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 .
No claim set in accordance with 37 CFR § 1.121 is provided in the instant response, therefore, the claim set presented on 21 July 2025 is considered to be current.
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 (i.e., changing from AIA to pre-AIA ) 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 nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-18 and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over US 20230319585 A1 to Zhang et al. (“Zhang”) in view of US 9413779 to Vasseur et al. (“Vasseur”).
Regarding claim 1, Zhang taught a method performed by a first network node, the method comprising:
receiving a first message from a second network node, the first message comprising information about how to format data for execution of at least a machine learning, ML, or artificial intelligence, AI, process, or ML or AI model thereof, that is available for execution at the first network node; (consider paragraph 0010, “receive, from the first node, a first set of configuration information including a set of model parameters for the local AI model stored in the memory, the local AI model being configured by the set of model parameters to generate inference data including at least one inferred control parameter for configuring the system for wireless communication”) (consider further paragraph 0147, “After selecting the global AI model(s) 216 for the task request, the AI management module 210 performs training of the global AI model(s) 216. For collaborative training, the AI management module 210 may use training data provided and/or identified in the task request for training of the global AI model(s) 216. For example, the AI management module 210 may use model data (e.g., locally trained model parameters) collected from one or more AI execution modules 220 managed by the AI management module 210 to update the parameters of the global AI model(s) 216... After training is complete (e.g., the loss function for each global AI model 216 has converged), model data extracted from the selected global AI model(s) 216 (e.g., the globally updated weights of the global AI model(s)) may be communicated to be used by local AI model(s) at the AI execution module 220. The global model parameter(s) may be communicated (e.g., using output functions of the AICF 214) to the AI execution module 220 as configuration information, for example in a configuration message.”) and
executing the at least a ML or AI process, or the ML or AI model thereof, based on the information comprised in the first message. (consider paragraph 0011 regarding the “execut[ion]” of the “local AI model”) (consider further paragraph 0147, “After selecting the global AI model(s) 216 for the task request, the AI management module 210 performs training of the global AI model(s) 216. For collaborative training, the AI management module 210 may use training data provided and/or identified in the task request for training of the global AI model(s) 216. For example, the AI management module 210 may use model data (e.g., locally trained model parameters) collected from one or more AI execution modules 220 managed by the AI management module 210 to update the parameters of the global AI model(s) 216... After training is complete (e.g., the loss function for each global AI model 216 has converged), model data extracted from the selected global AI model(s) 216 (e.g., the globally updated weights of the global AI model(s)) may be communicated to be used by local AI model(s) at the AI execution module 220. The global model parameter(s) may be communicated (e.g., using output functions of the AICF 214) to the AI execution module 220 as configuration information, for example in a configuration message.”) (consider further paragraph 0148, “At the AI execution module 220, the configuration information includes model parameter(s) that are used by the AI execution module 220 to update one or more corresponding local AI model(s) 226 (e.g., the AI model(s) that are the target(s) of the collaborative training, as identified in the collaborative task request).”)
Zhang may be interpreted as not expressly teaching wherein the information about how to format data comprises information about how to scale or descale values and ranges of values of input data to the ML or AI model.
However, in an analogous art relating to ML/AI modelling and processing/execution of said models, Vasseur taught that information about how to scale or descale values and ranges of values of input data to the ML or AI model may be included in information that is included within a message which is used to execute a ML or AI process or model. (consider column 7, lines 20-47, specifically regarding “Artificial Neural Networks” “whose underlying mathematical models were inspired by the hypothesis that mental activity consists primarily of electrochemical activity between interconnected neurons. ANNs are sets of computational units (neurons) connected by directed weighted links. By combining the operations performed by neurons and the weights applied by their links, ANNs are able to perform highly non-linear operations on their input data” wherein “[l]earning in ANNs is treated as an optimization problem where the weights of the links are optimized for minimizing a predefined cost function. This optimization problem is computationally very expensive, due to the high number of parameters to be optimized, but thanks to the backpropagation algorithm, the optimization problem can be performed very efficiently. Indeed, the backpropagation algorithm computes the gradient of the cost function with respect to the weights of the links in only one forward and backward pass throw the ANN. With this gradient, the weights of the ANN that minimize the cost function can be computed.”) (consider further column 12, line 66-column 13, line 11, “local model parameters are generated by training a machine learning model at a device in a computer network using a local data set. One or more other devices in the network are identified that have trained machine learning models (i.e., that may be identical or different) using remote data sets that are similar to the local data set. The local model parameters are provided to the one or more other devices to cause the one or more other devices to generate performance metrics using the provided model parameters with their local data sets. Performance metrics for model parameters are received from the one or more other devices and a global set of model parameters is selected for the device and the one or more other devices using the received performance metrics”) (consider further column 14, line 60-column 15, line 37, specifically “Once the FAR has joined the multicast group used for selection of the most efficient learning machine model, the FAR may export the information describing the learning machine configuration that has been obtained after the local training phase. For example, as shown in FIG. 8C, FAR-1 may export the model parameters for its trained machine learning model to FAR-2, FAR-3, etc. In the case of an ANN, for example, such information will include the connections between neurons and their corresponding weights. More specifically, such information may be a matrix of weights between all neurons called M_ANN (t), the set of weights computed by the FAR at time t. Such data may be included in a specific multicast message… In various embodiments, each element of the matrix may either be a scalar, a vector (e.g., precision and recall) or a more complex structure, such as a matrix of performance for a specific learning machine model.”) (consider further column 16, lines 20-32, “Procedure 900 then continues on to step 920 in which the local model parameters are provided to the other identified training device or devices. In response, the training devices may generate performance metrics using the model parameters with data sets that are collected locally by the other training devices. In step 925, performance metrics are received from the other training devices. As highlighted in more detail above, the received performance metrics may include metrics for the locally generated model parameters and/or for model parameters generated by any of the other training devices. In step 930, a global set of model parameters are then selected for the devices based on the received performance metrics and procedure 900 ends at step 935.”)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of these references such that their combination includes every element as claimed. One skilled in the art could have combined the teachings by known methods such as integration of software routines with no changes to the operation of either reference such that, in combination, each element merely performs the same function as it does separately. Additionally, the Examiner finds that, based on the references’ analogous disclosure regarding the use of messaging to include information regarding the formatting of data for execution of ML/AI processing or models thereof, further demonstrates that a combination of their features would have been known and obvious. Therefore, such a combination of the teachings of the references would have yielded nothing more than predictable results to one of ordinary skill in the art.
Regarding claim 2, the combined teachings of Zhang and Vasseur taught the method of claim 1.
Zhang taught wherein the ML or AI process, or the ML or AI model thereof, is trained at the second network node and provided to the first network node. (consider paragraph 0022, “In any of the above examples, the task request may be a request for collaborative training of the local AI model”) (consider further paragraph 0085, “In some examples, a task may be a collaborative task that involves cooperation among multiple nodes to perform an AI-related task. For example, a collaborative task may be to train an AI model (e.g., a global AI model) to perform a task (e.g., to perform object detection and recognition) that requires collection of a large amount of training data. The AI management module 210 may manage multiple AI execution modules 220 at respective system nodes 120 to collaboratively train a global AI model (e.g., similar to federated learning methods). Another example collaborative task may be for the AI management module 210 to train an AI model on behalf of an AI execution module 220, possibly using data collected by the AI execution module 220. For example, a system node 120 or UE 110 may wish to implement a local AI model that is trained on local data, but may request that the AI management module 210 (e.g., at the network node 131) perform the training (e.g., the system node 120 or UE 110 may have limited computing power and/or memory resources that are required for training an AI model).”) (consider further paragraph 0147, “After selecting the global AI model(s) 216 for the task request, the AI management module 210 performs training of the global AI model(s) 216. For collaborative training, the AI management module 210 may use training data provided and/or identified in the task request for training of the global AI model(s) 216. For example, the AI management module 210 may use model data (e.g., locally trained model parameters) collected from one or more AI execution modules 220 managed by the AI management module 210 to update the parameters of the global AI model(s) 216... After training is complete (e.g., the loss function for each global AI model 216 has converged), model data extracted from the selected global AI model(s) 216 (e.g., the globally updated weights of the global AI model(s)) may be communicated to be used by local AI model(s) at the AI execution module 220. The global model parameter(s) may be communicated (e.g., using output functions of the AICF 214) to the AI execution module 220 as configuration information, for example in a configuration message.”) (consider further paragraph 0148, “At the AI execution module 220, the configuration information includes model parameter(s) that are used by the AI execution module 220 to update one or more corresponding local AI model(s) 226 (e.g., the AI model(s) that are the target(s) of the collaborative training, as identified in the collaborative task request).”)
Regarding claim 3, the combined teachings of Zhang and Vasseur taught the method of claim 1.
Zhang taught wherein executing the at least a ML or AI process, or the ML or AI model thereof, based on the information comprised in the first message comprises formatting at least one input data provided to the ML or AI process, or the ML or AI model thereof, and/or at least one output data provided by the ML or AI process, or the ML or AI model thereof, based on the information comprised in the first message. (consider paragraph 0110, “Further, each AI model may be defined by a set of one or more input-related attributes (defining the type or characteristic of data that can be used as input by the AI model) and also may be defined by a set of one or more output-related attributes (defining the type or characteristic of data is generated by the AI model as output).”) (consider also paragraph 0187, “The task request message 652 may include a set of input-related attributes associated with a given target AI model and a set of output-related attributes associated with the given target AI model. For example, the set of input-related attributes associated with a given target AI model may include an identifier of the given target AI model, and/or any of the previously mentioned input-related attributes (e.g., what type(s) of raw data and/or AI-related data may be accepted as input data; one or more APIs for interacting with other software modules (e.g., to receive input data); which system node(s) 120 and/or UE(s) 110 can participate in providing input data to the AI model; and/or one or more data transfer protocols to be used for communicating input data; among others). The output-related attributes associated with the given target AI model may include any of the previously mentioned output-related attributes (e.g., the target of the inference output; and/or control parameter(s) that are the target of the inference output; among others).”)
Regarding claim 4, the combined teachings of Zhang and Vasseur taught the method of claim 1.
Zhang taught wherein executing the at least a ML or AI process, or the ML or AI model thereof, based on the information comprised in the first message comprises:
(a) formatting information used as input to the ML or AI process, or the ML or AI model thereof, based on scaling information comprised in the information comprised the first message;
(b) obtaining an output from the ML or AI process, or the ML or AI model thereof, by executing the ML or AI process, or the ML or AI model thereof, using input data formatted according to the information comprised in the first message;
(c) formatting an output provided by the ML or AI process, or the ML or AI model thereof, based on de-scaling information comprised in the formatting information comprised in the first message;
(d) applying an output provided by the ML or AI process, or the ML or AI model thereof, that is formatted according to the information comprised in the first message to an associated network function or communication device; or
(e) a combination of any two or more of (a) - (d). (consider paragraph 0116, “For example, if the AI management module 210 receives a task request for low latency service, a global AI model designed to control for latency sensitivity may be selected to infer control parameters for associated control modules (e.g., control parameters for MAC scheduling, power control, beamforming, mobility control, etc.). The AI management module 210 may perform baseline, non-RT training of the selected global AI model(s) to generate one or more globally inferred control parameters related to latency. The trained global model parameters (e.g., trained weights) and/or globally inferred control parameter(s) may then be communicated by the AI management module 210 to be implemented in one or more system nodes 120. For example, the global model parameters may be implemented in corresponding local AI model(s) by the AI execution module 220 at a given system node 120. The local AI model(s) may be executed (using the global model parameters) to generate local control parameter(s) related to latency. The local AI model(s) may be optionally updated (using near-RT training) using local network data collected at the system node 120. The updated local AI model(s) may then be executed to infer updated local control parameter(s) to control for latency, according to the dynamic local environment of the system node 120.”)
Regarding claim 5, the combined teachings of Zhang and Vasseur taught the method of claim 1.
Zhang taught wherein the ML or AI process, or the ML or AI model thereof, is trained to optimize one or more operations or functions of the first network node or to optimize one or more operations or configurations associated to a communication device connected to the first network node. (consider paragraph 0086, “Each AI model (which may be a global AI model or a local AI model) may be defined with input attributes (e.g., type and characteristics of data that can be accepted as input to the AI model) and output attributes (e.g., type and characteristics of data that is generated as inference output by the AI model), as well as one or more targeted tasks (e.g., the network problem or issue to be addressed by the inference data outputted by the AI model). The input attributes and output attributes of each AI model may be defined from a set of possible input attributes and a set of possible output attributes (respectively) that have been defined for the wireless system 100 as a whole (e.g., standardized according to a network standard). For example, a standard may specify that end-to-end latency can be used as input data to an AI model, but UE-AN latency cannot be used as input data; or a standard may specify that identification of a handover scheme may be inference output by an AI model, but a specific waveform cannot be inference output by an AI model.”) (consider further paragraph 0116, “For example, if the AI management module 210 receives a task request for low latency service, a global AI model designed to control for latency sensitivity may be selected to infer control parameters for associated control modules (e.g., control parameters for MAC scheduling, power control, beamforming, mobility control, etc.). The AI management module 210 may perform baseline, non-RT training of the selected global AI model(s) to generate one or more globally inferred control parameters related to latency. The trained global model parameters (e.g., trained weights) and/or globally inferred control parameter(s) may then be communicated by the AI management module 210 to be implemented in one or more system nodes 120. For example, the global model parameters may be implemented in corresponding local AI model(s) by the AI execution module 220 at a given system node 120. The local AI model(s) may be executed (using the global model parameters) to generate local control parameter(s) related to latency. The local AI model(s) may be optionally updated (using near-RT training) using local network data collected at the system node 120. The updated local AI model(s) may then be executed to infer updated local control parameter(s) to control for latency, according to the dynamic local environment of the system node 120.”)
Regarding claim 6, the combined teachings of Zhang and Vasseur taught the method of claim 1.
Zhang taught wherein the information comprised in the first message comprises:
(a) an indication of at least a data scaling and/or descaling criterion to be applied for at least one input data of the ML or AI process, or the ML or AI model thereof;
(b) an indication of at least a data format to be applied for at least one input data of the ML or AI process, or the ML or AI model thereof;
(c) an indication of at least a data scaling and/or de-scaling criterion to be applied for at least one output data of the ML or AI process, or the ML or AI model thereof;
(d) an indication of at least a data format to be used for at least one output data of the ML or AI process, or the ML or AI model thereof;
(e) an indication of at least a normalization criterion to be applied for at least one input data of the ML or AI process, or the ML or AI model thereof;
(f) an indication of at least a normalization criterion to be applied for at least one output data of the ML or AI process, or the ML or AI model thereof;
(g) an indication of at least one parameter to be utilized in a normalization function to be applied for at least one input data and/or to an output data of the ML or AI process, or the ML or AI model thereof; or
(h) a combination of any two or more of (a) - (g). (consider paragraph 0116, “For example, if the AI management module 210 receives a task request for low latency service, a global AI model designed to control for latency sensitivity may be selected to infer control parameters for associated control modules (e.g., control parameters for MAC scheduling, power control, beamforming, mobility control, etc.). The AI management module 210 may perform baseline, non-RT training of the selected global AI model(s) to generate one or more globally inferred control parameters related to latency. The trained global model parameters (e.g., trained weights) and/or globally inferred control parameter(s) may then be communicated by the AI management module 210 to be implemented in one or more system nodes 120. For example, the global model parameters may be implemented in corresponding local AI model(s) by the AI execution module 220 at a given system node 120. The local AI model(s) may be executed (using the global model parameters) to generate local control parameter(s) related to latency. The local AI model(s) may be optionally updated (using near-RT training) using local network data collected at the system node 120. The updated local AI model(s) may then be executed to infer updated local control parameter(s) to control for latency, according to the dynamic local environment of the system node 120.”)
Regarding claim 7, the combined teachings of Zhang and Vasseur taught the method of claim 1.
Zhang taught wherein the information comprised in the first message comprises information that indicates a linear or non-linear scaling function to be utilized by the first network node to scale at least one input data to the ML or AI process, or the ML or AI model thereof, and/or to descale at least one output data of the ML or AI process, or the ML or AI model thereof. (consider paragraph 0052, “In the present disclosure, an AI model encompasses neural networks, which are used in machine learning. A neural network is composed of a plurality of computational units (which may also be referred to as neurons), which are arranged in one or more layers. The process of receiving an input at an input layer and generating an output at an output layer may be referred to as forward propagation. In forward propagation, each layer receives an input (which may have any suitable data format, such as vector, matrix, or multidimensional array) and performs computations to generate an output (which may have different dimensions than the input). The computations performed by a layer typically involve applying a set of weights (also referred to as coefficients) to the input (e.g., by multiplying).”) (consider further paragraph 0053, “A neural network is trained to optimize the parameters (e.g., weights) of the neural network. This optimization is performed in an automated manner, and may be referred to as machine learning. Training of a neural network involves forward propagating an input data sample to generate an output value (also referred to as a predicted output value or inferred output value), and comparing the generated output value with a known or desired target value (e.g., a ground-truth value). A loss function is defined to quantitatively represent the difference between the generated output value and the target value, and the goal of training the neural network is to minimize the loss function. Backpropagation is an algorithm for training a neural network. Backpropagation is used to adjust (also referred to as update) a value of a parameter (e.g., a weight) in the neural network, so that the computed loss function becomes smaller. Backpropagation involves computing a gradient of the loss function with respect to the parameters to be optimized, and a gradient algorithm (e.g., gradient descent) is used to update the parameters to reduce the loss function. Backpropagation is performed iteratively, so that the loss function is converged or minimized over a number of iterations.”) (consider further paragraph 0147, “After selecting the global AI model(s) 216 for the task request, the AI management module 210 performs training of the global AI model(s) 216. For collaborative training, the AI management module 210 may use training data provided and/or identified in the task request for training of the global AI model(s) 216. For example, the AI management module 210 may use model data (e.g., locally trained model parameters) collected from one or more AI execution modules 220 managed by the AI management module 210 to update the parameters of the global AI model(s) 216... After training is complete (e.g., the loss function for each global AI model 216 has converged), model data extracted from the selected global AI model(s) 216 (e.g., the globally updated weights of the global AI model(s)) may be communicated to be used by local AI model(s) at the AI execution module 220. The global model parameter(s) may be communicated (e.g., using output functions of the AICF 214) to the AI execution module 220 as configuration information, for example in a configuration message.”) (consider further paragraph 0148, “At the AI execution module 220, the configuration information includes model parameter(s) that are used by the AI execution module 220 to update one or more corresponding local AI model(s) 226 (e.g., the AI model(s) that are the target(s) of the collaborative training, as identified in the collaborative task request).”)
Regarding claim 8, the combined teachings of Zhang and Vasseur taught the method of claim 1.
Zhang taught wherein the information comprised in the first message comprises:
(a) an indication of at least a maximum value or an upper bound value associated to at least one input data used for the ML or AI process, or the ML or AI model thereof;
(b) an indication of at least a minimum value or a lower bound value associated to at least one input data used for the ML or AI process, or the ML or AI model thereof;
(c) an indication of at least one statistical momentum associated to at least one input data used for the ML or AI process, or the ML or AI model thereof;
(d) an indication of at least a maximum value or an upper bound value associated to at least one output element of the ML or AI process, or the ML or AI model thereof;
(e) an indication of at least a minimum value or a lower bound value associated to at least one output element of the ML or AI process, or the ML or AI model thereof;
(f) an indication of at least one statistical momentum associated to at least one output element of the ML or AI process, or the ML or AI model thereof;
(g) an indication of at least one bias and/or scaling parameter to transform a distribution of at least one input or output element of the ML or AI process, or the ML or AI model thereof; or
(h) a combination of any two or more of (a) - (g). (consider paragraph 0052, “In the present disclosure, an AI model encompasses neural networks, which are used in machine learning. A neural network is composed of a plurality of computational units (which may also be referred to as neurons), which are arranged in one or more layers. The process of receiving an input at an input layer and generating an output at an output layer may be referred to as forward propagation. In forward propagation, each layer receives an input (which may have any suitable data format, such as vector, matrix, or multidimensional array) and performs computations to generate an output (which may have different dimensions than the input). The computations performed by a layer typically involve applying a set of weights (also referred to as coefficients) to the input (e.g., by multiplying).”) (consider further paragraph 0053, “A neural network is trained to optimize the parameters (e.g., weights) of the neural network. This optimization is performed in an automated manner, and may be referred to as machine learning. Training of a neural network involves forward propagating an input data sample to generate an output value (also referred to as a predicted output value or inferred output value), and comparing the generated output value with a known or desired target value (e.g., a ground-truth value). A loss function is defined to quantitatively represent the difference between the generated output value and the target value, and the goal of training the neural network is to minimize the loss function. Backpropagation is an algorithm for training a neural network. Backpropagation is used to adjust (also referred to as update) a value of a parameter (e.g., a weight) in the neural network, so that the computed loss function becomes smaller. Backpropagation involves computing a gradient of the loss function with respect to the parameters to be optimized, and a gradient algorithm (e.g., gradient descent) is used to update the parameters to reduce the loss function. Backpropagation is performed iteratively, so that the loss function is converged or minimized over a number of iterations.”) (consider further paragraph 0147, “After selecting the global AI model(s) 216 for the task request, the AI management module 210 performs training of the global AI model(s) 216. For collaborative training, the AI management module 210 may use training data provided and/or identified in the task request for training of the global AI model(s) 216. For example, the AI management module 210 may use model data (e.g., locally trained model parameters) collected from one or more AI execution modules 220 managed by the AI management module 210 to update the parameters of the global AI model(s) 216... After training is complete (e.g., the loss function for each global AI model 216 has converged), model data extracted from the selected global AI model(s) 216 (e.g., the globally updated weights of the global AI model(s)) may be communicated to be used by local AI model(s) at the AI execution module 220. The global model parameter(s) may be communicated (e.g., using output functions of the AICF 214) to the AI execution module 220 as configuration information, for example in a configuration message.”) (consider further paragraph 0148, “At the AI execution module 220, the configuration information includes model parameter(s) that are used by the AI execution module 220 to update one or more corresponding local AI model(s) 226 (e.g., the AI model(s) that are the target(s) of the collaborative training, as identified in the collaborative task request).”) (consider also paragraph 0186, “The optimization target(s) or requirement(s) may include characteristic descriptions of the optimization to be performed by the network node 131, for example, to minimize a defined cost function, maximize one or more KPIs, or maximize one or more parameters (such as distance), among other possibilities. The target AI model(s) may be (after training is complete) used to generate inferred data that may apply to control of various wireless functionalities (which may be controlled by configuration of control and signal components in the wireless system 100), such as MIMO, beamforming, channel encoding, waveform signal designs, power control, resource allocations, mobility modeling, channel rebuilding, spectrum utilization including carrier/band width part assignment, and/or TRP selection among others.”)
Regarding claim 9, the combined teachings of Zhang and Vasseur taught the method of claim 1.
Zhang taught wherein the information comprised in the first message comprises information that indicates:
(a) an extended validity period associated to information about how to format data for execution of at least a ML or AI process, or ML or AI model thereof, available at the first network node;
(b) a level of accuracy associated to information about how to format data for execution of at least a ML or AI process, or ML or AI model thereof, available at the first network node;
(c) an indication of an expected performance degradation associated to information about how to format data for execution of at least a ML or AI process, or ML or AI model thereof, available at the first network node; or
(d) a combination of any two or more of (a) - (c). (consider also paragraph 0186, “The optimization target(s) or requirement(s) may include characteristic descriptions of the optimization to be performed by the network node 131, for example, to minimize a defined cost function, maximize one or more KPIs, or maximize one or more parameters (such as distance), among other possibilities. The target AI model(s) may be (after training is complete) used to generate inferred data that may apply to control of various wireless functionalities (which may be controlled by configuration of control and signal components in the wireless system 100), such as MIMO, beamforming, channel encoding, waveform signal designs, power control, resource allocations, mobility modeling, channel rebuilding, spectrum utilization including carrier/band width part assignment, and/or TRP selection among others.”) (consider also paragraph 0196, “The optional signaling from 658 to 664 may be repeated to continue updating the target AI model(s). For example, 658 to 664 may be repeated until an acknowledgement (ACK) message from the requestor (e.g., system node 120 or UE 110) is transmitted to the network node 131 at 666. Alternatively, 658 to 664 may be repeated until an end point defined in the original task request (at 652). For example, the task request may define a time expiry or a maximum number of updates to be performed.”)
Regarding claim 10, the combined teachings of Zhang and Vasseur taught the method of claim 1.
Zhang taught further comprising, prior to receiving the first message, transmitting a second message (“collaborative task request”/”task request” for “collaborative training”) to the second network node, the second message comprising a request for information about how to format data for execution of the at least the ML or AI process, or the ML or AI model thereof. (consider paragraph 0010, “transmit, to the first node, a task request, the task request requiring configuration of at least one of a wireless communication functionality of the system or a local artificial intelligence (AI) model stored in the memory”) (consider paragraph 0145, “For example, the task request may be a request for collaborative training of an AI model, and may include an identifier of the AI model to be collaboratively trained, an identifier of data to be used and/or collected for training the AI model, a dataset to be used for training the AI model, locally trained model parameters to be used for collaboratively updating a global AI model, and/or a training target or requirement, among other possibilities. The task request may be received from a customer of the wireless system 100, from an external network 150, and/or from nodes within the wireless system 100 (e.g., from the system node 120 itself).”) (consider further paragraph 0177, “The signaling may begin with a task request at 602a from the core network 130 (e.g., a task request from the system node 120 may be relayed by the core network 130, or a task request may be generated by the core network 130 itself) or a task request at 602b from outside the core network 130 (e.g., from a customer of the wireless system 100). The network node 131 may receive different task requests from the core network 130 and from the customer, for example. The task request may be a network task request and may indicate a service to be provided, a task requirement, and may include one or more KPIs and/or traffic types, as discussed previously. The task request may be a collaborative task request and may indicate one or more AI models to be collaboratively trained, for example. The task request may also indicate one or more training targets and/or requirements for collaborative training. The task request may also include an identifier of training data to be used and/or may include training data to be used for collaborative training. The task request may also include model data (e.g., locally trained model parameters) to be updated by collaborative training. For example, collaborative training may be performed by the network node 131 training an AI model on behalf of one or more system nodes 120 and/or UEs 110. Collaborative training may also be performed by the network node 131 using locally trained model parameters to update a global AI model (e.g., a form of federated learning). Other such collaborative tasks are possible within the scope of the present disclosure.”)
Regarding claim 11, the combined teachings of Zhang and Vasseur taught the method of claim 10.
Zhang taught wherein the second message comprises:
(a) a request for at least a data scaling and/or descaling criterion to be applied for at least one input data of the ML or AI process, or the ML or AI model thereof;
(b) a request for at least a data format to be applied for at least one input data of the ML or AI process, or the ML or AI model thereof;
(c) a request for at least a data scaling and/or de-scaling criterion to be applied for at least one output data of the ML or AI process, or the ML or AI model thereof;
(d) a request for at least a data format to be used for at least one output data of the ML or AI process, or the ML or AI model thereof;
(e) a request for at least a normalization criterion to be applied for at least one input data of the ML or AI process, or the ML or AI model thereof;
(f) a request for at least a normalization criterion to be applied for at least one output data of the ML or AI process, or the ML or AI model thereof;
(g) a request for at least one parameter to be utilized in the normalization function to be applied for at least one input data and/or to an output data of the ML or AI process, or the ML or AI model thereof; or (h) a combination of any two or more of (a) - (h). (consider paragraph 0116, “For example, if the AI management module 210 receives a task request for low latency service, a global AI model designed to control for latency sensitivity may be selected to infer control parameters for associated control modules (e.g., control parameters for MAC scheduling, power control, beamforming, mobility control, etc.). The AI management module 210 may perform baseline, non-RT training of the selected global AI model(s) to generate one or more globally inferred control parameters related to latency. The trained global model parameters (e.g., trained weights) and/or globally inferred control parameter(s) may then be communicated by the AI management module 210 to be implemented in one or more system nodes 120. For example, the global model parameters may be implemented in corresponding local AI model(s) by the AI execution module 220 at a given system node 120. The local AI model(s) may be executed (using the global model parameters) to generate local control parameter(s) related to latency. The local AI model(s) may be optionally updated (using near-RT training) using local network data collected at the system node 120. The updated local AI model(s) may then be executed to infer updated local control parameter(s) to control for latency, according to the dynamic local environment of the system node 120.”)
Regarding claim 12, the combined teachings of Zhang and Vasseur taught the method of claim 10.
Zhang taught wherein the second message comprises:
(a) a request for at least a maximum value or an upper bound value associated to at least one input data used for the ML or AI process, or the ML or AI model thereof;
(b) a request for at least a minimum value or a lower bound value associated to at least one input data used for the ML or AI process, or the ML or AI model thereof;
(c) a request for at least one statistical momentum associated to at least one input data used for the ML or AI process, or the ML or AI model thereof;
(d) a request for at least a maximum value or an upper bound value associated to at least one output element of the ML or AI process, or the ML or AI model thereof;
(e) a request for at least a minimum value or a lower bound value associated to at least one output element of the ML or AI process, or the ML or AI model thereof;
(f) a request for at least one statistical momentum associated to at least one output element of the ML or AI process, or the ML or AI model thereof;
(g) a request for at least one bias and/or scaling parameter to transform a distribution of at least one input or output element of the ML or AI process, or the ML or AI model thereof; or (h) a combination of any two or more of (a) - (g). (consider paragraph 0052, “In the present disclosure, an AI model encompasses neural networks, which are used in machine learning. A neural network is composed of a plurality of computational units (which may also be referred to as neurons), which are arranged in one or more layers. The process of receiving an input at an input layer and generating an output at an output layer may be referred to as forward propagation. In forward propagation, each layer receives an input (which may have any suitable data format, such as vector, matrix, or multidimensional array) and performs computations to generate an output (which may have different dimensions than the input). The computations performed by a layer typically involve applying a set of weights (also referred to as coefficients) to the input (e.g., by multiplying).”) (consider further paragraph 0053, “A neural network is trained to optimize the parameters (e.g., weights) of the neural network. This optimization is performed in an automated manner, and may be referred to as machine learning. Training of a neural network involves forward propagating an input data sample to generate an output value (also referred to as a predicted output value or inferred output value), and comparing the generated output value with a known or desired target value (e.g., a ground-truth value). A loss function is defined to quantitatively represent the difference between the generated output value and the target value, and the goal of training the neural network is to minimize the loss function. Backpropagation is an algorithm for training a neural network. Backpropagation is used to adjust (also referred to as update) a value of a parameter (e.g., a weight) in the neural network, so that the computed loss function becomes smaller. Backpropagation involves computing a gradient of the loss function with respect to the parameters to be optimized, and a gradient algorithm (e.g., gradient descent) is used to update the parameters to reduce the loss function. Backpropagation is performed iteratively, so that the loss function is converged or minimized over a number of iterations.”) (consider further paragraph 0145, “Another example is now described in which the task request is a collaborative task request. For example, the task request may be a request for collaborative training of an AI model, and may include an identifier of the AI model to be collaboratively trained, an identifier of data to be used and/or collected for training the AI model, a dataset to be used for training the AI model, locally trained model parameters to be used for collaboratively updating a global AI model, and/or a training target or requirement, among other possibilities. The task request may be received from a customer of the wireless system 100, from an external network 150, and/or from nodes within the wireless system 100 (e.g., from the system node 120 itself).”) (consider further paragraph 0147, “After selecting the global AI model(s) 216 for the task request, the AI management module 210 performs training of the global AI model(s) 216. For collaborative training, the AI management module 210 may use training data provided and/or identified in the task request for training of the global AI model(s) 216. For example, the AI management module 210 may use model data (e.g., locally trained model parameters) collected from one or more AI execution modules 220 managed by the AI management module 210 to update the parameters of the global AI model(s) 216... After training is complete (e.g., the loss function for each global AI model 216 has converged), model data extracted from the selected global AI model(s) 216 (e.g., the globally updated weights of the global AI model(s)) may be communicated to be used by local AI model(s) at the AI execution module 220. The global model parameter(s) may be communicated (e.g., using output functions of the AICF 214) to the AI execution module 220 as configuration information, for example in a configuration message.”) (consider further paragraph 0148, “At the AI execution module 220, the configuration information includes model parameter(s) that are used by the AI execution module 220 to update one or more corresponding local AI model(s) 226 (e.g., the AI model(s) that are the target(s) of the collaborative training, as identified in the collaborative task request).”) (consider also paragraph 0186, “The optimization target(s) or requirement(s) may include characteristic descriptions of the optimization to be performed by the network node 131, for example, to minimize a defined cost function, maximize one or more KPIs, or maximize one or more parameters (such as distance), among other possibilities. The target AI model(s) may be (after training is complete) used to generate inferred data that may apply to control of various wireless functionalities (which may be controlled by configuration of control and signal components in the wireless system 100), such as MIMO, beamforming, channel encoding, waveform signal designs, power control, resource allocations, mobility modeling, channel rebuilding, spectrum utilization including carrier/band width part assignment, and/or TRP selection among others.”)
Regarding claim 13, the combined teachings of Zhang and Vasseur taught the method of claim 10.
Zhang taught wherein the second message comprises:
(a) a list of instructions to start, stop, pause, resume, or modify the reporting of assistance information associated to formatting data for at least the ML or AI process, or the ML or AI model thereof, available at the first network node;
(b) a list of at least one ML/AI process, and/or ML/AI models thereof, for which reporting of data formatting from the second network node is requested;
(c) a reporting periodicity;
(d) a request for one-time reporting;
(e) a reporting criteria; or
(f) a combination of any two or more of (a) - (e). (consider paragraph 0145, “Another example is now described in which the task request is a collaborative task request. For example, the task request may be a request for collaborative training of an AI model, and may include an identifier of the AI model to be collaboratively trained, an identifier of data to be used and/or collected for training the AI model, a dataset to be used for training the AI model, locally trained model parameters to be used for collaboratively updating a global AI model, and/or a training target or requirement, among other possibilities. The task request may be received from a customer of the wireless system 100, from an external network 150, and/or from nodes within the wireless system 100 (e.g., from the system node 120 itself).”) (consider also paragraph 0149, “This collaborative training, including communications between the AI management module 210 and the AI execution module 220, may be continued until an end condition is met (e.g., the model parameters have sufficiently converged, the target optimization and/or requirement of the collaborative training has been achieved, expiry of a timer, etc.). In some examples, the requestor of the collaborative task may transmit a message to the AI management module 210 to indicate that the collaborative task should end.”)
Regarding claim 14, the combined teachings of Zhang and Vasseur taught the method of claim 1.
Zhang taught further comprising receiving a second message from the second network node, the second message comprising a request or configuration for the first network node to provide at least one data sample generated by the first network node upon executing the ML or AI process, or the ML or AI model thereof. (consider paragraph 0109, “As will be discussed further below, communications between the AI management module 210 and the AI execution module 220 enable continuous collection of data and continuous updating of AI models, to enable responsive control of wireless functionality in a dynamically varying network environment.”) (consider further paragraph 0144, “In the example illustrated in FIG. 5A, the AI management module 210 performs continuous data collection, training of selected global AI model(s) 216 and execution of the trained global AI model(s) 216 to generate updated data (including updated globally inferred control parameter(s) and/or global model parameter(s)), to enable continuous satisfaction of the task request (e.g., satisfaction of one or more KPIs included as task requirements in the task request). The AI execution module 220 may similarly perform continuous updates of configuration parameter(s), continuous collection of local network data and optionally continuous training of the selected local AI model(s) 226, to enable continuous satisfaction of the task request (e.g., satisfaction of one or more KPIs included as task requirements in the task request). As illustrated in FIG. 5A, collection of local network data, training of global (or local) AI model(s) and generation of updated inference data (whether global or local) may be performed repeatedly as a loop, at least for the time duration indicated in the task request (or until the task request is updated or replaced), for example.”) (consider further paragraph 0149, “At the AI management module 210, local data collected from one or more AI execution modules 220 are received (e.g., using input functions provided by the AICF 214) and may be used for collaborative of the selected global AI model(s) 216. For example, if the local data from the AI execution module(s) 220 include the locally-trained weights of the local AI model(s) (if the local AI model(s) have been updated by near-RT training), the AI management module 210 may aggregate the locally-trained weights and use the aggregated result to collaboratively update the weights of the selected global AI model(s) 216. After the selected global AI model(s) 216 have been updated, updated model parameters may be communicated back to the AI execution module 220.”)
Regarding claim 15, the combined teachings of Zhang and Vasseur taught the method of claim 1.
Zhang taught further comprising transmitting a second message to the second network node, the second message comprising at least one data sample generated by the first network node upon executing the ML or AI process, or the ML or AI model thereof. (consider paragraph 0109, “As will be discussed further below, communications between the AI management module 210 and the AI execution module 220 enable continuous collection of data and continuous updating of AI models, to enable responsive control of wireless functionality in a dynamically varying network environment.”) (consider further paragraph 0144, “In the example illustrated in FIG. 5A, the AI management module 210 performs continuous data collection, training of selected global AI model(s) 216 and execution of the trained global AI model(s) 216 to generate updated data (including updated globally inferred control parameter(s) and/or global model parameter(s)), to enable continuous satisfaction of the task request (e.g., satisfaction of one or more KPIs included as task requirements in the task request). The AI execution module 220 may similarly perform continuous updates of configuration parameter(s), continuous collection of local network data and optionally continuous training of the selected local AI model(s) 226, to enable continuous satisfaction of the task request (e.g., satisfaction of one or more KPIs included as task requirements in the task request). As illustrated in FIG. 5A, collection of local network data, training of global (or local) AI model(s) and generation of updated inference data (whether global or local) may be performed repeatedly as a loop, at least for the time duration indicated in the task request (or until the task request is updated or replaced), for example.”) (consider further paragraph 0149, “At the AI management module 210, local data collected from one or more AI execution modules 220 are received (e.g., using input functions provided by the AICF 214) and may be used for collaborative of the selected global AI model(s) 216. For example, if the local data from the AI execution module(s) 220 include the locally-trained weights of the local AI model(s) (if the local AI model(s) have been updated by near-RT training), the AI management module 210 may aggregate the locally-trained weights and use the aggregated result to collaboratively update the weights of the selected global AI model(s) 216. After the selected global AI model(s) 216 have been updated, updated model parameters may be communicated back to the AI execution module 220.”)
Regarding claim 16, the combined teachings of Zhang and Vasseur taught the method of claim 14.
Zhang taught wherein the at least one data sample comprises:
(a) a data sample associated to the execution of the ML or AI process, or the ML or AI model thereof, prior to using the information about how the data is to be formatted comprised in the first message, (b) a data sample associated to the execution of the ML or AI process, or the ML or AI model thereof, when using the information about how the data is to be formatted comprised in the first message, or (c) both (a) and (b). (consider paragraph 0109, “As will be discussed further below, communications between the AI management module 210 and the AI execution module 220 enable continuous collection of data and continuous updating of AI models, to enable responsive control of wireless functionality in a dynamically varying network environment.”) (consider further paragraph 0144, “In the example illustrated in FIG. 5A, the AI management module 210 performs continuous data collection, training of selected global AI model(s) 216 and execution of the trained global AI model(s) 216 to generate updated data (including updated globally inferred control parameter(s) and/or global model parameter(s)), to enable continuous satisfaction of the task request (e.g., satisfaction of one or more KPIs included as task requirements in the task request). The AI execution module 220 may similarly perform continuous updates of configuration parameter(s), continuous collection of local network data and optionally continuous training of the selected local AI model(s) 226, to enable continuous satisfaction of the task request (e.g., satisfaction of one or more KPIs included as task requirements in the task request). As illustrated in FIG. 5A, collection of local network data, training of global (or local) AI model(s) and generation of updated inference data (whether global or local) may be performed repeatedly as a loop, at least for the time duration indicated in the task request (or until the task request is updated or replaced), for example.”)
Regarding claim 17, the combined teachings of Zhang and Vasseur taught the method of claim 14.
Zhang taught wherein the at least one data sample comprises: (a) at least one input data to the ML or AI process, or the ML or AI model thereof, (b) at least one output data provided by the ML or AI process, or the ML or AI model thereof, or (c) both (a) and (b). (consider paragraph 0109, “As will be discussed further below, communications between the AI management module 210 and the AI execution module 220 enable continuous collection of data and continuous updating of AI models, to enable responsive control of wireless functionality in a dynamically varying network environment.”) (consider further paragraph 0144, “In the example illustrated in FIG. 5A, the AI management module 210 performs continuous data collection, training of selected global AI model(s) 216 and execution of the trained global AI model(s) 216 to generate updated data (including updated globally inferred control parameter(s) and/or global model parameter(s)), to enable continuous satisfaction of the task request (e.g., satisfaction of one or more KPIs included as task requirements in the task request). The AI execution module 220 may similarly perform continuous updates of configuration parameter(s), continuous collection of local network data and optionally continuous training of the selected local AI model(s) 226, to enable continuous satisfaction of the task request (e.g., satisfaction of one or more KPIs included as task requirements in the task request). As illustrated in FIG. 5A, collection of local network data, training of global (or local) AI model(s) and generation of updated inference data (whether global or local) may be performed repeatedly as a loop, at least for the time duration indicated in the task request (or until the task request is updated or replaced), for example.”) (consider further paragraph 0149, “At the AI management module 210, local data collected from one or more AI execution modules 220 are received (e.g., using input functions provided by the AICF 214) and may be used for collaborative of the selected global AI model(s) 216. For example, if the local data from the AI execution module(s) 220 include the locally-trained weights of the local AI model(s) (if the local AI model(s) have been updated by near-RT training), the AI management module 210 may aggregate the locally-trained weights and use the aggregated result to collaboratively update the weights of the selected global AI model(s) 216. After the selected global AI model(s) 216 have been updated, updated model parameters may be communicated back to the AI execution module 220.”)
Regarding claim 18, Zhang taught a first network node comprising processing circuitry configured to cause the first network node to:
receive a first message from a second network node, the first message comprising information about how to format data for execution of at least a machine learning, ML, or artificial intelligence, AI, process, or ML or AI model thereof, that is available for execution at the first network node; (consider paragraph 0010, “receive, from the first node, a first set of configuration information including a set of model parameters for the local AI model stored in the memory, the local AI model being configured by the set of model parameters to generate inference data including at least one inferred control parameter for configuring the system for wireless communication”) (consider further paragraph 0147, “After selecting the global AI model(s) 216 for the task request, the AI management module 210 performs training of the global AI model(s) 216. For collaborative training, the AI management module 210 may use training data provided and/or identified in the task request for training of the global AI model(s) 216. For example, the AI management module 210 may use model data (e.g., locally trained model parameters) collected from one or more AI execution modules 220 managed by the AI management module 210 to update the parameters of the global AI model(s) 216... After training is complete (e.g., the loss function for each global AI model 216 has converged), model data extracted from the selected global AI model(s) 216 (e.g., the globally updated weights of the global AI model(s)) may be communicated to be used by local AI model(s) at the AI execution module 220. The global model parameter(s) may be communicated (e.g., using output functions of the AICF 214) to the AI execution module 220 as configuration information, for example in a configuration message.”) and
execute the at least a ML or AI process, or the ML or AI model thereof, based on the information comprised in the first message. (consider paragraph 0011 regarding the “execut[ion]” of the “local AI model”) (consider further paragraph 0147, “After selecting the global AI model(s) 216 for the task request, the AI management module 210 performs training of the global AI model(s) 216. For collaborative training, the AI management module 210 may use training data provided and/or identified in the task request for training of the global AI model(s) 216. For example, the AI management module 210 may use model data (e.g., locally trained model parameters) collected from one or more AI execution modules 220 managed by the AI management module 210 to update the parameters of the global AI model(s) 216... After training is complete (e.g., the loss function for each global AI model 216 has converged), model data extracted from the selected global AI model(s) 216 (e.g., the globally updated weights of the global AI model(s)) may be communicated to be used by local AI model(s) at the AI execution module 220. The global model parameter(s) may be communicated (e.g., using output functions of the AICF 214) to the AI execution module 220 as configuration information, for example in a configuration message.”) (consider further paragraph 0148, “At the AI execution module 220, the configuration information includes model parameter(s) that are used by the AI execution module 220 to update one or more corresponding local AI model(s) 226 (e.g., the AI model(s) that are the target(s) of the collaborative training, as identified in the collaborative task request).”)
Zhang may be interpreted as not expressly teaching wherein the information about how to format data comprises information about how to scale or descale values and ranges of values of input data to the ML or AI model.
However, in an analogous art relating to ML/AI modelling and processing/execution of said models, Vasseur taught that information about how to scale or descale values and ranges of values of input data to the ML or AI model may be included in information that is included within a message which is used to execute a ML or AI process or model. (consider column 7, lines 20-47, specifically regarding “Artificial Neural Networks” “whose underlying mathematical models were inspired by the hypothesis that mental activity consists primarily of electrochemical activity between interconnected neurons. ANNs are sets of computational units (neurons) connected by directed weighted links. By combining the operations performed by neurons and the weights applied by their links, ANNs are able to perform highly non-linear operations on their input data” wherein “[l]earning in ANNs is treated as an optimization problem where the weights of the links are optimized for minimizing a predefined cost function. This optimization problem is computationally very expensive, due to the high number of parameters to be optimized, but thanks to the backpropagation algorithm, the optimization problem can be performed very efficiently. Indeed, the backpropagation algorithm computes the gradient of the cost function with respect to the weights of the links in only one forward and backward pass throw the ANN. With this gradient, the weights of the ANN that minimize the cost function can be computed.”) (consider further column 12, line 66-column 13, line 11, “local model parameters are generated by training a machine learning model at a device in a computer network using a local data set. One or more other devices in the network are identified that have trained machine learning models (i.e., that may be identical or different) using remote data sets that are similar to the local data set. The local model parameters are provided to the one or more other devices to cause the one or more other devices to generate performance metrics using the provided model parameters with their local data sets. Performance metrics for model parameters are received from the one or more other devices and a global set of model parameters is selected for the device and the one or more other devices using the received performance metrics”) (consider further column 14, line 60-column 15, line 37, specifically “Once the FAR has joined the multicast group used for selection of the most efficient learning machine model, the FAR may export the information describing the learning machine configuration that has been obtained after the local training phase. For example, as shown in FIG. 8C, FAR-1 may export the model parameters for its trained machine learning model to FAR-2, FAR-3, etc. In the case of an ANN, for example, such information will include the connections between neurons and their corresponding weights. More specifically, such information may be a matrix of weights between all neurons called M_ANN (t), the set of weights computed by the FAR at time t. Such data may be included in a specific multicast message… In various embodiments, each element of the matrix may either be a scalar, a vector (e.g., precision and recall) or a more complex structure, such as a matrix of performance for a specific learning machine model.”) (consider further column 16, lines 20-32, “Procedure 900 then continues on to step 920 in which the local model parameters are provided to the other identified training device or devices. In response, the training devices may generate performance metrics using the model parameters with data sets that are collected locally by the other training devices. In step 925, performance metrics are received from the other training devices. As highlighted in more detail above, the received performance metrics may include metrics for the locally generated model parameters and/or for model parameters generated by any of the other training devices. In step 930, a global set of model parameters are then selected for the devices based on the received performance metrics and procedure 900 ends at step 935.”)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of these references such that their combination includes every element as claimed. One skilled in the art could have combined the teachings by known methods such as integration of software routines with no changes to the operation of either reference such that, in combination, each element merely performs the same function as it does separately. Additionally, the Examiner finds that, based on the references’ analogous disclosure regarding the use of messaging to include information regarding the formatting of data for execution of ML/AI processing or models thereof, further demonstrates that a combination of their features would have been known and obvious. Therefore, such a combination of the teachings of the references would have yielded nothing more than predictable results to one of ordinary skill in the art.
Regarding claim 20, Zhang taught a method performed by a second network node, the method comprising:
transmitting a first message to a first network node, the first message comprising information about how to format data for execution of at least a machine learning, ML, or artificial intelligence, AI, process, or ML or AI model thereof, that is available for execution at the first network node. (consider paragraph 0010, “receive, from the first node, a first set of configuration information including a set of model parameters for the local AI model stored in the memory, the local AI model being configured by the set of model parameters to generate inference data including at least one inferred control parameter for configuring the system for wireless communication”) (consider further paragraph 0147, “After selecting the global AI model(s) 216 for the task request, the AI management module 210 performs training of the global AI model(s) 216. For collaborative training, the AI management module 210 may use training data provided and/or identified in the task request for training of the global AI model(s) 216. For example, the AI management module 210 may use model data (e.g., locally trained model parameters) collected from one or more AI execution modules 220 managed by the AI management module 210 to update the parameters of the global AI model(s) 216... After training is complete (e.g., the loss function for each global AI model 216 has converged), model data extracted from the selected global AI model(s) 216 (e.g., the globally updated weights of the global AI model(s)) may be communicated to be used by local AI model(s) at the AI execution module 220. The global model parameter(s) may be communicated (e.g., using output functions of the AICF 214) to the AI execution module 220 as configuration information, for example in a configuration message.”)
Zhang may be interpreted as not expressly teaching wherein the information about how to format data comprises information about how to scale or descale values and ranges of values of input data to the ML or AI model.
However, in an analogous art relating to ML/AI modelling and processing/execution of said models, Vasseur taught that information about how to scale or descale values and ranges of values of input data to the ML or AI model may be included in information that is included within a message which is used to execute a ML or AI process or model. (consider column 7, lines 20-47, specifically regarding “Artificial Neural Networks” “whose underlying mathematical models were inspired by the hypothesis that mental activity consists primarily of electrochemical activity between interconnected neurons. ANNs are sets of computational units (neurons) connected by directed weighted links. By combining the operations performed by neurons and the weights applied by their links, ANNs are able to perform highly non-linear operations on their input data” wherein “[l]earning in ANNs is treated as an optimization problem where the weights of the links are optimized for minimizing a predefined cost function. This optimization problem is computationally very expensive, due to the high number of parameters to be optimized, but thanks to the backpropagation algorithm, the optimization problem can be performed very efficiently. Indeed, the backpropagation algorithm computes the gradient of the cost function with respect to the weights of the links in only one forward and backward pass throw the ANN. With this gradient, the weights of the ANN that minimize the cost function can be computed.”) (consider further column 12, line 66-column 13, line 11, “local model parameters are generated by training a machine learning model at a device in a computer network using a local data set. One or more other devices in the network are identified that have trained machine learning models (i.e., that may be identical or different) using remote data sets that are similar to the local data set. The local model parameters are provided to the one or more other devices to cause the one or more other devices to generate performance metrics using the provided model parameters with their local data sets. Performance metrics for model parameters are received from the one or more other devices and a global set of model parameters is selected for the device and the one or more other devices using the received performance metrics”) (consider further column 14, line 60-column 15, line 37, specifically “Once the FAR has joined the multicast group used for selection of the most efficient learning machine model, the FAR may export the information describing the learning machine configuration that has been obtained after the local training phase. For example, as shown in FIG. 8C, FAR-1 may export the model parameters for its trained machine learning model to FAR-2, FAR-3, etc. In the case of an ANN, for example, such information will include the connections between neurons and their corresponding weights. More specifically, such information may be a matrix of weights between all neurons called M_ANN (t), the set of weights computed by the FAR at time t. Such data may be included in a specific multicast message… In various embodiments, each element of the matrix may either be a scalar, a vector (e.g., precision and recall) or a more complex structure, such as a matrix of performance for a specific learning machine model.”) (consider further column 16, lines 20-32, “Procedure 900 then continues on to step 920 in which the local model parameters are provided to the other identified training device or devices. In response, the training devices may generate performance metrics using the model parameters with data sets that are collected locally by the other training devices. In step 925, performance metrics are received from the other training devices. As highlighted in more detail above, the received performance metrics may include metrics for the locally generated model parameters and/or for model parameters generated by any of the other training devices. In step 930, a global set of model parameters are then selected for the devices based on the received performance metrics and procedure 900 ends at step 935.”)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of these references such that their combination includes every element as claimed. One skilled in the art could have combined the teachings by known methods such as integration of software routines with no changes to the operation of either reference such that, in combination, each element merely performs the same function as it does separately. Additionally, the Examiner finds that, based on the references’ analogous disclosure regarding the use of messaging to include information regarding the formatting of data for execution of ML/AI processing or models thereof, further demonstrates that a combination of their features would have been known and obvious. Therefore, such a combination of the teachings of the references would have yielded nothing more than predictable results to one of ordinary skill in the art.
Regarding claim 21, Zhang taught a second network node comprising processing circuitry configured to cause the second network node to:
transmit a first message to a first network node, the first message comprising information about how to format data for execution of at least a machine learning, ML, or artificial intelligence, AI, process, or ML or AI model thereof, that is available for execution at the first network node. (consider paragraph 0010, “receive, from the first node, a first set of configuration information including a set of model parameters for the local AI model stored in the memory, the local AI model being configured by the set of model parameters to generate inference data including at least one inferred control parameter for configuring the system for wireless communication”) (consider further paragraph 0147, “After selecting the global AI model(s) 216 for the task request, the AI management module 210 performs training of the global AI model(s) 216. For collaborative training, the AI management module 210 may use training data provided and/or identified in the task request for training of the global AI model(s) 216. For example, the AI management module 210 may use model data (e.g., locally trained model parameters) collected from one or more AI execution modules 220 managed by the AI management module 210 to update the parameters of the global AI model(s) 216... After training is complete (e.g., the loss function for each global AI model 216 has converged), model data extracted from the selected global AI model(s) 216 (e.g., the globally updated weights of the global AI model(s)) may be communicated to be used by local AI model(s) at the AI execution module 220. The global model parameter(s) may be communicated (e.g., using output functions of the AICF 214) to the AI execution module 220 as configuration information, for example in a configuration message.”)
Zhang may be interpreted as not expressly teaching wherein the information about how to format data comprises information about how to scale or descale values and ranges of values of input data to the ML or AI model.
However, in an analogous art relating to ML/AI modelling and processing/execution of said models, Vasseur taught that information about how to scale or descale values and ranges of values of input data to the ML or AI model may be included in information that is included within a message which is used to execute a ML or AI process or model. (consider column 7, lines 20-47, specifically regarding “Artificial Neural Networks” “whose underlying mathematical models were inspired by the hypothesis that mental activity consists primarily of electrochemical activity between interconnected neurons. ANNs are sets of computational units (neurons) connected by directed weighted links. By combining the operations performed by neurons and the weights applied by their links, ANNs are able to perform highly non-linear operations on their input data” wherein “[l]earning in ANNs is treated as an optimization problem where the weights of the links are optimized for minimizing a predefined cost function. This optimization problem is computationally very expensive, due to the high number of parameters to be optimized, but thanks to the backpropagation algorithm, the optimization problem can be performed very efficiently. Indeed, the backpropagation algorithm computes the gradient of the cost function with respect to the weights of the links in only one forward and backward pass throw the ANN. With this gradient, the weights of the ANN that minimize the cost function can be computed.”) (consider further column 12, line 66-column 13, line 11, “local model parameters are generated by training a machine learning model at a device in a computer network using a local data set. One or more other devices in the network are identified that have trained machine learning models (i.e., that may be identical or different) using remote data sets that are similar to the local data set. The local model parameters are provided to the one or more other devices to cause the one or more other devices to generate performance metrics using the provided model parameters with their local data sets. Performance metrics for model parameters are received from the one or more other devices and a global set of model parameters is selected for the device and the one or more other devices using the received performance metrics”) (consider further column 14, line 60-column 15, line 37, specifically “Once the FAR has joined the multicast group used for selection of the most efficient learning machine model, the FAR may export the information describing the learning machine configuration that has been obtained after the local training phase. For example, as shown in FIG. 8C, FAR-1 may export the model parameters for its trained machine learning model to FAR-2, FAR-3, etc. In the case of an ANN, for example, such information will include the connections between neurons and their corresponding weights. More specifically, such information may be a matrix of weights between all neurons called M_ANN (t), the set of weights computed by the FAR at time t. Such data may be included in a specific multicast message… In various embodiments, each element of the matrix may either be a scalar, a vector (e.g., precision and recall) or a more complex structure, such as a matrix of performance for a specific learning machine model.”) (consider further column 16, lines 20-32, “Procedure 900 then continues on to step 920 in which the local model parameters are provided to the other identified training device or devices. In response, the training devices may generate performance metrics using the model parameters with data sets that are collected locally by the other training devices. In step 925, performance metrics are received from the other training devices. As highlighted in more detail above, the received performance metrics may include metrics for the locally generated model parameters and/or for model parameters generated by any of the other training devices. In step 930, a global set of model parameters are then selected for the devices based on the received performance metrics and procedure 900 ends at step 935.”)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of these references such that their combination includes every element as claimed. One skilled in the art could have combined the teachings by known methods such as integration of software routines with no changes to the operation of either reference such that, in combination, each element merely performs the same function as it does separately. Additionally, the Examiner finds that, based on the references’ analogous disclosure regarding the use of messaging to include information regarding the formatting of data for execution of ML/AI processing or models thereof, further demonstrates that a combination of their features would have been known and obvious. Therefore, such a combination of the teachings of the references would have yielded nothing more than predictable results to one of ordinary skill in the art.
Response to Arguments
Applicant's arguments filed in the instant response have been fully considered but they are not persuasive.
Applicant argues that the combined teachings of Zhang and Vasseur fail to teach or reasonably suggest the limitations of wherein the information about how to format data comprises information about how to scale or descale values and ranges of values of input data to the ML or AI model.
Examiner respectfully disagrees.
Vasseur, as shown previously in the rejection, taught that such information is sent in a message received from another network node. Within column 16, lines 20-32 (which notably Applicant fails to address in the rejection), Vasseur expressly teaches that “Procedure 900 then continues on to step 920 in which the local model parameters are provided to the other identified training device or devices. In response, the training devices may generate performance metrics using the model parameters with data sets that are collected locally by the other training devices. In step 925, performance metrics are received from the other training devices. As highlighted in more detail above, the received performance metrics may include metrics for the locally generated model parameters and/or for model parameters generated by any of the other training devices”.
Applicant argues that “The model parameters are thus an output of a model, and are not used to scale or descale input data to the model”. However, these teachings of Vasseur clearly show that this is not the case. The network node that receives the information from the other network node provides such as an input into its own ML or AI model. As Vasseur teaches as shown in the rejection, these “model parameters” are used to train a ML or AI model for when input data is provided to the network node’s ML or AI model during execution (consider column 7, lines 20-47 of Vasseur, specifically “By combining the operations performed by neurons and the weights applied by their links, ANNs are able to perform highly non-linear operations on their input data”). Since Applicant fails to dispute that the “model parameters” of Vasseur are, in fact, “information about how to scale or descale values and ranges of values of input data to the ML or AI model”, Examiner finds that Vasseur does teach or reasonably suggest these limitations and the rejection under Zhang and Vasseur is maintained.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to G. C. Neurauter, Jr. whose telephone number is (571)272-3918. The examiner can normally be reached Monday-Friday 9am-5pm Eastern Time.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tonia Dollinger, can be reached at 571-272-4170. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/G. C. Neurauter, Jr./Primary Examiner, Art Unit 2459