SYSTEM AND METHOD FOR PROGRAMMING A MONITORING DEVICE

An apparatus of programming a monitoring device is disclosed. The apparatus includes at least a processor and memory communicatively connected to the processor where the memory contains instructions configuring the processor to obtain a user datum of a plurality of user datums from a monitoring device, and calculate a signal profile as a function of the user datum. The processor is configured to determine a vigor status as a function of the signal profile. The processor is configured to generate a vigor status improvement plan as a function of the vigor status. The processor is configured to identify a scan frequency correlated to the vigor status improvement plan. The processor is configured to generate a device scheme as a function of the scan frequency, and program the monitoring device as a function of the device scheme. A method of programming a monitoring device is also disclosed.

FIELD OF THE INVENTION

The present invention generally relates to the field of artificial intelligence. In particular, the present invention is directed to a system and method for programming a monitoring device.

BACKGROUND

Monitoring devices are constantly being upgraded and altered due to software updates, mechanical updates, and consumer preference, which is preventing efficient customization as each new iteration restarts the relationship between the consumer and the monitoring device. This lack of consistency reduces the monitoring device capability and results in consumer frustration.

SUMMARY OF THE DISCLOSURE

In an aspect, a system of programming a monitoring device includes at least a processor and memory communicatively connected to the processor where the memory contains instructions configuring the processor to, obtain a user datum of a plurality of user datums from a monitoring device, and calculate a signal profile as a function of the user datum. The processor is configured to determine a vigor status as a function of the signal profile. The processor is configured to generate a vigor status improvement plan as a function of the vigor status. The processor is configured to identify a scan frequency correlated to the vigor status improvement plan. The processor is configured to generate a device scheme as a function of the scan frequency and program the monitoring device as a function of the device scheme.

In another aspect, a method of programming a monitoring device includes obtaining, by a processor, a user datum of a plurality of user datums from a monitoring device, calculating, by the processor, a signal profile as a function of the user datum. The method comprises determining a vigor status as a function of the signal profile. The method comprises generating a vigor status improvement plan as a function of the vigor status. The method comprises identifying, by a computing device, a scan frequency correlated to the vigor status improvement plan. The method comprises generating, by the processor, a device scheme as a function of the scan frequency, and programming, by the processor, the monitoring device as a function of the device scheme.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed to systems and methods for programming a monitoring device. In an embodiment, this system programs a monitoring device as a function of a user datum. Aspects of the present disclosure can be used to program a monitoring device that at least alters the monitoring as a function of the user datums detected by the monitoring device. This is so, at least in part, because the system obtains a user datum from a monitoring device and generates, via a machine-learning process, an efficient monitoring strategy for the user. Exemplary embodiments illustrating aspects of the present disclosure are described below in the context of several specific examples.

With continued reference toFIG. 1, apparatus100includes memory112communicatively connected to the processor, memory112containing instructions configuring the at least a processor to receive instructions for programming a monitoring device. As used in this disclosure, “communicatively connected” means connected by way of a connection, attachment, or linkage between two or more relata which allows for reception and/or transmittance of information therebetween. For example, and without limitation, this connection may be wired or wireless, direct or indirect, and between two or more components, circuits, devices, systems, and the like, which allows for reception and/or transmittance of data and/or signal(s) therebetween. Data and/or signals therebetween may include, without limitation, electrical, electromagnetic, magnetic, video, audio, radio and microwave data and/or signals, combinations thereof, and the like, among others. A communicative connection may be achieved, for example and without limitation, through wired or wireless electronic, digital or analog, communication, either directly or by way of one or more intervening devices or components. Further, communicative connection may include electrically coupling or connecting at least an output of one device, component, or circuit to at least an input of another device, component, or circuit. For example, and without limitation, via a bus or other facility for intercommunication between elements of a computing device. Communicative connecting may also include indirect connections via, for example, and without limitation, wireless connection, radio communication, low power wide area network, optical communication, magnetic, capacitive, or optical coupling, and the like. In some instances, the terminology “communicatively coupled” may be used in place of communicatively connected in this disclosure.

Still referring toFIG. 1, computing device108obtains, a user datum116aof a plurality of user data116a-mfrom a monitoring device120. As used in this disclosure a “user datum” is a sign, symptom, element, or quality that relates to a user. For instance, a user datum may include, without limitation, heart rate, calories burned, steps walked, blood pressure, biochemicals detected, time spent exercising, seizures, physical strain, or the like thereof. As a further non-limiting example, user datum may include mood quality, anxiety levels, sleep quality, or the like thereof. As used in this disclosure “monitoring device” is an electronic device that is worn on the person of a user, such as without limitation close to and/or on the surface of the skin, wherein the device can detect, analyze, and transmit information concerning a body signal such as a vital sign, and/or ambient datum, wherein allowing immediate biofeedback to be sent to the user wearing the device. Monitoring device120may include, without limitation, any device that further collects, stores, and analyzes data associated with user datums. Monitoring device120my consist of, without limitation, near-body electronics, on-body electronics, in-body electronics, electronic textiles, smart watches, smart glasses, smart clothing, fitness trackers, body sensors, wearable cameras, head-mounted displays, body worn cameras, Bluetooth headsets, wristbands, smart garments, chest straps, sports watches, fitness monitors, and the like thereof. Monitoring device120may include, without limitation, earphones, earbuds, headsets, bras, suits, jackets, trousers, shirts, pants, socks, bracelets, necklaces, brooches, rings, jewelry, AR HMDs, VR HMDs, exoskeletons, location trackers, and gesture control wearables.

With continued reference toFIG. 1, computing device108calculates a signal profile124as a function of user datum116. As used in this disclosure a “signal profile” is a value associated with the effect of the user datum on a user. For instance, a user datum related to an increased heart rate may have either a positive value or a negative value. An increased heart rate may be the result of a user exercising. Alternatively, an increased heart rate may be the result of a serious medical condition that may impact the signal profile negatively. Signal profile124may be comprised of a vigor status128. As used in this disclosure a “vigor status” is a qualitative measure of a user health. For instance, a user datum related to an increased heart rate may have either a positive or negative influence on the vigor status of the user. For instance, an increased heart rate may be the result of a user exercising, which would add a positive influence on the vigor status. Alternatively, an increased heart rate may be the result of a serious medical condition that may impact the vigor status negatively. Vigor status may include, without limitation, a user condition, a user fitness status, a user wellness goal, a user medical goal, or the like thereof. As used in this disclosure a “user condition” is a list or collection of current or potential ailments and/or diseases, and/or precursor states to such ailments and/or diseases, including but not limited to physical, spiritual, and/or psychological ailments and/or diseases correlating to any resulting impact on the user. In an embodiment a physical ailment or disease may include, without limitation, Influenza, Rhinovirus, Obesity, COVID-19, EEE, CRE, Ebola, Enterovirus D68, Influenza, Hantavirus, Hepatitis A, Hepatitis A, HIV/AIDS, Diabetes (Type I or Type II), Multiple Sclerosis, Chron's Disease, Colitis, Lupus, Rheumatoid Arthritis, Allergies, Asthma, Relapsing Polychondritis, Scleroderma, Liver Disease, Heart Disease, Cancer, and the like thereof. In an embodiment, a spiritual ailment or disease may include, without limitation, religious conflicts, chakra blockages, existential crisis, or the like thereof. In an embodiment, a psychological ailment or disease may include, without limitation, Alzheimer's, Parkinson's, alcohol or substance abuse disorder, anxiety disorder, ADD, ADHD, bipolar disorder, depression, eating disorder, obsessive-compulsive disorder, opioid use disorder, PTSD, schizophrenia, depersonalization disorder, dissociative amnesia and/or fatigue, anorexia, bulimia, sleep disorders, wake disorders, paraphilic disorders, sexual disorders, child mental disorders, personality disorders, gender dysphoria, depression, and the like thereof. As used in this disclosure a “user fitness status” is an enumeration vector relating a user fitness to a fitness capability. For example, and without limitation, a user fitness status may indicate a user to have a low fitness status, wherein a low fitness status indicates the user to be below average for fitness levels. As used in this disclosure a “user wellness goal” is a set value or metric that a user would like to achieve relating to the user's wellness. For example, and without limitation, a user wellness goal may include increased sleep, enhanced meditation, increase positivity, or the like thereof. As used in this disclosure a “user medical goal” is a set value or metric that a user and/or physician would like the user to achieve to increase overall medical health. For example, and without limitation, a user medical goal may include decrease LDL, lower blood pressure, reduced heart rate, increased lung capacity, increased metabolic rate, or the like thereof. Signal profile124may be comprised of a vector enumeration relating to a user datum. As used in this disclosure a “vector enumeration” is a measurable value generated as a function of either a quantitative or qualitative user datum. For example, and without limitation, a vector enumeration may generate a value of 20 for a user datum related to high blood pressure values. As a further non-limiting example a value of 100 may be generated as a function of a user datum related to a myocardial infarction. Signal profile124may utilize vector enumeration to calculate the value of the user element in relation to a signal of a user condition, a frequency of the user datum occurrence and/or the identification of a secondary user datum. For example, and without limitation, a user datum of heart rate may be detected such that an arrythmia is found, wherein a signal profile relating to arrythmia may be calculated as a value of 50. As a further non-limiting example signal profile124may be calculated as a function of a frequency of the user datum, wherein a high blood glucose value may be detected more than 5 times within a given time period such that the signal profile may calculate a value of 50 for diabetes.

Alternatively or additionally, with continued reference toFIG. 1, in another embodiment, the computing device may calculate signal profile124as a function of a signal machine-learning process. As used in this disclosure “signal machine-learning process” is a machine learning process that automatedly uses training data and/or a training set to generate an algorithm that will be performed by a computing device and/or module to produce outputs given data provided as inputs; this is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language. Signal machine-learning process may consist of any supervised, unsupervised, or reinforcement machine-learning process that computing device108may or may not use in the determination of signal profile124. Signal machine-learning process may include, without limitation, machine learning processes such as simple linear regression, multiple linear regression, polynomial regression, support vector regression, ridge regression, lasso regression, elasticnet regression, decision tree regression, random forest regression, logistic regression, logistic classification, K-nearest neighbors, support vector machines, kernel support vector machines, naïve bayes, decision tree classification, random forest classification, K-means clustering, hierarchical clustering, dimensionality reduction, principal component analysis, linear discriminant analysis, kernel principal component analysis, Q-learning, State Action Reward State Action (SARSA), Deep-Q network, Markov decision processes, Deep Deterministic Policy Gradient (DDPG), or the like thereof.

With continued reference toFIG. 1, processor104may be configured to generate a vigor status improvement plan132as a function of vigor status128. As used herein, a “vigor status improvement plan” is a list of any activities, nutrition, wellness activities, relaxation activities, and the like that have a positive impact on vigor status128. As a non-limiting example, a list of activities may include exercising or other fitness-related activities which would positively impact vigor status128in, for instance, losing weight. Alternatively, or additionally, low fat diet may have a positive impact on vigor status128in, for instance, reducing blood sugar. Vigor status improvement plan132may be developed by a professional such as a medical professional, a health coach, a fitness coach, and the like. Vigor status improvement plan132may be developed from historical vigor improvement plans used to treat the same or similar ailments or conditions. Vigor status improvement plan132may be developed using artificial intelligence, such as, but not limited to, the use of an app in a computing device. Alternatively, or additionally, the user may access a voice bot or any type of chatbot. In an embodiment, vigor status improvement plan132, may be generated by the use of a machine-learning process. A “machine learning process,” as used in this disclosure, is a process that automatedly uses a body of data known as “training data” and/or a “training set” to generate an algorithm that will be performed by a computing device/module to produce outputs given data provided as inputs; this is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language. Training data will be described further later in this disclosure in the context ofFIG. 2. Vigor status improvement data may be received, where the vigor status improvement training data correlates vigor status improvement with vigor status. Vigor status improvement training data may be received as a function of user-entered valuations of potential vigor status improvement plans. Vigor status improvement training data may be received by correlations of vigor status improvement plans previously used and/or determined during a previous iteration of generating vigor improvement plans. Vigor status improvement training data may be received by one or more remote devices that at least correlate a vigor improvement plan, wherein a remote device is an external device to any computing device. Vigor status improvement training data may be received in the form of one or more user-entered correlations of a modification to a plurality of vigor improvement plans. A vigor status improvement machine-learning model may be trained based on the vigor status improvement training data. A vigor status improvement plan is generated as based on the vigor status improvement machine-learning model. Training machine learning models and using objective functions to optimize machine learning models may be performed as described in U.S. Nonprovisional Application Ser. No. 16/890,686, entitled “ARTIFICIAL INTELLIGENCE METHODS & SYSTEMS FOR CONSTITUTIONAL ANALYSIS USING OBJECTIVE FUNCTIONS,” which is incorporated by reference in its entirety.

In a further embodiment, signal machine-learning process may be calculated as a function of a signal training set. As used in this disclosure “signal training set” is a training set that correlates a monitoring element to a vigor adjustment outcome. As used in this disclosure a “monitoring element” is an element relating to one or more human physiological statuses, wherein human physiological statuses may include heartbeat, blood pressure, body temperature, electrocardiograms, arrhythmias, cancerous indicators, body fat composition, or the like thereof. As a non-limiting example, monitoring elements may include data collected from using one or more pressure sensors, humidity sensors, position sensors, piezo film sensors, force sensors, temperature sensors, optical sensors, or the like thereof. As a further non-limiting example monitoring elements may include data collected from using X-ray absorptiometry, hydrostatic weighing, air displacement plethysmography, bioelectrical impedance analysis, bioimpedance spectroscopy, electrical impedance myograph, 3-D scanners, and multi-compartment models. As used in this disclosure a “vigor adjustment outcome” is an effect, impact, consequence, result, reaction, or the like thereof that may result as a function of a monitoring element. For example, and without limitation, a vigor adjustment outcome of pneumonia may be related to the monitoring elements of decreased lung capacity, fever, shortness of breath, coughing, or the like thereof. A vigor adjustment outcome may include a monitoring element wherein the vigor adjustment outcome is positive, such as, and without limitation, body fat percentage loss related to increased heart stamina and/or efficiency. A vigor adjustment outcome may include a monitoring element wherein the vigor adjustment outcome is negative, such as, and without limitation, heart arrhythmias related to congenital heart disease. Additionally or alternatively, signal machine learning process may be generated as a function of a classifier, wherein the classifier may receive a monitoring device element of a plurality of monitoring elements and output one or more vigor adjustment outcomes that are related to at least one or more monitoring elements. As used in this disclosure a “classifier” is a machine-learning model, such as a mathematical model, neural net, or program generated by a machine-learning algorithm known as a “classification algorithm,” as described below, that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric as described below, or the like. Computing device108and/or another device may generate a classifier using a classification algorithm, defined as a process whereby a computing device108derives a classifier from training data. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher's linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers. For example, and without limitation, a classifier may receive an input of a monitoring element associated with increased pressure on the epidermal layer of a user, wherein a vigor adjustment outcome may identify increased blood flow, swelling, and/or infection. In an embodiment, processor104may be configured to generate a vigor status classifier by receiving vigor status training data correlating vigor status and category of datum associated with the vigor status to a vigor status severity score. A “severity score” as defined in this disclosure, is a numerical score based on the category of the origin of the vigor status. For instance, a user may suffer from a condition such as “excessive perspiration” which may belong to different categories of conditions. Excessive perspiration may belong to the category of “user fitness.” Additionally, “excessive perspiration” may belong to a category including medical conditions such as diabetic hypoglycemia. Excessive perspiration related to “user fitness” would receive a lower severity score as compared to “excessive perspiration” caused by diabetes. Vigor status training data may be received as a function of user-entered valuations of potential vigor status improvement plans. Vigor status training data may be received by correlations of vigor status previously used and/or determined during a previous iteration of generating vigor status plans. Vigor status training data may be received by one or more remote devices that at least correlate a vigor status, wherein a remote device is an external device to any computing device. Vigor status training data may be received in the form of one or more user-entered correlations of a modification to a plurality of vigor improvement plans. The vigor status classifier is trained using the vigor status training data. In another embodiment, computing device108is further configured to classify, using the vigor status classifier, the vigor status, and the category of datum associated with the vigor status to the vigor status severity score. As a non-limiting example, for instance, vigor status of “low energy” in the category of “fitness” may receive a medium severity score whereas a vigor status of “problem breathing” in the category of “medical conditions” may receive a higher severity score.

Still referring toFIG. 1, computing device108identifies a scan frequency136correlated to vigor status improvement plan132. As used in this disclosure a “scan frequency” is a number of scans that are conducted over a given period of time, wherein a period of time is comprised of milliseconds, seconds, minutes, hours, days, weeks, months, years, and the like thereof. Scan frequency136may be comprised of the number of scans required to monitor the user datum. For example, and without limitation, a scan frequency of 10 scans may be identified for a user datum of elevated anxiety. Scan frequency136may be comprised of a number of scans necessary to at least monitor a user condition in a time period. As used in this disclosure a “time period” is a given measure of time intervals. For example, a time period may include milliseconds, seconds, minutes, hours, days, weeks, months, years, or the like thereof. For example, and without limitation, a scan frequency of 30 scans per hour may be required for the user datum pertaining to blood clots.

Alternatively or additionally, and still referring toFIG. 1, computing device108identifies scan frequency through the use of a frequency machine-learning process. As used in this disclosure “frequency machine-learning process” is a machine learning process that automatedly uses training data and/or a training set to generate an algorithm that will be performed by a computing device and/or module to produce outputs given data provided as inputs; this is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language. Frequency machine-learning process may consist of any supervised, unsupervised, or reinforcement machine-learning process that computing device108may or may not use in the determination of scan frequency136. Frequency machine-learning process may include, without limitation, machine learning processes such as simple linear regression, multiple linear regression, polynomial regression, support vector regression, ridge regression, lasso regression, elasticnet regression, decision tree regression, random forest regression, logistic regression, logistic classification, K-nearest neighbors, support vector machines, kernel support vector machines, naive bayes, decision tree classification, random forest classification, K-means clustering, hierarchical clustering, dimensionality reduction, principal component analysis, linear discriminant analysis, kernel principal component analysis, Q-learning, State Action Reward State Action (SARSA), Deep-Q network, Markov decision processes, Deep Deterministic Policy Gradient (DDPG), or the like thereof.

Still referring toFIG. 1, the frequency machine-learning process may be generated as a function of a frequency training set. As used in this disclosure “frequency training set” is a training set that correlates vigor status improvement plans to frequency monitoring requirements. The frequency training set may be received as a function of user-entered valuations of potential scan frequencies based on vigor status improvement plans. The frequency training set may be received by correlations of the frequency of scans previously used and/or determined during a previous iteration of generating frequency scan training sets. The frequency training set may be received by one or more remote devices that at least correlate frequency scans, wherein a remote device is an external device to any computing device. Frequency training sets may be received in the form of one or more user-entered correlations of a modification to a plurality of frequency scans. As used in this disclosure a “vigor element” is an element relating to one or more human physiological, psychological, or spiritual states, wherein a physiological state relates to one or more metabolic and/or biological processes, a psychological state relates to one or more neurologic and/or emotional processes, and a spiritual state relates to one or more religious and/or existential elements. As a non-limiting example, vigor elements may include lung capacity, edema pressure, neural transmission, mood quality, religious quarrel, and/or religious hopelessness. As used in this disclosure a “frequency monitoring requirement” is a value denoting a necessary and/or useful frequency of monitoring for a given user datum. For example, and without limitation, a frequency requirement of pneumonia may be 40 scans per day due to the decreased lung capacity, fever, shortness of breath, coughing, or the like thereof. Additionally or alternatively, frequency machine learning process may be generated as a function of a classifier, described above, wherein the classifier may receive a vigor element of a plurality of vigor elements and output one or more frequency requirements in monitoring the vigor element. For example, and without limitation, a classifier may receive an input of a vigor element associated with macular degeneration, wherein a frequency requirement outcome may be 10 scans per minute to ensure effective monitoring.

Still referring toFIG. 1, computing device108generates a device scheme140as a function of scan frequency136. As used in this disclosure a “device scheme” is a schedule of scans that is generated as a result of a given scan frequency and a given time period that may be entered into a device such that the device monitors the user effectively. For example, and without limitation, a device scheme may generate a scan every 8.5 minutes for a scan frequency of210scans over 30 hours. Computing device108may generate device scheme140by receiving scan frequency136relating to user datum116. Computing device108may determine a given time period, as described above to include milliseconds, seconds, minutes, hours, days, weeks, months, years, and the like thereof, to fulfill the frequency requirement, as described above. As used in this disclosure a “scan frequency” is a required number of scans of a monitoring device to at least monitor a given user datum. Computing device108may generate a device scheme as a function of the scan frequency and time period. For example, and without limitation, a given scan frequency may include 40 scans to effectively monitor a microvascular cranial nerve palsy and the given time period is daily, wherein the device schedule may be generated to determine a scan needs to be performed every 36 minutes. In an embodiment, and without limitation, computing device108may generate device scheme140by outputting a first device scheme as a function of a first scan frequency relating to a first signal profile, wherein the first signal profile relates to a first user datum recorded by a monitoring device. For example, and without limitation, a device scheme of scanning every 8 hours and 24 minutes may be generated as a function of a scan frequency of 20 scans over a 1-week time period.

Still referring toFIG. 1, in another embodiment, computing device108may identify a second scan frequency as a function of user profile and first signal profile. A “user profile” as used in this disclosure is a characteristic uniquely belonging to a human subject. A user profile may include, without limitation, particular traits, qualities, behaviors, and/or habits relating to a user. User profile may be comprised of a medical record and/or user demographic, wherein a user demographic relates one or more elements of age, sex, gender, weight, height, geolocation, and/or ethnicity. User profile may be received as a function of physician input, self-report, familial report, or the like thereof. For example, and without limitation, a user's physician may input to computing device108that a user profile indicates high blood pressure and a high LDL value. As a further non-limiting example a user may self-report a user profile of anxiety to computing device108. For example, and without limitation, a first signal profile may indicate a presence of a heart arrythmia while a user profile may indicate the presence of congenital heart failure, wherein a second scan frequency may be generated as a function of both arrhythmia and congenital heart failure. Computing device108may generate a second device scheme as a function of the second device scheme, wherein the second device scheme incorporates both the user profile and the first signal profile. For example, and without limitation, a second device scheme of scanning every 5 minutes may be generated as a function of a scan frequency of 12 scans per hour, wherein the scan frequency was identified as a function of a combination between a user profile of COVID-19 and a first scan frequency of 10 scans per hour. Additionally or alternatively, computing device108may be configured to generate second device scheme as a function of second scan frequency relating to a second signal profile. The second signal profile may be calculated as a function of a second user datum116m.For example, and without limitation, a first user datum of jaundice may be obtained by a monitoring device, wherein a second user datum of fever is then obtained by monitoring device120. Computing device108may then calculate the second signal profile, wherein calculating results in identifying second scan frequency and second device scheme.

With continued reference toFIG. 1, computing device108is configured to program monitoring device120as a function of device scheme140. Monitoring device120is programmed by computing device such that the user datum and/or user profile can be effectively monitored according to device scheme140. Computing device108may receive a second device scheme, wherein the generation of a second device scheme is described above. Computing device108may identify a first device scheme on monitoring device120. For example, and without limitation, a previous device scheme may include the monitoring device providing a scan 36 times over a 6-hour time period. Computing device108may program monitoring device120as a function of the second device scheme, wherein the second device scheme modifies the monitoring of the first device scheme. For example, and without limitation, a first device scheme may have monitoring device120conducting a scan every 3 minutes over a 24-hour time period, while a second device scheme may have monitoring device120conducting a scan every 2 minutes over a 24-hour scan to increase the overall number of scans for a given user profile and/or user datum.

Alternatively, or additionally, and with continued reference toFIG. 1, computing device108may be configured to update the device scheme as a function of the classification. A classification of the vigor status and the category of datum associated with the vigor status using the vigor status classifier may result, for example, in vigor status under different categories with different severity scores. In this case, a vigor status with a higher severity score may result in monitoring device120conducting a scan more frequently than a vigor status with a lower severity score. For instance, a condition such as “excessive perspiration” under medical conditions would receive a higher severity score and require a higher frequency of scans than a condition of “excessive perspiration” under “fitness.” In another embodiment, processor104may be configured to update the device scheme based on a second vigor status. For example, a user with religious conflicts and bipolar may require more frequent scans than a user with only religious conflicts.

Alternatively or additionally, and continuing to refer toFIG. 2, training data204may include one or more elements that are not categorized; that is, training data204may not be formatted or contain descriptors for some elements of data. Machine-learning algorithms and/or other processes may sort training data204according to one or more categorizations using, for instance, natural language processing algorithms, tokenization, detection of correlated values in raw data and the like; categories may be generated using correlation and/or other processing algorithms. As a non-limiting example, in a corpus of text, phrases making up a number “n” of compound words, such as nouns modified by other nouns, may be identified according to a statistically significant prevalence of n-grams containing such words in a particular order; such an n-gram may be categorized as an element of language such as a “word” to be tracked similarly to single words, generating a new category as a result of statistical analysis. Similarly, in a data entry including some textual data, a person's name may be identified by reference to a list, dictionary, or other compendium of terms, permitting ad-hoc categorization by machine-learning algorithms, and/or automated association of data in the data entry with descriptors or into a given format. The ability to categorize data entries automatedly may enable the same training data204to be made applicable for two or more distinct machine-learning algorithms as described in further detail below. Training data204used by machine-learning module200may correlate any input data as described in this disclosure to any output data as described in this disclosure. As a non-limiting illustrative example, a frequency training set may be used as an input and a scan frequency output may be generated as a function of the frequency machine learning process.

Still referring toFIG. 2, models may be generated using alternative or additional artificial intelligence methods, including without limitation by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of “training” the network, in which elements from a training data204set are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning. This network may be trained using training data204.

Now referring toFIG. 3, an exemplary embodiment of300of user profile144generating a second device scheme304according to an embodiment of the invention. In an embodiment, computing device108may obtain user datum108a-mand calculate a first signal profile308as a function of user datum108a.For example, and without limitation, a first user datum of loss of sleep quality may calculate s first signal profile of 20 for sleep apnea. Computing device108may identify a first scan frequency312as a function of first signal profile308. For example, and without limitation a scan frequency of 40 scans per month may be identified for a fitness goal of increasing 10 pounds of muscle mass in a 6-month period. Computing device108may generate a first device scheme316as a function of first scan frequency312and monitoring device120as a function of first device scheme316. As a non-limiting example, a device scheme of conducting a scan every 18 minutes to monitor a medical goal of decreasing LDL concentration in a user's circulatory system may be sent to monitoring device120such that monitoring device120is programmed for those scans. Computing device108may identify a second scan frequency320as a function of user profile144and first signal profile. As a non-limiting example user profile144a first signal profile308may indicate a presence of chakra blockage while user profile144may indicate the presence of religious hopelessness, wherein second scan frequency320may be generated as a function of both chakra blockage and religious hopelessness. Computing device108may generate second device scheme304as a function second scan frequency320and monitoring device120as a function of second device scheme304. For example, and without limitation, a second device scheme of 30 scans per hour may be generated as a function of a combination between a user profile of Influenza and a first scan frequency of 20 scans per hour. Additionally or alternatively, computing device108may be configured to generate second device scheme304as a function of second scan frequency320relating to a second signal profile. The second signal profile may be calculated as a function of a second user datum108m.For example, and without limitation, a first user datum of anxiety has resulted in a device scheme of monitoring every 30 minutes for 30 days followed by a second user datum of increase elevated epinephrine, wherein second device scheme304may be altered to scan every5minutes for 116 days.

Now referring toFIG. 4, an exemplary embodiment of a method400of programming a monitoring device is disclosed. At step405a processor is configured to obtain a user datum from a processor and a monitoring device. This step may be performed, without limitations, as described inFIGS. 1-3. User datum may include a sign, symptom, element, or quality that relates to a user. For instance, a user datum may include, without limitation, neurologic function, sleep cycle, linguistic quality, heart rate, calories burned, steps walked, blood pressure, mood quality, anxiety levels, or the like thereof. Monitoring device includes any of the monitoring devices as described above in reference toFIGS. 1-3. Monitoring device may include, without limitation, any smart electronic device that is worn close to and/or on the surface of the skin, wherein the device can detect, analyze, and transmit information concerning a body signal such as a vital sign, and/or ambient datum, wherein allowing immediate biofeedback to be sent to the user wearing the device. Monitoring device may include devices such as smart watches, head mounts, electronic textiles, smart glasses, smart clothing, smart jewelry, and the like thereof. For example, and without limitation, a monitoring device of a smart watch containing an optical sensor may provide a user datum of heart rate to computing device.

With continued reference toFIG. 4, at step410, a processor is configured to calculate, a signal profile as a function of user datum116. This step may be performed, without limitations, as described inFIGS. 1-3. Signal profile includes any of the signal profile as described above in reference toFIGS. 1-3. Signal profile may be calculated as a function of a signal of a user condition, a frequency of the user datum occurrence, the identification of a secondary user datum that differs from a previous signal profile. For instance and without limitation, signal profile may calculate a value of 75 for influenza due to the user datums of sneezing, coughing, shortness of breath, and decreased O2saturation levels. A processor may be configured to calculate signal profile124as a function of one or more machine-learning processes as described above in reference toFIGS. 1-3. A processor in a computing device may be configured to calculate a signal profile as a function of a signal machine-learning process. Signal machine-learning process includes any of the signal machine-learning process as described above in reference toFIGS. 1-3. For instance, and without limitation, signal machine-learning process may include a supervised machine-learning process or an unsupervised machine-learning process. Signal machine learning process may include a classification process, such as for example naïve Bayes, k-nearest neighbor, decision tree, and/or random forest. Classification processes include any of the classification processes as described above in reference toFIGS. 1-3. Signal machine-learning process may be configured using a signal training set. Signal training set includes any of the signal training set as described above in reference toFIGS. 1-3. Signal training set may include, without limitation, a monitoring element correlated to a vigor adjustment outcome, wherein a monitoring element relates to one or more human physiological statuses and a vigor adjustment outcome is any effect, impact, consequence, result, or reaction that may result from that monitoring element.

With continued reference toFIG. 4, at step415, a processor may be configured to determine a vigor status as a function of the signal profile. This step may be performed, without limitations, as described inFIGS. 1-3. In an embodiment, the vigor status may include a user condition.

With continued reference toFIG. 4, at step420, a processor may be configured to generate a vigor status improvement plan as a function of the vigor status. This step may be performed, without limitations, as described inFIGS. 1-3. In an embodiment, the vigor status improvement plan may be generated by a vigor status improvement machine-learning model. The processor receives vigor status improvement training data correlating vigor status improvement with vigor status. The vigor status improvement machine-learning model is trained based on the vigor status improvement training data. The vigor status improvement plan is generated as a function of the vigor status improvement plan.

Still referring toFIG. 4, at step425, the processor may be configured to identify a scan frequency correlated to the vigor status improvement plan. This step may be performed, without limitations, as described inFIGS. 1-3. Scan frequency includes any of the scan frequency as described above in reference toFIGS. 1-3. Scan frequency may include a number of scans that are conducted over a given period of time, wherein a period of time is comprised of milliseconds, seconds, minutes, hours, days, weeks, months, years, and the like thereof. For example, and without limitation, a scan frequency for weight loss may include 12 scans per day. Scan frequency may be comprised of the number of scans required to monitor the user datum. For example and without limitation, 40 scans may be required to monitor the user datum of premature ventricular contractions. Scan frequency may be comprised of a number of scans necessary to at least monitor a user condition in a time period. For example, and without limitation a number of 5 scans may be necessary to monitor a user condition of a fever over a 24-hour time period. Computing device108may calculate scan frequency136as a function of one or more machine-learning processes as described above in reference toFIGS. 1-3. A processor may calculate a scan frequency as a function of a frequency machine-learning process. A frequency machine-learning process includes any of the frequency machine-learning process as described above in reference toFIGS. 1-3. For instance, and without limitation, a frequency machine-learning process may include a supervised machine-learning process or an unsupervised machine-learning process. Frequency machine learning process may include a classification process, such as for example naïve Bayes, k-nearest neighbor, decision tree, and/or random forest. Classification processes include any of the classification processes as described above in reference toFIGS. 1-3. In an embodiment, a vigor status classifier is generated. The processor receives vigor status training data correlating vigor status and category of datum associated with the vigor status to a vigor status severity score. The vigor status classifier is trained using the vigor status training data. The vigor status classifier is used to classify the vigor status and the category of datum associated with the vigor status to vigor status severity score. Frequency machine-learning process may be configured using a frequency training set. A frequency training set includes any of the frequency training set as described above in reference toFIGS. 1-3. A Frequency training set may include, without limitation, at least a first element of a vigor element to at least a first frequency requirement, wherein a vigor element is an element relating to one or more human physiological, psychological, or spiritual states and a frequency requirement may consist of a value denoting a necessary frequency of monitoring for a given user profile and/or user datum. For example, and without limitation, a frequency training set may correlate rhabdomyolysis with a frequency requirement of 10 scans per second. In an embodiment, the scan frequency may be identified as a function of the vigor status improvement plan and the frequency machine-learning model.

Still referring toFIG. 4, at step430, a processor may be configured to generate a device scheme as a function of scan frequency. This step may be performed, without limitation, as described inFIGS. 1-3. A device scheme includes the device scheme as described above in reference toFIGS. 1-3. Device scheme may include a schedule that may or may not be generated as a function of a given scan frequency and a given time period that may be entered into a device such that the device monitors the user effectively. A processor may generate a second device scheme as a function of user profile. A processor may output a first device scheme as a function of a first scan frequency, wherein first scan frequency is identified as a function of a first signal profile. A processor104may then identify a second scan frequency as a function of the user profile and first signal profile. A processor may be configured to then generate a second device scheme as a function of second scan frequency. For example, and without limitation, a first device scheme of a scan every 20 minutes over a 4 day time period may be altered to a second device scheme of a scan every 5 minutes over a 12 day time period due to the user profile input of previous myocardial infarction. Additionally or alternatively, a computing device generates a second device scheme as a function of a second scan frequency relating to a second signal profile. The second signal profile may be calculated as a function of a second user datum108m.For example, and without limitation, a first user datum of high blood pressure may be obtained by a monitoring device, wherein a second user datum of myocardial infarction is then obtained by a monitoring device. A processor may be configured to calculate the second signal profile, wherein calculating results in identifying second scan frequency and second device scheme. In an embodiment, the device scheme is updated as a function of the classification involving the vigor status classifier. In another embodiment, the device scheme is updated as a function of a second vigor status.

Still referring toFIG. 4, at step435, a processor is configured to program a monitoring device as a function of a device scheme. A processor is configured to program monitoring device such that the user datum and/or user profile can be effectively monitored according to device scheme. A processor may receive a second device scheme, wherein the generation of a second device scheme is described above, identify a first device scheme on a monitoring device, and a program monitoring device as a function of a second device scheme, wherein the second device scheme modifies a first device scheme. For example, and without limitation, a first device scheme may have a monitoring device conducting a scan every 3 minutes over a 24 hour time period, while a second device scheme may have monitoring device120conducting a scan every 2 minutes over a 24 hour scan to increase the overall number of scans for a given user profile and/or user datum.

Computer system500may also include a storage device524. Examples of a storage device (e.g., storage device524) include, but are not limited to, a hard disk drive, a magnetic disk drive, an optical disc drive in combination with an optical medium, a solid-state memory device, and any combinations thereof. Storage device524may be connected to bus512by an appropriate interface (not shown). Example interfaces include, but are not limited to, SCSI, advanced technology attachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and any combinations thereof. In one example, storage device524(or one or more components thereof) may be removably interfaced with computer system500(e.g., via an external port connector (not shown)). Particularly, storage device524and an associated machine-readable medium528may provide nonvolatile and/or volatile storage of machine-readable instructions, data structures, program modules, and/or other data for computer system500. In one example, software520may reside, completely or partially, within machine-readable medium528. In another example, software520may reside, completely or partially, within processor504.

Computer system500may also include an input device532. In one example, a user of computer system500may enter commands and/or other information into computer system500via input device532. Examples of an input device532include, but are not limited to, an alpha-numeric input device (e.g., a keyboard), a pointing device, a joystick, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), a cursor control device (e.g., a mouse), a touchpad, an optical scanner, a video capture device (e.g., a still camera, a video camera), a touchscreen, and any combinations thereof. Input device532may be interfaced to bus512via any of a variety of interfaces (not shown) including, but not limited to, a serial interface, a parallel interface, a game port, a USB interface, a FIREWIRE interface, a direct interface to bus512, and any combinations thereof. Input device532may include a touch screen interface that may be a part of or separate from display536, discussed further below. Input device532may be utilized as a user selection device for selecting one or more graphical representations in a graphical interface as described above.

A user may also input commands and/or other information to computer system500via storage device524(e.g., a removable disk drive, a flash drive, etc.) and/or network interface device540. A network interface device, such as network interface device540, may be utilized for connecting computer system500to one or more of a variety of networks, such as network544, and one or more remote devices548connected thereto. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. A network, such as network544, may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software520, etc.) may be communicated to and/or from computer system500via network interface device540.

Computer system500may further include a video display adapter552for communicating a displayable image to a display device, such as display device536. Examples of a display device include, but are not limited to, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display, a light emitting diode (LED) display, and any combinations thereof. Display adapter552and display device536may be utilized in combination with processor504to provide graphical representations of aspects of the present disclosure. In addition to a display device, computer system500may include one or more other peripheral output devices including, but not limited to, an audio speaker, a printer, and any combinations thereof. Such peripheral output devices may be connected to bus512via a peripheral interface556. Examples of a peripheral interface include, but are not limited to, a serial port, a USB connection, a FIREWIRE connection, a parallel connection, and any combinations thereof