DETAILED ACTION
Status
This communication is in response to Applicant’s application filed on July 14, 2025. Claims 1-20 are pending and presented for examination. Of the pending claims, Claims 1, 8 and 15 are independent.
Claims 1-20 are originally presented by Applicant and, therefore, have been constructively elected by original presentation for prosecution on the merits per MPEP § 819 and MPEP § 821.03.
The present application, filed after March 16, 2013, is being examined under the first inventor to file (FITF) provisions of the America Invents Act (AIA ).
Priority/Benefit Claim
No claim(s) for benefit or priority exists in this application and, therefore, the effective filing date of this application is its filing date of March 3, 2025.
Information Disclosure Statement (IDS)
No information disclosure statement (IDS) has been filed.
Applicant is notified of 37 CFR 1.51(d): “Applicants are encouraged to file an information disclosure statement in nonprovisional applications.”
Applicant is notified of 37 C.F.R. 1.56, which states that each inventor named in the application has a duty to disclose information material to patentability.
Applicant is notified of MPEP § 2001.06(b): “prior art references from one application must be made of record in another subsequent application if such prior art references are ‘material to patentability’ of the subsequent application”.
CPC Classification Notes
Examiner notes the following CPC classification symbols:
G06Q 30/00 Commerce
G06Q 30/02 • Marketing
G06Q 30/0201 •• Market modelling; Market analysis; Collecting market data
G06Q 30/02011 ••• Profiling or inferring profiles of users … based on their behavior
G06Q 30/00 Commerce
G06Q 30/02 • Marketing
G06Q 30/0207 •• Discounts or incentives, e.g. …rebates
G06Q 30/0226 ••• Incentive systems for frequent usage, e.g. …point systems
G06Q 30/0229 •••• Multi-merchant loyalty card systems
G06Q 30/00 Commerce
G06Q 30/02 • Marketing
G06Q 30/0241 •• Advertisements
G06Q 30/0242 ••• Determining effectiveness of advertisements
G06Q 30/0243 •••• Comparative campaigns
G06Q 30/0244 •••• Optimization
G06Q 30/0251 ••• Targeted advertisements
G06Q 30/0254 •••• based on statistics
G06Q 30/0255 •••• based on user history
G06Q 30/0276 ••• Advertisement creation
G06Q 30/00 Commerce
G06Q 30/06 • Buying… transactions
G06Q 30/0601 •• Electronic shopping [e-shopping]
G06Q 30/0631 ••• Recommending goods or services
G06Q 30/06311 •••• based on purchase… history
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b) of the America Invents Act (AIA ):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) of the America Invents Act (AIA ) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. “A claim is indefinite when it contains words or phrases whose meaning is unclear” (MPEP § 2173.05(e)).
Regarding Claims 1, 3, 6, 8, 10, 13, 15 and 17, since it is unclear as to what the phrase “the loyalty consumer activity data” makes antecedent reference to in each corresponding independent claim, each of Claims 1, 3, 6, 8, 10, 13, 15 and 17 is rejected under AIA 35 U.S.C. 112(b) as being indefinite. More specifically, it is unclear as to whether the phrase “the loyalty consumer activity data” references first-recited loyalty consumer activity data which may or may not describe transactions, references second-introduced loyalty consumer activity data which may or may not describe attributes, or references both. There is insufficient antecedent basis for the phrase “the loyalty consumer activity data” recited in each of Claims 1, 3, 6, 8, 10, 13, 15 and 17. Therefore, Claims 1, 3, 6, 8, 10, 13, 15 and 17 are rejected under AIA 35 U.S.C. 112(b) as being indefinite. Appropriate correction(s) is required.
Claims 2-7 depend from independent Claim 1, but do not resolve the above issues and inherit the deficiencies of Claim 1; therefore, Claims 2-7 are rejected under 35 U.S.C. 112(b) of the AIA . Similarly, Claims 9-14 depend from independent Claim 8, but do not resolve the above issues and inherit the deficiencies of Claim 8; therefore, Claims 9-14 are rejected under AIA 35 U.S.C. 112(b) of the AIA . Similarly, Claims 16-20 depend from independent Claim 15, but do not resolve the above issues and inherit the deficiencies of Claim 15; therefore, Claims 16-20 are rejected under 35 U.S.C. 112(b) of the AIA . Appropriate correction(s) is required.
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. During patent examination, the pending claims must be “given their broadest reasonable interpretation consistent with the specification” (MPEP § 2111). In view of this standard and based upon consideration of all of the relevant factors with respect to each claim as a whole, Claims 1-20 are rejected as ineligible subject matter under 35 U.S.C. 101.
Step 1: Claims 1-14 satisfy Step 1 enunciated in Alice Corp. v. CLS Bank International, 573 U.S. __, 134 S. Ct. 2347 (2014) in view of Applicant’s disclosure having a processor being a hardware processor. However, independent Claim 15 (and corresponding dependent Claims 16-20) encompass a signal per se. Examiner notes a signal per se is ineligible subject matter under 35 U.S.C. 101. Independent Claim 15 is drawn to a “computer storage device…”; however, the broadest reasonable interpretation of a claim drawn to “computer storage device”, as recited in Claim 15, covers forms of a signal per se. More specifically, Examiner understands that the phrase “computer storage device” as encompassing a transitory storage device, such as a transitory propagating signal(s) per se. {See, for example, In re Nuijten, 500 F.3d at 1356-57 where volatile computer memory was a computer readable medium ("CRM"). Because the CRM was considered transitory, data stored in computer memory was held in Nuijten to be ineligible/transitory}. Thus, the broadest reasonable interpretation of a claim drawn to a “computer storage device” encompasses a transitory storage device, such as a transitory propagating signal per se. Since independent Claim 15 encompasses one or more signals per se {i.e., with “instructions stored thereon” being software per se encoded on the transitory signal(s)}, Claim 15 is rejected under 35 U.S.C. 101 as being drawn to non-statutory subject matter. In an effort to expedite prosecution and to assist Applicant regarding step 1 enunciated in Alice Corp. v. CLS Bank, it is suggested that Applicant make clear that Claim 15’s recited “computer storage device” is non-transitory, such as, for example, by amending independent Claim 15 as follows: “A non-transitory computer storage device…” (underlining emphases added by Examiner).
Step 2A: Claims 1-20 are rejected under § 101 because Applicant’s claimed subject matter is directed to an abstract idea without significantly more. The rationale for this finding is that Applicant’s claims recite assisting commercial/marketing decisions (e.g., “inform decision-making” per Spec. ¶ [0002]; “user 502 asks… if it can recommend ways to improve its loyalty program” per Spec. ¶ [0075] and Figure 5A of Applicant’s drawings; “assist in determining the effectiveness of loyalty programs” per Spec. ¶ [0048]; etc.) by designing/recommending loyalty-program options {e.g., offering “proposed loyalty program [that is] above the threshold propensity” and “wherein the threshold propensity is a minimum acceptable propensity for consumers to make transactions in any loyalty program”; “proposes loyalty programs that are customized to a user's dataset” per Spec. ¶ [0021]; “receive only the proposed loyalty programs that pass/survive the consumer propensity analysis” per Spec. ¶ [0022]; “recommends an increase in the budget” per Spec. ¶ [0079]; “I would recommend targeting customers who have not made a cross border transaction in the past 6 months” per Figure 5A of Applicant’s drawings; “I would recommend a budget of $50K” per Figure 5A of Applicant’s drawings; “Here are the recommended promotion details” per Figure 5B of Applicant’s drawings; “I would recommend increasing the budget of $35K to offset the cap increase” per Figure 5B of Applicant’s drawings; etc.} based on past consumer activity {e.g., “consumer transactions”; “loyalty consumer activity”; “loyalty program activity”; “customer spend patterns” noted in Figure 5A of Applicant’s drawings; “a consumer’s response to the proposed loyalty programs”; “historical data”; “consumer segmentation”; “attributes of previously offered loyalty programs”; etc.} in an effort to improve business performance {e.g., Figure 5A of Applicant’s drawings showing “improve my loyalty program” such as to “(increase revenue by $100K over 6 months)”, “(increase revenue by $70K over 6 months)” and/or “(increase revenue by $30K over 6 months)”; “consumers to make transactions in any loyalty program”; “effectiveness of… loyalty program”; “and thus improve the quality and effectiveness of the proposed loyalty programs” per Spec. ¶ [0043]; etc.}, as more particularly recited in the pending claims save for recited (non-abstract claim elements): a database storing data; a generative pre-trained transformer (GPT); a graphical user interface (GUI) with icons; each of Applicant’s recited operations/processes of retrieving from the database, presenting an icon in the GUI, moving icons in the GUI, and providing; (only Claim 1 and corresponding dependent claims) a system comprising: a processor and a computer storage medium storing instructions operative upon execution by the processor; (only Claims 2, 9 and 16) a configuration manager tool configured to allow an operator to modify; (only Claims 4, 11 and 18), a text-based natural-language UI, and a user to interface (with the system) using text-based queries with the UI; (only Claims 5, 12 and 19) graphical representations, and presenting the graphical representations on the GUI; (only Claim 15 and corresponding dependent claims) a computer storage device having computer-executable instructions stored thereon, which, upon execution by a computer, cause the computer to perform. However, assisting business decisions or marketing decisions (“inform decision-making”, “recommend ways to improve… loyalty program”, etc.) by designing/recommending loyalty program options (“proposed loyalty program above the threshold propensity”, “proposes loyalty programs that are customized to a [marketing] user's dataset”, “recommends an increase in the budget”, etc.) based on past consumer activity (“consumer transactions”, etc.) in an effort to improve business performance (“improve my loyalty program”, “increase revenue”, “improve the quality and effectiveness of… loyalty programs”, etc.), as currently recited in Applicant’s pending claims and further explained below, is within a certain method of organizing human activity — (i) fundamental economic principle or practice; and/or (ii) commercial interaction (including advertising, marketing or sales activities or behaviors; business relations). MPEP 2106.04(a)(2)(II)(A) provides examples of “fundamental economic principles or practices” and MPEP 2106.04(a)(2)(II)(B) provides additional discussion and examples of commercial or legal interactions. This judicial exception (i.e., abstract idea exception) is not integrated into a practical application because each claim as a whole, having the combination of additional elements beyond the judicial exception(s), does not integrate the exception into a practical application of the exception and, therefore, the pending claims are “directed to” a judicial exception under USPTO Step 2A. More specifically, each claim as a whole does not appear to reflect the combination of additional elements as: (1) improving the functioning of a computer itself or improving another technology or technical field, (2) applying the judicial exception with, or by use of, a particular machine/manufacture that is integral to the claim, (3) effecting a transformation or reduction of a particular article to a different state or thing, or (4) applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Instead, any improvement is to the underlying abstract idea of utilizing past consumer activity to recommend loyalty-program options to assist with business/marketing decisions and improve commercial/marketing performance. SAP Am., Inc. v. InvestPic, LLC, No. 2017-2081, 2018 U.S. App. LEXIS 12590, Slip. Op. 13 (Fed. Cir. May 15, 2018) (“What is needed is an inventive concept in the non-abstract realm.”). Examiner notes that Applicant's recited use or usage of “a generative pre-trained transformer (GPT)” in the independent claims, as well as “a natural language processing module” in Claims 4, 11 and 18, each appear as a high-level black box with no detail about either the GPT algorithm or Natural Language Processing (NLP) algorithm, or any GPT or NLP processes, such as how Applicant's model/tool operates on input data to produce an output(s), such as Applicant’s “a plurality of proposed loyalty programs” recited in each of independent Claims 1, 8 and 15. In addition, Examiner notes that no detail of any training algorithm appears to be mentioned in Applicant's disclosure and, therefore, no specific way of training the algorithm/model exists within Applicant's recited use of a GPT or NLP. See analysis in Recentive Analytics, Inc. v. Fox Corp., Case No. 2023-2437 (Fed. Cir. Apr. 18, 2025), which found “The machine learning technology described…is conventional, as the…specifications demonstrate” (page 11 of Recentive Analytics, Inc. v. Fox Corp.). Similarly, Applicant’s claims “do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning model[] to be applied” (see page 18 of Recentive Analytics, Inc. v. Fox Corp.). Consequently, Applicant's mere recitation to GPT and NLP concepts, as currently recited, is not sufficient to amount to a practical application under Step 2A, Prong 2 of the Subject Matter Eligibility (SME) analysis. In addition, although Applicant’s claims require “comparing the proposed propensity… with a threshold propensity, wherein the threshold propensity is a minimum…”, “determine a relative effectiveness”, “list …in descending order of relative effectiveness”, etc., these techniques encompass mathematical concepts in the form of formulas, equations, and calculations which also have been determined to constitute abstract ideas. See Memorandum, "Grouping of Abstract Ideas" and cases cited in footnote 12, such as enumerated in Section I of the 2019 Revised Patent Subject Matter Eligibility Guidance (84 Fed. Reg. 50). As noted on page 4 of the “October 2019 Update: Subject Matter Eligibility” issued by the USPTO, Examiner notes that a claim does not have to recite the word “calculating” in order to be considered a mathematical calculation. For example, a step of “determining” a variable or number using mathematical methods or “performing” a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation (BRI) of the claim, in light of the specification, encompasses one or more mathematical calculations. Applicant’s additional elements, taken individually and in combination, do not appear to be integrated into a practical application since they embody mere instructions to implement the abstract idea on a computer or mere use of a computer as a tool to perform the abstract idea, do no more than generally linking the use of the abstract idea to a particular technological environment or field of use {e.g., see “Exemplary Operating Environment” illustrated in Figure 6 of Applicant’s drawings, “described in connection with an exemplary computing system environment” (Spec. ¶ [0089]), and “well-known computing systems, environments, and/or configurations that may be suitable for use with aspects of the disclosure include… mobile or portable computing devices (e.g., smartphones), personal computers, server computers,… network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. In general, the disclosure is operable with any device with processing capability such that it can execute instructions such as those described herein” (Spec. ¶ [0090])}, and amount to no more than combining the abstract idea with insignificant extra-solution activity including each of Applicant’s recited operations/processes of retrieving from a database, presenting in a GUI, moving icons in the GUI and providing, as further explained below. For the reasons discussed above, Applicant’s pending claims are directed to an abstract idea that is not integrated into a practical application under Step 2A, Prong 2 of the Subject Matter Eligibility (SME) analysis of 35 U.S.C. 101.
Step 2B: Under Step 2B enunciated in Alice Corp. v. CLS Bank International, 573 U.S. __, 134 S. Ct. 2347 (2014), Applicant’s instant claims do not recite limitations, taken individually and in combination, that are sufficient to amount to “significantly more” than the abstract idea because Applicant’s claims do not recite, as further explained in detail below, an improvement to another technology or technical field, an improvement to the functioning of a computer itself, an application with or by a particular machine, a transformation or reduction of a particular article to a different state or thing, unconventional steps confining the claim to a particular useful application, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. Examiner notes that each of Claims 8-14 is drawn to a method; however, the method steps do not recite, require, or indicate implementation by a particular machine since none of limitations recited in Applicant’s method claims are performed by any computer or processing device — this encompasses a situation where any computing device does no more than assist/help a person perform such steps/processes or thoughts when the person is using the computing device. Even if a computer/machine was implied, such as via use of a GPT, Applicant’s method claim limitations taken individually and in combination would be merely instructions to implement the abstract idea on a computer and would require no more than generally linking the use of an abstract idea to a particular technological environment or field of use {e.g., see “Exemplary Operating Environment” illustrated in Figure 6 of Applicant’s drawings, “described in connection with an exemplary computing system environment” (Spec. ¶ [0089]), and “well-known computing systems, environments, and/or configurations that may be suitable for use with aspects of the disclosure include… mobile or portable computing devices (e.g., smartphones), personal computers, server computers,… network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. In general, the disclosure is operable with any device with processing capability such that it can execute instructions such as those described herein” (Spec. ¶ [0090])}, and having the abstract idea combined with insignificant extra-solution activity including each of Applicant’s recited operations/processes of retrieving from a database, presenting in a GUI, moving icons in the GUI and providing, as further explained below. Examiner also notes that albeit limitations recited in the Claims 1-7 are performed by a generically recited “a processor”, while Claims 15-20 are performed by a generically recited “a computer”, these claim limitations taken individually and in combination are merely instructions to implement the abstract idea on a computer and require no more than a generic computer to generally link the abstract idea to a particular technological environment or field of use {e.g., see “Exemplary Operating Environment” illustrated in Figure 6 of Applicant’s drawings, “described in connection with an exemplary computing system environment” (Spec. ¶ [0089]), and “well-known computing systems, environments, and/or configurations that may be suitable for use with aspects of the disclosure include… mobile or portable computing devices (e.g., smartphones), personal computers, server computers,… network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. In general, the disclosure is operable with any device with processing capability such that it can execute instructions such as those described herein” (Spec. ¶ [0090])}, and no more than a combination of the abstract idea with insignificant extra-solution activity including each of Applicant’s recited operations/processes of retrieving from a database, presenting in a GUI, moving icons in the GUI and providing, as further explained below. As mentioned above, the claim elements in addition to the abstract idea arguably include: a database storing data; a generative pre-trained transformer (GPT); a graphical user interface (GUI) with icons; each of Applicant’s recited operations/processes of retrieving from the database, presenting an icon in the GUI, moving icons in the GUI, and providing; (only Claim 1 and corresponding dependent claims) a system comprising: a processor and a computer storage medium storing instructions operative upon execution by the processor; (only Claims 2, 9 and 16) a configuration manager tool configured to allow an operator to modify; (only Claims 4, 11 and 18), a text-based natural-language UI, and a user to interface (with the system) using text-based queries with the UI; (only Claims 5, 12 and 19) graphical representations, and presenting the graphical representations on the GUI; (only Claim 15 and corresponding dependent claims) a computer storage device having computer-executable instructions stored thereon, which, upon execution by a computer, cause the computer to perform. However, each of these components is recited at a high level of generality that taken individually and in combination perform corresponding generic computer functions of retrieving from a database, presenting in a GUI, moving icons in the GUI and providing — there is no indication that the combination of elements improves the functioning of a computer or improves any other technology since the additional elements taken individually and collectively merely provide conventional computer implementations known to the industry. Furthermore, Examiner notes that none of the processes/steps recited in the pending claims taken individually and in combination impose a meaningful limit on the claim’s scope since none of recited processes/steps taken individually and in combination involve activity that amounts to more than generic computer functions/activity. The steps/processes of retrieving from a database, presenting in a GUI, moving icons in the GUI and providing, as currently recited individually and in combination in Applicant’s claims, are considered to be generic computer functions since they involve having the abstract idea combined with insignificant extra-solution activity, and generally linking the use of an abstract idea to a particular technological environment or field of use previously known to the industry — each of the steps of retrieving from a database encompasses a data input/loading or receiving function performed by virtually all general purpose computers {see Alice Corp., 134 S. Ct. at 2360; see Ultramercial, 772 F.3d at 716‐17; see buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014); see Cyberfone Systems, LLC v. CNN Interactive Group, Inc., 558 Fed. Appx. 988, 993 (Fed. Cir. 2014); and see Mayo Collaborative Serv. v. Prometheus Labs., Inc., 566 U.S. __, 132 S.Ct. 1289, 101 USPQ2d 1961 (2012)}; each of the steps of retrieving from a database encompasses a data recognition/inquiry function or retrieving function performed by virtually all general purpose computers {see Content Extraction and Transmission LLC v. Wells Fargo Bank, N.A., 776 F.3d 1343, 113 U.S.P.Q.2d 1354 (Fed. Cir. 2014), hereinafter “Content Extraction”, for data recognition); each of the steps of encoding encompasses a data saving or depositing function performed by virtually all general purpose computers {see Alice Corp., 134 S. Ct. at 2360; Cyberfone Systems, LLC v. CNN Interactive Group, Inc., 558 Fed. Appx. 988 (Fed. Cir. 2014), hereinafter “Cyberfone”; and Content Extraction and Transmission LLC v. Wells Fargo Bank, N.A., 776 F.3d 1343, 113 U.S.P.Q.2d 1354 (Fed. Cir. 2014), hereinafter “Content Extraction”, for data storage}; and each of the steps of providing as well as presenting and moving in a GUI encompasses a data output/transmittal function performed by virtually all general purpose computers {see Ultramercial, 772 F.3d at 716‐17; see buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014); and see Cyberfone Systems, LLC v. CNN Interactive Group, Inc., 558 Fed. Appx. 988, 993 (Fed. Cir. 2014)}. In addition, Examiner notes that ¶ [0090] of Applicant’s specification mentions that “well-known computing systems, environments, and/or configurations that may be suitable for use with aspects of the disclosure include… mobile or portable computing devices (e.g., smartphones), personal computers, server computers,… network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. In general, the disclosure is operable with any device with processing capability such that it can execute instructions such as those described herein” (Spec. ¶ [0090]). Examiner notes that it may be worth being mindful of the “July 2015 Update: Subject Matter Eligibility” document, at page 7, second and sixth bullet points (July 30, 2015) regarding various well‐understood, routine, and conventional functions of a computer. Employing well-known computer functions individually and in combination to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not add significantly more, similar to how limiting the computer-implemented abstract idea in Flook (Parker v. Flook, 437 U.S. 584, 19 U.S.P.Q. 193 (1978)) to petrochemical and oil-refining industries was insufficient. For the reasons discussed above, Applicant’s pending claims do not satisfy Step 2B enunciated in Alice Corp. v. CLS Bank International, 573 U.S. __, 134 S. Ct. 2347 (2014).
Consequently, based upon consideration of all of the relevant factors with respect to each claim as a whole, Claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. For information regarding 35 U.S.C. 101, please see Subject Matter Eligibility (SME) guidance and instructional materials at https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility, which includes guidance, memoranda, and updates regarding SME under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 (AIA ) which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 of the AIA as being unpatentable over U.S. Patent Application Publication No. 2018/0276710 of TIETZEN et al. (hereinafter “Tietzen”) in view of U.S. Patent Application Publication No. 2025/0317632 of Galynsky et al. (hereinafter “Galynsky”).
Regarding Claim 1, Tietzen discloses a system for automatically designing loyalty programs, the system comprising: a processor; and a computer storage medium storing instructions that are operative upon execution by the processor to:
retrieve, from a database of historical transactions, loyalty data comprising loyalty program activity data describing attributes of loyalty programs and loyalty consumer activity data describing consumer transactions associated with a loyalty program (e.g., “merchants… provide benefits, incentives, and rewards to cardholders in… a loyalty program” and “ ‘merchant’… participates in a loyalty program to build loyalty with customers, and potentially acquire new business, and in exchange is willing to provide a loyalty ‘benefit’ ” —Tietzen at ¶¶ [0081] and [0003]; “loyalty system 26… employ data mining… to use cardholder transaction data and analytics along with historical product barcode scan data of the cardholder to generate an attractive offer for the cardholder. Each such attractive offer… generated by a recommendation engine…. and will…. recommend incentives for merchants…. loyalty system 26 to… operate an artificial conversational entity to conduct conversations… simulating how a human would behave as a conversational partner… to provide… information acquisition for merchants” and “Loyalty system 26 may include a merchant interface 52…. for merchant system 40 to create, customize, and manage loyalty programs and incentives” —Tietzen at ¶¶ [0232] and [0224]; “Customer profiler 602 classifies customers according to… customer profile categories… referred to as “personas”. Each profile category (or persona) defines a grouping of customers who share particular attributes such as behavioural and/or motivation attributes. Customer profile 602 analyzes data for each customer to determine the customer's attributes and to classify the customer into… profile categories. This data may include the cardholder data and transaction data discussed above…. Recommendation engine 60 may recommend incentives targeting customers classified into a particular profile categories” —Tietzen at ¶¶ [0253]; as well as Tietzen at ¶¶ [0091]–[0092], [0229], [0253], [0262], [0342] and [0346]);
anonymize the loyalty data, wherein any individual identifying information is masked and wherein the loyalty consumer activity data is aggregated (e.g., “aggregated transactions” —Tietzen at ¶ [0098]; “aggregate the transaction information, and supply… results to… the merchant” —Tietzen at ¶ [0150]; and Tietzen at ¶¶ [0150], [0188], [0231], [0253], [0346]–[0347], [0491] and [0543]);
encode the anonymized loyalty data into representations for transformation by artificial intelligence (e.g., “an artificial intelligence engine upon transaction data… to predict an offer that is likely to incent… loyalty program members to conduct transactions with a merchant” and “using an artificial intelligence engine to predict an offer that is likely to incent a loyalty program member to conduct a transaction with a merchant” —Tietzen at ¶¶ [0014] and [0013]; “artificial intelligence engine… solve problems normally done by humans, albeit with natural intelligence, by way of data mining, recognizing patterns in the mined data, and using probabilities to predict the likelihood of success for a particular recommendation” —Tietzen at ¶ [0226]; “recommendation engine… implements a conventional artificial neural network or fuzzy logic to determine when the criteria of rules are met” —Tietzen at ¶ [0239]; as well as Tietzen at ¶¶ [0084], [0114]–[0116], [0148], [0150], [0188], [0231], [0253], [0346]–[0347], [0358], [0491] and [0543]);
decode the representations from the artificial intelligence into a plurality of proposed loyalty programs and a proposed propensity for each proposed loyalty program, wherein the proposed propensity is a likelihood for consumers to make transactions for a given loyalty program (e.g., “artificial intelligence… upon transaction data… to predict an offer that is likely to incent… loyalty program members to conduct transactions with a merchant” and “using an artificial intelligence engine to predict an offer that is likely to incent a loyalty program member to conduct a transaction with a merchant” —Tietzen at ¶¶ [0014] and [0013]; “recognizing patterns in the mined data, and using probabilities to predict the likelihood of success for a particular recommendation” and “predicts the likelihood that an offer having an incentive will be accepted by a registered customer by conducting a transaction with the registered merchant” —Tietzen at ¶¶ [0226] and [0015]; “goal… by the use of artificial intelligence is to increase purchases from merchants by incenting such purchases with offers” and “incentives… with conditional transactions with merchants (e.g. the purchase of a particular good… is required… to receive the special offer or prize). This encourages cardholders to conduct transactions with merchants” —Tietzen at ¶¶ [0226] and [0146]; “The deal would… fluctuate based on the number and type of similar products the cardholder has scanned, which are used to calculate the… likelihood of the cardholder buying the products, etc.” —Tietzen at ¶ [0232]; “incentivize them to make purchases. Any such incentives may be customized for…the particular merchant…. for example, incentives may be customized based on the customer's… transaction history, persona… and any preferences specified by… the merchant” —Tietzen at ¶ [0337]; “enable merchants to monitor, predict… 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” —Tietzen at ¶ [0081]; and Tietzen at ¶¶ [0091]–[0092], [0150], [0188], [0229], [0231], [0253], [0262], [0342], [0346]–[0347], [0358], [0491] and [0543]);
compare the proposed propensity for each proposed loyalty program with a threshold propensity, wherein the threshold propensity is a minimum acceptable propensity for consumers to make transactions in any loyalty program (e.g., “predicts the likelihood that an offer having an incentive will be accepted by a registered customer by conducting a transaction with the registered merchant” and “artificial intelligence… used to engage in predictive modeling … in the optimization of incentives. For example,… neural networks, decision trees, CHAID, CART, fuzzy logic…” can be used to optimize incentives —Tietzen at ¶¶ [0015] and [0084]; “uses artificial intelligence to the benefit of and assistance to loyalty system 26 by way of automatically generating or modify operations, parameters, and outputs with respect to a goal, for example, maximizing or increasing merchant revenue or profitability, and automatically adapts the generation or modification operations, parameters, and outputs to feedback. As such,… loyalty system 26 with the functionalities of self-learning and self-adapting with respect to generating or modifying operations, parameters, and outputs” —Tietzen at ¶ [0115]; “alert…include a recommended incentive” and “recommended incentives and associated transactions are likely to be of interest to the targeted segment based on data mining and correlations of cardholder…attributes” and “merchant… able to sort the list of alerts that they have received by…parameter or attribute” and “attributes include… spending (total, average monthly, etc.)… number of transactions… transaction history… redeemed incentives… etc.” —Tietzen at ¶¶ [0086], [0147], [0679] and [0233]; “artificial intelligence engines assist loyalty system 26 in generating alerts that trigger [based on] a… threshold…” and “alert…include a recommended incentive” when, for example, “merchant's affinity score for a particular profile category… rises above… a pre-defined threshold” —Tietzen at ¶¶ [0116], [0086], [0321] and [0536]; “thresholds may be modified and selected to generate incentives for customers that fall meet the threshold” —Tietzen at ¶ [0899]; “thresholds for alert triggers” —Tietzen at ¶ [0536]; “recommended incentives based on data analysis, trends based on thresholds…” such as “an offer that is likely to incent… loyalty program members to conduct transactions with a merchant” —Tietzen at ¶¶ [0554] and [0014]; as well as Tietzen at ¶¶ [0091]–[0095], [0146], [0150], [0188], [0226], [0229], [0231], [0253], [0262], [0337], [0342], [0346]–[0348], [0358], [0491], [0536], [0543] and [0684]);
based on comparing the proposed propensity for each proposed loyalty program with the threshold propensity, determine a relative effectiveness of each proposed loyalty program (e.g., “predicts the likelihood that an offer having an incentive will be accepted by a registered customer by conducting a transaction with the registered merchant” and “artificial intelligence… used to engage in predictive modeling … in the optimization of incentives. For example,… neural networks, decision trees, CHAID, CART, fuzzy logic…” can be used to optimize incentives such as for “maximizing or increasing merchant revenue or profitability” —Tietzen at ¶¶ [0015], [0084] and [0115]; “uses artificial intelligence to the benefit of and assistance to loyalty system 26 by way of automatically generating or modify operations, parameters, and outputs with respect to a goal, for example, maximizing or increasing merchant revenue or profitability, and automatically adapts the generation or modification operations, parameters, and outputs to feedback. As such,… loyalty system 26 with the functionalities of self-learning and self-adapting with respect to generating or modifying operations, parameters, and outputs” —Tietzen at ¶ [0115]; “use artificial intelligence engines to provide a merchant interface for management of incentive programs, for review of incentive performance indicators, and for managing alerts…. provide dynamic and iterative incentive planning tools…to obtain decision support in building incentives, such as recommendations of incentives, alerts, target cardholders, and the associated transactions…. calibrate incentive attributes…. use artificial intelligence engines to provide incentive segmenting criteria and allow the user to modify the criteria and immediately and see a refresh of the various components of the ‘impact’ display segments” and “enable a merchant to modify incentive attributes and receive recommendations” —Tietzen at ¶¶ [0088]–[0089]; “artificial intelligence… identify incentive performance indicators… enable selection of attributes to filter the incentive performance indicators” —Tietzen at ¶ [0089]; “alert…include a recommended incentive” and “recommended incentives and associated transactions are likely to be of interest to the targeted segment based on data mining and correlations of cardholder…attributes” and “merchant… able to sort the list of alerts that they have received by…parameter or attribute” and “attributes include… spending (total, average monthly, etc.)… number of transactions… transaction history… redeemed incentives… etc.” —Tietzen at ¶¶ [0086], [0147], [0679] and [0233]; as well as Tietzen at ¶¶ [0091]–[0095], [0146], [0150], [0188], [0226], [0229], [0231], [0253], [0262], [0337], [0342], [0346]–[0348], [0358], [0491], [0536], [0543] and [0684]);
present each proposed loyalty program above the threshold propensity as a natural language icon in a graphical user interface (GUI) (e g., “manage loyalty programs and incentives” —Tietzen at ¶ [0224]; “provide a merchant interface for management of incentive programs, for review of incentive performance indicators, and for managing alerts…. provide dynamic and iterative incentive planning tools… to obtain decision support in building incentives, such as recommendations of incentives, alerts, target cardholders, and the associated transactions…. calibrate incentive attributes…. use artificial intelligence engines to provide incentive segmenting criteria and allow the user to modify the criteria and immediately and see a refresh of the various components of the ‘impact’ display segments” and “enable a merchant to modify incentive attributes and receive recommendations” —Tietzen at ¶¶ [0088]–[0089]; “artificial intelligence… identify incentive performance indicators… enable selection of attributes to filter the incentive performance indicators” —Tietzen at ¶ [0089]; GUI of Figures 22A listing selectable alert icons and “alert…include a recommended incentive” and “recommended incentives and associated transactions are likely to be of interest to the targeted segment based on data mining and correlations of cardholder…attributes” and “merchant… able to sort the list of alerts that they have received by…parameter or attribute” and “attributes include… spending (total, average monthly, etc.)… number of transactions… transaction history… redeemed incentives… etc.” —Figure 22A of Tietzen and Tietzen at ¶¶ [0086], [0147], [0679] and [0233]; GUI of Figure 26 illustrating selectable alert icons and relative revenue performance of three loyalty reward incentives —Figure 26 of Tietzen; “user may select an example reward, such as 10% Off Any Bottle reward” and GUI illustrating selected alert icons and relative performance of four loyalty reward incentives — Tietzen at ¶ [0762] and Figure 27 of Tietzen; Figures 11A and 11B of Tietzen; “Increase sales by offering Rewards to the segment whose average purchase is less than others. Based on your objective to increase spend we recommend targeting customers whose average transaction amount is below the average ($50) to get them to increase their purchase amount. The graph illustrates this customer group” — Figure 11A of Tietzen; “FIG. 11A illustrates…a custom incentive with the object to increase spending” —Tietzen at ¶ [0029]; “TARGET CUSTOMER GROUPS WHO AREN’T SHOPPING AT YOUR STORE. Based on your objective to Bring in New Customers we recommend targeting the customer types (males aged…) that are not currently shopping at your store.” — Figure 11B of Tietzen; “FIG. 11B illustrates… a custom incentive with the object to bring in new customers to one or more location” —Tietzen at ¶ [0030]; “REWARD HIGH-SPENDING CUSTOMERS TO SHOW YOUR APPRECIATION. Based on your objective to Reward Spending we recommend targeting customers whose spend $51 or more to show your appreciation and build loyalty. The graph illustrates this customer group” — Figure 19 of Tietzen; as well as Tietzen at ¶¶ [0015], [0084], [0115] and [0550]); and
automatically move the natural language icons to a list in the GUI in descending order of relative effectiveness (e.g., “sorts in descending order” —Tietzen at ¶ [0675]; “attributes to filter the incentive performance indicators” —Tietzen at ¶ [0089]; “The list is sorted…in descending order” and “…sort the list of alerts… by…parameter or attribute” and “attributes include… spending (total, average monthly, etc.)… number of transactions… transaction history… redeemed incentives… etc.” —Tietzen at ¶¶ [0747], [0086], [0147], [0679] and [0233]; “grouping of customers who share particular attributes” and “artificial intelligence engines to suggest a relevant incentive objective, and based on the objective may suggest or recommend a particular segment of customers or cardholders to target…. targeting that segment based on performance of that attribute” —Tietzen at ¶¶ [0253] and [0093]; “customer attributes based on … predicted data” such as “an offer that is likely to incent … loyalty program members to conduct transactions with a merchant” and/or “the likelihood that an offer having an incentive will be accepted by a registered customer by conducting a transaction with the registered merchant” —Tietzen at ¶¶ [0896], [0014] and [0015]; “using probabilities to predict the likelihood of success for a particular recommendation” —Tietzen at ¶¶ [0226]; “predictive modeling or data mining in the optimization of incentives” —Tietzen at ¶¶ [0084]; Figure 22A of Tietzen; and Tietzen at ¶¶ [0015], [0084], [0115] and [0550]), but Tietzen fails to explicitly disclose the artificial intelligence including a generative pre-trained transformer (GPT). However, Galynsky teaches: artificial intelligence including a generative pre-trained transformer (GPT), such as ChatGPT which is an AI chatbot developed by OpenAI that launched in 2022 (e.g., Galynsky at ¶ [0016]); that ChatGPT is a member of the generative pre-trained transformer (GPT) family of language models, and that ChatGPT is based on GPT-3.5 and GPT-4 families of LLMs (e.g., Galynsky at ¶ [0017]); and “Jasper is an AI virtual assistant and copilot assistant … to help produce marketing content with GPT-3.5” (Galynsky at ¶ [0038]); “ChatGPT is basically an AI-powered chatbot… it's a natural language processing tool, powered by Artificial Intelligence, which enables users to have a human-like conversation” (Galynsky at ¶ [0053]); “the chatbot is trained to automatically provide… customized responses to requests or prompts: … 4) create targeted advertisements” (Galynsky at ¶ [0069]); and “Trainers of ChatGPT deploy Reinforcement Learning via Human Feedback (RLHF), in which, actual human responses and feedback are induced in the training loop…. ChatGPT is able to produce human-like conversations with users” (Galynsky at ¶ [0056]). Therefore, it would have been obvious to one skilled in the art, before the effective filing date of the claimed invention, to incorporate the artificial intelligence including a generative pre-trained transformer (GPT), as taught by Galynsky, into the method/system disclosed by Tietzen, which is directed toward making intelligent recommendations based on data utilizing artificial intelligence, predictive modeling and automatic learning (e.g., Tietzen at ¶¶ [0013]–[0015], [0084], [0091]–[0095], [0114]–[0116] and [0358]), because such incorporation would be applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (see MPEP § 2143).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Tietzen in view of Galynsky as applied to Claim 1 above and Tietzen teaching: a configuration manager tool, wherein the configuration manager tool is configured to allow an operator to modify a weight of the proposed propensity for each proposed loyalty program (e.g., Tietzen at ¶¶ [0088]–[0089], [0115], [0190], [0563], [0565] and [0901]).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Tietzen in view of Galynsky as applied to Claim 1 above and Tietzen teaching wherein the instructions are further operative to: incorporate the loyalty consumer activity data into decoding the representations into the proposed propensity for each proposed loyalty program, wherein the proposed loyalty programs are compatible with a user's consumer data (e.g., “artificial intelligence… upon transaction data… to predict an offer that is likely to incent… loyalty program members to conduct transactions with a merchant” and “using an artificial intelligence engine to predict an offer that is likely to incent a loyalty program member to conduct a transaction with a merchant” —Tietzen at ¶¶ [0014] and [0013]; “recognizing patterns in the mined data, and using probabilities to predict the likelihood of success for a particular recommendation” and “predicts the likelihood that an offer having an incentive will be accepted by a registered customer by conducting a transaction with the registered merchant” —Tietzen at ¶¶ [0226] and [0015]; “goal… by the use of artificial intelligence is to increase purchases from merchants by incenting such purchases with offers” and “incentives… with conditional transactions with merchants (e.g. the purchase of a particular good… is required… to receive the special offer or prize). This encourages cardholders to conduct transactions with merchants” —Tietzen at ¶¶ [0226] and [0146]; “The deal would… fluctuate based on the number and type of similar products the cardholder has scanned, which are used to calculate the… likelihood of the cardholder buying the products, etc.” —Tietzen at ¶ [0232]; “incentivize them to make purchases. Any such incentives may be customized for…the particular merchant…. for example, incentives may be customized based on the customer's… transaction history, persona… and any preferences specified by… the merchant” —Tietzen at ¶ [0337]; “enable merchants to monitor, predict… 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” —Tietzen at ¶ [0081]; and Tietzen at ¶¶ [0091]–[0092], [0150], [0188], [0229], [0231], [0253], [0262], [0342], [0346]–[0347], [0358], [0491] and [0543]); and
encode the user's consumer data into the artificial intelligence, wherein the proposed loyalty programs are compatible with the user's consumer data (e.g., “an artificial intelligence engine upon transaction data… to predict an offer that is likely to incent… loyalty program members to conduct transactions with a merchant” and “using an artificial intelligence engine to predict an offer that is likely to incent a loyalty program member to conduct a transaction with a merchant” —Tietzen at ¶¶ [0014] and [0013]; “artificial intelligence engine… solve problems normally done by humans, albeit with natural intelligence, by way of data mining, recognizing patterns in the mined data, and using probabilities to predict the likelihood of success for a particular recommendation” —Tietzen at ¶ [0226]; “recommendation engine… implements a conventional artificial neural network or fuzzy logic to determine when the criteria of rules are met” —Tietzen at ¶ [0239]; as well as Tietzen at ¶¶ [0084], [0114]–[0116], [0148], [0150], [0188], [0231], [0253], [0346]–[0347], [0358], [0491] and [0543]), but Tietzen fails to explicitly disclose the artificial intelligence including a generative pre-trained transformer (GPT). However, Galynsky teaches: artificial intelligence including a generative pre-trained transformer (GPT), such as ChatGPT which is an AI chatbot developed by OpenAI that launched in 2022 (e.g., Galynsky at ¶ [0016]); that ChatGPT is a member of the generative pre-trained transformer (GPT) family of language models, and that ChatGPT is based on GPT-3.5 and GPT-4 families of LLMs (e.g., Galynsky at ¶ [0017]); and “Jasper is an AI virtual assistant and copilot assistant … to help produce marketing content with GPT-3.5” (Galynsky at ¶ [0038]); “ChatGPT is basically an AI-powered chatbot… it's a natural language processing tool, powered by Artificial Intelligence, which enables users to have a human-like conversation” (Galynsky at ¶ [0053]); “the chatbot is trained to automatically provide… customized responses to requests or prompts: … 4) create targeted advertisements” (Galynsky at ¶ [0069]); and “Trainers of ChatGPT deploy Reinforcement Learning via Human Feedback (RLHF), in which, actual human responses and feedback are induced in the training loop…. ChatGPT is able to produce human-like conversations with users” (Galynsky at ¶ [0056]). Therefore, it would have been obvious to one skilled in the art, before the effective filing date of the claimed invention, to incorporate the artificial intelligence including a generative pre-trained transformer (GPT), as taught by Galynsky, into the method/system taught by Tietzen in view of Galynsky, which is directed toward making intelligent recommendations based on data utilizing artificial intelligence, predictive modeling and automatic learning (e.g., Tietzen at ¶¶ [0013]–[0015], [0084], [0091]–[0095], [0114]–[0116] and [0358]), because such incorporation would be applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (see MPEP § 2143).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Tietzen in view of Galynsky as applied to Claim 1 above and Tietzen teaching: a text-based natural-language UI (e.g., “loyalty system 26 to… operate an artificial conversational entity to conduct conversations via auditory or textual methods, thereby convincingly simulating how a human would behave as a conversational partner… to provide… information acquisition for merchants” and “Loyalty system 26 may include a merchant interface 52…. for merchant system 40 to create, customize, and manage loyalty programs and incentives” —Tietzen at ¶¶ [0232] and [0224]); and a natural language processing module configured to enable a user to interface with the system using text-based queries (e.g., “loyalty system 26 to… operate an artificial conversational entity to conduct conversations via auditory or textual methods, thereby convincingly simulating how a human would behave as a conversational partner… to provide… information acquisition for merchants” and “Loyalty system 26 may include a merchant interface 52…. for merchant system 40 to create, customize, and manage loyalty programs and incentives” —Tietzen at ¶¶ [0232] and [0224]), wherein the natural language processing module is further configured to modify a proposed loyalty program in response to a user query (e.g., “loyalty system 26 to… operate an artificial conversational entity to conduct conversations via auditory or textual methods, thereby convincingly simulating how a human would behave as a conversational partner… to provide… information acquisition for merchants” and “Loyalty system 26 may include a merchant interface 52…. for merchant system 40 to create, customize, and manage loyalty programs and incentives” —Tietzen at ¶¶ [0232] and [0224]; as well as Tietzen at ¶¶ [0088]–[0089], [0115], [0190], [0563], [0565] and [0901]).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Tietzen in view of Galynsky as applied to Claim 1 above and Tietzen teaching wherein the instructions are further operative to: generate graphical representations of analytics of historical data, consumer segmentation, propensity models, and spend impact (e.g., Figures 11A, 11B, 18-19 and 26-27 of Tietzen; and Tietzen at ¶¶ [0642], [0646], [0689], [0746] and [0763]); and present the graphical representations on the GUI (e.g., Figures 11A, 11B, 18-19 and 26-27 of Tietzen; and Tietzen at ¶¶ [0642], [0646], [0689], [0746] and [0763]).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Tietzen in view of Galynsky as applied to Claim 1 above and Tietzen teaching wherein the loyalty consumer activity data includes transaction data following a consumer decision to accept or reject previously offered loyalty programs including a result of the consumer decision (e.g., Tietzen at ¶¶ [0098], [0227], [0229], [0260], [0275], [0279], [0348]–[0349], [0357], [0427]–[0428] and [0550]).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Tietzen in view of Galynsky as applied to Claim 1 above and Tietzen teaching: wherein the instructions are further configured to: analyze a consumer's response to the proposed loyalty programs (e.g., Tietzen at ¶¶ [0098], [0227], [0229], [0260], [0275], [0279], [0348]–[0349], [0357], [0427]–[0428] and [0550]); and model consumer responses based on attributes of previously offered loyalty programs (e.g., Tietzen at ¶¶ [0098], [0227], [0229], [0260], [0275], [0279], [0348]–[0349], [0357], [0427]–[0428] and [0550]).
Regarding Claim 8, Tietzen discloses a method for automatically designing loyalty programs, the method comprising:
encoding anonymized loyalty data into representations for transformation by artificial intelligence (e.g., “an artificial intelligence engine upon transaction data… to predict an offer that is likely to incent… loyalty program members to conduct transactions with a merchant” and “using an artificial intelligence engine to predict an offer that is likely to incent a loyalty program member to conduct a transaction with a merchant” —Tietzen at ¶¶ [0014] and [0013]; “artificial intelligence engine… solve problems normally done by humans, albeit with natural intelligence, by way of data mining, recognizing patterns in the mined data, and using probabilities to predict the likelihood of success for a particular recommendation” —Tietzen at ¶ [0226]; “recommendation engine… implements a conventional artificial neural network or fuzzy logic to determine when the criteria of rules are met” —Tietzen at ¶ [0239]; as well as Tietzen at ¶¶ [0084], [0114]–[0116], [0148], [0150], [0188], [0231], [0253], [0346]–[0347], [0358], [0491] and [0543]), the anonymized loyalty data comprising loyalty program activity data describing attributes of loyalty programs and loyalty consumer activity data describing consumer transactions associated with a loyalty program (e.g., “merchants… provide benefits, incentives, and rewards to cardholders in… a loyalty program” and “ ‘merchant’… participates in a loyalty program to build loyalty with customers, and potentially acquire new business, and in exchange is willing to provide a loyalty ‘benefit’ ” —Tietzen at ¶¶ [0081] and [0003]; “loyalty system 26… employ data mining… to use cardholder transaction data and analytics along with historical product barcode scan data of the cardholder to generate an attractive offer for the cardholder. Each such attractive offer… generated by a recommendation engine…. and will…. recommend incentives for merchants…. loyalty system 26 to… operate an artificial conversational entity to conduct conversations… simulating how a human would behave as a conversational partner… to provide… information acquisition for merchants” and “Loyalty system 26 may include a merchant interface 52…. for merchant system 40 to create, customize, and manage loyalty programs and incentives” —Tietzen at ¶¶ [0232] and [0224]; “Customer profiler 602 classifies customers according to… customer profile categories… referred to as “personas”. Each profile category (or persona) defines a grouping of customers who share particular attributes such as behavioural and/or motivation attributes. Customer profile 602 analyzes data for each customer to determine the customer's attributes and to classify the customer into… profile categories. This data may include the cardholder data and transaction data discussed above…. Recommendation engine 60 may recommend incentives targeting customers classified into a particular profile categories” —Tietzen at ¶¶ [0253]; as well as Tietzen at ¶¶ [0091]–[0092], [0229], [0253], [0262], [0342] and [0346]);
decoding the representations from the artificial intelligence into a plurality of proposed loyalty programs and a proposed propensity for each proposed loyalty program, wherein the proposed propensity is a likelihood for consumers to make transactions for a given loyalty program and based on the proposed propensity for each proposed program, recurrently weighting the artificial intelligence (e.g., “artificial intelligence… upon transaction data… to predict an offer that is likely to incent… loyalty program members to conduct transactions with a merchant” and “using an artificial intelligence engine to predict an offer that is likely to incent a loyalty program member to conduct a transaction with a merchant” —Tietzen at ¶¶ [0014] and [0013]; “recognizing patterns in the mined data, and using probabilities to predict the likelihood of success for a particular recommendation” and “predicts the likelihood that an offer having an incentive will be accepted by a registered customer by conducting a transaction with the registered merchant” —Tietzen at ¶¶ [0226] and [0015]; “goal… by the use of artificial intelligence is to increase purchases from merchants by incenting such purchases with offers” and “incentives… with conditional transactions with merchants (e.g. the purchase of a particular good… is required… to receive the special offer or prize). This encourages cardholders to conduct transactions with merchants” —Tietzen at ¶¶ [0226] and [0146]; “The deal would… fluctuate based on the number and type of similar products the cardholder has scanned, which are used to calculate the… likelihood of the cardholder buying the products, etc.” —Tietzen at ¶ [0232]; “incentivize them to make purchases. Any such incentives may be customized for…the particular merchant…. for example, incentives may be customized based on the customer's… transaction history, persona… and any preferences specified by… the merchant” —Tietzen at ¶ [0337]; “enable merchants to monitor, predict… 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” —Tietzen at ¶ [0081]; and Tietzen at ¶¶ [0091]–[0092], [0150], [0188], [0229], [0231], [0253], [0262], [0342], [0346]–[0347], [0358], [0491] and [0543]);
comparing the proposed propensity for each proposed loyalty program with a threshold propensity, wherein the threshold propensity is a minimum acceptable propensity for consumers to make transactions in any loyalty program (e.g., “predicts the likelihood that an offer having an incentive will be accepted by a registered customer by conducting a transaction with the registered merchant” and “artificial intelligence… used to engage in predictive modeling … in the optimization of incentives. For example,… neural networks, decision trees, CHAID, CART, fuzzy logic…” can be used to optimize incentives —Tietzen at ¶¶ [0015] and [0084]; “uses artificial intelligence to the benefit of and assistance to loyalty system 26 by way of automatically generating or modify operations, parameters, and outputs with respect to a goal, for example, maximizing or increasing merchant revenue or profitability, and automatically adapts the generation or modification operations, parameters, and outputs to feedback. As such,… loyalty system 26 with the functionalities of self-learning and self-adapting with respect to generating or modifying operations, parameters, and outputs” —Tietzen at ¶ [0115]; “alert…include a recommended incentive” and “recommended incentives and associated transactions are likely to be of interest to the targeted segment based on data mining and correlations of cardholder…attributes” and “merchant… able to sort the list of alerts that they have received by…parameter or attribute” and “attributes include… spending (total, average monthly, etc.)… number of transactions… transaction history… redeemed incentives… etc.” —Tietzen at ¶¶ [0086], [0147], [0679] and [0233]; “artificial intelligence engines assist loyalty system 26 in generating alerts that trigger [based on] a… threshold…” and “alert…include a recommended incentive” when, for example, “merchant's affinity score for a particular profile category… rises above… a pre-defined threshold” —Tietzen at ¶¶ [0116], [0086], [0321] and [0536]; “thresholds may be modified and selected to generate incentives for customers that fall meet the threshold” —Tietzen at ¶ [0899]; “thresholds for alert triggers” —Tietzen at ¶ [0536]; “recommended incentives based on data analysis, trends based on thresholds…” such as “an offer that is likely to incent… loyalty program members to conduct transactions with a merchant” —Tietzen at ¶¶ [0554] and [0014]; as well as Tietzen at ¶¶ [0091]–[0095], [0146], [0150], [0188], [0226], [0229], [0231], [0253], [0262], [0337], [0342], [0346]–[0348], [0358], [0491], [0536], [0543] and [0684]);
based on comparing the proposed propensity for each proposed loyalty program with the threshold propensity, determining a relative effectiveness of each proposed loyalty program (e.g., “predicts the likelihood that an offer having an incentive will be accepted by a registered customer by conducting a transaction with the registered merchant” and “artificial intelligence… used to engage in predictive modeling … in the optimization of incentives. For example,… neural networks, decision trees, CHAID, CART, fuzzy logic…” can be used to optimize incentives such as for “maximizing or increasing merchant revenue or profitability” —Tietzen at ¶¶ [0015], [0084] and [0115]; “uses artificial intelligence to the benefit of and assistance to loyalty system 26 by way of automatically generating or modify operations, parameters, and outputs with respect to a goal, for example, maximizing or increasing merchant revenue or profitability, and automatically adapts the generation or modification operations, parameters, and outputs to feedback. As such,… loyalty system 26 with the functionalities of self-learning and self-adapting with respect to generating or modifying operations, parameters, and outputs” —Tietzen at ¶ [0115]; “use artificial intelligence engines to provide a merchant interface for management of incentive programs, for review of incentive performance indicators, and for managing alerts…. provide dynamic and iterative incentive planning tools…to obtain decision support in building incentives, such as recommendations of incentives, alerts, target cardholders, and the associated transactions…. calibrate incentive attributes…. use artificial intelligence engines to provide incentive segmenting criteria and allow the user to modify the criteria and immediately and see a refresh of the various components of the ‘impact’ display segments” and “enable a merchant to modify incentive attributes and receive recommendations” —Tietzen at ¶¶ [0088]–[0089]; “artificial intelligence… identify incentive performance indicators… enable selection of attributes to filter the incentive performance indicators” —Tietzen at ¶ [0089]; “alert…include a recommended incentive” and “recommended incentives and associated transactions are likely to be of interest to the targeted segment based on data mining and correlations of cardholder…attributes” and “merchant… able to sort the list of alerts that they have received by…parameter or attribute” and “attributes include… spending (total, average monthly, etc.)… number of transactions… transaction history… redeemed incentives… etc.” —Tietzen at ¶¶ [0086], [0147], [0679] and [0233]; as well as Tietzen at ¶¶ [0091]–[0095], [0146], [0150], [0188], [0226], [0229], [0231], [0253], [0262], [0337], [0342], [0346]–[0348], [0358], [0491], [0536], [0543] and [0684]);
presenting each proposed loyalty program above the threshold propensity as a natural language icon in a graphical user interface (GUI) (e.g., “manage loyalty programs and incentives” —Tietzen at ¶ [0224]; “provide a merchant interface for management of incentive programs, for review of incentive performance indicators, and for managing alerts…. provide dynamic and iterative incentive planning tools… to obtain decision support in building incentives, such as recommendations of incentives, alerts, target cardholders, and the associated transactions…. calibrate incentive attributes…. use artificial intelligence engines to provide incentive segmenting criteria and allow the user to modify the criteria and immediately and see a refresh of the various components of the ‘impact’ display segments” and “enable a merchant to modify incentive attributes and receive recommendations” —Tietzen at ¶¶ [0088]–[0089]; “artificial intelligence… identify incentive performance indicators… enable selection of attributes to filter the incentive performance indicators” —Tietzen at ¶ [0089]; GUI of Figures 22A listing selectable alert icons and “alert…include a recommended incentive” and “recommended incentives and associated transactions are likely to be of interest to the targeted segment based on data mining and correlations of cardholder…attributes” and “merchant… able to sort the list of alerts that they have received by…parameter or attribute” and “attributes include… spending (total, average monthly, etc.)… number of transactions… transaction history… redeemed incentives… etc.” —Figure 22A of Tietzen and Tietzen at ¶¶ [0086], [0147], [0679] and [0233]; GUI of Figure 26 illustrating selectable alert icons and relative revenue performance of three loyalty reward incentives —Figure 26 of Tietzen; “user may select an example reward, such as 10% Off Any Bottle reward” and GUI illustrating selected alert icons and relative performance of four loyalty reward incentives — Tietzen at ¶ [0762] and Figure 27 of Tietzen; Figures 11A and 11B of Tietzen; “Increase sales by offering Rewards to the segment whose average purchase is less than others. Based on your objective to increase spend we recommend targeting customers whose average transaction amount is below the average ($50) to get them to increase their purchase amount. The graph illustrates this customer group” — Figure 11A of Tietzen; “FIG. 11A illustrates…a custom incentive with the object to increase spending” —Tietzen at ¶ [0029]; “TARGET CUSTOMER GROUPS WHO AREN’T SHOPPING AT YOUR STORE. Based on your objective to Bring in New Customers we recommend targeting the customer types (males aged…) that are not currently shopping at your store.” — Figure 11B of Tietzen; “FIG. 11B illustrates… a custom incentive with the object to bring in new customers to one or more location” —Tietzen at ¶ [0030]; “REWARD HIGH-SPENDING CUSTOMERS TO SHOW YOUR APPRECIATION. Based on your objective to Reward Spending we recommend targeting customers whose spend $51 or more to show your appreciation and build loyalty. The graph illustrates this customer group” — Figure 19 of Tietzen; as well as Tietzen at ¶¶ [0015], [0084], [0115] and [0550]); and
automatically moving the natural language icons to a list in the GUI in descending order of relative effectiveness (e.g., “sorts in descending order” —Tietzen at ¶ [0675]; “attributes to filter the incentive performance indicators” —Tietzen at ¶ [0089]; “The list is sorted…in descending order” and “…sort the list of alerts… by…parameter or attribute” and “attributes include… spending (total, average monthly, etc.)… number of transactions… transaction history… redeemed incentives… etc.” —Tietzen at ¶¶ [0747], [0086], [0147], [0679] and [0233]; “grouping of customers who share particular attributes” and “artificial intelligence engines to suggest a relevant incentive objective, and based on the objective may suggest or recommend a particular segment of customers or cardholders to target…. targeting that segment based on performance of that attribute” —Tietzen at ¶¶ [0253] and [0093]; “customer attributes based on … predicted data” such as “an offer that is likely to incent … loyalty program members to conduct transactions with a merchant” and/or “the likelihood that an offer having an incentive will be accepted by a registered customer by conducting a transaction with the registered merchant” —Tietzen at ¶¶ [0896], [0014] and [0015]; “using probabilities to predict the likelihood of success for a particular recommendation” —Tietzen at ¶¶ [0226]; “predictive modeling or data mining in the optimization of incentives” —Tietzen at ¶¶ [0084]; Figure 22A of Tietzen; and Tietzen at ¶¶ [0015], [0084], [0115] and [0550]), but Tietzen fails to explicitly disclose the artificial intelligence including a generative pre-trained transformer (GPT) and the recurrently weighting including recurrently weighting the GPT using reinforced learning from human feedback (RLHF). However, Galynsky teaches: artificial intelligence including a generative pre-trained transformer (GPT), such as ChatGPT which is an AI chatbot developed by OpenAI that launched in 2022 (e.g., Galynsky at ¶ [0016]); that ChatGPT is a member of the generative pre-trained transformer (GPT) family of language models, and that ChatGPT is based on GPT-3.5 and GPT-4 families of LLMs (e.g., Galynsky at ¶ [0017]); and “Jasper is an AI virtual assistant and copilot assistant … to help produce marketing content with GPT-3.5” (Galynsky at ¶ [0038]); “ChatGPT is basically an AI-powered chatbot… it's a natural language processing tool, powered by Artificial Intelligence, which enables users to have a human-like conversation” (Galynsky at ¶ [0053]); “the chatbot is trained to automatically provide… customized responses to requests or prompts: … 4) create targeted advertisements” (Galynsky at ¶ [0069]); and “Trainers of ChatGPT deploy Reinforcement Learning via Human Feedback (RLHF), in which, actual human responses and feedback are induced in the training loop…. ChatGPT is able to produce human-like conversations with users” (Galynsky at ¶ [0056]). Therefore, it would have been obvious to one skilled in the art, before the effective filing date of the claimed invention, to incorporate the artificial intelligence including a generative pre-trained transformer (GPT) and the recurrently weighting including recurrently weighting the GPT using reinforced learning from human feedback (RLHF), as taught by Galynsky, into the method/system disclosed by Tietzen, which is directed toward making intelligent recommendations based on data utilizing artificial intelligence, predictive modeling and automatic learning (e.g., Tietzen at ¶¶ [0013]–[0015], [0084], [0091]–[0095], [0114]–[0116] and [0358]), because such incorporation would be applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (see MPEP § 2143).
Claims 9-14 recite substantially similar subject matter to that of respective Claims 2-7 and, therefore, Claims 9-14 are rejected on the same basis(es) as Claims 2-7, respectively.
Regarding Claim 15, Tietzen discloses a computer storage device having computer-executable instructions stored thereon, which, upon execution by a computer, cause the computer to perform operations comprising: retrieving from a database of historical transactions, loyalty data comprising loyalty program activity data describing attributes of loyalty programs and loyalty consumer activity data describing consumer transactions associated with a loyalty program (e.g., “merchants… provide benefits, incentives, and rewards to cardholders in… a loyalty program” and “ ‘merchant’… participates in a loyalty program to build loyalty with customers, and potentially acquire new business, and in exchange is willing to provide a loyalty ‘benefit’ ” —Tietzen at ¶¶ [0081] and [0003]; “loyalty system 26… employ data mining… to use cardholder transaction data and analytics along with historical product barcode scan data of the cardholder to generate an attractive offer for the cardholder. Each such attractive offer… generated by a recommendation engine…. and will…. recommend incentives for merchants…. loyalty system 26 to… operate an artificial conversational entity to conduct conversations… simulating how a human would behave as a conversational partner… to provide… information acquisition for merchants” and “Loyalty system 26 may include a merchant interface 52…. for merchant system 40 to create, customize, and manage loyalty programs and incentives” —Tietzen at ¶¶ [0232] and [0224]; “Customer profiler 602 classifies customers according to… customer profile categories… referred to as “personas”. Each profile category (or persona) defines a grouping of customers who share particular attributes such as behavioural and/or motivation attributes. Customer profile 602 analyzes data for each customer to determine the customer's attributes and to classify the customer into… profile categories. This data may include the cardholder data and transaction data discussed above…. Recommendation engine 60 may recommend incentives targeting customers classified into a particular profile categories” —Tietzen at ¶¶ [0253]; as well as Tietzen at ¶¶ [0091]–[0092], [0229], [0253], [0262], [0342] and [0346]);
anonymizing the loyalty data, wherein any individual identifying information is masked and wherein the loyalty consumer activity data is aggregated (e.g., “aggregated transactions” —Tietzen at ¶ [0098]; “aggregate the transaction information, and supply… results to… the merchant” —Tietzen at ¶ [0150]; and Tietzen at ¶¶ [0150], [0188], [0231], [0253], [0346]–[0347], [0491] and [0543]);
encoding the anonymized loyalty data into representations for transformation by artificial intelligence (e.g., “an artificial intelligence engine upon transaction data… to predict an offer that is likely to incent… loyalty program members to conduct transactions with a merchant” and “using an artificial intelligence engine to predict an offer that is likely to incent a loyalty program member to conduct a transaction with a merchant” —Tietzen at ¶¶ [0014] and [0013]; “artificial intelligence engine… solve problems normally done by humans, albeit with natural intelligence, by way of data mining, recognizing patterns in the mined data, and using probabilities to predict the likelihood of success for a particular recommendation” —Tietzen at ¶ [0226]; “recommendation engine… implements a conventional artificial neural network or fuzzy logic to determine when the criteria of rules are met” —Tietzen at ¶ [0239]; as well as Tietzen at ¶¶ [0084], [0114]–[0116], [0148], [0150], [0188], [0231], [0253], [0346]–[0347], [0358], [0491] and [0543]);
decoding the representations from the artificial intelligence into a plurality of proposed loyalty programs (e.g., “artificial intelligence… upon transaction data… to predict an offer that is likely to incent… loyalty program members to conduct transactions with a merchant” and “using an artificial intelligence engine to predict an offer that is likely to incent a loyalty program member to conduct a transaction with a merchant” —Tietzen at ¶¶ [0014] and [0013]; “recognizing patterns in the mined data, and using probabilities to predict the likelihood of success for a particular recommendation” and “predicts the likelihood that an offer having an incentive will be accepted by a registered customer by conducting a transaction with the registered merchant” —Tietzen at ¶¶ [0226] and [0015]; “goal… by the use of artificial intelligence is to increase purchases from merchants by incenting such purchases with offers” and “incentives… with conditional transactions with merchants (e.g. the purchase of a particular good… is required… to receive the special offer or prize). This encourages cardholders to conduct transactions with merchants” —Tietzen at ¶¶ [0226] and [0146]; “The deal would… fluctuate based on the number and type of similar products the cardholder has scanned, which are used to calculate the… likelihood of the cardholder buying the products, etc.” —Tietzen at ¶ [0232]; “incentivize them to make purchases. Any such incentives may be customized for…the particular merchant…. for example, incentives may be customized based on the customer's… transaction history, persona… and any preferences specified by… the merchant” —Tietzen at ¶ [0337]; “enable merchants to monitor, predict… 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” —Tietzen at ¶ [0081]; and Tietzen at ¶¶ [0091]–[0092], [0150], [0188], [0229], [0231], [0253], [0262], [0342], [0346]–[0347], [0358], [0491] and [0543]);
determining a proposed propensity for each proposed loyalty program based on a spend impact analysis and consumer response modeling, wherein the proposed propensity is a likelihood for consumers to make transactions for a given loyalty program (e.g., “artificial intelligence… upon transaction data… to predict an offer that is likely to incent… loyalty program members to conduct transactions with a merchant” and “using an artificial intelligence engine to predict an offer that is likely to incent a loyalty program member to conduct a transaction with a merchant” —Tietzen at ¶¶ [0014] and [0013]; “recognizing patterns in the mined data, and using probabilities to predict the likelihood of success for a particular recommendation” and “predicts the likelihood that an offer having an incentive will be accepted by a registered customer by conducting a transaction with the registered merchant” —Tietzen at ¶¶ [0226] and [0015]; “goal… by the use of artificial intelligence is to increase purchases from merchants by incenting such purchases with offers” and “incentives… with conditional transactions with merchants (e.g. the purchase of a particular good… is required… to receive the special offer or prize). This encourages cardholders to conduct transactions with merchants” —Tietzen at ¶¶ [0226] and [0146]; “The deal would… fluctuate based on the number and type of similar products the cardholder has scanned, which are used to calculate the… likelihood of the cardholder buying the products, etc.” —Tietzen at ¶ [0232]; “incentivize them to make purchases. Any such incentives may be customized for…the particular merchant…. for example, incentives may be customized based on the customer's… transaction history, persona… and any preferences specified by… the merchant” —Tietzen at ¶ [0337]; “enable merchants to monitor, predict… 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” —Tietzen at ¶ [0081]; and Tietzen at ¶¶ [0091]–[0092], [0150], [0188], [0229], [0231], [0253], [0262], [0342], [0346]–[0347], [0358], [0491] and [0543]);
comparing the proposed propensity for each proposed loyalty program with a threshold propensity, wherein the threshold propensity is a minimum acceptable propensity for consumers to make transactions in any loyalty program (e.g., “predicts the likelihood that an offer having an incentive will be accepted by a registered customer by conducting a transaction with the registered merchant” and “artificial intelligence… used to engage in predictive modeling … in the optimization of incentives. For example,… neural networks, decision trees, CHAID, CART, fuzzy logic…” can be used to optimize incentives —Tietzen at ¶¶ [0015] and [0084]; “uses artificial intelligence to the benefit of and assistance to loyalty system 26 by way of automatically generating or modify operations, parameters, and outputs with respect to a goal, for example, maximizing or increasing merchant revenue or profitability, and automatically adapts the generation or modification operations, parameters, and outputs to feedback. As such,… loyalty system 26 with the functionalities of self-learning and self-adapting with respect to generating or modifying operations, parameters, and outputs” —Tietzen at ¶ [0115]; “alert…include a recommended incentive” and “recommended incentives and associated transactions are likely to be of interest to the targeted segment based on data mining and correlations of cardholder…attributes” and “merchant… able to sort the list of alerts that they have received by…parameter or attribute” and “attributes include… spending (total, average monthly, etc.)… number of transactions… transaction history… redeemed incentives… etc.” —Tietzen at ¶¶ [0086], [0147], [0679] and [0233]; “artificial intelligence engines assist loyalty system 26 in generating alerts that trigger [based on] a… threshold…” and “alert…include a recommended incentive” when, for example, “merchant's affinity score for a particular profile category… rises above… a pre-defined threshold” —Tietzen at ¶¶ [0116], [0086], [0321] and [0536]; “thresholds may be modified and selected to generate incentives for customers that fall meet the threshold” —Tietzen at ¶ [0899]; “thresholds for alert triggers” —Tietzen at ¶ [0536]; “recommended incentives based on data analysis, trends based on thresholds…” such as “an offer that is likely to incent… loyalty program members to conduct transactions with a merchant” —Tietzen at ¶¶ [0554] and [0014]; as well as Tietzen at ¶¶ [0091]–[0095], [0146], [0150], [0188], [0226], [0229], [0231], [0253], [0262], [0337], [0342], [0346]–[0348], [0358], [0491], [0536], [0543] and [0684]);
based on comparing the proposed propensity for each proposed loyalty program with the threshold propensity, determining a relative effectiveness of each proposed loyalty program (e.g., “predicts the likelihood that an offer having an incentive will be accepted by a registered customer by conducting a transaction with the registered merchant” and “artificial intelligence… used to engage in predictive modeling … in the optimization of incentives. For example,… neural networks, decision trees, CHAID, CART, fuzzy logic…” can be used to optimize incentives such as for “maximizing or increasing merchant revenue or profitability” —Tietzen at ¶¶ [0015], [0084] and [0115]; “uses artificial intelligence to the benefit of and assistance to loyalty system 26 by way of automatically generating or modify operations, parameters, and outputs with respect to a goal, for example, maximizing or increasing merchant revenue or profitability, and automatically adapts the generation or modification operations, parameters, and outputs to feedback. As such,… loyalty system 26 with the functionalities of self-learning and self-adapting with respect to generating or modifying operations, parameters, and outputs” —Tietzen at ¶ [0115]; “use artificial intelligence engines to provide a merchant interface for management of incentive programs, for review of incentive performance indicators, and for managing alerts…. provide dynamic and iterative incentive planning tools…to obtain decision support in building incentives, such as recommendations of incentives, alerts, target cardholders, and the associated transactions…. calibrate incentive attributes…. use artificial intelligence engines to provide incentive segmenting criteria and allow the user to modify the criteria and immediately and see a refresh of the various components of the ‘impact’ display segments” and “enable a merchant to modify incentive attributes and receive recommendations” —Tietzen at ¶¶ [0088]–[0089]; “artificial intelligence… identify incentive performance indicators… enable selection of attributes to filter the incentive performance indicators” —Tietzen at ¶ [0089]; “alert…include a recommended incentive” and “recommended incentives and associated transactions are likely to be of interest to the targeted segment based on data mining and correlations of cardholder…attributes” and “merchant… able to sort the list of alerts that they have received by…parameter or attribute” and “attributes include… spending (total, average monthly, etc.)… number of transactions… transaction history… redeemed incentives… etc.” —Tietzen at ¶¶ [0086], [0147], [0679] and [0233]; as well as Tietzen at ¶¶ [0091]–[0095], [0146], [0150], [0188], [0226], [0229], [0231], [0253], [0262], [0337], [0342], [0346]–[0348], [0358], [0491], [0536], [0543] and [0684]);
presenting each proposed loyalty program above the threshold propensity as a natural language icon in a graphical user interface (GUI) (e.g., “manage loyalty programs and incentives” —Tietzen at ¶ [0224]; “provide a merchant interface for management of incentive programs, for review of incentive performance indicators, and for managing alerts…. provide dynamic and iterative incentive planning tools… to obtain decision support in building incentives, such as recommendations of incentives, alerts, target cardholders, and the associated transactions…. calibrate incentive attributes…. use artificial intelligence engines to provide incentive segmenting criteria and allow the user to modify the criteria and immediately and see a refresh of the various components of the ‘impact’ display segments” and “enable a merchant to modify incentive attributes and receive recommendations” —Tietzen at ¶¶ [0088]–[0089]; “artificial intelligence… identify incentive performance indicators… enable selection of attributes to filter the incentive performance indicators” —Tietzen at ¶ [0089]; GUI of Figures 22A listing selectable alert icons and “alert…include a recommended incentive” and “recommended incentives and associated transactions are likely to be of interest to the targeted segment based on data mining and correlations of cardholder…attributes” and “merchant… able to sort the list of alerts that they have received by…parameter or attribute” and “attributes include… spending (total, average monthly, etc.)… number of transactions… transaction history… redeemed incentives… etc.” —Figure 22A of Tietzen and Tietzen at ¶¶ [0086], [0147], [0679] and [0233]; GUI of Figure 26 illustrating selectable alert icons and relative revenue performance of three loyalty reward incentives —Figure 26 of Tietzen; “user may select an example reward, such as 10% Off Any Bottle reward” and GUI illustrating selected alert icons and relative performance of four loyalty reward incentives — Tietzen at ¶ [0762] and Figure 27 of Tietzen; Figures 11A and 11B of Tietzen; “Increase sales by offering Rewards to the segment whose average purchase is less than others. Based on your objective to increase spend we recommend targeting customers whose average transaction amount is below the average ($50) to get them to increase their purchase amount. The graph illustrates this customer group” — Figure 11A of Tietzen; “FIG. 11A illustrates…a custom incentive with the object to increase spending” —Tietzen at ¶ [0029]; “TARGET CUSTOMER GROUPS WHO AREN’T SHOPPING AT YOUR STORE. Based on your objective to Bring in New Customers we recommend targeting the customer types (males aged…) that are not currently shopping at your store.” — Figure 11B of Tietzen; “FIG. 11B illustrates… a custom incentive with the object to bring in new customers to one or more location” —Tietzen at ¶ [0030]; “REWARD HIGH-SPENDING CUSTOMERS TO SHOW YOUR APPRECIATION. Based on your objective to Reward Spending we recommend targeting customers whose spend $51 or more to show your appreciation and build loyalty. The graph illustrates this customer group” — Figure 19 of Tietzen; as well as Tietzen at ¶¶ [0015], [0084], [0115] and [0550]); and
automatically moving the natural language icons to a list in the GUI in descending order of relative effectiveness (e.g., “sorts in descending order” —Tietzen at ¶ [0675]; “attributes to filter the incentive performance indicators” —Tietzen at ¶ [0089]; “The list is sorted…in descending order” and “…sort the list of alerts… by…parameter or attribute” and “attributes include… spending (total, average monthly, etc.)… number of transactions… transaction history… redeemed incentives… etc.” —Tietzen at ¶¶ [0747], [0086], [0147], [0679] and [0233]; “grouping of customers who share particular attributes” and “artificial intelligence engines to suggest a relevant incentive objective, and based on the objective may suggest or recommend a particular segment of customers or cardholders to target…. targeting that segment based on performance of that attribute” —Tietzen at ¶¶ [0253] and [0093]; “customer attributes based on … predicted data” such as “an offer that is likely to incent … loyalty program members to conduct transactions with a merchant” and/or “the likelihood that an offer having an incentive will be accepted by a registered customer by conducting a transaction with the registered merchant” —Tietzen at ¶¶ [0896], [0014] and [0015]; “using probabilities to predict the likelihood of success for a particular recommendation” —Tietzen at ¶¶ [0226]; “predictive modeling or data mining in the optimization of incentives” —Tietzen at ¶¶ [0084]; Figure 22A of Tietzen; and Tietzen at ¶¶ [0015], [0084], [0115] and [0550]), but Tietzen fails to explicitly disclose the artificial intelligence including a generative pre-trained transformer (GPT). However, Galynsky teaches: artificial intelligence including a generative pre-trained transformer (GPT), such as ChatGPT which is an AI chatbot developed by OpenAI that launched in 2022 (e.g., Galynsky at ¶ [0016]); that ChatGPT is a member of the generative pre-trained transformer (GPT) family of language models, and that ChatGPT is based on GPT-3.5 and GPT-4 families of LLMs (e.g., Galynsky at ¶ [0017]); and “Jasper is an AI virtual assistant and copilot assistant … to help produce marketing content with GPT-3.5” (Galynsky at ¶ [0038]); “ChatGPT is basically an AI-powered chatbot… it's a natural language processing tool, powered by Artificial Intelligence, which enables users to have a human-like conversation” (Galynsky at ¶ [0053]); “the chatbot is trained to automatically provide… customized responses to requests or prompts: … 4) create targeted advertisements” (Galynsky at ¶ [0069]); and “Trainers of ChatGPT deploy Reinforcement Learning via Human Feedback (RLHF), in which, actual human responses and feedback are induced in the training loop…. ChatGPT is able to produce human-like conversations with users” (Galynsky at ¶ [0056]). Therefore, it would have been obvious to one skilled in the art, before the effective filing date of the claimed invention, to incorporate the artificial intelligence including a generative pre-trained transformer (GPT), as taught by Galynsky, into the method/system disclosed by Tietzen, which is directed toward making intelligent recommendations based on data utilizing artificial intelligence, predictive modeling and automatic learning (e.g., Tietzen at ¶¶ [0013]–[0015], [0084], [0091]–[0095], [0114]–[0116] and [0358]), because such incorporation would be applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (see MPEP § 2143).
Claims 16-19 recite substantially similar subject matter to that of respective Claims 2-5 and, therefore, Claims 16-19 are rejected on the same basis(es) as Claims 2-5, respectively.
Claim 20 recites substantially similar subject matter to that of Claim 7 and, therefore, Claim 20 is rejected on the same basis(es) as Claim 7.
Conclusion
The following references are considered pertinent to Applicant's disclosure, and are being made of record albeit the references are not being relied upon as a basis for rejection in this Office action:
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U.S. Patent Application Publication No. 2026/0080432 of Miao et al. (“Miao”).
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/Mathew Syrowik/ Primary Examiner, Art Unit 3621
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