Method for scheduling and usage of satellite bandwidth

• US 7,809,655 B1
• Filed: 09/17/2003
• Issued: 10/05/2010
• Est. Priority Date: 09/17/2003
• Status: Expired due to Fees

• Current Assignee: StarSat, LLC
• Original Assignee: StarSat, LLC
• Inventors: Anderson, Ken W., Schwager, Stephen A.
• Primary Examiner(s): Sparks; Donald
• Assistant Examiner(s): Brown, Jr.; Nathan H
• Application Number:
  US10/664,481
• Time in Patent Office: 2,575 Days
• Field of Search: 706/1
• US Class Current: 706/1
• CPC Class Codes: H04N 21/23805 Controlling the feeding rate to the network, e.g. by controlling the video pump
• CPC Class Codes: H04N 21/2385 Channel allocation H04N21/266 takes precedence; Bandwidth allocation H04N21/24 takes precedence ; allocation of channels according to the instantaneous demands of the users in time-division multiplex systems H04J3/1682; admission control, resource allocation in open networks H04L12/5692; arrangements for maintenance or administration in data switching networks involving bandwidth and capacity management H04L41/0896; negotiating bandwidth in wireless networks H04W28/16
• CPC Class Codes: H04N 21/2743 Video hosting of uploaded data from client
• CPC Class Codes: H04N 21/466 Learning process for intelligent management, e.g. learning user preferences for recommending movies services using the results of monitoring in broadcast systems H04H60/61
• CPC Class Codes: H04N 21/6143 involving transmission via a satellite
• CPC Class Codes: H04N 7/17318 Direct or substantially direct transmission and handling of requests

Abstract

A method for scheduling and usage of satellite bandwidth wherein data is transmitted to an artificial intelligence model for analysis and assigned a ranking. An event resource allocation model analyzes the ranking and determines how to compress the associated video. The event resource allocation model also determines when to compress the remotely stored video and transmit via satellite to a central data center where the video may be reviewed.

Claims

1. A method of scheduling and usage of bandwidth comprising the steps of:

a. capturing digital video and storing said video on a computer;

b. capturing digital data associated with said digital video;

c. transmitting said digital data associated with said digital video to a central data server;

d. developing at least one compression curve for a customer;

e. instructing said computer how to compress said digital video;

f. transmitting said digital video; and

, creating a list of possible compression methods.

2. A method of scheduling and usage of bandwidth comprising the steps of:

a. capturing digital video and storing said video on a computer;

b. capturing digital data associated with said digital video;

c. transmitting said digital data associated with said digital video to a central data server;

d. developing at least one compression curve for a customer;

e. instructing said computer how to compress said digital video;

f. transmitting said digital video; and

determining the amount of bandwidth a customer can use.

3. A method of scheduling and usage of bandwidth comprising the steps of:

a. capturing digital video and storing said video on a computer;

b. capturing digital data associated with said digital video;

c. transmitting said digital data associated with said digital video to a central data server;

d. developing at least one compression curve for a customer;

e. instructing said computer how to compress said digital video;

f. transmitting said digital video; and

, computing an average event size based on a customer budget.

4. A method of scheduling and usage of bandwidth comprising the steps of:

a. capturing digital video and storing said video on a computer;

b. capturing digital data associated with said digital video;

c. transmitting said digital data associated with said digital video to a central data server;

d. developing at least one compression curve for a customer;

e. instructing said computer how to compress said digital video;

f. transmitting said digital video; and

, computing a weighted average event size based on a probability distribution curve.

5. The method of claim 4, computing said weighted average event size based on historical data.

6. The method of claim 5, computing said weighted average event size by determining probability of probable event size being less than or equal to an average event size.

7. The A method of scheduling and usage of bandwidth comprising the steps of:

a. capturing digital video and storing said video on a computer;

b. capturing digital data associated with said digital video;

c. transmitting said digital data associated with said digital video to a central data server;

d. developing at least one compression curve for a customer;

e. instructing said computer how to compress said digital video;

f. transmitting said digital video; and

, generating multiple compression curves depending on said customer'"'"'s market segment.

8. A method of scheduling and usage of bandwidth comprising the steps of:

a. capturing digital video and storing said video on a computer;

b. capturing digital data associated with said digital video;

c. transmitting said digital data associated with said digital video to a central data server;

d. developing at least one compression curve for a customer;

e. instructing said computer how to compress said digital video;

f. transmitting said digital video; and

, defining a look up table.

9. The method of claim 8, redefining said look up table at pre-selected intervals.

10. The method of claim 8, further comprising the step of determining a compression method in a look up table based on ranking of an event.

11. The method of claim 10, further comprising the step of determining a compression method based on type of unit monitored.

12. The method of claim 11, further comprising the step of determining a compression method based on market segment.

13. The method of claim 12, further comprising the step of determining a compression method based on customer preference.

14. The method of claim 12, further comprising the step of forming a compression curve connecting minimal video size to maximum video size using a standard linear equation.

15. A method of scheduling and usage of bandwidth comprising the steps of:

a. capturing digital video and storing said video on a computer;

b. capturing digital data associated with said digital video;

c. transmitting said digital data associated with said digital video to a central data server;

d. developing at least one compression curve for a customer;

e. instructing said computer how to compress said digital video;

f. transmitting said digital video; and

, storing each of said captured videos for a preselected time.

16. A method of scheduling and usage of bandwidth comprising the steps of:

a. capturing digital video and storing said video on a computer;

b. capturing digital data associated with said digital video;

c. transmitting said digital data associated with said digital video to a central data server;

d. developing at least one compression curve for a customer;

e. instructing said computer how to compress said digital video;

f. transmitting said digital video; and

, storing each captured video in a separate file.

17. A method of scheduling and usage of bandwidth comprising the steps of:

a. capturing digital video and storing said video on a computer;

b. capturing digital data associated with said digital video;

c. transmitting said digital data associated with said digital video to a central data server;

d. developing at least one compression curve for a customer;

e. instructing said computer how to compress said digital video;

f. transmitting said digital video; and

, capturing said data in a raw format and storing said data on a hard drive.

18. The method of claim 17, storing said data in ASCII format.

19. The method of claim 17, uploading said data at regular intervals to said central data center.

20. The method of claim 17, uploading said data immediately to said central data center.

21. A method of scheduling and usage of bandwidth comprising the steps of:

a. capturing digital video and storing said video on a computer;

b. capturing digital data associated with said digital video;

c. transmitting said digital data associated with said digital video to a central data server;

d. developing at least one compression curve for a customer;

e. instructing said computer how to compress said digital video;

f. transmitting said digital video; and

, said central data server receiving said digital data.

22. The method of claim 21, said central data center statistically ranking said data.

23. A method of scheduling and usage of bandwidth comprising the steps of:

a. capturing digital video and storing said video on a computer;

b. capturing digital data associated with said digital video;

c. transmitting said digital data associated with said digital video to a central data server;

d. developing at least one compression curve for a customer;

e. instructing said computer how to compress said digital video;

f. transmitting said digital video; and

, instructing said computer on a method of video compression.

24. A method of scheduling and usage of bandwidth comprising the steps of:

a. capturing digital video and storing said video on a computer;

b. capturing digital data associated with said digital video;

c. transmitting said digital data associated with said digital video to a central data server;

d. developing at least one compression curve for a customer;

e. instructing said computer how to compress said digital video;

f. transmitting said digital video; and

, inserting information into a scheduling table on the central data server.

25. The method of claim 24 said scheduling table holding all requirements for said video compression.

26. The method of claim 24, said table continually updating dates and times for uploading said compressed video.

27. The method of claim 24, a user'"'"'s computer continually polling said central data server to determine if uploading necessary.

28. The method of claim 24, said central data server managing video uploads and inhibiting collisions.

29. A method for scheduling and usage of satellite bandwidth, comprising the steps of:

a. continuously deriving an artificial intelligence model for ranking data captured by electronic devices;

b. continuously deriving an event resource allocation model for determining compression routines and managing transmission of compressed video from a remote site to a central data center;

c. capturing said data from an electronic device at a remote site;

d. capturing video associated with said data at said remote site; and

, storing said captured video on a computer at said remote site.

30. A method for scheduling and usage of satellite bandwidth, comprising the steps of:

a. continuously deriving an artificial intelligence model for ranking data captured by electronic devices;

b. continuously deriving an event resource allocation model for determining compression routines and managing transmission of compressed video from a remote site to a central data center;

c. capturing said data from an electronic device at a remote site;

d. capturing video associated with said data at said remote site; and

, said data continuously transmitted to said central data center.

31. The method of claim 30, said artificial intelligence model reviewing said data and statistically ranking an event represented by said data.

32. A method for scheduling and usage of satellite bandwidth, comprising the steps of:

a. continuously deriving an artificial intelligence model for ranking data captured by electronic devices;

b. continuously deriving an event resource allocation model for determining compression routines and managing transmission of compressed video from a remote site to a central data center;

c. capturing said data from an electronic device at a remote site;

d. capturing video associated with said data at said remote site; and

, said artificial intelligence model utilizing a two step analysis.

33. The method of claim 32, analyzing said data using a normal distribution model.

34. The method of claim 33, analyzing said data using a linear regression model.

35. The method of claim 34, said data ranked according to criticality of the event.

36. A method for scheduling and usage of satellite bandwidth, comprising the steps of:

a. continuously deriving an artificial intelligence model for ranking data captured by electronic devices;

b. continuously deriving an event resource allocation model for determining compression routines and managing transmission of compressed video from a remote site to a central data center;

c. capturing said data from an electronic device at a remote site;

d. capturing video associated with said data at said remote site; and

, including a central data server defining a lookup table.

37. The method of claim 36, populating said lookup table using at least one algorithm including various pre-selected modifiers.

38. A method for scheduling and usage of satellite bandwidth, comprising the steps of:

a. continuously deriving an artificial intelligence model for ranking data captured by electronic devices;

b. continuously deriving an event resource allocation model for determining compression routines and managing transmission of compressed video from a remote site to a central data center;

c. capturing said data from an electronic device at a remote site;

d. capturing video associated with said data at said remote site; and

, said event resource allocation model managing transmission from a land based satellite dish at said remote site to orbiting satellites and on to said central data center.

39. A method for scheduling and usage of satellite bandwidth, comprising the steps of:

a. continuously deriving an artificial intelligence model for ranking data captured by electronic devices;

b. continuously deriving an event resource allocation model for determining compression routines and managing transmission of compressed video from a remote site to a central data center;

c. capturing said data from an electronic device at a remote site;

d. capturing video associated with said data at said remote site; and

, said event resource allocation model managing deriving a compression curve for appropriate video compression.

40. The method of claim 39, said event resource allocation model statistically minimizing weighted averages to achieve a probable event size less than or equal to an average event size.

41. A method for scheduling and usage of satellite bandwidth, comprising the steps of:

a. continuously deriving an artificial intelligence model for ranking data captured by electronic devices;

b. continuously deriving an event resource allocation model for determining compression routines and managing transmission of compressed video from a remote site to a central data center;

c. capturing said data from an electronic device at a remote site;

d. capturing video associated with said data at said remote site; and

, said event resource allocation model utilizing a scheduling table having compression information therein.

42. The method of claim 41, continuously updating said scheduling table.

43. The method of claim 41, a computer polling said scheduling table and query instruction on compression and transmission of said captured video.

44. A method for scheduling and usage of satellite bandwidth, comprising:

a. capturing data from an electronic device;

b. capturing video associated with said electronic device;

c. transmitting said data to a central data server;

d. said central data server analyzing said data and ranking event represented by said data;

e. said central data server instructing a computer to compress pre-selected video;

f. said computer transmitting said video to said central data server; and

utilizing an artificial intelligence model to rank said event.

45. The method of claim 44, said artificial intelligence model utilizing a normal distribution model.

46. The method of claim 45, said artificial intelligence model utilizing a linear regression model.

47. A method for scheduling and usage of satellite bandwidth, comprising:

a. capturing data from an electronic device;

b. capturing video associated with said electronic device;

c. transmitting said data to a central data server;

d. said central data server analyzing said data and ranking event represented by said data;

e. said central data server instructing a computer to compress pre-selected video;

f. said computer transmitting said video to said central data server; and

said central data server utilizing an event resource allocation model.

48. The method of claim 47, said event resource allocation model determining an appropriate compression method.

49. The method of claim 47, said event resource allocation model instructing said computer how to compress said captured video and when to transmit said captured video.