System and method for automated multi-dimensional network management

• US 10,346,454 B2
• Filed: 04/17/2018
• Issued: 07/09/2019
• Est. Priority Date: 04/17/2017
• Status: Active Grant

• Current Assignee: Mammoth Medical, LLC
• Original Assignee: Mammoth Medical, LLC
• Inventors: Moeller-Bertram, Tobias, McDonald, Christopher A.
• Primary Examiner(s): Jacobs, Lashonda T
• Application Number:
  US15/955,540
• Publication Number:
  US 20180302300A1
• Time in Patent Office: 448 Days
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/022 by applying pressure to close blood vessels, e.g. against the skin; Ophthalmodynamometers
• CPC Class Codes: A61B 5/4848 Monitoring or testing the effects of treatment, e.g. of medication
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: G06F 15/76 Architectures of general purpose stored program computers with program plugboard G06F15/08; multicomputers G06F15/16
• CPC Class Codes: G06F 16/337 Profile generation, learning or modification
• CPC Class Codes: G06F 18/2113 by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation
• CPC Class Codes: G06F 18/213 Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
• CPC Class Codes: G06F 9/30036 Instructions to perform operations on packed data, e.g. vector, tile or matrix operations
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: H04L 41/16 using machine learning or artificial intelligence
• CPC Class Codes: H04L 41/5054 Automatic deployment of services triggered by the service manager, e.g. service implementation by automatic configuration of network components
• CPC Class Codes: H04L 63/1433 Vulnerability analysis
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/52 specially adapted for the location of the user terminal
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

Systems, methods, and devices for automated provisioning are disclosed herein. The system can include a memory including a user profile database having n-dimension attributes of a user. The system can include a user device and a source device. The system can include a server that can: generate and store a user profile in the user profile database and generate and store a characterization vector from the user profile. The server can identify a service for provisioning, receive updates to at least some of the attributes of the first user, and trigger regeneration of the characterization vector from the received inputs. The server can: regenerate the characterization vector, determine an efficacy of the provisioned services, and automatically identify a second service for provisioning for a second user based on the efficacy of the provisioned services to the first user.

Claims

1. An automated multi-dimensional network management system comprising:

a memory comprising;

a user profile database comprising user profiles in the form of electronic health records (EHR), each comprising n-dimension attributes of a user; and

a multi-dimensional network comprising a plurality of networks linked by a plurality of edges, the multi-dimensional network comprising a first network comprising a first plurality of nodes linked by a first plurality of edges, each of the first plurality of nodes corresponding to a user state, a second network comprising a second plurality of nodes linked by a second plurality of edges, each of the second plurality of nodes corresponding to a user characteristic, and a third network comprising a third plurality of nodes linked by a third plurality of edges, each of the third plurality of nodes corresponding to a medical remediation;

a user device configured to receive input from a first user and transmit the received input;

a source device configured to receive source inputs and transmit the received source inputs; and

a server configured to;

receive user inputs associated with the first user from the user device and source inputs associated with the first user from the source device;

generate and store an EHR for the first user in the user profile database, wherein the EHR identifies n-dimension attributes of the first user;

determine a current state of the first user based on the EHR;

generate a risk profile according to the current state of the first user, wherein the risk profile identifies a likelihood of an adverse outcome within a time frame;

identify a remediation via an artificial intelligence (AI) machine-learning model to mitigate the likelihood of the adverse outcome, wherein the remediation is identified based on the EHR and the current state of the first user;

determine a data insufficiency based on missing data corresponding to attributes in the EHR;

identify the data insufficiency, wherein the data insufficiency prevents at least one of;

complete determination of the current state of the first user;

complete generation of the risk profile;

or complete identification of the remediation;

select a medical service from a plurality of potential medical services for provisioning to the first user, wherein the medical service is identified to result in generation of data to remedy the data insufficiency, the medical service comprising a digital component and a non-digital component;

provision the selected medical service corresponding to the missing data to thereby resolve the data insufficiency;

receive electronic data generated from the provisioned medical service;

automatically update the EHR based on the received electronic data;

automatically determine resolution of the data insufficiency based on the updated EHR;

upon determining resolution of the data insufficiency, complete the at least one of;

determination of the current state of the first user;

generation of the risk profile;

or identification of the remediation; and

provide a remediation to the first user.

2. The system of claim 1, wherein the server is further configured to automatically deliver the digital component of the medical service for provisioning.

3. The system of claim 2, wherein the digital component of the medical service for provisioning is automatically delivered subsequent to automated determination of fulfillment of at least one delivery criteria.

4. The system of claim 3, wherein the server is further configured to automatically schedule provising of the medical service.

5. The system of claim 4, wherein scheduling the provisioning of the medical service comprises:

retrieving user location information from the user device;

determining a classification of the medical service for provisioning;

identifying potential medical service locations based on a combination of location of the potential medical service locations, user location information, and medical service location attributes.

6. The system of claim 5, wherein the user location information retrieved from the user device comprises current location information and location history information.

7. The system of claim 6, wherein the location history information identifies historic trends in user location.

8. The system of claim 5, wherein the medical service location attributes identify medical service types provided at the medical service location.

9. The system of claim 5, wherein the server is further configured to:

receive inputs indicating updates to at least some of the n-dimension attributes of the first user, wherein the updates are received at least from the source device;

trigger updating of the EHR based on the received inputs; and

automatically identify a second medical service for provisioning to the first user, wherein the second medical service is identified based on the updated EHR.

10. The system of claim 9, wherein the second medical service comprises a plurality of timed and automatically triggered reminders directing the first user to complete an action.

11. A method of multi-dimensional network management, the method comprising:

receiving a user input associated with a first user from a first user device and source inputs associated with the first user from at least one source device;

generating and storing an electronic health record (EHR) for the first user in a user profile database comprising user profiles in the form of EHRs, each EHR comprising n-dimension attributes of a user;

determining a current state of the first user based on the EHR;

generating a risk profile according to the current state of the first user, wherein the risk profile identifies a likelihood of an adverse medical outcome occurring within a time frame;

identifying a remediation via an artificial intelligence (AI) machine-learning model to mitigate the likelihood of the adverse medical outcome, wherein the remediation is identified based on the EHR and the current state of the first user, wherein the remediation is identified from a multi-dimensional network comprising a plurality of networks linked by a plurality of edges, the multi-dimensional network comprising a first network comprising a first plurality of nodes linked by a first plurality of edges, each of the first plurality of nodes corresponding to a user state, a second network comprising a second plurality of nodes linked by a second plurality of edges, each of the second plurality of nodes corresponding to a user characteristic, and a third network comprising a third plurality of nodes linked by a third plurality of edges, each of the third plurality of nodes corresponding to a medical remediation;

determine a data insufficiency based on missing data corresponding to attributes in the EHR;

identifying the data insufficiency, wherein the data insufficiency prevents at least one of;

complete determination of the current state of the first user;

complete generation of the risk profile;

or complete identification of the remediation;

selecting a medical service from a plurality of potential medical services for provisioning to the first user, wherein the medical service is identified to result in generation of data to remedy the data insufficiency, the medical service comprising a digital component and a non-digital component;

provisioning the selected medical service corresponding to the missing data to thereby resolve the data insufficiency;

receiving electronic data generated from the provisioned medical service;

automatically updating the EHR based on the received electronic data;

automatically determining resolution of the data insufficiency based on the updated EHR;

upon determining resolution of the data insufficiency, completing the at least one of;

determination of the current state of the first user;

generation of the risk profile;

or identification of the remediation; and

providing a remediation to the first user via the first user device.

12. The method of claim 11, further comprising automatically delivering the digital component of the medical service for provisioning.

13. The method of claim 12, wherein the digital component of the medical service for provisioning is automatically delivered subsequent to automated determination of fulfillment of at least one delivery criteria.

14. The method of claim 13, further comprising automatically scheduling provising of the medical service.

15. The method of claim 14, wherein scheduling the provisioning of the medical service comprises:

retrieving user location information from the user device;

determining a classification of the medical service for provisioning;

identifying potential medical service locations based on a combination of location of the potential medical service locations, user location information, and medical service location attributes.

16. The method of claim 15, wherein the user location information retrieved from the user device comprises current location information and location history information.

17. The method of claim 16, wherein the location history information identifies historic trends in user location.

18. The method of claim 15, wherein the service location attributes identify medical service types provided at the medical service location.

19. The method of claim 15, further comprising:

receiving inputs indicating updates to at least some of the n-dimension attributes of the first user, wherein the updates are received at least from the source device;

triggering updating of the EHR based on the received inputs; and

automatically identifying a second medical service for provisioning to the first user, wherein the second medical service is identified based on the updated EHR.

20. The method of claim 19, wherein the second medical service comprises a plurality of timed and automatically triggered reminders directing the first user to complete an action.