DETAILED ACTION
Acknowledgements
This action is in response to Applicant’s filing on Jun. 4, 2026, and is made Final. This action is being examined by James H. Miller, who is in the eastern time zone (EST), and who can be reached by email at James.Miller1@uspto.gov or by telephone at (469) 295-9082.
Interviews
Interviews are “indispensable to advance the prosecution of a patent application.” MPEP § 713. Accordingly, the following Examiner’s guidance and suggested workflow maximizes this benefit to Applicant by: (1) avoiding back and forth telephone calls for scheduling, (2) permitting Examiner out-of-office notifications to the Applicant when emailing the agenda, and (3) permitting real-time document collaboration and screen sharing.
Interviews are available by telephone or, preferably, by video conferencing using the USPTO’s web-based collaboration platform. Applicants are strongly encouraged to schedule via the USPTO Automated Interview Request (AIR) portal at http://www.uspto.gov/interviewpractice. If an interview is needed more quickly than permitted by the AIR scheduling tool, note this in the AIR remarks for consideration. The Examiner routinely considers such urgent requests when practicable.
An agenda submitted when filing the AIR is strongly encouraged, because Examiners use agendas when determining whether to grant an interview. The AIR has character limits, so send the agenda contemporaneously to James.Miller1@uspto.gov and reference the AIR.
After-Final Interviews Requests are granted only at the Examiner’s discretion and only if disposal or clarification for appeal may be accomplished with only nominal further consideration. MPEP § 713.09. An advance agenda explaining how the interview advances prosecution—e.g., through targeted arguments, identified Examiner error, or proposed claim amendments—is strongly suggested.
For GRANTED requests, expect an email within two (2) business days confirming a date/time slot and collaboration tool access instructions. For DENIED requests, the record will include an explanation for the denial.
The examiner is generally available for interviews, Monday through Friday, 10:00 a.m. to 4:00 p.m. ET.
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 Status
The status of claims is as follows:
Claims 21–40 remain pending and examined with Claims 21, 29, and 36 in independent form.
Claims 21, 22, 23, 29, 30, 31, 36, 37, and 38 are presently amended.
No Claims are presently cancelled or added.
Response to Amendment
Applicant's Amendment has been reviewed against Applicant’s Specification filed Jan. 9, 2023, [“Applicant’s Specification”] and accepted for examination.
Response to Arguments
35 U.S.C. § 101 Argument
Applicant argues the amended claims integrate any abstract idea into a practical application because they improve technology, i.e., in how the predictive model itself operates. Applicant’s Reply at 11, 13. Amended Independent Claim 21 recites “a specific technical process in which the system (1) generates predictions and confidence estimates using the trained deep-learning neural-network; (2) prompts the user to adjust or accept those predicted values and stores the user-validated values in memory; (3) compares the predicted values to actual reimbursement amounts to identify prediction errors; (4) derives corrected training examples from those identified errors; (5) retrains the deep-learning neural- network with the corrected examples; and (6) stores the updated model in memory for use in subsequent predictions. This ordered combination constitutes an improvement to how the machine learning model itself operates. For example, it provides a self-improving predictive system that uses real-world outcomes as ground truth to systematically identify and correct prediction errors, then persists the improved model, which is not merely the application of generic machine learning to a new data environment.” Applicant’s Reply at 13. Applicant further relies on Ex parte Desjardins and contends that Recentive is distinguishable because the claims specify how the model achieves improved prediction accuracy. Applicant’s Reply at 13–14.
Examiner respectfully disagrees. The claimed feedback loop is a results-oriented application of supervised learning, using observed outcomes to adjust/retrain a model, but does not require a particular network architecture, parameter update technique, loss function, optimization algorithm, data structure, or other technical feature that improves the operation of a computer or the ML model itself. The Specification teaches “[t]he possession value model may be generated based at least in part upon any suitable technique.” Spec. ¶ 24. The Specification further teaches that the machine learning model may be trained based at least in part on supervised or unsupervised machine learning and may employ a neural network including “a convolutional neural network, a deep learning neural network, or a combined learning module or program that learns in two or more fields or areas of interest.” Spec. ¶ 72. Further, models “may be created based at least in part upon historical policyholder data to predict what personal possessions are associated with users, and more specifically, the personal data of users (e.g., demographics and/or location data).” Id. NPL Leskovec of record describes neural network training as using weighted layers, choosing a cost/loss function, and applying an optimization algorithm, including gradient descent, to reduce training error. NPL, Leskovec, §§ 13.1.4, 13.2, 13.2.7, 13.2.8, 13.3, 13.3.4.
Applicant argues the claimed “a closed-loop feedback system in which prediction errors are automatically identified by comparing model outputs to actual outcomes, corrected training examples are derived from those errors, the deep-learning neural-network is retrained, and the updated model is stored in memory for subsequent use … is not practically performable in the human mind or with pen and paper, as it involves computational resources to process the comparison, error identification, example derivation, and network retraining across potentially thousands of parameters.”
Applicant’s argument is persuasive. The recited training and retraining of “a deep- learning neural-network machine learning model” are not properly characterized as a mental process because NPL Leskovec of record describes its hand worked Figure 13.1 example as “much simpler than anything that would be a useful application of neural nets.” NPL Leskovec, § 13.1.1. However, the claims remain directed to the abstract idea of processing insurance claims, a commercial interaction under the organizing human activity grouping, and mathematical concepts used to train, apply, evaluate, and update a predictive model.
Applicant argues that the recited combination of supervised deep learning training, corrected training examples, retraining, and persistent storage is not WURC and therefore is an inventive concept. Applicant’s Reply at 15.
Examiner respectfully disagrees. The Specification describes machine learning, neural networks, deep learning, sample data, and supervised learning at a general level without identifying the argued sequence as an unconventional implementation. Spec. ¶¶ 24, 72, 73, 74. NPL Leskovec teaches that neural network training uses weighted layers, training examples, error/loss functions, and repeated optimization or gradient descent updates to reduce loss. NPL Leskovec, §§ 13.1.1, 13.1.4, 13.2, 13.2.7, 13.2.8, 13.3. 13.3.4. The claims do not recite a particular technical implementation for the generic ML operations they involve. Further, the absence of complete preemption does not demonstrate eligibility. MPEP § 2106 (citing Ariosa Diagnostics, Inc. v. Sequenom, Inc., 788 F.3d 1371, 1379 (Fed. Cir. 2015) (“While preemption may signal patent ineligible subject matter, the absence of complete preemption does not demonstrate patent eligibility.”)
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 21–40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Analysis
Step 1: Claims 21–40 are directed to a statutory category. Claims 21–28 recite a “computing system” and are therefore, directed to the statutory category of a “machine.” Claims 29–35 recite a “method” and are therefore, directed to the statutory category of a “process.” Claims 36–40 recite a “one or more non-transitory computer-readable media” and are therefore, directed to the statutory category of an "article of manufacture.”
Representative Claim
Claim 21 is representative [“Rep. Claim 21”] of the subject matter under examination and recites, in part, emphasis added by Examiner to identify limitations with normal font indicating the abstract idea exception, bold limitations indicating additional elements. Each limitation is identified by a letter for later use as a shorthand notation in referencing/describing each limitation. Portions of the claim use italics to identify intended use limitations1 and underline, as needed, in further describing the abstract idea exception:
[A] 21. A computing system for generating one or more predicted values of one or more personal property items owned by a user, the computing system including one or more processors in communication with at least one memory device, the one or more processors configured to:
[B] generate a predictive possession value model based at least in part upon a plurality of historical policyholder records associated with a plurality of policyholders, comprising:
[C] training a deep-learning neural-network machine learning model with a first set of training data comprising (i) training input data comprising respective personal data and respective property data for each policyholder of the plurality of policyholders, and
[D] (ii) training output data comprising one or more respective item values of one or more respective items owned by each policyholder of the plurality of policyholders, wherein training the deep-learning neural-network machine learning model comprises recognizing patterns that map the input training data to the output training data through supervised machine learning;
[E] receive personal data and property data associated with the user;
[E1] prompt the user to at least one of adjust the one or more predicted values or accept the one or more predicted values;
[E2] store, in the at least one memory device, at least one of the one or more predicted values, as adjusted, or the one or more predicted values, as accepted;
[F] determine, based at least in part upon the predictive possession value model, as generated, the one or more predicted values of the one or more personal property items owned by the user and one or more confidence estimates for the one or more predicted values of the one or more personal property items based at least in part upon the first personal data and the first property data;
[G] receive a claim associated with the user in response to a claim event, wherein the claim includes a list of lost items and a list of spared items;
[H] estimate a claim value associated with the claim based at least in part on the list of lost items and the list of spared items;
[I] determine an actual reimbursement amount for the user based at least in part upon the one or more predicted values of the one or more personal property items and the claim value, as estimated, associated with the claim; and
[J] updating the predictive possession value model using a second set of training data in which the first set of training data is updated with one or more item values for the one or more personal property items based on the actual reimbursement amount, continually retrieved additional historical policyholder data, and feedback associated with accuracy of predictions made by the predictive possession value model, wherein updating the predictive possession value model comprises:
[K] comparing the one or more predicted values to the actual reimbursement amount to identify prediction errors; and
[L] retraining the deep-learning neural-network machine learning model with corrected training examples derived from the prediction errors, as identified.
[M] and store in the at least one memory device, the updated predicted possession value model.
Claims are directed to an abstract idea exception.
Step 2A, Prong One: Rep. Claim 21 recites a computer system “for generating one or more predicted values of one or more personal property items owned by a user” in the preamble, Limitation A, and “determine an actual reimbursement amount for the user based at least in part upon the one or more predicted values of the one or more personal property items and the claim value, as estimated, associated with the claim” in Limitation I, which recites commercial or legal interactions under the organizing human activity exception because these limitations describe processing an insurance claim. MPEP § 2106.04(a)(2)(II)(B)(ii) (“processing insurance claims for a covered loss or policy event under an insurance policy (i.e., an agreement in the form of a contract”)). Limitations E, E1, E2, F, G, H, and I further recite collecting policyholder information, estimating claim value, determining reimbursement, and user validation in the context of insurance coverage and claim processing and thus, are part of the same commercial interaction.
Alternatively, Limitations B, C, D, F, J, K, L, M recite a mathematical concepts, including mathematical calculations and algorithms, because "a process that employs mathematical algorithms [“generate a … model,” “training a … model … with training input data … and training output data … through supervised machine learning,” use the model to “determine … predictive values,” “updating the … model,” by “comparing the one or more predicted values,” and “retraining the … model”] to manipulate existing information to generate additional information [“an actual reimbursement amount”] is an abstract idea.” Digitech Image Techs. LLC v. Elecs. For Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014); MPEP § 2106.04(a)(2) (citing Digitech (“The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula.”)
Step 2A, Prong Two: Rep. Claim 21 does not contain additional elements that integrate the abstract idea exception into a practical application because the additional elements are mere instructions to apply the abstract idea exception. MPEP § 2106.05(f). The additional elements are limited to the computer components and indicated in bold, supra. The additional elements are: A computing system … including one or more processors in communication with at least one memory device; a predictive possession value mode comprising a deep-learning neural-network machine learning model;
Regarding the computing system including one or more processors in communication with at least one memory device; a predictive possession value mode comprising a deep-learning neural-network machine learning model, Applicant’s Specification does not otherwise describe them or describes them using exemplary language as a general-purpose computer, as a part of a general-purpose computer, or as any known and exemplary (generic) computer component known in the prior art. Thus, Applicant takes the position that such hardware/software is so well known to those of ordinary skill in the art that no explanation is needed under 35 U.S.C. § 112(a). Lindemann Maschinenfabrik GMBH v. Am. Hoist & Derrick Co., 730 F.2d 1452, 1463 (Fed. Cir. 1984) (citing In re Meyers, 410 F.2d 420, 424 (CCPA 1969) (“[T]he specification need not disclose what is well known in the art”). E.g., Spec. ¶ 24 (“The possession value model may be generated based at least in part upon any suitable technique”); ¶¶ 37, 76 (“The methods and systems described herein may be implemented based at least in part upon computer programming or engineering techniques including computer software, firmware, hardware, or any combination thereof”); ¶ 41 (“User computing device 108 may be any device capable of accessing the Internet”); ¶ 42 (“Insurance provider device 110 may be any device capable of accessing the Internet”); ¶ 48 (“Memory 310 maybe any device allowing information such as executable instructions and/or transaction data to be stored and retrieved.”); ¶ 48 (“Media output component 315 may be any component capable of conveying information to user 301.”); ¶ 56 (“Storage device 434 may be any computer-operated hardware suitable for storing and/or retrieving data”); ¶ 58 (“Storage interface 420 may be any component capable of providing processor 405 with access to storage device 434.”); ¶ 76 (“any transmitting/receiving medium”); ¶ 77 (“”machine readable medium" "computer-readable medium" refers to any computer program product, apparatus and/or device … used to provide machine instructions and/or data to a programmable processor.”); ¶ 77 (“The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor”); ¶ 78 (“a processor may include any programmable system”); ¶ 79 (“the terms "software" and "firmware" are interchangeable, and include any computer program stored in memory for execution by a processor”); ¶ 80 (“The application is flexible and designed to run in various different environments without compromising any major functionality”). The generic processor, here, appears to perform calculations (functions) that are programmed by software. Spec. ¶¶ 79, 48. This is a computer doing what it is designed to do—performing directions it is given to follow.
Regarding the predictive possession value model comprising a deep-learning neural-network machine learning model, Applicant’s Specification describes the model “may be generated based at least in part upon any suitable technique.” Spec.¶ 24. The claims do not specify a particular network architecture, parameter updating technique, loss function, optimization algorithm, or data structure by which the model itself is technically improved. The possession model or ML model do not pose any meaningful limits on the abstract idea exception and amounts to “apply it” with a generic computer. MPEP § 2106.05(f).
The Specification and NPL Leskovec describe supervised machine learning from input-output examples, prediction error/loss evaluation, and iterative model parameter updating at a general level. Spec. ¶¶ 72, 73, 74; NPL Leskovec, §§ 13.1.4, 13.2.7, 13.2.8, 13.33, 13,3,4. Claim 21 does not specify any non-conventional network architecture, loss function, optimization technique, or data structure that improves the model’s operation beyond ordinary practice.
Neither the claim language nor the Specification identifies a particular technical mechanism by which the claimed model operations are improve the machine learning model, the computer, or any other technology. MPEP § 2106.05(a). Recentive Analytics, Inc. v. Fox Corp., 2025 U.S.P.Q.2d 628 (Fed. Cir. 2025) (“[P]atents 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.”).
Limitation A describes the processor communicating with memory to perform the steps of the claimed invention. This takes a generic piece of hardware and describe the functions of receiving, storing, and sending data (instructions) between the processor and memory, which merely invokes computers or other machinery in its ordinary capacity to receive, store, or transmit data. MPEP § 2106.05(f)(2).
Limitations B–M describe the processor and memory performing the steps of the claimed invention, which represents the abstract idea exception itself. Performing the steps of the abstract idea exception itself simply adds a general-purpose computer after the fact to an abstract idea exception, MPEP § 2106.05(f)(2), or generically recites an effect of the judicial exception. MPEP § 2106.05(f)(3). The generic processor, here, appears to perform calculations that are programmed by software. This is a computer doing what it is designed to do—performing directions it is given to follow.
Therefore, the claim as a whole, looking at the additional elements individually and in combination, are no more than mere instructions to apply the exception using generic computer components and is not a practical application. MPEP § 2106.05(f). The additional elements do not integrate the abstract idea exception into a practical application because they do not impose any meaningful limits on the abstract idea exception. Accordingly, Rep. Claim 21 is directed to an abstract idea.
Rep. Claim 21 is not substantially different than Independent Claims 29 and 36 and includes all the limitations of Rep. Claim 21. Independent Claims 29 and 36 contain no additional elements. Therefore, Independent Claims 29 and 36 are also directed to the same abstract idea.
The claims do not provide an inventive concept.
Step 2B: Rep. Claim 21 fails Step 2B because the claim as whole, looking at the additional elements individually and in combination, are not sufficient to amount to significantly more than the recited judicial exception. As discussed with respect to Step 2A, Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer and/or generic computer components. MPEP § 2106.05(f). The same analysis applies here in Step 2B. Mere instructions to apply an exception using a generic computer and/or generic computer components cannot provide an inventive concept. MPEP § 2106.05(I).
The additional elements, taken individually and in combination, do not result in the claim, as a whole, amounting to significantly more than the identified judicial exception.
The pending claims in their combination of additional elements do not provide an inventive concept. The processor, memory, storage, and network components are described generically in the Specification, and the claimed ML operations are recited without a particular technical implementation that improves the ML-model or computer. Spec. ¶¶ 24, 37, 41, 42, 48, 56, 58, 71, 72, 73, 74, 76, 77, 78, 79, 80, 81.
Thus, Examiner finds the generic processor, memory, storage, and network components to be well-understood, routine, and conventional (“WRC”) based on Applicant’s own disclosure2. Spec. ¶¶ 24, 37, 41, 42, 48, 56, 58, 71, 72, 73, 74, 76, 77, 78, 79, 80, 81; MPEP § 2106.05(d). Specifically, Applicant’s Specification discloses the recited additional elements (i.e., a computing system … including one or more processors in communication with at least one memory device; a predictive possession value mode comprising a deep-learning neural-network machine learning model) are generic computer components. These elements do no more than “apply” the recited abstract idea(s) on a known computer (e.g., processor) and computer-related components (e.g., memory).
Examiner finds the machine learning technology and techniques of “supervised machine learning,” “deep-learning,” and “neural-network,” as recited by the claims, are described in the Specification at a general level. Spec. ¶¶ 72, 73, 74. NPL Leskovec teaches that neural network training uses weighted layers, training examples, error/loss functions, and repeated optimization or gradient descent updates to reduce loss. NPL Leskovec, §§ 13.1.1, 13.1.4, 13.2, 13.2.7, 13.2.8, 13.3. 13.3.4. The claims do not recite a particular technical implementation for the generic ML operations they involve.
The Examiner also finds the functions of receiving, storing, transmitting, and processing (e.g., performing mathematical operations on) data, described in Limitations A–M are all normal programmed functions of a generic computer.
There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the additional elements in combination adds nothing that is not already present when looking at the elements individually. Their collective functions merely provide conventional computer implementation of the abstract idea at a high level of generality. Thus, Rep. Claim 21 does not provide an inventive concept.
Rep. Claim 21 is not substantially different than Independent Claims 29 and 36 and includes all the limitations of Rep. Claim 21. Independent Claims 29 and 36 contain no additional elements. Therefore, Independent Claims 29 and 36 also do not recite an inventive concept.
Dependent Claims Not Significantly More
The dependent claims have been given the full two-part analysis including analyzing the additional limitations both individually and in combination. The dependent claim(s) when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. § 101. Dependent claims are dependent on Independent Claims and include all the limitations of the Independent Claims. Therefore, all dependent claims recite the same Abstract Idea. Dependent claims do not contain additional elements that integrate the abstract idea exception into a practical application or recite an inventive concept because the additional elements: (1) are mere instructions to apply the abstract idea exception; and/or (2) further limit the abstract idea exception of the Independent Claims. The abstract idea itself cannot provide the inventive concept or practical application. MPEP §§ 2106.05(I), 2106.04(d)(III).
Dependent Claims 22, 23, 24, 25, 26, 27, 28, 34, 35, 37, 38, 39, and 40 all recite “wherein” clauses or limitations that further limit the abstract idea of the Independent Claims and contain no additional elements. An inventive concept or practical application cannot be furnished by an abstract idea exception itself. MPEP §§ 2106.05(I), 2106.04(d)(III).
Dependent Claims 30, 31, 32, and 33 recite additional limitations that form part of the same abstract idea exception as recited in Independent Claims and contain no additional elements. An inventive concept or practical application cannot be furnished by an abstract idea exception itself. MPEP §§ 2106.05(I), 2106.04(d)(III).
Conclusion
Claims 21–40 are therefore drawn to ineligible subject matter as they are directed to an abstract idea without significantly more. The analysis above applies to all statutory categories of invention. As such, the presentment of Rep. Claim 21 otherwise styled as another statutory category is subject to the same analysis.
Examiner Statement of Prior Art—No Prior Art Rejections
Based on the prior art search results conducted to date, the references reviewed do not anticipate or render obvious the claimed subject matter of Claims 21–40. Specifically, the references reviewed do not disclose or suggest “updating the predictive possession value model using a second set of training data in which the first set of training data is updated with one or more item values for the one or more personal property items based on the actual reimbursement amount,” wherein updating comprises “comparing the one or more predicted values to the actual reimbursement amount to identify prediction errors” and “retraining the deep-learning neural-network machine learning model with corrected training examples derived from the prediction errors, as identified.”
The prior art most closely resembling the applicant’s claimed invention are:
Hayward et al. (U.S. Pat. No. 11,373,249) is pertinent because it discloses “the value of a new claim may be predicted directly by a neural network model trained on historical data 108,” including prediction of “the value of a new claim may be predicted directly by a neural network model trained on historical data 108,” and that the model may be trained with new training data “until the predicted dollar values and the correct dollar values converge.” Hayward, col. 22:1–21. The “model that has been trained on a set of electronic claim records from historical data 270 may be updated dynamically, such that the model may be updated on a much shorter time scale,” and that “the settlement of a claim may trigger an immediate update of one or more neural network models.” Col. 17:60–67. However, Hayward does not teach reimbursement based, prediction error, and corrected training example sequence.
FOR: (Int. Pat. Pub. No. WO 2019/106136 A1) is pertinent because it discloses that a neural network model may be trained using training data and then iteratively subjected to “testing the trained neural network model using the test data to determine a loss function … adjusting the initial regularization parameter based on the loss function … [and] re-training the neural network model using the training data, based on the adjusted regularization parameter … [until] the training data and the loss function for the test data have both converged to a steady state.” FOR p. 5, ll. 7–13. However, FOR does not disclose the insurance specific reimbursement based, prediction error, and corrected training example sequence.
NPL: Leskovec, Jure, Anand Rajaraman, and Jeffrey David Ullman. "Mining of Massive Datasets." (2019) is pertinent because it discloses a neural network, “associated with each input to each node is a weight,” (§ 13.1.1) and that training includes selecting “what cost function should we minimize” (§ 13.1.4, ¶ 4) and “what algorithm do we use to exploit the training examples in order to optimize the weights.” NPL Leskovec, §13.1.4, ¶ 6. NPL Leskovec further discloses that, during gradient descent, “at each iteration we update each parameter vector in the direction opposite to its gradient, so the loss will tend to decrease.” NPL Leskovec, § 13.3.4. However, Leskovec does not disclose the insurance specific reimbursement based, prediction error, and corrected training example sequence.
Conclusion
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 JAMES H MILLER whose telephone number is (469)295-9082. The examiner can normally be reached M-F: 10- 4 PM (EST).
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, Bennett M Sigmond can be reached at (303) 297-4411. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAMES H MILLER/Primary Examiner, Art Unit 3694
1 Statements of intended use fail to limit the scope of the claim under BRI. MPEP § 2103(I)(C).
2 See Changes in Examination Procedure Pertaining to Subject Matter Eligibility, Recent Subject Matter Eligibility Decision (Berkheimer v. HP, Inc.), 3-4, https://www.uspto.gov/sites/default/files/documents/memo-berkheimer-20180419.PDF (April, 18, 2018) (That additional elements are well-understood, routine, or conventional may be supported by various forms of evidence, including "[a] citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates the well-understood, routine, conventional nature of the additional element(s).").
Prosecution Insights