DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The Information Disclosure Statement filed 10/07/2025 was considered. An initialed copy of the Form PTO-1449 is enclosed herewith.
Acknowledgements
This Office Action addresses the response filed on 11/25/2025.
Claims 13-16 and 19-25 were amended.
Claims 1-12 and 26-48 were withdrawn.
Claims 13-25 are pending.
Claims 13-25 were examined.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 13-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The independent claims recite:
(a) training a machine learning model to predict a subset of benefits to represent to a user, and using the machine learning model to predict which one or more benefits, of a plurality of benefits associated with use of a tangible object/physical card and attributed to an account of the user, to include for representation as part of a design for depiction on the tangible object/physical card; and(b) generating and printing the design for depiction on the tangible object/physical card, the design including representations of the one or more benefits
Analysis
Under the broadest reasonable interpretation, the terms of the claim are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP 2111. to MPEP 2106 II, It is essential that the broadest reasonable interpretation (BRI) of the claim be established prior to examining a claim for eligibility. Further, MPEP 2103 I C establishes that the subject matter of a properly construed claim is defined by the terms that limit the scope of the claim when given their broadest reasonable interpretation. It is this subject matter that must be examined. Regarding the independent claims, claims 13 and 21 recite “ the design including representations of the one or more benefits”, language directed to non-functional descriptive material. See MPEP 2111.05. In addition, claims 13 and 21 recite “training a machine learning model to predict a subset of benefits to represent to a user”; “using the machine learning model to predict which one or more benefits, of a plurality of benefits associated with use of a tangible object and attributed to an account of the user, to include for representation as part of a design for depiction on the tangible object...” , statements of intended use or field use. See MPEP 2114 II.
Step (a) recites training a machine learning model. The term is recognized as having its plain meaning of training data. The claim does not impose any limits on “training a machine learning model”. Step (b) recites generating and printing a design. The claim does not impose any limits on “generating and printing a design”. Steps (a) and (b) are all recited as being performed by a computing system. The recited computing system is recited at a high level of generality, i.e., as a generic computer performing generic computer functions. Based on the plain meaning of the words in the claim, the broadest reasonable interpretation encompasses training a machine learning model and generating and printing a design. The claimed training step encompasses performing mathematical calculations.
Step 1:
This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claims recite at least one step or act, including training a machine learning model. Further, claims 13-20 are directed to a system, and claims 21-25 are directed to a method. Therefore, these claims fall within the four statutory categories of invention. (Step 1: YES).
Step 2A, Prong One:
This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. As discussed above, the broadest reasonable interpretation of step (a) falls within the mathematical concepts grouping of abstract ideas.Specifically, Step (a) recites training a machine learning model to predict a subset of benefits to represent to a user, and using the machine learning model to predict which one or more benefits, of a plurality of benefits associated with use of a tangible object/physical card and attributed to an account of the user, to include for representation as part of a design for depiction on the tangible object/physical card, encompassing mathematical concepts.
Step 2A, Prong Two:
This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). The claims recite the additional elements of “generating and printing the design for depiction on the tangible object/physical card, the design including representations of the one or more benefits” in limitation (b) and “using the machine learning model” in limitation (a). The claims also recite that steps (a) and (b) are performed by a computing system, at least in system claim 13. The Limitation “(b) generating and printing the design for depiction on the tangible object/physical card, the design including representations of the one or more benefits” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”).In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
Further, limitations (a) and (b) are recited as being performed by a computer. The computer is recited at a high level of generality. In limitation (a), the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). In limitation (b), the computer is used as a tool to perform the generic computer function. See MPEP 2106.05(f). The limitation in (a) reciting “using the machine learning model” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f).
MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
The judicial exception of “training a machine learning model to predict a subset of benefits to represent to a user, and using the machine learning model to predict which one or more benefits, of a plurality of benefits associated with use of a tangible object/physical card and attributed to an account of the user, to include for representation as part of a design for depiction on the tangible object/physical card” includes “using the machine learning model”.
The machine learning model is used to generally apply the abstract idea without placing any limits on how the machine learning model functions. Rather, these limitations only recite the outcome of “training a machine learning model to predict a subset of benefits to represent to a user, and using the machine learning model to predict which one or more benefits, of a plurality of benefits associated with use of a tangible object/physical card and attributed to an account of the user, to include for representation as part of a design for depiction on the tangible object/physical card” and do not include any details about how the “training” is accomplished. See MPEP 2106.05(f).
The recitation of “using the machine learning model” in limitation (a) also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using the machine learning model” limits the identified judicial exceptions “training a machine learning model to predict a subset of benefits to represent to a user, and using the machine learning model to predict which one or more benefits, of a plurality of benefits associated with use of a tangible object/physical card and attributed to an account of the user, to include for representation as part of a design for depiction on the tangible object/physical card” this type of limitation merely confines the use of the abstract idea to a particular technological environment and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception.
Step 2B:
This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. As explained with respect to Step 2A, Prong Two, there are four additional elements. The additional element of “using the machine learning model” in limitation (a), are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).Additional element “(b) generating and printing the design for depiction on the tangible object/physical card, the design including representations of the one or more benefits” was found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and outputting. However, a conclusion that an additional element is insignificant extra-solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A.
At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). As discussed in Step 2A, Prong Two above, the recitations of “(b) generating and printing the design for depiction on the tangible object/physical card, the design including representations of the one or more benefits” is recited at a high level of generality. This element amounts to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. As discussed in Step 2A, Prong Two above, the recitation of a computing system to perform limitations (a) and (b) amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO). The claims are ineligible.
Examiner notes that, for elements recited in the dependent claims which were previously analyzed as additional elements of the independent claims above (i.e. ), the assessment of these elements under step 2A and step 2B for the dependent claims is inherited from the analysis of the independent claims and omitted for brevity, unless noted by Examiner below. Dependent claims 12-20 and 22-25 further recite the following additional language, in which elements which merely further define the identified abstract idea are recited below:
c) wherein the tangible object is a physical card and the printing prints the representations of the one or more benefits on a surface of the physical card. d) wherein the printing prints the design on a removable indicator and the tangible object is a physical card, and wherein the removable indicator is configured as a sticker for adherence to a surface of the physical card and the representations of the one or more benefits are depicted on a surface of the removable indicator. e) wherein the executable code, when executed, further causes the processor to establish an automated protocol to reinitiate the generating and printing the design on the removable indicator according to a predefined schedule. f) wherein the predefined schedule is based on a predicted longevity of the removable indicator. g) wherein the predefined schedule is based on periodic changes to the benefits. ; h) wherein the executable code, when executed, further causes the processor to terminate the automated protocol in response to a request from a user device. i) wherein the executable code, when executed, further causes the processor to receive a request to reinitiate the using the machine learning model and the generating and printing the design, wherein the reinitiating the using the machine learning model results in an updated prediction by the machine learning model that produces a change in the one or more benefits to include for representation for depiction on the tangible object, and the reinitiating the generating and printing the design generates and prints an updated design that includes representations of the changed one or more benefits. j) wherein the printing prints the representations of the one or more benefits on a surface of the physical card and the method further includes establishing an automated protocol that reinitiates the generating and printing the design when an updated prediction by the machine learning model produces a change in the one or more benefits to include for representation for depiction on the physical card, and the reinitiated generating and printing generates and prints an updated design that includes representations of the changed one or more benefits. k) wherein the printing prints the design on a removable indicator configured as a sticker for adherence to a surface of the physical card. l) further comprising establishing an automated protocol to reinitiate the using the machine learning model and the generating and printing the design.
With respect to claim 14, the claim includes language c), which do not introduce additional elements/functions. The additional language merely represents statements directed to directed to non-functional descriptive material by describing what the tangible object is and the printing prints. Those statements are insufficient to significantly alter the eligibility analysis. This language further elaborates the abstract idea identified in the analysis of independent claims 13 and 21. The additional elements/functions, alone or in combination, are insufficient to integrate the abstract idea into a practical application because the additional elements/functions do not pertain to an improvement to the functioning of a computer or to another technology. The additional elements/functions, alone or in combination, do not offer significantly more than the abstract idea, because the additional elements/functions merely further recite additional instructions to implement the abstract idea on a computer.
With respect to claim 15, the claim includes language d), which do not introduce additional elements/functions. The additional language merely represents statements directed to directed to non-functional descriptive material by describing what the printing prints and what the indicator is. Those statements are insufficient to significantly alter the eligibility analysis. This language further elaborates the abstract idea identified in the analysis of independent claims 13 and 21. The additional elements/functions, alone or in combination, are insufficient to integrate the abstract idea into a practical application because the additional elements/functions do not pertain to an improvement to the functioning of a computer or to another technology. The additional elements/functions, alone or in combination, do not offer significantly more than the abstract idea, because the additional elements/functions merely further recite additional instructions to implement the abstract idea on a computer.
With respect to the eligibility analysis of claim 16, the claim recites item e) above, which represents the additional elements/functions of reinitiate generating and printing. This language further elaborates the abstract idea identified in the analysis of independent claims 13 and 21. The additional elements/functions, alone or in combination, are insufficient to integrate the abstract idea into a practical application because the additional elements/functions do not pertain to an improvement to the functioning of a computer or to another technology. The additional elements/functions, alone or in combination, do not offer significantly more than the abstract idea, because the additional elements/functions merely further recite additional instructions to implement the abstract idea on a computer.
With respect to claim 17, the claim includes language f), which do not introduce additional elements/functions. The additional language merely represents statements directed to directed to non-functional descriptive material by describing what the schedule is based on. Those statements are insufficient to significantly alter the eligibility analysis. This language further elaborates the abstract idea identified in the analysis of independent claims 13 and 21. The additional elements/functions, alone or in combination, are insufficient to integrate the abstract idea into a practical application because the additional elements/functions do not pertain to an improvement to the functioning of a computer or to another technology. The additional elements/functions, alone or in combination, do not offer significantly more than the abstract idea, because the additional elements/functions merely further recite additional instructions to implement the abstract idea on a computer.
With respect to claim 18, the claim includes language g), which do not introduce additional elements/functions. The additional language merely represents statements directed to directed to non-functional descriptive material by describing what the schedule is based on. Those statements are insufficient to significantly alter the eligibility analysis. This language further elaborates the abstract idea identified in the analysis of independent claims 13 and 21. The additional elements/functions, alone or in combination, are insufficient to integrate the abstract idea into a practical application because the additional elements/functions do not pertain to an improvement to the functioning of a computer or to another technology. The additional elements/functions, alone or in combination, do not offer significantly more than the abstract idea, because the additional elements/functions merely further recite additional instructions to implement the abstract idea on a computer.
With respect to the eligibility analysis of claim 19, the claim recites item h) above, which represents the additional elements/functions of terminate a protocol. This language further elaborates the abstract idea identified in the analysis of independent claims 13 and 21. The additional elements/functions, alone or in combination, are insufficient to integrate the abstract idea into a practical application because the additional elements/functions do not pertain to an improvement to the functioning of a computer or to another technology. The additional elements/functions, alone or in combination, do not offer significantly more than the abstract idea, because the additional elements/functions merely further recite additional instructions to implement the abstract idea on a computer.
With respect to claims 20 and 25, the claims include language directed to intended use by describing the reinitiating the using the machine learning model "results" in. Those statements are insufficient to significantly alter the eligibility analysis. Further, the claims recite item i) above, which represents the additional elements/functions of receiving a request. This language further elaborates the abstract idea identified in the analysis of independent claims 13 and 21. The additional elements/functions, alone or in combination, are insufficient to integrate the abstract idea into a practical application because the additional elements/functions do not pertain to an improvement to the functioning of a computer or to another technology. The additional elements/functions, alone or in combination, do not offer significantly more than the abstract idea, because the additional elements/functions merely further recite additional instructions to implement the abstract idea on a computer.
Examiner notes that claim 22 recites “ establishing an automated protocol that reinitiates the generating and printing the design when an updated prediction by the machine learning model produces a change in the one or more benefits…”, language directed to contingent limitations. The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met. The broadest reasonable interpretation of a system (or apparatus or product) claim having structure that performs a function, which only needs to occur if a condition precedent is met, requires structure for performing the function should the condition occur. See Ex parte Schulhauser, Appeal 2013-007847 (PTAB April 28, 2016) (precedential) for an analysis of contingent claim limitations in the context of both method claims and system claims. See also MPEP 2111.04. With respect to the eligibility analysis of claim 22, This language further elaborates the abstract idea identified in the analysis of independent claims 13 and 21. The additional elements/functions, alone or in combination, are insufficient to integrate the abstract idea into a practical application because the additional elements/functions do not pertain to an improvement to the functioning of a computer or to another technology. The additional elements/functions, alone or in combination, do not offer significantly more than the abstract idea, because the additional elements/functions merely further recite additional instructions to implement the abstract idea on a computer.
With respect to the eligibility analysis of claim 23, This language further elaborates the abstract idea identified in the analysis of independent claims 13 and 21. The additional elements/functions, alone or in combination, are insufficient to integrate the abstract idea into a practical application because the additional elements/functions do not pertain to an improvement to the functioning of a computer or to another technology. The additional elements/functions, alone or in combination, do not offer significantly more than the abstract idea, because the additional elements/functions merely further recite additional instructions to implement the abstract idea on a computer.
With respect to the eligibility analysis of claim 24, the claim recites item l) above, which represents the additional elements/functions of establishing a protocol. This language further elaborates the abstract idea identified in the analysis of independent claims 13 and 21. The additional elements/functions, alone or in combination, are insufficient to integrate the abstract idea into a practical application because the additional elements/functions do not pertain to an improvement to the functioning of a computer or to another technology. The additional elements/functions, alone or in combination, do not offer significantly more than the abstract idea, because the additional elements/functions merely further recite additional instructions to implement the abstract idea on a computer.
Therefore, while the additional language c)-l) of dependent claims 14-20 and 22-25 slightly modify the analysis provided with respect to independent claims 13 and 21, these additional elements/functions are insufficient to render the dependent claims eligible, as detailed above. Therefore, these dependent claims are also ineligible.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 20 and 25 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 20 and 25 recite the language “the reinitiating the using the machine learning model” in lines 5 and 7; 4 and 7. There is insufficient antecedent basis for this language in the claims.
Claim 25 recites “the tangible object” in line 6. There is insufficient antecedent basis for this language in the claim.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 13-18 and 20-25 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Mupkala et al. (US 2021/0065279 A1), hereinafter Mupkala, in view of Blank et al. (US 5,531,482 A), hereinafter Blank.
With respect to claims 13 and 21, Mupkala teaches a computing system for tangible object design generation, comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and a memory device; and a method (Systems and methods for recommending personalized rewards based on customer profiles and customer preferences) comprising:
training a machine learning model to predict a subset of benefits to represent to a user, and using the machine learning model to predict which one or more benefits, of a plurality of benefits associated with use of a tangible object and attributed to an account of the user, to include for representation as part of a design... (see paragraph [0024]: “A recommendation model, communicatively coupled to or included within service provider system 104, is constructed in advance using the customer specific information stored in database 106. System 100 may use one or more of a machine learning process or neural networks to construct the recommendation model to recommend one or more type of redeemable reward categories to customer 114(1) and to further recommend personalized rewards. System 100 may also have a machine learning algorithm incorporated such that the recommendation model may be updated each time customer 114(1) makes a purchase or financial transaction using a credit card provided by service provider system 104. The customer specific information is used to iteratively train and update the recommendation model to recommend the personalized rewards to first customer 114(1).”; paragraph [0072]: “Service provider system 104 applies a machine learning algorithm to determine personalized rewards associated with the identified redeemable reward categories for first customer 114(1) (608)..."); and
generating and [outputting] the design including representations of the one or more benefits (see paragraph [0072]: “... Further, service provider system 104 generates a personalized reward that includes division of reward percentages in the corresponding redeemable reward categories that have been previously defined by the first customer 114(1). Service provider system 104 then transmits the personalized rewards to first customer 114(1). In this example, the personalized reward includes 50% of reward percentage for travel rewards and 50% for Grocery rewards (610).”).
Mupkala does not explicitly disclose a system and method comprising: [outputting the design] is printing the design for depiction on the tangible object.
However, Blank discloses a system and method (Card with removable reusable element) comprising:
[outputting the design] is printing the design for depiction on the tangible object (see col. 6, line 60 to col. 7, line 22: “Referring to FIGS. 3 and 4a-4b, there is illustrated a promotional card and label set suitable for use as a promotional vehicle. In FIG. 3, the label 18 is printed with indicia 19 representative of a redeemable coupon. In FIG. 3, the redeemable coupon label 18 is affixed to the first planar side 12 of the transaction card 10. Alternatively, referring to FIG. 4b, the redeemable coupon label 18 can be affixed to the second major planar side 14 of the transaction card 10. Although it is shown that the redeemable coupon is affixed to either side of the promotional card, it is contemplated by the present invention that a number of such coupon labels may be used and affixed to either or both sides of the promotional card. For example in FIG. 4b, there is illustrated a second coupon label 18' attached to the second planar side of the promotional card. Examples of promotional uses for which the promotional card and label set can be used include frequent flyer membership cards and associated promotions, video membership cards and associated promotions, preferred customer cards and associated promotions, credit cards and associated promotions, and the like. In addition, the removable and reusable label can also be used as an enhancement to existing transactional or promotional cards by affixing a coupon label to a card for later redemption. Therefore, the promotional card and coupon set can be supplied to potential customers to replace existing separately issued coupons which are sent to customers, for example as is seen in the airline industry with frequency flyer upgrades, car rental discounts, hotel upgrade coupons, and the like...”).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the transaction card and label set as disclosed by Blank in the system and method of Mupkala, the motivation being to reduce the costs of producing the multiple element sets of cards, labels and application forms by providing a method of reducing the number of steps and the costs associated with producing the set (see Blank, background of the invention).
With respect to the BRI of the claims, Examiner notes that claims 13 and 21 recite “ the design including representations of the one or more benefits.”, language directed to non-functional descriptive material. See MPEP 2111.05. In addition, claims 13 and 21 recite “training a machine learning model to predict a subset of benefits to represent to a user”; “using the machine learning model to predict which one or more benefits, of a plurality of benefits associated with use of a tangible object and attributed to an account of the user, to include for representation as part of a design for depiction on the tangible object...”, statements of intended use or field use. See MPEP 2114 II.
With respect to claim 14, the combination of Mupkala and Blank teaches all the subject matter of the system as described above with respect to claim 13. Furthermore, Blank disclose a system wherein the tangible object is a physical card and the printing prints the representations of the one or more benefits on a surface of the physical card (see col. 5, line 54 to col. 6, line 11: “As described above, the prior art method of manufacturing a set of transaction cards and associated labels is a multi-step, labor intensive, and expensive process. In accordance with the present invention, the number of steps and the cost of manufacturing can be significantly reduced. For example, prior to printing, the label or labels can be affixed to either planar side of the transaction card and thereafter the labels and transaction card can be printed with registration indicia, and personalized graphic fields by a single printer. In a specific embodiment of the invention printing of the labels and cards is accomplished in sequence (i.e. in a single pass). In addition, it is contemplated by the present invention that the label and card can either be pre-printed with static information prior to the printing step or personalized at the same time as the printing step."). Regarding the BRI of the claim, Examiner notes that claim 14 recites “wherein the tangible object is a physical card and the printing prints the representations of the one or more benefits on a surface of the physical card”, language directed to non-functional descriptive material. The motivation for combining the references remain unaltered from the motivation described above in conjunction with the rejection of the independent claims.
With respect to claim 15, the combination of Mupkala and Blank teaches all the subject matter of the system as described above with respect to claim 13. Furthermore, Blank disclose a system wherein the printing prints the design on a removable indicator and the tangible object is a physical card, and wherein the removable indicator is configured as a sticker for adherence to a surface of the physical card and the representations of the one or more benefits are depicted on a surface of the removable indicator (see col. 5 lines 1-7: “Either planar side of the card can be supplied with at least one label 18 affixed to the card, wherein the label has two major planar sides, a first side suitable for printing with indicia thereon and a second side coated with an adhesive layer suitable for affixing to the card and for removal from the card without leaving a residue on the card. The label 18 can be made of paper, plastic or other material.”;). Regarding the BRI of the claim, Examiner notes that claim 15 recites “the printing prints the design on a removable indicator and the tangible object is a physical card”; “the representations of the one or more benefits are depicted on a surface of the removable indicator”, language directed to non-functional descriptive material. In addition, claim 15 recites “the removable indicator is configured as a sticker for adherence to a surface of the physical card”, statements of intended use or field use. See MPEP 2114 II. The motivation for combining the references remain unaltered from the motivation described above in conjunction with the rejection of the independent claims.
With respect to claim 16, the combination of Mupkala and Blank teaches all the subject matter of the system as described above with respect to claim 15. Furthermore, Mupkala disclose a system wherein the executable code, when executed, further causes the processor to establish an automated protocol to reinitiate the generating and printing the design on the removable indicator according to a predefined schedule (see paragraph [0065]: “...After a pre-defined time period, service provider system 104 then evaluates if first customer 114(1) has utilized already accrued personalized rewards (See FIG. 4B step 418). The time period may include 1 hour, 1 day, 1 week, or 1 month, although any other time period may also be included). By way of example, server 300 determines if the Gas rewards have been utilized for gasoline purchases and if the Grocery rewards have been utilized for grocery purchases. When it is determined that the rewards have been utilized, the first machine learning algorithm is updated. This updated first machine learning algorithm is then utilized for identifying second customers as described above in step 406 (420). In some embodiments, recommendation model 312 may utilize machine learning to update and adjust its algorithm using the evaluation of utilization of the personalized rewards by the customers in real-time.”). Regarding the BRI of the claim, Examiner notes that claim 16 recites “establish an automated protocol to reinitiate the generating and printing the design on the removable indicator according to a predefined schedule” , statements of intended use or field use. See MPEP 2114 II. The motivation for combining the references remain unaltered from the motivation described above in conjunction with the rejection of the independent claims.
With respect to claim 17, the combination of Mupkala and Blank teaches all the subject matter of the system as described above with respect to claim 16. Furthermore, Mupkala disclose a system wherein the predefined schedule is based on a predicted longevity of the removable indicator (see any time period, paragraph [0065]: “...After a pre-defined time period, service provider system 104 then evaluates if first customer 114(1) has utilized already accrued personalized rewards (See FIG. 4B step 418). The time period may include 1 hour, 1 day, 1 week, or 1 month, although any other time period may also be included). By way of example, server 300 determines if the Gas rewards have been utilized for gasoline purchases and if the Grocery rewards have been utilized for grocery purchases. When it is determined that the rewards have been utilized, the first machine learning algorithm is updated. This updated first machine learning algorithm is then utilized for identifying second customers as described above in step 406 (420). In some embodiments, recommendation model 312 may utilize machine learning to update and adjust its algorithm using the evaluation of utilization of the personalized rewards by the customers in real-time.”). The motivation for combining the references remain unaltered from the motivation described above in conjunction with the rejection of the independent claims.
With respect to claim 18, the combination of Mupkala and Blank teaches all the subject matter of the system as described above with respect to claim 16. Furthermore, Mupkala disclose a system wherein the predefined schedule is based on periodic changes to the benefits. (see paragraph [0065]: “...When it is determined that the rewards have been utilized, the first machine learning algorithm is updated...”). The motivation for combining the references remain unaltered from the motivation described above in conjunction with the rejection of the independent claims.
With respect to claims 20 and 25, the combination of Mupkala and Blank teaches all the subject matter of the system and method as described above with respect to claims 15 and 24. Furthermore, Mupkala disclose a system and method wherein the executable code, when executed, further causes the processor to receive a request to reinitiate the using the machine learning model and the generating and printing the design, wherein the reinitiating the using the machine learning model results in an updated prediction by the machine learning model that produces a change in the one or more benefits to include for representation for depiction on the tangible object, and the reinitiating the generating and printing the design generates and prints an updated design that includes representations of the changed one or more benefits (see paragraph [0065]: “Upon receiving the first recommendation, first customer 114(1) selects one or more of the redeemable reward categories for which they would be interested in receiving the rewards based on their preferences. Service provider system 104 receives the selection of redeemable reward categories from the recommended list of categories from user device 102(1) of first customer 114(1) (see FIG. 4A step 412). Service provider system 104 applies a second machine learning algorithm to determine personalized rewards associated with the selected redeemable reward categories for the first customer 114(1) (414). Service provider system 104 analyzes the customer parameter values of first customer 114(1). For example, service provider system 104 analyzes bank account statements of first customer 114(1) and determines rewards collected and/or accrued by first customer 114(1) based on the amount money spent by first customer 114(1) for purchase transactions in the most recent billing period. Further, service provider system 104 generates a personalized reward that includes division of reward percentages among the corresponding redeemable reward categories selected indicated by first customer 114(1). Service provider system 104 then transmits the personalized rewards to the first customer 114(1) (See FIG. 4B step 416). In this example, the personalized reward includes 50% of reward percentage for gas rewards and 50% for Grocery rewards (416). After a pre-defined time period, service provider system 104 then evaluates if first customer 114(1) has utilized already accrued personalized rewards (See FIG. 4B step 418). The time period may include 1 hour, 1 day, 1 week, or 1 month, although any other time period may also be included). By way of example, server 300 determines if the Gas rewards have been utilized for gasoline purchases and if the Grocery rewards have been utilized for grocery purchases. When it is determined that the rewards have been utilized, the first machine learning algorithm is updated. This updated first machine learning algorithm is then utilized for identifying second customers as described above in step 406 (420). In some embodiments, recommendation model 312 may utilize machine learning to update and adjust its algorithm using the evaluation of utilization of the personalized rewards by the customers in real-time.”;). Regarding the BRI of the claims, Examiner notes that claims 20 and 25 recite “the reinitiating the using the machine learning model results in an updated prediction by the machine learning model that produces a change in the one or more benefits to include for representation for depiction on the tangible object” , statements of intended use or field use. See MPEP 2114 II. Lastly, claims 20 and 25 is a method claim and recites “the reinitiating the generating and printing the design generates and prints an updated design that includes representations of the changed one or more benefits...”, language directed to not positively recited method steps. The motivation for combining the references remain unaltered from the motivation described above in conjunction with the rejection of the independent claims.
With respect to claim 22, the combination of Mupkala and Blank teaches all the subject matter of the method as described above with respect to claim 21. Furthermore, Mupkala disclose a method wherein the printing prints the representations of the one or more benefits on a surface of the physical card and the method further includes establishing an automated protocol that reinitiates the generating and printing the design when an updated prediction by the machine learning model produces a change in the one or more benefits to include for representation for depiction on the physical card, and the reinitiated generating and printing generates and prints an updated design that includes representations of the changed one or more benefits (see paragraph [0057]: “Server 300 then evaluates if first customer 114(1) has utilized the personalized rewards. By way of example, server 300 determines if the Gas rewards have been utilized for gasoline purchases and if the Grocery rewards have been utilized for grocery purchases. When it is determined that the rewards have been utilized, the first machine learning algorithm is updated. This updated first machine learning algorithm is then utilized for identifying second customers as described above. In some embodiments, recommendation model 312 may utilize machine learning to update and adjust its algorithm using the evaluation of utilization of the personalized rewards by the customers in real time.”). Regarding the BRI of the claim, Examiner notes that Further, claim 22 recites “establishing an automated protocol that reinitiates the generating and printing the design when an updated prediction by the machine learning model produces a change in the one or more...” , language directed to contingent limitations. The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met. The broadest reasonable interpretation of a system (or apparatus or product) claim having structure that performs a function, which only needs to occur if a condition precedent is met, requires structure for performing the function should the condition occur. See Ex parte Schulhauser, Appeal 2013-007847 (PTAB April 28, 2016) (precedential) for an analysis of contingent claim limitations in the context of both method claims and system claims. See also MPEP 2111.04. Lastly, claim 22 is a method claim and recites “ the printing prints the representations of the one or more benefits on a surface of the physical card...”, language directed to not positively recited method steps. The motivation for combining the references remain unaltered from the motivation described above in conjunction with the rejection of the independent claims.
With respect to claim 23, the combination of Mupkala and Blank teaches all the subject matter of the method as described above with respect to claim 21. Furthermore, Blank disclose a method wherein the printing prints the design on a removable indicator configured as a sticker for adherence to a surface of the physical card (see col. 5 lines 1-7: “Either planar side of the card can be supplied with at least one label 18 affixed to the card, wherein the label has two major planar sides, a first side suitable for printing with indicia thereon and a second side coated with an adhesive layer suitable for affixing to the card and for removal from the card without leaving a residue on the card. The label 18 can be made of paper, plastic or other material.”;). Regarding the BRI of the claim, Examiner notes that claim 23 recites “a removable indicator configured as a sticker for adherence to a surface of the physical card” , statements of intended use or field use. See MPEP 2114 II. The motivation for combining the references remain unaltered from the motivation described above in conjunction with the rejection of the independent claims.
With respect to claim 24, the combination of Mupkala and Blank teaches all the subject matter of the method as described above with respect to claim 23. Furthermore, Mupkala disclose a method further comprising establishing an automated protocol to reinitiate the using the machine learning model and the generating and printing the design (see paragraph [0057]: “Server 300 then evaluates if first customer 114(1) has utilized the personalized rewards. By way of example, server 300 determines if the Gas rewards have been utilized for gasoline purchases and if the Grocery rewards have been utilized for grocery purchases. When it is determined that the rewards have been utilized, the first machine learning algorithm is updated. This updated first machine learning algorithm is then utilized for identifying second customers as described above. In some embodiments, recommendation model 312 may utilize machine learning to update and adjust its algorithm using the evaluation of utilization of the personalized rewards by the customers in real time.”).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Mupkala (US 2021/0065279 A1), in view of Blank (US 5,531,482 A), in view of Whitworth (US 2001/0034717 A1)
With respect to claim 19, the combination of Mupkala and Blank teaches all the subject matter of the system as described above with respect to claim 16. The combination of Mupkala and Blank does not explicitly teach a system wherein the executable code, when executed, further causes the processor to terminate the automated protocol in response to a request from a user device.
However, Whitworth discloses a system (Fraud resistant credit card using encryption, encrypted cards on computing devices) wherein the executable code, when executed, further causes the processor to terminate the automated protocol in response to a request from a user device (see paragraph [0165]: “At 1109, accounts eligible for reactivation are checked to see if the user would like to use a new device. For an additional device advance to 1111. A user might want the same account on multiple devices, as described below in FIG. 12. An additional device can be authorized in a manner similar to the current device. To activate the additional device, the process continues at 1009 and a check is made to see if the old device should get a new encryption key 1119.”; paragraph [0166]: “If no new device is being activated, a no at 1109 advances to 1119. It may be desirable to provide a new key to the current device at 1119. If yes at 1119, advance to 1121. At 1121 a new key is provided to the software using the activation process at 1009. If no, the process is finished at 1123”).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the scheduled expiration dates and unscheduled deactivation\ as disclosed by Whitworth in the system of Mupkala and Blank, the motivation being to increase security by allowing the plastic credit or debit cards to be replaced periodically and/or unscheduled deactivation (see Whitworth, paragraphs [0157] and [0160]).
Response to Arguments/Amendments
Claim rejections - 35 USC § 101
Applicant’s amendments and arguments (see remarks, pages 14 and 15, filed on 11/25/2025), with respect to the rejection of claims 13-25 under 35 USC § 101 as being directed to an abstract idea have been fully considered but are not persuasive. in claims 13-25, Applicant asserts “Applicant agrees and respectfully submits that neither training and using the machine learning model, nor generating and printing a design, recite abstract ideas.”. Examiner respectfully disagrees. Training a machine learning model is fundamentally a mathematical operation.. Therefore, upon review of the proposed amendments, Examiner is unpersuaded by Applicant's arguments in view of the analysis provided above. . The new and amended claims do not offer significantly more than the abstract idea itself, therefore the claims are still rejected under 35 USC § 101 as further detailed above.
Claim rejections - 35 USC § 112(b)
Applicant’s amendments and arguments (see remarks, page 15, filed on 11/25/2025), with respect to the rejection of claim 19 under 35 USC § 112(b) have been fully considered. in claim 19Examiner finds Applicant's arguments persuasive in view of the submitted amendments, therefore the rejection was withdrawn.
Claim rejections - 35 USC § 103
Applicant’s amendments and arguments (see remarks, pages 15 and 16, filed on 11/25/2025), with respect to the rejection of claims 13-25 under 35 USC § 103 have been fully considered, but are moot because the arguments do not apply to the reference being used in the current rejection of the amended claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Patent Literature
Crawford et al. (US 10,636,020 B1) disclose system for adding identification element to card, including creating an identification element to be added to an exterior surface of a personal card.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDUARDO D CASTILHO whose telephone number is (571)270-1592. The examiner can normally be reached Mon-Fri 8-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patrick McAtee can be reached at (571) 272-7575. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/EDUARDO CASTILHO/Primary Examiner, Art Unit 3698