TRANSFER ROUTING AND APPOINTMENT OFFERING BASED ON SCHEDULED OR DYNAMIC ROUTES

• US 20180374021A1
• Filed: 06/26/2017
• Published: 12/27/2018
• Est. Priority Date: 06/26/2017
• Status: Abandoned Application

• Current Assignee: WayForth, Inc.
• Original Assignee: Livible, Inc. (WayForth, Inc.)
• Inventors: Drayton, John Terrence, Hatfield, Rodney Joe, Jones, JR., Robert Allen, Smith, Michael Laverne
• Application Number:
  US15/633,671
• Publication Number:
  US 20180374021A1
• Time in Patent Office: Days
• CPC Class Codes: G01C 21/343 Calculating itineraries, i.e. routes leading from a starting point to a series of categorical destinations using a global route restraint, round trips, touristic trips travelling salesman problem G06Q10/04; optimisation of routes G06Q10/047
• CPC Class Codes: G01C 21/3484 Personalized, e.g. from learned user behaviour or user-defined profiles
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 20/20 Ensemble learning
• CPC Class Codes: G06N 3/02 Neural networks
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 5/043 Distributed expert systems; Blackboards
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 10/06315 Needs-based resource requirements planning or analysis
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders

Abstract

Embodiments are directed towards a platform that enables users to schedule transfers of items to store at an off-premises storage stations or to deliver items that are currently stored off-premises. The platform may be selectively offer pickup/delivery slots to users. The platform may be arranged select slots to offer to users based on job size, location, item type, transfer agent capacity, or the like. The platform may be arranged to generate distribution routes for one or more transfer agents that perform the pickup/delivery jobs. The routes may be optimized to account real-time transfer agent capacity. For example, if pickups are cancelled additional pickups may be added to a route to use the excess capacity. Likewise, if deliveries are cancelled, additional transfer agent capacity may be added to accommodate the increase volume of items.

Claims

1. A method for managing an inventory of items over a network using a network computer that includes one or more processors that execute instructions to perform the method, comprising:

instantiating an inventory engine to perform actions, including;

collecting information provided by a user of a client computer over a network and provide to the user one or more appointment slots based on item transfer information provided by the user and other item transfer information provided by one or more other users of one or more other client computers over the network; and

employing geolocation information provided by a separate global positioning system (GPS) device to selectively localize one or more included features for one or more of user interfaces, reports, or databases, wherein the localization features include time zones, languages, currencies, and calendar formatting, and wherein the one or more included features improve the user'"'"'s understanding of the user interfaces, reports or databases that are displayed to the user of the client computer when the client computer is located at a particular geo-location; and

instantiating a routing engine to perform actions, including;

providing one or more jobs based on each selection of the one or more appointment slots by the user;

characterizing the one or more jobs based on a type of job and a location included with the item transfer information;

providing one or more routes to perform the one or more jobs, wherein each route is associated with one or more transfer agents; and

providing metrics for the one or more transfer agents based on monitoring one or more actions of the one or more transfer agents, wherein the one or more monitored actions are stored in a remote data store; and

instantiating a learning engine to perform actions, including;

comparing the metrics to one or more predictive models, wherein the one or more predictive models are based on previously obtained metrics; and

employing the one or more predictive models to modify the one or more routes based on the comparison, wherein the modification of the one or more routes includes editing one or more characterizations of the one or more jobs to provide the one or more modified routes to the one or more transfer agents.

2. The method of claim 1, further comprising employing the inventory engine to perform further actions, including providing delivery instructions and pickup instructions to the one or more transfer agents, wherein the delivery instructions and the pickup instructions are based on the one or more jobs and the one or more routes.

3. The method of claim 1, wherein employing the one or more predictive models to modify the one or more routes, further comprises, modifying one or more paths to one or more of the jobs that are associated with the one or more routes based on the provided metrics.

4. The method of claim 1, further comprising employing the inventory engine to perform further actions, including:

rank ordering the one or more appointment slots based on the transfer information and the other transfer information; and

scoring appointment slots for item pickup based on their time window, wherein item pickup appointment slots that have time windows that are later in time than previously scheduled delivery jobs are scored higher than item pickup appointment slots that have time windows that occur before the previously scheduled delivery jobs.

5. The method of claim 1, further comprising employing the inventory engine to perform further actions, including:

rank ordering the one or more appointment slots based on the transfer information and the other transfer information; and

scoring appointment slots based on availability of the one or more transfer agents, wherein more transfer agent capacity is scored higher than less transfer agent capacity.

6. The method of claim 1, further comprising employing the inventory engine to perform further actions, including providing one or more appointment slots for item pickup that are associated with the one or more transfer agents that are currently performing a route, wherein the one or more transfer agents have excess available capacity to transfer one or more items.

7. The method of claim 1, further comprising employing the learning engine to perform further actions, including:

employing the one or more predictive models to predict one or more metrics for the one or more routes;

comparing the one or more predicted metrics to the provided metrics; and

retraining each predictive model that is associated with a variance that exceeds a defined value.

8. A system for managing an inventory of items, comprising:

a network computer, comprising;

a transceiver that communicates over the network;

a memory that stores at least instructions; and

one or more processor devices that execute instructions that perform actions, including;

instantiating an inventory engine to perform actions, including;

collecting information provided by a user of a client computer over a network and provide to the user one or more appointment slots based on item transfer information provided by the user and other item transfer information provided by one or more other users of one or more other client computers over the network; and

employing geolocation information provided by a separate global positioning system (GPS) device to selectively localize one or more included features for one or more of user interfaces, reports, or databases, wherein the localization features include time zones, languages, currencies, and calendar formatting, and wherein the one or more included features improve the user'"'"'s understanding of the user interfaces, reports or databases that are displayed to the user of the client computer when the client computer is located at a particular geo-location; and

instantiating a routing engine to perform actions, including;

providing one or more jobs based on each selection of the one or more appointment slots by the user;

characterizing the one or more jobs based on a type of job and a location included with the item transfer information;

providing one or more routes to perform the one or more jobs, wherein each route is associated with one or more transfer agents; and

providing metrics for the one or more transfer agents based on monitoring one or more actions of the one or more transfer agents, wherein the one or more monitored actions are stored in a remote data store; and

instantiating a learning engine to perform actions, including;

comparing the metrics to one or more predictive models, wherein the one or more predictive models are based on previously obtained metrics; and

employing the one or more predictive models to modify the one or more routes based on the comparison, wherein the modification of the one or more routes includes editing one or more characterizations of the one or more jobs to provide the one or more modified routes to the one or more transfer agents; and

the one or more client computers, comprising;

a client computer transceiver that communicates over the network;

a client computer memory that stores at least instructions; and

one or more processor devices that execute instructions that perform actions, including;

displaying the one or more appointment slots in rank order; and

providing the item transfer information to the network computer.

9. The system of claim 8, wherein the one or more network computer processor devices execute instructions that perform actions, further comprising employing the inventory engine to perform further actions, including providing delivery instructions and pickup instructions to the one or more transfer agents, wherein the delivery instructions and the pickup instructions are based on the one or more jobs and the one or more routes.

10. The system of claim 8, wherein employing the one or more predictive models to modify the one or more routes, further comprises, modifying one or more paths to one or more of the jobs that are associated with the one or more routes based on the provided metrics.

11. The system of claim 8, further comprising employing the inventory engine perform further actions, including:

rank ordering the one or more appointment slots based on the transfer information and the other transfer information; and

scoring appointment slots for item pickup based on their time window, wherein item pickup appointment slots that have time windows that are later in time than previously scheduled delivery jobs are scored higher than item pickup appointment slots that have time windows that occur before the previously scheduled delivery jobs.

12. The system of claim 8, wherein the one or more network computer processor devices execute instructions that perform actions, further comprising employing the inventory engine to perform further actions, including:

rank ordering the one or more appointment slots based on the transfer information and the other transfer information; and

scoring appointment slots based on availability of the one or more transfer agents, wherein more transfer agent capacity is scored higher than less transfer agent capacity.

13. The system of claim 8, wherein the one or more network computer processor devices execute instructions that perform actions, further comprising employing the inventory engine to perform further actions, including providing one or more appointment slots for item pickup that are associated with the one or more transfer agents that are currently performing a route, wherein the one or more transfer agents have excess available capacity to transfer one or more items.

14. The system of claim 8, wherein the one or more network computer processor devices execute instructions that perform actions, further comprising employing the learning engine to perform further actions, including:

employing the one or more predictive models to predict one or more metrics for the one or more routes;

comparing the one or more predicted metrics to the provided metrics; and

retraining each predictive model that is associated with a variance that exceeds a defined value.

15. A processor readable non-transitory storage media that includes instructions for managing an inventory of items, wherein execution of the instructions by one or more hardware processors performs actions, comprising:

instantiating an inventory engine to perform actions, including;

collecting information provided by a user of a client computer over a network and provide to the user one or more appointment slots based on item transfer information provided by the user and other item transfer information provided by one or more other users of one or more other client computers over the network; and

employing geolocation information provided by a separate global positioning system (GPS) device to selectively localize one or more included features for one or more of user interfaces, reports, or databases, wherein the localization features include time zones, languages, currencies, and calendar formatting, and wherein the one or more included features improve the user'"'"'s understanding of the user interfaces, reports or databases that are displayed to the user of the client computer when the client computer is located at a particular geo-location; and

instantiating a routing engine to perform actions, including;

providing one or more jobs based on each selection of the one or more appointment slots by the user;

characterizing the one or more jobs based on a type of job and a location included with the item transfer information;

providing one or more routes to perform the one or more jobs, wherein each route is associated with one or more transfer agents; and

providing metrics for the one or more transfer agents based on monitoring one or more actions of the one or more transfer agents, wherein the one or more monitored actions are stored in a remote data store; and

instantiating a learning engine to perform actions, including;

comparing the metrics to one or more predictive models, wherein the one or more predictive models are based on previously obtained metrics; and

employing the one or more predictive models to modify the one or more routes based on the comparison, wherein the modification of the one or more routes includes editing one or more characterizations of the one or more jobs to provide the one or more modified routes to the one or more transfer agents.

16. The media of claim 15, further comprising employing the inventory engine to perform further actions, including providing delivery instructions and pickup instructions to the one or more transfer agents, wherein the delivery instructions and the pickup instructions are based on the one or more jobs and the one or more routes.

17. The media of claim 15, wherein employing the one or more predictive models to modify the one or more routes, further comprises, modifying one or more paths to one or more of the jobs that are associated with the one or more routes based on the provided metrics.

18. The media of claim 15, further comprising employing the inventory engine to perform further actions, including:

rank ordering the one or more appointment slots based on the transfer information and the other transfer information; and

scoring appointment slots for item pickup based on their time window, wherein item pickup appointment slots that have time windows that are later in time than previously scheduled delivery jobs are scored higher than item pickup appointment slots that have time windows that occur before the previously scheduled delivery jobs.

19. The media of claim 15, further comprising employing the inventory engine to perform further actions, including:

rank ordering the one or more appointment slots based on the transfer information and the other transfer information; and

scoring appointment slots based on availability of the one or more transfer agents, wherein more transfer agent capacity is scored higher than less transfer agent capacity.

20. The media of claim 15, further comprising employing the inventory engine to perform further actions, including providing one or more appointment slots for item pickup that are associated with the one or more transfer agents that are currently performing a route, wherein the one or more transfer agents have excess available capacity to transfer one or more items.

21. The media of claim 15, further comprising employing the learning engine to perform further actions, including:

employing the one or more predictive models to predict one or more metrics for the one or more routes;

comparing the one or more predicted metrics to the provided metrics; and

retraining each predictive model that is associated with a variance that exceeds a defined value.

22. A network computer for managing an inventory of items, comprising:

a transceiver that communicates over the network;

a memory that stores at least instructions; and

one or more processor devices that execute instructions that perform actions, including;

instantiating art inventory engine to perform actions, including;

collecting information provided by a user of a client computer over a network and provide to the user one or more appointment slots based on item transfer information provided by the user and other item transfer information provided by one or more other users of one or more other client computers over the network; and

employing geolocation information provided by a separate global positioning system (GPS) device to selectively localize one or more included features for one or more of user interfaces, reports, or databases, wherein the localization features include time zones, languages, currencies, and calendar formatting, and wherein the one or more included features improve the user'"'"'s understanding of the user interfaces, reports or databases that are displayed to the user of the client computer when the client computer is located at a particular geo-location; and

instantiating a routing engine to perform actions, including;

providing one or more jobs based on each selection of the one or more appointment slots by the user;

characterizing the one or more jobs based on a type of job and a location included with the item transfer information;

providing one or more routes to perform the one or more jobs, wherein each route is associated with one or more transfer agents; and

providing metrics for the one or more transfer agents based on monitoring one or more actions of the one or more transfer agents, wherein the one or more monitored actions are stored in a remote data store; and

instantiating a learning engine to perform actions, including;

comparing the metrics to one or more predictive models, wherein the one or more predictive models are based on previously obtained metrics; and

employing the one or more predictive models to modify the one or more routes based on the comparison, wherein the modification of the one or more routes includes editing one or more characterizations of the one or more jobs to provide the one or more modified routes to the one or more transfer agents.

23. The network computer of claim 22, further comprising employing the inventory engine to perform further actions, including providing delivery instructions and pickup instructions to the one or more transfer agents, wherein the delivery instructions and the pickup instructions are based on the one or more jobs and the one or more routes.

24. The network computer of claim 22, wherein employing the one or more predictive models to modify the one or more routes, further comprises, modifying one or more paths to one or more of the jobs that are associated with the one or more routes based on the provided metrics.

25. The network computer of claim 22, further comprising employing the inventory engine to perform further actions, including:

rank ordering the one or more appointment slots based on the transfer information and the other transfer information; and

scoring appointment slots for item pickup based on their time window, wherein item pickup appointment slots that have time windows that are later in time than previously scheduled delivery jobs are scored higher than item pickup appointment slots that have time windows that occur before the previously scheduled delivery jobs.

26. The network computer of claim 22, further comprising employing the inventory engine to perform further actions, including:

rank ordering the one or more appointment slots based on the transfer information and the other transfer information; and

scoring appointment slots based on availability of the one or more transfer agents, wherein more transfer agent capacity is scored higher than less transfer agent capacity.

27. The network computer of claim 22, further comprising employing the inventory engine to perform further actions, including providing one or more appointment slots for item pickup that are associated with the one or more transfer agents that are currently performing a route, wherein the one or more transfer agents have excess available capacity to transfer one or more items.

28. The network computer of claim 22, further comprising employing the learning engine to perform further actions, including:

employing the one or more predictive models to predict one or more metrics for the one or more routes;

comparing the one or more predicted metrics to the provided metrics; and

retraining each predictive model that is associated with a variance that exceeds a defined value.