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 .
Information Disclosure Statement
The information disclosure statement submitted on 04/30/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Allowable Subject Matter
Claims 5, 8-9, 15, 17 and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 6, 10 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Johnsson et al. (US 20240135247 A1 hereinafter Johnsson) in view of Turgeman et al. (US 20210012187 A1 hereinafter Turgeman), Feuz et al. (US 20190163667 A1 hereinafter Feuz) and SOBOT et al. (US 20220343004 A1 hereinafter Sobot).
As to independent claim 1, Johnsson teaches a computer-implemented method of deploying a machine learning model, comprising: [deploying models ¶32-34]
receiving a user request for deploying a machine learning model, for an application, to an edge device; [receives a request for a model (to be deployed) ¶9 "receiving, by an apparatus, a request for a machine learning model solving a task T" ]
determining a device constraint type associated with the edge device, wherein the device constraint type is one of a number of device constraint types associated with a plurality of edge devices capable of running the application; [determines constraints for the execution environment (edge computer¶24) ¶9 "resource constraints of the execution environment."; four types (number) ¶53 "hardware constraints, software constraints, sampling requirements and resource usage of the execution environment."]
identifying a machine learning model corresponding to the device constraint type of the edge device, [selects suitable model based on constraints ¶9 "determining, from the first set of machine learning models, at least one suitable machine learning model to be deployed, wherein the determining is based on the calculated complexity and the resource constraints of the execution environment"]
deploying the machine learning model to the edge device. [deploys accordingly ¶61 "highest ranked ML model is deployed in the execution environment 102"]
Johnsson does not specifically teach identifying a machine learning model corresponding to the device constraint type of the edge device, wherein the machine learning model is identified from one of a number of tiers of machine learning models developed for the application according to a quantity of device constraint types, wherein the number of the tiers corresponds to the quantity of the device constraint types;
However, Turgeman teaches identifying a machine learning model corresponding to the device constraint type of the edge device, wherein the machine learning model is identified from one of a number of tiers of machine learning models developed for the application according to a quantity of device constraint types, wherein the number of the tiers corresponds to the quantity of the device constraint types; [set of model solutions (tiers) based on constraints and objectives ¶13 "set of non-dominated DL model solutions, each of which are optimal according to different trade-offs among multiple objectives. Users can then flexibly construct various predictive models from the solution set for a given edge, considering its resource constraints." … "different resource constraints of edge devices, such as DL model size, inference time, accuracy, and others"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the model development disclosed by Johnsson by incorporating the identifying a machine learning model corresponding to the device constraint type of the edge device, wherein the machine learning model is identified from one of a number of tiers of machine learning models developed for the application according to a quantity of device constraint types, wherein the number of the tiers corresponds to the quantity of the device constraint types disclosed by Turgeman because both techniques address the same field of machine learning and by incorporating Turgeman into Johnsson provide more user flexibility aligning to user preferences with improved performance [Turgeman ¶29]
Johnsson and Turgeman do not specifically teach wherein the machine learning models are trained based on user data reflecting one or more of user interests or user preferences of a user.
However, Feuz teaches wherein the machine learning models are trained based on user data reflecting one or more of user interests or user preferences of a user. [personalized training using user data such as likes, clicks ¶29 "leverage user data (e.g., which photos the user likes, clicks on, spends a lot of time on) to learn complex machine-learned models that capture the user preferences", ¶55 "update the machine-learned model over time as additional data (e.g., user-specific data) is received"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the learning process disclosed by Johnsson and Turgeman by incorporating the wherein the machine learning models are trained based on user data reflecting one or more of user interests or user preferences of a user by Feuz because all techniques address the same field of machine learning and by incorporating Feuz into Johnsson and Turgeman enhances the trust in models while maintaining high accuracy [Feuz ¶26-27]
Johnsson, Turgeman and Feuz fail to teach trained based on a private key associated with the user, and wherein the trained machine learning model generates inaccurate predictions when accessed without the private key, a password for the user, or a user-id for the user.
However, Sobot teaches trained based on a private key associated with the user, and wherein the trained machine learning model generates inaccurate predictions when accessed without the private key, a password for the user, or a user-id for the user. [key based model modifications (trained) makes model useful and accurate (inaccurate without key) ¶22 " The obfuscation key-based modifications to the model instead affect the quality or accuracy of the output of the derivative model (e.g., affect the predictions made based on the input data) to the extent the obfuscation key is available and applied at runtime. In other words, the derivative model is always operational to process the input data, but only practically useful (e.g., to achieve quality or accurate output) if the obfuscation key is available and applied to the derivative model at runtime."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the learning process disclosed by Johnsson, Turgeman and Feuz by incorporating the trained based on a private key associated with the user, and wherein the trained machine learning model generates inaccurate predictions when accessed without the private key, a password for the user, or a user-id for the user by Sobot because all techniques address the same field of machine learning and by incorporating Sobot into Johnsson, Turgeman and Feuz enables encryption of models with less computational processing and more specific to users [Sobot ¶2-3]
As to dependent claim 2, the rejection of claim 1 is incorporated, Johnsson, Turgeman, Feuz and Sobot further teach wherein the machine learning models are developed and trained on a cloud device. [Turgeman computation in a cloud server ¶11]
As to dependent claim 6, the rejection of claim 1 is incorporated, Johnsson, Turgeman, Feuz and Sobot further teach wherein the edge device is an enterprise server. [Johnsson server apparatus as edge node ¶28]
As to dependent claim 10, the rejection of claim 1 is incorporated, Johnsson, Turgeman, Feuz and Sobot further teach the inaccurate predictions render the machine learning model useless to an unauthorized user. [Sobot useless ¶24, ¶34]
As to independent claim 11, Johnsson teaches a system for deploying a machine learning model, comprising: [apparatus with a system for deploying models ¶32-34]
a processor; and [processing unit ¶33]
a memory, coupled to the processor, configured to store executable instructions that, when executed by the processor, cause the processor to perform operations including: [memory with instructions ¶33]
receiving a user request for deploying a machine learning model, for an application, to an edge device; [receives a request for a model (to be deployed) ¶9 "receiving, by an apparatus, a request for a machine learning model solving a task T"]
determining a device constraint type associated with the edge device, wherein the device constraint type is one of a number of device constraint types associated with a plurality of edge devices capable of running the application; [determines constraints for the execution environment (edge computer¶24) ¶9 "resource constraints of the execution environment."; four types (number) ¶53 "hardware constraints, software constraints, sampling requirements and resource usage of the execution environment."]
identifying a machine learning model corresponding to the device constraint type of the edge device, [selects suitable model based on constraints ¶9 "determining, from the first set of machine learning models, at least one suitable machine learning model to be deployed, wherein the determining is based on the calculated complexity and the resource constraints of the execution environment"]
deploying the machine learning model to the edge device. [deploys accordingly ¶61 "
highest ranked ML model is deployed in the execution environment 102"]
Johnsson does not specifically teach identifying a machine learning model corresponding to the device constraint type of the edge device, wherein the machine learning model is identified from one of a number of tiers of machine learning models developed for the application according to a quantity of device constraint types, wherein the number of the tiers corresponds to the quantity of the device constraint types;
However, Turgeman teaches identifying a machine learning model corresponding to the device constraint type of the edge device, wherein the machine learning model is identified from one of a number of tiers of machine learning models developed for the application according to a quantity of device constraint types, wherein the number of the tiers corresponds to the quantity of the device constraint types; [set of model solutions (tiers) based on constraints and objectives ¶13 "set of non-dominated DL model solutions, each of which are optimal according to different trade-offs among multiple objectives. Users can then flexibly construct various predictive models from the solution set for a given edge, considering its resource constraints." … "different resource constraints of edge devices, such as DL model size, inference time, accuracy, and others"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the model development disclosed by Johnsson by incorporating the identifying a machine learning model corresponding to the device constraint type of the edge device, wherein the machine learning model is identified from one of a number of tiers of machine learning models developed for the application according to a quantity of device constraint types, wherein the number of the tiers corresponds to the quantity of the device constraint types disclosed by Turgeman because both techniques address the same field of machine learning and by incorporating Turgeman into Johnsson provide more user flexibility aligning to user preferences with improved performance [Turgeman ¶29]
Johnsson and Turgeman do not specifically teach wherein the machine learning models are trained based on user data reflecting one or more of user interests or user preferences of a user.
However, Feuz teaches wherein the machine learning models are trained based on user data reflecting one or more of user interests or user preferences of a user. [personalized training using user data such as likes, clicks ¶29 "leverage user data (e.g., which photos the user likes, clicks on, spends a lot of time on) to learn complex machine-learned models that capture the user preferences", ¶55 "update the machine-learned model over time as additional data (e.g., user-specific data) is received"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the learning process disclosed by Johnsson and Turgeman by incorporating the wherein the machine learning models are trained based on user data reflecting one or more of user interests or user preferences of a user by Feuz because all techniques address the same field of machine learning and by incorporating Feuz into Johnsson and Turgeman enhances the trust in models while maintaining high accuracy [Feuz ¶26-27]
Johnsson, Turgeman and Feuz fail to teach trained based on a private key associated with the user, and wherein the trained machine learning model generates inaccurate predictions when accessed without the private key, a password for the user, or a user-id for the user.
However, Sobot teaches trained based on a private key associated with the user, and wherein the trained machine learning model generates inaccurate predictions when accessed without the private key, a password for the user, or a user-id for the user. [key based model modifications (trained) makes model useful and accurate (inaccurate without key) ¶22 " The obfuscation key-based modifications to the model instead affect the quality or accuracy of the output of the derivative model (e.g., affect the predictions made based on the input data) to the extent the obfuscation key is available and applied at runtime. In other words, the derivative model is always operational to process the input data, but only practically useful (e.g., to achieve quality or accurate output) if the obfuscation key is available and applied to the derivative model at runtime."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the learning process disclosed by Johnsson, Turgeman and Feuz by incorporating the trained based on a private key associated with the user, and wherein the trained machine learning model generates inaccurate predictions when accessed without the private key, a password for the user, or a user-id for the user by Sobot because all techniques address the same field of machine learning and by incorporating Sobot into Johnsson, Turgeman and Feuz enables encryption of models with less computational processing and more specific to users [Sobot ¶2-3]
As to dependent claim 12, the rejection of claim 11 is incorporated, Johnsson, Turgeman, Feuz and Sobot further teach wherein the machine learning models are developed and trained on a cloud device. [Turgeman computation in a cloud server ¶11]
Claims 3-4 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Johnsson in view of Turgeman, Feuz and Sobot, as applied in claim 1 and 11 above, and further in view of Choudhary et al. (US 20190385043 A1 hereinafter Choudhary)
As to dependent claim 3, Johnsson, Turgeman, Feuz and Sobot teach the method of claim 1 above that is incorporated,
Johnsson, Turgeman, Feuz and Sobot do not specifically teach wherein the machine learning model is trained on the edge device after deploying to the edge device.
However, Choudhary teaches wherein the machine learning model is trained on the edge device after deploying to the edge device. [trains local models on client devices (edge) ¶21-22 "trains a machine learning model across client devices that implement local versions of the model"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the learning process disclosed by Johnsson, Turgeman, Feuz and Sobot by incorporating the wherein the machine learning model is trained on the edge device after deploying to the edge device by Choudhary because all techniques address the same field of machine learning and by incorporating Choudhary into Johnsson, Turgeman, Feuz and Sobot provide more efficient consumption of computing resources [Choudhary ¶4]
As to dependent claim 4, Johnsson, Turgeman, Feuz and Sobot teach the method of claim 1 above that is incorporated,
Johnsson, Turgeman, Feuz and Sobot do not specifically teach wherein, prior to determining the device constraint type associated with the edge device, the method further comprises: receiving, from the edge device, device information for the edge device; and determining the device constraint type associated with the edge device based on the received device information for the edge device.
However, Choudhary teaches wherein, prior to determining the device constraint type associated with the edge device, the method further comprises: receiving, from the edge device, device information for the edge device; and [Lumpesko receives edge characteristics (device information) Col. 5 ln. 19-25 "receives edge device characteristics 721 including, for example, FLOPS, GPU RAM, CPU RAM, CPU speed, power, network capabilities (wired, wireless, and their types), memory, etc"]
determining the device constraint type associated with the edge device based on the received device information for the edge device. [Lumpesko determines a change to a model (constraint) according to information Col. 5 ln. 19-25]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the learning process disclosed by Johnsson, Turgeman, Feuz and Sobot by incorporating the wherein, prior to determining the device constraint type associated with the edge device, the method further comprises: receiving, from the edge device, device information for the edge device; and determining the device constraint type associated with the edge device based on the received device information for the edge device by Choudhary because all techniques address the same field of machine learning and by incorporating Choudhary into Johnsson, Turgeman, Feuz and Sobot provide more efficient consumption of computing resources [Choudhary ¶4]
As to dependent claim 13, Johnsson, Turgeman, Feuz and Sobot teach the method of claim 1 above that is incorporated,
Johnsson, Turgeman, Feuz and Sobot do not specifically teach wherein the machine learning model is trained on the edge device after deploying to the edge device.
However, Choudhary teaches wherein the machine learning model is trained on the edge device after deploying to the edge device. [trains local models on client devices (edge) ¶21-22 "trains a machine learning model across client devices that implement local versions of the model"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the learning process disclosed by Johnsson, Turgeman, Feuz and Sobot by incorporating the wherein the machine learning model is trained on the edge device after deploying to the edge device by Choudhary because all techniques address the same field of machine learning and by incorporating Choudhary into Johnsson, Turgeman, Feuz and Sobot provide more efficient consumption of computing resources [Choudhary ¶4]
As to dependent claim 14, Johnsson, Turgeman, Feuz and Sobot teach the method of claim 11 above that is incorporated,
Johnsson, Turgeman, Feuz and Sobot do not specifically teach wherein, prior to determining the device constraint type associated with the edge device, the method further comprises: receiving, from the edge device, device information for the edge device; and determining the device constraint type associated with the edge device based on the received device information for the edge device.
However, Choudhary teaches wherein, prior to determining the device constraint type associated with the edge device, the method further comprises: receiving, from the edge device, device information for the edge device; and [Lumpesko receives edge characteristics (device information) Col. 5 ln. 19-25 "receives edge device characteristics 721 including, for example, FLOPS, GPU RAM, CPU RAM, CPU speed, power, network capabilities (wired, wireless, and their types), memory, etc"]
determining the device constraint type associated with the edge device based on the received device information for the edge device. [Lumpesko determines a change to a model (constraint) according to information Col. 5 ln. 19-25]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the learning process disclosed by Johnsson, Turgeman, Feuz and Sobot by incorporating the wherein, prior to determining the device constraint type associated with the edge device, the method further comprises: receiving, from the edge device, device information for the edge device; and determining the device constraint type associated with the edge device based on the received device information for the edge device by Choudhary because all techniques address the same field of machine learning and by incorporating Choudhary into Johnsson, Turgeman, Feuz and Sobot provide more efficient consumption of computing resources [Choudhary ¶4]
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Johnsson in view of Turgeman, Feuz and Sobot, as applied in claim 1 and 11 above, and further in view of O'Neill (US 6832373 B2)
As to dependent claim 7, Johnsson, Turgeman, Feuz and Sobot teach the method of claim 1 above that is incorporated,
Johnsson, Turgeman, Feuz and Sobot do not specifically teach wherein a quantity of the device constraint types is determined based on device information of the plurality of edge devices capable of running the application.
However, O'Neill teaches wherein a quantity of the device constraint types is determined based on device information of the plurality of edge devices capable of running the application. [package or size of update (quantity) determined from client devices (edge) Col. 7 ln. 36-50 "receives the identity information 113 from the client device 104 and subsequently generates the desired update package 110"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the learning process disclosed by Johnsson, Turgeman, Feuz and Sobot by incorporating the wherein a quantity of the device constraint types is determined based on device information of the plurality of edge devices capable of running the application by O'Neill because all techniques address the same field of machine learning and by incorporating O'Neill into Johnsson, Turgeman, Feuz and Sobot provides a more convenient and reliable update process [O'Neill Col. 3 ln. 42-60]
As to dependent claim 16, Johnsson, Turgeman, Feuz and Sobot teach the method of claim 15 above that is incorporated,
Johnsson, Turgeman, Feuz and Sobot do not specifically teach wherein a quantity of the device constraint types is determined based on device information of the plurality of edge devices capable of running the application.
However, O'Neill teaches wherein a quantity of the device constraint types is determined based on device information of the plurality of edge devices capable of running the application. [package or size of update (quantity) determined from client devices (edge) Col. 7 ln. 36-50 "receives the identity information 113 from the client device 104 and subsequently generates the desired update package 110"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the learning process disclosed by Johnsson, Turgeman, Feuz and Sobot by incorporating the wherein a quantity of the device constraint types is determined based on device information of the plurality of edge devices capable of running the application by O'Neill because all techniques address the same field of machine learning and by incorporating O'Neill into Johnsson, Turgeman, Feuz and Sobot provides a more convenient and reliable update process [O'Neill Col. 3 ln. 42-60]
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Kuo et al. (US 20190156246 A1 hereinafter Kuo) in view of Beaudoin (US 11455572 B2)
As to independent claim 18, Kuo teaches a machine learning system, comprising: [machine learning system ¶18]
a cloud training pipeline, [trained/developed in the cloud ¶15 "Machine learning models may be trained in the cloud (e.g., by a provider network) "]
a deployment engine; and [deployment service ¶12]
an edge inference pipeline, wherein [inference application ¶12]
the edge inference pipeline is configured to access a machine learning model deployed to the edge device to process received input to generate a prediction. [deploys models ¶25, that generates inferences and predications based on data input (collected data) ¶28-29 "process the collected data to generate inference data (e.g., one or more inferences and/or one or more predictions)"
Kuo does not specifically teach wherein the cloud training pipeline is configured to receive device information from a plurality of edge devices capable of running an application, determine a quantity of device constraint types based on the received device information.
However, Turgeman teaches wherein the cloud training pipeline is configured to receive device information from a plurality of edge devices capable of running an application, determine a quantity of device constraint types based on the received device information, [cloud processed ¶11, edge related metrics including constraints and objectives ¶2, " store a training corpus comprising training data, a parameters vector, and a set of edge-related metrics", ¶13 "different resource constraints of edge devices, such as DL model size, inference time, accuracy, and others"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the model development disclosed by Kuo by incorporating the wherein the cloud training pipeline is configured to receive device information from a plurality of edge devices capable of running an application, determine a quantity of device constraint types based on the received device information disclosed by Turgeman because both techniques address the same field of machine learning and by incorporating Turgeman into Kuo provide more user flexibility aligning to user preferences with improved performance [Turgeman ¶29]
Kuo and Turgeman does not specifically train a structured set of a number of tiers of machine learning models for the application, the number of tiers of machine learning models corresponding to the quantity of device constraint types for the plurality of edge devices capable of running the application;
However, Beaudoin teaches train a structured set of a number of tiers of machine learning models for the application, the number of tiers of machine learning models corresponding to the quantity of device constraint types for the plurality of edge devices capable of running the application; [Trains tiers of models for particular types of systems and capabilities Col. 4 ln. 49-67 "the version installed on the various data processing systems may be stripped down versions of the trained model. As an example, the stripped down version of a fully trained neural network may have fewer hidden layers than the full version"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the model development disclosed by Kuo and Turgeman by incorporating the train a structured set of a number of tiers of machine learning models for the application, the number of tiers of machine learning models corresponding to the quantity of device constraint types for the plurality of edge devices capable of running the application disclosed by Beaudoin because both techniques address the same field of machine learning and by incorporating Beaudoin into Kuo and Turgeman reduces latency in learning systems for delivering model results [Beaudoin Col. 2 ln. 23-47]
Kuo, Turgeman and Beaudoin do not specifically teach the machine learning model is trained based on user data reflecting user interests and user preferences of a user.
However, Feuz teaches the machine learning model is trained based on user data reflecting user interests and user preferences of a user. [personalized training using user data such as likes, clicks ¶29 "leverage user data (e.g., which photos the user likes, clicks on, spends a lot of time on) to learn complex machine-learned models that capture the user preferences", ¶55 "update the machine-learned model over time as additional data (e.g., user-specific data) is received"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the learning process disclosed by Kuo, Turgeman and Beaudoin by incorporating the machine learning model is trained based on user data reflecting user interests and user preferences of a user by Feuz because all techniques address the same field of machine learning and by incorporating Feuz into Kuo, Turgeman and Beaudoin enhances the trust in models while maintaining high accuracy [Feuz ¶26-27]
Kuo, Turgeman, Beaudoin and Feuz fail to teach trained based on a private key associated with the user, and wherein the trained machine learning model generates inaccurate predictions when accessed without the private key, a password for the user, or a user-id for the user.
However, Sobot teaches trained based on a private key associated with the user, and wherein the trained machine learning model generates inaccurate predictions when accessed without the private key, a password for the user, or a user-id for the user. [key based model modifications (trained) makes model useful and accurate (inaccurate without key) ¶22 " The obfuscation key-based modifications to the model instead affect the quality or accuracy of the output of the derivative model (e.g., affect the predictions made based on the input data) to the extent the obfuscation key is available and applied at runtime. In other words, the derivative model is always operational to process the input data, but only practically useful (e.g., to achieve quality or accurate output) if the obfuscation key is available and applied to the derivative model at runtime."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the learning process disclosed by Kuo, Turgeman, Beaudoin and Feuz by incorporating the trained based on a private key associated with the user, and wherein the trained machine learning model generates inaccurate predictions when accessed without the private key, a password for the user, or a user-id for the user by Sobot because all techniques address the same field of machine learning and by incorporating Sobot into Kuo, Turgeman, Beaudoin and Feuz enables encryption of models with less computational processing and more specific to users [Sobot ¶2-3]
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Kuo in view of Turgeman, Beaudoin Feuz and Sobot, as applied in claim 18 above, and further in view of O'Neill.
As to dependent claim 19, Kuo, Turgeman, Beaudoin Feuz and Sobot teach the method of claim 18 above that is incorporated,
Kuo, Turgeman, Beaudoin Feuz and Sobot do not specifically teach wherein the quantity of device constraint types is determined based on device information of the plurality of edge devices capable of running the application.
However, O'Neill teaches wherein the quantity of device constraint types is determined based on device information of the plurality of edge devices capable of running the application. [package or size of update (quantity) determined from client devices (edge) Col. 7 ln. 36-50 "receives the identity information 113 from the client device 104 and subsequently generates the desired update package 110"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the learning process disclosed by Kuo, Turgeman, Beaudoin Feuz and Sobot by incorporating the wherein the quantity of device constraint types is determined based on device information of the plurality of edge devices capable of running the application by O'Neill because all techniques address the same field of machine learning and by incorporating O'Neill into Kuo, Turgeman, Beaudoin Feuz and Sobot provides a more convenient and reliable update process [O'Neill Col. 3 ln. 42-60]
Response to Arguments
Applicant's arguments filed 04/03/2026. In the remark, applicant argues that:
Johnsson and Lupesko fail to teach "identifying a machine learning model corresponding to the device constraint type of the edge device, wherein the machine learning model is identified from one of a number of tiers of machine learning models developed for the application according to a quantity of device constraint types, wherein the number of the tiers corresponds to the quantity of the device constraint types; and deploying the machine learning model to the edge device, wherein the machine learning model is trained based on user data reflecting user interests and user preferences of a user and based on a private key associated with the user, and wherein the trained machine learning model generates inaccurate predictions when accessed without the private key, a password for the user, or a user-id for the user," as recited by amended claim 1. See Col. 2, 11. 45-57, Col. 5 In. 50-66 and Col. 10, 11. 25-40 of Lupesko. See Johnsson,11 [0038]- [0040]
Kuo and Beaudoin fail to teach "wherein the machine learning model is trained based on user data reflecting user interests and user preferences of a user and based on a private key associated with the user, and wherein the trained machine learning model generates inaccurate predictions when accessed without the private key, a password for the user, or a user-id for the user." (See Kuo ¶5-7, Beaudoin Col. 1, 11. 50-60; Col. 3, 11. 40-65 Col. 5, 11. 55-67)
As to point (1) applicant’s arguments with respect to claim 1 and 11 have been considered but are moot in view of a new ground of rejection made under rejected under 35 U.S.C. 103 as being unpatentable over Johnsson, view of Turgeman, Feuz and Sobot as set forth above.
As to point (2) applicant’s arguments with respect to claim 18 have been considered but are moot in view of a new ground of rejection made under rejected under 35 U.S.C. 103 as being unpatentable over Kuo, view of Turgeman, Beaudoin, Feuz and Sobot as set forth above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
Pinel et al. (US 20210360082 A1) teaches nodes of different tiers and optimized deployment (see ¶5-6)
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BEAU SPRATT whose telephone number is (571)272-9919. The examiner can normally be reached M-F 8:30-5 EST.
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, Jennifer Welch can be reached at 5712127212. 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.
/BEAU D SPRATT/Primary Examiner, Art Unit 2143
Prosecution Insights