Patents by Inventor Jonathan Thomas Wolf
Jonathan Thomas Wolf has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11915151Abstract: Techniques are disclosed herein for improving the accuracy of test data obtained outside of a clinical setting. Using the technologies described herein, different techniques can be utilized to analyze, score and adjust test data associated with one or more “at home” tests. In some examples, computing systems are utilized to generate quality scores indicating the accuracy of the test data associated with a particular biomarker. In other examples, an authorized user, such as a data manager can analyze the test data utilizing a user interface to generate scores and/or adjust the test data.Type: GrantFiled: February 11, 2019Date of Patent: February 27, 2024Assignee: Zoe LimitedInventors: Jonathan Thomas Wolf, Richard James Davies, George Hadjigeorgiou
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Patent number: 11817200Abstract: Techniques are disclosed herein for generating personalized food guidance using predicted food responses. Using the technologies described herein, instead of providing food guidance that is generalized for a group of individuals, personalized food guidance is provided that takes into account an individual's personalized responses to foods, such as the individual's glucose and fat responses, the individual's micro biome, the individual's overall health, potential health risks for the individual, and/or other data associated with the individual. Providing an individual with personalized food guidance can make choosing food easier and healthier.Type: GrantFiled: August 4, 2020Date of Patent: November 14, 2023Assignee: Zoe LimitedInventors: Jonathan Thomas Wolf, Richard James Davies, Elco Bakker
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Publication number: 20220367050Abstract: Techniques are disclosed herein for generating predictions of gut microbiome diversity for a user. In some examples, a nutritional service may utilize data associated with a gut transit time and user data to generate a prediction of gut microbiome diversity for a user. For example, the nutritional service may perform an analysis of the data associated with gut transit time and the answers to questions to generate the prediction. In some examples, the nutritional service identifies a uniqueness of the microbiome, identify interesting species, and the like. The information determined and/or otherwise generated by the nutritional service may be presented to the user on a display.Type: ApplicationFiled: May 4, 2022Publication date: November 17, 2022Inventors: Jonathan Thomas Wolf, Richard James Davies
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Publication number: 20220290226Abstract: A deep metagenomic sequencing of more than 1000 individual gut microbiomes, coupled with detailed long-term diet, fasting, and same-meal postprandial cardiometabolic blood markers analyses, is described. Strong associations between a set of microbes and specific nutrients, foods, food groups, and general dietary indices are demonstrated. Microbial biomarkers of obesity were reproducible across cohorts, but blood markers of cardiovascular disease and impaired glucose tolerance were more strongly associated with microbiome structures. Panels of intestinal microbial species associated with different conditions and/or habits are identified, enabling stratification of the gut microbiome into generalizable health levels among individuals even without clinically manifest disease.Type: ApplicationFiled: March 16, 2021Publication date: September 15, 2022Applicant: Zoe Global LimitedInventors: Jonathan Thomas Wolf, Nicola Segata
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Patent number: 11348479Abstract: Techniques are disclosed herein for improving the accuracy of nutritional responses measured in a non-clinical setting. Using the technologies described herein, different techniques can be utilized to improve the accuracy of test data associated with one or more “at home” tests. In some examples, more than one test is utilized to improve the accuracy of test data associated with a particular biomarker. In other examples, a data accuracy service can programmatically analyze data received from an individual and determine whether the data is accurate. In some examples, a computing device is utilized to assist in determining what food item(s) are consumed, as well as determine whether a test protocol was followed.Type: GrantFiled: May 23, 2018Date of Patent: May 31, 2022Assignee: Zoe LimitedInventors: George Hadjigeorgiou, Jonathan Thomas Wolf, Richard James Davies
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Publication number: 20220130512Abstract: Techniques are disclosed herein for generating personalized food guidance using predicted food hunger. Using the technologies described herein, instead of providing food guidance that is generalized for a group of individuals, personalized food guidance is provided that takes into account an individual's personalized responses to foods, including the predicted hunger of an individual. A nutritional service generates a hunger score that predicts a hunger level of an individual at a time (or for more than one time) after the individual has or is planning to consume food. The nutritional service uses the hunger score to generate the food guidance. Providing an individual with personalized food guidance can make choosing food easier and healthier.Type: ApplicationFiled: October 26, 2020Publication date: April 28, 2022Inventors: Patrick James Wyatt, Jonathan Thomas Wolf
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Patent number: 11295860Abstract: Techniques are disclosed herein for using standardized meals and digital devices can be utilized outside a clinical setting to assist in determining/predicting one or more clinical states associated with one or more individuals. Using the technologies described herein, different techniques can be utilized to for using nutritional response measurements. For example, response measurements can be obtained for two fat meals instead of a single fat meal. Data associated with a circadian rhythm (e.g., sleep times, awake times) can also be utilized to improve the accuracy of the measured biomarkers. In other examples, biome data and other data can be utilized.Type: GrantFiled: June 6, 2019Date of Patent: April 5, 2022Assignee: Zoe LimitedInventors: Jonathan Thomas Wolf, Richard James Davies, George Hadjigeorgiou
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Patent number: 11183291Abstract: Techniques are disclosed herein for generating personalized nutritional recommendations using predicted values of target biomarkers. Using the technologies described herein, a programmatic analysis is performed on different data to predict values of target biomarkers that are associated with an individual. Personalized nutritional recommendations are then generated, using the predicted values, and provided to the individual. The predictions are based on data that is associated with the individual, such as microbiome data, ketone data, glucose data, nutritional data, questionnaire data, and the like. A prediction service can utilize a machine learning mechanism to generate the predicted value of the target biomarkers. A nutrition service utilizes the predicted value of the target biomarkers when generating the personalized nutritional recommendations.Type: GrantFiled: February 12, 2018Date of Patent: November 23, 2021Assignee: Zoe LimitedInventors: Jonathan Thomas Wolf, George Hadjigeorgiou, Richard James Davies
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Patent number: 11183080Abstract: Techniques are disclosed herein for generating personalized nutritional recommendations using predicted values of target biomarkers. Using the technologies described herein, a programmatic analysis is performed on different data to predict values of target biomarkers that are associated with an individual. Personalized nutritional recommendations are then generated, using the predicted values, and provided to the individual. The predictions are based on data that is associated with the individual, such as microbiome data, ketone data, glucose data, nutritional data, questionnaire data, and the like. A prediction service can utilize a machine learning mechanism to generate the predicted value of the target biomarkers. A nutrition service utilizes the predicted value of the target biomarkers when generating the personalized nutritional recommendations.Type: GrantFiled: February 12, 2018Date of Patent: November 23, 2021Assignee: Zoe LimitedInventors: Jonathan Thomas Wolf, George Hadjigeorgiou, Richard James Davies
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Publication number: 20210265029Abstract: Techniques are disclosed herein for generating microbiome fingerprints, dietary fingerprints, and microbiome ancestry. Microbiome data associated with an individual is analyzed to generate a microbiome fingerprint, and a dietary fingerprint for a user. A “microbiome fingerprint” uniquely identifies the microbiome of a user at a particular point in time and is based on a combination of different profiles generated from the microbiome data. The dietary fingerprint identifies how the microbiome of a user is associated with one or more different indexes, such as a dietary index and/or a particular characteristic (e.g., a Mediterranean diet index, a vegetarian diet index, a fast food index, an internal fat index, a fat-digesting index, a carbohydrate-digesting index, a health index, a fasting index, a ketogenic index, . . . ). The microbiome data may also be utilized to determine microbiome ancestry for the user that indicates other users to which the user is related to.Type: ApplicationFiled: February 18, 2021Publication date: August 26, 2021Inventors: Nicola Segata, Richard James Davies, Jonathan Thomas Wolf
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Publication number: 20210065873Abstract: Techniques are disclosed herein for generating personalized food guidance using predicted food responses. Using the technologies described herein, instead of providing food guidance that is generalized for a group of individuals, personalized food guidance is provided that takes into account an individual's personalized responses to foods, such as the individual's glucose and fat responses, the individual's micro biome, the individual's overall health, potential health risks for the individual, and/or other data associated with the individual. Providing an individual with personalized food guidance can make choosing food easier and healthier.Type: ApplicationFiled: August 4, 2020Publication date: March 4, 2021Inventors: Jonathan Thomas Wolf, Richard James Davies, Elco Bakker
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Publication number: 20200065681Abstract: Techniques are disclosed herein for improving the accuracy of test data obtained outside of a clinical setting. Using the technologies described herein, different techniques can be utilized to analyze, score and adjust test data associated with one or more “at home” tests. In some examples, computing systems are utilized to generate quality scores indicating the accuracy of the test data associated with a particular biomarker. In other examples, an authorized user, such as a data manager can analyze the test data utilizing a user interface to generate scores and/or adjust the test data.Type: ApplicationFiled: February 11, 2019Publication date: February 27, 2020Inventors: Jonathan Thomas Wolf, Richard James Davies, George Hadjigeorgiou
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Publication number: 20200066181Abstract: Techniques are disclosed herein for generating personalized nutritional recommendations for foods available from one or more food sources. Using the technologies described herein, a programmatic analysis is performed on different data to predict values of personalized nutrition data, such as one or more target biomarkers, that are associated with an individual after eating the foods. Personalized nutritional recommendations for foods available from the food sources are then generated, using the predicted values, and provided to the individual. The predictions are based on data that is associated with the individual, such as microbiome data, triglycerides data, glucose data, nutritional data, questionnaire data, and the like. A prediction service can utilize a machine learning mechanism to generate the predicted personalized nutrition data. A nutrition service utilizes the predicted personalized nutrition data when generating the personalized nutritional recommendations.Type: ApplicationFiled: August 31, 2018Publication date: February 27, 2020Inventors: George Hadjigeorgiou, Jonathan Thomas Wolf, Richard James Davies
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Publication number: 20190362848Abstract: Techniques are disclosed herein for using standardized meals and digital devices can be utilized outside a clinical setting to assist in determining/predicting one or more clinical states associated with one or more individuals. Using the technologies described herein, different techniques can be utilized to for using nutritional response measurements. For example, response measurements can be obtained for two fat meals instead of a single fat meal. Data associated with a circadian rhythm (e.g., sleep times, awake times) can also be utilized to improve the accuracy of the measured biomarkers. In other examples, biome data and other data can be utilized.Type: ApplicationFiled: June 6, 2019Publication date: November 28, 2019Inventors: Jonathan Thomas Wolf, Richard James Davies, George Hadjigeorgiou
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Publication number: 20190362648Abstract: Techniques are disclosed herein for improving the accuracy of nutritional responses measured in a non-clinical setting. Using the technologies described herein, different techniques can be utilized to improve the accuracy of test data associated with one or more “at home” tests. In some examples, more than one test is utilized to improve the accuracy of test data associated with a particular biomarker. In other examples, a data accuracy service can programmatically analyze data received from an individual and determine whether the data is accurate. In some examples, a computing device is utilized to assist in determining what food item(s) are consumed, as well as determine whether a test protocol was followed.Type: ApplicationFiled: May 23, 2018Publication date: November 28, 2019Inventors: George Hadjigeorgiou, Jonathan Thomas Wolf, Richard James Davies
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Publication number: 20190251861Abstract: Techniques are disclosed herein for generating personalized nutritional recommendations using predicted values of target biomarkers. Using the technologies described herein, a programmatic analysis is performed on different data to predict values of target biomarkers that are associated with an individual. Personalized nutritional recommendations are then generated, using the predicted values, and provided to the individual. The predictions are based on data that is associated with the individual, such as microbiome data, ketone data, glucose data, nutritional data, questionnaire data, and the like. A prediction service can utilize a machine learning mechanism to generate the predicted value of the target biomarkers. A nutrition service utilizes the predicted value of the target biomarkers when generating the personalized nutritional recommendations.Type: ApplicationFiled: February 12, 2018Publication date: August 15, 2019Inventors: Jonathan Thomas Wolf, George Hadjigeorgiou, Richard James Davies
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Publication number: 20190252058Abstract: Techniques are disclosed herein for generating personalized nutritional recommendations using predicted values of target biomarkers. Using the technologies described herein, a programmatic analysis is performed on different data to predict values of target biomarkers that are associated with an individual. Personalized nutritional recommendations are then generated, using the predicted values, and provided to the individual. The predictions are based on data that is associated with the individual, such as microbiome data, ketone data, glucose data, nutritional data, questionnaire data, and the like. A prediction service can utilize a machine learning mechanism to generate the predicted value of the target biomarkers. A nutrition service utilizes the predicted value of the target biomarkers when generating the personalized nutritional recommendations.Type: ApplicationFiled: February 12, 2018Publication date: August 15, 2019Inventors: Jonathan Thomas Wolf, George Hadjigeorgiou, Richard James Davies