Predictive model for voice/video over IP calls

• US 10,091,348 B1
• Filed: 07/25/2017
• Issued: 10/02/2018
• Est. Priority Date: 07/25/2017
• Status: Active Grant

• Current Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Original Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Inventors: Arunachalam, Chidambaram, Salgueiro, Gonzalo, Nainar, Nagendra Kumar, Chen, Eric, Griffin, Keith
• Primary Examiner(s): Tieu, Binh Kien
• Application Number:
  US15/659,356
• Time in Patent Office: 434 Days
• Field of Search: 379 3201, 379 3202, 379 3204, 37911201, 37911204, 37911205, 37911206, 37911207, 37911208, 3791121, 379133, 379137, 379138, 379139, 37922101, 37922102, 37922103, 37922105, 37922106, 37922107, 37922001
• CPC Class Codes: H04L 41/06 Management of faults, events, alarms or notifications
• CPC Class Codes: H04L 41/0896 Bandwidth or capacity management, i.e. automatically increasing or decreasing capacities flow or congestion control using dynamic resource allocation, e.g. in-call renegotiation, H04L47/76
• CPC Class Codes: H04L 41/147 for predicting network behaviour
• CPC Class Codes: H04L 41/22 comprising specially adapted graphical user interfaces [GUI]
• CPC Class Codes: H04L 41/5009 Determining service level performance parameters or violations of service level contracts, e.g. violations of agreed response time or mean time between failures [MTBF]
• CPC Class Codes: H04L 43/04 Processing captured monitoring data, e.g. for logfile generation
• CPC Class Codes: H04L 43/062 related to network traffic
• CPC Class Codes: H04L 43/08 Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
• CPC Class Codes: H04L 43/0829 Packet loss
• CPC Class Codes: H04L 43/0852 Delays
• CPC Class Codes: H04L 43/10 Active monitoring, e.g. heartbeat, ping or trace-route
• CPC Class Codes: H04L 47/82 Miscellaneous aspects
• CPC Class Codes: H04L 47/83 based on usage prediction
• CPC Class Codes: H04L 65/102 Gateways arrangements for connecting between networks having differing types of switching systems, e.g. gateways, H04L12/66
• CPC Class Codes: H04L 65/1023 Media gateways
• CPC Class Codes: H04L 65/403 Arrangements for multi-party communication, e.g. for conferences data switching systems for conference H04L12/18; arrangements for connecting several subscribers to a common circuit, i.e. affording conference facilities H04M3/56; television conferencing systems H04N7/15
• CPC Class Codes: H04L 65/80 Responding to QoS
• CPC Class Codes: H04L 67/52 specially adapted for the location of the user terminal
• CPC Class Codes: H04L 67/75 Indicating network or usage conditions on the user display
• CPC Class Codes: H04M 2203/556 Statistical analysis and interpretation
• CPC Class Codes: H04M 3/2218 : Call detail recording
• CPC Class Codes: H04M 3/2227 : Quality of service monitoring
• CPC Class Codes: H04M 7/0027 : Collaboration services where a computer is used for data transfer and the telephone is used for telephonic communication
• CPC Class Codes: H04M 7/006 : Networks other than PSTN/ISDN providing telephone service, e.g. Voice over Internet Protocol (VoIP) , including next generation networks with a packet-switched transport layer H04L65/00 takes precedence; aspects not specific to the type of network H04M3/00; special services in those networks H04M3/42
• CPC Class Codes: H04M 7/1285 : Details of finding and selecting a gateway for a particular call
• CPC Class Codes: H04Q 2213/13514 : quality of service - inc. grade of service

Abstract

Disclosed is a system and method for forecasting the expected quality of a call. In some examples, a system or method can generate a plurality of scenarios from network metrics, retrieve historical ratings for the network metrics from users, and assign the historical ratings for the network metrics to the plurality of scenarios. The system or method can also filter one or more users based on similarities of the historical ratings for the plurality of scenarios with current network metrics, and forecast an expected call quality based on the historical ratings of the one or more filtered users.

Claims

1. A computer-implemented method for forecasting expected call quality, the method comprising:

generating a plurality of scenarios from a plurality of network metrics;

retrieving historical ratings for the plurality of network metrics from a plurality of users;

assigning the historical ratings for the plurality of network metrics to the plurality of scenarios;

filtering one or more users of the plurality of users based on similarities of the historical ratings for the plurality of scenarios with one or more current network metrics;

forecasting an expected call quality based on the historical ratings of the one or more filtered users; and

routing a communication based on the forecasting of the expected call quality.

2. The computer-implemented method of claim 1, further comprising

generating an initial rating for the one or more users.

3. The computer-implemented method of claim 2, wherein generating the initial rating further comprises:

retrieving historical data from one or more users; and

performing regression modeling on the historical data.

4. The computer-implemented method of claim 3, wherein the historical data is feedback from the one or more users.

5. The computer-implemented method of claim 1, wherein each of the plurality of scenarios includes a combination of one or more of the plurality of networks metrics.

6. The computer-implemented method of claim 1, wherein assigning the historical ratings for the plurality of network metrics to the plurality of scenarios further comprises:

determining one or more network metrics for each scenario of the plurality of scenarios; and

averaging the historical ratings for each of the one or more network metrics for each scenario.

7. The computer-implemented method of claim 1, further comprising:

comparing the historical ratings of the one or more filtered users with ratings of a user initiating the expected call; and

updating the forecasting based on the comparison.

8. A system comprising:

one or more processors; and

at least one computer-readable storage medium having stored therein instructions which, when executed by the one or more processors, cause the system to;

generate a plurality of scenarios from a plurality of network metrics;

retrieve historical ratings for the plurality of network metrics from a plurality of users;

assign the historical ratings for the plurality of network metrics to the plurality of scenarios;

filter one or more users of the plurality of users based on similarities of the historical ratings for the plurality of scenarios with one or more current network metrics;

forecast an expected call quality based on the historical ratings of the one or more filtered users; and

route a communication based on the forecast of the expected call quality.

9. The system of claim 8, wherein the at least one computer-readable storage medium stores additional instructions which, when executed by the one or more processors, cause the system to:

generate an initial rating for the one or more users.

10. The system of claim 9, wherein generating the initial rating further comprises:

retrieving historical data from one or more users; and

performing regression modeling on the historical data.

11. The system of claim 10, wherein the historical data is feedback from the one or more users.

12. The system of claim 8, wherein each of the plurality of scenarios includes a combination of one or more of the plurality of networks metrics.

13. The system of claim 8, wherein assigning the historical ratings for the plurality of network metrics to the plurality of scenarios further comprises:

determining one or more network metrics for each scenario of the plurality of scenarios; and

averaging the historical ratings for each of the one or more network metrics for each scenario.

14. The system of claim 8, wherein the at least one computer-readable storage medium stores additional instructions which, when executed by the one or more processors, cause the system to:

compare the historical ratings of the one or more filtered users with ratings of a user initiating the expected call; and

update the forecasting based on the comparison.

15. A non-transitory computer-readable storage medium comprising:

instructions stored therein which, when executed by one or more processors, cause the one or more processors to;

generate a plurality of scenarios from a plurality of network metrics;

retrieve historical ratings for the plurality of network metrics from a plurality of users;

assign the historical ratings for the plurality of network metrics to the plurality of scenarios;

filter one or more users of the plurality of users based on similarities of the historical ratings for the plurality of scenarios with one or more current network metrics;

forecast an expected call quality based on the historical ratings of the one or more filtered users; and

route a communication based on the forecast of the expected call quality.

16. The non-transitory computer-readable storage medium of claim 15, storing additional instructions which, when executed by the one or more processors, cause the one or more processors to:

generate an initial rating for the one or more users.

17. The non-transitory computer-readable storage medium of claim 16, wherein generating the initial rating further comprises:

retrieving historical data from one or more users; and

performing regression modeling on the historical data.

18. The non-transitory computer-readable storage medium of claim 15, wherein each of the plurality of scenarios includes a combination of one or more of the plurality of networks metrics.

19. The non-transitory computer-readable storage medium of claim 15, wherein assigning the historical ratings for the plurality of network metrics to the plurality of scenarios further comprises:

determining one or more network metrics for each scenario of the plurality of scenarios; and

averaging the historical ratings for each of the one or more network metrics for each scenario.

20. The non-transitory computer-readable storage medium of claim 15, storing additional instructions which, when executed by the one or more processors, cause the one or more processors to:

compare the historical ratings of the one or more filtered users with ratings of a user initiating the expected call; and

update the forecasting based on the comparison.