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 7/28/2026.
Claims 1, 8, and 15 were amended by Applicant.
Claims 2 – 6, 9 – 13, and 16 – 19 were previously presented by Applicant.
Claims 7, 14, and 20 remain as original.
Claims 1 – 20 are currently pending and have been examined.
The prior 35 USC 101 claim rejections set forth in the Non-Final rejection of 4/29/2026 as to claims 1 – 20 are maintained in view of Applicant's arguments and amendments.
The prior 35 USC 103 claim rejections set forth in the Non-Final rejection of 4/29/2026 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.
With regard to the limitations of claims 1 – 20, Applicant argues that the claims as amended are patent eligible under 35 USC 101 because they meet the analysis set forth by the Supreme Court. Remarks 9 - 14. Examiner respectfully disagrees. The subject claims noted were analyzed pursuant to MPEP 2106, et seq., and are still considered ineligible. Step 1 is met because the claims are directed towards one of the four statutory categories; Part 2A-Prong1 of the test is trying to evaluate if the claims recite a judicial exception (an abstract idea enumerated in the MPEP 2106.04(a)); Part 2A-Prong 2 is to evaluate whether the subject claims recite additional elements that integrate the exception into a practical application, and, lastly, Part 2B checks whether the claims amount to significantly more than the abstract idea. A detailed and formal analysis pursuant to 35 USC 101 as the same applies to the amended claim set will follow below.
As respects 35 USC 101, the claims as a whole amount to a drafting effort designed to monopolize the exception. The additional limitations when taken individually and in combination are not sufficient to amount to significantly more than the judicial exception because the claims do not provide improvements to another technology or technical field nor improvements to the function of the computer itself. Accordingly, the claim(s) recite an abstract idea. A detailed and formal analysis pursuant to 35 USC 101 as the same applies to the specifics of the claims follows below.
Applicant argues per 35 USC 103 that neither Falkenberg nor Sela disclose identifying preferences based on patterns. Remarks 10. Examiner respectfully disagrees.
Falkenberg clearly states:
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 profile generator (121) to generate transaction profiles (127) based on the transaction data (109) to provide information/intelligence about user preferences and spending patterns.
Applicant next argues per 35 USC 103 that the last office action arguments / mapping “conflates” its claimed preference rankings with the importance of said rankings. Remarks 11. What is specifically meant by this argument is somewhat unclear. Examiner respectfully states however that he did not draft the amended claims of 7/28/2026, and that, based on those claims’ plain reading, “preference rankings / patterns” as it were are clearly based on inter alia “importance”. This is covered by Falkenberg as follows:
Thus, the transaction data (109) can be used to determine the purchase behavior, pattern, preference, tendency, frequency, trend, budget and/or propensity of the customers in relation to various types of businesses, services and/or products and in relation to time.”, Falkenberg at [0422]).
and that:
The opportunity information is generated based on rankings of the respective accounts, see Falkenberg Abstract, published 6/13/2013.
Applicant next argues per 35 USC 103 that Sela recommends products, not transactions. Examiner respectfully disagrees with any such distinction. Sela clearly states:
The invention disclosed provides a device and method for recommending at least a product or a plurality of products to a user based at least in part on opinion-based criteria. A product may be any device, service, piece of software, and so forth, available for purchase to a person using a device or method of the invention.”, see Sela at [0024]).
It's obvious that a recommended purchase transaction is ipso facto an obtaining of this or that recommended product.
Applicant argues per 35 USC 101 that examiner did not evaluate the claims of 7/28/2026 as a whole. Remarks 14. Examiner respectfully disagrees. Not only were the claims per 101 analyzed as a whole, each limitation was additionally separated and specifically analyzed. See the following 35 USC 101 detailed analysis.
Applicant next argues per 35 USC 101 that the claims improve processing in electronic marketplace matching. Remarks 15. Unfortunately, Applicant does not specify how this occurs, apart from simply citing amended claim language (which does not appear to address the above question). Specifically, one would at least need to know how “processing in electronic marketplace matching” is actually improved, and how is it actually improved as compared to what? Neither of these “hows” are clearly set forth in the arguments.
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 04/29/2026, and also respecting the pending amended claim set of 07/28/2026, 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 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 – 20 are rejected pursuant to 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 – 7 are directed to a method (process), claims 8 – 14 are directed to a system (machine), and claims 15 – 20 are directed to a non-transitory machine readable medium (composition). The claims are therefore eligible statutory categories of invention. (Step 1: YES).
Independent claims 1, 8, and 15 recite the limitations of (using claim 8):
Claim 8. A system, comprising: one or more network interfaces to communicate over a network; a processor coupled to the network interfaces and adapted to execute one or more processes; and a memory having instructions executable by the processor encoded thereon, the instructions, when executed by the processor, are operable to: receive transaction review data associated with a plurality of completed transactions of a marketplace, 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; identify 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; identify 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; rank each preference of the one or more preferences based on the importance for each preference; and provide a recommendation to the marketplace for a new person-to-person transaction between a first user and a second user in the marketplace based on the rankings of each preference.
The claims thus recite the abstract idea of:
Providing a recommendation … for … a transaction based on rankings of each preference.
The above abstract idea recites a fundamental economic practice and/or commercial interaction. These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic practice and/or commercial interaction, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, independent claims 1, 8, and 15 recite an abstract idea.
(Step 2A-Prong 1: YES. The independent claims recite an abstract idea).
The above bolded terms of independent claims 1, 8, and 15 recite: a system, network interfaces to communicat[ing] over a network; a processor coupled to the network interfaces and adapted to execute one or more processes; and a memory having instructions executable by the processor encoded thereon, and instructions … executed by the processor. These bolded terms representing generic computer components / computer driven systems are simply being applied to the abstract idea limitations, without more. The recitation of generic computer components in a claim does not necessarily preclude that claim from reciting an abstract idea. (Step 2A-Prong 1: YES. The claims recite an abstract idea).
As to the dependent claims 2 – 7, 9 – 14, and 16 – 20, they further refine the above noted abstract idea set forth by the independent claims as follows:
The system of claim 1, further comprising: receiving a new transaction request from the marketplace; identifying provider data from the transaction review data, the provider data associated with one or more providers of the marketplace; identifying unique characteristics associated with each of the one or more providers of the marketplace based on patterns in the provider data; 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 preference; and recommending at least one of the pairings to the marketplace to facilitate the new transaction request. (claims 2, 9, 16); The system of claim 1, further comprising: providing the rankings of each preference to the marketplace. (claims 3, 10); The system of claim 1, 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. (claims 4, 8, 17); The system of claim 1, 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. (claims 5, 12, 18); The system of claim 1, wherein identifying the importance for each preference further comprises: analyzing the characteristics associated with the goods or services of the marketplace. (claims 6, 13, 19); The system of claim 2, wherein the unique characteristics are associated with a product or a service offered by the one or more providers of the marketplace. (claims 7, 14, 20).
Examiner notes that the above bolded generic computer / computer driven terms set forth in the dependent claims, i.e., the systems, are also being applied as tools to the abstract idea, without more.
The computer hardware/software above bolded are recited at a high-level of generality (i.e., generic processors, computer systems, and memories all performing generic computer functions), and the same amounts to no more than mere instructions to apply the exception using a generic computer component(s). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. That said, the claims are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additionally claimed elements in the claims do not integrate the abstract idea into a practical application).
All claims above reviewed also do not include additional elements that are sufficient to amount to significantly more than the judicial exception. When considered separately and as an ordered combination, the claims do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to perform a judicial exception by applying generic computer components and thereby automating the process cannot provide an inventive concept.
This Application's lack of providing significantly more than the judicial exception is also referred to as its claims lacking an “inventive concept. See MPEP 2106.05(f) where applying a computer as a tool to the abstract idea is not indicative of significantly more. The above detailed non-computer related elements do not change the outcome of the analysis, as they simply further limit ways which the abstract idea may be performed. (Step 2B: NO. The claims do not provide significantly more than the judicial exception).
In summary, the claim set reviewed as above does not include any additional elements that integrate its abstract idea into a practical application, or that are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination.
The above analyses of the dependent claims 2 - 7, 9 – 14, and 16 – 20, demonstrate that they too are not patent-eligible pursuant to 35 USC 101, in addition to the above demonstrated ineligibility of the independent claims 1, 8, and 15.
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 Sela (US20090192808A1).
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 (“In one embodiment, an account classification model is applied to the transaction data (109) of a set of accounts for model validation. The accounts are selected from business accounts and personal accounts. The accounts are ranked based on the business spending score computed using the account classification model”, [0228]) and (“In one embodiment, the transaction data (109) generated by a transaction handler (103) is used for the targeting of products and services to consumers as individuals as opposed to business owners, or to business owners as opposed to consumers as individuals, due to their different needs and interests in terms of product features and support. [0205];
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 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 Sela teaches:
providing a recommendation to the marketplace for a new person-to-person transaction between a first and a second user in the marketplace based on the rankings of each preferences. Apart from the original claims of 05/01/2024, the only time that either the non-defined words of “[rank]” or “[rate]” are ever actually disclosed herein is found in Specification [048] wherein “rank” is used in accord with its common usage as indicating a high ranking, that said, in light of the Specification, the term “rank” is synomonomusly equated to “importance level” and/or “rate” here for the present purposes (“It is therefore an object of the present invention to provide a method and device capable of sorting through a large number of products in a category and provide a product recommendation to a user which incorporates importance level of a plurality of criteria, including opinion-based criteria.”, [010]) and (“In embodiments of the invention, the ratings of the various criteria and sub-criteria, the weight given to each review, and the importance level of various criteria as selected by a user receiving a recommendation are calculated by any reasonable method known in the art to provide at least one product recommendation to the user.”, [025]) and (“In step 120, the user (anyone who makes the selection or wishes to receive a product recommendation) selects an importance level of opinion-based criteria. In step 130, the user may optionally select an importance level of functionality-based criteria. … The importance level may be assigned a number to assist in a calculation of recommendability and may be on a scale from one to ten, inclusive; using whole numbers only; a linear scale; a logarithmic scale; or the like.”, [029]) and (“ With regard to consumer electronic devices, such users may be experts, or people who have purchased the product they are rating, note that product importance levels or product ratings/rankings, may also be assigned numbers to assist the recommendations”, [024]);
ratings provided by a plurality of users of the marketplace; (“The method of the invention proceeds by first choosing a plurality of products in a category. The choosing may be accomplished by a user and/or by a device capable of receiving a selection of a category from a user. At least one product in the category is associated with at least one rating. The rating comprises a rating of a plurality of criteria, and the plurality of criteria comprises opinion-based criteria. Opinion-based criteria are those related to the product which are dependent upon an evaluation by an expert or a user, and which are capable of receiving substantially different ratings by different users, and/or require the judgment of an expert or user of the product. In the next step of the method of the invention, a calculation of product recommendability of the plurality of products is made based on a selection by at least one user of an importance level of a plurality of criteria. Then at least one product recommendation is provided based on the selection by at least one user of an importance level of a plurality of criteria and the opinion-based criteria.”, [012]) and (“The at least one product recommendation to a user may be provided based on an average selection of importance level by a plurality of users,”, [015]) and (“Opinion-based criteria, as used in the disclosure of this invention and as in step 120 of FIG. 2, are criteria related to the product which are dependent upon an evaluation by an expert or a user, are ordinarily capable of receiving substantially different ratings or values by different experts or users, and/or require the judgment of an expert or user of the product. In this context, substantially different means that when rated by a plurality of users it is within reason for each user to rate an opinion-based criterion differently (outside a statistically calculated margin of error) than another user. For example, opinion-based criteria may include overall satisfaction with the product; satisfaction with technical support; ease of use; ease of use in comparison to other products in the category; ease of installation or implementation; whether the price is justified, given a quality or functionality characteristic of the product; an opinion of worksmanship with regard to the product; estimated cost of operation over time for the product; and so forth.”, [031]);
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 Sela because Falkenborg would be more efficient and user friendly and user accurate if it could recommend (a plurality of) user rated recommendations as done in Sela. (“The at least one product recommendation to a user may be provided based on an average selection of importance level by a plurality of users,”, see [015] of Sela)
Regarding claims 2, 9, and 16:
The combination of Falkenborg and Sela 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 (“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]);
recommending at least one of the pairings to the marketplace to facilitate the new transaction request. (“The portal (143) and/or the transaction handler (103) may recommend offers based on the transaction data (109). “, [0175]).
Regarding claims 3 and 10:
The combination of Falkenborg and Sela 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 Sela 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 Sela 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 Sela 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 Sela 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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached form 892.
Martin (US20090276368A1) - The present describes systems and methods to support personal financial management by recommending financial products and services responsive to a user's particular financial health or situation based on explicit and implicit user data and feedback.
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.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at (866) 217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call (800) 786-9199 (IN USA OR CANADA) or (571) 272-1000.
/MATTHEW COBB/Examiner, Art Unit 3661
/PETER D NOLAN/Supervisory Patent Examiner, Art Unit 3661
