Prosecution Insights
Last updated: April 19, 2026
Application No. 19/208,724

METHODS AND SYSTEMS FOR DYNAMIC COMPUTING RESOURCE ALLOCATION BASED ON IIOT DATA CENTER

Non-Final OA §103
Filed
May 15, 2025
Examiner
TRAN, ALEX HOANG
Art Unit
2453
Tech Center
2400 — Computer Networks
Assignee
Chengdu Qinchuan Iot Technology Co. Ltd.
OA Round
1 (Non-Final)
62%
Grant Probability
Moderate
1-2
OA Rounds
2y 10m
To Grant
92%
With Interview

Examiner Intelligence

Grants 62% of resolved cases
62%
Career Allow Rate
107 granted / 172 resolved
+4.2% vs TC avg
Strong +30% interview lift
Without
With
+29.9%
Interview Lift
resolved cases with interview
Typical timeline
2y 10m
Avg Prosecution
18 currently pending
Career history
190
Total Applications
across all art units

Statute-Specific Performance

§101
5.8%
-34.2% vs TC avg
§103
71.8%
+31.8% vs TC avg
§102
8.7%
-31.3% vs TC avg
§112
4.7%
-35.3% vs TC avg
Black line = Tech Center average estimate • Based on career data from 172 resolved cases

Office Action

§103
DETAILED ACTION This action is responsive to communications filed 15 May 2025. A Preliminary Amendment has been filed on 16 October 2025. Claims 4, 7, 11 and 14-15 have been canceled. Claims 1-3, 5-6, 8-10 and 12-13 are subject to examination. Note: This application is granted on a request to participate in the Patent Prosecution Highway (PPH) program and a petition under 37 CFR 1.102(a) filed October 16 2025. Claims 1-3, 5-6, 8-10 and 12-13 pertain to and sufficiently correspond to CN/OEE Application Claims 1-3, 4-5, 6-8 and 9-10. 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 . Priority Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Information Disclosure Statement The information disclosure statement (IDS) submitted on 25 July 2025 and 16 October 2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Interpretation The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: "IIot user platform being configured to receive ... monitoring module being configured to monitor ... control center being configured to ... determine/determine/generate ... resource allocation instruction being configured to ... create/bind" in claim 1, “production monitoring device being configured to obtain … control center is further configured to … determine” in claim 2, “control center is further configured to determine” in claim 3, “control center is further configured to … determine/determine/determine … resource allocation instruction is configured to … obtain/determine/adjust” in claim 5, “control center is further configured to … determine/determine/determine” in claim 6, “the resource allocation instruction being configured to … create/bind” in claim 7 and “the resource allocation instruction is configured to … obtain” in claim 12. Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Upon examination of the specification, it denotes: [0019] “IIoT user platform 110 is a platform for interacting with an enterprise user” [0027] “monitoring module includes a server monitoring hardware, a resource management software … or the like” [0028] “control center is a component for controlling a target terminal to perform computing and processing of data … includes a server-dependent computing device” [0072] “resource allocation instruction is a policy and mechanism by which resource is allocated” [0139] “implementation of various components described above may be embodied in a hardware device, it may also be implemented as a software-only solution” Such that, any component that is embodied in a hardware or software to perform the functionalities thereof is considered as the various components of the platform(s), module(s), center(s) and instruction(s). 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 and 8-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Amaro, Jr. et al. (US-12210329-B2) hereinafter Amaro in view of Siebel et al. (US-11954112-B2) hereinafter Siebel further in view of Mukundan et al. (US-20240069969-A1) hereinafter Mukundan. Regarding claim 1, Amaro discloses: A system for dynamic computing resource allocation based on an Industrial Internet of Things (IIoT) data center ([4:56-5:18] allocate, assign, re-allocate, re-assign, load-balance, etc. the SD application layer components (i.e. resource) to and among various nodes [15:31-42] software defined control system (SDCS) … included in the industrial process plant (i.e. industrial internet of things) [16:4-23] interchangeably refers to the computing platform 208 of the SDCS 200 as “data center clusters,” “computing platform nodes,” or “nodes”, see [68:3-30] other IOT protocols), wherein the system comprises an IIoT user platform ([8:8-20] user requests authorization to access a service … authorization service may determine (i.e. IIoT user platform) {note: specification defines the IIoT user platform in [0019] as “a platform for interacting with an enterprise user”}), an IIoT service platform ([68:40-60] certificate generation service (i.e. IIoT service platform) 1604 may generate the digital certificate for a physical or logical asset for example, upon receiving a request from the physical or logical asset {note: specification defines the IIoT service platform in [0022] as “a platform for processing a received business demand”}), an IIoT management platform ([87:52-88:13] system orchestrator 222 controls or manages the allocation of the various logical entities, see [16:54-17:32] SDCS Hyper Converged Infrastructure (HCI) operating system (i.e. IIoT management platform) … include … SD Orchestrator service 222 (i.e. IIoT management platform) {note: specification defines the IIoT management platform in [0024] as “a platform for managing and controlling the system”}), an IIoT sensing network platform ([60:63-61:9] networking service 220 may administer and manage the logical or virtual networking utilized by the logical process control system (i.e. IIoT sensing network platform) {note: specification defines the IIoT sensing network platform in [0035] as “a network for data transmission”}), and an IIoT sensing control platform ([31:42-32:10] SD HCI OS 210 may monitor performance parameters, resource usage, and/or criteria during run-time (i.e. IIoT sensing control platform) {note: specification defines the IIoT sensing control platform in [0037] as “a platform used for monitoring and controlling the execution of production processes”}); the IIoT user platform being configured to receive a business demand from an enterprise user ([8:8-20] user (i.e. enterprise user, e.g. of industrial process plant set forth above) requests authorization to access a service (i.e. business demand, e.g. accessing a service) … authorization service may determine (i.e. IIoT user platform, e.g. receiving user request to determine)) and the IIoT management platform comprising the data computing center ([16:54-17:32] SDCS HCI … executes on the computing platform 208 (i.e. data computing center)), a business management sub-platform ([16:54-17:32] SD Orchestrator service 222, see [87:52-88:13] system orchestrator 222 controls or manages (i.e. business management sub-platform, by controlling or managing) the allocation of the various logical entities {note: specification defines the business management sub-platform in [0027] as “a hardware device used to perform a computing task in the system”}), and a control center ([16:54-17:32] SD Orchestrator service 222, see [87:52-88:13] system orchestrator 222 controls or manages (i.e. business management sub-platform, by controlling or managing) the allocation of the various logical entities {note: specification defines business management sub-platform in [0027] as “a hardware device used to perform a computing task in the system”}, see also [37:59-38:6] orchestrator 222 has instantiated the four logical functions (i.e. control center) {note: specification defines the control center in [0028] as “a component for controlling a target computing terminal to perform computing and processing of data”}), the business management sub-platform comprising a monitoring module ([37:32-58] orchestrator 222 may monitor the QoS metrics of the various containers instantiated (i.e. comprising a monitoring module so as to monitor) {note: specification defines the monitoring module in [0047] where “resource data of the business management sub-platform is monitored by a monitoring module”}), the monitoring module being configured to monitor resource data of the business management sub-platform ([37:32-58] orchestrator 222 may monitor the QoS metrics of the various containers instantiated); Amaro does not explicitly disclose: send the business demand to a data computing center of the IIoT management platform via the IIoT service platform; the control center being configured to: determine a resource demand feature of the enterprise user based on the business demand; and determine a resource allocation parameter based on the resource demand feature and the resource data of the business management sub-platform, the resource allocation parameter including computing resources corresponding to a computing task; and the control center being further configured to generate a resource allocation instruction based on the resource allocation parameter, the resource allocation instruction being configured to: create a process corresponding to the computing task; and bind the process corresponding to the computing task to a resource core corresponding to the computing task, the resource core being determined based on the resource allocation parameter. However, Siebel discloses: send the business demand to a data computing center of the IIoT management platform via the IIoT service platform ([64:11-32] input requests can be naturally parallelized and distributed to a set of worker nodes for execution [109:9-67] actions 3018 may represent the tasks that are to be performed in response to requests that are provided to the enterprise Internet-of-Things application development platform (i.e. via the IIoT service platform to the data computing center of the IIoT management platform) … provided to the action queue … routed to an appropriate computing resource and then performed); the control center ([109:9-67] actions 3018 may represent the tasks that are to be performed in response to requests that are provided to the enterprise Internet-of-Things application development platform (i.e. via the IIoT service platform to the data computing center of the IIoT management platform) … provided to the action queue … routed to an appropriate computing resource and then performed) being configured to: determine a resource demand feature of the enterprise user based on the business demand ([108:61-109:67] automatically and dynamically scale a network of computing resources for the enterprise Internet-of-Things application development platform 3002 according to demand on the enterprise Internet-of-Things application development platform 3002 … actions 3018 may represent the tasks that are to be performed in response to requests that are provided to the enterprise Internet-of-Things application development platform (i.e. business demand) … provided to the action queue … distributed task queue and represents work that is to be routed to an appropriate computing resource and then performed (i.e. resource demand feature, e.g. appropriate resource is a resource demand)); and determine a resource allocation parameter based on the resource demand feature and the resource data of the business management sub-platform ([108:61-109:67] control routing of each queued action to a particular one (i.e. resource allocation parameter, e.g. which particular resource to be used is the allocation parameter) based on load balancing and other optimization considerations … provision new nodes when the current computing resources are at or below a threshold capacity (i.e. resource allocation parameter, e.g. adding resources based on capacity)), the resource allocation parameter including computing resources corresponding to a computing task ([108:61-109:67] each queued action to a particular one (e.g. particular resource, i.e. resources corresponding to a computing task)); and the control center being further configured to generate a resource allocation instruction based on the resource allocation parameter ([108:61-109:67] associate and hand-off a queued action to an engine that will execute the action (i.e. generating a resource allocation instruction, e.g. by handing off actions to an engine to execute actions; as set forth above to which particular resource, such as based on load balancing or other optimization considerations)), the resource allocation instruction being configured to: create a process corresponding to the computing task ([88:4-28] workflow tool … build applications that coordinate work across distributed components … create tasks that are long running, or that may fail, time out, or require restarts … stores tasks and assigns them to workers when they are ready (i.e. processes to tasks must be created to have the action and work be executed on resources)); and It would have been obvious to one of ordinary skill in the pertinent art before the effective filing date of the claimed invention to modify the invention of Amaro in view of Siebel to have sent the business demand to a data computing center of the IIoT management platform via the IIoT service platform, and the control center configured to determine a resource demand feature of the enterprise user based on the business demand, determine a resource allocation parameter based on the resource demand feature and the resource data of the business management sub-platform including computing resources corresponding to a computing task, and generate a resource allocation instruction based on the resource allocation parameter that is configured to create a process corresponding to the computing task. One of ordinary skill in the art would have been motivated to do so to automatically and dynamically scale a network of computing resources for the enterprise Internet-of-Things application development platform according to demand (Siebel, [108:61-109:67]). Amaro-Siebel do not explicitly disclose: bind the process corresponding to the computing task to a resource core corresponding to the computing task, the resource core being determined based on the resource allocation parameter. However, Mukundan discloses: bind the process corresponding to the computing task to a resource core corresponding to the computing task ([0334] distribute commands or other work items to a processing array … tasks are distributed to processing array, see [0076] process may be bound to CPUs that are not shared with other processes [0074] binds job processes associated to CPU cores), the resource core being determined based on the resource allocation parameter ([0334] distribute commands or other work items to a processing array … tasks are distributed to processing array, see [0083] binding option may indicate preferences associated with isolation of GPUs … isolation of CPUs). It would have been obvious to one of ordinary skill in the pertinent art before the effective filing date of the claimed invention to modify the invention of Amaro-Siebel in view of Mukundan to have bound the process corresponding to the computing task to a resource core corresponding to the computing task which is determined based on the resource allocation parameter. One of ordinary skill in the art would have been motivated to do so to ensure that a process may be bound to CPUs that are not shared with other processes (Mukundan, [0076]). Regarding claim 2, Amaro-Siebel-Mukundan disclose: The system according to claim 1, set forth above, Amaro discloses: wherein the IIoT sensing control platform further includes a production monitoring device ([31:42-32:10] SD HCI OS 210 may monitor performance parameters, resource usage, and/or criteria during run-time (i.e. IIoT sensing control platform monitors, during run-time is equated to during production, to monitor) {note: specification defines the IIoT sensing control platform in [0037] as “a platform used for monitoring and controlling the execution of production processes”}), the production monitoring device being deployed in the enterprise user ([91:52-92:15] visualization service may create the hierarchy so that the hierarchy indicates … the temporarily configured (e.g. user assignable, i.e. in the enterprise user) relationships between physical and logical elements …), the production monitoring device being configured to obtain production status data of the enterprise user ([88:14-30] user … able to view the current operational status of one or more logical elements of the control system in order to view and/or diagnose the current operational status of the process control system); wherein Amaro does not explicitly disclose: the resource demand feature further includes a peak feature; and the control center is further configured to: determine the resource demand feature of the enterprise user based on the production status data and the business demand of the enterprise user. However, Siebel discloses: the resource demand feature further includes a peak feature ([46:26-36] metered electric peak demand); and the control center is further configured to: determine the resource demand feature of the enterprise user based on the production status data and the business demand of the enterprise user ([108:61-109:67] automatically and dynamically scale a network of computing resources for the enterprise Internet-of-Things application development platform 3002 according to demand on the enterprise Internet-of-Things application development platform 3002 … actions 3018 may represent the tasks that are to be performed in response to requests that are provided to the enterprise Internet-of-Things application development platform (i.e. business demand) … provided to the action queue … distributed task queue and represents work that is to be routed to an appropriate computing resource and then performed (i.e. resource demand feature, e.g. appropriate resource is a resource demand), see also [69:14-22] business requirement will be to have availability of real time energy consumption for all consumers … acquire energy load profile and device status data every fifteen minutes from all meter installed on the field (i.e. demand feature of resources based on energy load profile and device status data, such as metered electric peak demand in [46:26-36])). It would have been obvious to one of ordinary skill in the pertinent art before the effective filing date of the claimed invention to modify the invention of Amaro in view of Siebel to have the resource demand feature include a peak feature and the control center to determine the resource demand feature based on the production status and the business demand of the enterprise user. One of ordinary skill in the art would have been motivated to do so to automatically and dynamically scale a network of computing resources for the enterprise Internet-of-Things application development platform according to demand (Siebel, [108:61-109:67]). Regarding claim 3, Amaro-Siebel-Mukundan disclose: The system according to claim 2, wherein the control center, set forth above, is further configured to Amaro does not explicitly disclose: determine, based on the production status data of the enterprise user and the business demand, the resource demand feature of the enterprise user using a demand estimation model, the demand estimation model being a machine learning model. However, Siebel discloses: determine, based on the production status data of the enterprise user and the business demand ([108:61-109:67] automatically and dynamically scale a network of computing resources for the enterprise Internet-of-Things application development platform 3002 according to demand on the enterprise Internet-of-Things application development platform 3002 … actions 3018 may represent the tasks that are to be performed in response to requests that are provided to the enterprise Internet-of-Things application development platform (i.e. business demand) … provided to the action queue … distributed task queue and represents work that is to be routed to an appropriate computing resource and then performed (i.e. resource demand feature, e.g. appropriate resource is a resource demand), see also [69:14-22] business requirement will be to have availability of real time energy consumption for all consumers … acquire energy load profile and device status data every fifteen minutes from all meter installed on the field (i.e. demand feature of resources based on energy load profile and device status data, such as metered electric peak demand in [46:26-36])), the resource demand feature of the enterprise user using a demand estimation model ([61:49-62:3] if there is data missing, estimation may be used to fill in the missing fields … interpolation, an average of historical data, or the like may be used to fill in missing data … processing nodes may perform machine learning, see [83:55-84:18] machine learning models), the demand estimation model being a machine learning model ([61:49-62:3] perform machine learning, see [83:55-84:18] machine learning models). It would have been obvious to one of ordinary skill in the pertinent art before the effective filing date of the claimed invention to modify the invention of Amaro in view of Siebel to have determine, based on the production status data of the enterprise user and the business demand, the resource demand feature using a demand estimation model. One of ordinary skill in the art would have been motivated to do so to automatically and dynamically scale a network of computing resources for the enterprise Internet-of-Things application development platform according to demand (Siebel, [108:61-109:67]). Regarding claims 8-10, they do not further define nor teach over the limitations of claims 1-3, therefore, claims 8-10 are rejected for at least the same reasons set forth above as in claims 1-3. Allowable Subject Matter Claims 5-6 and 12-13 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Cella et al. (WO-2025160415-A1) SYSTEMS, METHODS, DEVICES, AND PLATFORMS FOR INDUSTRIAL INTERNET OF THINGS; Liang et al. (WO-2024187479-A1) COMPUTING POWER RESOURCE SCHEDULING METHOD AND APPARATUS; Sun et al. (WO-2024125251-A1) RESOURCE ALLOCATION METHOD AND APPARATUS; Wang et al. (CN-117149441-B) A Task Scheduling Optimization Method Applied To IoT; Xia et al. (CN-118760505-A) Task Processing Method And Device. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Alex Tran whose telephone number is (571)272-8173. The examiner can normally be reached Monday-Friday 10AM-6PM ET. 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, Kamal Divecha can be reached at (571)272-5863. 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. /Alex Tran/Primary Examiner, Art Unit 2453
Read full office action

Prosecution Timeline

May 15, 2025
Application Filed
Jan 21, 2026
Non-Final Rejection — §103 (current)

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Prosecution Projections

1-2
Expected OA Rounds
62%
Grant Probability
92%
With Interview (+29.9%)
2y 10m
Median Time to Grant
Low
PTA Risk
Based on 172 resolved cases by this examiner. Grant probability derived from career allow rate.

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