DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Office action is in reply to filing by applicant on 07/11/2025.
Claims 1 – 6, 8 – 13, and 15 – 19 were amended by Applicant.
Claims 7, 14, and 20 remain as original.
Claims 1 – 20 are currently pending and have been examined.
The prior 35 USC 103 claim rejections set forth in the Non-Final rejection of 04/11/2025 as to claims 1 – 20 are maintained in view of Applicant's arguments and amendments.
THIS ACTION IS MADE FINAL.
Response to Arguments
There are no new grounds of rejection herein as to any of the claims.
Applicant argues pursuant to 35 USC 103 that the combination of Falkenberg and Martin do not adequately map the claims. Remarks 8 – 10. Examiner respectfully disagrees. Specifically, Applicant cites exact (amended) claim language, then concludes, for one, that Falkenberg does not teach “transaction review”. Remarks 9. Falkenberg does in fact teach the same:
(“In one embodiment, offers are based on the point-of-service to offeree distance to allow the user (101) to obtain in-person services. In one embodiment, the offers are selected based on transaction history and shopping patterns in the transaction data (109) and/or the distance between the user (101) and the merchant. In one embodiment, offers are provided in response to a request from the user (101), or in response to a detection of the location of the user (101).”, [064]).
As above, the transaction is clearly reviewed. Examiner is entitled to interpret claim language (i.e, “transaction review”) pursuant to its broadest reasonable interpretation, as was done. Please see 35 USC 103 analysis below.
Applicant further cites exact (amended) claim language, then concludes that Falkenberg also does not teach “ratings provided by a plurality of users in the marketplace”. Remarks 9. Examiner respectfully disagrees. Falkenberg does in fact teach the same:
(“In one embodiment, transaction data (and hence actual spending behavior) is used to compute a score to identify the likelihood of an account being primarily being used for business purposes, based on spending patterns reflected in the transaction data associated with the use of payment accounts. In one embodiment, the account holders who are determined to have an account of a type different from a type as indicated by the score are identified and targeted for an account re-alignment effort, such as an offer to migrate to a different payment product, an offer to adjust or add account features, etc”, [030]) and (“Some of the transaction profiles (127) are specific to the user (101), or to an account of the user (101), or to a group of users of which the user (101) is a member, such as a household, family, company, neighborhood, city, or group identified by certain characteristics related to online activities, offline purchase activities, merchant propensity, etc.”, [53]) and (“In one embodiment, the products and/or services purchased by the user (101) are also identified by the information transmitted from the merchants or service providers. Thus, the transaction data (109) may include identification of the individual products and/or services, which allows the profile generator (121) to generate transaction profiles (127) with fine granularity or resolution.”, [423]);
As above, users do in fact provide ratings (a.k.a., broadly interpreted as user scores). Please see 35 USC 103 analysis below.
Applicant next concludes that Falkenberg also does not teach “identifying an importance for each preference … based on patterns in the ratings”. Remarks 9. Examiner respectfully disagrees. Falkenberg does in fact teach the same:
(“In one embodiment, transaction data (and hence actual spending behavior) is used to compute a score to identify the likelihood of an account being primarily being used for business purposes, based on spending patterns reflected in the transaction data associated with the use of payment accounts.”, [030]) and (“For example, the factor values (344) and/or the cluster ID (343) in the aggregated spending profile (341) can be used to determine the spending preferences of the user (101)”, [072]).
As noted above, the system ID’s the importance of scores / ratings of users based on their spending transactions via scores. Please see 35 USC 103 analysis below.
Applicant next concludes that the secondary reference, Martin, does not teach “feedback” (to ID spending patterns). Remarks 9. Examiner notes that “feedback” as it were, is taught by the primary reference:
(“In one embodiment, the user specific advertisement data (119) is associated with the identity or characteristics of the user (101),”, [0109]) and (“Thus, the user (101) may use the account identifier (181) to access privileges afforded to the members of the loyalty programs, such as rights to access a member only area, facility, store, product or service, discounts extended only to members, or opportunities to participate in certain events, buy certain items, or receive certain services reserved for members.”, [0145]) and (“In one embodiment, offers are based on the point-of-service to offeree distance to allow the user (101) to obtain in-person services. In one embodiment, the offers are selected based on transaction history and shopping patterns in the transaction data (109) and/or the distance between the user (101) and the merchant. In one embodiment, offers are provided in response to a request from the user (101), or in response to a detection of the location of the user (101).”, [064]).
All of the above primary data analyzes user transaction “feedback”. Please see 35 USC 103 analysis below.
Generally as to obviousness, examiner submits that it is determined on the basis of the evidence as a whole and the relative persuasiveness of the arguments. See In re Oetiker, 977 F.2d 1443, 1445, 24 USPQ2d 1443, 1444 (Fed. Cir. 1992); In re Hedges, 783 F.2d 1038, 1039, 228 USPQ 685,686 (Fed. Cir. 1992); In re Piasecki, 745 F.2d 1468, 1472, 223 USPQ 785,788 (Fed. Cir. 1984); and In re Rinehart, 531 F.2d 1048, 1052, 189 USPQ 143,147 (CCPA 1976). Using this standard, examiner submits that the burden of presenting a prima facie case of obviousness was successfully established in the prior Office Action of 04/11/2025, and also respecting the pending amended claim set of 07/11/2025, as seen below.
Examiner recognizes that references cannot be arbitrarily altered or modified, and that there must be some reason why a person having ordinary skill in the relevant art would be motivated to make the proposed modifications. Although the motivation or suggestion to make modifications must be articulated, it is respectfully submitted that there is no requirement that the motivation to make modifications must be expressly articulated within the references themselves. References are evaluated by what they suggest to one versed in the art, rather than by their specific disclosures, In re Bozek, 163 USPQ 545 (CCPA 1969).
Examiner also notes that the motivation to combine the applied references is, where appropriate in the below detailed analysis pursuant to 35 USC 103, additionally accompanied by select passages from the respective references which specifically support that particular motivation. It is also respectfully submitted that motivation based on the logic and scientific reasoning of one ordinarily skilled in the art at the time of the invention, which evidence can also support a finding of obviousness, is otherwise provided in the detailed 35 USC 103 analysis of the claim set below. In re Nilssen, 851 F.2d 1401, 1403, 7 USPQ2d 1500, 1502 (Fed. Cir. 1988) (references do not have to explicitly suggest combining teachings); Ex parte Clapp, 227 USPQ 972 (Bd. Pat. App. & Inter. 1985) (examiner must present convincing line of reasoning supporting rejection); and Ex parte Levengood, 28 USPQ2d 1300 (Bd. Pat. App. & Inter. 1993) (reliance on logic and sound scientific reasoning).
Examiner recognizes that obviousness can only be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to a person of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988) and In re Jones, 958 F.2d 347.
Claim Rejections – 35 USC 103
In the event the determination of the status of the application as subject to AIA 35 USC 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 USC 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 USC 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.
Claims 1 – 20 are rejected pursuant to 35 USC 103 as being unpatentable over Falkenborg (US20130151388A1) in view of Martin (US20090276368A1).
Regarding claims 1, 8, and 15:
Falkenborg discloses:
receiving transaction review data associated with a plurality of completed transactions of a marketplace, (“In one embodiment, offers are based on the point-of-service to offeree distance to allow the user (101) to obtain in-person services. In one embodiment, the offers are selected based on transaction history and shopping patterns in the transaction data (109) and/or the distance between the user (101) and the merchant. In one embodiment, offers are provided in response to a request from the user (101), or in response to a detection of the location of the user (101).”, [064]);
the transaction review data including characteristics associated with products or services of the marketplace and ratings provided by a plurality of users of the marketplace; (“In one embodiment, transaction data (and hence actual spending behavior) is used to compute a score to identify the likelihood of an account being primarily being used for business purposes, based on spending patterns reflected in the transaction data associated with the use of payment accounts. In one embodiment, the account holders who are determined to have an account of a type different from a type as indicated by the score are identified and targeted for an account re-alignment effort, such as an offer to migrate to a different payment product, an offer to adjust or add account features, etc”, [030]) and (“Some of the transaction profiles (127) are specific to the user (101), or to an account of the user (101), or to a group of users of which the user (101) is a member, such as a household, family, company, neighborhood, city, or group identified by certain characteristics related to online activities, offline purchase activities, merchant propensity, etc.”, [53]) and (“In one embodiment, the products and/or services purchased by the user (101) are also identified by the information transmitted from the merchants or service providers. Thus, the transaction data (109) may include identification of the individual products and/or services, which allows the profile generator (121) to generate transaction profiles (127) with fine granularity or resolution.”, [423]);
identifying one or more preferences associated with each of the plurality of users based on the characteristics associated with the products or services of the marketplace; (“FIG. 1 illustrates a system to provide services based on transaction data according to one embodiment. 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).”, [037]) and (“In one embodiment, the aggregated spending profile (341) is used to provide intelligence information about the spending patterns, preferences, and/or trends of the user (101).”, [072]);
identifying an importance for each preference of the one or more preferences based on patterns in the ratings provided by the plurality of users of the marketplace; (“In one embodiment, transaction data (and hence actual spending behavior) is used to compute a score to identify the likelihood of an account being primarily being used for business purposes, based on spending patterns reflected in the transaction data associated with the use of payment accounts.”, [030]) and (“For example, the factor values (344) and/or the cluster ID (343) in the aggregated spending profile (341) can be used to determine the spending preferences of the user (101)”, [072]);
ranking each preference of the one or more preferences based on the importance for each (“In the above example, a total of 42 variables are used in the regression model for the classification of an account. Even though the 42 variables are statistically significant, some of the variables are a relatively stronger indicator of an account being a consumer account or a business account than others. In one embodiment, the most significant variables in the model are identified as:”, [0301]) and (“In one embodiment, in at least one of the merchant categories (e.g., “restaurant”), the ranking is based on the highest transaction amount that is recorded in a single transaction among transactions in each respective account (e.g., 146) account in the plurality of accounts, such as ranking accounts according to the largest ticket size of restaurant transactions.”, [0405]) and (“In one embodiment, the transaction profiles (127) provide intelligence information on the behavior, pattern, preference, propensity, tendency, frequency, trend, and budget of the user (101) in making purchases. In one embodiment, the transaction profiles (127) include information about what the user (101) owns, such as points, miles, or other rewards currency, available credit, and received offers, such as coupons loaded into the accounts of the user (101). In one embodiment, the transaction profiles (127) include information based on past offer/coupon redemption patterns. In one embodiment, 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.”, [051]);
Falkenborg does not expressly disclose, but Martin teaches:
providing a recommendation for a new person-to-person transaction in the marketplace based on the rankings of each preferences. (“The tag recommender 882 ranks products, and more specifically, financial products, according to predetermined rules applied to user profiles.”, [0153]) and (“The present describes systems and methods to support personal financial management. More particularly, the present describes systems and methods for providing personalized recommendations of financial products and services.”, [003]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Falkenborg to incorporate the teachings of Martin because Falkenborg would be more efficient and versatile if it could recommend financial products and services. (“A need remains, therefore, for improved systems and methods for recommending financial products and services. Additional aspects and advantages of this invention will be apparent from the following detailed description of preferred embodiments, which proceeds with reference to the accompanying drawings.”, see [007] of Martin).
Regarding claims 2, 9, and 16:
The combination of Falkenborg and Martin disclose the limitations of claims 1, 8, and 15, respectively:
Falkenborg further teaches:
receiving a new transaction request from the marketplace; (“In one embodiment, offers are provided in response to a request from the user (101), or in response to a detection of the location of the user (101).”, [064]);
identifying provider data from the transaction review data, the provider data associated with one or more providers of the marketplace; (“In one embodiment, the products and/or services purchased by the user (101) are also identified by the information transmitted from the merchants or service providers.”, [0423]);
identifying unique characteristics associated with each of the one or more providers of the marketplace based on patterns in the rovider data; (“In one embodiment, transaction data (and hence actual spending behavior) is used to compute a score to identify the likelihood of an account being primarily being used for business purposes, based on spending patterns reflected in the transaction data associated with the use of payment accounts.”, [030]);
pairing at least one of the one or more providers with the new transaction request based on matching the unique characteristics associated with each of the one or more providers with the rankings each preferences; and (“In FIG. 1, the user tracker (113) obtains and generates context information about the user (101) at the point of interaction (107), including user data (125) that characterizes and/or identifies the user (101). The profile selector (129) selects a user specific profile (131) from the set of transaction profiles (127) generated by the profile generator (121), based on matching the characteristics of the transaction profiles (127) and the characteristics of the user data (125). For example, the user data (125) indicates a set of characteristics of the user (101); and the profile selector (129) selects the user specific profile (131) that is for a particular user or a group of users and that best matches the set of characteristics specified by the user data (125).”, [076]);
and Martin further teaches:
recommending at least one of the pairings to the marketplace to facilitate the new transaction request. (“The tag recommender 882 ranks products, and more specifically, financial products, according to predetermined rules applied to user profiles.”, [0153]) and (“The present describes systems and methods to support personal financial management. More particularly, the present describes systems and methods for providing personalized recommendations of financial products and services.”, [003]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Falkenborg to incorporate the teachings of Martin because Falkenborg would be more efficient and versatile if it could recommend financial products and services. (“A need remains, therefore, for improved systems and methods for recommending financial products and services. Additional aspects and advantages of this invention will be apparent from the following detailed description of preferred embodiments, which proceeds with reference to the accompanying drawings.”, see [007 of Martin]).
Regarding claims 3 and 10:
The combination of Falkenborg and Martin disclose the limitations of claims 1 and 8, respectively:
Falkenborg further teaches:
providing the rankings of each preferences to the marketplace. (“ranking and dividing the accounts into deciles based on the aggregated amount of travel and entertainment (409)”, [0389]); and see Abstract.
Regarding claims 4, 11, and 17:
The combination of Falkenborg and Martin disclose the limitations of claims 1, 8, and 15, respectively:
Falkenborg further teaches:
wherein the transaction review data includes user identity data, data associated with a product sold in the marketplace, data associated with a service sold in the marketplace, marketplace data, or feedback information. (“In one embodiment, the user specific advertisement data (119) is associated with the identity or characteristics of the user (101),”, [0109]) and (“Thus, the user (101) may use the account identifier (181) to access privileges afforded to the members of the loyalty programs, such as rights to access a member only area, facility, store, product or service, discounts extended only to members, or opportunities to participate in certain events, buy certain items, or receive certain services reserved for members.”, [0145]) and (“In one embodiment, offers are based on the point-of-service to offeree distance to allow the user (101) to obtain in-person services. In one embodiment, the offers are selected based on transaction history and shopping patterns in the transaction data (109) and/or the distance between the user (101) and the merchant. In one embodiment, offers are provided in response to a request from the user (101), or in response to a detection of the location of the user (101).”, [064]).
Regarding claims 5, 12, and 18:
The combination of Falkenborg and Martin disclose the limitations of claims 1, 8, and 15, respectively:
Falkenborg further teaches:
wherein the one or more preferences associated with each of the plurality of users includes product attributes associated with a product of the marketplace or service attributes associated with a service of the marketplace. (“In one embodiment, a computing apparatus is configured to determine a score to identify accounts that are more likely to be held by affluent users and which therefore represent opportunities for offers to upgrade to a different product type,”, [031]) and (“By data mining transactional data (109) and evaluating account holder spending patterns, the payment processor or other entity can develop insights into the ways in which the spending behaviors of a consumer may be differentiated from those of a business and thus, provide an opportunity for product re-alignment and offering a new or improved value proposition to an account holder, whether they are a consumer or a business.”, [0223]) and (“In one embodiment, transaction data, such as records of transactions made via credit accounts, debit accounts, prepaid accounts, bank accounts, stored value accounts and the like, is processed to provide information for various services, such as reporting, benchmarking, advertising, content or offer selection, customization, personalization, prioritization, etc.”, [028]);
Regarding claims 6, 13, and 19:
The combination of Falkenborg and Martin disclose the limitations of claims 1, 8, and 15, respectively:
Falkenborg further teaches:
wherein identifying the importance for each preference further comprises: analyzing the characteristics associated with the goods or services of the marketplace. (“Based on the personal account number, the profile selector (129) may select a user specific profile (131) that constitutes the SKU-level profile associated specifically with the user (101). The SKU-level profile may reflect the individual, prior purchases of the user (101) specifically, and/or the types of goods and services that the user (101) has purchased.”, [0187]) and (“A computing apparatus of, or associated with, the transaction handler uses the transaction data and/or other data, such as account data, merchant data, search data, social networking data, web data, etc., to develop intelligence information about individual customers, or certain types or groups of customers. The intelligence information can be used to select, identify, generate, adjust, prioritize, and/or personalize advertisements/offers to the customers.”, [029).
Regarding claims 7, 14, and 20:
The combination of Falkenborg and Martin disclose the limitations of claims 2, 9, and 16, respectively:
Falkenborg further teaches:
wherein the unique characteristics are associated with a product or a service offered by the one or more providers of the marketplace. (“In one embodiment, the products and/or services purchased by the user (101) are also identified by the information transmitted from the merchants or service providers.”, [0423]) and (“In one embodiment, the user data (125) includes an identifier of the user (101), such as a global unique identifier (GUID), a personal account number (PAN) (e.g., credit card number, debit card number, or other card account number), or other identifiers that uniquely and persistently identify the user (101) within a set of identifiers of the same type.”, [079]).
CONCLUSION
THIS ACTION IS MADE FINAL. 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 extension fee 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.
Wu (US9727616B2) – A method and a system are disclosed for predicting sales of item listings on a network-based system. For example, historical transaction data generated by the network-based system is accessed to create a prediction model. A feature predictive of an item being sold through the network-based system is selected. A training set is created by extracting the predictive feature from the historical transaction data. The prediction model is trained based on the training data set to predict the probability of an item listing being sold through the network-based system. The prediction model is used to rank search results, and the search results can be presented through the network-based system.
Frazer (US20140222506A1) - A method for selecting a next action includes reading transaction data, determining insights and relationships between a first entity and a second entity from the collected transaction data. Once these relationships and insights have been determined, the possibility of a future event occurring in one of a number of selected time periods can be determined using a predictive time-to-event component. A system for selecting a next action includes a memory for storing transaction data, an insight/relationship determination module, and a predictive time-to-event module. The memory, the insight/relationship determination module and the predictive time-to-event module carry out the above method. A programmable media having an instruction set can also cause a machine to carry out the above method.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW COBB whose telephone number is (571) 272-3850. The examiner can normally be reached 9 - 5, M - F.
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 call examiner Cobb as above, or 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, Peter Nolan, can be reached at (571) 270-7016. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MATTHEW COBB/Examiner, Art Unit 3661
/PETER D NOLAN/Supervisory Patent Examiner, Art Unit 3661