Intelligent adaptive navigation optimization

• US 20030030666A1
• Filed: 08/07/2001
• Published: 02/13/2003
• Est. Priority Date: 08/07/2001
• Status: Abandoned Application

• Current Assignee: Leegur OY
• Original Assignee: Leegur OY
• Inventors: Najmi, Amir, Siddiqui, Ali
• Application Number:
  US09/923,916
• Publication Number:
  US 20030030666A1
• Time in Patent Office: Days
• US Class Current: 345/745
• CPC Class Codes: G06F 3/0482 Interaction with lists of selectable items, e.g. menus
• CPC Class Codes: H04M 1/72445 for supporting Internet browser applications
• CPC Class Codes: H04M 1/72451 according to schedules, e.g. using calendar applications
• CPC Class Codes: H04M 1/72457 according to geographic location
• CPC Class Codes: H04M 1/72472 wherein the items are sorted according to specific criteria, e.g. frequency of use

Abstract

A method and apparatus for optimizing the navigation of lists or other hierarchies of alternatives, as presented to the user by electronic devices and computer networks, by automatically recommending the alternatives of the next list to be presented. Each alternative is recommended on the basis of the importance of that alternative to the user, or to the operator of the service provided to the user, or to providers of applications that can be selected by the user. The optimization is based upon probabilities estimated by mathematical functions on several variables, statistics, and parameters, including but not limited to the user location, time and date, user'"'"'s expressed personal preferences, service operators'"'"' and application providers'"'"' expressed commercial intentions, service operators'"'"' and application providers'"'"' expressed business rules, implied application relationships, personal information of the user, usage statistics of the user, usage statistics of the general public, and embedded parameters which refine a statistical model of the user'"'"'s behavior. Such optimization reduces the efforts required of a user to select one item, out of a structure of lists of items, by reorganizing the structure so that the user is likely to use fewer keystrokes or spoken words to select an item of combined higher importance to the user, the service operator, and the application provider. The optimization is personalized to each user by presenting the lists and structures to each user in a way that is automatically adapted to the statistical and deterministic factors pertaining to that individual user, so that users of different personalities and preferences can see or hear differently organized lists from which to choose. The number of keystrokes, or spoken words, is further reduced by intelligent data sharing between applications to avoid requiring the user'"'"'s reentry of the same data to one application that was already entered in another.

Claims

1. A method for presenting an optimized selection of alternatives from a plurality of alternative choices to a user through an electronic device, comprising the steps of:

providing a statistical model for said plurality of alternative choices, said statistical model reflecting user-specific and non-user-specific weighting factors;

applying said statistical model to a first ordered selection of choices to derive a recommended selection of choices of optimized order, said optimized order being determined by said statistical model; and

presenting said recommended selection of choices to said user substantially in said optimized order.

2. The method of claim 1 wherein access to said plurality of alternative choices is provided by a service operator and said method further comprises the step of including in said statistical model a weighting factor reflecting preferences of said service operator in presenting alternatives to said user.

3. The method of claim 2 wherein at least one of said alternative choices is provided by a third-party advertiser and said method further comprises the step of including in said statistical model a weighting factor reflecting preferences of said third-party advertiser in presenting alternatives to said user.

4. A method for presenting an optimized selection of alternatives from a plurality of alternative choices to a user through an electronic device, comprising the steps of:

providing a statistical model for said plurality of alternative choices;

applying said statistical model to a first ordered selection of choices to derive a recommended selection of choices of optimized order, said optimized order being determined by said statistical model;

presenting said recommended selection of choices to said user substantially in said optimized order for further selection of an end alternative by said user;

when said end alternative calls for entry of data by said user, applying said statistical model to derive a recommended selection of prefill data objects; and

presenting said recommended selection of prefill data objects to said user.

5. A method for presenting an optimized selection of alternatives from a plurality of alternative choices to a user through an electronic device, comprising the steps of:

providing a statistical model for said plurality of alternative choices, wherein said statistical model is an Empirical Bayesian statistical model;

applying said Empirical Bayesian statistical model to a first ordered selection of choices to derive a recommended selection of choices of optimized order, said optimized order being determined by said statistical model; and

presenting said recommended selection of choices piecemeal to said user substantially in said optimized order.

6. A method for presenting an optimized selection of alternatives from a plurality of alternative choices to a user through an electronic device having a display capable of showing only a characteristic number of choices to the user at a time, comprising the steps of:

providing a statistical model for said plurality of alternative choices;

applying said statistical model to a first ordered selection of alternative choices to derive a recommended selection of alternative choices of optimized order, said optimized order being determined by said statistical model; and

presenting at least a portion of said recommended selection of alternative choices to said user a fixed number at a time substantially in said optimized order, said fixed number being at most the characteristic number said device is capable of displaying at a time.

7. The method of claim 6 wherein said statistical model reflects both user-specific and non-user-specific weighting factors.

8. The method of claim 6 wherein said statistical model is an Empirical Bayesian statistical model.