Method and system for automatic task time estimation and scheduling

Abstract

A method and system for automatic task time estimation and scheduling comprising the steps of: (1) storing a plurality of media items; (2) defining an aggregate task; (3) storing participant data and historical time data; (4) determining a plurality of metadata attributes; and (5) determining a final time estimate.

We claim: 
     
         1 . A computer-implemented method for automatic task time estimation and scheduling, using a computing device comprising at least one processor and at least one storage device, the method comprising the steps of:
 storing a plurality of media items on a computer-readable storage device of the computing device, each media item comprising content information;   defining an aggregate task comprising a plurality of component tasks, wherein each task in the plurality of component tasks is defined in relation to at least one corresponding item in the plurality of media items;   storing participant data for at least one participant on the computer-readable storage device;   storing historical time data for the at least one participant on the computer-readable storage device;   operating a processor of the computing device to determine a plurality of corresponding metadata attributes for the aggregate task by, for each component task in the aggregate task, deriving at least one corresponding metadata attribute from the at least one corresponding item for that component task; and   operating the processor to determine a final time estimate for a participant in the at least one participants to complete the aggregate task based on at least the plurality of corresponding metadata attributes for that aggregate task, the participant data for the at least one participant, and the historical time data for the at least one participant.   
     
     
         2 . The method as defined in  claim 1  wherein the at least one participant comprises only a single participant, the participant data being a participant profile for that participant, such that operating the processor to determine the final time estimate for completing the aggregate task comprises determining a final time estimate for that single participant to complete the aggregate task. 
     
     
         3 . The method as defined in  claim 2  further comprising:
 storing a plurality of media item categories on the computer-readable storage device; 
 for each media item category in the plurality of media item categories, storing corresponding metadata derivation instructions on the computer-readable storage device for configuring the processor to derive corresponding metadata attributes from media items of that media item category; and 
 for each component task, determining a corresponding media item category for the at least one corresponding item in the plurality of media items, and operating the processor to derive at least one metadata attribute for that component task based on the corresponding metadata derivation instructions. 
 
     
     
         4 . The method as defined in  claim 3  further comprising:
 for each participant in the at least one participant, the historical time data comprises a plurality of historical completion time values stored on the computer-readable storage device, wherein each historical completion time value is stored in relation to a plurality of factors of a media item comprising at least the following: a media item category, a difficulty level, and an average rate per media item(s) per difficulty level of the media item; and 
 for each participant in the at least one participant, the participant data further comprises a plurality of participant-specific time variation factors on the computer-readable storage device comprising at least one of the following: a settling time factor, an idle time factor, a packing-up time factor, and a task-dependent time factor. 
 
     
     
         5 . The method as defined in  claim 4  further comprising:
 configuring the processor to receive an input value representing a media item category and to derive metadata attributes from the media item based on the input value, wherein:
 if the input value indicates that the media item category is video, then the processor is configured to derive the difficulty level and a length variable based on the content information of the video; 
 if the input value indicates that the media item category is audio, then the processor is configured to derive the difficulty level and a length variable based on the content information of the audio; 
 if the input value indicates that the media item category is English text, then the processor is configured to derive the difficulty level based on the vocabulary in the text, and a length variable based on the word count of the text, wherein both the vocabulary and the word count are derived from the content information of the English text; 
 if the input value indicates that the media item category is mathematical text, then the processor is configured to derive the difficulty level based on the mathematical operators in the text, and a length variable based on the word count of the text, wherein both the mathematical operators and the word count are derived from the content information of the mathematical text; 
 if the input value indicates that the media item category is an English problem set, then the processor is configured to derive the difficulty level based on the vocabulary in the English problem set, and a length variable based on the total number of questions in the English problem set, wherein both the vocabulary and the total number of questions are derived from the content information of the English problem set; 
 if the input value indicates that the media item category is a mathematical problem set, then the processor is configured to derive the difficulty level based on the mathematical operators in the mathematical problem set, and a length variable based on the total number of questions in the mathematical problem set, wherein both the vocabulary and the total number of questions are derived from the content information of the mathematical problem set; and 
 if the input value indicates that the media item category is hybrid, then the processor is configured to derive the difficulty level based on user input. 
 
 
     
     
         6 . The method as defined in  claim 5  further comprising:
 operating the processor to determine a plurality of component time estimates, wherein each component time estimate in the plurality of the component time estimates is the time estimate for the participant to complete a component task in the aggregate task, each component time estimate being determined based on the media item category, the at least one corresponding metadata attribute for the corresponding component task, the participant data of the participant, and the historical time data of the participant or other participants in the at least one participant; and 
 operating the processor to determine the final time estimate for the participant to complete the aggregate task based on the sum of the plurality of component times estimates and an additional average total time between the component tasks. 
 
     
     
         7 . The method as defined in  claim 6 , wherein operating the processor to determine a component time estimate for the participant to complete a component task in the aggregate task further comprises operating the processor to:
 determine the at least one corresponding item based on the component task;   for each corresponding item, determine the length variable, the difficulty level, and the media item category;   for each corresponding item, determine the average rate per media item(s) per difficulty level of the corresponding item based on the historical time data; and   determine the component time estimate by multiplying the length variable of the corresponding item with the average rate per media item(s) per difficulty level of the corresponding item.   
     
     
         8 . The method as defined in  claim 7 , wherein operating the processor to determine a component time estimate for the participant to complete a component task in the aggregate task further comprises the steps of:
 if the processor locates the participant data for the participant in the computer-readable storage device:
 if the processor locates the media item category of the at least one corresponding item of the component task for the participant, operating the processor to determine the component time estimate based on the located media item category and the corresponding participant data; or 
 if the processor fails to locate the media item category of the at least one corresponding media item of the component task for the participant, operating the processor to determine the component time estimate based on available participant data relating to the other media item categories for the participant, or on user input; or 
   if the processor fails to locate the participant data for the participant in the computer-readable storage device, operating the processor to determine the component time estimate based on a class of alternative participant data, wherein the class of alternative participant data is determined from user input or available participant data of the other participants in the at least one participant in the computer-readable storage device, the other participants sharing at least one characteristic with the participant.   
     
     
         9 . The method as defined in  claim 8 , wherein the at least one characteristic shared between the participant and the other participants is determined based on user input. 
     
     
         10 . The method as defined in  claim 9 , further comprising operating the processor to update the final time estimate for the participant to complete the aggregate task based on a numeric value representing a variance factor, wherein the numeric value representing the variance factor is determined based on the historical completion time values and their respective final or component time estimate for the participant. 
     
     
         11 . The method as defined in  claim 10  further comprising:
 the processor receiving a request to determine an estimated workload for the one participant; and 
 in response to the request, operating the processor to determine an estimated workload for the participant based on the final time estimate for the participant to complete an aggregate task corresponding to the workload. 
 
     
     
         12 . The method as defined in  claim 11  further comprising:
 the processor receiving a request to schedule an aggregate task for the participant; and 
 in response to the request, operating the processor to determine a schedule for the participant based on the estimated workload and calendar information of the participant. 
 
     
     
         13 . The method as defined in  claim 1  wherein the computer-readable storage device comprises a first computer-readable storage device for storing the plurality of media items, and a second computer-readable storage device for storing the participant data. 
     
     
         14 . A system for providing automatic task time estimation and scheduling, the system comprising:
 at least one computer-readable storage device configured to store i) a plurality of media items, each media item comprising content information, ii) at least one aggregate task comprising a plurality of component tasks, wherein each task in the plurality component tasks is defined in relation to at least one corresponding item in the plurality of media items; iii) participant data for at least one participant in a participant data module, and iv) historical time data for the at least one participant in a historical data module; and   at least one processor linked for communication with the at least one computer-readable storage device and configured to i) determine a plurality of corresponding metadata attributes for the aggregate task by, for each component task in the aggregate task, deriving at least one corresponding metadata attribute from the at least one corresponding item for that component task, and ii) provide a time estimation module for determining a final time estimate for completing the aggregate task based on at least the plurality of corresponding metadata attributes for that aggregate task, the participant data of the at least one participant, and the historical time data of the at least one participant.   
     
     
         15 . The system as defined in  claim 14 , wherein
 the computer-readable storage device is further configured to:
 store a plurality of media item categories, and 
 for each media item category in the plurality of media item categories, store corresponding metadata derivation instructions for configuring the processor to derive corresponding metadata attributes from media items of that media item category; and, 
   the at least one processor is further configured to:
 for each component task, determine a corresponding media item category for the at least one corresponding item in the plurality of media items, and derive at least one metadata attribute for that component task based on the corresponding metadata derivation instructions. 
   
     
     
         16 . The system as defined in  claim 15 , wherein,
 for each participant in the at least one participant, the historical data module is further configured to store historical time data further comprising at least a plurality of historical completion time values on the computer-readable storage device, wherein each historical completion time value is stored in relation to a plurality of factors of a media item comprising at least the following: a media item category, a difficulty level, and an average rate per media item(s) per difficulty level of the media item; and   for each participant in the at least one participant, the participant data module is further configured to store participant data further comprising a plurality of participant-specific time variation factors on the computer-readable storage device, wherein the plurality of participant-specific time variation factors comprise at least one of the following: a settling time factor, an idle time factor, a packing-up time factor, and a task-dependent time factor.   
     
     
         17 . The system as defined in  claim 16 , wherein the at least one processor is further configured to receive an input value representing a media item category and to derive metadata attributes from the media item based on the input value, wherein,
 if the input value indicates that the media item category is video, then the processor is configured to derive the difficulty level and a length variable based on the content information of the video;   if the input value indicates that the media item category is audio, then the processor is configured to derive the difficulty level and a length variable based on the content information of the audio;   if the input value indicates that the media item category is English text, then the processor is configured to derive the difficulty level based on the vocabulary in the text, and a length variable based on the word count of the text, wherein both the vocabulary and the word count are derived from the content information of the English text;   if the input value indicates that the media item category is mathematical text, then the processor is configured to derive the difficulty level based on the mathematical operators in the text, and a length variable based on the word count of the text, wherein both the mathematical operators and the word count are derived from the content information of the mathematical text;   if the input value indicates that the media item category is English problem set, then the processor is configured to derive the difficulty level based on the vocabulary in the English problem set, and a length variable based on the total number of questions in the English problem set, wherein both the vocabulary and the total number of questions are derived from the content information of the English problem set;   if the input value indicates that the media item category is mathematical problem set, then the processor is configured to derive the difficulty level based on the mathematical operators in the mathematical problem set, and a length variable based on the total number of questions in the mathematical problem set, wherein both the vocabulary and the total number of questions are derived from the content information of the mathematical problem set; and   if the input value indicates that the media item category is hybrid, then the processor is configured to derive the difficulty level based on user input.   
     
     
         18 . The system as defined in  claim 17 , wherein the time estimation module configured to operate the at least one processor to determine a final time estimate for completing the aggregate task comprises:
 a set of machine-readable instructions configured to operate the at least one processor to determine a plurality of component time estimates, wherein each component time estimate in the plurality of the component time estimates is the time estimate for the participant to complete a component task in the aggregate task, each component time estimate being determined based on the media item category, the at least one corresponding metadata attribute for the corresponding component task, the participant data of the participant, and the historical time data of the participant or other participants in the at least one participant; and   another set of machine-readable instructions configure to operate the at least one processor to determine the final time estimate for the participant to complete the aggregate task based on the sum of the plurality of component times estimates and an additional average total time between the component tasks.   
     
     
         19 . The system as defined in  claim 18 , wherein operating the at least one processor to determine a component time estimate for the participant to complete a component task in the aggregate task comprises a set of machine-readable instructions configured to:
 determine the at least one corresponding item based on the component task;   for each corresponding item, determine the length variable, the difficulty level variable, and the media item category;   for each corresponding item, determine the average rate per media item(s) per difficulty level of the corresponding item based on the historical time data; and   determine the component time estimate by multiplying the length variable of the corresponding item with the average rate per media item(s) per difficulty level of the corresponding item.   
     
     
         20 . The system as defined in  claim 19 , wherein operating the at least one processor to further determine a component time estimate for a participant to complete a component task in the aggregate task further comprises a set of machine-readable instructions configured to:
 if the processor locates the participant data of the participant in the computer-readable storage device:
 if the processor locates the media item category of the at least one corresponding item of the component task for the participant, operating the processor to determine the component time estimate based on the located media item category and the corresponding participant data; or 
 if the processor fails to locate the media item category of the at least one corresponding media item of the component task for the participant, operating the processor to determine the component time estimate based on available participant data relating to the other media item categories for the participant, or on user input; or 
   if the processor fails to locate the participant data of the participant in the computer-readable storage device, operating the processor to determine the component time estimate based on a class of alternative participant data, wherein the class of alternative participant data is determined from user input or available participant data of the other participants in the at least one participant in the computer-readable storage device, the other participants sharing at least one characteristic with the participant.   
     
     
         21 . The system as defined in  claim 20 , wherein the at least one characteristic shared between the participant and the other participants is determined based on user input. 
     
     
         22 . The system as defined in  claim 21 , wherein the time estimation module further comprises a set of machine-readable instructions configured to operate the processor to update the final time estimate for the participant to complete the aggregate task based on a numeric value representing a variance factor, wherein the numeric value representing the variance factor is determined based on the historical completion time values and their respective final or component time estimate for the participant. 
     
     
         23 . The system as defined in  claim 22 , further comprising a workload estimation module, the workload estimation module comprising a set of machine-readable instructions configured to:
 receive a request to determine an estimated workload for the participant; and   in response to the request, operate the at least one processor to determine an estimated workload for the participant based on the final time estimate for the participant to complete an aggregate task corresponding to the workload.   
     
     
         24 . The system as defined in  claim 23 , further comprising a scheduling module, the scheduling module comprising a set of machine-readable instructions configured to receive a request to schedule an aggregate task for the participant, and in response to the request to operate the at least one processor to determine a schedule for the participant based on the estimated workload and calendar information of the participant. 
     
     
         25 . The system as defined in  claim 14  wherein the at least one computer-readable storage device comprises a first computer-readable storage device for storing the plurality of media items, and a second computer-readable storage device for storing the participant data.