Systems and articles for enhancing wellness associated with habitable environments

• US 10,712,722 B2
• Filed: 02/25/2015
• Issued: 07/14/2020
• Est. Priority Date: 02/28/2014
• Status: Active Grant

• Current Assignee: Delos Living LLC
• Original Assignee: Delos Living LLC
• Inventors: Pillai, Dana S., Stodola, Nathan B., Macary, Richard A., Granger, Trevor S., Stewart, Shaun B.
• Primary Examiner(s): Tecklu, Isaac T
• Application Number:
  US15/121,953
• Publication Number:
  US 20170068782A1
• Time in Patent Office: 1,966 Days
• Field of Search: None
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/14542 for measuring blood gases A61B5/14551 takes precedence
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: F24F 11/30 for purposes related to the operation of the system, e.g. for safety or monitoring
• CPC Class Codes: F24F 11/64 using pre-stored data
• CPC Class Codes: F24F 2110/00 Control inputs relating to air properties
• CPC Class Codes: F24F 2120/20 Feedback from users
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 19/048 Monitoring; Safety
• CPC Class Codes: G05B 2219/2614 HVAC, heating, ventillation, climate control
• CPC Class Codes: G06Q 50/10 Services
• CPC Class Codes: G06Q 50/12 Hotels or restaurants
• CPC Class Codes: G06Q 50/16 Real estate
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 20/60 relating to nutrition control, e.g. diets
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: Y02A 90/10 : Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

Environmental characteristics of habitable environments (e.g., hotel or motel rooms, spas, resorts, cruise boat cabins, offices, hospitals and/or homes, apartments or residences) are controlled to eliminate, reduce or ameliorate adverse or harmful aspects and introduce, increase or enhance beneficial aspects in order to improve a “wellness” or sense of “wellbeing” provided via the environments. Control of intensity and wavelength distribution of passive and active Illumination addresses various issues, symptoms or syndromes, for instance to maintain a circadian rhythm or cycle, adjust for “jet lag” or season affective disorder, etc. . Air quality and attributes are controlled. Scent(s) may be dispersed. Noise is reduced and sounds (e.g., masking, music, natural) may be provided. Environmental and biometric feedback is provided. Experimentation and machine learning are used to improve health outcomes and wellness standards.

Claims

1. An environmental control system, comprising:

a plurality of active subsystems operable to effect a condition in a habitable space;

a first number of sensors that collect habitable space wellness data that includes, for at least the habitable space, at least one wellness parameter indicative of a wellness associated with the respective habitable space; and

a control subsystem including at least one processor and at least one nontransitory processor-readable medium communicatively coupled to the at least one processor and which stores at least one of instructions or data including at least collected personal wellness data, the at least one of instructions or data executable by the at least one processor to control the active subsystems, the first number of sensors that collect habitable space wellness data communicatively coupled to the at least one processor, the control subsystem which;

identifies at least one pattern in the collected personal wellness data via at least one machine learning technique, identifies at least one pattern in the collected habitable space wellness data via at least one machine learning technique, determines at least one adjustment to at least one operational parameter based on the identified at least one pattern in the collected personal wellness data and the collected habitable space wellness data, dynamically adjusts at least one operational parameter of at least one of the active subsystems based on both the collected habitable space wellness data and on collected personal wellness data, and adjusts a baseline set of operational parameters of the one or more active subsystems based on at least one of the collected habitable space wellness data or the collected personal wellness data.

2. The environmental control system of claim 1, further comprising:

a second number of sensors that collect the collected personal wellness data from at least one individual.

3. The environmental control system of claim 2 wherein the second number of sensors includes one or more biometric sensors, and the at least one processor evaluates the collected personal wellness data from time-to-time.

4. The environmental control system of claim 3 wherein the one or more biometric sensors includes at least one of a temperature sensor operable to detect a temperature of the at least one individual, a scale operable to detect a weight of the at least one individual, a heart rate sensor operable to detect a heart rate of the at least one individual, a blood oxygen sensor operable to detect a level of blood oxygen of the at least one individual, a respiratory cycle sensor operable to detect at least one characteristic of a respiratory cycle of the at least one individual, or an electroencephalography (EEG) sensor operable to detect at least one brainwave pattern of the at least one individual.

5. The environmental control system of claim 3 wherein the one or more biometric sensors has at least one motion sensor operable to detect a motion of the at least one individual in the habitable space.

6. The environmental control system of claim 5 wherein the at least one processor assesses an amount of motion of the at least one individual based at least in part on information collected by the at least one motion sensor;

and in response to an assessment of the amount of motion, stimulates the at least one individual to increase physical activity.

7. The environmental control system of claim 1, further comprising:

a user interface that in use collects the collected personal wellness data via at least one survey completed by individuals who from time-to-time occupy the respective habitable space.

8. The environmental control system of claim 7 wherein the at least one survey collects self-reported personal wellness data indicative of at least one of:

eating habits of the at least one individual, sleep habits of the at least one individual, exercise habits of the at least one individual, or self-assessed sense of wellness of the at least one individual.

9. The environmental control system of claim 1 wherein the first number of sensors includes at least one of an air quality sensor, a temperature sensor, or a humidity sensor to detect air quality parameters in the respective habitable space.

10. The environmental control system of claim 1 wherein the first number of sensors includes at least one audio transducer to detect ambient sound levels in the respective habitable space.

11. The environmental control system of claim 1 wherein the first number of sensors includes at least one light sensor to detect at least one of a light level or a color index of light in the respective habitable space.

12. An environmental wellness evaluation system, comprising:

at least one processor; and

at least one nontransitory processor-readable medium communicatively coupled to the at least one processor and which stores at least one of instructions or data executable by the at least one processor, which cause the at least one processor to;

collect wellness data that includes, for each of a plurality of habitable spaces, at least one wellness parameter indicative of a wellness associated with the respective habitable space;

identify at least one pattern in the collected wellness data via at least one machine learning circuit;

update at least one wellness standard for habitable spaces based on the identified at least one pattern; and

dynamically adjust an operational parameter of one or more active subsystems operably associated with the respective habitable space to effect a condition in the respective habitable space based on the collected wellness data; and

adjust a baseline set of operational parameters of the one or more active subsystems based on the collected wellness data.

13. The environmental wellness evaluation system of claim 12,

wherein the at least one processor:

from time-to-time, causes at least one of the active subsystems to change at least one characteristic in each of a number of the plurality of habitable spaces;

subsequently collects wellness data that includes, for each of at least the number of the plurality of habitable spaces, at least one wellness parameter indicative of a wellness associated with the respective habitable space after the change; and

performs machine learning based at least in part on the subsequently collected wellness data.

14. The environmental wellness evaluation system of claim 13 wherein the at least one processor causes at least one characteristic in a subset of the plurality of habitable spaces to change, and performs the machine learning with a first set of wellness data collected from the habitable spaces in which the at least one characteristic was changed and with a second set of wellness data collected from the habitable spaces in which the at least one characteristic was not changed.

15. The environmental wellness evaluation system of claim 13 wherein the at least one processor performs the machine learning with a first set of wellness data collected from the habitable spaces before the at least one characteristic was changed in the respective habitable space and with a second set of wellness data collected from the habitable spaces after the at least one characteristic was changed in the respective habitable space.

16. The environmental wellness evaluation system of claim 13 wherein the at least one processor causes at least one habitable space characteristic to randomly change.

17. A machine learning system, comprising:

at least one circuit; and

at least one nontransitory memory that stores at least one of executable instructions or data, the at least one nontransitory memory communicatively coupled to the at least one circuit, where in operation the at least one circuit;

for each of a number of changes in at least one environmental characteristic of at least one habitable space, receives wellness results information by the machine learning system, the received wellness information indicative of at least one of a measurement or an assessment of a level of wellness of the at least one habitable space or at least one individual that inhabits the at least one habitable space;

identifies at least one relationship between the changes in the at least one environmental characteristic and a measured or assessed level of wellness;

dynamically adjusts an operational parameter of one or more active subsystems operably associated with the at least one habitable space based on the assessment of the level of wellness of the at least one habitable space and the at least one individual that inhabits the at least one habitable space; and

adjusts a baseline set of operational parameters of the one or more active subsystems based on the level of wellness of the at least one habitable space or the at least one individual that inhabits the at least one habitable space.

18. The machine learning system of claim 17 where in operation the at least one circuit further:

updates at least one wellness standard for habitable spaces based on the identified at least one relationship.

19. The machine learning system of claim 18 where in operation the at least one circuit further:

evaluates at least one of the habitable spaces based on the at least one updated wellness standard for habitable spaces via at least one processor.

20. The machine learning system of claim 19, where in operation the at least one circuit further:

provides certification to at least one building having habitable spaces that meet the at least one updated wellness standard.

21. The machine learning system of claim 17 wherein the at least one circuit is communicatively coupled to receive the wellness results information collected by at least one sensor in a respective one of each of a plurality of habitable spaces.

22. The machine learning system of claim 17 wherein the at least one circuit is communicatively coupled to receive the wellness results information collected via at least one survey completed by individuals who from time-to-time occupy a respective one of a plurality of habitable spaces.

23. The machine learning system of claim 17 wherein the at least one circuit is communicatively coupled to receive the wellness results information collected via at least one assessment completed by individuals who from time-to-time inspect the respective habitable space.

24. The machine learning system of claim 17 wherein the at least one circuit is communicatively coupled to cause a respective active component of each of a plurality of active components to,

from time-to-time, cause at least one environmental characteristic in each of a plurality of habitable spaces to change.

25. The machine learning system of claim 24 wherein the at least one circuit causes at least one environmental characteristic in a subset of a plurality of habitable spaces to change, and performs the machine learning with a first set of wellness results information collected from the habitable spaces in which the at least one characteristic was changed and with a second set of wellness results information collected from habitable spaces in which the at least one characteristic was not changed.

26. The machine learning system of claim 24 wherein the at least one circuit performs the machine learning with a first set of wellness results information collected from the habitable spaces before the at least one environmental characteristic was changed in the respective habitable space and with a second set of wellness results information collected from the habitable spaces after the at least one environmental characteristic was changed in the respective habitable space.

27. The machine learning system of claim 24 wherein the at least one circuit causes the at least one habitable space environmental characteristic to randomly change.