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 communication is in response to application 18/526,978 filed on 01/14/2026. Claims 1-2, 4-7, 9 and 15-20 have been amended. Claim 3 is canceled. Claims 1-2 and 4-20 are pending and examined in the instant office action. The rejections are as stated below.
Response to Arguments
Applicant’s arguments, see page 6, filed 01/14/2026, with respect to the previous claim objections have been fully considered. Applicant has amended claims 1, 2, 4-7, 9 and 15-20, thereby rendering previous objections moot.
Applicant’s arguments, see pages 6-8, filed 01/14/2026, with respect to 35 U.S.C. 102 and 103 rejections of claims 1-2 and 4-20 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made Yoshihito Ishibashi, US 20180340786 A1, in view of Sun et al., US 20190064793A1.
Claim Interpretation
Claim 13 includes the limitation “wherein the component is configured to facilitate autonomous driving of the vehicle, to heat the vehicle, to condition air in the vehicle, to ventilate the vehicle, to present audio content in the vehicle, or to present vehicle content in the vehicle.” (Emphasis added).
This limitation requires that the component is configured to facilitate autonomous driving of the vehicle, to heat the vehicle, to condition air in the vehicle, to ventilate the vehicle, to present audio content in the vehicle, or to present vehicle content in the vehicle. Because only one of autonomous driving of the vehicle, to heat the vehicle, to condition air in the vehicle, to ventilate the vehicle, to present audio content in the vehicle, or to present vehicle content in the vehicle is required, the claim only requires one of these conditions to be met and does not require all of the conditions to be met. Examiner has chosen one of these conditions as defined in the rejection stated 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.
Claim(s) 1, 2, 4, 7-13 and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Yoshihito Ishibashi, US 20180340786 A1, in view of Sun et al., US 20190064793 A1, hereinafter referred to as Ishibashi and Sun, respectively.
Regarding claim 1, Ishibashi discloses a vehicle (Electric vehicle – See at least ¶32), comprising:
a battery stack (The electric vehicle having a battery including a secondary battery, i.e. battery stack – See at least ¶33);
a plurality of components powered by the battery stack, including a motor (A battery including a secondary battery, a drive processing unit including a motor, a power consuming device including an air conditioner, a car audio system, or the like, a driving unit including wheels – See at least ¶33. The power consuming device includes an air conditioner, a car audio system, or the like, as described above, and operates on electric power stored in the battery – See at least ¶40); and
a computer system configured to (A computer program – See at least ¶7):
determine a destination (According to another embodiment of the present invention, there is provided a route guidance method, including the steps of searching for at least one route, to a predetermined destination – See at least ¶18);
determine a first amount of available energy in the battery stack (When the electric vehicle is driven, capacity of the battery is then to be taken into account – See at least ¶55. For example, in the above embodiment, a memory having information on a travel amount of the electric vehicle per unit power and power consumption of the electric vehicle per gradient stored therein is provided in the navigation system – See at least ¶99);
estimate, for each of a plurality of predetermined routes to the destination, a second amount of energy to be used during driving of the vehicle to the destination (When the navigation control unit calculates the amount of electric power which needs to be consumed, (i.e. second amount of energy to be used) in traveling for each route, the navigation control unit uses a travel amount per unit power, and power consumption or a generation amount of regenerative energy per gradient. By referring to such information stored in the memory, the navigation control unit can roughly calculate an amount of electric power to be consumed in traveling each route, which was searched for by the route search unit – See at least ¶100).
Ishibashi fails to disclose dynamically control operating conditions to facilitate autonomous driving of the vehicle using artificial intelligence to cause the plurality of components to operate more efficiently based at least in part on keeping the second amount of energy to be used smaller than the first amount of available energy by selecting one of the plurality of predetermined routes that keeps the second amount of energy to be used smaller than the first amount of available energy.
However, Sun teaches dynamically control operating conditions to facilitate autonomous driving of the vehicle using artificial intelligence to cause the plurality of components to operate more efficiently based at least in part on keeping the second amount of energy to be used smaller than the first amount of available energy by selecting one of the plurality of predetermined routes that keeps the second amount of energy to be used smaller than the first amount of available energy (In an example embodiment, the vehicle energy consumption model can be dynamically learned vehicle energy consumption model corresponding to the typical energy consumption characteristics of a particular vehicle, a particular vehicle type, or an aggregate energy consumption characteristics for a class of vehicles. The vehicle energy consumption model can be configured to provide a predicted energy consumption rate for each of the potential routings and motion controls to cause the vehicle to transit from its current position to the desired destination. These predicted energy consumption rates for each of the potential routings and motion controls can be used to score and rank the plurality of potential routings and motion controls based on a level of energy consumption – See at least ¶42. In one example embodiment, these predicted energy consumption levels for each potential routing and vehicle motion control can be determined based on machine learning techniques (artificial intelligence) configured from training scenarios produced from prior real-world test data collections - See at least ¶43. The vehicle motion control processing module can select the potential routing and related motion controls with the lowest predicted energy consumption score. If the lowest predicted energy consumption score is not within a pre-defined acceptability range, the vehicle motion control processing module can modify the related vehicle motion control operations to lower the vehicle's energy consumption over the corresponding potential routing – See at least ¶44).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Ishibashi and include the feature of dynamically control operating conditions to facilitate autonomous driving of the vehicle using artificial intelligence to cause the plurality of components to operate more efficiently based at least in part on keeping the second amount of energy to be used smaller than the first amount of available energy by selecting one of the plurality of predetermined routes that keeps the second amount of energy to be used smaller than the first amount of available energy, as taught by Sun, to lower energy costs related to the operation of autonomous vehicles (See at least ¶5 of Sun).
Regarding claim 2, Ishibashi discloses wherein the plurality of components being controlled based at least in part on keeping the second amount of energy to be used smaller than the first amount of available energy includes at least one sensor configured in the vehicle (Examples of countermeasures necessary for achieving the reduction of electric power may include control over operation of the power consuming device – See at least ¶74. When an amount of electric power to be consumed by the power consuming device during traveling has been varied, the navigation control unit may perform again route search, after the variation results has been reflected. The amount of electric power to be consumed varies due to, for example, a variation in number of persons on board, and variation in loading weight. The variation in number of persons on board may be detected by a sensor, which is provided in a seat. The variation in loading weight may be detected by a sensor which measures loading weight – See at least ¶76. Examiner construed the claimed “sensor” as an occupant sensor as defined by Applicant’s specification as “occupant sensor devices 208 to monitor the weight of the ADV occupants such that the weight of the occupant(s) can be taken into account when determining the total electrical energy required to navigate the car along a route – See at least ¶34 of Applicant’s Specification).
Regarding claim 4, Ishibashi discloses wherein the plurality of components being controlled based at least in part on keeping the second amount of energy to be used smaller than the first amount of available energy include an air conditioning unit (The electric vehicle having the navigation system according to the embodiment of the present invention mounted thereon includes an air conditioner – See at least ¶33 and FIG. 1. Examples of countermeasures necessary for achieving the reduction of electric power may include control over operation of the power consuming device, that is, for example, turn-off of an air conditioner, regulation of temperature of the air conditioner – See at least ¶74).
Regarding claim 7, Ishibashi discloses wherein the plurality of components being controlled based at least in part on keeping the second amount of energy to be used smaller than the first amount of available energy include a multimedia system (Examples of countermeasures necessary for achieving the reduction of electric power may include control over operation of the power consuming device, that is, for example, turn-off of an audio-visual device – See at least ¶74. When an amount of electric power to be consumed by the power consuming device during traveling is reflected in an amount of electric power which needs to be consumed in traveling to a destination, the navigation control unit may display on the display unit information on what to do in order to enable power consumption of the electric vehicle to be reduced. As such information, there are given, for example, ban on the use of a car audio system – See at least ¶75).
Regarding claim 8, Ishibashi discloses wherein the operating conditions of the plurality of components include a speed of the vehicle (In this rough calculation of power consumption by the navigation control unit, time taken in traveling from a current location to a destination may be determined by rough calculation, using a traveling speed of the electric vehicle – See at least ¶73).
Regarding claim 9, Ishibashi discloses a method, comprising:
powering, by a battery stack, a plurality of components of a vehicle (A battery including a secondary battery, a drive processing unit including a motor, a power consuming device including an air conditioner, a car audio system, or the like, a driving unit including wheels – See at least ¶33. The power consuming device includes an air conditioner, a car audio system, or the like, as described above, and operates on electric power stored in the battery – See at least ¶40);
determining a destination of the vehicle (According to another embodiment of the present invention, there is provided a route guidance method, including the steps of searching for at least one route, to a predetermined destination – See at least ¶18);
determining a first amount of available energy in the battery stack (When the electric vehicle is driven, capacity of the battery is then to be taken into account – See at least ¶55. For example, in the above embodiment, a memory having information on a travel amount of the electric vehicle per unit power and power consumption of the electric vehicle per gradient stored therein is provided in the navigation system – See at least ¶99);
estimating, for each of a plurality of predetermined routes to the destination, a second amount of energy to be used during driving of the vehicle to the destination (When the navigation control unit calculates the amount of electric power which needs to be consumed, (i.e. second amount of energy to be used) in traveling for each route, the navigation control unit uses a travel amount per unit power, and power consumption or a generation amount of regenerative energy per gradient. By referring to such information stored in the memory, the navigation control unit can roughly calculate an amount of electric power to be consumed in traveling each route, which was searched for by the route search unit – See at least ¶100).
Ishibashi fails to disclose dynamically controlling, by the vehicle, operating conditions to facilitate autonomous driving of the vehicle using artificial intelligence to cause the plurality of components to operate more efficiently, based at least in part on keeping the second amount of energy to be used smaller than the first amount of available energy, by selecting one of the plurality of predetermined routes that keeps the second amount of energy to be used smaller than the first amount of available energy.
However, Sun teaches dynamically controlling, by the vehicle, operating conditions to facilitate autonomous driving of the vehicle using artificial intelligence to cause the plurality of components to operate more efficiently, based at least in part on keeping the second amount of energy to be used smaller than the first amount of available energy, by selecting one of the plurality of predetermined routes that keeps the second amount of energy to be used smaller than the first amount of available energy (In an example embodiment, the vehicle energy consumption model can be dynamically learned vehicle energy consumption model corresponding to the typical energy consumption characteristics of a particular vehicle, a particular vehicle type, or an aggregate energy consumption characteristics for a class of vehicles. The vehicle energy consumption model can be configured to provide a predicted energy consumption rate for each of the potential routings and motion controls to cause the vehicle to transit from its current position to the desired destination. These predicted energy consumption rates for each of the potential routings and motion controls can be used to score and rank the plurality of potential routings and motion controls based on a level of energy consumption – See at least ¶42. In one example embodiment, these predicted energy consumption levels for each potential routing and vehicle motion control can be determined based on machine learning techniques (artificial intelligence) configured from training scenarios produced from prior real-world test data collections - See at least ¶43. The vehicle motion control processing module can select the potential routing and related motion controls with the lowest predicted energy consumption score. If the lowest predicted energy consumption score is not within a pre-defined acceptability range, the vehicle motion control processing module can modify the related vehicle motion control operations to lower the vehicle's energy consumption over the corresponding potential routing – See at least ¶44).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Ishibashi and include the feature of dynamically controlling, by the vehicle, operating conditions to facilitate autonomous driving of the vehicle using artificial intelligence to cause the plurality of components to operate more efficiently, based at least in part on keeping the second amount of energy to be used smaller than the first amount of available energy, by selecting one of the plurality of predetermined routes that keeps the second amount of energy to be used smaller than the first amount of available energy, as taught by Sun, to lower energy costs related to the operation of autonomous vehicles (See at least ¶5 of Sun).
Regarding claim 10, Ishibashi discloses wherein the controlling of the operating conditions of the plurality of components includes selecting a route to the destination (Further, for example, the navigation system may select, in route search, whether to search for a route in a mode with priority on a route in which less electric power of the electric vehicle is to be consumed. Further, the navigation system may automatically select priority factor by which the search is to be performed, according to a remaining amount of electric power of the battery. For example, when the remaining amount of electric power of the battery is less than or equal to a predetermined amount, the navigation system may search for a route in a mode with priority on a route in which less electric power of the electric vehicle is to be consumed – See at least ¶103).
Regarding claim 11, Ishibashi discloses wherein the controlling of the operating conditions of the plurality of components includes changing the destination of the vehicle (Further, when, as a result of re-calculation, a route in which the least electric power is to be consumed, among routes to a destination, has been changed to another route, the navigation system may display on the display unit such other route to a destination, or when a destination to which the least electric power is to be consumed has been changed to another destination, the navigation system may display on the display unit a route to such other destination – See at least ¶100).
Regarding claim 12, Ishibashi discloses wherein the controlling of the operating conditions of the plurality of components includes turning off a component of the vehicle (Examples of countermeasures necessary for achieving the reduction of electric power may include control over operation of the power consuming device, that is, for example, turn-off of an air conditioner and turn-off of an audio-visual device – See at least ¶74).
Regarding claim 13, Ishibashi discloses wherein the component is configured to facilitate autonomous driving of the vehicle, to heat the vehicle, to condition air in the vehicle (Examples of countermeasures necessary for achieving the reduction of electric power may include control over operation of the power consuming device, that is, for example, regulation of temperature of the air conditioner – See at least ¶74), to ventilate the vehicle, to present audio content in the vehicle, or to present vehicle content in the vehicle.
Regarding claim 15, Ishibashi discloses a non-transitory computer readable storage medium storing instructions which, when executed by a computer system, cause the computer system to perform a method (The navigation control unit controls operation of the navigation system and includes a Central Processing Unit (CPU), for example. The navigation control unit may control the operation of the navigation system, for example, by reading out computer programs stored in the memory and sequentially executing the computer programs – See at least ¶42), comprising:
powering, using a battery stack, a plurality of components of a vehicle (A battery including a secondary battery, a drive processing unit including a motor, a power consuming device including an air conditioner, a car audio system, or the like, a driving unit including wheels – See at least ¶33. The power consuming device includes an air conditioner, a car audio system, or the like, as described above, and operates on electric power stored in the battery – See at least ¶40);
determining a destination of the vehicle (According to another embodiment of the present invention, there is provided a route guidance method, including the steps of searching for at least one route, to a predetermined destination – See at least ¶18);
determining a first amount of available energy in the battery stack (When the electric vehicle is driven, capacity of the battery is then to be taken into account – See at least ¶55. For example, in the above embodiment, a memory having information on a travel amount of the electric vehicle per unit power and power consumption of the electric vehicle per gradient stored therein is provided in the navigation system – See at least ¶99);
estimating, for each of a plurality of predetermined routes to the destination, a second amount of energy to be used during driving of the vehicle to the destination (When the navigation control unit calculates the amount of electric power which needs to be consumed, (i.e. second amount of energy to be used) in traveling for each route, the navigation control unit uses a travel amount per unit power, and power consumption or a generation amount of regenerative energy per gradient. By referring to such information stored in the memory, the navigation control unit can roughly calculate an amount of electric power to be consumed in traveling each route, which was searched for by the route search unit – See at least ¶100).
Ishibashi fails to disclose dynamically controlling operating conditions to facilitate autonomous driving of the vehicle using artificial intelligence to cause the plurality of components to operate more efficiently, based at least in part on keeping the second amount of energy to be used smaller than the first amount of available energy, by selecting one of the plurality of predetermined routes that keeps the second amount of energy to be used smaller than the first amount of available energy.
However, Sun teaches dynamically controlling operating conditions to facilitate autonomous driving of the vehicle using artificial intelligence to cause the plurality of components to operate more efficiently, based at least in part on keeping the second amount of energy to be used smaller than the first amount of available energy, by selecting one of the plurality of predetermined routes that keeps the second amount of energy to be used smaller than the first amount of available energy (In an example embodiment, the vehicle energy consumption model can be dynamically learned vehicle energy consumption model corresponding to the typical energy consumption characteristics of a particular vehicle, a particular vehicle type, or an aggregate energy consumption characteristics for a class of vehicles. The vehicle energy consumption model can be configured to provide a predicted energy consumption rate for each of the potential routings and motion controls to cause the vehicle to transit from its current position to the desired destination. These predicted energy consumption rates for each of the potential routings and motion controls can be used to score and rank the plurality of potential routings and motion controls based on a level of energy consumption – See at least ¶42. In one example embodiment, these predicted energy consumption levels for each potential routing and vehicle motion control can be determined based on machine learning techniques (artificial intelligence) configured from training scenarios produced from prior real-world test data collections - See at least ¶43. The vehicle motion control processing module can select the potential routing and related motion controls with the lowest predicted energy consumption score. If the lowest predicted energy consumption score is not within a pre-defined acceptability range, the vehicle motion control processing module can modify the related vehicle motion control operations to lower the vehicle's energy consumption over the corresponding potential routing – See at least ¶44).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Ishibashi and include the feature of dynamically controlling operating conditions to facilitate autonomous driving of the vehicle using artificial intelligence to cause the plurality of components to operate more efficiently, based at least in part on keeping the second amount of energy to be used smaller than the first amount of available energy, by selecting one of the plurality of predetermined routes that keeps the second amount of energy to be used smaller than the first amount of available energy, as taught by Sun, to lower energy costs related to the operation of autonomous vehicles (See at least ¶5 of Sun).
Regarding claim 16, Ishibashi discloses wherein the second amount of energy to be used is estimated based at least in part on inclination information of a route to the destination (The navigation system according to an embodiment of the present invention performs, in search for a route to a destination, searching processing with priority on a route in which less electric power of the electric vehicle is to be consumed. Further, when the navigation system preferentially searches for a route in which less electric power of the electric vehicle is to be consumed, the navigation system takes into account information on a gradient, i.e. inclination, on a route – See at least ¶58).
Regarding claim 17, Ishibashi discloses wherein the second amount of energy to be used is estimated based at least in part on traffic information of a route to the destination (In this rough calculation of power consumption by the navigation control unit, time taken in traveling from a current location to a destination may be determined by rough calculation, traffic jam information – See at least ¶73).
Regarding claim 18, Ishibashi discloses wherein the second amount of energy to be used is estimated based at least in part on speed information of a route to the destination (In this rough calculation of power consumption by the navigation control unit, time taken in traveling from a current location to a destination may be determined by rough calculation, using a legal speed of a road on a route – See at least ¶73).
Regarding claim 19, Ishibashi discloses wherein the second amount of energy to be used is estimated based at least in part on temperature information of a route to the destination (Further, for example, the navigation system may take into account external circumstances such external temperature, in calculating power consumption in traveling to a destination – See at least ¶101).
Claims 5, 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Yoshihito Ishibashi, US 20180340786 A1, in view of Sun et al., US 20190064793 A1, as applied to claims 1 and 9 above and further in view of Christopher M. Higgins, US 20170355371 A1, hereinafter referred to as Ishibashi, Sun and Higgins, respectively.
Regarding claim 5, Ishibashi discloses the limitations contained in claim 1 for the reasons discussed above. In addition, Ishibashi discloses “wherein the plurality of components being controlled based at least in part on keeping the second amount of energy to be used smaller than the first amount of available energy” (See at least ¶103 of Ishibashi).
The combination of Ishibashi and Sun do not appear to disclose “a heating system”.”
However, Higgins teaches “a heating system” (FIG. 2 is a schematic that shows exemplary vehicle control systems according to one example. The vehicle may include a HVAC (heating system) – See at least ¶23 of Higgins).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Ishibashi and Sun and include the feature of a heating system, as taught by Higgins, to minimize energy consumption in order to extend the operation range of the vehicle to reach the energy station (See at least ¶52 of Higgins).
Regarding claim 6, Ishibashi discloses the limitations contained in claim 1 for the reasons discussed above. In addition, Ishibashi discloses “wherein the plurality of components being controlled based at least in part on keeping the second amount of energy to be used smaller than the first amount of available energy” (See at least ¶103 of Ishibashi).
The combination of Ishibashi and Sun do not appear to disclose “a ventilation system”.”
However, Higgins teaches “a ventilation system” (FIG. 2 is a schematic that shows exemplary vehicle control systems according to one example. The vehicle may include a HVAC (ventilation system) – See at least ¶23 of Higgins).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Ishibashi and Sun and include the feature of a ventilation system, as taught by Higgins, to minimize energy consumption in order to extend the operation range of the vehicle to reach the energy station (See at least ¶52 of Higgins).
Regarding claim 14, the combination of Ishibashi and Sun do not appear to disclose “wherein the controlling of the operating conditions of the plurality of components includes reducing a speed of the vehicle.”
However, Higgins teaches “wherein the controlling of the operating conditions of the plurality of components includes reducing a speed of the vehicle” (The autonomous/semi-autonomous vehicle may operate under a plurality of operation modes. The operation modes may include an economy mode. The economy mode may involve several changes to the driving or operating behavior of the autonomous/semi-autonomous vehicle. Such changes may include, but are not limited to, limiting vehicle speed – See at least ¶22 of Higgins).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Ishibashi and Sun and include the feature of wherein the controlling of the operating conditions of the plurality of components includes reducing a speed of the vehicle, as taught by Higgins, to alter operations of one or more vehicle control systems to extend the operating range of the vehicle in order to reach an energy station identified based on a plurality of factors without running out of fuel (See at least ¶66 of Higgins).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Yoshihito Ishibashi, US 20180340786 A1, in view of Sun et al., US 20190064793 A1, as applied to claim 15 above and further in view of Mason et al., US 20170307391 A1, hereinafter referred to as Ishibashi, Sun and Mason, respectively.
Regarding claim 20, the combination of Ishibashi and Sun do not appear to disclose “wherein the second amount of energy to be used is estimated based at least in part on estimated stop time on a route to the destination.”
However, Gusikhin teaches “wherein the second amount of energy to be used is estimated based at least in part on estimated stop time on a route to the destination” (Route calculation module can analyze the feasible routes based on an energy use cost function and overlay the energy-related costs on each route. In some embodiments, the overlay of routes includes calculating or otherwise identifying an energy use cost based on the cost function for each of the feasible routes. The energy use cost function can be an objective function or the like that is based on one or more energy use factors such as estimated stop or idling time – See at least ¶51 of Mason).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Ishibashi and Sun and include the feature of wherein the second amount is estimated based at least in part on estimated stop time on a route to the destination, as taught by Mason, to identify a plurality of alternative feasible routes based, at least in part, on shortest distance or shortest transit time (See at least ¶11 of Mason).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Uyeki et al., (US 20120109515 A1) discloses a vehicle navigation system is provided to determine a traveling route from a current vehicle location to a destination location at least partially according to the present state of charge of the vehicle battery. If the present SOC is insufficient to reach the destination using shortest time or shortest distance routes, the navigation system preferentially selects low speed routes over higher speed routes in determining the traveling route.
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 MAHMOUD M KAZIMI whose telephone number is (571)272-3436. The examiner can normally be reached M-F 7am-5pm.
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, Erin Bishop can be reached at 5712703713. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.M.K./Examiner, Art Unit 3665
/David P. Merlino/Primary Examiner, Art Unit 3665
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