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 .
Status of Claims
The present application is being examined under the claims filed 6/22/2026. Claims 1, 11, and 20 are amended. Claims 4, 8-9, 14-15, and 18-19 are canceled.
Response to Arguments
Applicant's arguments filed 6/22/2026 have been fully considered but are moot because the arguments allege that the newly added limitations are not taught by the prior art of record. It should be noted that a new prior art reference to Zhang teaches the newly added limitations as shown in the rejections below.
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.
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 1-3,5-7,10-13,16-17 and 20-23 are rejected under 35 U.S.C. 103 as being unpatentable over DeCia et. al. (US 2018/0304836 A1) [previously cited] in view of Moeller et. al. (US 2022/0161758 A1) [previously cited], Miller et. al. (US 2016/0303968 A1) [previously cited], and Zhang (US 10518750 B1).
Regarding Claim 1, DeCia discloses a vehicle (DeCia par. 9, vehicle (shown in FIG. 1) in a key-off-state in accordance with the teachings disclosed) comprising:
a transceiver configured to receive a charging station location (DeCia FIG. 1, GPS receiver 110 and par. 26, GPS receiver 110 of the illustrated example receives a signal from a global positioning system (GPS) to determine a location of the vehicle 100 relative to landmarks (e.g., stadiums, restaurants, fueling stations) [i.e., location of changing station is needed to determine vehicle location relative to a charging station, also see par. 22, vehicle may be an electric vehicle]); and
a processor communicatively coupled to the transceiver (DeCia FIG. 2 and par. 48, e.g., remote processor 218 collects or determines the location of the vehicle 100 via the communication module 108 after GPS receiver 110 determines a location of the vehicle 100 [i.e., remote processor communicatively coupled with remote processor 218]), wherein the processor is configured to:
determine that the vehicle is in a key-off mode (DeCia par. 48, the remote processor 218 determines whether the vehicle 100 is in a key-off state);
determine an identity of a user based on inputs from a […] system of the vehicle (DeCia par. 53, the remote processor 218 accesses a personal calendar of the driver (e.g., via communication with the vehicle 100 and/or a mobile device of the driver) [i.e., identity of driver is determined via personal calendar/mobile device]);
determine, at a first time, a vehicle State of Charge (SOC) level (DeCia par. 81, the remote processor 218 determines a current charge level of the battery 104 of the vehicle 100);
determine, at the first time, a vehicle distance from the [fuel] station location based on a current vehicle location (DeCia par. 26, GPS receiver 110 of the illustrated example receives a signal from a global positioning system (GPS) to determine a location of the vehicle 100 relative to landmarks (e.g., stadiums, restaurants, fueling stations) [i.e., current vehicle location (GPS) is used to determine distance from landmarks, which includes fueling stations, also see par. 22, vehicle may be an electric vehicle]);
determine that the vehicle SOC level is above a first threshold (DeCia par. 82, the remote processor 218 determines whether the current charge level of the battery 104 is greater than a predetermined charge threshold);
determine that the vehicle distance from the [fuel] station is below a second threshold (DeCia par. 80, remote processor 218 determines a type of location at which the vehicle 100 is in the key-off state; and DeCia par. 26, GPS receiver 110 of the illustrated example receives a signal from a global positioning system (GPS) to determine a location of the vehicle 100 relative to landmarks [i.e., threshold needed to determine if the vehicle is "at" a location]);
select a vehicle feature from a plurality of vehicle features based on historical usage pattern of the [vehicle] in the key-off mode associated with the identified user (DeCia par. 54, event associated with the location is used to determine which of the ECUs 206 [i.e., vehicle features; see par. 44, ECUs 206 monitor and control the subsystems of the vehicle 100] are to be activated, and in which order, while the vehicle 100 remains in the key-off state; and par. 53, personal calendar of the driver may be used to determine the event, e.g., a work shift [i.e., historical pattern of usage associated with the identified user]; and the remote processor 218 determines when to activate one or more of the ECUs 206 of the vehicle 100 based on the end time of the event),
responsive to the vehicle SOC level being above the first threshold and the vehicle distance from the [fuel] station being below the second threshold (DeCia FIG. 4 and par. 55-59, the remote processor 218 determines vehicle location (step 402) and whether the current charge level of the battery 104 is greater than a predetermined charge threshold (step 414) before activating the telematic control unit [i.e., a vehicle feature]; or DeCia FIG. 5 and par. 66-68, flowchart determines characteristics based on the vehicle location (step 502), then checks battery charge compared to a threshold in order to decide whether to activate vehicle sensors; or DeCia FIG. 6 and par. 84, remote processor 218 prioritizes the ECUs 206 based upon the charge level of the battery 104, the location of the vehicle 100, and/or the corresponding parking duration); and
enable a high-power mode of the vehicle feature responsive to the selection (DeCia par. 84, the remote processor 218 prioritizes activation of the ECUs 206 [i.e., vehicle features] identified at block 61, e.g., if the vehicle 100 is parked at a fueling station, the remote processor 218 prioritizes the infotainment head unit 124 to enable media (e.g., music) to continue to be presented to the user while the vehicle 100 is being refilled at the fueling station [i.e., activating a feature that was formerly slept/off is a high-power mode, see par. 89, processor determines whether to set another one of the ECUs 206 in an active mode]).
DeCia does not explicitly teach:
determine an identity of a user based on inputs from a sensory system of the vehicle;
determine, at the first time, a vehicle distance from the charging station location based on a current vehicle location;
determine that the vehicle distance from the charging station is below a second threshold;
select a vehicle feature from a plurality of vehicle features based on historical usage pattern of the plurality of vehicle features in the key-off mode associated with the identified user, wherein different vehicle features are selected for different identified users based on each identified user's respective historical usage pattern of vehicle features in the key-off mode, responsive to the vehicle SOC level being above the first threshold and the vehicle distance from the charging station being below the second threshold.
In the analogous art of using geographic location of a vehicle to select features to activate, Moeller teaches:
a processor communicatively coupled to the transceiver (Moeller par. 79, security device 103 includes a computing device with a processor to execute the monitor application 215; and Moeller FIG. 2, transceiver 221 a), wherein the processor is configured to:
determine, at the first time, a vehicle distance from the charging station location based on a current vehicle location (Moeller par. 67 the security device 103 can determine that a current location corresponds to one or more points of interest [see par. 17 or 64, points of interest (POI 102) can be an electric vehicle charging station]);
select a vehicle feature from a plurality of vehicle features based on historical usage pattern of the plurality of vehicle features in the key-off mode […] (Moeller par. 153-154, mode of operation selected based on device profile associated with the POI category [historical use at point of interest] and par. 117 and 126, data store 236 includes historical data POI data, par. 103, the monitor application 215 compares a current location to a relational database of POIs, locations, and corresponding categories (including setting appropriate security level, also see par. 139 and 152; and Moeller par. 69, security device 103 determines that the vehicle 106 is parked [i.e., key-off, operations occur in parked state]), responsive to a vehicle [fuel level] being above the first threshold and the vehicle distance from the charging station being below the second threshold (Moeller par. 153, based on the POI category [i.e., location determination] [ex. at a refueling station, which requires a threshold to determine, see par. 114] security device enables sensors of the vehicle (e.g., fuel level sensors) and e.g. par. 40, at a “gas station” category, security device activates increased security features in response to being fuel being added [i.e., based on fuel and location], such as motion data analysis);
Therefore, it would have been obvious of one of ordinary skill in the art, having the teachings of DeCia and Moeller before him, before the effective filing date of the claimed invention, to combine DeCia’s system for selecting vehicle features to enable in a key-off mode based on geographic location with Moeller’s system utilizing geographic location to select features to enable (including a charging station as a POI category), the motivation being to tailor system behaviors for different locations (Moeller par. 85).
DeCia in view of Moeller does not explicitly teach:
determine an identity of a user based on inputs from a sensory system of the vehicle;
select a vehicle feature from a plurality of vehicle features based on historical usage pattern of the plurality of vehicle features in the key-off mode associated with the identified user, wherein different vehicle features are selected for different identified users based on each identified user's respective historical usage pattern of vehicle features in the key-off mode, responsive to the vehicle SOC level being above the first threshold and the vehicle distance from the charging station being below the second threshold.
In the analogous art of determining features of a vehicle to activate based on user and geographic location, Miller teaches:
determine an identity of a user based on inputs from a [sensory] system of the vehicle (Miller par. 30, user may be identified via some form of biometric data such as fingerprints, voice recognition);
select a vehicle feature from a plurality of vehicle features based on historical usage pattern of the plurality of vehicle features in the key-[on] mode associated with the identified user (Miller FIG. 2 and par. 24, prediction module 120 is used to align various current attributes with past attributes that are cataloged in database 130 to determine if a certain feature has been historically selected under similar vehicle conditions [also see par. 33, e.g., if the user has historically turned on the vehicle radio each day on his way to work at approximately two minutes after the vehicle key-on, then if the current conditions are similar (i.e., it is a weekday and the vehicle was started approximately two minutes ago), then the prediction module 120 may determine that there is a high probability that the user will again turn on the vehicle radio]), responsive to the vehicle SOC level […] and the vehicle [location] (Miller par. 25, GPS location and state of charge may be used to generate a feature probability);
Therefore, it would have been obvious of one of ordinary skill in the art, having the teachings of DeCia, Moeller, and Miller before them, before the effective filing date of the claimed invention, to combine DeCia and Moeller’s user-dependent vehicle system management in a key-off mode with Miller’s consideration of probability in regards to using each vehicle feature, the motivation being to improve user experience by considering historical feature use for each user (Miller par. 15-16).
DeCia in view of Moeller and Miller does not explicitly teach:
select a vehicle feature from a plurality of vehicle features based on historical usage pattern of the plurality of vehicle features in the key-off mode associated with the identified user, wherein different vehicle features are selected for different identified users based on each identified user's respective historical usage pattern of vehicle features in the key-off mode.
In the analogous art of determining features of a vehicle to activate based on user and geographic location, Zhang teaches:
select a vehicle feature from a plurality of vehicle features based on historical usage pattern of the plurality of vehicle features in the key-off mode associated with the identified user, wherein different vehicle features are selected for different identified users based on each identified user's respective historical usage pattern of vehicle features in the key-off mode (Zhang FIG. 2 & C5:L3-41 teach upon the vehicle being parked, the vehicle system may retrieve a user profile at step 203, where the user profile may include data regarding a user's preferences, tendencies, historic data, etc. The user profile may be associated with a user automatically based on various vehicle settings (e.g. seat settings) associated with that user, a mobile phone paired with the vehicle associated with the user, a key fob associated with the user, a car-sharing application (e.g. commercial or non-commercial setting). The user may be an owner of the car or a designated driver.
Once a user profile is retrieved, the vehicle system may determine the category of the location that the vehicle is parked at in step 205. For example, the vehicle system may determine whether the vehicle is parked at a home, work, or another category of the POI (e.g. restaurant, gas station, airport, coffee shop, mall, etc.). The vehicle system may utilize the GPS coordinates of the map and compare it to those of nearby POIs. If the GPS coordinates match with a nearby POI, an algorithm of the vehicle processor can identify that the vehicle is parked under the assumption of visiting that POI.
At step 207, the vehicle system may determine if the parked area or POI is a new stop or non-routine stop of the vehicle. The vehicle system may use the vehicle's coordinates to determine whether the POI is a routine place like home, work, a commonly visited POI, or a new POI. The vehicle system may have historic data of a vehicle's behavior of stopping points based on the user's schedule. For example, the vehicle may identify that the user typically leaves home for an office at 7 AM Monday thru Friday, as well as head home from the office at 6 PM on Monday thru Friday. Additional details regarding whether the parked location is a non-routine POI is shown in FIG. 3, while additional details regarding whether the parked location is a routine POI is shown in FIG. 4).
Therefore, it would have been obvious of one of ordinary skill in the art, having the teachings of DeCia, Moeller, Miller, and Zhang before them, before the effective filing date of the claimed invention, to combine DeCia, Moeller, and Miller’s user-dependent vehicle system management in a key-off mode with Zhang’s anti-theft system by location prediction based on heuristics and learning, the motivation being to allow drivers to secure their parked cars from theft by providing users with a secure way to protect their vehicle using connectivity, location sensors, and machine learning methods to identify unauthorized use of their vehicle by predicting location and an estimated departure time threshold (Zhang C2:L30-38).
Regarding Claim 2, DeCia in view of Moeller, Miller, and Zhang discloses the vehicle of claim 1, wherein the processor is further configured to:
determine a probability of use of the vehicle feature in the key-off mode based on the historical usage pattern of the plurality of vehicle features in the key-off mode (Miller FIG. 2 and par. 24, prediction module 120 is used to align various current attributes with past attributes that are cataloged in database 130 to determine if a certain feature has been historically selected under similar vehicle conditions; also see DeCia par. 17, on operating features in a key-off state without causing a battery of the vehicle to become discharged or drained); and
select the vehicle feature when the probability of using the vehicle feature in the key-off mode is greater than a first predetermined threshold (Miller par. 36-37, features with probability above a predetermined threshold may be selected).
The same motivation that was utilized for combining DeCia, Moeller, and Miller as set forth in claim 1 is equally applicable to claim 2.
Regarding Claim 3, DeCia in view of Moeller, Miller, and Zhang discloses the vehicle of claim 1, wherein the selection of the vehicle feature is further based on a power consumption associated with each vehicle feature of the plurality of vehicle features (DeCia par. 71, determination to select an additional sensor [i.e., vehicle feature] to activate is based on the upon the current charge level of the battery; or DeCia par. 81-82, determine the current charge level of the vehicle and determine whether the current charge level of the battery 104 is greater than a predetermined charge threshold, which corresponds to a minimum charge level that enables one or more of the ECUs).
Regarding Claim 5, DeCia in view of Moeller, Miller, and Zhang discloses the vehicle of claim 1, wherein the charging station location comprises a location associated with at least one of an electric vehicle charging station or a fuel station for a hybrid vehicle (Moeller par. 64, POI [i.e., location affecting vehicle mode] can be a gas station or electric vehicle charging station; also see DeCia par. 22, the vehicle 100 may be an electric vehicle and DeCia par. 26, determine the location of the vehicle 100 relative to landmarks (e.g., stadiums, restaurants, fueling stations)).
The same motivation that was utilized for combining DeCia and Moeller as set forth in claim 1 is equally applicable to claim 5.
Regarding Claim 6, DeCia in view of Moeller, Miller, and Zhang discloses wherein the selection of the vehicle feature is further based on additional information comprising one or more of:
the vehicle current location (DeCia par. 84, remote processor 218 prioritizes the ECUs 206 based upon the location of the vehicle 100), an ambient temperature, a time of a day, a day of week (DeCia par. 55, e.g., the driver typically leaves the office at 5:45 PM on Thursdays), a vehicle user calendar (DeCia FIG. 4 and par. 53, remote processor 218 accesses a personal calendar of the driver), an identity of a user who last drove the vehicle (Moeller par. 118 and 126, user data used by security service 233), and an identity of a user who is around the vehicle (Moeller par. 75, movement of a subject 112 within a predetermined proximity of the vehicle 106 may trigger security functions).
Regarding Claim 7, DeCia in view of Moeller, Miller, and Zhang discloses wherein the selection of the vehicle feature is further based on user inputs (Moeller par. 121, configuration data 245 can be configured by a user [i.e., user input]; or Miller par. 18, user interface device 105 may be configured to receive user inputs from the vehicle occupants… inputs provided the user interface device 105 may be passed to the controller 110 to control various aspects of the vehicle).
The same motivation that was utilized for combining DeCia, Moeller, and Miller as set forth in claim 1 is equally applicable to claim 7.
Regarding Claim 10, DeCia in view of Moeller, Miller, and Zhang discloses the vehicle of claim 1,
wherein the processor is further configured to select an operational level of the vehicle feature based on at least one of the vehicle SOC level (DeCia FIG. 5 and par. 68, remote processor 218 determines the current charge level of the battery 104 and compares it to the predetermined charge threshold, the sensors are not activated or fewer sensors are activated if threshold is not met) or the vehicle distance from the charging station location (DeCia FIG. 5 and par. 66-68, flowchart determines characteristics based on the vehicle location (step 502, location can be a fueling station, see par. 20), then checks battery charge compared to a threshold in order to decide whether to activate vehicle sensors; or Moeller par. 153, based on the determination of location [ex. at a refueling station], security device activates increased security features, such as continuous recording and activating a passive infrared detector).
Regarding Claim 11, DeCia discloses a method (DeCia par. 6 and 40).
The remaining limitations of claim 11 are similar in scope to claim 1 as addressed above and are thus rejected under the same rationale.
Regarding Claim 12, the claim is similar in scope to claim 2 as addressed above and is thus rejected under the same rationale.
Regarding Claim 13, the claim is similar in scope to claim 3 as addressed above and is thus rejected under the same rationale.
Regarding Claim 16, the claim is similar in scope to claim 6 as addressed above and is thus rejected under the same rationale.
Regarding Claim 17, the claim is similar in scope to claim 7 as addressed above and is thus rejected under the same rationale.
Regarding Claim 20, DeCia discloses a non-transitory computer-readable storage medium having instructions stored thereupon (DeCia par. 40, memory 212 is computer readable media on which one or more sets of instructions, such as the software for operating the methods of the present disclosure, can be embedded) which, when executed by a processor, cause the processor to:
determine, at a first time, a first vehicle State of Charge (SOC) level and a first vehicle distance from a charging station location based on a current vehicle location, responsive to determining that the vehicle is in the key-off mode (DeCia par. 48, the remote processor 218 determines whether the vehicle 100 is in a key-off state (see FIG. 3, key-off state is determined, and block 310 is triggered as a response) and DeCia FIG. 6 and par. 79, FIG. 6 is a flowchart of an example method 600 to implement block 310, device determines location type (block 602) and determines charge of battery (block 606); or Moeller par. 69, in response to the determination that the vehicle is parked [i.e., key-off], security device 103 initiates one or more actions including determining one or more categories with which the POI 102 [i.e., determining distance to charging station] and receiving readings from the remote device);
The remaining limitations are similar in scope to claim 1 as addressed above and is thus rejected under the same rationale.
Regarding Claim 21, DeCia in view of Moeller, Miller, and Zhang discloses the vehicle of claim 1, wherein the processor is further configured to:
determine, at a second time, a second vehicle SOC level (DeCia FIG. 4, step 412 and 430, check charge level against a threshold; and par. 53, vehicle determines location and event the user is attending [e.g., work shift, based upon analysis of driving history of the driver and/or the vehicle, which requires analyzing multiple times]);
determine, at the second time, a second vehicle distance from the charging station location (DeCia par. 52, location of the vehicle utilized to check for events occurring [which requires knowing the vehicle's location from FIG. 3 step 304; see par. 48, communication module 108 sends a vehicle status signal that includes the charge level and the location of the vehicle 100 to processor 218]; or Moeller par. 69 and par. 102, monitoring application can find exact distance between locations and can determine position of the vehicle [e.g., par. 69, the security device 103 determines a current location of the vehicle 106, compares the current location to one or more point of interest databases, and, based on the comparison, determines that the vehicle 106 is parked at the POI 102 [i.e. charging station; POI category includes gas stations and electric vehicle charging stations, see par. 17]);
determine that the second vehicle SOC level is below a third threshold (DeCia FIG. 4, step 412-414, check current battery state of charge against a threshold);
determine that the second vehicle distance from the charging station is above a fourth threshold (DeCia par. 52, location of the vehicle utilized to check for events occurring [which requires knowing the vehicle's location from FIG. 3 step 304; or Moeller par. 69 and par. 102, monitoring application determines a current location of the vehicle 106, compares the current location to one or more point of interest databases [i.e., determines vehicle distance from the charging station], and, based on the comparison [i.e., based on a threshold that the vehicle is close enough to the POI, see par. 114, rules include proximity thresholds]; exceeding a distance for being "at" a POI is exceeding a threshold);
select, a second vehicle feature from the plurality of vehicle features (DeCia par. 66, device uses location information [i.e., historical usage pattern of the vehicle when parked at a location] to select sensors to activate; also see par. 70 and FIG. 5 steps 510-516, on deciding monitoring level/duration; or Moeller par. 153-154, mode of operation selected based on device profile associated with the POI category [historical use at point of interest]); and
select a low power mode for the second vehicle feature (DeCia FIG. 5 and par. 67, based on the location of the vehicle, monitoring for damage [i.e., vehicle feature] is not enabled [i.e., low power mode]; or Moeller par. 35 and 110, the monitoring application 215 can send instructions to the remote device 104 to decrease the power level to preserve battery life [e.g., lower signal transmission strength]).
Regarding Claim 22, the claim is similar in scope to claim 21 as addressed above and is thus rejected under the same rationale.
Regarding Claim 23, the claim is similar in scope to claim 21 as addressed above and is thus rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is reminded that in amending in response to a rejection of claims, the patentable novelty must be clearly shown in view of the state of the art disclosed by the references cited and the objections made. Applicant must also show how the amendments avoid such references and objections. See 37 CFR §1.111(c).
METHOD FOR PROCESSING A PREDETERMINED COMPUTING TASK BY A DISTRIBUTED, VEHICLE-BASED COMPUTING SYSTEM AND A CORRESPONDING COMPUTING SYSTEM – Hauschild et al. (US 2021/0316632 A1) - generating a user profile concerning historic and/or statistically determined travel times, the said user profile can describe at least one custom of the user, for instance at least one stay location visited once or several times or regularly in the past and the duration of times spent there, and calendar data can describe a current and/or a future timetable and/or plan of stays of the user, and therefrom a prospective duration of the ready state in each case, in particular a prospective remaining parking duration of the motor vehicle can be determined. Consequently the motor vehicle can signal its prospective parking time or parking period. Hereby the advantage is rendered that by the server device the data packets are generated or arranged in such a way that a remaining duration of the ready state is considered and/or exploited. The ability to define a prospective remaining parking duration is advantageous to managing task specifications and/or orientation. Statistical usage data of one or more charging stations may also be used in order to predict within a predefined range that a predefined minimum number of motor vehicles are parked and connected to a/their respective charging station(s) at a given point of the day.
Trip-configurable content – Herz et. al. (US 2022/0224963 A1) – Herz discloses a vehicle with a sensory output device include receiving, by one or more processors, a signal indicating an identity or passenger profile of a detected passenger in or boarding the vehicle, accessing preference data and geographic location data for the passenger, and selecting sensory content for delivery to the passenger in the vehicle.
History-based And Location-based Control of Vehicle Key-off Loads - Chen et. al. (US 2022/0324399 A1) – Chen discloses a vehicle in a key-off mode comprising an analyzer to identify Key-Off events sharing a repetitive time span and a common geographic location. A scheduler activates a timed key-off load sequence according to the identified Key-Off events so that repetitive time slots of vehicle usage can be used to reduce battery drain during times when vehicle usage is less likely.
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 extension fee 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 ANDREW JUNG whose telephone number is (571)270-3779. The examiner can normally be reached 9:30am-5:30pm ET (Mon-Fri).
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/ANDREW J JUNG/Supervisory Patent Examiner, Art Unit 2175
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