DETAILED ACTION
This is a response to Application # 18/9257,135 filed on October 25, 204 in which claims 1-20 were presented for examination.
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 .
Status of Claims
Claims 1-20 are pending, of which claims 1-3, 6, 7, and 9-11 are rejected under 35 U.S.C. § 102(a)(2) and claims 4, 5, 8, and 12-20 are rejected under 35 U.S.C. § 103.
Information Disclosure Statement
The information disclosure statement filed November 5, 2024 complies with the provisions of 37 C.F.R. § 1.97, 1.98 and MPEP § 609. It has been placed in the application file and the information referred to therein has been considered as to the merits.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. § 119(e) or under 35 U.S.C. §§ 120, 121, or 365(c) is acknowledged.
Claim Rejections - 35 U.S.C. § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §§ 102 and 103 (or as subject to pre-AIA 35 U.S.C. §§ 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. § 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, , 6, 7, and 9-11 are rejected under 35 U.S.C. § 102(a)(2) as being anticipated by Dickey et al., US Patent 12,591,160 (hereinafter Dickey).
Regarding claim 1, Dickey discloses a method of deploying machine learning models in building management systems, comprising “receiving, by one or more processors of a building management system, data from one or more devices of the building management system” (Dickey col. 12, ll. 25-57) where the system receives data from a plurality of sensors, which are part of a building management system, as discussed below. Additionally, Dickey discloses “the one or more devices corresponding to a plurality of computational resources associated with the building management system” (Dickey col. 10, ll. 51-58) where the devices may include an occupancy sensor that is part of a building management system. Further, Dickey discloses “the plurality of computational resources including an edge device associated with the building management system and a cloud server associated with the building management system” (Dickey col. 5, ll. 33-54) where the computation resources include sensor associated with the building management system and a cloud server associated with the building management system. A person of ordinary skill in the art prior to the effective filing date would have understood an sensor to be an edge device.1 Moreover, Dickey discloses “generating, by the one or more processors, a first output responsive to the data” (Dickey col. 27, l. 48-col. 28, l. 7) where predictive output is generated.
Likewise, Dickey discloses “detecting, by the one or more processors, that a trigger condition is satisfied responsive to detecting a target characteristic from the first output” (Dickey col. 6, l. 52-col. 7, l. 10) by detecting a trigger event. Dickey also discloses “triggering, by the one or more processors responsive to detecting that the trigger condition is satisfied, a machine learning model deployment process” (Dickey col. 4, ll. 22-35; col. 4, l. 62-col. 5, l. 32) where the predictive data potentially triggers a corrective action (Dickey col. 4, ll. 22-35), where the corrective action includes the deployment of a trained machine learning model (Dickey col. 4, l. 62-col. 5, l. 32). In addition, Dickey discloses “a machine learning model deployment process “comprising: identifying, by the one or more processors, at least one machine learning model for processing at least one of the data or the first output” (Dickey col. 32, l. 63-col. 33, l. 28) where a model is selected based on various scores related to the data received. Furthermore, Dickey discloses “selecting, by the one or more processors, according to one or more resource criteria regarding the at least one machine learning model, from the plurality of computational resources associated with the building management system, one or more computational resources on which to deploy the at least one machine learning model on the one or more computational resources” (Dickey col. 4, l. 62-col. 5, l. 32; col. 49, l. 62-col. 50, l. 7) by disclosing that the machine learning model is deployed to a client device, the identity of which must necessarily be selected, (Dickey col. 4, l. 62-col. 5, l. 32) and by giving an example of the machine learning models being deployed to specific locations based on the training data, such as machine learning models trained on chemicals being deployed to chemical factories. (Dickey col. 49, l. 62-col. 50, l. 7). Finally, Dickey discloses “causing the at least one machine learning model to process the at least one of the data or the first output using the one or more computational resources” (Dickey col. 36, ll. 8-16) where the machine learning model performs the particular functions.
Regarding claim 2, Dickey discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Dickey discloses “wherein the one or more resource criteria represent, for the plurality of computational resources, at least one of an energy usage for operating the at least one machine learning model, an environmental impact score for operating the at least one machine learning model, a device capability relative to operating the at least one machine learning model, a resource availability relative to operating the at least one machine learning model, or a network resource usage for communicating the at least one of the data or the first output to the one or more computational resources” (Dickey col. 19, l. 64-col. 20, l. 18) where the resource criteria represents an energy consumption of the machine learning model (i.e., an energy usage for operating the at least one machine learning model).
Regarding claim 3, Dickey discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Dickey discloses “monitoring, by the one or more processors, completion of processing of the at least one of the data or the first output, by the at least one machine learning model” (Dickey col. 23, ll. 31-45) where the sensor data is collected during a time period, meaning that the end of the time period is the monitored completion processing of the data. Further, Dickey discloses “modifying, by the one or more processors, the one or more computational resources according to the monitoring” (Dickey col. 23, l. 46-64) where a corrective action (i.e., modification) is performed on a device based on the collected data (i.e., the monitoring).
Regarding claim 6, Dickey discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Dickey discloses “evaluating, by the one or more processors, execution of the at least one machine learning model” (Dickey col. 34, ll. 13-32) where the model is validated against testing data. Further, Dickey discloses “updating, by the one or more processors, the one or more resource criteria according to the evaluation” (Dickey col. 39, ll. 15-37) where, responsive to the accuracy of the model against the testing data, the model is retrained (i.e., updated).
Regarding claim 7, Dickey discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Dickey discloses “wherein the plurality of computational resources comprise the one or more processors” (Dickey col. 53, l. 21-45) where any computation resource may be a machine with a processor.
Regarding claim 9, Dickey discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Dickey discloses “wherein the target characteristic comprises a type of data indicated by the first output” (Dickey col. 32, l. 63-col. 33, l. 28) where the raw data of the events are used to generate features (i.e., types of data), meaning that those events must necessarily indicate a type of feature to be assigned in some manner.
Regarding claim 10, Dickey discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Dickey discloses “further comprising transmitting, by the one or more processors, the at least one machine learning model to the one or more computational resources” (Dickey col. 4, l. 62-col. 5, l. 32) by deploying (i.e., transmitting) the machine learning model to the client device.
Regarding claim 11, Dickey discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Dickey discloses “wherein the data comprises at least one of sensor data generated by a sensor of the one or more devices, a input received via a user interface of the one or more devices, equipment data generated by an item of equipment of the one or more devices, an output from a machine learning model operated on the one or more devices, or an output from a generative artificial intelligence (AI) machine learning model implemented by the one or more devices” (Dickey col. 12, ll. 25-57) where the system receives data from a plurality of sensors.
Claim Rejections - 35 U.S.C. § 103
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 4, 5, 8, and 12-20 are rejected under 35 U.S.C. § 103 as being unpatentable over Dickey in view of Balakrishnan et al., US Publication 2022/03776145 (hereinafter Balakrishnan).
Regarding claim 4, Dickey discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Dickey does not appear to explicitly disclose “receiving, by the one or more processors, feedback indicative of a capability of the one or more computational resources to deploy the at least one machine learning model; and updating, by the one or more processors, the selection of the one or more computational resources responsive to the feedback.
However, Balakrishnan discloses a machine learning processing system configured to perform “receiving, by the one or more processors, feedback indicative of a capability of the one or more computational resources to deploy the at least one machine learning model; and updating, by the one or more processors, the selection of the one or more computational resources responsive to the feedback” (Balakrishnan ¶¶ 119, 179) where a client node is selected based on “energy consumption/budget/connectivity status of the client nodes, one or more client nodes from each cluster in one round of gradient updates” (i.e., capabilities), which must necessarily have been known and, therefore, received, as part of the Federated Learning process (Balakrishnan ¶ 179) that is a process where client devices receive the machine learning model. (Balakrishnan ¶ 119). Thus, when selecting a client to deploy the machine learning model as part of the federated learning process, Balakrishnan is making that selection based on the computational resources including “energy consumption/budget/connectivity status.”
Dickey and Balakrishnan are analogous art because they are from the “same field of endeavor,” namely that of machine learning model deployment methods.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Dickey and Balakrishnan before him or her to modify the deployment of the machine learning model of Dickey to include the consideration of computation capabilities of the clients when deploying a machine learning model of Balakrishnan.
The motivation for doing so would have been to improve the energy consumption of the overall network. (Balakrishnan ¶ 35).
Regarding claim 5, Dickey discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Dickey does not appear to explicitly disclose “wherein selecting the one or more computational resources comprises retrieving, by the one or more processors, a priority score assigned to the one or more computational resources, the priority score corresponding to a capability of the one or more computational resources to execute the at least one machine learning model relative to one or more additional processes being performed by the one or more computational resources.
However, Balakrishnan discloses a machine learning processing system configured to perform “wherein selecting the one or more computational resources comprises retrieving, by the one or more processors, a priority score assigned to the one or more computational resources, the priority score corresponding to a capability of the one or more computational resources to execute the at least one machine learning model relative to one or more additional processes being performed by the one or more computational resources” (Balakrishnan ¶ 41) where resources are accessed based on priorities that include performance sensitivity of the computer (i.e., the capability to execute the data stream, which includes a machine learning model, see Balakrishnan ¶ 119).
Dickey and Balakrishnan are analogous art because they are from the “same field of endeavor,” namely that of machine learning model deployment methods.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Dickey and Balakrishnan before him or her to modify the deployment of the machine learning model of Dickey to include the consideration of computation capabilities of the clients when deploying a machine learning model of Balakrishnan.
The motivation for doing so would have been to improve the energy consumption of the overall network. (Balakrishnan ¶ 35).
Regarding claim 8, Dickey discloses the limitations contained in parent claim 1 for the reasons discussed above. In addition, Dickey discloses “wherein the plurality of computational resources comprise: an edge device coupled with an internal network of the building management system” (Dickey col. 12, ll. 25-57) where the edge devices may be connected via a gateway that is limited to devices “within the building.” Further, Dickey discloses “a cloud server coupled with an external network coupled with the internal network” (Dickey col. 27, ll. 25-30) where the cloud server is coupled to multiple facilities, meaning it is coupled to an external network.
Although implied, Dickey does not appear to explicitly disclose “the cloud server having a greater computational capacity than the edge device.”
However, Balakrishnan discloses a machine learning processing system wherein “the cloud server having a greater computational capacity than the edge device.” (Balakrishnan ¶ 118).
Dickey and Balakrishnan are analogous art because they are from the “same field of endeavor,” namely that of machine learning model deployment methods.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Dickey and Balakrishnan before him or her to modify the computational capacity of the devices of Dickey so that the cloud server had greater computational capacity, as disclosed by Balakrishnan.
The motivation/rationale for doing so would have been that such a modification is obvious to try. See KSR Int’l Co. v. Teleflex Inc., 550 US 398, 82 USPQ2d 1385, 1397 (U.S. 2007) and MPEP § 2143(I)(E). At the time of invention, there was a recognized problem or need in the art, namely pushing machine learning models from a cloud server device to an edge device. Further, there were only four identified, predictable potential options: (1) both the server and edge devices have the same computational power, (2) the edge devices have greater computational power than the server, (3) the server has greater computational power than the edge devices, and (4) a some edge devices have the same computational power as the server, some edge devices having more computational power than the server, and some edge devices having less computational power than the server. One of ordinary skill in the art could have pursued the known potential solutions with a reasonable expectation of success because at least two of the four options meet the claim requirements, but it further generally being understood in the art that servers traditionally have greater computational power than client devices. Further, Balakrishnan explicitly teaches that its use of federated learning overcomes the drawback overcomes the longer lag times often associated with such architecture. (Balakrishnan ¶ 118).
Regarding claim 12, Dickey discloses a building management system, comprising “a plurality of computational resources arranged as a plurality of nodes coupled by a network, each computational resource of the plurality of computational resources comprising one or more processors and memory for use by one or more corresponding nodes of the plurality of nodes” (Dickey col. 4, l. 62-col. 5, l. 32) where the network comprise a series of nodes which are servers and client devices, each of which are known to include processors and memory. Additionally, Dickey discloses “the plurality of nodes comprising a deployment node configured to: evaluate an output from a given node of the plurality of nodes to determine that a trigger condition for deployment of at least one machine learning model is satisfied, the at least one machine learning model to process the output” (Dickey col. 4, ll. 22-35; col. 4, l. 62-col. 5, l. 32) by evaluating whether the predictive data triggers a corrective action (Dickey col. 4, ll. 22-35), where the corrective action includes the deployment of a trained machine learning model (Dickey col. 4, l. 62-col. 5, l. 32). Finally, Dickey discloses “cause the one or more nodes to operate the at least one machine learning model to process the output” (Dickey col. 36, ll. 8-16) where the machine learning model performs the particular functions.
Dickey does not appear to explicitly disclose “determine, responsive to the trigger condition being satisfied, a capability of each node of the plurality of nodes to deploy the at least one machine learning model to process the output; select, according to the capability of each node and one or more resource criteria, from the plurality of nodes, one or more nodes on which to deploy the at least one machine learning model.”
However, Balakrishnan discloses a machine learning processing system configured to perform “determine … a capability of each node of the plurality of nodes to deploy the at least one machine learning model to process the output; select, according to the capability of each node and one or more resource criteria, from the plurality of nodes, one or more nodes on which to deploy the at least one machine learning model” (Balakrishnan ¶¶ 119, 179) where a client node is selected based on “energy consumption/budget/connectivity status of the client nodes, one or more client nodes from each cluster in one round of gradient updates” (i.e., capabilities), which must necessarily have been known and, therefore, determined, as part of the Federated Learning process (Balakrishnan ¶ 179) that is a process where client devices receive the machine learning model. (Balakrishnan ¶ 119). Thus, when selecting a client to deploy the machine learning model as part of the federated learning process, Balakrishnan is making that selection based on the computational resources including “energy consumption/budget/connectivity status.”
A person of ordinary skill in the art prior to the effective filing date would have recognized that when Balakrishnan was combined with Dickey, the determining and selecting of Balakrishnan would necessarily be “responsive to the trigger condition being satisfied” because the trigger condition is what causes the need to select the node. Therefore, the combination of Dickey and Balakrishnan at least teaches and/or suggests the claimed limitation “determine, responsive to the trigger condition being satisfied, a capability of each node of the plurality of nodes to deploy the at least one machine learning model to process the output,” rendering it obvious.
Dickey and Balakrishnan are analogous art because they are from the “same field of endeavor,” namely that of machine learning model deployment systems.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Dickey and Balakrishnan before him or her to modify the deployment of the machine learning model of Dickey to include the consideration of computation capabilities of the clients when deploying a machine learning model of Balakrishnan.
The motivation for doing so would have been to improve the energy consumption of the overall network. (Balakrishnan ¶ 35).
Regarding claim 13, the combination of Dickey and Balakrishnan discloses the limitations contained in parent claim 12 for the reasons discussed above. In addition, the combination of Dickey and Balakrishnan discloses “wherein the plurality of nodes comprise a sensor, an edge device, and a cloud server.” (Dickey col. 4, l. 62-col. 5, l. 32).
Regarding claim 14, the combination of Dickey and Balakrishnan discloses the limitations contained in parent claim 12 for the reasons discussed above. In addition, the combination of Dickey and Balakrishnan discloses “wherein the one or more resource criteria represent, for deployment of the at least one machine learning model on the one or more nodes, at least one of an energy usage, an environmental impact score, or a network resource usage for communication of the output to the one or more nodes” (Dickey col. 19, l. 64-col. 20, l. 18) where the resource criteria represents an energy consumption of the machine learning model (i.e., an energy usage for operating the at least one machine learning model).
Regarding claim 15, the combination of Dickey and Balakrishnan discloses the limitations contained in parent claim 12 for the reasons discussed above. In addition, the combination of Dickey and Balakrishnan discloses “wherein the deployment node is configured to: monitor completion of the operation of the at least one machine learning model” (Dickey col. 23, ll. 31-45) where the sensor data is collected during a time period, meaning that the end of the time period is the monitored completion processing of the data. Further, the combination of Dickey and Balakrishnan discloses “modify the selection of the one or more nodes according to the monitoring of the completion” (Dickey col. 23, l. 46-64) where a corrective action (i.e., modification) is performed on a device based on the collected data (i.e., the monitoring).
Regarding claim 16, the combination of Dickey and Balakrishnan discloses the limitations contained in parent claim 12 for the reasons discussed above. In addition, the combination of Dickey and Balakrishnan discloses “wherein the deployment node is configured to select the one or more nodes according to a priority score assigned to the one or more nodes, the priority score corresponding to a capability of the one or more nodes to execute the at least one machine learning model relative to one or more additional processes being performed by the one or more nodes” (Balakrishnan ¶ 41) where resources are accessed based on priorities that include performance sensitivity of the computer (i.e., the capability to execute the data stream, which includes a machine learning model, see Balakrishnan ¶ 119).
Regarding claim 17, the combination of Dickey and Balakrishnan discloses the limitations contained in parent claim 12 for the reasons discussed above. In addition, the combination of Dickey and Balakrishnan discloses “wherein the deployment node is configured to determine that the trigger condition is satisfied responsive to the output having a characteristic matching a target characteristic for use of the at least one machine learning model” (Dickey col. 33, ll. 29-51) where the models correspond to a distinct set of features (i.e., characteristics), such as a particular sensor from which the output is received.
Regarding claim 18, Dickey discloses a system, comprising one or more processors (Dickey col. 53, ll. 35-45) configured to “receive data from an item of equipment of a building management system” (Dickey col. 12, ll. 25-57) where the system receives data from a plurality of sensors, which are part of a building management system, as discussed below. Additionally, Dickey discloses “determine that the data is to be processed by at least one machine learning model” (Dickey col. 18, ll. 11-26; col. 20, l. 65-col. 21, l. 21) by giving multiple examples of determining that the sensor data (i.e., the data) is to be processed by a machine learning model. For instance, Dickey first discloses that predictive actions (i.e., processing by machine learning models) is performed on specific ranges of sensor values (Dickey col. 18, ll. 11-26) and later discloses that specific models are selected based on the labels of the sensor data. (Dickey col. 20, l. 65-col. 21, l. 21). Further Dickey discloses “select … one or more computing devices of the plurality of computing devices”(Dickey col. 4, l. 62-col. 5, l. 32; col. 49, l. 62-col. 50, l. 7) by disclosing that the machine learning model is deployed to a client device, the identity of which must necessarily be selected, (Dickey col. 4, l. 62-col. 5, l. 32) and by giving an example of the machine learning models being deployed to specific locations based on the training data, such as machine learning models trained on chemicals being deployed to chemical factories. (Dickey col. 49, l. 62-col. 50, l. 7). Finally, Dickey discloses “cause the one or more computing devices to deploy the at least one machine learning model to process the data from the item of equipment using the at least one machine learning model” (Dickey col. 36, ll. 8-16) where the machine learning model performs the particular functions.
Dickey does not appear to explicitly disclose “retrieve, for each computing device of a plurality of computing devices of the building management system, an indication of a workload capability of each computing device of the plurality of computing devices during at least one of a current period or a future period; retrieve one or more performance criteria regarding execution of the at least one machine learning model; select, according to (1) the indication of the workload capability of each computing device of the plurality of computing devices and (2) the one or more performance criteria, one or more computing devices of the plurality of computing devices.”
However, Balakrishnan discloses a machine learning processing system configured to perform “retrieve, for each computing device of a plurality of computing devices of the building management system, an indication of a workload capability of each computing device of the plurality of computing devices during at least one of a current period or a future period; retrieve one or more performance criteria regarding execution of the at least one machine learning model; select, according to (1) the indication of the workload capability of each computing device of the plurality of computing devices and (2) the one or more performance criteria, one or more computing devices of the plurality of computing devices” (Balakrishnan ¶¶ 119, 179) where a client node is selected based on “energy consumption/budget/connectivity status of the client nodes, one or more client nodes from each cluster in one round of gradient updates” (i.e., capabilities), which must necessarily have been known and, therefore, received, as part of the Federated Learning process (Balakrishnan ¶ 179) that is a process where client devices receive the machine learning model. (Balakrishnan ¶ 119). Thus, when selecting a client to deploy the machine learning model as part of the federated learning process, Balakrishnan is making that selection based on the computational resources including “energy consumption/budget/connectivity status.”
Dickey and Balakrishnan are analogous art because they are from the “same field of endeavor,” namely that of machine learning model deployment systems.
Prior to the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Dickey and Balakrishnan before him or her to modify the deployment of the machine learning model of Dickey to include the consideration of computation capabilities of the clients when deploying a machine learning model of Balakrishnan.
The motivation for doing so would have been to improve the energy consumption of the overall network. (Balakrishnan ¶ 35).
Regarding claim 19, the combination of Dickey and Balakrishnan discloses the limitations contained in parent claim 18 for the reasons discussed above. In addition, the combination of Dickey and Balakrishnan discloses “wherein the plurality of computing devices comprise a sensor, one or more edge devices of the building management system, and a cloud server coupled with the one or more edge devices” (Dickey col. 4, l. 62-col. 5, l. 32).
Regarding claim 20, the combination of Dickey and Balakrishnan discloses the limitations contained in parent claim 18 for the reasons discussed above. In addition, the combination of Dickey and Balakrishnan discloses “wherein the one or more performance criteria comprise at least one of an energy usage score, an environmental impact score, or a network communication score” (Dickey col. 19, l. 64-col. 20, l. 18) where the resource criteria represents an energy consumption of the machine learning model (i.e., an energy usage for operating the at least one machine learning model).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Stone et al., US Publication 2021/0287521, System and method for deploying machine learning models to edge devices in a cloud network of a building management system.
Schönfeld, US Publication 2022/0101456, System and method for deploying machine learning models to edge devices in a cloud network of a building management system.
Schmidt et al., US Publication 2024/0338610, System and method for deploying machine learning models to edge devices in a cloud network.
Alaas et al., US Publication 2024/0412107, System and method for deploying machine learning models to edge devices in a cloud network.
Sullivan, US Publication 2025/0138490, System and method for deploying machine learning models to edge devices in a cloud network of a building management system.
Mitura, US Publication 2024/0370437, System and method for deploying machine learning models to edge devices in a cloud network of a building management system.
Albanese et al., US Patent 11,915,050, System and method for deploying machine learning models to edge devices in a cloud network.
Heydari, US Patent 12,563,704, System and method for deploying machine learning models to edge devices in a cloud network.
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/ANDREW R DYER/Primary Examiner, Art Unit 3662
1 Henry Osborne, Edge computer devices: what are they?, December 3, 2022, stilpartners.com, Pages 1-2.
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