PATEX 2: Patent Exploration for Artificial Intelligence in Medicine (AIM) Experiment 2 - The Search for Deep Learning Impact

PATEX 2: Patent Exploration for Artificial Intelligence in Medicine (AIM)

Experiment 2 -The Search for Deep Learning Impact

This continues the Patent Exploration Experiments.   The question is what's really happening in the real world of actual patents granted?

PATEX-1 explored the recent granted patent portfolios that highlighted the terms "Artificial Intelligence" and Medicine.  THIS experiment explores recent USPTO granted patents that contain the terms "Deep Learning" and Medicine.  I excluded results that just included the terms but were clearly not specifically focused on medical / health science applications. My evaluation is admittedly subjective, a more detailed analysis will take time, and may be done in the future, or under some specific tasking.

I included some results that suggest specific promise to medicine and/or health services, and are not generic.  Obviously generic techniques are broadly useful, but they provide little insight that suggests profound medical or biological knowledge being used in construction of the invention.

Additional insights obtained:

  • Medical image analysis (MRI, lesions, cell abnormalities) seems to be a core interest
  • Computer Aided Diagnostics CADx is becoming 'a thing'.
  • NLP (Natural Language Processing) / Semantic Processing / Corpus processing are increasing.

To be complete, one needs to look at World Patents, the Japanese, Chinese, and European databases, and other databases.   Currently this is out of scope.  If you are interested in supporting such effort, let me know.

FOR FUN: the search returned results for the following patent granted to IBM.  ALL IS POSSIBLE! Pursue your dreams :-)

 US-10040551-B2. - Drone delivery of coffee based on a cognitive state of an individual 

Coffee or other drink, for example a caffeine containing drink, is delivered to individuals that would like the drink, or who have a predetermined cognitive state, using an unmanned aerial vehicle (UAV)/drone. The drink is connected to the UAV, and the UAV flies to an area including people, and uses sensors to scan the people for an individual who has gestured that they would like the drink, or for whom an electronic analysis of sensor data indicates to be in a predetermined cognitive state. The UAV then flies to the individual to deliver the drink. The analysis can include profile data of people, including electronic calendar data, which can be used to determine a potentially predetermined cognitive state.


USPTO Patent ID Title Assignee Inventor Link

filing date grant date
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US-10292768-B2 Augmented reality guidance for articular procedures  Philipp K. Lang Philipp K. Lang https://patents.google.com/patent/US10292768B2/en 5/24/18 5/21/19
US-10296555-B1 Methods, systems, and computer readable media for non-parametric dependence detection using bitwise operations in a computing system  The University Of North Carolina At Chapel Hill, The Board Of Trustees Of The Leland Stanford Junior University, Temple University‚ÄîOf the Commonwealth System of Higher Education Kai Zhang, Michael Thomas Max Baiocchi, Zhigen Zhao https://patents.google.com/patent/US10296555B1/en 3/20/18 5/21/19
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AIM Related Projects

The Experiment above was motivated by exploring the impact of  IBM Watson and Watson Paths. - see discussions  here

SOME BOOKS ON IBM WATSON

(unless otherwise specified, these are of general domain applicability)

  1. Rob High  and Tanmay Bakshi,  (2019) Cognitive Computing with IBM Watson: Build smart applications using artificial intelligence as a service 
  2. IBM Redbooks, IBM Watson Content Analytics: Discovering Actionable Insight from Your Content. 3rd Edition
  3. Steven Baker, (2011), Final Jeopardy: Man vs. Machine and the Quest to Know Everything 


RELEVANT SOURCES INCLUDING THOSE CITED IN PATENTS:

  1. IBM's DeepQA Research Team Publications
  2. Ferrucci et al., "Towards the Open Advancement of Question Answering Systems," IBM Technical Report RC24789, Computer Science, Apr. 22, 2009.
  3. David Ferrucci, Eric Brown, Jennifer Chu-Carroll, James Fan, David Gondek, Aditya A. Kalyanpur, Adam Lally, J. William Murdock, Eric Nyberg, John Prager, Nico Schlaefer, and Chris Welty, (2010) *Building Watson: An Overview of the DeepQA Project, AI Magazine Fall, 2010. 
  4. William Murdock (2015), Decision Making in IBM Watson Question Answering Web presentation: Ontology Summit 2015
  5. M. Devarakonda, Dongyang Zhang, Ching-Huei Tsou, M. Bornea, Problem-oriented patient record summary: An early report on a Watson application, e-Health Networking, Applications and Services (Healthcom), 2014 IEEE 16th International Conference on, pp. 281-286
  6. WatsonPaths: Scenario-based Question Answering and Inference over Unstructured Information,IBM Research Report RC25489, IBM, 2014
  7. Nico Schlaefer, (2011),Statistical Source Expansion for Question Answering, PHD Thesis,CMU-LTI-11-019


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