Patent Publication Number: US-2021183520-A1

Title: Machine learning based health outcome recommendation engine

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Application No. 62/948,658, filed Dec. 16, 2019, which is incorporated by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     The disclosure generally relates to the field of health recommendations, and specifically to a machine learning based health outcome recommendation engine. 
     BACKGROUND 
     A user may use applications on a client device to track fitness activities, diet, and vital signs, such as heart rate, among other health characteristics. While the user may be able to identify changes in and trends associated with these characteristics, conventional applications often fail to contextualize the user&#39;s health, and accordingly, do not provide feasible suggestions on how to improve the user&#39;s health. 
     SUMMARY 
     A method for training and applying a machine-learned model configured to provide recommendations for achieving health outcomes is disclosed. The method includes accessing a training set of information comprising, for each of a plurality of training users, health characteristics of the training user, actions taken by the training user, an environment of the training user, a set of products used by the training user, and a set of health outcomes associated with the training user. The method includes training a machine-learned model based on the accessed training set of information. The trained machine-learned model is applied to a set of user information and a set of health goals received from a user. The user information describes one or more of health characteristics of the user, actions taken by the user, and an environment of the user. The machine-learned model is configured to identify actions that, if performed by the user, increase a likelihood that the user achieves the received set of health goals. The method subsequently includes modifying an interface displayed by a device of the user to include the identified actions. In some embodiments, a system and/or a non-transitory computer readable storage medium performs the steps described above. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       The disclosed embodiments have other advantages and features which will be more readily apparent from the detailed description, the appended claims, and the accompanying figures (or drawings). A brief introduction of the figures is below. 
         FIG. 1  illustrates a system environment of a health outcome recommendation engine, in accordance with one or more embodiments. 
         FIG. 2  illustrates training and applying a machine-learned model configured to provide recommendations for achieving health outcomes, in accordance with one or more embodiments. 
         FIG. 3  illustrates an example process for providing a user with recommendations for achieving health outcomes, in accordance with one or more embodiments. 
         FIGS. 4A-C  illustrate example user interfaces through which the user may interact with the health outcome recommendation engine, in accordance with one or more embodiments. 
     
    
    
     DETAILED DESCRIPTION OF DRAWINGS 
     The Figures (FIGS.) and the following description relate to preferred embodiments by way of illustration only. It should be noted that from the following discussion, alternative embodiments of the structures and methods disclosed herein will be readily recognized as viable alternatives that may be employed without departing from the principles of what is claimed. 
     Reference will now be made in detail to several embodiments, examples of which are illustrated in the accompanying figures. It is noted that wherever practicable similar or like reference numbers may be used in the figures and may indicate similar or like functionality. The figures depict embodiments of the disclosed system (or method) for purposes of illustration only. One skilled in the art will readily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles described herein. 
     Overview 
     A user may track activities and health characteristics, such as diet, exercise, heart rate, and weight, via one or more applications on a client device. Use of these applications is often limited to viewing trends of and/or changes in the tracked activities and health characteristics. The method and system included herein describe a health outcome recommendation engine that takes in the user&#39;s health characteristics and actions, as well as an environment of the user, to recommend actions and/or products that would increase a likelihood of the user achieving a set of intended health goals. The health outcome recommendation engine selects recommendations for the user using a machine-learned model trained on a training set of information (such as other users&#39; health information). 
     System Environment 
       FIG. 1  illustrates a system environment of a health outcome recommendation engine, in accordance with one or more embodiments. The health outcome recommendation engine receives information about a user and provides the user with recommendations (such as product recommendations, action recommendations, and the like) that increase a likelihood of the user achieving the user&#39;s health goals. The system environment includes a user  110 , a client device  120 , a plurality of training users  140 , a plurality of training user client devices  150 , the health outcome recommendation engine  155 , and a network  190 . 
     The health outcome recommendation engine  155  provides recommendations to the user  110  to improve the likelihood that the user  110  will achieve the user&#39;s health goals. The health outcome recommendation engine  155  takes, as input, information about the user  110 , such as health characteristics, actions, and environmental conditions around the user  110 , and the user&#39;s health goals. In response to receiving the information about the user  110  and the user&#39;s health goals, the health outcome recommendation engine  155  generates recommendations for the user  110 . The recommendations may include product suggestions (e.g., dietary supplements, topical serums, vitamins, etc.) as well as other action suggestions (e.g., meditation, physical activity, etc.). 
     The client device  120  couples the user  110  to the health outcome recommendation engine  155 . The client device  120  is a computing device capable of transmitting and/or receiving data over the network  190 . The client device  120  may be a conventional computer (e.g., a laptop or a desktop computer), a cellphone, or a similar device that communicates with the health outcome recommendation engine  155 . In some embodiments, the client device  120  provides the health outcome recommendation engine  155  with the user&#39;s information and health goals. The client device  120  may be a device worn by the user  110  (e.g., a smart watch and/or a fitness tracker) that automatically collects data about the user&#39;s health characteristics, actions, and environmental conditions. In some embodiments, the user  110  inputs information about themselves to the health outcomes recommendation engine, via the client device  120 . In some embodiments, another device, such as an Internet enabled blood sugar monitor, may couple to the client device  120  and provide information, in this case, the user&#39;s blood sugar, to the health outcome recommendation engine  155 . In some embodiments, user health data can be provided by wearable fitness trackers or monitors, from health or monitoring applications running on a user&#39;s smart device, or from any other information source worn by, used by, or associated with a user. In some embodiments, multiple client devices  120  provide the health recommendation engine with information about the user. The client device  120  presents the recommendations to the user  110  as well, via a user interface displayed on the client device  120 . In some embodiments, the health outcome recommendation engine  155  accesses a subset of the user&#39;s information from an external data source (e.g., environmental conditions at the user&#39;s location may be retrieved from a weather database and/or an air quality tracking system). In some embodiments, the health outcome recommendation engine  155  accesses a subset of the user&#39;s information from a social network profile of the user  110 . 
     The health outcome recommendation engine  155  generates recommendations for the user  110  using a trained machine-learned model  170 . The machine-learned model  170  is stored by the server  160  and trained using a training set of data including information about a plurality of training users  140 . The training users  140  may be people other than the user  110  that use the health outcome recommendation engine. The training set includes, for each training user  140 , health characteristics, actions, environmental conditions, one or more actions taken (such as health products used, exercises performed, and the like) and one or more health outcomes achieved by the training users  140 . The training and application of the machine-learned model  170  is further described with respect to  FIG. 2 . 
     The training user client devices  150  provide information about the plurality of training users  140  to the server  160 , over the network  190 . The training user client devices  150  may be substantially similar to the client devices  120 , and may be, for example, conventional computers or cellphones owned by each of the training users  140 . In some embodiments, the training users  140  self-report a subset of their information to the health outcomes recommendation engine via their training user client device  150 . In some embodiments, each training user&#39;s client device  150  automatically determines a subset of the training user&#39;s information (e.g., by receiving the data from a wearable fitness or health tracking device or from a health monitoring application running on the training user client device) and adds it to the training set. In some embodiments, the training user client devices  150  may automatically report health outcomes achieved by the training users  140 , wherein the health outcomes are data received by the client devices  150 , such as a heart rate, measure of blood pressure, etc. In some embodiments, the training users  140  self-report their health outcomes. 
     The server  160  stores and receives information from the client device  120  and the training user client devices  150 . The server  160  hosts the machine-learned model  170  and the database  180 . The server  160  may be located on a local or remote physical computer and/or may be located within a cloud-based computing system. 
     The database  180  stores information relevant to the recommendation engine. The database  180  stores information about the user  110 , the user&#39;s health goals, and the training set comprising information about the plurality of training users  140 . 
     The network  190  transmits data from the client device  120  and the training user client devices  150  to the server  160  and vice versa. The network  190  may be a local area and/or wide area network using wireless and/or wired communication systems, such as the Internet. In some embodiments, the network  190  transmits data over a single connection (e.g., a data component of a cellular signal, or WiFi, among others) and/or over multiple connections. The network  190  may include encryption capabilities to ensure the security of consumer data. For example, encryption technologies may include secure sockets layer (SSL), transport layer security (TLS), virtual private networks (VPNs), Internet Protocol security (IPsec), etc. 
     Training and Application of Machine-Learned Model 
       FIG. 2  illustrates training and applying the machine-learned model  170 , the machine-learned model  170  configured to provide recommendations for achieving health outcomes, in accordance with one or more embodiments. As described in  FIG. 1 , the machine-learned model  170  takes in information about a user (e.g., the user  110 ) and the user&#39;s health goals, and provides recommendations that will improve the likelihood that the user will achieve the user&#39;s health goals. 
     The machine-learned model  170  is trained using a set of training data (“training set  200 ”). The training set  200  includes training user information  210  (e.g., information about the training users  140 ), training user product information  220  (e.g., information about one or more products used by the training users  140 ), and training user health outcomes  230  (e.g., health outcomes of the training users  140 ). As described with respect to  FIG. 1 , the training user information  210 , the training user product information  220 , and/or the training user health outcomes  230  may be self-reported and/or automatically determined by client devices (e.g., the training user client devices  150 ) coupled to the health outcome recommendation engine  155 . In some embodiments, the health outcome recommendation engine  155  incentivizes training users to provide the training user information  210 , the training user product information  220 , and/or the training user health outcomes  230  via gamification, rewards (such as social network status awards), and/or product offers. For example, a training user may receive an offer on a product if they provide information to the health outcome recommendation engine  155  for one month. 
     The training user information  210  includes information about a plurality of training users (e.g., the training users  140 ). For each training user, the training user information  210  includes at least one of health characteristics of the training user, actions taken by the training user, and an environment of the training user. Health characteristics may be measurements of and/or describe the training user&#39;s age, mental health, productivity, sleep, pollution, sexual and reproductive health, fertility, performance in sports, gastrointestinal microbiome, pain, cardiovascular health, pregnancy, post-natal health, immunity, disposition to and/or state of cancer, chronic inflammation, weight and/or obesity, eating disorders, substance use, access to healthcare, injury, vaccines, HIV and/or AIDS, nervous system, disposition to and/or history of stroke, lung disease, blood health (e.g., blood sugar, blood pressure), and non-communicable diseases (e.g., autoimmune disorders, heart disease, diabetes). The training user&#39;s actions may describe a lifestyle of the user, including a duration and quality of sleep, type and nature of physical activity (e.g. stretching, yoga, cardio, etc.), meditation, type and nature of employment, among others. The environment of the training user may be described by conditions of air quality (e.g., carbon dioxide concentrations, volatile organic compounds), temperature, ultraviolet radiation level, and humidity, among other parameters. In some embodiments, the training user information  210  includes data obtained via the training user&#39;s client device, such as a location of the training user via a GPS receiver and an itinerary of the training user via a calendar coupled to the training user&#39;s client device. For example, the health outcome recommendation engine  155  may determine conditions describing the environment around the training user based on training user&#39;s location via a GPS on the training user&#39;s client device. The health characteristics, actions, and environmental conditions associated with a training user may be recorded at set intervals over a period of time (e.g., every day for  5  weeks, every few hours, once every week). 
     The training user product information  220  includes information about one or more products used by the plurality of training users. In some embodiments, a product may correspond to a subset of the training user information  210 . For example, the training user may consume a sleep aid (e.g., melatonin) to address poor sleep quality. The products included in the training user product information  220  may be nutritional supplements, vitamins, dietary supplements, neutraceutical supplements, topical creams and/or serums, lotions (such as lotions with sun protection factor or SPF), effervescent tablets and/or powders, essential oils, over the counter pharmaceuticals and/or medical devices, and diagnostic tools. 
     The training user health outcomes  230  describe health outcomes associated with the training user. Health outcomes may be associated with one or more health characteristics, actions, or environmental conditions of the training user. In some embodiments, health outcomes may be indirectly and/or directly related to the products used by the training user. Health outcomes may be positive or negative. Examples of positive health outcomes include reduced pain, reduced stress, greater sleep quality, reduced blood pressure, reduced inflammation, and improved energy levels, for example. In some embodiments, health outcomes may be negative side effects due to one or more products used by the user, such as anxiety, pain, nausea, dizziness, and skin conditions, among others. For example, a training user with a cardiovascular condition may react negatively to a particular product promoting anxiety relief. 
     The training users&#39; information in the training set  200  may be considered a part of a positive training set or a negative training set. The positive training set includes health outcomes that positively impact the training user. For example, the training user information  210  training set may include health characteristics that indicate that a training user previously had trouble staying asleep for more than an hour at a time. When they used a sleep aid (e.g., melatonin), as indicated by the training user product information  220 , the training user health outcomes  230  showed a significant increase in the training user&#39;s quality and duration of sleep. Thus, the health outcome associated with the sleep aid was positive for the training user. 
     The negative training set includes health outcomes that negatively impact the training user and/or have no impact on the training user. Continuing the above example, a different training user may react negatively to the sleep aid, resulting in a more disturbed sleep than usual. Thus, the health outcome associated with the sleep aid for a different training user may be negative. In another example, the health outcome may be neutral, neither positively nor negatively affecting the training user. For example, a training user with high blood sugar and decent sleep patterns may not experience any effects from consuming the sleep aid. Accordingly, the training set  200  provides the machine-learned model  170  with information about a training user, information describing the training user, products used by the training user, and associated health outcomes. The training set  200  may be categorized into a positive training set and a negative training set. 
     The health outcome recommendation engine  155  uses supervised or unsupervised machine learning to train the machine-learned model  170  using the positive and/or negative training sets of the training set  200  to enable the machine-learned model to identify correlations and relationships between health outcomes (e.g., an ability or likelihood of a user to meet a health goal) and user information, product use information, action information, and environmental information. Different machine learning techniques may be used in various embodiments, such as linear support vector machine (linear SVM), boosting for other algorithms (e.g., AdaBoost), neural networks, logistic regression, naive Bayes, memory-based learning, random forests, bagged trees, decision trees, boosted trees, or boosted stumps. 
     In one embodiment, the machine-learned model  170  creates a matrix, based on the training set  200 , describing each training user&#39;s overall state of health. The matrix tracks the user information  240  across time, where each row of the matrix corresponds to a health characteristic, action, or environmental condition, and each column corresponds to a point in time. The machine-learned model  170  creates vectors for each product of the training user product information  220 , the vectors identifying the benefits that the product has demonstrated for each health characteristic, action, or environmental condition. Accordingly, the trained machine-learned model  170  identifies relationships between the training user information  210 , the training user product information  220  and the associated training user health outcomes  230  and uses the matrix and generated product vectors to provide recommendations to users. It should be noted that the machine-learned model can be trained using vectors corresponding to actions taken by training users other than product usage according to the principles described herein. 
     The trained machine-learned model  170 , when applied to another user&#39;s (e.g., the user  110 ) information  240  and health goals  250 , outputs recommendations  260  that increase the likelihood of the user achieving the user&#39;s health goals. As described with respect to  FIG. 1 , the user&#39;s information  240  and health goals  250  may be reported to the health outcome recommendation engine  155  by the user via a client device (e.g., the client device  120 ) and/or automatically determined by the client device. The user may be incentivized to provide the user information  240  and the user health goals  250  via gamification, rewards, and/or product offers. 
     The user information  240  includes the user&#39;s health characteristics, actions, and environment, and can include examples substantially similar to those described with respect to the training user. The user health goals  250  may be associated with the user information  240 . For example, the user health goals  250  may include the user&#39;s aim to sleep at least 8 hours per night, exercise 5 times per week, lose at least 5 pounds in 2 months, among others. In some embodiments, the user health goals  250  are associated generally with a health characteristic, action, or environment. For example, the user may set a health goal to reduce a level of stress the user is experiencing. In some embodiments, the user health goals  250  may be generalized across a demographic due to research data. For example, research may show that 8 hours of sleep is necessary for people under the age of 20. Accordingly, the health outcome recommendation engine  155  may automatically set at least one user health goal  250  to achieve 8 hours of sleep each night. 
     Based on the input user information  240 , the trained machine-learned model  170  may generate a matrix that describes the user&#39;s overall state of health. Similar to the matrix built from the training set  200 , the matrix of the user&#39;s state of health includes rows corresponding to a health characteristic, action, or environmental condition, and each column corresponds to a point in time. The machine-learned model  170  calculates a dot product of each product vector and the user matrix; the resultant dot product with the highest value for each health characteristic, action or environmental condition indicates which product has the highest likelihood of improving the respective characteristic. In some embodiments, the machine-learned model  170  recommends the product with the biggest overall impact on the user&#39;s health (e.g., the maximum resultant dot product). Alternatively, the user information  240  may be formatted into any suitable format used required by the machine-learned model  170  (such as the format of the training information used to train the machine-learned model). 
     The recommendations  260  include suggested products and other actions that will help the user achieve the user&#39;s health goals  250 . Recommended products may be similar to the products used by the training users, such as those included in the training user product information. Recommended actions include suggested meditation sessions, social activities, physical activities, stretches, reminders to drink water, and food suggestions, among others. For example, in response to receiving user information  240  with health characteristics that indicate that the user has poor mental health and user health goals  250  of wanting to feel less anxiety, the machine-learned model  170  may provide recommendations  260  prompting the user to go for a walk in a nearby park. In another example, the user information  240  indicates that the user has a poor gastrointestinal microbiome, and the user health goals  250  indicate that the user wants to be able to eat without a gastrointestinal reaction. The machine-learned model  170  outputs recommendations  260  for dietary supplements, such as probiotics, that will assist with achieving the user&#39;s health goals  250 . 
     In some embodiments, the machine-learned model presents recommendations  260  based on the user information  240 , independent of the user health goals  250 . For example, in response to determining that air pollution at the user&#39;s location (e.g., determined via the user&#39;s client device) is at unhealthy levels, the machine-learned model&#39;s recommendations  260  may include products associated with reducing asthma. 
     The recommendations  260  are displayed on the client device, in some embodiments, by modifying a display of the client device. In some embodiments, the client device notifies the user of recommendations  260 . 
     Process for Presenting Recommendations 
       FIG. 3  illustrates an example process for providing a user with recommendations for achieving health outcomes, in accordance with one or more embodiments. A health outcome recommendation engine accesses  310  a training set (e.g., the training set  200 ) associated with a plurality of training users (e.g., the training users  140 ). For each training user, the training set includes information about health characteristics, actions, and environmental conditions, as well as any products used, and associated health outcomes. 
     The health outcome recommendation engine trains  320  a machine-learned model (e.g., the machine-learned model  170 ) with the training set. The machine-learned model determines relationships between the actions taken and products used by training users and their health outcomes. 
     The health outcome recommendation engine receives  330  information about a user (e.g., the user information  240 ) and the user&#39;s health goals (e.g., the user health goals  250 ). The user (e.g., the user  110 ) is distinct from the training users. The user information can include health characteristics, actions, and environmental conditions tracked over a period of time. 
     The health outcome recommendation engine applies  340  the trained machine-learned model to the received user information and health goals. The trained machine-learned model identifies products and/or actions that increase a likelihood of the user achieving the user&#39;s health goals, and outputs  350  the identified products and/or actions as recommendations. 
     The health outcome recommendation engine  360  modifies a display of a client device of the user (e.g., the client device  120 ) to include the recommended products and/or actions that will aid the user in achieving the user&#39;s health goals. 
     In some embodiments, the user presents feedback to the health outcome recommendation engine as to whether the recommended products and/or actions helped achieve the user&#39;s health goals. The presented feedback is added to the training set to improve the machine-learned model&#39;s recommendations, for instance by retraining the machine-learned model. 
     Example User Interface 
       FIGS. 4A-C  illustrate an example user interfaces through which the user may interact with the health outcome recommendation engine, in accordance with one or more embodiments. In some embodiments, the user accesses the health outcome recommendation engine via an application executed on a client device (e.g., the client device  120 ). The client device displays the user interfaces of the health outcome recommendation engine, and enables the user to provide input to and/or interact with the health outcome recommendation engine. 
     In  FIG. 4A , the user interface  400  enables the user to input health goals via a plurality of user input elements  410 . As described above, health goals include, for example, a desire for better sleep, more exercise, and/or a balanced diet. In some embodiments, the user interface  400  allows the user to input custom health goals. The user may opt to automatically import health goals into the health outcome recommendation engine from, for example, another application on the client device or from a wearable fitness device (such as a smart watch) worn by the user. In some embodiments, the health outcome recommendation engine may contact a medical professional associated with the user to determine the user&#39;s health goals. In other embodiments, the medical professional may provide input, via the user input elements  410 , on the user&#39;s health goals. 
     In  FIG. 4B , the user interface  420  enables the user to input information about the user&#39;s health characteristics, activities, and environment via the user input elements  430 . In some embodiments, the user interface  420  includes an import data element  440 . When interacted with, the import data element  440  extracts the user&#39;s information from another application hosted and/or executed on the client device (e.g., a fitness tracking application, a social networking application). In some embodiments, the health outcome recommendation engine automatically receives the user&#39;s information from the client device and/or another application on the client device. 
     In  FIG. 4C , the user interface  450  presents recommendations to the user on actions and/or products that increase a likelihood that the user achieves the input health goals. In  FIG. 4C , the user interface  450  includes user input elements  460  that allow the user to buy the suggested products, share suggested actions and/or products with friends (e.g., via social media), and track progress over time (e.g., by displaying one or more interfaces or visualizations of a user&#39;s progress in achieving one or more health goals). In some embodiments, the user may provide feedback to the health outcome recommendation engine on whether the suggested actions and/or products helped achieve the health goals and/or how likely the user is to follow through on the recommendations, and the user interface  450  can, in response, display new or additional recommendations identified by the machine-learned model to increase the likelihood that the user achieves the identified health goals. 
     Additional Configuration Considerations 
     The foregoing description of the embodiments has been presented for the purpose of illustration; it is not intended to be exhaustive or to limit the patent rights to the precise forms disclosed. Persons skilled in the relevant art can appreciate that many modifications and variations are possible in light of the above disclosure. 
     Some portions of this description describe the embodiments in terms of algorithms and symbolic representations of operations on information. These algorithmic descriptions and representations are commonly used by those skilled in the data processing arts to convey the substance of their work effectively to others skilled in the art. These operations, while described functionally, computationally, or logically, are understood to be implemented by computer programs or equivalent electrical circuits, microcode, or the like. 
     Furthermore, it has also proven convenient at times, to refer to these arrangements of operations as modules, without loss of generality. The described operations and their associated modules may be embodied in software, firmware, hardware, or any combinations thereof. 
     Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described. 
     Embodiments may also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, and/or it may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a non-transitory, tangible computer readable storage medium, or any type of media suitable for storing electronic instructions, which may be coupled to a computer system bus. Furthermore, any computing systems referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability. 
     Embodiments may also relate to a product that is produced by a computing process described herein. Such a product may comprise information resulting from a computing process, where the information is stored on a non-transitory, tangible computer readable storage medium and may include any embodiment of a computer program product or other data combination described herein. 
     Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the patent rights. It is therefore intended that the scope of the patent rights be limited not by this detailed description, but rather by any claims that issue on an application based hereon. Accordingly, the disclosure of the embodiments is intended to be illustrative, but not limiting, of the scope of the patent rights, which is set forth in the following claims.