DETAILED ACTION
This is a non-final, first office action on the merits. Claims 1-20 are pending. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1-20 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
With respect to Step 2A Prong One of the framework, claims 1, 10, and 17 recite an abstract idea. Claims 1, 10, and 17 include “in response to a request including a card value proposition (CVP) from a relying institution, accessing data, the data including card comparison data and benefit data; filtering, the data based on a geographic limitation; compiling, a data structure, from the filtered data, the data structure including data for one or more segments including card peers for the CVP, a peers as defined by the geographic limitations, and the CVP, for each of multiple parameters; calculating, representative values for the multiple parameters included in the data structure; generating, a graphic having a spoke specific to each of the multiple parameters and a line based on the calculated values for each of the card peers, peers defined by the graphic limitation, and CVP; and presenting, the graphic to the relying institution, in response to the request”.
The limitations above recite an abstract idea under Step 2A Prong One. More particularly, the elements above recite mental processes-concepts performed in the human mind (including an observation, evaluation, judgment, opinion), certain methods of organizing human activity-commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations), and mathematical calculations because the elements describe a process for defining dynamic insights for a proposition. As a result, claims 1, 10, and 17 recite an abstract idea under Step 2A Prong One.
Claims 2-9, 11-16, and 18-20 further describe the process for defining dynamic insights for a proposition. As a result, claims 2-9, 11-16, and 18-20 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claims 1, 10, and 17.
With respect to Step 2A Prong Two of the framework, claims 1, 10, and 17 do not include additional elements that integrate the abstract idea into a practical application. Claims 1, 10, and 17 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 1, 10, and 17 include an intelligence platform, a computing device, a processor, and a non-transitory computer readable medium. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 1, 10, and 17 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2-3, 5-9, 12, 14-16, 18, and 20 do not include any additional elements beyond those recited with respect to claims 1, 10, and 17. As a result, claims 2-3, 5-9, 12, 14-16, 18, and 20 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above with respect to claims 1, 10, and 17.
Claims 4, 11, 13, and 19 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 4, 11, 13, and 19 include databases and intelligence platform computing device, . When considered in view of the claims as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements do no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claims 4, 11, 13, and 19 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, claims 1, 10, and 17 do not include additional elements amounting to significantly more than the abstract idea. As noted above, claims 1, 10, and 17 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 1, 10, and 17 include an intelligence platform, a computing device, a processor, and a non-transitory computer readable medium. The additional elements do not amount to significantly more than the abstract idea because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, independent claims 1, 10, and 17 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 2-3, 5-9, 12, 14-16, 18, and 20 do not include any additional elements beyond those recited with respect to claims 1, 10, and 17. As a result, claims 2-3, 5-9, 12, 14-16, 18, and 20 do not include additional elements that amount to significantly more than the abstract idea under Step 2B for the same reasons as stated above with respect to claims 1, 10, and 17.
Claims 4, 11, 13, and 19 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 4, 11, 13, and 19 include a proximal sensor, a remote sensor, a sensor, a remote sensing satellite, an airplane, an unmanned aerial vehicle (UAV). The additional elements do not amount to significantly more than the abstract idea because the additional computing elements do no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 4, 11, 13, and 19 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1-20 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.
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 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 8-13, and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Tietzen et al. (US Pub No. 2018/0276710) (hereinafter Tietzen et al.) in view of Henby et al. (US Pub No. 2009/0138334) (hereinafter Henby et al.).
Regarding claims 1, 10, and 17, Tietzen discloses a computer-implemented method for use in defining dynamic insights for a proposition, the method comprising:
Regarding claim 17 recites additional features a non-transitory computer-readable storage medium comprising executable instructions, which when executed by at least one processor of an intelligence platform computing device (see Tietzen, para [0486] & [0116]-[0117], wherein Fig 2), cause the at least one processor to:
in response to a request including a card value proposition (CVP) from a relying institution, accessing, by a computing device, data from one or more databases, the data including card comparison data and benefit data (see Tietzen, paras [0768] & [0834], wherein requests for reviews may be presented to particular customers based on the customer's attributes (e.g., BIN ranges of financial card(s) held by that customer)….. ; para [0834], wherein the card issuer may propose to encourage a specific demographic (as defined by a BIN range) to join the loyalty program by tailoring benefits and incentives to the specific segment of cardholders. Loyalty system 26 may recommend incentives based on attributes of the segment of cardholders (i.e., card value propositions mix of benefits, rewards, and services that makes a card attractive to customers)1; para [0126], wherein access to different aspects and account records of the database 32 may be provided by an administration utility (not shown) that enables hierarchical access to the database 32, depending on permissions assigned by the operator of the loyalty system 26, and to each of the members, merchants, card issuers; and paras [0218] & [0284], wherein recommending incentives to target customers by profile category may impose a lower computational burden compared to recommending incentives to target each customer individual);
filtering, by the computing device, the data based on a geographic limitation (see Tietzen, para [0568], wherein Item 5 enables selection of a distance from store filter to allow merchants to limit reward recipients by the distance of their home address from a store location. The maximum distance from a location may be the region (State) it is located in);
compiling, by the computing device, a data structure, from the filtered data, the data structure including data for one or more segments including card peers for the CVP, a peers as defined by the geographic limitations, and the CVP, for each of multiple parameters (see Tietzen, paras [0429]-[0430], wherein compile a database of facial images from with member profiles associated with a particular persona….; para [0568], wherein a customize incentive display view (e.g. "Customize Reward") may create a data structure for maintaining data regarding the incentive in a persistent store; para [0568], wherein Item 5 enables selection of a distance from store filter to allow merchants to limit reward recipients by the distance of their home address from a store location. The maximum distance from a location may be the region (State) it is located in; para [0356], wherein incentives may also be presented to customers based on geographic proximity, as shown in FIG. 60D. In particular, incentives may be presented on a map showing the location of the customer and the location where incentives are being offered. Optionally, this map may also show the locations of other customers to whom incentives have been offered. Thus, for example, this map may indicate that a large number of customers are nearby and that a "care mob" is forming. Loyalty system 26 may be configured to provide locations of customers only upon requesting and receiving permission to do so. A customer may select an incentive shown on this map to receive further information regarding the incentive, as shown in FIG. 60E; and para [0190], wherein the loyalty system 26 may adjust the parameters associated with reward generation and change incentives (based on e.g. recommended incentives) in connection with specific members);
calculating, by the computing device, representative values for the multiple parameters included in the data structure (see Tietzen, paras [0258]-[0259], wherein customer profiler 602 may calculate a set of affinity scores, each proportional to a degree of affinity of a particular customer with a particular profile category. The score may, for example, reflect the degree to which a particular customer exhibits the attributes associated with the particular profile category; para [0190], wherein the loyalty system 26 may adjust the parameters associated with reward generation and change incentives (based on e.g. recommended incentives) in connection with specific members; and para [0900], wherein the data structure may define different data fields for the incentive with corresponding values, such as for example, reward identification number);
generating, by the computing device, a graphic and a line based on the calculated values for each of the card peers, peers defined by the graphic limitation, and CVP (see Tietzen, para [0530], wherein offers a graphical bar-chart providing a comparison of published and redeemed rewards (which may be referred to as incentives). Alongside the graph are the numerical values associated with each item; paras [0258]-[0259], wherein customer profiler 602 may calculate a set of affinity scores, each proportional to a degree of affinity of a particular customer with a particular profile category. The score may, for example, reflect the degree to which a particular customer exhibits the attributes associated with the particular profile category; para [0190], wherein the loyalty system 26 may adjust the parameters associated with reward generation and change incentives (based on e.g. recommended incentives) in connection with specific members; and para [0356], wherein incentives may also be presented to customers based on geographic proximity, as shown in FIG. 60D. In particular, incentives may be presented on a map showing the location of the customer and the location where incentives are being offered. Optionally, this map may also show the locations of other customers to whom incentives have been offered. Thus, for example, this map may indicate that a large number of customers are nearby and that a "care mob" is forming. Loyalty system 26 may be configured to provide locations of customers only upon requesting and receiving permission to do so. A customer may select an incentive shown on this map to receive further information regarding the incentive, as shown in FIG. 60E; para [0568], wherein Item 5 enables selection of a distance from store filter to allow merchants to limit reward recipients by the distance of their home address from a store location. The maximum distance from a location may be the region (State) it is located in); and
presenting, by the computing device, the graphic to the relying institution, in response to the request (see Tietzen, para [0530], wherein offers a graphical bar-chart providing a comparison of published and redeemed rewards (which may be referred to as incentives). Alongside the graph are the numerical values associated with each item; and para [0834], wherein requests for reviews may be provided to particular customers based on customer profile categories (personas) determined for those customers).
Tietzen et al. fails to explicitly disclose a graphic having a spoke specific to each of the multiple parameters.
Analogous art Henby discloses a graphic having a spoke specific to each of the multiple parameters (see Henby, paras [0192] & [0208], wherein The "spokes" on spider chart 894 may, as a result, represent the associated principles of each subsystem 300, respectively. For example, spider chart 894a may provide information assessing how well facility 114 is performing within the operating subsystem 310 with respect to its associated principle 340, Chase Waste; principle 342, Pull; principle 344, Make Value Flow; principle 346, Drive Standard Work; principle 348, Even the Load; and principle 350, Validate Our Processes (see FIG. 3). Referring to FIG. 8b, each "spoke" of each spider chart 894 may include six levels chart 894 may, as a result, represent the associated principles of each subsystem 300, respectively. For example, spider chart 894a may provide information assessing how well facility 114 is performing within the operating subsystem 310 with respect to its associated principle 340, Chase Waste; principle 342, Pull; principle 344, Make Value Flow; principle 346, Drive Standard Work; principle 348, Even the Load; and principle 350, Validate Our Processes (see FIG. 3). Referring to FIG. 8b, each "spoke" of each spider chart 894 may include six levels; and para [012], wherein one or more assessment parameters is altered by organization 100, such that a new baseline is desired prior to formally executing the year-end assessment).
Tietzen directed to a system for offering an incentive will be accepted by a customer. Henby directed to improving for the production of products within an organization. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Tietzen, regarding the System for Artificial Intelligence Engine Incenting Merchant Transaction With Consumer Affinity, to have included a graphic having a spoke specific to each of the multiple parameters because both inventions teach improving performance and customer service. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claims 2, 12, and 18, Tietzen discloses the computer-implemented method of claim 1, wherein the geographic limitation is one of a region and a sub-region, in which the relying institution is located, and a card program included in the CVP (see Tietzen, para [0011], wherein many regional or small business co-branded financial card arrangements; para [0289], wherein within a pre-defined geographic span (e.g., partial or full ZIP code, neighborhood, city); and para [0834], wherein the card issuer may propose to encourage a specific demographic (as defined by a BIN range) to join the loyalty program by tailoring benefits and incentives to the specific segment of cardholders. Loyalty system 26 may recommend incentives based on attributes of the segment of cardholders (i.e., card value propositions mix of benefits, rewards, and services that makes a card attractive to customers)2).
Regarding claims 3 and 12, Tietzen discloses the computer-implemented method of claim 2, wherein filtering the data is further based on a card program included in the CVP (see Tietzen, para [0081], wherein deriving from benefits being provided to cardholders who are members of the loyalty program, thereby encouraging the merchants to increase the level of benefits that they provide).
Regarding claims 4, 13, and 19, Tietzen discloses the computer-implemented method of claim 1, wherein the one or more databases include multiple databases, each of the multiple databases associated with a financial institution (see Tietzen, para [0834], wherein requests for reviews may be presented to particular customers based on the customer's attributes (e.g., BIN ranges of financial card(s) held by that customer);
wherein compiling the data structure includes compiling multiple data structures (see Tietzen, para [0568], wherein a customize incentive display view (e.g. "Customize Reward") may create a data structure for maintaining data regarding the incentive in a persistent store; and para [0138], wherein data may be transferred in a variety of formats, including for example, comma-delimited text (CSV) files, SQL data files, JSON data files, or the like); and
wherein the multiple data structures include a data structure specific to benefits, a data structure specific to technical features, and/or a data structure specific to pricing (see Tietzen, para [0568], wherein a customize incentive display view (e.g. "Customize Reward") may create a data structure for maintaining data regarding the incentive in a persistent store; and para [0081], wherein provide benefits, incentives, and rewards to cardholders in the context of a loyalty program is enhanced).
Regarding claims 5, 14, and 20, Tietzen discloses the computer-implemented method of claim 4, wherein the parameters include insurance, travel, lifestyle and rewards for the data structure specific to benefits (see Tietzen, para [0133], wherein features of a travel card, such as points for travel and/or specialized services ( e.g. travel insurance, lost baggage coverage) to facilitate needs and wants of persons who travel regularly; and para [0134], wherein such as spending habits, interests, needs, wants, preferred or associated charities, social habits, etc.).
Regarding claims 8, 15, and 19, Tietzen discloses the computer-implemented method of claim 4, wherein the calculated values are percentages for at least one of the one or more segments (see Tietzen, para [0259], wherein calculate a particular customer's affinity scores as a percentage, with the scores for that customer totaling 100%. For example, customer profile 602 may determine a customer's scores to be 70% Gamer, 20% Discounter, and 10% Giver; and para [0133], wherein Card issuers typically market card types to certain segments of the population based upon demographic data such as credit scores, income, age, location, and anticipated card use); and
wherein the graphic, as set forth above with claim 1.
Tietzen et al. fails to explicitly disclose includes a spider graph for each of the multiple data structures.
Analogous art Henby discloses wherein the graphic includes a spider graph for each of the multiple data structures (see Henby, paras [0192] & [0208], wherein spider chart 894a may provide information assessing how well facility 114 is performing within the operating subsystem 310 with respect to its associated principle 340, Chase Waste; principle 342, Pull; principle 344, Make Value Flow; principle 346, Drive Standard Work; principle 348, Even the Load; and principle 350, Validate Our Processes (see FIG. 3). Referring to FIG. 8b, each "spoke" of each spider chart 894 may include six levels chart 894 may, as a result, represent the associated principles of each subsystem 300, respectively. For example, spider chart 894a may provide information assessing how well facility 114 is performing within the operating subsystem 310 with respect to its associated principle 340, Chase Waste; principle 342, Pull; principle 344, Make Value Flow; principle 346, Drive Standard Work; principle 348, Even the Load; and principle 350, Validate Our Processes (see FIG. 3). Referring to FIG. 8b, each "spoke" of each spider chart 894 may include six levels; and para [0169], wherein each metric may be used identically within the hierarchical levels).
One of ordinary skill in the art would have recognized that applying the known technique of Henby would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Regarding claims 9 and 16, Tietzen discloses the computer-implemented method of claim 1, wherein the graphic, as set forth above with claim 1.
Tietzen et al. fails to explicitly disclose includes a spider graph.
Analogous art Henby discloses the graphic includes a spider graph (see Henby, paras [0192] & [0208], wherein Referring to Fig. 8b and Fig. 9b).
One of ordinary skill in the art would have recognized that applying the known technique of Henby would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Regarding claim 11, Tietzen discloses the system of claim 10, wherein the intelligence platform computing device is configured, by the executable instructions, to:
filter the data based on a geographic limitation, as set forth above with claim 1; and
compile the data structure from the filtered data, as set forth above with claim 1.
Claims 6, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Tietzen et al. (US Pub No. 2018/0276710) (hereinafter Tietzen et al.), in view of Henby et al. (US Pub No. 2009/0138334) (hereinafter Henby et al.), in view of Ghosh et al. (US Pub No. 2010/0076813) (hereinafter Ghosh et al.), and further in view of Woodrick et al. (US Pub No. 2022/0092627) (hereinafter Woodrick et al.).
Regarding claims 6, 14, and 20, Tietzen discloses the computer-implemented method of claim 4, wherein the parameters include alerts and digital wallet for the data structure specific to technical features (see Tietzen, para [0086], wherein systems and methods described herein may use artificial intelligence engines to provide alerts for a loyalty program; para [0466], wherein a digital wallet when transacting with a member merchant).
Tietzen et al. and Henby et al. combined fail to explicitly disclose the parameters include contactless.
Analogous art Ghosh discloses the parameters include contactless (see Ghosh, para [029], wherein transaction data 126 could be obtained from contactless payments).
Tietzen directed to a system for offering an incentive will be accepted by a customer. Ghosh directed to providing a market-dynamics network that assesses social and economic market dynamics in specific geographic regions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Tietzen, regarding the System for Artificial Intelligence Engine Incenting Merchant Transaction With Consumer Affinity, to have included the parameters include contactless because both inventions teach improving transaction and customer service. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Tietzen et al. and Henby et al. combined fail to explicitly disclose the parameters include virtual card.
Analogous art Woodrick discloses the parameters include virtual card (see Woodrick, para [049], wherein type of virtual card (e.g. virtual cards generated by issuers).
Tietzen directed to a system for offering an incentive will be accepted by a customer. Woodrick directed to providing payment card transactions and the determined estimated benefit value. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Tietzen, regarding the System for Artificial Intelligence Engine Incenting Merchant Transaction With Consumer Affinity, to have included the parameters include virtual card because both inventions teach improving transaction and customer service. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 7, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Tietzen et al. (US Pub No. 2018/0276710) (hereinafter Tietzen et al.), in view of Henby et al. (US Pub No. 2009/0138334) (hereinafter Henby et al.), and further in view of Gopalan et al. (US Pub No. 2020/0310888) (hereinafter Gopalan et al.).
Regarding claims 7, 14, and 20, Tietzen discloses the computer-implemented method of claim 4, wherein the parameters include annual fee, interest rate for the data structure specific to pricing (see Tietzen, para [0200], wherein cardholders an annual fee to carry a financial card; para [0843], wherein Item 2 provides an interest rating; para [0843], wherein
Tietzen et al. and Henby et al. combined fail to explicitly disclose the parameters include late payment fee.
Analogous art Gopalan discloses the parameters include late payment fee (see Gopalan, para [0272], wherein charges levied by the bank for late payment on the total outstanding amount).
Tietzen directed to a system for offering an incentive will be accepted by a customer. Gopalan directed to matching sentiment of the user query, based on the determined confidence scores. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Tietzen, regarding the System for Artificial Intelligence Engine Incenting Merchant Transaction With Consumer Affinity, to have included the parameters include late payment fee because both inventions teach improving transaction and customer service. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior arts made of record and not relied upon is considered pertinent to applicant's disclosure. (US Pub No. 2013/0238447; US Pat No. 11,238,480; US Pub No. 2004/0107131; US Pub No. 2004/0002870; US Pub No. 2014/0108437; US Pub No. 2007/0239523; US Pat No. 2013/0297446; US Pub No. 2013/0073340; US Pub No. 2014/0040044; US Pub No. 2010/0076813; US Pub No. 2014/0108437; and A Mekonnen, F Harris, A Laing (Linking products to a cause or affinity group: does this really make them more attractive to consumers?) - European Journal of Marketing, 2008 - emerald.com.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAFIZ A KASSIM whose telephone number is (571)272-8534. The examiner can normally be reached 9:00 - 5:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached at 571-272-6045. 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.
/HAFIZ A KASSIM/Primary Examiner, Art Unit 3623 07/29/2026
1 A Mekonnen, F Harris, A Laing (Linking products to a cause or affinity group: does this really make them more attractive to consumers?) - European Journal of Marketing, 2008 - emerald.com discloses affinity card value propositions for cardholders can be categorized along two dimensions. The first dimension is the focus of benefit; that is, whether the benefit outcome is focused on the group or the individual. Here the counterpoint of financial return for the group (e.g. donation) is the financial advantage for the individual..
2 A Mekonnen, F Harris, A Laing (Linking products to a cause or affinity group: does this really make them more attractive to consumers?) - European Journal of Marketing, 2008 - emerald.com discloses affinity card value propositions for cardholders can be categorized along two dimensions. The first dimension is the focus of benefit; that is, whether the benefit outcome is focused on the group or the individual. Here the counterpoint of financial return for the group (e.g. donation) is the financial advantage for the individual..
Prosecution Insights