DETAILED ACTION
Claims 1-20 are 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 .
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.
Claim(s) 1-3, 6-14, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ho (US PG Pub No. 2023/0198771 A1), in view of Santamala et al. (US PG Pub No. 2022/0198839 A1), and further in view of Scott et al. (US PG Pub No. 2011/0055205 A1).
Regarding claim Ho teaches a method for dynamically allocating computing resources for processing data, the method comprising:
obtaining, over a network, data from one or more units associated with a manufacturing plant (Fig 1B; [0007], wherein data from sensors is received at a manufacturing plant), the data indicating addition of a new process or asset to the manufacturing plant ([0041-42], wherein new assets are added to the network);
determining one or more calculations to be performed for the new process or asset ([0042]; [0014]);
determining an amount of processing resources for performing the one or more calculations ([0008], wherein processing resources are consumed to manage IoT data, therefore it is inherent that an amount of resources is determined if they are to be consumed);
processing, by the combination of resources, the data obtained from the one or more units associated with the manufacturing plant to generate one or more performance indicators associated with the manufacturing plant ([0014], wherein performance indicators are calculated based on the data).
Ho does not teaches generating one or more signals configured to commission the amount of processing resources as a combination of resources from (i) processing resources associated with a cloud-computing system and (ii) processing resources located at a site of the manufacturing plant.
Santamala teaches the use of edge and cloud processing resources for the processing of tasks ([0030]). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to commission the amount of processing resources as a combination of cloud and edge processing resources. One would be motivated by the desire to utilize all available processing resources to speed up execution of the tasks.
Ho and Santamala do not teaches providing, to a user device, the one or more performance indicators.
It is old and well known to display performance indicators or metrics in user interface dashboards for managers to view. For example, Scott teaches the use of dashboards of management reports related to metrics tracking operational and functional effectiveness and efficiencies in manufacturing plants ([0082]). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to display performance indicators in a user display. One would be motivated by the desire to communicate pertinent information to a plant manager.
Regarding claim 2, Ho teaches obtaining data from the one or more units associated with the manufacturing plant over the network comprises: obtaining data from one or more computers communicably connected to manufacturing devices at the manufacturing plant ([0007]).
Regarding claim 3, Ho teaches wherein determining the one or more calculations to be performed for the new process or asset comprises: parsing a configuration file that includes one or more metrics to be calculated for the new process or asset ([0014], wherein it is inherent that key performance indicators are configured ahead of time such that they are to be calculated).
Regarding claim 6, Santamala teaches generating the one or more signals configured to commission (i) the processing resources associated with the cloud-computing system and (ii) the processing resources located at the site of the manufacturing plant comprises: generating a signal configured to turn on a processing component of the processing resources associated with the cloud-computing system or the processing resources located at the site of the manufacturing plant ([0030]).
Regarding claim 7, Ho teaches obtaining, over the network, data generated by the combination of resources encoded in signals transmitted by the combination of resources, wherein the data includes the one or more performance indicators ([0007]).
Regarding claim 8, Ho teaches providing a portion of the data from the one or more units associated with the manufacturing plant to the processing resources associated with the cloud-computing system ([0014]).
Regarding claim 9, Ho teaches providing, to the user device, the one or more performance indicators comprises: generating a signal encoded with data generated by the combination of resources ([0014]); and providing the signal to a transmitting antenna ([0066]).
Regarding claim 10, Ho teaches after obtaining the data indicating addition of the new process or asset to the manufacturing plant: obtaining incoming values from the new process or asset during operation at the manufacturing plant; and providing the incoming values to temporary cache storage ([0012-13]).
Regarding claim 11, Ho teaches wherein the data from the one or more units associated with the manufacturing plant include the incoming values from the new process or asset ([0041-42], wherein new assets are added to the network).
Regarding claim 12-14 and 17-20, they are the medium and system claims of claims 1-3 and 5-8 above. Therefore, they are rejected for the same reasons as claims 1-3 and 5-8 above.
Claim(s) 4 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ho (US PG Pub No. 2023/0198771 A1), in view of Santamala et al. (US PG Pub No. 2022/0198839 A1), further in view of Wen et al. (US PG Pub No. 2021/0278825 A1).
Regarding claim 4, Ho and Santamala do not teach updating a digital twin model representing the manufacturing plant to include the new process or asset.
Wen teaches simulating a manufacturing plant to simulate production schedules to simulate states and changes over time ([0025]; [0057]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to update a digital twin model representing the manufacturing plant to include the new process or asset. One would be motivated by the desire to bridge the gap between the simulator and actual manufacturing processes as taught by Wen ([0025])
Regarding claim 15, it is the medium claim of claim 4 above. Therefore, it is rejected for the same reasons as claim 4 above.
Claim(s) 5 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ho (US PG Pub No. 2023/0198771 A1), in view of Santamala et al. (US PG Pub No. 2022/0198839 A1), in view of Scott et al. (US PG Pub No. 2011/0055205 A1), further in view of Saxena et al. (US PG Pub No. 2021/0034374 A1).
Regarding claim 5, Ho, Santamala, and Scott do not teaches determining the amount of processing resources for performing the one or more calculations comprises: generating one or more values representing an amount of sub-calculations to perform per calculation and an amount of data to process for the one or more calculations; and determining the amount of processing resources for performing the one or more calculations using the one or more values representing the amount of sub-calculations to perform per calculation and the amount of data to process for the one or more calculations.
Saxena teaches “obtaining a size of an input dataset, a size of a model, and a set of batch sizes corresponding to a job to be processing using a distributed computing system” then “computing, based at least in part on the set of batch sizes, one or more node counts corresponding to a number of nodes that can be used for processing said job,” and “estimating, for each given one of said node counts, an execution time to process” the job where the estimating step comprises determining an average computation time for a batch of said input dataset and an average communication time for said batch of said input dataset (Abstract). In other words, Saxena separately generates a data-size-driven value (i.e. amount of sub- calculation per calculation) and a computation/iteration-driven value (i.e. amount of data to process), then uses both together to determine the compute resource count needed.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate Saxena wherein resource determination is based on both a batch-size derived computation value and an independently obtained input-dataset-size value. One of ordinary skill would have been motivated to make this modification because Saxena teaches that accounting for both the computational workload per batch and the size of the dataset being processed allows for more accurate estimation of the number of nodes that can be used for processing said job, thereby avoiding the inefficiency. This modification would have yielded the predictable result of a resource allocation method that more precisely matches computing resources to actual workload demands, reducing computation cost and processing delay as taught by Saxena.
Regarding claim 16, it is the medium claim of claim 5 above. Therefore, it is rejected for the same reasons as claim 5 above.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 and 5 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Choi et al. (US PG Pub No. 2021/0124617 A1) teaches an edge computing method that allocates resources in a manufacturing plant based on a model predicting expected required resource amounts.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC C WAI whose telephone number is (571)270-1012. The examiner can normally be reached Monday - Friday 9-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aimee Li can be reached at (571) 272-4169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Eric C Wai/Primary Examiner, Art Unit 2195
Prosecution Insights