DETAILED ACTION
This office action is in response to the communication received on September 10, 2025 concerning application No. 18/201,036 filed on May 23, 2023.
Claims 1-20 are currently pending.
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 .
Response to Arguments
Applicant's arguments filed 09/10/2025 regarding the drawings objections have been fully considered. The amendments to the drawings have been entered and overcome the drawings objections previously set forth.
Applicant's arguments filed 09/10/2025 regarding the claim objections have been fully considered. The amendments to the claims have been entered and overcome the claim objections of claim 9 previously set forth.
Applicant's arguments filed 09/10/2025 regarding the 35 USC 112 rejections have been fully considered. The amendments to the claims have been entered and overcome the 35 USC 112b rejection of claims 9, 13, 18, and 19 previously set forth.
Applicant's arguments filed 09/10/2025 regarding the double patenting rejection have been fully considered. The amendments to the claims have been entered and overcome the double patenting rejection previously set forth.
Applicant's arguments filed 09/10/2025 regarding the 35 USC 102/103 rejections have been fully considered but they are not persuasive. In response to the applicant’s arguments that the prior art fails to teach the newly filed claim amendments, specifically, in response to determining that the amount of time is less than the defined time period…applying the interference classification model, examiner respectfully disagrees. [0069] and [0071] of Wojcik disclose determining whether the breath sample is received by the offender before a grace period expires and continuing to analyze the breath sample when it is determined the breath sample was taken before the grace period expires. Therefore Wojcik teaches at least the argued limitation recited above.
Regarding the limitation, in response to the breath sample including at least a minimum volume of gas and applying the interference classification model when the breath sample includes at least the minimum volume of gas, a new prior art reference is being applied to teach the limitation, therefore applicant’s arguments regarding the limitation are considered moot. Please see the rejection below for how the newly applied reference teaches the limitation and why it would have been obvious to combine the reference with the teaching of Wojcik.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-6, 9-14, and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable by Wojcik et al. (US 20150212063, hereinafter Wojcik) in view of Jung et al. (US 20230044709, hereinafter Jung).
Regarding claim 1, Wojcik teaches a system for determining proper use of a breath tester (Abstract and [0071] disclose a breath alcohol monitoring device that determines whether a breath sample is valid (proper)), the system comprising:
a camera ([0091] “camera 18”);
a breath tube ([0069] “breath tube 14”);
a breath sensor configured to receive a breath sample of an individual via the breath tube ([0090] discloses the breath sample is received by the fuel cell (breath sensor)) and to generate an alcohol level based on the breath sample ([0090] “the electrical signal registered in the fuel cell, whose signal strength is proportional to the alcohol content of the breath sample” and [0014] “RBAM employs an ethanol fuel cell to determine breath alcohol content (BrAC)”);
one or more processors (the electronic circuitry of the device 200 shown in figs. 5A-C. also the main circuit board and processor circuit board in fig. 8 are made of processors, see [0090]-[0091]) configured to receive an image from the camera ([0091] “the digital image taken by camera 18 is sent to processor circuit board assembly 8”) of a monitored individual blowing into the breath tube ([0095] “the user’s mouth and begins to blow. Camera 18 takes a digital image 1000 of the user”. Also [0094] “while offender 202 is delivering a breath sample, camera 18 in camera circuit board assembly 9 takes an image of offender 202”); and
a non-transient computer readable medium coupled to the one or more processors, and having stored therein instructions which when executed by the one or more processors ([0090] the part of the main circuit board and processor circuit board that store the processes performed by the processors is considered the non-transient computer readable medium and the stored processes are considered the instructions), causes the one or more processors to:
transmit a request to deliver the breath sample to the breath tube ([0069] discloses displaying a message to offender 202 to begin blowing into breath tube 14),
measure an amount of time between transmitting the request and receiving the breath sample ([0069] and [0071] disclose starting a timer to determine the grace period time and determining when the offender delivers the breath sample. [0030] discloses “‘Grace Period’ means the time allowed from when the breath test is supposed to be performed to when the offender must start blowing. E.g., if the grace period is ten minutes, an offender can start blowing for a 10:00 AM test as late as 10:10 AM”, thereby determining the time between when the request is sent and when the breath has been received),
determine whether the amount of time is less than a defined time period ([0071] discloses the determining if the breath sample was received before the grace period expired),
in response to determining that the amount of time is less than the defined time period ([0071] discloses continuing to analyze the breath sample when it is determined the breath sample was taken before the grace period expires):
apply an interference classification model to the image to yield a probability that the monitored individual is interfering with gas flowing from the monitored individual's mouth via the breath tube ([0110] the facial recognition software (interference classification model) analyzes the facial image and determines a match score (probability) and quality score (probability) for the image, the match score corresponds to whether or not there is a facial match. [0113] further discloses the facial match corresponds to whether an obstruction is present or not within the image. The presence of an obstruction corresponds to an individual interfering with gas flow);
indicate interference when the probability exceeds a first threshold ([0110] discloses it is determined whether or not the quality score meets a threshold. When it is determined the quality score does not meet the threshold, the probability of interference exceeds a first threshold and a retest is required. [0111] discloses outputting a message to the user to retest which is an indication that there is interference); and
indicate no interference when the probability is less than a second threshold ([0110]-[0112] discloses at step 730 there is a determination of whether to retest or not based on if the match score is acceptable (above a threshold), by having the match score be above a threshold, a probability of interference is less than a threshold and the system is indicating there is a likelihood of no interference. The results of the test are then sent to the monitor network, thereby indicating no interference).
Wojcik does not specifically teach detecting whether the breath sample includes at least a minimum volume of gas and in response to determining that the breath sample includes a minimum volume of gas, applying an interference classification model.
However,
Jung in a similar field of endeavor teaches detecting whether the breath sample includes at least a minimum volume of gas ([0055] discloses “the sensor unit 120 may measure the breath volume pulled into the inhaling unit 110. [0056] further discloses ensuring the breath volume is sufficient) and in response to determining that the breath sample includes a minimum volume of gas ([0015] “the control module may only complete the breath-checking when the breath volume of the diver supplied in the sensor module is equal to or greater than a preset first reference breath volume”), applying an interference classification model ([0238] and [0244] disclose using determination logic (model) to determine whether an unauthorized object is present within the image as part of the breath-checking procedure).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the known technique of detecting whether the breath sample includes at least a minimum volume of gas and in response to determining that the breath sample includes a minimum volume of gas, applying the interference classification model of Jung to the instructions of Wojcik to allow for the predictable results of adding extra precautions in order to ensure the user is not trying to trick the device, thereby increasing the accuracy of the system.
Regarding claim 9, Wojcik teaches a method for determining proper application of a breath based impairment test (Abstract and [0071] disclose a breath alcohol monitoring device that determines whether a breath sample is valid (proper application)), the method comprising:
transmitting a request to deliver a breath sample by blowing into a breath tube ([0069] discloses displaying a message to offender 202 to begin blowing into breath tube 14),
measuring an amount of time between transmitting the request and receiving the breath sample ([0069] and [0071] disclose starting a timer to determine the grace period time and determining when the offender delivers the breath sample. [0030] discloses “‘Grace Period’ means the time allowed from when the breath test is supposed to be performed to when the offender must start blowing. E.g., if the grace period is ten minutes, an offender can start blowing for a 10:00 AM test as late as 10:10 AM”, thereby determining the time between when the request is sent and when the breath has been received),
determining whether the amount of time is less than a defined time period ([0071] discloses the determining if the breath sample was received before the grace period expired),
capturing an image using a camera of a monitored individual blowing into a breath tube ([0095] “the user’s mouth and begins to blow. Camera 18 takes a digital image 1000 of the user”. Also [0094] “while offender 202 is delivering a breath sample, camera 18 in camera circuit board assembly 9 takes an image of offender 202”);
in response to determining that the amount of time is less than the defined time period ([0071] discloses continuing to analyze the breath sample when it is determined the breath sample was taken before the grace period expires):
applying, by a hardware processing system (the electronic circuitry of the device 200 shown in figs. 5A-C. also the main circuit board and processor circuit board in fig. 8 are made of processors, see [0090]-[0091]), an interference classification model to the image to yield a probability that the monitored individual is interfering with gas flowing from the monitored individual's mouth via the breath tube ([0110] the facial recognition software (interference classification model) analyzes the facial image and determines a match score (probability) and quality score (probability) for the image, the match score corresponds to whether or not there is a facial match. [0113] further discloses the facial match corresponds to whether an obstruction is present or not within the image. The presence of an obstruction corresponds to an individual interfering with gas flow);
comparing, by the hardware processing system, the probability with a first threshold and generating an indication of interference when the probability exceeds the first threshold ([0110] discloses it is determined whether or not the quality score meets a threshold. When it is determined the quality score does not meet the threshold, the probability of interference exceeds a first threshold and a retest is required. [0111] discloses outputting (generating an indication) a message to the user to retest which is an indication that there is interference); and
comparing, by the hardware processing system, the probability with a second threshold and generating an indication of no interference when the probability is less than the second threshold ([0110]-[0112] discloses at step 730 there is a determination of whether to retest or not based on if the match score is acceptable (above a threshold), by having the match score be above a threshold, a probability of interference is less than a threshold and the system is indicating there is a likelihood of no interference. The results of the test are then sent to the monitor network, thereby generating an indication of no interference).
Wojcik does not specifically teach detecting whether the breath sample includes at least a minimum volume of gas and in response to determining that the breath sample includes a minimum volume of gas, applying an interference classification model.
However,
Jung in a similar field of endeavor teaches detecting whether the breath sample includes at least a minimum volume of gas ([0055] discloses “the sensor unit 120 may measure the breath volume pulled into the inhaling unit 110. [0056] further discloses ensuring the breath volume is sufficient) and in response to determining that the breath sample includes a minimum volume of gas ([0015] “the control module may only complete the breath-checking when the breath volume of the diver supplied in the sensor module is equal to or greater than a preset first reference breath volume”), applying an interference classification model ([0238] and [0244] disclose using determination logic (model) to determine whether an unauthorized object is present within the image as part of the breath-checking procedure).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the known technique of detecting whether the breath sample includes at least a minimum volume of gas and in response to determining that the breath sample includes a minimum volume of gas, applying the interference classification model of Jung to the method of Wojcik to allow for the predictable results of adding extra precautions in order to ensure the user is not trying to trick the device, thereby increasing the accuracy of the system.
Regarding claim 19, Wojcik teaches a non-transient computer readable medium having stored therein instructions, which when executed by a hardware processing system ([0090] the part of the main circuit board and processor circuit board that store the processes performed by the processors is considered the non-transient computer readable medium and the stored processes are considered the instructions), cause the hardware processing system to:
transmit a request to deliver a breath sample by blowing into a breath tube ([0069] discloses displaying a message to offender 202 to begin blowing into breath tube 14),
measure an amount of time between transmitting the request and receiving the breath sample ([0069] and [0071] disclose starting a timer to determine the grace period time and determining when the offender delivers the breath sample. [0030] discloses “‘Grace Period’ means the time allowed from when the breath test is supposed to be performed to when the offender must start blowing. E.g., if the grace period is ten minutes, an offender can start blowing for a 10:00 AM test as late as 10:10 AM”, thereby determining the time between when the request is sent and when the breath has been received),
determine whether the amount of time is less than a defined time period ([0071] discloses the determining if the breath sample was received before the grace period expired),
receive an image from a camera, wherein the image shows a monitored individual blowing into the breath tube ([0091] “the digital image taken by camera 18 is sent to processor circuit board assembly 8”, [0095] “the user’s mouth and begins to blow. Camera 18 takes a digital image 1000 of the user”. Also [0094] “while offender 202 is delivering a breath sample, camera 18 in camera circuit board assembly 9 takes an image of offender 202”);
in response to determining that the amount of time is less than the defined time period ([0071] discloses continuing to analyze the breath sample when it is determined the breath sample was taken before the grace period expires):
apply an interference classification model to the image to yield a probability that the monitored individual is interfering with gas flowing from the monitored individual's mouth via the breath tube ([0110] the facial recognition software (interference classification model) analyzes the facial image and determines a match score (probability) and quality score (probability) for the image, the match score corresponds to whether or not there is a facial match. [0113] further discloses the facial match corresponds to whether an obstruction is present or not within the image. The presence of an obstruction corresponds to an individual interfering with gas flow);
compare the probability with a first threshold and generating an indication of interference when the probability exceeds the first threshold ([0110] discloses it is determined whether or not the quality score meets a threshold. When it is determined the quality score does not meet the threshold, the probability of interference exceeds a first threshold and a retest is required. [0111] discloses outputting (generating an indication) a message to the user to retest which is an indication that there is interference);
compare the probability with a second threshold and generating an indication of no interference when the probability is less than the second threshold ([0110]-[0112] discloses at step 730 there is a determination of whether to retest or not based on if the match score is acceptable (above a threshold), by having the match score be above a threshold, a probability of interference is less than a threshold and the system is indicating there is a likelihood of no interference. The results of the test are then sent to the monitor network, thereby generating an indication of no interference);
perform an impairment test of the monitored individual based at least in part on the indication of no interference and a breath sample of the monitored individual received via the breath tube ([0073]-[0075] disclose the system analyzes the BrAC value in response to a determination that a valid sample (no interference) has been received, the analysis comprising comparing the BrAC value to the lower limit of detection (LLOD) and if the BrAC is above the LLOD it is considered a failed test and the offender is positive for alcohol and is impaired (see 296 in fig. 3D and [0028]-[0029]); and
report an impairment result of the impairment test to a recipient device apart from the hardware processing system ([0112] discloses the results of the test (BrAC – Alcohol level/likelihood of impairment) is sent to the monitor network 206. Fig. 2 shows the monitor network is separate from the processor (RBAM 200)).
Wojcik does not specifically teach detecting whether the breath sample includes at least a minimum volume of gas and in response to determining that the breath sample includes a minimum volume of gas, applying an interference classification model.
However,
Jung in a similar field of endeavor teaches detecting whether the breath sample includes at least a minimum volume of gas ([0055] discloses “the sensor unit 120 may measure the breath volume pulled into the inhaling unit 110. [0056] further discloses ensuring the breath volume is sufficient) and in response to determining that the breath sample includes a minimum volume of gas ([0015] “the control module may only complete the breath-checking when the breath volume of the diver supplied in the sensor module is equal to or greater than a preset first reference breath volume”), applying an interference classification model ([0238] and [0244] disclose using determination logic (model) to determine whether an unauthorized object is present within the image as part of the breath-checking procedure).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the known technique of detecting whether the breath sample includes at least a minimum volume of gas and in response to determining that the breath sample includes a minimum volume of gas, applying the interference classification model of Jung to the instructions of Wojcik to allow for the predictable results of adding extra precautions in order to ensure the user is not trying to trick the device, thereby increasing the accuracy of the system.
Regarding claims 2 and 10, Wojcik in view of Jung teaches the system of claim 1 and method of claim 9, as set forth above. Wojcik further teaches forwarding the image to a user classification when the probability is both less than the first threshold and greater than the second threshold ([0074] “if the facial match is negative for this test, the test is labeled as pending review, and monitor network 206 sends a message to supervising agency 210…the test results are stored in a sever in monitor network 206”. [0118] “all test results are uploaded to monitor network 206 upon completion of the test and stored and are immediately available for review by supervising agency 210 via the website. An agent at supervising agency 210 may review the uploaded data”. Additionally, [0110] discloses “if the quality score of the image does not meet a threshold value, no facial recognition attempt will be made”, therefore in this case, the probability is less than the first threshold and greater than the second threshold since the image passed the quality test but did not pass the facial recognition test).
Regarding claims 3 and 11, Wojcik in view of Jung teaches the system of claim 1 and method of claim 9, as set forth above. Wojcik further teaches requesting that the monitored individual adjust the breath tube when the probability is both less than the first threshold and greater than the second threshold ([0111] discloses in response to a negative facial match a message is displayed to a user including “breath tube must be level” which corresponds to a request to adjust the breath tube. Additionally, [0110] discloses “if the quality score of the image does not meet a threshold value, no facial recognition attempt will be made”, therefore in this case, the probability is less than the first threshold and greater than the second threshold since the image passed the quality test but did not pass the facial recognition test).
Regarding claims 4 and 12, Wojcik in view of Jung teaches the system of claim 1 and method of claim 9, as set forth above. Wojcik further teaches performing an impairment test of the monitored individual when the probability is less than the second threshold, wherein the impairment test is based upon a breath sample of the monitored individual received via the breath tube ([0073]-[0075] disclose the system analyzes the BrAC (breath alcohol content) value in response to a determination that a valid sample (no interference) has been received, the analysis comprising comparing the BrAC value to the lower limit of detection (LLOD) and if the BrAC is above the LLOD it is considered a failed test and the offender is positive for alcohol and is impaired (see 296 in fig. 3D and [0028]-[0029]).
Regarding claims 5 and 13, Wojcik in view of Jung teaches the system of claim 4 and method of claim 12, as set forth above. Wojcik further teaches reporting an impairment result of the impairment test to a recipient device apart from the one or more processors ([0112] discloses the results of the test (BrAC – Alcohol level/likelihood of impairment) is sent to the monitor network 206. Fig. 2 shows the monitor network is separate from the processor (RBAM 200)).
Regarding claims 6 and 14, Wojcik in view of Jung teaches the system of claim 4 and method of claim 12, as set forth above. Wojcik further teaches the impairment test is selected from a group consisting of: a breath based drug impairment test, and a breath based alcohol impairment test ([0073] discloses the test is a breath based alcohol impairment test by determining if the BrAC is greater than or equal to the LLOD).
Regarding claim 17, Wojcik in view of Jung teaches the method of claim 9, as set forth above. Wojcik further teaches the hardware processing system includes:
a first processor in a central monitoring station (the electronic circuitry of the monitor network 206 and monitoring station 208 in fig. 2);
a second processor in a breath based impairment detection device remote from the central monitoring station (the electronic circuitry in RBAM 200 (remote breath alcohol monitor) in fig. 2. Fig. 2 shows the RBAM and monitoring network are remote from one another);
wherein the camera is included in the breath based impairment detection device (fig. 5B shows the camera 18 is included in the RBAM 200); and wherein applying the interference classification model to the image is done by the first processor ([0091] discloses “the facial matching software is stored on a server at monitoring network 206 and the image may be uploaded to monitoring station 208 to perform facial matching”, the facial matching is considered the application of the interference classification model);
wherein the method further comprises transmitting, by the second processor, the image to the central monitoring station via a wireless communication network (claims 24-25 discloses sending the facial image to the monitor network from the remote breath alcohol monitor using a wireless cellular phone module).
Regarding claim 18, Wojcik in view of Jung teaches the method of claim 9, as set forth above. Wojcik further teaches the hardware processing system includes:
a processor in a breath based impairment detection device remote from a central monitoring station (the electronic circuitry in RBAM 200 (remote breath alcohol monitor) in fig. 2. Fig. 2 shows the RBAM and monitoring network are remote from one another);
wherein the camera is included in the breath based impairment detection device (fig. 5B shows the camera 18 is included in the RBAM 200);
wherein applying the interference classification model to the image is done by the processor ([0091] “the software for performing facial matching is run on processor circuit board assembly 8 in RBAM 200”, the facial matching is considered the application of the interference classification model); and
wherein the method further comprises transmitting, by the second processor, the image to the central monitoring station via a wireless communication network (claims 24-25 discloses sending the facial image to the monitor network from the remote breath alcohol monitor using a wireless cellular phone module).
Claim(s) 7-8, 15-16, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wojcik in view of Jung as applied to claims 1, 9 and 19 above, and further in view of Reddy et al. (“Real-time Face Mask Detection Using Machine Learning/ Deep Feature-Based Classifiers For Face Mask Recognition”, hereinafter Reddy).
Regarding claims 7 and 15, Wojcik in view of Jung teaches the system of claim 1 and method of claim 9, as set forth above. Wojcik in view of Jung does not specifically teach the interference classification model is a machine learning model trained using at least one hundred images that have each been classified as exhibiting interference or not exhibiting interference.
However,
Reddy in a similar field of determining whether an interference is present teaches a interference classification model that is a machine learning model (Pg. 1, Abstract discloses a model that utilizes machine learning and deep learning to categorize whether a user is wearing a facemask and to what extent they are wearing the facemask. The presence of a facemask is considered the interference) trained using at least one hundred images that have each been classified as exhibiting interference or not exhibiting interference (pg. 3, “Experimental Results” discloses the model was trained using 767 images that were classified into a face without a mask (not exhibiting interference) and face with a mask (exhibiting interference)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention substitute the interference classification model of Wojcik in view of Jung for the machine learning model of Reddy because it amounts to simple substitution of one known element for another to obtain the predictable results of determining whether an interference is present on an individual’s face that is performing a breath test.
Regarding claims 8 and 16, Wojcik in view of Jung and Reddy teaches the system of claim 7 and method of claim 15, as set forth above. Reddy further teaches the at least one hundred images depict at least ten different individuals performing a task (figs. 1 and 2 show examples of the dataset being used for training data; the datasets include at least ten different individuals performing the task). As set forth above, Wojcik teaches the task is a breath based impairment test. Therefore applying the teachings of Reddy to Wojcik would result in the training data depicting at least ten different individuals performing a breath based impairment test).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the known technique of having the training data depict at least ten different individuals performing the task of Reddy to the system and method of Wojcik in view of Jung and Reddy to allow for the predictable results of increasing the accuracy of the model by training the model with a more diverse training set.
Regarding claim 20, Wojcik in view of Jung teaches the non-transient computer readable medium of claim 19, as set forth above. Wojcik in view of Jung does not specifically teach the interference classification model is a machine learning model trained using at least one hundred images that have each been classified as exhibiting interference or not exhibiting interference; and wherein the at least one hundred images depict at least ten different individuals undergoing a breath based impairment test.
However,
Reddy in a similar field of determining whether an interference is present teaches a interference classification model that is a machine learning model (Pg. 1, Abstract discloses a model that utilizes machine learning and deep learning to categorize whether a user is wearing a facemask and to what extent they are wearing the facemask. The presence of a facemask is considered the interference) trained using at least one hundred images that have each been classified as exhibiting interference or not exhibiting interference (pg. 3, “Experimental Results” discloses the model was trained using 767 images that were classified into a face without a mask (not exhibiting interference) and face with a mask (exhibiting interference)) and the at least one hundred images depict at least ten different individuals performing a task (figs. 1 and 2 show examples of the dataset being used for training data, the datasets include at least ten different individuals performing the task). As set forth above, Wojcik teaches the task is a breath based impairment test. Therefore applying the teachings of Reddy to Wojcik would result in the training data depicting at least ten different individuals performing a breath based impairment test).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention substitute the interference classification model of Wojcik in view of Jung for the machine learning model of Reddy because it amounts to simple substitution of one known element for another to obtain the predictable results of determining whether an interference is present on an individual’s face that is performing a breath test.
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 ANDREW BEGEMAN whose telephone number is (571)272-4744. The examiner can normally be reached Monday-Thursday 8:30-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Keith Raymond can be reached at 5712701790. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW W BEGEMAN/Examiner, Art Unit 3798
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