In the IoT (Internet of Things) age, AI (Artificial Intelligence) is considered a major accelerator to smarten up electronics products, leading to the emergence of relevant applications such as smart cars, smart elevators, smart home appliances, smart pets, smart robots, and smartphones. Following the widespread adoption of AI technology in emerging applications, Amazon which started as an online bookshop has been dedicated to M&A activities and AI patent portfolio developments over the years. Meanwhile, Qualcomm also has come with its own AI patent portfolio strategies following a noticeable decline in its revenue and patent royalty from the mobile phone market. Meanwhile, the Chinese AI industry is led by Tencent which has invested over US$10 billion into AI in the past two years. Tencent's AI ambitions over the next few years are defined by a strategy called 'Make AI Everywhere'. This report provides an overview of AI patent portfolios, and examines the patent portfolio development strategies of Amazon and Qualcomm; looks into the AI investment plans of Tencent.
List of Topics
- AI technology overview and includes trend analysis with breakdowns by country, sector, and field, key and correlative technology, and R&D readiness analysis
- Patent portfolio analysis of several major AI assignees, including IBM, Microsoft, Google, Siemens, and Rockwell Automation
- Detailed AI patent portfolio analysis of Amazon with patent mining techniques
- Amazon's AI patent counts with a detailed breakdown by field, core technology, and intelligent application
- Analysis of Amazon's investment projects and M&A activities
- Detailed AI patent portfolio analysis of Qualcomm with patent mining techniques
- Qualcomm's AI patent counts with a detailed breakdown by field, core technology, and intelligent application
- Analysis of Amazon's investment projects and M&A activities
Table of Contents
1. AI Technology Overview
1.1 Neural Network
1.2 Expert System
1.3 Fuzzy Logic
1.4 Genetic Algorithm
2. Patent Mining
2.1 Patent Search
2.1.1 Selecting a Patent Database
2.1.2 Identifying Search Keywords
2.1.3 Data Selection
2.1.4 Data Analysis
3. Trend Analysis
3.1 Data Mining
3.1.1 Patent Distribution by Country
3.1.2 Patent Distribution by Sector and Field
3.1.3 Patent Distribution by Key Technology Field
3.1.4 Patent Distribution by Correlative Technology
3.2 R&D Readiness Analysis
3.2.1 R&D Intensity
4. Patent Portfolio Analysis
4.1 International Assignees
4.2 Development of Major Patent Assignees
4.2.1 IBM
4.2.2 Microsoft
4.2.3 Google
4.2.4 Siemens
4.2.5 Rockwell Automation
5. Perspective
6. Company Profile of Amazon
7. Amazon Patent Portfolio Deployment
7.1 Patent Mining
7.2 Amazon Patent Portfolio Analysis
7.2.1 Analysis by Field
7.2.2 Analysis by Core Technology
7.2.3 Analysis by Intelligent Application
7.3.4 Matrix Analysis of AI Technology with Intelligent Applications
8. Analysis of Amazon's Investment Projects
9. Analysis of Amazon's M&;A Activities
10. Perspective
11. Company Profile of Qualcomm
12. Qualcomm Patent Portfolio Deployment
12.1 Patent Mining
12.2 Patent Analysis
12.2.1 Analysis by Field
12.2.2 Analysis by Core Technology
12.2.3 Analysis by Intelligent Application
12.2.4 Matrix Analysis of AI Technologies and Intelligent Applications
13. Analysis of Qualcomm's Investment Projects
14. Analysis of Qualcomm's M&A Activities
15. Perspectives
16. Company Profile of Tencent
17. Organizational Development and AI Team
17.1 Organizational Resource Integration
17.2 AI Teams
17.2.1 Tencent AI Lab
17.2.2 Youtu Lab
17.3 Public Security
17.4 Facial Recognition
17.5 Online Content Review
17.6 Smart Retail
17.6.1 WeChat AI
18. Tencent's AI Products and Technologies
18.1 The Six Resources of Tencent's AI Ecosystem
18.2 AI Products
18.2.1 Smart Hardware
18.2.2 Smart Hiring
19. Tencent's AI Accelerator and Investment Strategy
19.1 AI Accelerator
19.2 Tencent's AI Investment Strategy
20. Perspective
- Appendix
- Glossary of Terms
- List of Companies
List of Tables
Table 1: AI Patent Core Technology/Correlative Technology Matrix Analysis
Table 2: R&D Intensity of Top 20 International AI Patent Assignees
Table 3: R&D Intensity of Top 20 Taiwanese AI Patent Assignees
Table 4: AI Patent/International Assignee Matrix Analysis
Table 5: Amazon's Patent Counts by Technology, 2006 - 2017
Table 6: Patent Count by Application, 2006 - 2017
Table 7: Matrix Analysis of Amazon's AI Core Technologies with Intelligent Applications
Table 8: Amazon's Investments in Emerging Application Areas, 2013 - 2017
Table 9: Amazon's M&As and Deployments in Emerging Application Areas, 2013-2017
Table 10: Development of Qualcomm's Core AI Technologies
Table 11: Smart Application Areas of Qualcomm's AI Technologies
Table 12: Matrix Analysis of Qualcomm's Core AI Technologies and Intelligent applications
Table 13: Qualcomm Ventures' Investment between 2013 and 2017
Table 14: Qualcomm's M&A between 2013 and 2017
Table 15: Basic Information on Tencent
Table 16: Organizational Structure of Tencent
Table 17: Applications of Tencent AI Lab
Table 18: Research Plans of Tencent AI Lab
Table 19: Shared Technologies on the Tencent Open AI Platform
Table 20: Smart Hardware Application Scenarios
Table 21: Investment Targets of Tencent's AI Accelerator
Table 22: Tencent's AI Investment Strategy
List of Figures
Figure 1: AI Patent Distribution Share by Country
Figure 2: AI Patent Distribution Share of International Assignees by Sector
Figure 3: AI Patent Distribution Share of Taiwanese Assignees by Sector
Figure 4: AI Patent Distribution Share of International Assignees by Field
Figure 5: AI Patent Distribution Share of Taiwanese Assignees by Field
Figure 6: AI Correlative Patent Distribution Share by Technology Type: International Assignees
Figure 7: AI Correlative Patent Distribution Share by Technology Type: Taiwanese Assignees
Figure 8: Amazon's Business Model Built around Customer Experience
Figure 9: Amazon's Revenue Breakdowns by Business Unit, 2012 - 2016
Figure 10: Amazon's Patent Count by Field
Figure 11: Qualcomm's Business Model
Figure 12: Qualcomm's Revenue Structure, 2012 - 2016
Figure 13: Qualcomm's AI Patent Distribution Share by Field
Figure 14: The Grandeye Security Platform
Figure 15: Facial Recognition Verification Process
Figure 16: Youtu's AI Applications in Smart Retail
Figure 17: The Six Resources of Tencent's AI Ecosystem
Figure 18: Smart Hardware Program
Figure 19: Features of Tencent's Smart Recruitment Technology
Companies Mentioned (Partial List)
A selection of companies mentioned in this report includes, but is not limited to:
- 2lemetry
- 58.com
- Academia Sinica
- Adobe Systems
- AISEC
- Alibaba
- Amazon
- Amazon Technologies
- Amiato
- Angel.ai
- Annapurna Labs
- Apple
- AppThwack
- Arteris
- AT&T
- Atman
- Atomwise
- Baidu
- Biba
- Black Sand Technologies
- Blackberry
- Blippar
- BMW
- Body Labs
- Brain Corporation
- Byton
- Canon
- Capsule Tech
- Career Executive
- Chi Mei Communication Systems
- China Everbright Bank
- China Unicom
- Chunghwa Telecom
- Cloud9 IDE
- Cloudbrain
- Clusterk
- Comixology
- Cowarobot
- CSR
- DefinedCrowd
- Delta Electronics
- DiDi Chuxing
- Do
- Double Helix Games
- Element AI
- Elemental Technologies
- Embodied
- Empowered Careers
- Emvantage Payments
- Equota
- Euvision Technologies
- Evi
- Fujitsu
- GameSparks
- General Electric
- GJS Robot
- GMEMS
- Goodreads
- Graphiq
- Harbin Institute of Technology
- Harvest.ai
- Hitachi
- Hon Hai Precision
- HP
- HTC
- Huiyihuiying
- IBM
- iCarbonX
- InnoPath Software
- Inotera Memories
- Institute for Information Industry
- Institute of Nuclear Energy Research Atomic Energy Council
- Intel
- Inventec
- ITRI
- Ivona Text-To-Speech
- Kitt.ai
- Kiwi
- kooaba
- L'Oréal
- Lancôme
- Lavector
- Leju Robotics
- Leshi Zhixin
- LG
- Liquavista
- Marble
- Market Intelligence & Consulting Institute
- McGill University
- MediaTek
- Mi Live
- Microsoft
- Mininglamp
- Moji
- National Central University
- National Cheng Kung University
- National Chiao Tung University
- National Pingtung University of Science and Technology
- National Taiwan University
- National Taiwan University of Science and Technology
- NEC
- Nice
- NIO
- Nuance Communications
- NXP Semiconductors
- OpenDNA
- Oracle
- Orb Networks
- Orbeus
- Pacewear
- Prospera Technologies
- Qualcomm
- Qzone
- Roadstar.ai
- Rockwell Automation
- Rooftop Media
- Safaba Translation Solutions
- Samsung
- SAP
- Scyfer
- SF Express
- Shangri-La Hotels & Resorts
- Shoefitr
- Siemens
- Sinovation Ventures
- Sony
- SoundHound
- Souq.com
- Stonestreet One
- Stratifyd
- Summit Microelectronics
- Tempo AI
- Tencent
- TenMarks Education
- Tensorise
- Tesla
- Thinkbox Software
- TI.
- Toshiba
- TSMC
- Tu
- Twitch
- UBTECH
- Ultra-Scan Corporation
- United States Patent and Trademark Office
- University of Montreal
- Versa Inc.
- VoxelCloud
- WeBank
- Weltmeister
- Whole Foods Market
- Wilocity
- WIPO
- Wistron
- Wonder Workshop
- Xeros
- Yahoo!
- Yingyongbao
- Yunji Technology
- Zhuiyi
Methodology
Primary research with a holistic, cross-domain approach
The exhaustive primary research methods are central to the value that the analyst delivers. A combination of questionnaires and on-site visits to the major manufacturers provides a first view of the latest data and trends. Information is subsequently validated by interviews with the manufacturers' suppliers and customers, covering a holistic industry value chain. This process is backed up by a cross-domain team-based approach, creating an interlaced network across numerous interrelated components and system-level devices to ensure statistical integrity and provide in-depth insight.
Complementing primary research is a running database and secondary research of industry and market information. Dedicated research into the macro-environmental trends shaping the ICT industry also allows the analyst to forecast future development trends and generate foresight perspectives. With more than 20 years of experience and endeavors in research, the methods and methodologies include:
Method
- Component supplier interviews
- System supplier interviews
- User interviews
- Channel interviews
- IPO interviews
- Focus groups
- Consumer surveys
- Production databases
- Financial data
- Custom databases
Methodology
- Technology forecasting and assessment
- Product assessment and selection
- Product life cycles
- Added value analysis
- Market trends
- Scenario analysis
- Competitor analysis
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