DETAILED ACTION
Status of the Application
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 the Claims
This action is in response to the applicant’s filing on June 09, 2025. Claims 1 – 20 are pending and examined below.
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words. The form and legal phraseology often used in patent claims, such as "means" and "said," should be avoided. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, "The disclosure concerns," "The disclosure defined by this invention," "The disclosure describes," etc.
4. The disclosure is objected to because of the following informalities: (FP 7.29)
On page 11, in paragraph [0003], line 3, “In some embodiments, the the redundancy verification module eliminates” should read “In some embodiments, the redundancy verification module eliminates”. Emphasis added.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. § 102 and 103 (or as subject to pre-AIA 35 U.S.C. § 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. § 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 – 20 are rejected under 35 U.S.C. § 103 as being unpatentable over U.S. Patent Application Publication No. US 2019/0051159 A1 to WANG et al. (herein after "Wang") in view of U.S. Patent Application Publication No. US 2020/0010077 A1 to CORMACK et al. (herein after "Cormack").
(Note: Claim language is in bold typeface, and the Examiner’s comments and cited passages from the prior art reference(s) are in normal typeface.)
As to Claim 1,
Wang’s cooperative autonomous driving for traffic congestion avoidance discloses a vehicle intelligent unit (VIU) (see Fig. 3 ~ illustrates a cooperative autonomous driving system,
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see Table 1 ~ outlines speed negotiation formulas which the cooperative autonomous driving system uses to perform collision avoidance in deadlock (traffic congestion) scenarios,
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see ¶0029 ~ cooperative autonomous driving for traffic congestion avoidance through vehicle-to-vehicle communication, ¶0039 ~ Figs. 11A-11F illustrates another example scenario in which a deadlock condition is resolved through cooperative autonomous driving, and ¶0115 ~ "Through speed negotiation, the front autonomous vehicle may be guaranteed to be able to change lanes into the target lane after a short period of time") comprising:
a collaborative decision-making module (see Fig. 3 ~ process method step 308 ~ vehicle collaborative coordination to resolve traffic deadlock / congestion, Fig. 7 ~ outlines a process flow chart that further defines process method steps of collaborative decision making between vehicle groups to resolve traffic deadlock conditions, and
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see ¶0054 ~ "in order to avoid the traffic congestion caused by the deadlock condition, the systems described herein may implement a cooperative autonomous driving strategy called Altruistic Cooperative Driving (ACD)… to detect and resolve congestion conditions collaboratively" and ¶0059); and
an intelligent control instruction / auxiliary module (see Fig. 2 ~ illustrates a block control schematic of a cooperative driving system 210), wherein:
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As shown above, Wang’s cooperative autonomous driving system for traffic congestion avoidance teaches an autonomous vehicle intelligent unit (VIU) with his cooperative autonomous driving system which mitigates and/or avoids deadlock traffic conditions. (See Fig. 3, ¶0029, ¶0039, and ¶0115; Wang). Wang stops short in teaching though, the essential sensing and perception fusion module needed to inform elevation of the VIU through autonomy levels.
Therefore, Cormack’s proactive vehicle safety system is then relied upon to disclose a sensing and perception fusion module (see Fig. 2 ~ illustrates an autonomous driving system 210 comprising a sensor fusion module 236
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wherein a process flow chart Fig. 10 outlines process method steps for sensing and perception operations through a sensor fusion 1012, Fig. 10, and ¶0068 ~ "sensor processing module 1011 that receives and processes sensor data from a plurality of sensors... the sensor processing module 1011... then passed to the sensor fusion module 1012); the VIU is configured to elevate a vehicle intelligence level and wherein an intelligence level of a vehicle having intelligence level 1 is elevated to intelligence level 2, 3, 4, or 5;the collaborative decision-making module generates decisions that support longitudinal and/or lateral vehicle control to provide partial automated driving for a vehicle of intelligence level 1 (see FIG . 4 ~ illustrates a block diagram of autonomous driving example levels ¶0018. and ¶0050 ~ autonomy levels within autonomous vehicles' "operational design domain" for "safety-critical driving" operations; respective to lateral and longitudinal vehicle control as a function of autonomous collision avoidance as taught in Figs. 6, 9, and ¶0064);
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the collaborative decision-making module provides trajectory planning decisions and detailed driving decisions (see Figs. 4 - 5, ¶0018. and ¶0050 - ¶0051); and
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transmits driver override decisions using information for a vehicle of intelligence level 2 (see Figs. 4 - 5, ¶0018, ¶0050 - ¶0051, and ¶0054 ~ "override driver inputs... to an autonomous vehicle... In parallel with another high- performance computer system… focused on identifying safest, minimal loss/ damage strategies for that vehicle");
the collaborative decision-making module collaborates with external systems to generate decisions for a vehicle of intelligence level 3;
the collaborative decision-making module generates decisions in collaboration with the external systems to address long-tail scenarios for a vehicle of intelligence level 4 (see Figs. 4 - 5, ¶0018. ¶0037 ~ external computing systems may be provided and leveraged , which are hosted in road - side units or fog based edge devices (e.g.,140 ) , other ( e.g. , higher - level ) vehicles (e.g.,115 ) , and cloud - based systems 150 (e.g., accessible through various network access points (e.g.,145)) . A roadside unit 140 or cloud - based system 150... for... an autonomous driving system 210... rely on the machine learning training... and/or models... for certain tasks and handling certain scenarios; ¶0050 - ¶0051, and ¶0071, thereby teaching a combination of event scenarios which could occur during the course of autonomous driving vehicle subsequently prompting the ADV to adjust autonomy driving levels while integrating external based data to address long-tail scenarios); and/or
the collaborative decision-making module improves predictive decisions and trajectory planning based on perception results for a vehicle of intelligence level 5 (see Fig. 5 and ¶0051 ~ "localization , performed using one or more machine learning models . A planning and decision stage 510 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"); and
wherein the sensing and perception fusion module fuses sensing and perception information provided by the vehicle subsystem and sensing and perception information provided by external systems to provide fused sensing and perception information. (See Fig. 2, 10, ¶0051 ~ "During a sensing and perception stage 505 data is generated… collected for use by the autonomous driving system... include data filtering and receiving sensor from external sources... include sensor fusion operations and object recognition and other perception tasks, and ¶0068 ~ sensor processing module 1011 outputs sensing data to sensing module 236 and performs sensing and perception fusion).
As shown above, Wang’s cooperative autonomous driving system for traffic congestion avoidance teaches an autonomous vehicle intelligent unit (VIU) with his cooperative autonomous driving system which mitigates and/or avoids deadlock traffic conditions. (See Fig. 3, ¶0029, ¶0039, and ¶0115; Wang).
Cormack’s proactive vehicle safety system
As a result, It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide Wang’s cooperative autonomous driving system for traffic congestion avoidance with the sensing and perception fusion module, as taught by Cormack, where the resultant combination would successfully provide sensing and perception data extracted and filtered from external sources for spatial mapping, thereby enabling benefits, including but not limited to: reliable and enhanced “real world” vision systems for navigating complex traffic deadlock / congestion environments.
environments; enhancing real world vision for autonomous driving vehicles.
As to Claim 2,
Wang/Cormack discloses the VIU of claim 1,
wherein elevating the vehicle intelligence level comprises integrating data from a cloud system. (See Figs. 4 - 5, ¶0018. ¶0020, and ¶0037; Cormack).
As to Claim 3,
Wang/Cormack discloses the VIU of claim 2,
wherein the data includes
high-definition map information, traffic information, driving information from surrounding vehicles, route planning information, and/or driving decision instructions. (See Fig. 4; Cormack ~ illustrates a block diagram of autonomous driving example levels ¶0018; Cormack ~ "autonomous driving stacks may allow vehicles to self - control... detect obstacles and hazards (e.g., 120), and road conditions (e.g., traffic, road conditions , weather conditions, etc.), and adjust control and guidance of the vehicle", ¶0050; Cormack ~ autonomy levels within autonomous vehicles”, and ¶0051; Cormack ~ route and path planning).
As to Claim 4,
Wang/Cormack discloses the VIU of claim 2,
wherein the collaborative decision-making module generates decisions that support longitudinal and/or lateral vehicle control to provide partial automated driving for the vehicle of intelligence level 1. (See FIG. 4; Cormack ~ illustrates a block diagram of autonomous driving example levels ¶0018. and ¶0050; Cormack ~ autonomy levels within autonomous vehicles' "operational design domain" for "safety-critical driving" operations; respective to lateral and longitudinal vehicle control as a function of autonomous collision avoidance as taught in Figs. 6, 9, and ¶0064; Cormack).
As to Claim 5,
Wang/Cormack discloses the VIU of claim 4,
wherein the collaborative decision-making module is assisted by the sensing and perception fusion module,
which processes the data from the cloud system for elevating the vehicle intelligence level. (See Figs. 4 - 5, ¶0018. ¶0037, ¶0050 - ¶0051, and ¶0071; Cormack).
As to Claim 6,
Wang/Cormack discloses the VIU of claim 5,
wherein in the event of an interruption in the connection between the VIU and the cloud system, the VIU is configured to assume control of the vehicle. (See Figs. 4 - 5, ¶0018. ¶0037, ¶0050 - ¶0051, and ¶0071; Cormack).
As to Claim 7,
Wang/Cormack discloses the VIU of claim 1,
wherein the intelligence level of the vehicle having intelligence level 1 is elevated to intelligence level 2, 3, 4, or 5 by the sensing and perception fusion module and the collaborative decision-making module providing
additional perception functions and making driving decisions for vehicle longitudinal and lateral control. (See Fig. 4; Cormack ~ illustrates a block diagram of autonomous driving example levels ¶0018, and ¶0050; Cormack ~ autonomy levels within autonomous vehicles' "operational design domain" for "safety-critical driving" operations; respective to lateral and longitudinal vehicle control as a function of autonomous collision avoidance as taught in Figs. 6, 9, and ¶0064; Cormack).
As to Claim 8,
Wang/Cormack discloses the VIU of claim 1,
wherein the collaborative decision-making module
receives resources from a Collaborative Automated Driving System (CADS) (see Fig. 3, ¶0029, ¶0039, and ¶0115; Wang) to
reduce long- tail risks (see Figs. 4 - 5, ¶0018. ¶0037, ¶0050 - ¶0051, and ¶0071; Cormack) and
extend an Operational Design Domain (ODD). (See Fig. 4; Cormack ~ illustrates a block diagram of autonomous driving example levels ¶0018, and ¶0050; Cormack ~ autonomy levels within autonomous vehicles' "operational design domain" for "safety-critical driving" operations).
As to Claim 9,
Wang discloses a vehicle intelligent unit (VIU) (see Fig. 3 ~ illustrates a cooperative autonomous driving system, Table 1 ~ outlines speed negotiation formulas which the cooperative autonomous driving system uses to perform collision avoidance in deadlock (traffic congestion) scenarios, ¶0029 ~ cooperative autonomous driving for traffic congestion avoidance through vehicle-to-vehicle communication, ¶0039 ~ Figs. 11A-11F illustrates another example scenario in which a deadlock condition is resolved through cooperative autonomous driving, ¶0115 ~ "Through speed negotiation, the front autonomous vehicle may be guaranteed to be able to change lanes into the target lane after a short period of time") comprising:
a collaborative decision-making module (see Fig. 3 ~ process method step 308 ~ vehicle collaborative coordination to resolve traffic deadlock / congestion, Fig. 7 ~ outlines a process flow chart that further defines process method steps of collaborative decision making between vehicle groups to resolve traffic deadlock conditions, and ¶0054 ~ "in order to avoid the traffic congestion caused by the deadlock condition, the systems described herein may implement a cooperative autonomous driving strategy called Altruistic Cooperative Driving (ACD)… to detect and resolve congestion conditions collaboratively" and ¶0059); and
an intelligent control instruction / auxiliary module (see Fig. 2 ~ illustrates a block control schematic of a cooperative driving system 210).
As shown above, Wang’s cooperative autonomous driving system for traffic congestion avoidance teaches an autonomous vehicle intelligent unit (VIU) with his cooperative autonomous driving system which mitigates and/or avoids deadlock traffic conditions. (See Fig. 3, ¶0029, ¶0039, and ¶0115; Wang). Wang stops short in teaching though, the essential sensing and perception fusion module needed to inform elevation of the VIU through autonomy levels.
However, Cormack’s proactive vehicle safety system discloses a sensing and perception fusion module (see Fig. 2 ~ illustrates an autonomous driving system 210 comprising a sensor fusion module 236 wherein a process flow chart Fig. 10 outlines process method steps for sensing and perception operations through a sensor fusion 1012, Fig. 10, and ¶0068 ~ "sensor processing module 1011 that receives and processes sensor data from a plurality of sensors... the sensor processing module 1011... then passed to the sensor fusion module 1012 );
the VIU is configured to elevate a vehicle intelligence level and wherein an intelligence level of a vehicle having intelligence level 2 is elevated to intelligence level 3, 4, or 5 (see FIG . 4 ~ illustrates a block diagram of autonomous driving example levels ¶0018. and ¶0050 ~ autonomy levels within autonomous vehicles' "operational design domain" for "safety-critical driving" operations);
the collaborative decision-making module provides trajectory planning decisions and detailed driving decisions; and transmits driver override decisions using information for a vehicle of intelligence level 2 (see Figs. 4 - 5, ¶0018, ¶0050 - ¶0051, and ¶0054 ~ "override driver inputs... to an autonomous vehicle... In parallel with another high- performance computer system… focused on identifying safest, minimal loss/ damage strategies for that vehicle");
the collaborative decision-making module collaborates with external systems to generate decisions for a vehicle of intelligence level 3 (see Figs. 4 - 5, ¶0018. and ¶0050 - ¶0051);
the collaborative decision-making module generates decisions in collaboration with the external systems to address long-tail scenarios for a vehicle of intelligence level 4 (see Figs. 4 - 5, ¶0018. ¶0037 ~ external computing systems may be provided and leveraged , which are hosted in road - side units or fog based edge devices (e.g.,140 ) , other ( e.g. , higher - level ) vehicles (e.g.,115 ) , and cloud - based systems 150 (e.g., accessible through various network access points (e.g.,145)) . A roadside unit 140 or cloud - based system 150... for... an autonomous driving system 210... rely on the machine learning training... and/or models... for certain tasks and handling certain scenarios; ¶0050 - ¶0051, and ¶0071, thereby teaching a combination of event scenarios which could occur during the course of autonomous driving vehicle subsequently prompting the ADV to adjust autonomy driving levels while integrating external based data to address long-tail scenarios); and/or
the collaborative decision-making module improves predictive decisions and trajectory planning based on perception results for a vehicle of intelligence level 5 (see Fig. 5 and ¶0051 ~ "localization , performed using one or more machine learning models . A planning and decision stage 510 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"); and
wherein the sensing and perception fusion module fuses sensing and perception information provided by the vehicle subsystem and sensing and perception information provided by external systems to provide fused sensing and perception information. (See Fig. 2, 10, ¶0051 ~ "During a sensing and perception stage 505 data is generated… collected for use by the autonomous driving system... include data filtering and receiving sensor from external sources... include sensor fusion operations and object recognition and other perception tasks, and ¶0068 ~ sensor processing module 1011 outputs sensing data to sensing module 236 and performs sensing and perception fusion).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide Wang’s cooperative autonomous driving system for traffic congestion avoidance with the sensing and perception fusion module, as taught by Cormack, where the resultant combination would successfully provide sensing and perception data extracted and filtered from external sources for spatial mapping, thereby enabling benefits, including but not limited to: reliable and enhanced “real world” vision systems for navigating complex traffic deadlock / congestion environments.
As to Claim 10,
Wang/Cormack discloses the VIU of claim 9,
wherein elevating the vehicle intelligence level comprises integrating data from a cloud system. (See Figs. 4 - 5, ¶0018. ¶0037, ¶0050 - ¶0051, and ¶0071; Cormack).
As to Claim 11,
Wang/Cormack discloses the VIU of claim 10,
wherein the collaborative decision-making module is assisted by the sensing and perception fusion module,
which processes data from the cloud system for elevating the vehicle intelligence level. (See Figs. 4 - 5, ¶0018. ¶0037, ¶0050 - ¶0051, and ¶0071; Cormack).
As to Claim 12,
Wang/Cormack discloses the VIU of claim 10,
wherein in the event of an interruption in the connection between the VIU and the cloud system, the VIU is configured to assume control of the vehicle. (See Figs. 4 - 5, ¶0018. ¶0037, ¶0050 - ¶0051, and ¶0071; Cormack).
As to Claim 13,
Wang/Cormack discloses the VIU of claim 9,
wherein the intelligence level of the vehicle having intelligence level 2 is elevated to intelligence level 3, 4, or 5 by the sensing and perception fusion module and the collaborative decision-making module
providing supplemental sensing information for trajectory planning and detailed driving decisions. (See Fig. 4; Cormack ~ illustrates a block diagram of autonomous driving example levels ¶0018; Cormack ~ "autonomous driving stacks may allow vehicles to self - control... detect obstacles and hazards (e.g., 120), and road conditions (e.g., traffic, road conditions , weather conditions, etc.), and adjust control and guidance of the vehicle", ¶0050; Cormack ~ autonomy levels within autonomous vehicles”, and ¶0051; Cormack ~ route and path planning).
As to Claim 14,
Wang/Cormack discloses the VIU of claim 9,
wherein the collaborative decision-making module receives resources from a Collaborative Automated Driving System (CADS) (see Fig. 3, ¶0029, ¶0039, and ¶0115; Wang) to
reduce long-tail risks (see Figs. 4 - 5, ¶0018. ¶0037, ¶0050 - ¶0051, and ¶0071; Cormack) and
extend an Operational Design Domain (ODD). (See Fig. 4; Cormack ~ illustrates a block diagram of autonomous driving example levels ¶0018, and ¶0050; Cormack ~ autonomy levels within autonomous vehicles' "operational design domain" for "safety-critical driving" operations).
As to Claim 15,
Wang discloses a vehicle intelligent unit (VIU) (see Fig. 3 ~ illustrates a cooperative autonomous driving system, Table 1 ~ outlines speed negotiation formulas which the cooperative autonomous driving system uses to perform collision avoidance in deadlock (traffic congestion) scenarios, ¶0029 ~ cooperative autonomous driving for traffic congestion avoidance through vehicle-to-vehicle communication, ¶0039 ~ Figs. 11A-11F illustrates another example scenario in which a deadlock condition is resolved through cooperative autonomous driving, ¶0115 ~ "Through speed negotiation, the front autonomous vehicle may be guaranteed to be able to change lanes into the target lane after a short period of time") comprising:
a collaborative decision-making module (see Fig. 3 ~ process method step 308 ~ vehicle collaborative coordination to resolve traffic deadlock / congestion, Fig. 7 ~ outlines a process flow chart that further defines process method steps of collaborative decision making between vehicle groups to resolve traffic deadlock conditions, and ¶0054 ~ "in order to avoid the traffic congestion caused by the deadlock condition, the systems described herein may implement a cooperative autonomous driving strategy called Altruistic Cooperative Driving (ACD)… to detect and resolve congestion conditions collaboratively" and ¶0059); and
an intelligent control instruction / auxiliary module (see Fig. 2 ~ illustrates a block control schematic of a cooperative driving system 210).
As shown above, Wang’s cooperative autonomous driving system for traffic congestion avoidance teaches an autonomous vehicle intelligent unit (VIU) with his cooperative autonomous driving system which mitigates and/or avoids deadlock traffic conditions. (See Fig. 3, ¶0029, ¶0039, and ¶0115; Wang). Wang stops short in teaching though, the essential sensing and perception fusion module needed to inform elevation of the VIU through autonomy levels.
However, Cormack’s proactive vehicle safety system discloses a sensing and perception fusion module (see Fig. 2 ~ illustrates an autonomous driving system 210 comprising a sensor fusion module 236 wherein a process flow chart Fig. 10 outlines process method steps for sensing and perception operations through a sensor fusion 1012, Fig. 10, and ¶0068 ~ "sensor processing module 1011 that receives and processes sensor data from a plurality of sensors... the sensor processing module 1011... then passed to the sensor fusion module 1012);
the VIU is configured to elevate a vehicle intelligence level and wherein
an intelligence level of a vehicle having intelligence level 3 is elevated to intelligence level 4 or 5 (see FIG . 4 ~ illustrates a block diagram of autonomous driving example levels ¶0018. and ¶0050 ~ autonomy levels within autonomous vehicles' "operational design domain" for "safety-critical driving" operations);
the collaborative decision-making module collaborates with external systems to generate decisions for a vehicle of intelligence level 3 (see Figs. 4 - 5, ¶0018. and ¶0050 - ¶0051);
the collaborative decision-making module generates decisions in collaboration with the external systems to address long-tail scenarios for a vehicle of intelligence level 4; and/or the collaborative decision-making module improves predictive decisions and trajectory planning based on perception results for a vehicle of intelligence level 5 (see Figs. 4 - 5, ¶0018. ¶0037 ~ external computing systems may be provided and leveraged , which are hosted in road - side units or fog based edge devices (e.g.,140 ) , other ( e.g. , higher - level ) vehicles (e.g.,115 ), and cloud - based systems 150 (e.g., accessible through various network access points (e.g.,145)) . A roadside unit 140 or cloud - based system 150... for... an autonomous driving system 210... rely on the machine learning training... and/or models... for certain tasks and handling certain scenarios; ¶0050 - ¶0051, and ¶0071, thereby teaching a combination of event scenarios which could occur during the course of autonomous driving vehicle subsequently prompting the ADV to adjust autonomy driving levels while integrating external based data to address long-tail scenarios); and
wherein the sensing and perception fusion module fuses sensing and perception information provided by the vehicle subsystem and sensing and perception information provided by external systems to provide fused sensing and perception information. (See Fig. 2, 10, ¶0051 ~ "During a sensing and perception stage 505 data is generated… collected for use by the autonomous driving system... include data filtering and receiving sensor from external sources... include sensor fusion operations and object recognition and other perception tasks, and ¶0068 ~ sensor processing module 1011 outputs sensing data to sensing module 236 and performs sensing and perception fusion).
To that end, It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide Wang’s cooperative autonomous driving system for traffic congestion avoidance with the sensing and perception fusion module, as taught by Cormack, where the resultant combination would successfully provide sensing and perception data extracted and filtered from external sources for spatial mapping, thereby enabling benefits, including but not limited to: reliable and enhanced “real world” vision systems for navigating complex traffic deadlock / congestion environments.
As to Claim 16,
Wang/Cormack discloses the VIU of claim 15, wherein elevating the vehicle intelligence level comprises
integrating data from a cloud system. (See Figs. 4 - 5, ¶0018. ¶0020, and ¶0037; Cormack).
As to Claim 17,
Wang/Cormack discloses the VIU of claim 16,
wherein the collaborative decision-making module is assisted by the sensing and perception fusion module,
which processes data from the cloud system for elevating the vehicle intelligence level. (See Figs. 4 - 5, ¶0018. ¶0037, ¶0050 - ¶0051, and ¶0071; Cormack).
As to Claim 18,
Wang/Cormack discloses the VIU of claim 15,
wherein the collaborative decision-making module receives resources from a Collaborative Automated Driving System (CADS) (see Fig. 3, ¶0029, ¶0039, and ¶0115; Wang) to reduce long- tail risks (see Figs. 4 - 5, ¶0018. ¶0037, ¶0050 - ¶0051, and ¶0071; Cormack) and extend an Operational Design Domain (ODD). (See Fig. 4; Cormack ~ illustrates a block diagram of autonomous driving example levels ¶0018, and ¶0050; Cormack ~ autonomy levels within autonomous vehicles' "operational design domain" for "safety-critical driving" operations).
As to Claim 19,
Wang/Cormack discloses the VIU of claim 15,
wherein the intelligence level of the vehicle having intelligence level 3 is elevated to intelligence level 4 or 5 by the sensing and perception fusion module providing extra sensing and monitoring of the driver in real-time. (See Figs. 4 - 5, ¶0018. ¶0037, ¶0050 - ¶0051, and ¶0071; Cormack ~ real-time path planning and monitoring of driver actions).
As to Claim 20,
Wang/Cormack discloses the VIU of claim 15,
wherein the intelligence level of the vehicle having intelligence level 3 is elevated to intelligence level 4 or 5 by the collaborative decision-making module (see Fig. 4; Cormack ~ illustrates a block diagram of autonomous driving example levels ¶0018, and ¶0050; Cormack)
generating driving decisions that cooperate with other vehicles and an Intelligent Road Infrastructure System (IRIS) or an IRIS subsystem. (See Fig. 3; Wang ~ illustrates a cooperative autonomous driving system, Table 1 ~ outlines speed negotiation formulas which the cooperative autonomous driving system uses to perform collision avoidance in deadlock (traffic congestion) scenarios, ¶0029; Wang ~ cooperative autonomous driving for traffic congestion avoidance through vehicle-to-vehicle communication, and ¶0039; Wang ~ Figs. 11A-11F illustrates another example scenario in which a deadlock condition is resolved through cooperative autonomous driving).
Conclusion
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to ASHLEY L. REDHEAD, JR. whose telephone number is (571) 272 - 6952. The Examiner can normally be reached on weekdays, Monday through Thursday, between 7 a.m. and 5 p.m.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s Supervisor, Peter Nolan can be reached Monday through Friday, between 9 a.m. and 5 p.m. at (571) 270 – 7016. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ASHLEY L REDHEAD JR./Primary Examiner, Art Unit 3661
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