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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “image attribute acquisition unit”, “image conversion unit”, and “image determination unit” in claim(s) 1 and 11 and dependent claim(s) 2-10.
See para. 29 “FIG. 2 is a diagram showing an example of a functional configuration of the image processing device 100 according to the present embodiment. The image processing device 100 includes, for example, a communication unit 110, a transmission/reception control unit 120, an image processing unit 130, an image conversion unit 140, an image determination unit 150, a trained model generation unit 160, and a storage unit 170. For example, these components are implemented by a hardware processor such as a central processing unit (CPU) executing a program (software). Also, some or all of these components may be implemented by hardware
(including a circuit; circuitry) such as a large-scale integration (LSI) circuit, an
application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a graphics processing unit (GPU) or may be implemented by software and hardware in cooperation. The program may be pre-stored in a storage device (a storage device
including a non-transitory storage medium) such as a hard disk drive (HDD) or flash memory. The program may be stored in a removable storage medium (a non-transitory storage medium) such as a DVD or a CD-ROM and installed in the storage device when the storage medium is mounted in a drive device. The storage unit 170 is, for example,
an HDD, a flash memory, a random-access memory (RAM), and the like. The storage
unit 170 stores, for example, captured image data 172, converted image data 174,
annotation image data 176, annotated image data 178, and a trained model 180.
Although the image processing device 100 includes the trained model generation unit
160 and the storage unit 170 for storing the trained model 180 for ease of description, the function of generating the trained model and the generated trained model may be held by a server device different from the image processing device 100.”
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 11-13 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by Van Beek et al. (US 20220161815 A1), hereinafter Van.
Regarding claim 1, Van teaches An image processing device comprising: (Para. 50 see "FIG. 46 shows an example image collected from a vehicle before and after applying a face blurring policy to the image." Para. 101 see "With reference now to FIG. 2, a simplified block diagram 200 is shown illustrating an example implementation of a vehicle (and corresponding in-vehicle computing system) 105 equipped with autonomous driving functionality. In one example, a vehicle 105 may be equipped with one or more processors 202, such as central processing units (CPUs), graphical processing units (GPUs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), digital signal processors (DSPs), tensor processors and other matrix arithmetic processors, among other examples. Such processors 202 may be coupled to or have integrated hardware accelerator devices (e.g., 204), which may be provided with hardware to accelerate certain processing and memory access functions, such as functions relating to machine learning inference or training (including any of the machine learning inference or training described below), processing of particular sensor data (e.g., camera image data, LIDAR point clouds, etc.), performing certain arithmetic functions pertaining to autonomous driving (e.g., matrix arithmetic, convolutional arithmetic, etc.), among other examples. One or more memory elements (e.g., 206) may be provided to store machine-executable instructions implementing all or a portion of any one of the modules or sub-modules of an autonomous driving stack implemented on the vehicle, as well as storing machine learning models (e.g., 256), sensor data (e.g., 258), and other data received, generated, or used in connection with autonomous driving functionality to be performed by the vehicle (or used in connection with the examples and solutions discussed herein)." Para. 677 see "Example I13 is an apparatus that includes memory and processing circuitry coupled to the memory to perform one or more of the methods of Examples I1-I12."). an image attribute acquisition unit configured to acquire an image attribute that is a capturing aspect of an input image; (Para. 51 see "FIG. 47 is a simplified flowchart that illustrates a high-level possible flow of operations associated with tagging data collected at a vehicle in an on-demand privacy compliance system." Para. 101 see "processing of particular sensor data (e.g., camera image data, LIDAR point clouds, etc.)," Para. 105 see "A perception engine 238 may be provided in some examples, which may take as inputs various sensor data (e.g., 258) including data, in some instances, from extraneous sources and/or sensor fusion module 236 to perform object recognition and/or tracking of detected objects, among other example functions corresponding to autonomous perception of the environment encountered (or to be encountered) by the vehicle 105." Para. 680 see "receiving a dataset comprising data collected by a vehicle, wherein one or more tags are associated with the dataset; determining a first policy to be applied to the dataset based on the one or more tags;"). an image conversion unit configured to perform an anonymization process on the input image; (Para. 332 see "Examples of processes include, but are not necessarily limited to, applying a data anonymization script to personally identifying information (e.g., GPS location, etc.), blurring personally identifying information or images (e.g., faces, license plates, private or sensitive property addresses, etc.), pixelating sensitive data, and redacting sensitive data." Para. 665 see "generating a disguised image based, at least in part, on applying the first neural network to the input image, wherein a gaze attribute of the face depicted in the input image is included in the disguised image, and wherein one or more other attributes of the face depicted in the input image are modified in the disguised image." Para. 681 see "applying the first policy to the dataset includes at least one of obscuring one or more faces in an image in the dataset, obscuring one or more license plates in an image in the dataset, anonymizing personal identifying information in the dataset, or modifying location information in the dataset."). and an image determination unit configured to determine whether or not the input image on which the anonymization process has been performed satisfies a predetermined requirement, (Para. 34 see "FIG. 31 shows example disguised images generated by a StarGAN based model from an input image of a real face and results of a face recognition engine that evaluates the real and disguised images." Para. 334 see "A policy, such as policy 4030, associates one or more tags to one or more processes. For example, a dataset that is tagged with ‘suburban’ as previously described could be subject to a policy that associates the ‘suburban’ tag with a privacy process to anonymize (e.g., blur, redact, pixelate, etc.) faces of people and private property information. The tag in that case enables the right processes to be matched to the right dataset based on the nature of that dataset and the potential privacy implications that it contains." Para. 673 see "where the GAN model is preconfigured with the target domain based on the GAN model generating disguised images from test images and a facial recognition model being unable to identify at least a threshold number of the disguised images."). wherein the image determination unit performs a predetermined process on the input image on which the anonymization process has been performed when it is determined that the input image on which the anonymization process has been performed satisfies the predetermined requirement, (Para. 334 see "A policy, such as policy 4030, associates one or more tags to one or more processes. For example, a dataset that is tagged with ‘suburban’ as previously described could be subject to a policy that associates the ‘suburban’ tag with a privacy process to anonymize (e.g., blur, redact, pixelate, etc.) faces of people and private property information. The tag in that case enables the right processes to be matched to the right dataset based on the nature of that dataset and the potential privacy implications that it contains." Para. 675 see "sending the disguised image to a data collection system associated with the vehicle, wherein the data collection system is to detect an emotion based on the emotion attribute in the disguised image." Para. 676 see "providing the disguised image to a computer vision application of the vehicle, wherein the computer vision application is to detect a gaze based on a gaze attribute in the disguised image and identify a trajectory of a human represented in the disguised image based on the detected gaze." Examiner Note: Further processes are performed on the image if the GAN determines it as successfully anonymized. The tags associated with an image determine the subsequent processes.). and wherein the predetermined requirement is determined in accordance with the acquired image attribute. (Para. 334 see "A policy, such as policy 4030, associates one or more tags to one or more processes. For example, a dataset that is tagged with ‘suburban’ as previously described could be subject to a policy that associates the ‘suburban’ tag with a privacy process to anonymize (e.g., blur, redact, pixelate, etc.) faces of people and private property information. The tag in that case enables the right processes to be matched to the right dataset based on the nature of that dataset and the potential privacy implications that it contains." Para. 680 see "determining a first policy to be applied to the dataset based on the one or more tags" Examiner Note: Tags determine which processes to perform on an image. A disguising process of a face occurs if the image is tagged with an indication of the presence of a face (the predetermined requirement).).
Claim 11 is rejected under the same analysis as claim 1 above.
Claim 12 is rejected under the same analysis as claim 1 above.
Claim 13 is rejected under the same analysis as claim 1 above.
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.
Claims 2-4 are rejected under 35 U.S.C. 103 as being unpatentable over Van Beek et al. (US 20220161815 A1), hereinafter Van, in view of Nagai et al. (JP 2017187850 A), hereinafter Nagai.
Regarding claim 2, Van teaches The image processing device according to claim 1.
While Van teaches tagged (annotated) images and saving images, Van does not teach wherein the predetermined process is a process of saving the input image on which the anonymization process has been performed as an annotation work target image.
However, Nagai teaches wherein the predetermined process is a process of saving the input image on which the anonymization process has been performed as an annotation work target image. (Para. 10 see "a classification information acquisition means for transmitting the protection image to a second information processing apparatus and acquiring classification information relating to the classification of the protection image from the second information processing apparatus" Para. 19 see "the person in charge 6 a operates the person in charge PC 6 to request a protected image from the image management apparatus 5. Since the image management apparatus 5 requests the protected image from the image processing server 4, the image processing server 4 associates the original image with the protected image and transmits only the protected image to the image managing apparatus 5. The person in charge 6a visually recognizes the protected image and labels the protected image with respect to the presence / absence of a person and the operation of the person." Para. 154 see "Thus, since the image seen by the person in charge 6a is the protected image 702, the person in charge 6a is less likely to specify an individual.").
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Van to incorporate the teachings of Nagai to save the anonymized input image as an annotation work target image. Doing so would predictably increase accuracy of the model by facilitating machine learning training data generation by providing labeled images that protect confidential information.
Regarding claim 3, Van teaches The image processing device according to claim 1.
While Van teaches saving images, an anonymization process, and detecting gaze of people, Van does not teach wherein the predetermined process is a process of saving the input image on which the anonymization process has been performed as learning information for generating a behavior prediction model for predicting behavior of a person shown in the input image.
However, Nagai teaches wherein the predetermined process is a process of saving the input image on which the anonymization process has been performed as learning information for generating a behavior prediction model for predicting behavior of a person shown in the input image. (Para. 17 see "when the information processing apparatus detects a person or a person's movement by image recognition, for example, in the learning phase, the image data is a person, not a person, sitting, or reaching out. The person in charge performs the labeling." Para. 19 see "The person in charge 6a visually recognizes the protected image and labels the protected image with respect to the presence / absence of a person and the operation of the person ... The image processing server 4 has a learning unit 45, and the learning unit 45 performs machine learning by deep learning, for example, using the original image and the label ... it recognizes the presence of a person and the movement of a person" Para. 22 see "In the present embodiment, classification is performed according to the presence / absence of a person in the image and the action of the person in the image when the person is shown, and the operation content will be described as classification information.").
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Van to incorporate the teachings of Nagai to save the input image, after anonymization, as learning information for generating a behavior prediction model for predicting a person's behavior. Doing so would predictably improve the utility of the anonymized data by enabling the creation of predictive models without compromising privacy.
Regarding claim 4, Van teaches The image processing device according to claim 1.
While Van teaches transmitting images to a server, Van does not teach wherein the predetermined process is a process of transmitting the input image on which the anonymization process has been performed to an image server through a communication means.
However, Nagai teaches wherein the predetermined process is a process of transmitting the input image on which the anonymization process has been performed to an image server through a communication means. (Para. 10 see "a classification information acquisition means for transmitting the protection image to a second information processing apparatus and acquiring classification information relating to the classification of the protection image from the second information processing apparatus" Para. 19 see "the image processing server 4 associates the original image with the protected image and transmits only the protected image to the image managing apparatus 5.").
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Van to incorporate the teachings of Nagai to transmit the image to a server. Doing so would predictably enable remote storage and analysis of image data by offloading the images to a server for further processing or machine learning, thereby reducing the need for computational hardware on a vehicle.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Van Beek et al. (US 20220161815 A1), hereinafter Van, in view of Lin et al. (US 20190325219 A1), hereinafter Lin.
Regarding claim 5, Van teaches The image processing device according to claim 1.
While Van teaches imaging inside and outside of the vehicle, Van does not teach wherein the image attribute is information indicating at least whether the input image is an image obtained by capturing an interior of a vehicle equipped with a camera that has captured the input image or an image obtained by capturing an exterior of the vehicle.
However, Lin teaches wherein the image attribute is information indicating at least whether the input image is an image obtained by capturing an interior of a vehicle equipped with a camera that has captured the input image or an image obtained by capturing an exterior of the vehicle. (Abstract see "One example method for enhancing a passenger experiences includes capturing a first set of images of an area around the vehicle using an external camera system, capturing a second set of images of one or more passengers inside the vehicle using an internal camera system, recognizing at least one gesture made by the one or more passengers based on the second set of images, identifying an object or a location external to the vehicle based on the first set of images and the at least one gesture" Para. 37 see "The internal and external camera systems are used to continually monitor the inside and outside of the car. The in-cabin views are used to determine the position and orientation of each passenger face. The outside cameras record the views of the outside world continuously." Para. 39 see "the Pose component 435 may use standard machine learning algorithms to determine the location of faces on images obtained from the internal camera system 430. Once faces are extracted, standard machine learning algorithms can further be used to extract facial landmarks. These facial landmarks may be used to compute the position and orientation (or “pose”) of the face relative to the camera").
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Van to incorporate the teachings of Lin to include an image attribute that specifies whether an input image is captured from inside or outside the vehicle. Doing so would predictably increase the system's situational awareness by allowing distinct processing of interior and exterior environmental factors.
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Van Beek et al. (US 20220161815 A1), hereinafter Van, in view of Tamura (JP 2020061081 A), hereinafter Tamura.
Regarding claim 9, Van teaches The image processing device according to claim 1.
While Van teaches an anonymization process and verifying if a model can produce anonymized images based on a numeric threshold, Van does not teach wherein the image conversion unit performs the anonymization process on the input image again when the image determination unit determines that the input image on which the anonymization process has been performed does not satisfy the predetermined requirement.
However, Tamura teaches wherein the image conversion unit performs the anonymization process on the input image again when the image determination unit determines that the input image on which the anonymization process has been performed does not satisfy the predetermined requirement. (Abstract see "Achieve privacy protection in images. An image processing device includes an object detection means for detecting an object included in an image, and a generation means for generating a mask image to be superimposed on the image in order to obstruct detection of the object by the object detection means." Para. 35 see "In step S305, the object detection unit 204 determines whether or not a predetermined end condition is satisfied, and determines whether or not to end the process. The predetermined termination condition is, for example, that the characteristic value exceeds (above / below) the threshold value. Here, the characteristic value is the “detection number of objects detected by the detector” or the “calculation upper limit”. The calculation upper limit may be “time required for calculation of correction processing” or “number of times of repeating processing of S304 to S307”." Para. 50 see "When S307 ends, the process proceeds to S304, and the object detection unit 204 performs the object detection process again on the image I to which the updated correction pattern δ is added. When the object is detected again, the correction pattern is updated based on the area of the detected object. By repeating S304 to S307, if the number of objects finally detected becomes “0” or if the predetermined ending condition described in S305 is satisfied, the image I ′ is output and the process ends." Para. 84 see "When the process of S307 ends, the process proceeds to S304, and the object detection process is performed again on the image obtained by multiplying the image obtained by adding the input image I and the updated correction pattern δ by the target mask function T. If an object is still detected, the correction pattern is updated based on the detected area of the object. Finally, when the number of detected objects becomes “0” or when the predetermined termination condition described in S305 of the first embodiment is satisfied, the processing is completed and the image is output." Examiner Note: S305 determines whether to continue the process of masking the objects in the image again if objects are detected.).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Van to incorporate the teachings of Tamura to iteratively adjust the image when the anonymization process fails to meet a requirement. Doing so would predictably improve privacy protection effectiveness by ensuring sufficient anonymization of an image.
Regarding claim 10, Van teaches The image processing device according to claim 1.
While Van teaches an anonymization process and verifying if a model can produce anonymized images based on a numeric threshold, Van does not teach wherein the image conversion unit does not perform the predetermined process on the input image on which the anonymization process has been performed when the image determination unit determines that the input image on which the anonymization process has been performed does not satisfy the predetermined requirement.
However, Tamura teaches wherein the image conversion unit does not perform the predetermined process on the input image on which the anonymization process has been performed when the image determination unit determines that the input image on which the anonymization process has been performed does not satisfy the predetermined requirement. (Abstract see "Achieve privacy protection in images. An image processing device includes an object detection means for detecting an object included in an image, and a generation means for generating a mask image to be superimposed on the image in order to obstruct detection of the object by the object detection means." Para. 35 see "In step S305, the object detection unit 204 determines whether or not a predetermined end condition is satisfied, and determines whether or not to end the process. The predetermined termination condition is, for example, that the characteristic value exceeds (above / below) the threshold value. Here, the characteristic value is the “detection number of objects detected by the detector” or the “calculation upper limit”. The calculation upper limit may be “time required for calculation of correction processing” or “number of times of repeating processing of S304 to S307”." Para. 50 see "When S307 ends, the process proceeds to S304, and the object detection unit 204 performs the object detection process again on the image I to which the updated correction pattern δ is added. When the object is detected again, the correction pattern is updated based on the area of the detected object. By repeating S304 to S307, if the number of objects finally detected becomes “0” or if the predetermined ending condition described in S305 is satisfied, the image I ′ is output and the process ends." Para. 84 see "When the process of S307 ends, the process proceeds to S304, and the object detection process is performed again on the image obtained by multiplying the image obtained by adding the input image I and the updated correction pattern δ by the target mask function T. If an object is still detected, the correction pattern is updated based on the detected area of the object. Finally, when the number of detected objects becomes “0” or when the predetermined termination condition described in S305 of the first embodiment is satisfied, the processing is completed and the image is output." Examiner Note: S305 determines whether to end the process of masking the objects if the number of times repeating S304 to S307 reach a threshold.).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Van to incorporate the teachings of Tamura to incorporate a determination step that checks whether an anonymized image satisfies a predetermined requirement, and to conditionally skip a subsequent predetermined process when the requirement is not met. Doing so would predictably enhance privacy protection and computational efficiency by preventing the execution of processes on improperly anonymized images.
Allowable Subject Matter
Claim(s) 6-8 is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claim 6, none of the above cited references teach changing a face of a person in an image to a face of another person and comparing a visual-line direction of the original face to the other face when the image attribute indicates it as an image obtained from the interior of the vehicle.
Regarding claim 7, none of the above cited references teach claim 6 nor do they teach comparing the visual-line direction of the faces when the image attribute indicates it as an image obtained from the exterior of the vehicle.
Regarding claim 8, none of the above cited references teach claim 6 nor do they teach the predetermined requirement not including the comparison of the faces when the image is obtained from the exterior of the vehicle.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Tamrakar et al. (US 20180239975 A1) discloses a method and system for monitoring driving conditions and protecting privacy of the driver and passengers.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDER VAUGHN whose telephone number is (571) 272-5253. The examiner can normally be reached M-F 11am-7pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JENNIFER MEHMOOD can be reached on (571) 272-2976. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALEXANDER VAUGHN/Examiner, Art Unit 2675
/XIAO LIU/Primary Examiner, Art Unit 2664
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