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
Claims 1-21 are eligible.
Step 1: Independent claims fall within one of the statutory classes.
Step 2A, Prong one: the independent claims do no recite any limitation that falls within the groupings of abstract ideas enumerated in MPEP 2106.04(a)(2). Claims 1, 20, and 21 recite a method/system/computer program product for sensing air quality with a sensor platform, comprising: directing an air quality measurement system to a predicted high information region and to a low info region, but preferentially to the high info region. Directing a system to go somewhere isn’t a mental process that can be carried out in the human mind, it is an act of controlling the movement of a sensor platform.
Therefore, the claims are eligible.
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 1-4, 11, 12, and 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over Pariseau (USPAP. 20200378940).
Regarding claims 1, 20, and 21, Pariseau discloses a method for sensing air quality with a sensor platform (air quality sensor network 310 including sensors 302 in Fig. 3. See Par. 75), comprising:
directing an air quality measurement system to a predicted high information region wherein the predicted high information region is determined based on high variance of prior air quality measurements taken at different times (Pars. 16-25: deploying fleet of sensors to various regions that react in real-time to variances in the environment, indoor, outdoor, industrial/manufacturing concerns or municipalities etc…during data collection which includes geographic location (i.e. GPS or equivalent), air quality metrics (particulate concentrations, particulate mass, VOC, CO2, other gases, etc.) and it might also include environmental information (temperature, humidity, air velocity and direction, etc. Par. 28 discloses autonomous system in which some fixed sensors or non-directed sensors or a small number of periodic audits with directed sensors can monitor general air quality conditions and if some of the air quality parameters being monitored exceeded some minimum threshold, a larger fleet of sensors can be deployed to that specific area to investigate and map the event in much finer detail than is possible with a fixed installation of sensors or even a pre-defined grid route of mobile sensors. Pars 29-31 discloses boundary line in large settings and the system can be tasked dynamically, e.g. periodic monitoring of this fence line/boundary, optimizing for points in the path of the current wind direction, If the air quality on any point is above threshold X (75 ug/m3, TVOCs >100 ppb, etc.) then triple monitoring density with points (continuous 1 minute samples) until threshold falls for 1 hour, and map the contour, and deploy sensors to identify the source(s) and map/monitor it/them (continuous 1 minutes samples). The monitoring period can be manually or automatically adjusted based on the sensed conditions, and is preferably 1 hour or less, 15 minutes or less, five minutes or less depending upon the particular environmental conditions. Pars. 75-79: sensors 303 would be released and strewn throughout the environment or can be dropped by air using a drone. The sensors have onboard air quality sensor and a transmitter and periodically report air quality information using the integrated transmitters. The sensors have receivers with precise clocks and positions sensors would timestamp these received messages and communicate with local network and controller. The sensor’s position information can be used to create a dynamic aggregate map of air quality conditions across a geographic area. Therefore, Pariseau directs the sensor network in the system of Fig. 3 to a predicted high information region wherein the predicted high information region is determined based on high variance of prior air quality measurements taken at different times)
and directing the air quality measurement system to a predicted low information region wherein the predicted low information region is determined based on low variance of prior air quality measurements taken at different times (As explained above, it is the Examiner’s position that Pariseau can direct the system to a predicted low information region wherein the predicted low information region is determined based on low variance of prior air quality measurements taken at different times. See examples of low particulates detection at Par. 82);
Pariseau does not explicitly disclose “wherein the air quality measurement system is preferentially directed to the predicted high information region”.
Pariseau discloses at Par. 28 discloses autonomous system in which some fixed sensors or non-directed sensors or a small number of periodic audits with directed sensors can monitor general air quality conditions and if some of the air quality parameters being monitored exceeded some minimum threshold, a larger fleet of sensors can be deployed to that specific area to investigate and map the event in much finer detail than is possible with a fixed installation of sensors or even a pre-defined grid route of mobile sensors. Further, Par. 156 discloses Mobile sensors can be mounted on drones (or mobile robots, rovers, etc.) that navigate the complex on pre-assigned routes or they could be mounted on campus vehicles or transports (shuttles, golf carts. Segways, etc.). The collected data from these can be aggregated to provide real-time air quality information. If an air quality event occurred (or air quality from a neighboring quarter impinged) on the campus mobile sensors can be tasked to survey the area in question to provide more finely detailed information or to help trace the source of the air quality issue. Par. 102 discloses applying a weight to the value of the sensor data based on the accuracy of particular sensors, aging the sensor data, or factoring in outside information like meteorological sources, or historical or model-based data.
Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filling the Application to arrive at the claimed invention to use the variance of prior air quality measurements to identify high information regions to go collect more data from.
Note: Mobile sensors can be mounted on drones (or mobile robots, rovers, etc.) that navigate the complex on pre-assigned routes or they could be mounted on campus vehicles or transports (shuttles, golf carts. Segways, etc.). The collected data from these can be aggregated to provide real-time air quality information. If an air quality event occurred (or air quality from a neighboring quarter impinged) on the campus mobile sensors can be tasked to survey the area in question to provide more finely detailed information or to help trace the source of the air quality issue). Pariseau discloses at Par. 117 “the system can assign different sensors for different size ranges, and thereby provide a sensor with a much larger dynamic range” and at Par. 98 that the maps can have regions and sub-regions or can comprise a fixed grid enabling display of combined real time weather and air quality and particle count data. Par. 78 discloses a configuration that allows for calibration and configuration information to flow. It can also allow the system to provide direction information to the various sensors 302 in order to more evenly deploy or direct them perhaps redeploying them based on changing conditions within an environment and also keep them within the desired area.
Regarding claim 2, Pariseau discloses wherein the air quality measurement system comprises a set of mobile sensors mounted to a vehicle (Par. 156: sensors can be mounted on drones, vehicles, mobile robots, rovers etc).
Regarding claim 3, Pariseau discloses determining for each session, a set of target regions (region of interest) at which air quality measurements are to be obtained during the session, the set of target regions comprising the predicted high information region and the predicted low information region (Par. 72: in a case might involve a sensor network which might be deployed in an area, perhaps as part of a response to some environmental event. For example, in the aftermath of some event sensors could be supplied to first responders, these would likely have associated displays. Pars. 14-16 discloses the system can utilize bi-directional communication to control operation of the airborne particulate sensors that are geographically distributed in a region to be monitored. The system can use one or more controllers that can remotely control operating parameters of the individual sensor devices and also control geographic position or communicate with users of sensor devices to direct movement and operation of such sensors. The sensors can be deployed on various tasks and regions that react in real time to variances in the environment during data collection. Sensors are placed in region of interest. Par. 27 discloses that machine learning methods can be applied to generate improved geographic mapping of air quality information by utilizing real time weather data including wind velocity and precipitation data over the particle detection spatial region. Par. 28 discloses monitoring general air quality conditions and if some of the air quality parameters being monitored exceeded some threshold, a larger fleet of sensors can be deployed to that specific area to investigate and map the event in much finer detail than is possible with a fixed installation of sensors or even a pre-defined grid route of mobile sensors).
Regarding claim 4, Pariseau discloses determining a navigational guidance for navigating the air quality measurement to the set of target regions during the session (Pars. 22 and 26-29. Pars. 75-77 discloses that the sensors would be realeased and move about the environment or be strewn throughout the environment perhaps dropped by air using a drone. The position information would be determined by triangulation with 3 sensor receivers 311 providing enough information to determine relative position in 2 dimensions, and 4 sensor receivers 311 providing enough information to determine relative position in 4 dimensions for example, like GPS. Since each sensor receiver 311 would also have accurate position information due to their local position sensor 356 the relative position of the sensors could be converted to an absolute position. Once position information is derived, that information can be attached to the sensor air quality information and then communicated via a cloud connected device 351 via its cloud communications controller 354 through a network cloud 381 to at least one server 391. Sensors in the sensor network can have both transmitters and receivers to enable bidirectional communications. This allows This allows for calibration and configuration information to flow. It can also allow the system to provide direction information to the various sensors 302 in order to more evenly deploy or direct them perhaps redeploying them based on changing conditions within an environment and also keep them within the desired area. Therefore, the reference meets the claimed limitation of claim 4: determining a navigational guidance for navigating the air quality measurement to the set of target regions during the session).
Regarding claim 11, Pariseau discloses wherein the set of target regions is selected from a set of partitions of a geographic region. (Par. 28: Such a system can be autonomous in that based on some fixed sensors or non-directed sensors or a small number of periodic audits with directed sensors can monitor general air quality conditions and if some of the air quality parameters being monitored exceeded some minimum threshold, a larger fleet of sensors can be deployed to that specific area to investigate and map the event in much finer detail than is possible with a fixed installation of sensors or even a pre-defined grid route of mobile sensors).
Regarding claim 12, Pariseau discloses wherein the set of partitions are determined based on a spatial variance of prior air quality measurements across the geographic region (Par. 27: A further application of machine learning methods can be applied to generate improved geographic mapping of air quality information by utilizing real time weather data including wind velocity and precipitation data over the particle detection spatial region).
Regarding claim 19, Pariseau discloses wherein the predicted high information region and the predicted low information region are comprised in a service area assigned to one of a plurality of hubs that service a geographic region, and the air quality measurement system is assigned to the hub (Pariseau: Par. 90 sensors for indoor and outdoor (hubs)).
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 5 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Pariseau and Nagao (USPAP. 20210396546).
Regarding claim 5, Pariseau discloses wherein the navigational guidance (Par. 80: The position data might be sourced directly from a GPS receiver, but it might also be derived from a street address perhaps on installation and then communicated thereafter as the same derived position information); but not the turn-by-turn guidance.
Nagao teaches the turn by turn guidance (Nagao: Pars. 58-60: a driver of a sensor platform 102d comprising a vehicle may enter an intended destination 302 into a mapping and/or route finding interface of a telematics system 304 and/or an in-vehicle-infotainment system (“IVI”) 306 associated with the vehicle. Information regarding the destination 302 and/or a planned route to the destination proposed by the mapping and/or route finding interface of the telematics 304 and/or IVI system 306 may be transmitted to the environmental information service provider system 110 from the mobile sensor platform 102d. ased in part on the received destination and/or planned route information, the environmental information acquisition engine 308 may identify environmental information of interest that may be obtained by the mobile sensor platform 102d either along the planned route or along a different alternative route. A proposed alternative route and/or travel time may be displayed to the user based on the received route information 314 (e.g., via the telematics 304 and/or IVI system 306), thereby allowing the user to travel to the intended destination 302 via the alternative route and facilitating the collection of the environmental information of interest (e.g., environmental information 106d).
Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filling the Application to modify 's invention using 's invention to arrive at the claimed invention specified in claim 5 to allow the user to travel to the intended destination and facilitating the collection of the environmental information of interest (see Nagao: Par. 59).
Regarding claim 10, Pariseau and Nagao disclose everything as applied above (see claim 5). In addition, Pariseau discloses providing the navigational guidance to the air quality measurement system (Par. 80: The position data might be sourced directly from a GPS receiver, but it might also be derived from a street address perhaps on installation and then communicated thereafter as the same derived position information).
Note: Claims 5 and 10 are also rejected as being unpatentable over Pariseau and Langland (see rejection below in this Office Action).
Claim 6, 8, and 15-17, as best understood, are rejected under 35 U.S.C. 103 as being unpatentable over Pariseau and Lection (USPAP. 20160316334).
Regarding claim 6, Pariseau discloses everything as applied above. However, Pariseau does not explicitly disclose wherein determining the navigational guidance includes selecting a set of road segments over which the air quality measurement system is to travel between the set of target regions.
Lection teaches wherein determining the navigational guidance includes selecting a set of road segments over which the air quality measurement system is to travel between the set of target regions (Lection: Par. 34 teaches that Map 310 is divided into regions shown as cells C1-C8. Providing and non-providing devices could be shown with priorities as illustrated in FIG. 3A above. As shown, each region or cell is irregular in shape and delineated by borders. Some regions or cells are larger than others. Some regions may be sized based on a variety of factors such as population density, distance from a central location, statistical significance to a data item of interest, etc. Also see Figs. 5 and 6. Par. 33 teaches that the road segments as being hexagonal cells).
It would have been obvious to one of ordinary skilled in the art at the time of filling the Application to modify Pariseau 's invention using Lection's invention to arrive at the claimed invention specified in claim 6 to produce a map showing missing coverage; prioritizing acquisition of mobile devices in locations of missing coverage; and aggregating the transmitted desired data and providing the aggregated data to an entity (Lection: Par. 6).
Regarding claim 8, Pariseau and Lection disclose everything as applied above. In addition, Lection teaches wherein the set of road segments are selected based at least in part on a drive time (Lection: Par. 46).
Regarding claim 15, Pariseau and Lection disclose everything as applied above. In addition Lection teaches wherein the plurality of partitions corresponds to a plurality of hexagons (see Par. 33: teaches that the road segments as being hexagonal cells).
Regarding claim 16, Pariseau and Lection disclose everything as applied above. In addition, Lection teaches wherein directing the air quality measurement system to the predicted high information region comprises selecting a selected location within the predicted high information region (Pars. 33 and 34).
Regarding claim 17, Pariseau and Lection disclose everything as applied above. In addition, Lection teaches wherein the selecting the selected location within the predicted high information region comprises selecting a road segment along which the air quality measurement system is to obtain an air quality measurement at a selected location (Pars. 33 and 34).
Note: Claim 16 is also rejected under 35 USC 103 as being unpatentable over Pariseau and Langland Claim 17 is rejected under 102 103 by Parisian (see the rejection below in this Office Action).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Pariseau, Lection and further in view of Shire (USPAP. 20230243678).
Regarding claim 7, Pariseau and Lection disclose everything as applied above. However, Pariseau and Lection do not explicitly disclose wherein the set of road segments are selected based at least in part on a pass count corresponding to a number of prior air quality measurements taken within the particular target region.
Shire teaches “wherein the set of road segments are selected based at least in part on a pass count corresponding to a number of prior air quality measurements taken within the particular target region” (Pars. 47-52 teaches one possible approach is to perform a random or pseudo-random assignment of sampling locations, which is evaluated against predefined criteria and repeated if needed. The criteria may include: completeness of time coverage, avoidance of clustering, observance of refractory periods, completeness of spatial coverage of the desired measuring locations, resilience (e.g., the number of independent sensors that are engaged to cover each measuring location. It is the Examiner’s position that the completeness as taught by Shire meets the claimed “pass count”).
It would have been obvious to one of ordinary skilled in the art at the time of filling the Application to modify Pariseau and Lection's invention using Shire's invention to arrive at the claimed invention specified in claim 7 to control sampling at a plurality of mobile ambient sensors which would allow precise control of both sensors and more complete coverage of the measuring location (Shire: Par. 10).
Claim 9 (dependent from claim 6) is rejected under 35 U.S.C. 103 as being unpatentable over Pariseau, Lection and further in view of Anastassov (USPAP. 20220163347).
Regarding claim 9, Pariseau and Lection disclose everything as applied above (see claim 6). Pariseau discloses at Par. 102 applying a weight to the value of the sensor data based on the accuracy of particular sensors, aging the sensor, data. However, Pariseau and Lection do not explicitly “wherein the set of road segments are randomly selected based on a scoring function that scores a road segment using a weighted travel time value and a weighted pass count value”.
Anastassov teaches wherein the set of road segments are randomly selected based on a scoring function (scoring function at Par. 73) that scores a road segment using a weighted travel time value and a weighted pass count value (Anastassov teaches identifying special areas and cleaning-up map data. Pars. 24, 70, and 81 discloses weighted feature ranking of road segment to identify road segments).
It would have been obvious to one of ordinary skilled in the art at the time of filling the Application to modify Pariseau and Lection's inventions using Anastassov's invention to arrive at the claimed invention specified in claim 9 for creating a road geometry representation (e.g., a base map with double digitized roads representing actual travel paths and connectivity) and identifying special areas with significant vehicle activities that do not represent established roads (e.g., construction areas) (Anastassov: Par. 2).
Claim 5, 10, 13, 14, 16, 17, as best understood, are rejected under 35 USC 103 as being unpatentable over Pariseau and Langland et al. (USPAP. 20210140769)(hereinafter “Langland”).
Regarding claim 5, Pariseau discloses everything as applied above. However, Pariseau does not explicitly disclose that wherein the navigational guidance comprises turn-by-turn guidance.
Langland teaches wherein the navigational guidance comprises turn-by-turn guidance (Pars. 52-55: road maps that sensor platform traverses and tables 1-5 showing trajectories and corrected locations, map feature (road segment) sensor data values. The sensor data may be associated with map features).
It would have been obvious to one of ordinary skilled in the art at the time of filling the Application to modify Pariseau's invention using Langland's invention to arrive at the claimed invention specified in claim 5 to provide data that is aggregated over time to provide confidence in the environmental data presented (Par. 58).
Regarding claim 10, Pariseau and Langland disclose everything as applied above. In addition, Langland teaches providing the navigational guidance to the air quality measurement system (Langland: Pars. 52-55: the sensor data values are assigned to map features corresponding to the positions of mobile sensor platform 102A that are in the same time interval. For this sensor having a time interval of one second, GPS data within 0.5 seconds of the sensor timestamp is considered to be in the same interval. Thus, the first point of the GPS data is in the same interval as the first point of sensor data. The second and third points of GPS data are in the same interval as the second sensor data point. The fourth and fifth points of GPS data are in the same interval as the third sensor data point. The sixth and seventh points of GPS data are in the same interval as the fourth sensor data point. Based on the GPS data and timing shown, the second sensor value is place in road segment).
Regarding claim 13, Pariseau discloses everything as applied above. However, Pariseau does not explicitly disclose wherein the set of partitions comprises a plurality of partitions of varying sizes.
Langland teaches wherein the set of partitions comprises a plurality of partitions of varying sizes (hexagon, cell shown in Figs. 5A and 5B and Par. 48 and their sizes and shapes can be changed).
It would have been obvious to one of ordinary skilled in the art at the time of filling the Application to modify 's invention using 's invention to arrive at the claimed invention specified confidence in the environmental data presented (Par. 58).
Regarding claim 14, Pariseau and Langton disclose everything as applied above. In addition, Langton teaches “wherein a size of a particular partition in the set of partitions is
Regarding claim 16, Pariseau and Langland disclose everything as applied above. In addition, Langland teaches wherein directing the air quality measurement system to the predicted high information region comprises selecting a selected location within the predicted high information region (Pars. 63 and 64: weighing the sensor data values for map features intersecting the region is determined for each sensor of interest. Par. 66 discloses road segments are weighted and road segment has the highest weight. Server determines the region including the map location. Par. 67: Weighted averages of the sensor data values for road segments are determined).
Regarding claim 17, Pariseau and Langland disclose everything as applied above. In addition, Langland teaches wherein the selecting the selected location within the predicted high information region comprises selecting a road segment along which the air quality measurement system is to obtain an air quality measurement at a selected location (Pars. 63-68: Explanation in claim 16 is incorporated by reference. In addition, Langland teaches at Par. 68 that sensor data values assigned to surrounding map features may be interpolated to obtain sensor data values for an address or other map locations. As discussed above, the sensor data values and map features are hyper-local in nature. Consequently, the sensor data values generated for the map location are also hyper-local. Thus, the environmental data specific to the map location may be provided. Adjacent addresses (e.g. adjacent map locations) may have different sensor data values generated using method 600 because different map features may intersect the regions corresponding to the adjacent addresses. As a result, a user may be able to investigate the environmental quality particular to their address).
Claim 18 is rejected under 35 U.S.C. 102(a)(1) as anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over Pariseau.
Regarding claim 18, Pariseau discloses “determining a set of target regions (Par. 98: maps have regions and sub-regions comprise a fixed grid enabling display of combined real time weather and air quality and particle count data) at which air quality measurements are to be obtained by a plurality of vehicles during a corresponding set of sessions, wherein the plurality of vehicles are comprised in the air quality measurement system” (see Pariseau: Par. 156: Mobile sensors can be mounted on drones (or mobile robots, rovers, etc.) that navigate the complex on pre-assigned routes or they could be mounted on campus vehicles or transports (shuttles, golf carts. Segways, etc.). The collected data from these can be aggregated to provide real-time air quality information. If an air quality event occurred (or air quality from a neighboring quarter impinged) on the campus mobile sensors can be tasked to survey the area in question to provide more finely detailed information or to help trace the source of the air quality issue).
Pariseau does not explicitly disclose “assigning different subsets of the set of target regions to different vehicles for the vehicles to obtain air quality measurements during respective driving sessions in the set of sessions.”
However, Pariseau discloses at Par. 117 “the system can assign different sensors for different size ranges, and thereby provide a sensor with a much larger dynamic range” and at Par. 98 that the maps can have regions and sub-regions or can comprise a fixed grid enabling display of combined real time weather and air quality and particle count data. Par. 78 discloses a configuration that allows for calibration and configuration information to flow. It can also allow the system to provide direction information to the various sensors 302 in order to more evenly deploy or direct them perhaps redeploying them based on changing conditions within an environment and also keep them within the desired area.
Therefore, it is the Examiner’s position that it would have been obvious to one of ordinary skilled in the art at the time of filling the Application to “assign different subset of the set of target regions to different vehicles for the vehicles to obtain air quality measurements during respective driving sessions in the set of sessions” and that Pariseau discloses the claimed invention as Pariseau has the ability mounting sensors to mobile vehicles such as drones, robots, rovers navigate the complex on pre-assigned routes or they could be mounted on campus vehicles or transports (shuttles, golf carts. Segways, etc.). The collected data from these can be aggregated to provide real-time air quality information. If an air quality event occurred (or air quality from a neighboring quarter impinged) on the campus mobile sensors can be tasked to survey the area in question to provide more finely detailed information or to help trace the source of the air quality issue. Further, Pariseau has the ability of assign different sensors for different size ranges, and thereby provide a sensor with a much larger dynamic range” and at Par. 98 that the maps can have regions and sub-regions or can comprise a fixed grid enabling display of combined real time weather and air quality and particle count data and that Pariseau discloses redeploying sensors based on changing conditions within an environment and also keep them within the desired area (see explanation above and Pars. 78, 98, 117, and 156).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHUONG HUYNH whose telephone number is (571)272-2718. The examiner can normally be reached M-F: 9:00AM-5:30PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew M Schechter can be reached at 571-272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/PHUONG HUYNH/Primary Examiner, Art Unit 2857 April 2, 2026
Prosecution Insights