DETAILED ACTION
This action is in response to the Applicant Response filed 17 March 2026 for application 18/026,207 filed 14 March 2023.
Claim(s) 1, 20 is/are currently amended.
Claim(s) 19 is/are cancelled.
Claim(s) 1-18, 20 is/are pending.
Claim(s) 1-18, 20 is/are rejected.
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 .
Response to Arguments
Applicant’s arguments regarding the 35 U.S.C. 101 rejection of claim 20 as software per se have been fully considered and are persuasive. The 35 U.S.C. 101 software per se rejection of claim 20 is withdrawn.
Applicant’s arguments regarding the 35 U.S.C. 101 rejection of claims 1-18, 20 have been fully considered but are not persuasive.
Applicant first argues that the claims cannot be performed in the human mind. Specifically, applicant argues that acquire data related to mobile devices and determine matching routes of devices based on the data. While examiner agrees that acquiring data is not a mental process, as noted below, once that data is acquired a person can easily look ad the data and determine which devices have matching routes and times.
Applicant next argues that the claims recites a particular solution to a particular problem. Examiner respectfully disagrees. The claims simply recite examining data to determine a schedule. This is done mentally on a daily basis. Moreover, as detailed below, the claims do not provide elements that integrate the abstract idea into a practical application or provide significantly more. Further, as noted in the MPEP, it is important to keep in mind that an improvement in the abstract idea itself is not an improvement in technology. MPEP 2106.05(a). Therefore, even if an improvement were to exist, the improvement would be in the scheduling of a task and, therefore, be an improvement in the abstract idea.
The remainder of applicant’s arguments regarding the 35 U.S.C. 101 rejection of the claims are based on the newly amended subject matter. These arguments are addressed in the 35 U.S.C. 101 rejection of the claims below.
Therefore, the 35 U.S.C. 101 rejection of claims 1-18, 20 is maintained.
Applicant’s arguments regarding the 35 U.S.C 103 rejections of claim 1-18, 20 have been fully considered but are not persuasive. Applicant argues that Yu does not teach determining an amount of required processing resources at the base station and at the mobile device. Examiner respectfully disagrees. First, the claims recites that an amount of processing resources be determined for one of the base state or the mobile device. Further, Yu teaches that vehicles are selected based on the amount of time in the coverage area and that vehicles for a short period of time in the coverage area are not selected (Yu, section IV.A). Therefore, the system has determined the required resources necessary for training and the amount of time needed and selects vehicles that are in the coverage area for the needed amount of time. Therefore, Yu does, in fact, teach determining an amount of required processing resources at the mobile device. The 35 U.S.C. 103 rejections of claim 1-18, 20 are maintained.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-18, 20 is/are rejected under 35 U.S.C. 101, because the claim(s) is/are directed to an abstract idea, and because the claim elements, whether considered individually or in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. V. CLS Bank International et al., 573 US 208 (2014).
Regarding claim 1, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for scheduling federated learning tasks for training a federated learning model for object detection and prediction of actions of mobile communication devices for autonomous vehicles.
The limitation of determining a subset of mobile communication devices which share a same route for a given amount of time based on the data associated with the routes of the plurality of mobile communication devices, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of determining one or more base stations located along the shared route, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of estimating, for the one or more base stations, points of time at which the subset of mobile communication devices are in coverage areas of respective base stations, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of determining at least one of: an amount of required processing resources at the base stations and an amount of required processing resources at the subset of mobile communication devices, wherein the determination is based on the estimated points of time at which the subset of mobile communication devices are in coverage areas of respective base stations, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generating a schedule for a plurality of federated learning tasks to be performed at the subset of mobile communication devices and the one or more base stations, wherein the generation of the schedule is based on priority levels associated with the plurality of federated learning tasks, estimated points of time at which the subset of mobile communication devices are in coverage areas of the one or more base stations, and at least one of: the amount of required processing resources at the one or more base stations and the amount of required processing resources at the subset of mobile communication devices, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – computer-implemented, plurality of mobile communication devices, one or more base stations. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – object detection, autonomous vehicles, plurality of federated learning tasks, federated learning model. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites acquiring data associated with routes of a plurality of mobile communication devices, which is simply acquiring data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
computer-implemented, plurality of mobile communication devices, one or more base stations amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
acquiring data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
object detection, autonomous vehicles, plurality of federated learning tasks, federated learning model amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 2, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for scheduling federated learning tasks for training a federated learning model for object detection and prediction of actions of mobile communication devices for autonomous vehicles. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 2 carries out the method of claim 1 but for the recitation of additional element(s) of communicating the generated schedule to at least one of: the one or more base stations and the subset of mobile communication devices.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites communicating the generated schedule to at least one of: the one or more base stations and the subset of mobile communication devices, which is simply transmitting data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
transmitting data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network (MPEP 2016.05(d))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 3, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for scheduling federated learning tasks for training a federated learning model for object detection and prediction of actions of mobile communication devices for autonomous vehicles.
The limitation of assigning a worker role or a semi-master role to at least one of the subset of mobile communication devices, wherein a worker role indicates that model training is to be performed at the respective mobile communication device, and a semi-master role indicates that gathering of local model parameters from nearby mobile communication devices is to be performed at the respective mobile communication device, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated
into a practical application. The claim does not recite any additional elements which integrate the
abstract idea into a practical application and, therefore, does not impose any meaningful limits on
practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to the integration of the
abstract idea into a practical application, the claim does not recite any additional elements which
provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 4, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for scheduling federated learning tasks for training a federated learning model for object detection and prediction of actions of mobile communication devices for autonomous vehicles. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 4 carries out the method of claim 1 but for the recitation of additional element(s) of wherein the schedule is generated such that when a respective mobile communication device is estimated to be in a coverage area of a respective base station, communication between the mobile communication device and the base station is triggered to perform at least a part of a scheduled federated learning task.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein the schedule is generated such that when a respective mobile communication device is estimated to be in a coverage area of a respective base station, communication between the mobile communication device and the base station is triggered to perform at least a part of a scheduled federated learning task which is simply additional information regarding the federated learning, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites wherein the schedule is generated such that when a respective mobile communication device is estimated to be in a coverage area of a respective base station, communication between the mobile communication device and the base station is triggered to perform at least a part of a scheduled federated learning task, which is simply transmitting data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
transmitting data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network (MPEP 2016.05(d))
additional information regarding the federated learning do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 5, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for scheduling federated learning tasks for training a federated learning model for object detection and prediction of actions of mobile communication devices for autonomous vehicles. The Step 2A Prong One Analysis for claim 4 is applicable here since claim 5 carries out the method of claim 4 but for the recitation of additional element(s) of wherein communication between the mobile communication device and the base station comprises at least one of: transmitting initial model parameters for the federated learning model from the base station to the mobile communication device, and transmitting local model parameters for the federated learning model from the mobile communication device to the base station.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein communication between the mobile communication device and the base station comprises at least one of: transmitting initial model parameters for the federated learning model from the base station to the mobile communication device, and transmitting local model parameters for the federated learning model from the mobile communication device to the base station which is simply additional information regarding the communication, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites wherein communication between the mobile communication device and the base station comprises at least one of: transmitting initial model parameters for the federated learning model from the base station to the mobile communication device, and transmitting local model parameters for the federated learning model from the mobile communication device to the base station, which is simply transmitting data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
transmitting data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network (MPEP 2016.05(d))
additional information regarding the communication do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 6, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for scheduling federated learning tasks for training a federated learning model for object detection and prediction of actions of mobile communication devices for autonomous vehicles.
The limitation of wherein an aggregation algorithm based on the received local model parameters is performed ... to generate an updated federated learning model, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical concept. The limitation encompasses aggregating numerical data.
If a claim limitation, under its broadest reasonable interpretation, covers performance of mathematical concepts, then it falls within the "Mathematical Concepts" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein local model parameters from at least one of the subset of mobile communication devices are received at a base station, which is simply transmitting data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
transmitting data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network (MPEP 2016.05(d))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 7, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for scheduling federated learning tasks for training a federated learning model for object detection and prediction of actions of mobile communication devices for autonomous vehicles. The Step 2A Prong One Analysis for claim 6 is applicable here since claim 7 carries out the method of claim 6 but for the recitation of additional element(s) of wherein the updated federated learning model is transmitted from the base station to at least one of the subset of mobile communication devices or at least one of the plurality of mobile communication devices.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein the updated federated learning model is transmitted from the base station to at least one of the subset of mobile communication devices or at least one of the plurality of mobile communication devices, which is simply transmitting data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
transmitting data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network (MPEP 2016.05(d))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 8, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for scheduling federated learning tasks for training a federated learning model for object detection and prediction of actions of mobile communication devices for autonomous vehicles. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 8 carries out the method of claim 1 but for the recitation of additional element(s) of wherein the data associated with the routes of the plurality of mobile communication devices includes at least one of: current route data of the plurality of mobile communication devices, and inferred route data of the plurality of mobile communication devices.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 9, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 9 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for scheduling federated learning tasks for training a federated learning model for object detection and prediction of actions of mobile communication devices for autonomous vehicles. The Step 2A Prong One Analysis for claim 8 is applicable here since claim 9 carries out the method of claim 8 but for the recitation of additional element(s) of wherein the current route data of the plurality of mobile communication devices is acquired from a navigation application associated with respective mobile communication devices, and/or wherein the inferred route data is acquired from an external artificial intelligence component.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the data and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 10, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for scheduling federated learning tasks for training a federated learning model for object detection and prediction of actions of mobile communication devices for autonomous vehicles. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 10 carries out the method of claim 1 but for the recitation of additional element(s) of wherein locally scheduling of communication with at least one of the subset of mobile communication devices is performed at the one or more base stations, and wherein the local scheduling is based on at least one of: a current distance between the respective base station and the respective mobile communication device, a speed of motion of the at least of the respective mobile communication device, a direction of motion of the respective mobile communication device.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the scheduling and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the scheduling do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 11, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for scheduling federated learning tasks for training a federated learning model for object detection and prediction of actions of mobile communication devices for autonomous vehicles. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 11 carries out the method of claim 1 but for the recitation of additional element(s) of acquiring configuration management data associated with the shared route, and wherein determining one or more base stations located along the shared route is based on the configuration management data.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites wherein determining one or more base stations located along the shared route is based on the configuration management data which is simply additional information regarding the base stations, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites acquiring configuration management data associated with the shared route, which is simply acquiring data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
acquiring data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
additional information regarding the base stations do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 12, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 12 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for scheduling federated learning tasks for training a federated learning model for object detection and prediction of actions of mobile communication devices for autonomous vehicles. The Step 2A Prong One Analysis for claim 11 is applicable here since claim 12 carries out the method of claim 11 but for the recitation of additional element(s) of wherein the configuration management data is acquired from an operations support system.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – operations support system. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites wherein the configuration management data is acquired from an operations support system which is simply additional information regarding the data, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
The claim recites wherein the configuration management data is acquired from an operations support system, which is simply acquiring data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
operations support system amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
acquiring data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 13, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 13 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for scheduling federated learning tasks for training a federated learning model for object detection and prediction of actions of mobile communication devices for autonomous vehicles.
The limitation of wherein the estimation of points of time at which the subset of mobile communication devices are in the coverage areas of the respective base stations comprises analysing ... the data associated with the routes of the plurality of mobile communication devices, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – machine learning model. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites wherein data associated with the routes of the plurality of mobile communication devices comprises at least one of: a location of a mobile communication device, a speed of a mobile communication device, a direction of a mobile communication device, and one or more environmental features associated with a mobile communication device, wherein the one or more environmental features includes one or more of: road conditions, traffic conditions, and weather conditions which is simply additional information regarding the data, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
machine learning model amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
additional information regarding the data do(es) not apply the exception in a meaningful way (MPEP 2106.05(e))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 14, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 14 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for scheduling federated learning tasks for training a federated learning model for object detection and prediction of actions of mobile communication devices for autonomous vehicles. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 14 carries out the method of claim 1 but for the recitation of additional element(s) of wherein determining an amount of required processing resources for a base station or for a mobile communication device is further based on an estimated amount of processing resources required by the federated learning model.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the resources and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the resources do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 15, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 15 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for scheduling federated learning tasks for training a federated learning model for object detection and prediction of actions of mobile communication devices for autonomous vehicles. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 15 carries out the method of claim 1 but for the recitation of additional element(s) of wherein the amount of required processing resources for a base station or for a mobile communication device is associated with at least one of: an amount of required computational resources, an amount of required storage resources, and an amount of required networking resources.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the resources and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the resources do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 16, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 16 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for scheduling federated learning tasks for training a federated learning model for object detection and prediction of actions of mobile communication devices for autonomous vehicles. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 16 carries out the method of claim 1 but for the recitation of additional element(s) of wherein the generation of the schedule is further based on at least one of: a type of connectivity between respective mobile communication devices and/or between respective base stations and respective mobile communication devices, a quality of connectivity between respective mobile communication devices and/or between respective base stations and respective mobile communication devices, radio quality metrics of the one or more base stations and/or of one or more of the subset of mobile communication devices, a type of device required for performing a respective federated learning task, available processing power at a respective base station, and available processing power at a respective mobile communication devices.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the schedule and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the schedule do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 17, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 17 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for scheduling federated learning tasks for training a federated learning model for object detection and prediction of actions of mobile communication devices for autonomous vehicles. The Step 2A Prong One Analysis for claim 1 is applicable here since claim 17 carries out the method of claim 1 but for the recitation of additional element(s) of wherein a priority level associated with a federated learning task is based on at least one of: a priority level of the federated learning model, a type of data required for the federated learning model, existing data already available for the federated learning model, and a level of subscription associated with a mobile communication device at which the federated learning task is to be performed.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the federated learning task and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the federated learning task do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 18, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 18 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) method for scheduling federated learning tasks for training a federated learning model for object detection and prediction of actions of mobile communication devices for autonomous vehicles. The Step 2A Prong One Analysis for claim 17 is applicable here since claim 18 carries out the method of claim 17 but for the recitation of additional element(s) of wherein the priority level of the federated learning model is set by a controlling entity and is contained in the metadata of the federated learning model.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. In particular, the claim recites additional information regarding the federated learning task and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of additional information regarding the federated learning task do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Not applying the exception in a meaningful way does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 20, the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 20 is directed to a system [interpreted to have a processor for compact prosecution], which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The claim recites a(n) system for scheduling federated learning tasks for training a federated learning model for object detection and prediction of actions of mobile communication devices for autonomous vehicles.
The limitation of determine a subset of mobile communication devices which share a same route for a given amount of time based on the data associated with the routes of the plurality of mobile communication devices, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of determine one or more base stations located along the shared route, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of estimate, for the one or more base stations, points of time at which the subset of mobile communication devices are in coverage areas of respective base stations, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of determine at least one of: an amount of required processing resources at the base stations and an amount of required processing resources at the subset of mobile communication devices, wherein the determination is based on the estimated points of time at which the subset of mobile communication devices are in coverage areas of respective base stations, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
The limitation of generate a schedule for a plurality of federated learning tasks to be performed at the subset of mobile communication devices and the one or more base stations, wherein the generation of the schedule is based on priority levels associated with the plurality of federated learning tasks, estimated points of time at which the subset of mobile communication devices are in coverage areas of the one or more base stations, and at least one of: the amount of required processing resources at the one or more base stations and the amount of required processing resources at the subset of mobile communication devices, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application.
The claim recites additional element(s) – processing circuit, memory, computer readable program instructions, plurality of mobile communication devices, one or more base stations. The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)).
The claim recites additional element(s) – object detection, autonomous vehicles, plurality of federated learning tasks, federated learning model. The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)).
The claim recites acquire data associated with routes of a plurality of mobile communication devices, which is simply acquiring data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)).
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of:
processing circuit, memory, computer readable program instructions, plurality of mobile communication devices, one or more base stations amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b))
acquiring data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d))
object detection, autonomous vehicles, plurality of federated learning tasks, federated learning model amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h))
The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible.
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.
The factual inquiries 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.
Claim(s) 1-12, 14-18, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al. (Mobility-Aware Proactive Edge Caching for Connected Vehicles Using Federated Learning, hereinafter referred to as “Yu”).
Regarding claim 1 (Currently Amended), Yu teaches a computer-implemented (Yu, section V.A. – teaches HP workstations, base stations, vehicles, MSBs and RSUs) method for scheduling federated learning tasks for training a federated learning model (Yu, section IV – teaches scheduling federated learning training using vehicles), for object detection and prediction of actions (Yu, section III – teaches a FL model for content popularity and content caching) of mobile communication devices for autonomous vehicles (Yu, section III – teaches vehicle communication with RSUs [base stations]), the method comprising:
acquiring data associated with routes of a plurality of mobile communication devices (Yu, section IV – teaches the system having knowledge of vehicle trajectories);
determining a subset of mobile communication devices which share a same route for a given amount of time based on the data associated with the routes of the plurality of mobile communication devices (Yu, section IV.A. – teaches selecting a set of vehicles within the coverage area of an RSU for a given amount of time);
determining one or more base stations located along the shared route (Yu, section IV.A – teaches selecting an RSU [base station] at a road entrance for the vehicles);
estimating , for the one or more base stations, points of time at which the subset of mobile communication devices are in coverage areas of respective base stations (Yu, section IV.A. – teaches selecting vehicles with a long standing time in the RSU’s coverage area);
determining at least one of: an amount of required processing resources at the base stations and an amount of required processing resources at the subset of mobile communication devices (Yu, section IV.A. – teaches vehicle selection based on channel condition and network stability), wherein the determination is based on the estimated points of time at which the subset of mobile communication devices are in coverage areas of respective base stations (Yu, section IV.A. – teaches selecting vehicles with a long standing time in the RSU’s coverage area); and
generating a schedule for a plurality of federated learning tasks to be performed at the subset of mobile communication devices and the one or more base stations (Yu, section IV.A. – teaches scheduling vehicles and RSU to perform federating learning), wherein the generation of the schedule is based on priority levels associated with the plurality of federated learning tasks (Yu, section III – teaches scheduling federated learning training based on content popularity for content caching), estimated points of time at which the subset of mobile communication devices are in coverage areas of the one or more base stations (Yu, section IV.A. – teaches selecting vehicles for federated learning training with a long standing time in the RSU’s coverage area), and at least one of: the amount of required processing resources at the one or more base stations and the amount of required processing resources at the subset of mobile communication devices (Yu, section IV.A. – teaches vehicle selection based on channel condition and network stability).
Regarding claim 2 (Original), Yu teaches all of the limitations of the method of claim 1 as noted above. Yu further teaches communicating the generated schedule to at least one of: the one or more base stations and the subset of mobile communication devices (Yu, section IV.A. – teaches selected vehicles download the model [Therefore, the selected vehicle is notified of the scheduled task]).
Regarding claim 3 (Currently Amended), Yu teaches all of the limitations of the method of claim 1 as noted above. Yu further teaches assigning a worker role or a semi-master role to at least one of the subset of mobile communication devices, wherein a worker role indicates that model training is to be performed at the respective mobile communication device, and a semi-master role indicates that gathering of local model parameters from nearby mobile communication devices is to be performed at the respective mobile communication device (Yu, section IV.A. – teaches that a selected vehicle trains the downloaded model on local data [worker]).
Regarding claim 4 (Currently Amended), Yu teaches all of the limitations of the method of claim 1 as noted above. Yu further teaches wherein the schedule is generated such that when a respective mobile communication device is estimated to be in a coverage area of a respective base station, communication between the mobile communication device and the base station is triggered to perform at least a part of a scheduled federated learning task (Yu, section IV.A. – teaches selected vehicles download the model [communication is triggered]).
Regarding claim 5 (Original), Yu teaches all of the limitations of the method of claim 4 as noted above. Yu further teaches wherein communication between the mobile communication device and the base station comprises at least one of: transmitting initial model parameters for the federated learning model from the base station to the mobile communication device, and transmitting local model parameters for the federated learning model from the mobile communication device to the base station (Yu, section IV.A. – teaches selected vehicles download the model and upload the local model once trained).
Regarding claim 6 (Currently Amended), Yu teaches all of the limitations of the method of claim 5 as noted above. Yu further teaches
wherein local model parameters from at least one of the subset of mobile communication devices are received at a base station (Yu, section IV.A. – teaches uploading the trained local model from the vehicle to the RSU [base station] and the RSU computing a weighted sum of all received local models); and
wherein an aggregation algorithm based on the received local model parameters is performed at the base station to generate an updated federated learning model (Yu, section IV.A. – teaches the RSU computing a weighted sum of all received local models to generate new global model).
Regarding claim 7 (Currently Amended), Yu teaches all of the limitations of the method of claim 6 as noted above. Yu further teaches wherein the updated federated learning model is transmitted from the base station to at least one of the subset of mobile communication devices or at least one of the plurality of mobile communication devices (Yu, section IV.A. – teaches the new updated global model is then sent to vehicles for the next round of training).
Regarding claim 8 (Currently Amended), Yu teaches all of the limitations of the method of claim 1 as noted above. Yu further teaches wherein the data associated with the routes of the plurality of mobile communication devices includes at least one of: current route data of the plurality of mobile communication devices, and inferred route data of the plurality of mobile communication devices (Yu, section IV.A. – teaches that the vehicles are allowed to use previously downloaded models for training due to the knowledge of historic and current trajectory data).
Regarding claim 9 (Original), Yu teaches all of the limitations of the method of claim 8 as noted above. Yu further teaches wherein the current route data of the plurality of mobile communication devices is acquired from a navigation application associated with respective mobile communication devices, and/or wherein the inferred route data is acquired from an external artificial intelligence component (Yu, section IV.A. – teaches selecting vehicles based on speed and position [requiring a navigation application]).
Regarding claim 10 (Currently Amended), Yu teaches all of the limitations of the method of claim 1 as noted above. Yu further teaches
wherein locally scheduling of communication with at least one of the subset of mobile communication devices is performed at the one or more base stations (Yu, section IV.A – teaches the RSU selects the vehicles; see also Yu, Algorithm 1), and
wherein the local scheduling is based on at least one of: a current distance between the respective base station and the respective mobile communication device, a speed of motion of the at least of the respective mobile communication device, a direction of motion of the respective mobile communication device (Yu, section IV.A. – teaches selecting vehicles based on speed and position).
Regarding claim 11 (Currently Amended), Yu teaches all of the limitations of the method of claim 1 as noted above. Yu further teaches acquiring configuration management data associated with the shared route (Yu, section III – teaches a vehicular network consisting of several MBSs location at the edge of the network, a set of RSUs within the coverage area of a MBS placed equidistantly on both sides of the road and a set of connected vehicles; see also Yu, Fig. 1), and wherein determining one or more base stations located along the shared route is based on the configuration management data (Yu, section IV.A – teaches selecting an RSU [base station] at a road entrance for the vehicles).
Regarding claim 12 (Original), Yu teaches all of the limitations of the method of claim 11 as noted above. Yu further teaches wherein the configuration management data is acquired from an operations support system (Yu, section III – teaches the MBSs connected to the Internet via a backhaul link; see also Yu, Fig. 1).
Regarding claim 14 (Currently Amended), Yu teaches all of the limitations of the method of claim 1 as noted above. Yu further teaches wherein determining an amount of required processing resources for a base station or for a mobile communication device is further based on an estimated amount of processing resources required by the federated learning model (Yu, section IV.A. – teaches that the vehicle selection is based on the amount of time in the coverage area [processing time needed to train the model]).
Regarding claim 15 (Currently Amended), Yu teaches all of the limitations of the method of claim 1 as noted above. Yu further teaches wherein the amount of required processing resources for a base station or for a mobile communication device is associated with at least one of: an amount of required computational resources, an amount of required storage resources, and an amount of required networking resources (Yu, section IV.A. – teaches vehicle selection based on channel condition and network stability).
Regarding claim 16 (Currently Amended), Yu teaches all of the limitations of the method of claim 1 as noted above. Yu further teaches wherein the generation of the schedule is further based on at least one of: a type of connectivity between respective mobile communication devices and/or between respective base stations and respective mobile communication devices, a quality of connectivity between respective mobile communication devices and/or between respective base stations and respective mobile communication devices, radio quality metrics of the one or more base stations and/or of one or more of the subset of mobile communication devices, a type of device required for performing a respective federated learning task, available processing power at a respective base station, and available processing power at a respective mobile communication devices (Yu, section IV.A. – teaches vehicle selection based on channel condition and network stability).
Regarding claim 17 (Currently Amended), Yu teaches all of the limitations of the method of claim 1 as noted above. Yu further teaches wherein a priority level associated with a federated learning task is based on at least one of: a priority level of the federated learning model, a type of data required for the federated learning model, existing data already available for the federated learning model, and a level of subscription associated with a mobile communication device at which the federated learning task is to be performed (Yu, section III – teaches scheduling federated learning training based on content popularity for content caching; Yu, section IV.A. – teaches sufficient local data for training).
Regarding claim 18 (Currently Amended), Yu teaches all of the limitations of the method of claim 17 as noted above. Yu further teaches wherein the priority level of the federated learning model is set by a controlling entity (Yu, section III – teaches the MBS controls the list of cached contents and dynamically updates the caching contents of the RSUs) and is contained in metadata of the federated learning model (Yu, sections III-IV.A. – teaches that the results of the federated learning model show content popularity and which contents are cached/replaced).
Regarding claim 20, it is the system embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found in claim 1. Yu further teaches a system for scheduling federated learning tasks for training a federated learning model (Yu, section IV – teaches scheduling federated learning training using vehicles) for object detection and prediction of actions (Yu, section III – teaches a FL model for content popularity and content caching) of mobile communication devices for autonomous vehicles (Yu, section III – teaches vehicle communication with RSUs [base stations]), the system comprising:
a processing circuit (Yu, section V.A. – teaches HP workstations, base stations, vehicles, MSBs and RSUs); and
a memory coupled to the processing circuit and comprising computer readable program instructions that, when executed by the processing circuit, cause the system to perform (Yu, section V.A. – teaches HP workstations, base stations, vehicles, MSBs and RSUs) …
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yu in view of Abdalla et al. (DeepMotions: A Deep Learning System for Path Prediction Using Similar Motions, hereinafter referred to as “Abdalla”).
Regarding claim 13 (Currently Amended), Yu teaches all of the limitations of the method of claim 1 as noted above. Yu further teaches
wherein data associated with the routes of the plurality of mobile communication devices comprises at least one of: a location of a mobile communication device, a speed of a mobile communication device, a direction of a mobile communication device, and one or more environmental features associated with a mobile communication device, wherein the one or more environmental features includes one or more of: road conditions, traffic conditions, and weather conditions (Yu, section IV.A. – teaches selecting vehicles based on speed and position),
While Yu teaches estimating the time in the coverage area, Yu does not explicitly teach wherein the estimation of points of time at which the subset of mobile communication devices are in the coverage areas of the respective base stations comprises analysing, using a machine learning model, the data associated with the routes of the plurality of mobile communication devices.
Abdalla teaches wherein the estimation of points of time at which the subset of mobile communication devices are in the coverage areas of the respective base stations comprises analysing, using a machine learning model, the data associated with the routes of the plurality of mobile communication devices (Abdalla, section V – teaches using machine learning to predict a path of a of an object based on the motions of similar objects).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Yu with the teachings of Abdalla in order to predict the future path of a query object without any prior knowledge of the object historical motions by observing the moving objects in the vicinity that have similar motion patterns of the query object in the field of path planning for mobility-aware federated learning (Abdalla, Abstract – “Trajectory prediction techniques play a serious role in many location-based services such as mobile advertising, carpooling, taxi services, traffic management, and routing services. These techniques rely on the object's motion history to predict the future path(s). As a consequence, these techniques fail when history is unavailable. The unavailability of history might occur for several reasons such as; history might be inaccessible, a recently registered user with no preceding history, or previously logged data is preserved for confidentiality and privacy. This paper presents a Bi-directional recurrent deep-learning based prediction system, named DeepMotions, to predict the future path of a query object without any prior knowledge of the object historical motions. The main idea of DeepMotions is to observe the moving objects in the vicinity that have similar motion patterns of the query object. Then use those similar objects to train and predict the query object's future steps. To compute similarity, we propose a similarity function that is based on the KNN algorithm. Extensive experiments conducted on real data sets confirm the efficient performance and the quality of prediction in DeepMotions with up to 96% accuracy.”).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communication from the examiner should be directed to MARSHALL WERNER whose telephone number is (469) 295-9143. The examiner can normally be reached on Monday – Thursday 7:30 AM – 4:30 PM ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar, can be reached at (571) 272-7796. The fax number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MARSHALL L WERNER/ Primary Examiner, Art Unit 2125
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