DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, anycorrection of the statutory basis for the rejection will not be considered a new ground ofrejection if the prior art relied upon, and the rationale supporting the rejection, would bethe same under either status.
Response to Amendment
The proposed reply filed on December 11th, 2025 has been entered. Claims 1 and 11-13 have been amended. Claims 1-20 are pending in the application.
Claim Rejections - 35 USC § 101
Amended claims and arguments filed on 12/11/2025 are found to be persuasive, thus, 35 USC § 101 rejection of claims 11-20 are withdrawn.
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 non-obviousness.
Claim(s) 1-2, 4-5, 11-12 and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Garcia Morchon (US 2024/0373312 A1) in view of IEEE, Zhang et al. (5G-Enabled Vehicular Networks) and Ding et al. (US 12,1852,30 B2).
Regarding claim 1, Garcia Morchon teaches a method, comprising: receiving a request from a user equipment (UE) in an endpoint environment, the request indicating a demand for processing resources on an edge server to perform a task for the UE (Fig. 1, [0062, 0107], a mobile access device 30 (5G enabled vehicle) is provided for relaying data between a donor access device (e.g. gNB) 20 and a terminal device (e.g. wireless communication device such as a UE) 10. The donor access device 20 provides connectivity to a 5G core network 40. The UE 10 may also request the required resources (data) again, triggering then the local download, from the mobile base station 30 or from an edge server, e.g., collocated with the donor gNB 20),
Garcia Morchon teaches the task comprising at least one of video encoding, video decoding, audio encoding, audio decoding, graphics processing, graphics rendering, video analytics, computer vision, object recognition, data caching, augmented reality computing, virtual reality computing, distributed computing tasks, video or audio storage, data storage, signal processing, system modeling, simulations, machine learning, or software application tasks (Figs. 1 and 9, [0292-0295], base station may be any network access device (such as a base station, Node B (eNB, eNodeB, gNB, gNodeB, ng-eNB, etc.), access point or the like) that provides a geographical service area. At least some of the above embodiments may be based on a 5G New Radio (5G NR) radio access technology. Moreover, the invention can be applied in medical applications or connected healthcare in which multiple wireless (e.g. 4G/5G) connected sensor or actuator nodes participate, in medical applications or connected healthcare in which a wireless (e.g. 4G/5G) connected equipment consumes or generates occasionally a continuous data stream of a certain average data rate, for example video, ultrasound, X-Ray, Computed Tomography (CT) imaging devices, real-time patient sensors, audio or voice or video streaming devices used by medical staff),
Garcia Morchon teaches in response to the request, identifying a first 5G-enabled vehicle in proximity to the endpoint environment (Fig. 1, [0026], it may be determined which of a plurality of mobile access devices 30 will be in a predetermined range of the target device taking into account current traveling schedules of the mobile access devices and it may be identified which of the plurality of mobile access devices can deliver the transmission data to the target device taking into account the position information of the target device at a predetermined time, the positions of each of the plurality of mobile access devices at the predetermined time, and communication requirements of a user of the target device at the predetermined time. Thereby, a best suited mobile access device can be selected for effective transfer of the transmission data),
Garcia Morchon teaches determining an operational state and a power state of the first 5G-enabled vehicle (Fig. 1, [0105], the CN 50 can inform the UE 10 through the macro cell (i.e. macro base station 20) about the timing when the mobile base station 30 will be in reach and available for delivery (i.e. the operational state of the mobile access station 30). Optionally, the CN 50 may also inform the UE 10 about the physical cell identifier and beam index to use to speed up cell acquisition),
Garcia Morchon teaches in response to the determination that the operational state is idle and that the power state is above a predetermined threshold, determining availability of a first processing resource carried by the first 5G-enabled vehicle (Fig. 1, [0097-0100], the CN 50 knows the route of mobile base stations as well as the location of the user and his macro cell. The CN 50 plans which of the mobile base stations will be in close vicinity of the user taking into account their current schedules. Based thereon, the CN 50 can identify which mobile base station can deliver which load to which UE taking into account at least one of the positions of each UE at time t, the positions of each mobile base station at time t, and the communication requirements of a user at time t. A simple exemplary strategy may consist in checking which mobile base station is going to be close to the UE 10 next, and whether the available communication link between the UE 10 and a potential mobile base station will be enough to handle the communication requirements of the UE 10. The available communication link refers to the available communication resources when the UE 10 and the potential mobile base station encounter taking into account the distance and expected duration of the communication link. The CN 50 informs a chosen mobile base station 30 about a presence of a user along the route of the mobile base station 30, data requirements of the user of the UE 10, and/or the position of the UE 10, so that the mobile base station 30 can pre-optimize transmission parameters, e.g., beamforming or frequency or pre-allocate transmission resources. The CN 50 allocates resources at the mobile base station 30, e.g., triggered by the DN request. These resources can be storage resources to cache the data transfer or communication resources to perform the data transfer. The CN 50 starts a local download of the resources required by the user to the mobile base station 30 (e.g. caching, as in a content delivery network)),
Garcia Morchon teaches in response to the determination that the first 5G-enabled vehicle carries the first processing resource that is available, determining whether the first processing resource carried by the first 5G-enabled vehicle meets the demand of the user equipment (Fig. 1, the mobile base station 30 pre-allocates and configures communication resources for the transmission of the requested data taking into account at least one of the required amount of data to transfer, the known position of the user, the expected trajectory of the mobile base station 30 and expected time during which the UE 10 and the mobile base station 30 will be in a predetermined range. These communication resources might include coding (e.g. scrambling code, channelization code), timing and frequency resources and/or beam forming over time. Some of these parameters can be configured in advance since the mobile base station 30 has knowledge of the positioning information of the mobile base station 30 and historical data related to, e.g., a reported channel quality indication (CQI). Parameters such as beam forming can be optimized and pre-configured since the position of the UE 10 may be known (in some cases, it can be assumed to be static) and the trajectory of the mobile base station 30 may also be known [0102-0103]),
Garcia Morchon teaches and in response to the determination that the first processing resource meets the demand of the user equipment, allocating the first processing resource carried by the first 5G- enabled vehicle and causing the first 5G-enabled vehicle to serve as an edge server to perform the task for the user equipment using the allocated first processing resource (Fig. 1, [0027, 0108], once the UE 10 and the mobile base station 30 are connected to each other, data transfer can occur with minimal latency since communication parameters have been preconfigured and the UE 10 is able to download data directly from the mobile base station 30 or a close edge server, e.g., collocated with the donor gNB, that has been caching the data when approaching the UE 10. The selected mobile access device may be informed about at least one of a presence of the target device along a route of the mobile access device, data requirements of the user of the target device, and a position of the target device, so that the selected mobile access device can pre-optimize transmission parameters or pre-allocate transmission resources. Thereby, effective transfer of the transmission data can be ensured at the mobile access device).
Regarding claim 4, Garcia Morchon teach wherein the determining availability of 29 a first processing resource carried by the first 5G-enabled vehicle further comprises: determining whether a current level of processing resources carried by the first 5G-enabled vehicle meets both the demand by the user equipment and a self-demand by the first 5G-enabled vehicle; and in response to the determination that the current level of processing resources carried by the first 5G-enabled vehicle meets both the demand by the user equipment and a self- demand by the first 5G-enabled vehicle, determining that the processing resource requested by the user equipment is available (Fig. 1, [0027, 0097-0100, 0102-0103, 0108], the available communication link refers to the available communication resources when the UE 10 and the potential mobile base station encounter taking into account the distance and expected duration of the communication link. The CN 50 informs a chosen mobile base station 30 about a presence of a user along the route of the mobile base station 30, data requirements of the user of the UE 10, and/or the position of the UE 10, so that the mobile base station 30 can pre-optimize transmission parameters, e.g., beamforming or frequency or pre-allocate transmission resources. The CN 50 allocates resources at the mobile base station 30, e.g., triggered by the DN request. These resources can be storage resources to cache the data transfer or communication resources to perform the data transfer. The CN 50 starts a local download of the resources required by the user to the mobile base station 30. The mobile base station 30 pre-allocates and configures communication resources for the transmission of the requested data taking into account at least one of the required amount of data to transfer, the known position of the user, the expected trajectory of the mobile base station 30 and expected time during which the UE 10 and the mobile base station 30 will be in a predetermined range. These communication resources might include coding (e.g. scrambling code, channelization code), timing and frequency resources and/or beam forming over time. Some of these parameters can be configured in advance since the mobile base station 30 has knowledge of the positioning information of the mobile base station 30 and historical data related to, e.g., a reported channel quality indication (CQI). Parameters such as beam forming can be optimized and pre-configured since the position of the UE 10 may be known (in some cases, it can be assumed to be static) and the trajectory of the mobile base station 30 may also be known. Once the UE 10 and the mobile base station 30 are connected to each other, data transfer can occur with minimal latency since communication parameters have been preconfigured and the UE 10 is able to download data directly from the mobile base station 30 or a close edge server, e.g., collocated with the donor gNB, that has been caching the data when approaching the UE 10. The selected mobile access device may be informed about at least one of a presence of the target device along a route of the mobile access device, data requirements of the user of the target device, and a position of the target device, so that the selected mobile access device can pre-optimize transmission parameters or pre-allocate transmission resources. Thereby, effective transfer of the transmission data can be ensured at the mobile access device).
Regarding claim 5, Garcia Morchon teach wherein the determining availability of a first processing resource carried by the first 5G-enabled vehicle further comprises: determining whether a current usage of processing resource by the first 5G- enabled vehicle to perform primary tasks for the 5G-enabled vehicle is below a pre-established threshold level; and in response to the determination that the current usage of processing resource is below the pre-established threshold level, determining that the first processing resource carried by the 5G-enabled vehicle is available (Fig. 1, [0027, 0097-0100, 0102-0103, 0108], the CN 50 informs a chosen mobile base station 30 about a presence of a user along the route of the mobile base station 30, data requirements of the user of the UE 10, and/or the position of the UE 10, so that the mobile base station 30 can pre-optimize transmission parameters, e.g., beamforming or frequency or pre-allocate transmission resources. The CN 50 allocates resources at the mobile base station 30, e.g., triggered by the DN request. These resources can be storage resources to cache the data transfer or communication resources to perform the data transfer. The CN 50 starts a local download of the resources required by the user to the mobile base station 30. Some of these parameters can be configured in advance since the mobile base station 30 has knowledge of the positioning information of the mobile base station 30 and historical data related to, e.g., a reported channel quality indication (CQI). Parameters such as beam forming can be optimized and pre-configured since the position of the UE 10 may be known and the trajectory of the mobile base station 30 may also be known. Once the UE 10 and the mobile base station 30 are connected to each other, data transfer can occur with minimal latency since communication parameters have been preconfigured and the UE 10 is able to download data directly from the mobile base station 30 or a close edge server, e.g., collocated with the donor gNB, that has been caching the data when approaching the UE 10. The selected mobile access device may be informed about at least one of a presence of the target device along a route of the mobile access device, data requirements of the user of the target device, and a position of the target device, so that the selected mobile access device can pre-optimize transmission parameters or pre-allocate transmission resources. Thereby, effective transfer of the transmission data can be ensured at the mobile access device).
Garcia Morchon teaches about a user equipment, in an endpoint environment, requesting an edge server for processing resources. Garcia Morchon, however, fails to expressly teach of identifying, by the edge server to perform one of the tasks listed as video encoding or decoding, audio encoding or decoding or data storage etc. and identifying a 5G-enabled vehicle in the network. (Emphasis added).
Garcia Morchon does not expressly disclose the following features: regarding claim 2, further comprising: performing user authentication to authenticate the user of the user equipment to use the first processing resource of the first 5G-enabled vehicles and verify that the user equipment is eligible to use the first processing resource.
Regarding claim 1, Zhang et al. teach identifying a first 5G-enabled vehicle in proximity to the endpoint environment (Fig. 2, [see page 7942, 2nd col, sub-chap A 2nd para, Page 7943, 1st col, 2nd para, page 7944, 2nd col, para 1st, page 7946, 2nd col, para 1st], the TA (edge server) is consist of redundant TAs and multiple reliable server devices, such as registration servers key generation server, tracing server and so on. There are two kinds of vehicles, i.e., edge computing vehicles (ECVs) and ordinary vehicles (OVs). In special, ECVs act as a role like a group manager which is responsible for constructing a group and interact with OVs. After interacting with TA, ECVs generate a group secret key and a group identity so that vehicles in some groups can directly communicate without the help of TA. In essence, ECVs alleviate the burden of TA. As for OVs, they act only as of the normal manner in general schemes. The proposed protocol consists of five phases as (1) the system initialization phase, (2) participating vehicles registration phase, (3) election strategy and verification of ECV phase, (4) key agreement and batch authentication of vehicles phase, and (5) dynamic addition of vehicles phase. In brief, the system initialization phase is that the TA allocates public parameters to the system; participating vehicles registration phase is that TA preloads a secret and a long-term certificate into each vehicle; election strategy and verification of ECV phase is that TA uses fuzzy logic to select some edge computing vehicles and authenticate these edge computing vehicles; the fourth phase is that the edge computing vehicle is responsible for generating the key for the vehicles in the group, and fully prepares for the subsequent intra-group verification the TA calculates a fitness value of each vehicle within a certain range and then chooses ECV which has the maximal fitness value).
Regarding claim 2, Zhang et al. teach further comprising: performing user authentication to authenticate the user of the user equipment to use the first processing resource of the first 5G-enabled vehicles and verify that the user equipment is eligible to use the first processing resource (Fig. 2, [see page 7944, 2nd col, para 1st], dynamic addition of vehicles phase. In brief, the system initialization phase is that the TA allocates public parameters to the system; participating vehicles registration phase is that TA preloads a secret and a long-term certificate into each vehicle; election strategy and verification of ECV phase is that TA uses fuzzy logic to select some edge computing vehicles and authenticate these edge computing vehicles; the fourth phase is that the edge computing vehicle is responsible for generating the key for the vehicles in the group, and fully prepares for the subsequent intra-group verification. In the last phase, we discuss how to dynamically add vehicles to 5G vehicular networks. Fig. 2 shows the flow diagram of our protocol).
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Garcia Morchon by incorporating the features as taught by Zhang et al. in order to provide a more effective and efficient system that is capable of identifying a first 5G-enabled vehicle in proximity to the endpoint environment, and performing user authentication to authenticate the user of the user equipment to use the first processing resource of the first 5G-enabled vehicles and verify that the user equipment is eligible to use the first processing resource. The motivation is to support an improved method to increase the computing power of the network (see [page 7941, 1st col, 1st para]).
Garcia Morchon and Zhang et al. teaches about a user equipment, in an endpoint environment, requesting an edge server for processing resources. Garcia Morchon and Zhang et al., however, fails to expressly teach of identifying, by the edge server to perform one of the tasks listed as video encoding or decoding, audio encoding or decoding or data storage etc. (Emphasis added).
Regarding claim 1, Ding et al. teach the task comprising at least one of video encoding, video decoding, audio encoding, audio decoding, graphics processing, graphics rendering, video analytics, computer vision, object recognition, data caching, augmented reality computing, virtual reality computing, distributed computing tasks, video or audio storage, data storage, signal processing, system modeling, simulations, machine learning, or software application tasks (Figs. 1-2 and 12, [col 2, ln 54-67, col 3, ln 1-16, col 29, ln 35-38, col 31, ln 60-66], Automotive Edge Computing Consortium (AECC) system 100 according to various embodiments. AECC system 100 is an end-to-end system including vehicle systems 121, access/core networks, and cloud computing infrastructure that realizes the AECC use cases. The AECC system 100 may be built on a distributed computing and networking architecture, which includes the vehicle system 121; one or more networks including the cellular network 140, the wireless local area network (WLAN) 130, an MSP enterprise network (not shown by FIG. 1); and MSP servers including the MSP center server 150 and the MSP edge servers 136A and 136B (collectively referred to as “MSP edge servers 136” or “MSP edge server 136”). MSP edge servers 136A and 136B are respectively disposed at an edge of a communication network. The term “MSP” may refer to “Mobility Service Provider” or “Managed Services Provider”, which is a platform-independent (service) provider that provides customers with access to one or more Connected Vehicle services, for example. For purposes of the present disclosure, “edge computing” refers to a type of distributed computing paradigm where the computing process is allocated to computing instances and data storage located at the Network Edge (closer to) in order to provide desired service levels, for example by improving response times and/or to conserve bandwidth. The “edge” of the communication network refers to the outermost part of a communication network that a client or user equipment connects to, and does not include the client or user equipment itself. A plurality of edge compute nodes 1236a-c (collectively referred to as “edge compute nodes 1236” or the like) within an edge computing system 1235. Unlike the traditional cloud computing model, in some implementations, the cloud 1244 may have little or no computational capabilities and only serves as a repository for archiving data recorded and processed by the fog. In these implementations, the cloud 1244 centralized data storage system and provides reliability and access to data by the computing resources in the fog and/or edge devices).
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Garcia Morchon with Zhang et al. by incorporating the features as taught by Ding et al. in order to provide a more effective and efficient system that is capable of performing a task of data storage. The motivation is to support an improved method to Edge Computing technologies for supporting vehicle-to-everything (V2X) communications (see [col 1, ln 17-18]).
Regarding claim 11, Garcia Morchon teaches a system comprising: one or more processors; and a non-transitory computer-readable storage media storing computer-executable instructions that, when executed by the one or more processors, causes the system to (Fig. 1, [[0041-0042, 0298], it is noted that the above apparatus may be implemented based on discrete hardware circuitries with discrete hardware components, integrated chips, or arrangements of chip modules, or based on signal processing devices or chips controlled by software routines or programs stored in memories, written on a computer readable media, or downloaded from a network, such as the Internet. The wireless communication system of claim 7, the terminal device of claim 15, the method of claim 16, and the computer program product may have similar and/or identical preferred embodiments, in particular, as defined in the dependent claims. As described in fig. 3, it can be implemented as program code means of a computer program and/or as dedicated hardware of the related network device or function, respectively. The computer program may be stored and/or distributed on a suitable medium, such as an optical storage medium or a solid-state medium, supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems),
Garcia Morchon teaches receive a request from a user equipment (UE) in an endpoint environment, the request indicating a demand for processing resources on an edge server to perform a task for the UE (Fig. 1, [0062, 0107], a mobile access device 30 (5G enabled vehicle) is provided for relaying data between a donor access device (e.g. gNB) 20 and a terminal device (e.g. wireless communication device such as a UE) 10. The donor access device 20 provides connectivity to a 5G core network 40. The UE 10 may also request the required resources (data) again, triggering then the local download, from the mobile base station 30 or from an edge server, e.g., collocated with the donor gNB 20),
Garcia Morchon teaches the task comprising at least one of video encoding, video decoding, audio encoding, audio decoding, graphics processing, graphics rendering, video analytics, computer vision, object recognition, location services, data caching, augmented reality computing, virtual reality computing, distributed computing tasks, video or audio storage, data storage, signal processing, system modeling, simulations, machine learning, software application tasks, computing tasks (Figs. 1 and 9, [0292-0295], base station may be any network access device (such as a base station, Node B (eNB, eNodeB, gNB, gNodeB, ng-eNB, etc.), access point or the like) that provides a geographical service area. At least some of the above embodiments may be based on a 5G New Radio (5G NR) radio access technology. Moreover, the invention can be applied in medical applications or connected healthcare in which multiple wireless (e.g. 4G/5G) connected sensor or actuator nodes participate, in medical applications or connected healthcare in which a wireless (e.g. 4G/5G) connected equipment consumes or generates occasionally a continuous data stream of a certain average data rate, for example video, ultrasound, X-Ray, Computed Tomography (CT) imaging devices, real-time patient sensors, audio or voice or video streaming devices used by medical staff),
Garcia Morchon teaches in response to the request, identify a first 5G-enabled vehicle in proximity to the endpoint environment (Fig. 1, [0105], it may be determined which of a plurality of mobile access devices 30 will be in a predetermined range of the target device taking into account current traveling schedules of the mobile access devices and it may be identified which of the plurality of mobile access devices can deliver the transmission data to the target device taking into account the position information of the target device at a predetermined time, the positions of each of the plurality of mobile access devices at the predetermined time, and communication requirements of a user of the target device at the predetermined time. Thereby, a best suited mobile access device can be selected for effective transfer of the transmission data [0026]), determine an operational state and a power state of the first 5G-enabled vehicle (i.e. the CN 50 can inform the UE 10 through the macro cell (i.e. macro base station 20) about the timing when the mobile base station 30 will be in reach and available for delivery (i.e. the operational state of the mobile access station 30). Optionally, the CN 50 may also inform the UE 10 about the physical cell identifier and beam index to use to speed up cell acquisition),
Garcia Morchon teaches in response to the determination that the operational state is idle and that the power state is above a predetermined threshold, determine availability of a first processing resource carried by the first 5G-enabled vehicle (Fig. 1, [0097-0100], the CN 50 knows the route of mobile base stations as well as the location of the user and his macro cell. The CN 50 plans which of the mobile base stations will be in close vicinity of the user taking into account their current schedules. Based thereon, the CN 50 can identify which mobile base station can deliver which load to which UE taking into account at least one of the positions of each UE at time t, the positions of each mobile base station at time t, and the communication requirements of a user at time t. A simple exemplary strategy may consist in checking which mobile base station is going to be close to the UE 10 next, and whether the available communication link between the UE 10 and a potential mobile base station will be enough to handle the communication requirements of the UE 10. The available communication link refers to the available communication resources when the UE 10 and the potential mobile base station encounter taking into account the distance and expected duration of the communication link. The CN 50 informs a chosen mobile base station 30 about a presence of a user along the route of the mobile base station 30, data requirements of the user of the UE 10, and/or the position of the UE 10, so that the mobile base station 30 can pre-optimize transmission parameters, e.g., beamforming or frequency or pre-allocate transmission resources. The CN 50 allocates resources at the mobile base station 30, e.g., triggered by the DN request. These resources can be storage resources to cache the data transfer or communication resources to perform the data transfer. The CN 50 starts a local download of the resources required by the user to the mobile base station 30 (e.g. caching, as in a content delivery network)),
Garcia Morchon teaches in response to the determination that the first 5G-enabled vehicle carries the first processing resource that is available, determine whether the first processing resource carried by the first 5G-enabled vehicle meets the demand of the user equipment (Fig. 1, [0102-0103], the mobile base station 30 pre-allocates and configures communication resources for the transmission of the requested data taking into account at least one of the required amount of data to transfer, the known position of the user, the expected trajectory of the mobile base station 30 and expected time during which the UE 10 and the mobile base station 30 will be in a predetermined range. These communication resources might include coding (e.g. scrambling code, channelization code), timing and frequency resources and/or beam forming over time. Some of these parameters can be configured in advance since the mobile base station 30 has knowledge of the positioning information of the mobile base station 30 and historical data related to, e.g., a reported channel quality indication (CQI). Parameters such as beam forming can be optimized and pre-configured since the position of the UE 10 may be known (in some cases, it can be assumed to be static) and the trajectory of the mobile base station 30 may also be known),
Garcia Morchon teaches and in response to the determination that the first processing resource meets the demand of the user equipment, allocate the first processing resource carried by the first 5G-enabled vehicle and cause the first 5G-enabled vehicle to serve as an edge server to perform the task for the user equipment using the allocated first processing resource (Fig. 1, [0027, 0108] once the UE 10 and the mobile base station 30 are connected to each other, data transfer can occur with minimal latency since communication parameters have been preconfigured and the UE 10 is able to download data directly from the mobile base station 30 or a close edge server, e.g., collocated with the donor gNB, that has been caching the data when approaching the UE 10. The selected mobile access device may be informed about at least one of a presence of the target device along a route of the mobile access device, data requirements of the user of the target device, and a position of the target device, so that the selected mobile access device can pre-optimize transmission parameters or pre-allocate transmission resources. Thereby, effective transfer of the transmission data can be ensured at the mobile access device).
Regarding claim 14, Garcia Morchon teaches wherein, when executed by one or more processors, the computer-executable instructions further cause the system to: determine whether a current level of processing resources carried by the first 5G- enabled vehicle meets both the demand by the user equipment and a self-demand by the first 5G- enabled vehicle; and in response to the determination that the current level of processing resources carried by the first 5G-enabled vehicle meets both the demand by the user equipment and a self- demand by the first 5G-enabled vehicle, determine that the processing resource requested by the user equipment is available (Fig. 1, [0027, 0097-0100, 0102-0103, 0108], the available communication link refers to the available communication resources when the UE 10 and the potential mobile base station encounter taking into account the distance and expected duration of the communication link. The CN 50 informs a chosen mobile base station 30 about a presence of a user along the route of the mobile base station 30, data requirements of the user of the UE 10, and/or the position of the UE 10, so that the mobile base station 30 can pre-optimize transmission parameters, e.g., beamforming or frequency or pre-allocate transmission resources. The CN 50 allocates resources at the mobile base station 30, e.g., triggered by the DN request. These resources can be storage resources to cache the data transfer or communication resources to perform the data transfer. The CN 50 starts a local download of the resources required by the user to the mobile base station 30. The mobile base station 30 pre-allocates and configures communication resources for the transmission of the requested data taking into account at least one of the required amount of data to transfer, the known position of the user, the expected trajectory of the mobile base station 30 and expected time during which the UE 10 and the mobile base station 30 will be in a predetermined range. These communication resources might include coding (e.g. scrambling code, channelization code), timing and frequency resources and/or beam forming over time. Some of these parameters can be configured in advance since the mobile base station 30 has knowledge of the positioning information of the mobile base station 30 and historical data related to, e.g., a reported channel quality indication (CQI). Parameters such as beam forming can be optimized and pre-configured since the position of the UE 10 may be known (in some cases, it can be assumed to be static) and the trajectory of the mobile base station 30 may also be known. Once the UE 10 and the mobile base station 30 are connected to each other, data transfer can occur with minimal latency since communication parameters have been preconfigured and the UE 10 is able to download data directly from the mobile base station 30 or a close edge server, e.g., collocated with the donor gNB, that has been caching the data when approaching the UE 10. The selected mobile access device may be informed about at least one of a presence of the target device along a route of the mobile access device, data requirements of the user of the target device, and a position of the target device, so that the selected mobile access device can pre-optimize transmission parameters or pre-allocate transmission resources. Thereby, effective transfer of the transmission data can be ensured at the mobile access device).
Regarding claim 15, Garcia Morchon teaches wherein, when executed by one or more processors, the computer-executable instructions further cause the system to: determine whether a current usage of processing resource by the first 5G-enabled vehicle to perform primary tasks for the 5G-enabled vehicle is below a pre-established threshold level; and in response to the determination that the current usage of processing resource is below the pre-established threshold level, determine that the first processing resource carried by the 5G-enabled vehicle is available (Fig. 1, [0027, 0097-0100, 0102-0103, 0108], the CN 50 informs a chosen mobile base station 30 about a presence of a user along the route of the mobile base station 30, data requirements of the user of the UE 10, and/or the position of the UE 10, so that the mobile base station 30 can pre-optimize transmission parameters, e.g., beamforming or frequency or pre-allocate transmission resources. The CN 50 allocates resources at the mobile base station 30, e.g., triggered by the DN request. These resources can be storage resources to cache the data transfer or communication resources to perform the data transfer. The CN 50 starts a local download of the resources required by the user to the mobile base station 30. Some of these parameters can be configured in advance since the mobile base station 30 has knowledge of the positioning information of the mobile base station 30 and historical data related to, e.g., a reported channel quality indication (CQI). Parameters such as beam forming can be optimized and pre-configured since the position of the UE 10 may be known and the trajectory of the mobile base station 30 may also be known. Once the UE 10 and the mobile base station 30 are connected to each other, data transfer can occur with minimal latency since communication parameters have been preconfigured and the UE 10 is able to download data directly from the mobile base station 30 or a close edge server, e.g., collocated with the donor gNB, that has been caching the data when approaching the UE 10. The selected mobile access device may be informed about at least one of a presence of the target device along a route of the mobile access device, data requirements of the user of the target device, and a position of the target device, so that the selected mobile access device can pre-optimize transmission parameters or pre-allocate transmission resources. Thereby, effective transfer of the transmission data can be ensured at the mobile access device).
Garcia Morchon teaches about a user equipment, in an endpoint environment, requesting an edge server for processing resources. Garcia Morchon, however, fails to expressly teach of identifying, by the edge server to perform one of the tasks listed as video encoding or decoding, audio encoding or decoding or data storage etc. and identifying a 5G-enabled vehicle in the network. (Emphasis added).
Garcia Morchon does not expressly disclose the following features: regarding claim 12, wherein, when executed by one or more processors, the computer-executable instructions further cause the system to: perform user authentication to authenticate the user of the user equipment to use the first processing resource of the first 5G-enabled vehicles and verify that the user equipment is eligible to use the first processing resource.
Regarding claim 11, Zhang et al. teach identifying a first 5G-enabled vehicle in proximity to the endpoint environment (Fig. 2, [see page 7942, 2nd col, sub-chap A 2nd para, Page 7943, 1st col, 2nd para, page 7944, 2nd col, para 1st, page 7946, 2nd col, para 1st], the TA (edge server) is consist of redundant TAs and multiple reliable server devices, such as registration servers key generation server, tracing server and so on. There are two kinds of vehicles, i.e., edge computing vehicles (ECVs) and ordinary vehicles (OVs). In special, ECVs act as a role like a group manager which is responsible for constructing a group and interact with OVs. After interacting with TA, ECVs generate a group secret key and a group identity so that vehicles in some groups can directly communicate without the help of TA. In essence, ECVs alleviate the burden of TA. As for OVs, they act only as of the normal manner in general schemes. The proposed protocol consists of five phases as (1) the system initialization phase, (2) participating vehicles registration phase, (3) election strategy and verification of ECV phase, (4) key agreement and batch authentication of vehicles phase, and (5) dynamic addition of vehicles phase. In brief, the system initialization phase is that the TA allocates public parameters to the system; participating vehicles registration phase is that TA preloads a secret and a long-term certificate into each vehicle; election strategy and verification of ECV phase is that TA uses fuzzy logic to select some edge computing vehicles and authenticate these edge computing vehicles; the fourth phase is that the edge computing vehicle is responsible for generating the key for the vehicles in the group, and fully prepares for the subsequent intra-group verification the TA calculates a fitness value of each vehicle within a certain range and then chooses ECV which has the maximal fitness value).
Regarding claim 12, Zhang et al. teach wherein, when executed by one or more processors, the computer-executable instructions further cause the system to: perform user authentication to authenticate the user of the user equipment to use the first processing resource of the first 5G-enabled vehicles and verify that the user equipment is eligible to use the first processing resource (Fig. 2, [see page 7944, 2nd col, para 1st], dynamic addition of vehicles phase. In brief, the system initialization phase is that the TA allocates public parameters to the system; participating vehicles registration phase is that TA preloads a secret and a long-term certificate into each vehicle; election strategy and verification of ECV phase is that TA uses fuzzy logic to select some edge computing vehicles and authenticate these edge computing vehicles; the fourth phase is that the edge computing vehicle is responsible for generating the key for the vehicles in the group, and fully prepares for the subsequent intra-group verification. In the last phase, we discuss how to dynamically add vehicles to 5G vehicular networks. Fig. 2 shows the flow diagram of our protocol).
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Garcia Morchon by incorporating the features as taught by Zhang et al. in order to provide a more effective and efficient system that is capable of identifying a first 5G-enabled vehicle in proximity to the endpoint environment, and performing user authentication to authenticate the user of the user equipment to use the first processing resource of the first 5G-enabled vehicles and verify that the user equipment is eligible to use the first processing resource. The motivation is to support an improved method to increase the computing power of the network (see [page 7941, 1st col, 1st para]).
Garcia Morchon and Zhang et al. teaches about a user equipment, in an endpoint environment, requesting an edge server for processing resources. Garcia Morchon and Zhang et al., however, fails to expressly teach of identifying, by the edge server to perform one of the tasks listed as video encoding or decoding, audio encoding or decoding or data storage etc. (Emphasis added).
Regarding claim 11, Ding et al. teach the task comprising at least one of video encoding, video decoding, audio encoding, audio decoding, graphics processing, graphics rendering, video analytics, computer vision, object recognition, location services, data caching, augmented reality computing, virtual reality computing, distributed computing tasks, video or audio storage, data storage, signal processing, system modeling, simulations, machine learning, software application tasks, computing tasks (Figs. 1-2 and 12, [col 2, ln 54-67, col 3, ln 1-16, col 29, ln 35-38, col 31, ln 60-66], Automotive Edge Computing Consortium (AECC) system 100 according to various embodiments. AECC system 100 is an end-to-end system including vehicle systems 121, access/core networks, and cloud computing infrastructure that realizes the AECC use cases. The AECC system 100 may be built on a distributed computing and networking architecture, which includes the vehicle system 121; one or more networks including the cellular network 140, the wireless local area network (WLAN) 130, an MSP enterprise network (not shown by FIG. 1); and MSP servers including the MSP center server 150 and the MSP edge servers 136A and 136B (collectively referred to as “MSP edge servers 136” or “MSP edge server 136”). MSP edge servers 136A and 136B are respectively disposed at an edge of a communication network. The term “MSP” may refer to “Mobility Service Provider” or “Managed Services Provider”, which is a platform-independent (service) provider that provides customers with access to one or more Connected Vehicle services, for example. For purposes of the present disclosure, “edge computing” refers to a type of distributed computing paradigm where the computing process is allocated to computing instances and data storage located at the Network Edge (closer to) in order to provide desired service levels, for example by improving response times and/or to conserve bandwidth. The “edge” of the communication network refers to the outermost part of a communication network that a client or user equipment connects to, and does not include the client or user equipment itself. A plurality of edge compute nodes 1236a-c (collectively referred to as “edge compute nodes 1236” or the like) within an edge computing system 1235. Unlike the traditional cloud computing model, in some implementations, the cloud 1244 may have little or no computational capabilities and only serves as a repository for archiving data recorded and processed by the fog. In these implementations, the cloud 1244 centralized data storage system and provides reliability and access to data by the computing resources in the fog and/or edge devices).
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Garcia Morchon with Zhang et al. by incorporating the features as taught by Ding et al. in order to provide a more effective and efficient system that is capable of performing a task of data storage. The motivation is to support an improved method to Edge Computing technologies for supporting vehicle-to-everything (V2X) communications (see [col 1, ln 17-18]).
Claim(s) 3 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Garcia Morchon (US 2024/0373312 A1) in view of IEEE, Zhang et al. (5G-Enabled Vehicular Networks) and Ding et al. (US 12,1852,30 B2) as applied to claims 1 and 11 above, and further in view of Falla Cepeda (US 2022/0394557 A1).
Garcia Morchon, Zhang et al. and Ding et al. disclose the claimed limitations as described in paragraph 6 above. Garcia Morchon, Zhang et al. and Ding et al. do not expressly disclose the following features: regarding claim 3, wherein the determining availability of a first processing resource carried by the first 5G-enabled vehicle further comprises: determining that a central processing unit (CPU) of the first 5G-enabled vehicle is idle and predicted to continue to be idle for a particular time period; and determining that the CPU is included in the first processing resource to be allocated for and used by the user equipment for the particular time period; regarding claim 13, wherein, when executed by one or more processors, the computer-executable instructions further cause the system to: determine that a central processing unit (CPU) of the first 5G-enabled vehicle is 113 idle and predicted to continue to be idle for a particular time period; and 114 determine that the CPU is included in the first processing resource to be allocated 115 for and used by the user equipment for the particular time period.
Regarding claim 3, Falla Cepeda teaches wherein the determining availability of a first processing resource carried by the first 5G-enabled vehicle further comprises: determining that a central processing unit (CPU) of the first 5G-enabled vehicle is idle and predicted to continue to be idle for a particular time period; and determining that the CPU is included in the first processing resource to be allocated for and used by the user equipment for the particular time period (Fig. 1, [0027, 0033, 0035], system 100 capable is of providing mobile cloud computing resources (e.g., vehicles one-board resources) using high-speed data connections. Vehicles one-board resources can include hardware, execution environment capabilities, network capabilities, etc. For instance, the hardware may include CPU cores. The connected vehicles 101 have idle computing resources for use when stationary to offer cloud computing services. On the data transmission side, the 5G infrastructure can provide the connected vehicles 101, mobile devices, etc., with fiber-optic-like data speeds (in the Gigabits), especially 5G Millimeter Wave/High Band connections, to receive data for processing and/or to transmit processed data. As such, the system 100 can incorporate mobile cloud resources (e.g., in vehicles, mobile devices, etc.) into the cloud infrastructure which includes hardware, abstracted resources, storage, network resources, etc. The system 100 can retrieve road link attribute data that indicates road link(s) served by 5G high-band signals (e.g., 5G Millimeter Wave map attributes stored in a map database and/or collected by vehicles 101), and determine where and when the vehicle 101a can perform high volume data transmission for a given timeframe (e.g., a data transmission/connectivity schedule). The system 100 can then provide and/or publish the data transmission schedule to a mobile processing allocation service (e.g., a mobile processing allocation platform 107), indicating where and when 5G high-band connections will be available to upload and/or download data to the vehicle 101a. The indications correspond to the times when the vehicle 101a is expected to be within a 5G coverage).
Regarding claim 13, Falla Cepeda teaches wherein, when executed by one or more processors, the computer-executable instructions further cause the system to: determine that a central processing unit (CPU) of the first 5G-enabled vehicle is idle and predicted to continue to be idle for a particular time period; and determine that the CPU is included in the first processing resource to be allocated for and used by the user equipment for the particular time period (Fig. 1, [0027, 0033, 0035], system 100 capable is of providing mobile cloud computing resources (e.g., vehicles one-board resources) using high-speed data connections. Vehicles one-board resources can include hardware, execution environment capabilities, network capabilities, etc. For instance, the hardware may include CPU cores. The connected vehicles 101 have idle computing resources for use when stationary to offer cloud computing services. On the data transmission side, the 5G infrastructure can provide the connected vehicles 101, mobile devices, etc., with fiber-optic-like data speeds (in the Gigabits), especially 5G Millimeter Wave/High Band connections, to receive data for processing and/or to transmit processed data. As such, the system 100 can incorporate mobile cloud resources (e.g., in vehicles, mobile devices, etc.) into the cloud infrastructure which includes hardware, abstracted resources, storage, network resources, etc. The system 100 can retrieve road link attribute data that indicates road link(s) served by 5G high-band signals (e.g., 5G Millimeter Wave map attributes stored in a map database and/or collected by vehicles 101), and determine where and when the vehicle 101a can perform high volume data transmission for a given timeframe (e.g., a data transmission/connectivity schedule). The system 100 can then provide and/or publish the data transmission schedule to a mobile processing allocation service (e.g., a mobile processing allocation platform 107), indicating where and when 5G high-band connections will be available to upload and/or download data to the vehicle 101a. The indications correspond to the times when the vehicle 101a is expected to be within a 5G coverage).
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Garcia Morchon with Zhang et al. and Ding et al. al. by incorporating the features as taught by Li et al. in order to provide a more effective and efficient system that is capable of determining that a central processing unit (CPU) of the first 5G-enabled vehicle is idle and predicted to continue to be idle for a particular time period; and determining that the CPU is included in the first processing resource to be allocated for and used by the user equipment for the particular time period. The motivation is to support an improved method for enabling remote use of a vehicle's computational resources (see [0002]).
Claim(s) 6-7 and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Garcia Morchon (US 2024/0373312 A1) in view of IEEE, Zhang et al. (5G-Enabled Vehicular Networks) and Ding et al. (US 12,1852,30 B2) as applied to claims 1 and 11 above, and further in view of Li et al. (US 2025/0097286 A1).
Garcia Morchon, Zhang et al. and Ding et al. disclose the claimed limitations as described in paragraph 6 above.
Regarding claim 7, Zhang et al. teach identifying a second 5G-enabled vehicle in proximity to the endpoint environment; determining that the second 5G-enabled vehicle carries a second processing resource that is available and meets the demand of the user equipment; and allocating the second processing resource carried by the second 5G-enabled vehicle and causing the second 5G-enabled vehicle to serve as a second edge server to continue performing the task for the user equipment using the allocated second processing resource (Fig. 2, [see Page 7943, 1st col, 2nd para, page 7944, 2nd col, para 1st, page 7945, 1st col, chap C, para 1st], there are two kinds of vehicles, i.e., edge computing vehicles (ECVs) and ordinary vehicles (OVs). In special, ECVs act as a role like a group manager which is responsible for constructing a group and interact with OVs. After interacting with TA, ECVs generate a group secret key and a group identity so that vehicles in some groups can directly communicate without the help of TA. In essence, ECVs alleviate the burden of TA. As for OVs, they act only as of the normal manner in general schemes).
regarding claim 17, Zhang et al. teach wherein, when executed by one or more processors, the computer-executable instructions further cause the system to: identify a second 5G-enabled vehicle in proximity to the endpoint environment; determine that the second 5G-enabled vehicle carries a second processing resource that is available and meets the demand of the user equipment; and allocate the second processing resource carried by the second 5G-enabled vehicle and cause the second SG-enabled vehicle to serve as a second edge server to continue performing the task for the user equipment using the allocated second processing resource (Fig. 2, [see Page 7943, 1st col, 2nd para, page 7944, 2nd col, para 1st, page 7945, 1st col, chap C, para 1st], there are two kinds of vehicles, i.e., edge computing vehicles (ECVs) and ordinary vehicles (OVs). In special, ECVs act as a role like a group manager which is responsible for constructing a group and interact with OVs. After interacting with TA, ECVs generate a group secret key and a group identity so that vehicles in some groups can directly communicate without the help of TA. In essence, ECVs alleviate the burden of TA. As for OVs, they act only as of the normal manner in general schemes. For the election strategy of ECV, we will choose some vehicles within the TA that are close to the 5G_BS and to have enough computational capacities. We assume that the vehicle can get the GPS location and available computational resource information extracted by hello message/beacon. But the two metrics are always contradictory. Such as, a vehicle is close to the 5G_BS, but it has a low computational resource. Besides, distance and computational resources are always imprecise. The system initialization phase is that the TA allocates public parameters to the system; participating vehicles registration phase is that TA preloads a secret and a long-term certificate into each vehicle; election strategy and verification of ECV phase is that TA uses fuzzy logic to select some edge computing vehicles and authenticate these edge computing vehicles).
Garcia Morchon, Zhang et al. and Ding et al. do not expressly disclose the following features: regarding claim 6, further comprising: receiving a request by the first 5G-enabled vehicle to cease the using of the allocated first processing resource; and in response to the request, causing the first 5G-enabled vehicle to cease the use of the first processing resource; regarding claim 16, wherein, when executed by one or more processors, the computer-executable instructions further cause the system to: receive a request by the first 5G-enabled vehicle to cease the using of the allocated first processing resource; and in response to the request, cause the first 5G-enabled vehicle to cease the using of the first processing resource.
Regarding claim 6, Li et al. teach further comprising: receiving a request by the first 5G-enabled vehicle to cease the using of the allocated first processing resource; and in response to the request, causing the first 5G-enabled vehicle to cease the use of the first processing resource (Fig. 2, [0012, 0143-0144], Edge Cloud Infrastructures (ECIs) that are available and usually have more powerful resources/capabilities. They are provided by edge computing resource providers or those ECIs are deployed in the 3GPP networks by the mobile operators such as in the 5G edge network. Some of the new requests may be processed by the instance on D-SH 215 (fig. 11B) and the remaining ones may still be process by S-SH 216. At a later time, D-SH 215 may also send another request to terminate the instance of X hosted by D-SH 215 based on the policy as described in step 301 (e.g., when S-SH 216 is able to serve the requests by itself again). Accordingly, the instance hosted on D-SH 215 may be terminated and the resources may be released. In the meantime, the LSA 202 may further send requests to the 3GPP network so that the requests may be sent to S-SH 216 for processing).
Regarding claim 16, Li et al. teach wherein, when executed by one or more processors, the computer-executable instructions further cause the system to: receive a request by the first 5G-enabled vehicle to cease the using of the allocated first processing resource; and in response to the request, cause the first 5G-enabled vehicle to cease the using of the first processing resource (Fig. 2, [0012, 0143-0144], Edge Cloud Infrastructures (ECIs) that are available and usually have more powerful resources/capabilities. They are provided by edge computing resource providers or those ECIs are deployed in the 3GPP networks by the mobile operators such as in the 5G edge network. Some of the new requests may be processed by the instance on D-SH 215 (fig. 11B) and the remaining ones may still be process by S-SH 216. At a later time, D-SH 215 may also send another request to terminate the instance of X hosted by D-SH 215 based on the policy as described in step 301 (e.g., when S-SH 216 is able to serve the requests by itself again). Accordingly, the instance hosted on D-SH 215 may be terminated and the resources may be released. In the meantime, the LSA 202 may further send requests to the 3GPP network so that the requests may be sent to S-SH 216 for processing”)
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Garcia Morchon with Zhang et al. and Ding et al. by incorporating the features as taught by Li et al. in order to provide a more effective and efficient system that is capable of receiving a request by the first 5G-enabled vehicle to cease the using of the allocated first processing resource; and in response to the request, causing the first 5G-enabled vehicle to cease the use of the first processing resource. The motivation is to support an improved method for local service QoS management via service relocation (see [0012]).
Claim(s) 8 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Garcia Morchon (US 2024/0373312 A1) in view of IEEE, Zhang et al. (5G-Enabled Vehicular Networks) and Ding et al. (US 12,1852,30 B2) as applied to claims 1 and 11 above, and further in view of Trim et al. (US 2023/0171576 A1).
Garcia Morchon, Zhang et al. and Ding et al. disclose the claimed limitations as described in paragraph 6 above. Garcia Morchon, Zhang et al. and Ding et al. do not expressly disclose the following features: regarding claim 8, further comprising: performing a check on the power state of the first 5G-enabled vehicle to determine whether a current battery power level is below a pre-established threshold level; and in response to the determination that the current battery power level is below the pre-established threshold level, causing the first 5G-enabled vehicle to cease the use of the allocated first processing resource; regarding claim 18, wherein, when executed by one or more processors, the computer-executable instructions further cause the system to: perform a check on the power state of the first 5G-enabled vehicle to determine whether a current battery power level is below a pre-established threshold level; and in response to the determination that the current battery power level is below the pre-established threshold level, cause the first 5G-enabled vehicle to cease the use of the allocated first processing resource.
Regarding claim 8, Trim et al. teach further comprising: performing a check on the power state of the first 5G-enabled vehicle to determine whether a current battery power level is below a pre-established threshold level; and in response to the determination that the current battery power level is below the pre-established threshold level, causing the first 5G-enabled vehicle to cease the use of the allocated first processing resource (Fig. 1, [0050, 0075], it is anticipated that a 5G-enabled mobile device will run multiple applications at the same time consuming more battery power to operate. Because of this increased battery power consumption, the battery of a 5G-enabled mobile device will drain more quickly. Under normal circumstances, this battery power drain is not hampering. However, this battery power drain can become an issue when a user of the 5G-enabled mobile device is in an emergency situation and needs to use the mobile device to request assistance but has very little battery power left (e.g., less than 5%). Current emergency applications run according to a defined location-based policy (e.g., an identified start location where the emergency application is automatically activated and an identified end location where the emergency application is automatically deactivated) during user mobility. Further, emergency monitoring application mobility manager 510 can instruct the power control unit of the operating system on client mobile device 506 to decrease processor cycles and/or shut down applications on client mobile device 506 to maintain the reserved battery power requirement for the emergency monitoring application above a minimum threshold level during operation in an emergency situation).
Regarding claim 18, Trim et al. teach wherein, when executed by one or more processors, the computer-executable instructions further cause the system to: perform a check on the power state of the first 5G-enabled vehicle to determine whether a current battery power level is below a pre-established threshold level; and in response to the determination that the current battery power level is below the pre-established threshold level, cause the first 5G-enabled vehicle to cease the use of the allocated first processing resource (Fig. 1, [0050, 0075], it is anticipated that a 5G-enabled mobile device will run multiple applications at the same time consuming more battery power to operate. Because of this increased battery power consumption, the battery of a 5G-enabled mobile device will drain more quickly. Under normal circumstances, this battery power drain is not hampering. However, this battery power drain can become an issue when a user of the 5G-enabled mobile device is in an emergency situation and needs to use the mobile device to request assistance but has very little battery power left (e.g., less than 5%). Current emergency applications run according to a defined location-based policy (e.g., an identified start location where the emergency application is automatically activated and an identified end location where the emergency application is automatically deactivated) during user mobility. Further, emergency monitoring application mobility manager 510 can instruct the power control unit of the operating system on client mobile device 506 to decrease processor cycles and/or shut down applications on client mobile device 506 to maintain the reserved battery power requirement for the emergency monitoring application above a minimum threshold level during operation in an emergency situation).
It would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Garcia Morchon with Zhang et al. and Ding et al. by incorporating the features as taught by Trim et al. in order to provide a more effective and efficient system that is capable of performing a check on the power state of the first 5G-enabled vehicle to determine whether a current battery power level is below a pre-established threshold level, and in response to the determination that the current battery power level is below the pre-established threshold level, causing the first 5G-enabled vehicle to cease the use of the allocated first processing resource. The motivation is to support an improved method to reserve a portion of battery power on the mobile device (see [0001]).
Allowable Subject Matter
Claims 9-10 and 19-20 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-8 and 11-18 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/SYED M BOKHARI/Examiner, Art Unit 2473 3/6/2026
/KWANG B YAO/Supervisory Patent Examiner, Art Unit 2473