DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-6, 8-11, and 13-19 are pending. Claims 7, 12, and 20 are cancelled.
Response to Arguments
Regarding 35 U.S.C. 101:
Applicant’s amendments and arguments regarding the rejection of claims 1-20 under 35 U.S.C. 101 have been fully considered and are found to be persuasive. The rejections of claims 1-20 under 35 U.S.C. 101 are withdrawn. The judicial exception of resource allocation/scheduling is integrated into a practical application through the amended recitation of performance of the tasks by the selected edge devices.
Regarding: Prior Art Rejections:
Applicant’s amendments and arguments regarding the rejection of claims 1, 2, 14, and 15 under 35 U.S.C. 102 and the rejection of claims 3, 4, 16, 17, 5-7, 18-20, 8, and 9 under 35 U.S.C 103 have been fully considered and are moot due to new grounds of rejection necessitated by amendment. Claims 7 and 20 are cancelled.
Applicant’s amendments and arguments regarding the rejection of claims 10-13 under 35 U.S.C. 103 have been fully considered and are found to be not persuasive. The rejections of claims 10, 11, and 13 under 35 U.S.C. 103 are maintained. Claim 12 is cancelled.
Applicant’s Remarks recite:
“In other words, in Wang, the only device capable of operating the artificial intelligence program 62 of Yadav is the edge agent 523 that sends the task request to the edge devices, not the edge devices that receive such task request. As such, there is no suggestion or motivation in Wang and Yadav to implement any such artificial intelligence program 62 to be performed by the edge devices (in Wang) or the sensors 14 (in Yadav). As a result, Wang and Yadav, collectively or individually, fail to teach or suggest at least the process of…”
Applicant’s claims are not limited to the duties of resource allocation and model selection being performed at edge devices and are not being interpreted as such. The standing combination is obvious to one of ordinary skill in the art to make because the common objective of both Wang and Yadav is optimizing resource allocations using learned task and device information ultimately to better perform tasks at selected devices. Wang Fig 5 discloses an Edge Agent to disperse tasks to execute on different containers and to organize the return of execution results from the containers (i.e., a module to orchestrate tasks). From examiner’s interpretation of applicant’s claims, the task orchestrator is picking which models are best suited to run on the selected edge devices based on some form of observation pattern/data. The containers in Wang receive tasks from the edge device based on task-container suitability ([0080] upon receiving a task flow or task as shown in FIG. 6A, the definer module 5231 may obtain tags for each subtask of the task flow. The tags may indicate basic requirements for the attributes of the edge devices. In other words, the task flow may indicate which devices are adapted to execute the task in the tags). Yadav discloses an assessment artificial intelligence program which objective is to determine algorithms necessary to run upon selected sensors to perform certain tasks based on collected data quality monitored from the sensor ([0050] selecting for each sensor 14a, 14b, 14c of the plurality of sensors 14 selected, via the assessment artificial intelligence program 62, a machine learning algorithm 44a, 44b, 44c, 44d from a predetermined set of machine learning algorithms 44 based on the determined quality metric 46a, 46b, 46c of the sensor data 18a, 18b, 18c that yields the desired accuracy for performing the given task 16 (step 240). Method 200 optionally includes the steps of receiving a classification output 50a, 50b from each selected machine learning algorithm 44a, 44b, 44c, 44d for each selected sensor 14a, 14b, 14c, wherein the classification output 50a, 50b is a result generated by performing the task 16 (optional step 250); and combining classification outputs 50a, 50b using a decision-level fusion rule 56 (optional step 260)).
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 1, 2, 14, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. US 20220129306 A1 in view of Kapoor et al. US 12210898 B1.
Wang is cited in a previous office action.
Regarding claim 1, Wang teaches the invention substantially as claimed including:
A method for device constellation, the method comprising:
receiving a request, the request including a plurality of request parameters ([0079] attributes of the task flow; [0095] the definer module 5231 receives a request for executing a task flow … the definer module 5231 retrieves metadata information for the task flow;);
decomposing the request into one or more tasks ([0060] A process of production or service may be a process of executing the task flow, including a series of subtasks executed on the edge devices);
selecting one or more edge devices based at least in part on the plurality of request parameters ([0096] the definer module may determine a cluster of edge devices to execute the task flow from a set of edge devices, which comprises: the definer module retrieving attributes of the task flow and a set of edge devices respectively; selecting a group of edge devices from the set of edge devices as a cluster of edge devices to execute the task flow based on a mapping relationship of the attributes between the task flow and the set of edge devices);
assigning the one or more tasks to the one or more selected edge devices to cause the one or more selected edge devices to perform the one or more tasks ([0098] a sending module sending a request for executing the task flow with the metadata information to one or more starting edge devices in the cluster) includes:
identifying one or more first models that run on a first edge device of the one or more selected edge devices ([0060] It can be understood that industrial/intelligent production or service functions can be carried out or realized in the edge computing environment. The edge devices may be requested to perform various tasks to fulfil a workload, either to accomplish production missions or to implement service functions; [0081] edge devices have their own attributes or characteristics and may be adapted to execute various tasks. The attributes of the edge devices may be properties, type, power, parameter, index, configuration and the like. For purpose of simplicity, the attributes of the devices may also be marked as tags. The definer module 5231 may determine the cluster of edge devices for execution of the task flow based on a mapping relationship of the tags between the task flow and the edge devices);
causing the one or more selected edge devices to perform the one or more tasks ([0098] a sending module sending a request for executing the task flow with the metadata information to one or more starting edge devices in the cluster); and
receiving one or more task results from the one or more selected edge devices (([0098] in response to one or more last subtasks being completed by one or more last edge devices, the one or more last edge devices sending one or more final running results to a receiver module); wherein the method is performed using one or more processors ([0104] a processor to carry out aspects of the present disclosure).
Wang does not explicitly teach identifying one or more first models that run on a first edge device of the one or more selected edge devices; determining a second model based on the plurality of request parameters, the second model being different from any one of the one or more first models; deploying the second model to the first edge device;
However, Kapoor teaches identifying one or more first models that run on a first edge device of the one or more selected edge devices (the existing model (e.g., the version prior to the update) is currently deployed (also referred to herein as the current shard, Col 4 38-40; Examiner notes: Wang teaches the edge devices of the claimed invention. Kapoor teaches the concept of identifying existing models deployed on shards. The combination of Wang and Kapoor results in edge devices hosting models of which the system is able to identify for execution/model updating purposes);
determining a second model based on the plurality of request parameters, the second model being different from any one of the one or more first models (at 410, a determination is made that a model is to be added to a shard. According to various embodiments, the system determines that the model is to be added to the shard in response to determining that the model is created or updated (e.g., that the model is an updated model of a model currently deployed, Col 18 4-9);
deploying the second model to the first edge device (the new or updated model may be allocated (e.g., allocated based on a setting in a configuration mapping of models to shards, and/or copied/downloaded) to the selected shard on which the new or updated model is to be deployed, Col 2 67 – Col 3 4);
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to have combined Kapoor’s model management system with the system of Wang to provide the edge devices of Wang with new or updated machine learning models in order to execute assigned tasks. A person of ordinary skill in the art would have been motivated to make this combination to provide Wang’s system with the advantage of model scalability in big data processing systems (see Kapoor Col 1 13-21 At scale, the number of accesses or queries performed against the one or more datasets is very large, the number of organizations for which one or more datasets is stored is very large, and the models used in connection with analyzing the data become resource intensive as the models become more sophisticated as additional features are introduced. This creates a problem for maintaining models in memory for analyzing the applicable one or more datasets).
Regarding claim 2, Wang and Kapoor teach the method of claim 1.
Wang further teaches fusing the one or more task results received from the one or more selected edge devices to generate a course of actions (Fig 6A; [0076] The running results of the two subtasks may need to be sent as the input to a third device to run the next subtask).
Regarding claim 14, it is the system of claim 1. Therefore, it is rejected for the same reasons as claim 1.
Wang further teaches one or more memories comprising instructions stored thereon; and one or more processors configured to execute the instructions and perform operations ([0104] The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure).
Regarding claim 15, it is the system of claim 2. Therefore, it is rejected for the same reasons as claim 2.
Claims 3, 4, 16, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. US 20220129306 A1 in view of Kapoor et al. US 12210898 B1 in view of Levien et al. US 20130081049 A1.
Levien is cited in a previous office action.
Regarding claim 3, Wang and Kapoor teach the method of claim 1.
Wang further teaches wherein the plurality of request parameters include one or more collection parameters ([0080] the definer module 5231 may obtain tags for each subtask of the task flow. The tags may indicate basic requirements for the attributes of the edge devices; [0081] attributes of the edge devices may be properties, type, power, parameter, index, configuration and the like).
Wang does not explicitly teach wherein the one or more collection parameters include at least one selected from a group consisting of a location parameter, a field-of-view parameter, a sensor parameter, and a timing parameter.
However, Levien teaches wherein the one or more collection parameters include at least one selected from a group consisting of a location parameter, a field-of-view parameter, a sensor parameter, and a timing parameter ([0069] task portion two-or-more discrete interface subtask acquiring module 52 acquiring one or more subtasks (e.g., "take a picture of the Eiffel Tower from your location") that correspond to portions of a task (e.g. "take a 360-degree picture of the Eiffel Tower at night") requested by a task requestor (e.g., a person using a smartphone in Centerville, Ohio, requests a 360-degree picture of the Eiffel Tower for a grade school science project), wherein the task of acquiring data (e.g., the 360-degree image data of the location) is configured to be carried out by two or more discrete interface devices (e.g., two or more cellular phones, smartphones, network-connected cameras will provide image data to carry out the task of acquiring data; [0087] the task is "take a 360-degree picture of Times Square when the new Reebok ad pops up at 8:01:32 a.m).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to have combined Levien’s request parameters specifying request location, field of view, timing, and sensor information with the system of Wang. A person of ordinary skill in the art would have been motivated to make this combination to provide Wang’s system with the advantage of providing edge devices with instructions of data collection tasking (see Levien [0009] the task of acquiring data is configured to be carried out by two or more discrete interface devices, transmitting at least one of the one or more subtasks to at least two of the two or more discrete interface devices).
Regarding claim 4, Wang, Kapoor, and Levien teach the method of claim 3.
Wang further teaches wherein the selecting one or more edge devices comprises selecting the one or more edge devices based at least in part on at least one of the collection parameters ([0081] The definer module 5231 may determine the cluster of edge devices for execution of the task flow based on a mapping relationship of the tags between the task flow and the edge devices; Examiner notes: based upon instructions in the request specifying the task and resource requirements, the definer module selects edge devices that fit the request).
Regarding claims 16 and 17, they are the systems of claims 3 and 4 respectively. Therefore, they are rejected for the same reasons as claims 3 and 4 respectively.
Claims 5, 6, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. US 20220129306 A1 in view of Kapoor et al. US 12210898 B1 in view of Yadav US 20210201091 A1.
Yadav is cited in a previous office action.
Regarding claim 5, Wang and Kapoor teach the method of claim 1.
Wang does not explicitly teach wherein the plurality of request parameters include one or more monitoring parameters, wherein the one or more monitoring parameters include at least one selected from a group consisting of a model parameter, a fusion function parameter, and a target parameter.
However, Yadav teaches wherein the plurality of request parameters include one or more monitoring parameters, wherein the one or more monitoring parameters include at least one selected from a group consisting of a model parameter, a fusion function parameter, and a target parameter ([0038] a task may involve determining that an environment 2 is occupied by people 4a, 4b. If the environment 2 is occupied, the luminaires 6a, 6b, 6c, 6d may be turned on or the light 10 from the luminaires 6a, 6b, 6c, 6d may be brightened. As another example, a task may involve determining whether people 4a, 4b in the environment 2 are engaging in a type of activity which requires particular light 10 characteristics 8, such as an activity which requires people 4a, 4b to focus for an extended time while working. In response to determining that the task has been performed, light 10 characteristics 8 of luminaires 6a, 6b, 6c, 6d which promote alertness may be set. The plurality of sensors 14 may comprise sensors 14a, 14b, 14c of one type or multiple types and are arranged to provide data to perform a set of preselected tasks. The plurality of sensors 14 may be selected from a passive infrared (“PIR”) sensor, a thermopile sensor, a microwave sensor, an image sensor, a sound sensor, and/or any other sensor modality).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to have combined Yadav’s process of machine learning algorithm and resource allocation for a task of targeting of some object to monitor with the system of Wang. A person of ordinary skill in the art would have been motivated to make this combination to provide Wang’s system with the advantage of determining the necessary sensors and algorithms to best perform a computer vision task (see Yadav [0005] the system selects a specific sensor or a combination of sensors along with a corresponding machine learning algorithm to achieve enhanced performance in performing a task).
Regarding claim 6, Wang, Kapoor, and Yadav teach the method of claim 5.
Yadav further teaches wherein the selecting one or more edge devices comprises selecting the one or more edge devices based at least in part on at least one of the monitoring parameters ([0038] a task may involve determining whether people 4a, 4b in the environment 2 are engaging in a type of activity which requires particular light 10 characteristics 8, such as an activity which requires people 4a, 4b to focus for an extended time while working … The plurality of sensors 14 may be selected from a passive infrared (“PIR”) sensor, a thermopile sensor, a microwave sensor, an image sensor, a sound sensor, and/or any other sensor modality; Examiner notes: in order to detect the presence of people performing some activity, the sensors listed are selected based on the task).
Regarding claims 18 and 19, they are the systems of claims 5 and 6 respectively. Therefore, they are rejected for the same reasons as claims 5 and 6 respectively.
Claims 10, 11, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. US 20220129306 A1 in view of Yadav US 20210201091 A1.
Regarding claim 10, Wang teaches the invention substantially as claimed including:
A method for device constellation, the method comprising:
receiving a task assignment via a task orchestrator (Fig 5 Edge Agent receives task flows to schedule, edge devices receive task assignments from Edge Agent), the task assignment including one or more task parameters ([0079] attributes of the task flow; [0095] the definer module 5231 receives a request for executing a task flow … the definer module 5231 retrieves metadata information for the task flow; [0071] the sender module 5232 may be configured to send a request with the metadata information to one or more edge devices involved in cluster 1 to start the execution of the task), the one or more task parameters including a set of collection parameters ([0080] the definer module 5231 may obtain tags for each subtask of the task flow. The tags may indicate basic requirements for the attributes of the edge devices; [0081] attributes of the edge devices may be properties, type, power, parameter, index, configuration and the like);
conducting a task according to the task assignment including the one or more task parameters to collect data ([0072] The containers in each of the edge devices may run the assigned subtask and the one or more proxy modules in each of the devices may manage the data of task flow. The one or more proxy modules in each of the edge devices may manage or route the task flow according to the metadata information; [0073] the receiver module 5233 may be configured to receive a final running result/s from a corresponding last edge device/s in the cluster, rather than receiving a running result of each subtask from each device; Examiner notes: running a subtask on the edge devices accumulates data as part of calculations/measurements required to output results); and
transmitting the task result to a computing device ([0062] each of the edge devices may receive a task request from the edge agent 423 and may output the running result to the edge agent 423); wherein the method is performed using one or more processors ([0104] a processor to carry out aspects of the present disclosure).
Wang does not explicitly teach the one or more task parameters including a set of monitoring parameters; activating one or more models based at least in part on the monitoring parameters includes: receiving at least one model of the one or more models via the task orchestrator; and activating the at least one received model; and generating a task result by applying the one or more models to the collected data
However, Yadav teaches wherein the one or more task parameters including a set of monitoring parameters ([0038] a task may involve determining that an environment 2 is occupied by people 4a, 4b. If the environment 2 is occupied, the luminaires 6a, 6b, 6c, 6d may be turned on or the light 10 from the luminaires 6a, 6b, 6c, 6d may be brightened. As another example, a task may involve determining whether people 4a, 4b in the environment 2 are engaging in a type of activity which requires particular light 10 characteristics 8, such as an activity which requires people 4a, 4b to focus for an extended time while working. In response to determining that the task has been performed, light 10 characteristics 8 of luminaires 6a, 6b, 6c, 6d which promote alertness may be set. The plurality of sensors 14 may comprise sensors 14a, 14b, 14c of one type or multiple types and are arranged to provide data to perform a set of preselected tasks. The plurality of sensors 14 may be selected from a passive infrared (“PIR”) sensor, a thermopile sensor, a microwave sensor, an image sensor, a sound sensor, and/or any other sensor modality); activating one or more models based at least in part on the monitoring parameters ([0050] selecting for each sensor 14a, 14b, 14c of the plurality of sensors 14 selected, via the assessment artificial intelligence program 62, a machine learning algorithm 44a, 44b, 44c, 44d from a predetermined set of machine learning algorithms 44 based on the determined quality metric 46a, 46b, 46c of the sensor data 18a, 18b, 18c that yields the desired accuracy for performing the given task 16 (step 240); Examiner notes: the contents of the given task are considered when selecting which model to utilize on each sensor) includes: receiving at least one model of the one or more models via the task orchestrator ([0049] For every sensor (e.g., 14a, 14b, 14c) of the plurality of sensors 14 which the assessment artificial intelligence program 62 determines to use, a machine learning algorithm (from the predefined set of machine learning algorithms 44 (shown in FIG. 3)) is used to provide a classification output (e.g., 50a, 50b)); and activating the at least one received model ([0049] A classification output 50a, 50b is an output from the machine learning algorithm (e.g., 44a, 44b, 44c, 44d) for a particular sensor (e.g., 14a, 14b, 14c) and is a result generated by performing the task 16);
and generating a task result by applying the one or more models to the collected data (([0009] receiving a classification output from each selected machine learning algorithm for each selected sensor, wherein the classification output is a result generated by performing the task; Examiner notes: machine learning algorithm is applied to collected sensor data);
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to have combined Yadav’s process of machine learning algorithm and resource allocation for a task of targeting of some object to monitor with the system of Wang. A person of ordinary skill in the art would have been motivated to make this combination to provide Wang’s system with the advantage of determining the necessary sensors and algorithms to best perform a computer vision task (see Yadav [0005] the system selects a specific sensor or a combination of sensors along with a corresponding machine learning algorithm to achieve enhanced performance in performing a task).
Regarding claim 11, Wang and Yadav teach the method of claim 10.
Yadav further teaches the task orchestrator includes an indication of a model pipeline, the model pipeline including the one or more models ([0043] the assessment artificial intelligence program 62 can determine which machine learning algorithm from a predetermined set of machine learning algorithms 44 (shown in FIG. 3) should be used to perform the task).
Regarding claim 12, Wang and Yadav teach the method of claim 11.
Yadav further teaches wherein the activating one or more models comprises: receiving at least one model of the one or more models ([0049] For every sensor (e.g., 14a, 14b, 14c) of the plurality of sensors 14 which the assessment artificial intelligence program 62 determines to use, a machine learning algorithm (from the predefined set of machine learning algorithms 44 (shown in FIG. 3)) is used to provide a classification output (e.g., 50a, 50b)); and
activating the at least one received model ([0049] A classification output 50a, 50b is an output from the machine learning algorithm (e.g., 44a, 44b, 44c, 44d) for a particular sensor (e.g., 14a, 14b, 14c) and is a result generated by performing the task 16).
Regarding claim 13, Wang and Yadav teach the method of claim 11.
Wang further teaches wherein the transmitting the task result to a computing device comprises transmitting the task result via the task orchestrator ([0073] the receiver module 5233 may be configured to receive a final running result/s from a corresponding last edge device/s in the cluster, rather than receiving a running result of each subtask from each device).
Claims 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. US 20220129306 A1 in view of Kapoor et al. US 12210898 B1 in view of Reyes US 20230034835 A1.
Reyes is cited in a previous office action.
Regarding claim 8, Wang and Kapoor teach the method of claim 1.
Wang further teaches receiving one or more additional requests ([0061] multiple task flows can be run in parallel; Examiner notes: system receives requests to run multiple task flows); and
decomposing the request and the one or more additional requests into a plurality of sub requests ([0061] one of the task flows may comprise a series of subtasks which are to be run on devices B, C, and A … The other task flow may comprise a series of subtasks which are to be run on devices D, E, and A);
Wang does not explicitly teach generating a plurality of request queues for the request and the one or more additional requests, the plurality of request queues including a first request queue for data collection and a second queue for data processing; and storing the plurality of sub-requests into one of the plurality of request queues.
However, Reyes teaches generating a plurality of request queues for the request and the one or more additional requests, the plurality of request queues including a first request queue for data collection and a second queue for data processing ([0054] The task management system may be configured to receive requests to execute various types of tasks. For example, the task management system may receive requests to back up databases, perform computations on data present in databases, test new applications, retrieve data from databases, etc; [0055] Each task queue 412, 414, or 416 may be configured for a particular type of task; Examiner notes: system creates queues such that data collection tasks have a dedicated queue and data processing tasks have a dedicated queue);
storing the plurality of sub-requests into one of the plurality of request queues ([0091] The selected task may be added to a task queue that stores tasks of the task type of the selected task).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to have combined Reyes’ dedicated task queues for task types with the system of Wang. A person of ordinary skill in the art would have been motivated to make this combination to provide Wang’s system with the advantage of separating queues task types allowing for granularity in the task scheduling system (see Reyes [0010] In some examples, the system may comprise multiple task queues where each task queue is configured to perform a particular type of task. When the request to execute the task is received, the task queue may be selected based on the type of task requested. Additionally, a second task queue may be selected for a subsequent task, where the completion of the original task is a prerequisite for the initiation of execution of the subsequent task. The second task queue may be selected based on the task type of the subsequent task. The subsequent task may then be added to the second task queue).
Regarding claim 9, Wang, Kapoor, and Reyes teach the method of claim 8.
Wang further teaches wherein at least one task of the one or more tasks is generated based on the plurality of sub-requests ([0061] The devices B, C, and A may respectively receive the assigned subtask from the edge agent 423 and turn back the running result to the edge agent 423 respectively as shown by lines 431, 432 and 433. The other task flow may comprise a series of subtasks which are to be run on devices D, E, and A. Similarly, devices D, E, and A may communicate with the edge agent 423 to execute the task flow as shown by dotted lines 434, 435 and 436).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/H.L./
Examiner, Art Unit 2195
/BRADLEY A TEETS/Supervisory Patent Examiner, Art Unit 2197
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