Data processing: artificial intelligence – Neural network – Learning task
Reexamination Certificate
2000-02-17
2003-02-11
Black, Thomas (Department: 2121)
Data processing: artificial intelligence
Neural network
Learning task
C706S048000, C707S793000
Reexamination Certificate
active
06519576
ABSTRACT:
FIELD OF INVENTION
The present invention provides a method and system for predicting transactions, in particular, to aid in providing personalisation for Internet transactions.
BACKGROUND OF THE INVENTION
It has long been a goal of data mining applications to attempt to predict individual's preferences. Such applications usually operate by gathering user data and perhaps expert information to divide populations into a suitable number of clusters, each cluster being associated with a respective plurality of attribute values, for example, individuals who are married with children, vote democrat, live in suburbia, drink black coffee and earn over $60,000 per annum might form one cluster. When data mining applications are presented with incomplete data for an individual, they find the most suitable cluster for such an individual and use the associated attribute values of the cluster to predict the individuals unknown attributes.
U.S. Pat. No. 5,704,017 discloses a system for collaborative filtering utilizing a belief network. This involves defining a belief network, then cumulatively adding a number of expert supplied and user data to the network, and finally using the network to predict unknown values in the network for a new user. As an example, a belief network with attributes concerning a user's age, sex, income bracket, lifestyle (“sedentary”, “active”, etc.), coffee preference, and preferred music style can be defined, and user data for these attributes can be added to the network. Then, for a new user, if age, sex, income bracket and lifestyle are supplied the network predicts the user's likely coffee preference and preferred music style. Such a network, however, relies on having access to all (or at least a large portion of) user data before it can make a prediction relating to a single user.
Internet transaction processing systems, for example, Banking sites, which provide access to a range of standard transactions for their customers, including inter-account transfers, bill payment, ordering new cheques, are known. If such transactions were performed in a bank branch environment where the customer is well-known, and has been visiting regularly for a number of years, it is likely that the staff in the branch will get to know the sort of things which the customer wants to do, and will anticipate the customer's requests: for example, the first time the customer visits the branch after payday, they may usually want to take out 250 dollars in cash. So, when the customer approaches the teller, the teller may anticipate this, and have the cash ready at hand. Of course, the customer may not want to perform this transaction, so they can still perform anything else, but they usually do, so this in general will bring a number of advantages: it shortens the transaction time for the customer, increases their loyalty to the bank, and increases the throughput of the bank teller.
It is an object of the present invention to provide the same level of anticipation for transactions performed over the Internet based only on prior customer activity.
DISCLOSURE OF THE INVENTION
Accordingly the present invention provides a personalisation sub-system as claimed in claim
1
.
So, for example, when the customer clicks to bring up an “inter-account transfer” panel, the system of the invention may recognise that usually, with a given balance in their savings and cheque accounts, and at this time of the month, at this point in the financial year, the customer will want to transfer 500 dollars to their cheque account. So, as well as providing a window enabling the customer to manually enter the details for an inter-account transfer, the system also provides a personalised window containing the system's anticipated reason for the visit, with a single-click option to complete the current transaction, FIG.
2
.
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Black Thomas
Booker Kelvin
Doudnikoff Gregory M.
International Business Machines - Corporation
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