6127 Keswick Dr, Seattle, WA 98105 • 2065237613 • 2069858375
6065 NE Kelden Pl, Seattle, WA 98105
200 NE 42Nd St, Seattle, WA 98105
5820 57Th St, Seattle, WA 98105 • 2065237613
Kiona, WA
Washington, DC
Work
Company:
Allen institute for artificial intelligence
Jan 2014
Position:
Chief executive officer
Education
Degree:
Doctorates, Doctor of Philosophy
School / High School:
Carnegie Mellon University
1986 to 1991
Specialities:
Computer Science, Philosophy
Skills
Machine Learning • Artificial Intelligence • Data Mining • Big Data • Natural Language Processing • Computer Science • Algorithms • Text Mining • Information Retrieval • Entrepreneurship • Pattern Recognition • Text Analytics • Information Extraction
Interests
Machine Learning • Artificial Intelligence • Natural Language Processing • Data Mining
An information search device capable of selecting topic search engines (i. e. , search engines that focus on specific topics) that are appropriate to a users search keywords when searching the Web on the Internet. Terms having relevance to each topic search engine are collected from, for example, the Web, and a DB selection index for selecting search engines is produced in advance by an index generator. When a search keyword is supplied from a user, terms having relevance to the search keyword are acquired from a general-purpose Web search engine by means of a query expansion unit. The thus-acquired terms are matched with terms stored in the DB selection index, and topic search engines having a high incidence of matching are presented to the user.
Performing Predictive Pricing Based On Historical Data
Oren Etzioni - Seattle WA, US Alexander Yates - Seattle WA, US Craig A. Knoblock - El Segundo CA, US Rattapoom Tuchinda - Los Angeles CA, US
Assignee:
University of Washington - Seattle WA
International Classification:
G06F 17/60
US Classification:
705 1, 705 10, 705400, 705412
Abstract:
Techniques are described for using predictive pricing information for items to assist in evaluating buying and/or selling decisions in various ways, such as on behalf of end-user item acquirers and/or intermediate item providers. The predictive pricing for an item may be based on an analysis of historical pricing information for that item and/or related items, and can be used to make predictions about future pricing information for the item. Such predictions may then be provided to users in various ways to enable comparison of current prices to predicted future prices. In some situations, predictive pricing information is used to assist customers when purchasing airline tickets and/or to assist travel agents when selling airline tickets. This abstract is provided to comply with rules requiring an abstract, and it is submitted with the intention that it will not be used to interpret or limit the scope or meaning of the claims.
Performing Predictive Pricing Based On Historical Data
Oren Etzioni - Seattle WA, US Alexander Yates - Seattle WA, US Craig A. Knoblock - El Segundo CA, US Rattapoom Tuchinda - Los Angeles CA, US
Assignee:
University of Washington - Seattle WA
International Classification:
G06Q 99/00
US Classification:
705 1, 705 10, 705400, 705412
Abstract:
Techniques are described for using predictive pricing information for items to assist in evaluating buying and/or selling decisions in various ways, such as on behalf of end-user item acquirers and/or intermediate item providers. The predictive pricing for an item may be based on an analysis of historical pricing information for that item and/or related items, and can be used to make predictions about future pricing information for the item. Such predictions may then be provided to users in various ways to enable comparison of current prices to predicted future prices. In some situations, predictive pricing information is used to assist customers when purchasing airline tickets and/or to assist travel agents when selling airline tickets. This abstract is provided to comply with rules requiring an abstract, and it is submitted with the intention that it will not be used to interpret or limit the scope or meaning of the claims.
Hugh Crean - Seattle WA, US Jay Bartot - Seattle WA, US David Hsu - Santa Monica CA, US Oren Etzioni - Seattle WA, US Michael Fridgen - Seattle WA, US
Assignee:
Farecast, Inc. - Seattle WA
International Classification:
G06F 17/30 G06F 17/50 G06F 17/00 G06F 30/00
US Classification:
705 10, 705400, 705 26, 705 141, 705 7, 705 5
Abstract:
A method and system for protecting prices is provided. The price protection system increases consumer confidence when making purchases by reducing the risk associated with fluctuating prices. The price protection system receives a purchase specification from a consumer. Next, the price protection system determines the risk that the prices of items matching the purchase specification will change and reports a protected price to the consumer that represents the price that the price protection system will protect based on the determined risk for a protection period. Finally, the price protection system receives a request from the consumer to purchase protection of the protected price.
Michael J. Cafarella - Seattle WA, US Michele Banko - Seattle WA, US Oren Etzioni - Seattle WA, US
Assignee:
University of Washington through its Center for Commercialization - Seattle WA
International Classification:
G06E 1/00 G06F 15/18
US Classification:
706 20, 705 5, 382104
Abstract:
To implement open information extraction, a new extraction paradigm has been developed in which a system makes a single data-driven pass over a corpus of text, extracting a large set of relational tuples without requiring any human input. Using training data, a Self-Supervised Learner employs a parser and heuristics to determine criteria that will be used by an extraction classifier (or other ranking model) for evaluating the trustworthiness of candidate tuples that have been extracted from the corpus of text, by applying heuristics to the corpus of text. The classifier retains tuples with a sufficiently high probability of being trustworthy. A redundancy-based assessor assigns a probability to each retained tuple to indicate a likelihood that the retained tuple is an actual instance of a relationship between a plurality of objects comprising the retained tuple. The retained tuples comprise an extraction graph that can be queried for information.
Performing Predictive Pricing Based On Historical Data
Oren Etzioni - Seattle WA, US Alexander Yates - Seattle WA, US Craig A. Knoblock - El Segundo CA, US Rattapoom Tuchinda - Los Angeles CA, US
Assignee:
University of Washington - Seattle WA
International Classification:
G06Q 99/00
US Classification:
705 5, 705 11, 705 731
Abstract:
Techniques are described for using predictive pricing information for items to assist in evaluating buying and/or selling decisions in various ways, such as on behalf of end-user item acquirers and/or intermediate item providers. The predictive pricing for an item may be based on an analysis of historical pricing information for that item and/or related items, and can be used to make predictions about future pricing information for the item. Such predictions may then be provided to users in various ways to enable comparison of current prices to predicted future prices. In some situations, predictive pricing information is used to assist customers when purchasing airline tickets and/or to assist travel agents when selling airline tickets.
Use Of Lexical Translations For Facilitating Searches
Oren Etzioni - Seattle WA, US Kobi Reiter - Seattle WA, US Marcus Sammer - Seattle WA, US Michael Schmitz - Seattle WA, US Stephen Soderland - Bainbridge Island WA, US
Assignee:
University of Washington - Seattle WA
International Classification:
G06F 17/27 G06F 17/28
US Classification:
704 2, 704 9
Abstract:
A translation graph is created using a plurality of reference sources that include translations between a plurality of different languages. Each entry in a source is used to create a wordsense entry, and each new word in a source is used to create a wordnode entry. A pair of wordnode and wordsense entries corresponds to a translation. In addition, a probability is determined for each wordsense entry and is decreased for each translation entry that includes more than a predefined number of translations into the same language. Bilingual translation entries are removed if subsumed by a multilingual translation entry. Triangulation is employed to identify pairs of common wordsense translations between a first, second, and third language. Translations not found in reference sources can also be inferred from the data comprising the translation graph. The translation graph can then be used for searches of a data collection in different languages.
Performing Predictive Pricing Based On Historical Data
Oren Etzioni - Seattle WA, US Alexander Yates - Seattle WA, US Craig A. Knoblock - El Segundo CA, US Rattapoom Tuchinda - Los Angeles CA, US
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06Q 99/00
US Classification:
705 735, 705 11, 705400
Abstract:
Techniques are described for using predictive pricing information for items to assist in evaluating buying and/or selling decisions in various ways, such as on behalf of end-user item acquirers and/or intermediate item providers. The predictive pricing for an item may be based on an analysis of historical pricing information for that item and/or related items, and can be used to make predictions about future pricing information for the item. Such predictions may then be provided to users in various ways to enable comparison of current prices to predicted future prices. In some situations, predictive pricing information is used to assist customers when purchasing airline tickets and/or to assist travel agents when selling airline tickets. This abstract is provided to comply with rules requiring an abstract, and it is submitted with the intention that it will not be used to interpret or limit the scope or meaning of the claims.
Allen Institute For Artificial Intelligence
Chief Executive Officer
Decide 2010 - Sep 2013
Co-Founder and Chief Technology Officer
Farecast 2003 - 2008
Founder
Madrona Venture Group 2003 - 2008
Venture Partner
Go2Net 1999 - 2000
Chief Technology Officer
Education:
Carnegie Mellon University 1986 - 1991
Doctorates, Doctor of Philosophy, Computer Science, Philosophy
Harvard University 1982 - 1986
Bachelors, Bachelor of Arts, Bachelor of Science, Computer Science
Carnegie Mellon University
Master of Science, Masters, Computer Science
Skills:
Machine Learning Artificial Intelligence Data Mining Big Data Natural Language Processing Computer Science Algorithms Text Mining Information Retrieval Entrepreneurship Pattern Recognition Text Analytics Information Extraction
Interests:
Machine Learning Artificial Intelligence Natural Language Processing Data Mining
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