DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This communication is in response to applicant’s filing dated 07/22/2025. Claims 3-4 are canceled. Claims 6-8 are new claims. Claims 1-2 and 5-8 are currently pending.
Priority
Acknowledgment is made of applicant’s claim for foreign priority for Application No. JP 2022125565, filed on 08/05/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/26/2025 has been considered by the examiner.
Response to Arguments
Applicant’s arguments, filed 07/22/2025, with respect to Double Patenting rejection has been fully considered and are persuasive. The Double Patenting rejection has been withdrawn.
Applicant’s arguments, filed 07/22/2025, with respect to the previous 35 U.S.C. 101 have been fully considered and are unpersuasive.
With respect to the previous 35 U.S.C. 101 rejection of claim 1, Applicant argues:
Claim 1 is not directed to an abstract idea under Step 2A, Prong One of the 2019 Revised Patent Subject Matter Eligibility Guidance. Even if it were, the claim integrates any alleged abstract idea into a practical application and recites additional elements that amount to significantly more, thereby satisfying both Step 2A, Prong Two and Step 2B.
Examiner respectfully disagrees. The 2019 Revised Guidance explains that “mental processes” include acts that people can perform in their minds or using pen and paper, even if the claim recites that a generic computer component performs the acts. The claims are directed to determining sound data that determines whether an abnormal sound had occurred using a frequency spectrum. The claims disclose the steps of generating process and evaluation process that compares target operation data with the generated data to evaluate the target vehicle based on a magnitude of deviation between the generated data and the target operation data, the target operation data collected simultaneously with the evaluation sound data and indicating the operation status of the target vehicle. Hence, examiner has indicated that these identified limitations are directed to “mental process” and has provided a justification for why these limitations fall within one of the enumerated groupings of abstract ideas. Furthermore, using a computer as a tool to implement the abstract ideas does not make it less abstract. This is sufficient under the guidelines of the 2019 PEG and October 2019 Update as cited above. Accordingly, it seems reasonable to examiner to group the abstract idea under “mental process” as enumerated in Section I of the 2019 PEG. Integration into a practical application requires an additional element(s) or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. Limitations that are not indicative of integration into a practical application are those that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. -see MPEP 2106.05(f). A claim may be found to be eligible if it integrates a judicial exception into a practical application as cited by Applicant. However, claiming improved data processing efficiency inherent with applying any improvement to the judicial exception itself on a computer does not provide an inventive concept. The claims do not integrate the judicial exception into a practical application.
Applicant’s citation of McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299 (Fed. Cir. 2016) is non-persuasive because the claims at issue in McRo are readily distinguishable over the instant claims.
In McRO, the Federal Circuit held the claimed methods of automatic lip synchronization and facial expression animation using computer-implemented rules patent eligible under 35 U.S.C. § 101, because they were not directed to an abstract idea. The basis for the McRO court's decision was that the claims were directed to an improvement in computer-related technology (allowing computers to produce "accurate and realistic lip synchronization and facial expressions in animated characters" that previously could only be produced by human animators), and thus did not recite a concept similar to previously identified abstract ideas. In McRO, timing phonemes in milliseconds represents both the problem being solved and the inventive solution. On the other hand, timing in the instant application represents nothing more than a sequence of events that occur time – a feature common to most process/method patent applications. The timing is not critical to accomplishing the process. For example, a few second delay in a network transmission will not materially affect the outcome of the ordered combination of method steps. Timing is not a problem introduced by the technology itself or arising in the realm of computer networks that the instant application seeks to solve...” In contrast, the instant claims are incomparable to the claims at issue in McRO. The present case is different: the focus of the claims is not on such an improvement in computers as tools, but on certain independently abstract ideas that use computers as tools. The claims here are not directed to a specific improvement to computer functionality nor an inventive solution to any computer specific problem.
Applicant’s citation of Thales Visionix Inc. v. United States, 850 F. 3d 1343 (Fed. Cir. 2017) is non-persuasive because the claims at issue in Thales are readily distinguishable over the instant claims.
The claims specify a particular configuration of inertial sensors and a particular method of using the raw data from the sensors in order to more accurately calculate the position and orientation of an object on a moving platform. The mathematical equations are a consequence of the arrangement of the sensors and the unconventional choice of reference frame in order to calculate position and orientation. Far from claiming the equations themselves, the claims seek to protect only the application of physics to the unconventional configuration of sensors as disclosed. As such, these claims are not directed to an abstract idea and thus the claims survive Alice step one. The present case is different: the focus of the claims is not on such an improvement in computers as tools, but on certain independently abstract ideas that use computers as tools. The claims here are not directed to a specific improvement to computer functionality nor an inventive solution to any computer specific problem.
Applicant’s citation of SiRF Tech. v. International Trade Commission, 601 F. 3d 1319 (Fed. Cir. 2010) is non-persuasive because the claims at issue in SiRF are readily distinguishable over the instant claims.
As found by the courts “In order for the addition of a machine (processor) to impose a meaningful limit on the scope of a claim, it must play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly.” The present case is different: the focus of the claims is not on such an improvement in computers as tools, but on certain independently abstract ideas that use computers as tools. The claims here are not directed to a specific improvement to computer functionality nor an inventive solution to any computer specific problem.
Applicant’s arguments, filed 07/22/2025, with respect to the rejection(s) of claim(s) 1-2 and 5-8 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Kim et al. US 20210335064 A1, in view of Goto et al., JP2014222189 A and in view of Aso et al., US 20210390412 A1.
For these reasons the rejection under 35 USC § 101 directed to non-statutory subject matter set forth in this office action is maintained.
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 1-2 and 5-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 2A – Prong 1:
Claim 1 recites the abstract idea concept of system (Claim 1) of determining sound data that determines whether an abnormal sound has occurred using a frequency spectrum. This abstract idea is described in at least claim 1 by, generating process and evaluation process that compares target operation data with the generated data to evaluate the target vehicle based on a magnitude of deviation between the generated data and the target operation data, the target operation data collected simultaneously with the evaluation sound data and indicating the operation status of the target vehicle, are considered mental process steps. For example, the limitation of evaluation process is merely evaluating a data set. These limitations can be done with the aid of pen and paper. The identified claim limitations that recite an abstract idea fall within the enumerated groupings of abstract ideas in Section 1 of the 2019 Revised Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019.
The limitations of generating process and evaluation process that compares target operation data with the generated data to evaluate the target vehicle based on a magnitude of deviation between the generated data and the target operation data, the target operation data collected simultaneously with the evaluation sound data and indicating the operation status of the target vehicle as drafted, are process that, under its 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 system for generating process and evaluation process, nothing in the claim elements precludes the step from practically being performed in the mind. 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. Accordingly, claim 1 recites an abstract idea.
Under Step 2A – Prong 2:
The claim recites additional elements to the abstract idea. However, these additional elements fail to integrate into a practical application.
Claim 1 recites, processing circuitry; and a storage device, wherein the storage device stores data of a learned model that has been trained using training data, the training data includes: training sound data recorded while operating a reference vehicle in a state serving as an evaluation reference for a predetermined period of time; and reference operation data collected simultaneously with the training sound data and indicating an operation status of the reference vehicle, the learned model has been trained by supervised learning using the training data, to generate, from the sound data, generated data, which is prediction data of an operation status of the target vehicle, and the processing circuit is configured to execute: a generation process that generates the generated data by inputting, to the learned model, evaluation sound data recorded while operating the target vehicle for the predetermined period of time, the learned model is a neural network that sets a feature of data extracted from data corresponding to the predetermined period of time as an explanatory variable and sets the operation status at a point of time corresponding to the extracted data as an objective variable, the evaluation process includes calculating, as an evaluation index value, a total sum of the deviation that has been output for each piece of the data repeatedly extracted while changing an extraction start time, the total sum being a total sum of the deviation for the predetermined period of time, and the evaluation index value is used as a quantitative measure of vehicle health, such that the larger the evaluation index value, the more the state of the vehicle deviates from the state of the healthy reference vehicle. Which are mere data gathering that is simply employed as a tool to collect information and reporting results, which is insignificant extra solution activity as the step simply gathers data necessary to perform the abstract idea. These additional steps amount necessary data gathering and reporting results, wherein all uses of the recited abstract idea require such data gathering or data output. See MPEP 2106.05(g).
Under Step 2B:
Regarding Step 2B of the 2019 PEG, independent claim 1 does not include additional elements (considered both individually or in combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of processing circuitry; and a storage device, wherein the storage device stores data of a learned model that has been trained using training data, the training data includes: training sound data recorded while operating a reference vehicle in a state serving as an evaluation reference for a predetermined period of time, and the learned model is a neural network, the examiner submits that these limitations are insignificant extra- solution activities.
Further, a conclusion that an additional element is insignificant extra solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood routine and convention activity in the field. The additional limitations of processing circuitry; and a storage device, wherein the storage device stores data of a learned model that has been trained using training data, the training data includes: training sound data recorded while operating a reference vehicle in a state serving as an evaluation reference for a predetermined period of time, are well-understood, routine and conventional activities because the background recites that the processor may, for example, be configured to perform the operations by performing hardware implemented logical functions, executing stored instructions, or executing algorithms for performing each of the operations. Further the mere collection of data or receipt of data over a network is a well-understood, routine and conventional function when it is claimed in a merely generic manner (as itis here). See MPEP 2106.05(d). Therefore, claim 1 is ineligible under 35 U.S.C. 101.
Regarding claims 2 and 5-8:
Dependent claims 2 and 5-8 only recite limitations further defining the mental processes and recite further data gathering. These limitations are considered mental processes without significantly more elements to the abstract idea.
Claims 1-2 and 5-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 5, 6 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. US 20210335064 A1, in view of Goto et al., JP2014222189 A and in view of Aso et al., US 20210390412 A1, hereinafter referred to as Kim, Goto and Aso, respectively.
Regarding to claim 1, Kim teaches a vehicle evaluation system configured to evaluate a target vehicle, which is a vehicle to be evaluated, using sound data obtained by recording sound emitted from the target vehicle, the vehicle evaluation system comprising:
processing circuitry; (processor – See at least ¶71) and
a storage device, (storage device – See at least ¶87) wherein
the storage device stores data of a learned model that has been trained using training data, (the trained machine learned model may reside in memory that is stored locally at the vehicle, such as on a vehicle computing device, and/or may reside in memory that is stored remotely from the vehicle, such as on a server computing device or a computing device associated with a remote monitoring system of the vehicle – See at least ¶119) and
the training data includes: training sound data recorded while operating a reference vehicle in a state serving as an evaluation reference for a predetermined period of time, (As such, the first audio data (as well as other audio data associated with other vehicles), may be used as training data. The training data may include a designation of an operating status of the component any of the operating status indications and may be based on the previously stored log data – See at least ¶118. the process may determine whether the condition satisfies a threshold. For example, the condition may be compared against a threshold prior to commissioning the vehicle – See at least ¶128);
reference operation data collected simultaneously with the training sound data and indicating an operation status of the reference vehicle, (the audio data associated with a component that failed may be classified for use by the machine learning model to identify faults in the future. As such, the first audio data (as well as other audio data associated with other vehicles), may be used as training data. The training data may include a designation of an operating status of the component (e.g., fair, poor, healthy, defective, etc.) that the training data is representative of. The designation of the operating status may be any of the operating status indications and may be based on the previously stored log data and/or fault logs associated with the component – See at least ¶118. The process may include receiving second audio data associated with the component. The second audio data may be used for further training the machine learning model to identify a health of the component. In some instances, the second audio data may be received from other vehicles, or the same vehicle as the first audio data. As such, the machine learned model may be continuously trained to identify components that are properly functioning or are not properly functioning – See at least ¶120)
the learned model has been trained by supervised learning using the training data to generate, from the sound data, generated data, which is prediction data of an operation status of the target vehicle (Supervised learning – See at least ¶101)
the processing circuitry is configured to execute a generation process that generates the generated data by inputting, to the learned model, evaluation sound data recorded while operating the target vehicle for the predetermined period of time (the machine learned model may be trained via labels applied by a human labeler. For example, a human labeler may analyze the first audio data to detect sounds, or portions of the first audio data, that are indicative of faults of the component (e.g., energy level) and/or components that are not properly functioning. In other words, a health of the component may be determined by a human labeler to generate labeled training data to train the machine learned model. Moreover, the labels may be applied based on training data indicative of previous faults and/or replaced components. For example, the audio data associated with a component that failed may be classified for use by the machine learning model to identify faults in the future – See at least ¶118).
Kim fails to explicitly disclose an evaluation process that compares target operation data with the generated data to evaluate the target vehicle based on a magnitude of deviation between the generated data and the target operation data, the target operation data collected simultaneously with the evaluation sound data and indicating the operation status of the target vehicle.
However, Goto teaches an evaluation process that compares target operation data with the generated data to evaluate the target vehicle based on a magnitude of deviation between the generated data and the target operation data, the target operation data collected simultaneously with the evaluation sound data and indicating the operation status of the target vehicle (That is, after calculating the excess area above the threshold level S and comparing it with a predetermined judgment value, the presence or absence of the occurrence of abnormal noise represented by the low-quality sound or the magnitude thereof is calculated – See at least ¶18. the processing in steps S17 and S18 functions as an area calculating unit that calculates the area of the portion exceeding the threshold level S in the frequency-sound pressure level waveform as the excess area corresponding to the abnormal noise area – See at least ¶36).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the invention of Kim and include the feature of an evaluation process that compares target operation data with the generated data to evaluate the target vehicle based on a magnitude of deviation between the generated data and the target operation data, the target operation data collected simultaneously with the evaluation sound data and indicating the operation status of the target vehicle, as taught by Goto, to improve the operating status of a component of a vehicle.
The combination of Kim and Goto fails to explicitly disclose the learned model is a neural network that sets a feature of data extracted from data corresponding to the predetermined period of time as an explanatory variable and sets the operation status at a point of time corresponding to the extracted data as an objective variable, the evaluation process includes calculating, as an evaluation index value, a total sum of the deviation that has been output for each piece of the data repeatedly extracted while changing an extraction start time, the total sum being a total sum of the deviation for the predetermined period of time, and the evaluation index value is used as a quantitative measure of vehicle health, such that the larger the evaluation index value, the more the state of the vehicle deviates from the state of the healthy reference vehicle.
However, Aso teaches:
the learned model is a neural network that sets a feature of data extracted from data corresponding to the predetermined period of time as an explanatory variable and sets the operation status at a point of time corresponding to the extracted data as an objective variable (FIG. 15 is a view illustrating an example of time series data acquired in a real environment and expanded time series data extracted therefrom. In this example, the operation period of the NN model was 0.8 seconds, and the time series data of the manipulating variables and the controlled variable were acquired at intervals of 0.08 seconds, i.e., a shorter period than the operation period of the NN model. Three sets of time series data having the same period as the operation period of the NN model, i.e., 0.8 seconds, were extracted at phases acquired by shifting the extraction start time by 0.08 seconds, 0.32 seconds, and 0.64 seconds, respectively, from the acquired time series data and these data were included in the training data – See at least ¶72),
the evaluation process includes calculating, as an evaluation index value, a total sum of the deviation that has been output for each piece of the data repeatedly extracted while changing an extraction start time, the total sum being a total sum of the deviation for the predetermined period of time (Processor executes an evaluation on the NN model. During NN model evaluation, the processor evaluates the trained NN model using, for instance, evaluation data, which are separate from the training data but include input data and output data, similarly to the training data. More specifically, the processor inputs the input data of the evaluation data into the NN model, compares output data calculated therefrom with correct answer data (the output data) of the evaluation data, and determines whether or not the error therebetween is less than a predetermined reference value – See at least ¶47), and
the evaluation index value is used as a quantitative measure of vehicle health, such that the larger the evaluation index value, the more the state of the vehicle deviates from the state of the healthy reference vehicle (The engine inference unit is the trained NN model. Time series data of the manipulating variable output to the actual engine system are input into the NN model, and data indicating the internal state of the actual engine and environment data (temperature, pressure, and so on) may also be input into the NN model – See at least ¶52).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Kim and Goto and include the feature of the learned model is a neural network that sets a feature of data extracted from data corresponding to the predetermined period of time as an explanatory variable and sets the operation status at a point of time corresponding to the extracted data as an objective variable, the evaluation process includes calculating, as an evaluation index value, a total sum of the deviation that has been output for each piece of the data repeatedly extracted while changing an extraction start time, the total sum being a total sum of the deviation for the predetermined period of time, and the evaluation index value is used as a quantitative measure of vehicle health, such that the larger the evaluation index value, the more the state of the vehicle deviates from the state of the healthy reference vehicle, as taught by Aso, to improve the operating status of a component of a vehicle.
Regarding to claim 2, Kim, as modified, discloses wherein the learned model is configured to generate the generated data from evaluation sound data including image data of a spectrogram obtained by performing frequency analysis on the sound data (To compare the first audio signature and the second audio signature, frequencies, magnitudes, tonalities, visual appearances of a wave form, etc. of the first audio
signature and the second audio signature may be compared. If at 516, the first audio signature and the second audio signature are similar – See at least ¶110).
Regarding claim 5, Kim, as modified, discloses wherein each of the reference operation data and the target operation data includes data of a rotation speed of a rotation shaft in a power train (In additional examples, the first audio signature may be associated with a speed of the vehicle, speed of the component (e.g., translational or rotational) – See at least ¶24).
Regarding claim 6, Kim as modified discloses the system of claim 1, accordingly, the rejection of claim 1 above is incorporated.
Kim as modified does not teach wherein the evaluation process includes selecting and outputting an evaluation rank that corresponds to a magnitude of the evaluation index value from among evaluation ranks divided according to the magnitude of the evaluation index value.
However, Goto teaches wherein the evaluation process includes selecting and outputting an evaluation rank that corresponds to a magnitude of the evaluation index value from among evaluation ranks divided according to the magnitude of the evaluation index value (That is, after calculating the excess area above the threshold level S and comparing it with a predetermined judgment value, the presence or absence of the occurrence of abnormal noise represented by the low-quality sound or the magnitude thereof is calculated – See at least ¶18. the processing in steps S17 and S18 functions as an area calculating unit that calculates the area of the portion exceeding the threshold level S in the frequency-sound pressure level waveform as the excess area corresponding to the abnormal noise area – See at least ¶36).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the invention of Kim and include the feature of wherein the evaluation process includes selecting and outputting an evaluation rank that corresponds to a magnitude of the evaluation index value from among evaluation ranks divided according to the magnitude of the evaluation index value, as taught by Goto, to improve the operating status of a component of a vehicle.
Regarding claim 8, the combination of Kim and Goto fails to explicitly disclose wherein the evaluation index value is used in a component-specific diagnosis to evaluate health of a particular component of the target vehicle.
However, Aso teaches wherein the evaluation index value is used in a component-specific diagnosis to evaluate health of a particular component of the target vehicle (The engine inference unit is the trained NN model. Time series data of the manipulating variable output to the actual engine system are input into the NN model, and data indicating the internal state of the actual engine and environment data (temperature, pressure, and so on) may also be input into the NN model – See at least ¶52).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the combination of Kim and Goto and include the feature of wherein the evaluation index value is used in a component-specific diagnosis to evaluate health of a particular component of the target vehicle, as taught by Aso, to improve the operating status of a component of a vehicle.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. US 20210335064 A1, in view of Goto et al., JP2014222189 A, in view of Aso et al., US 20210390412 A1, as applied to claim 1 above and further in view of Campanella et al., US 20200234517 A1, hereinafter referred to as Kim, Goto, Aso and Campanella, respectively.
Regarding claim 7, the combination of Kim, Goto and Aso fails to explicitly disclose wherein the evaluation index value is used to estimate resale value of the target vehicle.
However, Campanella teaches wherein the evaluation index value is used to estimate resale value of the target vehicle (In accordance with the present disclosure, each of the various types of data can be used in various combinations for training a model which can in turn be used to automatically identify features in vehicle audio data. This may include automatically suggesting potential tags associated with features detected in the vehicle audio data, for optional confirmation by a user. Such automatically identified features may be the basis for adjusting an estimated value for a vehicle, or determining an expected sale price of the vehicle based on sales prices of vehicles having similar make/model information – See at least ¶100).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the combination of Kim, Goto and Aso and include the feature of wherein the evaluation index value is used to estimate resale value of the target vehicle, as taught by Campanella, to provide an accurate assessment of a vehicle's condition data provided to dealers in wholesale automobiles (See at least ¶4 of Campanella)
Conclusion
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 MAHMOUD M KAZIMI whose telephone number is (571)272-3436. The examiner can normally be reached M-F 7am-5pm.
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, Erin Bishop can be reached at 5712703713. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.M.K./Examiner, Art Unit 3665
/David P. Merlino/Primary Examiner, Art Unit 3665