Abstract:
An adaptive learning system learns and adapts to behavior of a user enjoying media content via a handheld device. The system includes a user interface provided to the handheld device and operable to receive user input, and a media delivery mechanism provided to the user interface and operable to deliver media content to the user in response to the user input. In further aspects, the system includes a data store provided to the handheld device and operable to record information relating to user consumption of media content, wherein the user consumption occurs in connection with delivering electronic media content.

Description:
FIELD OF THE INVENTION  
         [0001]    The present invention relates to the optimization of advertising viewership based on a user&#39;s behavior. More specifically, the present invention discloses a method and system for adapting advertising content based on a user&#39;s interaction with a handheld device.  
         BACKGROUND AND SUMMARY OF THE INVENTION  
         [0002]    The present invention implements an adaptive learning system cooperating with a handheld control device to capture a user&#39;s viewing habits and to optimize a user&#39;s interaction with programming content. The handheld device, which provides remote control and interactive television functionality, uses an adaptive learning algorithm to interpret viewing habits and use the acquired data to adjust advertising accordingly. Furthermore, the handheld device is operable to use adaptive learning functions to adjust its own interactive components based on a one or more user&#39;s behavior, and can adjust to preferences of a particular users among multiple users of the handheld control device.  
           [0003]    Generally, a major obstacle to optimized broadcasting and advertising is a user&#39;s ability to quickly change the viewed channel using a remote control, especially when advertising is being aired. Ideally, both advertisers and broadcasters want as many viewers as possible during advertising content. In order to maximize viewership during advertising, it is advantageous to all parties involved to know the typical viewer responses to various content.  
           [0004]    By using an adaptive learning system on a handheld device that is designed to capture a user&#39;s viewing habits, a broadcaster or advertiser can better appreciate the value of different programming content. The user&#39;s viewing habits are captured by the handheld device and then conveyed to the broadcaster or advertiser for analysis. With this data readily available, a broadcaster or advertiser is more knowledgeable of a viewership&#39;s characteristics and can dynamically customize advertisements to suit a viewer&#39;s interests.  
           [0005]    The adaptive learning system according to the present invention is advantageous over previous adaptive learning systems in that it enables multiple users to control media delivery devices and consume media content according to the preferences of a particular user. It is further advantageous in that it provides the aggregated and/or individual user preferences to providers of media content, and user profiles can be associated with the preferences by virtue of the device being able to identify a particular user employing the handheld device to consume media content.  
           [0006]    Further areas of applicability of the present invention will become apparent from the detailed description provided hereinafter. It should be understood that the detailed description and specific examples, while indicating the preferred embodiment of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.  
       
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0007]    [0007]FIG. 1 is one embodiment of the handheld control device.  
         [0008]    [0008]FIG. 2 is one embodiment of the system architecture of the present invention.  
         [0009]    [0009]FIGS. 3 and 4 describe one embodiment of an advertisement reward system used with the present invention.  
         [0010]    [0010]FIG. 5 is one embodiment of an adaptive learning system for a customizable handheld device.  
         [0011]    FIGS.  6 - 10  are flow diagrams describing an adaptive handwriting search method according to the present invention. 
     
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS  
       [0012]    The following description of the preferred embodiments is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses.  
         [0013]    With reference to FIG. 1, one embodiment of a handheld control device operable to implement the present invention is illustrated. The control device generally includes a housing assembly  10 , a user interface  12 , and a display screen  14 . A user interacts with the control device by way of the user interface  12 . The user interface may provide means for manipulating applications and data on the control device itself, as well as conventional interaction with electronic devices such as televisions, VCR&#39;s, and DVD players. In addition, a user may interact with the control device by direct contact with touch elements on the display screen  14  using a stylus pen. The handheld control device also includes a communication means  16  for transmitting and receiving wireless data.  
         [0014]    In one embodiment, the handheld control device is a personal data assistant (PDA).  
         [0015]    In another embodiment, the handheld control device includes a PDA stylus pen for handwriting input.  
         [0016]    In another embodiment, the handheld control device includes additional communications means  18  for uploading and downloading data to and from a personal computer.  
         [0017]    In yet another embodiment, the handheld control device includes additional communication means for transmitting and receiving wireless Internet data.  
         [0018]    With reference to FIG. 2, one embodiment of the system architecture is described. The handheld device  19  comprises a graphical user interface (GUI) application  20 , an adaptive learning algorithm  22 , an adaptive learning database  24 , and an IEEE 802.11b or Bluetooth interface  26 . In yet another embodiment, the handheld includes peripherals to access external media such as an SD card. In yet another embodiment, the handheld includes a TV tuner and supplementary data decoder as further described in U.S. Provisional Application No. 60/430,292, filed on Dec. 2, 2002; the disclosure of the above application is incorporated herein by reference.  
         [0019]    The user interacts with the handheld device  19  through the GUI application  20 . The applications  20  present media content extracted from a broadcast signal, such as program data, or downloaded from the Internet to the user for viewing and manipulation. Using the applications  20 , the user can request such information as electronic program guides (EPG&#39;s), supplementary program information, advertisement or product information, news highlights, or sporting event scores and statistics. In addition, the applications  20  may provide the user with games related to currently viewed content, such as trivia, coupon opportunities, and the ability to play along with game shows.  
         [0020]    The adaptive learning algorithm  22  intercepts application requests and commands from the user. The algorithm  22  is a software module that compiles data relating to a user&#39;s behavior. For instance, the algorithm  22  can determine what program a user was viewing during which an advertisement was viewed, whether or not the user changed the channel during this advertisement, what channel the viewer changed to, or what advertisements a user regularly watches. The algorithm  22  analyzes this data and organizes it for optimal storage in the adaptive learning database  24 .  
         [0021]    In one embodiment, the algorithm further identifies a particular user during operation based, for example, on biometric handwriting analysis of handwritten user search queries input via a touch screen and stylus; more information on the handwriting search process and biometric identification can be found in U.S. Provisional Application No. 60/370,496, filed on Apr. 5, 2002; the disclosure of the above application is incorporated herein by reference. Yet further information on the handwriting search process and biometric identification can be found in U.S. Provisional Application No. 60/370,561, filed on Apr. 5, 2002; the disclosure of the above application is incorporated herein by reference. It should be readily understood that the user identification can alternatively take place through use of fingerprint analysis or retinal scan, or through speech recognition-based search and biometric voiceprint analysis. It should further be readily understood that the identification can alternatively take place through user selection of an enrolled profile icon displayed on the device touchscreen that the user employs to activate user preferences.  
         [0022]    The device can use this identification to store user behavior data in association with a particular, identified user, and can even collect user profile information (age, sex, occupation) for storage as well; thus database  24  may be partitioned as needed to store information for different users. The adaptive learning database  24  stores the user behavior data on the handheld device  19  for later application by the algorithm  22 . Alternatively, the user behavior data can be stored on the network and can be shared by other devices on the network.  
         [0023]    The wireless interface  26  transmits user requests and commands to the television  28 . In one embodiment, the handheld device  19  transmits user requests directly to the television  28 . In another embodiment, the handheld device  19  transmits requests to an interface unit  30 , which in turn relays requests to the television  28  in an IR format. The interface unit  30  is a hardware device that resides in a fixed location relative to the television  30  and processes handheld device requests. In addition, the compiled user behavior data and any associated user profile from the algorithm database  24  is transmitted to the interface unit  30 , from which it is then sent back to the broadcaster for analysis. In yet another embodiment, the handheld device communicates the information to advertisers via the Internet or other communications network. With access to this information, a broadcaster or advertiser can dynamically adjust advertising content to correspond to a user&#39;s viewing habits.  
         [0024]    In one embodiment, the advertiser develops different advertising content for different user demographics, and the device is adapted to identify the particular user, identify a user demographic associated with received advertising content, and deliver received advertising content by matching a user profile of the particular user to the user demographic of the advertising content. In another embodiment, the device communicates an identification of a particular user, such as a user profile, currently consuming media content to an advertiser, and the advertiser adjusts the advertising content in real time based on a particular user profile, and/or based on a user demographic developed from an aggregate of current user profiles.  
         [0025]    In another embodiment, the adaptive algorithm  22  resides on the interface unit  30  to conserve processing resources on the handheld device  19 .  
         [0026]    With reference to FIGS. 3 and 4, a method for enticing users to view an advertisement is described. When an advertisement broadcast begins, supplementary data  32  is routed through the interface unit and transmitted to the user via the handheld device  19 . The data is presented to the user through the GUI application  20 . This data  32  can take the form of coupons that are available upon completion of the commercial, extra information about the current advertisement, or interactive games that reward the user with free or discounted products. In another embodiment, a user may acquire points as at  34  for each viewing as at  36  of particular advertisements. Upon reaching certain point totals, a user may redeem as at  38  points for free or discounted products as at  40 . In another embodiment, a user may qualify for a randomly awarded prize upon completion of the advertisement. In yet another embodiment, a user may gain access to products not normally advertised by viewing the entire commercial. This advertising data can be used in conjunction with the adaptive algorithm  22  to further determine the effects of the advertisements and the supplementary data  32  on viewership.  
         [0027]    With reference to FIG. 5, a method for using the adaptive algorithm  22  to customize the behavior of the handheld device according to user viewing habits is described. The handheld device implements a data flow system architecture and a data store  44  to capture information about the user, such as prior viewing habits, channel selections, and other information indicative of the user&#39;s environment. This information can come from diverse sources  42  such as biometric sources and other digital data sources such as DVD players. The adaptive algorithm access the data store  44  and then customizes the performance of the device to better suit the user&#39;s needs. This customized performance may be realized in applications such as advertisements and supplementary program information. One possible application is GPS interaction to determine a user&#39;s travel habits. Another possible application is interaction with a DVD player to determine what types of movies a user typically watches. Yet another application is mobile telephone interaction to determine a user&#39;s general telephone usage. Based on user data gathered in this manner, the handheld device  19  can analyze this data in conjunction with the adaptive learning algorithm  22  and database  24 . The device can then alter advertising content and offers, EPG format, the GUI application&#39;s  20  presentation, or command/request functions according to a user&#39;s typical behavior.  
         [0028]    FIGS.  6 - 10  describe an adaptive handwriting search method according to the present invention, wherein the user writing behavior and user viewing behavior are used together to achieve a more efficient handwriting search of Electronic Programming Guide information, stored advertisements, and/or other information the user accesses via the handheld device. Operation of the handwriting interpreter  74  is described in detail in FIGS. 6 through 10. As seen in FIGS. 6 and 7, handwriting may be analyzed character by character using a progressive search. After first character  76  is written it is analyzed by a handwriting recognition device  78 . Then the process proceeds directly to the word spotting matching engine  84  with one-character string. When the second character or subsequent characters are entered, previously analyzed characters are combined into a multi-character string  82 . Once a group of characters have been assembled, the process proceeds to the word spotting and matching engine  84 .  
         [0029]    The word spotting and matching engine  84  compares the query string to keywords found in keyword database  86  formed from program related contents  88  to return a list of keywords approximating that entered by the user. The user must then scan the list of returned keywords to determine if the expected keyword or result is listed at step  90 . If the expected keyword is not listed, the process proceeds to block  92  where the user is prompted to enter an additional character. The above process then repeats from step  78 . If the expected result is listed, it is selected by the user at  94 . The desired content associated with the handwritten entry is then obtained from the program related contents  88  at  96  and the character by character analysis of the handwriting input is complete.  
         [0030]    An example of a progressive search is illustrated in FIG. 8. As seen in FIG. 8, if a user desires to locate a particular channel and inputs the letter “e” at  76  and the character is recognized at  78 , the methodology proceeds to word spotting matching engine  84 . At word spotting matching engine  84  the recognized input is compared to the channel names within channel name database  98  to return ranked list  100 . The user may then select the appropriate channel from the list  100  and the channel selected will be displayed. If the user input is not recognized, the input is combined into a string at  82  with an input  76  that is recognized at  78 . The letters of the string are then associated with a channel name within channel database  98  by matching engine  84  to return ranked list  100 . The user may then select the desired channel from the ranked list  100  at  102  and the selected channel will then be displayed at  104 .  
         [0031]    Handwriting may also be analyzed using a word-based search as illustrated in FIG. 8. After the user writes the word command at  106 , the word undergoes segmentation at  108 . The segmented word is then analyzed by handwriting recognition engine  110  and compared by word matching engine  112  to the words of keyword database  114 , the words derived from program related contents  180 . Word matching engine  112  then ranks the keywords of keyword database  114  according to the keywords that most closely approximate the query word  106  at  118 . The user then confirms his/her desired keyword at  120  and the content associated with the user keyword is displayed at  122 . Finally, any other actions associated with the entered keyword are also performed at  122 .  
         [0032]    The handwriting interpreter  74  may also be self-training as seen in FIGS. 9 and 10. With reference to FIG. 9, training step  124  may be inserted into either the progressive search system (FIG. 6) or the word-based search system (FIG. 8). Specifically, at training step  124  the item selected by the user from the ranked list of results returned by the matching engine  84 / 112  is used to train the matching engine  84 / 112  to learn particular patterns of the handwriting recognition engine  78 / 110 . These patterns may identify mistakes that the handwriting recognition engine  78 / 110  is likely to make, and consequently use such patterns to better guess when the handwriting recognition engine  78 / 110  generates invalid results. A simple example is that when handwriting recognition engine  78 / 110  often recognizes “c” as “e,” this pattern is learned and used next time by the matching engine  84 / 112 . If confusion exists between “c” and “e”, the matching engine  84 / 112  can make a better guess based on the previous pattern it learned.  
         [0033]    An additional hybrid self-training mechanism is illustrated in FIG. 10. The hybrid method employs the concept of self learning and records the user&#39;s previous handwriting query. When the user confirms a generated ranked list, his/her handwritten query is associated with the selected keyword text. For an incoming handwritten query, a handwritten recognition and a handwritten matching engine can be combined. The handwriting matching engine compares the handwritten query with previous handwritten queries, and finds the best match. Through a previous handwritten query that has been matched, its associated text keyword can be successfully located. The ink based handwriting matching is limited to user dependent matching and this limitation is resolved in the hybrid method, while a cursive handwritten query can also be handled. Further, the ink based handwriting matching requires user handwriting (ink database) to be entered in advance. When combining into the hybrid method, this ink database is accumulated through the self training process.  
         [0034]    The description of the invention is merely exemplary in nature and, thus, variations that do not depart from the general substance of the invention are intended to be within the scope of the invention. Such variations are not to be regarded as a departure from the spirit and scope of the invention.