IP Library › Granted Patent US 12,191,009
Granted Patent B2
US 12,191,009 · App. 17/705,148 · Granted Jan 7, 2025

Personalized health system, method and device having a sleep function

Inventors: James Kaput (Madison, WI); Corrado Priami (Follonica, IT); Melissa Morine (Avonport, CA); Terry Carlone (Sacramento, CA); John Green (Burke, VA)
Assignee: Vydiant, Inc.
G16H10/60A61B5/742A61B5/7465A61B5/7475G06F3/04847G06F9/453G06N5/04G16H10/20G16H15/00G16H20/17G16H20/30G16H20/60G16H20/70G16H40/67G16H50/30G16H70/40G16H70/60
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Quick Facts
Patent No.
US 12,191,009
App. No.
17/705,148
Granted
Jan 7, 2025
Kind
B2
Abstract

A personal health system, method and device that maintains a health knowledge base, inputs user characteristics, generates health scores based on the user characteristics and provides recommendations based on the user characteristics, health scores and knowledge base, wherein the recommendations are indicated by the knowledge base to be likely to improve the user's health.

Claims (75)

1. A personal health system for promoting healthy choices to one or more users, the personal health system comprising:

a non-transitory computer-readable memory storing a knowledge base, the knowledge base including a plurality of tuples, wherein each tuple includes a condition, one or more factors that affect the condition, and a relationship that describes how the one or more factors affect the condition, wherein one or more of the conditions relate to sleep;

at least one computing device including a processor and a non-transitory computer-readable medium coupled with the processor and storing a personal health platform, wherein when executed by the processor the personal health platform is operable to generate an input graphical user interface on the device that provides a series of digital prompts guiding a user to input personal characteristics of the user, the personal characteristics including an average nightly sleep duration, a time to fall asleep, a number of times the user wakes up a night, a daytime nap duration and a daytime tiredness value, wherein the daytime tiredness value indicates how tired the user feels during daytime as evaluated by the user; and

a set of data on a non-transitory computer-readable memory, the set of data including a duration score for the user that is based on the average nightly sleep duration of the user, an adjusted duration score that is based on the daytime tiredness value of the user, a sleep score for the user that is based on the adjusted duration score, a normalized time to fall asleep value that is based on the time to fall asleep normalized to be within a specified range, a normalized number of times the user wakes value that is based on the number of times the user wakes up a night normalized to be within the specified range, and a normalized daytime nap value that is based on the daytime nap duration normalized to be within the specified range;

wherein when executed by the processor the personal health platform is operable to provide a navigation user interface comprising:

a condition selection feature that enables the user to select one or more of the conditions as desired conditions;

one or more relationship strength features and a network web, wherein the network web graphically illustrates nodes representing the desired conditions and visible connections between each of the nodes and one or more related factors of the factors that have an identified relationship with the desired conditions according to the tuples that include the desired conditions;

wherein the relationship strength features enable the user to selectively input changes to one or more relationship strength threshold values associated with the desired conditions;

wherein the navigation user interface dynamically alters the network web as concurrently displayed based on the input changes to the relationship strength threshold values, wherein the altering of the network web is such that the one or more related factors included in the network web is modified to only be a set of the related factors whose identified relationship to the desired conditions, according to the tuples that include the desired conditions, falls within one or more relationship strength ranges defined by the relationship strength threshold values for the desired conditions;

a highlight feature that displays a list of the related factors and the desired conditions of the network web as concurrently displayed, and enables selection of a subset of the related factors and the desired conditions;

wherein the navigation user interface dynamically alters the network web as concurrently displayed based on the subset such that the related factors and the desired conditions of the subset are emphasized within the network web with respect to the related factors and the desired conditions that are not a part of the subset; and

an excerpt feature that displays one or more excerpts upon which the relationships of the related factors and the desired conditions were based and a document identifier link that links to a document from which the excerpt was taken.

2. The personal health system of claim 1 , wherein the adjusted duration score is double the duration score when the tiredness value is within a first range.

3. The personal health system of claim 2 , wherein the adjusted duration score is half the duration score when the tiredness value is within a second range that is different from the first range.

4. The personal health system of claim 3 , wherein the personal characteristics include a sleep medication status.

5. The personal health system of claim 4 , wherein the time to fall asleep indicates how long the user takes to fall asleep and the sleep medication status indicates whether the user currently uses medication for falling asleep.

6. The personal health system of claim 5 , wherein the sleep score is further based on a combination of the normalized time to fall asleep value, the normalized daytime nap value and the normalized number of times the user wakes value.

7. The personal health system of claim 6 , wherein the personal health platform is operable to lower the sleep score based on the sleep medication status indicating that the user currently uses medication for falling asleep.

8. The personal health system of claim 7 , wherein the personal health platform is operable to assign a sleep condition to the user based on the sleep score of the user.

9. The personal health system of claim 8 , further comprising a health knowledge base coupled with the device, stored on a non-transitory computer-readable medium, and including a plurality of tuples that each include a tuple condition, a factor that affects the condition, and relationship that defines how the factor affects the condition.

10. The personal health system of claim 9 , wherein the personal health platform is operable to determine a set of the tuples whose tuple condition matches the sleep condition assigned to the user, and generate a recommendation for the user based on at least one of the set, wherein the recommendation identifies the factors of the at least one of the set and whether to increase or decrease each of the factors.

11. A set of data stored on a non-transitory computer-readable medium, the set of data including a personal health platform, a duration score for a user, an adjusted duration score for the user, a sleep score for the user, a normalized time to fall asleep value, and a normalized number of times the user wakes up value and a normalized daytime nap value, wherein the personal health platform is for operating with a knowledge base, the knowledge base including a plurality of tuples, wherein each tuple includes a condition, one or more factors that affect the condition, and a relationship that describes how the one or more factors affect the condition, wherein one or more of the conditions relate to sleep, and further wherein the duration score is based on the average nightly sleep duration of the user, the adjusted duration score is based on the daytime tiredness value of the user, the sleep score is based on the adjusted duration score, the normalized time to fall asleep value is based on the time to fall asleep normalized to be within a specified range, the normalized number of times the user wakes value is based on the number of times the user wakes up a night normalized to be within the specified range, and the normalized daytime nap value is based on the daytime nap duration normalized to be within the specified range, wherein when executed by a processor the personal health platform is operable to:

generate an input graphical user interface with the processor that provides a series of digital prompts guiding the user to input personal characteristics of the user, the personal characteristics including an average nightly sleep duration, a time to fall asleep, a number of times the user wakes up a night, a daytime nap duration and a daytime tiredness value, wherein the daytime tiredness value indicates how tired the user feels during daytime as evaluated by the user; and

provide a navigation graphical user interface comprising:

a condition selection feature that enables the user to select one or more of the conditions as desired conditions;

one or more relationship strength features and a network web, wherein the network web graphically illustrates nodes representing the desired conditions and visible connections between each of the nodes and one or more related factors of the factors that have an identified relationship with the desired conditions according to the tuples that include the desired conditions;

wherein the relationship strength features enable the user to selectively input changes to one or more relationship strength threshold values associated with the desired conditions;

wherein the navigation user interface dynamically alters the network web as concurrently displayed based on the input changes to the relationship strength threshold values, wherein the altering of the network web is such that the one or more related factors included in the network web is modified to only be a set of the related factors whose identified relationship to the desired conditions, according to the tuples that include the desired conditions, falls within one or more relationship strength ranges defined by the relationship strength threshold values for the desired conditions;

a highlight feature that displays a list of the related factors and the desired conditions of the network web as concurrently displayed, and enables selection of a subset of the related factors and the desired conditions;

wherein the navigation user interface dynamically alters the network web as concurrently displayed based on the subset such that the related factors and the desired conditions of the subset are emphasized within the network web with respect to the related factors and the desired conditions that are not a part of the subset; and

an excerpt feature that displays one or more excerpts upon which the relationships of the related factors and the desired conditions were based and a document identifier link that links to a document from which the excerpt was taken.

12. The medium of claim 11 , wherein the adjusted duration score is double the duration score when the tiredness value is within a first range.

13. The medium of claim 12 , wherein the adjusted duration score is half the duration score when the tiredness value is within a second range that is different from the first range.

14. The medium of claim 13 , wherein the personal characteristics include a sleep medication status.

15. The medium of claim 14 , wherein the time to fall asleep indicates how long the user takes to fall asleep and the sleep medication status indicates whether the user currently uses medication for falling asleep.

16. The medium of claim 15 , wherein the sleep score is further based on a combination of the normalized time to fall asleep value, the normalized daytime nap value and the normalized number of times the user wakes value.

17. The medium of claim 16 , wherein the personal health platform is operable to lower the sleep score based on the sleep medication status indicating that the user currently uses medication for falling asleep.

18. The medium of claim 17 , wherein the personal health platform is operable to assign a sleep condition to the user based on the sleep score of the user.

19. The medium of claim 18 , further comprising a health knowledge base stored on the non-transitory computer-readable medium and including a plurality of tuples that each include a tuple condition, a factor that affects the condition, and relationship that defines how the factor affects the condition.

20. The medium of claim 19 , wherein the personal health platform is operable to determine a set of the tuples whose tuple condition matches the sleep condition assigned to the user, and generate a recommendation for the user based on at least one of the set, wherein the recommendation identifies the factors of the at least one of the set and whether to increase or decrease each of the factors.

21. A method of implementing a personal health system for promoting healthy choices to one or more users, the method comprising:

providing a non-transitory computer-readable medium storing a knowledge base, the knowledge base including a plurality of tuples, wherein each tuple includes a condition, one or more factors that affect the condition, and a relationship that describes how the one or more factors affect the condition, wherein one or more of the conditions relate to sleep;

with at least one computing device including a non-transitory computer-readable memory:

generating an input graphical user interface on the device that provides a series of digital prompts guiding a user to input personal characteristics of the user, the personal characteristics including an average nightly sleep duration, a time to fall asleep, a number of times the user wakes up a night, a daytime nap duration and a daytime tiredness value, wherein the daytime tiredness value indicates how tired the user feels during daytime as evaluated by the user; and

providing a navigation graphical user interface comprising:

a condition selection feature that enables the user to select one or more of the conditions as desired conditions;

one or more relationship strength features and a network web, wherein the network web graphically illustrates nodes representing the desired conditions and visible connections between each of the nodes and one or more related factors of the factors that have an identified relationship with the desired conditions according to the tuples that include the desired conditions;

wherein the relationship strength features enable the user to selectively input changes to one or more relationship strength threshold values associated with the desired conditions;

wherein the navigation user interface dynamically alters the network web as concurrently displayed based on the input changes to the relationship strength threshold values, wherein the altering of the network web is such that the one or more related factors included in the network web is modified to only be a set of the related factors whose identified relationship to the desired conditions, according to the tuples that include the desired conditions, falls within one or more relationship strength ranges defined by the relationship strength threshold values for the desired conditions;

a highlight feature that displays a list of the related factors and the desired conditions of the network web as concurrently displayed, and enables selection of a subset of the related factors and the desired conditions;

wherein the navigation user interface dynamically alters the network web as concurrently displayed based on the subset such that the related factors and the desired conditions of the subset are emphasized within the network web with respect to the related factors and the desired conditions that are not a part of the subset; and

an excerpt feature that displays one or more excerpts upon which the relationships of the related factors and the desired conditions were based and a document identifier link that links to a document from which the excerpt was taken;

wherein a set of data is stored on the non-transitory computer-readable memory, the set of data including a duration score for the user that is based on the average nightly sleep duration of the user, an adjusted duration score that is based on the daytime tiredness value of the user, a sleep score for the user that is based on the adjusted duration score, a normalized time to fall asleep value that is based on the time to fall asleep normalized to be within a specified range, a normalized number of times the user wakes value that is based on the number of times the user wakes up a night normalized to be within the specified range, and a normalized daytime nap value that is based on the daytime nap duration normalized to be within the specified range.

22. The method of claim 21 , wherein the adjusted duration score is double the duration score when the tiredness value is within a first range.

23. The method of claim 22 , wherein the adjusted duration score is half the duration score when the tiredness value is within a second range that is different from the first range.

24. The method of claim 23 , wherein the personal characteristics include a sleep medication status.

25. The method of claim 24 , wherein the time to fall asleep indicates how long the user takes to fall asleep and the sleep medication status indicates whether the user currently uses medication for falling asleep.

26. The method of claim 25 , wherein the sleep score is further based on a combination of the normalized time to fall asleep value, the normalized daytime nap value and the normalized number of times the user wakes value.

27. The method of claim 26 , further comprising lowering the sleep score based on the sleep medication status indicating that the user currently uses medication for falling asleep.

28. The method of claim 27 , further comprising assigning a sleep condition to the user based on the sleep score of the user.

29. The method of claim 28 , wherein the personal health platform is coupled with a health knowledge base coupled with the device, stored on a non-transitory computer-readable medium and including a plurality of tuples that each include a tuple condition, a factor that affects the condition, and relationship that defines how the factor affects the condition.

30. The method of claim 29 , further comprising determining a set of the tuples whose tuple condition matches the sleep condition assigned to the user, and generating a recommendation for the user based on at least one of the set, wherein the recommendation identifies the factors of the at least one of the set and whether to increase or decrease each of the factors.

31. A personal health system for promoting healthy choices to one or more users, the personal health system comprising:

a non-transitory computer-readable medium storing a knowledge base, the knowledge base including a plurality of tuples, wherein each tuple includes a condition, one or more factors that affect the condition, and a relationship that describes how the one or more factors affect the condition, wherein one or more of the conditions relate to sleep;

at least one computing device including a processor and a non-transitory computer-readable medium coupled with the processor and storing a personal health platform, wherein when executed by the processor the personal health platform is operable to:

generate an input graphical user interface on the device that provides a series of digital prompts guiding a user to input personal characteristics of the user, wherein one or more of the prompts include a button that upon selection by the user displays an explanation of one of the personal characteristics associated with that prompt, the personal characteristics including an average nightly sleep duration, a time to fall asleep, a number of times the user wakes up a night, a daytime nap duration and a daytime tiredness value, wherein the daytime tiredness value indicates how tired the user feels during daytime as evaluated by the user; and

generate a digital image including a sleep score of the user, the sleep score being based on a duration score for the user that is based on the average nightly sleep duration of the user, an adjusted duration score that is based on the daytime tiredness value of the user, a normalized time to fall asleep value that is based on the time to fall asleep normalized to be within a specified range, a normalized number of times the user wakes value that is based on the number of times the user wakes up a night normalized to be within the specified range, and a normalized daytime nap value that is based on the daytime nap duration normalized to be within the specified range;

generate a navigation user interface comprising:

a condition selection feature that enables the user to select one or more of the conditions as desired conditions;

one or more relationship strength features and a network web, wherein the network web graphically illustrates nodes representing the desired conditions and visible connections between each of the nodes and one or more related factors of the factors that have an identified relationship with the desired conditions according to the tuples that include the desired conditions;

wherein the relationship strength features enable the user to selectively input changes to one or more relationship strength threshold values associated with the desired conditions;

wherein the navigation user interface dynamically alters the network web as concurrently displayed based on the input changes to the relationship strength threshold values, wherein the altering of the network web is such that the one or more related factors included in the network web is modified to only be a set of the related factors whose identified relationship to the desired conditions, according to the tuples that include the desired conditions, falls within one or more relationship strength ranges defined by the relationship strength threshold values for the desired conditions;

a highlight feature that displays a list of the related factors and the desired conditions of the network web as concurrently displayed, and enables selection of a subset of the related factors and the desired conditions;

wherein the navigation user interface dynamically alters the network web as concurrently displayed based on the subset such that the related factors and the desired conditions of the subset are emphasized within the network web with respect to the related factors and the desired conditions that are not a part of the subset; and

an excerpt feature that displays one or more excerpts upon which the relationships of the related factors and the desired conditions were based and a document identifier link that links to a document from which the excerpt was taken.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 25, 2024
From: KAPUT, JAMES; PRIAMI, CORRADO; MORINE, MELISSA; CARLONE, TERRY; GREEN, JOHN
To: VYDIANT, INC.
Reel/Frame 069400/0889 →
Continuity (3)
Provisional Application 63166416 · Mar 26, 2021
Provisional Application 63166427 · Mar 26, 2021
Related Publication 20220310229A1 · Sep 29, 2022
References Cited (107)
US 6163781A · Wess, Jr. · 2000 [cited by applicant]
US 9483606B1 · Dean · 2016 [cited by applicant]
US 9727885B1 · Reier et al. · 2017 [cited by applicant]
US 20030074142A1 · Steeg · 2003 [cited by applicant]
US 20080046916A1 · Shivaji-Rao · 2008 [cited by applicant]
US 20080228699A1 · Kenedy et al. · 2008 [cited by applicant]
US 20120072233A1 · Hanlon · 2012 [cited by applicant]
US 20120089909A1 · Block · 2012 [cited by applicant]
US 20120203707A1 · Hungerford · 2012 [cited by applicant]
US 20120221350A1 · Kenedy · 2012 [cited by applicant]
US 20120290327A1 · Hanlon · 2012 [cited by applicant]
US 20120313776A1 · Utter, II · 2012 [cited by applicant]
US 20130030260A1 · Hale · 2013 [cited by applicant]
US 20130035949A1 · Saltzman · 2013 [cited by applicant]
US 20130138239A1 · Chen · 2013 [cited by applicant]
US 20130216982A1 · Bennett · 2013 [cited by applicant]
US 20140067730A1 · Kozloski · 2014 [cited by applicant]
US 20140074510A1 · McClung et al. · 2014 [cited by applicant]
US 20140076318A1 · Flower · 2014 [cited by examiner]
US 20140114680A1 · Mills · 2014 [cited by examiner]
US 20140156308A1 · Ohnemus · 2014 [cited by applicant]
US 20150025809A1 · Herron · 2015 [cited by applicant]
US 20150164409A1 · Benson · 2015 [cited by examiner]
US 20150194071A1 · Bennett · 2015 [cited by applicant]
US 20150220697A1 · Hunt · 2015 [cited by applicant]
US 20150235562A1 · Klein · 2015 [cited by applicant]
US 20150269321A1 · Soon-Shiong · 2015 [cited by applicant]
US 20160151603A1 · Shouldice · 2016 [cited by examiner]
US 20160217266A1 · Damani et al. · 2016 [cited by applicant]
US 20160270717A1 · Lung et al. · 2016 [cited by applicant]
US 20160275253A1 · Shimura · 2016 [cited by applicant]
US 20170147775A1 · Ohnemus · 2017 [cited by applicant]
US 20170249445A1 · Devries · 2017 [cited by applicant]
US 20170262604A1 · Francois · 2017 [cited by applicant]
US 20170277841A1 · Shanker · 2017 [cited by applicant]
US 20170291067A1 · Jang · 2017 [cited by applicant]
US 20170293722A1 · Valverde, Jr. · 2017 [cited by applicant]
US 20170300655A1 · Lane · 2017 [cited by applicant]
US 20170323078A1 · Michon et al. · 2017 [cited by applicant]
US 20180082317A1 · Reier · 2018 [cited by applicant]
US 20180110960A1 · Youngblood · 2018 [cited by examiner]
US 20180192136A1 · Grabowski · 2018 [cited by examiner]
US 20180233064A1 · Dunn · 2018 [cited by applicant]
US 20180233223A1 · Solari · 2018 [cited by applicant]
US 20180268821A1 · Levanon · 2018 [cited by applicant]
US 20180277248A1 · Nazam · 2018 [cited by applicant]
US 20180344215A1 · Ohnemus · 2018 [cited by applicant]
US 20190008577A1 · Lazarus · 2019 [cited by examiner]
US 20190065692A1 · Connelly · 2019 [cited by applicant]
US 20190099582A1 · Crow · 2019 [cited by examiner]
US 20190371452A1 · Mainardi · 2019 [cited by applicant]
US 20200118685A1 · Lee · 2020 [cited by applicant]
US 20200143947A1 · Choi · 2020 [cited by applicant]
US 20200163824A1 · Kim · 2020 [cited by examiner]
US 20200205728A1 · Molina · 2020 [cited by examiner]
US 20200321133A1 · Gonzales, Jr. · 2020 [cited by applicant]
US 20210012900A1 · Smith · 2021 [cited by applicant]
US 20210182918A1 · Trairattanapa · 2021 [cited by applicant]
US 20210287803A1 · Radrich · 2021 [cited by applicant]
US 20210365951A1 · Benkreira · 2021 [cited by applicant]
US 20220030382A1 · Klassen · 2022 [cited by applicant]
US 20220139570A1 · Hettig · 2022 [cited by applicant]
US 20230343459A1 · Prozorovscaia · 2023 [cited by applicant]
CN 107103177A · 2017 [cited by applicant]
WO WO2008037020A1 · 2008 [cited by examiner]
WO 2012090226A2 · 2012 [cited by applicant]
WO 2014069896 · 2014 [cited by applicant]
WO 2019246032A1 · 2019 [cited by applicant]
WO 2021130144A1 · 2021 [cited by applicant]
Karim Tabia et al., Data Analytics and visualization for connected objects: A case study for sleep and physical activity trackers, 10868 Lecture Notes in Computer Science , 685-696 (2018), https://link.springer.com/chap… [cited by examiner]
International Search Report and Written Opinion, mailed on Jul. 25, 2022, PCT/US2022/022046, Applicant: Vydiant, Inc., 12 pages. [cited by applicant]
International Search Report and Written Opinion, mailed Aug. 3, 2022, PCT/US2022/022045, Applicant: Vydiant, Inc., 26 pages. [cited by applicant]
Lu, “These are the occupations with the highest COVID-19 risk”, Visual Capitalist articles on World Economic Forum, Apr. 20, 2020.n Retrieved on Jun. 30, 2022. Retrieved from <URL:https://www.weforum.org/agenda/2020/04/… [cited by applicant]
Bakker et al., “Mental health smartphone apps: review and evidence-based recommendations for future developments”. JMIR mental health 3.1(2016): e4984.Jan. 3, 2016(Jan. 3, 2016). Retrieved on Jul. 17, 2022 (Jul. 17, 202… [cited by applicant]
“Mining Biomedical Text: Transfer Learning to the Rescue”, Dimitris Vamvourellis et al., Institute for Applied Computational Science, Dec. 29, 2020, 9 pages. [cited by applicant]
A knowledge graph of clinical trials (CTKG), Ziqi Chen et al., www.nature.com/scientificreports, 12:4724 (2022), 14 pages. [cited by applicant]
“A systematic comprehensive longitudinal evaluation of dietary factors associated with acute myocardial infarction and fatal coronary heart disease”, Soodabeh Milanlouei et al., www.nature.com/naturecommunications, 11:6… [cited by applicant]
“Creating a Dietary Recommendations Knowledge Graph with Timbr”, Timbr.Ai, Jul. 19, 2021, 16 pages. [cited by applicant]
“Applications of knowledge graphs for food science and industry”, Weiqing Min et al., Patterns 3, CellPress Open Access, May 13, 2022, 21 pages. [cited by applicant]
“Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph”, Yu Chen et al., Association for Computing Machinery, Jan. 5, 2021, 9 pages. [cited by applicant]
“Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph”, Yu Chen et al., WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining,… [cited by applicant]
“Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019”, www.thelancet.com, vol. 396, Oct. 17, 2020, pp. 1204-1222. [cited by applicant]
“HiPub: translating PubMed and PMC texts to networks for knowledge discovery”, Kyubum Lee et al., Bioinformatics, 32(18), Aug. 2, 2016, pp. 2886-2888. [cited by applicant]
“Individualized Knowledge Graph A Viable Informatics Path to Precision Medicine”, Peipei Ping et al., Circulation Research, Mar. 31, 2017, pp. 1078-1080. [cited by applicant]
“Consensus statement understanding health and malnutrition through a systems approach: the ENOUGH program for early life”, Jim Kaput et al., Genes Nutr (2014) 9:378, Dec. 22, 2013, 9 pages. [cited by applicant]
“Knowledge Graphs and Knowledge Networks: The Story in Brief”, Amit Sheth et al., IEEE Internet Computing, vol. 23, No. 4, pp. 67-84, Mar. 7, 2020. [cited by applicant]
“NETME: on-the-fly knowledge network construction from biomedical literature”, Alessandro Muscolino et al., Applied Network Science (2022) 7:1, Jan. 6, 2022, 24 pages. [cited by applicant]
“HINT: Hierarchical interaction network for clinical-trial-outcome predictions”, Tianfan Fu et al., Patterns 3, 100445, Apr. 8, 2022, 13 pages. [cited by applicant]
“Causal relationship extraction from biomedical text using deep neural models: A comprehensive survey”, Abbas Akkasi et al., Journal of Biomedical Informatics 119 (2021) 103820, 12 pages. [cited by applicant]
“NutriChem 2.0: exploring the effect of plant-based foods on human health and drug efficacy”, Yueqiong Ni et al., Database, 2017, pp. 1-6. [cited by applicant]
“NutriChem: a systems chemical biology resource to explore the medicinal value of plant-based foods”, Kasper Jensen et al., Nucleic Acids Research, 2015, vol. 43, Database issue, Aug. 8, 2014,pp. D940-D945. [cited by applicant]
“Analysis of Knowledge Graph Model of Exercise Physiology Based on Computer Information Technology”, Jinglian Chi, ICASIT 2021: 2021 International Conference on Aviation Safety and Information Technology, Dec. 2021, pp.… [cited by applicant]
“Visual Analysis of College Sports Performance Based on Multimodal Knowledge Graph Optimization Neural Network”, Nan Zheng et al., Computational Intelligence and Neuroscience, vol. 2022, Article ID 5398932, Jul. 1, 2022… [cited by applicant]
“Building A PubMed knowledge graph”, Jian Xu et al., Scientific Data, (2020) 7:205, Jun. 26, 2020, 15 pages. [cited by applicant]
“Discovering biomedical semantic relations in PubMed queries for information retrieval and database curation”, Chung-Chi Huang et al., Database, 2016, doi: 10.1093/database/baw025, Feb. 14, 2016, pp. 1-15. [cited by applicant]
“Deep Reasoning with Knowledge Graph for Social Relationship Understanding”, Zhouxia Wang et al., Jul. 2, 2018, 8 pages. [cited by applicant]
“Building and Using Personal Knowledge Graph to Improve Suicidal Ideation Detection on Social Media”, Lei Cao et al., IEEE Transactions on Multimedia, vol. 24, Dec. 25, 2020, 3 pages. [cited by applicant]
“Methods for Measuring and Monitoring Medication Regimen Adherence in Clinical Trials and Clinical Practice” Clinical Therapeutics, vol. 21, No. 6, 1999, Kevin C. Farmer, PhD., pp. 1074-1090. [cited by applicant]
“Individual and Community-Level Risk for COVID-19 mortality in the United States”, Jin Jin et al., Nature Medicine, vol. 27, Feb. 2021, pp. 264-269 plus letters. [cited by applicant]
Reis et al.; Development of a Health Lifestyle Assessment Toolkit for the General Public, Jun. 27, 2019, Frontiers in Medicine, 6:134 (Year 2019). [cited by applicant]
Escribano, J. et al., “Effect of Protein Intake and Weight Gain Velocity on Body Fat Mass at 6months of Age:The EU Childhood Obesity Programme.”, International Journal of Obesity, 36(4), 548-553. (Year:2012). [cited by applicant]
Heinz, Adrienne J., The Effects of Alcohol, Caffeine and Expectancies on Personal Agency, Impulsivity and Risk Taking: University of Illinois at Chicago, ProQuest Dissertations Publishing, 2012.3551879 (Year2012). [cited by applicant]
Yi, Weiying, Enabling Personalized Air Pollution and Health Monitoring Using Low-Cost Sensors ands Artificial Intelligence; The Chinese University of Hong Kong (Hing Kong), ProQuest Dissertations Publishing. 2021.291862… [cited by applicant]
International Preliminary Report dated Oct. 5, 2023 from the International Patent Application No. PCT/US2022/022045. [cited by applicant]
International Preliminary Report dated Oct. 5, 2023 from the International Patent Application No. PCT/US2022/022046 . [cited by applicant]
Georgina Prodhan, Extending the range of COVID-19 risk factors in a Bayesian network model for personalised risk assessment, School of Electronic Engineering and Computer Science, Queen Mary, University of London, Londo… [cited by applicant]
Escribano, J., Luque, V., Ferre, N., Mendez-Riera, G., Koletzko, B., Grote, V., & Closa-Monasterolo, R. (2012). Effect of protein intake and weight gain velocity on body fat mass at 6 months of age; the EU Childhood Obe… [cited by applicant]