Source: http://www.google.es/patents/US6606613
Timestamp: 2018-01-22 14:36:43
Document Index: 180052707

Matched Legal Cases: ['§4', '§4', '§4', '§4', '§4', '§4', '§4']

Patente US6606613 - Methods and apparatus for using task models to help computer users complete ... - Google Patentes
Methods and apparatus for analyzing tasks performed by computer users by (i) gathering usage data, (ii) converting logged usage data into a uniform format, (iii) determining or defining task boundaries, and (iv) determining a task analysis model by “clustering” similar tasks together. The task analysis...http://www.google.es/patents/US6606613?utm_source=gb-gplus-sharePatente US6606613 - Methods and apparatus for using task models to help computer users complete tasks
Número de publicación US6606613 B1
Número de solicitud US 09/325,168
Fecha de presentación 3 Jun 1999
Número de publicación 09325168, 325168, US 6606613 B1, US 6606613B1, US-B1-6606613, US6606613 B1, US6606613B1
Inventores Steven J. Altschuler, David Ingerman, Edward K. Jung, Greg Ridgeway, Lani F. Wu
Citas de patentes (13), Otras citas (7), Citada por (93), Clasificaciones (9), Eventos legales (5)
Methods and apparatus for using task models to help computer users complete tasks
US 6606613 B1
1. A method for using a predetermined model of clustered tasks, for providing help to a user attempting to perform a task, the method comprising steps of:
e) rendering help content to the user, wherein the help content is related to a task cluster associated with the minimum of the distances determined.
2. The method of claim 1 wherein the help content is a script of steps for accomplishing the task.
7. The method of claim 6 wherein the sub-step of determining distances between the run time graph and the graphs of tasks of the task clusters of the model includes steps of:
8. The method of claim 7 wherein the distance between the run time graph A and each of the graphs B of the tasks of the task clusters of the model, is defined by the expression: distance   ( A , B ) = ∑ i ∈ all   graph   pieces   v  c i 
where v is a parameter greater than 1 and ci is the number of connected elements in piece i of the difference graph A−B.
9. The method of claim 7 wherein the distance between the run time graph A and each of the graphs B of the tasks of the task clusters of the model, is defined by the expression: distance   ( A , B ) = max  [ d *  ( A , B ) , d *  ( B , A ) ] n intersect
wherein nintersect is the number of vertices and edges in A∩B,
wherein d *  ( A , B ) = ∑ i ∈ all   graph   pieces   v  e i 
where v is a parameter greater than 1 and ci is the number of connected elements in piece i of the difference graph A−B, and,
wherein d *  ( B , A ) = ∑ i ∈ all   graph   pieces   v  c i 1 
where v is a parameter greater than 1 and c′i is the number of connected elements in piece i of the difference graph B−A.
10. A method for using a predetermined model of clustered tasks, for providing help to a user attempting to perform a task, the method comprising steps of:
d) determining a minimum of the distances determined;
e) querying the user whether they want to help; and
f) if the user indicates that they want help, rendering help content to the user, wherein the help content is related to a task cluster associated with the minimum of the distances determined.
11. The method of claim 10 wherein the help content is a script of steps for accomplishing the task.
16. The method of claim 15 wherein the sub-step of determining distances between the run time graph and the graphs of tasks of the task clusters of the model includes steps of:
17. The method of claim 16 wherein the distance between the run time graph A and each of the graphs B of the tasks of the task clusters of the model, is defined by the expression: distance   ( A , B ) = ∑ i ∈ all   graph   pieces   v  c i 
18. The method of claim 16 wherein the distance between the run time graph A and each of the graphs B of the tasks of the task clusters of the model, is defined by the expression: distance   ( A , B ) = max  [ d *  ( A , B ) , d *  ( B , A ) ] n intersect
wherein nintersect is the number of vertices and edges in A∩B
19. A method for using a predetermined model of clustered tasks, for providing help to a user attempting to perform a task, the method comprising steps of:
e) if the minimum of the distances is less than a predetermined threshold, rendering help content to the user, wherein the help content is related to a task cluster associated with the minimum of the distances determined.
20. The method of claim 19 wherein the help content is a script of steps for accomplishing the task.
25. The method of claim 24 wherein the sub-step of determining distances between the run time graph and the graphs of tasks of the task clusters of the model includes steps of:
26. The method of claim 25 wherein the distance between the run time graph A and each of the graphs B of the tasks of the task clusters of the model, is defined by the expression: distance  ( A , B ) = ∑ i ∈  all   graph   pieces   v  c i 
27. The method of claim 25 wherein the distance between the run time graph A and each of the graphs B of the tasks of the task clusters of the model, is defined by the expression: distance  ( A , B ) = max  [ d *  ( A , B ) , d *  ( B , A ) ] n intersect
wherein d *  ( A , B ) = ∑ i ∈  all   graph   pieces   v  c i 
wherein d *  ( B , A ) = ∑ i ∈  all   graph   pieces   v  c i ′ 
e) means for rendering help content to the user, wherein the help content is related to a task cluster associated with the minimum of the distances determined.
29. The apparatus of claim 28 wherein the help content is a script of steps for accomplishing the task.
34. The apparatus of claim 33 wherein the means for determining distances between the run time graph and the graphs of tasks of the task clusters of the model include:
35. The apparatus of claim 34 wherein the distance between the run time graph A and each of the graphs B of the tasks of the task clusters of the model, is defined by the expression: distance  ( A , B ) = ∑ i ∈  all   graph   pieces   v  c i 
36. The apparatus of claim 34 wherein the distance between the run time graph A and each of the graphs B of the tasks of the task clusters of the model, is defined by the expression: distance  ( A , B ) = max  [ d *  ( A , B ) , d *  ( B , A ) ] n intersect
where v is a parameter greater than 1 and c′1 is the number of connected elements in piece i of the difference graph B−A.
37. An apparatus for using a predetermined model of clustered tasks, for providing help to a user attempting to perform a task, the apparatus comprising:
d) means for determining a minimum of the distances determined;
e) means for querying the user whether they want to help; and
f) means for, if the user indicates that they want help, rendering help content to the user, wherein the help content is related to a task cluster associated with the minimum of the distances determined.
38. The apparatus of claim 37 wherein the help content is a script of steps for accomplishing the task.
43. The apparatus of claim 42 wherein the means for determining distances between the run time graph and the graphs of tasks of the task clusters of the model include:
44. The apparatus of claim 43 wherein the distance between the run time graph A and each of the graphs B of the tasks of the task clusters of the model, is defined by the expression: distance  ( A , B ) = ∑ i ∈  all   graph   pieces   v  c i 
45. The apparatus of claim 43 wherein the distance between the run time graph A and each of the graphs B of the tasks of the task clusters of the model, is defined by the expression: distance  ( A , B ) = max  [ d *  ( A , B ) , d *  ( B , A ) ] n intersect
wherein d *  ( B , A ) = ∑ i ∈ all   graph   pieces   v  c i ′ 
46. An apparatus for using a predetermined model of clustered tasks, for providing help to a user attempting to perform a task, the apparatus comprising:
e) means for, if the minimum of the distances is less than a predetermined threshold, rendering help content to the user, wherein the help content is related to a task cluster associated with the minimum of the distances determined.
47. The apparatus of claim 46 wherein the help content is a script of steps for accomplishing the task.
52. The apparatus of claim 51 wherein the means for determining distances between the run time graph and the graphs of tasks of the task clusters of the model include:
53. The apparatus of claim 52 wherein the distance between the run time graph A and each of the graphs B of the tasks of the task clusters of the model, is defined by the expression: distance  ( A , B ) = ∑ i ∈ all   graph   pieces   v  c i 
54. The apparatus of claim 52 wherein the distance between the run time graph A and each of the graphs B of the tasks of the task clusters of the model, is defined by the expression: distance  ( A , B ) = max  [ d *  ( A , B ) , d *  ( B , A ) ] n intersect
wherein d *  ( A , B ) = ∑ i ∈ all   graph   pieces   v  c i 
55. A machine readable medium having machine executable instructions which, when executed by the machine, perform steps for using a predetermined model of clustered tasks to provide help to a user attempting to perform a task, the steps comprising:
56. A machine readable medium having machine executable instructions which, when executed by the machine, perform steps for using a predetermined model of clustered tasks, for providing help to a user attempting to perform a task, the steps comprising:
57. A machine readable medium having machine executable instructions which, when executed by the machine, perform steps for using a predetermined model of clustered tasks, for providing help to a user attempting to perform a task, the steps comprising:
a) logging inputs cf the user as steps;
Third, the present invention may function to determine or define tasks boundaries. That is, a computer user may interact with a computer to perform a number of tasks during a single session (a “session” may be defined as a predetermined period of activity followed by a predetermined period of inactivity) or may perform a single task over a number of sessions. Examples of this task boundary definition function are described in §4.2.3.3 below.
During a given session, more than one task may be performed or attempted. Moreover, one task may be performed over more than one session. Again, each task may have a number of steps. Thus, a task boundary determination process 340 uses task boundary model parameters 349 to define task boundaries within a session(s). Examples of this process 340 will be described in detail in §4.2.3.3 below. The defined tasks are stored as usage task.data 342. The usage task data 342 may include records 344, each of which include an optional user ID field 345, a sub-a-ERD field 346, an optional time/date stamp field 347, and a task ID field 348. The user ID field 345 of the usage task data records 344 corresponds to the user ID field 325 of the object (or information) usage log records 324 and the user ID field 335 of the object (or information) usage log in universal format records 334. The sub-a-ERD field 346 of the usage task data records 344 corresponds to the sub-a-ERD field 336 of the object usage log in universal format records 334. The time stamp field 347 of the usage task data records 344 corresponds to the time stamp field 337 of the object (or information) usage log in uniform format records 334 and the time stamp field 327 of the object (or information) usage log records 324. Finally, the task ID field 348 is generated by the task boundary determination process 340. To reiterate, examples of this. process 340 will be described in §4.2.3.3 below.
§4.2.3.2 Uniform (Universal) Object Reprentation Process
FIG. 15 is a flow diagram of an exemplary process 1320′ for performing the step of determining a distance between graphs wherein the distance considers a connectedness of graph differences. (Recall, e.g., step 1320 of FIG. 13.) First, as shown in step 1510, an intersection (e.g., common edges and vertices) of the graphs is determined. Vertices are common (i.e., are the same) if (a) they are labeled with the same relation, (b) they are labeled with the same attribute and have the same constant value, or (c) they are labeled with the same attribute and have two variable (e.g., wildcard) values. Edges are common (i.e., are the same ) if the y are labeled the same and they have the same end point vertices. Next, as shown in step 1520, differences between the graphs are determined. That is, elements of the graphs which are not common are determined. Next, as shown in step 1530, connected elements of the differences are determined. Thereafter, as shown in step 1540, intermediate distances between the graphs are determined based on properties of subgraphs and the ambient graph, such as the differences and connectedness of the symmetric difference. Alternatively, such properties could be the size and number of components in a symmetric difference of two subgraphs. For example, the intermediate distance, d*, may be defined as: d *  ( B , A ) = ∑ i ∈ all   graph   pieces   v  c i 1 
Other methods for penalizing connectedness in graph differences may also be used. Finally, as shown in step 1550, a final distance between the graphs is determined based on the determined intermediate distances and intersection. The final distance, d, may be defined as: d  ( A , B ) = max  [ d *  ( A , B ) , d *  ( B , A ) ] n intersect
where: n INTERSECT ≡ the   number   of   vertices   and   edges   in   A ⋂ B
Referring now to FIG. 18, first, as shown in step 1810, user inputs are accepted. Then, as shown in step 1820, the user inputs are formatted as a run-time graph. The run-time graph may be generated in the same manner as discussed in §4.2.3.4 above with reference to FIG. 14. Next, as shown in step 1830, distances between the run-time graph and the task clusters of the task model 352 are determined. This distance determination may be carried out in the same manner as discussed in §4.2.3.4 above with reference to FIG. 15. Next, as shown in step 1840, the closest task cluster to the run-time graph is determined. Next, as shown in decision step 1850, if the closest distance is not less than a predetermined threshold, then processing continues Is at step 1810. If, on the other hand, the closes distance is less than the predetermined threshold, then help related to accomplishing the task related to the closest task cluster is provided to the user as shown in steps 1850 and 1860. Processing then continues via return node 1870.
d* (22 a,22 c)=d* (22 c,22 a)=v3
d* (22 a,22 b)=d* (22 b,22 a)=v2+v2
Thus, for example, if the tunable parameter v is 10, d* (22 a,22 c)=d* (22 c,22 a)=103=1000 while d* (22 a,22 b)=d* (22 b,22 a)=102+102=200.
§4.3.3.3 Operation of the Task Clust Ring Process
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Clasificación de EE.UU. 706/21, 707/999.003, 707/999.006
Clasificación internacional G06Q10/10, G06N3/02
Clasificación cooperativa Y10S707/99933, Y10S707/99936, G06Q10/10
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:ALTSCHULER, STEVEN J.;INGERMAN, DAVID;JUNG, EDWARD K.;AND OTHERS;REEL/FRAME:010028/0081;SIGNING DATES FROM 19990510 TO 19990601