Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Applicant’s amendment filed on December 10, 2025 amends claims 1-2 and 11-12. Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments, filed on December 10, 2025, regarding the amendment to the independent claims have been fully considered and are moot as shown in the rejections that follow. The amended independent claims are taught by newly presented reference, AAPA (Applicant’s Admitted Prior Art), in combination with the previously cited references, in light of the new grounds of rejection.
Applicant alleges that “Oh describes using the Meta-lens Al system in a wireless communication system. However, Oh only briefly mentions vehicles to state that the WD could be a vehicle. Oh simply never discusses any vehicle maneuvers.” Examiner disagrees with Applicant’s assessment of what the Examiner stated in the non-final Office action because the Examiner used Vassilovski to show a teaching of “vehicle maneuvers” as was indicated in page 6 of the non-final Office action. Furthermore, even if Vassilovski was not used to show a teaching, Oh at pages 7-8 discloses that the vehicle may include a vehicle equipped with a wireless communication function, an autonomous vehicle, a vehicle capable of performing inter-vehicle communication, and the like and that the vehicles 100b-1 and 100b-2 may perform direct communication (e.g., vehicle to vehicle (V2V)/vehicle to everything (V2X) communication). Examiner notes that the purpose in performing V2V and V2X communications is to predict maneuvers of other vehicles.
Applicant argues that Vassilovski fails to cure the deficiencies of Oh and that what Vassilovski teaches is not the same as Applicant's "predict a vehicle maneuver, the prediction being based at least in part on a learning process associated with vehicle data; and schedule a transmission resource usable at least by the WO, the scheduling being based on the predicted vehicle maneuver" as recited in amended independent Claims 1 and 11.” Applicant further alleges that “Vassilovski's use of the maneuver request is limited to transmitting the request to other vehicles and is limited to "granting the maneuver request, denying it, or providing a counter proposal to the maneuver request". Like Oh, Vassilovski simply never mentions transmission resources being allocated based on predicted vehicle maneuvers.” Examiner disagrees with Applicant’s characterizations of the claimed language and the non-final Office action. Examiner had pointed to Vassilovski at [0027] to show a teaching of predict[ing] a vehicle maneuver, the prediction being based at least in part on a learning process associated with vehicle data, the scheduling being based on the predicted vehicle maneuver, as recited in each of the independent claims. With respect to Applicant’s argument regarding “transmission resources being allocated”, the Examiner notes that Oh, at pages 2 and 12, discloses the use of resource scheduling to allocate resources and that sharing of available system resources (bandwidth, transmission power, etc.) includes transmission resources such as bandwidth. Therefore, Oh appears to teach “schedule a transmission resource usable at least by the WD” as recited in each of the independent claims.
Examiner disagrees with Applicant’s remarks that: “Even if combined, Vassilovski and Oh would only suggest the use of the Metalens Al system of Oh in the vehicle message system of Vassilovski. Neither reference discloses, nor was cited as disclosing, the allocation of transmission resources based on a predicted vehicle maneuver. Thus, neither reference provides the latency and resource allocation benefits provided by Applicant's claimed arrangement. As such, Oh and Vassilovski fail to disclose, teach or suggest the features recited in amended independent Claims 1 and 11.” Examiner disagrees because it appears that the combination of Oh and Vassilovski may be used to teach the features recited in each of independent claims 1 and 11.
While Oh and Vassilovski appear to teach the amended independent claims, the Examiner directs the Applicant to Applicant’s admitted prior art (AAPA) at the Background section, [0003], which discloses that network resource allocation in cellular based V2X communications has been studied (e.g., where some studies include surveys about sharing resource blocks (RBs) based on user clustering) and that hybrid schemes in which DRSC is used to assist C-V2X, and Artificial Intelligence (AI) enabled predictive resource allocation based on mobility patterns have been proposed. Examiner maps mobility patterns to maneuvers of other vehicles. AAPA at [0005-0010] further discloses numerous instances of UL scheduling and coordinating the UL scheduling based on whether the WD has data to transmit. Furthermore, AAPA at [0005] discloses that in order to achieve collision-free UL scheduling, where any two wireless devices (WDs) in the same cell do not transmit on the same radio resource, both LTE and 5G traditionally rely on the network node (e.g., base station) to coordinate the UL scheduling. Examiner notes that AAPA’s uplink resource allocation and/or UL (uplink) scheduling teaches scheduling a transmission resource usable at least by the WD, as recited in independent claims 1 and 11. Based on the foregoing reasons, the Examiner rejects the amended independent claims under 35 U.S.C. 103 based on the combination of Oh, Vassilovski, and AAPA (Applicant’s Admitted Prior Art).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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.
Claims 1-4, 6, 8-14, 16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Oh et al. (WO-2023042937-A1) (English translation attached) in view of Vassilovski et al. (KR-20220124711-A) (English translation attached) and further in view of Applicant’s Admitted Prior Art (AAPA) (Background section of US 2024/0420566).
Regarding claim 1, Oh teaches a network node configured to communicate with a wireless device, WD, the WD corresponding to a vehicle, (see Oh, at page 7, which discloses that a communication system 100 applied to the present disclosure includes a wireless device, a base station, and a network, that the wireless device means a device that performs communication using a radio access technology (e.g., 5G NR, LTE), and may be referred to as a communication/wireless/5G device, and that although not limited thereto, the wireless device includes a robot 100a, a vehicle 100b-1 and 100b-2, an extended reality (XR) device 100c, a hand-held device 100d, and a home appliance appliance) 100e, Internet of Thing (IoT) device 100f, and artificial intelligence (AI) device/server 100g. Also, see Oh, at page 8, which discloses that the base station 120 and the network 130 may also be implemented as a wireless device, and a specific wireless device 120a may operate as a base station/network node to other wireless devices. Examiner notes that any one of the foregoing wireless devices, such as the vehicle, corresponds to the WD corresponding to a vehicle. Examiner maps base station to network node, noting that the specification at [0005] discloses that a network node is a base station, for example.)
the network node comprising processing circuitry configured to: [predict a vehicle maneuver, the prediction being based at least in part on a learning process associated with vehicle data;] and schedule a [transmission] resource usable at least by the WD, [the scheduling being based on the predicted vehicle maneuver] (see Oh, at page 4, which discloses that as an example of the present disclosure, a base station may include at least one function of a server and an access point; see Oh, at page 8, which discloses that the base station 120 and the network 130 may also be implemented as a wireless device, and a specific wireless device 120a may operate as a base station/network node to other wireless devices; furthermore, see Oh, at page 8, which discloses that the first wireless device 200a includes one or more processors 202a and one or more memories 204a and that the processor 202a and the memory 204a may be part of a communication modem / circuit / chip designed to implement a wireless communication technology (e.g., LTE, NR); see Oh, at page 10, in conjunction with Fig. 4, which discloses that a controller 620 (of the AI device 600) may determine at least one executable operation of the AI device 600 based on information determined or generated using a data analysis algorithm or a machine learning algorithm, and that control unit 620 of an AI device may request, retrieve, receive, or utilize data from the learning processor unit 640c or the memory unit 630, and may perform a predicted operation among at least one feasible operation or one determined to be desirable. Examiner notes that the learning processor unit functions to perform a learning process. Furthermore, see Oh, at page 12, which discloses that time-consuming tasks such as handover, network selection, and resource scheduling can be performed instantly by using AI, and that deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based multiple input multiple output (MIMO) mechanism, and that it may include AI-based resource scheduling and allocation. Examiner notes that a processor and/or circuit may be mapped to processing circuitry.)
Oh further discloses a learning process (see Oh, at pages 4 and 11, which discloses using a learning processor unit 640c which performs learning based on a learning model, and a predicted operation among at least one feasible operation or one determined to be desirable.) but does not expressly disclose predict[ing] a vehicle maneuver, the prediction being based at least in part on a learning process associated with vehicle data, the scheduling being based on the predicted vehicle maneuver which in a related art, Vassilovski teaches (see Vassilovski at [0027] which discloses that coordinated driving maneuvers between vehicles require predictions of future vehicle locations and motion conditions and that the accuracy of each prediction is a function of in-vehicle and out-of-vehicle parameters.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Oh to include predicting a vehicle maneuver, the prediction being based at least in part on a learning process associated with vehicle data, the scheduling being based on the predicted vehicle maneuver, as taught by Vassilovski.
One would have been motivated to make such a modification to improve the ability of vehicles 304 and 306 to successfully plan and negotiate a coordinated lane change maneuver, as suggested by Vassilovski at [0035].
The modified Oh does not expressly disclose and schedule a transmission resource usable at least by the WD, which in a related art, AAPA teaches (see AAPA at [0003] which discloses that network resource allocation in cellular based V2X communications has been studied (e.g., where some studies include surveys about sharing resource blocks (RBs) based on user clustering) and that hybrid schemes in which DRSC is used to assist C-V2X, and Artificial Intelligence (AI) enabled predictive resource allocation based on mobility patterns have been proposed. Furthermore, AAPA at [0005] discloses that in order to achieve collision-free UL scheduling, where any two wireless devices (WDs) in the same cell do not transmit on the same radio resource, both LTE and 5G traditionally rely on the network node (e.g., base station) to coordinate the UL scheduling and that this could be done by conveying the UL scheduling decision to the WDs in either dynamic manner, or semi-static manner. AAPA, at [0005-0010], further discloses numerous instances of UL scheduling and coordinating the UL scheduling based on whether the WD has data to transmit. Examiner notes that AAPA’s uplink resource allocation and/or UL scheduling teaches scheduling a transmission resource usable at least by the WD, as recited in each of independent claims 1 and 11.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Oh to include scheduling a transmission resource usable at least by the WD, as taught by AAPA.
One would have been motivated to make such a modification to support communication with multiple users by way of sharing available system resources, including bandwidth, as suggested by Oh at page 2.
Regarding claim 2, the modified Oh teaches the network node of claim 1, wherein the network node further comprises a radio interface in communication with the processing circuitry, (see Oh, at page 8, which discloses that the second wireless device 200b includes one or more processors 202b, one or more memories 204b, and may further include one or more transceivers 206b and/or one or more antennas 208b and that the processor 202b controls the memory 204b and/or the transceiver 206b and may be configured to implement the descriptions, functions, procedures, suggestions, methods and/or operational flow diagrams disclosed herein. Examiner maps one of the one or more transceivers 206b to the radio interface. Examiner notes that the processor 202b is communicatively coupled to the transceiver 206b as the processor controls the transceiver.)
the radio interface configured to at least one of: receive the vehicle data from the WD; transmit first signaling to the WD including the scheduled resource; receive second signaling from the WD based on the scheduled resource; and transmit third signaling to another WD based on the scheduled resource, the third signaling being usable by the other WD to determine that the vehicle maneuver has been predicted (see Oh, at page 8, which discloses that a first wireless device 200a and a second wireless device 200b may transmit and receive radio signals through various wireless access technologies (e.g., LTE and NR) and that [h]ere, {the first wireless device 200a, the second wireless device 200b} denotes the {wireless device 100x and the base station 120} of FIG. 1 and/or the {wireless device 100x and the wireless device 100x.} can correspond; Examiner maps the first device to the wireless device and the base station to the network node. Also, see Oh, at page 8, and at Fig. 2, which discloses that for example, the processor 202a may process information in the memory 204a to generate first information/signal, and transmit a radio signal including the first information/signal through the transceiver 206a. Examiner maps the first information to vehicle data. Examiner notes that the transmission of a radio signal including the first information/signal through the transceiver 206a corresponds to receiving the vehicle data from the WD.)
Regarding claim 3, the modified Oh teaches the network node of claim 1, wherein the scheduled resource is usable at least by the WD to at least one of: perform at least one action associated with vehicle to everything, V2X, communication; and trigger a cooperative driving action (see Oh, at page 9, which discloses that the one or more processors 202a and 202b may include one or more layers (e.g., PHY (physical), MAC (media access control), RLC (radio link control), PDCP (packet data convergence protocol), RRC (radio resource) control) and functional layers such as service data adaptation protocol (SDAP); see Oh, at page 12, which discloses that time-consuming tasks such as handover, network selection, and resource scheduling can be performed instantly by using AI and that AI can also play an important role in machine-to-machine, machine-to-human, and human-to-human communications; see Oh, at page 12, which discloses that recently, there have been attempts to integrate AI with wireless communication systems, but these are focused on the application layer, network layer, and especially deep learning, wireless resource management and allocation; see Vassilovski at [0027] which discloses that if all nearby vehicles are V2X-capable, all vehicles can exchange current and planned motion states via V2X messaging, which at least removes the ambiguity of predicted parameters for planned maneuvers with respect to the motion of other vehicles, but that however, non-V2X vehicles cannot communicate their planned motion state, and as a result, they can move in a way that affects the vehicle's planned maneuvering; see Vassilovski at [0059] which discloses that in one implementation, the vehicle-to-vehicle message is transmitted in a Device-to-Device (D2C) communication link, and that for example, the D2C communication link may include dedicated short-range communication (DSRC), cellular Vehicle-to-Everything (C-V2X) communication, or 5G New Radio (NR) communication. Examiner notes that V2X capable messaging may be performed by way of radio resource control and scheduling.)
Regarding claim 4, the modified Oh teaches the network node of claim 1, wherein the processing circuitry is further configured to: determine a probability of the vehicle maneuver to predict the vehicle maneuver (see Vassilovski at [0027] which discloses that coordinated driving maneuvers between vehicles require predictions of future vehicle locations and motion conditions, that the accuracy of each prediction is a function of in-vehicle and out-of-vehicle parameters, that the longer the time for which a maneuver is requested, the less accurate the predicted parameter values will be, that in environments including vehicles in which V2X is not enabled, prediction accuracy may be further reduced and that the accuracy with which a V2X-enabled vehicle can predict certain future parameters, such as the start time of a maneuver, may be further reduced before a maneuver is planned. Examiner maps prediction to probability. Examiner notes that the prediction or probability accuracy may be reduced when V2X is not enabled.)
Regarding claim 6, the modified Oh teaches the network node of claim 4, wherein the probability is determined based at least on an input associated with the learning process (see Oh, at page 4, which discloses that the Metalens artificial intelligence system performs learning based on a learning model, but the obtained user recognition information is an input of the learning model and a control value; also, see Oh, at page 5, which discloses that as an example of the present disclosure, the learning model operates based on reinforcement learning, and the reinforcement learning uses state information and reward information as inputs based on the acquired user recognition information and control value, and corresponds to the control value.)
Regarding claim 8, the modified Oh teaches the network node of claim 1, wherein the processing circuitry is further configured to: perform the learning process based at least in part on the vehicle data (see Oh, at page 5, which discloses that as an example of the present disclosure, the learning model operates based on reinforcement learning, and the reinforcement learning uses state information and reward information as inputs based on the acquired user recognition information and control value, and corresponds to the control value; see Oh, at page 9, which discloses that one or more processors 202a, 202b may generate messages, control information, data or information according to the descriptions, functions, procedures, proposals, methods and/or operational flow diagrams disclosed herein; see Oh, at page 23, which discloses that metalens control artificial intelligence system can derive the optimal control value as described above by obtaining reward values and changed state information for actions from the environment, using them for learning, and repeating the action selection process, and that the reinforcement learning-based artificial intelligence 2110 may include a reinforcement learning model 2120 and that the reinforcement learning model 2120 consists of a learning unit 2121 and a prediction unit 2122, and can predict the next action at the same time as learning, and that the user's recognition information (direction, distance, viewing direction, mobility), etc. may be used as the state factor. Examiner maps direction, distance, viewing direction, mobility to vehicle data.)
Regarding claim 9, the modified Oh teaches the network node of claim 1, wherein at least one of: the WD is a vehicular WD; the scheduled resource is at least one of an uplink grant and a downlink grant; and the predicted vehicle maneuver comprises at least one of: changing lanes; passing another vehicle; crossing an intersection; coordinating a physical maneuver with at least one neighboring vehicle; and a maneuver expected to be performed by the vehicle within a predetermined interval of time (see Oh, at page 7, which discloses that referring to FIG. 1 , a communication system 100 applied to the present disclosure includes a wireless device, a base station, and a network. Here, the wireless device means a device that performs communication using a radio access technology (e.g., 5G NR, LTE), and may be referred to as a communication/wireless/5G device. Although not limited thereto, the wireless device includes a robot 100a, a vehicle 100b-1 and 100b-2, an extended reality (XR) device 100c, a hand-held device 100d, and a home appliance) 100e, Internet of Thing (IoT) device 100f, and artificial intelligence (AI) device/server 100g and that for example, the vehicle may include a vehicle equipped with a wireless communication function, an autonomous vehicle, a vehicle capable of performing inter-vehicle communication, and the like. Oh, at page 8, further discloses that the XR device 100c includes augmented reality (AR)/virtual reality (VR)/mixed reality (MR) devices, and includes a head mounted device (HMD), a head-up display (HUD) installed in a vehicle. Examiner notes that the XR device may be mapped to the recited wireless device (WD). Examiner notes that the XR device is installed in a vehicle, which corresponds to a vehicular WD. Furthermore, see Vassilovski at [0020] which discloses that the vehicle that plans the maneuver or the infrastructure component (RSU) that plans the maneuver on behalf of the vehicle provides the maneuver start and stop times, the start and stop location, and in some cases the maneuver trajectory, that for example, a lane change requires specification of when a vehicle starts to enter a target lane and when the vehicle completes a lane change and that similarly, a vehicle approaching an unsigned intersection with the intention of turning left may include in its messaging when it will arrive at the intersection, when it will initiate a left turn, and the duration of time required to perform the left turn. Examiner notes that the foregoing passages from Vassilovski teaches that the predicted vehicle maneuver comprises changing lanes.)
Regarding claim 10, the modified Oh teaches the network node of claim 1, wherein the vehicle data comprises at least one of: historical data; vehicle coordinate and speed data; an interval of time associated with the historical data; a quantity of surrounding vehicles; and data about the surrounding vehicles at a predetermined time (see Vassilovski at [0027] which discloses that coordinated driving maneuvers between vehicles require predictions of future vehicle locations and motion conditions. Examiner maps predictions of future vehicle locations to vehicle data comprising a quantity of surrounding vehicles.)
Claims 11-14, 16, and 18-20 are directed toward methods that perform the steps recited in the network node of claims 1-4, 6, and 8-10. The cited portions of the reference(s) used in the rejections of claims 1-4, 6, and 8-10 teach the steps recited in the methods of claims 11-14, 16, and 18-20. Therefore, claims 11-14, 16, and 18-20 are rejected under the same rationale used in the rejection of claims 1-4, 6, and 8-10.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Oh et al. (WO-2023042937-A1) in view of Vassilovski et al. (KR-20220124711-A) in view of Applicant’s Admitted Prior Art (AAPA) (Background section of US 2024/0420566) and further in view of Qiang et al. (CN-104427626). (English translations attached)
Regarding claim 5, the modified Oh teaches the network node of claim 4, based on the determined probability and a probability threshold (see Vassilovski, at [0027], which discloses that the accuracy of a prediction is based on external and internal factors, such as road damage (e.g., potholes, slippery conditions, etc.) that is not detected when a maneuver is planned may modify the vehicle's speed. Examiner mapped prediction to probability. Examiner notes that the number of potholes and slippery conditions may contribute to increasing or decreasing the probability threshold.
The modified Oh does not expressly disclose wherein the processing circuitry is further configured to: one of activate and deactivate a semi-static scheduling of the resource, which in a related art, Qiang teaches (see Qiang, at page 2, which discloses that for this purpose, the invention claims a method for semi-static scheduling, comprising: initiating a service call, judging whether the downlink service flow is suitable for adopting the semi-static scheduling mechanism under the condition, searching at least one piece of user equipment corresponding to the service call; and the at least one user device to the searched establishing cluster service control channel, the at least one user equipment on the trunk service control channel receiving communication system control information, and the communication system control information added corresponding to the service call of the semipersistent scheduling period information and said at least one user device identification code; the at least one user equipment conducts corresponding configuration according to the semi-static scheduling period information received by the base station through a physical downlink control channel to activate or modify the semi-static scheduling resource block allocation, to enter the service call.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Oh to include wherein the processing circuitry is further configured to: one of activate and deactivate a semi-static scheduling of the resource, as taught by Qiang.
One would have been motivated to make such a modification to initiate a service call when a downlink service flow is suitable for adopting the semi-static scheduling mechanism, as suggested by Qiang at page 2.
Claim 15 is directed toward a method that performs the steps recited in the network node of claim 5. The cited portions of the reference(s) used in the rejection of claim 5 teach the steps recited in the method of claim 15. Therefore, claim 15 is rejected under the same rationale used in the rejection of claim 5.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Oh et al. (WO-2023042937-A1) in view of Vassilovski et al. (KR-20220124711-A) in view of Applicant’s Admitted Prior Art (AAPA) (Background section of US 2024/0420566) and further in view of Sevindik (US 2022/0210819).
Regarding claim 7, the modified Oh does not expressly disclose the network node of claim 1, wherein the resource is scheduled to be transmitted in advance of the vehicle maneuver occurring by at least a predetermined interval of time, which in a related art, Sevindik teaches (see Sevindik at [0242], for example, which discloses that in some embodiments, the wireless devices chose from a set of uplink resource grants having a predetermined set of resource blocks and predetermined recurring time interval.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Oh to include wherein the resource is scheduled to be transmitted in advance of the vehicle maneuver occurring by at least a predetermined interval of time, as taught by Sevindik.
One would have been motivated to make such a modification so that all resource blocks will overlap when two different wireless devices select the same uplink resource grant for use from the set of uplink resource grants, as suggested by Sevindik at [0242].
Claim 17 is directed toward a method that performs the steps recited in the network node of claim 7. The cited portions of the reference(s) used in the rejection of claim 7 teach the steps recited in the method of claim 17. Therefore, claim 17 is rejected under the same rationale used in the rejection of claim 7.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/ROY RHEE/Examiner, Art Unit 3664