received regarding Targeted Content and payout amount can be determined.”
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