Intuit, Inc.

Chris Lee

Marko Rukonic

James Robert Del Favero

Barron R Ernst

Benjamin R Weiss

John Reed Flora

George A Hansen

Financial data associated with one or more 'contributing consumers' is obtained. The financial data is then aggregated, analyzed, and/or categorized, according to one or more events and one or more criteria/parameters associated with the financial transaction data and/or the contributing consumer. The aggregated and/or categorized data is then stored. A 'user consumer' then initiates a request for predictive event cost data associated with one or more specified events and the aggregated and/or categorized data representing the event related changes in the financial data is searched based. Results data representing the changes in the financial data for one or more similarly situated contributing consumers associated the specified event is then obtained and presented to the user consumer.

Method and system for predictive event budgeting based on financial data from similarly situated consumers

A system and method for predictive bill estimation includes a process for predictive bill estimation whereby financial data associated with one or more 'contributing consumers' is obtained from one or more sources. The financial data obtained from the one or more contributing consumers is then aggregated, analyzed, and/or categorized, according to one or more criteria/parameters associated with the financial transaction data and/or the contributing consumer. The aggregated and/or categorized financial data is then stored. A user consumer then initiates a request for cost/bill data associated with a given area and/or event. The aggregated and/or categorized financial data obtained from one or more contributing consumers is then searched based on one or more search parameters and results data representing multiple financial transactions and/or purchases associated the specified area and/or event is obtained. The results data obtained is then presented to the user consumer in any one of various data and/or report formats, and the user consumer may be notified/alerted to predicted fluctuations in his or her bills automatically well in advance of the expected fluctuation in order to provide the user consumer the opportunity to budget for the predicted fluctuations.

Method and system for predictive bill estimation based on financial data from similar users

Financial data associated with one or more 'contributing consumers' is obtained from one or more sources. The financial data obtained from the one or more contributing consumers is then aggregated, analyzed, and/or categorized, according to one or more events causing the changes and one or more criteria/parameters associated with the financial transaction data and/or the contributing consumer. The aggregated and/or categorized data representing the event related changes in the financial data is then stored. A 'user consumer' then initiates a request for predictive event cost data associated with one or more specified events and the aggregated and/or categorized data representing the event related changes in the financial data is searched based. Results data representing the changes in the financial data for one or more similarly situated contributing consumers associated the specified event is then obtained and presented to the user consumer.

Financial data associated with one or more 'contributing consumers' is obtained from one or more sources and categorized and associated with a specific expense/income category. One or more attributes associated with the contributing consumers are then identified and used to analyze, aggregate, and categorize the financial data according to the attributes. Data representing a user financial transaction is then obtained for categorization and one or more specific user attributes associated with the user are identified. The user financial transaction is then categorized based, at least in part, on the categorization of similar contributing consumer financial transactions by contributing consumers having at least one of the specific user attributes.

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San Francisco, CA, United States of America

Chris Lee