Patent Application Titled “Generating Instructions for Physical Experiments Using Whole Body Digital Twin Technology” Published Online (USPTO 20230110674): Patent Application

2023 APR 27 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- According to news reporting originating from Washington, D.C., by NewsRx journalists, a patent application by the inventors Banerjee, Abhik (Milpitas, CA, US); Mohammed, Jahangir (Mountain View, CA, US); Poon, Terrence Chun Yin (Foster City, CA, US); Wilson, James (Sunnyvale, CA, US), filed on October 11, 2022, was made available online on April 13, 2023.

No assignee for this patent application has been made.

Reporters obtained the following quote from the background information supplied by the inventors: “

“Field of Art

“The disclosure relates generally to a patient health management platform, and more specifically, to a patient health management platform for simulating clinical trials for candidate metabolic treatment recommendations using digital representations of metabolic states for population of patients.

“Description of the Related Art

“Conventional medicine relies on clinical trials to validate medical treatments. Because such clinical trials are often expensive, time-intensive, and labor-intensive, the number of clinical trials that can be run during a given time period is limited. This is particularly challenging for lifestyle interventions such as improvements in nutrition, physical activity, sleep, and meditative breathing, because each intervention is highly complex with an extremely large number of possible treatments (e.g., a large number of possible nutrition plans based on a combination of many different foods, quantities, timings, etc.). Further complicating the problem, attempts to personalize these nutrition plans are inhibited by an insufficient amount of data or by an insufficient number of similar patients to perform a trial that would yield a significant result. Due to these issues, the results of clinical trials are often averaged across the trial population instead of being tailored towards individual patients. Further, the effectiveness of the trial may be affected by variations in physiological and medical conditions across the tested populations.

“Additionally, given the amount of time that lifestyle interventions take to affect the metabolism of a patient, clinical trials may take years and an excess of financial funding. As a result, traditional approaches to validating medical treatments often result in effective lifestyle treatments being disregarded because of the high resource cost for completing a clinical trial.”

In addition to obtaining background information on this patent application, NewsRx editors also obtained the inventors’ summary information for this patent application: “A Digital Twin clinical trial simulator simulates various aspects of clinical trials using digital models of individuals that each capture the biology of the individual’s body (e.g., a whole body digital twin or WBDT). The models are generated using an array of inputs, such as biomarkers from sensors (e.g., wearable sensors), parameters taken from laboratory or other testing (e.g., blood tests), symptoms and other information reported by a user, medications reported to be consumed by the user, etc., and that outputs. Using these digital models that act as representatives or twins of individuals, the Digital Twin clinical trial simulator effectively has a population of patients for whom it can perform various clinical trial simulations with a substantial savings in time, expense, and labor relative to what is typically required with conventional clinical trials where live tests are performed on actual patients. The Digital Twin clinical trial simulator can test a large number of scenarios without the negative consequences to patients that clinical trials sometimes entail. In addition, it allows for analysis across more controlled populations and has access to a larger population of patients and substantially more data than is possible in a conventional clinical trial, ultimately providing a more accurate and tailored result.

“In one embodiment, the Digital Twin clinical trial stimulator generates a pool of candidate treatments for effecting a target improvement in health (e.g., metabolic health). Each candidate treatment provides instructions for adjusting a distinct combination of one or more intervention parameters. Intervention parameters refer to the various aspects of patient data known to affect metabolic health, for example micronutrients, macronutrients, biota nutrients, lifestyle data, physical activity routines, and sleep habits.

“For each candidate treatment, the Digital Twin clinical trial simulator identifies a cohort of sensitive patients based on a likelihood that the candidate treatment will affect the patient’s metabolic health. Described differently, patients in the identified cohort are determined to have the strongest correlation between the intervention parameter(s) adjusted by the candidate treatment and their own metabolic health.

“The Digital Twin clinical trial simulator inputs a feature vector representation of each candidate treatment recommendation to patient-specific metabolic models of each patient in the cohort to generate a prediction of whether the candidate treatment will affect the metabolic health of the patient to achieve the target improvement. Accordingly, the Digital Twin clinical trial simulator predicts the efficacy of each candidate treatment. The Digital Twin clinical trial simulator may additionally identify new intervention parameters or new features of metabolic health by extracting novel correlations between the metabolic profiles of patients in the identified cohort and intervention parameters identified in effective or ineffective candidate treatments. The Digital Twin clinical trial simulator may additionally identify features of metabolic health where the Digital Twin clinical trial simulator lacks sufficient data for patient-specific metabolic models to generate accurate predictions. For such features, the Digital Twin clinical trial simulator may supplement the data with synthetically generated data and validate the candidate treatment using the supplemented data.

“Based on the predicted effectiveness of each candidate treatment, the Digital Twin clinical trial simulator identifies a shortlist of the most effective candidate treatments for further evaluation by physical experiments. The shortlist of candidate treatments may additionally be generated based on the accuracy of the predictions and the confidence intervals of the predictions. The Digital Twin clinical trial simulator may additionally define instructions/procedures and additional insight for performing the physical experiments.

“In one embodiment, the clinical trial simulator determines one or more trial parameters for a physical experiment to validate a candidate treatment recommendation. The one or more trial parameters comprise a duration of the experiment and a number of patients to be enrolled in the experiment. For each of one or more variations of the physical experiment, the clinical trial simulator determines an effectiveness of the variation in validating the candidate treatment recommendation. The effectiveness describes a likelihood that the physical experiment will provide insight regarding the effect of the candidate treatment recommendation on a metabolic state. For a selected variation of the physical experiment that satisfies a threshold effectiveness, the clinical trial simulator determines one or more metabolic features shared among a cohort of patients sensitive to the candidate treatment recommendation. The sensitivity of a patient represents a likelihood that adjustments to the one or more intervention parameters will affect the metabolic state of the patient. The clinical trial simulator generates instructions for a medical professional to perform the selected variation of the physical experiment by adjusting the trial parameters according to the selected variation and enrolling patients sharing at least one of the one or more metabolic features.

“The figures depict various embodiments of the presented invention for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles described herein.”

The claims supplied by the inventors are:

“1. A method comprising: determining one or more trial parameters for a physical experiment to validate a candidate treatment recommendation, the one or more trial parameters comprising: a duration of the experiment and a number of patients to be enrolled in the experiment; for each of one or more variations of the physical experiment, determining an effectiveness of the variation in validating the candidate treatment recommendation, the effectiveness describing a likelihood that the physical experiment will provide insight regarding the effect of the candidate treatment recommendation on a metabolic state; for a selected variation of the physical experiment satisfying a threshold effectiveness, determining one or more metabolic features shared among a cohort of patients sensitive to the candidate treatment recommendation, the sensitivity of a patient representing a likelihood that adjustments to the one or more intervention parameters will affect the metabolic state of the patient; and generating instructions for a medical professional to perform the selected variation of the physical experiment by adjusting the trial parameters according to the selected variation and enrolling patients sharing at least one of the one or more metabolic features.

“2. The method of claim 1, wherein the one or more trial parameters further comprise: a number of intervention parameters adjusted in the candidate treatment recommendation; adjustments to each intervention parameter; a composition of patients to be enrolled in the experiment; and a magnitude of the adjustment to each intervention parameter.

“3. The method of claim 1, further comprising: identifying the candidate treatment recommendation based on the effectiveness of the candidate treatment recommendation, the effectiveness determined by predicting the effect of each candidate treatment recommendation on a cohort of patients.

“4. The method of claim 1, further comprising: generating a shortlist of candidate treatment recommendations for validation by a physical experiment; and generating instructions for a medical professional to perform a physical experiment to validate each candidate treatment recommendation of the shortlist.

“5. The method of claim 1, further comprising: generating the one or more variations of the physical experiment by adjusting the one or more trial parameters, wherein each variation of the physical experiment represents a distinct combination of the one or more trial parameters.

“6. The method of claim 1, wherein determining the effectiveness of the variation comprises: identifying a target outcome of the candidate treatment recommendation; determining an acceptable risk of failure backed on past physical experiments designed to validate candidate treatment recommendations with the same target outcome; and determining a power calculation for the candidate treatment recommendation based on the acceptable risk of failure.

“7. The method of claim 1, wherein determining the one or more metabolic features shared among the cohort of patients sensitive to the candidate treatment recommendation comprises: identifying a cohort of patients sensitive to intervention parameters adjusted by the candidate treatment recommendation; identifying one or more metabolic features shared among all patients in the cohort of patients, wherein patients sharing the one or more metabolic features are predicted to experience improvements in metabolic health by adhering to the candidate treatment recommendation; and generating instructions for a medical professional to enroll patients in the physical experiment with at least one of the one or more identified metabolic features.

“8. The method of claim 1, wherein the one or more metabolic features comprise: metabolic features associated with binary values; and metabolic features associated with a range of values.

“9. The method of claim 1, wherein determining the one or more metabolic features shared among the cohort of patients sensitive to the candidate treatment recommendation comprises: identifying a cohort of patients sensitive to intervention parameters adjusted by the candidate treatment recommendation; determining a significance that each metabolic feature has on patients in the cohort of patients; ranking each of the one or more metabolic features based on the determined significance; and generating instructions for a medical professional to prioritize enrollment of patients sharing higher ranked metabolic features.

“10. A non-transitory computer-readable medium storing instructions encoded thereon that, when executed by a processor, cause the one or more processor to: determine one or more trial parameters for a physical experiment to validate a candidate treatment recommendation, the one or more trial parameters comprising: a duration of the experiment and a number of patients to be enrolled in the experiment; for each of one or more variations of the physical experiment, determine an effectiveness of the variation in validating the candidate treatment recommendation, the effectiveness describing a likelihood that the physical experiment will provide insight regarding the effect of the candidate treatment recommendation on a metabolic state; for a selected variation of the physical experiment satisfying a threshold effectiveness, determine one or more metabolic features shared among a cohort of patients sensitive to the candidate treatment recommendation, the sensitivity of a patient representing a likelihood that adjustments to the one or more intervention parameters will affect the metabolic state of the patient; and generate instructions for a medical professional to perform the selected variation of the physical experiment by adjusting the trial parameters according to the selected variation and enrolling patients sharing at least one of the one or more metabolic features.

“11. The non-transitory computer readable storage medium of claim 10, further comprising instructions that cause the processor to: identify the candidate treatment recommendation based on the effectiveness of the candidate treatment recommendation, the effectiveness determined by predicting the effect of each candidate treatment recommendation on a cohort of patients.

“12. The non-transitory computer readable storage medium of claim 10, further comprising instructions that cause the processor to: generate a shortlist of candidate treatment recommendations for validation by a physical experiment; and generate instructions for a medical professional to perform a physical experiment to validate each candidate treatment recommendation of the shortlist.

“13. The non-transitory computer readable storage medium of claim 10, further comprising instructions that cause the processor to: generate the one or more variations of the physical experiment by adjusting the one or more trial parameters, wherein each variation of the physical experiment represents a distinct combination of the one or more trial parameters.

“14. The non-transitory computer readable storage medium of claim 10, wherein instructions for determining the effectiveness of the variation further comprise instructions that cause the processor to: identify a target outcome of the candidate treatment recommendation; determine an acceptable risk of failure backed on past physical experiments designed to validate candidate treatment recommendations with the same target outcome; and determine a power calculation for the candidate treatment recommendation based on the acceptable risk of failure.

“15. The non-transitory computer readable storage medium of claim 10, wherein instructions for determining the one or more metabolic features shared among the cohort of patients sensitive to the candidate treatment recommendation further comprise instructions that cause the processor to: identify a cohort of patients sensitive to intervention parameters adjusted by the candidate treatment recommendation; identify one or more metabolic features shared among all patients in the cohort of patients, wherein patients sharing the one or more metabolic features are predicted to experience improvements in metabolic health by adhering to the candidate treatment recommendation; and generate instructions for a medical professional to enroll patients in the physical experiment with at least one of the one or more identified metabolic features.

“16. The non-transitory computer readable storage medium of claim 10, wherein instructions for determining the one or more metabolic features shared among the cohort of patients sensitive to the candidate treatment recommendation further comprise instructions that cause the processor to: identify a cohort of patients sensitive to intervention parameters adjusted by the candidate treatment recommendation; determine a significance that each metabolic feature has on patients in the cohort of patients; rank each of the one or more metabolic features based on the determined significance; and generate instructions for a medical professional to prioritize enrollment of patients sharing higher ranked metabolic features.”

There are additional claims. Please visit full patent to read further.

For more information, see this patent application: Banerjee, Abhik; Mohammed, Jahangir; Poon, Terrence Chun Yin; Wilson, James. Generating Instructions for Physical Experiments Using Whole Body Digital Twin Technology. U.S. Patent Application Number 20230110674, filed October 11, 2022 and posted April 13, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20230110674)&db=US-PGPUB&type=ids

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