SYSTEM AND METHOD FOR TEXT-BASED CONVERSATION WITH A USER, USING MACHINE LEARNING

A method of risk score comparison and analysis using wearable devices is illustrated. The method comprising receiving, at a computing device, user data associated with a first user, generating, using the computing device, a first risk score as a function of the user data, and matching, using the computing device, the first user to a second user having a second risk score as a function of user data associated with the second user. Matching the users comprises extracting, from the first risk score, a plurality of first risk score content elements, extracting, from the second risk score, a plurality of second risk score content elements, and matching the plurality of first risk score elements to the plurality of second risk score elements.

FIELD OF THE INVENTION

The present invention generally relates to the field of artificial intelligence, simulation, and modeling. In particular, the present invention is directed to risk score comparison and analysis using wearable devices.

BACKGROUND

Machine generated text-based conversation is commonly employed for a number of applications, including commerce, education, entertainment, finance, health, news, and productivity. Commonly, machine generated text-based conversation is with a random chatbot, thus the two users do not have any sort of connection.

SUMMARY OF THE DISCLOSURE

In an aspect, a method of risk score comparison and analysis using wearable devices is illustrated. The method comprising receiving, at a computing device, user data associated with a first user, generating, using the computing device, a first risk score as a function of the user data, and matching, using the computing device, the first user to a second user having a second risk score as a function of user data associated with the second user. Matching the users comprises extracting, from the first risk score, a plurality of first risk score content elements, extracting, from the second risk score, a plurality of second risk score content elements, and matching the plurality of first risk score elements to the plurality of second risk score elements.

In another aspect, an apparatus for risk score comparison and analysis using wearable devices is presented. The apparatus comprises at least a processor and a memory communicatively connected to the processor. The memory contains instruction configuring the at least a processor to receive, at a computing device, user data associated with a first user, generate, using the computing device, a first risk score as a function of the user data, and match, using the computing device, the first user to a second user having a second risk score as a function of user data associated with the second user. Matching the users instructs the at least a processor to extract, from the first risk score, a plurality of first risk score content elements, extract, from the second risk score, a plurality of second risk score content elements, and match the plurality of first risk score elements to the plurality of second risk score elements.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed to an apparatus, systems, and methods for risk score comparison and analysis using wearable devices. In an embodiment, machine-learning may be used to determine a risk score from user data, such as without limitation a feature and/or a preference input. In some cases, risk score of a first user may be compared and matched to the risk score of another user. Text may then be used to interface conversationally between users, in a purposeful and user-specific manner.

Aspects of the present disclosure may be configured to receive user data associated with a first user at a computing device and from a wearable device. Aspects of the present disclosure may also include generating a first risk score as a function of the user data. Aspects of the present disclosure may include matching the first user to a second user having a second risk score.

Still referring toFIG. 1A, computing device104receives at least a feature108and at least a preference input112from a user. In some embodiments, at least a feature108may include one or more of a continuous value, a discrete value, an answer to an interview question, or a Boolean (e.g., True/False) value. In some embodiments, preference input112may include, without limitation, one or more of a prioritization ranking, a preferred selection, or a discrete value. In some embodiments, user may be communicating directly with a computing device104. Alternatively or additionally, in some cases, user may be communication by way of a user device116. An exemplary list of user devices may include a personal computer, fax, telephone, or smart phone. In some embodiments, communication between computing device104and user device116is performed by way of text-based messages, such as without limitation short message service (SMS) messages. Communication with user device116and computing device104may, in some cases, also be performed by way of one or more networks, such as without limitation the Internet.

Continuing with reference toFIG. 1A, computing device104inputs at least a feature to a probabilistic machine learning model120and generates a probabilistic output124using the probabilistic machine learning model120. In some embodiments, probabilistic machine learning model120may be accessed, for example from any of a storage component, a database, or another computing device. In some cases, probabilistic machine learning model120may be accessed from another computing device by way of one or more networks, such as without limitation the Internet. In some embodiments, computing device104may be additionally configured to train a probabilistic machine learning model120. Probabilistic machine-learning model may include any machine-learning model as described in this disclosure. Computing device104may train probabilistic machine learning model120using training data. Training data may include a plurality of training examples. Each training example of plurality of training examples may correlate one or more features to or with one or more probabilistic outcomes and/or other elements of data usable for computation of probabilistic outcomes. Computing device104may train probabilistic machine learning model120as a function of and/or using any machine learning algorithm as described in this disclosure. In some cases, and as a non-limiting example, probabilistic machine learning algorithm may include one or more of a supervised machine learning algorithm and an unsupervised machine learning algorithm. In some cases, and without limitation, plurality of features may include one or more of inputs or outputs used for training a machine learning algorithm. Training data may include any training data described in this disclosure. Training data and/or training examples may include at least a feature related to a user's condition. At least a feature related to a user's condition may, in some cases, be received from one or more evidential resources, such as without limitation refereed journal articles demonstrating a significant correlation between the at least a feature and the condition. Alternatively or additionally, in some embodiments, at least a feature related to a user's condition may be received from experiential resources, such as without limitation correlations derived through a medical practice involving treatment of the condition. Alternatively or additionally, in some embodiments, at least a feature related to a user's condition may be include assessment results of assessments used for determining a severity of a condition, such as without limitation a brief cognitive assessment. In some embodiments, probabilistic machine learning model120may be trained using an unsupervised machine learning algorithm and training data; and the training data includes a plurality of features. “Training data,” as used herein in this disclosure, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements. For instance, and without limitation, training data may include a plurality of features, each feature representing a set of data elements that were recorded, received, and/or generated together; data elements may be correlated by shared existence in a given data entry, by proximity in a given data entry, or the like. Multiple data entries in training data may evidence one or more trends in correlations between categories of data elements; for instance, and without limitation, a higher value of a first data element belonging to a first category of data element may tend to correlate to a higher value of a second data element belonging to a second category of data element, indicating a possible proportional or other mathematical relationship linking values belonging to the two categories. Multiple categories of data elements may be related in training data according to various correlations; correlations may indicate causative and/or predictive links between categories of data elements, which may be modeled as relationships such as mathematical relationships by machine-learning processes as described in further detail below. Training data may be formatted and/or organized by categories of data elements, for instance by associating data elements with one or more descriptors corresponding to categories of data elements. As an example, a feature selected from an evidential resource may be included as training data, where results presence of features significantly correlated with a health condition are provided as input elements, which are correlated to higher or lower probabilistic outputs. As another example, results from a mini-cog cognitive assessment may be included as training data, where results indicative of poor performance on the assessment are provided as input elements, which are correlated to higher or lower probabilistic outputs. As a non-limiting example, training data may include data entered in standardized forms by persons or processes, such that entry of a given data element in a given field in a form may be mapped to one or more descriptors of categories. Elements in training data may be linked to descriptors of categories by tags, tokens, or other data elements; for instance, and without limitation, training data may be provided in fixed-length formats, formats linking positions of data to categories such as comma-separated value (CSV) formats and/or self-describing formats such as extensible markup language (XML), JavaScript Object Notation (JSON), or the like, enabling processes or devices to detect categories of data.

Still referring toFIG. 1A, computing device104operates a classifying machine learning model128input with probabilistic output124and at least a feature to classify an intervention class132. In some embodiments, classifying machine learning model120is trained using any of a number of machine learning algorithms described in this disclosure, such as without limitation a supervised machine learning algorithm or an unsupervised machine learning algorithm. In some embodiments, classifying machine learning model128is trained using any of a number of optimization algorithms described in this disclosure, such as without limitation a linear optimization algorithm. In some embodiments, intervention class132may include, without limitation, a class of lifestyle change for user. In some embodiments, classifying machine learning model128may be trained using a supervised machine learning algorithm and training data; and the training data includes a plurality of features. In some cases, plurality of features may include one or more of inputs or outputs used for training a supervised machine learning algorithm. Training data may include any training data described in this disclosure. In some embodiments, classifying machine learning model128may be trained using an unsupervised machine learning algorithm and training data; and the training data includes a plurality of features. In some cases, plurality of features may include one or more of inputs or outputs used for training an unsupervised machine learning algorithm. Training data may include any training data described in this disclosure. In some embodiments, computing device104may additionally configured to select classifying machine learning model128from a plurality of classifying machine learning models, as a function of at least a feature; where, the classifying machine learning model128was trained using training data comprising a plurality of inputs. In some cases, selected classifying machine learning model128may be chosen based upon at least a feature directly. Alternatively or additionally, in some cases, selected classifying machine learning model128may be chosen based upon one or more calculations made using at least a feature. In some cases, plurality of features may include one or more of inputs or outputs used for training a machine learning algorithm. Training data may include any training data described in this disclosure. In some embodiments, computing device104may additionally be configured to select training data from a plurality of training data, as a function of at least one feature; and train classifying machine learning model128using the training data. In some cases, selected training may be chosen based upon at least a feature directly. Alternatively or additionally, in some cases, selected training data may be chosen based upon one or more calculations made using at least a feature. In some cases, plurality of features may include one or more of inputs or outputs used for training a machine learning algorithm. Training data may include any training data described in this disclosure. In some embodiments, classifying machine learning model128may be accessed, for example from any of a storage component, a database, or another computing device. In some cases, classifying machine learning model128may be accessed from another computing device by way of one or more networks, such as without limitation the Internet. In some embodiments, computing device104is additionally configured to train classifying machine learning model128, wherein training the classifying machine learning model128further comprises inputting training data to a machine learning algorithm, wherein the training data comprises a plurality of features; and training the classifying machine learning model128as a function of the machine learning algorithm. Training data may include any training data described in this disclosure, such as for example at least a feature related to a user's condition. At least a feature related to a user's condition may, in some cases, be selected from an evidential resources, such as without limitation refereed journal articles demonstrating a significant correlation between the at least a feature and an intervention class used to treat the condition. Alternatively or additionally, in some embodiments, at least a feature related to a user's condition may be selected from an experiential resources, such as without limitation correlations derived through a medical practice involving treatment of the condition. Alternatively or additionally, in some embodiments, at least a feature related to a user's condition may be include assessment results of assessments used for determining a severity of a condition and/or an intervention class useful in treating the condition.

Still referring toFIG. 1A, In some embodiments, classifying machine learning model128may include a classifier. A “classifier,” as used in this disclosure 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 in further detail 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 device104and/or another device may generate a classifier using a classification algorithm, defined as a processes whereby a computing device104derives 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.

Still referring toFIG. 1A, in some embodiments, computing device104may classify an intervention class132by using one or more optimization algorithms. For example, in some cases, an intervention class132is classified from a finite plurality of intervention classes, by way of an optimization algorithm.

Still referring toFIG. 1A, computing device104may compute a score associated with each of a plurality of intervention classes and select an intervention class to minimize and/or maximize the score, depending on whether an optimal result is represented, respectively, by a minimal and/or maximal score; a mathematical function, described herein as an “objective function,” may be used by computing device104to score each possible pairing. Objective function may be based on one or more objectives as described below. Computing device104may classify an intervention class132with a user, that optimizes objective function, such that without limitation the user's condition is maximally benefitted. In some embodiments, exemplary intervention classes132may include, without limitation interventions related to one of the user's diet, exercise, sleep, cognitive activity, and social engagement. In various embodiments a score of a particular intervention class132may be based on a combination of one or more factors, including without limitation a probabilistic output124and at least a feature. Each factor may be assigned a score based on predetermined variables. In some embodiments, the assigned scores may be weighted or unweighted.

Still referring toFIG. 1A, optimization of objective function may include performing a greedy algorithm process. A “greedy algorithm” is defined as an algorithm that selects locally optimal choices, which may or may not generate a globally optimal solution. For instance, computing device104may select an intervention class132so that scores associated therewith are the best score for each feature. In such an example, optimization may determine the combination of intervention class132that best addresses a user's condition, such that an intervention belonging to the intervention class132, when implemented will introduce greatest benefit to a user's condition.

Still referring toFIG. 1A, objective function may be formulated as a linear objective function, which computing device104may solve using a linear program such as without limitation a mixed-integer program. A “linear program,” as used in this disclosure, is a program that optimizes a linear objective function, given at least a constraint. For instance, in some embodiments, an intervention class132may be classified to minimize a user's risk score for a condition; the user's risk score may be represented as a probabilistic output124; and the intervention class132is classified to minimize the user's risk score when an intervention for the classified intervention class132is implemented. In various embodiments, system100may determine an intervention class that maximizes a total score subject to a constraint that precludes implementation of certain candidate intervention classes. A mathematical solver may be implemented to solve for an intervention class132that maximizes scores; mathematical solver may be implemented on computing device104and/or another device in system100, and/or may be implemented on third-party solver.

With continued reference toFIG. 1A, optimizing objective function may include minimizing a loss function, where a “loss function” is an expression of an output, which an optimization algorithm minimizes to generate an optimal result. As a non-limiting example, computing device104may assign variables relating to a set of parameters, which may correspond to score components as described above, calculate an output of mathematical expression using the variables, and select an intervention class132that produces an output having the lowest size, according to a given definition of “size,” of the set of outputs representing each of plurality of candidate ingredient combinations; size may, for instance, included absolute value, numerical size, or the like. Selection of different loss functions may result in identification of different potential pairings as generating minimal outputs.

Still referring toFIG. 1A, computing device104interfaces conversationally with user, for example without limitation by way of user device116, using text136generated as a function of intervention class132and a preference input112. In some embodiments, computing device104selects a particular intervention belonging to an intervention class132, using a preference input from user; and the particular intervention is a subject of text136communicated to the user. In some cases, computing device104may generate text136by accessing pre-prepared text, which is stored by the computing device104, such as without limitation within a database. According to some embodiments, computing device104may interface conversationally with user by using a chatbot, as described in this application. In some embodiments, computing device104may be configured to interface conversationally by performing natural language processing. Natural language processing may include any methods and/or processes for natural language processing described in this disclosure. Alternatively or additionally, in some embodiments, computing device104may interface conversationally with user by using a language processing module. Language processing module may include any hardware and/or software module. Language processing module may be configured to extract, from the one or more documents, one or more words. One or more words may include, without limitation, strings of one or more characters, including without limitation any sequence or sequences of letters, numbers, punctuation, diacritic marks, engineering symbols, geometric dimensioning and tolerancing (GD&T) symbols, chemical symbols and formulas, spaces, whitespace, and other symbols, including any symbols usable as textual data as described above. Textual data may be parsed into tokens, which may include a simple word (sequence of letters separated by whitespace) or more generally a sequence of characters as described previously. The term “token,” as used herein, refers to any smaller, individual groupings of text from a larger source of text; tokens may be broken up by word, pair of words, sentence, or other delimitation. These tokens may in turn be parsed in various ways. Textual data may be parsed into words or sequences of words, which may be considered words as well. Textual data may be parsed into “n-grams”, where all sequences of n consecutive characters are considered. Any or all possible sequences of tokens or words may be stored as “chains”, for example for use as a Markov chain or Hidden Markov Model.

Still referring toFIG. 1A, language processing module may operate to produce a language processing model. Language processing model may include a program automatically generated by computing device and/or language processing module to produce associations between one or more words extracted from at least a document and detect associations, including without limitation mathematical associations, between such words. Associations between language elements, where language elements include for purposes herein extracted words, relationships of such categories to other such term may include, without limitation, mathematical associations, including without limitation statistical correlations between any language element and any other language element and/or language elements. Statistical correlations and/or mathematical associations may include probabilistic formulas or relationships indicating, for instance, a likelihood that a given extracted word indicates a given category of semantic meaning. As a further example, statistical correlations and/or mathematical associations may include probabilistic formulas or relationships indicating a positive and/or negative association between at least an extracted word and/or a given semantic meaning; positive or negative indication may include an indication that a given document is or is not indicating a category semantic meaning. Whether a phrase, sentence, word, or other textual element in a document or corpus of documents constitutes a positive or negative indicator may be determined, in an embodiment, by mathematical associations between detected words, comparisons to phrases and/or words indicating positive and/or negative indicators that are stored in memory at computing device, or the like.

Still referring to1A, language processing module and/or diagnostic engine may generate the language processing model by any suitable method, including without limitation a natural language processing classification algorithm; language processing model may include a natural language process classification model that enumerates and/or derives statistical relationships between input terms and output terms. Algorithm to generate language processing model may include a stochastic gradient descent algorithm, which may include a method that iteratively optimizes an objective function, such as an objective function representing a statistical estimation of relationships between terms, including relationships between input terms and output terms, in the form of a sum of relationships to be estimated. In an alternative or additional approach, sequential tokens may be modeled as chains, serving as the observations in a Hidden Markov Model (HMM). HMMs as used herein are statistical models with inference algorithms that that may be applied to the models. In such models, a hidden state to be estimated may include an association between an extracted words, phrases, and/or other semantic units. There may be a finite number of categories to which an extracted word may pertain; an HMM inference algorithm. such as the forward-backward algorithm or the Viterbi algorithm, may be used to estimate the most likely discrete state given a word or sequence of words. Language processing module may combine two or more approaches. For instance, and without limitation, machine-learning program may use a combination of Naive-Bayes (NB), Stochastic Gradient Descent (SGD), and parameter grid-searching classification techniques; the result may include a classification algorithm that returns ranked associations.

Continuing to refer to FIG. IA, generating language processing model may include generating a vector space, which may be a collection of vectors, defined as a set of mathematical objects that can be added together under an operation of addition following properties of associativity, commutativity, existence of an identity element, and existence of an inverse element for each vector, and can be multiplied by scalar values under an operation of scalar multiplication compatible with field multiplication, and that has an identity element is distributive with respect to vector addition, and is distributive with respect to field addition. Each vector in an n-dimensional vector space may be represented by an n-tuple of numerical values. Each unique extracted word and/or language element as described above may be represented by a vector of the vector space. In an embodiment, each unique extracted and/or other language element may be represented by a dimension of vector space; as a non-limiting example, each element of a vector may include a number representing an enumeration of co-occurrences of the word and/or language element represented by the vector with another word and/or language element. Vectors may be normalized, scaled according to relative frequencies of appearance and/or file sizes. In an embodiment associating language elements to one another as described above may include computing a degree of vector similarity between a vector representing each language element and a vector representing another language element; vector similarity may be measured according to any norm for proximity and/or similarity of two vectors, including without limitation cosine similarity, which measures the similarity of two vectors by evaluating the cosine of the angle between the vectors, which can be computed using a dot product of the two vectors divided by the lengths of the two vectors. Degree of similarity may include any other geometric measure of distance between vectors.

Still referring toFIG. 1A, language processing module may use a corpus of documents to generate associations between language elements in a language processing module, and diagnostic engine may then use such associations to analyze words extracted from one or more documents and determine that the one or more documents indicate significance of a category. In an embodiment, language module and/or [computing device] may perform this analysis using a selected set of significant documents, such as documents identified by one or more experts as representing good information; experts may identify or enter such documents via graphical user interface, or may communicate identities of significant documents according to any other suitable method of electronic communication, or by providing such identity to other persons who may enter such identifications into [computing device]. Documents may be entered into a computing device by being uploaded by an expert or other persons using, without limitation, file transfer protocol (FTP) or other suitable methods for transmission and/or upload of documents; alternatively or additionally, where a document is identified by a citation, a uniform resource identifier (URI), uniform resource locator (URL) or other datum permitting unambiguous identification of the document, diagnostic engine may automatically obtain the document using such an identifier, for instance by submitting a request to a database or compendium of documents such as JSTOR as provided by Ithaka Harbors, Inc. of New York.

Still referring toFIG. 1A, in some embodiments, computing device104may additionally be configured to interface conversationally with a user, for example by way of a user device, wherein interfacing conversationally additionally includes: receiving a submission; recognizing at least a word from the submission, wherein recognizing the at least a word further comprises: inputting the submission to a language processing model; and, recognizing the at least a word as a function of the language processing model; and generating a response as a function of the at least a word. In some cases, computing device may be further configured to: train language processing model, wherein training the language processing model further comprises inputting training data to a natural language processing algorithm; and training the language processing model as a function of the natural language processing algorithm.

Still referring toFIG. 1A, in some embodiments, computing device104may additionally be configured to reperform one or more functions after having interfaced conversationally with a user. For instance, computing device104may be configured to iteratively reperform one or more functions, including those described immediately below, any number of times. In some embodiments, computing device104may additionally be configured to wait for a timeframe to elapse, receive at least a second feature associated with a user's condition and at least a second preference input112, generate a second probabilistic output124, and classify a second intervention class132. In some cases generating a second probabilistic output124may additionally include inputting at least a second feature to a probabilistic machine learning model120and generating the second probabilistic output124as a function of the probabilistic machine learning model120. In some cases, classifying a second intervention class may additionally include inputting a second probabilistic output124and an at least a second preference input112to a classifying machine learning model128and classifying the second probabilistic output124and the at least a second preference input112to the second intervention class132. In some cases, a timeframe, which computing device may be configured to wait, may be predetermined; for example, the timeframe may be within a range of between about 1 to 10 weeks, a range of between about 3 to 8 weeks, or a range of between about 6 to 8 weeks. Alternatively or additionally, in some cases, a timeframe may be determined during the timeframe; for example, in some cases, the timeframe may be determined as a function of user submissions, which are received while user device104is interfacing conversationally with user.

Still referring toFIG. 1A, in some embodiments, computing device104may be further configured to generate at least a metric, wherein generating the at least a metric further comprises: inputting the intervention class to a machine learning model; and generating the at least a metric as a function of the machine learning model. In some cases, computing device104may be further configured to receive a submission from the user; recognize at least a datum from the submission, wherein recognizing the at least a datum further comprises: inputting the submission to a language processing model; and, recognizing the at least a datum as a function of the language processing model; wherein the at least a datum is associated with the at least a metric. In some cases, at least a metric may be associated with an intervention class132; and the at least a metric may be a useful measure of progress made through implementing an intervention belonging to the intervention class132. Likewise, in some cases, at least a datum may be associated with at least a metric; and the at least a datum may be an actual measure of progress made through implementing an intervention belonging to the intervention class132.

Now referring toFIG. 1B, computing device104may also receive user data140associated with a first user. “User data,” as used in this disclosure, is data relating to a user. User data may contain a plurality of user data items. A “user data item” is an element of specific data concerning the user that is extracted from the computing device, or a wearable device. User data items may be, without limitation, the user's name, gender, blood type, heart rate, language, location, or any other piece of information given or detected. User data may include one or more test results such as test results from magnetic resonance imaging device, CT scanning device, PET scanning device, EEG scanner, MEG imaging device, NIRS imaging device and the like. User data may include any data input and/or generated by a physician and/or any other health care professional. User data may include any therapy data including any treatments and/or therapies a user may be currently undergoing and/or partaking in. User data may include any medical history data, any biomarkers, imaging results, scan results, behavioral history data, cognitive evaluations and/or test results and the like. User data may be retrieved from a user data database108, which may be implemented in any manner suitable for implementation of a database as described in this disclosure, and/or may be received from user input device. User database may include a user profile, which may be a profile of a user with goals, facts, or any other type of information about the user. User data140may also be received from a wearable device144. A “wearable device,” as used in this disclosure, is a device that a user wears on their person and/or within the proximity of the user and which is configured to detect user data. Wearable device144may include a sensor, such as a biosensor. Sensor may capture step, gait, and/or other mobility data, as well as data describing activity levels and/or physical fitness. Wearable device144may detect heart rate or the like. Wearable device144may detect any hematological parameter including blood oxygen level, pulse rate, heart rate, pulse rhythm, and/or blood pressure. Wearable device144may also include continuous glucose monitoring or any sort of health monitoring possible through the sensor. User data may include at least a feature and a preference input. In some embodiments, at least a feature may include one or more of a continuous value, a discrete value, an answer to an interview question, or a Boolean (e.g., True/False) value. In some embodiments, preference input may include, without limitation, one or more of a prioritization ranking, a preferred selection, or a discrete value. In some embodiments, user may be communicating directly with a computing device104. Alternatively or additionally, in some cases, user may be communication by way of a user input device or wearable device144. An exemplary list of user devices may include a personal computer, fax, telephone, or smart phone. In some embodiments, communication between computing device104and wearable device144is performed by way of text-based messages, such as without limitation short message service (SMS) messages. Communication with wearable device144and computing device104may, in some cases, also be performed by way of one or more networks, such as without limitation the Internet. User data may include biomarkers. A “biomarker” is a biological, measurable substance in an organism whose presence is indicative of some phenomenon. In this disclosure, biomarkers may be extracted from the user through wearable device144or another sort of blood or genetic test. Biomarker may include, without limitation, longitudinal blood-based Alzheimer's biomarkers such as serum amyloid, tau, neurofilament light chain, or GFAP. Biomarker may include one or more genetic markers such as APOE4, ABCA7, CLU, CR1, PICALM, PLD3, TREM2, SORL1, APP, PSEN1, PSEN2, and the like. Biomarker may include any physiological measurement of a user. Moreover, user data may also include goals. In this disclosure, “goals” refers to the object of a person's ambition or effort; in this embodiment, a user's goals may refer to their goals concerning their disease and/or health. Goals may be inputted by the user or generated at the processor as a function of the user data or received or risk score, as explained below. In an embodiment, one or more biomarkers may be tracked over time to determine effectiveness and/or clinical response to one or more interventions.

Still referring toFIG. 1B, computing device104may generate a first risk score152. In this disclosure, a “risk score” is a score given to a specific user associated with a severity of their illness or disease and/or probability of developing an illness or disease and/or of progressing to a more severe stage therein. Risk score may be a probabilistic output. Risk score124may be tracked over time to determine and/or evaluate effectiveness. Probabilistic output may be generated by inputting the user data to a probabilistic machine learning model and generating the probabilistic output as a function of the probabilistic machine learning model. For example but without limitation, risk score may score the severity of a user's condition, such as Alzheimer's or dementia. An example of a risk score may be any sort of number scale to reflect the user's risk of further developing their illness, such as but without limitation, a 1-10 scale.

Continuing with reference toFIG. 1B, computing device104inputs user data140to a scoring machine learning model148and generates a first risk score152using the scoring machine learning model148. In some embodiments, scoring machine learning model148may be accessed, for example from any of a storage component, a database, or another computing device. In some cases, scoring machine learning model148may be accessed from another computing device by way of one or more networks, such as without limitation the Internet. In some embodiments, computing device104may be additionally configured to train a scoring machine learning model148. Scoring machine-learning model120may include any machine-learning model as described in this disclosure. Computing device104may train scoring machine learning model148using training data. Training data may include a plurality of training examples. Each training example of plurality of training examples may correlate one or more features to or with one or more risk scores and/or other elements of data usable for computation of risk scores, such as goals, biomarkers, test result, etc. Computing device104may train scoring machine learning model148as a function of and/or using any machine learning algorithm as described in this disclosure. Training data could be received from any user or input based on outcomes in previous iterations of methods described here in this disclosure. In some cases, and as a non-limiting example, scoring machine learning algorithm may include one or more of a supervised machine learning algorithm and an unsupervised machine learning algorithm. In some cases, and without limitation, plurality of features may include one or more of inputs or outputs used for training a machine learning algorithm. Training data may include any training data described in this disclosure. Training data and/or training examples may include at least a feature, goals, biomarkers, or tests related to a user's condition. In some embodiments, scoring machine learning model148may be trained using an unsupervised machine learning algorithm and training data; and the training data includes a plurality of features. Training data may be any of the training data described herein with reference toFIG. 1A. Training data used by computing device104may correlate any input data as described in this disclosure to any output data as described in this disclosure. As a non-limiting illustrative example, input data includes user data140and first risk score152, such that the features and risks cores are correlated by a machine-learning model to corresponding to one another. Input data for training data may also include any biomarkers or goals received with user data, then the users are matched as a function of their user data from the wearable device, such as biomarker, goals, blood tests, genetic tests, or any other relevant data explained above.

Still referring toFIG. 1B, computing device104may match a first user to a second user as a function of their risk scores and user data received. Not only may risk scores be used to match users, but similarities in goals, biomarkers, genetic test, or blood tests may also be used to match users to one another. In an embodiment, a first user may have a first risk score of 6 and may be matched with a second user whose has a second risk score of 8, but first and second user may have similar goals, biomarkers, or other aspects of user data.

Still referring toFIG. 1B, computing device104may operate a classifying machine learning model128input with first risk score152and user data140in order to match it to a second risk score156and a second set of user data associated with a second user. In some embodiments, classifying machine learning model128is trained using any of a number of machine learning algorithms described in this disclosure, such as without limitation a supervised machine learning algorithm or an unsupervised machine learning algorithm. In some embodiments, classifying machine learning model128may be trained using any of a number of optimization algorithms described in this disclosure, such as without limitation a linear optimization algorithm. In some embodiments, risk score may include, without limitation, a number associated with the severity of the user's health. In some embodiments, classifying machine learning model128may be trained using a supervised machine learning algorithm and training data; and the training data includes a plurality of features. In some cases, plurality of features may include one or more of inputs or outputs used for training a supervised machine learning algorithm. Training data may include any training data described in this disclosure. In some embodiments, classifying machine learning model128may be trained using an unsupervised machine learning algorithm and training data; and the training data includes a plurality of features. In some cases, plurality of features may include one or more of inputs or outputs used for training an unsupervised machine learning algorithm. Training data may include any training data described in this disclosure. In some embodiments, computing device104may additionally configured to select classifying machine learning model128from a plurality of classifying machine learning models, as a function of user data140; where, the classifying machine learning model128was trained using training data comprising a plurality of inputs. In some cases, selected classifying machine learning model128may be chosen based upon user data140directly. Alternatively or additionally, in some cases, selected classifying machine learning model128may be chosen based upon one or more calculations made using user data140. In some cases, plurality of features may comprise user data and include one or more of inputs or outputs used for training a machine learning algorithm. Training data may include any training data described in this disclosure. In some embodiments, computing device104may additionally be configured to select training data from a plurality of training data, as a function of user data140; and train classifying machine learning model128using the training data. In some cases, selected training may be chosen based upon user data140directly. Alternatively or additionally, in some cases, selected training data may be chosen based upon one or more calculations made using user data140. In some cases, plurality of features may include one or more of inputs or outputs used for training a machine learning algorithm. Training data may include any training data described in this disclosure and may come from any of the users or inputs from previous iterations described herein. In some embodiments, classifying machine learning model128may be accessed, for example from any of a storage component, a database, or another computing device. In some cases, classifying machine learning model128may be accessed from another computing device by way of one or more networks, such as without limitation the Internet. In some embodiments, computing device104is additionally configured to train classifying machine learning model128, wherein training the classifying machine learning model128further comprises inputting training data to a machine learning algorithm, wherein the training data comprises a plurality of features; and training the classifying machine learning model128as a function of the machine learning algorithm. Training data may include any training data described in this disclosure, such as for example at least a feature, biomarkers, or goals related to a user's condition. Alternatively or additionally, in some embodiments, user data related to a user's condition may be include assessment results of assessments used for determining a severity of a condition and/or an intervention class useful in treating the condition. In some embodiments, classifying machine learning model128may include a classifier. Classifier may be any of the classifiers explained above.

Still referring toFIG. 1B, in some embodiments, computing device104may match first risk score152to second risk score156by using one or more optimization algorithms.

Still referring toFIG. 1B, computing device104may compute a score associated with each of a plurality of intervention classes and select an intervention class to minimize and/or maximize the score, depending on whether an optimal result is represented, respectively, by a minimal and/or maximal score; a mathematical function, described herein as an “objective function,” may be used by computing device104to score each possible pairing. Objective function may be based on one or more objectives as described below. Computing device104may classify a first risk score152with a user, that optimizes objective function, such that without limitation the user's condition is maximally benefitted. In some embodiments, exemplary risk scores may include, without limitation interventions related to one of the user's diet, exercise, sleep, cognitive activity, and social engagement. In various embodiments a score may be based on a combination of one or more factors, including without limitation a probabilistic output and at least a feature. Each factor may be assigned a score based on predetermined variables. In some embodiments, the assigned scores may be weighted or unweighted.

Still referring toFIG. 1B, computing device104may interface conversationally with user, for example without limitation by way of user device or wearable device144, using text generated as a function of first risk score152being matched to second risk score156. In some embodiments, computing device104selects a particular risk score associated with a first user, using a preference input from user; and the particular score is a subject of text communicated to the user. In some cases, computing device104may generate text by accessing pre-prepared text, which is stored by the computing device104, such as without limitation within a database. According to some embodiments, computing device104may interface conversationally with user by using a chatbot, as described in this application.

Still referring toFIG. 1B, computing device104may have an additional functionality to provide virtual visits or evaluations for dementia prevention. Virtual visits or evaluations may include connecting the user to any sort of medical professional that may help with their condition. For example, but without limitation, if a user who has dementia generates a fairly high risk score, computing device104will book appointments, visits or get them in contact with nearby neurologists or psychiatrists. Virtual visits may also be utilized to provide evaluations for dementia prevention and/or Alzheimer's disease management.

Still referring toFIG. 1B, computing device104matches the first user to a second user having a second risk score156. Matching the users comprises extracting, from the first risk score and the second risk score, a plurality of risk score content elements. A “content element” is an image, word, number, or anything shown in a risk score that may convey user data. Risk score content elements may include any user data associated with the respective user, including biomarkers, health information, goals, or any other information concerning the user. Once these content elements are extracted, computing device104classifies each content element of the plurality of risk score content elements to a risk score content element of a second user using a classifier. Classification may also occur as a function of a fuzzy inference system or doing some sort of average similarity score. “Fuzzy inference” is the process of formulating a mapping from a given input to an output using fuzzy logic. “Fuzzy logic” is a form of many-valued logic in which the truth value of variables may be any real number between 0 and 1. Fuzzy logic may be employed to handle the concept of partial truth, where the truth value may range between completely true and completely false. The mapping of a given input to an output using fuzzy logic may provide a basis from which decisions may be made and/or patterns discerned. A first fuzzy set may be represented, without limitation, according to a first membership function representing a probability that an input falling on a first range of values is a member of the first fuzzy set, where the first membership function has values on a range of probabilities such as without limitation the interval [0,1], and an area beneath the first membership function may represent a set of values within the first fuzzy set. A first membership function may include any suitable function mapping a first range to a probability interval, including without limitation a triangular function defined by two linear elements such as line segments or planes that intersect at or below the top of the probability interval. “Linguistic variables” may, in a non-limiting example, cover input value factors and the “defuzzified” output may represent a score or output indicating how likely a mission is to succeed or, via a functional output or threshold comparison, be used to make the “go/no go” determination. Linguistic variables may represent, for instance, degree of charge of batteries, external temperature, wind velocity, or any other variable that may affect a probability of successful completion of a flight. Combinations of input variables and/or member functions may be linked to and/or composed with output membership functions and/or functional output formulas such as TSK functions to generate a defuzzified probability of success, and/or score to be compared to a threshold. Any parameters, biases, weights or coefficients of membership functions may be tuned and/or trained using machine-learning algorithms as described in this disclosure.

Referring now toFIG. 2an exemplary embodiment of neural network200is illustrated. Neural network also known as an artificial neural network, is a network of “nodes,” or data structures having one or more inputs, one or more outputs, and a function determining outputs based on inputs. Such nodes may be organized in a network, such as without limitation a convolutional neural network, including an input layer of nodes204, one or more intermediate layers208, and an output layer of nodes212. Connections between nodes may be created via the process of “training” the network, in which elements from a training dataset are applied to input nodes204, 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 layers208of the neural network to produce the desired values at output nodes212. This process is sometimes referred to as deep learning.

Still referring toFIG. 3, a neural network may, for example without limitation, receive at least a feature and/or at least a probabilistic output128as inputs and output an intervention class132and a classification score representing a probability of classification to a predetermined class according to weights withat are derived using machine-learning processes as described in this disclosure.

Referring again toFIG. 1, computing device104, in some embodiments may be configured to interface conversationally with a user by way of a chatbot. Chatbot, in some cases, may be used to generate text136that is used to interface conversationally with user. In some versions, user may respond to computing device136, by way of a text-based interface, for example without limitation short message service (SMS) text message.

Referring toFIG. 4, a chatbot system400is schematically illustrated. According to some embodiments, a user interface404may be communicative with a computing device104that is configured to operate a chatbot. In some cases, user interface404may be local to computing device104. Alternatively or additionally, in some cases, user interface404may remote to computing device104and communicative with the computing device104, by way of one or more networks, such as without limitation the internet. Alternatively, or additionally, user interface404may communicate with user device404using telephonic devices and networks, such as without limitation fax machines, short message service (SMS), or multimedia message service (MMS). Commonly, user interface404communicates with computing device104using text-based communication, for example without limitation using a character encoding protocol, such as American Standard for Information Interchange (ASCII). Typically, a user interface404conversationally interfaces a chatbot, by way of at least a submission408, from the user interface404to the chatbot, and a response412, from the chatbot to the user interface404. In many cases, one or both of submission408and response412are text-based communication. Alternatively or additionally, in some cases, one or both of submission408and response412are audio-based communication. According to some embodiments, text-based communication may include at least a network location address, such as without limitation a hyperlink or uniform resource locator (URL), which may direct a user to more information, including without limitation videos, images, infographics, websites, audio, and text.

Continuing in reference toFIG. 4, a submission408once received by computing device104operating a chatbot, may be processed by a computing device104. In some embodiments, computing device104processes a submission4112using one or more of keyword recognition, pattern matching, and natural language processing. In some embodiments, processor employs real-time learning with evolutionary algorithms. In some cases, computing device104may retrieve a pre-prepared response from at least a storage component416, based upon submission408. Alternatively or additionally, in some embodiments, computing device104communicates a response412without first receiving a submission408, thereby initiating conversation. In some cases, computing device104communicates an inquiry to user interface404; and the processor is configured to process an answer to the inquiry in a following submission408from the user interface404. In some cases, an answer to an inquiry present within a submission408from a user device404may be used by computing device104as an input to another function, for example without limitation at least a feature or at least a preference input112.

Alternatively or additionally, and continuing to refer toFIG. 5, training data504may include one or more elements that are not categorized; that is, training data504may not be formatted or contain descriptors for some elements of data. Machine-learning algorithms and/or other processes may sort training data504according 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 data504to be made applicable for two or more distinct machine-learning algorithms as described in further detail below. Training data504used by machine-learning module500may correlate any input data as described in this disclosure to any output data as described in this disclosure. As a non-limiting illustrative example inputs may include at least a feature, a probabilistic output124, and/or at least a preference input112; and outputs may include a probabilistic output124and/or an intervention class132.

Referring toFIG. 6A, a method600of text-based conversation with a user, using machine learning is diagrammed by way of a flow chart. At step605, computing device104receives at least a feature associated with a user's condition and at least a preference input112; this may be implemented, without limitation, in any manner described above in reference toFIGS. 1-5. Computing device104may include any computing device104as described above in reference toFIGS. 1-5. A feature may include any feature described above in reference toFIGS. 1-5. A preference input112may include any preference input112described above in reference toFIGS. 1-5.

Still referring toFIG. 6A, at step610, computing device104generates a probabilistic output124, wherein generating a probabilistic output124includes inputting at least a feature104to a probabilistic machine learning model120; and generating the probabilistic output120as a function of the probabilistic machine learning model120. Probabilistic output124may include any probabilistic outputs described above, in reference toFIGS. 1-5. Probabilistic machine learning model120may include any machine learning model120described above, in reference toFIGS. 1-5. In some embodiments, probabilistic machine learning model120may be trained using a supervised machine learning algorithm and training data, comprising a plurality of features. A supervised machine learning algorithm may include any supervised machine learning algorithm described above, in reference toFIGS. 1-5. Training data may include any training data described above, in reference toFIGS. 1-5. A plurality of features may include any plurality of features described above, in reference toFIGS. 1-5. In some embodiments, probabilistic machine learning model120may be trained using an unsupervised machine learning algorithm and training data, comprising a plurality of features. An unsupervised machine learning algorithm may include any unsupervised machine learning algorithm described above, in reference toFIGS. 1-5.

Still referring toFIG. 6A, at step615, computing device104classifies an intervention class132, wherein classifying the intervention class132includes: inputting a probabilistic output124and at least a feature to a classifying machine learning model128input; and classifying the probabilistic output124and the at least a feature to the intervention class132. Intervention class132may include any intervention classes132described above, in reference toFIGS. 1-5. Classifying machine learning model128may include any machine learning model128described above, in reference toFIGS. 1-5. In some embodiments, classifying machine learning model may be trained using an unsupervised machine learning algorithm and training data, comprising a plurality of inputs. Plurality of inputs may include any plurality of inputs described above, in reference toFIGS. 1-5. In some embodiments, classifying machine learning model may be trained using a supervised machine learning algorithm and training data, comprising a plurality of inputs. In some embodiments, method600may additionally selecting classifying machine learning model128from a plurality of classifying machine learning models, as a function of at least a feature; wherein, the classifying machine learning model128was trained using training data, comprising a plurality of inputs. Inputs may include any inputs described above, in reference toFIGS. 1-5. Alternatively or additionally, in some embodiments, method600may additionally select training data, comprising a plurality of inputs, from a plurality of training data, as a function of at least one feature; and train classifying machine-learning model128using the training data.

Still referring toFIG. 6A, at step620, computing device104interfaces conversationally with a user, wherein interfacing conversationally includes: generating text136as a function of an intervention class132and at least a preference input112; and interfacing conversationally using the generated text. Text136may include any text136described above in reference toFIGS. 1-5. In some embodiments, step620may additionally include natural language processing. Natural language processing may include any natural language processing described above, in reference toFIGS. 1-5. In some embodiments, step620additionally includes receiving a submission; recognizing at least a word from the submission, wherein recognizing at least a word additionally includes: inputting the submission to a language processing model; and recognizing the at least a word as a function of the language processing model; and generating a response as a function of the at least a word. In some cases, step620further includes training a language processing model, wherein training the language processing model additionally includes: inputting training data to a natural language processing algorithm; and training the language processing model as a function of the natural language processing algorithm.

Still referring toFIG. 6A, in some embodiments, method600may additionally include training, using a computing device104, a probabilistic machine learning model120, wherein training the probabilistic machine learning model120includes: inputting training data to a machine learning algorithm, wherein the training data comprises a plurality of features; and training the probabilistic machine learning model120as a function of the machine learning algorithm.

Still referring toFIG. 6A, in some embodiments, method600may additionally include training, using a computing device104, a classifying machine learning model128, wherein training the probabilistic machine learning model128includes: inputting training data to a machine learning algorithm, wherein the training data comprises a plurality of features; and training the classifying machine learning model128as a function of the machine learning algorithm.

Still referring toFIG. 6A, in some embodiments, method600may additionally include waiting for a timeframe to elapse; receiving at least a second feature associated with a user's condition and at least a second preference input112; generating a second probabilistic output124, wherein generating the second probabilistic output124includes inputting the at least a second feature to a probabilistic machine learning model120; and generating the second probabilistic output124as a function of the probabilistic machine learning model120; and classifying a second intervention class132, wherein classifying a second intervention class132includes: inputting the second probabilistic output124and the at least a second feature to a classifying machine learning model128; and classifying the second probabilistic output124and the at least a second feature to a second intervention class132as a function of the classifying machine learning model128. Timeframe may include any timeframe described above, in reference toFIGS. 1-5.

Still referring toFIG. 6A, in some embodiments, method600may additionally include generating at least a metric by operating a machine learning model input with an intervention class132; and step620additionally may include processing a submission from user for at least a datum associated with the at least a metric. Metric may include any metric described above, in reference toFIGS. 1-5. Machine learning model may include any machine learning model described above, in reference toFIGS. 1-5. Datum may include any datum described above, in reference toFIGS. 1-5.

Still referring toFIG. 6A, in some embodiments, method600may additionally include: generating at least a metric, wherein generating the at least a metric additionally includes: inputting the intervention class to a machine learning model; and, generating the at least a metric as a function of the machine learning model. In some cases, step620may additionally include: receiving a submission from a user; recognizing, using the computing device, at least a datum from the submission, wherein recognizing the at least a datum additionally includes: inputting the submission to a language processing model; and, recognizing the at least a datum as a function of the language processing model; wherein the at least a datum is associated with the at least a metric.

Now referring toFIG. 6B, a method600of risk score comparison and analysis using wearable devices is diagrammed by way of a flow chart. At step605, computing device104receives from a wearable device144, user data140associated with a first user. Computing device may be any of the computing device explained herein with reference toFIGS. 1-5.

Still referring toFIG. 6B, at step610, computing device104generates a first risk score152as a function of the user data140. Generating the risk score further comprises generating, using the computing device, a probabilistic output. Generating the probabilistic output further comprises inputting the at least a feature to a probabilistic machine learning model and generating the probabilistic output as a function of the probabilistic machine learning model. Computing device may be any of the computing device explained herein with reference toFIGS. 1-5. Risk scores may be any of the risk scores described herein with reference toFIG. 1. User data may be any of the user data as explained herein with reference toFIG. 1.

Still referring toFIG. 6B, at step615, computing device104matches the first user to a second user having a second risk score156. Matching the plurality of first risk score elements to the plurality of second risk score elements further comprises matching using a classifier. Computing device104also trains a classifier, as a function of a machine learning algorithm and training data comprising user data correlated to risk score data. Matching the plurality of first risk score elements to the plurality of second risk score elements comprises comparing the first risk score of one user to the second risk score of another person in a database. Matching the plurality of first risk score elements to the plurality of second risk score elements further comprises interfacing conversationally. Interfacing conversationally further comprises generating text as a function of the user data and risk score. Computing device may be any of the computing device explained herein with reference toFIGS. 1-5. Risk scores may be any of the risk scores described herein with reference toFIG. 1.

Still referring toFIG. 6B, in some embodiments, method600may additionally include extracting, from the first risk score152, a plurality of first risk score content elements. Risk scores may be any of the risk scores described herein with reference toFIG. 1.

Still referring toFIG. 6B, in some embodiments, method600may additionally include extracting, from the second risk score156, a plurality of second risk score content elements. Risk scores may be any of the risk scores described herein with reference toFIG. 1.

Still referring toFIG. 6B, in some embodiments, method600may additionally include training, using a computing device104, a scoring machine learning model148, wherein training the scoring machine learning model148includes: inputting training data to a machine learning algorithm, wherein the training data comprises user data; and training the scoring machine learning model148as a function of the machine learning algorithm.

Still referring toFIG. 6B, in some embodiments, method600may additionally include training, using a computing device104, a classifying machine learning model128, wherein training the probabilistic machine learning model128includes: inputting training data to a machine learning algorithm, wherein the training data comprises a plurality of features; and training the classifying machine learning model128as a function of the machine learning algorithm.

Still referring toFIG. 6B, in some embodiments, method600may additionally include waiting for a timeframe to elapse; receiving user data140associated with a user's condition; generating a risk score, wherein generating the risk score includes inputting the user data to a scoring machine learning model148. Timeframe may include any timeframe described above, in reference toFIGS. 1-5.

Still referring toFIG. 6B, in some embodiments, method600may additionally include generating at least a metric by operating a machine learning model input with an intervention class; and step620additionally may include processing a submission from user for at least a datum associated with the at least a metric. Metric may include any metric described above, in reference toFIGS. 1-5. Machine learning model may include any machine learning model described above, in reference toFIGS. 1-5. Datum may include any datum described above, in reference toFIGS. 1-5.

Still referring toFIG. 6B, in some embodiments, method600may additionally include: generating at least a metric, wherein generating the at least a metric additionally includes: inputting the intervention class to a machine learning model; and, generating the at least a metric as a function of the machine learning model. In some cases, step620may additionally include: receiving a submission from a user; recognizing, using the computing device, at least a datum from the submission, wherein recognizing the at least a datum additionally includes: inputting the submission to a language processing model; and, recognizing the at least a datum as a function of the language processing model; wherein the at least a datum is associated with the at least a metric.

Referring now toFIG. 7, in some non-limiting embodiments, a system700is used for prophylactically treating and/or preventing a user's condition, such as without limitation Alzheimer's disease, other neurodegenerative disorders and/or other acute/chronic diseases, through one or more interventions. A computing device704may be communicative with a user device708by way of one or more networks. For example, in some cases communication over one or more networks may include communication by way of hypertext transfer protocol Secure (HTTPS)712and short message service (SMS)716. In some cases, different communication protocols may be used for different functional purposes. As a non-limiting example, HTTPS712may be used for receiving information, i.e., at least a feature, relevant to a user's condition and/or performance of an assessment module720configured to assess a user's risk score, i.e., probabilistic output124. Non-limiting examples of information108relevant to a user's condition may include one or more of responses to an interview (e.g., multiple choice selections), biometric data, genetic data, health information (e.g., at least a diagnosis), activity information (e.g., average number of steps per day), metrics indicating social connectivity (e.g., number of electronic human interactions per day), and cognitive test results or answers. In some embodiments, assessment module720may perform an assessment720using provided information108and any processes described above to generate a user's risk score124. Next, a classification module724classifies a domain, i.e., an intervention class132, in which to recommend an intervention. In some cases, a recommended intervention includes a lifestyle change within a domain132, non-limiting exemplary domains132include diet, exercise, sleep, cognitive activity, and social engagement. Classification module724may function according to any method described throughout this application. Next, in some embodiments, a user may select a particular intervention related to a classified intervention class132. For example, in some cases, an intervention class132relates to a user's behavior surrounding nutrition, or limiting sugar intake, and a user may be asked to select a single lifestyle change, i.e., preference input112, from a list of possible lifestyle changes, including without limitation (1) limit dessert to once a week; (2) eat only fruit for dessert; and (3) eat only dark chocolate for dessert. Next, a monitoring module728may monitor a user's progress with an intervention, such as without limitation instituting and habitualizing a lifestyle or behavior change. In some embodiments, monitoring module728may interact with user device708by way of a test message716. In some cases, text messages may be employed, because text-messages are intrusive and have an increased probability of response by a user. Monitoring module728may, for example, send a text message to a user device708asking “How many times in the past week did you eat dessert?” User may respond to this exemplary text message with a number. Positive progress, in this instance, may be indicated if a user had dessert no more than once in a past week. Ultimately, a habit confirmation module732may be implemented, for example after adequate progress has been made or a determined timeframe has elapsed. Habit confirmation732module may, in some embodiments, verify user has habitualized a lifestyle change or other intervention. In some cases, habit confirmation module732may additionally recollect information108related to a user's condition; as in some cases, assessment module720performs a re-assessment and treatment process iteratively progresses again.

Still referring toFIG. 7, in some embodiments a second user device may be in communication with computing device704. In some cases, a second user familiar with user, such as without limitation a friend or family member, may be involved in intervention. For example, in some cases, a second user may be informed of a user's current intervention or lifestyle change; the second user may be supportive of the user's compliance with the intervention.

Still referring toFIG. 7, in some embodiments before a user is asked to engage with a communication with a computing device704, the user may be asked if ready? Only after communicating that user is ready may computing device704engage with a communication with user, for example by taking a cognitive assessment.

Still referring toFIG. 7, in some embodiments communication between user and computing device704may include educational materials for a user. In some cases, educational materials may be related to an intervention or intervention class132that is presently being considered by user. Educational materials may attempt to educate a user as to why a particular intervention is worthwhile; how a particular intervention will work; or how a user may be able to be successful with a particular intervention.

Referring now toFIG. 8, some non-limiting embodiments may relate to a method800of prophylactically treating a user's condition through one or more interventions, such as without limitation Alzheimer's disease. At step805, a computing device704may receive information relevant a user's condition. Non-limiting examples of information108relevant to a user's condition may include one or more of responses to an interview (e.g., multiple choice selections), biometric data, genetic data, health information (e.g., at least a diagnosis), activity information (e.g., average number of steps per day), metrics indicating social connectivity (e.g., number of electronic human interactions per day), and cognitive test results or answers. At step810, computing system704may assess a user's risk score, i.e., probabilistic output124. Step810may include performing one or more of any processes described in this disclosure. At step815, computing device704may classify an intervention class132. Step815may include performing one or more of any processes described in this disclosure. At step820, computing device704may monitor an intervention. In some embodiments, step820may include conversationally interfacing with a user, such as without limitation by way of SMS. Step820may include performing one or more of any processes described in this disclosure. At step825, computing device may confirm success of an intervention. In some embodiments, step825may include waiting for a timeframe to elapse and/or one or more user inquiries. Step825may include one or more of any processes described in this disclosure.

Although present disclosure has been described in detailed above, it will be understood by those of ordinary skill in the art that the additional embodiments and applications are envisioned by the present disclosure. For example, in some additional embodiments, a user's risk score may be assessed based upon a series of question-and-answer routines. In some cases, categories and/or sub-categories of lifestyle choices, and the like, may be defined for example by an expert user such as without limitation a clinician. In some cases, a clinician may assign risk value to one or more classifications including, without limitation nutrition, exercise, sleep hygiene, and the like. In some cases, an expert may define categories and assign risks to them. In some cases, an expert may develop a patient question and answer routine. In some cases, question and answer routine may be developed in order to facilitate communication of information associated with patient risks. Clinicians can assign a risk value to individual questions and responses. In some cases, a hierarchy of questions, responses, and the like, is built according to their respective risk weight.

In some cases, a question-and-answer routine may be used to assess user risk, set priorities for user behavioral changes, make targeted instruction for lifestyle changes, and/or guide a user through text communication with lifestyle changes. For example, responses that correspond to a high-risk category may then lead system to prioritize that category for intervention. Question-and-answer routine may be repeated cyclically and/or periodically to determine and continually update risk scores, reset priorities, and the like. In some cases, risk-based hierarchies of categories, features and the like may contribute to increased scalability and ease of use.

In a non-limiting exemplary use case, topics may have relative weights as well as categories, so while exercise for instance may have more weight than “brain training,” extremely high-risk responses within the lower weight category could still cause that one to be prioritized. For example without limitation, patient does exercise, and all exercise question-and-answer responses are low-risk, while user responses are all high-risk in terms of cognitive activity (e.g., “I don't read, I don't challenge my brain, I don't do puzzles,” and the like) may cause interventions that prioritize cognitive to be suggested.

Computer system900may also include a storage device924. Examples of a storage device (e.g., storage device924) 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 device924may be connected to bus912by 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 device924(or one or more components thereof) may be removably interfaced with computer system900(e.g., via an external port connector (not shown 6. Particularly, storage device924and an associated machine-readable medium928may provide nonvolatile and/or volatile storage of machine-readable instructions, data structures, program modules, and/or other data for computer system900. In one example, software920may reside, completely or partially, within machine-readable medium928. In another example, software920may reside, completely or partially, within processor904.

Computer system900may also include an input device932. In one example, a user of computer system900may enter commands and/or other information into computer system900via input device932. Examples of an input device932include, 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 device932may be interfaced to bus912via 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 bus912, and any combinations thereof. Input device932may include a touch screen interface that may be a part of or separate from display936, discussed further below. Input device932may 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 system900via storage device924(e.g., a removable disk drive, a flash drive, etc.) and/or network interface device940. A network interface device, such as network interface device940, may be utilized for connecting computer system900to one or more of a variety of networks, such as network944, and one or more remote devices948connected 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 network944, may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software920, etc.) may be communicated to and/or from computer system900via network interface device940.

Computer system900may further include a video display adapter952for communicating a displayable image to a display device, such as display device936. 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 adapter952and display device936may be utilized in combination with processor904to provide graphical representations of aspects of the present disclosure. In addition to a display device, computer system900may 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 bus912via a peripheral interface956. 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.