Data processing: database and file management or data structures – Database design – Data structure types
Reexamination Certificate
2000-03-29
2003-12-09
Coby, Frantz (Department: 2171)
Data processing: database and file management or data structures
Database design
Data structure types
C707S793000, C705S001100, C705S014270, C705S005000
Reexamination Certificate
active
06662177
ABSTRACT:
BACKGROUND OF THE INVENTION
The present invention relates to search, retrieval, and organization of data from large data spaces such as the contents of CD ROMS, electronic program guides, the Internet, etc.
The vast amount of information available in CD-ROMS, the Internet, television programming guides, the proposed national information infrastructure, etc. spur the dream of easy access to many large information media sources. Such increased access to information is likely to be useful, but the prospect of such large amounts of information presents new challenges for the design of user interfaces for information access. For example, Internet users often struggle to find information sources or give up in the face of the difficulty of constructing search queries and visualizing the results of queries. Straight text lists such as provided by electronic program guides, Internet search engines, and text search tools such as Folio®, are tedious to work with, often hard to work with, and, because of the rather monotonous look, rather tiring to look at for long periods of time.
There are two major components to searching databases: filtering so irrelevant information is excluded, and sorting the filtered results by some priority schema. For example, an Internet search engine such as Google® uses a text query to filter and sort records in its database representing entry points in the World-Wide-Web. It uses certain implicit criteria such as an implied vote “cast” by pages that link to the candidates retrieved by the query (That is, pages that are linked to by more other pages, have more “votes”). Google also analyzes the pages that cast the votes and gives greater weight to pages that receive more votes by other pages.
Tools such as Google and most other database retrieval tools accept search queries in the form of text with connectors and results are presented in the form of lists sorted by some specific lump criterion which might be an operator involving multiple criteria (such as sort by A, then by B, etc).
SUMMARY OF THE INVENTION
Briefly, a user interface (UI) provides a mechanism for interacting with large databases. Search queries are constructed using various criteria. In addition, the permits implicit and explicit user profiles to selectively and separately incorporated into a search query. Both explicit and implicit profiles are sets of data by which predictions about the preferences of respective users can be made. For example, game software might be a clear preference for one user in one environment (e.g., free Internet downloads) and situation comedies may be clearly disfavored by another user in a different environment (e.g., broadcast tlelvision). The user's profile ranks the records of the database according to the preference prediction made from the profile. An implicit profile, as will be explained more below, is one that is derived automatically from the user's past consumption patterns. An explicit profile is one that is derived by the entry of explicit rule information, such as answers to questions like “Select which type of evening television show you like to watch.” In one embodiment of the invention, explicit profiles are created by allowing the user to select certain criteria, which are represented by tokens in a graphical scene, from various category groups by removing them from the category groups and adding them to a query group. The resulting query group is the set of criteria that define the explicit profile. Profiles may be saved and represented as tokens, just as the criteria, so that they can be manipulated (added to query groups) in further interactions in the same manner as criteria tokens.
This UI design addresses various problems with user interaction with database search devices in the “lean-back” environment. (In the “lean back” situation the user is being entertained and relaxes as when the user watches television, and in the “lean-forward” situation the user is active and focused as when the user uses a desktop computer.) For example, the invention may be used to interact with electronic program guides (EPGs) used with broadcast television. In such an application, the UI may be displayed as a layer directly on top of the recorded or broadcast program or selectively on its own screen. The UI may be accessed using a simple handheld controller. In a preferred embodiment, the controller has vertical and horizontal scroll buttons and only a few specialized buttons to access the various operating modes directly.
The UI generates three environments or worlds: a search world, a profiling world, and an overview world. Assuming an EPG environment, in the search world, the user enters, saves, and edits filtering and sorting criteria (time of day, day of week, genre, etc.). In the profiling world, the user generates and modifies the explicit (and some types of implicit) user profiles. In the overview world, the user views and selects among the results of the search, which is a result of the sorting, filtering, and profiling information.
The invention may be used in connection with various different searching functions. For example, in a preferred embodiment designed around EPGs, there are three basic searching functions provided: (1) Filtering, (2) Filtering and/or sorting by explicit profile, and (3) Sorting by implicit profile. These are defined as follows.
(1) Filtering—A set of criteria that defines the set of results to be displayed. These criteria choose exactly what records in the database will be chosen and which will be excluded from the overview world display.
(2) Filtering and/or sorting by explicit profile—A user is permitted to specify likes or dislikes by making selections from various categories. For example, the user can indicate that dramas and action movies are favored and that certain actors are disfavored. These criteria are then applied to sort the records returned by the filtering process. The degree of importance of the criteria may also be specified, although the complexity of adding this layer may make its addition to a system less worthwhile for the vast majority of users.
As an example of the second type of system, one EP application (EP 0854645A2) describes a system that enables a user to enter generic preferences such as a preferred program category, for example, sitcom, dramatic series, old movies, etc. The application also describes preference templates in which preference profiles can be selected, for example, one for children aged 10-12, another for teenage girls, another for airplane hobbyists, etc. This method of inputting requires that a user have the capacity to make generalizations about him/herself and that these be a true picture of his/her preferences. It can also be a difficult task for common people to answer questions about abstractions such as: “Do you like dramas or action movies?” and “How important is the ‘drama’ criteria to you?”
(3) Sorting by implicit profile—This is a profile that is generated passively by having the system “observe” user behavior. The user merely makes viewing (recording, downloading, or otherwise “using”) choices in the normal fashion and the system gradually builds a personal preference database by extracting a model of the user's behavior from the choices. This process can be enhanced by permitting the user to rate material (for example on a scale of one to five stars). The system uses this model to make predictions about what the user would prefer to watch in the future. The process of extracting predictions from a viewing history, or specification of degree of desirability, can follow simple algorithms, such as marking apparent favorites after repeated requests for the same item. It can be a sophisticated machine-learning process such as a decision-tree technique with a large number of inputs (degrees of freedom). Such models, generally speaking, look for patterns in the user's interaction behavior (i.e., interaction with the UI for making selections).
An example of this type of profile information is MbTV, a system that learns viewers' televisio
Camplin Alison F.
Martino Jacquelyn A.
Nikolovska Lira
Coby Frantz
Thai Hanh B
Thorne Gregory L.
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