User interface for correlation of analysis systems

• US 7,966,246 B2
• Filed: 10/21/2004
• Issued: 06/21/2011
• Est. Priority Date: 10/23/2003
• Status: Expired due to Fees

• Current Assignee: Alphacet, Inc.
• Original Assignee: Alphacet, Inc.
• Inventors: Ingargiola, Rosario M., Levinson, Robert
• Primary Examiner(s): PATEL, JAGDISH
• Application Number:
  US10/971,842
• Publication Number:
  US 20050090911A1
• Time in Patent Office: 2,434 Days
• Field of Search: 705/37, 705/35
• US Class Current: 705/37
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

A system for management, correlation, and combination of various analysis techniques is disclosed. Graphical user interface (GUI) controls and displays are provided to allow a user to configure different aspects of an analysis system. A financial analysis application allows a user to designate instruments whose data points, factors or other relevant data can be pre-processed with indicators. The outputs of the indicators are provided to advisors for calculation and grouping. A user is able to select the types of raw data to use, the instruments, the factors, the data feeds and other sources for data, the indicators that perform processing and the advisors that perform analysis, grouping and outputting of results for combining using machine learning mechanisms.

Claims

1. A computer implemented method for predicting the behavior of an instrument, the method using a processor coupled to a user input device and a display, the method comprising:

storing a machine learning mechanism and a plurality of identified combiner processes in the processor;

identifying via the processor a plurality of predictor instruments wherein each one of the plurality of predictor instruments includes associated data points defining properties of the one of the plurality of predictor instruments;

displaying the plurality of predictor instruments on the display;

receiving a selection of a predictor instrument from the displayed predictor instruments via the user input device;

displaying the plurality of combiner processes on the display;

receiving a selection of a combiner process from the displayed combiner processes via the user input device;

identifying, via the processor and using the selected combiner process, linear and non-linear relationships between the associated data points of the selected predictor instrument using a machine learning mechanism;

generating via the processor and based on the identified linear and non-linear relationships a prediction and a recommendation concerning an outcome of the selected predictor instrument;

displaying the prediction and the recommendation on the display.

2. The method of claim 1, wherein an instrument includes a financial security.

3. The method of claim 2, wherein the instrument includes a stock.

4. The method of claim 2, wherein the instrument includes a bond.

5. The method of claim 2, wherein the instrument includes an option.

6. The method of claim 2, wherein the instrument includes a commodity future.

7. The method of claim 1, further comprising accepting a signal from a user input device to designate one or more factors associated with the data points.

8. The method of claim 1, wherein each of the plurality of the predictor instruments include data points having data types, wherein a factor includes a data type designation, the method further comprising:

identifying a plurality of indicators;

displaying the plurality of indicators on the display;

receiving from the user input device a selection of at least one indicator of the plurality of indicators as at least one active indicator;

using factors to determine data points having data types in accordance with a factor'"'"'s data type; and

using the determined data points as inputs to the at least one active indicator.

9. The method of claim 8, wherein a data type includes an open value.

10. The method of claim 8, wherein a data type includes a close value.

11. The method of claim 8, wherein a data type includes a high value.

12. The method of claim 8, wherein a data type includes a low value.

13. The method of claim 8, wherein a data type includes a price/earnings ratio value.

14. The method of claim 8, wherein the accuracy of said predicting the outcome is fed back to the at least one active indicator for subsequent predictions of outcomes.

15. The method of claim 1, further comprising using at least one of a data feed and a database linked to the processor to provide an input as an active indicator.

16. The method of claim 15, further comprising storing and retrieving the record of the at least one active indicator;

and using the retrieved record of the at least one active indicator to predict the behavior of the selected predictor instrument.

17. An apparatus for predicting the behavior of a system, the apparatus comprising:

a processor coupled to a user input device and a display;

a non-transitory, machine-readable medium including code executable by the processor, the non-transitory machine-readable medium including;

code for a machine learning mechanism executable by the processor;

code for a plurality of identified combiner processes;

code for identifying a plurality of predictor instruments wherein each one of the plurality of predictor instruments includes associated data points defining properties of the one of the plurality of predictor instruments;

code for displaying the plurality of predictor instruments on the display;

code for receiving a selection of one of the predictor instruments via the user input device;

code for displaying the plurality of combiner processes on the display;

code for receiving a selection of a combiner process from the displayed combiner processes via the user input device;

code for identifying, via the processor and using the selected combiner process, linear and non-linear relationships between the associated data points of the selected predictor instrument using the machine learning mechanism;

code for generating via the processor and based on the identified linear and non-linear relationships, a prediction and a recommendation concerning an outcome of the selected predictor instrument;

and

code for displaying the prediction and the recommendation on the display.

18. A non-transitory machine-readable medium including code executable by a processor for predicting the behavior of an instrument, the non-transitory machine-readable medium comprising:

code for a machine learning mechanism executable by the processor;

code for a plurality of identified combiner processes;

code for identifying a plurality of predictor instruments wherein each one of the plurality of predictor instruments includes associated data points defining properties of the one of the plurality of predictor instruments;

code for displaying the plurality of predictor instruments on the display;

code for receiving a selection of one of the predictor instruments via the user input device;

code for displaying the plurality of combiner processes on the display;

code for receiving a selection of a combiner process from the displayed combiner processes via the user input device;

code for identifying, via the processor and using the selected combiner process, linear and non-linear relationships between the associated data points of the selected predictor instrument using the machine learning mechanism;

code for generating via the processor and based on the identified linear and non-linear relationships, a prediction and a recommendation concerning an outcome of the selected predictor instrument;

and

code for displaying the prediction and the recommendation on the display.