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 .
This is a Final Office Action of the merits. Claims 14, 15, 17, 18 and 29 are cancelled. Claims 13 and 28 have been amended. Therefore, claims 13, 16, and 19-28 are currently pending and are addressed below.
Response to Amendment/Arguments
The amendment filed 04/13/2026 has been entered.
Applicant's arguments with respect to the 35 USC 103 rejection of the claims, see pages 9-11 of Remarks, have been considered but are moot in view of the new grounds of rejection provided below, in light of newly found prior art, which was necessitated based on Applicant's amendments which changed the scope of the claims.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 13, 16, and 19-28 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 10198009 B2, hereinafter “Wang”), and further in view of Beaurepaire et al. (US 20180107216 A1, hereinafter “Beaurepaire”).
Regarding claims 13 and 28, Wang discloses a device and a method for determining a driving route for a vehicle having an automated driving mode in which the vehicle is longitudinally and/or laterally controlled in an at least partially automated manner, and a manual driving mode in which the vehicle is longitudinally and/or laterally controlled in an at least partially manual manner by a driver of the vehicle (Wang, see at least Fig. 1, col. 3, lines 44-47, “The vehicle 100 includes multiple levels of autonomous functionality, involving different respective levels of engagement on behalf of a driver or operator of the vehicle 100”), the device comprising:
a processor (Wang, see at least Fig. 1, col. 6, “the computer system of the controller 106 includes a processor 172”) , wherein the device is configured:
to determine clearance information with respect to a clearance of the automated driving mode on different roadway sections of a roadway network traveled by the vehicle (Wang, see at least Fig. 4, col. 11-12, determine level of vehicle automation are calculated and predict for each road segment throughout the selected route(s) to the destination (step 406), using the various data of step 404 such as roads, road conditions, traffic patterns, construction, weather, and so on; claim 17, “obtaining inputs pertaining to conditions of a plurality of potential routes to reach an operator-requested destination planned for a vehicle having autonomous operation capability; a sensor unit configured to at least facilitate: obtaining sensor data pertaining to an operator of the vehicle via one or more sensors; and a processor configured to at least facilitate: predicting a future level of engagement required for the operator of the vehicle, for each of the plurality of potential routes, based on the level of automated driving expected for the potential route, using the inputs”);
to determine and/or predict availability information with respect to an availability of the driver of the vehicle for manual longitudinal and/or lateral control of the vehicle during a journey from a starting point to a destination point (Wang, see at least Fig. 4, col. 11-12, lines 4-33, determine and/or predict the ability of manual driving of a driver based on a driver state and/or driver preferences, e.g. a current level of alertness of the driver, the history and/or preferences of step 404 may also be used to analyze expected driver preferences for the level of autonomous driving (and associated required level of driver engagement) during various segments of the vehicle drive for at least a desired period of time; claim 17, “monitoring a level of awareness of the operator of the vehicle based on the sensor data; and providing, for the operator, information relating, for each of the plurality of potential routes, (a) the future level of engagement required for the operator with (b) the level of awareness of the operator of the vehicle”); and
to determine the driving route through the roadway network from the starting point to the destination point based on the clearance information and the availability information (Wang, see at least Fig. 4, col. 13, lines 1-14, “Also in one embodiment, drivers are provided with a preview of automation levels and preview of drivers' responsibility on each road segment along a navigation route (or any time when not in a route), for automated vehicles. This method considers factors such as road conditions (e.g., lane marker visibility, lanes, present of other vehicles), weather conditions (e.g., snowing) and recorded other vehicles' automation system performance data. Also in one embodiment, drivers are provided with a method of selecting a schedule of preferred automation. In addition, in one embodiment, the most relevant options may be provided to the user (e.g. driver) using adaptive to variant environment and driver state information, as well as asking for the driver's confirmation”),
wherein the availability information indicates, for each time interval of a sequence of time intervals during the journey from the starting point to the destination point, whether the driver of the vehicle is available in the respective time interval to operate the vehicle in the manual driving mode (Wang, see at least Fig. 2, col. 8, lines 29-67, col. 9, lines 1-3, col. 12, lines 41-51, level of engagement of the driver for durations of time during the journey from the starting point to the destination point, e.g. a first color 210 (e.g. green) is used to depict road segments and/or durations of time in which little or no driver engagement is required when the driver appears be drowsy or relatively unresponsive; Fig. 4, col. 11, lines 19-67, col. 12, lines 1-59, driver state is repeatedly monitored to suggest a level of automation based on the driver state), and
wherein the device is further configured:
to determine, for a possible driving route, a correlation measure of a correlation between roadway sections in which the automated driving mode is cleared, and time intervals in which the driver is not available to operate the vehicle in the manual driving mode (Wang, see at least Fig. 4, col. 11, lines 19-67, col. 12, lines 1-59, driver state is repeatedly monitored to suggest a level of automation based on the driver state, “For example, by suggested alternate routes that may better comport with the driver's state or preferences (step 412). By way of example, in one embodiment, if a driver currently appears to be drowsy or relatively unresponsive, then an alternate route may be proposed and/or selected in which little or no driver engagement may be required for at least a desired period of time”; claim 15, “wherein the selected route is determined so as to match a desired level of automation for operating the vehicle at a particular time, per the preferred schedule from the operator, with the respective future level of engagement required for the operator of the vehicle at the particular time, per the predictions via the processor of the respective future levels of engagement required for the operator of the vehicle for each of the segments of each of the potential routes”);
to determine the driving route such that the correlation measure is increased (Wang, see at least col. 13, lines 59-67, col. 14, lines 1-5, ”potentially using the automation level information for other purposes such as route selection; allowing the driver to select schedules/routes with more or less automation based on current conditions (e.g., fastest, shortest, cheapest, and so on)”; col. 12, lines 41-51, “For example, by suggested alternate routes that may better comport with the driver's state or preferences (step 412). By way of example, in one embodiment, if a driver currently appears to be drowsy or relatively unresponsive, then an alternate route may be proposed and/or selected in which little or no driver engagement may be required for at least a desired period of time”); and
to cause the vehicle to travel along the driving route (Wang, see at least col. 8, operate the vehicle to travel along the selected driving route).
Wang teaches to determine a route for a vehicle based on the variant environment and driver state and improve cooperation between drivers and the automation system and thereby increase safety. However, Wang fails to explicitly teaches prioritize activities of the driver for use of the roadway sections in which the automated driving mode is cleared based on relative complexities of the activities of the driver.
Beaurepaire teaches to prioritize activities of the driver (Beaurepaire, see at least Fig. 3, par. [0033-0045, 0054-0076, 0112], The activity selection module 200 may generate a ranked list of candidate activities for one or more road segments. For example, from the list of activities the activity selection module 200 may select multiple activities that could be performed on each compatible road segment. The activities may be ranked according to future availability (whether or not a subsequent road segment could accommodate the activity), the duration of the activity (whether or not consecutive road segments have an estimated travel time sufficient to complete the activity), a user-configured priority (whether or not this activity has been set as a priority over one or more other activities by the user or another application such as a calendar or schedule) or an on-demand request (whether an incoming call or a request for meeting has been received)”) for use of the roadway sections in which the automated driving mode is cleared (Beaurepaire, see at least Figs. 2-4, 5, 8, 10, par. [0029-0032, 0046-0053], “The mobile device 122 or the server 125 may assign the activity sequence to the road segments of the route for activity planning for the example route based on autonomous driving availability”) based on relative complexities of the activities of the driver (Beaurepaire, see at least Figs. 2-4, 5, 8, par. [0046-0053, 0112], “The server 125 or the mobile device 122 may receive a selection of an activity or a series of activities from a user input. The activity selections may include individual activities or types of activities. The activity selections may include a priority of individual activities (e.g., eating a meal takes priority over reading a book, which takes priority over emailing) or a priority of types of activities (e.g., high attentive take priority over medium attentive activities, which take priority of low attentive activities). The activity selections may include an estimated time for each of the activities. The server 125 or the mobile device 122 may compare the activity selections to the possible routes. The activity selections may be matched to optimize the amount of activities that can be performed along the route”).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the method and system of Wang to include, prioritize activities of the driver for use of the roadway sections in which the automated driving mode is cleared based on relative complexities of the activities of the driver, as taught by Beaurepaire. This modification allows to match road-segment properties and vehicle assistance availability to user activities and further ensure the safety and comfort of the user.
Regarding claim 16, the combination of Wang and Beaurepaire teaches all the limitations of claim 13 as discussed above. The combination of Wang and Beaurepaire further teaches wherein the device is further configured: to determine the driving route such that the correlation measure is maximized (Wang, see at least col. 13, lines 59-67, col. 14, lines 1-5, ”potentially using the automation level information for other purposes such as route selection; allowing the driver to select schedules/routes with more or less automation based on current conditions (e.g., fastest, shortest, cheapest, and so on)”; col. 12, lines 41-51, “For example, by suggested alternate routes that may better comport with the driver's state or preferences (step 412). By way of example, in one embodiment, if a driver currently appears to be drowsy or relatively unresponsive, then an alternate route may be proposed and/or selected in which little or no driver engagement may be required for at least a desired period of time”).
Regarding claim 19, the combination of Wang and Beaurepaire teaches all the limitations of claim 13 as discussed above. The combination of Wang and Beaurepaire further teaches wherein the device is further configured: based on the availability information, to predict a time interval during the journey from the starting point to the destination point, in which the driver will not be available for the manual longitudinal and/or lateral control of the vehicle; and to determine the driving route based on the clearance information such that the vehicle is located in the predicted time interval in a roadway section which has clearance for the automated driving mode (Wang, see at least col. 12, lines 41-51, “For example, by suggested alternate routes that may better comport with the driver's state or preferences (step 412). By way of example, in one embodiment, if a driver currently appears to be drowsy or relatively unresponsive, then an alternate route may be proposed and/or selected in which little or no driver engagement may be required for at least a desired period of time”).
Regarding claim 20, the combination of Wang and Beaurepaire teaches all the limitations of claim 13 as discussed above. The combination of Wang and Beaurepaire further teaches wherein the device is further configured to determine and/or predict the availability information based on:
sensor data with respect to the driver, wherein the sensor data were acquired by one or more driver sensors of the vehicle (Wang, see at least Figs. 1, 3, col. 12, lines 4-33, “In addition, in certain embodiments, driver state monitoring is utilized in step 408 to suggest a level of automation based on a driver state and/or driver preferences. For example, in certain embodiments, driver monitoring (e.g. using the motion sensors 322, internal cameras 326, eye/head sensors 327, steering wheel sensors 328 of FIG. 3 and/or other sensors 103 of FIG. 1) may be used to monitor a current level of alertness of the driver. For example, in certain embodiments, if the operator has his or her eyes closed, is resting his or her head in a sleeping-type position, and/or has eyes that are not presently and/or actively focusing on the road, then this may indicate that the driver is not particularly alert”);
data of an infotainment system of the vehicle (Wang, see at least col. 10, lines 5-45, “user input devices 320 (e.g. switches, gesture input devices, and so on, for example corresponding to the user interface 105 of FIG. 1)”; col. 11, lines 24-46, “Various data is obtained pertaining to the vehicle drive (step 404). In one embodiment, crowd-sourced monitoring and data analytics and historical information are obtained as part of the data of step 404. In various embodiments, the data includes various data pertaining to the vehicle operator's preferences and/or history (e.g. as to a general time for leaving for work or other destination, preferred routes, preferred levels of autonomous driving and/or driver engagement requirements at different times and/or locations, and so), as well as various data pertaining to the vehicle 100 (including operation of the autonomous driving functionality), the operator (e.g. driver) thereof, and the surrounding environment (e.g. roads, road conditions, traffic patterns, construction, weather, and so on), for example corresponding to the various inputs 302, 308, 310, and 340 of FIG. 3”, col.12, lines 52-59, “In certain embodiments, the route recommendations are actively prompted to the driver through the in-vehicle infotainment system, and a request is made asking for the driver's confirmation”);
data from a personal user device of the driver (Wang, see at least col. 10, lines 21-45, “personal devices 318 such as smart phones, tablets, or other electronic devices (e.g. corresponding to device 109 of FIG. 1)”); and/or
data from a digital calendar of the driver (Beaurepaire, see at least par. [0069, 0074], user’s availability data based on various settings such as a schedule or calendar).
Regarding claim 21, the combination of Wang and Beaurepaire teaches all the limitations of claim 13 as discussed above. The combination of Wang and Beaurepaire further teaches wherein the device is further configured to determine and/or predict the availability information based on:
sensor data with respect to the driver, wherein the sensor data were acquired by one or more interior cameras of the vehicle (Wang, see at least Figs. 1, 3, col. 12, lines 4-33, “In addition, in certain embodiments, driver state monitoring is utilized in step 408 to suggest a level of automation based on a driver state and/or driver preferences. For example, in certain embodiments, driver monitoring (e.g. using the motion sensors 322, internal cameras 326, eye/head sensors 327, steering wheel sensors 328 of FIG. 3 and/or other sensors 103 of FIG. 1) may be used to monitor a current level of alertness of the driver. For example, in certain embodiments, if the operator has his or her eyes closed, is resting his or her head in a sleeping-type position, and/or has eyes that are not presently and/or actively focusing on the road, then this may indicate that the driver is not particularly alert”);
data of an infotainment system of the vehicle (Wang, see at least col. 10, lines 5-45, “user input devices 320 (e.g. switches, gesture input devices, and so on, for example corresponding to the user interface 105 of FIG. 1)”; col. 11, lines 24-46, “Various data is obtained pertaining to the vehicle drive (step 404). In one embodiment, crowd-sourced monitoring and data analytics and historical information are obtained as part of the data of step 404. In various embodiments, the data includes various data pertaining to the vehicle operator's preferences and/or history (e.g. as to a general time for leaving for work or other destination, preferred routes, preferred levels of autonomous driving and/or driver engagement requirements at different times and/or locations, and so), as well as various data pertaining to the vehicle 100 (including operation of the autonomous driving functionality), the operator (e.g. driver) thereof, and the surrounding environment (e.g. roads, road conditions, traffic patterns, construction, weather, and so on), for example corresponding to the various inputs 302, 308, 310, and 340 of FIG. 3”, col.12, lines 52-59, “In certain embodiments, the route recommendations are actively prompted to the driver through the in-vehicle infotainment system, and a request is made asking for the driver's confirmation”);
data from a personal user device of the driver (Wang, see at least col. 10, lines 21-45, “personal devices 318 such as smart phones, tablets, or other electronic devices (e.g. corresponding to device 109 of FIG. 1)”); and/or
data from a digital calendar of the driver (Beaurepaire, see at least par. [0069, 0074], user’s availability data based on various settings such as a schedule or calendar).
Regarding claim 22, the combination of Wang and Beaurepaire teaches all the limitations of claim 13 as discussed above. The combination of Wang and Beaurepaire further teaches wherein the device is further configured:
based on digital map information with respect to the roadway network (Wang, see at least col. 5, lines 34-60, a global positioning system (GPS) server providing vehicle 100 location information, a weather service and/or other service and/or server providing information regarding weather conditions, road conditions, road construction, traffic patterns, and so on), to predict a probable duration for the driving route (Wang, see at least Fig. 2, col. 8, lines 43-67, col. 9, lines 1-3, durations of time of the respective driver engagement for the driving route); and
to determine and/or predict the availability information for the probable duration (Wang, see at least col. 6, lines 4-37, determine predicted levels of engagement required by the driver for a current vehicle drive based on the types of roadways to be encountered (e.g. highways versus roads with stop signs and street lights, paved versus unpaved roads, traffic on various roads, construction on various roads, conditions of various roads [e.g. potholes, coefficient of friction, and so on], lane restrictions on various roads, accidents or events on the various roads).
Regarding claim 23, the combination of Wang and Beaurepaire teaches all the limitations of claim 13 as discussed above. The combination of Wang and Beaurepaire further teaches wherein the device is further configured:
to determine the driving route based on a routing algorithm, in which for each possible roadway section of different possible roadway sections of the roadway network, a section weight is taken into consideration, wherein the section weight indicates a value of the respective possible roadway section for the driving route (Beaurepaire, see at least Fig. 3, par. [0024], “The server 125 or the vehicle 124 may calculate a route from an origin to a destination … The route includes multiple segments stored in either of the databases. The route may be calculated according to the shortest distance, based on length or distance of the set of road segments, or according the estimated time to traverse the set of road segments. Example routing techniques include the A* algorithm and the Dijkstra algorithm”; Fig. 2, par. [0029-0032], “The dotted lines in the route of FIG. 2 indicate that segments A, C and E-H are enabled for autonomous driving. The mobile device 122 or the server 125 may access the database 123 or 133 respectively to determine whether particular segments are compatible with any type of autonomous or highly assisted driving. Each road segment may include a characteristic or flag that states whether or not the segment is compatible with any type of autonomous or highly assisted driving”); and
to determine the section weights of the different possible roadway sections based on the availability information (Beaurepaire, see at least Fig. 4, par. [0046-0053, 0069, 0074], the server 125 or mobile device 122 selects the route based on availability data of the user, i.e. activity selections, and further describe the section weight by using percentage or in terms of time. For example, the series of road segments A-B-C-D-E are associated with percentages, including 40% for activity 1 indicating that 40% of the series of road segments are compatible with activity type 1, 20% for activity 2 indicating that 20% of the series of road segments are compatible with activity type 2, and 40% for activity 3 indicating that 40% of the series of road segments are compatible with activity type 3).
Regarding claim 24, the combination of Wang and Beaurepaire teaches all the limitations of claim 13 as discussed above. The combination of Wang and Beaurepaire further teaches wherein:
the device is further configured to output route information with respect to the determined driving route to the driver of the vehicle (Wang, see at least Fig. 2, col. 8, lines 29-42, “a presentation is provided of two exemplary displays 200, 202 (referred to as the first display 200 and the second display 202, respectively) that may be presented on the display unit 108 for a presentation that may be provided via the display unit 108 (e.g. for viewing within the vehicle 100). The first display 200 provides the predicted levels of engagement along a route being taken by the vehicle 100 that is embedded along with a map of the route. The second display 202 provides the predicted levels of engagement instead via a linear presentation (with respect to time)”); and
the route information indicates one or more time intervals and/or one or more roadway sections during the journey along the driving route in which the vehicle can be operated in the automated driving mode or in which the vehicle has to be operated in the manual driving mode (Wang, see at least Fig. 2, col. 8, lines 43-67, col. 9, lines 1-3, durations of time of the respective driver engagement in which the vehicle can be operated in the automated driving mode or in which the vehicle has to be operated in the manual driving mode for the selected driving route)..
Regarding claim 25, the combination of Wang and Beaurepaire teaches all the limitations of claim 13 as discussed above. The combination of Wang and Beaurepaire further teaches wherein:
the device is further configured to determine digital map information with respect to the roadway network traveled by the vehicle (Wang, see at least col. 5, lines 34-60, a global positioning system (GPS) server providing vehicle 100 location information, a weather service and/or other service and/or server providing information regarding weather conditions, road conditions, road construction, traffic patterns, and so on), and to determine the driving route based on the digital map information (Wang, see at least Fig. 4, col. 11-12, determine the driving route based on the digital map information such as the types of roadways to be encountered (e.g. highways versus roads with stop signs and street lights, paved versus unpaved roads, traffic on various roads, construction on various roads, conditions of various roads [e.g. potholes, coefficient of friction, and so on], lane restrictions on various roads, accidents or events on the various roads, various weather conditions that may affect autonomous driving [e.g. snow, ice, rain, wind, fog, and so on], and/or various other factors that may affect autonomous driving); and
the digital map information comprises the clearance information for a plurality of different roadway sections of the roadway network (Wang, see at least col. 6, lines 4-37, “In various embodiments, the controller 106 determines the level of operator engagement required by analyzing how well the autonomous functionality and associated systems of the vehicle 100 are performing (e.g. how well the various sensors 103 are performing), as well as the types of roadways to be encountered (e.g. highways versus roads with stop signs and street lights, paved versus unpaved roads, traffic on various roads, construction on various roads, conditions of various roads [e.g. potholes, coefficient of friction, and so on], lane restrictions on various roads, accidents or events on the various roads, various weather conditions that may affect autonomous driving [e.g. snow, ice, rain, wind, fog, and so on], and/or various other factors that may affect autonomous driving)”).
Regarding claim 26, the combination of Wang and Beaurepaire teaches all the limitations of claim 13 as discussed above. The combination of Wang and Beaurepaire further teaches wherein the device is further configured, during the journey from the starting point to the destination point:
to determine and/or predict updated availability information with respect to the availability of the driver of the vehicle for the manual longitudinal and/or lateral control of the vehicle during the journey from a current location of the vehicle to the destination point (Wang, see at least Fig. 4, col. 12, lines 4-33, “driver state monitoring is utilized in step 408 to suggest a level of automation based on a driver state and/or driver preferences. For example, in certain embodiments, driver monitoring (e.g. using the motion sensors 322, internal cameras 326, eye/head sensors 327, steering wheel sensors 328 of FIG. 3 and/or other sensors 103 of FIG. 1) may be used to monitor a current level of alertness of the driver”); and
to adapt the driving route from the current location of the vehicle to the destination point based on the updated availability information (Wang, see at least Fig. 4, col. 12, lines 41-59, suggest alternate route from the current location of the vehicle to the destination point based on the current driver state of the drive).
Regarding claim 27, the combination of Wang and Beaurepaire teaches all the limitations of claim 13 as discussed above. The combination of Wang and Beaurepaire further teaches wherein the device is further configured, during the journey from the starting point to the destination point, repeatedly and/or periodically (Wang, see at least Fig. 4, col. 11-12, steps 406-408-414-404 are performed repeatedly after receiving a destination entry at step 402):
to determine and/or predict updated availability information with respect to the availability of the driver of the vehicle for the manual longitudinal and/or lateral control of the vehicle during the journey from a current location of the vehicle to the destination point (Wang, see at least Fig. 4, col. 12, lines 4-33, “driver state monitoring is utilized in step 408 to suggest a level of automation based on a driver state and/or driver preferences. For example, in certain embodiments, driver monitoring (e.g. using the motion sensors 322, internal cameras 326, eye/head sensors 327, steering wheel sensors 328 of FIG. 3 and/or other sensors 103 of FIG. 1) may be used to monitor a current level of alertness of the driver”); and
to adapt the driving route from the current location of the vehicle to the destination point based on the updated availability information (Wang, see at least Fig. 4, col. 12, lines 41-59, suggest alternate route from the current location of the vehicle to the destination point based on the current driver state of the drive).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TRANG DANG whose telephone number is (703)756-1049. The examiner can normally be reached Monday-Friday 8:00-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Khoi Tran can be reached at (571)272-6919. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TRANG DANG/ Examiner, Art Unit 3656 /KHOI H TRAN/Supervisory Patent Examiner, Art Unit 3656
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