Patent Publication Number: US-2021166803-A1

Title: Recommending actions for avoidance of food intolerance symptoms

Description:
BACKGROUND OF THE INVENTION 
     The present invention relates generally to the field of machine learning, and more particularly to a reinforcement learning model for recommending actions for avoiding food intolerance symptoms. 
     Reinforcement learning is an area of machine learning in which a model or algorithm is trained to take a suitable action to maximize reward in a particular situation. Reinforcement learning is employed by various software and machines to find the best possible behavior or path to take in a specific situation. Reinforcement learning differs from supervised learning. In supervised learning, the training data includes the correct answers. In reinforcement learning, there is no answer, but the reinforcement agent decides what to do to perform the given task. In the absence of training dataset, the model is bound to learn from experience. Reinforcement learning involves making decisions sequentially. In other words, the output depends on the state of the current input and the next input depends on the output of the previous input. 
     The internet of things (IoT) is the internetworking of physical devices (also referred to as “connected devices” and “smart devices”), vehicles, buildings, and other items, embedded with electronics, software, sensors, actuators, and network connectivity that enable these objects to collect and exchange data. The IoT allows objects to be sensed and/or controlled remotely across existing network infrastructure, creating opportunities for more direct integration of the physical world into computer-based systems, and resulting in improved efficiency, accuracy, and economic benefit in addition to reduced human intervention. Each thing is uniquely identifiable through its embedded computing system but is able to interoperate within the existing Internet infrastructure. 
     Food intolerance is a detrimental reaction, often delayed, to a food, beverage, food additive, or compound found in foods that produces symptoms in one or more body organs and systems, but generally refers to a difficulty in digesting certain foods. Foods most commonly associated with food intolerance include dairy products, grains that contain gluten, and foods that cause intestinal distress. Food intolerance reactions can include pharmacologic, metabolic, and gastro-intestinal responses to foods or food compounds. Symptoms of food intolerance may include, but are not limited to, digestive ailments, migraines, headaches, cough, runny nose, feeling under the weather, and hives. 
     SUMMARY 
     Embodiments of the present invention disclose a method, a computer program product, and a system for recommending actions for avoiding food intolerance symptoms. The method may include one or more computer processors receiving data, where the received data includes data associated with a food item viewed by a first user, data associated with the first user, and data associated with an environment of the first user. One or more computer processors determine a first health condition of the first user. One or more computer processors predict a first food intolerance reaction of the first user to the viewed food item based on the received data and the determined first health condition. One or more computer processors determine a first action recommendation for the first user corresponding to the first predicted food intolerance reaction. One or more computer processors determine a first action recommendation for the first user corresponding to the first predicted food intolerance reaction. One or more computer processors present the first action recommendation to the first user. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a functional block diagram illustrating a distributed data processing environment, in accordance with an embodiment of the present invention; 
         FIG. 2  is a flowchart depicting operational steps of a food intolerance system, on a server computer within the distributed data processing environment of  FIG. 1 , for training a food intolerance model, in accordance with an embodiment of the present invention; 
         FIG. 3  is a flowchart depicting operational steps of the food intolerance system, on the server computer within the distributed data processing environment of  FIG. 1 , for making recommendations regarding food intake, in accordance with an embodiment of the present invention; and 
         FIG. 4  depicts a block diagram of components of the server computer executing the food intolerance system within the distributed data processing environment of  FIG. 1 , in accordance with an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     A food intolerance, or a food sensitivity, occurs when a person&#39;s digestive system cannot tolerate certain foods and reacts with one or more physical symptoms as a result of the person consuming the food, thereby possibly restricting a person&#39;s food selections. In addition to intolerance of the food itself, a person&#39;s food intolerance may be affected by a current health condition, activities performed before or after consuming the food, weather conditions, and compatibility of food combinations, i.e., eating certain foods together or in a particular sequence. 
     Embodiments of the present invention recognize that providing a person with a recommendation to eat or not eat a selected food based on a knowledge corpus can prevent illness due to food intolerance. Embodiments of the present invention also recognize that efficiency may be gained by receiving data corresponding to a food selection via one or more sensors operably coupled to a user and/or operably coupled to one or more Internet of Things (IoT) devices to feed into a reinforcement learning model for predicting food intolerance and displaying an action recommendation to the user. Implementation of embodiments of the invention may take a variety of forms, and exemplary implementation details are discussed subsequently with reference to the Figures. 
       FIG. 1  is a functional block diagram illustrating a distributed data processing environment, generally designated  100 , in accordance with one embodiment of the present invention. The term “distributed” as used herein describes a computer system that includes multiple, physically distinct devices that operate together as a single computer system.  FIG. 1  provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environment may be made by those skilled in the art without departing from the scope of the invention as recited by the claims. 
     Distributed data processing environment  100  includes server computer  104 , client computing device  112 , and internet of things (IoT) platform  118 , all interconnected over network  102 . Network  102  can be, for example, a telecommunications network, a local area network (LAN), a wide area network (WAN), such as the Internet, or a combination of the three, and can include wired, wireless, or fiber optic connections. Network  102  can include one or more wired and/or wireless networks capable of receiving and transmitting data, voice, and/or video signals, including multimedia signals that include voice, data, and video information. In general, network  102  can be any combination of connections and protocols that will support communications between server computer  104 , client computing device  112 , IoT platform  118 , and other computing devices (not shown) within distributed data processing environment  100 . 
     Server computer  104  can be a standalone computing device, a management server, a web server, a mobile computing device, or any other electronic device or computing system capable of receiving, sending, and processing data. In other embodiments, server computer  104  can represent a server computing system utilizing multiple computers as a server system, such as in a cloud computing environment. In another embodiment, server computer  104  can be a laptop computer, a tablet computer, a netbook computer, a personal computer (PC), a desktop computer, a personal digital assistant (PDA), a smart phone, or any programmable electronic device capable of communicating with client computing device  112 , IoT platform  118 , and other computing devices (not shown) within distributed data processing environment  100  via network  102 . In another embodiment, server computer  104  represents a computing system utilizing clustered computers and components (e.g., database server computers, application server computers, etc.) that act as a single pool of seamless resources when accessed within distributed data processing environment  100 . Server computer  104  includes food intolerance system  106  and database  110   1-N . Server computer  104  may include internal and external hardware components, as depicted and described in further detail with respect to  FIG. 4 . 
     Food intolerance system  106  uses a reinforcement learning model (not shown), referred to herein as a food intolerance model, to determine one or more actions for a user to take in order to avoid symptoms of food intolerance when the user considers making a food selection. During a training phase, food intolerance system  106  receives data regarding a potential food selection by a user. Food intolerance system  106  also receives data specific to a user and any activities performed, or planned to be performed, by the user which may relate to the food selection. Food intolerance system  106  also receives data corresponding to the environment of the user. Food intolerance system  106  determines a health condition of the user. Based on the received data and the health condition of the user, food intolerance system  106  determines an action recommendation for the user with respect to the food selection. Food intolerance system  106  receives a user action and a resulting health condition. Food intolerance system  106  relates the health condition to the corresponding received data and user action. Food intolerance system  106  repeats the above-mentioned actions for a threshold number of iterations, at which time food intolerance system  106  determines an accuracy of the recommendations. If the accuracy of the recommendations, i.e., whether the recommendations would have resulted in a positive outcome for the user, exceeds a threshold, then food intolerance system  106  generates a food intolerance model. If the accuracy of the recommendations does not exceed the threshold, then food intolerance system  106  continues to process data, recommendations, and actions until the accuracy is at an acceptable level. Once trained, food intolerance system  106  receives data regarding a potential food selection by a user, along with associated user data, environmental data, and data specific to any activities performed, or planned to be performed, by the user which may relate to the food selection. Based on the received data, and using the food intolerance model, food intolerance system  106  predicts a food intolerance by the user associated with the potential food selection. Food intolerance system  106  determines a recommended action for the user and displays the recommendation to the user. Food intolerance system  106  receives a user action and a health condition result of the user action. Food intolerance system  106  stores the user action and health condition result of the user action with the corresponding user data, environmental data, and food-related activity, and feeds the stored data into the food intolerance model in order to continue to train the model for increasing accuracy of recommendations. Food intolerance system  106  includes knowledge corpus  108 . Food intolerance system  106  is depicted and described in further detail with respect to  FIG. 2  and  FIG. 3 . 
     Knowledge corpus  108  is a data repository for food intolerance system  106 . In the depicted embodiment, knowledge corpus  108  is integrated within food intolerance system  106 . In another embodiment, knowledge corpus  108  may reside elsewhere on server computer  104  or elsewhere within distributed data processing environment  100 , provided food intolerance system  106  has access to knowledge corpus  108 . In an embodiment, knowledge corpus  108  can be implemented with any type of storage device capable of storing data and configuration files that can be accessed and utilized by food intolerance system  106 , such as a database server, a hard disk drive, or a flash memory. Knowledge corpus  108  stores data received or retrieved by food intolerance system  106  for determining a food-related action recommendation for a user. Knowledge corpus  108  also stores the recommendations and the user actions in response to the recommendations, and the results of the user actions. In addition, knowledge corpus  108  stores the relationship determined by food intolerance system  106  between the received/retrieved data, the recommendations, the user actions, and the result of the actions. 
     Database  110   1-N , herein database(s)  110 , are each a repository for data used by food intolerance system  106 . As used herein, N represents a positive integer, and accordingly the number of scenarios implemented in a given embodiment of the present invention is not limited to those depicted in  FIG. 1 . Database(s)  110  may each represent one or more databases. In the depicted embodiment, database(s)  110  reside on server computer  104 . In another embodiment, database(s)  110  may each reside elsewhere within distributed data processing environment  100  provided food intolerance system  106  has access to database(s)  110 . A database is an organized collection of data. Database(s)  110  can each be implemented with any type of storage device capable of storing data and configuration files that can be accessed and utilized by food intolerance system  106 , such as a database server, a hard disk drive, or a flash memory. Database(s)  110  store food data retrieved by food intolerance system  106  in an effort to identify a user&#39;s food selection and the associated properties and ingredients. 
     Client computing device  112  can be one or more of a laptop computer, a tablet computer, a smart phone, smart watch, a smart speaker, or any programmable electronic device capable of communicating with various components and devices within distributed data processing environment  100 , via network  102 . Client computing device  112  may be a wearable computer. Wearable computers are miniature electronic devices that may be worn by the bearer under, with, or on top of clothing, as well as in or connected to glasses, hats, or other accessories. Wearable computers are especially useful for applications that require more complex computational support than merely hardware coded logics. In one embodiment, the wearable computer may be in the form of a head mounted display. The head mounted display may take the form-factor of a pair of glasses. In an embodiment, the glasses are capable of displaying augmented reality objects in the field of view of the user. In an embodiment, the wearable computer may be in the form of a smart watch. In an embodiment, client computing device  112  may be integrated into a vehicle of the user. In general, client computing device  112  represents one or more programmable electronic devices or combination of programmable electronic devices capable of executing machine readable program instructions and communicating with other computing devices (not shown) within distributed data processing environment  100  via a network, such as network  102 . Client computing device  112  includes an instance of food intolerance application  114  and sensor  116   1-N . 
     Food intolerance application  114  provides an interface between food intolerance system  106  on server computer  104  and a user of client computing device  112 . In one embodiment, food intolerance application  114  is mobile application software. Mobile application software, or an “app,” is a computer program designed to run on smart phones, tablet computers and other mobile devices. In one embodiment, food intolerance application  114  may be a graphical user interface (GUI) or a web user interface (WUI) and can display text, documents, web browser windows, user options, application interfaces, and instructions for operation, and include the information (such as graphic, text, and sound) that a program presents to a user and the control sequences the user employs to control the program. Food intolerance application  114  enables a user of client computing device  112  to provide preferences and health information to food intolerance system  106  to continually train food intolerance system  106  to recommend food intolerance related actions. Food intolerance application  114  also enables the user of client computing device  112  to receive food intolerance related recommendations from food intolerance system  106 . 
     Internet of things (IoT) platform  118  is a suite of components that enable a) deployment of applications that monitor, manage, and control connected devices and sensors; b) remote data collection from connected devices; and c) independent and secure connectivity between devices. The components may include, but are not limited to, a hardware architecture, an operating system, or a runtime library (not shown). In the depicted embodiment, IoT platform  118  includes sensor  120   1-N . In another embodiment, IoT platform  118  may include a plurality of other connected computing devices. For example, IoT platform  118  may include home security devices, electronic assistants, etc. In another example, IoT platform  118  may include a home climate control system or various kitchen appliances. In a further example, IoT platform  118  may be a restaurant or grocery store information system. 
     A sensor is a device that detects or measures a physical property and then records or otherwise responds to that property, such as vibration, chemicals, radio frequencies, environment, weather, humidity, light, etc. Sensor  116   1-N  and sensor  120   1-N , herein sensor(s)  116  and sensor(s)  120 , detect a plurality of attributes of a user of food intolerance application  114  and of food and food venues in a plurality of locations. As used herein, N represents a positive integer, and accordingly the number of scenarios implemented in a given embodiment of the present invention is not limited to those depicted in  FIG. 1 . Sensor(s)  116  and sensor(s)  120  may be one or more of a plurality of types of camera, including, but not limited to, pin-hole, stereo, omni-directional, non-central, infrared, video, digital, three dimensional, panoramic, filter-based, wide-field, narrow-field, telescopic, microscopic, etc. In some embodiments, sensor(s)  116  and sensor(s)  120  include any device capable of imaging a portion of the electromagnetic spectrum. If client computing device  112  is a wearable device, then sensor(s)  116  may include biometric sensors for detecting the physical condition of the user, such as blood pressure, heart rate, respiratory rate, calories burned, calories consumed, pulse, oxygen levels, blood oxygen level, glucose level, blood pH level, salinity of user perspiration, skin temperature, galvanic skin response, electrocardiography (ECG or EKG) data, body temperature, eye tracking data, etc. A biometric sensor may also be an ingestible gut sensor for detecting issues in the user&#39;s digestive tract. Sensor(s)  116  and sensor(s)  120  may be one or more of a plurality of types of microphone for detecting speech and other audible sounds. Sensor(s)  116  and sensor(s)  120  may be able to detect weather conditions, such as air temperature, relative humidity, presence and type of precipitation, wind speed, etc., as food intolerance may depend on the weather conditions. Sensor(s)  116  and sensor(s)  120  may be global positioning system (GPS) sensors or other sensors that can determine a location of client computing device  112 . Sensor(s)  116  and sensor(s)  120  may also track a user&#39;s mobility pattern, for example, whether the user lies down after eating a particular food. Sensor(s)  116  may be integrated into the vehicle of the user. 
     The present invention may contain various accessible data sources, such as database(s)  110 , that may include personal data, content, or information the user wishes not to be processed. Personal data includes personally identifying information or sensitive personal information as well as user information, such as tracking or geolocation information. Processing refers to any, automated or unautomated, operation or set of operations such as collection, recording, organization, structuring, storage, adaptation, alteration, retrieval, consultation, use, disclosure by transmission, dissemination, or otherwise making available, combination, restriction, erasure, or destruction performed on personal data. Food intolerance system  106  and food intolerance application  114  enable the authorized and secure processing of personal data. Food intolerance system  106  and food intolerance application  114  provide informed consent, with notice of the collection of personal data, allowing the user to opt in or opt out of processing personal data. Consent can take several forms. Opt-in consent can impose on the user to take an affirmative action before personal data is processed. Alternatively, opt-out consent can impose on the user to take an affirmative action to prevent the processing of personal data before personal data is processed. Food intolerance system  106  and food intolerance application  114  provide information regarding personal data and the nature (e.g., type, scope, purpose, duration, etc.) of the processing. Food intolerance system  106  and food intolerance application  114  provide the user with copies of stored personal data. Food intolerance system  106  and food intolerance application  114  allow the correction or completion of incorrect or incomplete personal data. Food intolerance system  106  and food intolerance application  114  allow the immediate deletion of personal data. 
       FIG. 2  is a flowchart depicting operational steps of food intolerance system  106 , on server computer  104  within distributed data processing environment  100  of  FIG. 1 , for training a food intolerance model, in accordance with an embodiment of the present invention. 
     Food intolerance system  106  receives food data (step  202 ). In an embodiment, as a user views and considers selecting a food, for example scanning a menu in a restaurant, looking at a buffet table, viewing shelves in a grocery store, etc., food intolerance system  106  receives an image of the selected food via food intolerance application  114 . In another embodiment, food intolerance system  106  may receive food data by scanning the text of a food menu and analyzing the text. Food intolerance system  106  receives food data by matching the image or text associated with the selected food with the food identification and data associated with the food in one or more of database(s)  110 . Data associated with a food selection includes, but is not limited to, the type of food, the quantity of food chosen, the ingredients of the food, one or more properties of the food, frequency of consumption of the food, the quality of the food, one or more certifications associated with the food, such as whether the food is “certified organic,” etc. In an embodiment, food data can also include the time, sequence, and/or frequency of other food items consumed by the user within a threshold duration of time in order to enable food intolerance system  106  to observe any correlation of a food intolerance to an interaction between different foods. For example, a user may avoid discomfort from consuming a cup of coffee if the user consumes a glass of water first. In an embodiment in which client computing device  112  is a wearable computer in the form of glasses operably coupled to a camera, food intolerance system  106  receives food data by capturing one or more images of the food selection within the gaze of the user and retrieving any data stored in database(s)  110  associated with the food selection. In the embodiment, if the user is viewing items in a grocery store, food intolerance system  106  may receive food data by instructing client computing device  112  to scan a QR code or a bar code on the food package or on the shelf In an embodiment in which client computing device  112  is a smart phone, food intolerance system  106  may instruct the user to capture one or more images of the food and food packaging, as well as scanning a QR code or a bar code on the food package or on the shelf with client computing device  112  and retrieving any data stored in database(s)  110  associated with the food selection. In another embodiment, food intolerance system  106  may receive images of the food selection from one or more of sensor(s)  120 . In an embodiment, food intolerance system  106  stores the received food data in knowledge corpus  108 . In an embodiment where sensor(s)  116  can track hand movements or gestures of the user, food intolerance system  106  may initiate food data gathering based on receiving hand movements or gestures that indicate the user is about to select a food item. 
     Food intolerance system  106  receives user data (step  204 ). In an embodiment, food intolerance system  106  receives user data via food intolerance application  114 . In an embodiment, a user completes a user profile in food intolerance application  114 . The user data entered in the user profile may include physical data about a user, for example, the age, height, and weight of a user, and a medical history of a user which may include medical test results. In an embodiment, a user may enter specifics about food intolerances and/or food allergies into food intolerance application  114  to be included in user data. Additional user data may include other dietary restrictions, such as due to a health concern. In addition, user data may include food likes and dislikes of a user. In an embodiment, food intolerance system  106  may receive user data via crowdsourcing from, for example, one or more social networks. For example, food intolerance system  106  may determine that a number of users were ill after eating at a particular restaurant. In an embodiment, food intolerance system  106  stores the received user data in knowledge corpus  108 . 
     Food intolerance system  106  receives environmental data (step  206 ). In an embodiment, food intolerance system  106  receives environmental data from sensor(s)  116  or sensor(s)  120 , or both. For example, food intolerance system  106  may receive current weather conditions, including, but not limited to, a temperature, a wind speed, a presence and type of precipitation, a sky condition, such as whether it is sunny or cloudy, etc. Food intolerance system  106  may also receive data indicating the current location of client computing device  112 . For example, if one or more of sensor(s)  116  include a GPS sensor, then food intolerance system  106  may determine the physical location of client computing device  112 . In another example, food intolerance system  106  may determine whether the user is in a grocery store or in a restaurant by analyzing images received from sensor(s)  116  and/or sensor(s)  120 . In an embodiment, food intolerance system  106  stores the received environmental data in knowledge corpus  108 . 
     Food intolerance system  106  determines food-related activity data (step  208 ). The sequence of activities a user participates in before or after consuming a particular food can impact a food intolerance response. Food intolerance system  106  determines one or more activities of the user that may correspond to or impact a food intolerance response. In an embodiment, food intolerance system  106  retrieves data from an electronic calendar on client computing device  112  (not shown) which indicates one or more activities the user participated in prior to the food selection or activities the user plans to participate in following the food selection. For example, food intolerance system  106  may determine that the user just completed a work day where the user was in several meetings, and therefore sedentary for most of the day. In another example, food intolerance system  106  may determine that after the food selection activity, the user is going to participate in a vigorous physical activity. In another embodiment, food intolerance system  106  may determine a prior physical activity by receiving data from one or more of sensor(s)  116 . For example, food intolerance system  106  may detect an elevated heart rate or perspiration which indicates the user just completed a physical activity. In a further embodiment, food intolerance system  106  may determine a prior or future activity of the user by receiving one or more images of the user from one or more or sensor(s)  116  and/or sensor(s)  120 . For example, food intolerance system  106  may receive an image which depicts the clothing the user is wearing which may indicate a prior or future activity. In an embodiment, the food-related activity may also be a meal or snack the user had previously. In an embodiment, the food-related activity may also be an adverse reaction to a food the user ingested prior to the current food selection. In yet another embodiment, food intolerance system  106  may determine a food-related activity by retrieving data associated with the user from one or more social networks. In an embodiment, food intolerance system  106  stores the determined food-related activity data in knowledge corpus  108 . 
     Food intolerance system  106  determines current health condition (step  210 ). In an embodiment, food intolerance system  106  may receive user data from one or more of sensor(s)  116  that indicates the current health conditions of the user. For example, food intolerance system  106  may receive biometric data, including, but not limited to, blood pressure, heart rate, respiratory rate, calories burned, calories consumed, pulse, oxygen levels, blood oxygen level, glucose level, blood pH level, salinity of user perspiration, skin temperature, galvanic skin response, electrocardiography (ECG or EKG) data, body temperature, eye tracking data, mobility data, etc. In another embodiment, food intolerance system  106  may receive current health conditions from one or more of sensor(s)  120 . For example, food intolerance system  106  may receive images of a user that depict the physical condition of the user, such as whether the user is sweating or shivering. In another example, food intolerance system  106  may receive images of a user that depict a change in the user&#39;s mobility, i.e., the user lies down. In an embodiment where one or more of sensor(s)  116  and/or sensor(s)  120  is a microphone, food intolerance system  106  may receive current health condition by detecting speech by the user and using one or more natural language processing (NLP) techniques to understand what the user is saying. For example, if the user states “I have a bad stomachache,” then food intolerance system  106  determines the user&#39;s current health condition from the speech analysis. In an embodiment, food intolerance system  106  stores the determined health condition data in knowledge corpus  108 . 
     Food intolerance system  106  determines an action recommendation (step  212 ). Based on the received food data, user data, and environmental data, and on the one or more determined food-related activities and the determined current health condition of the user, food intolerance system  106  determines an action recommendation corresponding to the food item the user is viewing or has selected. In an embodiment, food intolerance system  106  retrieves the data from knowledge corpus  108 . As data is accumulated and knowledge corpus  108  grows, food intolerance system  106  begins a process of reinforcement learning in order to determine an action to recommend to the user. In an embodiment, the recommended action is either to eat the selected food or to not eat the selected food. Over time, based on the available data, food intolerance system  106  determines which foods, or combinations of foods, or combinations of foods with food-related activities, or combinations of food with one or more weather conditions, can result in an adverse effect on the user. In an embodiment, food intolerance system  106  stores the determined action recommendations in knowledge corpus  108 . 
     Food intolerance system  106  receives user action (step  214 ). Using data received from sensor(s)  116  and/or sensor(s)  120 , food intolerance system  106  receives the user action regarding the food selection. For example, food intolerance system  106  may receive one or more images of the user placing the food in a shopping cart or on a plate. In another example, food intolerance system  106  may receive an image of the user putting the food back on the grocery store shelf. In a further example, food intolerance system  106  may receive an indication that the user ate the selected food. In an embodiment, food intolerance system  106  may receive the user action when the user inputs the action in food intolerance application  114 . In an embodiment, food intolerance system  106  stores the received user action data in knowledge corpus  108 . 
     Food intolerance system  106  receives health condition result (step  216 ). In an embodiment, food intolerance system  106  receives an updated health condition of the user in response to the user action. In one embodiment, food intolerance system  106  waits for a threshold duration of time to pass before receiving the health condition result in order to take into account a delayed response a food selection might have on a user&#39;s health. For example, food intolerance system  106  may receive the health condition result a half hour after the user action if the user action was to select food from a buffet and place the selected food on a plate. Food intolerance system  106  may receive the health condition result in a plurality of ways, as discussed with respect to step  210 . If the user ate the selected food, then food intolerance system  106  determines whether the health of the user was positively or adversely affected. For example, food intolerance system  106  can determine whether the user grimaced and rubbed the user&#39;s stomach after eating the food. If the user did not eat the food, then food intolerance system  106  determines whether the health of the user was positively or adversely affected. For example, if the user appeared ill prior to the food selection and the user did not eat the selected food, then food intolerance system  106  can determine whether or not the user&#39;s health improved. In an embodiment, food intolerance system  106  stores the received health condition result in knowledge corpus  108 . Food intolerance system  106  uses the determination of whether the user was positively or adversely affected by an action recommendation in the reinforcement learning process by “rewarding” a positive outcome and “penalizing” a negative outcome, enabling optimization for the reward function. 
     Food intolerance system  106  relates health condition result to received data and user action (step  218 ). In an embodiment, food intolerance system  106  creates or updates an existing matrix of food data, user data, environmental data, corresponding food-related activities, and the health condition of the user before and after the food selection, with the determined action recommendation and user action in an effort to predict the effect a recommended action has in response to a food selection. For example, food intolerance system  106  may determine how the user&#39;s health condition changes as a result of eating a particular food. In another example, food intolerance system  106  may determine how a change in health condition is related to a quantity of food intake. In a further example, food intolerance system  106  may determine how a change in health condition is related to a sequence of food intake, such as the user avoiding discomfort from consuming a cup of coffee when the user consumes a glass of water first. In yet another example, food intolerance system  106  may determine how a user&#39;s medical condition is related to a food intake-based illness. In another example, food intolerance system  106  may determine how weather is related to a food intake-based illness. In general, food intolerance system  106  determines which foods can create a health problem for a user. In an embodiment, food intolerance system  106  relates this data within knowledge corpus  108 . 
     Food intolerance system  106  determines whether a number of iterations exceeds a threshold (decision block  220 ). In an embodiment, a system administrator pre-defines a quantity of iterations of steps  202  through  218  for food intolerance system  106  to complete in order to ensure that food intolerance system  106  has enough data to generate a food intolerance model with required accuracy. 
     If food intolerance system  106  determines the number of iterations does not exceed a threshold (“no” branch, decision block  220 ), then food intolerance system  106  returns to step  202  to increase the number of iterations through the food intolerance model training process. 
     If food intolerance system  106  determines the number of iterations exceeds a threshold (“yes” branch, decision block  220 ), then food intolerance system  106  determines whether the recommendation accuracy exceeds a threshold (decision block  222 ). Food intolerance system  106  analyzes the previously recommended actions from each iteration of the food intolerance model training process with respect to the actual actions taken by the user to determine whether the recommended actions would have produced a desired outcome for the user. In an embodiment, the measure of accuracy equals the number of actions that would have produced desirable results divided by the total number of determined action recommendations. In an embodiment, a system administrator pre-defines the threshold for accuracy. For example, the threshold may be ninety percent, such that if the accuracy is greater than ninety percent, then the accuracy exceeds the threshold. 
     If food intolerance system  106  determines the recommendation accuracy does not exceed a threshold (“no” branch, decision block  222 ), then food intolerance system  106  returns to step  202  to increase the number of iterations through the food intolerance model training process. 
     If food intolerance system  106  determines the recommendation accuracy exceeds a threshold (“yes” branch, decision block  222 ), then food intolerance system  106  generates a food intolerance model (step  224 ). Once the accuracy of the recommendations is sufficient, food intolerance system  106  generates a food intolerance model. Food intolerance system  106  feeds the gathered feature data, as described with respect to step  218 , into the food intolerance model. In an embodiment, food intolerance system  106  bases the food intolerance model on the data in knowledge corpus  108 . Initially, the food intolerance model is a baseline model because the data in knowledge corpus  108  is gathered from a variety of general users and from crowdsourcing. As will be discussed with respect to  FIG. 3 , as each additional specific user adopts the use of food intolerance application  114 , food intolerance system  106  retrieves data specific to each user, and, through learning transfer, food intolerance system  106  creates and trains a specific version of the food intolerance model for each user by removing a general user classification and providing a user specific classification. 
       FIG. 3  is a flowchart depicting operational steps of food intolerance system  106 , on server computer  104  within distributed data processing environment  100  of  FIG. 1 , for making recommendations regarding food intake, in accordance with an embodiment of the present invention. 
     Food intolerance system  106  receives food data (step  302 ). Once food intolerance system  106  completes initial training of the food intolerance model using a variety of users, a specific user can adopt food intolerance system  106  by using food intolerance application  114 . Food intolerance system  106  receives food data from the specific user as discussed with respect to step  202  of  FIG. 2 . 
     Food intolerance system  106  receives user data (step  304 ). Food intolerance system  106  receives user data from the specific user as discussed with respect to step  204  of  FIG. 2 . 
     Food intolerance system  106  receives environmental data (step  306 ). Food intolerance system  106  receives environmental data associated with the specific user as discussed with respect to step  206  of  FIG. 2 . 
     Food intolerance system  106  receives food-related activity data (step  308 ). Food intolerance system  106  receives food-related activity data associated with the specific user as discussed with respect to step  208  of  FIG. 2 . 
     Food intolerance system  106  determines a current health condition (step  310 ). Food intolerance system  106  determines the current health condition of the specific user as discussed with respect to step  210  of  FIG. 2 . 
     Food intolerance system  106  predicts food intolerance (step  312 ). Food intolerance system  106  feeds the received food data, received user data, received environmental data, and received food-related activity data into the food intolerance model to predict whether the user may experience food intolerance symptoms as a result of eating the selected food. In an embodiment, food intolerance system  106  may predict the timing of the food intolerance symptoms. For example, food intolerance system  106  may determine that the user will begin experiencing food intolerance symptoms within an hour of consuming the food. In another embodiment, food intolerance system  106  may predict a relationship between the user consuming the selected food and the next activity the user plans to perform. For example, if food intolerance system  106  determines the food intolerance is exercise-dependent, then food intolerance system  106  may predict that the user will experience food intolerance symptoms if the user rides a bicycle within an hour of consuming the selected food. 
     Food intolerance system  106  determines an action recommendation (step  314 ). Based on the information in knowledge corpus  108 , the data received in steps  302  through  308 , and the predicted food intolerance, food intolerance system  106  determines an action recommendation. In one embodiment, the recommendation is either eat the food or do not eat the food. In another embodiment, the recommendation may include one or more caveats. For example, the recommendation may be do not eat the food until after exercise. In another example, the recommendation may be to eat the food before the temperature outside reaches 80 degrees Fahrenheit. In a further example, the recommendation may be to allow a time gap between the previous meal and consuming the selected food. In yet another example, the recommendation may be for a quantity of food to eat. 
     Food intolerance system  106  presents the action recommendation to the user (step  316 ). In an embodiment where client computing device  112  is a head mounted display in the form of glasses, food intolerance system  106  displays the action recommendation as augmented reality in the field of view of the user. For example, food intolerance system  106  may display text such as “Do not eat the meatloaf! Its ingredients include eggs.” In another example, food intolerance system  106  may overlay text or symbols over various foods in the view of the user which indicate which foods to eat and which foods to avoid, such as a circle around recommended foods to eat and an “X” over foods to avoid. In an embodiment where client computing device  112  is a smart phone, food intolerance system  106  displays the action recommendation on the screen of the smart phone. In an embodiment where client computing device  112  is a smart watch, food intolerance system  106  displays the action recommendation on the screen of the smart watch. In another embodiment, food intolerance system  106  may display the action recommendation by sending a text message to client computing device  112  for the user to read. In a further embodiment, food intolerance system  106  may display the action recommendation via food intolerance application  114 . In an embodiment, in addition to the action recommendation, food intolerance system  106  may display, for example, food ingredients, food properties, potential food intolerance symptoms, food-related activities to avoid, food-related activities to engage in, timing of food consumption, sequence of food consumption, quantity of food consumption, combinations of food consumption, etc. In an embodiment where client computing device  112  includes a speaker, food intolerance system  106  may speak the action recommendation to the user. In another embodiment, food intolerance system  106  may sound an alarm, such as a beeping noise, to alert the user to a recommendation. 
     Food intolerance system  106  receives the user action and health condition result (step  318 ). Food intolerance system  106  receives the user action regarding the food selection, in light of the action recommendation, as discussed with respect to step  214  of  FIG. 2 . Food intolerance system  106  also receives the health condition of the user, as a result of taking or not taking the action recommendation, as discussed with respect to step  216  of  FIG. 2 . 
     Food intolerance system  106  stores the user action and health condition result with corresponding data and action recommendation (step  320 ). In an embodiment, food intolerance system  106  stores the received food data, the received user data, the received environmental data, the received food-related activity data, as well as the determined current health condition, with the predicted food intolerance symptoms, the recommended action, the user action and the result of the user action. In an embodiment, food intolerance system  106  stored the above described information in knowledge corpus  108 . 
     Food intolerance system  106  feeds the stored data into the food intolerance model (step  322 ). As part of the learning transfer process of reinforcement learning, food intolerance system  106  feeds any newly acquired data and information relating to the specific user&#39;s food intolerance into the food intolerance model such that the food intolerance model continues to learn and increasingly improves the accuracy of food intolerance predictions and action recommendations, thereby personalizing a food intolerance model to the specific user. Learning transfer models have the benefit of often requiring less than ten percent of the data and code to fit a new domain or specialized model. 
       FIG. 4  depicts a block diagram of components of server computer  104  within distributed data processing environment  100  of  FIG. 1 , in accordance with an embodiment of the present invention. It should be appreciated that  FIG. 4  provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments can be implemented. Many modifications to the depicted environment can be made. 
     Server computer  104  can include processor(s)  404 , cache  414 , memory  406 , persistent storage  408 , communications unit  610 , input/output (I/O) interface(s)  412  and communications fabric  402 . Communications fabric  402  provides communications between cache  414 , memory  406 , persistent storage  408 , communications unit  410 , and input/output (I/O) interface(s)  412 . Communications fabric  402  can be implemented with any architecture designed for passing data and/or control information between processors (such as microprocessors, communications and network processors, etc.), system memory, peripheral devices, and any other hardware components within a system. For example, communications fabric  402  can be implemented with one or more buses. 
     Memory  406  and persistent storage  408  are computer readable storage media. In this embodiment, memory  406  includes random access memory (RAM). In general, memory  406  can include any suitable volatile or non-volatile computer readable storage media. Cache  414  is a fast memory that enhances the performance of processor(s)  404  by holding recently accessed data, and data near recently accessed data, from memory  406 . 
     Program instructions and data used to practice embodiments of the present invention, e.g., food intolerance system  106  and database(s)  110 , are stored in persistent storage  408  for execution and/or access by one or more of the respective processor(s)  404  of server computer  104  via cache  414 . In this embodiment, persistent storage  408  includes a magnetic hard disk drive. Alternatively, or in addition to a magnetic hard disk drive, persistent storage  408  can include a solid-state hard drive, a semiconductor storage device, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, or any other computer readable storage media that is capable of storing program instructions or digital information. 
     The media used by persistent storage  408  may also be removable. For example, a removable hard drive may be used for persistent storage  408 . Other examples include optical and magnetic disks, thumb drives, and smart cards that are inserted into a drive for transfer onto another computer readable storage medium that is also part of persistent storage  408 . 
     Communications unit  410 , in these examples, provides for communications with other data processing systems or devices, including resources of client computing device  112  and IoT platform  118 . In these examples, communications unit  410  includes one or more network interface cards. Communications unit  410  may provide communications through the use of either or both physical and wireless communications links. Food intolerance system  106 , database(s)  110 , and other programs and data used for implementation of the present invention, may be downloaded to persistent storage  408  of server computer  104  through communications unit  410 . 
     I/O interface(s)  412  allows for input and output of data with other devices that may be connected to server computer  104 . For example, I/O interface(s)  412  may provide a connection to external device(s)  416  such as a keyboard, a keypad, a touch screen, a microphone, a digital camera, and/or some other suitable input device. External device(s)  416  can also include portable computer readable storage media such as, for example, thumb drives, portable optical or magnetic disks, and memory cards. Software and data used to practice embodiments of the present invention, e.g., food intolerance system  106  and database(s)  110 , can be stored on such portable computer readable storage media and can be loaded onto persistent storage  408  via I/O interface(s)  412 . I/O interface(s)  412  also connect to a display  418 . 
     Display  418  provides a mechanism to display data to a user and may be, for example, a computer monitor. Display  418  can also function as a touch screen, such as a display of a tablet computer. 
     The programs described herein are identified based upon the application for which they are implemented in a specific embodiment of the invention. However, it should be appreciated that any particular program nomenclature herein is used merely for convenience, and thus the invention should not be limited to use solely in any specific application identified and/or implied by such nomenclature. 
     The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be any tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, a special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, a segment, or a portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. The terminology used herein was chosen to best explain the principles of the embodiment, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.