APPARATUS AND METHODS FOR DATA COLLECTION AND VALIDATION

• US 20120005527A1
• Filed: 07/01/2010
• Published: 01/05/2012
• Est. Priority Date: 07/01/2010
• Status: Active Grant

• Current Assignee: Time Warner Cable Enterprises LLC (Charter Communications Incorporated)
• Original Assignee: Time Warner Cable Enterprises LLC (Charter Communications Incorporated)
• Inventors: Tidwell, Justin, Engel, Craig, Goldberg, Craig, Samame, Eduardo
• Granted Patent: US 8,484,511 B2
• Time in Patent Office: Days
• US Class Current: 714/15
• CPC Class Codes: G06F 11/008 Reliability or availability analysis
• CPC Class Codes: H04H 60/33 Arrangements for monitoring the users' behaviour or opinions
• CPC Class Codes: H04H 60/46 for recognising users' preferences
• CPC Class Codes: H04H 60/64 for providing detail information
• CPC Class Codes: H04N 21/2408 Monitoring of the upstream path of the transmission network, e.g. client requests monitoring data switching networks utilization H04L43/0876; scheduling or organising the servicing of application requests H04L67/60
• CPC Class Codes: H04N 21/44204 Monitoring of content usage, e.g. the number of times a movie has been viewed, copied or the amount which has been watched monitoring of user activities for profile generation for accessing a video database G06F16/739; protecting generic digital content where the protection is independent of the precise nature of the content G06F21/10; arrangements for monitoring the use made of the broadcast services in broadcast systems H04H60/31
• CPC Class Codes: H04N 21/44224 Monitoring of user activity on external systems, e.g. Internet browsing
• CPC Class Codes: H04N 21/4524 involving the geographical location of the client retrieval from the Internet by querying based on geographical locations G06F16/9537; systems specially adapted for using geographical information in broadcast systems H04H60/70; protocols in which the network application is adapted for the location of the user terminal in communication control or processing H04L67/52; services making use of the location of users or terminals in wireless networks H04W4/02; locating users or terminals in wireless networks H04W64/00
• CPC Class Codes: H04N 21/4532 involving end-user characteristics, e.g. viewer profile, preferences monitoring of user activities for profile generation for accessing a video database G06F16/739; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/458 Scheduling content for creating a personalised stream, e.g. by combining a locally stored advertisement with an incoming stream; Updating operations, e.g. for OS modules ; time-related management operations arrangements for replacing or switching information during the broadcast or during the distribution H04H20/10
• CPC Class Codes: H04N 21/4667 Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
• CPC Class Codes: H04N 21/6582 Data stored in the client, e.g. viewing habits, hardware capabilities, credit card number arrangements where receivers interact with the broadcast H04H20/38

Abstract

Methods and apparatus for collection, validation, analysis, and automated error correction of data regarding user interaction with content. In one embodiment, statistical methods are used to arrive at expected values for the collected data. The data is compared to the expected value and must meet one or more acceptance criteria (e.g., be within a prescribed range) to be considered valid. The prescribed range is determined by the network operator, or a computer program adapted to generate this value. The invention enables a network operator to assess a large volume of data without requiring significant amounts of manual monitoring and/or error correction. The ability to collect, validate and analyze data across multiple platforms is also provided. Still further, an automated system capable of learning evaluation and error correction patterns is disclosed.

Claims

1. A method of providing data collection in a content delivery network, said method comprising:

collecting a plurality of data relating to interaction of a plurality of users with content or apparatus controlling said content;

deriving an expected value for said plurality of data;

based at least in part on said expected value, configuring a range of acceptable values for said plurality of data;

comparing said plurality of data to said range of acceptable values;

if said plurality of data is within said range, transmitting said plurality of data to at least one entity for analyzing said plurality of data; and

if said plurality of data is not within said range,

generating at least one error message;

implementing an error correction process to correct said plurality of data; and

transmitting corrected data to at least one entity for analyzing.

2. The method of claim 1, wherein said act of deriving said expected value for said plurality of data comprises utilizing statistical regression analysis.

3. The method of claim 1, wherein said act of configuring a range of acceptable values for said plurality of data comprises utilizing sample data to determine instances of error given various operator-designated ranges.

4. The method of claim 1, wherein said content delivery network comprises an operator-managed network, and said error correction process comprises manual correction of said plurality of data by said operator.

5. The method of claim 4, wherein said error correction process further comprises:

generating a record of said manual correction of said plurality of data; and

when a second plurality of data is determined to not be within said range, utilizing said record of said manual correction to automatically correct said second plurality of data.

6. In a content delivery network comprising a plurality of users receiving a plurality of content from a plurality of platforms, a method of collecting data comprising:

collecting data relating to an interaction of said plurality of users with said plurality of content received from said plurality of platforms;

comparing values of individual ones of a plurality of data fields in said data to one or more acceptance criteria for each of said individual ones of said plurality of data fields; and

if said one or more criteria are met, transmitting said data to at least one entity for analyzing said data.

7. The method of claim 6, wherein said plurality of platforms include at least one of:

a video on demand (VOD) server, a linear broadcast content source, and an Internet content source.

8. The method of claim 6, further comprising anonymizing said data via at least one cryptographic hash function.

9. The method of claim 6, wherein said act of performing one or more integrity checks comprises:

deriving expected values for each of said individual ones of said plurality of data fields; and

based at least in part on said expected values, configuring a range of acceptable values for said of said individual ones of said plurality of data fields.

10. The method of claim 6, further comprising implementing an error correction process to correct said data having values of individual ones of said plurality of data fields which do not meet said one or more acceptance criteria.

11. A method of correcting errors in one or more fields of a plurality of collected data, said method comprising:

accessing an error indication, said error indication identifying said one or more fields of an individual one of said plurality of collected data having one or more errors;

determining whether an error correction record exists for correction of said one or more fields;

if said error correction record exists for correction of said one or more fields, automatically correcting said one or more fields; and

if said error correction record does not exist for correction of said one or more fields, performing a secondary error correction process.

12. The method of claim 11, wherein said secondary error correction process comprises:

generating said error correction record for said one or more fields; and

storing said error correction record for said one or more fields for subsequent substantially automatic correction of errors in said one or more fields.

13. The method of claim 11, wherein said error indication comprises an error message, and said secondary process comprises manual correction by an operator.

14. Computer readable apparatus comprising a medium adapted to store a computer program thereon, said computer program configured to:

collect data relating to interaction of a plurality of users with a plurality of content received from a plurality of platforms, said data each comprising a plurality of data fields having values contained therein capable of being validated;

compare said values of said plurality of data fields for each of said data to one or more acceptance criteria;

if said one or more criteria of said plurality of data fields are met, transmit said plurality of data to at least one entity for analysis; and

if said one or more criteria of said plurality of data fields are not met, implement a substantially automated error correction process.

15. The apparatus of claim 14, wherein said plurality of platforms include at least one of a video on demand (VOD) server, a linear broadcast content source, and an Internet content source.

16. The apparatus of claim 14, wherein said collection of said plurality of data comprises receipt of said plurality of data from a plurality of user devices.

17. The apparatus of claim 14, wherein said one or more acceptance criteria are derived using statistical log-linear, log-log, or linear regression analysis.

18. The apparatus of claim 14, wherein said implementation of said error correction process comprises said computer program being further configured to utilize an existing error correction profile to automatically correct said plurality of data fields.

19. The apparatus of claim 14, wherein said implementation of said error correction process comprises said computer program being further configured to notify an administrator that manual error correction is necessary.