System and method for generating transaction based recommendations

• US 20040153389A1
• Filed: 01/23/2004
• Published: 08/05/2004
• Est. Priority Date: 01/23/2003
• Status: Active Grant

• Current Assignee: Frank Duane Jr. Lortscher
• Original Assignee: Frank Duane Jr. Lortscher
• Inventors: Lortscher, Frank Duane Jr.
• Granted Patent: US 7,720,732 B2
• Time in Patent Office: Days
• US Class Current: 705/36
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

A system for generating recommendation data includes an assessment unit and an evaluation unit. The assessment unit is configured to receive transaction data for a plurality of transactions and to assess each transaction and generate assessment data based thereon. The evaluation unit is coupled with the assessment unit and configured to receive an evaluation request including a proposed transaction, and generate recommendation data including a certainty indicator which indicates a level of certainty that the proposed transaction will be successful according to predetermined criteria.

Claims

1. A system for generating investment recommendation data, comprising:

an assessment unit configured to receive transaction data for a plurality of transactions, said transaction data including at least object data of the transaction, entity making the transaction, and at least one or more object characteristics, for each transaction, and to assess each transaction of said plurality and generate assessment data based on said transaction data;

an evaluation unit coupled with said assessment unit and configured to receive a proposed transaction data and generate recommendation data including a certainty indicator which indicates a level of confidence that said proposed transaction will meet predetermined criteria, said proposed transaction data including at least proposed object data of the transaction, entity making the transaction, and at least one or more object characteristics, said recommendation data being based on said assessment data.

2. The system as recited in claim 1, wherein said evaluation unit is configured to generate said recommendation data for said proposed transaction based on assessment data relating to transactions of said plurality of transactions having a common object with said proposed transaction.

3. The system as recited in claim 1, wherein said evaluation unit is configured to generate said recommendation data for said proposed transaction based on assessment data relating to transactions of said plurality of transactions for objects having a common object characteristic with the object of said proposed transaction.

4. The system as recited in claim 3, wherein said object is an equity and said object characteristic comprises at least one of object type, object size, P/E ratio, object industry, market cap, dividend history, age of company, and whether the equity is closely held or not.

5. The system as recited in claim 3, wherein said object is a real estate object for a real estate transaction and said object characteristic comprises at least one of object type, object size, type of property, purpose of property, zoning restrictions, and location of property.

6. The system as recited in claim 3, wherein said object is a corporate object for a commercial transaction and said object characteristic comprises at least one of object type, object industry, related product, stage in funding, and age of company.

7. The system as recited in claim 3, wherein said object is a wager and said object characteristic comprises at least one of object type, field, level of competition, and team size.

8. The system as recited in claim 1, wherein said assessment unit is configured to weight said transaction data based on the time each transaction occurred relative to the time the assessment data is generated.

9. The system as recited in claim 1, wherein said assessment unit is configured to generate said assessment data for each object and for each entity.

10. The system as recited in claim 1, wherein said assessment engine is configured to generate a competence indicator for each entity and object combination, said competence indicator indicating relative demonstrated ability, and

said evaluation unit is configured to generate said recommendation data further based on the competence indicator for transaction data sharing a common object with said proposed transaction.

11. The system as recited in claim 1, wherein said assessment engine is configured to generate a confidence indicator for each transaction of said plurality of transactions, said confidence indicator indicating relative aggressiveness associated with a transaction, and

said evaluation unit is configured to generate said recommendation data further based on the confidence indicator for transaction data sharing a common object with said proposed transaction.

12. The system as recited in claim 11, wherein said assessment engine is configured to generate a conviction indicator which is an aggregate assessment of one or more confidence indicators for entity and object combination of transactions, and

said evaluation unit is configured to generate said recommendation data further based on the conviction indicator for transaction data sharing a common object with said proposed transaction.

13. The system as recited in claim 10, wherein said assessment engine is configured to generate a expertise indicator which is an aggregate assessment of one or more competence indicators for entity and object combination of transactions, and

said evaluation unit is configured to generate said recommendation data further based on the expertise indicator for transaction data sharing a common object with said proposed transaction.

14. The system as recited in claim 12, wherein said assessment engine is configured to generate a competence indicator for each entity and object combination, said competence indicator indicating relative demonstrated ability, and

said evaluation unit is configured to generate said recommendation data further based on the competence indicator for transaction data sharing a common object with said proposed transaction.

15. The system as recited in claim 14, wherein said system is configured to administer a test to each entity, and said competence indicator is further based upon results of said test taken each respective entity.

16. The system as recited in claim 14, wherein said assessment engine is configured to generate a expertise indicator which is an aggregate assessment of one or more competence indicators for entity and object combination of transactions, and

said evaluation unit is configured to generate said recommendation data further based on the expertise indicator for transaction data sharing a common object with said proposed transaction.

17. The system as recited in claim 16, wherein said evaluation engine is configured to generate a certainty indicator which is combination of said expertise and said conviction indicators.

18. The system as recited in claim 1, further comprising:

a user profile unit configured to manage a plurality of subscribers from whom transaction data is received and assessed by said assessment unit, said user profile unit being coupled with said assessment and evaluation units and configured to provide a bonus to a user when transaction data of said user is used to generate said recommendation data.

19. The system as recited in claim 18, wherein said bonus comprises a free commission on a future transaction.

20. The system as recited in claim 19, where said bonus comprises monetary compensation.

21. The system as recited in claim 19, where said bonus comprises a bonus credit.

22. A securities trading system, comprising:

a trading platform coupled with at least one electronic securities exchange and configured to generate and execute trade orders within said exchange;

a transaction data facility coupled with said trading platform and configured to capture transaction data related to trade orders generated and executed by said trading platform, to store, maintain and to analyze said transaction data including at least the object of the trade order, size of the order, time of order and user making the transaction, said transaction facility comprising;

an assessment unit configured to assess each order and generate assessment data based on said transaction data, and

an evaluation unit coupled with said assessment unit and configured to receive an evaluation request, said request including at least a proposed object of the trade order, proposed size of the order, and proposed time of the order, and to generate recommendation data in response to the evaluation request based on assessment data generated from transaction data for execute trade orders for a same object as said proposed object; and

at least one user interface coupled with said trading platform and said transaction data facility and configured to request trade orders to be executed by said trade platform, to request an evaluation from said transaction data facility, and to receive and display said recommendation data generated by said transaction data facility.

23. The system as recited in claim 22, wherein said evaluation unit is configured to generate said recommendation data for said proposed transaction based on assessment data relating to transactions of said plurality of transactions having a common object with said proposed transaction.

24. The system as recited in claim 22, wherein said evaluation unit is configured to generate said recommendation data for said proposed transaction based on assessment data relating to transactions of said plurality of transactions for objects having a common industry with an object of said proposed transaction.

25. The system as recited in claim 22, wherein said assessment unit is configured to weight said transaction data based on the time of each transaction relative to the time the assessment data is generated.

26. The system as recited in claim 22, wherein said assessment unit is configured to generate said assessment data based for each object and for each trading entity.

27. The system as recited in claim 22, wherein said assessment engine is configured to generate a competence indicator for each trading entity and object combination, said competence indicator indicating relative demonstrated ability, and

said evaluation unit is configured to generate said recommendation data further based on the competence indicator for transaction data sharing a common object with said proposed transaction.

28. The system as recited in claim 22, wherein said assessment engine is configured to generate a confidence indicator for each transaction of said plurality of transactions, said confidence indicator indicating relative aggressiveness associated with a transaction, and

said evaluation unit is configured to generate said recommendation data further based on the confidence indicator for transaction data sharing a common object with said proposed transaction.

29. The system as recited in claim 28, wherein said assessment engine is configured to generate a conviction indicator which is an aggregate assessment of one or more confidence indicators for trading entity and object combination of transactions, and

said evaluation unit is configured to generate said recommendation data further based on the conviction indicator for transaction data sharing a common object with said proposed transaction.

30. The system as recited in claim 27, wherein said assessment engine is configured to generate a expertise indicator which is an aggregate assessment of one or more competence indicators for trading entity and object combination of transactions, and

said evaluation unit is configured to generate said recommendation data further based on the expertise indicator for transaction data sharing a common object with said proposed transaction.

31. The system as recited in claim 29, wherein said assessment engine is configured to generate a competence indicator for each trading entity and object combination, said competence indicator indicating relative demonstrated ability, and

said evaluation unit is configured to generate said recommendation data further based on the competence indicator for transaction data sharing a common object with said proposed transaction.

32. The system as recited in claim 31, wherein said assessment engine is configured to generate a expertise indicator which is an aggregate assessment of one or more competence indicators for trading entity and object combination of transactions, and

said evaluation unit is configured to generate said recommendation data further based on the expertise indicator for transaction data sharing a common object with said proposed transaction.

33. The system as recited in claim 32, wherein said evaluation engine is configured to generate a certainty indicator which is an aggregation of said expertise and said conviction indicators.

34. The system as recited in claim 22, further comprising:

a user profile unit configured to manage a plurality of subscribers from whom transaction data is received and assessed by said assessment unit, said user profile unit being coupled with said assessment and evaluation units and configured to provide a bonus to a user when transaction data of said user is used to generate said recommendation data.

35. The system as recited in claim 34, wherein said bonus comprises a free commission on a future transaction.

36. The system as recited in claim 34, where said bonus comprises monetary compensation.

37. The system as recited in claim 34, wherein said bonus comprises a bonus credit.

38. A method for generating an investment recommendation for a proposed transaction, said method comprising the following steps:

a. a step for receiving an evaluation request, said evaluation request including data related to said proposed transaction and a user making the request;

b. a step for selecting relevant transaction data from a transaction data source, said transaction data including data relating to a plurality of executed transaction, including associated user profile and object characteristic data;

c. a step for calculating a competence rating for each transaction of said transaction data selected in step b, said competence rating indicating a level of success related to the corresponding transaction;

d. a step for calculating an overall competency score for each user of said transaction data selected in step b;

e. a step for determining a level of similarity between for each user of said transaction data and said user making the request based on said user profile data;

f. a step for calculating a confidence rating for each transaction, said confidence rating indicated relative aggressiveness of the transaction; and

g. a step for storing the results of each of steps a-f.

39. The method as recited in claim 38, further comprising the step for generating a recommendation in response to said request based on the stored results.

40. The method as recited in claim 38, further comprising the following steps:

h. a step for selecting data from the stored results relevant to the evaluation request;

i. a step for calculating an expertise rating based on the selected data of step h, said expertise rating being an aggregate assessment of one or more users'"'"' relative demonstrated ability associated with one or more transactions;

j. a step for calculating a conviction rating based on the selected data of step h, said conviction rating being an aggregate assessment of one or more users'"'"' relative aggressiveness associated with one or more transactions; and

k. calculate a certainty rating for the proposed transaction based on the expertise and conviction ratings calculated in steps i-j, wherein said certainty rating is an indicator of whether to execute said proposed transaction.

41. The method as recited in claim 40, further comprising the following steps:

l. a step for generating a recommendation in response to said recommendation request.

42. The method as recited in claim 41, wherein relevancy in step b is determined by selecting data having at least one of a same object, industry an action in common with said evaluation request.

43. The method as recited in claim 41, wherein transaction data is weighted based on the corresponding data of execution relative to the date of said proposed transaction of said evaluation.

44. A method for generating a recommendation, comprising steps of:

receiving transaction data relating to a plurality of transactions, the transaction data including an object name, object price, a size of the transaction, and at least one transaction entity identifying a party to the transaction;

receiving data relating to a proposed transaction, the proposed transaction data including proposed object name, object price, transaction size, and at least one transaction entity identifying a party to the proposed transaction;

determining which transactions of said transaction data are relevant to said proposed transaction; and

generating a recommendation relating to said proposed transaction based on said transaction data of the transactions determined to be relevant.

45. The method as recited in claim 44, wherein said recommendation is generated by weighting each transaction of said transactions determined to be relevant based on at least a level of expertise of the trading entity that decided to make the transaction, and aggregating the weighted data.

46. The method as recited in claim 44, wherein said recommendation is generated by weighting each transaction data of said transactions determined to be relevant based on a level of expertise of the trading entity that decided to make the transaction for transactions being of a same type as the a type of the proposed transaction, and aggregating the weighted data.

47. The method as recited in claim 44, wherein said recommendation is generated by weighting each transaction data of said transactions determined to be relevant based on a level of expertise of the entity that decided to make the transaction for transactions of a same size as the proposed size of the proposed transaction, and aggregating the weighted data.

48. The method as recited in claim 44, wherein said recommendation is generated by weighting each transaction data of said transactions determined to be relevant based on a level of expertise of the trading entity that decided to make the transaction for transactions in a same industry as the industry of the proposed transaction, and aggregating the weighted data.

49. The method as recited in claim 44, wherein said recommendation is generated by weighting each transaction data of said transactions determined to be relevant based on a level of expertise of the trading entity that decided to make the transaction, on each of transaction action type, on industry type, and on a recency of the transaction relative to said proposed transaction, and aggregating the weighted data.

50. The method as recited in claim 44, wherein said transactions comprise securities transactions and said step of receiving data relating to a proposed transaction includes a step of making a market order, said proposed transaction being based on said market order.

51. The method as recited in claim 44, wherein said transactions comprise real estate transactions and said proposed transaction includes a proposed real estate contract.

52. The method as recited in claim 44, wherein said transactions comprise gambling transactions and said step of receiving data relating to a proposed transaction includes a step of making a proposed bet, said proposed transaction being based on said proposed bet.

53. The method as recited in claim 50, further comprising a step of executing said market order if said recommendation relating to said proposed transaction meets a predetermined criteria.

54. The method as recited in claim 53, wherein said recommendation generated comprises a numeric indicator and said market order is executed if said recommendation exceeds a predetermined value.

55. A method for generating a gaming recommendation, comprising steps of:

receiving transaction data relating to a plurality of gambling transactions, the transaction data including transaction and object characteristic data including at least type, odds, amount of the wager and gambler name;

receiving data relating to a proposed wager, the proposed gamble data at least proposed type, odds, wager amount and gambler name;

determining which gambling transactions of said transaction data are relevant to said proposed wager; and

generating a recommendation relating to said proposed wager based on said transaction data of the gambling transactions determined to be relevant.

56. The method as recited in claim 55, wherein said recommendation is generated by weighting each transaction of said gambling transactions determined to be relevant based on at least a level of expertise of the gambler that decided to make the transaction, and aggregating the weighted data.

57. The method as recited in claim 55, wherein said recommendation is generated by weighting each transaction data of said gambling transactions determined to be relevant based on a level of expertise of the gambler that decided to make the transaction for transactions have a same type as the type of the proposed wager, and aggregating the weighted data.

58. The method as recited in claim 55, wherein said recommendation is generated by weighting each transaction data of said gambling transactions determined to be relevant based on a level of expertise of the gambler that decided to make the transaction for transactions for wagers on a same event type as the event type of the proposed wager, and aggregating the weighted data.

59. The method as recited in claim 55, wherein said recommendation is generated by weighting each transaction data of said gambling transactions determined to be relevant based on a level of expertise of the entity that decided to make the wager for a same wager amount as the wager amount of the proposed transaction, and aggregating the weighted data.

60. The method as recited in claim 55, wherein said recommendation is generated by weighting each transaction data of said security transactions determined to be relevant based on a plurality of object and transaction characteristics as compared to the object and transaction characteristics of said proposed wager, and aggregating the weighted data.

61. The method as recited in claim 60, wherein said recommendation generated comprises a numeric indicator and said proposed wager is executed if said recommendation exceeds a predetermined value.

62. A method of generating a recommendation associated with a proposed transaction, comprising:

a step for assessing data for a plurality of exercised market transactions to generate weighted assessment data;

a step for determining relevant assessment data of said weighted assessment data that are relevant to said proposed transaction; and

a step for aggregating said relevant assessment data to generate an indicator indicating a level of certainty relating to said proposed transaction.

63. The method of claim 62, wherein said step for assessing data includes the steps:

a step for calculating a return value for each of said exercised market transactions; and

a step for generating a weighted assessment record for each of said exercised market transactions proportional to said return value calculated.

64. The method of claim 62, wherein said step for assessing data includes the steps:

a step for calculating a return value for each of said exercised market transactions; and

a step for generating a weighted assessment record for each of said exercised market transactions proportional to said return value calculated.