The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/03/2025 has been entered.
Notice to Applicant
In response to the communication received on 12/03/2025, the following is a Non-Final Office Action for Application No. 18607995.
Status of Claims
Claims 1-2, 4-8, 10-11 and 13-25 are pending.
Claims 3, 9 and 12 are cancelled.
Claims 24-25 are new.
Response to Amendments
Applicant’s amendments have been fully considered.
Response to Arguments
Applicant’s arguments with respect to the claims have been considered but are moot in light of the new grounds of rejection, as necessitated by amendment.
As per the 101 rejection, Applicant argues that the claims are in favor of eligibility per Prong One of Step 2A, however Examiner respectfully disagrees. Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion) and/or Certain Methods of Organizing Human Activity including managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules of instructions). Since the recitation of the claims falls into at least one of the above Groupings, there is a basis for providing further analysis with regard to Prong Two of Step 2A to determine whether the recitation of an abstract idea is deduced to being directed to an abstract idea. Thus, the rejection is maintained.
Applicant argues that the claims are in favor of eligibility per Prong Two of Step 2A, however Examiner respectfully disagrees. Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The sensor is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of collecting data. This generic processor server limitation is no more than mere instructions to apply the exception using a generic computer component. Further, check-in-data collected via one or more of social media applications, venue rating applications, point-of-sale systems, mobile applications, venue check-in apps, sensors at the venues and photo applications is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. In other words, the present claims use check-in-data collected via one or more of social media applications, venue rating applications, point-of-sale systems, mobile applications, venue check-in apps, sensors at the venues and photo applications which is a concept that can be performed in the human mind. The processor is merely used to perform the function(s), and the processor does not integrate the abstract idea into a practical application since there are no meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B. Thus, the rejection is maintained.
Applicant argues that the claims are in favor of eligibility per Step 2B, however Examiner respectfully disagrees. Therein, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of: sensors. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, check-in-data collected via one or more of social media applications, venue rating applications, point-of-sale systems, mobile applications, venue check-in apps, sensors at the venues is mere instruction to apply an exception using a generic computer component/application which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic sensors and/or computer/memory type structure. Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include the non-limiting or non-exclusive examples of MPEP § 2106.05. Thus, the rejection is maintained.
Priority
As required by M.P.E.P. 201.14(c), acknowledgement is made of applicant’s claim for priority based on: 18607995 filed 03/18/2024 is a Continuation of 17572252 , filed 01/10/2022 ,now U.S. Patent # 11935082 and having 1 RCE-type filing therein; 17572252, filed 01/10/2022 is a division of 16927671, filed 07/13/2020 ,now U.S. Patent #11222349 and having 1 RCE-type filing therein; 16927671 is a continuation of 15845203, filed 12/18/2017 ,now U.S. Patent #10713672; 15845203 is a division of 14015506, filed 08/30/2013 ,now U.S. Patent #9846887 and having 1 RCE-type filing therein; 14015506 Claims Priority from Provisional Application 61743263, filed 08/30/2012.
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 1-2, 4-8, 10-11 and 13-25 are rejected under 35 U.S.C. 101 as directed to non-statutory subject matter.
Claims 1-2, 4-8, 10-11 and 13-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. In adhering to the 2019 PEG, Step 1 is directed to determining whether or not the claims fall within a statutory class. Herein, the claims fall within statutory class of a process. Hence, the claims qualify as potentially eligible subject matter under 35 U.S.C §101. With Step 1 being directed to a statutory category, the 2019 PEG flowchart is directed to Step 2. Step 2 is the two-part analysis from Alice Corp. (also called the Mayo test). The 2019 PEG makes two changes in Step 2A: It sets forth new procedure for Step 2A (called “revised Step 2A”) under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. The two-prong inquiry is as follows: Prong One: evaluate whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon). If claim recites an exception, then Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. The claim(s) recite(s) the following abstract idea indicated by non-boldface font and additional elements indicated by boldface font (note: there are no additional elements recited):
collecting venue check-in data indicative of a physical or virtual presence of venue visitors at a plurality of venues, the venue check-in-data collected via one or more of mobile applications, point-of-sale systems, and sensors at the venues; determining a social similarity of pairs of venues selected from the one or more venues ; and determining one or more clusters of venues based on the social similarities, wherein the social similarity between venues is determined based on the number of common users checking into the venues or a large number of users checking to venues at a similar time.
[or]
collecting venue check-in data indicative of a physical or virtual presence of venue visitors at a plurality of venues, the venue check-in-data collected via one or more of mobile applications, point-of-sale systems, and sensors at the venues; generating one or more vector representations of one or more venues, wherein the vector representations are check-in intensity vectors having components reflective of a number of times a user has checked into the venue to which the vector representation applies; determining a social similarity of venues selected from the one or more venues based on a comparison of the one or more vector representations for each venue of the pair; and determining one or more neighborhood clusters of venues based on the social similarities; wherein the social similarity is determined based on a similarity of the check-in intensity vectors of the venues.
Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion) and/or Certain Methods of Organizing Human Activity including managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules of instructions). Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. A generic computer and/or database limitation is no more than mere instructions to apply the exception using a generic computer component. Further, determining one or more clusters of venues based on the social similarities by a computer and/or database is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B. Therein, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise sensors, however sensors are one out of a plurality of the ways check-in data is collected. Broadest Reasonable Interpretation (BRI) includes an instance(s) where sensors are not initialized or used for data collection, e.g., check-in-data collected via one or more of social media applications, venue rating applications, point-of-sale systems, mobile applications, venue check-in apps, sensors at the venues and photo applications. Further, determining one or more clusters of venues based on the social similarities is a mental process. To further advance prosecution, if the determining step is by a generic computer and/or database, then it is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic computer/memory type structure at Page 6 wherein “computer system 24 may comprise multiple processors and/or multiple memory units”. Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include, as a non-limiting or non-exclusive examples: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
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ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
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iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or
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v. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook. The courts have recognized the following computer functions inter alia to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations; receiving, processing, and storing data (e.g., the present claims); electronically scanning or extracting data; electronic recordkeeping; automating mental tasks (e.g., process/machine/manufacture for performing the present claims); and receiving or transmitting data (e.g., the present claims). The dependent claims do not cure the above stated deficiencies, and in particular, the dependent claims further narrow the abstract idea without reciting additional elements that integrate the exception into a practical application of the exception or providing significantly more than the abstract idea. Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101. Thus, viewed 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. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 of this title, 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 1-2, 4-8, 10-11 and 13-24 are rejected under 35 U.S.C. 103 as being unpatentable over Switzer (US 20120066065 A1) hereinafter referred to as Switzer in view of Blume et al. (US 20070244741 A1) hereinafter referred to as Blume.
Switzer teaches:
Claim 1. A computer-implemented method comprising:
collecting visitor check-in data indicative of a physical or virtual presence of venue visitors at a plurality of venues, the check-in-data collected via one or more of social media applications, venue rating applications, point-of-sale systems, mobile applications, venue check-in apps, sensors at the venues (¶0130 In another example, the transaction terminal (105) is a POS terminal at the checkout station in a retail store (e.g., a self-service checkout register). When the user (101) pays for a purchase via a payment card (e.g., a credit card or a debit card), the transaction handler (103) provides a targeted advertisement having a coupon obtained from an advertisement network. The user (101) may load the coupon into the account of the payment card and/or obtain a hardcopy of the coupon from the receipt. When the coupon is used in a transaction, the advertisement is linked to the transaction ¶0154 the user (101) may provide the account identifier (181) (e.g., the account number of a credit card) to the transaction terminal (105) to initiate an authorization process for a special transaction which is designed to check the member status of the user (101), in a manner similar to using the account identifier (181) to initiate an authorization process for a payment transaction. The special transaction is designed to verify the member status of the user (101) via checking whether the account data (111) is associated with the loyalty benefit offeror (183). If the account identifier (181) is associated with the corresponding loyalty benefit offeror (183), the transaction handler (103) provides an approval indication in the authorization process to indicate that the user (101) is a member of the loyalty program.);
determining a social similarity of pairs of venues selected from the one or more venues (¶0068 In one embodiment, the transaction records (301) are analyzed in frequency domain to identify periodic features in spending events. The periodic features in the past transaction records (301) can be used to predict the probability of a time window in which a similar transaction will occur. For example, the analysis of the transaction data (109) can be used to predict when a next transaction having the periodic feature will occur, with which merchant, the probability of a repeated transaction with a certain amount, the probability of exception, the opportunity to provide an advertisement or offer such as a coupon, etc. In one embodiment, the periodic features are detected through counting the number of occurrences of pairs of transactions that occurred within a set of predetermined time intervals and separating the transaction pairs based on the time intervals ¶0197 The identification of the other person may be based on a variety of factors including, for example, demographic similarity and/or purchasing pattern similarity between the user (101) and the other person. As one example, the common purchase of identical items or related items by the user (101) and the other person may result in an association between the user (101) and the other person, and a resulting determination that the user (101) and the other person are similar. Once the other person is identified, the transaction profile constituting the SKU-level profile for the other person may be analyzed. Through predictive association and other modeling and analytical techniques, the historical purchases reflected in the SKU-level profile for the other person may be employed to predict the future purchases of the user (101).)); and
determining one or more clusters of venues based on the social similarities (¶0419 Once the cluster definitions (333) are obtained from the cluster analysis (329), the identity of the cluster (e.g., cluster ID (343)) that contains the entity ID (322) can be used to characterize spending behavior of the entity represented by the entity ID (322). The entities in the same cluster are considered to have similar spending behaviors…Similarities and differences among the entities, such as accounts, individuals, families, etc., as represented by the entity ID (e.g., 322) and characterized by the variable values (e.g., 323, 324, . . . , 325) can be identified via the cluster analysis (329). In one embodiment, after a number of clusters of entity IDs are identified based on the patterns of the aggregated measurements, a set of profiles can be generated for the clusters to represent the characteristics of the clusters. Once the clusters are identified, each of the entity IDs (e.g., corresponding to an account, individual, family) can be assigned to one cluster; and the profile for the corresponding cluster may be used to represent, at least in part, the entity (e.g., account, individual, family). Alternatively, the relationship between an entity (e.g., an account, individual, family) and one or more clusters can be determined (e.g., based on a measurement of closeness to each cluster). Thus, the cluster related data can be used in a transaction profile (127 or 341) to provide information about the behavior of the entity (e.g., an account, an individual, a family) ¶0468 For example, in one embodiment, the profile generator (121) is to identify clusters of entities (e.g., accounts, cardholders, families, businesses, cities, regions, etc.) based on the spending patterns of the entities. The clusters represent entity segments identified based on the spending patterns of the entities reflected in the transaction data (109) or the transaction records (301)…In one embodiment, the clusters correspond to cells or regions in the mathematical space that contain the respective groups of entities. For example, the mathematical space representing the characteristics of users (101) may be divided into clusters (cells or regions).).
Although not explicitly taught by Switzer, Blume teaches in the analogous art of predictive modeling of consumer financial behavior using supervised segmentation and nearest-neighbor matching:
collecting visitor check-in data indicative of a physical or virtual presence of venue visitors at a plurality of venues […]determining a social similarity of pairs of venues […]wherein the social similarity between venues is determined based on the number of common users checking into the venues of a large number of user checking to venues at a similar time (ABSTRACT The merchant segments are derived from the consumer transaction data based on co-occurrences of merchants in sequences of transactions. Merchant vectors represent specific merchants, and are aligned in a vector space as a function of the degree to which the merchants co-occur more or less frequently than expected. Supervised segmentation is applied to merchant vectors to form the merchant segments. Merchant segment predictive models provide predictions of spending in each merchant segment for any particular consumer, based on previous spending by the consumer. Consumer profiles describe summary statistics of each consumer's spending in the merchant segments, and across merchant segments. The consumer profiles include consumer vectors derived as summary vectors of selected merchants patronized by the consumer. Predictions of consumer behavior are made by applying nearest-neighbor analysis to consumer vectors FIGS. 10-15 and ¶0017 Merchants may also be segmented according to a supervised segmentation technique, such as Kohonen's Learning Vector Quantization (LVQ) algorithm, as described in T. Kohonen, "Improved Versions of Learning Vector Quantization," in IJCNN San Diego, 1990; and T. Kohonen, Self-Organizing Maps, 2d ed., Springer-Verlag, 1997. Supervised learning allows characteristics of segments to be directly specified, so that segments may be defined, for example, as "art museums," "book stores," "Internet merchants," and the like. Segment boundaries can be defined by the training algorithm based on training exemplars with known membership in classes ¶0018 the analysis of consumer spending uses spending data, such as credit card statements, retail data, or any other transaction data, and processes that data to identify co-occurrences of purchases within defined co-occurrence windows, which may be based on either a number of transactions, a time interval, or other sequence related criteria. Each merchant is associated with a vector representation; in one embodiment, the initial vectors for all of the merchants are randomized to present a quasi-orthogonal set of vectors in a merchant vector space. ¶0019 Each consumer's transaction data reflecting their purchases (e.g. credit card statements, bank statements, and the like) is chronologically organized to reflect the general order in which purchases were made at the merchants. Analysis of each consumer's transaction data in various co-occurrence windows identifies which merchants co-occur. For each pair of merchants, their respective merchant vectors are updated in the vector space as a function of their frequency of their co-occurrence. After processing of the spending data, the merchant vectors of merchants that are frequented together are generally aligned in the same direction in the merchant vector space. ¶0023 both consumers and merchants are represented in a common vector space. This means that given a consumer vector, the merchant vectors that are "similar" to this consumer vector can be readily determined (that is, they point in generally the same direction in the merchant vector space), for example using dot product analysis. Thus, merchants who are "similar" to the consumer can be easily determined, these being merchants who would likely be of interest to the consumer, even if the consumer has never purchased from these merchants before. ¶0024 Given the merchant segments, the present invention then creates a predictive model of future spending in each merchant segment, based on transaction statistics of historical spending in the merchant segment by those consumers who have purchased from merchants in the segments, in other segments, and data on overall purchases);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the predictive modeling of consumer financial behavior using supervised segmentation and nearest-neighbor matching of Blume with the system for segmenting customers of Switzer for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Switzer ¶0003 teaches that millions of transactions occur daily through the use of payment cards;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Switzer Abstract teaches a data warehouse configured to store transaction data, geo-demographic data, attitudinal data and lifestyle data, and Blume Abstract teaches merchant vectors represent specific merchants and are aligned in a vector space as a function of the degree to which the merchants co-occur; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Switzer at least the above cited paragraphs, and Blume at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the predictive modeling of consumer financial behavior using supervised segmentation and nearest-neighbor matching of Blume with the system for segmenting customers of Switzer. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Switzer teaches:
Claim 2. The method of claim 1 wherein the social similarity of a pair of venues is further based on geographic proximity of the venues to each other (¶0057 the transaction profiles (127) include information on shopping patterns in retail stores as well as online, including frequency of shopping, amount spent in each shopping trip, distance of merchant location (retail) from the address of the account holder(s), etc. ¶0087 In one embodiment, the user tracker (113) determines certain characteristics of the user (101) to describe a type or group of users of which the user (101) is a member. The transaction profile of the group is used as the user specific profile (131). Examples of such characteristics include geographical location or neighborhood, types of online activities, specific online activities, or merchant propensity. In one embodiment, the groups are defined based on aggregate information (e.g., by time of day, or household), or segment (e.g., by cluster, propensity, demographics, cluster IDs, and/or factor values).
Switzer teaches:
Claim 4. The method of claim 1 wherein the visitor check-in data reflects a temporal check-in pattern (¶0086 In one embodiment, the identification reference table is used to identify the account information (142) (e.g., account number (302)) based on characteristics of the user (101) captured in the user data (125), such as browser cookie ID, IP addresses, and/or timestamps on the usage of the IP addresses. In one embodiment, the identification reference table is maintained by the operator of the transaction handler (103). Alternatively, the identification reference table is maintained by an entity other than the operator of the transaction handler ¶0457 In one embodiment, the transaction data (109) are aged to provide more weight to recent data than older data. In other embodiments, the transaction data (109) are reverse aged. In further embodiments, the transaction data (109) are seasonally adjusted. ¶0130 In another example, the transaction terminal (105) is a POS terminal at the checkout station in a retail store (e.g., a self-service checkout register). When the user (101) pays for a purchase via a payment card (e.g., a credit card or a debit card), the transaction handler (103) provides a targeted advertisement having a coupon obtained from an advertisement network).
Switzer teaches:
Claim 5. The method of claim 1 further comprising:computing elements of a pairwise venue similarity matrix having elements comprising scores indicative of the social similarity between pairs of venues; and creating a graph representation of the matrix having nodes representing venues, wherein a venue node is connected with an undirected edge to its m nearest neighbor venue nodes by geographic distance, and wherein the edges are weighted according to the socialsimilarity measure (¶0265 in one embodiment, when the geo-demographic data, behavior data, attitudinal data and/or lifestyle data are characterized via a profile having N parameters, using factor definitions (331) and/or cluster definitions (333) in a way as illustrated in FIG. 2, the need profile (213) dimension as illustrated in FIG. 12 is replaced with N-dimensions corresponding to the N parameters to represent the N-dimensional need profile for the segmentation space (210). The location of the customer (e.g., user (101)) in the N-dimensional subspace for the need profile (213) is indicative of the need of the customer ¶0431 When the diversity index (342) indicates that the diversity of the spending data is under a predetermined threshold level, the variable values (e.g., 323, 324, . . . , 325) for the corresponding entity ID (322) may be excluded from the cluster analysis (329) and/or the factor analysis (327) due to the lack of diversity. When the diversity index (342) of the aggregated spending profile (341) is lower than a predetermined threshold, the factor values (344) and the cluster ID (343) may not accurately represent the spending behavior of the corresponding entity).
Switzer teaches:
Claim 6. The method of claim 5 wherein the one or more clusters are derived using spectral clustering, or a variation thereof, of the graph representation (¶0431 When the diversity index (342) indicates that the diversity of the spending data is under a predetermined threshold level, the variable values (e.g., 323, 324, . . . , 325) for the corresponding entity ID (322) may be excluded from the cluster analysis (329) and/or the factor analysis (327) due to the lack of diversity. When the diversity index (342) of the aggregated spending profile (341) is lower than a predetermined threshold, the factor values (344) and the cluster ID (343) may not accurately represent the spending behavior of the corresponding entity).
Switzer teaches:
Claim 7. The method of claim 5 wherein the one or more clusters are derived using one or more of hierarchial clustering, density-based clustering, centroid-based clustering, distribution or model-based clustering, graph partition clustering, social network community detection and graph layout-based clustering (¶0431 When the diversity index (342) indicates that the diversity of the spending data is under a predetermined threshold level, the variable values (e.g., 323, 324, . . . , 325) for the corresponding entity ID (322) may be excluded from the cluster analysis (329) and/or the factor analysis (327) due to the lack of diversity. When the diversity index (342) of the aggregated spending profile (341) is lower than a predetermined threshold, the factor values (344) and the cluster ID (343) may not accurately represent the spending behavior of the corresponding entity).
Switzer teaches:
Claim 8. The method of claim 1 further comprising:generating vector representations of each cluster based on the similarities of visitors to all venues within each cluster; and comparing clusters based on a cosine similarity between the vector representation of each cluster (¶0431 When the diversity index (342) indicates that the diversity of the spending data is under a predetermined threshold level, the variable values (e.g., 323, 324, . . . , 325) for the corresponding entity ID (322) may be excluded from the cluster analysis (329) and/or the factor analysis (327) due to the lack of diversity. When the diversity index (342) of the aggregated spending profile (341) is lower than a predetermined threshold, the factor values (344) and the cluster ID (343) may not accurately represent the spending behavior of the corresponding entity).
Switzer teaches:
Claim 10. The method of claim 1 wherein the visitor check-in data incudes one or more of a user ID, a venue ID, and a time stamp (¶0086 In one embodiment, the identification reference table is used to identify the account information (142) (e.g., account number (302)) based on characteristics of the user (101) captured in the user data (125), such as browser cookie ID, IP addresses, and/or timestamps on the usage of the IP addresses. In one embodiment, the identification reference table is maintained by the operator of the transaction handler (103). Alternatively, the identification reference table is maintained by an entity other than the operator of the transaction handler)).
Switzer teaches:
Claim 11. The method of claim 1 wherein the visitor check-in data and the vector representations are stored in a data store (¶0356 The memory (429) may contain a collection of program and/or database components and/or data such as, but not limited to: operating system component(s) (415) (operating system); information server component(s) (416) (information server); user interface component(s) (417) (user interface); Web browser component(s) (418) (Web browser); database(s) (419); mail server component(s) (421); mail client component(s) (422); cryptographic server component(s) (420) (cryptographic server); the CS PLATFORM component(s) (435); and/or the like (i.e., collectively a component collection). These components may be stored and accessed from the storage devices (414) and/or from storage devices accessible through an interface bus (407)).
Switzer teaches:
Claim 13. The method of claim 1 wherein the vector representations are compared using cosine similarity (¶0431 When the diversity index (342) indicates that the diversity of the spending data is under a predetermined threshold level, the variable values (e.g., 323, 324, . . . , 325) for the corresponding entity ID (322) may be excluded from the cluster analysis (329) and/or the factor analysis (327) due to the lack of diversity. When the diversity index (342) of the aggregated spending profile (341) is lower than a predetermined threshold, the factor values (344) and the cluster ID (343) may not accurately represent the spending behavior of the corresponding entity).
Switzer teaches:
Claim 14. The method of claim 1 wherein the vector representations are compared using Jaccard similarity or vector-distance similarity with a non-increasing delay function (¶0431 When the diversity index (342) indicates that the diversity of the spending data is under a predetermined threshold level, the variable values (e.g., 323, 324, . . . , 325) for the corresponding entity ID (322) may be excluded from the cluster analysis (329) and/or the factor analysis (327) due to the lack of diversity. When the diversity index (342) of the aggregated spending profile (341) is lower than a predetermined threshold, the factor values (344) and the cluster ID (343) may not accurately represent the spending behavior of the corresponding entity).
Switzer teaches:
Claim 15. The method of claim 1 wherein the social similarity between venues is only determined if the venues are one of the m geographically closest venues to each other, wherein m is a predetermined threshold (¶0431 When the diversity index (342) indicates that the diversity of the spending data is under a predetermined threshold level, the variable values (e.g., 323, 324, . . . , 325) for the corresponding entity ID (322) may be excluded from the cluster analysis (329) and/or the factor analysis (327) due to the lack of diversity. When the diversity index (342) of the aggregated spending profile (341) is lower than a predetermined threshold, the factor values (344) and the cluster ID (343) may not accurately represent the spending behavior of the corresponding entity).
Switzer teaches:
Claim 16. The method of claim 1 wherein an element of the pairwise venue similarity matrix is 0 if the venues are not one of the m geographically closest venues to each other, wherein m is apredetermined threshold (¶0431 When the diversity index (342) indicates that the diversity of the spending data is under a predetermined threshold level, the variable values (e.g., 323, 324, . . . , 325) for the corresponding entity ID (322) may be excluded from the cluster analysis (329) and/or the factor analysis (327) due to the lack of diversity. When the diversity index (342) of the aggregated spending profile (341) is lower than a predetermined threshold, the factor values (344) and the cluster ID (343) may not accurately represent the spending behavior of the corresponding entity).
Switzer teaches:
Claim 17. The method of claim 1 wherein the vector representations are check-in intensity vectors wherein each vector entry indicates if a venue visitor checked into the venue a threshold number of times or more in a given time period (¶0200 In one embodiment, SKU-level profiles of others who are identified to be similar to the user (101) may be used to identify a user (101) who may exhibit a high propensity to purchase goods and services. For example, if the SKU-level profiles of others reflect a quantity or frequency of purchase that is determined to satisfy a threshold, then the user (101) may also be classified or predicted to exhibit a high propensity to purchase. Accordingly, the type and frequency of advertisements that account for such propensity may be appropriately tailored for the user (101).
Switzer teaches:
Claim 18. The method of claim 1 wherein the vector representations are intensity vectors having components that are a function of a rating for the venue by a venue visitor provided in a venue rating application (¶0475 the relationship of a pair of values from two different clusters provides an indication of the likelihood that the user (101) is in one of the two cells, if the user (101) is shown to be in the other cell. For example, if the likelihood of the user (101) to purchase each of two types of products is known, the scores can be used to determine the likelihood of the user (101) buying one of the two types of products if the user (101) is known to be interested in the other type of products. In one embodiment, a map of the values for the clusters is used in a profile (e.g., 127 or 341) to characterize the spending behavior of the user (101) (or other types of entities, such as a family, company, neighborhood, city, or other types of groups defined by other aggregate parameters, such as time of day, etc).
Switzer teaches:
Claim 19. The method of claim 4 wherein the temporal check-in data is measured during different times of a day or different days of a week (¶0087 In one embodiment, the user tracker (113) determines certain characteristics of the user (101) to describe a type or group of users of which the user (101) is a member. The transaction profile of the group is used as the user specific profile (131). Examples of such characteristics include geographical location or neighborhood, types of online activities, specific online activities, or merchant propensity. In one embodiment, the groups are defined based on aggregate information (e.g., by time of day, or household), or segment (e.g., by cluster, propensity, demographics, cluster IDs, and/or factor values ¶0475 the relationship of a pair of values from two different clusters provides an indication of the likelihood that the user (101) is in one of the two cells, if the user (101) is shown to be in the other cell. For example, if the likelihood of the user (101) to purchase each of two types of products is known, the scores can be used to determine the likelihood of the user (101) buying one of the two types of products if the user (101) is known to be interested in the other type of products. In one embodiment, a map of the values for the clusters is used in a profile (e.g., 127 or 341) to characterize the spending behavior of the user (101) (or other types of entities, such as a family, company, neighborhood, city, or other types of groups defined by other aggregate parameters, such as time of day, etc.)).
Switzer teaches:
Claim 20. The method of claim 1 wherein the temporal check-in data is measured seasonally (¶0457 In one embodiment, the transaction data (109) are aged to provide more weight to recent data than older data. In other embodiments, the transaction data (109) are reverse aged. In further embodiments, the transaction data (109) are seasonally adjusted.).
Switzer teaches:
Claim 21. The method of claim 1 wherein the vector representations are intensity vectors representing check-in data for groups of venue visitors at each venue (¶0130 In another example, the transaction terminal (105) is a POS terminal at the checkout station in a retail store (e.g., a self-service checkout register). When the user (101) pays for a purchase via a payment card (e.g., a credit card or a debit card), the transaction handler (103) provides a targeted advertisement having a coupon obtained from an advertisement network).
Switzer teaches:
Claim 22. The method of claim 21 wherein the groups of venue visitors are members of an organization (¶0057 the transaction profiles (127) include information on shopping patterns in retail stores as well as online, including frequency of shopping, amount spent in each shopping trip, distance of merchant location (retail) from the address of the account holder(s), etc. ¶0087 In one embodiment, the user tracker (113) determines certain characteristics of the user (101) to describe a type or group of users of which the user (101) is a member. The transaction profile of the group is used as the user specific profile (131). Examples of such characteristics include geographical location or neighborhood, types of online activities, specific online activities, or merchant propensity. In one embodiment, the groups are defined based on aggregate information (e.g., by time of day, or household), or segment (e.g., by cluster, propensity, demographics, cluster IDs, and/or factor values).).
Switzer teaches:
Claim 23. The method of claim 1 wherein the clusters are emblematic of an urban or neighborhood typology (¶0249 The result of a segmentation analysis according to one embodiment allows an offeror (e.g., an issuer, a merchant, a manufacturer, a service provider) to improve revenue, market share, customer loyalty, and/or brand position through strategies such as identifying opportunities to develop products and/or services that address unmet needs of certain segments, establishing or improving positioning and messaging among appealing segments, devising marketing strategies to enhance appeal across multiple market segments, etc. The segmentation results can be used for business case development, new product development, marketing strategy and tactics development, design of communications and offers, etc).
Switzer teaches:
Claim 24. The method of claim 1 wherein the one or more neighborhood clusters are used for one or more of marketing, making venue recommendations, urban design,city planning, business analytics, political uses, public health and safety matters,navigation, banking and national security matters (¶0053 In one embodiment, the centralized data warehouse (149) provides centralized management but allows decentralized execution. For example, a third party strategic marketing analyst, statistician, marketer, promoter, business leader, etc., may access the centralized data warehouse (149) to analyze customer and shopper data, to provide follow-up analyses of customer contributions, to develop propensity models for increased conversion of marketing campaigns, to develop segmentation models for marketing, etc. The centralized data warehouse (149) can be used to manage advertisement campaigns and analyze response profitability).
Claim 25 is rejected under 35 U.S.C. 103 as being unpatentable over Switzer (US 20120066065 A1) hereinafter referred to as Switzer in view of Elliott, JR. et al. (US 20090307049 A1) hereinafter referred to as Elliott, JR.in further view of Blume et al. (US 20070244741 A1) hereinafter referred to as Blume.
Switzer teaches:
Claim 25. A computer-implemented method comprising:
collecting venue check-in data indicative of a physical or virtual presence of venue visitors at a plurality of venues, the venue check-in-data collected via one or more of mobile applications, point-of-sale systems, and sensors at the venues (¶0130 In another example, the transaction terminal (105) is a POS terminal at the checkout station in a retail store (e.g., a self-service checkout register). When the user (101) pays for a purchase via a payment card (e.g., a credit card or a debit card), the transaction handler (103) provides a targeted advertisement having a coupon obtained from an advertisement network. The user (101) may load the coupon into the account of the payment card and/or obtain a hardcopy of the coupon from the receipt. When the coupon is used in a transaction, the advertisement is linked to the transaction ¶0154 the user (101) may provide the account identifier (181) (e.g., the account number of a credit card) to the transaction terminal (105) to initiate an authorization process for a special transaction which is designed to check the member status of the user (101), in a manner similar to using the account identifier (181) to initiate an authorization process for a payment transaction. The special transaction is designed to verify the member status of the user (101) via checking whether the account data (111) is associated with the loyalty benefit offeror (183). If the account identifier (181) is associated with the corresponding loyalty benefit offeror (183), the transaction handler (103) provides an approval indication in the authorization process to indicate that the user (101) is a member of the loyalty program.);
generating one or more vector representations of one or more venues, wherein the vector representations are check-in intensity vectors having components reflective of a number of times a user has checked into the venue to which the vector representation applies (Fig. 2 and ¶0265 In one embodiment, a profile (e.g., 127, 341) of a customer includes a plurality of profile parameters; and the space for segmentation is more than three dimensional. For example, in one embodiment, when the geo-demographic data, behavior data, attitudinal data and/or lifestyle data are characterized via a profile having N parameters, using factor definitions (331) and/or cluster definitions (333) in a way as illustrated in FIG. 2, the need profile (213) dimension as illustrated in FIG. 12 is replaced with N-dimensions corresponding to the N parameters to represent the N-dimensional need profile for the segmentation space (210). The location of the customer (e.g., user (101)) in the N-dimensional subspace for the need profile (213) is indicative of the need of the customer ¶0086 In one embodiment, the identification reference table is used to identify the account information (142) (e.g., account number (302)) based on characteristics of the user (101) captured in the user data (125), such as browser cookie ID, IP addresses, and/or timestamps on the usage of the IP addresses. In one embodiment, the identification reference table is maintained by the operator of the transaction handler (103). Alternatively, the identification reference table is maintained by an entity other than the operator of the transaction handler ¶0457 In one embodiment, the transaction data (109) are aged to provide more weight to recent data than older data. In other embodiments, the transaction data (109) are reverse aged. In further embodiments, the transaction data (109) are seasonally adjusted. ¶0130 In another example, the transaction terminal (105) is a POS terminal at the checkout station in a retail store (e.g., a self-service checkout register). When the user (101) pays for a purchase via a payment card (e.g., a credit card or a debit card), the transaction handler (103) provides a targeted advertisement having a coupon obtained from an advertisement network ¶0200 In one embodiment, SKU-level profiles of others who are identified to be similar to the user (101) may be used to identify a user (101) who may exhibit a high propensity to purchase goods and services. For example, if the SKU-level profiles of others reflect a quantity or frequency of purchase that is determined to satisfy a threshold, then the user (101) may also be classified or predicted to exhibit a high propensity to purchase. Accordingly, the type and frequency of advertisements that account for such propensity may be appropriately tailored for the user (101);
determining a social similarity of pairs of venues selected from the one or more venues based on a comparison of the one or more vector representations for each venue of the pair (¶0068 In one embodiment, the transaction records (301) are analyzed in frequency domain to identify periodic features in spending events. The periodic features in the past transaction records (301) can be used to predict the probability of a time window in which a similar transaction will occur. For example, the analysis of the transaction data (109) can be used to predict when a next transaction having the periodic feature will occur, with which merchant, the probability of a repeated transaction with a certain amount, the probability of exception, the opportunity to provide an advertisement or offer such as a coupon, etc. In one embodiment, the periodic features are detected through counting the number of occurrences of pairs of transactions that occurred within a set of predetermined time intervals and separating the transaction pairs based on the time intervals ¶0197 The identification of the other person may be based on a variety of factors including, for example, demographic similarity and/or purchasing pattern similarity between the user (101) and the other person. As one example, the common purchase of identical items or related items by the user (101) and the other person may result in an association between the user (101) and the other person, and a resulting determination that the user (101) and the other person are similar. Once the other person is identified, the transaction profile constituting the SKU-level profile for the other person may be analyzed. Through predictive association and other modeling and analytical techniques, the historical purchases reflected in the SKU-level profile for the other person may be employed to predict the future purchases of the user (101).)); and
determining one or more clusters of venues based on the social similarities (¶0419 Once the cluster definitions (333) are obtained from the cluster analysis (329), the identity of the cluster (e.g., cluster ID (343)) that contains the entity ID (322) can be used to characterize spending behavior of the entity represented by the entity ID (322). The entities in the same cluster are considered to have similar spending behaviors…Similarities and differences among the entities, such as accounts, individuals, families, etc., as represented by the entity ID (e.g., 322) and characterized by the variable values (e.g., 323, 324, . . . , 325) can be identified via the cluster analysis (329). In one embodiment, after a number of clusters of entity IDs are identified based on the patterns of the aggregated measurements, a set of profiles can be generated for the clusters to represent the characteristics of the clusters. Once the clusters are identified, each of the entity IDs (e.g., corresponding to an account, individual, family) can be assigned to one cluster; and the profile for the corresponding cluster may be used to represent, at least in part, the entity (e.g., account, individual, family). Alternatively, the relationship between an entity (e.g., an account, individual, family) and one or more clusters can be determined (e.g., based on a measurement of closeness to each cluster). Thus, the cluster related data can be used in a transaction profile (127 or 341) to provide information about the behavior of the entity (e.g., an account, an individual, a family) ¶0468 For example, in one embodiment, the profile generator (121) is to identify clusters of entities (e.g., accounts, cardholders, families, businesses, cities, regions, etc.) based on the spending patterns of the entities. The clusters represent entity segments identified based on the spending patterns of the entities reflected in the transaction data (109) or the transaction records (301)…In one embodiment, the clusters correspond to cells or regions in the mathematical space that contain the respective groups of entities. For example, the mathematical space representing the characteristics of users (101) may be divided into clusters (cells or regions).).
Although not explicitly taught by Switzer, Elliott, JR. teaches in the analogous art of soft co-clustering of data:
generating one or more vector representations of one or more venues, wherein the vector representations are check-in intensity vectors having components reflective of a number of times a user has checked into the venue to which the vector representation applies (¶0039 Referring to FIG. 3A, co-clustering can be described using a graph illustration. A graph is a collection of vertices and edges. The vertices, usually drawn as closed curves, can represent entities (e.g., people, business, abstractions, etc.) and the edges can represent relationships between entities. For example, in social networks the entities are people and the edges represent personal relationships between people. A minimum number of vertices necessary to traverse in order to travel from person "A" to person "B" can be called the degree of separation. ¶0044 In some implementations, mathematically clustering subjects based on such probability vectors (e.g., probabilities in a row of a table like table 420) identifies similarities between subjects based on their relationships with objects. For example, the clustering system 200 may identify that subjects 1 and 2 have similar probability vectors, whereas subject 3 has a different probability vector than either subject 1 or subject 2. ¶0056 Object "i" can be completely characterized by probability vector {right arrow over (p)}.sub.i just as subject "i" can be characterized by the frequency vector {right arrow over (m)}.sub.i in the example phase I DMM 450. This demonstrates that for any object "i," the phase II DM model 510 can provide a soft clustering… Referring to FIG. 2, in some implementations, the clustering system 200 can implement the soft co-clustering as described above. In some implementations, the clustering system 200 can include a clusterer 204 that clusters data sets. The clusterer 204 can include a purchaser clusterer 206 for generating clusters of purchasers and a merchant clusterer 208 for generating clusters of merchants. CL.9. The method of claim 1, wherein calculating the merchant clusters further comprises generating, for each merchant, a probability vector p that the merchant is associated with each of the purchase clusters and clustering the merchants based on similarities in probability vectors..).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the soft co-clustering of data of Elliott, JR. with the system for segmenting customers of Switzer for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Switzer ¶0003 teaches that millions of transactions occur daily through the use of payment cards;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Switzer Abstract teaches a data warehouse configured to store transaction data, geo-demographic data, attitudinal data and lifestyle data, and Elliott, JR. Abstract teaches accessing a data structure that includes information about purchasers, merchants, and financial transactions between the purchasers and the merchants and generating purchaser clusters; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Switzer at least the above cited paragraphs, and Elliott, JR. at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the soft co-clustering of data of Elliott, JR. with the system for segmenting customers of Switzer. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Although not explicitly taught by Switzer in view of Elliott, JR., Blume teaches in the analogous art of predictive modeling of consumer financial behavior using supervised segmentation and nearest-neighbor matching:
collecting visitor check-in data indicative of a physical or virtual presence of venue visitors at a plurality of venues[…]wherein the vector representations are check-in intensity vectors having components reflective of a number of times a user has checked into the venue[…]determining a social similarity of pairs of venues[…]based on a comparison of the one or more vector representations[…]wherein the social similarity is determined based on a similarity of the check-in intensity vectors of the venues (ABSTRACT The merchant segments are derived from the consumer transaction data based on co-occurrences of merchants in sequences of transactions. Merchant vectors represent specific merchants, and are aligned in a vector space as a function of the degree to which the merchants co-occur more or less frequently than expected. Supervised segmentation is applied to merchant vectors to form the merchant segments. Merchant segment predictive models provide predictions of spending in each merchant segment for any particular consumer, based on previous spending by the consumer. Consumer profiles describe summary statistics of each consumer's spending in the merchant segments, and across merchant segments. The consumer profiles include consumer vectors derived as summary vectors of selected merchants patronized by the consumer. Predictions of consumer behavior are made by applying nearest-neighbor analysis to consumer vectors FIGS. 10-15 and ¶0017 Merchants may also be segmented according to a supervised segmentation technique, such as Kohonen's Learning Vector Quantization (LVQ) algorithm, as described in T. Kohonen, "Improved Versions of Learning Vector Quantization," in IJCNN San Diego, 1990; and T. Kohonen, Self-Organizing Maps, 2d ed., Springer-Verlag, 1997. Supervised learning allows characteristics of segments to be directly specified, so that segments may be defined, for example, as "art museums," "book stores," "Internet merchants," and the like. Segment boundaries can be defined by the training algorithm based on training exemplars with known membership in classes ¶0018 the analysis of consumer spending uses spending data, such as credit card statements, retail data, or any other transaction data, and processes that data to identify co-occurrences of purchases within defined co-occurrence windows, which may be based on either a number of transactions, a time interval, or other sequence related criteria. Each merchant is associated with a vector representation; in one embodiment, the initial vectors for all of the merchants are randomized to present a quasi-orthogonal set of vectors in a merchant vector space. ¶0019 Each consumer's transaction data reflecting their purchases (e.g. credit card statements, bank statements, and the like) is chronologically organized to reflect the general order in which purchases were made at the merchants. Analysis of each consumer's transaction data in various co-occurrence windows identifies which merchants co-occur. For each pair of merchants, their respective merchant vectors are updated in the vector space as a function of their frequency of their co-occurrence. After processing of the spending data, the merchant vectors of merchants that are frequented together are generally aligned in the same direction in the merchant vector space. ¶0023 both consumers and merchants are represented in a common vector space. This means that given a consumer vector, the merchant vectors that are "similar" to this consumer vector can be readily determined (that is, they point in generally the same direction in the merchant vector space), for example using dot product analysis. Thus, merchants who are "similar" to the consumer can be easily determined, these being merchants who would likely be of interest to the consumer, even if the consumer has never purchased from these merchants before. ¶0024 Given the merchant segments, the present invention then creates a predictive model of future spending in each merchant segment, based on transaction statistics of historical spending in the merchant segment by those consumers who have purchased from merchants in the segments, in other segments, and data on overall purchases);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the predictive modeling of consumer financial behavior using supervised segmentation and nearest-neighbor matching of Blume with the system for segmenting customers of Switzer in view of Elliott, JR. for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Switzer ¶0003 teaches that millions of transactions occur daily through the use of payment cards;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Switzer Abstract teaches a data warehouse configured to store transaction data, geo-demographic data, attitudinal data and lifestyle data, and Elliott, JR. Abstract teaches accessing a data structure that includes information about purchasers, merchants, and financial transactions between the purchasers and the merchants and generating purchaser clusters, and Blume Abstract teaches merchant vectors represent specific merchants and are aligned in a vector space as a function of the degree to which the merchants co-occur; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Switzer in view of Elliott, JR. at least the above cited paragraphs, and Blume at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the predictive modeling of consumer financial behavior using supervised segmentation and nearest-neighbor matching of Blume with the system for segmenting customers of Switzer in view of Elliott, JR.. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Conclusion
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/KURTIS GILLS/
Primary Examiner, Art Unit 3624