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 .
Response to Amendment
The amendment filed June 24, 2026 has been entered. Claims 1-20 remain pending in the instant application. Applicant’s amendments to the Claims have overcome the 112(b) rejections previously set forth in the Non-Final Office Action mailed April 8, 2026. However, the amendments have introduced new issues under 112(b).
Response to Arguments
Applicant’s arguments, filed June 24, 2026, regarding rejections under 35 U.S.C 103 with respect to claim(s) 6 and 13 have been fully considered, but they are not persuasive.
Applicant argues that the frequency at which data is obtained is not “result-effective,” i.e., Applicant argues that one of ordinary skill in the art would not have been motivated to optimize a data acquisition frequency in the prior art. Specifically, Applicant argues that camera frame rates and LIDAR scan rates relate to different physical principles than the mechanism by which the accelerometer disclosed in Amorim measures data. Applicant argues that an accelerometer’s output data rate cannot be interchanged with a camera or LIDAR acquisition rate.
Regarding this argument, the Examiner notes that the claim does not recite a camera frame rate or scan rate as being 800Hz. Claim 6 merely recites that the sensor data is obtained at 800Hz. Given its broadest reasonable interpretation, obtaining sensor data could refer to merely receiving data transmitted from a sensor, not the specific method by which the sensor obtains the data. Furthermore, Amorim recites that the data acquisition rate may be adjusted to improve accuracy (e.g., Amorim; paragraph [0168] discusses increasing a sampling rate of a GPS position system to improve positioning accuracy). The primary reference, Kuehner, teaches a camera and LIDAR; one of ordinary skill in the art could have adjusted frequency at which data is obtained from the camera or LIDAR system as in Amorim, and one of ordinary skill in the art would have been motivated to do so as the frequency at which the data is obtained is recited as affecting accuracy as in Amorim.
Applicant further argues that the specified data acquisition rate of 800Hz is not an arbitrarily chosen design parameter. Regarding this argument, the Examiner points to paragraph [0109] of the instant specification, “In some examples, the image data may be obtained at an average of approximately 1600 Hz to 3200 Hz. In some examples, the image data may be obtained at less than 100 Hz.” In light of the specification, the specific data acquisition range recited in the claims is not considered to be critical by achieving unexpected results, as the specification recites a different range of data acquisition frequencies as being acceptable.
Applicant’s other arguments have been considered but are moot because the new ground of rejection, necessitated by Applicant’s amendment, does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2, 3, 9, 10, 16, and 17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claim 2, the term “vibration frequency” is indefinite because the claim is unclear as to whether “vibration frequency” refers to how often haptic vibrations are generated, a physical characteristic of the vibration (i.e., a low versus high rumble), or a haptic signature. The Examiner notes that “vibration frequency” is not recited in the original specification. For the purposes of compact prosecution, “vibration frequency” is interpreted as referring to a physical characteristic of the vibration.
Claim 2 is also unclear as to what is being adjusted in the first image data to produce said vibration frequency. The claim recites “adjusting a timing of the first image data.” However, the claim is unclear as to whether the timing refers to the camera frame rate, the rate at which data is received from the camera, the rate at which lines per frame are processed, or some other quantity. The claim is also unclear as to how the timing relates to the vibration frequency. For instance, Claim 1, on which this claim depends, uses the first and second image data to determine a road surface. However, Claim 2 appears to recite that the acquisition of the first image data directly controls the generated haptic vibrations without input from the second image data. Claim 2 may thus be considered incomplete for omitting an essential structural cooperative relationship between the first image data and a vibration frequency, see MPEP § 2172.01, and Claim 2 is considered indefinite.
Regarding Claim 3, the term “vibration frequency” is indefinite because the claim is unclear as to whether “vibration frequency” refers to how often haptic vibrations are generated, a physical characteristic of the vibration (i.e., a low versus high rumble), or a haptic signature. The Examiner notes that “vibration frequency” is not recited in the original specification. For the purposes of compact prosecution, “vibration frequency” is interpreted as referring to a physical characteristic of the vibration.
Claim 3 is also unclear as to what is being adjusted in the first image data to produce said vibration frequency. The claim recites “adjusting a timing of the first image data.” However, the claim is unclear as to whether the timing refers to the camera frame rate, the rate at which data is received from the camera, the rate at which lines per frame are processed, or some other quantity. The claim is also unclear as to how the timing relates to the vibration frequency. For instance, Claim 1, on which this claim depends, uses the first and second image data to determine a road surface. However, Claim 3 appears to recite that the acquisition of the first image data directly controls the generated haptic vibrations without input from the second image data. Claim 3 may thus be considered incomplete for omitting an essential structural cooperative relationship between the first image data and a vibration frequency, see MPEP § 2172.01, and Claim 3 is considered indefinite.
Regarding Claims 9, 10, 16, and 17, the claims recite substantially similar limitations to Claims 2, 3, 2, and 3, respectively, and the claims are rejected under 35 U.S.C 112(b) for the same reasons.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-5, 7-12, and 14-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuehner et al. (U.S. Pub. No. 2023/0311923 A1, filed March 29, 2022), hereinafter Kuehner; in view of Longuemere et al. (U.S. Pub. No. 2019/0152515 A1), hereinafter Longuemere; further in view of Zhong et al. (Zhong, Chuanchuan, Bowen Li, and Tao Wu. "Off-road drivable area detection: A learning-based approach exploiting lidar reflection texture information." Remote Sensing 15, no. 1 (2022): 27.), hereinafter Zhong; further in view of Chen et al. (Chen, Ning, Zijin Xu, Zhuo Liu, Yihan Chen, Yinghao Miao, Qiuhan Li, Yue Hou, and Linbing Wang. "Data augmentation and intelligent recognition in pavement texture using a deep learning." IEEE Transactions on Intelligent Transportation Systems 23, no. 12 (2022): 25427-25436. Published January 25, 2022.), hereinafter Chen.
Regarding Claim 1, Kuehner teaches A method for use in a vehicle (“FIG. 9 illustrates a flowchart of a method 900 that is associated with simulating a vehicle response to a rumble strip.”) (e.g., paragraph [0078]).
the method comprising: obtaining first image data from a camera (“At 910, the rumble strip simulator system 170 determines that a virtual boundary corresponding to a real world location in proximity of the vehicle 100 has been crossed based upon first sensor data generated by the vehicle 100 [...] As an example, in one or more arrangements, the sensor system 120 can include one or more radar sensors 123, one or more LIDAR sensors 124, one or more sonar sensors 125, and/or one or more cameras 126.”) (e.g., paragraphs [0079] and [0095]).
obtaining second image data from a light detection and ranging (LIDAR) sensor (“At 920, the rumble strip simulator system 170 determines information about the environment of the vehicle 100 as the virtual boundary is crossed based upon second sensor data generated by the vehicle 100 [...] As an example, in one or more arrangements, the sensor system 120 can include one or more radar sensors 123, one or more LIDAR sensors 124, one or more sonar sensors 125, and/or one or more cameras 126.”) (e.g., paragraphs [0080] and [0095]).
and generating haptic vibrations based on the determined road surface to create a road feel (“At 930, the rumble strip simulator system 170 activates an actuator of a seat of the vehicle 100 such that haptic feedback is delivered to the seat. The seat may be a seat in which the operator of the vehicle 100 sits. The haptic feedback is based upon the information about the environment of the vehicle 100 and a type of a virtual rumble strip.”) (e.g., paragraph [0080]).
However, Kuehner does not appear to teach wherein the first image data is used to determine a surface type of a road and the second image data is used to determine a three-dimensional (3D) surface of the road; determining a road surface based on the first image data and the second image data using a generative adversarial network (GAN) machine learning (ML) model;
On the other hand, Longuemere, which relates similarly as a method for vision based torque feedback, does teach wherein the first image data is used to determine a surface type of a road (“The method further includes detecting the road surface using the captured image data, as shown at 520. In one or more examples, the road surface detection module 210 determines the type of the road surface and sends a notification of the detected type to the control module 26. As described herein, determining the road surface may include using sensor fusion and/or determining the road surface based on road surfaces detected by multiple sources, such as a first based on image data, a second based on radar-data, a third based on steering system internal control signals, and so on.”) (e.g., paragraph [0040]).
However, neither Kuehner nor Longuemere teaches the second image data is used to determine a three-dimensional (3D) surface of the road.
On the other hand, Zhong, which relates similarly as a method for recognizing drivable areas in off-road scenes, does teach the second image data is used to determine a three-dimensional (3D) surface of the road (“We use the Robosense 80 LiDAR as our LiDAR sense, which was mounted at a height of 1.6m from the ground and operated at a rate of 10 FPS.” Figure 4 discloses the information obtained by the LiDAR, comprising a point cloud of a road surface, wherein the point cloud is interpreted as a 3D surface of a road.) (e.g., page 6, paragraph 2 and figure 4).
However, neither Kuehner, Longuemere, nor Zhong appear to specifically teach determining a road surface based on the first image data and the second image data using a generative adversarial network (GAN) machine learning (ML) model;
On the other hand, Chen, which relates to determining pavement texture for autonomous vehicles, does teach determining a road surface based on the first image data and the second image data using a generative adversarial network (GAN) machine learning (ML) model (“First, the pavement texture images were preprocessed. Then the traditional methods and WGAN-GP network were used for data augmentation. Finally, RF algorithm and the deep learning model DenseNet were applied for pavement texture image classification. The detailed computation process is shown in Fig. 1.”) (e.g., page 2, column 2, paragraph 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the Applicant's claimed invention to combine Kuehner with Longuemere. The claimed invention is considered to be merely combining prior art elements according to known methods to yield predictable results, see MPEP § 2143(I)(A). Kuehner teaches a method using cameras and LIDAR sensors. However, Kuehner does not specifically teach wherein the camera data is used to determine a surface type. On the other hand, Longuemere does teach a method comprising determining a road surface type using a camera. As both Kuehner and Longuemere relate to delivering haptic feedback to a driver (e.g., Kuehner, paragraph [0001]; Longuemere, abstract), one of ordinary skill in the art could have used the camera of Kuehner to determine a road surface type as in Longuemere. In combination, each element merely performs the same function as it does separately, and one of ordinary skill in the art would have recognized the results of the combination as predictable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the Applicant's claimed invention to combine Kuehner with Longuemere to provide more detailed road information to the rumble strip simulator of Kuehner.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the Applicant's claimed invention to combine the modified reference of Kuehner in view of Longuemere with Zhong. The claimed invention is considered to be merely combining prior art elements according to known methods to yield predictable results, see MPEP § 2143(I)(A). Kuehner teaches a method using cameras and LIDAR sensors. However, Kuehner does not specifically teach wherein the LIDAR data is used to determine a 3D surface of a road. On the other hand, Zhong does teach a method comprising determining a 3D road surface using LIDAR. Kuehner is merely silent on specifically how the LIDAR sensor operates, and one of ordinary skill in the art could have merely implemented the LIDAR of Kuehner using the specific system of Zhong to produce 3D road surface data. Furthermore, Kuehner already discloses capturing sensor data in the form of a LIDAR point cloud (e.g., Kuehner, paragraph [0043]), and the 3D road surface information collected by the LIDAR of Zhong is also a point cloud (e.g., Zhong, page 5, figure 2). Thus, one of ordinary skill in the art would have recognized that the result of combing Kuehner and Zhong as predictable. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the Applicant's claimed invention to combine the modified reference of Kuehner in view of Longuemere with Zhong to expand the LIDAR point cloud data of Kuehner to also include 3D road surface information.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the Applicant's claimed invention to combine Kuehner with Chen. The claimed invention is considered to be using a known technique to improve similar devices (methods, or products) in the same way, see MPEP § 2143(I)(C). Kuehner teaches a method for collecting camera and LIDAR data and generating haptic vibrations to simulate a vehicle crossing a nearby rumble strip. However, Kuehner does not appear to specifically teach using a GAN model to determine the road surface. On the other hand, Chen, which relates similarly as a method for use in autonomous vehicles, does teach training a GAN model and determining a road surface using the GAN model. Furthermore, Kuehner discloses that “one or more of the modules described herein can include artificial or computational intelligence elements, e.g., neural network, fuzzy logic or other machine learning algorithms” (e.g., Kuehner; paragraph [0104]). Thus, one of ordinary skill in the art could have applied the improvement of using a GAN model as in Chen to the haptic feedback system of Kuehner, and the results of the improvement would have been predictable to one of ordinary skill in the art. Therefore, it would have been obvious to a person of ordinary skill in the art to combine the modified reference of Kuehner in view of Longuemere and Zhong with Chen in order to more accurately assess a road texture.
Regarding Claim 2, Kuehner in view of Longuemere, Zhong, and Chen teaches The method of claim 1. Kuehner further teaches the method further comprising: adjusting a timing of the first image data such that the road feel created is based on a vibration frequency below a predetermined threshold (“In an example, if the vehicle 100 is traveling relatively fast when the virtual boundary is crossed, the actuator 340 delivers vibrations at a relatively high frequency. In contrast, if the vehicle 100 is traveling relatively slow when the virtual boundary is crossed, the actuator 340 delivers vibrations at a relatively low frequency.” The sampling of the sensor data may be slowed to match the vehicle speed.) (e.g., paragraph [0058]).
Regarding Claim 3, Kuehner in view of Longuemere, Zhong, and Chen teaches The method of claim 1. Kuehner further teaches the method further comprising: adjusting a timing of the first image data such that the road feel created is based on a vibration frequency that meets a predetermined threshold corresponding to a vehicle speed above a threshold speed (“In an example, if the vehicle 100 is traveling relatively fast when the virtual boundary is crossed, the actuator 340 delivers vibrations at a relatively high frequency. In contrast, if the vehicle 100 is traveling relatively slow when the virtual boundary is crossed, the actuator 340 delivers vibrations at a relatively low frequency.” The sampling of the sensor data may be sped up to match the vehicle speed.) (e.g., paragraph [0058]).
Regarding Claim 4, Kuehner in view of Longuemere, Zhong, and Chen teaches The method of claim 1. Kuehner further teaches wherein the haptic vibrations are generated on a steering wheel of the vehicle, a pedal of the vehicle, a seat of the vehicle, or a gear shifter of the vehicle (The Examiner notes the use of or, and the prior art provides haptic vibrations generated on a seat of the vehicle. “At 930, the rumble strip simulator system 170 activates an actuator of a seat of the vehicle 100 such that haptic feedback is delivered to the seat. The seat may be a seat in which the operator of the vehicle 100 sits. The haptic feedback is based upon the information about the environment of the vehicle 100 and a type of a virtual rumble strip.”) (e.g., paragraph [0081]).
Regarding Claim 5, Kuehner in view of Longuemere, Zhong, and Chen teaches The method of claim 1. Kuehner further teaches the method further comprising: obtaining sensor data from one or more sensors of a test vehicle (“As an example, in one or more arrangements, the sensor system 120 can include one or more radar sensors 123, one or more LIDAR sensors 124, one or more sonar sensors 125, and/or one or more cameras 126.”) (e.g., paragraph [0095]).
obtaining third image data associated with a camera image of a training data set (“At 910, the rumble strip simulator system 170 determines that a virtual boundary corresponding to a real world location in proximity of the vehicle 100 has been crossed based upon first sensor data generated by the vehicle 100 [...] As an example, in one or more arrangements, the sensor system 120 can include one or more radar sensors 123, one or more LIDAR sensors 124, one or more sonar sensors 125, and/or one or more cameras 126.” Camera data may be collected again in the same way to form a training data set.) (e.g., paragraphs [0079] and [0095]).
obtaining fourth image data associated with a LIDAR image of the training data set (“At 920, the rumble strip simulator system 170 determines information about the environment of the vehicle 100 as the virtual boundary is crossed based upon second sensor data generated by the vehicle 100 [...] As an example, in one or more arrangements, the sensor system 120 can include one or more radar sensors 123, one or more LIDAR sensors 124, one or more sonar sensors 125, and/or one or more cameras 126.” LIDAR data may be collected again in the same way to form a training data set.) (e.g., paragraphs [0080] and [0095]).
Chen further teaches correlating the sensor data with the third image data and the fourth image data to train the GAN ML model (“Before training, the image processing methods were used to extract the contour information, texture features and histogram information from the pavement texture datasets […] In this study, WGAN-GP was employed for pavement texture data augmentation. After the generator and discriminator were updated continuously, the trained model was able to generate new pavement texture images.” The image processing methods may be used to correlate the camera, LIDAR, and other sensor data of Kuehner. Updating the generator and discriminator describes training the GAN model, wherein the training is performed using the processed data.) (e.g., page 2, column 2, paragraph 3; page 3, column 2, paragraph 2).
Regarding Claim 7, Kuehner in view of Longuemere, Zhong, and Chen teaches The method of claim 5. Kuehner further teaches the method further comprising: generating a three-dimensional (3D) terrain model based on the LIDAR image (“The autonomous driving module(s) 160 can be configured to receive data from the sensor system 120 and/or any other type of system capable of capturing information relating to the vehicle 100 and/or the external environment of the vehicle 100. In one or more arrangements, the autonomous driving module(s) 160 can use such data to generate one or more driving scene models.”) (e.g., paragraph [0105]).
Chen further teaches correlating the sensor data with the 3D terrain model to train the GAN ML model (“Before training, the image processing methods were used to extract the contour information, texture features and histogram information from the pavement texture datasets […] In this study, WGAN-GP was employed for pavement texture data augmentation. After the generator and discriminator were updated continuously, the trained model was able to generate new pavement texture images.” The image processing methods may be used to correlate the driving scenes and sensor data of Kuehner. Updating the generator and discriminator describes training the GAN model, wherein the training is performed using the processed data.) (e.g., page 2, column 2, paragraph 3; page 3, column 2, paragraph 2).
Regarding Claim 8, Kuehner teaches A vehicle (“Referring to FIG. 1, an example of a vehicle 100 is illustrated.”) (e.g., paragraph [0023]).
The remaining limitations of Claim 8 recite substantially similar material to Claim 1, and the claim is rejected under 35 U.S.C 103 for the same reasons.
Regarding Claims 9-12 and 14, the Claims recite substantially similar limitations to Claims 2-5 and 7, respectively, and the claims are rejected under 35 U.S.C 103 for the same reasons.
Regarding Claim 15, Kuehner teaches A non-transitory computer-readable medium comprising instructions stored in a memory, that when executed by a processor, cause the processor to perform operations (“The vehicle 100 can include one or more data stores 115 for storing one or more types of data. The data store 115 can include volatile and/or non-volatile memory.”) (e.g., paragraph [0084]).
The remaining limitations of Claim 15 recite substantially similar material to Claim 1, and the claim is rejected under 35 U.S.C 103 for the same reasons.
Regarding Claims 16-19 and 20, the claims recite substantially similar limitations to Claims 2-5 and 7, respectively, and the claims are rejected under 35 U.S.C 103 for the same reasons.
Claim(s) 6 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuehner in view of Longuemere, Zhong, and Chen, further in view of Amorim de Faria Cardote et al. (U.S. Pub. No. 2017/0195953 A1), hereinafter Amorim.
Regarding Claim 6, Kuehner in view of Longuemere, Zhong, and Chen teaches The method of Claim 5. However, neither Kuehner nor Chen appear to specifically teach wherein the third image data and the fourth image data are obtained at 800 Hz.
On the other hand, Amorim, which relates configuring hardware for mobile internet of things (IoT) devices, does teach wherein the third image data and the fourth image data are obtained at 800 Hz (“In an example implementation in accordance with various aspects of the present disclosure, the internal sensor(s) 708 and/or external sensors 710 may comprise an accelerometer with the following features: […] Output Data Rates (ODR) in the range of from about 1.56 Hz to about 800 Hz.”) (e.g., paragraph [0167]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the Applicant's claimed invention to combine the modified reference of Kuehner in view of Longuemere, Zhong, and Chen with Amorim. The claimed invention is considered to be merely combining prior art elements according to known methods to yield predictable results, see MPEP § 2143(I)(A). Kuehner teaches a method using cameras and LIDAR sensors. However, Kuehner does not specifically teach wherein the camera and LIDAR data is captured at a minimum of 800 Hz. On the other hand, Amorim does teach a method comprising a sensor with a sampling rate of up to 800 Hz. Kuehner is merely silent on specifically how the camera and LIDAR sensors function, and one of ordinary skill in the art could have merely implemented the sensors using the specific sampling rate of Amorim, and the results would have been predictable to one of ordinary skill in the art. Furthermore, while the range of sampling rates in Amorim is not exactly the same as in the instant claims, a prima facie case of obviousness exists where the claimed ranges and prior art ranges do not overlap but are close enough that one skilled in the art would have expected them to have the same properties. “[W]here the general conditions of a claim are disclosed in the prior art, it is not inventive to discover the optimum or workable ranges by routine experimentation.” See In re Aller, 220 F.2d 454, 456, 105 USPQ 233, 235 (CCPA 1955). The discovery of an optimum value of a known result effective variable, without producing any new or unexpected results, is within the ambit of a person of ordinary skill in the art. See In re Boesch, 205 USPQ 215 (CCPA 1980) (see MPEP § 2144.05, II). In the instant claims, the 800 Hz sampling frequency does not produce any new or expected results in the training of the GAN model. Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to combine the modified reference of Kuehner in view of Longuemere, Zhong, and Chen with Amorim in order to select a specific sampling rate for the sensors of Kuehner.
Regarding Claim 13, the claim recites substantially similar limitations to Claim 6, and the claim is rejected under 35 U.S.C 103 for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lopatine et al. (U.S. Pub. No. 2024/0095418 A1, filed September 15, 2022), teaches a system for virtual reality driving simulation comprising adjusting image data according to the speed of a simulated vehicle.
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 KYLE HWA-KAI TSENG whose telephone number is (571)272-3731. The examiner can normally be reached M-F 9A-5P PST.
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/K.H.T./ Examiner, Art Unit 2189
/REHANA PERVEEN/ Supervisory Patent Examiner, Art Unit 2189
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