System, method, and computer program product for scoring items based on user sentiment and for determining the proficiency of predictors

• US 20060218179A1
• Filed: 11/07/2005
• Published: 09/28/2006
• Est. Priority Date: 03/25/2005
• Status: Active Grant

• Current Assignee: The Motley Fool LLC (The Motley Fool Holdings, Inc.)
• Original Assignee: The Motley Fool LLC (The Motley Fool Holdings, Inc.)
• Inventors: Sigler, Tracy Randall, Etter, Robert Glenn Jr., Gardner, David Herr, Etter, Todd Lewis
• Granted Patent: US 7,882,006 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

The present invention provides systems, methods, computer program products, and combinations and subcombinations thereof for scoring items based on user sentiment and for determining the proficiency of a predictor and for aiding an investment decision on an item by an individual. The invention includes one or more user devices and a prediction system server having a sentiment rating module, a user proficiency ranking module, a content creation module, and a database. Devices access the prediction system server directly via a communications medium or indirectly through links provided on a third party server.

Claims

1. A method for determining the proficiency of a first predictor, wherein the first predictor is a member of a contest having a plurality of additional predictors, comprising:

(a) receiving a plurality of predictions from the first predictor;

(b) for each prediction, calculating a prediction score, wherein the prediction score is a measure of the prediction against an objective baseline for the prediction; and

(c) calculating a proficiency score for the first predictor based on the set of prediction scores calculated in step (b), wherein step (c) includes;

(i) calculating a total score component for the first predictor, wherein the total score is the summation of the plurality of prediction scores generated in step (b),

(ii) calculating an accuracy component for the first predictor, wherein the accuracy component is based at least in part on the number of predictions the first predictor determined correctly and the total number of predictions made by the first predictor, and

(iii) generating the proficiency score for the user, wherein the proficiency score is the sum of the total score component and the accuracy component.

2. The method of claim 1, wherein each additional user has a proficiency score, further comprising:

(d) ordering the first predictor and the plurality of additional predictors in descending order of proficiency scores.

3. The method of claim 2, further comprising:

(e) assigning a predictor rank to the first predictor and each of the plurality of additional predictors based on the position in the ordering determined in step (d).

4. The method of claim 1, wherein step (b) further comprises:

for each prediction

(i) generating a weighting factor for the first predictor and the prediction; and

(ii) adjusting the prediction score by the weighting factor.

5. The method of claim 4, wherein the weighting factor is based at least in part on the difficulty of the prediction.

6. The method of claim 4, wherein the weighting factor is based at least in part on an action by the first predictor.

7. The method of claim 1, wherein a first weighting factor is applied to the total score component and a second weighting factor is applied to the accuracy component.

8. The method of claim 1, wherein step (c) further comprises:

(iv) generating a third weighting factor for the first predictor; and

(v) adjusting the proficiency score by the third weighting factor.

9. The method of claim 8, wherein the third weighting factor is based at least in part on the risk associated with predictions made by the first predictor.

10. The method of claim 8, wherein the third weighting factor is based at least in part on the total number of predictions made by the first predictor.

11. The method of claim 8, wherein the third weighting factor is based at least in part on the reputation of the first predictor with the plurality of additional predictors.

12. The method of claim 3, wherein step (e) further comprises:

(i) generating an adjustment factor for the first predictor; and

(ii) adjusting the predictor rank of the first predictor based on the adjustment factor.

13. The method of claim 12, wherein the adjustment factor is based at least in part on the past performance of the first predictor.

14. The method of claim 12, wherein the adjustment factor is based at least in part on the reputation of the first predictor with the plurality of additional predictors.

15. The method of claim 1, wherein the first predictor is a recognized pundit and the plurality of predictions are predictions made publicly by the recognized pundit.

16. The method of claim 1, further comprising:

(d) providing an informational message to the first predictor.

17. The method of claim 16, wherein step (d) comprises:

(i) initiating the generation of the informational message;

(ii) determining the content of the informational message; and

(iii) providing the informational message to the first predictor.

18. The method of claim 17, wherein the initiation of generation of an informational message is the receipt of a request for the informational message from the first prediction.

19. The method of claim 17, wherein the initiation of generation of an informational message is the occurrence of a message generation event.

20. The method of claim 17, wherein the initiation of generation of an informational message is the receipt from a third party of a request for an informational message for the first predictor.

21. The method of claim 16, wherein the content of the informational message is customized for the first predictor.

22. The method of claim 16, wherein the content of the informational message includes a prediction made by a predictor with a higher rank.

23. The method of claim 16, wherein the content of the informational message includes an offer, wherein the offer is based at least in part on a prediction made by the first predictor.

24. The method of claim 1, further comprising:

(d) providing advertising to the first predictor, wherein the content of the advertising is based at least in part on one of the plurality of predictions made by the first predictor.

25. The method of claim 1, further comprising:

(d) associating a status attribute with the first predictor.

26. The method of claim 25, wherein the type of status attribute is based on the proficiency score of the first predictor.

27. The method of claim 1, further comprising:

(d) displaying the plurality of predictions of the first user on a prediction display for the first user.

28. The method of claim 3, further comprising:

(f) generating a ranked list of predictors; and

(g) displaying at least a portion of the ranked list on a contest summary page.

29. The method of claim 1, further comprising:

(d) receiving subjective commentary from the first predictor for at least one prediction.

30. The method of claim 1, further comprising:

(d) analyzing the predictions, prediction scores, and proficiency score of the first predictor; and

(e) generating a report based on the analysis.

31. The method of claim 1, wherein the plurality of predictions are based on holdings in an investment portfolio of the first predictor.

32. The method of claim 1, wherein the prediction is a prediction of the performance of an asset.

33. The method of claim 1, wherein the prediction is a prediction of the performance of a player in a sports league.

34. The method of claim 1, wherein the prediction is a prediction of the performance of a candidate in an election.

35. The method of claim 1, wherein the prediction is a prediction of the outcome of an awards competition.

36. The method of claim 1, wherein the prediction is a prediction of a news event.

37. A computer program product comprising a computer useable medium including control logic stored therein, the control logic enabling the determination of the proficiency of a first predictor, wherein the first predictor is a member of a contest having a plurality of additional predictors, comprising:

receiving means for enabling a processor to receive a plurality of predictions from the first predictor;

first calculating means for enabling a processor to calculate, for each prediction, a prediction score, wherein the prediction score is a measure of the prediction against an objective baseline for the prediction; and

second calculating means for enabling a processor to calculate a proficiency score for the first predictor based on the set of prediction scores calculated by the first calculating means, wherein the second calculating means includes;

third calculating means for enabling the processor to calculate a total score component for the first predictor, wherein the total score is the summation of the plurality of prediction scores generated by the first calculation means,

fourth calculating means for enabling the processor to calculate an accuracy component for the first predictor, wherein the accuracy component is based at least in part on the number of predictions the first predictor determined correctly and the total number of predictions made by the first predictor, and

generation means for enabling the processor to generate the proficiency score for the user, wherein the proficiency score is the sum of the total score component and the accuracy component.

38. The computer program product of claim 37, the control logic further comprises:

ordering means for enabling the processor to order the first predictor and the plurality of additional predictors in descending order of proficiency scores.

39. The computer program product of claim 38, the control logic further comprises:

assigning means for enabling the processor to assign a predictor rank to the first predictor and each of the plurality of additional predictors based on the position in the ordering determined by the ordering means.

40. The computer program product of claim 37, the control logic further comprises:

informational message means for enabling the processor to provide an informational message to the first predictor.

41. The computer program product of claim 40, wherein the information message means comprises:

means for enabling the processor to initiate the generation of the informational message;

means for enabling the processor to determine the content of the informational message; and

means for enabling the processor to provide the informational message to the first predictor.

42. The computer program product of claim 37, the control logic further comprising:

means for enabling the processor to provide advertising to the first predictor, wherein the content of the advertising is based at least in part on one of the plurality of predictions made by the first predictor.

43. The computer program product of claim 37, the control logic further comprising:

means for enabling the processor to associate a status attribute with the first predictor.

44. The computer program product of claim 37, the control logic further comprising:

means for enabling the processor to display the plurality of predictions of the first user on a prediction display for the first user.

45. The computer program product of claim 37, the control logic further comprising:

means for enabling the processor to receive subjective commentary from the first predictor for at least one prediction.

46. The computer program product of claim 37, the control logic further comprising:

means for enabling the processor to analyze the predictions, prediction scores, and proficiency score of the first predictor; and

means for enabling the processor to generate a report based on the analysis.

47. A method for aiding an investment decision on an item by an individual, comprising:

(a) receiving a plurality of predictions on the item from a plurality of users of a prediction system, wherein each user makes a single prediction on the item;

(b) for each user, calculating a user component, wherein the value of the user component is based on whether the user predicted a specified outcome;

(c) calculating a community sentiment score for the item wherein the community sentiment score is the summation of user components calculated in step (b) divided by the total number of users making a prediction on the item; and

(d) displaying the community sentiment score for the item, a listing of each prediction made for the item on an item display, and at least one brief informational tag for the item.

48. The method of claim 47, wherein for at least one user, step (a) comprises:

(i) generating a weighting factor, wherein the weighting factor is based at least in part on the proficiency score of the user; and

(ii) applying the weighting factor to the user component.

49. The method of claim 47, wherein each prediction includes a proficiency score of the user making the prediction.

50. The method of claim 49, further comprising:

(e) ranking predictions in the prediction listing in descending order of proficiency score.

51. The method of claim 47, further comprising:

(e) receiving subjective commentary on the item from one or more users; and

(f) displaying subjective commentary on the item display, wherein the subjective commentary includes a proficiency score of the user making the commentary.

52. The method of claim 47, further comprising:

(e) receiving a brief informational tag for the item;

(f) adding the brief informational tag to a set of brief informational tags for the item;

(g) receiving a plurality of votes for the brief informational tag, wherein the value of a vote is selected from the group comprising positive and negative; and

(h) displaying the brief informational tag on the item display when a predetermined number of positive votes has been received.

53. The method of claim 47, further comprising:

(e) repeating steps (a) through (d) for a plurality of items.

54. The method of claim 53, further comprising:

(f) receiving a request from an individual to view predictions of a user having a high proficiency score; and

(g) displaying the predictions of the requested user to the individual.

55. The method of claim 54, further comprising:

(h) charging a fee to display the predictions of the requested user.

56. The method of claim 53, further comprising:

(f) generating a publication, wherein the content of the publication includes a listing of predictions made by a plurality of users with high proficiency scores and community sentiment scores for a plurality of items; and

(g) providing the publication to an individual.

57. The method of claim 53, wherein the individual is a user of the prediction system, further comprising:

(f) generating a publication, wherein the content of the publication is customized to the user based on the user'"'"'s actions in the prediction system; and

(g) providing the publication to an individual.

58. The method of claim 47, further comprising:

(e) providing a forum for one of the users of the prediction system to discuss predictions.

59. The method of claim 58, wherein the forum is a discussion board.

60. The method of claim 58, wherein the forum is a blog.

61. The method of claim 58, wherein the forum is a publication.

62. The method of claim 58, further comprising:

(f) charging a fee for an individual to access the forum.

63. The method of claim 62, further comprising:

(f) providing a portion of the fee to the user providing the forum as compensation.

64. A computer program product comprising a computer useable medium including control logic stored therein, the control logic enabling the aid of an investment decision on an item by an individual, comprising:

first receiving means for enabling a processor to receive a plurality of predictions on the item from a plurality of users of a prediction system, wherein each user makes a single prediction on the item;

first calculating means for enabling a processor to calculate, for each user, a user component, wherein the value of the user component is based on whether the user predicted a specified outcome;

second calculating means for enabling a processor to calculate a community sentiment score for the item wherein the community sentiment score is the summation of user components calculated by the first calculating means divided by the total number of users making a prediction on the item; and

first display means for enabling the processor to display the community sentiment score for the item, a listing of each prediction made for the item on an item display, and at least one brief informational tag for the item.

65. The computer program product of claim 64, further comprising:

second receiving means for enabling the processor to receive subjective commentary on the item from one or more users; and

second display means for enabling the processor to display subjective commentary on the item display, wherein the subjective commentary includes a proficiency score of the user making the commentary.

66. The computer program product of claim 64, further comprising:

second receiving means for enabling the processor to receive a brief informational tag for the item;

means for enabling the processor to add the brief informational tag to a set of brief informational tags for the item;

third receiving means for enabling the processor to receive a plurality of votes for the brief informational tag, wherein the value of a vote is selected from the group comprising positive and negative; and

second display means for enabling the processor to display the brief informational tag on the item display when a predetermined number of positive votes has been received.

67. The computer program product of claim 64, further comprising:

second receiving means for enabling the processor to receive a request from an individual to view predictions of a user having a high proficiency score; and

second display means for enabling the processor to display the predictions of the requested user to the individual.

68. The computer program product of claim 64, further comprising:

generation means for enabling the processor to generate a publication, wherein the content of the publication includes a listing of predictions made by a plurality of users with high proficiency scores and community sentiment scores for a plurality of items; and

means for enabling the processor to provide the publication to an individual.

69. The computer program product of claim 64, further comprising:

generation means for enabling the processor to generate a publication, wherein the content of the publication is customized to the user based on the user'"'"'s actions in the prediction system; and

means for enabling the processor to provide the publication to an individual.

70. The computer program product of claim 64, further comprising:

means for enabling the processor to provide a forum for one of the users of the prediction system to discuss predictions.