DETAILED ACTION
Continued Examination Under 37 CFR 1.114
This Office Actions is in response to communication (Amendment) filed on 04/07/2026.
Claims 45 – 64 are pending. Claims 1 – 44 are cancelled. Claims 45, 56, 63, and 64 are in independent form. Claims 45, 56, 58, 63, and 64 are amended.
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 .
Response to Amendment
This Office Action is in response to the applicant’s remarks and arguments filed on 04/07/2026.
Claims 45, 56, 58, 63, and 64 were amended. Claims 45 – 64 remain pending in the application. Claims 45 – 64 are being considered on the merits.
The objection of claims 45, 58, 63, and 64 has been withdrawn due to the amendment to the claim filed on 04/07/2026.
Applicant’s argument filed on 04/07/2026 regarding 35 U.S.C. §101 Rejections have been fully considered and they are persuasive. The examiner agreed with all of the argument made to the claim 64. The 35 U.S.C. §101 Rejections has been withdrawn.
Applicant’s argument filed on 04/07/2026 regarding 35 U.S.C. §112(b) Rejections have been fully considered and they are persuasive. The examiner agreed with all of the argument made to the claims 45 – 63. The 35 U.S.C. §112 Rejections has been withdrawn.
The Rejection of claims 45-64 under 35 U.S.C § 103 has been withdrawn due to the amendment to the claims filed on 04/07/2026. However, upon further consideration, a new ground(s) of rejection is made in view of a newly found prior art Ravichandran et al. US Pat. No. US 10447741 B2 and in view of the previously cited prior art(s). Reference Ravichandran, in combination with previously cited prior art(s), discloses each element of the claims highlighted by applicant.
Response to Arguments
The applicant’s remarks and/or arguments, filed on 04/07/2026 have been fully considered with the following result(s).
The examiner is entitled to give claim limitations their broadest reasonable interpretation in light of the specification. See MPEP 2111 [R-1] Interpretation of Claims-Broadest Reasonable Interpretation. The applicant always has the opportunity to amend the claims during prosecution, and broad interpretation by the examiner reduces the possibility that the claim, once issued, will be interpreted more broadly than is justified. In re Prater, 162 USPQ 541,550-51 (CCPA 1969).
Response to Claims Objection Remarks
Applicant’s argument filed on 04/07/2026 regarding Claims Objection has been fully considered and are persuasive. The Claims Objection has been withdrawn.
Response to 35 USC §101 Remarks
Applicant’s argument filed on 04/07/2026 regarding 35 USC § 101 rejections have been fully considered and are persuasive. The 35 USC §101 rejection of the claim 64 has been withdrawn.
Response to 35 USC §112 Remarks
Applicant’s argument filed on 04/07/2026 regarding 35 USC § 112 rejections have been fully considered and are persuasive. The examiner agreed with all of the argument made to the claims 45 – 63. The 35 USC §112 rejection of the claims 45 – 63 has been withdrawn.
Response to 35 USC §103 Remarks
Applicant's arguments in the applicant’s remarks and amendments of independent claims 45, 56, 63, and 64, found on pages 12 – 14 and filed on 04/07/2026, have been fully considered and are persuasive. Therefore, the previous claim(s) rejection under 35 U.S.C § 103 has been withdrawn.
However, upon further consideration, a new ground(s) of rejection is made in view of a newly found prior art Ravichandran et al. US Pat. No. US 10447741 B2, and in view of the previously cited prior art(s). Reference Ravichandran, in combination with previously cited prior art(s), discloses each element of the claims highlighted by applicant.
For further details, please see below claims rejections under 35 U.S.C § 103.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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 45, 49, 52, 54 – 56, 58, 59, and 61 – 64 are rejected under 35 U.S.C. 103 as being unpatentable over BHANDARU et al. US Pub. No. US 20210073047 A1 (hereafter BHANDARU), in further view of SAARNIVALA et al. US Pub. No. US 20220086045 A1 (hereafter SAARNIVALA), Katre et al. US Pub. No. US 20210136170 A1 (hereafter Katre), GEORGE et al. US Pub. No. US 20170149928 A1 (hereafter GEORGE), and Ravichandran et al. US Pat. No. US 10447741 B2
Regarding claim 45, BHANDARU teaches the invention substantially as claimed: A computing node ......... comprising processing circuitry configured to cause the computing node to ([0031]: “In operation, the memory 304 may store various data and software used during operation of the node compute device 104 such as operating systems, applications, programs, libraries, and drivers. The memory 304 is communicatively coupled to the processor 302 via the I/O subsystem 306, which may be embodied as circuitry and/or components to facilitate input/output operations with the processor 302” and [0079]: “the OMA Lightweight M2M (LWM2M) protocol”) The citation discloses the node compute device/computing node includes the processor 302, which may be embodied as circuitry, and [0079] disclose LWM2M.
receive a message comprising configuration information for a resource of a data object that is hosted at the computing node. ([0020]: “A task may be any task suitable for being performed on an accelerator device 308, such as training a deep learning algorithm, performing a block chain computation, performing k-means clustering, etc. …... Task parameters may include a specification of the accelerator image or bitstream to be implemented, task data to be processed, specific hardware requirements, and/or the like.…... the cloud resource manager 102 may directly assign a task to a node compute device 104 by sending task parameters and other relevant information directly to the node compute device 104.”) The citation discloses at the node compute device 104 receive the task parameters/configuration information, which includes a specification of the accelerator image/a resource that locates inside the node compute device/hosted at the computing node, and the task data to be processed/a data object.
and is associated with a computational operation which comprises a machine learning model, which computational operation is executable by the computing node ([0020]: “A task may be any task suitable for being performed on an accelerator device 308, such as training a deep learning algorithm, performing a block chain computation, performing k-means clustering, etc.” and [0082]: “In this fashion, the fog 1120 may be considered a distributed platform that provides computing and storage resources to perform processing or data-intensive tasks such as data analytics, data aggregation, and machine-learning, among others.” and [0051]: “node compute device 104 to perform the task with an accelerator device”) The citations disclose at [0020] the tasks/computational operation is being performed on an accelerator device of the node compute device and at [0082] the data-intensive tasks could include the machine learning tasks, and [0051] the compute device perform the task.
configure the resource of the data object on the computing node in accordance with the received configuration information ([0061]: “In block 734, the node compute device 104 determines whether there is an accelerator image already loaded where the task is to be performed. If the node compute device 104 determines that there is not an accelerator image already loaded where the task is to be performed, the node compute device 104 loads the accelerator image in block 736. If the node compute device 104 determines the accelerator image is already loaded, the method 700 advances to block 738 of FIG. 9.” and [0062]: “In block 738, in FIG. 9, the node compute device 104 prepares for the task to be performed. To do so, the node compute device 104 may load task parameters. In addition, the node compute device 104 may load a virtual machine (VM) or container to interact with the accelerator device.”) The citations disclose at the node computer prepares itself for the task to be performed by checking for the current accelerator images and configure the appropriate accelerator image, and load the task parameters.
execute the computational operation in accordance with the configured resource. ([0063]: “In block 740, the node compute device 104 performs the task on the accelerator device. In some embodiments, the node compute device 104 may send a notification to the cloud resource manager 102 and/or the requesting device that the task has been launched.”) The citation discloses the node compute device performs the task/execute the computational operation on the accelerator device/accordance with the configured resource.
However, BHANDARU does not explicitly teach A computing node operable to run a Lightweight Machine to Machine (LwM2M) client; wherein the computational operation is associated with a transformation operation to be performed on an output value of an output for the computational operation before the output value is written to an external resource identified for the output for the computational operation, wherein the configuration information comprises an identifier for an LwM2M object and a second identifier, the first identifier mapping a sensor value of a sensor at the computing node to an input label of the machine learning model, and the second identifier mapping an output label of the machine learning model to the external resource, wherein the data object comprises a resource associated with an update time, and wherein writing a time value to the resource associated with the update time causes the computing node, after expiration of the time value, to check for and obtain an updated computational operation from a specified location.
SAARNIVALA teaches A computing node operable to run a Lightweight Machine to Machine (LwM2M) client (e.g. FIG. 2a and [0046]: “The client device 2 comprises client 21 which may be integrated as a software library or a built-in function of a module and which is used in communications with the LwM2M server 4. The client 21 may be an LwM2M client.”)
wherein the configuration information comprises a first identifier for an LwM2M object (e.g. FIG. 2c and [0069]: “In the hierarchy shown in FIG. 2c, an object may represent an LwM2M object. Instances of such objects are created according to the requirements of the system being implemented.” and [0088]: “The registration request will include the template identifier to enable the LwM2M server 4 store the objects, object instances and/or resources of the identified resource template in the resource directory.”) The citation discloses at FIG. 2c and [0069] the LWM2M objects, and at [0088] discloses the template identifier/identifier, for the LWM2M object to be stored.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add A computing node operable to run a Lightweight Machine to Machine client, wherein the configuration information comprises an identifier for an LwM2M object, as taught in SAARNIVALA’s invention into BHANDARU’s invention because by running the light weight machine to machine, the system can process the computational operation faster, easier, and more power efficient, since the LwM2M reduces the bandwidth usage and operational cost when performing the computational operation.
However, Katre teaches wherein the computational operation is associated with a transformation operation to be performed on an output value of an output for the computational operation before the output value is written to an external resource identified for the output for the computational operation. (FIG. 2 and [0046]: “Conversely, after processing by ML cores 240, post-processing 234 adapts or changes the format of the ML processing output data to a format understood by the applicable ML client application 205 configured to implement the applicable machine learning to the IoT device 120 based on the ML processing output data.” and [0063]: “Post-processing 234 can convert the ML processing output data from a format specific to the ML model and ML cores 240 into a format that can be utilized by the ML client application 205 originally requesting the ML processing.”) The citation discloses the output from the ML cores 240 is changed to the format that can be utilized by the ML client application. FIG. 2 shows the ML client application 205 located on the IoT devices 120/external, outside of the ML proxy device 110.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the wherein the computational operation is associated with a transformation operation to be performed on an output value of an output for the computational operation before the output value is written to an external resource identified for the output for the computational operation, as taught in Katre’s invention into BHANDARU’s invention because this would which improve quality and consistency of the output data, and ensure that the output is in a form that is more accurate, relevant, and useful for its intended purpose.
However, GEORGE teaches and a second identifier, the first identifier mapping a sensor value of a sensor at the computing node to an input label of the machine learning model, and the second identifier mapping an output label of the machine learning model to the external resource. (e.g. FIG. 2 and [0042]: “For example, in one embodiment, the usage classifier 231 is configure to implement an artificial neural network trained using values of physical parameters known to represent instances of particular IoT product usages. In this embodiment, the usage classifier 231 is configured to provide newly acquired sensor data to the artificial neural network and to receive a usage classification as output. In this embodiment, the usage classifier 231 is further configured to store and/or transmit usage data that associates the newly acquired sensor data with the usage classification where the output usage classification is associated with a degree of confidence that exceeds a threshold value. For example, the usage classifier may transmit usage data that includes an identifier of the usage classification in association with one or more identifiers of measurements included in sensor data. This usage data may be transmitted to, for example, the sensor/usage interface 230 (e.g., via the interface(s) 206). As described below, the sensor/usage interface 230 may store this association as usage data in the user profile data store 232.” and [0044]: “This sensor data may include one or more identifiers of the sensor 134 (e.g., a serial number, a dynamic or static internet protocol address, etc.), one or more identifiers of measurements acquired by the sensor 134, and one or more values of physical parameters measured by the sensor 134. Additionally or alternatively, the sensor data may include an identifier of the IoT product 102. In some embodiments, as described above, the sensor 134 is also configured to provide sensor data to the usage classifier 231 for usage classification processing. Such provision may be via communication of the sensor data to the usage classifier 231 and/or via storage of the sensor data in the memory 204.”) The citation discloses at [0042] that the newly acquired sensor data is provided to an artificial neural network/machine learning model, of the classifier 231 as an input. After that, the usage classification is provided by the artificial neural network of the classifier 231 as an output. The output then sent to the usage interface 230, and FIG. 2 discloses that the usage interface 230 is external to the component 102. At [0044] discloses the sensor data include one or more identifiers/first identifier for the sensor, and one or more identifiers/second identifier for the measurements acquired by the sensor. Since the sensor data comprises identifiers, and the sensor data is sent to the artificial neural network of the classifier 231, it would imply that the identifiers map the sensor data to the artificial neural network/machine learning model.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the and a second identifier, the first identifier mapping a sensor value of a sensor at the computing node to an input label of the machine learning model, and the second identifier mapping an output label of the machine learning model to the external resource, as taught in GEORGE’s invention into BHANDARU’s invention because the additional features would clearly identify which sensor value maps to each model input and how the model output links to an external resource, which makes the configuration more consistent and reliable while allowing the model to be executed correctly and efficiently on the node.
However, Ravichandran teaches wherein the data object comprises a resource associated with an update time, and wherein writing a time value to the resource associated with the update time causes the computing node, after expiration of the time value, to check for and obtain an updated computational operation from a specified location. (e.g. 11 – Col 2 lines 64 – 67, and Col 3 lines 1 – 7: “Responsive to determining that a local timer of the one or more local timers is expired (e.g., a lapse of a predetermined time interval associated with the local timer), the application can send a request for updated data to a database storing the updated data. That is, responsive to determining that a local timer of the one or more local timers is expired, the application can retrieve updated data. In some examples, the application can return to the inactive state shortly after it retrieves the updated data. Such functionality enables the application to access updated data without the application having to be in an active state at all times.”) The citation discloses the concept when a timer (which indicate a data update is needed) expired, the application can send the request to retrieve/check and obtain, updated data at the database.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add wherein the data object comprises a resource associated with an update time, and wherein writing a time value to the resource associated with the update time causes the computing node, after expiration of the time value, to check for and obtain an updated computational operation from a specified location, as taught in Ravichandran’s invention into BHANDARU’s invention because the additional features would enables the system to control when it checks for an updated operation, in which improves system efficiency resource usage, and ensuring that the updated operation are obtained and executed in a timely and controlled manner.
Regarding claim 49, BHANDARU, in view of SAARNIVALA, Katre, Ravichandran, and GEORGE, teaches the computing node as claimed in claim 45, and BHANDARU further teaches wherein a value of a resource of the data object that is hosted at the computing node comprises at least one of: a resource identification; (BHANDARU - [0048]: “Reception of the task parameters include receiving task data such as …… which accelerator image should be used to perform the task,”) The citation discloses the task data/data object includes which accelerator image/resource identification.
and Katre further teaches a transformation operation; (Katre – [0046]: “pre-processing 232 ...... post-processing 234”)
a computational operation identification
for at least one of an input or an output of the computational operation.
Regarding claim 52, BHANDARU, in view of SAARNIVALA, Katre, Ravichandran, and GEORGE, discloses the computing node as claimed in claim 49, and Katre further teaches wherein the transformation operation is further configured to be performed on: a resource value identified as an input to the computational operation before the resource value is input to the computational operation. (Katre – e.g. FIG. 2 and [0046]: “In particular, ML pre-processing 232 receives the data including any ML requests from ML server and adapts or changes the format to conform to the format understood by the ML core 240 receiving the ML request.” and [0062]: “Upon receiving the incoming ML request at ML proxy device 110, ML Server 230 evaluates the ML request and determines if any reformatting of the ML request, including the collected input data, is needed by pre-processing 232 (635). Pre-processing 232 can be performed to transform and normalize the collected input data into a format as required by the ML model that will be used to process the collected input and by ML cores 240) The citation discloses the input data is changed by the pre-processing 232 to the format understood by the ML core 240.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add wherein the transformation operation is further configured to be performed on: a resource value identified as an input to the computational operation before the resource value is input to the computational operation, as taught in Fry’s invention into BHANDARU, SAARNIVALA, Katre, Ravichandran, and GEORGE’s invention because this would ensure that the computational operation only receive the compatible data, which helps to improve execution efficiency, and enhances system performance by reducing redundant computations and ensuring smother execution.
Regarding claim 54, BHANDARU, in view of SAARNIVALA, Katre, Ravichandran, and GEORGE, teaches the computing node as claimed in claim 45, and BHANDARU further teaches wherein a value of a condition resource of the data object that is hosted at the computing node indicates a condition for execution of the computational operation, (BHANDARU – e.g.[0044]: “The node compute device 104 may store metadata for the accelerator images 506 that include a size, power usage”) and BHANDARU - [0043]: “The accelerator usage monitor 510 may monitor and report usage, fragmentation, which accelerator images are deployed where, power usage levels, etc., for accelerator devices 308. If the node compute device 104 is over a power budget, the accelerator usage monitor 510 may trigger an alert, cancel operations, or take other appropriate actions.”) The citations disclose at [0044] the node computing device 104/computing node store metadata includes the power usage/condition resource, and at [0043] the accelerator usage monitor may indicate that the node compute device is over a power budget/condition for execution, it may cancel the operation.
and wherein the processing circuitry is further configured to cause the computing node to: determine whether the condition is fulfilled; (BHANDARU- [0064]: “y. If the node compute device 104 determines the power usage is not above a threshold, the node compute device 104 proceeds with performing the task.”) The citation discloses the compute device determine whether the power usage is not above a threshold/condition is fulfilled.
and if the condition is not fulfilled, perform at least one of postponing or cancelling execution of the computational operation. (BHANDARU – e.g. [0064]: “In block 744, the node compute device 104 determines whether the power usage is above a threshold. If the node compute device 104 determines the power usage is above a threshold, the node compute device triggers a power alarm in block 746. In response to the power alarm, the node compute device 104 may stop the task, pause the task, or take other appropriate action.”) The citation discloses when the power usage is above a threshold/condition is not fulfill, the node compute device may stop or pause/cancel the task.
Regarding claim 55, BHANDARU, in view of SAARNIVALA, Katre, Ravichandran, and GEORGE, teaches the computing node as claimed in claim 45, and BHANDARU further teaches wherein the processing circuitry is further configured to cause the computing node to: detect a change in a value of a resource identified as an input for the computational operation; (BHANDARU-[0044]: “The node compute device 104 may store metadata for the accelerator images 506 that include a size, power usage, and whether the corresponding accelerator image is permitted to be shared.” And BHANDARU - [0063]: “While performing the task on the accelerator device, the node compute device 104 may monitor the power usage in block 742.”) The citations disclose at [0044] the power usage/value is included in the metadata/input of the accelerator images/resource identified, and at [0063] the node compute device monitors the power usage/detect the change in value.
and execute the computational operation (BHANDARU - [0064]: “If the node compute device 104 determines the power usage is not above a threshold, the node compute device 104 proceeds with performing the task”)
Regarding claim 56, BHANDARU teaches the invention substantially as claimed: A server node ....... and comprising processing circuitry configured to cause the server node to ([0022]: “The cloud resource manager 102 may be embodied as any type of computation or computer device capable of performing the functions described herein, including, without limitation, a computer, a server”) and [0026]: “the processor 202 via the I/O subsystem 206, which may be embodied as circuitry”)
generate configuration information for a resource of a data object that is hosted at the computing node. ([0020]: “A task may be any task suitable for being performed on an accelerator device 308, such as training a deep learning algorithm, performing a block chain computation, performing k-means clustering, etc. …... Task parameters may include a specification of the accelerator image or bitstream to be implemented, task data to be processed, specific hardware requirements, and/or the like.…... the cloud resource manager 102 may directly assign a task to a node compute device 104 by sending task parameters and other relevant information directly to the node compute device 104.”) The citation discloses at the node compute device 104 receive the task parameters/configuration information, which includes a specification of the accelerator image/a resource that locates inside the node compute device/hosted at the computing node, and the task data to be processed/a data object. The teaching of BHANDARU does not clearly indicate the configuration is generated by the server node. The teaching of the “generate configuration information” will be discussed below.
and is associated with a computational operation which comprises a machine learning model, which computational operation is executable by the computing node ([0020]: “A task may be any task suitable for being performed on an accelerator device 308, such as training a deep learning algorithm, performing a block chain computation, performing k-means clustering, etc.” and [0082]: “In this fashion, the fog 1120 may be considered a distributed platform that provides computing and storage resources to perform processing or data-intensive tasks such as data analytics, data aggregation, and machine-learning, among others.” and [0051]: “node compute device 104 to perform the task with an accelerator device”) The citations disclose at [0020] the tasks/computational operation is being performed on an accelerator device of the node compute device and at [0082] the data-intensive tasks could include the machine learning tasks, and [0051] the compute node perform the task.
send to the computing node a message comprising the generated configuration information ([0020]: “A task may be any task suitable for being performed on an accelerator device 308, such as training a deep learning algorithm, performing a block chain computation, performing k-means clustering, etc. …... Task parameters may include a specification of the accelerator image or bitstream to be implemented, task data to be processed, specific hardware requirements, and/or the like.…... the cloud resource manager 102 may directly assign a task to a node compute device 104 by sending task parameters and other relevant information directly to the node compute device 104.”) The citation discloses at the node compute device 104 receive the task parameters/configuration information, which includes a specification of the accelerator image/a resource that locates inside the node compute device/hosted at the computing node, and the task data to be processed/a data object.
wherein in response to the message being received by the computing node, the generated configuration information causes the computing node to configure the resources for the data object and execute the computational operation in accordance with the generated configuration information (e.g. [0062]: “In block 738, in FIG. 9, the node compute device 104 prepares for the task to be performed. To do so, the node compute device 104 may load task parameters. In addition, the node compute device 104 may load a virtual machine (VM) or container to interact with the accelerator device.” and [0063]: “In block 740, the node compute device 104 performs the task on the accelerator device. In some embodiments, the node compute device 104 may send a notification to the cloud resource manager 102 and/or the requesting device that the task has been launched.”) The citation discloses at [0062], the node computing device prepares for the task to be performed/causes the computing node to configure the resources for the data object, by load the task parameters/configuration information. [0063] the node compute device performs the task/execute the computational operation on the accelerator device/accordance with the configured resource.
However, BHANDARU does not explicitly teach A server node operable to run a Lightweight Machine to Machine (LwM2M) server; and generate configuration information; wherein the computational operation is associated with a transformation operation to be performed on an output value of an output for the computational operation before the output value is written to an external resource identified for the output for the computational operation, wherein the configuration information comprises a first identifier for an LwM2M object and a second identifier, the first identifier mapping a sensor value of a sensor at the computing node to an input label of the machine learning model, and the second identifier mapping an output label of the machine learning model to the external resource, wherein the data object comprises a resource associated with an update time, and wherein writing a time value to the resource associated with the update time causes the computing node, after expiration of the time value, to check for and obtain an updated computational operation from a specified location
However, SAARNIVALA teaches A server node operable to run a Lightweight Machine to Machine (LwM2M) server (e.g. FIG. 2a and [0045]: “In the following examples the server 4 is depicted as a LwM2M server, such that the LwM2M server 4”)
generate configuration information ([0091]: “Embodiments of the present techniques provide for creating new resource templates for a particular device type, whereby the LwM2M server 4, bootstrap server 6, or another server or service may generate the new resource template.” and [0098]: “at S410 the LwM2M server 4 (or another entity) generates a resource template based on or in response to the instructions and at S412 stores the resource template in template storage.”) The citations disclose the LwM2M server generates resource template/configuration information.
wherein the configuration information comprises a first identifier for an LwM2M object ((e.g. FIG. 2c and [0069]: “In the hierarchy shown in FIG. 2c, an object may represent an LwM2M object. Instances of such objects are created according to the requirements of the system being implemented.” and [0088]: “The registration request will include the template identifier to enable the LwM2M server 4 store the objects, object instances and/or resources of the identified resource template in the resource directory.”) The citation discloses at FIG. 2c and [0069] the LWM2M objects, and at [0088] discloses the template identifier/identifier, for the LWM2M object to be stored.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add A server node operable to run a Lightweight Machine to Machine (LwM2M) server, generate configuration information, wherein the configuration information comprises an identifier for an LwM2M object, as taught in SAARNIVALA’s invention into BHANDARU’s invention because by running the light weight machine to machine, the system can process the computational operation faster, easier, and more power efficient, since the LwM2M reduces the bandwidth usage and operational cost when performing the computational operation.
However, Katre teaches wherein the computational operation is associated with a transformation operation to be performed on an output value of an output for the computational operation before the output value is written to an external resource identified for the output for the computational operation. (FIG. 2 and [0046]: “Conversely, after processing by ML cores 240, post-processing 234 adapts or changes the format of the ML processing output data to a format understood by the applicable ML client application 205 configured to implement the applicable machine learning to the IoT device 120 based on the ML processing output data.” and [0063]: “Post-processing 234 can convert the ML processing output data from a format specific to the ML model and ML cores 240 into a format that can be utilized by the ML client application 205 originally requesting the ML processing.”) The citation discloses the output from the ML cores 240 is changed to the format that can be utilized by the ML client application. FIG. 2 shows the ML client application 205 located on the IoT devices 120/external, outside of the ML proxy device 110.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the wherein the computational operation is associated with a transformation operation to be performed on an output value of an output for the computational operation before the output value is written to a resource identified for the output for the computational operation, as taught in Katre’s invention into BHANDARU’s invention because this would which improve quality and consistency of the output data, and ensure that the output is in a form that is more accurate, relevant, and useful for its intended purpose.
However, GEORGE teaches a second identifier, the first identifier mapping a sensor value of a sensor at the computing node to an input label of the machine learning model, and the second identifier mapping an output label of the machine learning model to the external resource. (e.g. FIG. 2 and [0042]: “For example, in one embodiment, the usage classifier 231 is configure to implement an artificial neural network trained using values of physical parameters known to represent instances of particular IoT product usages. In this embodiment, the usage classifier 231 is configured to provide newly acquired sensor data to the artificial neural network and to receive a usage classification as output. In this embodiment, the usage classifier 231 is further configured to store and/or transmit usage data that associates the newly acquired sensor data with the usage classification where the output usage classification is associated with a degree of confidence that exceeds a threshold value. For example, the usage classifier may transmit usage data that includes an identifier of the usage classification in association with one or more identifiers of measurements included in sensor data. This usage data may be transmitted to, for example, the sensor/usage interface 230 (e.g., via the interface(s) 206). As described below, the sensor/usage interface 230 may store this association as usage data in the user profile data store 232.” and [0044]: “This sensor data may include one or more identifiers of the sensor 134 (e.g., a serial number, a dynamic or static internet protocol address, etc.), one or more identifiers of measurements acquired by the sensor 134, and one or more values of physical parameters measured by the sensor 134. Additionally or alternatively, the sensor data may include an identifier of the IoT product 102. In some embodiments, as described above, the sensor 134 is also configured to provide sensor data to the usage classifier 231 for usage classification processing. Such provision may be via communication of the sensor data to the usage classifier 231 and/or via storage of the sensor data in the memory 204.”) The citation discloses at [0042] that the newly acquired sensor data is provided to an artificial neural network/machine learning model, of the classifier 231 as an input. After that, the usage classification is provided by the artificial neural network of the classifier 231 as an output. The output then sent to the usage interface 230, and FIG. 2 discloses that the usage interface 230 is external to the component 102. At [0044] discloses the sensor data include one or more identifiers/first identifier for the sensor, and one or more identifiers/second identifier for the measurements acquired by the sensor. Since the sensor data comprises identifiers, and the sensor data is sent to the artificial neural network of the classifier 231, it would imply that the identifiers map the sensor data to the artificial neural network/machine learning model.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the identifier mapping a sensor value of a sensor at a computing node to an input label of the machine learning model, and mapping an output label of the machine learning model to the external resource, as taught in GEORGE’s invention into BHANDARU’s invention because the additional features would clearly identifying which sensor value maps to each model input and how the model output links to an external resource, which makes the configuration more consistent and reliable while allowing the model to be executed correctly and efficiently on the node.
However, Ravichandran teaches wherein the data object comprises a resource associated with an update time, and wherein writing a time value to the resource associated with the update time causes the computing node, after expiration of the time value, to check for and obtain an updated computational operation from a specified location. (e.g. 11 – Col 2 lines 64 – 67, and Col 3 lines 1 – 7: “Responsive to determining that a local timer of the one or more local timers is expired (e.g., a lapse of a predetermined time interval associated with the local timer), the application can send a request for updated data to a database storing the updated data. That is, responsive to determining that a local timer of the one or more local timers is expired, the application can retrieve updated data. In some examples, the application can return to the inactive state shortly after it retrieves the updated data. Such functionality enables the application to access updated data without the application having to be in an active state at all times.”) The citation discloses the concept when a timer (which indicate a data update is needed) expired, the application can send the request to retrieve/check and obtain, updated data at the database.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add wherein the data object comprises a resource associated with an update time, and wherein writing a time value to the resource associated with the update time causes the computing node, after expiration of the time value, to check for and obtain an updated computational operation from a specified location, as taught in Ravichandran’s invention into BHANDARU’s invention because the additional features would enables the system to control when it checks for an updated operation, in which improves system efficiency resource usage, and ensuring that the updated operation are obtained and executed in a timely and controlled manner.
Regarding claim 58, BHANDARU, in view of SAARNIVALA, Katre and GEORGE, teaches the server node as claimed in claim 56, and SAARNIVALA further teaches wherein the processing circuitry is further configured to cause the server node to generate the configuration information based on at least one of: a state of the computing node; ([0093]: “When, at S304 the bootstrap server 6 determines that a resource template for that device type is not available (e.g. not in template storage), the bootstrap server 6, at S306, generates a resource template for the device type and, at S308, stores the resource template in template storage 32.”) The citation discloses the generation of the resource template when for the device type when the resource template is not available/state of the computing node.
or a value of a reference resource hosted at the computing node.
Regarding claim 59, it is a server node claim having similar limitations cited in claim 49. Thus, claim 59 is also rejected under the same rational as cited in the rejection of rejected claim 49.
Regarding claim 61, it is a server node claim having similar limitations cited in claim 52. Thus, claim 61 is also rejected under the same rational as cited in the rejection of rejected claim 52.
Regarding claim 63, it is a method claim having similar limitations cited in claim 45. Thus, claim 63 is also rejected under the same rational as cited in the rejection of rejected claim 45.
Regarding claim 64, it is a method claim having similar limitations cited in claim 56. Thus, claim 64 is also rejected under the same rational as cited in the rejection of rejected claim 56.
Claims 46, 48, 50, 51, 53, 57, 60, and 62 are rejected under 35 U.S.C. 103 as being unpatentable over BHANDARU, SAARNIVALA, Katre, Ravichandran, and GEORGE, in further view of Sem et al. US Pat. No. US 11138033 B1 (hereafter Sem)
Regarding claim 46, BHANDARU, in view of SAARNIVALA, Katre, Ravichandran, and GEORGE, discloses the computing node as claimed in claim 45, but does not explicitly teach wherein the processing circuitry is further configured to cause the computing node to: expose the resource of the data object for which the configuration information is received.
However, Sem teaches wherein the processing circuitry is further configured to cause the computing node to: expose the resource of the data object for which the configuration information is received. (Col 5, lines 54-57: “In other examples, the bulk task data 120 generally can be stored at any location that is accessible to the application or service used to execute the computing tasks described in the data.”) The citation discloses bulk task data/the resource of the data object can be stored at any accessible location/expose for the application or service used to run the tasks.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add wherein the processing circuitry is further configured to cause the computing node to: expose the resource of the data object for which the configuration information is received, as taught in Sem’s invention into BHANDARU, SAARNIVALA , Katre and GEORGE’s invention because by making the resource accessible to other computing devices when needed, it helps to reduce the unnecessary steps (send the request for the resources and receive the requested resources) when performing the computational operation, which would increase the speed to process the computational operation.
Regarding claim 48, BHANDARU, in view of SAARNIVALA, Katre, Ravichandran, and GEORGE, discloses the computing node as claimed in claims 45, but does not explicitly teach wherein the configuration information specifies a location from which the computational operation may be obtained, and wherein the processing circuitry is further configured to cause the computing node to: obtain the computational operation from the specified location.
However, Sem teaches wherein the configuration information specifies a location from which the computational operation may be obtained (Col 2, lines 57-62: “Each submitted compute job includes configuration information specifying, for example, a name or other identifier of the job, the job's memory and processing requirements, and an identifier of a location where the compute job is located (for example, a location of a shell script, executable, or container image).”) The citation discloses the compute job/computational operation includes the configuration information that has the location where the compute job is located.
and wherein the processing circuitry is further configured to cause the computing node to: obtain the computational operation from the specified location. (Col 3, lines 48-50: “The application or service can then obtain the bulk task data from the identified storage location” and Col 10, lines 65-67: “In one embodiment, the computing tasks are compute jobs to be executed by a batch processing service of a service provider network.) The citation discloses the task/job/computational operation can be obtained at the identified storage location/specified location
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the wherein the configuration information specifies a location from which the computational operation may be obtained, and wherein the processing circuitry is further configured to cause the computing node to: obtain the computational operation from the specified location, as taught in Sem’s invention into BHANDARU, SAARNIVALA, Katre, Ravichandran, and GEORGE’s invention because it helps the system be able to locate the corresponding computational operation to obtain it, which helps to improve the performance optimization by reducing the memory access latency and ensuring better memory alignment for faster processing.
Regarding claim 50, BHANDARU, in view of SAARNIVALA, Katre, Ravichandran, and GEORGE, discloses the computing node as claimed in claim 49, and BHANDARU further teaches comprises an identification of a resource that is: hosted on a node other than the computing node; (BHANDARU-[0039]: “The accelerator images 408 may be stored on the cloud resource manager 102 or the node compute devices 104” and BHANDARU-[0039]: “metadata for the accelerator images 408 that include a size, power usage, and whether the corresponding accelerator image is permitted to be shared.”) The citation discloses the metadata/identification of the accelerator image/a resource, and the accelerator image can be stored on the cloud resource manager or the node compute device/hosted on a node other than the computing node.
or of another instance of the data object that is hosted at the computing node.
However, BHANDARU, in view of SAARNIVALA, Katre, Ravichandran, and GEORGE, does not explicitly teach wherein at least one of a resource identification for an input for the computational operation; or a resource identification for an output for the computational operation
Sem teaches wherein at least one of a resource identification for an input for the computational operation; (Col 8, lines 56: “an identifier of the bulk task data” and Col 5, lines 54-57: “In other examples, the bulk task data 120 generally can be stored at any location that is accessible to the application or service used to execute the computing tasks described in the data.”) The citation discloses the identifier/resource identification for the bulk task data/input, which is used to execute the computing tasks/the computational operation.
or a resource identification for an output for the computational operation
Regarding claim 51, BHANDARU, in view of SAARNIVALA, Katre, Ravichandran, and GEORGE, discloses the computing node as claimed in claim 49, but does not explicitly teach wherein at least one of a resource identification for an input for the computational operation identifies a resource that comprises an output for a different computational operation; or a resource identification for an output for the computational operation identifies a resource that comprises an input for a different computational operation.
However, Sem teaches wherein at least one of a resource identification for an input for the computational operation identifies a resource that comprises an output for a different computational operation; (Col 9, lines 37-41: “As indicated above, a bulk task status API request 500A can include, among other possible parameters, an identifier of a previously submitted bulk task API request generated by the batch processing service 102.”) the citation discloses the identifier/a resource identification of a previously submitted bulk task API request generated by the batch processing service 102/ input for the computational operation identifies a resource that comprises an output for a different computational operation;
or a resource identification for an output for the computational operation identifies a resource that comprises an input for a different computational operation.
Regarding claim 53, BHANDARU, in view of SAARNIVALA, Katre, Ravichandran, and GEORGE, discloses the computing node as claimed in claim 49, but does not explicitly teach wherein the configuration information comprises a value of at least one of a resource identification; a transformation operation; a computational operation identification for at least one of an input or an output of the computational operation.
However, Sem teaches wherein the configuration information comprises a value of at least one of (Col 4, line 67 and Col 5 lines 1-2: “Each submitted compute job is associated with configuration information specifying”)
a resource identification; (Col 5, lines 3-5: “an identifier of a location where the compute job is located (for example, a location of a shell script, executable, or container image).”) identifier of a location/resource identification.
a transformation operation;
a computational operation identification for at least one of an input or an output of the computational operation. (Col 5, lines 2: “a name or other identifier of the job” and Col 5 lines 8-10: “Jobs can also reference other jobs by name or by identifier, and can be dependent on the successful completion of other jobs.”) The citation discloses the identifier of the job/computational operation identification
Regarding claim 57, it is a server node claim having similar limitations cited in claim 46. Thus, claim 57 is also rejected under the same rational as cited in the rejection of rejected claim 46.
Regarding claim 60, it is a server node claim having similar limitations cited in claim 51. Thus, claim 60 is also rejected under the same rational as cited in the rejection of rejected claim 51.
Regarding claim 62, BHANDARU, in view of SAARNIVALA, Katre, Ravichandran, and GEORGE, teaches A server node as claimed in claim 46, and BHANDARU further teaches wherein a value of a condition resource of the data object that is hosted at the computing node indicates a condition for execution of the computational operation, (BHANDARU - [0044]: “The node compute device 104 may store metadata for the accelerator images 506 that include a size, power usage”) and BHANDARU - [0043]: “The accelerator usage monitor 510 may monitor and report usage, fragmentation, which accelerator images are deployed where, power usage levels, etc., for accelerator devices 308. If the node compute device 104 is over a power budget, the accelerator usage monitor 510 may trigger an alert, cancel operations, or take other appropriate actions.”) The citations disclose at [0044] the node computing device 104/computing node store metadata includes the power usage/condition resource, and at [0043] the accelerator usage monitor may indicate that the node compute device is over a power budget/condition for execution, it may cancel the operation.
Claims 47 are rejected under 35 U.S.C. 103 as being unpatentable over BHANDARU, SAARNIVALA, Katre, Ravichandran, and GEORGE, in further view of BAI et al. US Pub. No. US 20200151023 A1 (hereafter BAI)
Regarding claim 47, BHANDARU, in view of SAARNIVALA, Katre, Ravichandran, and GEORGE, teaches the computing node as claimed in claims 45, and BHANDARU further teaches wherein the configuration information identifies the computational operation (BHANDARU-[0040]: “Reception of the tasks includes task parameters such as task data, which accelerator image 408 should be used to perform the task, hardware resource requirements, resources required besides accelerator devices 308 such as a virtual machine to be run during execution of the accelerator devices 308, etc.”) The citation discloses the task parameters/configuration information is related to the task/computational operation.
BHANDARU, in view of SAARNIVALA, Katre, Ravichandran, and GEORGE, does not explicitly teach wherein the processing circuitry is further configured to cause the computing node to: instantiate the identified computational operation.
However, BAI teaches wherein the processing circuitry is further configured to cause the computing node to: instantiate the identified computational operation. ([0041]: “Indeed, the resource bindings 218 may include any information that, when provided to a control plane, enables the control plane to schedule or instantiate a cloud-native service identified by a corresponding resource identifier.”) the citation discloses the control plane/computing node to instantiate the cloud-native service/ instantiate the identified computational operation
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add wherein the processing circuitry is further configured to cause the computing node to: instantiate the identified computational operation, as taught in BAI’s invention into BHANDARU, SAARNIVALA, Katre, Ravichandran, and GEORGE’s invention because instantiating the computational operation helps to optimize the resource allocation, improve execution efficiency, and enhances system performance by reducing redundant computations and ensuring smother execution.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
US 20210409922 A1: a method of re-establishing a connection between a LWM2M client and an LWM2M server following a reconnection of the LWM2M client includes determining, at the LWM2M client, a state of the LWM2M client device prior to reconnection of the LWM2M client, transmitting, to the LWM2M server, an indication of the state of the LWM2M client prior to reconnection of the LWM2M client, and receiving a response from the LWM2M indicating whether the indicated state of the LWM2M client is the expected state of the LWM2M client or an unexpected state.
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.
Examiner has cited particular columns/paragraphs/sections and line numbers in the references applied and not relied upon to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
When responding to the Office action, applicant is advised to clearly point out the patentable novelty the claims present in view of the state of the art disclosed by the reference(s) cited or the objections made. A showing of how the amendments avoid such references or objections must also be present. See 37 C.F.R. 1.111(c).
When responding to this Office action, applicant is advised to provide the line and page numbers in the application and/or reference(s) cited to assist in locating the appropriate paragraphs
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/TUAN M NGUYEN/Examiner, Art Unit 2198
/PIERRE VITAL/Supervisory Patent Examiner, Art Unit 2198
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