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 Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6 and 8-16 are rejected are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without integration into a practical application or recitation of significantly more.
In the analysis below, the method of independent claim 1 is considered representative of independent claim 8 and 11 since all of the independent claims recite identical steps despite being directed to different statutory matter. Furthermore, independent claims 1, 8, and 11 are directed to one of the four statutory categories of eligible subject matter (a process for independent claim 1, a non-transitory computer readable medium storing instructions for claim 8, and an apparatus for claim 11.); thus, the claims pass Step 1 of the Subject Matter Eligibility Test (See flowchart in MPEP 2106).
Step 2A, prong 1 analysis:
The independent claims are directed to receiving environmental information about an environment of the vehicle, the environmental information being generated based in part on processing of images using a first configuration; analyzing the received environmental information to determine a scenario as represented by the environmental information; determining, and based on the analyzing, a second configuration that corresponds to the scenario; and providing a prompt indicative of a need to change the processing of incoming images captured along the determined scenario using the second configuration.
Each of the above steps can be performed mentally. In particular, there is a driver and a passenger in a vehicle driven by the driver; the passenger and driver observe environmental data outside the car with their own human vision with no added eye protection (first configuration), such as determining the sun is out and it is warm, for example; then, based on the determined environmental data using human vision, the passenger determines that there is a scenario of that the car is driving toward the direction of the sun and that the sun is blocking/impairing the vision of the driver; then, the passenger prompts the driver to lower the car visor and put on sunglasses (second configuration) to change the process of seeing during driving and allowing the driver to see more accurately; therefore, this process can all be done mentally.
As such, the description in independent claims 1, 8 and 11 is an abstract idea – namely, a mental process. Accordingly, the analysis under prong one of step 2A of the Subject Matter Eligibility Test does not result in a conclusion of eligibility (See flowchart in MPEP 2106).
Additional elements:
The additional element recited in independent claims 1, 8, and 11 are a processing circuit, image signal processing configuration, second image signal processing configuration, processing circuit, and a memory.
Step 2A, prong 2 analysis:
The above-identified additional elements do not integrate the judicial exception into a practical application.
Each of the other additional elements (a processing circuit, first image signal processing configuration, second image signal processing configuration, processing circuit, and a memory) amounts to merely using different devices as tools to perform the claimed mental process. Implementing an abstract idea on a computer or using known generic devices does not integrate a judicial exception into a practical application (See MPEP 2106.05(f)).
Moreover, the additional elements of the claims do not recite an improvement in the functioning of a computer or other technology or technical field, the claimed steps are not performed using a particular machine, the claimed steps do not effect a transformation, and the claims do not apply the judicial exception in any meaningful way beyond generically linking the use of the judicial exception to a particular technological environment (See MPEP 2106.04(d)). Therefore, the analysis under prong two of step 2A of the Subject Matter Eligibility Test does not result in a conclusion of eligibility (See flowchart in MPEP 2106).
Step 2B:
Finally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Each of the other additional elements (a processing circuit, first image signal processing configuration, second image signal processing configuration, processing circuit, and a memory) are generic computer features which perform generic computer functions that are well-understood, routine, and conventional and do not amount to more than implementing the abstract idea with a computerized system. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea).
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation, and mere implementation on a generic computer does not add significantly more to the claims. Accordingly, the analysis under step 2B of the Subject Matter Eligibility Test does not result in a conclusion of eligibility (See flowchart in MPEP 2106).
For all of the foregoing reasons, independent claims 1, 8, and 11 do not recite eligible subject matter under 35 USC 101.
Claims 2, 9 and 12 recite where the providing of the prompt involves automatically triggering an activation of an image signal process with the second image signal processing configuration during the processing of the incoming images. Image signal process is akin to a process carried out by a human with their own vision such as putting sunglasses on in response to viewing the sun directly while driving; this is done automatically as the human sees the sun blocking their vision of the road they are driving on; therefore, this process can all be done mentally.
Claim 3 and 13 recite activating the image signal process with the second image signal processing configuration. Image signal process is generic computing akin to a process carried out by a human with their own vision such as putting sunglasses on in response to viewing the sun directly while driving; therefore, this process can all be done mentally.
Claims 4 and 14 recite wherein activating the image signal process with the second image signal processing configuration involves selecting an image signal processing engine, out of a group of image signal processing engines, for use during the processing of the image captured along the determined scenario. Image signal process is generic computing akin to a process carried out by a human with their own vision such as putting sunglasses on in response to viewing the sun directly while driving; further, image signal processing engines are akin to human determining what to do in response to the sun in their eyes; first option (engine 1) would be to put on sunglasses; second option would be to put down the visor to help the driver see better; image signal processing engines are generic computers and simply implement the abstract idea with a computing system; therefore, this process can all be done mentally.
Claims 5 and 15 recite wherein activating the image signal process with the second image signal processing configuration involves selecting a parameter of an image signal processing engine to be used during the processing of the image captured along the determined scenario. Image signal process is generic computing akin to a process carried out by a human with their own vision such as putting sunglasses on in response to viewing the sun directly while driving; image signal processing engines are akin to human determining what to do in response to the sun in their eyes; first option (engine 1) would be to put on sunglasses; second option would be to put down the visor to help the driver see better; image signal processing engines are generic computers and simply implement the abstract idea with a computing system; the parameters that are changed, are for example what type of sunglasses should be worn (polarized or not polarized) or how low the visor in the car needs to be lowered depending on how sunny it is to the driver; therefore, this process can all be done mentally.
Claims 6 and 16 recite wherein the determining of the second image signal processing configuration is also based on a parameter related to the vehicle. The parameter that is changed is how low the visor in the car needs to be lowered depending on how sunny it is to the driver; the visor is a parameter related to the vehicle in this case; therefore, this process can all be done mentally.
Claim 10 recites storing instructions for uploading second image signal processing configuration metadata defining the second image signal processing configuration from a remote memory unit. The passenger human in the car (remote memory separate from driver operating vehicle) remembers to use the vehicle sun visor or to use sunglasses (second configuration) when the sun is shining at the direction of the driver when operating the vehicle; therefore, this process can all be done mentally.
Therefore, dependent claims 2-6, 9-10 and 12-16 recite the same abstract idea of a mental process which can be performed in the mind with the aid of pen and paper, and are therefore also rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-17 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No.: 2022/0058402 (Hunt), in view of Chinese Patent Publication No.: CN 11086518 (Hu et al.) (hereinafter Hu).
Regarding claim 1, Hunt teaches a method of processing adaptable image signal for a vehicle, the method comprising: (Hunt, abstract: “Systems, methods, and other embodiments described herein relate to selectively adapting settings of a sensor according to a driving context.”)
receiving, by a processing circuit, environmental information about an environment of the vehicle, the environmental information being generated based in part on processing of images using a first image signal processing configuration (Hunt, para. [0034]; FIG. 1: “At 310, the sensor module 220 acquires sensor data 250 from at least one sensor of the vehicle 100. In one embodiment, the sensor module 220 acquires the sensor data 250 about a surrounding environment of the vehicle 100. As previously noted, the sensor module 220, in one or more implementations, iteratively acquires the sensor data 250 from one or more sensors of the sensor system 120. The sensor data 250 includes observations of a surrounding environment of the subject vehicle 100, including specific regions that are relevant to functions executed by systems of the vehicle 100, such as assistance system 160 (e.g., activation zones, scanning zones, etc.), an automated driving module (e.g., autonomous driving, semi-autonomous driving, etc.), and so on. Moreover, the sensor module 220 acquires the sensor data 250, in at least one embodiment, from a visible light camera. As such, the sensor data 250 can include images having attributes (e.g., resolution, coloring, etc.) defined according to the parameters 260. Of course, as noted previously, the sensor module 220 may also acquire data from other sensors, such as a LiDAR.”; see steps 310 in FIG. 3 below:
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analyzing, by the processing circuit, the received environmental information to determine a scenario as represented by the environmental information (Hunt, para. [0035]-[0036]; para. [0040]; FIG. 3: “At 320, the sensor module 220 determines a driving context for the ego vehicle 100 according to the sensor data 250. The driving context generally defines, in at least one approach, characteristics about the surrounding environment of the ego vehicle 100. For example, in one approach, the driving context defines weather conditions about adverse weather in the surrounding environment and context characteristics about a type of driving environment, such as highway or urban. In regard to the context characteristics, the monitoring system 170 determines this aspect since an urban driving environment is generally more complex to navigate. That is, an urban environment often includes more dynamic agents moving at different trajectories than the ego vehicle 100 and also includes more complex traffic patterns … By contrast, a highway context is generally limited to other vehicles moving in a same direction as the ego vehicle 100 with limited additional hazards and no traffic signals/signs other than those indicating exits. Accordingly, the difference in complexity between highway driving and urban driving generally results in additional computational resources in order to understand and effectively navigate the urban environment. Thus, the sensor module 220, in one approach, includes a set of machine learning algorithms that the sensor module 220 applies to the sensor data 250 to extract information about the surrounding environment so that the monitoring system 170 can distinguish between urban and highway contexts, and/or other aspects of the environment.”; “At 330, the sensor module 220 determines whether the driving context satisfies a sensor threshold for adjusting the parameters 260. In one approach, the sensor threshold includes multiple different criteria for different actions. Thus, in relation to highway/urban contexts, the sensor threshold may define a critical number of elements that lead to a conclusion of a particular context”; see steps 320 and 330 in FIG. 3 above);
determining, by the processing circuit and based on the analyzing, a second image signal processing configuration that corresponds to the scenario; and changing the processing of incoming images captured along the determined scenario using the second image signal processing configuration (Hunt, para. [0042]-[0043]; FIG. 3: “At 340, when the driving context indicates that the ego vehicle 100 is in a highway context, the sensor module 220 adjusts the parameters 260 to cause the camera to produce images in a low resolution (e.g., 4 megapixels to 2 megapixels). In various configurations, the sensor module 220 may also adjust the frame rate, color selection (e.g., full color to monochrome), and so on. In this way, the sensor module 220 reduces the computational resources associated with data handling and processing of the images, thereby freeing resources for other uses and reducing power consumption. In the case where the sensor module 220 is adapting the functioning of additional sensors at block 340, then the sensor module 220 adjusts the parameters 260 accordingly. For example, the sensor module 220 may adapt a scan speed of a LiDAR, a number of scan lines for the LiDAR, and so on. In a further aspect, the sensor module 220 may also control a radar, a number of cameras providing images, ultrasonic sensors, IR cameras, and so on. At 350, when the driving context indicates that the ego vehicle 100 is in a highway context, the sensor module 220 adjusts the parameters 260 to cause the camera to produce images at a high resolution (e.g., a change of 2 to 4 or more megapixels). As noted above, other attributes of the camera may also be adjusted to provide higher quality images, such as frame rate (e.g., 30 to 60 FPS), color selection (e.g., monochrome to color), and so on. Regarding other sensors, the sensor module 220 may similarly adjust attributes of the sensors to increase quality of acquired data at block 350.”; see steps 340 and 350 in FIG. 3 above).
Hunt fails to teach
providing a prompt indicative of a need to change the current driving state.
Hu teaches
providing a prompt indicative of a need to change the current driving state (Hu, page 6, para. 5; FIG. 4: “Therefore, when the obtained road condition information of the road where the car is located is preset road condition information, a virtual image for indicating the driver's driving operation is displayed, and the virtual image is used to prompt the car to change the current driving state. The virtual image may be an image with a prompting or indicating effect. For example, as shown in FIG. 4, a preset image A may be displayed on the display component of the vehicle head-up display device 100 on the car 200 to prompt or instruct the user to change the current driving state, of course, the specific image content may not be limited in this embodiment of the present application. The vehicle head-up display device can project the virtual image into the display component, and the displayed virtual image is superimposed with the road in the real world to realize the AR display effect, which can make the prompt effect more realistic, thereby improving the driver's alertness and ensuring driving. Safety.”;
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It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the method, as taught by Hunt, to include the step of providing a prompt indicative of a need to change the current driving state, as taught by Hu.
The suggestion/motivation for doing so would have been that “the virtual image is used to prompt the car to change the current driving state, so as to realize the intelligent prompting of the driver during the driving process and improve the driving safety” (Hu, page 19, para. 4).
Hunt, in view of Hu teaches providing a prompt indicative of a need to change the processing of incoming images captured along the determined scenario using the second image signal processing configuration (Hunt, para. [0042]-[0043]; FIG. 3; Hu, page 6, para. 5; FIG. 4; Hunt teaches changing the processing of incoming images captured along the determined scenario using the second image signal processing configuration by detecting a highway scenario and adjusting the cameras to low resolution or by detecting an urban scenario and adjusting the camera to high resolution; Hu teaches providing a prompt indicative of a need to change that has a display/user interface in the car the prompts/asks the driver to make changes to their driving to maintain safety based on identified road/environmental conditions; therefore, when Hunt is modified by Hu, a prompt on a display in the car during driving shows the driver that a highway scenario has been detected or the urban scenario detected and prompts the driver that the cameras settings must be changed to go into either low resolution mode or high resolution mode, respectively).
Therefore, it would have been obvious to combine Hunt, with Hu, to obtain the invention as specified in claim 1.
Regarding claim 2, Hunt, in view of Hu, teaches the method according to claim 1, where the providing of the prompt involves automatically triggering an activation of an image signal process with the second image signal processing configuration during the processing of the incoming images (Hunt, para. [0042]-[0043]; FIG. 3 Hu, page 6, para. 5; FIG. 4; see rejection of claim 1 above; triggering an automatic activation is implicit to the combination; Hunt, in view of Hu, teaches the automatic triggering of the image signal process of changing the resolution of the vehicles’ external environment cameras with the prompt of the need for the cameras to change resolution displayed to the driver when a highway scenario or an urban scenario is detected; Hunt, para. [0058]: “The one or more sensors can be configured to operate in real-time. As used herein, the term “real-time” means a level of processing responsiveness that a user or system senses as sufficiently immediate for a particular process or determination to be made, or that enables the processor to keep up with some external process.”).
Regarding claim 3, Hunt, in view of Hu, teaches the method according to claim 1, further comprising activating the image signal process with the second image signal processing configuration (Hunt, para. [0042]-[0043]; FIG. 3; see rejection of claim 1 above; steps 340 and 350 of FIG. 3 adjusting the cameras to either low or high resolution depending on if a highway scenario or an urban scenario is detected).
Regarding claim 4, Hunt, in view of Hu, teaches the method according to claim 3, wherein activating the image signal process with the second image signal processing configuration involves selecting an image signal processing engine, out of a group of image signal processing engines, for use during the processing of the image captured along the determined scenario (Hunt, para. [0042]-[0043]; FIG. 3; see rejection of claim 1 above; Hunt analyzes the sensor and image data of the environment around the vehicle while in operation an determines which algorithmic image process to modify the sensors and camera; the two algorithmic image processes are indicative of the determined two scenarios (highway mode or urban mode); both these modes operate with different images, sensor data, and different speeds, for example; Examiner is interpreting the term “image signal processing engine” as including algorithmic processes not necessarily having different hardware per se but rather using the hardware in a different configuration; the highway mode is chosen with a different “engine” (i.e. algorithm) to adjust the camera’s resolution and frame rate, as well as other setting including LIDAR, RADAR, etc.; Hunt, para. [0036]; para. [0048]: “Accordingly, the difference in complexity between highway driving and urban driving generally results in additional computational resources in order to understand and effectively navigate the urban environment.”; “In this case, the monitoring system 170 can adapt the settings of the camera via the parameters 260 to provide less/reduced data, thereby alleviating a burden on computational resources of the vehicle 100.”).
Regarding claim 5, Hunt, in view of Hu, teaches the method according to claim 3, wherein activating the image signal process with the second image signal processing configuration involves selecting a parameter of an image signal processing engine to be used during the processing of the image captured along the determined scenario (Hunt, para. [0042]-[0043]; FIG. 3; see rejection of claim 1 above; The image signal process is changing the resolution of the camera to match the determined scenario of either highway or urban by lowering the resolution or raising the resolution; the parameter is the resolution that is selected and changed based on the determined scenario; other parameters changed during the process include shutter speed of the camera as well).
Regarding claim 6, Hunt, in view of Hu, teaches the method according to claim 1, wherein the determining of the second image signal processing configuration is also based on a parameter related to the vehicle (Hunt, para. [0042]-[0043]; FIG. 3; see rejection of claim 1 above discussing the camera resolution as the parameter being changed in the second configuration; Hunt, para. [0038]-[0040]: “Regarding highway/urban determinations, the sensor module 220 analyzes the sensor data 250 to identify context characteristics of the surrounding environment that correspond with highway and urban environments. The context characteristics include aspects, such as pedestrians, highway signs, traffic signals, traffic signs, lane markers, trajectories of nearby objects, and surrounding structures. Thus, the sensor module 220 populates the driving context with the context characteristics as determined according to the analysis of the sensor data 250. In this way, the sensor module 220 can subsequently make determinations about the context. In yet a further approach, the sensor module 220 can acquire a location of the ego vehicle 100 using a GPS or other form of localization. The sensor module 220 can then compare a current location against a map that identifies different contexts. Accordingly, the sensor module 220 derives the driving context, in this example, from urban and highway context labels in the map that correspond with different locations … Thus, in relation to highway/urban contexts, the sensor threshold may define a critical number of elements that lead to a conclusion of a particular context. For example, in one approach, the sensor module 220 may assign values to different types of elements (e.g., traffic signals, pedestrians, etc. versus highway signs, common trajectories, etc.) and accumulate the values to determine the context. In one configuration, the sensor module may assign positive values to urban elements and negative values to highway elements, and a final accumulated value that is positive or negative may indicate the context. In this case, the sensor threshold itself may simply be a defined value, such as zero.”; determining of the high- or low-resolution adjustment of the camera on the car in either highway scenario or urban scenario is based on parameters related to the vehicle such as pedestrians, highway signs, traffic signals, traffic signs, lane markers, trajectories of nearby objects, and surrounding structures, etc.).
Regarding claim 7, Hunt, in view of Hu, teaches the method according to claim 1, wherein the determining of the second image signal processing configuration is also based on a performance indicator for an automated driving application (Hunt, para. [0049]; para. [0061]: “As shown in FIG. 4B, the monitoring system 170 identifies pedestrians, buildings, and a surface street with just a single lane, which are elements that correspond with an urban context. Thus, there are no indicators associated with a highway, and according to the identified components, the system 170 indicates that the scene 410 is associated with an urban environment. In this case, the monitoring system 170 indicates that the driving context is urban and proceeds to adjust the parameters 260 to provide additional data (e.g., higher resolution, higher frame rate, etc.). In this way, the monitoring system 170 improves autonomous navigation through the environment by using higher quality information.”; “Alternatively, or in addition, the sensor system 120 can include one or more environment sensors 122 configured to acquire and/or sense driving environment data. “Driving environment data” includes data or information about the external environment in which an autonomous vehicle is located or one or more portions thereof. For example, the one or more environment sensors 122 can be configured to detect and/or sense obstacles in at least a portion of the external environment of the vehicle 100 and/or information/data about such obstacles. Such obstacles may be stationary objects and/or dynamic objects. The one or more environment sensors 122 can be configured to detect, and/or sense other things in the external environment of the vehicle 100, such as, for example, lane markers, signs, traffic lights, traffic signs, lane lines, crosswalks, curbs proximate the vehicle 100, off-road objects, etc.”; the decision to change camera resolution or frame rate in response to a detected scenario or highway or urban is based on how well objects are detected by the autonomous vehicle detection system (i.e. performance)).
Regarding claim 8, Hunt teaches a non-transitory computer readable medium for processing adaptable image signal that stores instruction that once executed by a processing circuit causes the processing circuit to: (Hunt, para. [0051]: “In another embodiment, the described methods and/or their equivalents may be implemented with computer-executable instructions. Thus, in one embodiment, a non-transitory computer-readable medium is configured with stored computer-executable instructions that, when executed by a machine (e.g., processor, computer, and so on), cause the machine (and/or associated components) to perform the method.”).
With regards to the remaining limitations of claim 8 and dependent claim 9, they recite the functions of the process of claims 1-2 respectively, as non-transitory computer-readable media storing instructions. Thus, the analyses in rejecting claims 1-2, under Hunt, in view Hu, are equally applicable to the remaining limitations of claim 8 and dependent claim 9, respectively.
Regarding claim 10, Hunt, in view of Hu, teaches the non-transitory computer readable medium according to claim 8, further comprising storing instructions for uploading second image signal processing configuration metadata defining the second image signal processing configuration from a remote memory unit (Hunt, para. [0020]: The vehicle 100 also includes various elements. It will be understood that, in various embodiments, the vehicle 100 may not have all of the elements shown in FIG. 1. The vehicle 100 can have different combinations of the various elements shown in FIG. 1. Further, the vehicle 100 can have additional elements to those shown in FIG. 1. In some arrangements, the vehicle 100 may be implemented without one or more of the elements shown in FIG. 1. While the various elements are shown as being located within the vehicle 100 in FIG. 1, it will be understood that one or more of these elements can be located external to the vehicle 100. Further, the elements shown may be physically separated by large distances and provided as remote services (e.g., cloud-computing services).”;
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As shown in FIG. 1, the monitoring system 170 and data stores 115 can be outside and remote from the vehicle which stores the different configurations for different detected scenarios from the on-vehicle sensors and cameras, such as highway mode and urban mode which then send the data to change the camera resolution or shutter speed to the car to be received and make the image signal processing changes accordingly).
Regarding claim 11, Hunt teaches a system for processing adaptable image signal for a vehicle, comprising a processing circuitry; and a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to: (Hunt, para. [0023]-[0024]; FIG. 2: “With reference to FIG. 2, one embodiment of the monitoring system 170 is further illustrated. As shown, the monitoring system 170 includes a processor 110 … In one embodiment, the monitoring system 170 includes a memory 210 that stores the sensor module 220 … The sensor module 220 is, for example, computer-readable instructions that, when executed by the processor 110, cause the processor 110 to perform the various functions disclosed herein. While, in one or more embodiments, the module 220 is instructions embodied in the memory 210, in further aspects, the module 220 includes hardware, such as processing components (e.g., controllers), circuits, etc. for independently performing one or more of the noted functions.”;
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With regards to the remaining limitations of claim 11 and dependent claims 12-17, they recite the functions of the process of claims 1-7 respectively, as apparatuses. Thus, the analyses in rejecting claims 1-7, under Hunt, in view of Hu, are equally applicable to the remaining limitations of claim 11 and dependent claims 12-17, respectively.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL ADAM SHARIFF whose telephone number is 571-272-9741. The examiner can normally be reached M-F 8:30-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sumati Lefkowitz can be reached on 571-272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL ADAM SHARIFF/
Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672
Prosecution Insights