Data processing: database and file management or data structures – Database design – Data structure types
Reexamination Certificate
2001-02-07
2003-11-04
Mizrahi, Diane D. (Department: 2171)
Data processing: database and file management or data structures
Database design
Data structure types
C707S793000
Reexamination Certificate
active
06643639
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates generally to the field of customer self service systems for resource search and selection, and more specifically, to a novel mechanism for providing a response set based on user queries and derived user contexts and that is adaptable for modifying output response sets in accordance with different user contexts and user interactions as they change over time.
2. Discussion of the Prior Art
Currently there exist many systems designed to perform search and retrieval functions.
These systems may be classified variously as knowledge management systems, information portals, search engines, data miners, etc. However, providing effective customer self service systems for resource search and selection presents several significant challenges. The first challenge for current systems with query capability is that serving queries intelligently requires a large amount of user supplied contextual information, while at the same time the user has limited time, patience, ability and interest to provide it. The second challenge is that searching without sufficient context results in a very inefficient search (both user time and system resource intensive) with frequently disappointing results (overwhelming amount of information, high percentage of irrelevant information). The third challenge is that much of a user's actual use and satisfaction with search results differ from that defined at the start of the search: either because the users behave contrary to their own specifications, or because there are other contextual issues at play that have not been defined into the search.
The prior art has separately addressed the use of the history of interaction with the user or their current service environment to provide context for building a resource response set. The prior art also assumes the shallow context of a single user query stream focused on a single topic.
As will be hereinafter explained in greater detail, some representative prior art search and retrieval systems include Feldman, Susan, “The Answering Machine,” in Searcher: The Magazine for Database Professionals, 1, 8, Jan, 2000/58; U.S. Pat. No. 5,974,412 entitled “Intelligent Query System for Automatically Indexing Information in a Database and Automatically Categorizing Users”; U.S. Pat. No. 5,600, 835 entitled “Adaptive Non-Literal Text String Retrieval”; U.S. Pat. No. 6,105,023 entitled “System and Method for Filtering a Document Stream”; and, U.S. Pat. No. 5,754,939 entitled “System for Generation of User Profiles For a System For Customized Electronic Identification of Desirable Objects.”
For example, the article by Feldman, Susan entitled “The Answer Machine,” discusses generally how the use of learning may make systems dynamic, however, the systems related to learning appear to be focused on learning a taxonomy or relationships among document categories or topics. Such learning systems may detect the rise of new terms. For example, the Semio system (http://www.semio.com/products/semiotaxonomy.html) creates taxonomies or hierarchies automatically. However, none of the systems for learning in the prior art are focused on or uses user contexts. Moreover, no system in the prior art is directed to discovering clusters in user behaviors (user context clusters).
U.S. Pat. No. 5,974,412 describes an adaptive retrieval system that uses a vector of document and query features to drive the retrieval process. Specifically described is an Intelligent Query Engine (IQE) system that develops multiple information spaces in which different types of real-world objects (e.g., documents, users, products) can be represented. Machine learning techniques are used to facilitate automated emergence of information spaces in which objects are represented as vectors of real numbers. The system then delivers information to users based upon similarity measures applied to the representation of the objects in these information spaces. The system simultaneously classifies documents, users, products, and other objects with documents managed by collators that act as classifiers of overlapping portions of the database of documents. Collators evolve to meet the demands for information delivery expressed by user feedback. Liaisons act on the behalf of users to elicit information from the population of collators. This information is then presented to users upon logging into the system via Internet or another communication channel.
U.S. Pat. No. 5,600,835 describes a method and system for selectively retrieving information contained in a stored document set using a non-literal, or “fuzzy”, search strategy, and particularly implements an adaptive retrieval approach. A text string query is transmitted to a computer processor, and a dissimilarity value Di is assigned to selected ones of stored text strings representative of information contained in a stored document set, based upon a first set of rules. A set of retrieved text strings representative of stored information and related to the text string query is generated, based upon a second set of rules. Each of the retrieved text strings has an associated dissimilarity value Di, which is a function of at least one rule Rn from the first set of rules used to retrieve the text string and a weight value wn associated with that rule Rn. The retrieved text strings are displayed preferably in an order based on their associated dissimilarity value Di. Once one or more of the retrieved text strings is chosen, the weight value wn associated with at least one rule of the first set of rules is adjusted and stored.
U.S. Pat. No. 6,105,023 entitled “System and Method for Filtering A Document Stream” is directed to a robust document retrieval system, albeit it is not adaptive. Particularly, it describes a method for filtering incoming documents that includes the steps of receiving an incoming document and parsing it to produce an inverted list of terms contained in the incoming document. The inverted list is then used to retrieve user queries. Any user queries matching less than a pre-determined number of terms are immediately discarded. The remaining user queries are scored and user queries having a score less than a predetermined threshold are discarded. The remaining user queries are the queries which the incoming document matches.
U.S. Pat. No. 5,754,939 describes a method for 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 target 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.
A major limitation of these prior art approaches however, is their inability to apply specific user context to improve resource selection for other users on the same topic and their inability to adaptively respond to the same search query by the same user over time based on changes in user context and the user's history of prior interaction with the resource search and selection system. These approaches are also limited in their ability to dynamically generate inclusionary and exclusionary content filters as a bi-product of building the response set.
By returning the same response set to the same query regardless of the user's current context and previous selections, current self service search a
Biebesheimer Debra L.
Jasura Donn P.
Keller Neal M.
Oblinger Daniel A.
Rolando Stephen J.
Mizrahi Diane D.
Morris, Esq. Daniel P.
Scully Scott Murphy & Presser
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