DETAILED ACTION
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 .
Remarks
This Office Action is in response to amendment and remarks filed on 07/02/2026.
Claims 1, 10 and 15 are currently amended via Applicant’s amendment.
Claims 4-6, 13, 17 and 18 have been canceled via previous amendments.
Claims 1-3, 7-12, 14-16 and 19-20 are pending.
Claims 1, 10 and 15 are independent claims.
This Office Action is made final.
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
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.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/17/2026 is acknowledged and the cited references have been considered.
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 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.
Claims 1-3, 7, 9-12, 14-16 and 20 are rejected under AIA 35 U.S.C. 103 as being unpatentable over Andreas Meier (US 2019/0258243 A1) (hereinafter Meier) in view of Ahn et al. (US 2016/0119410 A1) (hereinafter Ahn) and Song et al. (US 2019/0318245 A1) (Song) and further in view of Zerhouni et al. (US 2021/0056463 A1) (Zerhouni).
As per claim 1, Meier discloses (Currently amended) An artificial intelligence operation processing method, performed by a terminal (e.g. Meier: [0035][0042]), comprising: receiving, by the terminal, indication information sent by a network device, wherein the indication information is used for indicating information about an Artificial Intelligence/Machine Learning (AI/ML) task to be performed by the terminal (e.g. Meier: [Figs. 1 and 2] [0041-0042] discloses a computation module of vehicle receives a partial task of a distributed data processing from a communication module of central office. The data processing corresponds to a distributed machine learning algorithm. The partial task can comprise information about instructions of the partial task and information about data of the partial task. [0049] discloses vehicle receives program to be processed and the data required thereof from a TSP. [0057] discloses the TSP pushes the program and data directly to the vehicle. [0074] discloses selected vehicle receives a job from the TSP which provides the partial tasks to be performed by the selected vehicle. [Fig. 4] [0087-0088] discloses computing system of central office sends a job retrieval of program and data to a vehicle and the vehicle receives information indicating the task that should be performed by the vehicle.); wherein the indication information is used for indicating part or all of operations to be performed by the terminal in the AI/ML task and an AI/ML model used by the terminal to perform the AI/ML task (e.g. Meier: [0042] [0049] [0057] discloses providing information comprising instructions, program code and data required to perform the partial task, the information indicates which instructions to perform to process the assigned partial task. Thus, by providing information comprising instructions, program code and data require to perform the partial task, Meier implies providing information that indicates part of operations to be performed by the terminal.).
Meier does not expressly disclose wherein the indication information used for indicating part or all of AI/ML acts to be performed by the terminal comprises a ratio between acts to be performed by the network device and the terminal in the AI/ML task; and wherein the method further comprises: switching the AI/ML model, only according to varying of an AI/ML computing power of the terminal.
However, Ahn discloses wherein the indication information used for indicating part or all of AI/ML acts to be performed by the terminal comprises a ratio between acts to be performed by the network device and the terminal in the AI/ML task (e.g. Ahn: [0018-0019] discloses the task performing patterns may include a task allocation ratio between the host device and the selected guest device [terminal and network device]. [0072] discloses a task performing pattern includes information whether to perform the task jointly, a task allocation ratio, and information regarding guest device to which to allocate the task. [0092-0093] discloses determining a task allocation ratio between the host device and the guest device. The task allocation ratio may be determined based on various factors. After the task allocation ratio is determined, the host device request the guest device to perform the divided task according to the determined ratio. Also see [0027-0028] [0085][0115].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method/system of determining a task allocation ratio between host device and a guest device, and including the task allocation ratio in the task performing pattern as taught by Ahn into Meier because it would allow dividing task between the host and the guest device according to measured network performances of the devices and also provide indication for the guest to only download a portion of the content required to perform the fraction of the task allocated to the guest device according to the ratio (See Ahn: [0092-0093][0115]).
The combination of Meier and Ahn does not expressly disclose wherein the method further comprises: switching the AI/ML model, only according to varying of an AI/ML computing power of the terminal; or switching the AI/ML model, according to varying of a realizable communication rate.
However, Song discloses wherein the method further comprises: switching the AI/ML model, according to varying [different] of an AI/ML computing capability [power of the terminal] (e.g. Song: [0005-0006] discloses terminal-side device receives a second neural network model that is obtained by trimming a first neural network model such that when the second neural network model runs is within an available hardware capability range of the terminal-side device. [0127] discloses trimming the neural network model, so a hardware resource required when the neural network model (that is, the second neural network model) delivered to the terminal-side device runs within the available hardware resource capability range of the terminal-side device. [0032] disclose receiving indication information used to indicate an available hardware resource capability of the terminal-side device. [0033] discloses trimming a first neural network model based on the available hardware resource capability of the terminal side device, and delivering the trimmed neural network model (second model) to the terminal-side device, so that the hardware resource required when the trimmed neural network model delivered to the terminal device is within the available hardware resource capability range of the terminal device. [0077-0080] discloses receiving an available hardware resource capability of the terminal-device including a computing capability related to CPU performance information, and a storage capability related to storage performance information. [0088-0092] additionally teaches that the terminal-side device may update its neural network basic platform using the second neural-network model, including updating its neural-network parameter component and/or neural-network architecture component, and then processes the task using the updated platform. [0097] Song teaches that the terminal may send a request when a hardware resource required for a neural-network model on the terminal side exceeds the terminal’s available hardware-resource capability range. [0101-0102] Song teaches determining the available hardware resource capability based on change status of resources used while the terminal neural-network basic platform runs. Song identifies Ccpu as a current computing capability of the terminal-side device and states that it may be represented by CPU usage. [0103-0104] Song also teaches that thresholds and/or change ranges may be used to measure computing capability and storage capability at the terminal-side device. When a computing-capability threshold is reached, or when such capability falls within a change range, the resulting state is used as a parameter trigger condition and to indicate degree of trimming of the cloud-side neural-network model. And the cloud trims a neural-network model based on terminal’s available hardware-resource capability, delivers the trimmed model to the terminal, and the terminal invokes that model based on terminal’s available hardware resources. Accordingly, Song teaches that the terminal transitions from an existing or first neural-network model to a different, second resource-conforming neural-network model, and thereafter performs the cognitive-computing task with that second model. Such replacement and subsequent use of the second model teaches or at least renders obvious, switching from the first AI/ML model to a second AI/ML model, wherein the second AI/ML model is trimmed to fit within the changing range of resource capability/availability of the device. [0169-0176] discloses dynamically updating [switching] neural network model on the terminal-side device. Also see [0018-0021] [0042][0094-0104]. Thus, Song teaches that the terminal-side model is adapted/replaced/switched with a second model, lower-resource model in view of terminal’s CPU-related computing capability and resource-condition changes. Song therefore discloses switching the neural network model based on varying [different] computing capability of terminal-device by trimming a first neural network model according to resource capability range of the terminal and delivering the trimmed (a second neural network model) to the terminal-side device.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method/system of dynamically updating or switching neural network model based on remaining or available hardware resource capability of the terminal-side device as taught by Song into the combination of Meier and Ahn because it would improve performance of processing a neural network-related application on the terminal-side device, and help enhance expansion of an intelligent application capability of the terminal-side device. (See Song: [0004, 0006] [0019] [0033] [0042] [0086] [0104]).
The combination of Meier, Ahn and Song strongly implies that an available hardware resource or computing capability may include varying computing power of the terminal device. As discussed above, Song refers to a “change status,” current CPU-related computing capability, thresholds, and change ranges, and teaches creating/delivering a second resource-conforming model. However, Song does not state as directly that a detected change in the current available computational power of the terminal is itself used as the explicit selection condition for choosing a different model. Therefore, the combination does not expressly disclose “switching the AI/ML model, only according to varying [changing] of an AI/ML computing power of the terminal” in the particular sense.
However, Zerhouni explicitly teaches switching the AI/ML model, only according to varying of an AI/ML computing power of the terminal (e.g. Zerhouni: [0011] teaches generating/providing multiple machine learned models having different levels of computational complexity to an on-premise computing device. “the on premise computing device detects current available computational resource at the on premise computing device and selects a machine learned model from all the generated machine learned models having a level of computational complexity that corresponds to the current available computation resources at the on premise computing device.” [0012] “changes in the current available computational resource at the on premise computing device can be detected. Based on the detected change in the available computational resources, a different machine learned model can be selected that has a level of computational complexity corresponding to the detected change.” Thus, Zerhouni expressly teaches switching the model according to varying of an AI/ML computing power of the terminal by selecting a different machine learned model based on detected changes in the current available computational resources at the on-premise computing device. The model is switched based on the variation detected in computational resources. Also see [Abstract] [0016] [0021-0024][0031] [0035-0037] that describe that an on-premise computing device is provided with multiple machine learned models having different level of computational complexity. The device selects an appropriate model based on its available computation resources; as those resources change over time, different models can be chosen. More specifically, Zerhouni teaches detecting a change in the device’s current available computational resources and, “based on the detected change,” selecting a different machine learned model having computational complexity corresponding to that change. This expressly teaches switching the AI/ML model according to variation in the terminal’s available AI/ML computing power.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Zerhouni’s dynamic model-selection or model switching technique into the combination of Meier, Ahn and Song because it would enable selection of an appropriate machine learned model based on detected changes in the available computational resources of the terminal that matches with computational complexity of the model (See Zerhouni: [0022-0024]). This enables the device to adapt to resource availability without requiring an updated model to be requested from the cloud each time the currently being used model becomes unsuitable based on changing capability of resources.
A POSITA would have been motivated to apply Zerhouni’s model-switching technique to Song so that Song terminal can select an appropriate model as its currently available computation resources change. Such modification would improve the system by avoiding use of a model whose computational complexity would interfere with concurrent terminal operations, while allowing use of a higher-complexity model when more computing resources become available. The modification would have involved applying Zerhouni’s known model-selection mechanism to Song’s resource-conforming terminal model framework for the predictable result of dynamically matching terminal-side AI/ML model complexity to currently available terminal computational resources.
As per claim 2, the combination of Meier, Ahn, Song and Zerhouni discloses The method according to claim 1 [See rejection to claim 1 above], further comprising: performing, by the terminal, part or all of operations in the AI/ML task to be performed according to the indication information (e.g. Meier: [0041-0042] [0045] discloses selected vehicle executes partial tasks according to received instructions. The computation module fetches the partial task and the data of the partial task on the basis of provided reference to the instructions and the reference to the data. The computation module can carry out the computation of the result of the partial task at least (or wholly) on the compute units for specialized computations. [Abstract] discloses apparatuses/method for processing partial task, the computation module receives a partial task and computes the partial task of the distributed data processing to obtain a result of the partial task. [0012-0013] discloses providing the partial tasks to the vehicle and the partial tasks are processed by the compute units of the vehicle. Also see [0018] [0026-0027] [0049] [0053] [0058] [0079] [0088][Figs. 2A, 4 and related description]. Ahn: [0018-0019] also discloses the task performing patterns may include a task allocation ratio between the host device and the selected guest device [terminal and network device]. [0072] discloses a task performing pattern includes information whether to perform the task jointly, a task allocation ratio, and information regarding guest device to which to allocate the task. [0092-0093] discloses determining a task allocation ratio between the host device and the guest device. The task allocation ratio may be determined based on various factors. After the task allocation ratio is determined, the host device requests the guest device to perform the divided task according to the determined ratio. Also see [0027-0028] [0085][0115].).
As per claim 3, the combination of Meier, Ahn, Song and Zerhouni discloses The method according to claim 1 [See rejection to claim 1 above], wherein the indication information is further used for indicating: a parameter set of the AI/ML model used by the terminal to perform the AI/ML task (e.g. Meier: [0042] discloses the partial task comprises information about instructions of the partial task and information about data of the partial task. The information about the partial task can comprise reference to the instruction of the partial task and reference to the data of the partial task. [0049] [0057] discloses TSP can initiate the job in the vehicle by providing or downloading the program to be processed and the data required therefor. [0088] disclose providing a job for retrieval of program and data to the vehicle, to be processed by the vehicle. Thus, the information provided to the vehicle includes program code, data and instructions required to perform the partial tasks. Song: [0005-0008] [0024-0026] further discloses updating neural network parameter of the neural network model and delivering the updated parameter and neural network model to terminal-side device for processing cognitive task on the terminal-side device.).
As per claim 7, the combination of Meier, Ahn, Song and Zerhouni discloses The method according to claim 1 [See rejection to claim 1 above], further comprising: sending, by the terminal, at least one piece of following information to the network device for generating the indication information by the network device: a computing power of the terminal for performing the AI/ML task, a storage space of the terminal for performing the AI/ML task, a battery resource of the terminal for performing the AI/ML task, or a communication requirement of the terminal for performing the AI/ML task (e.g. Meier: [0046] discloses computation module of vehicle is configured to provide a notification about an availability or non-availability of the vehicle to computer of the central office. [0047] discloses a vehicle regularly sends a heartbeat to make it clear to the TSP that it is still available. The heartbeat could also be supplemented by further information, such as, e.g., available CPU time. [0059] [0061] discloses receiving information about vehicle, the information includes system capacity utilization of computation module of the vehicle, energy capacity of the vehicle, performance of the computation module of the vehicle, connectivity of the vehicle, position of the vehicle, expected availability of the vehicle, previous processing of a partial task by the vehicle and prioritization of vehicle, to be selected for partial tasks. [0064-0065] discloses vehicle may regularly report the current system capacity utilization of their relevant control units, information concerning the present energy capacity, etc. Song: [0077-0080] discloses receiving an available hardware resource capability of the terminal-device including a computing capability related to CPU performance information, a storage capability related to storage performance information. Also see [0094-0104].).
As per claim 9, the combination of Meier, Ahn, Song and Zerhouni discloses The method according to claim 3 [See rejection to claim 3 above], Song further discloses wherein the AI/ML model is a neural network-based model (e.g. Song: [Abstract] [0005-0007] discloses terminal-side receives a neural network model for processing cognitive task on the terminal-side based on the neural network model. Also see [0009-0013] [0017-0020] [0022-0023] [0068] [0074] [0085].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine method/system of providing a neural network-based model to terminal-device as taught by Song into Meier because it would enable providing a technical solution that terminal-side device specific neural network model based on available hardware resource of the terminal-side device to process the cognitive task. This will ensure and improve accuracy of processing the cognitive task by the terminal-side device. (See Song: [0011-0013] [0023]).
As per claim 10, Meier discloses An artificial intelligence operation processing method, performed by a network device (e.g. Meier: [0035][0042]), comprising: determining, by the network device, information about an Artificial Intelligence/Machine Learning (AI/ML) task to be performed by a terminal (e.g. Meier: [0046] discloses computation module of vehicle is configured to provide a notification about an availability or non-availability of the vehicle to computer of the central office. [0047] discloses a vehicle regularly sends a heartbeat to make it clear to the TSP that it is still available. The heartbeat could also be supplemented by further information, such as, e.g., available CPU time. [0059] [0061] discloses receiving information about vehicle, the information includes system capacity utilization of computation module of the vehicle, energy capacity of the vehicle, performance of the computation module of the vehicle, connectivity of the vehicle, position of the vehicle, expected availability of the vehicle, previous processing of a partial task by the vehicle and prioritization of vehicle, to be selected for partial tasks. [0064-0065] discloses vehicle may regularly report the current system capacity utilization of their relevant control units, information concerning the present energy capacity, etc. [0073] [0087] discloses task requirements may include the number of necessary compute units, stable network connection, etc. The partial tasks to be performed by the selected vehicle is determined based on task requirement and available capacity of the vehicle.); and sending, by the network device, indication information to the terminal, wherein the indication information is used for indicating the information about the AI/ML task to be performed by the terminal (e.g. Meier: [Figs. 1 and 2] [0041-0042] discloses a computation module of vehicle receives a partial task of a distributed data processing from a communication module of central office. The data processing corresponds to a distributed machine learning algorithm. The partial task can comprise information about instructions of the partial task and information about data of the partial task. [0049] discloses vehicle receives program to be processed and the data required thereof from a TSP. [0057] discloses the TSP pushes the program and data directly to the vehicle. [0074] discloses selected vehicle receives a job from the TSP which provides the partial tasks to be performed by the selected vehicle. [Fig. 4] [0087-0088] discloses computing system of central office sends a job retrieval of program and data to a vehicle and the vehicle receives information indicating the task that should be performed by the vehicle.); wherein the indication information is used for indicating part or all of operations to be performed by the terminal in the AI/ML task and an AI/ML model used by the terminal to perform the AI/ML task (e.g. Meier: [0042] [0049] [0057] discloses providing information comprising instructions, program code and data required to perform the partial task, the information indicates which instructions to perform to process the assigned partial task. Thus, by providing information comprising instructions, program code and data require to perform the partial task, Meier implies providing information that indicates part of operations to be performed by the terminal.).
Meier does not expressly disclose wherein the indication information used for indicating part or all of AI/ML acts to be performed by the terminal comprises a ratio between acts to be performed by the network device and the terminal in the AI/ML task; and wherein the method further comprises: indicating, only according to varying of an AI/ML computing power of a terminal, the terminal to switch the AI/ML model.
However, Ahn discloses wherein the indication information used for indicating part or all of AI/ML acts to be performed by the terminal comprises a ratio between acts to be performed by the network device and the terminal in the AI/ML task (e.g. Ahn: [0018-0019] discloses the task performing patterns may include a task allocation ratio between the host device and the selected guest device [terminal and network device]. [0072] discloses a task performing pattern includes information whether to perform the task jointly, a task allocation ratio, and information regarding guest device to which to allocate the task. [0092-0093] discloses determining a task allocation ratio between the host device and the guest device. The task allocation ratio may be determined based on various factors. After the task allocation ratio is determined, the host device request the guest device to perform the divided task according to the determined ratio. Also see [0027-0028] [0085][0115].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method/system of determining a task allocation ratio between host device and a guest device, and including the task allocation ratio in the task performing pattern as taught by Ahn into Meier because it would allow dividing task between the host and the guest device according to measured network performances of the devices and also provide indication for the guest to only download a portion of the content required to perform the fraction of the task allocated to the guest device according to the ratio (See Ahn: [0092-0093][0115]).
The combination of Meier and Ahn does not expressly disclose wherein the method further comprises: indicating, according to varying of an AI/ML computing power of a terminal, the terminal to switch the AI/ML model.
However, Song discloses wherein the method further comprises: indicating, according to varying of an AI/ML computing capability [computing power of a terminal], the terminal to switch the AI/ML mode (e.g. Song: [0127] discloses trimming the neural network model, so a hardware resource required when the neural network model (that is, the second neural network model) delivered to the terminal-side device runs is within the available hardware resource capability range of the terminal-side device. [0005-0006] discloses terminal-side device receives a second neural network model that is obtained by trimming a first neural network model such that when the second neural network model runs is within an available hardware capability range of the terminal-side device. [0032] disclose receiving indication information used to indicate an available hardware resource capability of the terminal-side device. [0033] discloses trimming a first neural network model based on the available hardware resource capability of the terminal side device, and delivering the trimmed neural network model (second model) to the terminal-side device, so that the hardware resource required when the trimmed neural network model delivered to the terminal device is within the available hardware resource capability range of the terminal device. [0077-0080] discloses receiving an available hardware resource capability of the terminal-device including a computing capability related to CPU performance information, a storage capability related to storage performance information. [0169-0176] discloses dynamically updating [switching] neural network model on the terminal-side device. Also see [0018-0021] [0042] [0094-0104]. Thus, Song discloses switching the neural network model based on varying computing capability of terminal-device by trimming a first neural network model according to resource capability range of the terminal and delivering the trimmed (a second neural network model) to the terminal-side device. It is implied that the available hardware resource capability may be any computing resource including remaining computing power of the terminal device.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method/system of dynamically updating or switching neural network model based on remaining or available hardware resource capability of the terminal-side device as taught by Song into the combination of Meier and Ahn because it would improve performance of processing a neural network-related application on the terminal-side device, and help enhance expansion of an intelligent application capability of the terminal-side device. (See Song: [0004, 0006] [0019] [0033] [0042] [0086] [0104]).
The combination of Meier, Ahn and Song strongly implies that an available hardware resource or computing capability may include varying computing power of the terminal device. As discussed above, Song refers to a “change status,” current CPU-related computing capability, thresholds, and change ranges, and teaches creating/delivering a second resource-conforming model. However, Song does not state directly that a detected change in the current available computational power of the terminal is itself used as the explicit selection condition for choosing a different model. Therefore, the combination does not expressly disclose indicating, only according to varying of an AI/ML computing power of a terminal, the terminal to switch the AI/ML model.
However, Zerhouni explicitly discloses indicating, only according to varying of an AI/ML computing power of a terminal, the terminal to switch the AI/ML model (e.g. Zerhouni: [0011] teaches generating/providing multiple machine learned models having different levels of computational complexity to an on-premise computing device. “the on premise computing device detects current available computational resource at the on premise computing device and selects a machine learned model from all the generated machine learned models having a level of computational complexity that corresponds to the current available computation resources at the on premise computing device.” [0012] “changes in the current available computational resource at the on premise computing device can be detected. Based on the detected change in the available computational resources, a different machine learned model can be selected that has a level of computational complexity corresponding to the detected change.” Thus, Zerhouni expressly teaches switching the model according to varying of an AI/ML computing power of the terminal by selecting a different machine learned model based on detected changes in the current available computational resources at the on-premise computing device. The model is switched based on the variation detected in computational resources. Also see [Abstract] [0016] [0021-0024][0031] [0035-0037] that describe that an on-premise computing device is provided with multiple machine learned models having different level of computational complexity. The device selects an appropriate model based on its available computation resources; as those resources change over time, different models can be chosen. More specifically, Zerhouni teaches detecting a change in the device’s current available computational resources and, “based on the detected change,” selecting a different machine learned model having computational complexity corresponding to that change. This expressly teaches switching the AI/ML model according to variation in the terminal’s available AI/ML computing power.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Zerhouni’s dynamic model-selection or model switching technique into the combination of Meier, Ahn and Song because it would enable selection of an appropriate machine learned model based on detected changes in the available computational resources of the terminal that matches with computational complexity of the model (See Zerhouni: [0022-0024]). This enables the device to adapt to resource availability without requiring an updated model to be requested from the cloud each time the currently being used model becomes unsuitable based on changing capability of resources.
A POSITA would have been motivated to apply Zerhouni’s model-switching technique to Song so that Song terminal can select an appropriate model as its currently available computation resources change. Such modification would improve the system by avoiding use of a model whose computational complexity would interfere with concurrent terminal operations, while allowing use of a higher-complexity model when more computing resources become available. The modification would have involved applying Zerhouni’s known model-selection mechanism to Song’s resource-conforming terminal model framework for the predictable result of dynamically matching terminal-side AI/ML model complexity to currently available terminal computational resources.
As per claim 11, the combination of Meier, Ahn, Song and Zerhouni discloses The method according to claim 10 [See rejection to claim 10 above], wherein determining, by a network device, information about an AI/ML task to be performed by a terminal, comprises: acquiring at least one piece of following information: a computing power of the terminal for performing the AI/ML task, a storage space of the terminal for performing the AI/ML task, a battery resource of the terminal for performing the AI/ML task, or a communication requirement of the terminal for performing the AI/ML task; and determining, by the network device according to the acquired information, the information about the AI/ML task to be performed by the terminal (e.g. Meier: [0046] discloses computation module of vehicle is configured to provide a notification about an availability or non-availability of the vehicle to computer of the central office. [0047] discloses a vehicle regularly sends a heartbeat to make it clear to the TSP that it is still available. The heartbeat could also be supplemented by further information, such as, e.g., available CPU time. [0059] [0061] discloses receiving information about vehicle, the information includes system capacity utilization of computation module of the vehicle, energy capacity of the vehicle, performance of the computation module of the vehicle, connectivity of the vehicle, position of the vehicle, expected availability of the vehicle, previous processing of a partial task by the vehicle and prioritization of vehicle, to be selected for partial tasks. [0064-0065] discloses vehicle may regularly report the current system capacity utilization of their relevant control units, information concerning the present energy capacity, etc. [0073] [0087] discloses task requirements may include the number of necessary compute units, stable network connection, etc. The partial tasks to be performed by the selected vehicle is determined based on task requirement and available capacity of the vehicle. Song: [0032] further discloses receiving indication information used to indicate an available hardware resource capability of the terminal-side device. [0077-0080] discloses receiving an available hardware resource capability of the terminal-device including a computing capability related to CPU performance information, a storage capability related to storage performance information.).
As per claim 12, the combination of Meier, Ahn, Song and Zerhouni discloses The method according to claim 10 [See rejection to claim 10 above], wherein the indication information is further used for indicating: a parameter set of the AI/ML model used by the terminal to perform the AI/ML task (e.g. Meier: [0042] discloses the partial task comprises information about instructions of the partial task and information about data of the partial task. The information about the partial task can comprise reference to the instruction of the partial task and reference to the data of the partial task. [0049] [0057] discloses TSP can initiate the job in the vehicle by providing or downloading the program to be processed and the data required therefor. [0088] disclose providing a job for retrieval of program and data to the vehicle, to be processed by the vehicle. Thus, the information provided to the vehicle includes program code, data and instructions required to perform the partial tasks. Song: [0005-0008] [0024-0026] further discloses updating neural network parameter of the neural network model and delivering the updated parameter and neural network model to terminal-side device for processing cognitive task on the terminal-side device.).
As per claim 14, the combination of Meier, Ahn, Song and Zerhouni discloses The method according to claim 10 [See rejection to claim 10 above], further comprising: after sending the indication information to the terminal, performing, by the network device, an AI/ML operation that matches an AI/ML operation performed by the terminal; wherein an AI/ML operation that matches an AI/ML operation performed by the terminal, comprising: a part of AI/ML operations of the AM/ML task are performed by the terminal, and a remaining part of the AI/ML task is performed by the network device (e.g. Meier: [Figs. 1 and 2] [0041-0042] discloses a computation module of vehicle receives a partial task of a distributed data processing from a communication module of central office. The data processing corresponds to a distributed machine learning algorithm. The partial task can comprise information about instructions of the partial task and information about data of the partial task. [0049] discloses vehicle receives program to be processed and the data required thereof from a TSP. [0057] discloses the TSP pushes the program and data directly to the vehicle. [0074] discloses selected vehicle receives a job from the TSP which provides the partial tasks to be performed by the selected vehicle. [Fig. 4] [0087-0088] discloses computing system of central office sends a job retrieval of program and data to a vehicle and the vehicle receives information indicating the task that should be performed by the vehicle. Ahn: [0018-0019] further discloses the task performing patterns may include a task allocation ratio between the host device and the selected guest device. [0072] discloses a task performing pattern includes information whether to perform the task jointly, a task allocation ratio, and information regarding guest device to which to allocate the task. [0092-0093] discloses determining a task allocation ratio between the host device and the guest device. The task allocation ratio may be determined based on various factors. After the task allocation ratio is determined, the host device request the guest device to perform the divided task according to the determined ratio. Also see [0027-0028] [0085][0115]. Song: [0005] also discloses cloud side device trims neural network model based on available hardware resource capability of the terminal-side device and sends it to the terminal-side device. The first neural network model is used on the cloud-side device to process cognitive computing task, and the second neural network model is used on the terminal-side device to process the cognitive computing task. [0011] discloses matching the cognitive accuracy tolerance that represents the expected accuracy of processing the computing task by the terminal side meets the expected accuracy of cloud-side device. [0013] discloses matching/determining accuracy of processing the cognitive computing task by using the second neural network model delivered by the cloud-side device to the terminal-side device is consistent with accuracy corresponding to the cognitive accuracy tolerance.).
As per claims 15, 16 and 20, these are apparatus/system claims having similar limitations as cited in method claims 1, 3 and 9, respectively. Thus, claims 15, 16 and 20 are also rejected under the same rationale as cited in the rejection of rejected claims 1, 3 and 9, respectively.
Claims 8 and 19 are rejected under AIA 35 U.S.C. 103 as being unpatentable over the combination of Meier, Ahn, Song and Zerhouni in view of Pang et al. (US 2019/0327593 A1) (hereinafter Pang).
As per claim 8, the combination of Meier, Ahn, Song and Zerhouni discloses The method according to claim 1 [See rejection to claim 1 above], but does not expressly disclose wherein the indication information sent by the network device is received by receiving at least one piece of following information: Downlink Control Information (DCI), a Medium Access Control Control Element (MAC CE), high layer configuration information, or application layer control information.
However, Pang discloses wherein the indication information sent by the network device is received by receiving at least one piece of following information: Downlink Control Information (DCI), a Medium Access Control Control Element (MAC CE), high layer configuration information, or application layer control information (e.g. Pang: [0076] discloses the D2D communication method includes: sending, by the network device, downlink control information to the receiving device, where the downlink control information is used to indicate configuration information for downlink data transmission of the network device. [0127-0128] discloses the network device sends downlink control information to the receiving device, where the downlink control information is used to indicate configuration information for downlink data transmission of the network device. [0156] discloses receiving unit is configured to receive downlink control information sent by the network device.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine well-known method/system of D2D communication that includes sending, by the network device, downlink control information to the receiving device as taught by Pang into the combination of Meier, Ahn, Song and Zerhouni because it would enable communication between network device and receiving device, where the downlink control information is used to indicate configuration information for downlink data transmission of the network device (See Pang: [0076] [0127-0128]).
As per claim 19, this is an apparatus/system claim having similar limitations as cited in method claim 8. Thus, claim 19 is also rejected under the same rationale as cited in the rejection of rejected claim 8.
Response to Arguments
Applicant’s arguments with respect to 35 U.S.C. § 103 have been fully considered but they are moot in view of new grounds of rejections necessitated by the amendment.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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September 1, 2026
/HIREN P PATEL/Primary Examiner, Art Unit 2196
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