Prosecution Insights
Last updated: October 02, 2026
Application No. 18/967,681

TRAINING A MODEL TO PREDICT LIKELIHOODS OF USERS PERFORMING AN ACTION AFTER BEING PRESENTED WITH A CONTENT ITEM

Final Rejection §101
Filed
Dec 04, 2024
Priority
Jun 09, 2021 — continuation of 11/593,819 +1 more
Examiner
WAESCO, JOSEPH M
Art Unit
3625
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Maplebear Inc.
OA Round
2 (Final)
46%
Grant Probability
Moderate
3-4
OA Rounds
1y 5m
Est. Remaining
89%
With Interview

Examiner Intelligence

Grants 46% of resolved cases
46%
Career Allowance Rate
219 granted / 471 resolved
-5.5% vs TC avg
Strong +43% interview lift
Without
With
+42.6%
Interview Lift
resolved cases with interview
Typical timeline
3y 3m
Avg Prosecution
45 currently pending
Career history
525
Total Applications
across all art units

Statute-Specific Performance

§101
48.4%
+8.4% vs TC avg
§103
34.9%
-5.1% vs TC avg
§102
2.7%
-37.3% vs TC avg
§112
12.9%
-27.1% vs TC avg
Black line = Tech Center average estimate • Based on career data from 471 resolved cases

Office Action

§101
DETAILED ACTION The following is a Final Office action. In response to Non-Final communications received 3/4/2026, Applicant, on 6/4/2026, amended Claims 1-2, 7, 9-10, 15, and 17. Claims 1-20 are pending in this action, have been considered in full, and are rejected below. Response to Arguments Arguments regarding Double Patenting – The rejection remains and Applicant has stated they will consider filing a terminal disclaimer. Arguments regarding 35 USC §101 Alice – Applicant states the claims recite a specific improvement in the operation of a computerized content-serving platform by requiring concrete telemetry framework tied to identified opportunities to display content, recites the amended limitations of the claims, stating that this is a particular machine-implemented data collection architecture, and also states the claims recite a technical solution to a technical problem arising in computerized content delivery. Examiner disagrees as there are two clearly stated identified abstract ideas, that of a “Mental Process” and a “Certain Method of Organizing Human Activity”, which are detailed in the rejection below. The claims are not practically integrated as they merely utilize current technologies such as a computing system with a processor and medium, to perform the abstract limitations of the claims. Further, these claims are not directed to an improvement in any additional element, combination, a technology, or technological field, but rather recites claims directed at an abstraction (abstractions here). The use of a computing system, processor, medium, etc. does not make the claim eligible, and this is utilization of current technologies to perform the abstract limitations of the Claims. Again the claims as a whole do not improve any claimed addition element, such as by generally linking the claim to a computer with a processor, etc., and the whole of the rest, including the amended limitations, are part of the abstraction, as per the rejection below, as they recite merely receiving, analyzing, and transmitting steps which are observations, evaluations, and judgments and also can be designated as a Certain Method of Organizing Human Activity. These are not practically integrated, as the claim limitations merely utilize current technologies, such as processing circuitry, to perform the abstract limitations of the claims, similar to that of Alice, essentially “Applying It”. There is no improvement to any technology or any technological process, and any inventive concept would be contained wholly within the abstraction. Therefore, the arguments are non-persuasive, the Claims are ineligible as there is no inventive concept, and the rejection of the Claims and their dependents are maintained under 35 USC 101. Double Patenting The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp. Claims 1, 9, and 17 of the current application (Hereby known as ‘681) are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 11,593,819 (Hereby known as ‘819). Although the claims at issue are not identical, they are not patentably distinct from each other because: Regarding Claims 1, 9, and 17, Claims 1, 9, and 17 of the current application (‘681) recite substantially similar steps of '681 - Claim 1. Claims 1, 9, and 17 of ‘681 recite the steps of: Identifying an opportunity to display one or more content items to a first user; displaying one or more content items to the first user via the identified opportunity; storing information identifying the one or more content items displayed to the first user via the identified opportunity; storing a display time indicating when the one or more content items were displayed to the first user via the identified opportunity; receiving one or more interactions from the first user after the identified opportunity; storing information describing the one or more interactions in association with the first user; storing an interaction time indicating when the one or more interactions were received from the first user after the identified opportunity; measuring, from interactions received after the identified opportunity, a first number of interactions performed by the first user after the one or more content items are displayed to the first user; measuring a second number of interactions performed by a second user after an identified opportunity to display content items to the second user when none of the one or more content items are displayed to the second user; determining a measured difference between the first number of interactions and the second number of interactions; accessing a user interaction model that comprises a plurality of layers of a neural network, wherein the user interaction model is trained by: applying the user interaction model to generate a predicted difference, the predicted difference indicating a difference between a first predicted number of interactions performed by the first user after the one or more content items are displayed to the first user and a second predicted number of interactions performed by the second user when none of the one or more content items are displayed to the second user, and backpropagating one or more error terms obtained from one or more loss functions to update a set of parameters of the user interaction model, the backpropagating performed through the neural network and one or more of the error terms based on the measured difference and the predicted difference; identifying an additional opportunity to display content items to an additional user; applying the user interaction model to determine a predicted value of the additional user when a particular content item is displayed to the additional user; determining, using the predicted value, whether to display the particular content item to the additional user; and causing, based on determining to display the particular content item to the additional user, the particular content item to be displayed to the additional user. Whereas Claim 1 of ‘819 states: obtaining training data that comprises identifiers of a plurality of users of an online concierge system, with a label applied to each identifier of a user, the label applied to an identifier of a user comprising a value representing a difference between a number of an interaction performed by the user after one or more content items are displayed to the user and a number of the interaction performed by the user when none of the one or more content items are displayed to the user; initializing a user interaction network that comprises a plurality of layers of a neural network; for each of a plurality of the examples of the training data: applying the user interaction network to the identifier of the user to generating a predicted value of a user corresponding to the identifier of the user, the predicted value indicating a difference between a predicted number of the interaction performed by the user after one or more content items are displayed to the user by the online concierge system and a predicted number of the interaction performed by the user when none of the one or more content items are displayed to the user, and backpropagating one or more error terms obtained from one or more loss functions to update a set of parameters of the user interaction network, the backpropagating performed through the neural network and one or more of the error terms based on a difference between a label applied to the user identifier and the generated predicted value of the user; and storing the set of parameters of the layers of the user interaction network on the computer readable storage medium as parameters of the user interaction model, wherein the user interaction model is configured to receive an identifier of an additional user and to generate a predicted value of the additional user when a content item is displayed to the additional user by the online concierge system. These are obvious variants of each other as both recite substantially the same limitations. Further, elimination of an element or its functions is deemed to be obvious in light of prior art teachings of at least the recited element or its functions (see In re Karlson, 136 USPQ 184, 186; 311 F2d 581 (CCPA 1963)), thereby rendering the elimination of any elements recited in the claims of the related patent (that are not recited in the instant claims) obvious. Thus, Claims 1, 9, and 17 of the current application are obvious variants of claim 1 in ‘819. Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 9, and 17 recite limitations for identifying an opportunity to display one or more content items to a first user (Analyzing and Transmitting the Analyzed Information, an observation and judgment, a Mental Process; a Fundamental Economic Practice, i.e. data analytics for predicting user value; a Certain Method of Organizing Human Activity), displaying one or more content items to the first user via the identified opportunity (Transmitting Information, a judgment, a Mental Process; a Fundamental Economic Practice, i.e. data analytics for predicting user value; a Certain Method of Organizing Human Activity), storing information identifying the one or more content items displayed to the first user via the identified opportunity (Collecting/Storing and Analyzing the Information, an observation and evaluation, a Mental Process; a Fundamental Economic Practice, i.e. data analytics for predicting user value; a Certain Method of Organizing Human Activity), storing a display time indicating when the one or more content items were displayed to the first user via the identified opportunity (Collecting/Storing and Analyzing the Information, an observation and evaluation, a Mental Process; a Fundamental Economic Practice, i.e. data analytics for predicting user value; a Certain Method of Organizing Human Activity), receiving one or more interactions from the first user after the identified opportunity (Collecting Information, an observation, a Mental Process; a Fundamental Economic Practice, i.e. data analytics for predicting user value; a Certain Method of Organizing Human Activity), storing information describing the one or more interactions in association with the first user (Collecting/Storing and Analyzing the Information, an observation and evaluation, a Mental Process; a Fundamental Economic Practice, i.e. data analytics for predicting user value; a Certain Method of Organizing Human Activity), storing an interaction time indicating when the one or more interactions were received from the first user after the identified opportunity (Collecting/Storing and Analyzing the Information, an observation and evaluation, a Mental Process; a Fundamental Economic Practice, i.e. data analytics for predicting user value; a Certain Method of Organizing Human Activity), measuring, from interactions received after the identified opportunity, a first number of interactions performed by the first user after the one or more content items are displayed to the first user (Collecting and Analyzing the Information, an observation and evaluation, a Mental Process; a Fundamental Economic Practice, i.e. data analytics for predicting user value; a Certain Method of Organizing Human Activity), measuring a second number of interactions performed by a second user after an identified opportunity to display content items to the second user when none of the one or more content items are displayed to the second user (Collecting and Analyzing the Information, an observation and evaluation, a Mental Process; a Fundamental Economic Practice, i.e. data analytics for predicting user value; a Certain Method of Organizing Human Activity), determining a measured difference between the first number of interactions and the second number of interactions (Analyzing the Information, an evaluation, a Mental Process; a Fundamental Economic Practice, i.e. data analytics for predicting user value; a Certain Method of Organizing Human Activity), accessing a user interaction model that comprises a plurality of layers of a neural network, wherein the user interaction model is trained by: applying the user interaction model to generate a predicted difference, the predicted difference indicating a difference between a first predicted number of interactions performed by the first user after the one or more content items are displayed to the first user and a second predicted number of interactions performed by the second user when none of the one or more content items are displayed to the second user, and backpropagating one or more error terms obtained from one or more loss functions to update a set of parameters of the user interaction model, the backpropagating performed through the neural network and one or more of the error terms based on the measured difference and the predicted difference; identifying an additional opportunity to display content items to an additional user (Collecting and Analyzing the Information, an observation and evaluation, a Mental Process; a Fundamental Economic Practice, i.e. data analytics for predicting user value; a Certain Method of Organizing Human Activity), applying the user interaction model to determine a predicted value of the additional user when a particular content item is displayed to the additional user (Analyzing the Information, an evaluation, a Mental Process; a Fundamental Economic Practice, i.e. data analytics for predicting user value; a Certain Method of Organizing Human Activity), determining, using the predicted value, whether to display the particular content item to the additional user (Analyzing the Information, an evaluation, a Mental Process; a Fundamental Economic Practice, i.e. data analytics for predicting user value; a Certain Method of Organizing Human Activity), and causing, based on determining to display the particular content item to the additional user, the particular content item to be displayed to the additional user (Transmitting the Information, a judgment, a Mental Process; a Fundamental Economic Practice, i.e. data analytics for predicting user value; a Certain Method of Organizing Human Activity), which under their broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a computer system, processor, computer-readable medium, and memory, nothing in the claim element precludes the step from practically being performed or read into the mind for the purposes of presenting content. For example, but for the use of computer system, processor, etc. language, determining a measured difference between the first number of interactions and second number of interactions encompasses a manager, supervisor, data analyst, anyone really, watch someone interacting with a computer and recording it, and then noting a difference between types of interactions, an observation, evaluation, and judgment. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Further, as described above, these processes recite limitations for a Fundamental Economic Process, a “Method of Organizing Human Activity”. Accordingly, the claim recites an abstract idea. This judicial exception is not integrated into a practical application. In particular, the claim recites the above stated additional elements to perform the abstract limitations as above. The system, processor, memory, and medium are recited at a high-level of generality (i.e., as a generic processor/module performing a generic computer function of storing, retrieving, sending, and processing data) such that they amounts no more than mere instructions to apply the exception using generic computer components. Even if taken as an additional element, the receiving and transmission steps above would be insignificant extra-solution activity as these are receiving, storing, and transmitting data as per the MPEP 2106.05(d). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered both individually and as an ordered combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements being used to perform the abstract limitations stated above amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible. Applicant’s specification states: “[0078] Embodiments of the invention may also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, and/or it may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a tangible computer readable storage medium, which include any type of tangible media suitable for storing electronic instructions and coupled to a computer system bus. Furthermore, any computing systems referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability. “ Which shows any general-purpose computing device can be used, as per the specification above, and from this interpretation, one would reasonably deduce the aforementioned steps are all functions that can be done on generic components, such as a laptop, desktop, tablet, etc., and thus application of an abstract idea on a generic computer, as per the Alice decision and not similar to Berkheimer, but for edification the Applicant’s specification has been used as above satisfying any such requirement. For the collecting and transmission steps that were considered extra-solution activity in Step 2A above, if they were to be considered an additional element, they have been re-evaluated in Step 2B and determined to be well-understood, routine, conventional, activity in the field. The background does not provide any indication that the additional elements, such as the system, memory, processor, etc., nor the receiving and transmitting steps as above, are anything other than a generic, and the MPEP Section 2106.05(d) indicates that mere collection or receipt, storing, or transmission of data is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept. The claim is not patent eligible. Claims 2-8, 10-16, and 18-20 contain the identified abstract ideas, further narrowing them, with no new additional elements to be considered under prong 2 as part of the Alice analysis of the MPEP for a practical or under 2B, and thus not significantly more for the same reasons and rationale as above. After considering all claim elements, both individually and in combination, Examiner has determined that the claims are directed to the above abstract ideas and do not amount to significantly more. Therefore, the claims and dependent claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank International, No. 13–298. Allowable Subject Matter Claims 1-20 have overcome the prior art and would be allowable if amended to overcome the 35 USC 101 rejections. The closest prior art of record is Helenius (U.S. Publication No. 2020/031,1585), Bhatia (U.S. Publication No. 2020/010,4697), and George (U.S. Publication No. 2019/033,5006). Helenius, a system and method for multi-model based account/product sequence recommendations, teaches obtaining training data that comprises identifiers of a plurality of users, a label used as well as an identifier of a user, initializing a user interaction network that comprises a plurality of layers of a neural network, and for each of a plurality of the examples of the training data, using machine learning, but it does not explicitly state it is configured to the label applied to an identifier of a user comprising a value representing a difference between a number of an interaction performed by the user after one or more content items are displayed to the user and a number of the interaction performed by the user when none of the one or more content items are displayed to the use, nor does it teach the backpropagation being performed in the manner claimed below. Bhatia, a system and method for generating homogenous user embedding representations from heterogenous user interaction data using a neural network, teaches the label applied to an identifier of a user comprising a value representing a difference between a number of an interaction performed by the user after one or more content items are displayed to the user and a number of the interaction performed by the user when none of the one or more content items are displayed to the user, but does not teach the backpropagating in relation to a generated predicted value of a user corresponding to the user, nor does it teach the updating of the parameters used with the backpropagation and the neural network. George, a method and system for dynamic customization of structure, teaches storing the set of parameters, layers, a computer readable storage medium, the user interaction model is configured to receive an identifier of an additional user, to generate a predicted value of a user and additional user additional user, but not when a content item is displayed to the additional user by the online concierge system, nor does it teach specifically applying the user interaction network to the identifier of the user to generating a predicted value of a user corresponding to the identifier of the user, the predicted value indicating a difference between a predicted number of the interaction performed by the user after one or more content items are displayed to the user by the online concierge system and a predicted number of the interaction performed by the user when none of the one or more content items are displayed to the user, and backpropagating one or more error terms obtained from one or more loss functions to update a set of parameters of the user interaction network, the backpropagating performed through the neural network and one or more of the error terms based on a difference between a label applied to the user identifier and the generated predicted value of the user. None of the above prior art explicitly teaches applying the user interaction network to the identifier of the user to generating a predicted value of a user corresponding to the identifier of the user, the predicted value indicating a difference between a predicted number of the interaction performed by the user after one or more content items are displayed to the user by the online concierge system and a predicted number of the interaction performed by the user when none of the one or more content items are displayed to the user, and backpropagating one or more error terms obtained from one or more loss functions to update a set of parameters of the user interaction network, the backpropagating performed through the neural network and one or more of the error terms based on a difference between a label applied to the user identifier and the generated predicted value of the user, and these are the reasons which adequately reflect the Examiner's opinion as to why Claims 1-20 are allowable over the prior art of record. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20200311585 A1 Helenius; Jere Armas Michael et al. MULTI-MODEL BASED ACCOUNT/PRODUCT SEQUENCE RECOMMENDER US 20200104697 A1 Bhatia; Vidit et al. GENERATING HOMOGENOUS USER EMBEDDING REPRESENTATIONS FROM HETEROGENEOUS USER INTERACTION DATA USING A NEURAL NETWORK US 20190335006 A1 George; William Brandon et al. DYNAMIC CUSTOMIZATION OF STRUCTURED INTERACTIVE CONTENT ON AN INTERACTIVE COMPUTING SYSTEM US 20220398605 A1 Chen; Changyao et al. TRAINING A MODEL TO PREDICT LIKELIHOODS OF USERS PERFORMING AN ACTION AFTER BEING PRESENTED WITH A CONTENT ITEM US 20220139535 A1 KUSHNIR; Nataliya M. et al. EFFICIENT DETERMINATION OF A DATA ENTITY STORING HEALTHCARE DATA THROUGH MAPPED ENTRY AND/OR TRAVERSAL OF A SEMANTIC DATA STRUCTURE US 20210326718 A1 Olson; Brian S. et al. MACHINE LEARNING TECHNIQUES TO SHAPE DOWNSTREAM CONTENT TRAFFIC THROUGH HASHTAG SUGGESTION DURING CONTENT CREATION US 20210118440 A1 Peng; Fuchun et al. Speech Recognition Accuracy with Natural-Language Understanding based Meta-Speech Systems for Assistant Systems US 20210117712 A1 Huang; Lisa Xiaoyi et al. Smart Cameras Enabled by Assistant Systems US 20210117623 A1 Aly; Ahmed et al. On-device Convolutional Neural Network Models for Assistant Systems US 20210117214 A1 Presant; William Crosby et al. Generating Proactive Content for Assistant Systems US 20200202195 A1 Patel; Sanjay NEURAL NETWORK PROCESSING USING MIXED-PRECISION DATA REPRESENTATION US 20200151570 A1 Ravi; Sathya Narayanan et al. Training System for Artificial Neural Networks Having a Global Weight Constrainer US 20190147342 A1 Goulding; John R. et al. DEEP NEURAL NETWORK PROCESSOR WITH INTERLEAVED BACKPROPAGATION US 20190102694 A1 Yates; Andrew Donald et al. CONTENT DELIVERY BASED ON CORRECTIVE MODELING TECHNIQUES US 20190095796 A1 CHEN; LI et al. METHODS AND ARRANGEMENTS TO DETERMINE PHYSICAL RESOURCE ASSIGNMENTS US 20190080019 A1 Young; Andrew Richard et al. Predicting Non-Observable Parameters for Digital Components US 20190042945 A1 Majumdar; Somdeb et al. METHODS AND ARRANGEMENTS TO QUANTIZE A NEURAL NETWORK WITH MACHINE LEARNING US 20170154259 A1 Burr; Geoffrey et al. NEURON-CENTRIC LOCAL LEARNING RATE FOR ARTIFICIAL NEURAL NETWORKS TO INCREASE PERFORMANCE, LEARNING RATE MARGIN, AND REDUCE POWER CONSUMPTION US 11445252 B1 Kim; Taein et al. Prioritizing encoding of video data received by an online system to maximize visual quality while accounting for fixed computing capacity US 11443120 B2 Poddar; Shivani et al. Multimodal entity and coreference resolution for assistant systems US 11270159 B1 Gao; Tianshi et al. Training content selection models with reduced bias 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 JOSEPH M WAESCO whose telephone number is (571)272-9913. The examiner can normally be reached on 8 AM - 5 PM M-F. 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, BETH BOSWELL can be reached on (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-1348. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /JOSEPH M WAESCO/Primary Examiner, Art Unit 3625B 9/5/2026
Read full office action

Prosecution Timeline

Dec 04, 2024
Application Filed
Mar 04, 2026
Non-Final Rejection mailed — §101
Jun 04, 2026
Response Filed
Sep 11, 2026
Final Rejection mailed — §101 (current)

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Prosecution Projections

3-4
Expected OA Rounds
46%
Grant Probability
89%
With Interview (+42.6%)
3y 3m (~1y 5m remaining)
Median Time to Grant
Moderate
PTA Risk
Based on 471 resolved cases by this examiner. Grant probability derived from career allowance rate.

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