CONTEXTUALLY-AWARE RESOURCE MANAGER

• US 20170316022A1
• Filed: 04/29/2016
• Published: 11/02/2017
• Est. Priority Date: 04/29/2016
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Inventors: Joshi, Neel, Holmes, William Hart, Tischhauser, Paul David, Jain, Chandresh K., Persett, Tor-Helge, Burlin, Eva Britta Karolina, Lee, Dana Anne, Li, Joan Ching
• Application Number:
  US15/142,574
• Publication Number:
  US 20170316022A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: G06F 16/90324 using system suggestions
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 10/1095 Meeting or appointment
• CPC Class Codes: G06Q 30/0631 Item recommendations

Abstract

Techniques provide a contextually-aware resource manager. In response to one or more events, such as the creation or modification of a calendar event, one or more contextually-aware recommendations are generated and displayed to a user. For example, a recommendation can include the names of service providers, the names of customers, time slots for one or more calendar events, and notifications of one or more conditions. The recommendation can be based on data defining a level of eligibility for service providers and customers. The level of eligibility can be determined by a wide range of contextual data, including but not limited to traffic data, payment data, location data, map data, preference data, scheduling data, workload data, work history data, status data, skill set data, or weather data. The techniques assist user interaction with a computing device, and among other benefits, saves computing resources and reduce the number of inadvertent user entries.

Claims

1. A computer-implemented method comprising:

receiving, at a computing device, input data indicating a service category;

receiving, at the computing device, contextual data including at least one of traffic data, location data, map data, preference data, scheduling data, workload data, work history data, status data, skill set data, or weather data;

determining, at the computing device, a level of eligibility associated with individual providers of a plurality of providers based, at least in part, on the contextual data;

selecting at least one provider of the plurality of providers based, at least in part, on the level of eligibility;

generating at least one recommendation identifying the at least one provider; and

generating data for at least one data object based on the at least one recommendation.

2. The method of claim 1, wherein the method further comprises analyzing the skill set data to generate data defining a degree of alignment between a skillset associated with the at least one provider and the service category, and wherein the level of eligibility associated with the at least one provider is based, at least in part, on the data defining the degree of alignment between the skillset associated with the at least one provider and the service category.

3. The method of claim 1, wherein the method further comprises analyzing the preference data to generate data defining a degree of alignment between a performance metric defined in the preference data and a performance indicator defined in the work history data associated with the at least one provider, and wherein the level of eligibility associated with the at least one provider is based, at least in part, on the data defining the degree of alignment between the performance metric and the performance indicator.

4. The method of claim 1, wherein the method further comprises analyzing the scheduling data to generate data defining a severity of a scheduling conflict associated with the at least one provider, and wherein the level of eligibility associated with the at least one provider is based, at least in part, on the data defining the severity of the scheduling conflict.

5. The method of claim 4, wherein the severity of the scheduling conflict is based, at least in part, on the location data, the traffic data, the map data, or the weather data.

6. The method of claim 1, wherein the level of eligibility associated with the least one provider is determined by:

determining a first probability of a commute associated with the at least one provider;

determining a second probability of a commute associated with at least one other provider; and

determining the level of eligibility associated with the least one provider based, at least in part, on a comparison of the first probability and the second probability.

7. The method of claim 1, wherein the method further comprises analyzing the preference data to generate data defining a degree of alignment between a threshold defined in the preference data and a workload indicator defined in the workload data, the workload indicator associated with the at least one provider, and wherein the level of eligibility associated with the at least one provider is based, at least in part, on the data defining the degree of alignment between the threshold and the workload indicator.

8. The method of claim 1, wherein the method further comprises analyzing the workflow data to generate data defining a degree of alignment between the service category and a stage of the workflow data, and wherein the level of eligibility associated with the at least one provider is based, at least in part, on the data defining the degree of alignment between the service category and the stage of the workflow data.

9. A system, comprising:

a processor; and

a memory in communication with the processor, the memory having computer-readable instructions stored thereupon that, when executed by the processor, cause the processor to perform a method comprising

receiving input data indicating a service category;

receiving contextual data including at least one of traffic data, location data, specialty data, map data, preference data, payment data, scheduling data, workload data, work history data, status data, skill set data, or weather data;

determining a level of eligibility associated with individual providers of a plurality of providers based, at least in part, on the contextual data;

generating a ranked list of at least one recommendation identifying the at least one provider, wherein a ranking of the at least one recommendation is based, at least in part, on a level of eligibility associated with the at least one provider;

obtaining data indicating a selection of the at least one recommendation; and

generating data for at least one data object based on the at least one recommendation in response to the selection of the at least one recommendation.

10. The system of claim 9, wherein the method further comprises analyzing the skill set data to generate data defining a degree of alignment between a skillset associated with the at least one provider and the service category, and wherein the level of eligibility associated with the at least one provider is based, at least in part, on the data defining the degree of alignment between the skillset associated with the at least one provider and the service category.

11. The system of claim 9, wherein the method further comprises analyzing the preference data to generate data defining a degree of alignment between a performance metric defined in the preference data and a performance indicator defined in the work history data associated with the at least one provider, and wherein the level of eligibility associated with the at least one provider is based, at least in part, on the data defining the degree of alignment between the performance metric and the performance indicator.

12. The system of claim 9, wherein the method further comprises analyzing the scheduling data to generate data defining a severity of a scheduling conflict associated with the at least one provider, and wherein the level of eligibility associated with the at least one provider is based, at least in part, on the data defining the severity of the scheduling conflict.

13. The system of claim 12, wherein the severity of the scheduling conflict is based, at least in part, on the location data, the traffic data, the map data, or the weather data.

14. The system of claim 9, wherein the level of eligibility associated with the least one provider is determined by:

determining a first probability of a commute associated with the at least one provider;

determining a second probability of a commute associated with at least one other provider; and

determining the level of eligibility associated with the least one provider based, at least in part, on a comparison of the first probability and the second probability.

15. The system of claim 9, wherein the method further comprises analyzing the preference data to generate data defining a degree of alignment between a threshold defined in the preference data and a workload indicator defined in the workload data, the workload indicator associated with the at least one provider, and wherein the level of eligibility associated with the at least one provider is based, at least in part, on the data defining the degree of alignment between the threshold and the workload indicator.

16. The system of claim 9, wherein the method further comprises analyzing the workflow data to generate data defining a degree of alignment between the service category and a stage of the workflow data, and wherein the level of eligibility associated with the at least one provider is based, at least in part, on the data defining the degree of alignment between the service category and the stage of the workflow data.

17. The system of claim 9, wherein the data object comprises at least one of a message, a notification, and a calendar event.

18. A system, comprising:

a processor; and

a memory in communication with the processor, the memory having computer-readable instructions stored thereupon that, when executed by the processor, cause the processor to perform a method comprising

receiving input data defining aspects of a calendar event;

receiving contextual data including at least one of traffic data, location data, map data, preference data, payment data, scheduling data, workload data, work history data, status data, skill set data, or weather data;

determining a level of eligibility associated with individual customers of a plurality of customers based, at least in part, on the contextual data;

selecting at least one customer of the plurality of customers based, at least in part, on the level of eligibility;

generating at least one recommendation identifying the at least one customer; and

generating data for at least one data object based on the at least one recommendation, wherein the data object comprises at least one of a message, a notification, and a calendar event.

19. The system of claim 18, wherein the method further comprises analyzing the payment data to generate data defining a degree of alignment between a payment history associated with the at least one customer and one or more provider-defined preferences, and wherein the level of eligibility associated with the at least one customer is based, at least in part, on the data defining the degree of alignment between the payment history and the one or more provider-defined preferences.

20. The system of claim 18, wherein the method further comprises analyzing the work history data to generate data defining a customer rating associated with the at least one customer, and wherein the level of eligibility associated with the at least one customer is based, at least in part, on the data defining the customer rating associated with the at least one customer.

21. The system of claim 18, wherein the method further comprises analyzing the scheduling data to generate data defining a severity of a scheduling conflict associated with the at least one customer, and wherein the level of eligibility associated with the at least one customer is based, at least in part, on the data defining the severity of the scheduling conflict.

22. The system of claim 21, wherein the severity of the scheduling conflict is based, at least in part, on the location data, the traffic data, the map data, or the weather data.

23. The system of claim 18, wherein the level of eligibility associated with the least one customer is determined by:

determining a first probability of a commute associated with the at least one customer;

determining a second probability of a commute associated with at least one other customer; and

determining the level of eligibility associated with the least one customer based, at least in part, on a comparison of the first probability and the second probability.

24. The system of claim 23, wherein the first probability and the second probability is based, at least in part, on the traffic data or the weather data, wherein the traffic data provides a forecast of the traffic at a time of the calendar event, and wherein the weather data provides a forecast of the traffic at the time of the calendar event.