DETAILED ACTION
Claims 1-20 are pending.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/18/2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Joly et al. (US 2022/0179411 A1) in view of Wright et al. (US 2021/0174678 A1).
Regarding claim 1, Joly teaches the invention substantially as claimed including a computer-implemented method for providing non-critical resources for requested activity of a riding passenger of an autonomous vehicle ([0003] According to embodiments, disclosed are a method, system, and computer program product.; [0062] The ECAVC 460 may be configured to instruct, allocate, assign, or otherwise partition the various computing resources of the autonomous vehicles 430, based on the consumption of computing resources from the providers 420 and/or the CRI 440. The ECAVC 460 may assign and re-assign the computing resources of the autonomous vehicles 430 based on changing needs and during transportation operations of autonomous vehicles.; [0064]; [0075] The ECAVC 460 may assign, at 470-3, only those resources that are not used or are not critical to resources needed to perform autonomous navigation by the autonomous vehicles 430), the method comprising:
identifying, by one or more processors, available non-critical resources of one or more autonomous vehicles by a communicative connection to the one or more autonomous vehicles ([0064] The autonomous vehicle 430-2 may have free storage resources. The free resources of an autonomous vehicle 430-2 may not overlap with the type and/or amount of computing resources used for autonomous movement by the autonomous vehicle.; [0090] causing a processor to carry out aspects of the present invention.).
receiving, by the one or more processors, an autonomous vehicle travel request ([0018] For example, a user may consume media on a smart television in the home, the user may then plan to travel on a bus or in a ride sharing vehicle, and may continue to consume the media through a smartphone at the same visual and audio fidelity. One solution to reduce the limitations and inabilities of client devices is to utilize cloud computing to perform, or assist in performing, some or all of the computing tasks for client devices. [0057] The autonomous vehicles 430 may be a plurality of autonomous vehicles configured with a set of computing resources. In some embodiments, system 400 may be configured to operate with a single (i.e., one) autonomous vehicle (e.g., autonomous vehicle 430-1). The autonomous vehicles 430 may be configured to receive passengers and perform one or more transportation operations throughout various locations. For example, autonomous vehicle 430-1 may be a privately owned autonomous vehicle configured to drive an owner to a destination of the owner's choosing. In another example, autonomous vehicle 430-2 may be a vehicle owned by a ridesharing or taxi-cab company, configured to accept requests from users or from a central server of the company.) and a request for use of at least some of the available non-critical resources, wherein the request is received from a computing device of a requesting user ([0070]; [0073]; [0083] From start 505, method 500 begins by monitoring for one or more computer resource disparities at 510. The computer resources disparities may be monitored by listening to requests for computing resources from a client device. The compute resource disparities may be monitored by listening to requests for hosting of computing resources and/or data precaching requests by a provider or content resource intermediary. A computer resource disparity may be a lack of memory for processing by a client device, a lack of processing cycles for processing by the client device, a lack of input/output bandwidth or latency, or other relevant disparity between the number of computing resources needed by a client device to perform a computing task and the number of computing resources that the client device has access to for performing the computing task.);
determining, by the one or more processors, the one or more autonomous vehicles having the available non-critical resources to fulfill the request ([0069]; [0074] The ECAVC 460 may identify, based on the information received at 470-1, a subset of the autonomous vehicles 430 that are available to perform edge computing.; [0075] The ECAVC 460 may assign, at 470-3, only those resources that are not used or are not critical to resources needed to perform autonomous navigation by the autonomous vehicles 430.; [0084] If a computer resource disparity is detected at 515:Y, a set of one or more autonomous vehicles may be identified based on location at 520. Specifically, and by way of example, a set of one or more autonomous vehicles may be identified based on their adjacency, proximity, or present location. The location of the set of autonomous vehicles may be in relation to the location of a client device having a computer resource disparity, as detected at 515.; [0085] If there is an autonomous vehicle that is adjacent to the location identified at 515:Y, then an autonomous vehicle computing inquiry may be generated at 540. The autonomous vehicle computing inquiry may be generated based on the location of the computer resource disparity and based on the type of computer resource disparity. For example, if a client device is detected (at 515) as lacking sufficient processing power, then the inquiry may be generated that includes a request for processing cycles of a free autonomous vehicle.; [0087]);
generating, by the one or more processors, a resource stack configuration including the set of available non-critical resources ([0075] At 470-4, the ECAVC 460 may communicate the identity and security information of logical partitions of the subset of autonomous vehicles 430, to the provider 420-1 and/or the CRI 440. Responsively, the provider 420-1 and/or CRI 440 may transmit, at 470-5 the augmented reality data and objects to the identified subset. At 470-6 the subset of autonomous vehicles 430 may host edge computing resources (e.g., resource stack) to perform the task of augmented reality object hosting for the client devices 410 during the concert.); and
granting, by the one or more processors, access to the generated resource stack configuration based on the selected set of available, non-critical resources requested from the computing device of the requesting user ([0075] The ECAVC 460 may assign, at 470-3, only those resources that are not used or are not critical to resources needed to perform autonomous navigation by the autonomous vehicles 430.; [0088] At 570 a first autonomous vehicle may be assigned to perform the computing task. The first autonomous vehicle may be selected from the set of autonomous vehicles that are at the location. The first autonomous vehicle may be selected from a set of autonomous vehicles that will soon be at the location. For example, an autonomous vehicle may be selected that is on a route that will put the autonomous vehicle intersecting with the first location in an acceptable time frame to address and/or perform the computing task. The first autonomous vehicle may be assigned the performance of the computing task based on the set of computing resources. For example, only a subset of the set of computing resources returned (as part of the status received at 560) may be identified, selected, and assigned based on having the computing resources to alleviate a part or all of the computer resource disparity.).
Joly teaches selecting/assigning autonomous vehicles based on a user request but does not expressly teach presenting to the requesting user, by the one or more processors, information regarding the available non-critical resources matching the request;
receiving from the requesting user, by the one or more processors, a selection of a set of the available non-critical resources.
In a similar field of endeavor Wright in [0002] teaches “The present disclosure relates generally to allocating unused or under-utilized computational resources in autonomous vehicles. More particularly, the present disclosure relates to systems and methods that allocate excess computational capacity associated with current or forecasted autonomous vehicle routes towards processing operations for participation in a distributed ledger.” .
Further, Wright teaches presenting to the requesting user, by the one or more processors, information regarding the available non-critical resources matching the request ([0038] In some implementations, the route selection information can include an actual gain when presented to the user. More particularly, an actual gain can be determined based on the estimated gain and presented to the user in the route information. As an example, an actual gain can be determined from the actual value generated by the allocated computational resources and can be presented to the user. It should be noted that, due to the inherent fluctuations in value associated with distributed ledger processing, the actual gain can be either greater or lower than the estimated gain. In some implementations, the actual gain can be calculated in real-time and presented to the user in a game-like fashion as the route service is provided. As an example, an actual value indicator can be displayed to the user during route service that indicates the actual value being generated by the excess computational resources as the route service progresses.; [0041] Excess computational capacity of autonomous vehicles can be allocated to processing operations. More particularly, at least a portion of the excess computational capacity can be allocated to processing operations associated with participation in a distributed ledger. It will be appreciated by those skilled in the art that such processing operations can generally include performing hash operations on a block in a cryptographic blockchain (e.g., writing a new transaction to a section of a distributed ledger, etc.), but can also include any other manner of calculations or processing operations related to participation in a distributed ledger. In some implementations, the computing resources of each autonomous vehicle of one or more autonomous vehicle can be allocated independently. More particularly, each autonomous vehicle can perform processing operations in participation with an independently selected distributed ledger. As an example, a first autonomous vehicle can perform processing operations associated with participation in a first distributed ledger while a second autonomous vehicle can perform operations associated with participation in a second distributed ledger. As another example, the excess computational capacity of both a first and a second autonomous vehicle can be allocated towards participation in the same distributed ledger.);
receiving from the requesting user, by the one or more processors, a selection of a set of the available non-critical resources ([0050] Additional technical effects and benefits can be achieved by selecting routes based at least in part on the estimated gain associated with excess computational resource allocation. Autonomous vehicle service providers can generate additional value from autonomous vehicles by selecting routes that allow for more allocation of computational resources towards participation in distributed ledgers. Further, an autonomous vehicle service provider can choose to pass this value on to users, reducing costs associated with using their respective services (e.g., a rideshare service).; [0051] Further, presenting to users the estimated gains associated with allocation of excess computational resources allows service providers to reduce prices and incentivize users to choose their platform. As an example, by presenting a user with reward points associated with a less computationally complex alternative route, vehicle service providers can reward users with lower costs and/or reward points. As such, the improved methods for allocation of unused computational resources disclosed herein leads to more efficient utilization of expensive and sophisticated computational resources used for implementing autonomous driving. Further, the disclosed technology can achieve reduced route costs, improved user retention, and improved autonomous vehicle route planning services.; [0105] At 706, the method 700 includes selecting, based on a user selection input, a route from the base route and the one or more alternative routes for navigation by the autonomous vehicle. The user can select a route after viewing the displayed route selection information. After receiving the user selection input, the method can select the route based on the user selection input and implement the route for autonomous driving services (e.g., autonomous vehicle rideshare services).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Wright with the teachings of Joly to allow for a user to select an alternative that provides the necessary resources for the user’s request. The modification would have been motivated by the desire of improving methods for allocation of unused computational resources that would lead to more efficient utilization of expensive and sophisticated computational resources used for implementing autonomous driving. Further, the disclosed technology can achieve reduced route costs, improved user retention, and improved autonomous vehicle route planning services. (See Wright’s [0051])
Regarding claim 2, Joly teaches wherein the available non-critical resources include at least one or more central processing unit (CPU), graphical processing unit (GPU), memory, storage, network access, and software applications comprising the resource stack configuration ([0016] Computer systems may have computing resources. For example, computing resources may include memory, processing, input/output (I/O), and power source resources. Computing resources may include the amount of memory, processing, I/O, and/or power source resources available to perform computing tasks. Computing resources may include the type of memory, processing, I/O, and or power source resources available to perform a computing task. For example, a particular type of neural networking operation or other machine learning technique may require the use of a particular multicore processor, or a specific graphical processing unit.; [0064] The autonomous vehicle 430-2 may have free storage resources. The free resources of an autonomous vehicle 430-2 may not overlap with the type and/or amount of computing resources used for autonomous movement by the autonomous vehicle; [0075]).
Regarding claim 3, Joly teaches wherein the selection of a set of the available non-critical resources includes receiving a request from the requesting user for resources to perform an activity while riding on the autonomous vehicle ([0018] For example, a user may consume media on a smart television in the home, the user may then plan to travel on a bus or in a ride sharing vehicle, and may continue to consume the media through a smartphone at the same visual and audio fidelity. One solution to reduce the limitations and inabilities of client devices is to utilize cloud computing to perform, or assist in performing, some or all of the computing tasks for client devices.).
Regarding claim 4, Joly teaches wherein an analysis of a travel route current and historical conditions determines whether the requested available resources remain available for a duration of the travel ([0057] For example, autonomous vehicle 430-1 may be a privately owned autonomous vehicle configured to drive an owner to a destination of the owner's choosing. In another example, autonomous vehicle 430-2 may be a vehicle owned by a ridesharing or taxi-cab company, configured to accept requests from users or from a central server of the company. [0064] Responsive to an identified pattern, the ECAVC 460 may be configured to assign computing resources before there is a determined disparity, such as a future computing task to be performed for a client device 410. For example, a pattern of usage is identified of client devices 410 in a first location, and a particular computer task event (e.g., a community gathering to play a shared online game). Based on determining the event will happen in the future, the ECAVC 460 may be configured to instruct autonomous vehicle 430-2 to store one or more assets, or precache certain routings for execution at the time of the event. The autonomous vehicle 430-2 may have free storage resources. The free resources of an autonomous vehicle 430-2 may not overlap with the type and/or amount of computing resources used for autonomous movement by the autonomous vehicle. The autonomous vehicle 430-2 can perform edge computing for the client devices 410 at the first location and at the same time safely navigate along a route that is proximate or within a radius of the first location during the event.; [0066] The ECAVC 460 may coordinate with the autonomous vehicles 430 and/or the VP 450 to direct the autonomous vehicles 430 for transportation functions and for computing resource functions. For example, a first autonomous vehicle 430-1 may be performing autonomous movement along a first route for a user that is riding in the autonomous vehicle 430-1. The first route may facilitate the user to arrive at the proper location within a set duration (e.g., 30 minutes). The ECAVC 460 and/or the VP 450 may reroute the autonomous vehicle 430-1 to travel along a second route that is adjacent, proximate, or otherwise near client device 410-1 to facilitate the performance of a computing task for the client device. The second route may prioritize the computing task over autonomous movement (e.g., the second route may be towards more traffic, the second route may be a longer duration, the second route may take the autonomous vehicle a longer duration of 35 minutes to complete).; [0070])
Regarding claim 5, Joly teaches wherein the available non-critical resources are determined based on dynamically receiving resource usage data from sensors on operating autonomous vehicles operating within an autonomous vehicle transport management system (AVTMS) ([0021] An edge computing autonomous vehicle infrastructure (ECAVI) may alleviate the various issues and drawbacks to providing computing resources to client devices. ECAVI may utilize the computing resources of autonomous vehicles to provide edge computing resources. The ECAVI may operate without impacting the function or operation of autonomous vehicles. Specifically, an edge computing autonomous vehicle controller (ECAVC) may be configured to determine the current resource usage of one or more autonomous vehicles, and based on identifying free resources, assign, allocate, or otherwise distribute the resources for use by client devices. The ECAVI may operate in real-time, near real-time (e.g., minutes, seconds), or predictively (e.g., based on the analysis of processing, network, and data-usage trends) to assign computing resources of autonomous vehicles to the performance of computing tasks. [0025] The ECAVI may be configured to augment processing or data capacity needs where appropriate. For example, if a number of users are at a sporting event and each user has an AR device, the ECAVI may be configured to host the networking infrastructure of the AR devices. Further, the ECAVI may be configured to provide other operations in real-time to the users at the sporting event. For example, data storage of various objects (such as AR object polygonal models), and processing (such as running image detection algorithms to align the AR models within a real-world space of the field of the sporting event). The ECAVI may respond quicker and with more appropriate computing resources for these type of tasks, and may be able to scale to temporary spikes in demands, such as a computer resource disparity (e.g., a lack of memory, storage, processing, and/or I/O computing resources). The ECAVI may also facilitate the offloading of processing, storage, and streaming from both client devices, cloud servers, CDNs and other computing providers.).
Regarding claim 6, Joly teaches wherein identifying the available non-critical resources include identifying a corresponding autonomous vehicle associated with the respective available non- critical resources ([0021] Specifically, an edge computing autonomous vehicle controller (ECAVC) may be configured to determine the current resource usage of one or more autonomous vehicles, and based on identifying free resources, assign, allocate, or otherwise distribute the resources for use by client devices).
Regarding claim 7, Joly teaches wherein the request received from the computing device of the user further comprises:
receiving, by the one or more processors, the request by the requesting user inputting requested available non-critical resources to a communicatively connected application (app) operating on the computing device of the user, and determining, by the one or more processors, whether the request identifies a particular resource stack configuration or a request for a functional activity ([0018] For example, a user may consume media on a smart television in the home, the user may then plan to travel on a bus or in a ride sharing vehicle, and may continue to consume the media through a smartphone at the same visual and audio fidelity.; [0021]; [0073] Responsively the ECAVC 460 may use the request to determine a resource disparity for performing augmented reality operations (e.g., AR device and associated app running on client devices 410 of users attending the concert may require access to a large catalog of virtual objects with near real-time latency).).
Regarding claim 8, Joly teaches wherein a token can be established between two or more autonomous vehicles in advance, such that the two or more autonomous vehicles travel to a common destination at an approximately simultaneous timeframe, and wherein the token reserves portions of the available non-critical resources from the two or more autonomous vehicles to fulfill the request for the use of at least some of the available non-critical resources ([0065] The ECAVC 460 may be configured to apportion or allocate computing resources of a plurality of autonomous vehicles 430 for a single computing task. For example, the ECAVC 460 may determine by transmitting an autonomous vehicle inquiry the availability and computing resources. Responsively, autonomous vehicles 430-1 and 430-2 may be passing by a first location (at a first time and a second time, respectively). The first location may be where an identified resource disparity exists for client device 410-1. Based on the movement of autonomous vehicles 430-1 and 430-2 from one location to another location proximate to the first location, cached data and to-be performed computing algorithms may be transferred from autonomous vehicle 430-1 to autonomous vehicle 430-2, so that the autonomous vehicles 430-1 and 430-2 can collectively perform edge computing. Simultaneously the autonomous vehicles 430 may be communicating to the VP 450, the ECAVC 460, or each other, so that the autonomous vehicles 430 can provide required location specific data during edge computing. [0070] In some embodiments, the system 400 may be configured to determine a route of an autonomous vehicle. In detail, the ECAVC 460 (e.g., by request), and/or the VP 450 (e.g., by broadcast) may communicate with each other to identify any necessary needs of client devices 410 in relation to the location and route of the autonomous vehicles 430. For example, the ECAVC 460 may identify time sensitive data using any relevant method, which is to be accessed in a specific location, a particular region of contiguous locations, or along a particular route. The ECAVC 460 may further identify the capabilities and available computing resources of the autonomous vehicles 430 that are near the location, region, or route. Using pattern analysis, the ECAVC 460 may predict types of data that may be required to perform the computing task and may also predict the number of client devices 410 that are anticipated to need to perform the computing task. Using historical learning the proposed system will be predicting how many datacenter nodes are required to provide the required data. Using historical learning, the ECAVC 460 may predict a duration of data consumption in or around a location, region, or route. Based on time sensitive data and historical pattern analysis, the ECAVC 460 may predict the data consumption needed around the location and may also be identifying the actual data consumption needed. Then, a transportation system can identify the appropriate route such that at least one of the autonomous vehicles 430 (based on the requirement of edge computing datacenter nodes for performing computing tasks) have the relevant data.; [0084-88]; [0088]For example, an autonomous vehicle may be selected that is on a route that will put the autonomous vehicle intersecting with the first location in an acceptable time frame to address and/or perform the computing task. The first autonomous vehicle may be assigned the performance of the computing task based on the set of computing resources. For example, only a subset of the set of computing resources returned (as part of the status received at 560) may be identified, selected, and assigned based on having the computing resources to alleviate a part or all of the computer resource disparity.; Claim 11).
Regarding claim 9, it is a media/product type claim having similar limitations as claim 1 above. Therefore, it is rejected under the same rationale above.
Regarding claim 10, it is a media/product type claim having similar limitations as claim 2 above. Therefore, it is rejected under the same rationale above.
Regarding claim 11, it is a media/product type claim having similar limitations as claim 4 above. Therefore, it is rejected under the same rationale above.
Regarding claim 12, it is a media/product type claim having similar limitations as claim 5 above. Therefore, it is rejected under the same rationale above.
Regarding claim 13, it is a media/product type claim having similar limitations as claim 7 above. Therefore, it is rejected under the same rationale above.
Regarding claim 14, it is a media/product type claim having similar limitations as claim 8 above. Therefore, it is rejected under the same rationale above.
Regarding claim 15, it is a system type claim having similar limitations as claim 1 above. Therefore, it is rejected under the same rationale above.
Regarding claim 16, it is a system type claim having similar limitations as claim 2 above. Therefore, it is rejected under the same rationale above.
Regarding claim 17, it is a system type claim having similar limitations as claim 4 above. Therefore, it is rejected under the same rationale above.
Regarding claim 18, it is a system type claim having similar limitations as claim 5 above. Therefore, it is rejected under the same rationale above.
Regarding claim 19, it is a system type claim having similar limitations as claim 7 above. Therefore, it is rejected under the same rationale above.
Regarding claim 20, it is a system type claim having similar limitations as claim 8 above. Therefore, it is rejected under the same rationale above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORGE A CHU JOY-DAVILA whose telephone number is (571)270-0692. The examiner can normally be reached Monday-Friday, 6:00am-5:00pm.
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/JORGE A CHU JOY-DAVILA/Primary Examiner, Art Unit 2195