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Menlo Park, CA, United States

Ayasdi Inc. | Date: 2014-09-09

An example method includes receiving text from a plurality of documents, segmenting text received text of the plurality of documents, calculating a frequency statistic for each segment of each document, determining segments of potential interest of each document based on calculated frequency statistic, calculating distances between each document of the plurality of documents based on a text metric, and storing segments of potential interest of each document and the distances in a search database. The method may further include receiving a search query and performing a search of information contained in the search database, partitioning documents of search results using the distances, for each partition, determining labels of segments of potential interest for documents of that particular partition, the labels being determined based on a plurality of frequency statistics, and providing determined labels of segments of potential interest for documents of each partition.

In various embodiments, a system comprises a map and a patient data assessment module. The map includes a plurality of groupings and interconnections of the groupings, each grouping having one or more patient members that share biological similarities, each interconnection interconnecting groupings that share at least one common patient member, the map identifying a set of groupings and a set of interconnections having a medical characteristic of a set of medical characteristics. The patient data assessment module may be configured to receive sensor data from a users mobile device and to assess the sensor data to generate user medical attributes, to determine whether the user shares the biological similarities with the one or more patient members of each grouping based, at least in part, on the user medical attributes, thereby enabling association of the user with one or more of the set of medical characteristics.

Ayasdi Inc. | Date: 2014-11-04

An exemplary method may comprise receiving a matrix for a set of documents, each cell of the matrix including a frequency value indicating a number of instances of a corresponding text segment in a corresponding document, receiving an indication of a relationship between two text segments, each of the two text segments associated with a first column and a second column, respectively, of the matrix, adjusting, for each document, a frequency value of the second column based on the frequency value of the first column, projecting each frequency value into a reference space to generate a set of projection values, identifying a plurality of subsets of the reference space, clustering, for each subset of the plurality of subsets, at least some documents that correspond to projection values, and generating a graph of nodes, each of the nodes identifying one or more of the documents corresponding to each cluster.

Ayasdi Inc. | Date: 2015-10-15

An exemplary method comprises receiving data points, selecting a first subset of the data points to generate an initial set of landmarks, each data point of the first subset defining a landmark point and for each non-landmark data point: calculating first data point distances between a respective non-landmark data point and each landmark point of the initial set of landmarks, identifying a first shortest data point distance from among the first data point distances between the respective non-landmark data point and each landmark point of the initial set of landmarks, and storing the first shortest data point distance as a first landmark distance for the respective non-landmark data point. The method further comprising identifying a non-landmark data point with a longest first landmark distance in comparison with other first landmark distances and adding the identified non-landmark data point associated as a first landmark point to the initial set of landmarks.

News Article | March 25, 2015
Site: blogs.wsj.com

Ayasdi Inc., whose data analytics software is based on a branch of mathematics that is more than a century old, has raised another $55 million in a funding round led by Kleiner Perkins Caufield & Byers. The company also reported 400% growth in bookings for the fiscal year ended Jan. 30 and a long list of Fortune 1000 companies as customers, including Citigroup, Credit Suisse, Siemens, Lockheed Martin and several others in financial services, technology, health care, government and life sciences. Ayasdi was started in 2008 by three mathematicians. They include two Stanford University Ph.D.s–Chief Executive Gurjeet Singh, whose degree is from the Institute for Computational and Mathematical Engineering, and Harlan Sexton, whose degree is in mathematics—and Gunnar Carlsson, a former chair of the Department of Mathematics at Stanford who recently joined Ayasdi full time. Dr. Carlsson pioneered the use of a branch of mathematics called topology, which is the study of shapes, to solve real-world problems. In the 2000s, he received $10 million in research grants from the National Science Foundation and DARPA to study problems of interest to the U.S. government, and that work led to Ayasdi. Ayasdi’s specialty is its ability to apply Topological Data Analysis–which includes statistical, machine learning and geometric algorithms–to complex sets of data and expose patterns that humans may miss. The key here is data complexity, Dr. Singh said, not data size. One of the earliest uses of Ayasdi’s software was on a set of 12-year-old data on breast-cancer tumors. By representing the data visually, as a color-coded shape, it exposed correlations between the disease and patients’ outcomes that researchers had never noticed, giving clues to more effective treatments for breast cancer, Dr. Singh said. Ayasdi has now branched into several more industries, and is adding what it calls “sandwiches”–apps on top of the Ayasdi platform–for use by customers in those industries. A health-care sandwich, for instance, has its own user interface and understands how to talk to data from electronic medical records. Customers are reporting millions of dollars in savings, Dr. Singh said. Mercy Health System, for instance, is using Ayasdi to discover “care paths” for its patients–best practices in treatment based on their diseases and accepted medical procedures that staff can access on computers or iPads. Lockheed, a government contractor, uses Ayasdi to monitor the status of projects to make sure they are on track. None of these places could hire enough data scientists or analysts to do this type of work at this scale, Dr. Singh said, and they would also need luck to find some of the patterns in the data that Ayasdi is able to detect. The new money will be invested in sales and marketing as well as the software. “Machine intelligence for the enterprise is still in its infancy,” he said. “There’s still a lot more to do.” Kleiner Perkins Partner Ted Schlein, who becomes a board observer, said his firm invested in Ayasdi because it has shown that it is commercially able “to go from big data to important data, and in essence make companies smarter.” Current investors Institutional Venture Partners, Khosla Ventures, Floodgate and Citi Ventures also participated in the round, along with two new investors, Centerview Capital and Draper Nexus Ventures. Total funding in Ayasdi is now close to $100 million. Dr. Singh said he hopes that the current round will be the company’s last one. He added that he expects the company to be worth $1 billion or more by next year. Write to Deborah Gage at deborah.gage@wsj.com. Follow her on Twitter at @deborahgage

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