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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/05/2026 has been entered.
Status of claims
Claims 1,4,8,13,16 and 19 are amended.
Claim 12 is cancelled.
Claims 1-11, 13-22 are pending.
No new claim is added.
Response to arguments
With respect to Applicant’s remarks filed on 08/05/2026; Applicant's “Amendments and Remarks” have been fully considered. Applicant’s remarks will be addressed in sequential order as they were presented.
Applicant remarks:
Reconsideration of the rejection under 35 U.S.C 112(a) is respectfully requested based on the arguments that para [0034]-[0046] describe of a classifier for determining a destination for performing a computation.
Qian does not teach “a computing module configured to maintain a local dynamic map representing the vehicle and a surrounding environment, the LDM further representing at least one of the remote devices”.
Hall fails to cure the deficiencies of Qian w.r.t claims 1 and 13, Manku fails to cure the deficiencies of Quian w.r.t claims10,12 and 21
Office Response:
The argument overcome 112(a) understanding that classifier can be AI based algorithm, or mathematical optimization operated by the OM 420, which may solve the VEIP problem.
Please see the new mapping for the rejections.
Please see the new mapping for the rejections for claims 1,13, 10, 12 and 21 specifically where previous deficiencies overcome by Graefe in view of Hall.
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 1-11, 13-22 are rejected under 35 U.S.C. 103 as being unpatented over by US20190132709 A1 to Graefe et al. (herein after “Graefe”) in view of WO2021071621 A1 to Hall et al. (herein after “Hall”).
Regarding claim 1, Graefe teaches A system for managing operation of a vehicle, comprising: (See Graefe abstract Systems, methods, and computer-readable media are provided for wireless sensor networks (WSNs), including vehicle-based WSNs.)
a wireless network interface configured to communicate with remote devices of a wireless network via at least a first wireless channel (See Graefe para[0003] Dedicated short-range communications (DSRC) and/or cellular vehicle-to-everything (C-V2X) protocols provide communications between vehicles and the roadside infrastructure. ) and a second wireless channel( see Graefe para[0097] The response messages can be sent on a separate point-to-point connection using a suitable wireless/V2X communication technology, which can include broadcast or multicast transmissions ), (see Graefe para[0021] The objects 64a, 64b may include wireless communication technology to communicate with the infrastructure equipment 61a, 61b, and with each other. The infrastructure equipment 61a, 61b may also exchange information about the vehicles 64a, 64b that they are tracking and may support collaborative decision making. )
the first and second wireless channels having distinct spectrum bands; (see Graefe DSRC and mmWave channel are different)
a computing module configured to maintain a local dynamic map (LDM) representing the vehicle and a surrounding environment, (see Graefe para[0024] The real-time mapping of dynamic environments is used for high-reliability decision-making systems, such as when vehicles 64 are CA/AD vehicles 64. In Intelligent Transport Systems (ITS), the real-time mapping may be used for a real-time traffic status map called the Local Dynamic Map (LDM), that structures all relevant data for vehicle operation and that also provides information about highly dynamic objects, such as vehicles 64 on the road 63. The input for the LDM can be provided by user equipment (UEs) equipped with sensors, such as one or more vehicles 64)
the LDM further representing at least one of the remote devices, the LDM further representing one or more of a network topology of the wireless network (See Graefe para[0174] The network topology 1000 may include any number of types of IoT networks, such as a mesh network 1056 using BLE links 1022 ) and a computational load, wireless channel load metrics, and an expected latency of the at least one of the remote devices (See para[0062] the tracking of objects 64 using sensor data from the individual sensors 262 allows the infrastructure equipment 61 a, 61 b to broadcast or multicast map data helping to minimize latency in wireless information exchange); and
communicate with the remote devices via the first wireless channel (See Graefe para[0035] The term “user equipment” or “UE” may be considered synonymous to, and may be referred to as client, mobile, mobile device, mobile terminal, user terminal, mobile unit, mobile station, mobile user, subscriber, user, remote station, access agent, user agent, receiver, radio equipment, reconfigurable radio equipment, reconfigurable mobile device, etc. Furthermore, the term “user equipment” or “UE” may include any type of wireless/wired device or any computing device including a communications interface) to determine a status of remote computational resources (See Graefe para [0138] The computer program code for carrying out operations of the present disclosure may also be written in any combination of the programming languages discussed herein. The program code may execute entirely on the user's wearable device, partly on the user's wearable device, as a stand-alone software package, partly on the user's wearable device and partly on a remote computer or entirely on the remote computer or server, see paras[0163]-[0164], [0178]).
However, Graefe does not expressly mention or otherwise teach a controller configured to: generate a navigation task from sensor data corresponding to the surrounding environment, and with the computing module to determine a status of on-board computational resources; determine, based on the LDM and the status of on-board computational resources, a destination to process the navigation task, the destination being one of a set of resources including the computing module and the at least one of the remote devices
Nevertheless, Hall same field of endeavor teaches a controller configured to: generate a navigation task from sensor data corresponding to the surrounding environment; (see Hall para[0149] In block 1204, the processor may determine from the received data whether any information should be integrated into the LDM data model. For example, the processor may select mobile device sensor data, image data, audio data, and/or operating state data to determine LDM data that will augment, update or otherwise enhance the LDM data model. In some embodiments, the processor may determine highly dynamic LDM information, as such information is defined
and with the computing module to determine a status of on-board computational resources; (Hall para [0041] In some embodiments, a mobile device may be a computing device in a vehicle, such as a navigation unit or vehicle control system of an autonomous vehicle or semi-autonomous vehicle. In some embodiments, providing the determined second LDM data to vehicles and mobile devices may include generating a digital map encompassing an area within a predetermined distance of each vehicle, and transmitting the generated, vehicle-specific digital map to the vehicle, see map generating module 416, see para[0110] The computer program modules may be configured to enable an expert or user associated with a given vehicle or mobile device 404 to interface with system 400 and/or external resources 430, and/or provide other functionality attributed herein to vehicles and mobile devices 404.)
determine, based on the LDM and the status of on-board computational resources (See Hall para[0103] The instruction modules may include computer program modules. The instruction modules may include one or more of an LDM data receiving module 408, an LDM data integration module 410, an LDM data determination module 412, an LDM data providing module 414, a map generating module 416, a map transmittal module 418, and/or other instruction modules), a destination to process the navigation task, the destination being one of a set of resources including the computing module and the at least one of the remote devices; (See Hall para [0117] The Edge application server 504 and the application client(s) 512 each may be configured to process computing tasks, and may communicate application data traffic (i.e., data related to a computing task) via the 3GPP core network 530, para [0114] Processor(s) 434 may be configured to provide information processing capabilities in Edge computing device 402. As such, processor(s) 434 may include one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information)
communicate the navigation task to the destination via at least one of an on-board channel and the second wireless channel (See Hall para[0067] Alternatively, or in addition, the navigation components 172b may include radio navigation receivers for receiving navigation beacons or other signals from radio nodes, such as Wi-Fi access points, cellular network sites, radio station, remote computing devices, ); and
update the LDM based on an output associated with execution of the navigation task (See Hall para[0039] an Edge computing device may receive new or updated (referred to as “first”) LDM data for a service area of the Edge computing device, para[0088] Such information may be used by Edge computing devices to update LDM data for relay to vehicles within the local area of each Edge computing device.)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Graefe’s apparatus for providing infrastructure service with Hall’s communicate navigation task to computing module and remote device; update LDM in order to allow to allow unrestricted access by mobile devices with service subscription (see Hall para[0046]) and reflect highly dynamic environmental conditions (See Hall para[0002]).
Regarding claim 2, Graefe and Hall remain applied as claim 1. Nevertheless, Hall teaches wherein the navigation task includes processing the sensor data to determine an update to the LDM (See Hall para[0039] an Edge computing device may receive new or updated (referred to as “first”) LDM data for a service area of the Edge computing device, para[0088] Such information may be used by Edge computing devices to update LDM data for relay to vehicles within the local area of each Edge computing device.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Graefe’s apparatus for providing infrastructure service with Hall’s communicate navigation task to computing module and remote device; update LDM in order to allow to allow unrestricted access by mobile devices with service subscription (see Hall para[0046]) and reflect highly dynamic environmental conditions (See Hall para[0002]).
Regarding claim 3, Graefe and Hall remain applied as claim 1. Graefe teaches wherein the navigation task is a deep learning (DL) task. (See Graefe para[0056] a deep learning object detection technique (e.g., fully convolutional neural network (FCNN), region proposal convolution neural network (R-CNN).
Regarding claim 4, Graefe and Hall remain applied as claim 1. Graefe teaches wherein the first wireless channel is a dedicated short-range communications (DSRC) channel (See Graefe para[0003] Dedicated short-range communications (DSRC) and/or cellular vehicle-to-everything (C-V2X) protocols provide communications between vehicles and the roadside infrastructure. ) and the second wireless channel is a point-to-point millimeter wave (mmWave) channel ( see Graefe para[0097] The response messages can be sent on a separate point-to-point connection using a suitable wireless/V2X communication technology, which can include broadcast or multicast transmissions ) or wherein the second wireless channel is the DSRC channel and the first wireless channel is the mmWave channel. (See Graefe mentioned communication system can be mmWave or DSRC)
Regarding claim 5, Graefe and Hall remain applied as claim 1. Graefe teaches wherein the computing module (see Hall’s computing module) is further configured to update the LDM based on communications from the remote devices via the DSRC channel. (See Graefe para[0102] Following the handshake procedure 500, the combination of location information from the object 64 and the consecutive object tracking using sensor data from the sensors 262 may be used to update the environmental map 324. , para[0178] Communications in the cellular network 1060 may be enhanced by systems that offload data, extend communications to more remote devices, or both.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Graefe’s apparatus for providing infrastructure service with Hall’s communicate navigation task to computing module and remote device; update LDM in order to allow to allow unrestricted access by mobile devices with service subscription (see Hall para[0046]) and reflect highly dynamic environmental conditions (See Hall para[0002]).
Regarding claim 6, Graefe and Hall remain applied as claim 1. Graefe teaches wherein the controller is further configured to communicate the navigation task to the destination via the mmWave channel, the destination being one of a remote vehicle and a road side unit (RSU). (See Graefe para[0034] The term “hop” may refer to an individual node or intermediary device through which data packets traverse a path between a source device and a destination device, para[0055] The main system controller 302 is configured to manage the RTMS 300, such as by scheduling tasks for execution, managing memory/storage resource allocations, routing inputs/outputs to/from various entities, and the like. The main system controller 302 may schedule tasks according to a suitable scheduling algorithm,).
Regarding claim 7, Graefe and Hall remain applied as claim 1. Graefe teaches wherein the RSU is further configured to communicate the navigation task to a cloud network resource for performing the navigation task. (See Graefe para[0045] MEC provides application developers and content providers with cloud-computing capabilities and an information technology (IT) service environment at the edge of the network. MEC is a network architecture that allows cloud computing capabilities and computing services to be performed at the edge of a network. MEC provides mechanisms that allow applications to be run and to perform related processing tasks closer to network subscribers (also referred to as “edge users” and the like). In this way, network congestion may be reduced and applications may have better performance.).
Regarding claim 8, Graefe and Hall remain applied as claim 1. Graefe teaches wherein the controller is further configured to:
generate a first feature set representing the set of resources; (See Graefe para[0049] a set of requirements (e.g., latency, processing resources, storage resources, network resources, location, network capability, security conditions/capabilities, etc.)
generate a second feature set representing the navigation task; (See Graefe para [0055] The main system controller 302 is configured to manage the RTMS 300, such as by scheduling tasks for execution, managing memory/storage resource allocations, routing inputs/outputs to/from various entities, and the like. The main system controller 302 may schedule tasks according to a suitable scheduling algorithm, and/or may implement a suitable message passing scheme to allocate resources.)
apply the first and second feature sets to a classifier to determine the destination based on an anticipated computational performance and an anticipated communication performance of the set of resources. (see para[0048] The compute-intensive tasks are offloaded to the MEC host 257 since MEC host 257 has higher/greater performance capabilities as compared to the vUE system 201 of the vehicles 64.)
Regarding claim 9, Graefe and Hall remain applied as claim 1. Graefe teaches wherein the controller is further configured to: incorporate a representation of the set of resources and a representation of the navigation task into a mathematical model (See Graefe grid-based environment model, this is one specific type of mathematical model, para[0041] In any of these embodiments, the computing system of the infrastructure equipment 61 a, 61 b calculates a grid-based environment model that is overlaid on top of the observed coverage area 63. The grid-based environment model allows the computing system of the infrastructure equipment 61 a, 61 b to target particular objects 64 in specific grid cells for purposes of requesting data from those targeted objects 64) ; and
process the mathematical model to determine the destination. (See Graefe Para[0041] the computing system of the infrastructure equipment 61a, 61b calculates a grid-based environment model that is overlaid on top of the observed coverage area 63. The grid-based environment model allows the computing system of the infrastructure equipment 61a, 61b to target particular objects 64 in specific grid cells for purposes of requesting data from those targeted objects 64.).
Regarding claim 10, Graefe and Hall remain applied as claim 1. Graefe teaches wherein at least one of the distinct spectrum bands is an unlicensed spectrum band. (See Graefe para[0043] Additionally, the communication circuitry of the infrastructure equipment 61 may provide a WiFi hotspot (2.4 GHz band), 2.4 GHz is unlicensed spectrum band).
Regarding claim 11, Graefe and Hall remain applied as claim 1. Graefe teaches
the movement including at least one of collision avoidance and self-driving operation. (See Graefe para[0034] For example, the vehicles 64, radio access nodes, pedestrian UEs, etc., may collect knowledge of their local environment (e.g., information received from other vehicles or sensor equipment in proximity) to process and share that knowledge in order to provide more intelligent services, such a cooperative collision warning, autonomous driving, and the like. The V2X cooperative awareness mechanisms are similar to the CA services provided by ITS system as discussed previously).
Nevertheless, Hall same field of endeavor teaches wherein the computing module is further configured to control movement of the vehicle based on the LDM, (See Hall para[0086] generate control signals for controlling the motion of the vehicle 101 and to verify that such control signals meet safety requirements for the vehicle 100. For example, based on route planning information, refined location in the roadway information, and relative locations and motions of other vehicles, the motion planning and control layer 214 may verify and pass various control commands or instructions to the DBW system/control unit 220.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Graefe’s apparatus for providing infrastructure service with Hall’s communicate navigation task to computing module and remote device; update LDM in order to allow to allow unrestricted access by mobile devices with service subscription (see Hall para[0046]) and reflect highly dynamic environmental conditions (See Hall para[0002]).
Regarding claim 13, Graefe teaches A method of managing operation of a vehicle, comprising: (See Graefe abstract Systems, methods, and computer-readable media are provided for wireless sensor networks (WSNs), including vehicle-based WSNs.)
communicating with remote devices of a wireless network via at least a first wireless channel (See Graefe para[0003] Dedicated short-range communications (DSRC) and/or cellular vehicle-to-everything (C-V2X) protocols provide communications between vehicles and the roadside infrastructure. ) and a second wireless channel( see Graefe para[0097] The response messages can be sent on a separate point-to-point connection using a suitable wireless/V2X communication technology, which can include broadcast or multicast transmissions ), (see Graefe para[0021] The objects 64a, 64b may include wireless communication technology to communicate with the infrastructure equipment 61a, 61b, and with each other. The infrastructure equipment 61a, 61b may also exchange information about the vehicles 64a, 64b that they are tracking and may support collaborative decision making. ), the first and second wireless channels having distinct spectrum bands; (see Graefe DSRC and mmWave channel are different)
maintaining a local dynamic map (LDM) representing the vehicle and a surrounding environment, , (see Graefe para[0024] The real-time mapping of dynamic environments is used for high-reliability decision-making systems, such as when vehicles 64 are CA/AD vehicles 64. In Intelligent Transport Systems (ITS), the real-time mapping may be used for a real-time traffic status map called the Local Dynamic Map (LDM), that structures all relevant data for vehicle operation and that also provides information about highly dynamic objects, such as vehicles 64 on the road 63. The input for the LDM can be provided by user equipment (UEs) equipped with sensors, such as one or more vehicles 64)
the LDM further representing at least one of the remote devices, the LDM further representing one or more of a network topology of the wireless network(See Graefe para[0174] The network topology 1000 may include any number of types of IoT networks, such as a mesh network 1056 using BLE links 1022 ) and a computational load, wireless channel load metrics, and an expected latency of the at least one of the remote devices; (See para[0062] the tracking of objects 64 using sensor data from the individual sensors 262 allows the infrastructure equipment 61 a, 61 b to broadcast or multicast map data helping to minimize latency in wireless information exchange)
communicating with the remote devices via the first wireless channel (See Graefe para[0003] Dedicated short-range communications (DSRC) and/or cellular vehicle-to-everything (C-V2X) protocols provide communications between vehicles and the roadside infrastructure. ) to determine a status of remote computational resources (See Graefe para [0055]
The main system controller 302 is configured to manage the RTMS 300, such as by scheduling tasks for execution, managing memory/storage resource allocations).
However, Graefe does not expressly mention or otherwise teach generating a navigation task from sensor data corresponding to the surrounding environment, and with an on-board computing module to determine a status of on-board computational resources, a destination to process the navigation task, the destination being one of a set of resources including the computing module and the at least one of the remote devices. Nevertheless, Hall same field of endeavor teaches generating a navigation task from sensor data corresponding to the surrounding environment; (see Hall para[0149] In block 1204, the processor may determine from the received data whether any information should be integrated into the LDM data model. For example, the processor may select mobile device sensor data, image data, audio data, and/or operating state data to determine LDM data that will augment, update or otherwise enhance the LDM data model. In some embodiments, the processor may determine highly dynamic LDM information, as such information is defined)
and with an on-board computing module to determine a status of on-board computational resources; (Hall para [0041] In some embodiments, a mobile device may be a computing device in a vehicle, such as a navigation unit or vehicle control system of an autonomous vehicle or semi-autonomous vehicle. In some embodiments, providing the determined second LDM data to vehicles and mobile devices may include generating a digital map encompassing an area within a predetermined distance of each vehicle, and transmitting the generated, vehicle-specific digital map to the vehicle, see map generating module 416, see para[0110] The computer program modules may be configured to enable an expert or user associated with a given vehicle or mobile device 404 to interface with system 400 and/or external resources 430, and/or provide other functionality attributed herein to vehicles and mobile devices 404.)
determine, based on the LDM and the status of on-board computational resources (See Hall para[0103] The instruction modules may include computer program modules. The instruction modules may include one or more of an LDM data receiving module 408, an LDM data integration module 410, an LDM data determination module 412, an LDM data providing module 414, a map generating module 416, a map transmittal module 418, and/or other instruction modules), a destination to process the navigation task, the destination being one of a set of resources including the computing module and the at least one of the remote devices; (See Hall para [0117] The Edge application server 504 and the application client(s) 512 each may be configured to process computing tasks, and may communicate application data traffic (i.e., data related to a computing task) via the 3GPP core network 530, para [0114] Processor(s) 434 may be configured to provide information processing capabilities in Edge computing device 402. As such, processor(s) 434 may include one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information)
communicating the navigation task to the destination via at least one of an on- board channel the second wireless channel (See Hall para[0067] Alternatively, or in addition, the navigation components 172b may include radio navigation receivers for receiving navigation beacons or other signals from radio nodes, such as Wi-Fi access points, cellular network sites, radio station, remote computing devices);
updating the LDM based on an output associated with execution of the navigation task. (See Hall para[0039] an Edge computing device may receive new or updated (referred to as “first”) LDM data for a service area of the Edge computing device, para[0088] Such information may be used by Edge computing devices to update LDM data for relay to vehicles within the local area of each Edge computing device.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Graefe’s apparatus for providing infrastructure service with Hall’s communicate navigation task to computing module and remote device; update LDM in order to allow to allow unrestricted access by mobile devices with service subscription (see Hall para[0046]) and reflect highly dynamic environmental conditions (See Hall para[0002]).
Regarding claim 14, Graefe and Hall remain applied as claim 13. Graefe teaches wherein the navigation task includes processing the sensor data to determine an update to the LDM. (see Hall para[0149] In block 1204, the processor may determine from the received data whether any information should be integrated into the LDM data model. For example, the processor may select mobile device sensor data, image data, audio data, and/or operating state data to determine LDM data that will augment, update or otherwise enhance the LDM data model. In some embodiments, the processor may determine highly dynamic LDM information, as such information is defined).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Graefe’s apparatus for providing infrastructure service with Hall’s communicate navigation task to computing module and remote device; update LDM in order to allow to allow unrestricted access by mobile devices with service subscription (see Hall para[0046]) and reflect highly dynamic environmental conditions (See Hall para[0002]).
Regarding claim 15, Graefe and Hall remain applied as claim 13. Graefe teaches wherein the navigation task is a deep learning (DL) task. (See Graefe para[0056] a deep learning object detection technique (e.g., fully convolutional neural network (FCNN), region proposal convolution neural network (R-CNN).
Regarding claim 16, Graefe teaches wherein the first wireless channel is a dedicated short-range communications (DSRC) channel (See Graefe para[0003] Dedicated short-range communications (DSRC) and/or cellular vehicle-to-everything (C-V2X) protocols provide communications between vehicles and the roadside infrastructure. ) and the second wireless channel is a point-to-point millimeter wave (mmWave) channel ( see Graefe para[0097] The response messages can be sent on a separate point-to-point connection using a suitable wireless/V2X communication technology, which can include broadcast or multicast transmissions ) or wherein the second wireless channel is the DSRC channel and the first wireless channel is the mmWave channel. (See Graefe mentioned communication system can be mmWave or DSRC).
Regarding claim 17, Graefe and Hall remain applied as claim 13. Graefe teaches further comprising updating the LDM based on communications from the remote devices via the DSRC channel. (See Graefe para[0102] Following the handshake procedure 500, the combination of location information from the object 64 and the consecutive object tracking using sensor data from the sensors 262 may be used to update the environmental map 324. , para[0178] Communications in the cellular network 1060 may be enhanced by systems that offload data, extend communications to more remote devices, or both).
Regarding claim 18, Graefe and Hall remain applied as claim 13. Graefe teaches further comprising communicating the navigation task to the destination via the mmWave channel, the destination being one of a remote vehicle and a road side unit (RSU). (See Graefe para[0034] The term “hop” may refer to an individual node or intermediary device through which data packets traverse a path between a source device and a destination device, para[0055] The main system controller 302 is configured to manage the RTMS 300, such as by scheduling tasks for execution, managing memory/storage resource allocations, routing inputs/outputs to/from various entities, and the like. The main system controller 302 may schedule tasks according to a suitable scheduling algorithm).
Regarding claim 19, Graefe and Hall remain applied as claim 13. Graefe teaches further comprising:
generating a first feature set representing the set of resources; (See Graefe para[0049] a set of requirements (e.g., latency, processing resources, storage resources, network resources, location, network capability, security conditions/capabilities, etc.)
generating a second feature set representing the navigation task; (See Graefe para [0055] The main system controller 302 is configured to manage the RTMS 300, such as by scheduling tasks for execution, managing memory/storage resource allocations, routing inputs/outputs to/from various entities, and the like. The main system controller 302 may schedule tasks according to a suitable scheduling algorithm, and/or may implement a suitable message passing scheme to allocate resources.)
applying the first and second feature sets to a classifier to determine the destination based on an anticipated computational performance and an anticipated communication performance of the set of resources. (see para[0048] The compute-intensive tasks are offloaded to the MEC host 257 since MEC host 257 has higher/greater performance capabilities as compared to the vUE system 201 of the vehicles 64.)
Regarding claim 20, Graefe and Hall remain applied as claim 13. Graefe teaches further comprising:
incorporating a representation of the set of resources and a representation of the navigation task into a mathematical model (See Graefe grid-based environment model, this is one specific type of mathematical model, para[0041] In any of these embodiments, the computing system of the infrastructure equipment 61 a, 61 b calculates a grid-based environment model that is overlaid on top of the observed coverage area 63. The grid-based environment model allows the computing system of the infrastructure equipment 61 a, 61 b to target particular objects 64 in specific grid cells for purposes of requesting data from those targeted objects 64); and
processing the mathematical model to determine the destination. (See Graefe Para[0041] the computing system of the infrastructure equipment 61a, 61b calculates a grid-based environment model that is overlaid on top of the observed coverage area 63. The grid-based environment model allows the computing system of the infrastructure equipment 61a, 61b to target particular objects 64 in specific grid cells for purposes of requesting data from those targeted objects 64).
Regarding claim 21, Graefe and Hall remain applied as claim 13. Graefe teaches wherein at least one of the distinct spectrum bands is an unlicensed spectrum band. (See Graefe para[0043] Additionally, the communication circuitry of the infrastructure equipment 61 may provide a WiFi hotspot (2.4 GHz band), 2.4 GHz is unlicensed spectrum band).
Regarding claim 22, Graefe and Hall remain applied as claim 13. Graefe teaches further comprising controlling movement of the vehicle based on the LDM (See Graefe para[0039] The infrastructure equipment 61a, 61b may also include internal data storage circuitry to store coverage area 63 map geometry and related data, traffic statistics, media, as well as applications/software to sense and control on-going vehicular and pedestrian traffic.)
, the movement including at least one of collision avoidance and self-driving operation (See Graefe para[0034] For example, the vehicles 64, radio access nodes, pedestrian UEs, etc., may collect knowledge of their local environment (e.g., information received from other vehicles or sensor equipment in proximity) to process and share that knowledge in order to provide more intelligent services, such a cooperative collision warning, autonomous driving, and the like. The V2X cooperative awareness mechanisms are similar to the CA services provided by ITS system as discussed previously).
Conclusion
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/NAZIA AFRIN/Examiner, Art Unit 3666
/SCOTT A BROWNE/Supervisory Patent Examiner, Art Unit 3666
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