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2 Doing More with Less: How efficient analysis can improve your vantage point on information Nils Newman Director of New Business Development Search Technology

3 PIUG Workshop Topics 1. The Tech Mining Framework 2. Using Macros to Save Time and Effort 3. Combining Data/Leveraging Excel 4. New Features in VantagePoint Q & A

4 TECH MINING: Innovation, Information and MOT Types of Innovation Framework of Influences Method to analyze information Process to integrate information

5 Types of Innovation Incremental Innovation Radical Innovation

6 A Linear view of Innovation Processes Functionality Maturation Incremental Innovation Adoption Commercial Introduction New Product Development Licensing, Collaborative Innovation Development; Patenting Basic to Applied Research Time

7 The Linear View: A Gross Simplification The Linear or Pipeline view of Innovation is a gross oversimplification. Innovation theory has added chain-linked and feed back models as well as other complex models of innovation. However, in most management contexts, the Linear Model still works pretty well as a starting frame of reference.

8 Radical Innovation Functionality New Markets New Products Radical Innovation Early Signal Detection Challenges for CTI Time

9 Radical Innovation: A Tougher Challenge Discontinuous change implies unfamiliarity Increasingly sciencebased technologies (challenge to predict breakthroughs) Need to address sociotechnical systems contextual factors less stationary

10 TECH MINING: Innovation, Information and MOT Models of Innovation Framework of Influences Method to analyze information Process to integrate information

11 Technology Delivery System (TDS) Push vs. Pull Pressure of Scientific Discovery Suction of Societal Need Scientific Push Societal Pull

12 TDS: Mapping External Influences Public Interests Stakeholders Competitors Government Bodies Customers Impact Assessment Management/Organization Resources needed: Capital Skills Materials Software Products Impacts

13 Mapping the TDS Mapping the TDS requires information Competitive Technical Intelligence (CTI) T= Science and Technology Take a first pass with internal knowledge to identify key players and leverage points Enrich with external information

14 TECH MINING: Innovation, Information and MOT Models of Innovation Framework of Influences Method to analyze information Process to integrate information

15 The Problem Welcome to the age of information overload Anyone can now easily access vast quantities of scientific, technical, IP, business and market information How can one effectively use information if you are drinking from a fire hose?

16 Dealing with information overload External sources such as Science, Technical, Business and Intellectual Property databases will produce more hits than you can absorb. To integrate these sources into a TDS, you need to be able to systematically mine, process and analyze information.

17 But if we want to use all this information, what do we do? Change our perspective on textbased information. Get away from our need to read Treat text as DATA Use data mining techniques to analyze text (TEXT MINING)

18 What does Text Mining give us? Text Mining leverages text and data mining to analyze information. Since tech mining allows us to use computers to read the information, we can digest far more information than we could traditionally absorb.

19 When does Text Mining Work? When what you seek is a pattern (not a specific document) There is a distinct difference between search and retrieval and text mining When your information is available in electronic machine readable form PDF images introduce an added layer of complexity due to OCR issues When your electronic information is accessible via bulk download If you have to download one/few records at a time, adding a bot to do the downloads is possible but usually adds additional technical and licensing issues

20 What are Patterns in Text? Patterns in text are the relationships between words or phrases that repeat across many different documents. For example, if one document mentions sodium chloride and salt and then another document mentions sodium chloride and salt and then another and another etc You begin to assume that sodium chloride and salt are related.

21 How do we find a pattern? Use Co-word Bibliometrics/Co-occurrence statistics to find relationships Count the number of times words appear together in a set of documents The higher the co-occurrence, the stronger the potential relationship Word 1 Word 2

22 Patterns have Meaning Patterns that we find represent higher order abstractions within the large text collection. In our salt example, we can induce that Sodium Chloride is a salt Meaning

23 The Big Why Patterns = Knowledge

24 Types of Knowledge Text mining techniques are good at addressing: WHO? WHAT? WHEN? WHERE? The How and Why questions are better left to the experts.

25 TECH MINING: Innovation, Information and MOT Models of Innovation Framework of Influences Method to analyze information Process to integrate information

26 How do you deal with all this information and put it in a framework? Tech Mining Alan L. Porter and Scott W. Cunningham John Wiley & Sons Inc., 2005

27 The Tech Mining Steps Define the Management of Technology (MOT) Issues Break out particular MOT Questions Identify candidate empirical Indicators Identify appropriate Data Source(s) Identify appropriate Analytical Tool(s) Design Effective composite Representations that can be rapidly built one-pagers in one day.

28 Example Indicators: Lists and Trends Top Researchers Top Organizations Time Trends

29 Comparisons

30 Profiles

31 Networks Knowledge Networks: Who invents with whom

32 Visualizations

33 Domain Mapping

34 Geo-coded Mapping

35 Script-driven Analytical Views

36 One Pagers

37 VantagePoint Macros Save time and effort by automating your analytical process

38 Automation is the key to ROI Unless you are working with high value-added issues, custom exploration can yield a relatively low rate of return A higher rate of return requires routinization of the questions and automation of the analysis

39 Macros Included with VantagePoint There are many Macros included with VantagePoint Macros are also downloadable via the support site The Library of Resources contains a description of each Macro

40 Classes of Macros Utility Automate Repetitive Functions Combine Groups Create EspaceNet Patent Links Analytical Modify data Terms by Year Calculate Lag Combine Author Networks Reporting Transfers data out to other tools such as Excel Plot Matrix Super Profile Visualization Launches other tools within VantagePoint Matrix Viewer Company Activity Gantt

41 Example 1: Create EspaceNet Patent Links

42 Example 2: Plot Matrix

43 Example 3: Terms by Year

44 Example 4: Company Activity Gantt

45 Custom Macros Most commands within VantagePoint can be executed using Visual Basic VantagePoint includes a scriptwriter reference manual This enables you to build your own customized macros to automate your specific tasks

46 Combining Data/Leveraging Excel Enhance your results by adding Excel content to your core data

47 Provider Content + Your Content Start with External content loaded into VantagePoint Transfer some of that content to Excel Use Excel to warehouse internal content Department Codes Technical Classifications Product Names, etc.. Add the Excel content to the provider content Return to VantagePoint for analysis Export out of VantagePoint for Presentation

48 Demo Example Take a collection of firm names out of VantagePoint and copy into Excel Look up the stock symbol for each company on Yahoo Finance and enter it into your new stock symbol field Import the new Excel file into VantagePoint Use Record Fusion to combine your original file with your stock symbol file Now you have stock symbol added to your original records!

49 Real World Example Analyzing a collection of Web of Science records relating to Nanotechnology The set contains ~500,000 records Needed to visualize where research was being done

50 The Process: Step Import ~500,000 WoS records into VantagePoint 2. Parse the affiliation address field to extract city-country pairs 3. Transfer city-country pairs out to Excel 4. Use Excel CSV format to batch transfer all the city-country pairs to a website that returns Geo-Code data for citycountry pairs 5. Transfer the city-country pair plus the geo-code data back into VantagePoint 6. Transfer the city-country pairs + geocode data + number of records out of VantagePoint and into Google Earth KLM format 7. Run the KLM file 8. Have graduate student write a VB macro to automate the analysis

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52 Features in Version 6

53 Improved Database Interoperability

54 Import tables and queries from MS Access

55 Improved Processing of Excel Additional processing while importing from database or Excel Preview the processing Save as an Import Filter

56 Enhanced Record Exporting Record export Export processed fields Support four different formats

57 Enhanced Export from Data Providers: Patbase

58 Import Directly from Databases (BETA)

59 Improved Performance

60 Multi-threading Multi-threaded processing for Detail windows and Title window Improves interactive performance on multicore processors when working with large fields and datasets

61 RAM Optimization Redesigned structure of VPT files to reduce RAM consumption Data is loaded into RAM only as needed Improved File Open speed after initial save Embedded Browser sheet dependency files

62 Improved Interface

63 Color Coding Color Coding in Matrices Cooccurrence Correlation Factor Make Heat Maps

64 Matrix Lists List cells of matrix Flood & sort Selecting a row selects row and column items in underlying matrix Use with Detail Window to find intersecting terms

65 Sheet Navigation in the Tool Bar

66 Parse fields after import Parsing After Import

67 Map Enhancements Move nodes Redraw Map Map Improvements Show/hide legend Open all information boxes

68 Aduna Cluster Maps Macro Alternative Visualization Java based application Contained in next release schedule for June Aduna Cluster Maps

69 For More Information Search Technology, Inc

70 Questions?

71 Thank You!

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