trends of wellbeing AI, we analyze a massive amount of patent data. • We use Panoramic View Analytics that is our unique data visualization method. • We show the main technology areas, growing and fusion areas, as well as the effectiveness of panoramic view analytics.
that allows the user to “see and understand” the entirety of large amounts of data rather than “search and read” individual pieces of data. A B C Clustering docs by calculating similarities among them Visualization of the similarity among docs Original indicators for mining insights Patent / Paper / SNS / News / Annual Report etc Big Data Clustering Visualization Analytics
insights for strategy-making by analyzing information (distance, density, white space, distribution) on the radar chart. The size of each cluster is proportional to the number of documents in it. The distance between clusters indicates the similarities between them. The axis has no meanings. Each circle is called a “cluster”. They contain similar documents. Sparse area Density area Far = Low similarity Near = High similarity Figure 1: Understanding the Radar Chart.
Calculating Similarities between Documents Visualization The system divides sentence into words. Calculating each word’s weight based on the term frequency and the inverse document frequency. e.g.) The words included in documents Doc.A：aaa | aaa | bbb | ccc Doc.B：aaa | bbb | bbb | ddd Doc.C：aaa | bbb | ddd | ddd Word TF IDF Weight (TF*IDF) aaa 2 1/3 2/3 bbb 1 1/3 1/3 ccc 1 1/1 1 Vectorizing each document based on the each word's weight, and calculating the similarities based on the inner product. Doc.A Vector aaa bbb ccc Doc.A Vector θ1 Doc.C Vector Doc.B Vector θ2 Visualizing by original algorithm based on Multi Dimensional Scaling method θ1 θ2 The system visualize the documents searched. Documents(1,2,3,…,n) Documents(1,2,3,…,n) The / system / visualize / the / documents / searched. Doc.A Doc.C Doc.B Doc.A Vector Doc.C Vector Doc.B Vector Word importance in Doc. A High-dimensional data representation (Tens of thousands) Modified MDS in order to express close relationship
dataset S003 for analyzing Wellbeing /Healthcare and AI field. • Wellbeing / Healthcare: Keyword search • AI: IPC search (G06N) * IPC means International Patent Classification. And, G06N is “Computer systems based on specific computational models”. So, it is the IPC that represents the core technology area of AI. Also, it is used in JPO’s report on AI. No Content Search Type Search Formula Count (Rough estimate) S001 Wellbeing / Healthcare Keyword search “Wellbeing” OR “well-being” OR “happy” OR “happiness” OR “healthcare” // Title, Abstract, Claims 7,000 S002 AI IPC search G06N 14,000 S003 Total Logical expression S001 OR S002 21,000 Search condition DB： PatentSQUARE Period： Jan. 1, 2001 – Oct. 26, 2017 (publication date) Authority： US (published application) Language： English
containing 50% to 80% G06N patents in AI side • The below list shows examples of invention names included in the extracted clusters. No. Publication no. Title (example) A1 20150273697 ROBOT FOR MEDICAL ASSISTANCE A2 20160104486 Methods and Systems for Communicating Content to Connected Vehicle Users Based Detected Tone/Mood in Voice Input A3 20070005621 Information system using healthcare ontology A4 20120290516 Habituation-compensated predictor of affective response A5 20140149128 HEALTHCARE FRAUD DETECTION WITH MACHINE LEARNING A6 20120278064 SYSTEM AND METHOD FOR DETERMINING SENTIMENT FROM TEXT CONTENT A7 20120215555 SYSTEMS AND METHODS FOR HEALTHCARE SERVICE DELIVERY LOCATION RELATIONSHIP MANAGEMENT A8 20160156781 SECURELY AND EFFICIENTLY TRANSFERRING SENSITIVE INFORMATION VIA A TELEPHONE A9 20060129427 Systems and methods for predicting healthcare related risk events A10 20080294692 Synthetic Events For Real Time Patient Analysis Table 1: Fusion fields of list A.
containing 20% to 50% G06N patents in wellbeing / healthcare side • The below list shows examples of invention names included in the extracted clusters. No. Publication no. Title (example) B1 20100185456 Medication Management System B2 20160156781 SECURELY AND EFFICIENTLY TRANSFERRING SENSITIVE INFORMATION VIA A TELEPHONE B3 20150324693 PREDICTING DRUG-DRUG INTERACTIONS BASED ON CLINICAL SIDE EFFECTS B4 20120190937 EMOTION SCRIPT GENERATING , EXPERIENCING , AND EMOTION INTERACTION B5 20030050797 System and user interface for processing healthcare related event information B6 20080164998 Location Sensitive Healthcare Task Management System B7 20140108025 COLLECTING AND TRANSFERRING PHYSIOLOGICAL DATA B8 20090248744 TRANSACTIONAL STORAGE SYSTEM FOR HEALTHCARE INFORMATION B9 20100205001 SYSTEM AND METHOD FOR ASSISTING IN THE HOME TREATMENT OF A MEDICAL CONDITION B10 20140249848 DEFINING PATIENT EPISODES BASED ON HEALTHCARE EVENTS Table 2: Fusion fields of list B.
area, but several other applications. Robot Connected car Healthcare ontology Medical fraud detection Sentimental / emotion analysis Telemedicine / home care Medical information security Medication management Risk prediction Patient condition analysis Predicting drug-drug interactions Utilization of physiological data Medical Other application Keywords map in fusion area.
area, “Neural network”, “Neuromorphic chip” and “Q&A system” are detected as growing area. Medical (Prescription) information management Risk (finance, disease), Monitoring Medical fraud detection Rule base Ontology Inference Agent Recommendation Q&A system Neuromorphic chip Neural network Robot Wellbeing / Healthcare AI Classification Figure 2: Radar chart of wellbeing / healthcare and AI.
Keywords neuron, synaptic, firing, spike, neural, STDP, synapse, circuit, pulse, learning Year Count Count Assignee n Transition n Player n Example of title COMPUTED SYNAPSES FOR NEUROMORPHIC SYSTEMS n Example of figure
Robot (A1), Sentimental analysis (A6), Telemedicine / home care (A7) and Medication management (B1), etc are detected in growing area in the fusion area. Medical (Prescription) information management Risk (finance, disease), Monitoring Medical fraud detection Rule base Ontology Inference Agent Recommendation Q&A system Neuromorphic chip Neural network Robot Wellbeing / Healthcare AI Classification A6 A1 A7 B1 Figure 2: Radar chart of wellbeing / healthcare and AI.
Keywords noun, score, polarity, corpus, sentiment, topic, opinion, domain, content, advertisement Count Count Assignee n Transition n Player n Example of title System and method for determining sentiment from text content n Example of figure Year
Analytics to massive amounts of patent data for analyzing R&D trend in wellbeing / healthcare and AI field. n Methodology Panoramic View Analytics is our unique data visualization method of massive amounts of document data. You can easily find trends and create R&D strategy. n Result The fusion and growing areas: “robotics”, “sentimental analysis”, “telemedicine / home care” and “medication management systems”, etc. n Future work • To analyze scientific paper data on wellbeing AI. • To analyze specific field discussed in this symposium such as sleep, body motion etc.
Analysis 2.SWOT Analysis 3.Searching Application Areas for Client’s Technology 4.Investigating Alliance Strategy (Technology Synergy Analysis) n ASP service Panoramic View Analytics software that can analyze not only patent but also research paper, marketing data, etc. n API service API that can be embedded in client’s web site, internal system, etc. n Consultation We provide R&D strategy-making consultation by using panoramic view analytics. n Coaching We support the development of human capital & organizations to utilize data science.
Universities Other Manufacturing 23% 15% 15% 20% 13% 14% Industry (2013-15) We provide analytics services (ASP and over 100 consulting projects/year, etc.) to leading companies. About VALUENEX To name a few: • Honda motors • Panasonic • OMRON • Otsuka HD • Mitsubishi UFJ Morgan Stanley • LIXIL • Sumitomo Bakelite • The Chugoku Electric Power • IPICS • National Graduate Institute for Policy Studies Much more 150+