Prosecution Insights
Last updated: August 18, 2026
Application No. 18/542,239

SUGGESTING REPLACEMENT ITEMS BY INFERRING INTENT OF A USER OF AN ONLINE SYSTEM USING A TRAINED MODEL

Final Rejection §101
Filed
Dec 15, 2023
Examiner
WEINER, ARIELLE E
Art Unit
3689
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Maplebear Inc.
OA Round
4 (Final)
44%
Grant Probability
Moderate
5-6
OA Rounds
6m
Est. Remaining
97%
With Interview

Examiner Intelligence

Grants 44% of resolved cases
44%
Career Allowance Rate
104 granted / 237 resolved
-8.1% vs TC avg
Strong +53% interview lift
Without
With
+53.1%
Interview Lift
resolved cases with interview
Typical timeline
3y 2m
Avg Prosecution
34 currently pending
Career history
279
Total Applications
across all art units

Statute-Specific Performance

§101
31.0%
-9.0% vs TC avg
§103
43.0%
+3.0% vs TC avg
§102
6.2%
-33.8% vs TC avg
§112
17.4%
-22.6% vs TC avg
Black line = Tech Center average estimate • Based on career data from 237 resolved cases

Office Action

§101
DETAILED ACTION This action is in reply to the Amendments filed on 02/26/2026. Claims 6, 11, and 18 were previously cancelled. Claims 1-5, 7-10, 12-17, 19 and 20 are currently pending and have been examined Response to Amendment Applicant’s amendment, filed 02/26/2026, has been entered. Claims 1, 8, 13, and 20 have been amended. 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 . Claim Objections Claims 1-5, 7-10, 12-17, 19 and 20 are objected to because of the following informalities: -Claims 1, 13, and 20 read “transmitting … the ordering interface to the device associated with the user” but should likely read “transmitting … the ordering interface to a device associated with the user” -Claims 1, 13, and 20 read “responsive to the user pressing a user interface element of the one or more user interface elements” but should likely read “responsive to the user pressing a third user interface element of the one or more user interface elements” Claims 2-5, 7-10, 12, 14-17, and 19 inherit the deficiencies noted in claims 1 and 13, and are therefore objected to on the same basis. Appropriate correction 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 1-5, 7-10, 12-17, 19 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., law of nature, a natural phenomenon, or an abstract idea) without significantly more. Under Step 1 of the Subject Matter Eligibility Test for Products and Processes, the claims must be directed to one of the four statutory categories (see MPEP 2106.03). All the claims are directed to one of the four statutory categories (YES). Under Step 2A of the Subject Matter Eligibility Test, it is determined whether the claims are directed to a judicially recognized exception (see MPEP 2106.04). Step 2A is a two-prong inquiry. Under Prong 1, it is determined whether the claim recites a judicial exception (YES). Taking Claim 1 as representative, the claim recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including: -A method, performed at a computer system comprising a processor and a computer-readable medium, comprising: -receiving, via a network and from a device associated with a picker, a signal indicating that an item in a cart requested by a user of an online system is not available; -accessing a replacement model, wherein the replacement model is a machine-learning model trained to identify a set of one or more replacement items for replacing the item; -applying the replacement model to one or more features of the item to score each replacement item in the identified set of one or more replacement items; -responsive to identifying a failure of the replacement model to identify one or more suitable replacement items according to defined criteria, accessing a recipe prediction model, wherein the recipe prediction model is a machine-learning model trained to infer a recipe that is potentially associated with the item; -applying the recipe prediction model to the one or more features of the item and information about one or more ingredients in the cart to generate a name of the inferred recipe and a replacement item category associated with the inferred recipe; -collecting, from a database of recipes of the online system and using the name of the inferred recipe, a set of one or more recipes; -identifying, from the set of one or more recipes and using the replacement item category, a set of one or more candidate replacement items; -applying the replacement model to the name of the inferred recipe, the replacement item category and information about the set of one or more candidate replacement items to identify one or more replacement items from the set of one or more candidate replacement items; -responsive to inferring the name of the recipe and the replacement item category, generating an inquiry message for the user including the name of the recipe and the replacement item category; -generating, using information about the item that is not available, the inquiry message and the one or more replacement items, an ordering [display] interface; -transmitting, via the network and from a content presentation module of the online system, the ordering [display] interface to the device associated with the user, wherein transmitting the ordering [display] interface causing a causes the device associated with the user to display a user interface including the item that is not available, the inquiry message, the one or more replacement items for recommendation to the user to be included in the cart instead of the item, and one or more user interface elements each displayed partially overlaid onto a respective replacement item of the one or more replacement items, the inquiry message prompting the user to confirm whether the inference of the recipe is correct, -wherein the user [display] interface further includes a first user interface element overlaid onto the inquiry message, the first user interface element having a first functionality that generates, when the user presses [selects] the first user interface element, a confirmation that the inference of the recipe is correct, and -wherein the user [display] interface further includes a second user interface element overlaid onto the inquiry message and displayed adjacent to the first user interface element, the second user interface element having a second functionality that generates, when the user presses [selects] the second user interface element, a confirmation that the inference of the recipe is not correct; -responsive to the user [selecting] pressing the first user interface element or the user [selecting] pressing the second user interface element, generating a user feedback signal with information that the inference of the recipe is correct or with information that the inference of the recipe is not correct; -updating a set of parameters of the recipe prediction model using the user feedback signal; and -responsive to the user pressing [selecting] a user interface element of the one or more user interface elements, automatically updating the cart with a corresponding replacement item of the one or more replacement items that is associated with the user interface element The above limitations recite the concept of recommending replacement items to a user. The above limitations fall within the “Certain Methods of Organizing Human Activity” groupings of abstract ideas, enumerated in MPEP 2106.04(a). Certain methods of organizing human activity include: fundamental economic principles or practices (including hedging, insurance, and mitigating risk) commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; and business relations) managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) The limitations of applying the replacement model to one or more features of the item to score each replacement item in the identified set of one or more replacement items; applying the recipe prediction model to the one or more features of the item and information about one or more ingredients in the cart to generate a name of the inferred recipe and a replacement item category associated with the inferred recipe; identifying, from the set of one or more recipes and using the replacement item category, a set of one or more candidate replacement items; applying the replacement model to the name of the inferred recipe, the replacement item category and information about the set of one or more candidate replacement items to identify one or more replacement items from the set of one or more candidate replacement items; responsive to inferring the name of the recipe and the replacement item category, generating an inquiry message for the user including the name of the recipe and the replacement item category; and updating a set of parameters of the recipe prediction model using the user feedback signal are processes that, under their broadest reasonable interpretation, cover a commercial interaction. For example, “applying,” “applying,” “identifying,” “applying,” “generating,” and “updating” in the context of this claim encompass advertising, and marketing or sales activities. Similarly, the limitations of a method, performed at a computer system comprising a processor and a computer-readable medium, comprising: receiving, via a network and from a device associated with a picker, a signal indicating that an item in a cart requested by a user of an online system is not available; accessing a replacement model, wherein the replacement model is a machine-learning model trained to identify a set of one or more replacement items for replacing the item; responsive to identifying a failure of the replacement model to identify one or more suitable replacement items according to defined criteria, accessing a recipe prediction model, wherein the recipe prediction model is a machine-learning model trained to infer a recipe that is potentially associated with the item; collecting, from a database of recipes of the online system and using the name of the inferred recipe, a set of one or more recipes; generating, using information about the item that is not available, the inquiry message and the one or more replacement items, an ordering [display] interface; transmitting, via the network and from a content presentation module of the online system, the ordering [display] interface to the device associated with the user, wherein transmitting the ordering [display] interface causing a causes the device associated with the user to display a user interface including the item that is not available, the inquiry message, the one or more replacement items for recommendation to the user to be included in the cart instead of the item, and one or more user interface elements each displayed partially overlaid onto a respective replacement item of the one or more replacement items, the inquiry message prompting the user to confirm whether the inference of the recipe is correct, wherein the user [display] interface further includes a first user interface element overlaid onto the inquiry message, the first user interface element having a first functionality that generates, when the user presses [selects] the first user interface element, a confirmation that the inference of the recipe is correct, and wherein the user [display] interface further includes a second user interface element overlaid onto the inquiry message and displayed adjacent to the first user interface element, the second user interface element having a second functionality that generates, when the user presses [selects] the second user interface element, a confirmation that the inference of the recipe is not correct; responsive to the user [selecting] pressing the first user interface element or the user [selecting] pressing the second user interface element, generating a user feedback signal with information that the inference of the recipe is correct or with information that the inference of the recipe is not correct; and responsive to the user pressing [selecting] a user interface element of the one or more user interface elements, automatically updating the cart with a corresponding replacement item of the one or more replacement items that is associated with the user interface element are processes that, under their broadest reasonable interpretation, cover a commercial interaction. That is, other than reciting that the system is a computer system comprising a processor and a computer-readable medium, that the receiving is via a network and from a device associated with a picker, that the system is an online system, that the replacement model is a machine-learning model that is trained, that the recipe prediction model is a machine-learning model that is trained, that the collecting is from a database of recipes of the online system, that the ordering display is an ordering interface, that the transmitting is via the network and from a content presentation module of the online system, that the one or more elements is one or more user interface elements, that the one or more replacement items are displayed on a user interface of a device associated with a user, that the user display is a user interface, that the first element is a first user interface element, that the user presses the first element, that the second element is a the second user interface element, that the user presses the second element, that the user presses a user interface element, and that the updating is automatically updating nothing in the claim element precludes the step from practically being performed by people. For example, but for the “computer system,” “a processor,” “a computer-readable medium,” “online system,” “the replacement model is a machine-learning model,” “a network,” “a device associated with a picker,” “the recipe prediction model is a machine-learning model,” “trained,” “a database,” “a content presentation module,” “a device” “an ordering interface,” “a user interface,” “one or more user interface elements,” “first user interface element,” “second user interface element,” “press[ing] the first user interface element” “press[ing] the second user interface element,” “pressing a user interface element,” “automatically updating,” and “the user interface element” language, “receiving,” “accessing,” “accessing,” “collecting,” “generating,” “transmitting,” “causing display,” “generates,” “generating,” and “updating” in the context of this claim encompasses advertising, and marketing or sales activities. Under Prong 2, it is determined whether the claim recites additional elements that integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application (NO). -A method, performed at a computer system comprising a processor and a computer-readable medium, comprising: -receiving, via a network and from a device associated with a picker, a signal indicating that an item in a cart requested by a user of an online system is not available; -accessing a replacement model, wherein the replacement model is a machine-learning model trained to identify a set of one or more replacement items for replacing the item; -applying the replacement model to one or more features of the item to score each replacement item in the identified set of one or more replacement items; -responsive to identifying a failure of the replacement model to identify one or more suitable replacement items according to defined criteria, accessing a recipe prediction model, wherein the recipe prediction model is a machine-learning model trained to infer a recipe that is potentially associated with the item; -applying the recipe prediction model to the one or more features of the item and information about one or more ingredients in the cart to generate a name of the inferred recipe and a replacement item category associated with the inferred recipe; -collecting, from a database of recipes of the online system and using the name of the inferred recipe, a set of one or more recipes; -identifying, from the set of one or more recipes and using the replacement item category, a set of one or more candidate replacement items; -applying the replacement model to the name of the inferred recipe, the replacement item category and information about the set of one or more candidate replacement items to identify one or more replacement items from the set of one or more candidate replacement items; -responsive to inferring the name of the recipe and the replacement item category, generating an inquiry message for the user including the name of the recipe and the replacement item category; -generating, using information about the item that is not available, the inquiry message and the one or more replacement items, an ordering interface; -transmitting, via the network and from a content presentation module of the online system, the ordering interface to the device associated with the user, wherein transmitting the ordering interface causing a causes the device associated with the user to display a user interface including the item that is not available, the inquiry message, the one or more replacement items for recommendation to the user to be included in the cart instead of the item, and one or more user interface elements each displayed partially overlaid onto a respective replacement item of the one or more replacement items, the inquiry message prompting the user to confirm whether the inference of the recipe is correct, -wherein the user interface further includes a first user interface element overlaid onto the inquiry message, the first user interface element having a first functionality that generates, when the user presses the first user interface element, a confirmation that the inference of the recipe is correct, and -wherein the user interface further includes a second user interface element overlaid onto the inquiry message and displayed adjacent to the first user interface element, the second user interface element having a second functionality that generates, when the user presses the second user interface element, a confirmation that the inference of the recipe is not correct; -responsive to the user pressing the first user interface element or the user pressing the second user interface element, generating a user feedback signal with information that the inference of the recipe is correct or with information that the inference of the recipe is not correct; -updating a set of parameters of the recipe prediction model using the user feedback signal; and -responsive to the user pressing a user interface element of the one or more user interface elements, automatically updating the cart with a corresponding replacement item of the one or more replacement items that is associated with the user interface element These limitations are not indicative of integration into a practical application because: The additional elements of claim 1 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea) as supported by paragraph [0092] of Applicant’s specification – “Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In some embodiments, a software module is implemented with a computer program product comprising one or more computer-readable media storing computer program code or instructions, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.” Specifically, the additional elements of a computer system, a processor, a computer-readable medium, an online system, the replacement model is a machine-learning model, a network, a device associated with a picker, the recipe prediction model is a machine-learning model, training, a database, a device, a content presentation module, an ordering interface, a user interface, one or more user interface elements, a first user interface element, a second user interface element, pressing the first user interface element, pressing the second user interface element, pressing a user interface element, automatically updating, and the user interface element are recited at a high-level of generality (i.e. as a generic processor performing the generic computer functions of receiving data, accessing data, applying data, collecting data, identifying data, generating data, transmitting data, causing display of data, and updating data) such that they amount do no more than mere instructions to apply the exception using generic computer components. Accordingly, 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 an abstract idea. Further, the additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use (such as computers or computing networks). Employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application. Additionally, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to i) reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, ii) apply the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, iii) effect a transformation or reduction of a particular article to a different state or thing, or iv) apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Accordingly, the judicial exception is not integrated into a practical application. Under Step 2B, it is determined whether the claims recite additional elements that amount to significantly more than the judicial exception. The claims of the present application do not include additional elements that are sufficient to amount to significantly more than the judicial exception (NO). In the case of claim 1, taken individually or as a whole, the additional elements of claim 1 do not provide an inventive concept. As discussed above under step 2A (prong 2) with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed functions amount to no more than a general link to a technological environment. Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually. Claim 13 is a computer program product reciting similar functions as claim 1. Examiner notes that claim 13 recites the additional elements of a non-transitory computer readable storage medium having instructions encoded thereon, a processor, an online system, the replacement model is a machine-learning model, a network, a device associated with a picker, the recipe prediction model is a machine-learning model, training, a database, a device, a content presentation module, an ordering interface, a user interface, one or more user interface elements, a first user interface element, a second user interface element, pressing the first user interface element, pressing the second user interface element, pressing a user interface element, automatically updating, and the user interface element, however, claim 13 does not qualify as eligible subject matter for similar reasons as claim 1 indicated above. Claim 20 is a computer system reciting similar functions as claim 1. Examiner notes that claim 20 recites the additional elements of a computer system, a processor, a non-transitory computer-readable storage medium having instructions, an online system, the replacement model is a machine-learning model, a network, a device associated with a picker, the recipe prediction model is a machine-learning model, training, a database, a device, a content presentation module, an ordering interface, a user interface, one or more user interface elements, a first user interface element, a second user interface element, pressing the first user interface element, pressing the second user interface element, pressing a user interface element, automatically updating, and the user interface element, however, claim 20 does not qualify as eligible subject matter for similar reasons as claim 1 indicated above. Therefore, claims 1, 13, and 20 do not provide an inventive concept and do not qualify as eligible subject matter. Dependent claims 2-5, 7-10, 12, 14-17 and 19, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. § 101 because they do not add “significantly more” to the abstract idea. More specifically, dependent claims 2-5, 7-10, 12, 14-17 and 19 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas in that they recite commercial interactions. Dependent claims 2, 7, and 12 do not recite any farther additional elements, and as such are not indicative of integration into a practical application for at least similar reasons discussed above. Dependent claims 3-5, 8-10, 14-17 and 19 recite the additional elements of the availability model being a machine-learning model that is trained, device associated with a picker, the database, the online system, training a set of parameters of the recipe prediction model, re-training at least one of the replacement model or the recipe prediction model, and the processor, but similar to the analysis under prong two of Step 2A these additional elements are used as a tool to perform the abstract idea. As such, under prong two of Step 2A, claims 2-5, 7-10, 12, 14-17 and 19 are not indicative of integration into a practical application for at least similar reasons as discussed above. Thus, dependent claims 2-5, 7-10, 12, 14-17 and 19 are “directed to” an abstract idea. Next, under Step 2B, similar to the analysis of claims 1, 13, and 20, dependent claims 2-5, 7-10, 12, 14-17 and 19 when analyzed individually and as an ordered combination, merely further define the commonplace business method (i.e. recommending replacement items to a user) being applied on a general-purpose computer and, therefore, do not amount to significantly more than the abstract idea itself. Accordingly, the Examiner concludes that there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself. The analysis above applies to all statutory categories of invention. Subject Matter Allowable over the Prior Art In the present application, claims 1-5, 7-10, 12-17, 19 and 20 would be allowable if rewritten or amended to overcome the rejections under 35 USC § 101 set forth in this Office action. The following is the Examiner's statement of reasons of allowance: Regarding 35 U.S.C. §103, upon review of the evidence at hand, it is hereby concluded that the totality of the evidence, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of the applicant’s invention. Claims 1-5, 7-10, 12-17, 19 and 20 are allowable over the prior art as follows: Claims 1-5, 7-10, 12-17, 19 and 20 are allowable over 35 U.S.C. §103 as follows: Claims 1-5, 7-10, 12-17, 19 and 20 are allowable for the reasons detailed in the “Allowable Subject Matter” section of the Final Office Action dated 07/22/2025. The most relevant prior art made of record includes over Guo et al. (US 2017/0193582 A1), Durazo Almeida et al. (US 11,257,049 B1), and Nigul et al. (US 2022/0215061 A1). The most relevant NPL is cited NPL reference U (cited 07/18/2025, 10/10/2025, 01/05/2025, and 05/21/2026 on PTO-892) teaches providing recipe recommendations to a user based on the items in a user’s cart, but does not teach or suggest the recited limitations. Response to Arguments Rejections under 35 U.S.C. §101 Applicant argues that limitations of amended claim 1 integrate the alleged judicial exception into a practical application of a computer system that generates a specific user interface based on outputs of machine-learning models. These limitations do not merely link the judicial exception to a technical field, but instead add a meaningful limitation in that they employ information provided by the judicial exception (i.e., the inferred name of the recipe, the inferred replacement item category, and the one or more replacement items for recommendation) to generate the user interface with specific components displayed in a specific manner that allow for efficient user's interaction with the user interface. Moreover, limitations of amended claim 1 related to the user interface are not recited at a high level of generality. Instead, these limitations recite technical aspects of how user interface elements are displayed at the user interface (e.g., overlaid or partially overlaid onto other components of the user interface), what operations are performed to trigger displaying of the user interface with specific components (e.g., operations of "generating an ordering interface" and "transmitting the ordering interface"), and how the user interface enables selection of a specific user interface element to add the recommended item to the cart, as also suggested in the latest Office Action. (Office Action, p. 17.). Examiner respectfully disagrees. The claims do not recite generating the user interface with specific components displayed in a specific manner. The claims don’t include the technical aspects of how they are displayed, such as the technical means for how they are overlaid and what the various interface elements actually are. The recited additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use (such as computers or computing networks). Accordingly, the amended claims are ineligible. Applicant further argues that independent claims 13 and 20 are amended to recite similar limitations in claim 1, and, thus, independent claims 13 and 20, as amended herein, integrate the judicial exception into the practical application for at least the same reason as amended claim 1. Each of the remaining pending claims depends on claim 1 or claim 13; thus, these claims also integrate the judicial exception into the practical application (Remarks, page 18). Examiner respectfully disagrees. As detailed in response to the arguments above, claim 1 is not eligible. Accordingly, independent claims 13 and 20 and the dependent claims are ineligible. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. -Green et al. (US 2024/0257953 A1) teaches using a machine learning model to optimize distribution of surplus products. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARIELLE E WEINER whose telephone number is (571)272-9007. The examiner can normally be reached M-F 8:30-5:00. 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, Jason Dunham can be reached at 571-272-8109. 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. /ARIELLE E WEINER/ Primary Examiner, Art Unit 3689
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Prosecution Timeline

Show 5 earlier events
Oct 16, 2025
Final Rejection mailed — §101
Dec 04, 2025
Request for Continued Examination
Dec 29, 2025
Response after Non-Final Action
Jan 07, 2026
Non-Final Rejection mailed — §101
Feb 25, 2026
Examiner Interview Summary
Feb 25, 2026
Applicant Interview (Telephonic)
Feb 26, 2026
Response Filed
May 27, 2026
Final Rejection mailed — §101 (current)

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Prosecution Projections

5-6
Expected OA Rounds
44%
Grant Probability
97%
With Interview (+53.1%)
3y 2m (~6m remaining)
Median Time to Grant
High
PTA Risk
Based on 237 resolved cases by this examiner. Grant probability derived from career allowance rate.

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