DETAILED ACTION
This Office action is in response to the original application filed on 6/20/2024. Claims 1-20 are
pending in the application.
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 .
Claim Objections
3. Claims 6 and 10 are objected to because of the following informalities:
Claim 6, in line 2, “(EPA) model , an extended vehicular” should be replaced by “(EPA) model, an extended vehicular”
Claim 10, in line 3, “an in-external electronic device infotainment (IVI),” should be replaced by “an in-vehicle infotainment (IVI),”
Appropriate corrections are required.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 7-9, 11-15, and 18-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated
by ASHOUR et al. (US 2024/0064793 Al, hereinafter “Ashour”).
Regarding claim 1, Ashour discloses:
A user equipment comprising (In sidelink communications, a transmitting (Tx) UE initially achieves sidelink synchronization with a receiving (Rx) UE, Ashour: [0058]):
a communication circuit configured to perform a communication connection with an external electronic device (FIG. 4 is a block diagram of a first wireless communication device 410 in communication with a second wireless communication device 450, e.g., via V2V/V2X/D2D communication. Each transmitter 418TX may modulate an RF carrier with a respective spatial stream for transmission. Each receiver 418RX recovers information modulated onto an RF carrier and provides the information to a RX processor 470, Ashour: Fig. 4, [0103], [0105], [0109]);
at least one processor (The transmit (TX) processor 416 and the receive (RX) processor 470 implement layer 1 functionality associated with various signal processing functions. each receiver 454RX receives a signal through its respective antenna, Ashour: Fig. 4, [0104]- [0106]); and
a memory configured to store instructions executed during operations of the at least one processor (The controller/processor 459 can be associated with a memory 460 that stores program codes and data, Ashour: [0107]),
wherein the at least one processor is configured to (At least one of the TX processor 416, 468, the RX processor 456, 470, and the controller/processor 459, 475 may be configured to perform, Ashour: [0112]):
control the communication circuit to transmit, to the external electronic device, device information related to a first target task of the user equipment (each receiver 454RX receives a signal through its respective antenna 452. Each receiver 454RX recovers information modulated onto an RF carrier and provides the information to the receive (RX) processor 456. After determining the resources, the Tx UE may send sidelink control information (SCI) including the resource allocation in a physical sidelink control channel (PSCCH) to the Rx UE, Ashour: [0104]-[0106], [0132]); and
in response to receiving, from the external electronic device, first result data generated by using computing resources of the external electronic device, perform the first target task, based on the first result data (Channel estimates from a channel estimator 474 may be used to determine the coding and modulation scheme, as well as for spatial processing. The channel estimate may be derived from a reference signal and/or channel condition feedback transmitted by the device 450, Ashour: [0105]), and
wherein the first result data is generated based on the device information allocated to the computing resources of the external electronic device (participant nodes in the same cluster or peer-to-peer network may derive common SCI parameters from their updated ML models to apply in sidelink communications, thereby leading to maximized packet reception rate, maximized throughput, or minimized latency for mode 2 resource allocation, Ashour: [0065], [0069]).
Regarding claim 2, Ashour teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Ashour further discloses:
The user equipment of claim 1 (In sidelink communications, a transmitting (Tx) UE initially achieves sidelink synchronization with a receiving (Rx) UE, Ashour: [0058]),
wherein the at least one processor is further configured to, in response to receiving, from the external electronic device, a confirmation message indicating that an amount of idle computing resources of the external electronic device is equal to or greater than a threshold value, control the communication circuit to transmit the device information about the user equipment to the external electronic device (the learning nodes 906 transmit information corresponding to their local updated models W to the FL parameter server 902. To reduce transmission traffic, the corresponding information may be, for example, the modified weights, weights that changed more than a threshold, or the multiplicative or additive delta amounts or percentages for those weights. After determining the resources, the Tx UE may send sidelink control information (SCI) including the resource allocation in a physical sidelink control channel (PSCCH) to the Rx UE, Ashour: [0132], [0143]), and
wherein the first target task is a data processing task based on an online learning operation (providing coordination among UEs in sidelink communication can improve the overall network performance (e.g., by allowing UEs to efficiently improve MCS or other transmission parameters due to coordinated selection of SCI parameters by other UEs), particularly in automated use cases employing artificial intelligence or machine learning (AI/ML) in artificial neural networks (ANNs), Ashour: [0059]- [0061]).
Regarding claim 3, Ashour teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Ashour further discloses:
The user equipment of claim 1, wherein the at least one processor is further configured to (The transmit (TX) processor 416 and the receive (RX) processor 470 implement layer 1 functionality associated with various signal processing functions. each receiver 454RX receives a signal through its respective antenna, Ashour: Fig. 4, [0104]- [0106]):
control the communication circuit to receive information measured by the external electronic device (The MMIU component 498 of controller/processor 475 in device 410 may obtain the aggregated ML model information update from device 450 via RX processor 470, which may receive the aggregated ML model information update from device 450 via antennas 420, Ashour: [0113]- [0114]); and
perform a second target task of the user equipment, based on the information measured by the external electronic device (The base station may receive the beamformed signal from the UE in one or more receive directions. The base station/UE may perform beam training to determine the best receive and transmit directions for each of the base station/UE, Ashour: [0083]),
wherein the information measured by the external electronic device comprises at least one of information about a direction of movement of the external electronic device, a speed of movement of the external electronic device, and a location of the external electronic device (the CNN may be designed to recognize an ideal action a computational agent may take in any given state, such as a direction a vehicle may move in any given location when navigating to a specific destination, Ashour: [0127]), and
wherein the second target task comprises at least one task for improving device performance in the user equipment (providing coordination among UEs in sidelink communication can improve the overall network performance (e.g., by allowing UEs to efficiently improve MCS or other transmission parameters due to coordinated selection of SCI parameters by other UEs). provide coordination among UEs in sidelink communication to improve the overall network performance, particularly in automated use cases employing AI/ML in neural networks, Ashour: [0059], [0136]).
Regarding claim 4, Ashour teaches all the claimed limitations as set forth in the rejection of claim 3 above.
Ashour further discloses:
The user equipment of claim 3, wherein, to perform the second target task, the at least one processor is further configured to:
predict a location to which the user equipment moves, based on the information measured by the external electronic device (The base station may receive the beamformed signal from the UE in one or more receive directions. The base station/UE may perform beam training to determine the best receive and transmit directions for each of the base station/UE, Ashour: [0083]); and
perform a task of selecting a transmission/reception beam that ensures an optimal channel environment at the predicted location (UE 104 may also transmit a beamformed signal to the base station 180 in one or more transmit directions. Although beamformed signals are illustrated between UE 104 and base station 102/180, aspects of beamforming may similarly may be applied by UE 104 or RSU 107 to communicate with another UE 104 or RSU 107, such as based on V2X, V2V, or D2D communication. the CNN may be designed to recognize an ideal action a computational agent may take in any given state, such as a direction a vehicle may move in any given location when navigating to a specific destination, Ashour: [0083], [0127]).
Regarding claim 7, Ashour teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Ashour further discloses:
The user equipment of claim 1, wherein the at least one processor is further configured to:
control the communication circuit to receive, from the external electronic device, information related to a target task of the external electronic device (Channel estimates from a channel estimator 474 may be used to determine the coding and modulation scheme, as well as for spatial processing. The channel estimate may be derived from a reference signal and/or channel condition feedback transmitted by the device 450, Ashour: [0105]);
allocate the information related to the target task of the external electronic device to computing resources of the user equipment (the first FL information includes at least one of: a machine learning task of the apparatus, information associated with an available sensor coupled to the apparatus for the machine learning task, an available ML model associated with the machine learning task, or an available computation resource of the apparatus for the machine learning task, Ashour: [0011]-[0012]);
generate second result data based on the information related to the target task of the external electronic device by using the computing resources of the user equipment (the message 1806 may indicate model training-related information of the first node 1802. This information may include, for example, ML tasks that the first node 1802 is participating in or is interested in participating in, available sensors at the first node 1802 and their associated data input format, available ML models in training including model status, model architectures, model training parameters, and current performance (e.g., accuracy and loss), and available computation resources, Ashour: [0193]-[0194]); and
control the communication circuit to transmit the second result data to the external electronic device (each receiver 454RX receives a signal through its respective antenna 452. Each receiver 454RX recovers information modulated onto an RF carrier and provides the information to the receive (RX) processor 456. After determining the resources, the Tx UE may send sidelink control information (SCI) including the resource allocation in a physical sidelink control channel (PSCCH) to the Rx UE, Ashour: [0104]-[0106], [0132]).
Regarding claim 8, Ashour teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Ashour further discloses:
The user equipment of claim 1, wherein the at least one processor is further configured to:
control the communication circuit to transmit, to the external electronic device, a portion of the device information related to the first target task of the user equipment (each receiver 454RX receives a signal through its respective antenna 452. Each receiver 454RX recovers information modulated onto an RF carrier and provides the information to the receive (RX) processor 456. After determining the resources, the Tx UE may send sidelink control information (SCI) including the resource allocation in a physical sidelink control channel (PSCCH) to the Rx UE, Ashour: [0104]-[0106], [0132]);
generate, by using computing resources of the user equipment, third result data based on the remaining device information related to the first target task of the user equipment (the message 1806 may indicate model training-related information of the first node 1802. This information may include, for example, ML tasks that the first node 1802 is participating in or is interested in participating in, available sensors at the first node 1802 and their associated data input format, available ML models in training including model status, model architectures, model training parameters, and current performance (e.g., accuracy and loss), and available computation resources, Ashour: [0193]-[0194]); and
in response to receiving, from the external electronic device, fourth result data generated by using the computing resources of the external electronic device, perform the first target task by using the third result data and the fourth result data (Channel estimates from a channel estimator 474 may be used to determine the coding and modulation scheme, as well as for spatial processing. The channel estimate may be derived from a reference signal and/or channel condition feedback transmitted by the device 450, Ashour: [0105]), and
wherein the fourth result data is generated based on the portion of the device information, which is allocated to the computing resources of the external electronic device (participant nodes in the same cluster or peer-to-peer network may derive common SCI parameters from their updated ML models to apply in sidelink communications, thereby leading to maximized packet reception rate, maximized throughput, or minimized latency for mode 2 resource allocation, Ashour: [0065], [0069]).
Regarding claim 9, Ashour teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Ashour further discloses:
The user equipment of claim 1, wherein the device information comprises at least one of first measurement information about a state of a channel between the user equipment and a base station and second measurement information about a beam received by the user equipment (The RS may include demodulation RS (DM-RS) (indicated as Rx for one particular configuration, where lOOx is the port number, but other DM-RS configurations are possible) and channel state information reference signals (CSI-RS) for channel estimation at the UE. The RS may also include beam measurement RS (BRS), beam refinement RS (BRRS), and phase tracking RS (PT-RS), Ashour: [0096]-[0097]).
Regarding claim 11, Ashour teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Ashour further discloses:
The user equipment of claim 1, wherein the user equipment is configured to perform a wireless communication connection with a base station by using at least one of a terrestrial network and a non-terrestrial network (The UEs 1302 within a respective zone or geographic area covered by an RSU (or other network node) may be grouped into a same cluster. FIG. 13 illustrates two terrestrial UEs and two aerial UEs in communication with cluster leader 1306 in one of the clusters 1304 and five terrestrial UEs, Ashour: Fig. 13, [0154]).
Regarding claim 12, Ashour discloses:
A method performed by a user equipment, the method comprising (The method may be performed by a UE, Ashour: [0235]):
transmitting, to an external electronic device, device information related to a first target task of the user equipment (each receiver 454RX receives a signal through its respective antenna 452. Each receiver 454RX recovers information modulated onto an RF carrier and provides the information to the receive (RX) processor 456. After determining the resources, the Tx UE may send sidelink control information (SCI) including the resource allocation in a physical sidelink control channel (PSCCH) to the Rx UE, Ashour: [0104]-[0106], [0132]); and
in response to receiving, from the external electronic device, first result data generated by using computing resources of the external electronic device, performing the first target task, based on the first result data (Channel estimates from a channel estimator 474 may be used to determine the coding and modulation scheme, as well as for spatial processing. The channel estimate may be derived from a reference signal and/or channel condition feedback transmitted by the device 450, Ashour: [0105]), and
wherein the first result data is generated based on the device information allocated to the computing resources of the external electronic device (participant nodes in the same cluster or peer-to-peer network may derive common SCI parameters from their updated ML models to apply in sidelink communications, thereby leading to maximized packet reception rate, maximized throughput, or minimized latency for mode 2 resource allocation, Ashour: [0065], [0069]).
Regarding claim 13, Ashour teaches all the claimed limitations as set forth in the rejection of claim 12 above.
Ashour further discloses:
The method of claim 12,
wherein the transmitting of the device information to the external electronic device comprises, in response to receiving, from the external electronic device, a confirmation message indicating that an amount of idle computing resources of the external electronic device is equal to or greater than a threshold value, transmitting the device information to the external electronic device (the learning nodes 906 transmit information corresponding to their local updated models W to the FL parameter server 902. To reduce transmission traffic, the corresponding information may be, for example, the modified weights, weights that changed more than a threshold, or the multiplicative or additive delta amounts or percentages for those weights. After determining the resources, the Tx UE may send sidelink control information (SCI) including the resource allocation in a physical sidelink control channel (PSCCH) to the Rx UE, Ashour: [0132], [0143]), and
wherein the first target task is a data processing task based on an online learning operation (providing coordination among UEs in sidelink communication can improve the overall network performance (e.g., by allowing UEs to efficiently improve MCS or other transmission parameters due to coordinated selection of SCI parameters by other UEs), particularly in automated use cases employing artificial intelligence or machine learning (AI/ML) in artificial neural networks (ANNs), Ashour: [0059]- [0061]).
Regarding claim 14, Ashour teaches all the claimed limitations as set forth in the rejection of claim 12 above.
Ashour further discloses:
The method of claim 12, further comprising:
receiving information measured by the external electronic device (The MMIU component 498 of controller/processor 475 in device 410 may obtain the aggregated ML model information update from device 450 via RX processor 470, which may receive the aggregated ML model information update from device 450 via antennas 420, Ashour: [0113]- [0114]); and
performing a second target task of the user equipment, based on information measured by the external electronic device (The base station may receive the beamformed signal from the UE in one or more receive directions. The base station/UE may perform beam training to determine the best receive and transmit directions for each of the base station/UE, Ashour: [0083]),
wherein the information measured by the external electronic device comprises at least one of information about a direction of movement of the external electronic device, a speed of movement of the external electronic device, and a location of the external electronic device (the CNN may be designed to recognize an ideal action a computational agent may take in any given state, such as a direction a vehicle may move in any given location when navigating to a specific destination, Ashour: [0127]), and
wherein the second target task comprises at least one task for improving device performance in the user equipment (providing coordination among UEs in sidelink communication can improve the overall network performance (e.g., by allowing UEs to efficiently improve MCS or other transmission parameters due to coordinated selection of SCI parameters by other UEs). provide coordination among UEs in sidelink communication to improve the overall network performance, particularly in automated use cases employing AI/ML in neural networks, Ashour: [0059], [0136]).
Regarding claim 15, Ashour teaches all the claimed limitations as set forth in the rejection of claim 14 above.
Ashour further discloses:
The method of claim 14, wherein the performing of the second target task comprises:
predicting a location to which the user equipment moves, based on the information measured by the external electronic device (The base station may receive the beamformed signal from the UE in one or more receive directions. The base station/UE may perform beam training to determine the best receive and transmit directions for each of the base station/UE, Ashour: [0083]); and
selecting a transmission/reception beam that ensures an optimal channel environment at the predicted location (UE 104 may also transmit a beamformed signal to the base station 180 in one or more transmit directions. Although beamformed signals are illustrated between UE 104 and base station 102/180, aspects of beamforming may similarly may be applied by UE 104 or RSU 107 to communicate with another UE 104 or RSU 107, such as based on V2X, V2V, or D2D communication. the CNN may be designed to recognize an ideal action a computational agent may take in any given state, such as a direction a vehicle may move in any given location when navigating to a specific destination, Ashour: [0083], [0127]).
Regarding claim 18, Ashour teaches all the claimed limitations as set forth in the rejection of claim 12 above.
Ashour further discloses:
The method of claim 12, further comprising:
receiving, from the external electronic device, information related to a target task of the external electronic device (Channel estimates from a channel estimator 474 may be used to determine the coding and modulation scheme, as well as for spatial processing. The channel estimate may be derived from a reference signal and/or channel condition feedback transmitted by the device 450, Ashour: [0105]);
allocating the information related to the target task of the external electronic device to computing resources of the user equipment (the first FL information includes at least one of: a machine learning task of the apparatus, information associated with an available sensor coupled to the apparatus for the machine learning task, an available ML model associated with the machine learning task, or an available computation resource of the apparatus for the machine learning task, Ashour: [0011]-[0012]);
generating, by using the computing resources of the user equipment, second result data based on the information related to the target task of the external electronic device (the message 1806 may indicate model training-related information of the first node 1802. This information may include, for example, ML tasks that the first node 1802 is participating in or is interested in participating in, available sensors at the first node 1802 and their associated data input format, available ML models in training including model status, model architectures, model training parameters, and current performance (e.g., accuracy and loss), and available computation resources, Ashour: [0193]-[0194]); and
transmitting the second result data to the external electronic device (each receiver 454RX receives a signal through its respective antenna 452. Each receiver 454RX recovers information modulated onto an RF carrier and provides the information to the receive (RX) processor 456. After determining the resources, the Tx UE may send sidelink control information (SCI) including the resource allocation in a physical sidelink control channel (PSCCH) to the Rx UE, Ashour: [0104]-[0106], [0132]).
Regarding claim 19, Ashour teaches all the claimed limitations as set forth in the rejection of claim 12 above.
Ashour further discloses:
The method of claim 12, further comprising:
transmitting, to the external electronic device, a portion of the device information related to the first target task of the user equipment (each receiver 454RX receives a signal through its respective antenna 452. Each receiver 454RX recovers information modulated onto an RF carrier and provides the information to the receive (RX) processor 456. After determining the resources, the Tx UE may send sidelink control information (SCI) including the resource allocation in a physical sidelink control channel (PSCCH) to the Rx UE, Ashour: [0104]-[0106], [0132]);
generating, by using the computing resources of the user equipment, third result data based on the remaining device information related to the first target task of the user equipment (the message 1806 may indicate model training-related information of the first node 1802. This information may include, for example, ML tasks that the first node 1802 is participating in or is interested in participating in, available sensors at the first node 1802 and their associated data input format, available ML models in training including model status, model architectures, model training parameters, and current performance (e.g., accuracy and loss), and available computation resources, Ashour: [0193]-[0194]); and
in response to receiving, from the external electronic device, fourth result data generated by using t e computing resources of the external electronic device, performing the first target task by using the third result data and the fourth result data (Channel estimates from a channel estimator 474 may be used to determine the coding and modulation scheme, as well as for spatial processing. The channel estimate may be derived from a reference signal and/or channel condition feedback transmitted by the device 450, Ashour: [0105]), and
wherein the fourth result data is generated based on the portion of the device information, allocated to the computing resources of the external electronic device (participant nodes in the same cluster or peer-to-peer network may derive common SCI parameters from their updated ML models to apply in sidelink communications, thereby leading to maximized packet reception rate, maximized throughput, or minimized latency for mode 2 resource allocation, Ashour: [0065], [0069]).
Regarding claim 20, Ashour discloses:
A method performed by a wireless communication system, the method comprising (method of wireless communication. The method may be performed by a leader node, Ashour: [0271]):
transmitting, to an external electronic device, first data related to a target task of a user equipment (each receiver 454RX receives a signal through its respective antenna 452. Each receiver 454RX recovers information modulated onto an RF carrier and provides the information to the receive (RX) processor 456. After determining the resources, the Tx UE may send sidelink control information (SCI) including the resource allocation in a physical sidelink control channel (PSCCH) to the Rx UE, Ashour: [0104]-[0106], [0132]);
transmitting, to the user equipment, second data related to a target task of the external electronic device (resource allocation may be determined through a sensing procedure conducted autonomously by the Tx UE (in a mode 2 resource allocation). After determining the resources, the Tx UE may send sidelink control information (SCI) including the resource allocation to the Rx UE. the first FL information may comprise at least one of: a machine learning task of the apparatus; information associated with an available sensor coupled to the apparatus for the machine learning task; an available ML model associated with the machine learning task; or an available computation resource of the apparatus for the machine learning task., Ashour: [0058], [0272]);
performing the target task of the user equipment, based on first computation result data received from the external electronic device (Channel estimates from a channel estimator 474 may be used to determine the coding and modulation scheme, as well as for spatial processing. The channel estimate may be derived from a reference signal and/or channel condition feedback transmitted by the device 450, Ashour: [0105]); and
performing the target task of the external electronic device, based on second computation result data received from the user equipment (achieve coordination between network nodes applying AI/ML tasks to obtain sidelink communication parameters is federated learning (FL). FL refers to a distributed machine learning technique in which multiple decentralized nodes holding local data samples may train a global ML model (e.g., a classifier, a navigation system recommendation system, a digital assistant, a diagnostic system, or other model applied by multiple network nodes) without exchanging the data samples themselves between nodes to perform the training, Ashour: [0060]-[0063]),
wherein the first computation result data is generated by using computing resources of the external electronic device, based on the first data (participant nodes in the same cluster or peer-to-peer network may derive common SCI parameters from their updated ML models to apply in sidelink communications, thereby leading to maximized packet reception rate, maximized throughput, or minimized latency for mode 2 resource allocation, Ashour: [0065], [0069]), and
wherein the second computation result data is generated by using computing resources of the user equipment, based on the second data (providing coordination among UEs in sidelink communication can improve the overall network performance (e.g., by allowing UEs to efficiently improve MCS or other transmission parameters due to coordinated selection of SCI parameters by other UEs), particularly in automated use cases employing artificial intelligence or machine learning (AI/ML) in artificial neural networks (ANNs), Ashour: [0059]).
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 5-6 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Ashour in view of Kwon (US 2021/0314198 Al, hereinafter “Kwon”).
Regarding claim 5, Ashour teaches all the claimed limitations as set forth in the rejection of claim 3 above.
Ashour does not explicitly disclose:
wherein, to perform the second target task, the at least one processor is further configured to perform a task of selecting a channel model and characteristics of the channel model that ensure an optimal channel environment at the location of the external electronic device, based on the information measured by the external electronic device.
However, in the same field of endeavor, Kwon teaches:
wherein, to perform the second target task, the at least one processor is further configured to perform a task of selecting a channel model and characteristics of the channel model that ensure an optimal channel environment at the location of the external electronic device, based on the information measured by the external electronic device (the receiver further includes a channel estimator that utilizes a neural network to estimate the channel, that is, the channel impulse response (CIR), for each bundle of transmitted signal and provides the CIR to the detector. the channel estimatorincludes an edge expander, a neural network, a post-processor, and a narrowband channel estimator (NBCE). he EPA, EVA, and ETU are multipath fading channel model delay profiles that represent a low, medium, and high delay spread environment, respectively., Kwon: [0047], [0064]-[0065], [0114]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Ashour in view of Kwon in order to further modify performing a task of selecting a channel model and characteristics of the channel model that ensure an optimal channel environment at the location of the external electronic device, based on the information measured by the external electronic device from the teachings of Kwon.
One of ordinary skill in the art would have been motivated because the channel estimator using A2C reduces (e.g., minimizes) the MSE of channel estimation, which may lead to the performance enhancement (Kwon: [0117]).
Regarding claim 6, Ashour in view of Kwon teaches all the claimed limitations as set forth in the rejection of claim 5 above.
Ashour does not explicitly disclose:
wherein the channel model comprises at least one of an extended pedestrian A (EPA) model, an extended vehicular A (EVA) model, and an extended typical urban (ETU) model.
However, in the same field of endeavor, Kwon teaches:
wherein the channel model comprises at least one of an extended pedestrian A (EPA) model, an extended vehicular A (EVA) model, and an extended typical urban (ETU) model (FIGS. 8A, 8B, and 8C illustrate the block error rate (BLER) versus signal to noise ratio (SNR) performance gain of the channel estimator for a rank 2 extended pedestrian A model (EPA) channel, EVA channel, and extended typical urban model (ETA) channel. training may be performed with samples from all of extended pedestrian A model (EPA), extended vehicular A model (EVA), and extended typical urban model (ETA) channels, Kwon: [0039], [0112]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Ashour in view of Kwon in order to further modify the channel model which comprises at least one of an extended pedestrian A (EPA) model, an extended vehicular A (EVA) model, and an extended typical urban (ETU) model from the teachings of Kwon.
One of ordinary skill in the art would have been motivated because by utilizing the model and a supervised machine learning algorithm, such as a one of various known regression or back propagation algorithms, the neural network estimates the autocorrelation which is the estimated frequency autocorrelation of an unprecoded channel for a given bundle (Kwon: [0086]).
Regarding claim 16, Ashour teaches all the claimed limitations as set forth in the rejection of claim 14 above.
Ashour does not explicitly disclose:
wherein the performing of the second target task comprises selecting a channel model and characteristics of the channel model that ensure an optimal channel environment at the location of the external electronic device, based on the information measured by the external electronic device.
However, in the same field of endeavor, Kwon teaches:
wherein the performing of the second target task comprises selecting a channel model and characteristics of the channel model that ensure an optimal channel environment at the location of the external electronic device, based on the information measured by the external electronic device (the receiver further includes a channel estimator that utilizes a neural network to estimate the channel, that is, the channel impulse response (CIR), for each bundle of transmitted signal and provides the CIR to the detector. the channel estimator includes an edge expander, a neural network, a post-processor, and a narrowband channel estimator (NBCE). he EPA, EVA, and ETU are multipath fading channel model delay profiles that represent a low, medium, and high delay spread environment, respectively., Kwon: [0047], [0064]-[0065], [0114]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Ashour in view of Kwon in order to further modify performing a task of selecting a channel model and characteristics of the channel model that ensure an optimal channel environment at the location of the external electronic device, based on the information measured by the external electronic device from the teachings of Kwon.
One of ordinary skill in the art would have been motivated because the channel estimator using A2C reduces (e.g., minimizes) the MSE of channel estimation, which may lead to the performance enhancement (Kwon: [0117]).
Regarding claim 17, Ashour in view of Kwon teaches all the claimed limitations as set forth in the rejection of claim 16 above.
Ashour does not explicitly disclose:
wherein the channel model comprises at least one of an extended pedestrian A (EPA) model, an extended vehicular A (EV A) model, and an extended typical urban (ETU) model.
However, in the same field of endeavor, Kwon teaches:
wherein the channel model comprises at least one of an extended pedestrian A (EPA) model, an extended vehicular A (EV A) model, and an extended typical urban (ETU) model (FIGS. 8A, 8B, and 8C illustrate the block error rate (BLER) versus signal to noise ratio (SNR) performance gain of the channel estimator for a rank 2 extended pedestrian A model (EPA) channel, EVA channel, and extended typical urban model (ETA) channel. training may be performed with samples from all of extended pedestrian A model (EPA), extended vehicular A model (EVA), and extended typical urban model (ETA) channels, Kwon: [0039], [0112]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Ashour in view of Kwon in order to further modify the channel model which comprises at least one of an extended pedestrian A (EPA) model, an extended vehicular A (EVA) model, and an extended typical urban (ETU) model from the teachings of Kwon.
One of ordinary skill in the art would have been motivated because by utilizing the model and a supervised machine learning algorithm, such as a one of various known regression or back propagation algorithms, the neural network estimates the autocorrelation which is the estimated frequency autocorrelation of an unprecoded channel for a given bundle (Kwon: [0086]).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Ashour in view of Banuli Nanje
Gowda (US 2021/0320825 Al, hereafter “Banuli”).
Regarding claim 10, Ashour teaches all the claimed limitations as set forth in the rejection of claim 1 above.
Ashour does not explicitly disclose:
wherein the first result data comprises data generated by using idle computing resources of at least one of a telematic controller (TCU), an in-external electronic device infotainment (IVI), and an advanced driver assistance system (ADAS), of the external electronic device.
However, in the same field of endeavor, Banuli teaches:
wherein the first result data comprises data generated by using idle computing resources of at least one of a telematic controller (TCU), an in-external electronic device infotainment (IVI), and an advanced driver assistance system (ADAS), of the external electronic device (vehicle 1200 may include ADAS system 1238. conventional ADAS systems alert driver and allow driver to decide whether a safety condition truly exists and act accordingly. ADAS system 1238 may include a secondary computer that performs ADAS functionality using traditional rules of computer vision. vehicle 1200 may further include infotainment SoC 1230 (e.g., an in-vehicle infotainment system (IVI)). a machine learning model may be trained by calculating weight parameters according to a neural network architecture using software and computing resources, Banuli: [0211], [0219], [0222], [0224], [0507]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Ashour in view of Banuli in order to further modify the first result data which comprises data generated by using idle computing resources of at least one of a telematic controller (TCU), an in-external electronic device infotainment (IVI), and an advanced driver assistance system (ADAS), of the external electronic device from the teachings of Banuli.
One of ordinary skill in the art would have been motivated because outputs from ADAS system may be provided to a supervisory MCU. In at least one embodiment, if outputs from primary computer and secondary computer conflict, supervisory MCU determines how to reconcile conflict to ensure safe operation (Banuli: [0219]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
References considered relevant to this application are listed in the attached “Notice of References Cited” (PTO-892).
Sydir et al. (US 2022/0326757 Al); See Fig. 6, [0063], [0067] -[0072].
YEH et al. (US 2023/0072769 Al); See Fig. 9, [0182] -[0195].
MUECK (US 2022/0345863 Al); See Fig. 23, [0183] -[0194].
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/S.C.L./Examiner, Art Unit 2467
/HASSAN A PHILLIPS/Supervisory Patent Examiner, Art Unit 2467
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