Bibliographic record
Abstract
Corporate Strategy With its new IP Ventures initiative, Microsoft Corp. hopes to get a bigger bang from the more than $6 billion its spends annually on research and development. Its intellectual property (IP) licensing program, launched in 2005, sells non-core technology to start-ups or incubating companies in an effort to earn money from discoveries that would otherwise collect dust. In January, the program was extended to include collaboration with governments and public-sector development organizations. April marked the launch of Wallop Inc., the first spinoff to come out of the venture's incubating efforts. Microsoft has six labs on three continents with more than 700 researchers working on projects ranging from consumer-oriented mobile phones to high-performance grid computing. But not all of the research ends in products. That's the mission of IP Ventures: to recoup some of the billions that Microsoft pours annually into RD a start-up could, for instance, turn around and work with a rival. That's a gamble Microsoft is prepared to take in return for the opportunity to bring technologies to market and generate additional revenue, according to David Harnett, senior director of IP Ventures, which is located at Microsoft's headquarters in Redmond, Washington. The company expects to release about five patented technologies every quarter through the program, he says. Last September, Seattle, Washington-based Inrix Inc. became the first start-up to take part in the effort, paying Microsoft a revenue-based royalty in exchange for exclusive use of its traffic-analysis software. With Wallop, Microsoft has formed a new company around a social-networking technology developed by researchers working at its labs in Beijing (China), Cambridge (England), Bangalore (India), Redmond, San Francisco and Mountain View (California). The software maker has installed Karl Jacob, a serial entrepreneur, as chief executive and brought in Pay Partners, a venture capital company, to help fund the 12-person startup based in San Francisco. Research as Profit Center What's interesting about IP Ventures in general and the Wallop deal in particular, according to Joe Wilcox with New York-based JupiterResearch, is that Microsoft is beginning to look at its research as a profit center. Until now, the company has not been known for spinoffs. The research behind Wallop's technologies began in 1999 when Microsoft began its research on social-networking technology, which allows users to personalize their online profiles and add Web logs (blogs), music and photos. Those early efforts led to the launch of the Wallop project in 2002 in which Microsoft researchers have been tinkering with the technology ever since. After IP Ventures was established, Wallop was quickly identified as a spinoff candidate. Wallop hopes to use Microsoft's social-network technology to take some steam away from heavy hitters such as MySpace, Friendster and Facebook. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.197 | 0.241 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".