DETAILED ACTION
This communication is a Final Rejection Office Action in response to the 9/23/2025 filling of Application 18/488,878.
Claims 1, 10 have been amended. Claims 1-11 are now presented.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The communication corrects an error made in the previous Non-Final Office action mailed on 6/1//025. The conclusion paragraph of the office action stated that the action was made final. This was an error and the finality paragraph has been removed.
Response to Arguments
Applicant’s arguments with respect to the prior art have been considered but are moot because they do not apply to the new grounds of rejection that was necessitated by amendment.
Applicant's remaining arguments filed have been fully considered but they are not persuasive.
Regarding the rejections under 101, the Applicant argues “One of ordinary skill in the art would understand that the claimed steps of "acquir[ing] ... customer information,""generat[ing] ...a customer representation,""generat[ing] ... a store representation," and "cluster[ing] stores" are rooted in technological improvements to data analysis and do not encompass "fundamental economic principles or practices (including hedging, insurance, mitigating risk),""commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations)," or "managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions)," as defined in MPEP § 2106.04(a)(2)(II). Thus, amended claim 1 cannot be considered as reciting "certain methods of organizing human activity" as defined in M.P.E.P. § 2106.04(a)(2)(II).”
The Examiner respectfully disagrees. The Applicant does not agree that the limitations are rooted in technological improvements to data analysis. The Examiner maintains that generating, on a customer-by-customer basis, a customer representation representing an action pattern of a customer, based on the action time; generating, on a store-by-store basis, a store representation representing a representation of a customer coming to a store, based on the customer representation; and clustering stores by using the store representation is directed to performing market analysis which amounts to marketing or sales activities or behaviors.
Regarding the rejections under 101, the Applicant further argues “These operations involve the use of the processing circuitry to analyze large volumes of purchase data, extract behavioral patterns, and perform clustering computations-tasks that are beyond the practical capabilities of the human mind. Thus, the claimed features of amended independent claim 1 cannot be considered to encompass "mental processes" as defined in M.P.E.P.§ 2106.04(a)(2)(ll). Therefore, amended independent claim 1 is patent eligible at Step 2A - Prong 1.”
The Examiner respectfully disagrees. The processing circuitry is recited at a high-level of generality (i.e., as a generic processor performing a generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Further, nothing in the claims prohibits the analysis of data for each customer and each merchant from being performed in the human mind.
Regarding the rejections under 101, the Applicant further argues “As explained above, amended claim 1 recites specific technical improvements that go beyond merely implementing an abstract idea on generic computer components. In particular, the claimed invention includes "acquir[ing], on a customer-by-customer basis, customer information including an action time of a purchase-related action relating to purchase, the action time being indicative of a time range in which the purchase-related action has been performed among a plurality of time ranges,""generat[ing], on a customer-by-customer basis, a customer representation representing an action pattern of a customer, based on the action time,""generat[ing], on a store-by- store basis, a store representation representing a representation of a customer coming to a store, based on the customer representation," and "cluster[ing] stores by using the store representation." This approach leverages concrete data structures and ordered processing steps that are rooted in technological practice, resulting in more accurate and meaningful store clustering than was previously possible. As described in the specification, these additional elements are not conventional or routine, but instead provide a technological solution to the shortcomings of prior systems that relied solely on sales data and failed to capture the true characteristics of stores and their customers. Id. at [0088]-[0098]. Thus, the claims integrate the alleged abstract idea into a practical application that improves the functioning of purchase data analysis technology itself.”
The Examiner respectfully disagrees. Limitations that can be classified into one of the abstract idea grouping cannot also provide a technological solution. As such, the "generat[ing], on a customer-by-customer basis, a customer representation representing an action pattern of a customer, based on the action time,""generat[ing], on a store-by- store basis, a store representation representing a representation of a customer coming to a store, based on the customer representation," and "cluster[ing] stores by using the store representation” do not represent a technical improvement.
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 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
When considering subject matter eligibility under 35 U.S.C. 101, in step 1 it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, in step 2A prong 1 it must then be determined whether the claim is recite a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). If the claim recites a judicial exception, under step 2A prong 2 it must additionally be determined whether the recites additional elements that integrate the judicial exception into a practical application. If a claim does not integrate the Abstract idea into a practical application, under step 2B it must then be determined if the claim provides an inventive concept.
In the Instant case, Claims 1-9 are directed toward an apparatus to cluster stores by using a store representation. Claim 10 is directed toward an method to cluster stores by using a store representation. Claims 11 is directed toward a computer program to cluster stores by using a store representation. As such, each of the Claims is directed to one of the four statutory categories of invention.
MPEP 2106.04 II. A. explains that in step 2A prong 1 Examiners are to determine whether a claim recites a judicial exception. MPEP 2106.04(a) explains that:
To facilitate examination, the Office has set forth an approach to identifying abstract ideas that distills the relevant case law into enumerated groupings of abstract ideas. The enumerated groupings are firmly rooted in Supreme Court precedent as well as Federal Circuit decisions interpreting that precedent, as is explained in MPEP § 2106.04(a)(2). This approach represents a shift from the former case-comparison approach that required examiners to rely on individual judicial cases when determining whether a claim recites an abstract idea. By grouping the abstract ideas, the examiners’ focus has been shifted from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types.
The enumerated groupings of abstract ideas are defined as:
1) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);
2) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II); and
3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
As per step 2A prong 1 of the eligibility analysis, claim 1 recites the abstract idea of generating a customer representation representing an action pattern of a customer; generating a store representation representing a representation of a customer coming to a store, based on the customer representation; and clustering stores by using the store representation which falls into the abstract idea categories of certain methods of organizing human activity and mental processes. The elements of Claim 1 that represent the Abstract idea include:
A purchase data analysis apparatus being configured to:
generate, on a customer-by-customer basis, a customer representation representing an action pattern of a customer, based on the action time;
generate, on a store-by-store basis, a store representation representing a representation of a customer coming to a store, based on the customer representation; and
cluster stores by using the store representation.
MPEP 2106.04(a)(2) II. states:
The phrase "methods of organizing human activity" is used to describe concepts relating to:
fundamental economic principles or practices (including hedging, insurance, mitigating risk);
commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); and
managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions).
The Supreme Court has identified a number of concepts falling within the "certain methods of organizing human activity" grouping as abstract ideas. In particular, in Alice, the Court concluded that the use of a third party to mediate settlement risk is a ‘‘fundamental economic practice’’ and thus an abstract idea. 573 U.S. at 219–20, 110 USPQ2d at 1982. In addition, the Court in Alice described the concept of risk hedging identified as an abstract idea in Bilski as ‘‘a method of organizing human activity’’. Id. Previously, in Bilski, the Court concluded that hedging is a ‘‘fundamental economic practice’’ and therefore an abstract idea. 561 U.S. at 611–612, 95 USPQ2d at 1010.
In the instant case, generating, on a customer-by-customer basis, a customer representation representing an action pattern of a customer, based on the action time; generating, on a store-by-store basis, a store representation representing a representation of a customer coming to a store, based on the customer representation; and clustering stores by using the store representation is directed to performing market analysis which amounts to marketing or sales activities or behaviors.
MPEP 2106.04(a)(2) states:
The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012) ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions
The instant claims recite mental processes including observation, evaluation, judgment, opinion. For example, the steps directed to generating, on a customer-by-customer basis, a customer representation representing an action pattern of a customer, based on the action time; generating, on a store-by-store basis, a store representation representing a representation of a customer coming to a store, based on the customer representation; and clustering stores by using the store representation. There is nothing is nothing the claims that preclude these steps from being performed mentally. As such, the claims recite abstract ideas.
Under step 2A prong 2 the examiner must then determine if the recited abstract idea is integrated into a practical application. MPEP 2106.04 states:
Limitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include:
• An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
• Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
• Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
• Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
• Applying or using 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, as discussed in MPEP § 2106.05(e)
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
• Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
• Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
• Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
In the instant case, this judicial exception is not integrated into a practical application. In particular, Claim 1 recites the additional elements of:
processing circuitry, the processing circuitry being configured to perform the abstract idea
acquire, on a customer-by-customer basis, customer information including an action time of a purchase-related action relating to purchase the action time being indicative of a time range in which the purchase-related action has been performed among a plurality of time ranges;
However, the computer elements (the processing circuitry) is recited at a high-level of generality (i.e., as a generic processor performing a generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Further MPEP 2105.05(g) explains that data gathering and data output can be considered pre-solution activity and post-solution activity. See MPEP 2106.05(g) that states:
An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent.
In the instant case, the acquiring data, on a customer-by-customer basis, customer information is considered mere data gathering which is incidental to the primary process in a similar way that obtaining information about credit card transactions to be analyzed was incidental to the primary process explained above. The amendments directed to the action time being indicative of a time range in which the purchase-related action has been performed among a plurality of time ranges does not save the claim. This elements still amounts to mere data gathering. Further, MPEP 2106.05 also states Examiner should evaluate whether the extra-solution limitation is well known. In this case, the broadly recited receipt of data is well known. The MPEP also cites several examples of mere data gathering that have been found to be insignificant extra-solution activity including gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price (see OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93); and obtaining information about transactions using the Internet to verify credit card transactions (see CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011))
Viewing the generic data gathering in combination with the generic computer does not add more than when viewing the elements individually. 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.
In step 2B, the examiner must be determine whether the claim adds a specific limitation other than what is well-understood, routine, conventional activity in the field - see MPEP 2106.05(d). As discussed with respect to Step 2A Prong Two, the processing circuitry in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Further, the receipt data is recited broadly in the claims. MPEP 2106.05(d) states receiving or transmitting data over a network, e.g., using the Internet to gather data is conventional when claimed generically (see 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)). As such, the broadly claimed receipt of data is considered well-known and conventional as established by the MPEP and relevant case law.
Viewing the conventional data gathering in combination with the generic computer does not add more than when viewing the elements individually. Accordingly, the additional elements do provide and inventive concept.
Further Claims 2-9 further limit the mental processes and marketing activities recited in the parent claim, but fail to remedy the deficiencies of the parent claim as they do not impose any additional elements that amount to significantly more than the abstract idea itself.
Accordingly, the Examiner concludes that there are no meaningful limitations in claims 1-9 that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself.
The analysis above applies to all statutory categories of invention. The presentment of claim 1 otherwise styled as a method, or system, for example, would be subject to the same analysis. As such, claims 10, 11 are also rejected.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Hu 2019/0147469 A1 in view of Laserson US 20200410534 A1.
As per Claim 1 Hu teaches A purchase data analysis apparatus comprising processing circuitry, the processing circuitry being configured to:
acquire, on a customer-by-customer basis, customer information including an action time of a purchase-related action relating to purchase; Hu para. 48 teaches FIG. 3 is a diagram illustrating a table 300 of transaction history data identifying the number of times each consumer has conducted transactions at a given merchant location. The system 200 can identify from the consumer transaction database 208, a plurality of different merchants at which different customers have shopped and can generate and/or retrieve a table 300 identifying such information.
generate, on a customer-by-customer basis, a customer representation representing an action pattern of a customer, based on the action time; Hu para. 48 teaches By determining that a particular consumer (e.g., Acct 2) has visited a particular set of stores frequently (e.g., store1, store2, and store3) and determining that other consumers (e.g., Acct 1 and Acct 5) have visited the same three merchant locations, the merchant clustering module 203 can determine that these three stores should be part of the same merchant cluster since there exists a high degree of correlation in shopping activity of the same customer at each of these stores.
generate, on a store-by-store basis, a store representation representing a representation of a customer coming to a store, based on the customer representation; and Hu para. 48 teaches 48 FIG. 3 is a diagram illustrating a table 300 of transaction history data identifying the number of times each consumer has conducted transactions at a given merchant location. The system 200 can identify from the consumer transaction database 208, a plurality of different merchants at which different customers have shopped and can generate and/or retrieve a table 300 identifying such information. As shown in FIG. 3, table 300 indicates how many transactions a particular consumer account (e.g., Acct 1, Acct 2, etc.) has conducted at a particular merchant (e.g., store1, store2, etc.). As depicted by the embodiment illustrated in FIG. 3, table 300 illustrates customer transaction activity by fifteen different consumers at twenty different merchants located in the same geographic region. By determining that a particular consumer (e.g., Acct 2) has visited a particular set of stores frequently (e.g., store1, store2, and store3) and determining that other consumers (e.g., Acct 1 and Acct 5) have visited the same three merchant locations, the merchant clustering module 203 can determine that these three stores should be part of the same merchant cluster since there exists a high degree of correlation in shopping activity of the same customer at each of these stores. The system 200 can further determine whether these consumers (i.e., Acct 1, Acct 2, and Acct 5) have similar demographic profiles (e.g., spending power and/or financial status of consumers, age groups of consumers, gender of consumers, geographic location, etc.) and if so, associate the generated merchant cluster (i.e., merchant cluster having Acct 1, Acct 2, and Acct 5 as members) with a particular parameter identified to be common from the different consumers (i.e., Acct 1, Acct 2, and Acct 5). In some embodiments, the table 300 can be used to generate a regional merchant cluster by regional merchant clustering engine 218 upon determining that the merchants in table 300 are located in the same geographic region as determined by merchant distance calculation engine 214
cluster stores by using the store representation. Hu para. 48 teaches by determining that a particular consumer (e.g., Acct 2) has visited a particular set of stores frequently (e.g., store1, store2, and store3) and determining that other consumers (e.g., Acct 1 and Acct 5) have visited the same three merchant locations, the merchant clustering module 203 can determine that these three stores should be part of the same merchant cluster since there exists a high degree of correlation in shopping activity of the same customer at each of these stores.
Hu does not explicitly disclose the action time being indicative of a time range in which the purchase-related action has been performed among a plurality of time ranges; However, Laserson para. 22 teaches The modeler 180 processes the transaction histories provided in the data store 110 (each of 120-150 can update transaction data in real time to the data store 110). The modeler 180 generates, derives, and creates a shopping pattern for each customer of the enterprise using the transaction data. The shopping pattern is used for both training the predictor 170 (via the trainer 160) and used when the predictor is fully trained as input to receive as output (provided to the reporter/notifier 190) indications on a customer that is predicted to be nearing a point of abandoning or substantially abandoning business with the enterprise along with an indication as to whether remedial efforts made by the store are going to be likely successful in preventing the customer from abandoning or substantially abandoning business with the enterprise. Furter, para. 99 teaches the input modeler 402, when executed by the processor(s) of the device 401, is configured to: 1) generate a sales pattern for a given customer from transaction data obtained for a given period of time, 2) generate tabulations from the transaction data, and 3) provide as input to the deviation-remediation manager 403: a customer identifier for the given customer, the transaction data, the sales pattern, and the tabulations. Both Hu and Laserson are directed to consumer analytics. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Hu to include the action time being indicative of a time range in which the purchase-related action has been performed among a plurality of time ranges as taught by Laserson to leveraging the significance of shopping patterns in conjunction with other conventional approaches to provide an enhanced and more accurate prediction of customer churn at an early enough stage where the customer can be retained through promotions or other remediation (see para. 26)
As per Claim 2 Hu teaches the purchase data analysis apparatus of Claim 1, wherein the processing circuitry is further configured to acquire customer master data, and generate the customer representation, based on the customer information and the customer master data. Hu para. 38 teaches by analyzing the consumer shopping behavior of different groups of consumers against the consumer profile data of the consumers whose shopping data is being analyzed, the consumer shopping behavior engine 216 can identify each of these parameters (e.g., spending power and/or financial status of consumers, age groups of consumers, gender of consumers, etc.) that affects customer shopping behavior in different geographic regions.
Claim 10 recites similar limitation to claim 1 and is rejected for similar reasons. Further, Hu teaches a method that performs the recited steps (see para. 5).
Claim 11 recites similar limitation to claim 1 and is rejected for similar reasons. A non-transitory computer-readable storage medium storing a program for causing a computer to execute the recited steps (see para. 70)
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hu US 2019/0147469 A1 in view of Laserson US 20200410534 A1 as applied to claim 1 and in further view of Moriya US 2011/0199486 A1.
As per Claim 3 Hu does not teach The purchase data analysis apparatus of Claim 1, wherein the customer representation includes a periodic explanatory variable, an elapsed time from an entry to the store to the purchase-related action, a value obtained by discretizing the explanatory variable, or a value obtained by discretizing the elapsed time. However, Moriya para.75 teaches FIG. 15 shows a file in which various data has been written by the accounting area camera process. As shown in FIG. 15, after recording information indicating the movement path of the customer in the store (stopping locations, stopping times) and information related to viewing digital signage advertising images (staying time, image information) in the file, the POS information acquired in step S24 is recorded (information relating to the names and volume of products purchase by that customer). That is to say, when and where that customer who entered the store 1 stopped, for what length of time the customer watched what advertising images and finally what the customer purchased are recorded. Para. 77 teaches With the customer behavior recording device 11, it is possible to accumulate information relating to the track (movement line) in the store 1 from when the customer enters the store 1, purchases products and until the customer leaves the store, and the names and volume of products purchased by the customer, the contents of images on the digital signage viewed by the customer while in the store 1 and the sex and age estimation results for that customer. In addition, analyzing this information is useful in improving product displays in the store 1, the arrangement of shelves 3 and images displayed on the digital signage. Both Hu and Moriya are directed to consumer analytics. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Hu to include wherein the customer representation includes an elapsed time from an entry to the store to the purchase-related action as taught by Moriya to improve product displays in the store, the arrangement of shelves and images displayed on the digital signage (see para. 77).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hu US 2019/0147469 A1 Laserson US 20200410534 A1as applied to claim 1 and in further view of Scheidelman US 20160321705 A1.
As per Claim 4 Hu does not teach the purchase data analysis apparatus of Claim 1, wherein the processing circuitry is configured to cluster the customers, based on the customer representations, and clusters the stores, based on a clustering result of the customers. However, Scheidelman para. 98 teaches in one embodiment, the offer engine (511) and/or the profile generator (121) is configured to perform automated cluster analysis of the transaction data to identify merchant micro-categories (305, . . . , 307). For example, the cluster analysis is performed based on the clustering of consumer micro-segments that patron each merchant and incorporating the relative merchant ticket size distribution. As a result of the cluster analysis of merchant micro-categories (305, . . . , 307), different merchant micro-categories (305, . . . , 307) are identified to be associated with different strength levels of consumer micro-segments (e.g., 331, . . . , 333) which represent the customer patterns of the merchants in the respective merchant micro-categories (305, . . . , 307). Para. 101 teaches for example, one merchant micro-category (e.g., 305) may have a first set of consumer micro-segments and have a first ticket distribution pattern; and another merchant micro-category (e.g., 307) may have a second set of consumer micro-segments and have a second ticket distribution pattern. Both Hu and Scheidelman are directed to consumer analytics. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Hu to include wherein the processing circuitry is configured to cluster the customers, based on the customer representations, and clusters the stores, based on a clustering result of the customers as taught by Scheidelman to generate more effective offers and greatly simplify merchant's offer sourcing process (see para. 86).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hu US 2019/0147469 A1 in view of Laserson US 20200410534 A1 as applied to claim 1 and in further view of Rohrbasser US 2011/0106581 A1.
As per Claim 5 Hu does not teaches the purchase data analysis apparatus of Claim 1, wherein the processing circuitry is configured to execute totalization of the customer information in regard to each of store clusters, based on a clustering result of the stores, and displays a totalization result of the customer information. However, Rohrbasser para. 74 FIG. 3 illustrates a visualized key measure report 16. The key measure report 16 is divided into three main areas 23, 24 and 25 and visualizes a comparison of a product performance between two time periods. A first area 23 includes a list of KPIs 26 (key performance indicators), such as total sales, sales per customer, etc. The KPIs are given for the first time period in column 27 and the second time period 28. In a third column 29, the development of the respective KPI is given in percent. This development is also graphically visualized in area 24, showing the development of each KPI of the list of KPIs 26 as a bar diagram. Both Hu and Rohrbasser are directed to consumer analytics. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Hu to include wherein the processing circuitry is configured to execute totalization of the customer information in regard to each of store clusters, based on a clustering result of the stores, and displays a totalization result of the customer information as taught by Rohrbasser to harness customer insights to drive better strategic decisions as well as supporting day-to-day operations (see para. 20).
Claim(s) 6, 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hu US 2019/0147469 A1 in view of Laserson US 20200410534 A1 in view of Rohrbasser US 2011/0106581 A1 as applied to claim 5 and in further view of Lazarus US 2005/0159996 A1.
As per Claim 6 Hu does not teaches the purchase data analysis apparatus of Claim 5, wherein the processing circuitry is configured to execute, based on a sales result of a designated store cluster or a store cluster to which a designated store belongs, a sales prediction of the designated store cluster or the designated store. However, Lazarus para. 22 teaches to predict financial behavior, the consumer profile of a consumer, using preferably the same type of summary statistics for a recent, past time period, is input into the predictive models for the different merchant clusters. The result is a prediction of the amount of money that the consumer is likely to spend in each merchant cluster in a future time interval, for which no actual spending data may yet be available. Both Hu and Lazarus are directed to consumer analytics. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Hu to include wherein the processing circuitry is configured to execute, based on a sales result of a designated store cluster or a store cluster to which a designated store belongs, a sales prediction of the designated store cluster or the designated store as taught by Lazarus to accurately predict future spending behavior and likely responses to particular marketing efforts, in specifically identified data-driven industry segments (see para 3).
As per Claim 7 Hu does not teaches the purchase data analysis apparatus of Claim 5, further comprising a store cluster storage unit configured to store a store cluster label, wherein the processing circuitry is configured to manage a store cluster label including one or more of the store clusters, and further executes totalization of the customer information in regard to each of the store cluster labels. However, Lazarus para. 172 teaches (Lazarus 172) Prediction window: The dependent variables are generally any measure of amount or rate of spending by the consumer in the segment in the prediction window. A simple measure is the total dollar amount that was spent in the segment by the consumer in the transactions in the prediction window. Another measure may be average amount spent at merchants (e.g. total amount divided by number of transactions). Both Hu and Lazarus are directed to consumer analytics. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Hu to include wherein the processing circuitry is configured to execute, based on a sales result of a designated store cluster or a store cluster to which a designated store belongs, a sales prediction of the designated store cluster or the designated store as taught by Lazarus to accurately predict future spending behavior and likely responses to particular marketing efforts, in specifically identified data-driven industry segments (see para 3).
Claim(s) 8, 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hu US 2019/0147469 A1 in view of Laserson US 20200410534 A1 in view of Rohrbasser US 2011/0106581 A1 in view of Lazarus US 2005/0159996 A1 as applied to claim 7 and in further view of Saarenvirta US 2021/0279753 A1.
As per Claim 8 Hu does not teaches the purchase data analysis apparatus of Claim 7, wherein the processing circuitry is configured to accept an input of a name of the store cluster or the store cluster label, and updates a name of the store cluster or the store cluster label to the input name. However, Saarenvirta para.136 teaches The data input 288 may be configured to receive the retail environment data using the communication unit 272, i.e. from one or more retail stores of the retailer or ecommerce organization. The received retail data may be received and parsed by the data input 288, and may be processed using the data correction 289. The received retail data from the one or more retail stores may be received as is known, for example by File-Transfer-Protocol using flat files, Excel® files, Comma Separated Value (CSV) files, or other data interchange formats as is known. The format of the received retail data may be standardized, or may be an agreed upon format. Para. 141 teaches The retail data received by the data input 288 may include: [0210] 10) Store Groupings [0211] a) Store Cluster Identifier [0212] b) Store Cluster Description Both Hu and Saarenvirta are directed to consumer analytics. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Hu to include wherein the processing circuitry is configured to accept an input of a name of the store cluster or the store cluster label, and updates a name of the store cluster or the store cluster label to the input name as taught by Saarenvirta to allow retailers to input preferred description for clusters.
As per Claim 9 Hu does not teach the purchase data analysis apparatus of Claim 8, wherein the processing circuitry is further configured to accept an input of information of the store cluster or the store cluster label. However, Saarenvirta para.136 teaches The data input 288 may be configured to receive the retail environment data using the communication unit 272, i.e. from one or more retail stores of the retailer or ecommerce organization. The received retail data may be received and parsed by the data input 288, and may be processed using the data correction 289. The received retail data from the one or more retail stores may be received as is known, for example by File-Transfer-Protocol using flat files, Excel® files, Comma Separated Value (CSV) files, or other data interchange formats as is known. The format of the received retail data may be standardized, or may be an agreed upon format. Para. 141 teaches The retail data received by the data input 288 may include: [0210] 10) Store Groupings [0211] a) Store Cluster Identifier [0212] b) Store Cluster Description. Both Hu and Saarenvirta are directed to consumer analytics. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Hu to include wherein the processing circuitry is further configured to accept an input of information of the store cluster or the store cluster label as taught by Saarenvirta to allow retailers to input preferred description for clusters.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEIRDRE D HATCHER whose telephone number is (571)270-5321. The examiner can normally be reached Monday-Friday 8-4:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached at 571-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DEIRDRE D HATCHER/Primary Examiner, Art Unit 3625