Prosecution Insights
Last updated: April 19, 2026
Application No. 18/624,234

DYNAMIC MODIFICATION OF DIGITAL REDEMPTION TRANSACTIONS

Final Rejection §101
Filed
Apr 02, 2024
Examiner
WOODWORTH, II, ALLAN J
Art Unit
3622
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Loop Commerce Inc.
OA Round
4 (Final)
39%
Grant Probability
At Risk
5-6
OA Rounds
3y 11m
To Grant
80%
With Interview

Examiner Intelligence

Grants only 39% of cases
39%
Career Allow Rate
91 granted / 232 resolved
-12.8% vs TC avg
Strong +41% interview lift
Without
With
+41.1%
Interview Lift
resolved cases with interview
Typical timeline
3y 11m
Avg Prosecution
26 currently pending
Career history
258
Total Applications
across all art units

Statute-Specific Performance

§101
37.7%
-2.3% vs TC avg
§103
35.9%
-4.1% vs TC avg
§102
9.1%
-30.9% vs TC avg
§112
12.5%
-27.5% vs TC avg
Black line = Tech Center average estimate • Based on career data from 232 resolved cases

Office Action

§101
Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Status of the Application This final rejection is in response to the arguments filed 2/23/2026. Claims 2, 9, and 15 have been amended. Claims 22-27 have been cancelled. Claims 2-21 are currently pending and have been examined below. Claim Rejections – 35 U.S.C. 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 2-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Per step 1 of the eligibility analysis set forth in MPEP § 2106, subsection III, the claims are directed towards a process, machine, or manufacture. Per step 2A Prong One, independent claim 2 recites specific limitations which fall within at least one of the groupings of abstract ideas enumerated in MPEP 2106.04(a)(2) as follows: receiving a gift order, wherein the gift order is associated with an initial amount; generate redemption options including an alternate redemption option; generating an initial gift notification based on the gift order, wherein the initial gift notification includes the redemption options and the alternate redemption option; providing the initial gift notification, wherein when a recipient receives the initial gift notification, transmits an alternate redemption request that includes a selection of the alternate redemption option; receiving the alternate redemption request; and performing an alternate redemption action based on the alternate redemption request. As noted above, these limitations fall within at least one of the groupings of abstract ideas enumerated in the MPEP 2106.04(a)(2). Specifically, these limitations fall within the group Certain Methods of Organizing Human Activity (i.e., fundamental economic principles or practices (including hedging, insurance, mitigating risk; commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). That is, the limitations recited above describe a sales activity of receiving a gift order and providing alternative and additional gift redemption options to a consumer which is a method of organizing human activities. Thus, the claim recites an abstract idea. Per step 2A Prong 2, the Examiner finds that the judicial exception is not integrated into a practical application. Claim 1 recites the additional limitations of: training in real-time a redemption options algorithm, wherein the redemption options algorithm is a deep artificial neural network trained using historical consumer information associated with one or more operations performed in response to receiving previous redemption information; applying the redemption options algorithm to consumer information [to generate redemption options] wherein applying the redemption options algorithm is associated with an initial operational cost corresponding to resources maintained by a resource provider; recipient device associated with the recipient; and dynamically updating the redemption options algorithm as the alternate redemption option is generated, wherein updating the redemption options algorithm reduces the initial operational cost maintained by the resource provider. With respect to training in real-time a redemption options algorithm, wherein the redemption options algorithm is a deep artificial neural network trained using historical consumer information associated with one or more operations performed in response to receiving previous redemption information and applying the redemption options algorithm to consumer information [to generate redemption options], Examiner notes that these limitations are recited at a high level of generality utilizing a generic neural network to train an algorithm based on historical consumer and redemption information. Examiner adds that Applicant’s published specification paragraph [0296] recites that “[s]uch a machine learning or artificial intelligence algorithm may be trained using supervised, unsupervised, reinforcement, or other such training techniques . . . [o]ther examples of machine learning or artificial intelligence algorithms include, but are not limited to, genetic algorithms, backpropagation, reinforcement learning, decision trees, liner classification, artificial neural networks, anomaly detection, and such. More generally, machine learning or artificial intelligence methods may include regression analysis, dimensionality reduction, metalearning, reinforcement learning, deep learning, and other such algorithms and/or methods.” This is the only reference to the training of a neural network specifically in the entire specification. Examiner notes that the claimed recitation of training and using generic models is similar to Claim 2 of Example 47 in the July 2024 Subject Matter Eligibility Examples published by the USPTO available at https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility where the training of an artificial neural network was not found to integrate the abstract idea into a practical application. Additionally, see Recentive Analytics, Inc. v. Fox Corp. et al., No. 2023-2437, slip op. at 18 (Fed. Cir. Apr. 18, 2025) holding that claims “that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” Here, Examiner takes the position training a generic neural network to identify alternative redemption options and applying the model to identify the options is the mere application of generic machine learning to a new data environment. Because no improvement to the underlying machine learning models is disclosed, this limitation does not integrate the abstract idea into a practical application. With respect to wherein applying the redemption options algorithm is associated with an initial operational cost corresponding to resources maintained by a resource provider and dynamically updating the redemption options algorithm as the alternate redemption option is generated, wherein updating the redemption options algorithm reduces the initial operational cost maintained by the resource provider, Examiner notes that these limitations are recited at a high level of generality and that the only discussion of operational cost is in the background section (paragraph [0006]) of Applicant’s specification. These limitations merely recite the outcome of updating the algorithm to reduce initial operational cost without reciting how the algorithm is actually updated to result in the reduction (beyond the application of generic business rules). For example, paragraph [0097] provides the example of a retailer desiring to reduce the inventory level of certain products and therefore the algorithm including logic to not present the donation option depending on the level of product inventory (e.g., to prevent the donee from opting for the donation rather than accepting the gift and lowering inventory). At most, this describes applying a business rule (e.g., do not offer a donation option if product inventory exceeds a certain level), as opposed to an improvement to the underlying machine learning model. Finally, with respect to a recipient device associated with the recipient, this limitation merely generally links the abstract idea to a particular technological environment or utilizes a recipient device as a tool to perform the abstract idea. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea. Alice Corp. also establishes that the same analysis should be used for all categories of claims (e.g., product and process claims). Therefore, independent system claim 9 and independent computer-readable medium claim 15 are also rejected as ineligible subject matter under 35 U.S.C. 101 for substantially the same reasons as independent method claim 2. The additional limitations in claim 9 (i.e., one or more processors and memory) and the additional limitations in claim 15 (i.e., a non-transitory computer readable storage medium) add nothing of substance to the underlying abstract idea. The components are merely providing a particular technological environment to implement the abstract idea. Dependent claims 3-8, 10-14, and 16-21 are rejected on a similar rational to the claims upon which they depend. Specifically, dependent claims 3-8, 10-14, and 16-21 merely further narrow the abstract idea. Response to Arguments 35 U.S.C. 101 Applicant's arguments, see page 11, filed 2/23/2026, with respect to the rejection(s) of claims 2-21 under 35 U.S.C. 101 have been fully considered but are not persuasive. Applicant argues that: By dynamically updating the redemption options algorithm, the redemption options algorithm can be used to further increase "the likelihood that the gift redemption will reduce the retailer's inventory of the product." See Specification at [0054] and [0097]. "As an example, a combination of rules may be evaluated by the redemption options algorithm 128 that indicate that a regift option may be presented when a gift order explicitly includes an indication that the gifting entity wants to allow regifting, the selected gift exists in sufficient quantity in the inventory of a retailer, and the price of the gift is over $25." See id. at [0055]. Because the Specification identifies that returns/exchanges drive high operational costs for merchants, reducing return/exchange events necessarily reduces operational costs (including the operational burden and associated computing/system overhead involved in managing inventory exceptions, reversals, and post-transaction remediation workflows) associated with the resource provider (remarks page 9). Examiner respectfully replies that the limitations directed to updating the redemption algorithm (and cited paragraphs of the specification) describe the outcome of updating the algorithm to reduce initial operational cost without reciting how the algorithm is actually updated (other than applying simple business rules) to result in the reduction. For example, paragraph [0097] provides the example of a retailer desiring to reduce the inventory level of certain products and therefore the algorithm including logic to not present the donation option depending on the level of product inventory (e.g., to prevent the donee from opting for the donation rather than accepting the gift and lowering inventory). At most, this describes applying a business rule (e.g., do not offer a donation option if product inventory exceeds a certain level), as opposed to an improvement to the underlying machine learning model that integrates the abstract idea into a practical application. Further, paragraph [0055] similarly describes applying a combination of simple business rules to determine whether to offer the user an option regift. This describes the application of business rules, not an improvement to the underlying machine learning algorithm. Finally, so the extent that applying the business rules results in less returns/exchanges, this is at most the achievement of a business objective, not a technological improvement that integrates the abstract idea into a practical application. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US Patent Application Publication Number 20220343306 (“Jeong”) discloses counting the number of selections of a donation menu selected by a customer, so that if the customer selects the donation menu more than a certain number of times the donation is prohibited US Patent Application Publication Number 20060253320 (“Heywood”) discloses available redemption options may include gift certificates or charitable contributions and those types of redemption options may be available to the user and displayed. Heywood also discloses that user may interact with a display to select one of the available redemption options. US Patent Application Publication 20210398092 (“Boruhovin”) discloses transmitting a notice of selected gift options to a recipient and providing alternative redemption options (e.g., regifting) However, the prior art fails to teach each and every limitation as claimed, and would involve hindsight reasoning to arrive at the claimed invention. Therefore, the claims are considered allowable over the prior art. 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 ALLAN J WOODWORTH, II whose telephone number is (571)272-6904. The examiner can normally be reached Mon-Fri 9:00-5:30. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ilana Spar can be reached at (571) 270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ALLAN J WOODWORTH, II/Primary Examiner, Art Unit 3622
Read full office action

Prosecution Timeline

Apr 02, 2024
Application Filed
Nov 13, 2024
Non-Final Rejection — §101
Mar 08, 2025
Interview Requested
Mar 17, 2025
Applicant Interview (Telephonic)
Mar 17, 2025
Examiner Interview Summary
Mar 18, 2025
Response Filed
Jun 20, 2025
Final Rejection — §101
Aug 21, 2025
Interview Requested
Sep 10, 2025
Examiner Interview Summary
Sep 22, 2025
Examiner Interview Summary
Sep 22, 2025
Applicant Interview (Telephonic)
Sep 29, 2025
Request for Continued Examination
Oct 05, 2025
Response after Non-Final Action
Oct 18, 2025
Non-Final Rejection — §101
Feb 12, 2026
Interview Requested
Feb 21, 2026
Examiner Interview Summary
Feb 23, 2026
Response Filed
Mar 21, 2026
Final Rejection — §101 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

5-6
Expected OA Rounds
39%
Grant Probability
80%
With Interview (+41.1%)
3y 11m
Median Time to Grant
High
PTA Risk
Based on 232 resolved cases by this examiner. Grant probability derived from career allow rate.

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