Machine learned optimizing of health activity for participants during meeting times

Patent metadata

Field	Value
Publication number	US-2018116599-A1
Application number	US-201615341597-A
Country	US
Kind code	A1
Filing date	Nov 2, 2016
Priority date	Nov 2, 2016
Publication date	May 3, 2018
Grant date	—

Abstract

Providing heath activity for a participant in a conference call may include receiving data associated with the conference call and location data specifying a location of the participant conducting the conference call. An engagement level of the participant that is to participate in the conference call may be predicted based on the received data and the location data. Sensor data associated with the participant may be received, the sensor data comprising at least current physiological data associated with the participant. The participant's fitness goal may be identified. Based on the predicted engagement level, the sensor data and the participant's fitness goal, an exercise for the participant to perform during the conference call may be determined. A notification signal may be transmitted to the participant to perform the exercise.

First claim

1 . A method of providing heath activity for a participant in a conference call, the method performed by a hardware processor, comprising:
receiving data associated with the conference call and location data specifying a location of the participant conducting the conference call; predicting an engagement level of the participant that is to participate in the conference call based on the received data and the location data by executing a trained machine model with the received data and the location data as input to the trained machine model, wherein the trained machine model is trained based on labeled historical data comprising historical data associated with historical conference calls and associated historical location data and historical engagement levels; receiving sensor data associated with the participant, the sensor data comprising at least current physiological data associated with the participant; identifying the participant's fitness goal stored in a network; based on the predicted engagement level, the sensor data and the participant's fitness goal, determining an exercise for the participant to perform during the conference call from a predefined lookup table of suggested exercises; and transmitting a notification signal to the participant to perform the exercise responsive to determining the exercise, wherein the hardware processor is a component of a smartphone device, the smartphone device interfaced with a plurality of wearable devices worn by the participant, the plurality of wearable devices continuing to communicate real-time physiological sensor data to the smartphone device during the conference call, wherein the hardware processor further monitors during the conference call, audio sensors coupled to the smartphone to determine current engagement level of the participant during the conference call, wherein the determining an exercise for the participant to perform during the conference call comprises: automatically updating a decision determined based on the predicted engagement level that an exercise should not be recommended, based on the real-time physiological sensor data and the current engagement level of the participant during the conference call, and the transmitting a notification signal to the participant to perform the exercise comprises transmitting the notification signal to the participant during the conference all.

2 . The method of claim 1 , wherein the data comprises a type of the conference meeting, a number of conference meetings being conducted at a time, agenda participation, context of the conference meeting, and a type of equipment used in the conference meeting.

3 . The method of claim 1 , further comprising training the machine model based on parameters labeled by analyzing historical pattern of engagement.

4 . The method of claim 3 , wherein the parameters comprise a type of a conference meeting, a number of conference meetings being conducted at a time, agenda participation, location, context of the conference meeting, and a type of equipment used in the conference meeting.

5 . The method of claim 4 , wherein the machine model is further trained based on sensor parameter data.

6 .- 7 . (canceled)

8 . The method of claim 1 , wherein the sensor data further comprises acceleration sensor coupled to the smartphone device that measures acceleration.

9 . The method of claim 1 , wherein the machine model comprises a decision tree trained based on supervised machine learning algorithm.

10 . A computer readable storage medium storing a program of instructions executable by a machine to perform a method of providing heath activity for a participant in a conference call, the method comprising:
receiving data associated with the conference call and GPS location data specifying a location of the participant conducting the conference call; predicting an engagement level of the participant that is to participate in the conference call based on the received data and the GPS location data by executing a trained machine model with the received data and the location data as input to the trained machine model, wherein the trained machine model is trained based on labeled historical data comprising historical data associated with historical conference calls and associated historical location data and historical engagement levels; receiving sensor data associated with the participant, the sensor data comprising at least current physiological data associated with the participant; identifying the participant's fitness goal stored in a network; based on the predicted engagement level, the sensor data and the participant's fitness goal, determining an exercise for the participant to perform during the conference call from a predefined lookup table of suggested exercises; and transmitting a notification signal to the participant to perform the exercise responsive to determining the exercise, wherein the machine comprises a hardware processor coupled to a smartphone device, the smartphone device interfaced with a plurality of wearable devices worn by the participant, the plurality of wearable devices continuing to communicate real-time physiological sensor data to the smartphone device during the conference call, wherein the hardware processor further monitors during the conference call, audio sensors coupled to the smartphone device to determine current engagement level of the participant during the conference call, wherein the determining an exercise for the participant to perform during the conference call comprises: automatically updating a decision determined based on the predicted engagement level that an exercise should not be recommended, based on the real-time physiological sensor data and the current engagement level of the participant during the conference call, and the transmitting a notification signal to the participant to perform the exercise comprises transmitting the notification signal to the participant during the conference all.

11 . The computer readable storage medium of claim 10 , wherein the data comprises a type of the conference meeting, a number of conference meetings being conducted at a time, agenda participation, context of the conference meeting, and a type of equipment used in the conference meeting.

12 . The computer readable storage medium of claim 10 , further comprising training the machine model based on parameters labeled by analyzing historical pattern of engagement.

13 . The computer readable storage medium of claim 12 , wherein the parameters comprise a type of a conference meeting, a number of conference meetings being conducted at a time, agenda participation, location, context of the conference meeting, and a type of equipment used in the conference meeting.

14 . The computer readable storage medium of claim 13 , wherein the machine model is further trained based on sensor parameter data.

15 .- 16 . (canceled)

17 . The computer readable storage medium of claim 10 , wherein the sensor data further comprises acceleration sensor coupled to the smartphone device that measures acceleration.

18 . The computer readable storage medium of claim 10 , wherein the machine model comprises a decision tree trained based on supervised machine learning algorithm.

19 . A system of providing heath activity for a participant in a conference call, comprising:
a predictive system executing on at least one hardware processor, the predictive system operable to train a machine model based on parameters labeled by analyzing historical pattern of engagement, the parameters comprising a type of a conference m

Assignees

• IBM

Inventors

• Bastide Paul R
• Kiral-Kornek Filiz Isabell
• Mahapatra Dwarikanath
• Saha Susmita
• Vishwanath Arun
• von Cavallar Stefan

Classifications

• A61B5/6898

  Portable consumer electronic devices, e.g. music players, telephones, tablet computers · CPC title

• H04W4/027

  using movement velocity, acceleration information · CPC title

• A61B5/1118

  Determining activity level · CPC title

• H04M2250/12

  including a sensor for measuring a physical value, e.g. temperature or motion · CPC title

• G16H20/30

  relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising · CPC title