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 . Claims 79-98 are pending and examined below. This action is in response to the claims filed 9/4/25.
Response to Amendment
Applicant’s remarks, pages 9-10 filed on 9/4/25, asserting support for specification amendments on pages 14-15 of the present application’s specification are not found to be persuasive. While the general teachings of the different features are included in the different portions of the applications/patents incorporated by reference into the present application, the details presented deviate from those portions cited as providing support for the amendments. It is advised for amendments to the specification to only include explicit teachings from other documents incorporated by reference and not to include paraphrasing and restructuring of similar material in order to avoid potential new matter.
Applicant’s arguments, see Applicant Remarks 1.1 filed on 9/4/25, regarding obviousness rejections have been fully considered and are not found persuasive.
Applicant’s remarks, page 11, asserts Santoni only discusses autonomously toggling between levels based on the conditions of the road, for example, or based off of compromised sensors. Both of which are discussed within the reference, but are only exemplary situations in which the autonomous driving level may be adapted.
Santoni overall discloses a system which may “interface with and leverage information and services provided by other computing systems to enhance, enable, or otherwise support the autonomous driving functionality of the device 105” (¶36). The further disclosures of utilizing external computing systems and sensors to enable autonomous driving features which are not natively available to the vehicle, which directly correlates to the claims “level-to-level improvement of automated driving as expressly recited in amended claim 79”.
Applicant’s remarks pages 12-14 recite additional arguments as to why the secondary references do not recite the automation level improvement as claimed, however, none of those references are relied upon to disclose that material.
Therefore, the rejections are maintained and updated to address the newly amended subject matter.
Specification
The amendment filed 9/4/25 is objected to under 35 U.S.C. 132(a) because it introduces new matter into the disclosure. 35 U.S.C. 132(a) states that no amendment shall introduce new matter into the disclosure of the invention. The added material which is not supported by the original disclosure is as follows:
In some embodiments, vehicles operating within the IRT system comprise an autonomous vehicle (AV) control system configured to interface with the IRT infrastructure. The AV control system enables vehicles to receive and execute control instructions from the roadside infrastructure network, thereby extending the capabilities of vehicles with varying levels of automation. The AV control system comprises an RSU communication module that establishes and maintains communication with one or more roadside units (RSUs) in the IRT network. This communication module is configured to receive both information and command instructions from the RSUs. The RSU communication module handles the wireless communication protocols and data exchange between the vehicle and the roadside infrastructure, enabling real-time transmission of control instructions from the IRT system to individual vehicles.
The AV control system may further comprise a vehicle control module configured to execute control commands received through the RSU communication module. The vehicle control module interfaces directly with the vehicle's mechanical and electronic control systems to implement the received instructions. This module translates the high-level control instructions received from the RSUs into specific vehicle control actions, including throttle control, brake control, and steering control. The vehicle-specific control instructions transmitted from the RSUs to individual vehicles through the RSU communication module comprise control parameters for automated driving. These instructions include: (1) lateral and longitudinal position requests at certain times, enabling precise vehicle positioning within lanes and along the roadway;(2) advised speed parameters that optimize traffic flow while maintaining safety; and (3) steering and control information necessary for vehicle maneuvering, including lane changes and navigation through complex roadway geometries.
In some embodiments, the AV control system also comprises a data collection module configured to monitor the operational state of the vehicle. This module gathers vehicle status information including current longitudinal and lateral position, speed, acceleration, heading, and other dynamic parameters. The collected data is transmitted back to the RSUs through the RSU communication module, providing the IRT system with real-time vehicle state information for traffic optimization and control. The communication between the AV control system and the RSUs is bidirectional. While the primary flow of control instructions is from the RSUs to the vehicle, the vehicle also transmits its static information (such as vehicle ID, size, type, and capability information) and dynamic information (such as timestamp, position, speed, and destination information) back to the RSUs. This bidirectional communication enables the IRT system to maintain awareness of all proximate vehicles and to generate optimized, vehicle-specific control instructions based on real-time conditions.
Applicant is required to cancel the new matter in the reply to this Office Action.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim elements that are being interpreted utilizing 35 USC § 112(f) are:
roadside sensing devices, found in Specification ¶79
“the sensing devices comprise a camera, lidar, radar, microphone, motion sensor, and/or sound sensor”
roadside computation devices, found in Specification ¶79
“the computation devices comprise one or more of a central processing unit, a graphics processing unit, signal processor, or other microprocessor”
roadside supporting subsystems, found in Specification ¶80
“supporting subsystems comprise one or more of, e.g., high-resolution map data and/or database, satellite position data and/or satellite positioning receiver (e.g., Global Positioning System, BeiDou Navigation Satellite System, Galileo positioning system, GLONASS (Global Navigation Satellite System), etc.), storage devices, cloud services, cybersecurity devices, and/or power supply devices”
communication devices, found in Specification ¶79
“the communications devices comprise components for communicating over wired and/or wireless communications (e.g., cellular (e.g., 4G, 5G, or other cellular technology)), Dedicated Short Range Communication (DSRC), WiFi (e.g., IEEE 802.11), and/or Bluetooth)”
RSU communication module, only found in objected to Specification amendments of 9/4/25, no structural support found within references incorporated by reference
vehicle control module, only found in objected to Specification amendments of 9/4/25, no structural support found within references incorporated by reference
Computational unit, found in ¶95 and ¶98 with no structural support
Additional Specification support is drawn to the paragraphs discussed within the Remarks submitted 1/17/24.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 79-91, 93, and 95-98 are rejected under 35 U.S.C. 103 as being unpatentable over Santoni et al. (US 2020/0017114), in view of Tarkiainen et al. (US 2020/0064140).
Regarding claim 79, Santoni discloses an independent safety monitoring of automated driving systems including an Intelligent Roadside Toolbox (IRT) system comprising a roadside unit (RSU) network, wherein said RSU comprises (¶18-20 and Fig. 1 – roadside equipment, roadside units and access points corresponding to the recited an Intelligent Roadside Toolbox (IRT) system):
a roadside sensing device configured to receive driving environment data (¶19 - sensor devices may be embedded within the roadway itself (e.g., sensor 160), on roadside or overhead signage (e.g., sensor 165 on sign 125), sensors (e.g., 170, 175) attached to electronic roadside equipment or fixtures (e.g., traffic lights (e.g., 130), electronic road signs, electronic billboards, etc.), dedicated road side units (e.g., 140), among other examples);
a roadside computation device configured to process said driving environment data (¶36 - a cloud-based computing system, road side unit 140, or other computing system may include a machine learning engine supporting either or both model training and inference engine logic. For instance, such external systems may possess higher-end computing resources and more developed or up-to-date machine learning models, allowing these services to provide superior results to what would be generated natively on a vehicle's automated driving system 210);
a roadside supporting subsystem (¶35-36 - A roadside unit 140 or cloud-based system 150 (or other cooperating system, with which a vehicle (e.g., 105) interacts may include all or a portion of the logic illustrated as belonging to an example in-vehicle automated driving system (e.g., 210), along with potentially additional functionality and logic where sensors that may be onboard or external in roadside units may include GPS); and
a communication device configured to communicate with said roadside sensing device, said roadside computation device and said roadside supporting subsystem (¶20 and Fig. 1 – element 155 corresponding to the recited communication devices may be provided within an environment and used to facilitate communication over one or more local or wide area networks (e.g., 155) between cloud-based systems (e.g., 150) and various vehicles (e.g., 105, 110, 115)), wherein
said IRT system manages exchange of information and driving instructions between a vehicle and automated driving information entities and said roadside sensing device, said roadside computation device, and said roadside supporting subsystem (¶20-21, ¶36 and ¶42 - autonomous vehicle system 105 may interface with and leverage information and services including external computing systems may be provided and leveraged, which are hosted in road-side units corresponding to the recited automated driving information entities and said roadside sensing device, said roadside computation device, and said roadside supporting subsystem as may be controlled by one or more system management tools corresponding to the recited IRT, a cloud-based system (e.g., 150) may collect sensor data from a variety of devices in one or more locations and utilize this data to build and/or train machine-learning models which may be used at the cloud-based system (to provide results to various vehicles (e.g., 105, 110, 115) in communication with the cloud-based system 150, or to push to vehicles for use by their in-vehicle systems, among other example implementations, further disclosing computer systems may be distributed or centralized remotely, on board, or otherwise for managing the distribution and exchange of information between systems), thereby
providing a customized, on-demand, and dynamic virtual automated driving service that replaces one or more automated driving tasks for the vehicle, wherein the IRT system is configured to provide automated driving services to a vehicle operating at a first automated driving level, wherein the services improve the automated driving of a vehicle to allow a vehicle to operate at a second automated driving level, wherein the second automated driving level is higher than the first automated driving level (¶20-21, ¶36-37, and ¶97-99 – autonomous vehicles may call upon support infrastructure to supplement both sensing and processing capabilities either not present or not functioning to permit higher levels of autonomous functionalities such as L0-L5 corresponding to the recited providing customized higher level of enhanced customized on demand and dynamic virtual automated driving service permitting driving at a higher level than is present in the vehicle itself corresponding to the recited improving the automated driving of the vehicle allowing the vehicle to operate at a higher automated driving level where the supplemental sensing and processing may include replacing the automated driving task with a different automated driving task);
wherein said vehicle comprises an autonomous vehicle (AV) control system comprising: an RSU communication module communicating with one or more RSU and receiving vehicle-specific control instructions from said one or more RSU; and a vehicle control module controlling said vehicle comprising said AV control system according to said vehicle-specific control instructions, wherein said vehicle-specific control instructions comprise instructions for vehicle longitudinal and lateral position; speed; and steering and control (¶36-38 - external computing systems may be provided and leveraged, which are hosted in road-side units for processing vehicle specific controls corresponding to the recited an RSU communication module communicating with one or more RSU and receiving vehicle-specific control instructions from said one or more RSU where the autonomous functionality provided by external computing systems includes a control and action stage 415 may convert these determinations into actions, through actuators to manipulate driving controls including steering, acceleration, and braking, as well as secondary controls, such as turn signals, sensor cleaners, windshield wipers, headlights, etc);
wherein the customized, on-demand, and dynamic virtual automated driving services comprise dynamic utility management (DUM), transportation behavior prediction and management services and at least one of (¶20-21, ¶36, ¶38, and ¶40 - cloud-based computing system, road side unit 140, or other computing system may include a machine learning engine supporting either or both model training and inference engine logic. For instance, such external systems may possess higher-end computing resources and more developed or up-to-date machine learning models, allowing these services to provide superior results to what would be generated natively on a vehicle's automated driving system 210 where the driving information provides precise localization to a moving vehicle corresponding to the recited customized, on-demand, and dynamic virtual driving service where a planning and decision stage 410 may utilize the sensor data and results of various perception operations to make probabilistic predictions of the roadway(s) ahead and determine a real time path plan based on these predictions. Given that the computing resources and autonomous driving logic used to facilitate machine learning model training and use of such machine learning models may be provided on the in-vehicle computing systems entirely or partially on both the in-vehicle systems and some external systems (e.g., 140, 150), the virtual automated driving services do disclose the recited transportation behavior and prediction services. The element “at least one of” requires only one of the following to be present in order to disclose the elements as claimed, where the computer systems may be distributed or centralized remotely, on board, or otherwise for managing the distribution and exchange of information between systems):
sensing services (¶38 - Data collection, in some instances, may include data filtering and receiving sensor from external sources),
transportation behavior prediction and management services (¶38 - A planning and decision stage 410 may utilize the sensor data and results of various perception operations to make probabilistic predictions of the roadway(s) ahead and determine a real time path plan based on these predictions.),
planning and decision-making services (¶38 - A planning and decision stage 410 may utilize the sensor data and results of various perception operations to make probabilistic predictions of the roadway(s) ahead and determine a real time path plan based on these predictions.),
vehicle control services (¶38 - a control and action stage 415 may convert these determinations into actions, through actuators to manipulate driving controls including steering, acceleration, and braking, as well as secondary controls, such as turn signals, sensor cleaners, windshield wipers, headlights, etc.),
system security and backup services (¶34 - an external driver or system takes over control of the vehicle (e.g., based on an emergency event)),
computing and management services (¶20-21),
vehicle performance optimization services (¶40 - consider driving goals (e.g., system-level or user-customized goals) to deliver certain driving or user comfort expectations (e.g., speed, comfort, traffic avoidance, toll road avoidance, prioritization of scenic routes or routes that keep the vehicle within proximity of certain landmarks or amenities, etc.)), or
information provision services (¶17 - detect obstacles and hazards (e.g., 120), and road conditions (e.g., traffic, road conditions, weather conditions, etc.));
While the exemplary teachings of Santoni does disclose that the security companion system is in the vehicle part of the autonomous driving system 210, Santoni further discloses “computer resources and autonomous driving logic used to facilitate machine learning model training and use of such machine learning models may be provided on the in-vehicle computing systems entirely or partially on both the in-vehicle systems and some external systems (e.g., 140, 150)” (Santoni - ¶20).
From this disclosure of processing capabilities being able to be “entirely or partially on both the in-vehicle systems and some external systems”, it would have been obvious to one of ordinary skill in the art before the effective filing date to have the security companion system which replaces the autonomous driving task for the vehicle to be in an external system in order to support and improve autonomous driving results (Santoni - ¶18).
Santoni does not explicitly disclose optimizing resource use based on computation ability, number of computation unit cost etc. or multilevel planning however Tarkiainen discloses a platooning in a smart city system including transportation prediction and management services at a macroscopic level, mesoscopic level, and microscopic level, planning and decision-making services at a macroscopic level, mesoscopic level, and microscopic level (¶56-57, ¶63 and Fig. 6 – full route planning from an origin to a destination over a road network corresponding to the recited macroscopic level planning, lane organization between the platoon corresponding to the recited mesoscopic level planning, and specific vehicle controls such as speed/gap/etc. corresponding to the recited microscopic planning),
wherein the DUM optimizes the use of resources by connected and automated vehicles (CAVs) at various vehicle intelligence levels by assembling IRT functions provided to CAVs and balancing CAV onboard system costs (¶38-39, ¶57, and ¶63 – platoon formation designed for optimized driving corresponding to the recited optimized use of resources for CAVs where the automation level corresponding to the recited computation ability cost, the number of platooning vehicles corresponding to the recited number of computational units cost, minimizing fuel consumption corresponding to the recited fuel consumption cost, recommended speed corresponding to the recited driver comfort (e.g., acceleration and/or deceleration) cost. While the different focuses of Tarkiainen are not explicitly costs, the utilization of each element as input into platoon formation and control is inventively equivalent to a cost).
The combination of the integrated autonomous vehicle sensor augmentation system of Santoni with the platooning conditions/control assessment features of Tarkiainen fully disclose the elements as claimed.
It would have been obvious to one of ordinary skill in the art before the filing date to have combined the integrated autonomous vehicle sensor augmentation system of Santoni with the platooning conditions/control assessment features of Tarkiainen in order to make traffic management more efficient (Tarkiainen - ¶39).
Regarding claim 80, Santoni further discloses IRT further comprising at least one of a traffic control unit (TCU), traffic control center (TCC), or traffic operations center (TOC) (¶19 – roadside units include roadside equipment or fixtures (e.g., traffic lights (e.g., 130), electronic road signs, electronic billboards, etc.) corresponding to the recited traffic control unit, the element “at least one of” requires only one of the following to be present in order to disclose the elements as claimed).
Regarding claim 81, Santoni further discloses IRT system configured to provide status management services to maintain or change a status of the vehicle, wherein said status comprises at least one of (¶34-36 and Figs. 1-2 - sensors mounted or provided with other roadside elements, such as a roadside unit (e.g., 140), road sign, traffic light, streetlight, etc may generate sensor data including vehicle system status information corresponding to the recited vehicle status management services where the autonomous driving stack of a vehicle utilizing input from roadside sensors/processing corresponding to the recited maintain or change a vehicle status, the element “at least one of” requires only one of the following to be present in order to disclose the elements as claimed):
location, velocity, or acceleration of the vehicle (¶34-36 and Figs. 1-2 - accelerator/throttle controls corresponding to the recited velocity/acceleration );
route of the vehicle (¶38 - planning and decision stage 410 may utilize the sensor data and results of various perception operations to make probabilistic predictions of the roadway(s) ahead and determine a real time path plan based on these predictions);
longitudinal or lateral status of the vehicle (¶35 – GPS positioning); or
ventilation or climate control status of the vehicle (¶32 - utilize sensor data and outputs of other modules within the vehicle's autonomous driving stack to cause driving maneuvers and changes to the vehicle's cabin environment to enhance the experience of passengers within the vehicle based on the observations captured by the sensor data (e.g., 258)).
Regarding claim 82, Santoni further discloses the customized, on- demand, and dynamic virtual automated driving service enhances, completes, or replaces at least one of (¶36 - cloud-based computing system, road side unit 140, or other computing system may include a machine learning engine supporting either or both model training and inference engine logic. For instance, such external systems may possess higher-end computing resources and more developed or up-to-date machine learning models, allowing these services to provide superior results to what would be generated natively on a vehicle's automated driving system 210 where external processing systems performing autonomous vehicle functions corresponding to the recited virtual automated driving service):
sensing services provided by the vehicle with virtual sensing services provided by the IRT system (¶35-38 – sensing stage and sensor data 258 may also (or instead) be generated by sensors that are not integrally coupled to the vehicle, including sensors on other vehicles (e.g., 115) (which may be communicated to the vehicle 105 through vehicle-to-vehicle communications or other techniques), sensors on ground-based or aerial drones 180, sensors of user devices 215 (e.g., a smartphone or wearable) carried by human users inside or outside the vehicle 105, and sensors mounted or provided with other roadside elements, such as a roadside unit (e.g., 140), road sign, traffic light, streetlight, etc);
transportation behavior prediction and management services provided by a vehicle with virtual transportation behavior prediction and management services provided by the IRT system (¶36-40 – perception stage 405 corresponding to the recited transportation behavior prediction and management which may assess the information included in sensor data generated by one or a combination of the vehicle's sensors (or even external (e.g., roadside) sensors) and perform object detection (e.g., to identify potential hazards and road characteristics), classify the objects (e.g., to determine whether they are hazards or not), and track objects (e.g., to determine and predict movement of the objects and ascertain whether or when the objects should impact the driving of the vehicle), transportation behavior prediction and management is being interpreted utilizing BRI to be object detection/tracking/prediction);
planning and decision-making services provided by a vehicle with virtual planning and decision-making services provided by the IRT system (¶36-38 - a planning and decision stage 410 which may be performed processed externally corresponding to the recited virtual planning and decision making); or
vehicle control services provided by a vehicle with virtual vehicle control services provided by the IRT system (¶36-38 - a control and action phase 415 which may be performed processed externally corresponding to the recited virtual control services).
Regarding claim 83, Santoni further discloses configured and managed as an open platform comprising devices and subsystems owned or operated by different entities (¶18-19 – incorporation of data sources including public infrastructure such as traffic lights as well as personal computing devices corresponding to the recited open platform based on devices and subsystems owned or operated by different entities); or
as an open platform comprising physical or logical devices and subsystems that are shared by different entities (¶18-19 – incorporation of data sources including public infrastructure such as traffic lights as well as personal computing devices corresponding to the recited open platform based on devices and subsystems shared by different entities).
Regarding claim 84, Santoni further discloses a roadside unit (RSU) network comprises at least one of: said roadside sensing devices, said roadside computation devices, said roadside supporting subsystems (¶18-20 and ¶35-36 – roadside equipment, roadside units and access points including external sensors such as which may include GPS units corresponding to the recited roadside sensing devices and roadside supporting subsystems and a cloud-based computing system, road side unit 140, or other computing system corresponding to the recited roadside computation devices may include a machine learning engine supporting either or both model training and inference engine logic. For instance, such external systems may possess higher-end computing resources and more developed or up-to-date machine learning models, allowing these services to provide superior results to what would be generated natively on a vehicle's automated driving system 210); or
said communication devices (¶20 and Fig. 1 – element 155 corresponding to the recited communication devices may be provided within an environment and used to facilitate communication over one or more local or wide area networks (e.g., 155) between cloud-based systems (e.g., 150) and various vehicles (e.g., 105, 110, 115)).
Regarding claim 85, Santoni further discloses said roadside supporting subsystems comprise at least one of (¶18-20 and ¶35-36 – roadside equipment, roadside units and access points including external sensors such as which may include GPS units corresponding to the recited roadside supporting subsystems, the element “at least one of” requires only one of the following to be present in order to disclose the elements as claimed)
a map service, a satellite positioning service (¶35-36 – GPS corresponding to the recited map and satellite positioning service),
a data storage service (¶83 - Code 1204, which may be one or more instructions to be executed by processor 1200, may be stored in memory 1202, or may be stored in software, hardware, firmware, or any suitable combination thereof, or in any other internal or external component, device, element, or object where appropriate and based on particular needs where stored code corresponding to the recited data storage service),
a cloud service (¶20 - a connected vehicle may communicate with road-side units, edge systems, or cloud-based devices (e.g., 140) local to a particular segment of roadway, with such devices (e.g., 140) capable of providing data (e.g., sensor data aggregated from local sensors (e.g., 160, 165, 170, 175, 180) or data reported from sensors of other vehicles)),
real-time wired communication (¶36-38 – real time path planning within a processor inherently corresponding to the recited wired communication given that within the processor communications are localized through wired connections),
real-time wireless communication (¶18-19 and ¶36-38 - real time path planning through wireless communication with external processing corresponding to the recited real-time wireless communication),
a power supply network, or
a cyber safety and security system (¶50 - safety companion subsystem as part of the automated driving system which may be processed externally).
Regarding claim 86, Santoni further discloses said virtual automated driving service is provided to the vehicle to: enhance, complete, or replace one or more automated driving tasks of said vehicle operating at a first automated driving level; or operate said vehicle at a second automated driving level, wherein said second automated driving level is higher than said first automated driving level (¶36 and ¶64 - some autonomous driving features (including some of the example solutions discussed herein) may be enabled through services, computing logic, machine learning models, data, or other resources of computing systems external to a vehicle. an autonomous vehicle system 105 may interface with and leverage information and services provided by other computing systems to enhance, enable, or otherwise support the autonomous driving functionality of the device 105 corresponding to the recited enhance, complete, or replace one or more automated driving tasks of said vehicle operating at a first automated driving level where the systems may be utilized to satisfy driving automation levels L3, L4, and L5 corresponding to the recited higher level of automated driving capable. the element “at least one of” requires only one of the following to be present in order to disclose the elements as claimed).
Regarding claim 87, Santoni further discloses the automated driving functions and abilities of the vehicle are not sufficient to perform necessary, appropriate, or required automated driving tasks of said vehicle; and said virtual automated driving service replaces one or more automated driving functions and abilities of said vehicle (¶36 – processing performed externally in order to allow higher levels of autonomous driving corresponding to the recited autonomous driving levels of the vehicle are not sufficient where the virtual services replace the automated driving functions and abilities of said vehicle).
Regarding claim 88, Santoni further discloses IRT configured to produce sensing data, integrate sensing data, or manage sensing data sharing between said IRT system and the vehicle to improve a function of the vehicle based on a target system intelligence level (¶35-38 – automated driving sensing/integration/processing performed externally at a roadside unit to increase/improve autonomous driving functions based on driver selected level of autonomy for a given trip corresponding to the recited target intelligence level).
Regarding claim 89, Santoni further discloses to predict movements of a plurality of vehicles and to predict traffic for a transportation network (¶36-40 – perception stage 405 corresponding to the recited prediction of vehicle movements and traffic which may assess the information included in sensor data generated by one or a combination of the vehicle's sensors (or even external (e.g., roadside) sensors) and perform object detection (e.g., to identify potential hazards and road characteristics), classify the objects (e.g., to determine whether they are hazards or not), and track objects (e.g., to determine and predict movement of the objects and ascertain whether or when the objects should impact the driving of the vehicle)).
Regarding claim 90, Santoni further discloses to generate and send route planning and decision making information or commands to an onboard unit (OBU) or a vehicle control unit (VCU) of the vehicle, wherein generating route planning information comprises generating or adjusting a globally optimized route using predicted movements of a plurality of vehicles and predicted traffic; and wherein said route planning and decision making information or commands are specific for said vehicle (¶17 and ¶35-38 - a planning and decision stage 410 which may be performed processed externally corresponding to the recited generate and send route planning and decision making information where a control and action phase 415 creates commands to send to the one or more driving controls corresponding to the recited VCU to perform the driving actions, where path planning and transmission to driving control actuators inherently creates and sends route planning information to vehicles individually where the autonomous driving and object avoidance of the sensing and perception system is utilized to adjust control and guidance of the vehicle accordingly corresponding to the recited adjustment of the globally optimized route utilizing predicted vehicle movements and traffic).
Regarding claim 91, Santoni further discloses said route planning and decision making information is used to provide a driving behavior plan for a transportation network using said globally optimized route and predicted movements of a plurality of vehicles and predicted traffic, wherein said driving behavior plan is used to provide specific and instantaneous control instructions for a plurality of vehicles that are transmitted to an OBU or a VCU of each vehicle of the plurality of vehicles (¶35-38 – sensing and perception system is utilized to adjust control and guidance of the vehicle accordingly corresponding to the recited provide specific and instantaneous control instructions for individual vehicles using the globally optimized route utilizing predicted vehicle movements and traffic in the environment around the vehicle where external processing providing vehicle controls to the vehicle driving controls corresponding to the recited transmitting to the VCU of an individual vehicle. Given own Specification ¶14 and Fig. 2 defines transportation network as including microscopic information including longitudinal and lateral control instruction, the object tracking and path planning system of Santoni discloses the plan for a transportation network).
Regarding claim 93, Santoni further discloses to optimize a plurality of optimization goals comprising one or more of driver comfort, energy consumption, travel time, user route preferences, computing resources, safety, or performance of the vehicle (¶40 - Driving behavior planning logic (e.g., 650) may also be provided in some implementations to consider driving goals (e.g., system-level or user-customized goals) corresponding to the recited optimization goals to deliver certain driving or user comfort expectations (e.g., speed, comfort, traffic avoidance, toll road avoidance, prioritization of scenic routes or routes that keep the vehicle within proximity of certain landmarks or amenities, etc.), the element “one or more of” requires only one of the following to be present in order to disclose the elements as claimed).
Regarding claim 95, Santoni further discloses to provide customized software or hardware configurations based on user preferences or service provider requests to improve at least one of the automated driving level, safety, or stability of the vehicle (¶35-38 – user can select level of autonomy corresponding to the recited user preferences of automated driving level for which external processing may provide autonomous driving processing corresponding to the recited providing customized software to improve the automated driving level/safety of individual vehicles, the element “at least one of” requires only one of the following to be present in order to disclose the elements as claimed).
Regarding claim 96, Santoni further discloses IRT configured to manage and control resources and services provided by the IRT according to an optimization strategy, wherein said resources and services comprise at least one of (¶36-37 - such external systems may possess higher-end computing resources and more developed or up-to-date machine learning models, allowing these services to provide superior results to what would be generated natively on a vehicle's automated driving system 210. For instance, an automated driving system 210 may rely on the machine learning training, machine learning inference, and/or machine learning models provided through a cloud-based service for certain tasks and handling certain scenarios based on driver elected autonomy level corresponding to the recited optimization strategy, the element “at least one of” requires only one of the following to be present in order to disclose the elements as claimed)
power resources and services;
computing resources and services (¶36 - such external systems may possess higher-end computing resources);
communications resources and services (¶36 – external processing systems must include communication resources to transmit results to the vehicle); or
intelligence resources and services provided by the IRT according to an optimization strategy (¶36 - more developed or up-to-date machine learning models corresponding to the recited intelligence resources).
Regarding claim 97, Santoni further discloses a distributed driving system (DDS) comprising (¶36 and Fig. 1 – external computing systems include a plurality of external sources corresponding to the recited distributed driving system):
one or more connected and automated vehicles (CAV) (¶36 – external computing systems can include other (e.g., higher-level) vehicles (e.g., 115));
said IRT system (¶42 - one or more system management tools (e.g., 670)); and
communications media for transmitting data between said CAV and said IRT system (¶20 and Fig. 1 - Access points (e.g., 145), such as cell-phone towers, road-side units, network access points mounted to various roadway infrastructure, access points provided by neighboring vehicles or buildings, and other access points, may be provided within an environment and used to facilitate communication over one or more local or wide area networks (e.g., 155) between cloud-based systems (e.g., 150) and various vehicles (e.g., 105, 110, 115)), wherein
said DDS is configured to provide on-demand and dynamic virtual automated driving services of the IRT system to individual CAV of the one or more CAV (¶35-40 – utilization of external sources to provide autonomous driving sensing/perception/planning/control in real time to vehicles corresponding to the recited provide on-demand and dynamic virtual automated driving services of the IRT system to individual CAV).
Regarding claim 98, Santoni further discloses an automated driving services community based on an IRT system in which the automated driving services community provides a user interface for automated driving applications (¶36-39, ¶42 and Fig. 6 – inputs to the perception engine provided from sources external to the vehicle (e.g., through a network facilitating vehicle-to-everything (V2X) communications (e.g., 635)) corresponding to the recited automated driving services community or from the user of the vehicle (e.g., driving goals (e.g., 640) or other inputs provided by passengers within the vehicle (e.g., through human-machine interfaces (e.g., 230)) corresponding to the recited user interface for assessing/processing of automated driving applications utilizing one or more system management tools corresponding to the recited IRT).
Claim 92 is rejected under 35 U.S.C. 103 as being unpatentable over Santoni et al. (US 2020/0017114), as applied to claim 79, in view of Tarkiainen et al. (US 2020/0064140), further in view of Kelkar et al. (US 2020/0042017).
Regarding claim 92, Santoni does not disclose a fee collection however Kelkar discloses a system of shared autonomy including a fee collection component or subsystem configured to collect payments from users of said IRT system and to manage user access to services provided by said IRT system based on a subscription or fee-for-service payment system (¶155-159 – pecuniary arrangement may be pay per time, or pay per distance corresponding to the recited fee-for-service payment system as well as a potential subscription service to pay for increased autonomous driving capabilities).
It would have been obvious to one of ordinary skill in the art before the filing date to have combined the autonomous driving improvement system of Santoni in view of Tarkiainen with the shared autonomy pecuniary arrangement of Kelkar in order to provide an incentive to share higher levels of autonomy.
Claim 94 is rejected under 35 U.S.C. 103 as being unpatentable over Santoni et al. (US 2020/0017114), as applied to claim 93, in view of Tarkiainen et al. (US 2020/0064140), further in view of Cardoso (US 2011/0250015).
Regarding claim 94, Santoni does not disclose power distribution/allocation however Cardoso discloses a traffic infrastructure system including to allocate and distribute power to one or more components of said IRT system or to a connected automated vehicle highway (CAVH) system to optimize said optimization goals (¶35 – optimization of power distribution to highway infrastructure for optimizing traffic assistance corresponding to the recited distribution of power to optimize optimization goals. The combination of the roadside autonomous assistance units of Santoni in view of Tarkiainen with the highway infrastructure power optimization system of Cardoso fully discloses the elements as claimed).
It would have been obvious to one of ordinary skill in the art before the filing date to have combined the roadside autonomous assistance units of Santoni in view of Tarkiainen with the highway infrastructure power optimization system of Cardoso in order to adapt renewable energy sources to infrastructure without sacrificing impact on traffic signaling potentially resulting in accidents (Cardoso - ¶1-2).
Additional References Cited
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tao et al. (US 2021/0024095) discloses a system for controlling an autonomous vehicle including utilizing external assist sensing to enable non-autonomous vehicles or vehicles with weak autonomous driving capabilities to simply and cost-effectively improve the autonomous driving capabilities (¶21).
Frazzoli et al. (US 2021/0163021) discloses a redundancy system for autonomus veihcles including information collected or generated by the first autonomous vehicle can be enriched or supplemented with data originating at other autonomous vehicles to improve its overall operation (e.g., plan a more efficient route of travel, identify an object in the surrounding environment more accurately, evaluate a condition of a road more accurately, interpret signage in the environment of the autonomous vehicle more accurately, etc.). (¶450).
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 extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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