PREDICTING STORAGE NEED IN A DISTRIBUTED NETWORK

• US 20200134450A1
• Filed: 09/23/2019
• Published: 04/30/2020
• Est. Priority Date: 03/26/2017
• Status: Active Application

• Current Assignee: ShopFulfill IP LLC
• Original Assignee: ShopFulfill IP LLC
• Inventors: Chopp, Shlomo
• Application Number:
  US16/579,516
• Publication Number:
  US 20200134450A1
• Time in Patent Office: Days
• CPC Class Codes: G06N 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/044 Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: H04L 67/1097 for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04L 69/22 Parsing or analysis of headers

Abstract

Systems and methods are disclosed for predicting storage need for an acquisition system within a distributed data storage network. An example method may include receiving, from a distributed storage network, node data associated with multiple nodes on the distributed storage network; receiving, from a first node in the distributed storage network, first user data associated with one or more acquisitions at a non-mobile platform; receiving, from a second node in the distributed storage network, second user data associated with one or more acquisitions at a mobile platform; determining, using the node data, first user data, and second user data, an estimated future storage need for each of the multiple nodes; generating a data transition scheme based on the estimated future storage needs; and implementing the data transition scheme into the distributed storage network.

Claims

1. A system, comprising:

one or more data processors; and

a non transitory computer readable storage medium containing instructions which when executed on the one or more data processors, cause the one or more processors to perform operations including;

receiving, from a distributed storage network, node data associated with multiple nodes on the distributed storage network, wherein the node data includes data corresponding to characteristics of the multiple nodes and users that interact with the multiple nodes;

receiving, from a first node in the distributed storage network, first user data associated with one or more acquisitions at a non-mobile platform, wherein the first user data includes data corresponding to the acquisition at the non-mobile platform and an associated physical action taken by a first user;

receiving, from a second node in the distributed storage network, second user data associated with one or more acquisitions at a mobile platform, wherein the second user data includes data corresponding to the acquisition at the mobile platform and an associated action taken by a second user interacting with a mobile device;

determining, using the node data, first user data, and second user data, an estimated future storage need for each of the multiple nodes;

generating a data transition scheme based on the estimated future storage needs; and

implementing the data transition scheme into the distributed storage network, wherein implementing the data transition scheme includes re-routing one or more item deliveries from the first node to the second node.

2. The system of claim 1, wherein determining the estimated future storage need for each of the multiple nodes includes training and implementing an artificial neural network, wherein the node data, first user data, and second user data are inputs to the artificial neural network after the artificial neural network is trained.

3. The system of claim 2, further comprising operations including:

accessing a set of parameters for the artificial neural network, wherein the set of parameters includes weights associated with artificial neurons within the artificial neural network, and wherein the parameters are associated with the characteristics.

4. The system of claim 1, wherein the first user data and second user data are parsed into mobile-specific data, non-mobile-specific data, and hybrid data, and wherein data within the first user data and the second user data are tagged according to the parsing.

5. The system of claim 4, wherein the parsed mobile-specific data, non-mobile-specific data, and hybrid data are weighted based on the tagging, and wherein the weighted mobile-specific data, non-mobile-specific data, and hybrid data are analyzed to determine the data transition scheme.

6. The system of claim 1, wherein the data transition scheme includes amounts of data for transition, timing of transition, and method of transport for transition for each transition of data.

7. The system of claim 1, wherein the estimated future storage need for each of the multiple nodes includes an estimated number of items, a recommended number of items, and a confidence metric associated with the estimated number of items.

8. The system of claim 1, wherein the node data includes data specific to a geographic area around each node corresponding to the node data.

9. The system of claim 1, wherein the first user data associated with one or more acquisitions at a non-mobile platform is continuously captured via sensors located at the non-mobile platform.

10. The system of claim 9, wherein the data transition scheme is continuously updated based on new data from the sensors, and wherein the continuously updated data transition scheme causes periodic re-routing at predetermined times between nodes in the distributed storage network.

11. A method, comprising:

receiving, from a distributed storage network, node data associated with multiple nodes on the distributed storage network, wherein the node data includes data corresponding to characteristics of the multiple nodes and users that interact with the multiple nodes;

receiving, from a first node in the distributed storage network, first user data associated with one or more acquisitions at a non-mobile platform, wherein the first user data includes data corresponding to the acquisition at the non-mobile platform and an associated physical action taken by a first user;

receiving, from a second node in the distributed storage network, second user data associated with one or more acquisitions at a mobile platform, wherein the second user data includes data corresponding to the acquisition at the mobile platform and an associated action taken by a second user interacting with a mobile device;

determining, using the node data, first user data, and second user data, an estimated future storage need for each of the multiple nodes;

generating a data transition scheme based on the estimated future storage needs; and

implementing the data transition scheme into the distributed storage network, wherein implementing the data transition scheme includes re-routing one or more item deliveries from the first node to the second node.

12. The method of claim 11, wherein determining the estimated future storage need for each of the multiple nodes includes training and implementing an artificial neural network, wherein the node data, first user data, and second user data are inputs to the artificial neural network after the artificial neural network is trained.

13. The method of claim 12, further comprising:

accessing a set of parameters for the artificial neural network, wherein the set of parameters includes weights associated with artificial neurons within the artificial neural network, and wherein the parameters are associated with the characteristics.

14. The method of claim 11, wherein the first user data and second user data are parsed into mobile-specific data, non-mobile-specific data, and hybrid data, and wherein data within the first user data and the second user data are tagged according to the parsing.

15. The method of claim 14, wherein the parsed mobile-specific data, non-mobile-specific data, and hybrid data are weighted based on the tagging, and wherein the weighted mobile-specific data, non-mobile-specific data, and hybrid data are analyzed to determine the data transition scheme.

16. The method of claim 11, wherein the data transition scheme includes amounts of data for transition, timing of transition, and method of transport for transition for each transition of data.

17. The method of claim 11, wherein the estimated future storage need for each of the multiple nodes includes an estimated number of items, a recommended number of items, and a confidence metric associated with the estimated number of items.

18. The method of claim 11, wherein the node data includes data specific to a geographic area around each node corresponding to the node data.

19. The method of claim 11, wherein the first user data associated with one or more acquisitions at a non-mobile platform is continuously captured via sensors located at the non-mobile platform.

20. The method of claim 19, wherein the data transition scheme is continuously updated based on new data from the sensors, and wherein the continuously updated data transition scheme causes periodic re-routing at predetermined times between nodes in the distributed storage network.