With a successful business model, Qualcomm has dominated the CDMA (Code Division Multiple Access), 3G EV-DO (Evolution-Data Optimized), and 4G LTE (Long Term Evolution) baseband markets. But success comes with hidden worries. The company's excessive focus on the mobile phone market has led to a decline in its revenue and patent royalty.
Therefore, Qualcomm has tried to transform its business and drive sustainable growth through strategic investments in the development of emerging applications, acquisitions, and technologies and patents. These efforts, especially the emerging application markets that Qualcomm's AI (Artificial Intelligence) technology has aimed at, provide important clues for understanding Qualcomm's deployment plans. This report looks into what core AI technologies Qualcomm has focused on and examines smart applications that Qualcomm's AI technology has been applied to.
List of Topics:
- Background of Qualcomm, touching on its business model and revenue structure for the period 2012-2016
- Qualcomm's major technology fields, core technologies, and smart applications identified using the data mining technique; also included is the matrix analysis of core AI technologies and smart applications of Qualcomm for the period 2006-2017 (January to May)
- Analysis of Qualcomm's investment and acquisition projects
- Analysis of the worldwide sensor sales value and share in 2011 vs. 2016, by application, by region market
Table of Contents
1. Background
2. Patent Distribution Analysis
2.1 Patent Mining
2.2 Patent Analysis
2.2.1 Analysis by Field
2.2.2 Analysis by Core Technologies
2.2.3 Analysis by Smart Applications
2.2.4 Matrix Analysis of Core AI Technologies and Smart Applications
3. Investment Projects Analysis
4. Acquisition Projects Analaysis
5. Conclusions
5.1 Qualcomm Facing Revenue Dilemmas Due to Excessive Focus on Mobile Phone Market
5.2 Investment Focuses on Machine Learning Solutions for Car Electronics, Healthcare, Medical Technology, and Robot Applications
5.3 Core AI Patents Focus on Neural Networks and Natural Language Processing While Machine Learning Patents on the Rise
5.4 Communications Patents Account for Largest Share in AI Applications, Followed by Neuromorphic Computing Hardware, Transport, Healthcare, and Medical Technology
Appendix
Glossary of Terms
List of Companies
List of Tables
Table 1 Development of Qualcomm’s Core AI Technologies
Table 2 Smart Application Areas of Qualcomm’s AI Technologies
Table 3 Matrix Analysis of Qualcomm’s Core AI Technologies and Smart Applications
Table 4 Qualcomm Ventures’ Investment between 2013 and 2017
Table 5 Qualcomm’s M&A between 2013 and 2017
List of Figures
Figure 1 Qualcomm’s Business Model
Figure 2 Qualcomm’s Revenue Structure, 2012 - 2016
Figure 3 Qualcomm’s AI Patent Distribution Share by Field
Samples
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Companies Mentioned (Partial List)
A selection of companies mentioned in this report includes, but is not limited to:
- Arteris
- Black Sand Technologies
- Blippar
- Brain Corporation
- CSR
- Capsule Tech
- Empowered Careers
- Euvision Technologies
- InnoPath Software
- LG
- NXP Semiconductors
- Orb Networks
- Prospera Technologies
- Qualcomm
- Samsung
- Scyfer
- Stonestreet One
- Summit Microelectronics
- TI.
- Tempo AI
- Ultra-Scan Corporation
- Wilocit
- kooaba
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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