Oscar Health Insurance(OHI)是家成立於2012年的美國保險公司。雖然成立至今不過五年時間，但公司在去年（2016）第一季時完成了4億美元（約120億美元）的募資，是去年前三季中，美國金融科技（Fintech）新創公司的最高紀錄，而公司估值也達到17.5億美元（約530億台幣），是名符其實的新創獨角獸。無獨有偶，在去年Fintech新創公司的募資排行前五名中，包括Oscar Health Insurance在內，保險公司就佔了三名。保險科技（InsurTech）已經成為當紅炸子雞。
Big Data and AI: Taiwan Consumers in the Here and Now
Conor Stuart/IP Observer
Scott Hsia, CEO of Dentsu Aegis Network’s Taiwan subsidiary AAA, has been quoted as stating, “the biggest risk in product retail is guessing.” This is essentially the problem that big data goes towards resolving. Hsia was the keynote speaker at a recent big data conference hosted by Digitimes in Taipei.
Hsia approaches big data from a retail and marketing perspective. He described a departure in marketing from “teaching” consumers what is the best, what is fashionable and when to consume, to the new reality of the internet age, where you need to market your goods to the right people, in the right context and at the right time.
Another way to phrase this, is that in the digital era, companies should be seeking to find existing need, instead of creating need. Essentially the product or service needs to be delivered to the client in a way that is seamless and does not force them to change their daily schedule or force them to wait around unnecessarily.
This presents another problem, in that the data that you collect has to be constantly updated in order to allow you to be aware of whether the consumer will be receptive to your marketing when they are exposed to it. If you have this data in hand, it is then possible to automate the marketing process, in delivering advertisements, in whatever form they take, when the consumer is most receptive to them.
Smart sensors and tagging can play a role in this, and in China, the mega IDs associated with individuals on WeChat even allow you knowledge of whether certain kinds of people are within your circle or not.
Joe Chiang, from e-Cloud Valley, a cloud services firm which works closely with Amazon Web Services, pointed to the three big challenges of big data, namely volume, velocity and variety. She stated that different solutions are suitable depending on the “temperature of your data” and the amount latency permissible until you’re provided with an “answer”. Some functions require data to be processed instantly, while, with other forms of data, the output is not required right away. With different requirements, different forms of storage at different pricing points will be required.【Unfinished; For Further Reading: IP Observer 012: Virtual Designs: Big Data and AI: Taiwan Consumers in the Here and Now】
1980年代初期，分子生物學研究蓬勃發展，這波發展趨勢主要依賴生物研究人員所研發生物材料或生物資訊的散佈，使當時研究分子生物方法逐漸複雜化。這一波生物科技的發展，有很大一個因素是因為美國國家科學基金會（NSF）和國家衛生研究院（National Institutes of Health，以下簡稱NIH）以及私人公司在基因資源（genomic resources）上的投資，為其後的基因功能等基礎研究奠定基礎。【本文未完，完整內容請見《北美智權報》182期：淺談生物材料移轉契約條款】