Evening planner

• US 20040216168A1
• Filed: 05/08/2001
• Published: 10/28/2004
• Est. Priority Date: 05/08/2001
• Status: Active Grant

• Current Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Trovato, Karen I., Kurapati, Kaushal, Schaffer, James D.
• Granted Patent: US 7,131,134 B2
• Time in Patent Office: Days
• US Class Current: 725/135
• CPC Class Codes: H04N 21/44224 Monitoring of user activity on external systems, e.g. Internet browsing
• CPC Class Codes: H04N 21/4532 involving end-user characteristics, e.g. viewer profile, preferences monitoring of user activities for profile generation for accessing a video database G06F16/739; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/454 Content or additional data filtering, e.g. blocking advertisements
• CPC Class Codes: H04N 21/4668 for recommending content, e.g. movies
• CPC Class Codes: H04N 21/6581 Reference data, e.g. a movie identifier for ordering a movie or a product identifier in a home shopping application
• CPC Class Codes: H04N 7/163 by receiver means only

Abstract

An apparatus and method for recommending a schedule of events to a user is disclosed. In the preferred embodiment of the system and method, each channel schedule is broken down into time slices. A novel fuzzy-now recommendation-time value is calculated for each time slice. This fuzzy-now recommendation-time value is a two dimensional value measured in units of recommendation-time, or “enjoyment minutes”. By means of the calculated fuzzy-now recommendation-time values, recommended schedules may be generated using a wide variety of selection methods.

Claims

1. An apparatus for recommending a schedule of events to a user, wherein an event is a discrete item of media presentation, said schedule starting at a start time and ending at an end time, comprising:

at least one fuzzy-now recommendation system for generating a fuzzy-now recommendation function for each event in a plurality of event schedules, each of said event schedules being at least one of an event and a partial event presented on a channel from the start time to the end time;

a fuzzy-now recommendation-time value generator for dividing each generated fuzzy-now recommendation function into time slices, and for calculating a cumulative fuzzy-now recommendation-time value for each time slice, said fuzzy-now recommendation value being comprised of units of recommendation-time; and

a selector for selecting a series of at least one of an event and a partial event to comprise a schedule of recommended events, said selecting being based upon the calculated fuzzy-now recommendation-time values.

2. The apparatus as recited in claim 1, further comprising:

a scheduling information source for providing a plurality of event schedules to the at least one fuzzy-now recommendation system;

at least one media source for supplying at least one of an event and a partial event in the provided plurality of event schedules;

a receiver/tuner for tuning in a channel, for receiving at least one of an event and a partial event over the tuned channel from the at least one media source, and for providing the received at least one of an event and a partial event to a media output means;

said media output means for receiving at least one of an event and a partial event from the receiver/tuner and presenting said at least one of an event and a partial event to a user;

a controller for directing the receiver/tuner, for directing the reception of the plurality of event schedules from the scheduling information source, and for directing the creation of an Electronic Programming Guide (EPG) Graphical User Interface (GUI) which will be provided to an EPG GUI output means, said EPG GUI being created from the plurality of event schedules; and

an EPG GUI output means;

wherein the selected schedule of recommended events is output to the EPG GUI output means.

3. The apparatus as recited in claim 1, further comprising:

a user profile storage for storing a user profile, said user profile comprising recommendation values and fuzzy-now functions for channel events, wherein the at least one fuzzy-now recommendation system generates a fuzzy-now recommendation function for each event by applying a fuzzy-now function for said each event in the user profile to a recommendation value for said each event in the user profile.

4. The apparatus as recited in claim 2, further comprising:

an input means for a user to control the EPG GUI and to select the start and end times of the recommended schedule.

5. The apparatus as recited in claim 2, wherein the EPG GUI output means and the media output means are the same means.

6. The apparatus as recited in claim 2, wherein the at least one media source is one of CATV system, an RF broadcast television system, the Internet, and a storage/playback device.

7. The apparatus as recited in claim 2, wherein the receiver/tuner is one of a stereo AM/FM radio receiver, a digital radio receiver, a television UTHF/VHF receiver, a digital television receiver, a set-top box (STB), and a web browser.

8. The apparatus as recited in claim 2, wherein the media output means is one of a television monitor, a stereo speaker system, a holographic display, a screen on a PDA, and a screen on a mobile terminal.

9. The apparatus as recited in claim 1, wherein the time slices are one of equal size and varying sizes.

10. The apparatus as recited in claim 1, wherein the time slices correspond in size to events.

11. A method of generating a schedule of recommended events, wherein an event is a discrete item of media presentation, said schedule starting at a start time and ending at an end time, comprising the steps of:

receiving a plurality of event schedules, each of said event schedules being at least one of an event and a partial event presented on a channel;

generating a fuzzy-now recommendation function for each at least one of an event and a partial event in the received plurality of event schedules to create a series of at least one fuzzy-now recommendation function for each channel;

dividing at least a portion of each series of at least one generated fuzzy-now recommendation function into time slices, said at least a portion being from the start time to the end time;

calculating a fuzzy-now recommendation-time value for each time slice, said fuzzy-now recommendation-time value being comprised of units of recommendation-time; and

selecting a series of at least one of an event and a partial event to comprise a schedule of recommended events, said selecting being based upon the calculated fuzzy-now recommendation-time values.

12. The method as recited in claim 11, wherein the step of calculating a fuzzy-now recommendation-time value for each time slice comprises:

mathematically integrating the generated fuzzy-now recommendation function over each time slice.

13. The method as recited in claim 11, wherein the step of generating a fuzzy-now recommendation function for each at least one of an event and a partial event in the received plurality of event schedules comprises:

receiving a recommendation value for an event;

receiving a fuzzy-now function for the event; and

applying the fuzzy-now function of the event to the recommendation value of the event, thus generating a fuzzy-now recommendation function for the event.

14. The method as recited in claim 11, wherein the step of selecting a series of at least one of an event and a partial event to comprise a schedule of recommended events is based upon events with a highest total recommendation-time.

15. The method as recited in claim 14, wherein the step of selecting a series of at least one of an event and a partial event to comprise a schedule of recommended events comprises:

calculating a total fuzzy-now recommendation-time for each event and partial event within the start time and end time, said calculating being the addition of time slices comprising each event and partial event;

selecting, within the start time and end time, at least one of an event and partial event with a highest total fuzzy-now recommendation-time for its own time period in order to fill its own time period;

repeating the selecting step for any unfilled time period within the start time and end time, until a period from the start time to the end time is completely filled with at least a selected non-overlapping one of an event and a partial event; and

creating a schedule of recommended events with the at least a selected non-overlapping one of an event and a partial event.

16. The method as recited in claim 14, wherein the step of selecting a series of at least one of an event and a partial event to comprise a schedule of recommended events comprises:

(a) calculating a total fuzzy-now recommendation-time for each event and partial event within the start time and end time, said calculating being the addition of time slices comprising each event and partial event;

(b) selecting, within the start time and end time, one of an event and partial event with a highest total fuzzy-now recommendation-time;

(c) removing the selected event from a pool of events comprising all events and partial events in the plurality of event schedules;

(d) repeating the generating, dividing, and calculating a fuzzy-now recommendation-time value for the pool of events, wherein, in the repeated generating step, a fuzzy-now recommendation function for events that overlap in time the selected event is diminished by a weighting factor;

(e) repeating steps (a), (b), (c), and (d) until a period from the start time to the end time is completely filled with at least a selected non-overlapping one of an event and a partial event; and

(f) creating a schedule of recommended events with the at least a selected non-overlapping one of an event and a partial event.

17. The method as recited in claim 11, wherein the step of selecting a series of at least one of an event and a partial event to comprise a schedule of recommended events is based upon time slices with a highest total recommendation-time.

18. The method as recited in claim 17, wherein the step of selecting a series of at least one of an event and a partial event to comprise a schedule of recommended events comprises:

finding each event that has at least one time slice with a recommendation-time value above a given threshold;

calculating a ratio of time slices above the given threshold to time slices below the given threshold for each found event;

selecting, within the start time and end time, an event with a highest calculated ratio for its own time period in order to fill its own time period;

repeating the selecting step until a period from the start time to the end time is filled as it can be with whole events;

calculating, if there are any unfilled time segments between the start time and end time, a ratio of time slices above the given threshold to time slices below the given threshold for each partial event in each unfilled time segment within the period from the start time to the end time;

selecting, within the start time and end time, a partial event with a highest calculated ratio for its own time period to fill its own time period;

repeating the selecting step until a period from the start time to the end time is filled;

creating a schedule of recommended events with the at least a selected non-overlapping one of an event and a partial event.

19. The method as recited in claim 11, wherein the step of selecting a series of at least one of an event and a partial event to comprise a schedule of recommended events is based upon channel-surfing habits of a user.

20. The method as recited in claim 19, wherein the step of selecting a series of at least one of an event and a partial event to comprise a schedule of recommended events comprises:

determining a multiple of time slices based on an amount of time the user watches a channel event before switching to another channel event;

dividing each series of time slices into divisions, each division being comprised of one determined multiple of time slices;

calculating a fuzzy-now recommendation-time value for each division;

selecting, within the start time and end time, non-overlapping divisions with a highest calculated ratio for their own time period to fill their own time period, until a period between the start time and end time is filled;

creating a schedule of recommended events with the selected non-overlapping divisions.

21. The apparatus as recited in claim 11, wherein the time slices are one of equal size and varying sizes.

22. The apparatus as recited in claim 11, wherein the time slices correspond in size to events.