System and method for analyzing human interaction with electronic devices that access a computer system through a network

Abstract

A computer-implemented method of analyzing a series of events which may overlap but which can be characterized by various non-uniform starting times and varying durations such as the interactions of human beings with electronic devices that communicate with a computer system accessed through a network. The resulting metrics provide information useful for understanding human behavior; understanding various combinations of who uses the devices, when do they use the devices, and the purpose for which they use the devices; understanding resource consumption, and understanding device usage for the benefit of service providers. One embodiment teaches how to use set-top box channel tuning data to calculate metrics which provide detailed insight into who watches television, when they watch, and what they watch along with metrics needed to manage capacity in a Switched Digital Video system. Another embodiment relates to cell phone/personal communication device usage based on call detail records.

I claim: 
     
       1. A computer-implemented method, executed on a data analysis computer system including at least one data analysis computer of known type, of analyzing a plurality of human interactions by a plurality of humans interacting with a plurality of electronic devices, each interacting directly or indirectly with a computer system accessed through a network, said computer-implemented method comprisingthe steps of:
 a. Providing on said data analysis computer system a data analysis program, 
 b. creating, by a computing system, a data structure comprising data records representing discrete time periods associated with video-viewing events;  
 receivingin computer readable format electronic device usage, by the computing system, data resulting from said human interaction and making said electronic device usage data available to said data analysis program run on said data analysis computer system, indicating a first plurality of video-viewing events, associated with a plurality of devices, during a time range; 
 based on the first plurality of video-viewing events, populating a first set of data records of the data structure with values indicating device usage; 
 for a second plurality of video-viewing events of the first plurality of video-viewing events, populating a second set of data records of the data structure with data indicating one or more program attributes; 
 using the data structure to determine, by the computing system, and based on the second plurality of video-viewing events, a plurality of different video assets, associated with the one or ore program attributes, that were output during a time period, of a plurality of time periods, of the time range; 
 using the data structure to determine, based on the second plurality of video-viewing events and for each time period of the plurality of time periods of the time range, a quantity of devices, of the plurality of devices, that each output a video asset of the plurality of different video assets during the time period; 
 selecting, based on the quantity of devices determined for each time period of the plurality of time periods, a time period, of the plurality of time periods, as a peak time period corresponding to a peak in output of the plurality of different video assets during the time range; 
 determining, based on the peak time period, a video-viewing metric; and 
 outputting, to a computing device, the video-viewing metric  
 c. creating a data structure in said data analysis program run on said data analysis computer system containing identifying fields for things of interest for analysis, 
 d. creating in said data structure buckets representing individual seconds of time during a window of time of interest for analysis wherein said buckets are correlated with said identifying fields, 
 e. receiving in computer readable format and then loading to said identifying fields in said data structure identifying information for at least one member selected from the group of items of interest consisting of:
 (i) the identifier of said electronic device, 
 (ii) the identifier of said computer system accessed through said network, 
 (iii) the identifier of a resource consumed by said electronic device, 
 (iv) the amount of said resource consumed by said electronic device, 
 (v) demographic information about said human operating said electronic device, 
 (vi) information about the activity occurring on said electronic device, 
 (vii) information about the location of said electronic device, 
 (viii) program attribute information about the content being delivered to said electronic device, 
 
 f. using said electronic device usage data to determine the beginning date and time and the ending date and time of each said human interaction between said electronic device and said computer system accessed through said network and making said beginning date and time and said ending date and time available to said data analysis program run on said data analysis computer system, 
 g. loading values that identify second-by-second electronic device usage activity to selected buckets in said data structure based on said beginning date and time and said ending date and time of each said human interaction, where said buckets loaded correspond with said identifying fields in said data structure, and where each said bucket represents a second of time during which said data analysis program is tracking said electronic device usage activity against at least one said item of interest, 
 h. executing algorithms in said data analysis program running on said data analysis computer system to perform analytics on the data in said data structure, 
 i. outputting said analytics in a useful format, 
 
       whereby said analytics
 (i) provide insight into the amount of resource consumed by said human interaction with said electronic device interacting with said computer system accessed through said network, 
 (ii) provide insight into the electronic device usage pattern of said human interactions, and 
 (iii) provide insight into the behavior of said human interactions. 
 
     
     
       2. The computer-implemented method of  claim 1  wherein said human interaction includes both real time human interactions with said electronic device and interactions with said electronic device that occur as a result of a previous human action. 
     
     
       3. The computer-implemented method of  claim 1  wherein said useful format in which said analytics are output includes at least one member selected from the group consisting of: a data file that can be read by a computer program, a data base table, an electronic message, and a spreadsheet. 
     
     
       4. The computer-implemented method of  claim 1  wherein said data analysis program performs analytics on said data in said data structure to produce viewing metrics where said viewing metrics include at least one member selected from the group consisting of: STB-Channel-Viewing-seconds, STB-Channel-tune-ins, STB-Chan-Avg-viewing-duration, Stb-chan-stay-away-secs-total, Stb-chan-stay-away-tune-count, Stb-chan-avg-stay-away-secs, STB-Viewing-seconds, STB-tune-ins, STB-Average-viewing-duration, Channel-Viewing-seconds, Channel-Non-Viewing-seconds, Channel-one-STB-Viewing-seconds, Agg-Channel-Viewing-seconds, Pct-of-day-only-one-stb-viewg-chan, Pct-of-day-no-stb-viewing-channel, Pct-of-day-viewing-channel, Peak-viewing-second-for-chan, Peak-viewing-count-for-channel, Agg-viewing-at-this-chan-peak, Pct-of-peak-view-by-this-chanpeak. 
     
     
       5. The computer-implemented method of  claim 1  wherein said resource consumed includes at least one member selected from the group consisting of: channels, frequencies, radio frequencies, bandwidth, megabits per second of data transferred, internet protocol packets transferred, Ethernet packets transferred, computer equipment, network equipment, network capacity, cell towers, hubs, routers, switches, nodes, circuits, devices, switched digital video computer systems. 
     
     
       6. The computer-implemented method of  claim 1  wherein said data analysis program performs analytics on said data in said data structure to produce metrics on the resource consumed in supporting said human interaction with said electronic device interacting with said computer system accessed through said network. 
     
     
       7. The computer-implemented method of  claim 1  wherein said demographic information about said human operating said electronic device includes at least one member selected from the group consisting of: income, ethnicity, gender, age, marital status, location, geographic area, postal code, census data, occupation, social grouping, family status, any proprietary demographic grouping, segmentation, credit score, dwelling type, homeownership status, property ownership status, rental status, vehicle ownership, tax rolls, credit card usage, religious affiliation, sports interest, political party affiliation, cable subscriber type, cable subscriber package level, and cell phone service level. 
     
     
       8. The computer-implemented method of  claim 1  wherein said data analysis program performs analytics on said data in said data structure to produce demographic metrics where said demographic metrics include at least one member selected from the group consisting of: Demo-Viewing-seconds, Demo-Non-Viewing-seconds, Demo-one-STB-Viewing-seconds, Agg-Demo-Viewing-seconds, Pct-of-day-only-one-stb-viewg-demo, Pct-of-day-no-stb-viewing-demo, Pct-of-day-viewing-demo, Peak-viewing-second-for-demo, Peak-viewing-count-for-demo, Agg-viewing-at-this-demo-peak, Pct-of-peak-view-by-this-demopeak, Pct-of-peak-view-by-STB-viewng, Demo-viewed-during-peak-flag, Peak-period-duration-in-seconds, Demo-viewed-secs-during-peak, Agg-Demo-viewed-secs-during-peak, Pct-of-peak-period-demo-was-viewed, Pct-of-peak-view-by-STB-viewng, Chan-viewed-during-pea k-flag, Peak-period-duration-in-second, Chan-viewed-secs-during-peak, Agg-Chan-viewed-secs-during-peak, and Pct-of-peak-period-chan-was-viewed. 
     
     
       9. The computer-implemented method of  claim 1  wherein said program attribute information includes at least one member selected from the group consisting of: program type, program genre, program provider, video asset id, video asset name, program rating, producer, script writer, agency name, featured actor, featured actress, featured voice, actor celebrity status, language, informational content code, delivery format, audio track code, audience suitability rating, product category, episode identifier. 
     
     
       10. A computer-implemented method, executed on a data analysis computer system including at least one data analysis computer of known type, of analyzing a plurality of channel tuning events caused by a plurality of humans interacting with a plurality of set-top boxes, each interacting directly or indirectly with a cable television system, said computer-implemented method comprising the steps of:
 a. Providing on said data analysis computer system a data analysis program,   b. receiving in computer readable format channel tuning data resulting from said channel tuning events and making said channel tuning data available to said data analysis program run on said data analysis computer system,   c. creating a data structure in said data analysis program run on said data analysis computer system containing identifying fields for things of interest for analysis,   d. creating in said data structure buckets representing individual seconds of time during a window of time of interest for analysis wherein said buckets are correlated with said identifying fields,   e. receiving in computer readable format and then loading to said identifying fields in said data structure identifying information for at least one member selected from the group of items of interest consisting of:
 (i) the identifier of said set-top box, 
 (ii) the identifier of cable television system equipment serving said set-top box, 
 (iii) the identifier of a resource consumed by said set-top box, 
 (iv) the amount of said resource consumed by said set-top box, 
 (v) demographic information about said human operating said set-top box, 
 (vi) program attribute information about the content being delivered to said set-top 
 (vii) information about the activity occurring on said set-top box, 
 (viii) information about the location of said set-top box, 
   f. using said channel tuning data to determine the tune-in date and time and the tune-out date and time of each said channel tuning event and making said tune-in date and time and said tune-out date and time available to said data analysis program run on said data analysis computer system,   g. loading values that identify second-by-second channel viewing activity to selected buckets in said data structure based on said tune-in date and time and said tune-out date and time of each said channel tuning event, where said buckets loaded correspond with said identifying fields in said data structure, and where each said bucket represents a second of time during which said data analysis program is tracking said channel viewing activity against at least one said item of interest,   h. executing algorithms in said data analysis program running on said data analysis computer system to perform analytics on the data in said data structure,   i. outputting said analytics in a useful format,   
       whereby said analytics
 (i) provide insight into the amount of resource consumed by said human interaction with said set-top boxes interacting with said cable television system, 
 (ii) provide insight into the set-top box usage pattern of said human interactions, and 
 (iii) provide insight into the behavior of said human interactions. 
 
     
     
       11. The computer-implemented method of  claim 10  wherein said channel tuning event includes both real time channel tuning events and channel tuning events that occur as a result of a previous human action. 
     
     
       12. The computer-implemented method of  claim 10  wherein said useful format in which said analytics are output includes at least one member selected from the group consisting of: a data file that can be read by a computer program, a data base table, an electronic message, and a spreadsheet. 
     
     
       13. The computer-implemented method of  claim 10  wherein said data analysis program performs analytics on said data in said data structure to produce viewing metrics where said viewing metrics include at least one member selected from the group consisting of: STB-Channel-Viewing-seconds, STB-Channel-tune-ins, STB-Chan-Avg-viewing-duration, Stb-chan-stay-away-secs-total, Stb-chan-stay-away-tune-count, Stb-chan-avg-stay-away-secs, STB-Viewing-seconds, STB-tune-ins, STB-Average-viewing-duration, Channel-Viewing-seconds, Channel-Non-Viewing-seconds, Channel-one-STB-Viewing-seconds, Agg-Channel-Viewing-seconds, Pct-of-day-only-one-stb-viewg-chan, Pct-of-day-no-stb-viewing-channel, Pct-of-day-viewing-channel, Peak-viewing-second-for-chan, Peak-viewing-count-for-channel, Agg-viewing-at-this-chan-peak, Pct-of-peak-view-by-this-chanpeak. 
     
     
       14. The computer-implemented method of  claim 10  wherein said resource consumed includes at least one member selected from the group consisting of: channels, quadrature amplitude modulation signals, frequencies, radio frequencies, bandwidth, megabits per second of data transferred, internet protocol packets transferred, Ethernet packets transferred, computer equipment, network equipment, hubs, routers, switches, nodes, circuits, devices, network capacity, switched digital video computer systems, all in said cable television system. 
     
     
       15. The computer-implemented method of  claim 10  wherein said data analysis program performs analytics on said data in said data structure to produce resource consumption metrics where said resource consumption metrics include at least one member selected from the group consisting of: Pct-of-peak-view-by-STB-viewng, Chan-viewed-during-peak-flag, Peak-period-duration-in-seconds, Chan-viewed-secs-during-peak, Agg-Chan-viewed-secs-during-peak, Pct-of-pea k-period-chan-was-viewed, By-sec-chan-viewed-count, By-sec-no-chan-viewed-count, By-sec-agg-chan-viewed-count, By-sec-bandwidth-reqd-quantity, By-sec-SDV-chan-viewed-count, By-sec-bcast-chan-viewed-count, By-sec-Std-Def-chan-viewed-cnt, By-sec-High-Def-chan-view-cnt, Peak-usage-in-mbits-per-sec, Peak-usage-second-in-mbits-per, Pct-of-peak-to-be-near-threshold, Near-pea k-threshold-in-mbits-per, Count-of-sec-mbits-near-peak, Pct-of-day-mbits-near-peak, Max-tune-ins-per-second, Max-tune-ins-sec-of-day, Peak-usage-by-chan-viewed-cnt, Peak-usage-second-by-chan-view, Peak-usage-by-STB-viewing-cnt, Peak-usage-second-by-STB-view, Agg-STB-view-at-peak-sec-ofday, Peak-period-duration-in-seconds, Peak-period-most-chan-view-beg-sec, Peak-period-most-chan-view-end-sec, Peak-period-most-STB-activ-beg-sec, Peak-period-most-STB-activ-end-sec. 
     
     
       16. The computer-implemented method of  claim 10  wherein said demographic information about said human operating said electronic device includes at least one member selected from the group consisting of: income, ethnicity, gender, age, marital status, location, geographic area, postal code, census data, occupation, social grouping, family status, any proprietary demographic grouping, segmentation, credit score, dwelling type, homeownership status, property ownership status, rental status, vehicle ownership, tax rolls, credit card usage, religious affiliation, sports interest, political party affiliation, cable subscriber type, and cable subscriber package level. 
     
     
       17. The computer-implemented method of  claim 10  wherein said data analysis program performs analytics on said data in said data structure to produce demographic metrics where said demographic metrics include at least one member selected from the group consisting of: Demo-Viewing-seconds, Demo-Non-Viewing-seconds, Demo-one-STB-Viewing-seconds, Agg-Demo-Viewing-seconds, Pct-of-day-only-one-stb-viewg-demo, Pct-of-day-no-stb-viewing-demo, Pct-of-day-viewing-demo, Peak-viewing-second-for-demo, Peak-viewing-count-for-demo, Agg-viewing-at-this-demo-peak, Pct-of-peak-view-by-this-demopeak, Pct-of-peak-view-by-STB-viewng, Demo-viewed-during-peak-flag, Peak-period-duration-in-seconds, Demo-viewed-secs-during-peak, Agg-Demo-viewed-secs-during-peak, Pct-of-peak-period-demo-was-viewed, Pct-of-peak-view-by-STB-viewng, Chan-viewed-during-peak-flag, Peak-period-duration-in-second, Chan-viewed-secs-during-peak, Agg-Chan-viewed-secs-during-peak, and Pct-of-peak-period-chan-was-viewed. 
     
     
       18. The computer-implemented method of  claim 10  wherein said program attribute information includes at least one member selected from the group consisting of: program type, program genre, program provider, video asset id, video asset name, program rating, producer, script writer, agency name, featured actor, featured actress, featured voice, actor celebrity status, language, informational content code, delivery format, audio track code, audience suitability rating, product category, episode identifier. 
     
     
       19. The computer-implemented method of  claim 10  wherein said data analysis program performs analytics on said data in said data structure to produce program attribute metrics where said program attribute metrics include at least one member selected from the group consisting of: Prog-Viewing-seconds, Prog-Non-Viewing-seconds, Prog-one-STB-Viewing-seconds, Agg-Prog-Viewing-seconds, Pct-of-day-only-one-stb-viewg-prog, Pct-of-day-no-stb-viewing-prog, Pct-of-day-viewing-prog, Peak-viewing-second-for-prog, Peak-viewing-count-for-prog, Agg-viewing-at-this-prog-peak, Pct-of-peak-view-by-STB-viewng, Pct-of-peak-view-by-this-progpeak, Prog-viewed-during-peak-flag, Peak-period-duration-in-seconds, Prog-viewed-secs-during-peak, Agg-Prog-viewed-secs-during-peak, and Pct-of-peak-period-prog-was-viewed. 
     
     
       20. The computer-implemented method of  claim 1  wherein said data analysis program performs analytics on said data in said data structure to produce program attribute metrics where said program attribute metrics include at least one member selected from the group consisting of: Prog-Viewing-seconds, Prog-Non-Viewing-seconds, Prog-one-STB-Viewing-seconds, Agg-Prog-Viewing-seconds, Pct-of-day-only-one-stb-viewg-prog, Pct-of-day-no-stb-viewing-prog, Pct-of-day-viewing-prog, Peak-viewing-second-for-prog, Pea k-viewing-count-for-prog, Agg-viewing-at-this-prog-peak, Pct-of-peak-view-by-STB-viewng, Pct-of-peak-view-by-this-progpeak, Prog-viewed-during-peak-flag, Peak-period-duration-in-seconds, Prog-viewed-secs-during-peak, Agg-Prog-viewed-secs-during-peak, and Pct-of-peak-period-prog-was-viewed. 
     
     
       21. The method of  claim 1 , wherein the data indicates a time, in the time range, at which a video-viewing event of the first plurality of video-viewing events occurred.  
     
     
       22. The method of  claim 1 , further comprising:
 determining a second quantity of devices, of the plurality of devices, that each output a video asset of the plurality of different video assets during the plurality of time periods; and   determining, based on the second quantity of devices, a video-viewing metric associated with the one or more program attributes.    
     
     
       23. The method of  claim 1 , further comprising:
 determining a ratio between the quantity of devices determined for the peak time period and a quantity of the plurality of devices.    
     
     
       24. The method of  claim 1 , further comprising:
 associating, for each video-viewing event of the first plurality of video-viewing events and based on a service associated with the video-viewing event and a start time associated with the video-viewing event, the video-viewing event with one or more second program attributes, wherein the second plurality of video-viewing events is determined based on the one or more second program attributes, and wherein the determining the plurality of different video assets is based on the association.    
     
     
       25. The method of  claim 1 , wherein each time period of the plurality of time periods comprises a same time duration.  
     
     
       26. The method of  claim 1 , further comprising:
 based on determining that the quantity of devices is greater than a threshold value, incrementing a count of program viewing time.    
     
     
       27. The method of  claim 26 , further comprising:
 based on adding the quantity of devices determined for each time period of the plurality of time periods, determining a count of total viewing time.    
     
     
       28. The method of  claim 1 , wherein the one or more program attributes comprise one or more of a program type, a program genre, a program provider, a video asset identifier, a video asset name, a program rating, a producer, a script writer, an agency name, a featured actor, a featured actress, a featured voice, an actor celebrity status, a language, an informational content code, a delivery format, an audio track code, an audience suitability rating, a product category, or an episode identifier.  
     
     
       29. The method of  claim 1 , wherein the determining the quantity of devices comprises:
 based on determining that a first device output, during the time period, a first video asset of the plurality of different video assets, incrementing a count of devices corresponding to the quantity of devices.    
     
     
       30. The method of  claim 29 , wherein the determining the quantity of devices further comprises:
 based on determining that a second device output, during the time period, a second video asset of the plurality of different video assets while the first device was outputting the first video asset, incrementing the count of devices corresponding to a second quantity of devices.    
     
     
       31. The method of  claim 1 , wherein the determining the plurality of different video assets further comprises determining, separately for each video-viewing event of the second plurality of video-viewing events, that a video asset indicated in the video-viewing event is associated with the one or more program attributes, and wherein determining the quantity of devices, of the plurality of devices, that each output a video asset of the plurality of different video assets during the time period comprises incrementing, based on each video-viewing event of the second plurality of video-viewing events, a count of devices corresponding to the quantity of devices.  
     
     
       32. The method of  claim 1 , further comprising:
 determining, for each of the plurality of devices and based on the first plurality of video-viewing events, a number of instances that a device tuned to a first channel.    
     
     
       33. The method of  claim 32 , further comprising:
 determining, based on the first plurality of video-viewing events, a duration of time the device was tuned to the first channel.    
     
     
       34. The method of  claim 1 , further comprising:
 determining a quantity of devices that output the video asset during the peak time period; and   determining, based on the quantity of devices that output the video asset during the peak time period, a video-viewing metric.    
     
     
       35. The method of  claim 1 , further comprising:
 determining, based on a quantity of devices that output the video asset via a first channel during the peak time period, a video-viewing metric.    
     
     
       36. The method of  claim 1 , further comprising:
 selecting a time period, of the plurality of time periods, indicating a highest quantity of active devices of the plurality of devices; and   determining, based on the selected time period indicating the highest quantity of active devices, a video-viewing metric.    
     
     
       37. The method of  claim 1 , wherein each data record represents one second of time during which a program was output by a respective video viewing device.  
     
     
       38. The method of  claim 1 , wherein the plurality of time periods is determined based on user input associated with a window of time of interest for video viewing analysis.  
     
     
       39. The method of  claim 1 , wherein each data record indicates aggregate device usage for a first geographic area.  
     
     
       40. An apparatus comprising:
 one or more processors; and   memory storing instructions that, when executed by the one or more processors, cause the apparatus to:
 create a data structure comprising data records representing discrete time periods associated with video-viewing events; 
 receive data indicating a first plurality of video-viewing events, associated with a plurality of devices, during a time range; 
 based on the first plurality of video-viewing events, populate a first set of data records of the data structure with values indicating device usage; 
 for a second plurality of video-viewing events of the first plurality of video-viewing events, populate a second set of data records of the data structure with data indicating one or more program attributes; 
 use the data structure to determine, based on the second plurality of video-viewing events, a plurality of different video assets, associated with the one or more program attributes, that were output during a time period, of a plurality of time periods, of the time range; 
 use the data structure to determine, based on the second plurality of video-viewing events and for each time period of the plurality of time periods of the time range, a quantity of devices, of the plurality of devices, that each output a video asset of the plurality of different video assets during the time period; 
 select, based on the quantity of devices determined for each time period of the plurality of time periods, a time period, of the plurality of time periods, as a peak time period corresponding to a peak in output of the plurality of different video assets during the time range; 
 determine, based on the peak time period, a video-viewing metric; and 
 output, to a computing device, the video-viewing metric.  
   
     
     
       41. The apparatus of  claim 40 , wherein the instructions, when executed by the one or more processors, further cause the apparatus to:
 based on determining that the quantity of devices is greater than a threshold value, increment a count of program viewing time.    
     
     
       42. The apparatus of  claim 41 , wherein the instructions, when executed by the one or more processors, further cause the apparatus to:
 based on adding the quantity of devices determined for each time period of the plurality of time periods, determine a count of total viewing time.    
     
     
       43. A system comprising:
 a first computing device comprising:
 one or more first processors; and 
 memory storing first instructions that, when executed by the one or more first processors, cause the first computing device to:
 create a data structure comprising data records representing discrete time periods associated with video-viewing events; 
 
 receive data indicating a first plurality of video-viewing events, associated with a plurality of devices, during a time range; 
 based on the first plurality of video-viewing events, populate a first set of data records of the data structure with values indicating device usage; 
 for a second plurality of video-viewing events of the first plurality of video-viewing events, populate a second set of data records of the data structure with data indicating one or more program attributes; 
 use the data structure to determine, based on the second plurality of video-viewing events, a plurality of different video assets, associated with the one or more program attributes, there were output during a time period, of a plurality of time periods, of the time range; 
 use the data structure to determine, based on the second plurality of video-viewing events and for each time period of the plurality of time periods of the time range, a quantity of devices, of the plurality of devices, that each output a video asset of the plurality of different video assets during the time period; 
 select, based on the quantity of devices determined for each time period of the plurality of time periods, a time period, of the plurality of time periods, as a peak time period corresponding to a peak in output of the plurality of different video assets during the time range; 
 determine, based on the peak time period, a video-viewing metric; and 
 output, to a third computing device, the video-viewing metric; and 
   a second computing device comprising:
 one or more second processors; and 
 memory storing second instructions that, when executed by the one or more second processors, cause the second computing device to send at least a portion of the data.  
   
     
     
       44. The system of  claim 43 , wherein the first instructions, when executed by the one or more first processors, further cause the first computing device to:
 based on determining that the quantity of devices is greater than a threshold value, increment a count of program viewing time.    
     
     
       45. The system of  claim 44 , wherein the first instructions, when executed by the one or more first processors, further cause the first computing device to:
 based on adding the quantity of devices determined for each time period of the plurality of time periods, determine a count of total viewing time.