Data processing: database and file management or data structures – Database design – Data structure types
Reexamination Certificate
1997-12-05
2002-10-01
Metjahic, Safet (Department: 2171)
Data processing: database and file management or data structures
Database design
Data structure types
C707S793000, C725S014000, C709S217000, C705S014270
Reexamination Certificate
active
06460036
ABSTRACT:
FIELD OF INVENTION
This invention relates to customized electronic identification of desirable objects, such as news articles, in an electronic media environment, and in particular to a system that automatically constructs both a “target profile” for each target object in the electronic media based, for example, on the frequency with which each word appears in an article relative to its overall frequency of use in all articles, as well as a “target profile interest summary” for each user, which target profile interest summary describes the user's interest level in various types of tar get objects. The system then evaluates the target profiles against the users' target profile interest summaries to generate a user-customized rank ordered listing of target objects most likely to be of interest to each user so that the user can select from among these potentially relevant target objects, which were automatically selected by this system from the plethora of target objects that are profiled. on the electronic media. Users' target profile interest summaries can be used to efficiently organize the distribution of information in a large scale system consisting of many users interconnected by means of a communication network. Additionally, a cryptographically based proxy server is provided to ensure privacy of a user's target profile interest summary, by giving the user control over the ability of third parties to access this summary and to identify or contact the user.
PROBLEM
It is a problem in the field of electronic media to enable a user to access information of relevance and interest to the user without requiring the user to expend an excessive amount of time and energy searching for the information. Electronic media, such as on-line information sources, provide a vast amount of information to users, typically in the form of “articles,” each of which comprises a publication item, or document that relates to a specific topic. The difficulty with electronic media is that the amount of information available to the user is overwhelming and the article repository systems that are connected on-line are not organized in a manner that sufficiently simplifies access to only the articles-of interest to the user. Presently, a user either fails to access relevant articles because they are not easily identified or expends a significant amount of time and energy to conduct an exhaustive search of all articles to identify those most likely to be of interest to the user. Furthermore, even if the user conducts an exhaustive search, present information searching techniques do not necessarily accurately extract only the most relevant articles, but also present articles of marginal relevance due to the functional limitations of the information searching techniques. There is also no existing system which automatically estimates the inherent quality of a n article or other target object to distinguish among a number of articles or target objects identified as of possible interest to a user.
Therefore, in the field of information retrieval, there is a long-standing need for a system which enables users to navigate through the plethora of information. With commercialization of communication networks, such as the Internet, the growth of available information has increased. Customization of the information delivery process to the user's unique tastes and interests is the ultimate solution to this problem. However, the techniques which have been proposed to date either only address the user's interests on a superficial level or provide greater depth and intelligence at the cost of unwanted demands on the user's time and energy. While many researchers have agreed that traditional methods have been lacking in this regard, no one to date has successfully addressed these problems in a holistic manner and provided a system that can fully learn and reflect the user's tastes and interests. This is particularly true in a practical commercial context, such as on-line services available on the Internet. There is a need for an information retrieval system, that is largely or entirely passive, unobtrusive, undemanding of the user, and yet both precise and comprehensive in its ability to learn and truly represent the user's tastes and interests. Present information retrieval systems require the user to specify the desired information retrieval behavior through cumbersome interfaces.
Users may receive information on a computer network either by actively retrieving the information or by passively receiving information that is sent to them. Just as users of information retrieval systems face the problem of too much information, so do users who are targeted with electronic junk mail by individuals and organizations. An ideal system would protect the user from unsolicited advertising, both by automatically extracting only the most relevant messages received by electronic mail, and by preserving the confidentiality of the user's preferences, which should not be freely available to others on the network.
Researchers in the field of published article information retrieval have devoted considerable effort to finding efficient and accurate methods of allowing users to select articles of interest from a large set of articles. The most widely used methods of information retrieval are based on keyword matching: the user specifies a set of keywords which the user thinks are exclusively found in the desired articles and the information retrieval computer retrieves all articles which contain those keywords. Such methods are fast, but are notoriously unreliable, as users may not think of the right keywords, or the keywords may be used in unwanted articles in an irrelevant or unexpected context. As a result, the information retrieval computers retrieve many articles which are unwanted by the user. The logical combination of keywords and the use of wild-card search parameters help improve the accuracy of keyword searching but do not completely solve the problem of inaccurate search results. Starting in the 1960's, an alternate approach to information retrieval was developed: users were presented with an article and asked if it contained the information they wanted, or to quantify how close the information contained in the article was to what they wanted. Each article was described by a profile which comprised either a list of the words in the article or, in more advanced systems, a table of word frequencies in the article. Since a measure of similarity between articles is the distance between their profiles, the measured similarity of article profiles can be used in article retrieval. For example, a user searching for information on a subject can write a short description of the desired information. The information retrieval computer generates an article profile for the request and then retrieves articles with profiles similar to the profile generated for the request. These requests can then be refined using “relevance feedback”, where the user actively or passively rates the articles retrieved as to how close the information contained therein is to what is desired. The information retrieval computer then uses this relevance feedback information to refine the request profile and the process is repeated until the user either finds enough articles or tires of the search.
A number of researchers have looked at methods for selecting articles of most interest to users. An article titled “Social Information filtering: algorithms for automating ‘word of mouth’” was published at the CHi-95 Proceedings by Patti Maes et al and describes the Ringo information retrieval system which recommends musical selections. The Ringo system requires active feedback from the users—users must manually specify how much they like or dislike each musical selection. The Ringo system maintains a complete list of users ratings of music selections and makes recommendations by finding which selections were liked by multiple people. However, the Ringo system does not take advantage of any available descriptions of t
Hunn Melvin A.
Le Uyen
Metjahic Safet
Pinpoint Incorporated
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