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DESCRIPTION:IEEE NY WIE Day Webinar\, June 25\, 2026\n\nFrom Conversational
  Signals to Cognitive Awareness:\n\nDesigning Non-Clinical AI Systems for 
 Early Detection in Healthcare\n\nThis talk presents a novel approach to le
 veraging everyday conversational data for early cognitive awareness throug
 h non-clinical artificial intelligence systems. Unlike traditional diagnos
 tic tools that rely on structured clinical testing\, this work introduces 
 a human-centered AI framework that analyzes linguistic and acoustic signal
 s from natural speech interactions.\n\nThe speaker will introduce KinaBot\
 , an AI system designed to extract cognitive indicators such as response l
 atency\, speech rhythm\, and semantic richness\, transforming them into lo
 ngitudinal cognitive trend insights. The system emphasizes a baseline-rela
 tive modeling approach\, enabling personalized tracking without requiring 
 medical labeling or intrusive testing environments.\n\nThe talk will also 
 discuss system architecture\, real-world deployment considerations\, and e
 thical design principles\, including privacy-preserving “compute-and-dis
 card” strategies. By bridging human-computer interaction\, machine learn
 ing\, and healthcare awareness\, this work contributes a scalable paradigm
  for early cognitive insight that complements—but does not replace—cli
 nical diagnosis.\n\nCo-sponsored by: IEEE NY WIE and IEEE New York Section
 \n\nSpeaker(s): Minamoto\, \n\nAgenda: \n6:00 - 6:10PM Welcome Remark - IE
 EE NY WIE Group Chair\, Dr. Sarah H. Chung\, IEEE WIE Ambassador\,.\n\n6:1
 0 - 7:00PM Presentation: Ms. Aoi Minamoto\n\nFrom Conversational Signals t
 o Cognitive Awareness:\n\nDesigning Non-Clinical AI Systems for Early Dete
 ction in Healthcare\n\n7:00 - 7:15PM Q/A\n\nVirtual: https://events.vtools
 .ieee.org/m/561188
LOCATION:Virtual: https://events.vtools.ieee.org/m/561188
ORGANIZER:sarah.h.chung@ieee.org
SEQUENCE:53
SUMMARY:IEEE NY WIE Day Webinar\, June 25\, 2026
URL;VALUE=URI:https://events.vtools.ieee.org/m/561188
X-ALT-DESC:Description: &lt;br /&gt;&lt;p style=&quot;margin: 0in\; text-align: center\;&quot;
  align=&quot;center&quot;&gt;&lt;strong&gt;&lt;span style=&quot;font-size: 14.0pt\; color: #a02b93\; 
 mso-themecolor: accent5\;&quot;&gt;IEEE NY WIE Day Webinar\, June 25\, 2026&lt;/span&gt;
 &lt;/strong&gt;&lt;/p&gt;\n&lt;p style=&quot;margin: 0in\; text-align: center\;&quot; align=&quot;center
 &quot;&gt;&lt;strong&gt;&lt;span style=&quot;font-size: 14.0pt\; color: #a02b93\; mso-themecolor
 : accent5\;&quot;&gt;&amp;nbsp\;&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p style=&quot;margin: 0in\; text-ali
 gn: center\;&quot; align=&quot;center&quot;&gt;&lt;strong&gt;&lt;span style=&quot;font-size: 14.0pt\; colo
 r: #a02b93\; mso-themecolor: accent5\;&quot;&gt;From Conversational Signals to Cog
 nitive Awareness: &lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p style=&quot;margin: 0in\; text-align
 : center\;&quot; align=&quot;center&quot;&gt;&lt;strong&gt;&lt;em&gt;&lt;span style=&quot;color: black\;&quot;&gt;Design
 ing Non-Clinical AI Systems for Early Detection in Healthcare&lt;/span&gt;&lt;/em&gt;&lt;
 /strong&gt;&lt;/p&gt;\n&lt;p style=&quot;margin: 0in\; text-align: center\;&quot; align=&quot;center&quot;
 &gt;&lt;strong&gt;&lt;span style=&quot;font-size: 14.0pt\; color: #a02b93\; mso-themecolor:
  accent5\;&quot;&gt;&amp;nbsp\;&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p style=&quot;margin: 0in\;&quot;&gt;&lt;span st
 yle=&quot;font-size: 10.5pt\; font-family: Roboto\; mso-bidi-font-family: Arial
 \; color: black\;&quot;&gt;&amp;nbsp\;&lt;/span&gt;&lt;/p&gt;\n&lt;p style=&quot;margin: 0in\; text-align:
  justify\; text-justify: inter-ideograph\;&quot;&gt;&lt;span style=&quot;color: black\;&quot;&gt;T
 his talk presents a novel approach to leveraging everyday conversational d
 ata for early cognitive awareness through non-clinical artificial intellig
 ence systems. Unlike traditional diagnostic tools that rely on structured 
 clinical testing\, this work introduces a human-centered AI framework that
  analyzes linguistic and acoustic signals from natural speech interactions
 .&lt;/span&gt;&lt;/p&gt;\n&lt;p style=&quot;margin: 0in\; text-align: justify\; text-justify: 
 inter-ideograph\;&quot;&gt;&lt;span style=&quot;color: black\;&quot;&gt;The speaker will introduce
  &lt;strong&gt;KinaBot\,&lt;/strong&gt; an AI system designed to extract cognitive ind
 icators such as response latency\, speech rhythm\, and semantic richness\,
  transforming them into longitudinal cognitive trend insights. The system 
 emphasizes a baseline-relative modeling approach\, enabling personalized t
 racking without requiring medical labeling or intrusive testing environmen
 ts.&lt;/span&gt;&lt;/p&gt;\n&lt;p style=&quot;margin: 0in\; text-align: justify\; text-justify
 : inter-ideograph\;&quot;&gt;&lt;span style=&quot;color: black\;&quot;&gt;The talk will also discu
 ss system architecture\, real-world deployment considerations\, and ethica
 l design principles\, including privacy-preserving &amp;ldquo\;compute-and-dis
 card&amp;rdquo\; strategies. By bridging human-computer interaction\, machine 
 learning\, and healthcare awareness\, this work contributes a scalable par
 adigm for early cognitive insight that complements&amp;mdash\;but does not rep
 lace&amp;mdash\;clinical diagnosis.&lt;/span&gt;&lt;/p&gt;&lt;br /&gt;&lt;br /&gt;Agenda: &lt;br /&gt;&lt;p&gt;6:0
 0 - 6:10PM Welcome Remark - IEEE NY WIE Group Chair\, Dr. Sarah H. Chung\,
  IEEE WIE Ambassador\,.&lt;/p&gt;\n&lt;p&gt;6:10 - 7:00PM Presentation: Ms. &lt;strong&gt;&lt;s
 pan style=&quot;font-size: 14.0pt\; line-height: 115%\; font-family: &#39;Aptos&#39;\,s
 ans-serif\; mso-ascii-theme-font: minor-latin\; mso-fareast-font-family: P
 MingLiU\; mso-fareast-theme-font: minor-fareast\; mso-hansi-theme-font: mi
 nor-latin\; mso-bidi-font-family: &#39;Times New Roman&#39;\; mso-bidi-theme-font:
  minor-bidi\; color: black\; mso-ansi-language: EN-US\; mso-fareast-langua
 ge: ZH-TW\; mso-bidi-language: AR-SA\;&quot;&gt;Aoi Minamoto&lt;/span&gt;&lt;/strong&gt;&lt;span 
 style=&quot;font-size: 12.0pt\; line-height: 115%\; font-family: &#39;Aptos&#39;\,sans-
 serif\; mso-ascii-theme-font: minor-latin\; mso-fareast-font-family: PMing
 LiU\; mso-fareast-theme-font: minor-fareast\; mso-hansi-theme-font: minor-
 latin\; mso-bidi-font-family: &#39;Times New Roman&#39;\; mso-bidi-theme-font: min
 or-bidi\; color: black\; mso-ansi-language: EN-US\; mso-fareast-language: 
 ZH-TW\; mso-bidi-language: AR-SA\;&quot;&gt; &lt;/span&gt;&lt;/p&gt;\n&lt;p style=&quot;margin: 0in\; 
 text-align: left\;&quot; align=&quot;center&quot;&gt;&lt;strong&gt;&lt;span style=&quot;font-size: 14.0pt\
 ; color: #a02b93\; mso-themecolor: accent5\;&quot;&gt;From Conversational Signals 
 to Cognitive Awareness:&amp;nbsp\;&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p style=&quot;margin: 0in\
 ; text-align: left\;&quot; align=&quot;center&quot;&gt;&lt;strong&gt;&lt;em&gt;&lt;span style=&quot;color: black
 \;&quot;&gt;Designing Non-Clinical AI Systems for Early Detection in Healthcare&lt;/s
 pan&gt;&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p style=&quot;margin: 0in\; text-align: left\;&quot; align=
 &quot;center&quot;&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p style=&quot;margin: 0in\; text-align: left\;&quot; align=&quot;c
 enter&quot;&gt;&lt;span style=&quot;color: black\;&quot;&gt;7:00 - 7:15PM Q/A&lt;/span&gt;&lt;/p&gt;\n&lt;p&gt;&amp;nbsp
 \;&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;
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