IP Library › Granted Patent US 12,438,826
Granted Patent B1
US 12,438,826 · App. 18/176,311 · Granted Oct 7, 2025

Multi-tier resource, subsystem, and load orchestration

Inventors: William Gregg (Nashville, TN); Annabaker Garber (Nashville, TN); Aaron Montlary (Nashville, TN); Louis Davis (Nashville, TN)
Assignee: C/HCA, Inc.
H04L47/762H04L41/22H04L43/0882H04L47/125H04L47/781
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,438,826
App. No.
18/176,311
Granted
Oct 7, 2025
Kind
B1
Abstract

Electronic communications received via a network from a plurality of electronic devices may include signals of device interactions or data changes that correspond to process performances by process-performing resources, signals of conditions of loads, or signals of processes associated with the process-performing resources and the loads. Data composites may be formed from the electronic communications, with data portions collected and mapped to resource profile records and load profile records that may be updated with the collected data portions. For each load, at least one of the one or more resource profile records and/or the one or more load profile records may be used to map the process-performing resources to the load. Content nodes may be linked in a network of content nodes, including respective linked content, resource specifications or load specifications. Access to the network of content nodes may be allowed via a control interface.

Claims (99)

1. A system comprising:

one or more non-transitory, computer-readable storage media containing instructions which, when executed on one or more processors, cause the one or more processors to perform operations comprising:

processing a set of electronic communications received via a network from a plurality of electronic devices, the set of electronic communications comprising one or more of:

signals of device interactions or data changes that correspond to process-performing resources and/or process performances by the process-performing resources; signals of conditions of loads; and/or

signals of processes associated with the loads;

forming a plurality of data composites from the set of electronic communications at least in part by:

for each electronic communication of the set of electronic communications:

processing the electronic communication to identify one or more digital identifiers mapped to one or more of the process-performing resources and/or the loads; and

extracting a data portion from the electronic communication;

collecting the data portions and mapping the collected data portions to one or more resource profile records and/or one or more load profile records that are stored in one or more data storages; and

updating the one or more resource profile records and/or the one or more load profile records with the collected data portions;

processing a subsequent set of one or more electronic communications received via the network from one or more electronic devices;

determining that the set of one or more electronic communications corresponds to a trigger event mapped to a particular load; and

using at least one of the one or more resource profile records and/or the one or more load profile records to map a subset of one or more of the process-performing resources to the particular load, where the mapping is based at least in part on proximity of the subset of one or more of the process-performing resources to a location corresponding to the particular load, wherein using includes ranking the subset of one or more of the process-performing resources according to proximity to the particular load, and wherein the ranking specifies an order for serially sending an alert to the subset of one or more of the process-performing resources until a confirmation of the alert is received,

wherein the plurality of data composites are arranged and linked according to a hierarchical tree network, wherein the hierarchical tree network includes load nodes corresponding to leaf nodes of the hierarchical tree network and resource nodes corresponding to non-leaf nodes of the hierarchical tree network.

2. The system as recited in claim 1 , wherein the trigger event corresponds to a request from a device associated with the particular load.

3. The system as recited in claim 1 , wherein the trigger event corresponds to a selection of a particular interface element of one or more interface elements of an interface associated with the particular load.

4. The system as recited in claim 1 , the operations further comprising causing one or more communications to be transmitted to one or more control interfaces, one or more controlling devices, and/or one or more client devices associated with the subset of one or more of the process-performing resources.

5. The system as recited in claim 1 , where:

the mapping is further based at least in part on one or more metrics of the subset of one or more of the process-performing resources; and

the one or more metrics of the subset of one or more of the process-performing resources are based at least in part on identifying one or more patterns of effectiveness metrics of each process-performing resource of the subset of one or more of the process-performing resources mapped to one or more loads having common specifications.

6. The system as recited in claim 5 , where the identifying the one or more patterns of effectiveness metrics is based at least in part on mapping historical resource-load pairings to load metrics, wherein the load metrics comprise one or more metrics corresponding to one or both of a load outcome and a load condition.

7. The system as recited in claim 6 , where the mapping the subset of one or more of the process-performing resources to the particular load is based at least in part on resource specifications corresponding to the subset for effective pairing to load specifications of the particular load, wherein the mapping is based at least in part on an effectiveness score for one or more of the process performing resources and a criticality level for the particular load.

8. The system as recited in claim 7 , wherein the trigger event is one of a plurality of trigger events, each of the plurality of trigger events being assigned a weight as a function of a criticality score assigned to each trigger event and one or more thresholds corresponding to one or more categories of criticality.

9. The system as recited in claim 1 , wherein the ranking of the process-performing resources according to their proximity to the particular load is used to form a hierarchically scaled tree that specifies the order for serially alerting the subset of one or more of the process-performing resources.

10. One or more non-transitory, machine-readable media storing executable instructions that, when executed by one or more processing devices, cause the one or more processing devices to perform operations comprising:

processing a set of electronic communications received via a network from a plurality of electronic devices, the set of electronic communications comprising one or more of:

signals of device interactions or data changes that correspond to process-performing resources and/or process performances by the process-performing resources;

signals of conditions of loads; and/or

signals of processes associated with the loads;

forming a plurality of data composites from the set of electronic communications at least in part by:

for each electronic communication of the set of electronic communications:

processing the electronic communication to identify one or more digital identifiers mapped to one or more of the process-performing resources and/or the loads; and

extracting a data portion from the electronic communication;

collecting the data portions and mapping the collected data portions to one or more resource profile records and/or one or more load profile records that are stored in one or more data storages; and

updating the one or more resource profile records and/or the one or more load profile records with the collected data portions;

processing a subsequent set of one or more electronic communications received via the network from one or more electronic devices;

determining that the set of one or more electronic communications corresponds to a trigger event mapped to a particular load; and

using at least one of the one or more resource profile records and/or the one or more load profile records to map a subset of one or more of the process-performing resources to the particular load, where the mapping is based at least in part on proximity of the subset of one or more of the process-performing resources to a location corresponding to the particular load, wherein the mapping the subset of one or more of the process-performing resources to the particular load is based at least in part on resource specifications corresponding to the subset for effective pairing to load specifications of the particular load, wherein the mapping is based at least in part on an effectiveness score of one or more of the process-performing resources and a criticality level of the particular load,

wherein the plurality of data composites are arranged and linked according to a hierarchical tree network, wherein the hierarchical tree network includes load nodes corresponding to leaf nodes of the hierarchical tree network and resource nodes corresponding to non-leaf nodes of the hierarchical tree network.

11. The one or more non-transitory, machine-readable media as recited in claim 10 , wherein the trigger event corresponds to a request from a device associated with the particular load.

12. The one or more non-transitory, machine-readable media as recited in claim 10 , wherein the trigger event corresponds to a selection of a particular interface element of the one or more interface elements of an interface associated with the particular load.

13. The one or more non-transitory, machine-readable media as recited in claim 10 , the operations further comprising causing one or more communications to be transmitted to one or more control interfaces, one or more controlling devices, and/or one or more client devices associated with the subset of one or more of the process-performing resources.

14. The one or more non-transitory, machine-readable media as recited in claim 10 , where:

the mapping is further based at least in part on one or more metrics of the subset of one or more of the process-performing resources; and

the one or more metrics of the subset of one or more of the process-performing resources are based at least in part on identifying one or more patterns of effectiveness metrics of each process-performing resource of the subset of one or more of the process-performing resources mapped to one or more loads having common specifications.

15. The one or more non-transitory, machine-readable media as recited in claim 14 , where the identifying the one or more patterns of effectiveness metrics is based at least in part on mapping historical resource-load pairings to load metrics, wherein the load metrics comprise one or more metrics corresponding to one or both of a load outcome and a load condition.

16. The one or more non-transitory, machine-readable media as recited in claim 10 , wherein using includes ranking the subset of one or more of the process-performing resources according to proximity to the particular load, and wherein the ranking specifies an order for serially sending an alert to the subset of one or more of the process-performing resources until a confirmation of the alert is received.

17. A method comprising:

processing a set of electronic communications received via a network from a plurality of electronic devices, the set of electronic communications comprising one or more of:

signals of device interactions or data changes that correspond to process-performing resources and/or process performances by the process-performing resources;

signals of conditions of loads; and/or

signals of processes associated with the loads;

forming a plurality of data composites from the set of electronic communications at least in part by:

for each electronic communication of the set of electronic communications:

processing the electronic communication to identify one or more digital identifiers mapped to one or more of the process-performing resources and/or the loads; and

extracting a data portion from the electronic communication;

collecting the data portions and mapping the collected data portions to one or more resource profile records and/or one or more load profile records that are stored in one or more data storages; and

updating the one or more resource profile records and/or the one or more load profile records with the collected data portions;

processing a subsequent set of one or more electronic communications received via the network from one or more electronic devices;

determining that the set of one or more electronic communications corresponds to a trigger event mapped to a particular load; and

using at least one of the one or more resource profile records and/or the one or more load profile records to map a subset of one or more of the process-performing resources to the particular load, where the mapping is based at least in part on proximity of the subset of one or more of the process-performing resources to a location corresponding to the particular load, wherein using includes ranking the subset of one or more of the process-performing resources according to proximity to the particular load, and wherein the ranking specifies an order for serially sending an alert to the subset of one or more of the process-performing resources until a confirmation of the alert is received,

wherein the plurality of data composites are arranged and linked according to a hierarchical tree network, wherein the hierarchical tree network includes load nodes corresponding to leaf nodes of the hierarchical tree network and resource nodes corresponding to non-leaf nodes of the hierarchical tree network.

18. The method as recited in claim 17 , wherein the trigger event corresponds to a request from a device associated with the particular load.

19. The method as recited in claim 17 , wherein the trigger event corresponds to a selection of a particular interface element of one or more interface elements of an interface associated with the particular load.

20. The method as recited in claim 17 , further comprising causing one or more communications to be transmitted to one or more control interfaces, one or more controlling devices, and/or one or more client devices associated with the subset of one or more of the process-performing resources.

21. The method as recited in claim 17 , where:

the mapping is further based at least in part on one or more metrics of the subset of one or more of the process-performing resources; and

the one or more metrics of the subset of one or more of the process-performing resources are based at least in part on identifying one or more patterns of effectiveness metrics of each process-performing resource of the subset of one or more of the process-performing resources mapped to one or more loads having common specifications.

22. The method as recited in claim 21 , where the identifying the one or more patterns of effectiveness metrics is based at least in part on mapping historical resource-load pairings to load metrics, wherein the load metrics comprise one or more metrics corresponding to one or both of a load outcome and a load condition.

23. A method comprising:

processing a set of electronic communications received via a network from a plurality of electronic devices, the set of electronic communications comprising one or more of:

signals of device interactions or data changes that correspond to process-performing resources and/or process performances by the process-performing resources;

signals of conditions of loads; and/or

signals of processes associated with the loads;

forming a plurality of data composites from the set of electronic communications at least in part by:

for each electronic communication of the set of electronic communications:

processing the electronic communication to identify one or more digital identifiers mapped to one or more of the process-performing resources and/or the loads; and

extracting a data portion from the electronic communication;

collecting the data portions and mapping the collected data portions to one or more resource profile records and/or one or more load profile records that are stored in one or more data storages; and

updating the one or more resource profile records and/or the one or more load profile records with the collected data portions;

processing a subsequent set of one or more electronic communications received via the network from one or more electronic devices;

determining that the set of one or more electronic communications corresponds to a trigger event mapped to a particular load; and

using at least one of the one or more resource profile records and/or the one or more load profile records to map a subset of one or more of the process-performing resources to the particular load, where the mapping is based at least in part on proximity of the subset of one or more of the process-performing resources to a location corresponding to the particular load, wherein using includes ranking the subset of one or more of the process-performing resources according to proximity to the particular load, and wherein the ranking specifies an order for serially sending an alert to the subset of one or more of the process-performing resources until a confirmation of the alert is received, wherein the trigger event is one of a plurality of trigger events, each of the plurality of trigger events being assigned a weight as a function of a criticality score assigned to each trigger event and one or more thresholds corresponding to one or more categories of criticality.

24. One or more non-transitory, machine-readable media storing executable instructions that, when executed by one or more processing devices, cause the one or more processing devices to perform operations comprising:

processing a set of electronic communications received via a network from a plurality of electronic devices, the set of electronic communications comprising one or more of:

signals of device interactions or data changes that correspond to process-performing resources and/or process performances by the process-performing resources;

signals of conditions of loads; and/or

signals of processes associated with the loads;

forming a plurality of data composites from the set of electronic communications at least in part by:

for each electronic communication of the set of electronic communications:

processing the electronic communication to identify one or more digital identifiers mapped to one or more of the process-performing resources and/or the loads; and

extracting a data portion from the electronic communication;

collecting the data portions and mapping the collected data portions to one or more resource profile records and/or one or more load profile records that are stored in one or more data storages; and

updating the one or more resource profile records and/or the one or more load profile records with the collected data portions:

processing a subsequent set of one or more electronic communications received via the network from one or more electronic devices;

determining that the set of one or more electronic communications corresponds to a trigger event mapped to a particular load; and

using at least one of the one or more resource profile records and/or the one or more load profile records to map a subset of one or more of the process-performing resources to the particular load, where the mapping is based at least in part on proximity of the subset of one or more of the process-performing resources to a location corresponding to the particular load, wherein the mapping the subset of one or more of the process-performing resources to the particular load is based at least in part on resource specifications corresponding to the subset for effective pairing to load specifications of the particular load, wherein the mapping is based at least in part on an effectiveness score of one or more of the process-performing resources and a criticality level of the particular load, wherein the trigger event is one of a plurality of trigger events, each of the plurality of trigger events being assigned a weight as a function of a criticality score assigned to each trigger event and one or more thresholds corresponding to one or more categories of criticality.

25. The method as recited in claim 17 , where the mapping the subset of one or more of the process-performing resources to the particular load is based at least in part on resource specifications corresponding to the subset for effective pairing to load specifications of the particular load, wherein the mapping is based at least in part on an effectiveness score for one or more of the process performing resources and a criticality score for the particular load.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 31, 2024
From: GREGG, WILLIAM; GARBER, ANNABAKER; MONTLARY, AARON; DAVIS, LOUIS
To: C/HCA, INC.
Reel/Frame 069091/0173 →
Continuity (2)
Continuation 17364502 · Jun 30, 2021
Provisional Application 63047022 · Jul 1, 2020
References Cited (91)
US 6063028A · Luciano · 2000 [cited by applicant]
US 6117073A · Jones et al. · 2000 [cited by applicant]
US 7174381B2 · Gulko et al. · 2007 [cited by applicant]
US 7707289B1 · Skene et al. · 2010 [cited by applicant]
US 7734479B2 · Rosow et al. · 2010 [cited by applicant]
US 7743303B2 · Nobunaga et al. · 2010 [cited by applicant]
US 7890349B2 · Cole et al. · 2011 [cited by applicant]
US 8949082B2 · Farooq et al. · 2015 [cited by applicant]
US 9754335B2 · Jourdan et al. · 2017 [cited by applicant]
US 9805163B1 · Panch et al. · 2017 [cited by applicant]
US 9880881B1 · Perez · 2018 [cited by examiner]
US 10231085B1 · Kumar et al. · 2019 [cited by applicant]
US 10303519B1 · Perez · 2019 [cited by examiner]
US 10319056B1 · Perez et al. · 2019 [cited by applicant]
US 10652164B2 · Garcia · 2020 [cited by examiner]
US 10817342B1 · Perez · 2020 [cited by examiner]
US 10847261B1 · Neumann · 2020 [cited by applicant]
US 11201835B1 · Roberts · 2021 [cited by examiner]
US 11283690B1 · Mosier · 2022 [cited by examiner]
US 11283726B1 · Houston · 2022 [cited by examiner]
US 11317292B1 · Feldmann et al. · 2022 [cited by applicant]
US 11381506B1 · Jindal et al. · 2022 [cited by applicant]
US 11422830B1 · Hefley · 2022 [cited by examiner]
US 11595320B1 · Gregg · 2023 [cited by examiner]
US 11836664B2 · Ackerman et al. · 2023 [cited by applicant]
US 12003426B1 · Gregg · 2024 [cited by examiner]
US 20020178246A1 · Mayer · 2002 [cited by applicant]
US 20030229645A1 · Mogi et al. · 2003 [cited by applicant]
US 20040122702A1 · Sabol et al. · 2004 [cited by applicant]
US 20050075902A1 · Wager et al. · 2005 [cited by applicant]
US 20060112247A1 · Ramany et al. · 2006 [cited by applicant]
US 20070005257A1 · Cheng et al. · 2007 [cited by applicant]
US 20080154691A1 · Wellman et al. · 2008 [cited by applicant]
US 20080154712A1 · Wellman · 2008 [cited by applicant]
US 20080155386A1 · Jensen · 2008 [cited by applicant]
US 20080164998A1 · Scherpbier et al. · 2008 [cited by applicant]
US 20100036903A1 · Ahmad et al. · 2010 [cited by applicant]
US 20100250285A1 · Shelton · 2010 [cited by applicant]
US 20100332583A1 · Szabo · 2010 [cited by applicant]
US 20110199214A1 · Gawlick · 2011 [cited by applicant]
US 20110238949A1 · Archer · 2011 [cited by examiner]
US 20110295621A1 · Farooq et al. · 2011 [cited by applicant]
US 20110313788A1 · Amland et al. · 2011 [cited by applicant]
US 20120065987A1 · Farooq et al. · 2012 [cited by applicant]
US 20120303827A1 · Neystadt et al. · 2012 [cited by applicant]
US 20130144641A1 · Bessette · 2013 [cited by applicant]
US 20130191158A1 · Fillmore · 2013 [cited by applicant]
US 20140108033A1 · Akbay et al. · 2014 [cited by applicant]
US 20140108034A1 · Akbay et al. · 2014 [cited by applicant]
US 20140172459A1 · De Vries et al. · 2014 [cited by applicant]
US 20140180711A1 · Kamen et al. · 2014 [cited by applicant]
US 20140297301A1 · Rock · 2014 [cited by applicant]
US 20140350967A1 · Geleijnse et al. · 2014 [cited by applicant]
US 20150238270A1 · Raffy et al. · 2015 [cited by applicant]
US 20160013990A1 · Kulkarni et al. · 2016 [cited by applicant]
US 20160026762A1 · Radhakrishnan et al. · 2016 [cited by applicant]
US 20160042135A1 · Hogan et al. · 2016 [cited by applicant]
US 20160094410A1 · Anwar et al. · 2016 [cited by applicant]
US 20160125168A1 · Aagesen et al. · 2016 [cited by applicant]
US 20160125680A1 · White et al. · 2016 [cited by applicant]
US 20160277255A1 · Dasgupta et al. · 2016 [cited by applicant]
US 20170019264A1 · Nugent et al. · 2017 [cited by applicant]
US 20170054758A1 · Maino et al. · 2017 [cited by applicant]
US 20170063826A1 · Sundaresan et al. · 2017 [cited by applicant]
US 20170083626A1 · Kensel et al. · 2017 [cited by applicant]
US 20170169173A1 · Snow, Jr. et al. · 2017 [cited by applicant]
US 20170308648A1 · Clarke et al. · 2017 [cited by applicant]
US 20170310605A1 · Garcia · 2017 [cited by examiner]
US 20170364043A1 · Ganti et al. · 2017 [cited by applicant]
US 20170366558A1 · Liu · 2017 [cited by applicant]
US 20180075108A1 · Park et al. · 2018 [cited by applicant]
US 20180182471A1 · Yelton et al. · 2018 [cited by applicant]
US 20180225311A1 · Bandopadhyay et al. · 2018 [cited by applicant]
US 20180314802A1 · Dreyer · 2018 [cited by applicant]
US 20180330115A1 · Felton · 2018 [cited by applicant]
US 20190133445A1 · Eteminan et al. · 2019 [cited by applicant]
US 20190180868A1 · Makram et al. · 2019 [cited by applicant]
US 20190231280A1 · Tudor · 2019 [cited by applicant]
US 20190318026A1 · Joseph et al. · 2019 [cited by applicant]
US 20190378619A1 · Meyer et al. · 2019 [cited by applicant]
US 20200013490A1 · Rumoro · 2020 [cited by applicant]
US 20200296053A1 · Garcia · 2020 [cited by examiner]
US 20200320178A1 · Gallagher · 2020 [cited by applicant]
US 20200382617A1 · Trapane · 2020 [cited by examiner]
US 20210105518A1 · Kannan et al. · 2021 [cited by applicant]
US 20210168455A1 · Campbell et al. · 2021 [cited by applicant]
US 20210224634A1 · Chen et al. · 2021 [cited by applicant]
US 20220175325A1 · Fukushima et al. · 2022 [cited by applicant]
US 20220385581A1 · Delos Reyes et al. · 2022 [cited by applicant]
WO 2021087317A1 · 2021 [cited by applicant]
Gulshan, Varun, et al. “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.” Jama 316.22 (2016): 2402-2410. (Year: 2016). [cited by applicant]