Data processing: artificial intelligence – Knowledge processing system
Reexamination Certificate
1999-04-20
2003-04-22
Starks, Jr., Wilbert L. (Department: 2121)
Data processing: artificial intelligence
Knowledge processing system
C705S039000, C707S793000
Reexamination Certificate
active
06553358
ABSTRACT:
RELATED APPLICATIONS
This application is related to the coassigned and cofiled applications entitled “Systems and Methods for Directing Automated Services for Messaging and Scheduling”, and “Learning by Observing a User's Activity for Enhancing the Provision of Automated Services”, both of which are hereby incorporated by reference.
FIELD OF THE INVENTION
This invention relates generally to decision theory, and its application to automated decision making when used in conjunction with automated classification systems such as text classification systems, and more particularly to utilizing decision theory for guiding automated user interface, communication, and system actions.
BACKGROUND OF THE INVENTION
Computer applications such as messaging and scheduling applications have become important applications in many computer users' lives. Messaging programs generally allow a user to send and receive electronic mail (e.g., messages) to and from other computer users, for example, over a local- or a wide-area network, or over an intranet, extranet, or the Internet. Online schedule and calendar programs generally allow a user to create and to track appointments in a calendar. More sophisticated scheduling programs allow one user to schedule a group meeting with other computer users—checking the latter users' schedule availability, and receiving confirmation from the users upon them accepting or rejecting the group meeting appointment.
Within the prior art, however, messaging and scheduling programs are generally not very well integrated, even if they are components within the same computer program. For example, a user may receive a message from a colleague stating “Looking forward to seeing you at 2 on Thursday.” Generally, however, the prior art does not provide for automatically directing the scheduling program to make a meeting appointment at 2 p.m. on Thursday. Instead, typically the user who has received the message has to open the scheduling program, access Thursday's calendar, and manually enter an appointment at 2 p.m. on Thursday's calendar. Because of the many steps required to go from reading the message within the messaging program to entering the information into the scheduling program, many users choose not to even use scheduling programs, or to only use them sparingly.
For these and other reasons, there is a need for the present invention.
SUMMARY OF THE INVENTION
The invention relates to the use of decision theory for directing automated actions. In one embodiment, a computer-implemented method first determines a text to analyze. The method then determines an action probability based on the text and/or contextual information (e.g., information regarding recent user activity, organizational information, etc.), and based on the action probability, selects one of the following options: (1) inaction, (2) automatic action, or (3) suggested action with user approval. Upon the method selecting either the (1) automatic action option or the (2) suggested action with user approval option—the latter also in conjunction with receiving actual user approval—the method performs an action based on the text.
Embodiments of the invention provide for advantages not found within the prior art. For example, in the context of scheduling appointments based on the text input, the method can perform an action based on the text, upon determining the action probability of the text. Based on the action probability the method determines if it should do nothing (i.e., corresponding to a low probability), do something automatically (i.e., corresponding to a high probability), or suggest an action, but do not do it automatically (i.e., corresponding to a medium probability). Thus, one embodiment of the invention effectively links scheduling with messaging automatically. It is noted that the invention itself is not limited to the application of scheduling and messaging, however; for example, other actions that can be based on the text analyzed including extracting contact information and storing the information in an address book, forwarding, paging, routing and moving, as those of ordinary skill within the art can appreciate.
REFERENCES:
patent: 5696965 (1997-12-01), Dedrick
patent: 5864848 (1999-01-01), Horvitz et al.
patent: 5870723 (1999-02-01), Pare, Jr. et al.
patent: 6151600 (2000-11-01), Dedrick
“Principles of Mixed-Initiative User Interfaces”; Eric Horvitz; Microsoft Research; Aug. 27, 2000.
U.S. patent application Ser. No. 09/055,477, filed Apr. 6, 1998.
U.S. patent application Ser. No. 08/684,003, filed Jul. 19, 1996.
U.S. patent application Ser. No. 09/197,159, filed Nov. 20, 1998.
U.S. patent application Ser. No. 09/197,158, filed Nov. 20, 1998.
U.S. patent application Ser. No. 09/197,160, filed Nov. 20, 1998.
Judea Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (ISBN 1558604790), Apr. 1997.
Eric Horvitz, Matthew Barry, Display of Information for Time-Critical Decision-Making, Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, Aug. 1995.
Eric Horvitz, Jack Breese, David Heckerman, David Hovel, Koos Rommelse, The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users, Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI, Jul. 1998, Morgan Kaufmann Publishers, pp. 256-265.
Daivd Heckerman and Eric Horvitz, Inferring Informational Goals from Free-Text Queries: A Bayesian Approach, Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI, Jul. 1998, Morgan Kaufmann Publishers, pp. 230-237.
Susan Dumais, John Platt, David Heckerman, Mehran Sahami, Inductive Learning Algorithms and Representations for Text Categorization, Proceedings of ACM-CIKM98, Nov. 1998.
Ben Shneiderman, Pattie Maes, Direct Manipulation vs Interface Agents: Excerpts from debates at IUI 97 and CHI 97, interactions, Nov.-Dec. 1997, pp. 42-61.
M. Sahami, S. Dumais, D. Heckerman, E. Horvitz, A Bayesian Approach to Filtering Junk E-mail, AAAI Workshop on Text Classification, Jul. 1998, Madison, Wisconsin, AAAI Technical Report WS-98-05.
Amin & Turocy LLP
Starks, Jr. Wilbert L.
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