DETAILED ACTION
This Office action is in response to the amendment filed June 24, 2025.
Claims 1, 3, 4, 6-12, 14, 15, and 17-22 are pending and have been examined.
Claims 1, 3, 12, 14, and 15 have been amended.
The previous 35 U.S.C. § 112 rejections of claims 1, 3, 4, 6-12, 14, 15, and 17-22 are withdrawn in view of Applicant’s amendments to the claims.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 06/24/2025 has been entered.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
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.
Claims 1, 3, 4, 6-10, 12, 14, 15, and 17-22 are rejected under 35 U.S.C. 103 as being unpatentable over Fordyce (US 2011/0093327) in view of Nidamanuri (US 2017/0364916).
Regarding claim 1, Fordyce discloses:
receiving, in connection with a first transaction for a first object by an anonymous user using a first transaction tool, a first set of transaction information comprising at least a first object identifier and a first transaction tool information (see at least Abstract, In one aspect, a system includes a transaction handler to process transactions, a data warehouse to store transaction data recording the transactions processed at the transaction handler and to store mapping data between first user identifiers and first account identifiers, a profile generator to generate a profile of a user based on the transaction data, and a portal coupled to the transaction handler to receive a query identifying a second user identifier used by the first tracker to track online activities of a user; paragraphs 76 and 224, identify the user in an anonymous manner);
generating a profile for the anonymous user based on the first set of transaction information (see at least fig 1; paragraph 35, a correlator (117) to correlate user specific advertisement data (119) with transactions resulting from the user specific advertisement data (119). The correlation results (123) can be used by the profile generator (121) to improve the transaction profiles (127));
receiving, in connection with a second transaction for a second object, using a second transaction tool, a second set of transaction information comprising at least a second object identifier and a second transaction tool information (see at least fig 13; paragraph 252, In FIG. 13, a computing apparatus receives (241) first user data (e.g., 229) associated with first user identifiers (e.g., 215) used by a first user tracker (217) to track first online activities of users and receives (243) second user data (e.g., 227) associated with second online activities of respective first account identifiers (e.g., 211) that uniquely identify accounts for payment transactions processed by a transaction handler (103));
determining one or more of a plurality of partial matches, the plurality of partial matches comprising (a) a first partial match representing a similarity of a first object represented by the first object identifier and a second object represented by the second object identifier, (b) a second partial match between the first transaction tool information and the second transaction tool information (see at least paragraph 246, a match between two online activities represented by user data indicates a link between account identifier and user identifier with a certain probability; paragraph 79, user data matching different profile granularity; paragraph 96, various matching data such as transaction data or user data; paragraphs 150 and 179, transactions and data include purchase data and specific items; paragraph 188, identifying other people include common purchases of identical or related items. NOTE: partial match meaning part of the data matches for instance the user data or transaction data matches);
determining, based on the weighted partial matches, a probability that the second transaction was performed by the anonymous user (see at least paragraph 246, when there are a large number of matches, the probability of the link increases, when the probability is above a threshold or when the number of matches is above a threshold, the match between the account identifier and user identifier can be recorded in the mapping table);
determining, when the probability is above a threshold value, that the second transaction was performed by the anonymous user (see at least paragraph 246, when the probability is above a threshold or when the number of matches is above a threshold, the match between the account identifier and user identifier can be recorded in the mapping table; fig 10; paragraph 224, the user identifier (215) is used by the web server (201) to track anonymous users without requiring the user (101) to register); and
adding the second object identifier to the profile of the anonymous user (see at least fig 10; paragraph 249, When the user identifier (215) used by the user tracker B (217) to track anonymous user online behavior is mapped to the account identifier (211), the account identifier (211) can be used to obtain intelligence information about the user (101) for advertising, based on the transaction data (109))
However, Fordyce does not explicitly disclose, but Nidamanuri discloses
determining a plurality of weights, each weight associated with one of the plurality of partial matches, each weight representing an importance of the transaction information of that match; applying each of the plurality of weights to the partial match associated with that weight (see at least paragraph 56, cardholder transaction data includes data elements; paragraph 57, compare historical data elements to identify matching historical data elements which identifies, model historical transaction data 330 from model cardholders similar to the candidate cardholder may be used during authentication and authorization of a suspect purchase to avoid the transaction being declined as potentially fraudulent, applying weighting factors to the matching historical data elements to prioritize certain data elements over other, for example weighting factors may be applied to merchant identifier and location identifier to prioritize model cardholder purchases at the same merchant location as the candidate cardholder; paragraph 20, weighting factors; paragraph 22, compares suspect data elements of the first transaction data to peer data elements and when weighting factors are applied calculate a score based on the matching data elements);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Fordyce’s anonymous user by adapting the teachings of Nidamanuri to include weighing elements of the transaction in order to prioritize certain characteristics of the purchase to match to other purchases which can prevent valid purchases from being declined as fraudulent (Nidamanuri ¶57).
Regarding claim 3, the rejection of claim 1 is incorporated, and Fordyce further discloses:
determining whether the first transaction tool matches the second transaction tool by: determining a user identifier and a transaction tool identifier associated with each of the first and the second set of transaction information; and responsive to determining that the user identifier and the transaction tool identifier associated with each of the first and the second set of transaction information are the same, determining that the first transaction tool matches the second transaction tool (see at least fig 1; paragraph 225, the web server (201) may register the user (101) to obtain further information about the user (101). The registration information can also be used to link the user identifier (215) to the account identifier (211) of the user (101); paragraph 246)
Regarding claim 4, the rejection of claim 1 is incorporated, and Fordyce further discloses:
determining whether a time at which the first transaction occurred matches a time at which the second transaction occurred (see at least fig 11; paragraph 232, However, the IP address (221) of the user (101) typically changes from time to time; and it is desirable to track the user (101) even when the IP address (221) of the user (101) changes, so that activities of the user (101) at different IP addresses (e.g., 221) with different timestamps (e.g., 223) can be linked to the same online user (101), which may be an anonymous user, or a registered user)
Regarding claim 6, the rejection of claim 1 is incorporated, and Fordyce further discloses:
wherein determining that the second transaction was performed by the anonymous user occurs at a transaction terminal (see at least fig 1; paragraph 34, In FIG. 1, the system includes a transaction terminal (105) to initiate financial transactions for a user (101), a transaction handler (103) to generate transaction data (109) from processing the financial transactions of the user (101) (and the financial transactions of other users), a profile generator (121) to generate transaction profiles (127) based on the transaction data (109) to provide information/intelligence about user preferences and spending patterns, a point of interaction (107) to provide information and/or offers to the user (101), a user tracker (113) to generate user data (125) to identify the user (101) using the point of interaction (107), a profile selector (129) to select a profile (131) specific to the user (101) identified by the user data (125), and an advertisement selector (133) to select, identify, generate, adjust, prioritize and/or personalize advertisements for presentation to the user (101) on the point of interaction (107) via a media controller (115))
Regarding claim 7, the rejection of claim 1 is incorporated, and Fordyce further discloses:
wherein determining that the second transaction was performed by the anonymous user occurs at a server (see at least fig 1; paragraph 119, In one embodiment, the advertisement is forwarded to the ATM via the data stream for authorization. In another embodiment, the ATM makes a separate request to a server of the transaction handler (103) (e.g., a web portal) to obtain the advertisement)
Regarding claim 8, the rejection of claim 1 is incorporated, and Fordyce further discloses:
responsive to determining that the second transaction was performed by the anonymous user, identifying a third object relating to the first object and the second object; providing a recommendation including the third object to the anonymous user (see at least fig 1; paragraph 187, In one embodiment, the identifications for the user (101) may be additionally or alternatively based on transaction profiles associated with others. The recommendations may be determined by predictive association and other analytical techniques)
Regarding claim 9, the rejection of claim 8 is incorporated, and Fordyce further discloses:
wherein the anonymous user accesses the recommendation via a client device (see at least fig 1; paragraph 267, multiple different devices are used at the point of interaction (107) for interaction with the user (101); and some of the devices may not be capable of receiving input from the user (101))
Regarding claim 10, the rejection of claim 8 is incorporated, and Fordyce further discloses:
wherein the recommendation is provided to the anonymous user in response to a use of the first transaction tool or the second transaction tool in a third transaction (see at least fig 2; paragraph 264, In one embodiment, the transaction information includes a profile (e.g., 131 or 341) summarizing transaction data (109) of the user (101) using a plurality of values (e.g., 343, 344, 346) representing aggregated spending in various areas; and the values (e.g., 343, 344, 346) are computed based on factor definitions (331) identified from a factor analysis (327) of a plurality of variables (e.g., 311, 313, 315). In one embodiment, the factor analysis (327) is based on transaction data (109) associated with a plurality of users; and the variables (e.g., 311, 313, 315) aggregate the transactions based on merchant categories (e.g. 306). In one embodiment, the variables (e.g., 311, 313, 315) include spending frequency variables (313) and spending amount variables (315))
Regarding claim 21, the rejection of claim 1 is incorporated, and Fordyce further discloses:
wherein a partial match between the first transaction tool information and the second transaction tool information comprises a match between a user identifier associated with each of the first transaction tool information and the second transaction tool information (see at least paragraph 76, identifiers of the user; paragraph 31, mapping online user identifiers to account identifiers)
Regarding claim 22, the rejection of claim 1 is incorporated, and Fordyce further discloses:
wherein a partial match between the first transaction tool information and the second transaction tool information comprises a match between a transaction tool identifier associated with each of the first transaction tool information and the second transaction tool information (see at least 145, account identifiers; paragraph 31, mapping online user identifiers to account identifiers)
Regarding claims 12, 14, 15, 17-20, the scope of the instant claims does not differ substantially from that of claims 1, 3, 4, 6-8, and 10 and they are rejected for the same reasons, respectively.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Fordyce (US 2011/0093327), in view of Nidamanuri (US 2017/0364916), and further in view of Kravitz (2017/0147808).
Regarding claim 11, the rejection of claim 8 is incorporated. However, Fordyce and Nidamanuri do not explicitly disclose, but Kravitz discloses:
wherein the recommendation is provided to the anonymous user via a token memorializing the second transaction (see at least Abstract, One embodiment provides a method for token management in a multi-tenant transactional database, including: utilizing at least one processor to execute computer code that performs the steps of: receiving a request for one or more tokens to be used by an entity; verifying that the entity is qualified to receive the one or more tokens for use on the multi-tenant transactional database)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Fordyce and Nidamanuri by adapting the teachings of Kravitz to include token use in transactions. Fordyce and Kravitz both discuss transactional data and the combination allows for secure transaction data.
Response to Arguments
Rejection of claims under §103:
Applicant asserts the prior art does not disclose determining a partial match that represents a similarity of a first object and a second object each represented by object identifiers. Applicant states Fordyce teaches matching specific portions of data like IP addresses, timestamps, or transaction data elements, but doesn’t compare the actual objects being purchased to determine their similarity. Applicant states Nidamanuri teaches comparing historical transaction data but focuses on metadata rather than similarities between objects acquired in different transactions. Examiner respectfully disagrees. Fordyce does disclose matches made between specific portions of data or a partial match. For example, ¶96 discloses matching user data or transaction data, ¶86 discloses matching IP addresses or time period of transaction request, ¶196 discloses purchasing details and matching offers against items purchased, and ¶150, 179, and 188 disclose matching transaction data including purchase data and specific items purchased. Therefore the prior art does disclose the first and second partial matches. It is also noted that Nidamanuri ¶22 discusses determining how many model cardholders made similar purchases to the suspect purchase and how similar the purchases are.
Applicant also asserts Nidamanuri does not disclose determining a plurality of weights each associated with one of the partial matches each weight representing an importance of the transaction information of that match. Applicant states Nidamanuri deals with accounts with known identities such as account identifiers that associate the transaction with a consumer or account holder. Applicant states Nidamanuri performs fraud modeling by comparing historical transaction data to a suspect payment transaction. Applicant states Nidamanuri does not match two pieces of transaction information but matches a model of a cardholder to a candidate cardholder and discusses weighting factors that may be applied. Applicant states Nidamanuri describes applying weighting factors to matching historical data elements but does not disclose applying those weights to partial matches because the claimed matches are of two transaction information where Nidamanuri’s are a transaction to a model of a cardholder.
Examiner respectfully disagrees. Nidamanuri discloses applying weights to the partial matches and Nidamanuri uses these weights to prioritize certain matched data elements over others (some matching data might be more important than other matches). Nidamanuri ¶22 discloses comparing suspect data elements of first transaction data to peer data elements and counts the matching data elements. Nidamanuri ¶22 also discloses when weighting factors are applied, calculating a score based on the matching data elements. Paragraph 20 states weighting factors are applied to particular data elements to prioritize certain data elements over others. Therefore the prior art does disclose partial matches as well as applying weights to the partial matches.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KIMBERLY L JORDAN whose telephone number is (571)270-5481. The examiner can normally be reached Monday, Tuesday, and Thursday 9am-3pm.
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/KIMBERLY L JORDAN/Examiner, Art Unit 2194
