DETAILED ACTION
The applicant’s amendment on October 15, 2025 has been acknowledged. Claims 2, 3, 8-14, 17 and 21 have been canceled. Claims 22 and 23 have been added. Claims 1, 4-7, 15, 16, 18-20, 22 and 23, as amended, are currently pending and have been considered below.
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 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, 4-7, 15, 16, 18-20, 22 and 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites the actions related to determining the value of a vehicle, which is a method of organizing human activity. Under step 2A1, Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II). As noted by the applicant’s originally filed specification paragraph [0002], the determining a value of a vehicle is a typical practice. Thus the claim is directed toward a fundamental economic practice as it evaluates or determines the value a vehicle.
While the claims recite a computing device which receives fault codes and/or sensor data along with receiving usage data this is merely a data gathering step as part of the data transmission, as shown in MPEP 2106.05(g) and as such does not serve to render the claims into a practical application. As shown in the applied art collecting and using known fault codes, sensor data and usage data is a known concept in the prior art and the steps merely require gathering the data so future determinations can be made. The claims have been previously amended to establish that the receiving is performed via a network interface, however this is still merely data collection, which is an insignificant extra solution activity, see MPEP 2106.05(g). The analyzing steps are described by the specific module or software names; however this is still merely applying the abstract idea on a computer, see MPEP 2106.05(f).
The remaining elements of the claims recite analyzing and determining based on the received information matching pre-determined groups, however the claims are not specific as to how this occurs. Rather the claims only recite that is done “via a network interface” which allows for the server to merely receive and present the data to a person which performs the determination and visually inspects the images. The final determination of determining the value of the vehicle, however while it states what the determination is based on it is not specific on how any of these values are determined. As such these limitations do not render the claims into a practical application and as such as a whole or considered individually are still directed the abstract idea.
Claims recite receiving fault codes and/or sensor data along with usage data. These steps are generic functions of receiving data, see MPEP 2106.05(g). The limitation of “determining, using the trained machine learning algorithm, that the one or more generated fault codes and the generated sensor data together comprise a group of one or more generated fault codes and generated sensor data that matches a pred-determined grouping of the pre-determined groupings, wherein the group of one or more generated fault codes and generated sensor data includes a first fault code and particular sensor data and the pre-determined grouping includes the first fault code and the particular sensor data”, “determining, via a calculation module, a cost associated with the particular repair associated with the particular component” and “determining the value of the vehicle” are generic and broad in nature. That is to say they do not provide or state any specific way to perform these determinations. Thus this is any and all ways to determine the matching, cost and value of the vehicle. As such this allows for a person to be visually seeing the data and making the determinations. Thus they can be readily performed by a person or via a machine as an ordinary set of operations. The two steps of determining can be performed by a person and are generically claimed as such are not a practical application of the abstract idea. Thus when considered individually or in combination the claims continue to be directed toward merely an abstract idea without reciting a practical application.
The claims additionally recite “training a machine learning algorithm of an algorithm module using one or more of the historical fault codes and one or more of the historical sensor data to provide repair predictions using the plurality of pre-determined groupings” which as outlined in the July 2024 guidance from the Office is not considered sufficient to render the claims into a practical application. Example 47 claim 2 from the guidance establishes that training a machine learning system is merely applying the function on a computer as outlined by MPEP 2106.05(f). As such these limitations fail to render the abstract idea into a practical application.
The applicant has amended the claims further to recite elements previously recited in claims 2 and 21. Specifically the claims have been amended to recite, “wherein the one or more fault codes comprise On Board Diagnostics (OBD) codes”, “wherein each of the pre-determined groupings includes at least one of the historical fault codes and at least one of the historical sensor data that are mapped to at least one failed component;” and “receiving a repair history of the vehicle; determining a particular repair shop that previously performed a repair on the vehicle; determining that the particular repair shop does not meet one or more standards for repairs and/or does not achieve a predetermined level of quality of repairs; and reducing the value of the vehicle”. As previously stated the fact that the fault codes comprise On Board Diagnostic (OBD) codes, merely describes the type of fault codes it does not change the step as it is still merely receiving data. As for the limitations determining receiving the repair history determining the repair shop and that the repair shop does not meet the standards and/or level of quality, however does not explain or establish how this changes the value determination. As far as the limitations which establish that the mapping provides a combination of OBD fault code and sensor data this again is merely describing the data which is used and does not change or alter the steps but rather merely describes the data which is used. As such these limitations fail to render the abstract idea into a practical application.
While the applicant has amended the claims to recite “mapping historical fault codes and historical sensor data to historical data of failed vehicle components to create a plurality of pre-determined groupings, wherein each of the pre-determined groupings includes at least one of the historical fault codes and at least one of the historical sensor data that are mapped to the respective historical data of at least one failed vehicle component”, this fails to provide any specifics as to how the data is actually mapped. That is the provided section of the applicant’s originally filed specification outlines how this is merely an example of one possible way to map the data, but is not the only way the data can be mapped. Further the provided paragraph doesn’t establish how the fault code and the sensor data are shown to be the failed component. Was this the conclusion after repair? Is this always the case? Is this the most likely cause? This establishes that the claim recites the function rather than a specific way of mapping the collected data to the fault.
For the limitation of “training a machine learning algorithm of an algorithm module using one or more of the historical fault codes, one or more of the historical sensor data, and one or more of the historical data of failed vehicle components to provide repair predictions using the plurality of pre-determined groupings”, the applicant points to paragraphs [0018], [0048] and [0066] of the originally filed specification. Paragraph [0018] establishes that an algorithm can be trained, and points to a machine learning algorithm which “may be trained on historical repair data to recognize that the presence of particular sensor data and/or one or more fault codes comprising a particular grouping is an indication of a particular component failure”, but again doesn’t establish any particular manner of training the machine learning algorithm. As stated above this amounts to merely applying the function on a computer, see MPEP 2106.05(f). Paragraph [0048] establishes different ways the algorithm may make predictions, which highlights that this is a function rather than a specific manner of utilizing the data. That is the claim is broader and allows for multiple ways of performing the function rather than a specific manner of performing the function. As discussed in MPEP 2106.05(f), the claim limitations cover any solution to the problem with no restriction and as such does not integrate the abstract idea into a practical application. Paragraph [0066] also establishes that the scope of the claims is broader as it provides additional examples of how it could be implemented. As the scope of the claims is broader and does not recite a specific manner of implementing the algorithm the limitations do not integrate the abstract idea into a practical application.
The applicant appears to also argue the limitations “receiving…one or more fault codes generated by the vehicle, wherein the one or more fault codes comprise On Board Diagnostics (OBD) codes” which was discussed above, specifically merely describes the type of fault codes it does not change the step as it is still merely receiving data. Further it is also a data gathering step and while part of the abstract idea it does not render the abstract idea into a practical application. That is there is no specific manner of receiving the data, and the claims only recite a description that it is through a network interface.
The applicant appears to also argue the limitations “receiving, via the network interface, sensor data generated by one or more sensors of the vehicle”, as stated above these steps are generic functions of receiving data, see MPEP 2106.05(g). As such this does not render the claims into a practical application.
The applicant argues the limitation “determining, using the trained machine learning algorithm , that the one or more generated fault codes and the generated sensor data together comprise a group of one or more generated fault codes and generated sensor data that matches a pre-determined grouping of the pre-determined groupings, wherein the group of one or more generated fault codes and generated sensor data includes a first fault code and particular sensor data and the pre-determined grouping includes the first fault code and the particular sensor data”, as stated above the limitations do not provide or state any specific way to perform the determination. Thus this allows for any and all ways to determine the matching. As such this allows for a person to be visually seeing the data and making the determinations. Thus they can be readily performed by a person or via a machine as an ordinary set of operations. The step of determining can be performed by a person and is generically claimed as such are not a practical application of the abstract idea. Therefore, when considered individually or in combination the claims continue to be directed toward merely an abstract idea without reciting a practical application. While the applicant cites paragraph [0066] as stated above this provides examples of possible implementations, which helps establish that the claim is in fact broader and allows for any possible solution. While this includes the solutions recited in the specification it is not limited to those solutions and as such merely recites a function and applies it to computer, see MPEP 2106.05(f). Therefore the limitations fail to render the abstract idea into a practical application.
The applicant appears to argue the limitation of “receiving usage data associated with the vehicle”, as stated above the step is a generic function of receiving data, see MPEP 2106.05(g). Further when looking to the dependent claim 15, the data can be received from “an accelerometer, a gyroscope, a microphone, a vibration detector, an odometer, a driving analysis system, or a navigation system” highlighting that the scope of the claim is broad and allows for various types of information to be received and used without any specifics to how they are used to achieve the result. For example you would get different information from an accelerometer than you would a microphone but the claim as written describes the function broadly that it does not establish how the data is used or combined in the invention to achieve the result. As such it is merely applying the abstract idea on a computer as shown in MPEP 2106.05(f).
The applicant argues “based on the usage data and the pre-determined grouping, predict, using the trained machine learning algorithm, a particular repair associated with a particular component associated with the vehicle” citing to paragraphs [0020], [0048], [0059], [0066], [0077] and [0084]. Paragraph [0020], provides examples of how different data may be used to indicate a likely failure but also establishes that the usage data can be collected from anywhere and be any type of data. This can be combined with the sensor and fault data in anyway to indicate the failure. As such this does not establish that the limitation is rendered into a practical application. Paragraph [0048] as stated above establishes different ways the algorithm may make predictions, which highlights that this is a function rather than a specific manner of utilizing the data. That is the claim is broader and allows for multiple ways of performing the function rather than a specific manner of performing the function. As discussed in MPEP 2106.05(f), the claim limitations cover any solution to the problem with no restriction and as such does not integrate the abstract idea into a practical application. Paragraph [0059] establishes another example of how the system may be implemented and establishing the interaction with a user who is trouble shooting problems. Again the scope of the claims is broader and is not limited by the specification. The claim limitations cover any solution to the problem with no restriction and as such does not integrate the abstract idea into a practical application. Paragraph [0066] also establishes that the scope of the claims is broader as it provides additional examples of how it could be implemented. As the scope of the claims is broader and does not recite a specific manner of implementing the algorithm the limitations do not integrate the abstract idea into a practical application. Paragraph [0077] again provides examples of possible implementations and highlights again that the usage data can be any type of data used in any way to make predictions. This does not establish a specific implementation of the function but rather establishes that is merely reciting the end result with no specifics as to how to achieve that result. As the scope of the claims is broader and does not recite a specific manner of implementing the algorithm the limitations do not integrate the abstract idea into a practical application. Paragraph [0084] like the other paragraphs provides examples of possible implementations and highlights again that the usage data can be any type of data used in any way to make predictions. This does not establish a specific implementation of the function but rather establishes that is merely reciting the end result with no specifics as to how to achieve that result. As the scope of the claims is broader and does not recite a specific manner of implementing the algorithm the limitations do not integrate the abstract idea into a practical application. While the applicant argues that these paragraphs establish how the limitation is performed, the Examiner respectfully disagrees as the paragraphs provide examples of how it could but they are non-limiting and therefore do not establish specifically how the claims perform these functions. Rather as discussed above each of the paragraphs establishes that the data can be for any fault, any sensor data and any usage data and combined in any way to make predictions. Establishing that the limitation itself is merely applying the abstract idea on a computer, see MPEP 2106.05 (f). Therefore the limitations fail to render the abstract idea into a practical application.
The applicant argues claim limitations “determining, via a calculation module, a cost associated with the particular repair associated with the particular component; determining the value of the vehicle at a valuation module communicably coupled to the algorithm module, wherein the value of the vehicle is determined based on reducing a first value of the vehicle by the cost associated with the particular repair associated with the particular component” pointing to paragraphs [0046],[0068], [0077], and [0084]. As discussed above each of the paragraphs establishes that the data can be for any fault, any sensor data and any usage data and combined in any way to make predictions. Establishing that the limitation itself is merely applying the abstract idea on a computer, see MPEP 2106.05 (f). Therefore the limitations fail to render the abstract idea into a practical application.
The applicant argues claim limitations “receiving a repair history of the vehicle; determining a particular repair shop that previously performed a repair on the vehicle; determining that the particular repair shop does not meet one or more standards for repairs and/or does not achieve a predetermined level of quality for repairs; and reducing the value of the vehicle” pointing to paragraphs [0061]-[0063]. Paragraphs [0061]-[0063] establishes that there are various types of quality checks again establishing that the claims are broad and allow for the system to check any manner of quality standards. These standards can include industry standards as well as customer’s satisfaction surveys. Again establishing that this not limited to a specific manner of performing the function but broad enough to allow for any type of determination of quality standards. Establishing that the limitations themselves are merely applying the abstract idea on a computer, see MPEP 2106.05 (f). Therefore the limitations fail to render the abstract idea into a practical application.
As such each of the limitations have been considered both individually and in combination. The limitations do not amount to a practical application of the abstract idea as the merely apply the abstract idea on a computer as discussed in detail above.
Step 2(a)(II) considers the additional elements of the independent claims with respect to transforming the abstract idea into a practical application. As noted the above the determinations are generic and as such cannot be considered to be a practical application. The other steps of the independent claims amount to merely sending and receiving data, which again does not amount to be a practical application.
Step 2(b) considers the additional elements of the independent claims with respect to being significantly more than the identified abstract idea. As noted above there are not additional elements which indicate that the claims amount to significantly more than the abstract idea.
Dependent Claims 4 and 18, establishes that the data additionally indicates the mileage which has been added, but doesn’t establish how the mileage is used or how this changes the steps, as such this does not serve to render the claims into a practical application. Dependent Claims 5, and 19, recites determining that the data has been previously generated, but fails to establish how this data is used to determine the value of the vehicle. Additionally the limitations recite reducing the value of the vehicle but it doesn’t expand on how the calculations are made, further the act of reducing or subtracting values is a fundamental practice. As such these limitations fail to render the claims into a practical application. Dependent Claim 6, and 20 receiving repair history however there is no explanation of how the repair history changes the value of the vehicle or how it is used. Like claim 5, the claim recites determining that the data has been previously generated, but fails to establish how this data is used to determine the value of the vehicle. Additionally the limitations recite reducing the value of the vehicle but it doesn’t expand on how the calculations are made, further the act of reducing or subtracting values is a fundamental practice. As such these limitations fail to render the claims into a practical application. Dependent claim 7, like claim 6, recite receiving repair history however there is no explanation of how the repair history changes the value of the vehicle or how it is used. Like claims 5, the claims recite determining that the data has been previously generated, but fails to establish how this data is used to determine the value of the vehicle. Additionally the limitations recite reducing the value of the vehicle but it doesn’t expand on how the calculations are made, further the act of reducing or subtracting values is a fundamental practice. Claim 7 recite determining that the fault codes were cleared without a corresponding repair, however does not explain or establish how this changes the value determination. As such these limitations fail to render the claims into a practical application. Claim 15, recites “wherein the usage data is based on information received from an accelerometer, a gyroscope, a microphone, a vibration detector, an odometer, a driving analysis system, or a navigation system” which establishes various possible equipment and sensors which can collect and transmit data but does not establish how the data is used to achieve the result. Dependent claim 22 recites “wherein at least one pre-determined grouping of the plurality of pre-determined groupings includes a historical fault code for a fault of a headlight circuit and a historical sensor data for a headlight bulb out that are mapped to respective historical data of a failed central electronic module vehicle component”, which describes the data but does not establish how the data is used to achieve the result. Dependent claim 23 recites “wherein at least one pre-determined grouping of the plurality of pre-determined groupings includes a historical fault code for a misfire in a cylinder and a historical sensor data for low coolant level that are mapped to respective historical data for a failed head gasket vehicle component,” which describes the data but does not establish how the data is used to achieve the result. As such these limitations fail to render the claims into a practical application. Thus when considered individually or as a combination these elements do not amount to a practical application.
As state above the judicial exception is not integrated into a practical application. In particular, the claim recites additional elements – computing device and media. The hardware in claimed limitations is recited at a high-level of generality (i.e., as a generic component performing a generic functions of transmitting and receiving information) such that it amounts no more than mere instructions to apply the exception using a generic components. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Response to Arguments
Applicant's arguments filed October 15, 2025 have been fully considered but they are not persuasive.
In response to the applicant’s arguments on pages 9-11 regarding the 101 rejections specifically that, “The Office Action rejects Claims 1, 4-7, 15, 16, and 18-20 under 35 U.S.C. § 101 as allegedly being directed to an abstract idea without significantly more. Applicant respectfully traverses these rejections.”
“Without conceding to the merits of the analysis performed in the Office Action under Step 1 and Step 2A (prong one) with regard to Independent Claim 1, Applicant respectfully submits that Independent Claim 1 provides an improvement to another technology or technical field (i.e., vehicle diagnostics), and is therefore patent-eligible under 35 U.S.C. § 101. See M.P.E.P. §§ 2106.04(d)(l) (Step 2A, prong 2) and 2106.05(a) (Step 2B); see also Ex Parte Desjardins, Appeal No. 2024-000567, U.S. Application No. 16/319,040, at 7-10.”
“In particular, as is discussed above, the claimed mapping (and the resulting pre- determined groupings) and the claimed training of the machine learning algorithm allows for improved vehicle diagnostics. As one example, this may allow a user to be able to determine that sensor data that indicates that a headlight bulb is out (when combined with fault data for a fault of the headlight circuit) actually refers to a faulty central electronic module (CEM), as opposed to that a bulb has failed. See Paras. [0017], [0018], and [0051] of the Published Application. As another example, this may allow a user to be able to determine that sensor data that indicates that coolant level is low (when combined with a fault code for misfiring cylinder(s)) actually refers to a leaking head gasket, as opposed to simply that more coolant needs to be added. See Para. [0077] of the Published Application. As such, this mapping (and the resulting pre-determined groupings) and this training of the machine learning algorithm allows, in some examples, for improved vehicle diagnostics.”
“In fact, the case here appears to be similar to that in Ex Parte Desjardins, where the Appeal Review Panel found machine learning claims to be patentable under 35 U.S.C. § 101. See Ex Parte Desjardins, Appeal No. 2024-000567, U.S. Application No. 16/319,040, at 7-10. Specifically, in Ex Parte Desjardins, the claims were found patentable under 35 U.S.C. § 101 because the Specification discussed the improvement "in the functioning of a computer, or ... to other technology or technical field", and further because the claim itself "reflect[ ed] the improvement". See id Similarly, here, the Specification explains how the claimed mapping (and the resulting pre-determined groupings) and the claimed training of the machine learning algorithm allows for improved vehicle diagnostics. See Paras. [0017], [0018], [0051 ], and [0077] of the Published Application. Furthermore, this improvement is reflected in Independent Claim 1, itself, such as, for example, in at least the following emphasized limitations:”
[quoting the claim 1]
“( emphasis added). As such, like in Ex Parte Desjardins, Independent Claim 1 is patentable under 35 U.S.C. § 101 because it provides an improvement to another technology or technical field (i.e., vehicle diagnostics). See M.P.E.P. §§ 2106.04(d)(l) (Step 2A, prong 2) and 2106.05(a) (Step 2B); see also ExParte Desjardins, Appeal No. 2024-000567, U.S. Application No. 16/319,040, at 7-10.”
“For at least these reasons, Independent Claim 1 is patent-eligible under 35 U.S.C. § 101, as are its dependent claims. For at least certain analogous reasons, Independent Claim 16 is patent-eligible under 35 U.S.C. § 101, as are its dependent claims. Reconsideration and favorable action are respectfully requested.”
The Examiner respectfully disagrees.
As previously stated in the prior Office Action, while the applicant has alleged that the claims amount to “significantly more” than the abstract idea, the Examiner respectfully disagrees. As discussed in detail above and in the 101 rejection the limitations when considered individually or in combination fail to amount to significantly more than the abstract idea as they are merely applying the abstract idea to a computer, see MPEP 2106.05(f). As previously stated while the applicant has amended the claims to recite “mapping historical fault codes and historical sensor data to historical data of failed vehicle components to create a plurality of pre-determined groupings, wherein each of the pre-determined groupings includes at least one of the historical fault codes and at least one of the historical sensor data that are mapped to the responsive historical data of at least one failed vehicle component”, this fails to provide any specifics as to how the data is actually mapped. That is the provided section of the applicant’s originally filed specification paragraph [0051] outlines how this is merely an example of one possible way to map the data, but is not the only way the data can be mapped. Further the provided paragraph doesn’t establish how the fault code and the sensor data are shown to be the failed component. That is to say, was the failure the conclusion of a technician after repair? Is this always the case? Is this the most likely cause based on percentage? The Examiner asserts that the function is broad enough to allow for any of these possible outcomes. This establishes that the claim recites the function rather than a specific way of mapping the collected data to the fault. The Examiner notes that this is not a written description issue as the specification does establish possible ways it could be performed.
The applicant has argued that this allows “a user to be able to determine that sensor data that indicates that a headlight bulb is out (when combined with fault data for a fault of the headlight circuit) actually refers to a faulty central electronic module (CEM), as opposed to that a bulb has failed. See Paras. [0017], [0018], and [0051] of the Published Application” and “this may allow a user to be able to determine that sensor data that indicates that coolant level is low (when combined with a fault code for misfiring cylinder(s)) actually refers to a leaking head gasket, as opposed to simply that more coolant needs to be added. See Para. [0077] of the Published Application”. However this merely states possible examples of what may occur these are also found in the newly added dependent claims. These are merely examples and do not establish how the function it achieved but rather what is possible with the function. As such this continues to merely apply the abstract idea on a computer, see MPEP 2106.05(f) and as such it is not considered to be an improvement.
The applicant argues an Board decision; however the facts of the case do not apply to the current application. Further still the limitations do not amount to an improvement of the computer or equipment. Rather the applicant is alleging that the specification outlines improving diagnostics which is not improving the computer but the accuracy of the diagnostics, which has not been established by the claims or the applicant’s specification. While the applicant alleges the specification outlines specifically how the claimed mapping (and the resulting pre-determined groupings) and the claimed training of the machine learning algorithm allows for improved vehicle diagnostics, the applicant fails to establish how the specific functions are performed. That is the claims merely recite the data and the function with no specifics as to how the functions achieve the result. As such the claims are merely applying the abstract idea to a computer, see MPEP 2106.05(f) and as such it is not considered to be an improvement.
Lacking any additional arguments the Examiner has not been persuaded and the rejections have been maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Walton et al. (US 9,305,407 B1) – discusses fleet management by identifying services which are needed.
Chen et al. (US 2018/0081857 A9) – discusses prioritizing vehicle fixes accordance with ranked matches.
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 PAUL R FISHER whose telephone number is (571)270-5097. The examiner can normally be reached Monday - Friday 9 am to 5:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Yin-Chen Shaw can be reached at (571)272-8878. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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PAUL R. FISHER
Primary Examiner
Art Unit 2498
/PAUL R FISHER/Primary Examiner, Art Unit 2498 1/12/2026