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
This Final Office Action is in response to the applicant’s amendment/response of 30 April 2026.
Claims 1-20 are currently pending and addressed below.
Response to Arguments
Applicant's arguments/amendments with respect to the rejection of claims under 35 U.S.C. 112(b) have been fully considered but they are not persuasive therefore, same issue(s) remain(s) as detailed below.
Applicant’s arguments with respect to the rejections of claims under 35 U.S.C. 102 and 35 U.S.C. 103 have been considered but are moot because the new ground of rejection does not rely on the combination of references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Objections
Claims 1, 6, and 16 are objected to because of the following informalities: The claims recite “a plurality of second vehicle” should read as “plurality of second vehicles”. Appropriate correction is required.
Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required: antecedent basis should be provided for the amended new claim terminologies “the critical code comprises information required for autonomous driving of the vehicle” recited in claims 1, 6, and 16, and “operational data” recited in claims 4 and 12 without adding new matter.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As to claim 1, the claim recites “storing critical code in the volatile memory of the vehicle”. It is unclear to the Examiner what is meant by “critical code”. While applicant’s filed specification provides examples of “critical code”, it specifically states that it is merely an example of the critical code (e.g. “For example, critical code (e.g…data) for an autonomous driving application. The data can include data collected from vehicle sensors…”) and is not limited thereto, so it is unclear exactly what this term encompasses. Further, in view of the applicant’s specification, it is unclear to the Examiner what the metes and bounds of the “information required for autonomous driving of the vehicle” are.
As to claim 6, the claim is rejected for the same reasons as mentioned in the rejection of claim 1.
As to claim 16, the claim is rejected for the same reasons as mentioned in the rejection of claim 1.
Dependent claims are rejected as being dependent upon a rejected claim.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-14 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Lakshamanan et al. (US 20180300964 A1, cited in IDS filed 10/04/2024) in view of James et al. (US 20180293809 A1).
Regarding claim 1, and similarly with respect to claims 6 and 16, Lakshamanan et al.
discloses A method, comprising: method, comprising: collecting vehicle data from a sensor of a vehicle; ([0199] “a lead vehicle in a group of multiple autonomous vehicles can share inference determinations with other vehicles that may not have access to the sensor field of view of the lead vehicle. If the lead vehicle determines that an obstacle is present or a dangerous event is has occurred, the vehicle can share such determinations with nearby connected vehicles.”, and [0216] “autonomous vehicle sensors to assist in a distributed search. In one embodiment, one or more autonomous vehicles can receive a descriptor or via a cloud-based, V2I, and/or neighboring autonomous vehicle network. The descriptor identifies a target of search 1802. Autonomous vehicles that receive the descriptor can enable a background search work item that passively analyzes sensor data received via external sensors used to enable autonomous driving tasks (e.g., cameras, Radar, Lidar, etc.). Any object that tends the sensor detection area 1820, 1830 of autonomous vehicles that are executing the search task can be classified to determine if the object matches the details provided for the target of search 1802”, [0223] “The lead vehicle 2010 has a sensor detection area 2004 that includes areas that may not be detectible by the trailing vehicle 2020. When the inferencing module of the lead vehicle 2010 detects that an obstacle or dangerous event 2002 is present along a common path of the set of multiple autonomous vehicles, the inferencing determination can be shared with all vehicles in the set of multiple autonomous vehicles, including the trailing vehicle 2020. “, figure 18, figure 20, figure 24 and see at least [0228])
wherein the critical code comprises information required for autonomous driving of the
vehicle and transmitting, via wireless communication, the critical code between a first vehicle and a second vehicle or a first vehicle and a plurality of second vehicle, wherein the transmit is performed in response to determining that a processing capability or a memory capacity, or both, at the first vehicle is insufficient to perform a processing operation. (Figures 14 and 17, [0211] “the computation offload techniques are primarily optimized for use in situations in which on-vehicle computation resources are insufficient and cloud communication latency is too great. The decision to offload computation to either to neighboring trusted vehicles or to the cloud considers numerous factors including the amount of computation to be performed and the communication latencies from the local autonomous vehicle 1602 to neighboring AVs and V2I nodes 1604 or to an autonomous vehicle cloud and datacenter 1606.”, [0214] “the autonomous vehicle compute offload logic 1700 can determine if compute resources from neighboring vehicles available and sufficient at block 1706… If communication latency is acceptable at block 1708, the autonomous vehicle compute offload logic 1700 can dispatch at least a portion of the workload for processing on neighboring autonomous vehicles or V2I nodes, as shown at block 1710.”)
However, Lakshamanan et al. may be alleged to not explicitly disclose storing the vehicle
data in volatile memory of the vehicle; storing critical code in the volatile memory of the vehicle;
James et al. teaches storing the vehicle data in volatile memory of the vehicle; storing
critical code in the volatile memory of the vehicle; wherein the critical code comprises information required for autonomous driving of the vehicle; (115, Figure 1, and [0046] “The vehicle 100 can include one or more data stores 115 for storing one or more types of data. The data store 115 can include volatile and/or non-volatile memory. Examples of suitable data stores 115 include RAM (Random Access Memory), flash memory, ROM (Read Only Memory), PROM (Programmable Read-Only Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), registers, magnetic disks, optical disks, hard drives, or any other suitable storage medium, or any combination thereof.”, [0050] “The one or more data stores 115 can include sensor data 119. In this context, “sensor data” means any information about the sensors that the vehicle 100 is equipped with, including the capabilities and other information about such sensors. As will be explained below, the vehicle 100 can include the sensor system 120. The sensor data 119 can relate to one or more sensors of the sensor system 120. As an example, in one or more arrangements, the sensor data 119 can include information on one or more LIDAR sensors 124 of the sensor system 120.”, and see at least [0065])
It would have been obvious to one of ordinary skill in the art before the effective filling date
of the claimed invention with reasonable expectations of success to implement or modify the invention of Lakshamanan et al. to incorporate storing vehicle data in a volatile memory of the vehicle as taught by James et al. for the purpose of allowing the sensor data to be processed as intended by Lakshamanan et al.
Regarding claim 2, Lakshamanan et al. in view of James et al. discloses The method of claim
1,
James et al. teaches comprising storing the vehicle data in non-volatile memory. (115,
Figure 1, and [0046] “The vehicle 100 can include one or more data stores 115 for storing one or more types of data. The data store 115 can include volatile and/or non-volatile memory. Examples of suitable data stores 115 include RAM (Random Access Memory), flash memory, ROM (Read Only Memory), PROM (Programmable Read-Only Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), registers, magnetic disks, optical disks, hard drives, or any other suitable storage medium, or any combination thereof.”, and see at least [0065])
It would have been obvious to one of ordinary skill in the art before the effective filling date
of the claimed invention with reasonable expectations of success to implement or modify the invention of Lakshamanan et al. in combination with James et al. to incorporate the teachings of James et al. for the same reasons stated in the motivation statement of claim 1.
Regarding claim 3, Lakshamanan et al. in view of James et al. discloses The method of claim
1,
Lakshamanan et al. discloses comprising using the critical code for autonomous driving of the vehicle. (Abstract “the compute workload associated with autonomous operations of the autonomous vehicle, and offload logic configured to execute on the set of multiple processors, the offload logic to determine to offload one or more of the compute workloads to one or more autonomous vehicles within range of the wireless network device.”, Figures 14 and 17, [0211] “the computation offload techniques are primarily optimized for use in situations in which on-vehicle computation resources are insufficient and cloud communication latency is too great. The decision to offload computation to either to neighboring trusted vehicles or to the cloud considers numerous factors including the amount of computation to be performed and the communication latencies from the local autonomous vehicle 1602 to neighboring AVs and V2I nodes 1604 or to an autonomous vehicle cloud and datacenter 1606.”, [0214] “the autonomous vehicle compute offload logic 1700 can determine if compute resources from neighboring vehicles available and sufficient at block 1706… If communication latency is acceptable at block 1708, the autonomous vehicle compute offload logic 1700 can dispatch at least a portion of the workload for processing on neighboring autonomous vehicles or V2I nodes, as shown at block 1710.”, [0233] “The data from the vehicle sensors 2420 and the passenger health monitoring sensors 2432 can be provided to a machine learning framework 2240 that is executing on compute resources within the autonomous vehicle. The machine learning framework can provide the input data to a machine learning model 2442 that can output an inferred collision response 2450. The inferred collision response 2450 can include a set of actions that, if possible, the autonomous vehicle will automatically perform in response to the current post collision scenario.”)
Regarding claim 4, Lakshamanan et al. in view of James et al. discloses The method of claim
1,
Lakshamanan et al. discloses wherein the critical code further comprise specific
parameters, firmware, or operational data for autonomous driving of the vehicle. ([0199] “a lead vehicle in a group of multiple autonomous vehicles can share inference determinations with other vehicles that may not have access to the sensor field of view of the lead vehicle. If the lead vehicle determines that an obstacle is present or a dangerous event is has occurred, the vehicle can share such determinations with nearby connected vehicles.”, and [0216] “autonomous vehicle sensors to assist in a distributed search. In one embodiment, one or more autonomous vehicles can receive a descriptor or via a cloud-based, V2I, and/or neighboring autonomous vehicle network. The descriptor identifies a target of search 1802. Autonomous vehicles that receive the descriptor can enable a background search work item that passively analyzes sensor data received via external sensors used to enable autonomous driving tasks (e.g., cameras, Radar, Lidar, etc.). Any object that tends the sensor detection area 1820, 1830 of autonomous vehicles that are executing the search task can be classified to determine if the object matches the details provided for the target of search 1802”, [0223] “The lead vehicle 2010 has a sensor detection area 2004 that includes areas that may not be detectible by the trailing vehicle 2020. When the inferencing module of the lead vehicle 2010 detects that an obstacle or dangerous event 2002 is present along a common path of the set of multiple autonomous vehicles, the inferencing determination can be shared with all vehicles in the set of multiple autonomous vehicles, including the trailing vehicle 2020. “, figure 18, figure 20, figure 24 and see at least [0228])
Regarding claim 5, Lakshamanan et al. in view of James et al. discloses The method of claim
1,
Lakshamanan et al. discloses wherein collecting the vehicle data includes collecting photographic data. ([0216] “ Autonomous vehicles that receive the descriptor can enable a background search work item that passively analyzes sensor data received via external sensors used to enable autonomous driving tasks (e.g., cameras, Radar, Lidar, etc.). Any object that tends the sensor detection area 1820, 1830 of autonomous vehicles that are executing the search task can be classified to determine if the object matches the details provided for the target of search 1802. For example, a search descriptor can indicate that the target of search has a specific license plate type or license plate number. Alternatively, the descriptor for the target of search 1802 can be a more general description of a vehicle, such as a vehicle make, model, and color. Should a vehicle identified by the target of search descriptor pass into the sensor detection area 1820, 1830 of a connected autonomous vehicle, the vehicle can send information such as camera and sensor data back to the originator of the search. In one embodiment, a more general search can be enabled for the target of search 1802.”, and see at least figure 24)
Regarding claim 7, Lakshamanan et al. in view of James et al. discloses The apparatus of
claim 6,
Lakshamanan et al. discloses further comprising one or more sensors of a vehicle. (Figure 24)
Regarding claim 8, Lakshamanan et al. in view of James et al. discloses The apparatus of
claim 7,
Lakshamanan et al. discloses wherein collecting the vehicle data is performed by the one or more sensors. ([0199] “a lead vehicle in a group of multiple autonomous vehicles can share inference determinations with other vehicles that may not have access to the sensor field of view of the lead vehicle. If the lead vehicle determines that an obstacle is present or a dangerous event is has occurred, the vehicle can share such determinations with nearby connected vehicles.”, and [0216] “autonomous vehicle sensors to assist in a distributed search. In one embodiment, one or more autonomous vehicles can receive a descriptor or via a cloud-based, V2I, and/or neighboring autonomous vehicle network. The descriptor identifies a target of search 1802. Autonomous vehicles that receive the descriptor can enable a background search work item that passively analyzes sensor data received via external sensors used to enable autonomous driving tasks (e.g., cameras, Radar, Lidar, etc.). Any object that tends the sensor detection area 1820, 1830 of autonomous vehicles that are executing the search task can be classified to determine if the object matches the details provided for the target of search 1802”, [0223] “The lead vehicle 2010 has a sensor detection area 2004 that includes areas that may not be detectible by the trailing vehicle 2020. When the inferencing module of the lead vehicle 2010 detects that an obstacle or dangerous event 2002 is present along a common path of the set of multiple autonomous vehicles, the inferencing determination can be shared with all vehicles in the set of multiple autonomous vehicles, including the trailing vehicle 2020. “, figure 18, figure 20, figure 24 and see at least [0228])
Regarding claim 9, Lakshamanan et al. in view of James et al. discloses The apparatus of
claim 7,
Lakshamanan et al. discloses wherein the one or more sensors include a vehicle camera. (2422, figure 24)
Regarding claim 10, Lakshamanan et al. in view of James et al. discloses The apparatus of
claim 6,
James et al. teaches wherein the memory resource further includes non-volatile memory.
[0046] “The vehicle 100 can include one or more data stores 115 for storing one or more types of data. The data store 115 can include volatile and/or non-volatile memory. Examples of suitable data stores 115 include RAM (Random Access Memory), flash memory, ROM (Read Only Memory), PROM (Programmable Read-Only Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), registers, magnetic disks, optical disks, hard drives, or any other suitable storage medium, or any combination thereof.”, and see at least [0065])
It would have been obvious to one of ordinary skill in the art before the effective filling date
of the claimed invention with reasonable expectations of success to implement or modify the invention of Lakshamanan et al. in combination with James et al. to incorporate the teachings of James et al. for the same reasons stated in the motivation statement of claim 1.
Regarding claim 11, Lakshamanan et al. in view of James et al. discloses The apparatus of
claim 10,
James et al. teaches wherein the non-volatile memory is configured to store the vehicle data. (115, Figure 1, [0046] “The vehicle 100 can include one or more data stores 115 for storing one or more types of data. The data store 115 can include volatile and/or non-volatile memory. Examples of suitable data stores 115 include RAM (Random Access Memory), flash memory, ROM (Read Only Memory), PROM (Programmable Read-Only Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), registers, magnetic disks, optical disks, hard drives, or any other suitable storage medium, or any combination thereof.”, [0050] “The one or more data stores 115 can include sensor data 119… The sensor data 119 can relate to one or more sensors of the sensor system 120. As an example, in one or more arrangements, the sensor data 119 can include information on one or more LIDAR sensors 124 of the sensor system 120.”, and see at least [0065])
It would have been obvious to one of ordinary skill in the art before the effective filling date
of the claimed invention with reasonable expectations of success to implement or modify the invention of Lakshamanan et al. in combination with James et al. to incorporate the teachings of James et al. for the same reasons stated in the motivation statement of claim 1.
Regarding claim 12, Lakshamanan et al. in view of James et al. discloses The apparatus of
claim 6,
Lakshamanan et al. discloses wherein the critical code further comprises specific parameters, firmware, or operational data for an autonomous driving application. ([0199] “a lead vehicle in a group of multiple autonomous vehicles can share inference determinations with other vehicles that may not have access to the sensor field of view of the lead vehicle. If the lead vehicle determines that an obstacle is present or a dangerous event is has occurred, the vehicle can share such determinations with nearby connected vehicles.”, and [0216] “autonomous vehicle sensors to assist in a distributed search. In one embodiment, one or more autonomous vehicles can receive a descriptor or via a cloud-based, V2I, and/or neighboring autonomous vehicle network. The descriptor identifies a target of search 1802. Autonomous vehicles that receive the descriptor can enable a background search work item that passively analyzes sensor data received via external sensors used to enable autonomous driving tasks (e.g., cameras, Radar, Lidar, etc.). Any object that tends the sensor detection area 1820, 1830 of autonomous vehicles that are executing the search task can be classified to determine if the object matches the details provided for the target of search 1802”, [0223] “The lead vehicle 2010 has a sensor detection area 2004 that includes areas that may not be detectible by the trailing vehicle 2020. When the inferencing module of the lead vehicle 2010 detects that an obstacle or dangerous event 2002 is present along a common path of the set of multiple autonomous vehicles, the inferencing determination can be shared with all vehicles in the set of multiple autonomous vehicles, including the trailing vehicle 2020. “, figure 18, figure 20, figure 24 and see at least [0228])
Regarding claim 13, Lakshamanan et al. in view of James et al. discloses The apparatus of
claim 6,
Lakshamanan et al. discloses wherein the memory resource (1305, figure 13) and the
processing resource (Figure 13) are located on an autonomous vehicle. ([0189] “The processing components can be optimized for low power operation to enable deployment to a variety of machine learning platforms, including autonomous vehicles and autonomous robots. For example, one implementation of the SOC 1300 can be used as a portion of the main control system for an autonomous vehicle.”)
Regarding claim 14, Lakshamanan et al. in view of James et al. discloses The apparatus of
claim 6,
Lakshamanan et al. discloses wherein the memory resource is used by an active vehicle.
([0189] “inferencing system on a chip (SOC) 1300 suitable for performing inferencing using a trained model. The SOC 1300 can integrate processing components including a media processor 1302, a vision processor 1304, a GPGPU 1306 and a multi-core processor 1308. The SOC 1300 can additionally include on-chip memory 1305 that can enable a shared on-chip data pool that is accessible by each of the processing components. The processing components can be optimized for low power operation to enable deployment to a variety of machine learning platforms, including autonomous vehicles and autonomous robots. For example, one implementation of the SOC 1300 can be used as a portion of the main control system for an autonomous vehicle. Where the SOC 1300 is configured for use in autonomous vehicles the SOC is designed and configured for compliance with the relevant functional safety standards of the deployment jurisdiction.”)
Regarding claim 17, Lakshamanan et al. in view of James et al. discloses The vehicle of claim
16,
Lakshamanan et al. discloses wherein the sensor is a vehicle sensor. (2422, 2424, 2426,
2428, figure 24)
Regarding claim 18, Lakshamanan et al. in view of James et al. discloses The vehicle of claim
16,
Lakshamanan et al. discloses wherein the vehicle is an autonomous vehicle. (Figure 22 and
see at least abstract )
Claims 15 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lakshamanan et al. (US 20180300964 A1) in view of James et al. (US 20180293809 A1) and further in view of Sitzes (US 20100202346 A1).
Regarding claim 15, Lakshamanan et al. in view of James et al. discloses The apparatus of
claim 6,
However, Lakshamanan et al. in combination with James et al. fails to explicitly disclose
wherein the memory resource is accessed by another vehicle.
Sitzes teaches wherein the memory resource is accessed by another vehicle. ([0050] “The
shared memory 512 may include a memory location that is accessible by other communication devices on the network. For example, in one embodiment, other communication devices may be capable of mapping to the memory location to view and download information (e.g., files and messages) stored in the shared memory 512. In one embodiment, access to the shared memory location may be at least partially limited to external devices and/or users. For example, other communication device may be capable of viewing only certain information related to information stored in shared memory 512.”, and [0065] “communication device 112 may be adapted for use in various locations. For example, where the communication device is to be used as an inter-vehicle communication device, it may be mounted somewhere within a vehicle that is conducive to use by the vehicle occupants.”, [0054] “Transceiver 516 includes, in one embodiment, a device configured to wirelessly transmit and/or receive communication signal between one or more communication devices. The devices may include other communication devices on the network, such as those associated with vehicles and/or non-vehicles.”, see at least [0011] and etc.)
It would have been obvious to one of ordinary skill in the art before the effective filling date
of the claimed invention with reasonable expectations of success to modify the invention of Lakshamanan et al. in combination with James et al. to incorporate a shared memory that is accessible by other communication devices (e.g. vehicles) as taught by Sitzes for the purpose of allowing the vehicle to share data with other vehicles for navigation.
Regarding claim 19, Lakshamanan et al. in view of James et al. discloses The vehicle of claim
16,
However, Lakshamanan et al. in combination with James et al. may be alleged to not
explicitly disclose The vehicle… further comprising a transceiver.
Sitzes teaches The vehicle… further comprising a transceiver. ([0050] “The shared memory
512 may include a memory location that is accessible by other communication devices on the network. For example, in one embodiment, other communication devices may be capable of mapping to the memory location to view and download information (e.g., files and messages) stored in the shared memory 512. In one embodiment, access to the shared memory location may be at least partially limited to external devices and/or users. For example, other communication device may be capable of viewing only certain information related to information stored in shared memory 512.”, and [0065] “communication device 112 may be adapted for use in various locations. For example, where the communication device is to be used as an inter-vehicle communication device, it may be mounted somewhere within a vehicle that is conducive to use by the vehicle occupants.”, [0054] “Transceiver 516 includes, in one embodiment, a device configured to wirelessly transmit and/or receive communication signal between one or more communication devices. The devices may include other communication devices on the network, such as those associated with vehicles and/or non-vehicles.”, see at least [0011] and etc.)
It would have been obvious to one of ordinary skill in the art before the effective filling date
of the claimed invention with reasonable expectations of success to modify the invention of Lakshamanan et al. in combination with James et al. to incorporate a vehicle transceiver as taught by Sitzes for the purpose of allowing the vehicle to receive/transmit data from/to other vehicles.
Regarding claim 20, Lakshamanan et al. in view of James et al. discloses The vehicle of claim
19,
However, Lakshamanan et al. in combination with James et al. fails to explicitly disclose
wherein the transceiver is configured to enable access to another memory resource.
Sitzes teaches wherein the transceiver is configured to enable access to another memory
resource. ([0050] “The shared memory 512 may include a memory location that is accessible by other communication devices on the network. For example, in one embodiment, other communication devices may be capable of mapping to the memory location to view and download information (e.g., files and messages) stored in the shared memory 512. In one embodiment, access to the shared memory location may be at least partially limited to external devices and/or users. For example, other communication device may be capable of viewing only certain information related to information stored in shared memory 512.”, and [0065] “communication device 112 may be adapted for use in various locations. For example, where the communication device is to be used as an inter-vehicle communication device, it may be mounted somewhere within a vehicle that is conducive to use by the vehicle occupants.”, and [0054] “Transceiver 516 includes, in one embodiment, a device configured to wirelessly transmit and/or receive communication signal between one or more communication devices. The devices may include other communication devices on the network, such as those associated with vehicles and/or non-vehicles.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention with reasonable expectations of success to modify the invention of Lakshamanan et al. in combination with James et al. to incorporate a shared memory that is accessible by other communication devices (e.g. vehicles) as taught by Sitzes for the purpose of allowing the vehicle to share data with other vehicles for vehicle navigation.
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 MISA HUYNH NGUYEN whose telephone number is (571)270-5604. The examiner can normally be reached Monday-Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anne Antonucci can be reached at (313) 446-6519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MISA H NGUYEN/Examiner, Art Unit 3666
/ANNE MARIE ANTONUCCI/Supervisory Patent Examiner, Art Unit 3666
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