DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The IDS document submitted on 12/20/2024 has been received and considered by the Examiner.
The foreign priority claim to European patent application EP 24153945.1, dated 01/25/2024, is acknowledged, and the Examiner can confirm that the corresponding priority document has been received.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 4, 9-10, 13, and 18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kakosyan et al. (US 2023/0194279 A1, hereinafter “Kakosyan”).
As to Claim 1:
A method for providing a specific quality of service along a route of a vehicle
Paragraphs 0050-0055 of Kakosyan describe a prediction server responding to a vehicle UE’s request for resources along a desired route. Also, Fig. 2A in Kakosyan shows the signaling exchange between vehicles and the prediction server. Specifically, these paragraphs describe “[c]onnected vehicles and their operators” that “contract[] mobile communication network service with multiple mobile network operators (MNOs) with the expectation that, at any given time and location one of the MNOs will be able to meet the QoS requirements of the connected vehicle” (Kakosyan, 0043).
Here, “meet[ing] the QoS requirements of the connected vehicle” at “any given time and location” maps to “a method for providing a specific quality of service along a route of a vehicle”.
Receiving, by a service management server, from a user equipment (UE) of the vehicle, route information and a definition of a minimum required connectivity of service along the route
Paragraphs 0050-0055 of Kakosyan describe a prediction server responding to a vehicle UE’s request for resources along a desired route. Also, Fig. 2A in Kakosyan shows the signaling exchange between vehicles and the prediction server. Specifically, these paragraphs describe a “prediction system” that “receives from a connected vehicle ... a request for a route for a trip”. There is also “profile information” that “is also shared with the prediction system”, and “[t]he route” is subsequently “calculated based on the MNOs stored in the profile and ... various mobile communication network parameters ... such as ... required QoS” (Kakosyan, 0054).
Here, a “prediction system” that “receives ... a request for a route for a trip” maps to “receiving, by a service management server”,
“a connected vehicle” maps to “a user equipment (UE) of the vehicle”,
“a request for a route” maps to “route information”, and
“required QoS” maps to “a definition of a minimum required connectivity of service along the route”.
Managing and reserving, by the service management server, the required connectivity quality of service along the route of the vehicle by allocating communication resources
Paragraphs 0050-0055 of Kakosyan describe a prediction server responding to a vehicle UE’s request for resources along a desired route. Also, Fig. 2A in Kakosyan shows the signaling exchange between vehicles and the prediction server. Specifically, these paragraphs teach that the “prediction system” in Kakosyan can “determine a route to [a] destination” based on “the most likely QoS level to be experienced in each segment for each MNO along the route” (Kakosyan, 0055). Element 255 in Fig. 2B also specifies that the prediction system “localize[s] mobile communication network resources to segments”.
Here, “localiz[ing] mobile communication network resources” based on “QoS level to be experienced” maps to “managing and reserving, by the service management server, the required connectivity quality of service along the route of the vehicle”, and
“localiz[ing] mobile communication network resources” maps to “allocating communication resources”.
The service management server interfaces and communicates with a plurality of mobile network operators to allocate communication resources: (i) specific to regions along the route, and/or (ii) based on the route information, comprising at least one of arrival times, tolerance margins, or priority information transmitted by the vehicle
Paragraphs 0050-0055 of Kakosyan describe a prediction server responding to a vehicle UE’s request for resources along a desired route. Also, Fig. 2A in Kakosyan shows the signaling exchange between vehicles and the prediction server. Specifically, these paragraphs teach that “[t]he route prediction method and system” generates “a recommendation of which MNO to opt for on each segment of the route” (Kakosyan, 0050). Earlier, Kakosyan contextualizes this by describing “a system” that “can optimize routing options” using “the different Mobile Network Operators (MNOs) accessible to the connected vehicle” (Kakosyan, 0049).
Here, “the system ... optimiz[ing] routing options” using “the different Mobile Network Operators (MNOs)” maps to “the service management server interfaces and communicates with a plurality of mobile network operators”, and
“a recommendation of which MNO to opt for on each segment of the route” maps to “allocat[ing] communication resources: (i) specific to regions along the route” from the list of “allocat[ing] communication resources: (i) specific to regions along the route, and/or (ii) based on the route information, comprising at least one of arrival times, tolerance margins, or priority information transmitted by the vehicle”.
Transmitting, by the service management server, feedback information to the UE regarding the availability of the required connectivity quality of service along the route
Fig. 2A in Kakosyan shows the signaling exchange between vehicles and the prediction server that sends predicted SLIs to a vehicle UE. Kakosyan also specifies “SLIs” result from “network service QoS levels” that “get mapped into Service Level Indicators (SLIs)” (Kakosyan, 0053).
Here, element 209 and 210 in Fig. 2, “push[ing] predicted SLIs [service level indicators] for [the] route” map to “transmitting ... feedback information”,
the “route prediction block (RPB)”, element 203 in Fig. 2, maps to “the service management server”,
the “autonomous driving vehicle (ADV)” maps to “the UE”, and
“SLIs for [the] route” map to “the availability of the required connectivity quality of service along the route”.
The feedback information comprises at least one of a confirmation of resource availability or configuration details for achieving the required connectivity quality of service
Fig. 2A in Kakosyan shows the signaling exchange between vehicles and the prediction server that sends predicted SLIs to a vehicle UE. Kakosyan also specifies that “SLIs” result from “network service QoS levels” that “get mapped into Service Level Indicators (SLIs)”, and these SLIs are included in “configuration information” that “can further include the supported mobile communication service QoS levels” (Kakosyan, 0053).
Here, the “SLIs for [the] route” in Fig. 2A map to “the feedback information”,
“SLIs” map to “resource availability” because it is prediction of the resources that will be available, and
the “configuration information” that “can further include the supported mobile communication service QoS levels” maps to “configuration details for achieving the required connectivity quality of service”.
As to Claims 4 and 13:
From the list of:
The definition of the minimum required connectivity quality of service comprises at least one of: latency, reliability, or throughput requirements
Kakosyan at least teaches:
The definition of the minimum required connectivity quality of service comprises ... latency
Kakosyan describes “QoS information” as “throughput metrics, operational status, latency and similar information” (Kakosyan, 0067).
Here, “latency” maps to “the definition of the minimum required connectivity quality of service comprises ... latency”.
The definition of the minimum required connectivity quality of service comprises ... throughput requirements
Kakosyan describes “QoS information” as “throughput metrics, operational status, latency and similar information” (Kakosyan, 0067). Later, Kakosyan explicitly defines the “supported mobile communication network service QoS levels” as “at least 10 MBps or at least 5 Mbps of uplink or downlink throughput” (Kakosyan, 0053).
Here, “10 MBps or at least 5 Mbps of uplink or downlink throughput” maps to “the definition of the minimum required connectivity quality of service comprises ... throughput requirements”.
Claim 13 introduces the same new limitations as Claim 4 in the form of an apparatus claim.
As to Claims 9 and 18:
From the list of:
The feedback information comprises at least one of:
Identifiers to proceed with upcoming handovers or physical layer configurations necessary to achieve the required connectivity quality of service
Kakosyan at least teaches:
The feedback information comprises ... physical layer configurations necessary to achieve the required connectivity quality of service
Kakosyan describes “configuration information can further include the supported mobile communication network service QoS levels” that “then get mapped into Service Level Indicators (SLIs)” (Kakosyan, 0053).
Here, “SLIs” map to “the feedback information”, and
“configuration information” for “the supported mobile communication network service QoS levels” maps to “physical layer configurations necessary to achieve the required connectivity quality of service”.
Claim 18 introduces the same subject matter as Claim 9 in the form of an apparatus claim.
As to Claim 10:
Claim 10 encompasses substantially the same subject matter as Claim 1 in the form of an apparatus claim that also requires:
A system for providing a specific connectivity quality of service along a route of a vehicle, comprising: a communication interface, communicatively coupled to a network; and a service management server, operatively coupled to the communication interface, the service management server being configured
Paragraphs 0050-0055 of Kakosyan describe a prediction server responding to a vehicle UE’s request for resources along a desired route. Also, Fig. 2A in Kakosyan shows the signaling exchange between vehicles and the prediction server. Specifically, these paragraphs describe “[c]onnected vehicles and their operators” that “contract[] mobile communication network service with multiple mobile network operators (MNOs) with the expectation that, at any given time and location one of the MNOs will be able to meet the QoS requirements of the connected vehicle” (Kakosyan, 0043).
Here, “meet[ing] the QoS requirements of the connected vehicle” at “any given time and location” maps to “providing a specific quality of service along a route of a vehicle”, and
the connection between the “autonomous driving fleets” (element 101 in Fig. 1) and the “Route Prediction Blocks” (element 104 in Fig. 1) maps to “a service management server, operatively coupled to the communication interface, the service management server being configured”.
Receive, via the communication interface, route information and a definition of a minimum required connectivity of service along the route from a user equipment (UE) of the vehicle
Paragraphs 0050-0055 of Kakosyan describe a prediction server responding to a vehicle UE’s request for resources along a desired route. Also, Fig. 2A in Kakosyan shows the signaling exchange between vehicles and the prediction server. Specifically, these paragraphs describe a “prediction system” that “receives from a connected vehicle ... a request for a route for a trip”. There is also “profile information” that “is also shared with the prediction system”, and “[t]he route” is subsequently “calculated based on the MNOs stored in the profile and ... various mobile communication network parameters ... such as ... required QoS” (Kakosyan, 0054).
Here, a “prediction system” that “receives ... a request for a route for a trip” maps to “receive, via the communication interface”,
“a request for a route” maps to “route information”,
“required QoS” maps to “a definition of a minimum required connectivity of service along the route”, and
“a connected vehicle” maps to “a user equipment (UE) of the vehicle”,
Manage and reserve the required connectivity quality of service along the route of the vehicle by allocating communication resources
Paragraphs 0050-0055 of Kakosyan describe a prediction server responding to a vehicle UE’s request for resources along a desired route. Also, Fig. 2A in Kakosyan shows the signaling exchange between vehicles and the prediction server. Specifically, these paragraphs teach that the “prediction system” in Kakosyan can “determine a route to [a] destination” based on “the most likely QoS level to be experienced in each segment for each MNO along the route” (Kakosyan, 0055). Element 255 in Fig. 2B also specifies that the prediction system “localize[s] mobile communication network resources to segments”.
Here, “localiz[ing] mobile communication network resources” based on “QoS level to be experienced” maps to “manage and reserve the required connectivity quality of service along the route of the vehicle”, and
“localiz[ing] mobile communication network resources” maps to “allocating communication resources”.
The service management server interfaces and communicates, via the communication interface, with a plurality of mobile network operators to allocate communication resources: (i) specific to regions along the route, and/or (ii) based on the route information, comprising at least one of arrival times, tolerance margins, or priority information transmitted by the vehicle
Paragraphs 0050-0055 of Kakosyan describe a prediction server responding to a vehicle UE’s request for resources along a desired route. Also, Fig. 2A in Kakosyan shows the signaling exchange between vehicles and the prediction server. Specifically, these paragraphs teach that “[t]he route prediction method and system” generates “a recommendation of which MNO to opt for on each segment of the route” (Kakosyan, 0050). Earlier, Kakosyan contextualizes this by describing “a system” that “can optimize routing options” using “the different Mobile Network Operators (MNOs) accessible to the connected vehicle” (Kakosyan, 0049).
Here, “the system ... optimiz[ing] routing options” using “the different Mobile Network Operators (MNOs)” maps to “the service management server interfaces and communicates, via the communication interface, with a plurality of mobile network operators”, and
“a recommendation of which MNO to opt for on each segment of the route” maps to “allocat[ing] communication resources: (i) specific to regions along the route” from the list of “allocat[ing] communication resources: (i) specific to regions along the route, and/or (ii) based on the route information, comprising at least one of arrival times, tolerance margins, or priority information transmitted by the vehicle”.
Transmit, via the communication interface, feedback information to the UE regarding the availability of the required connectivity quality of service along the route
Fig. 2A in Kakosyan shows the signaling exchange between vehicles and the prediction server that sends predicted SLIs to a vehicle UE. Kakosyan also specifies “SLIs” result from “network service QoS levels” that “get mapped into Service Level Indicators (SLIs)” (Kakosyan, 0053).
Here, element 209 and 210 in Fig. 2, “push[ing] predicted SLIs [service level indicators] for [the] route” map to “transmit, via the communication interface, feedback information”,
the “route prediction block (RPB)”, element 203 in Fig. 2, maps to “the service management server”,
the “autonomous driving vehicle (ADV)” maps to “the UE”, and
“SLIs for [the] route” map to “the availability of the required connectivity quality of service along the route”.
The feedback information comprises at least one of a confirmation of resource availability or configuration details for achieving the required connectivity quality of service
Fig. 2A in Kakosyan shows the signaling exchange between vehicles and the prediction server that sends predicted SLIs to a vehicle UE. Kakosyan also specifies that “SLIs” result from “network service QoS levels” that “get mapped into Service Level Indicators (SLIs)”, and these SLIs are included in “configuration information” that “can further include the supported mobile communication service QoS levels” (Kakosyan, 0053).
Here, the “SLIs for [the] route” in Fig. 2A map to “the feedback information”,
“SLIs” map to “resource availability” because it is prediction of the resources that will be available, and
the “configuration information” that “can further include the supported mobile communication service QoS levels” maps to “configuration details for achieving the required connectivity quality of service”.
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.
Claim(s) 2-3, 11-12, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kakosyan (US 2023/0194279 A1) in view of MacNeille et al. (US 2015/0260537 A1, hereinafter “MacNeille”).
As to Claims 2 and 11:
Kakosyan teaches:
The route information comprises the route to be followed by the vehicle as a set of waypoints
Kakosyan describes the Route Prediction Block (element 104 in Fig. 1) which “receive[s]” a “request ... from the ADF 101 with a list of locations, segments, or similar identifiers for which a QoS score or rating is requested” (Kakosyan, 0065).
Here, “locations” map to “the route information comprises the route to be followed by the vehicle as a set of waypoints” because waypoints are specific locations along a desired route, as described here.
Kakosyan does not explicitly disclose:
The route information comprises ... the arrival time at which the vehicle will be at each waypoint
However, MacNeille does describe a vehicle designating “stopping points” along its route with a built in tolerance for the projected time when the vehicle will reach them.
Specifically, MacNeille teaches:
The route information comprises ... the arrival time at which the vehicle will be at each waypoint
MacNeille describes “set[ting] stopping points along a route” which “correspond ... to points at which a time surplus will be checked against an estimated stop length” (MacNeille, 0046). MacNeille adds that “some additional amount of tolerance” should be “add[ed] ... into the amount of time estimated to complete a journey” (MacNeille, 0046).
Here, “a journey” maps to “[t]he route information”, and
“points at which a time surplus will be checked against an estimated stop length” maps to “the arrival time at which the vehicle will be at each waypoint”.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate MacNeille’s practice of projecting specific arrival times for points along a route into Kakosyan’s method for maintaining quality of service for an autonomous vehicle. As MacNeille explains, this helps to “successfully negotiate[] ... traffic, lights, and other obstacles to travel”.
Claim 11 introduces the same new limitations as Claim 2 in the form of an apparatus claim.
As to Claims 3 and 12:
Kakosyan teaches:
The service management server allocates the communication resources based on the ... margins
Kakosyan describes “[p]rediction of path loss or degradation” using “machine learning models” for the purpose of “resource allocation” meant to “support[] mobility and provid[e] QoS in mobile communication networks” (Kakosyan, 0057).
Here, “resource allocation” maps to “the service management server allocates the communication resources”, and
“prediction of path loss or degradation” maps to “margins” because path loss and degradation are forms of error that can influence or change the performance of an autonomous vehicle.
Kakosyan does not explicitly disclose:
The route information further comprises a tolerance margin around the arrival time at each waypoint, and/or a tolerance margin is assigned to the arrival time at each waypoint
Kakosyan describes “some additional amount of tolerance” associated with “points at which a time surplus will be checked against an estimated stop length”, i.e. “stopping points” (Kakosyan, 0046).
Here, “some additional amount of tolerance” maps to “a tolerance margin”,
“stopping points” map to “the arrival time at each waypoint”,
“some additional amount of tolerance” maps to “a tolerance margin”, and
“an estimated stop length” at each “stopping point” maps to “a tolerance margin is assigned to the arrival time at each waypoint”.
Tolerance margins
MacNeille describes “set[ting] stopping points along a route” which “correspond ... to points at which a time surplus will be checked against an estimated stop length” (MacNeille, 0046). MacNeille adds that “some additional amount of tolerance” should be “add[ed] ... into the amount of time estimated to complete a journey” (MacNeille, 0046).
Here, “some additional amount of tolerance” maps to “tolerance margins”.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate MacNeille’s practice of projecting specific arrival times for points along a route into Kakosyan’s method for maintaining quality of service for an autonomous vehicle. As MacNeille explains, this helps to “successfully negotiate[] ... traffic, lights, and other obstacles to travel”
Claim 12 introduces the same new limitations as Claim 3 in the form of an apparatus claim.
As to Claim 19:
Kakosyan teaches:
A system for providing a specific connectivity quality of service along a route of a vehicle, comprising: a communication interface, communicatively coupled to a network; and a service management server, operatively coupled to the communication interface, the service management server being configured
Paragraphs 0050-0055 of Kakosyan describe a prediction server responding to a vehicle UE’s request for resources along a desired route. Also, Fig. 2A in Kakosyan shows the signaling exchange between vehicles and the prediction server. Specifically, these paragraphs describe “[c]onnected vehicles and their operators” that “contract[] mobile communication network service with multiple mobile network operators (MNOs) with the expectation that, at any given time and location one of the MNOs will be able to meet the QoS requirements of the connected vehicle” (Kakosyan, 0043).
Here, “meet[ing] the QoS requirements of the connected vehicle” at “any given time and location” maps to “providing a specific quality of service along a route of a vehicle”, and
the connection between the “autonomous driving fleets” (element 101 in Fig. 1) and the “Route Prediction Blocks” (element 104 in Fig. 1) maps to “a service management server, operatively coupled to the communication interface, the service management server being configured”.
Receive, via the communication interface, route information and a definition of a minimum required connectivity quality of service along the route from user equipment (UE) of the vehicle
Paragraphs 0050-0055 of Kakosyan describe a prediction server responding to a vehicle UE’s request for resources along a desired route. Also, Fig. 2A in Kakosyan shows the signaling exchange between vehicles and the prediction server. Specifically, these paragraphs describe a “prediction system” that “receives from a connected vehicle ... a request for a route for a trip”. There is also “profile information” that “is also shared with the prediction system”, and “[t]he route” is subsequently “calculated based on the MNOs stored in the profile and ... various mobile communication network parameters ... such as ... required QoS” (Kakosyan, 0054).
Here, a “prediction system” that “receives ... a request for a route for a trip” maps to “receive, via the communication interface”,
“a request for a route” maps to “route information”,
“required QoS” maps to “a definition of a minimum required connectivity of service along the route”, and
“a connected vehicle” maps to “a user equipment (UE) of the vehicle”.
The route information includes a plurality of waypoints along the route
Kakosyan describes the Route Prediction Block (element 104 in Fig. 1) which “receive[s]” a “request ... from the ADF 101 with a list of locations, segments, or similar identifiers for which a QoS score or rating is requested” (Kakosyan, 0065).
Here, “locations” map to “the route information includes a plurality of waypoints along the route” because waypoints are specific locations along a desired route, as described here.
The definition comprises at least one of latency, reliability, or throughput requirements
Kakosyan describes “QoS information” as “throughput metrics, operational status, latency and similar information” (Kakosyan, 0067).
Here, “latency” maps to “the definition of the minimum required connectivity quality of service comprises ... latency” from the list of “the route information includes a plurality of waypoints along the route and corresponding arrival times”.
Manage and reserve the required connectivity quality of service along the route of the vehicle by allocating communication resources
Paragraphs 0050-0055 of Kakosyan describe a prediction server responding to a vehicle UE’s request for resources along a desired route. Also, Fig. 2A in Kakosyan shows the signaling exchange between vehicles and the prediction server.
Specifically, these paragraphs teach that the “prediction system” in Kakosyan can “determine a route to [a] destination” based on “the most likely QoS level to be experienced in each segment for each MNO along the route” (Kakosyan, 0055). Element 255 in Fig. 2B also specifies that the prediction system “localize[s] mobile communication network resources to segments”.
Here, “localiz[ing] mobile communication network resources” based on “QoS level to be experienced” maps to “managing and reserving, by the service management server, the required connectivity quality of service along the route of the vehicle”, and
“localiz[ing] mobile communication network resources” maps to “allocating communication resources”.
The service management server interfaces and communicates, via the communication interface, with a plurality of mobile network operators to allocate communication resources (i) specific to regions corresponding to the waypoints along the route, and (ii) accounting for potential deviations in arrival times
Paragraphs 0050-0055 of Kakosyan describe a prediction server responding to a vehicle UE’s request for resources along a desired route. Also, Fig. 2A in Kakosyan shows the signaling exchange between vehicles and the prediction server. Specifically, these paragraphs teach that “[t]he route prediction method and system” generates “a recommendation of which MNO to opt for on each segment of the route” (Kakosyan, 0050). Earlier, Kakosyan contextualizes this by describing “a system” that “can optimize routing options” using “the different Mobile Network Operators (MNOs) accessible to the connected vehicle” (Kakosyan, 0049).
Here, “the system ... optimiz[ing] routing options” using “the different Mobile Network Operators (MNOs)” maps to “the service management server interfaces and communicates, via the communication interface, with a plurality of mobile network operators”, and
“a recommendation of which MNO to opt for on each segment of the route” maps to “allocat[ing] communication resources: (i) specific to regions corresponding to the waypoints along the route” from the list of “allocat[ing] communication resources: (i) specific to regions corresponding to the waypoints along the route, and (ii) accounting for potential deviations in arrival times” because “each segment of the route” corresponds to “regions corresponding to the waypoints along the route”.
Transmit, via the communication interface, feedback information to the UE regarding the availability of the required connectivity quality of service along the route
Fig. 2A in Kakosyan shows the signaling exchange between vehicles and the prediction server that sends predicted SLIs to a vehicle UE. Kakosyan also specifies “SLIs” result from “network service QoS levels” that “get mapped into Service Level Indicators (SLIs)” (Kakosyan, 0053).
Here, element 209 and 210 in Fig. 2, “push[ing] predicted SLIs [service level indicators] for [the] route” map to “transmit, via the communication interface, feedback information”,
the “autonomous driving vehicle (ADV)” maps to “the UE”, and
“SLIs for [the] route” map to “the availability of the required connectivity quality of service along the route”.
The feedback information comprises at least one of resource availability, handover identifiers, or configuration details for achieving the required connectivity quality of service
Fig. 2A in Kakosyan shows the signaling exchange between vehicles and the prediction server that sends predicted SLIs to a vehicle UE. Kakosyan also specifies that “SLIs” result from “network service QoS levels” that “get mapped into Service Level Indicators (SLIs)”, and these SLIs are included in “configuration information” that “can further include the supported mobile communication service QoS levels” (Kakosyan, 0053).
Here, the “SLIs for [the] route” in Fig. 2A map to “the feedback information”,
“SLIs” map to “resource availability” from the list of “at least one of resource availability, handover identifiers, or configuration details for achieving the required connectivity quality of service” because it is prediction of the resources that will be available, and
the “configuration information” that “can further include the supported mobile communication service QoS levels” maps to “configuration details for achieving the required connectivity quality of service” from the list of “at least one of resource availability, handover identifiers, or configuration details for achieving the required connectivity quality of service”.
Kakosyan does not explicitly disclose:
The route information includes a plurality of waypoints along the route and corresponding arrival times
However, MacNeille does teach:
The route information includes a plurality of waypoints along the route and corresponding arrival times
MacNeille describes “set[ting] stopping points along a route” which “correspond ... to points at which a time surplus will be checked against an estimated stop length” (MacNeille, 0046). MacNeille adds that “some additional amount of tolerance” should be “add[ed] ... into the amount of time estimated to complete a journey” (MacNeille, 0046).
Here, “a journey” including “stopping points” maps to “[t]he route information including a plurality of waypoints along the route”, and
“points at which a time surplus will be checked against an estimated stop length” maps to “corresponding arrival times”.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate MacNeille’s practice of projecting specific arrival times for points along a route into Kakosyan’s method for maintaining quality of service for an autonomous vehicle. As MacNeille explains, this helps to “successfully negotiate[] ... traffic, lights, and other obstacles to travel”
As to Claim 20:
Kakosyan teaches:
Allocate communication resources based on a ... margin
Kakosyan describes “[p]rediction of path loss or degradation” using “machine learning models” for the purpose of “resource allocation” meant to “support[] mobility and provid[e] QoS in mobile communication networks” (Kakosyan, 0057).
Here, “resource allocation” maps to “allocat[ing] the communication resources”, and
“prediction of path loss or degradation” maps to “margin” because path loss and degradation are forms of error that can influence or change the performance of an autonomous vehicle.
Kakosyan does not explicitly disclose:
A tolerance margin around the arrival time at each waypoint
Kakosyan describes “some additional amount of tolerance” associated with “points at which a time surplus will be checked against an estimated stop length”, i.e. “stopping points” (Kakosyan, 0046).
Here, “some additional amount of tolerance” maps to “a tolerance margin”, and
“stopping points” map to “the arrival time at each waypoint”.
The tolerance margin spans a time window of at least one minute in both directions relative to the arrival time
Kakosyan describes “stopping points” that may take, for example, “an estimated 8 minutes” or “fifteen minutes” (Kakosyan, 0042-0043). Kakosyan later clarifies that “it may be useful to add some additional amount of tolerance into the amount of time estimated to complete a journey” (Kakosyan, 0046).
Here, the stops of “8 minutes” and “15 minutes” representing an “additional amount of tolerance” for “the amount of time estimated to complete a journey” map to “the tolerance margin spans a time window of at least one minute in both directions relative to the arrival time” because both values would exceed the two minute threshold required to create a “one minute in both directions”.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate MacNeille’s practice of projecting specific arrival times for points along a route into Kakosyan’s method for maintaining quality of service for an autonomous vehicle. As MacNeille explains, this helps to “successfully negotiate[] ... traffic, lights, and other obstacles to travel”
Claim(s) 5 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kakosyan (US 2023/0194277 A1) in view of Orbay et al. (US 2023/0161623 A1, hereinafter “Orbay”).
As to Claims 5 and 14:
Kakosyan does not explicitly disclose:
Determining, by the service management server, based on the feedback information, whether to enable or disable an application or service associated with the vehicle
However, Orbay does describe a method to disable autonomous driving to save compute resources.
Specifically, Orbay teaches:
Determining, by the service management server, based on the feedback information, whether to enable or disable an application or service associated with the vehicle
Orbay describes a “a vehicle 120 can be engaged in autonomous driving and can concurrently receive a request to allocate at least some of the compute resources 122” and that this “can comprise discontinuing autonomous driving and/or disabling other systems of the vehicle 120 in order to make said compute resources available to satisfy the compute request” (Orbay, 0041).
Here, “a request to allocate at least some of the compute resources” maps to “beedback information”,
“discontinuing autonomous driving” maps to “determining, by the service management server ... whether to enable or disable an application or service associated with the vehicle”, and
“discontinuing” maps to “disable” from the list of “enable or disable an application”.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Orbay’s method for disabling autonomous driving into Kakosyan’s method for allocating resources for an autonomous driving route. As Orbay explains, this can help “make ... compute resources available to satisfy the compute request” (Orbay, 0041).
Claim 14 introduces the same new limitations as Claim 5 in the form of an apparatus claim.
Claim(s) 6-8 and 15-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kakosyan (US 2023/0194279 A1) in view of Katou et al. (US 2018/0151070 A1, hereinafter “Katou”).
As to Claims 6 and 15:
Kakosyan teaches:
Receiving, by the service management server, ... information from the vehicle
Kakosyan describes a “route prediction system” that “receiv[es] a route request originating from a connected vehicle” maps to “receiving, by the service management server, ... information from the vehicle”.
The allocation of communication resources
Kakosyan describes “localizing mobile communication network resources to the segments” using “a current offline model” for “sending predicted service level indicators (SLIs) for the segments to the connected vehicle” (Kakosyan, 0006).
Here, “localizing mobile communication network resources” maps to “the allocation of communication resources”.
Kakosyan does not explicitly disclose:
Priority information from the vehicle
The allocation of communication resources is based on the priority information
However, Katou does describe a method for predicting and avoiding collisions by assigning vehicles priority based on their vehicle type and work mode.
Specifically, Katou teaches:Priority information from the vehicle
Katou describes “values of the priority 443” in Fig. 6 corresponding to each “vehicle type 441” and “work mode 442” (Katou, 0076).
Here, “the priority 443” maps to “priority information from the vehicle”.
The allocation of communication resources is based on the priority information
Katou describes a “determination” based on a “priority management table” including “priorities set for every work modes [sic] with respect to various vehicles” to “determine” if “there is a risk of collision”, and this risk is used to “output[] warning information on another vehicle, which is determined to have a risk of collision with the own vehicle” (Katou, 0042-0043).
Here, “output[ting] warning information” maps to “the allocation of communication resources”, and
the “determination” based on “priority” maps to “based on the priority information”.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to prioritize vehicles based on their type and function, as taught in Katou, in Kakosyan’s method for maintaining QoS along a route. As Katou explains, certain types of vehicles are at higher risk for collision than others (see, for example, Katou 0042-0043), so elevating their priority helps them to mitigate this risk.
Claim 15 introduces the same new limitations as Claim 6 in the form of an apparatus claim.
As to Claims 7 and 16:
Kakosyan does not explicitly disclose:
The priority information comprises vehicle operating mode information
However, Katou does teach:
The priority information comprises vehicle operating mode information
Katou describes “values of the priority 443” in Fig. 6 corresponding to each “vehicle type 441” and “work mode 442” (Katou, 0076).
Here, “values of the priority 443” map to “the priority information”, and
“work mode 442” maps to “vehicle operating mode information”.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to prioritize vehicles based on their type and function, as taught in Katou, in Kakosyan’s method for maintaining QoS along a route. As Katou explains, certain types of vehicles are at higher risk for collision than others (see, for example, Katou 0042-0043), so elevating their priority helps them to mitigate this risk.
Claim 16 introduces the same new limitations as Claim 7 in the form of an apparatus claim.
As to Claims 8 and 17:
Kakosyan does not explicitly disclose:
The priority information comprises vehicle class information
However, Katou does teach:
The priority information comprises vehicle class information
Katou describes “values of the priority 443” in Fig. 6 corresponding to each “vehicle type 441” and “work mode 442” (Katou, 0076).
Here, “values of the priority 443” map to “the priority information”, and
“vehicle type 441” maps to “vehicle class information”.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to prioritize vehicles based on their type and function, as taught in Katou, in Kakosyan’s method for maintaining QoS along a route. As Katou explains, certain types of vehicles are at higher risk for collision than others (see, for example, Katou 0042-0043), so elevating their priority helps them to mitigate this risk.
Claim 17 introduces the same new limitations as Claim 8 in the form of an apparatus claim.
Conclusion
The prior art made of record in the IDS filed on (12/20/2024) and not relied upon is considered pertinent to applicant's disclosure. Nakata et al. (US 202/0221349 A1) describes a vehicle sending a resource allocation request including a proposed route and timeline. Kakosyan et al. (US 2023/0194277 A1) describes a vehicle sending predicted service level requirements along its planned route. Finally, Huber et al. (WO 2016/150494 A1) describes a server receiving a route request and allocating resources along the proposed route.
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BENJAMIN PETER WELTE
Examiner
Art Unit 2477
/CHIRAG G SHAH/Supervisory Patent Examiner, Art Unit 2477
