Patent Publication Number: US-2022237681-A1

Title: Artificial intelligence assisted technology for grocery recommendation and nutrient optimization

Description:
BACKGROUND 
     Historically, nutrition tracking required users to manually record the food they consumed. Users generally did this by writing consumed items in a notepad and referring to general food information to determine the corresponding nutrient content. This process required time and continued effort from the user, which could lead to omissions or abandoning the practice. To save time, users sometimes limit themselves to tracking only select nutrients such as fat or calories and therefore would not have a full understanding of their food&#39;s comprehensive nutrient content. Additionally, manual tracking required users to do their own calculations of total nutrient intake, which raised the risk of errors. Users also had to independently research and seek foods that optimized their nutrient intake, which could yield suboptimal results due a user&#39;s limited exposure to and knowledge of all possible foods and their nutrient contents. 
     Traditional app-based nutrition tracking has been adopted by some users. Traditional app-based nutrition tracking allowed users to enter what they were eating into a computer program or a phone app, rather than needing to carry a notepad, and alleviated the need to look up some corresponding nutritional information. In the last several years, app-based nutrition tracking evolved to incorporate rich nutritional information into apps available for various devices, i.e., FITBIT, MYFITNESSPAL. Some of these newer conventional apps provided more nutrition information than Calories and fat tracking, and at least one provided a static list of foods high in certain nutrients, i.e., SAMSUNG HEALTH. At least one conventional approach (SHOPWELL) recommends alternatives specific to a food product category that might be beneficial to the user: e.g., low sugar yogurt instead of high sugar yogurt. Some of these newer conventional apps provided different interfaces for users to record their consumption like the ability to scan bar codes from packaged foods and the ability to search previous records. Nevertheless, both traditional and newer conventional app-based nutrition tracking still relied on user effort, causing the process of tracking nutrition to still be time consuming and tedious for users, and prone to omissions. Additionally, recommended items were not tailored to optimize overall nutrition of an individual&#39;s food consumption behavior. Moreover, these conventional app-based methods requiring that the user enter their consumption, was and always will be limited to the food available to the user at the time of their meal or snack. 
     Recently online grocery shopping has been adopted by many people. Online grocery shopping makes it easy for users to purchase grocery items for pick up or delivery. However, since the user doing the online shopping does not go into a physical store to pick out their groceries, some users are more likely to forget items or select items that do not meet their nutritional needs. 
     Whether offline or online, users seeking to improve their nutrition are limited by their subjective knowledge and awareness of food nutrients. Accordingly, there is an opportunity to improve users&#39; nutrition by using artificial intelligence to provide recommendations to optimize nutrients based on items in a user&#39;s online grocery cart. 
     SUMMARY 
     This disclosure describes systems, methods, and computer-executable instructions on computer-readable media for an artificial intelligence (AI) assisted technology for grocery recommendations and nutrient optimization. This disclosure describes an intuitive application (app) that can use AI to improve grocery recommendations during grocery shopping to optimize nutrition and encourage users to purchase foods that can help the user receive more complete nutrition. Automatically capturing nutritional information while grocery shopping can be important for users seeking to improve their health because it can alleviate the need for users to separately record their consumption whether manually or through a conventional app. Nutritional optimization while grocery shopping as described herein can improve the use of resources including time and food costs because it can provide suggestions to address nutritional opportunities that represent nutritional surpluses and nutritional opportunities that represent nutritional deficits. 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key and/or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. The term “techniques,” for instance, can refer to system(s), method(s), computer-readable instructions, module(s), algorithms, hardware logic, and/or operation(s) as permitted by the context described above and throughout the document. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same reference numbers in different figures indicate similar and/or identical items. 
         FIG. 1  is a block diagram depicting an example environment for implementing AI assisted technology for grocery recommendations and nutrient optimization as described herein. 
         FIG. 2  is a flow diagram that illustrates an example process associated with AI assisted technology for grocery recommendations and nutrient optimization according to various examples described herein. 
         FIG. 3  is a flow diagram that illustrates an example process associated with AI assisted technology for grocery recommendations and nutrient optimization according to various examples described herein. 
         FIG. 4A  illustrates part of an example graphical user interface (GUI) associated with AI assisted technology grocery recommendations and nutrient optimization according to various examples described herein. 
         FIG. 4B  illustrates another part of an example graphical user interface (GUI) associated with AI assisted technology for grocery recommendations and nutrient optimization according to various examples described herein. 
         FIG. 5  is a block diagram depicting an example computing device configured to participate in AI assisted technology for grocery recommendations and nutrient optimization according to various examples described herein. 
     
    
    
     DETAILED DESCRIPTION 
     Overview 
     The goal of the innovation described herein is to make nutrition information and customized grocery recommendations effortlessly accessible to people. All of the previous nutrition-tracking methods required that users enter their consumption, which requires that the user either stop preparation of their meal or stop the meal itself, activate an application, then enter information about the meal including the ingredients and portion size, or capture information from bar codes on pre-packaged food using cameras. All of the previous nutrition-tracking methods are limited to focusing on the food available to the user at the time of their meal or snack. Meanwhile, online grocery shopping, as has been adopted by many people, is a platform that provides an opportunity to process nutrition information and generate customized recommendations at no effort to the user, which could encourage users to choose foods that can make their nutrition more complete. Examples described herein can leverage and expand on experiences of online grocery shopping, grocery membership clubs that tie purchases to membership accounts, and/or other shopping experiences, such as smart phone scanning functionalities, to make recommendations that can optimize nutrition while relieving people of the need to record consumption and provide more specific nutrient information for nutrition tracking. 
     In at least one example, an intuitive app can be installed on a device associated with a user as a standalone app, as an app providing an API for one or more other apps, e.g., grocery shopping apps or delivery apps such as INSTACART, POSTMATES, FRESHDIRECT, FOODKICK, PEAPOD, SHIPT, SCHWAN&#39;S, THRIVE MARKET, AMAZON FRESH, etc., or as a browser extension that can activate when the browser navigates to an online grocery portal such as those associated with grocery stores like KROGER, ALDI, SAFEWAY, WALMART, FRED MEYER, PUBLIX, TARGET, COSTCO, etc. other grocery providers such as GOOGLE SHOPPING, SCHWAN&#39;S, AMAZON FRESH, AMAZON, AMAZON PRIME PANTRY, AMAZON PRIME NOW, BOXED, etc. or meal subscription services like HUNGRYROOT, HELLO FRESH, EVERY PLATE, etc. The app can use AI to improve grocery recommendations during grocery shopping to optimize nutrition according to an identified nutritional opportunity. In at least one example, the app can participate in or broker payments associated with grocery shopping including one or more of charging the user a fee to customize grocery recommendations based on dietary preferences and purchase history, charging grocery providers a fee to access the API so their shoppers can have access to recommendations that improve nutrition, charging sponsors a fee to have their product prioritized in recommendations for a specific food category, charging a grocery retailer a percentage of their increased sales revenue due to the app recommendations, etc. 
     As used herein a nutritional opportunity represents a deficit or a surplus of one or more nutrients such as calcium, cholesterol, carbohydrates, vitamin A, vitamin C, vitamin D, fiber, iron, potassium, protein, saturated fat, sugar, sodium, etc. In examples, a food item can include various nutrients, such as vitamins, minerals, fiber, macronutrients, probiotics, and/or bacteria such as  I. acidophilus . In at least one example, nutritional opportunity can represent a relative deficit or a relative surplus compared to overall nutrients and/or recommended daily values from nutritional guidelines. 
     In some examples, nutrients in a food item include one or more vitamins. Examples of vitamins include vitamin A (e.g., retinol, retinal, alpha carotene, beta-carotene, gamma carotene, cryptoxanthin, or any combination thereof), vitamin B (e.g., thiamine, riboflavin, niacin, pantothenic acid, pyridoxine, pyridoxine 5′-phosphate, pyridoxal, pyridoxal 5′-phosphate, pyridoxamine, pyridoxamine 5′-phosphate, 4-pyridoxic acid, pyritinol, biotin, folic acid, cobalamins, or any combination thereof), vitamin C (e.g., L-ascorbic acid), vitamin D (e.g., cholecalciferol, ergocalciferol, 22-dihydroergocalciferol, sitocalciferol, or any combination thereof), vitamin E (e.g., alpha-tocopherol, beta-tocopherol, gamma-tocopherol, delta-tocopherol, tocopheryl acetate, or any combination thereof), vitamin K (e.g., phylloquinone, menaquinone, or a combination thereof), or any combination thereof. 
     In some examples, nutrients in a food item include one or more minerals. Examples of minerals include calcium (e.g., calcium carbonate, calcium citrate, calcium gluconate, or the like), chromium (e.g., chromium(III) picolinate), copper (e.g., copper gluconate), iodine (e.g., potassium iodate, calcium iodate, or the like), iron (e.g., iron(II) sulfate), magnesium (e.g., magnesium oxide, magnesium citrate, magnesium chloride, or the like), manganese (e.g., manganese gluconate), selenium (e.g., selenomethionine, sodium selenite, sodium selenite, or a combination thereof), sodium (e.g., sodium chloride), potassium (e.g., potassium chloride), zinc (e.g., zinc gluconate, zinc sulfate, zinc acetate, or the like), or a combination thereof. 
     In various examples, nutrients in a food item includes fiber. Fiber refers to a component of food that is no more than partially broken down in the human digestive tract. Examples of fiber that is water-soluble (also referred to as “soluble fiber”) include inulin, wheat dextrin, psyllium, beta-glucans, and guar gum. Examples of fiber that is water-insoluble (also referred to as “insoluble fiber”) includes cellulose and lignin. 
     In some cases, nutrients in a food item include one or more macronutrients (macros). Macronutrients include, for example, carbohydrates, fats, and proteins. As used herein, the term “carbohydrate,” and its equivalents, can refer to a saccharide. Examples of carbohydrates include sugars, starches, and cellulose. As used herein, the term “fat” and its equivalents, can refer to an ester of multiple fatty acids, and/or any other lipid. Fats can be saturated fats, unsaturated fats, trans fats, cis fats, or a combination thereof. Cholesterol, corticosteroids, and androgens, estrogens, and progestogens, are examples of lipids. As used herein, the term “protein,” and its equivalents, can refer to a chain of multiple amino acids. 
     Some nutrients are not synthesized in the human body or are synthesized in limited quantities. People may rely on their diets to obtain these nutrients. Fatty acids that cannot be synthesized or have lower than a threshold conversion efficiency in the human body are referred to as “essential fatty acids,” and include linoleic acid, alpha-linolenic acid, eicosatetraenoic acid, docosahexaenoic acid, as well as other omega-3 and omega-6 fatty acids. Amino acids that cannot be synthesized or have lower than a threshold conversion efficiency in the human body are referred to as “essential amino acids,” and include histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, and valine. Some amino acids with limited synthesis in the human body include conditionally essential amino acids, such as arginine, cysteine, glutamine, glycine, proline, and tyrosine. 
     In some examples, nutrient opportunity can be determined according to a recorded serving size of a food item and/or daily recommended value of a nutrient. In various examples, the nutritional opportunity can be identified based on food items in the current shopping order or in more than one shopping order associated with the user and/or within a period of time, e.g., from the previous shopping order, within the current shopping order and from the previous shopping order, within shopping orders from today and yesterday, within shopping orders from today and earlier this week, etc., and in a variety of examples, shopping orders from more than one grocery store. 
     The app can use AI to identify foods that can help the user receive complete nutrition, which the app can present to the user to encourage purchase of the identified foods. Automatically capturing nutritional information while grocery shopping can be important for improving users&#39; health because it can alleviate the need for users to separately record their consumption whether manually or through an app. 
     In at least one example, the app includes or can access nutritional guidelines, which are publicly available. In some examples, the app can be configured to include or access specialized nutritional guidelines such as vegetarian, vegan, pescatarian, Mediterranean, keto, etc., specialized nutritional guidelines tailored to avoid food allergies, e.g., gluten free, peanut free, dairy free, etc. and/or specialized nutritional guidelines that account for different needs based on age, sex, and/or activity level. For example, specialized nutritional guidelines can account for factors like premenopausal women need more iron than men, adults tend to need fewer calories than teenagers, athletes need more calories and protein than sedentary people, etc. In some examples, the app can access one or more nutritional guidelines, including the specialized nutritional guidelines like the mentioned example guidelines, via the internet and from one or more agencies of the United States and/or other governments, the American Heart Association, and/or other diet and nutrition related sites such as WEIGHT WATCHERS, etc. In examples, operation of the app can include storing a copy of and/or applying one or more nutritional guidelines. In some examples, the user can customize their own nutritional guideline relative to their own nutritional goals. 
     In at least one example, a system associated with the app includes a food datastore that includes records associated with a plurality of foods items. A food item can include any comestible item including raw food and/or spices, e.g., fruits, vegetables, nuts, grains, legumes, beef, fish, poultry, etc., types and/or varieties of food including granny smith apple, fuji apple, blood orange, mandarin orange, blueberries, strawberries, broccoli florets, bibb lettuce, walnuts, almonds, brown rice, jasmine rice, green lentils, beef steak, beef roast, hamburger 20% fat, hamburger 5% fat, salmon, cod, whole turkey, whole chicken, chicken breast boneless, chicken thigh, potato chips, tortilla chips, spices, and many more. A food item can include any comestible item including cooked food, particular restaurant dishes, and/or pre-packaged food. The records can include nutritional information including nutrients included in the food item such as calcium, vitamin A, vitamin C, vitamin D, fiber, iron, potassium, protein, saturated fat, sodium, etc. portion size, and/or type of preparation, e.g., raw, baked, poached in water, poached in dry white wine, air fried, fried in oil, etc. Food datastore can include default records associated general food terms for which nutritional information is not otherwise provided, e.g., default nutritional information for “apple” based on the most common type of apples that have been purchased, an average of nutritional information based on the types of apples available, etc. In some examples, if the food datastore does not contain nutritional information for a general food item, a system associated with the app can instigate a search for this information and create a record based on the search results and/or, the system can cause a user interface (UI) to request more specificity about the food item, e.g., “what type of apple?” In some instances where the type is not found in the food datastore or via a search, e.g., due to a typographical error, a system associated with the app can use an algorithm like Levenshtein distance or a similar algorithm to identify the closest match or a likely match. In various examples, the food datastore can be organized by food category. In some examples, a food category can be partitioned by name. For example, a general node “apples” could include Gala apples, Honeycrisp apples, Macintosh Apples, etc. In some examples, a food category can be partitioned using unsupervised learning or generative statistical models on nutrient information. For example, a food category partitioned using unsupervised learning can include kale and spinach because both have high values of vitamin K and vitamin A. 
     In at least one example, a system associated with the app includes a user datastore that includes records of parameters associated with users including one or more of user identifier, device identifier, family size and/or makeup (infants, children, teens, adults), user age or age range, sex, activity level (sedentary, moderately active, active, athlete), specialty diet(s), food purchase history, storage authorization, registration information, customized nutritional guideline, etc. In some examples, a system associated with the app can incorporate the option for a user to report meals and/or food items acquired without activating the app, e.g., one or more of a restaurant meal, a meal at which the user is a guest, food items acquired from a grocery store without interaction with the app, etc. 
     In some examples, algorithms for AI assisted grocery recommendations as described herein can be performed on a computing device, such as a smart phone, a tablet, a laptop computer, a hybrid computing device, a desktop computer, a server, a supercomputer, etc. having one or more input devices, such as a physical keyboard, a soft keyboard, a touch screen, a touch pad, microphone(s), and/or camera(s). 
     Various environments, configurations of computing devices, and methods that can enable AI assisted technology for grocery recommendations and nutrient optimization, are described further with reference to  FIGS. 1-5 . While many examples described herein refer to distributed computing resources and/or consumer electronic devices, other types of electronic devices can implement such examples, e.g., as discussed with reference to  FIG. 1 . 
     Illustrative Environment 
       FIG. 1  shows an example environment  100  in which examples of AI assisted technology for grocery recommendations and nutrient optimization, can operate and/or in which methods of AI assisted technology for grocery recommendations and nutrient optimization such as those described herein can be performed. In the illustrated example, the various devices and/or components illustrated in environment  100  includes various devices and or components. The illustrated environment includes computing device(s)  102 ( 1 )- 102 (N) (individually and/or collectively referred to herein with reference  102 ), where N is any integer greater than and/or equal to 1, e.g., server(s) and/or desktop computer(s). The illustrated environment includes computing devices  104 ( 1 )- 104 (K) (individually and/or collectively referred to herein with reference  104 ), where K is any integer greater than and/or equal to 1. In some examples, N=K; in other examples, N&gt;K or N&lt;K e.g., laptop computer(s), tablet computer(s), hybrid computing device(s), and/or smart phones. Computing device(s)  102  and/or  104  can include a diverse variety of device categories, classes, and/or types and are not limited to any of the particular types of devices illustrated. 
     In the illustrated example, computing device(s)  102 ( 1 )- 102 (N) can be computing nodes of distributed computing resources  106 , e.g., in a computing cluster, such as a cloud service such as MICROSOFT AZURE, VMWARE VCLOUD, RACKSPACE, Inc.&#39;s OPENSTACK, AMAZON WEB SERVICES (AWS), IBM SMARTCLOUD, ORACLE CLOUD, etc. In the illustrated example, computing device(s)  104  can include consumer devices and in some instances can operate as clients of distributed computing resources  106  that can submit jobs to distributed computing resources  106  and/or receive job results from distributed computing resources  106 . Computing devices  102 ( 1 )- 102 (N) in distributed computing resources  106  can, share resources, balance load, increase performance, and/or provide fail-over support and/or redundancy, etc. Computing devices  104  can additionally or alternatively operate in a cluster and/or grouped configuration via association with one or more user(s). 
     By way of example and not limitation, computing device(s)  102  can include, but are not limited to, server computers and/or blade servers such as Web servers, map/reduce servers and/or other computation engines, and/or network-attached-storage units (e.g.,  102 ( 1 )). By way of example and not limitation, computing device(s)  104  can include, but are not limited to, laptop computers (e.g.,  104 ( 1 )), tablet computers (e.g.,  104 ( 2 )), tablet hybrid computers  104 ( 3 ), smartphones (e.g.,  104 ( 4 )), and/or other telecommunication devices, desktop computers, and/or integrated components for inclusion in computing devices, appliances, and/or other computing device(s) configured to participate in and/or carry out AI assisted grocery recommendations and nutrient optimization as described herein. 
     In some examples, as indicated, computing device(s), e.g., computing devices  102  and  104 , can intercommunicate to participate in and/or carry out AI assisted grocery recommendations and nutrient optimization as described herein. For example, a computing device  104  can be a query and/or data source and computing device  102  can host components of an AI assisted grocery recommendations and nutrient optimization system to store data, provide recommendations, and/or be queried, as described below with reference to, e.g.,  FIGS. 2-5 . 
     Different devices and/or types of computing devices  102  and  104  can have different needs and/or ways of interacting with distributed computing resources  106 . For example, computing devices  104  can interact with distributed computing resources  106  with discrete request/response communications, e.g., for responses and/or updates using an already-trained nutritional model. Additionally and/or alternatively, computing devices  104  can be query sources and/or data sources and can interact with distributed computing resources  106  with discrete and/or ongoing transmissions of data to be used as input to a nutritional model. This can provide improved personalized optimizations by increasing the number or queries and/or amount of data input to the nutritional model. 
     In some examples, computing devices  102  and/or  104  can communicate with each other and/or with other computing devices via one or more network(s)  108 . In some examples, computing devices  102  and  104  can communicate with external devices via network(s)  108 . For example, network(s)  108  can include public networks such as the Internet, private networks such as an institutional and/or personal intranet, and/or combination(s) of private and public networks. Private networks can include networks connected to the Internet and/or other public network(s) via network address translation (NAT) devices, firewalls, network intrusion detection systems, and/or other devices that restrict and/or control the types of network packets permitted to flow between the private network and the public network(s). 
     Network(s)  108  can also include any type of wired and/or wireless network, including but not limited to local area networks (LANs), wide area networks (WANs), satellite networks, cable networks, Wi-Fi networks, WiMAX networks, mobile communications networks (e.g., 3G, 4G, 5G, and so forth), any combination thereof, etc. Network(s)  108  can utilize communications protocols, such as, for example, packet-based and/or datagram-based protocols such as Internet Protocol (IP), Transmission Control Protocol (TCP), User Datagram Protocol (UDP), other types of protocols, and/or combinations thereof. Moreover, network(s)  108  can also include a number of devices that facilitate network communications and/or form a hardware infrastructure for the networks, such as switches, routers, gateways, access points, firewalls, base stations, repeaters, backbone devices, and the like. Network(s)  108  can also include devices that facilitate communications between computing devices  102  and/or  104  using bus protocols of various topologies, e.g., crossbar switches, INFINIBAND switches, FIBRE CHANNEL switches and/or hubs, etc. 
     In some examples, network(s)  108  can further include devices that enable connection to a wireless network, such as a wireless access point (WAP). Examples support connectivity through WAPs that send and receive data over various electromagnetic frequencies (e.g., radio frequencies), including WAPs that support Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (e.g., 802.11g, 802.11n, and so forth), and/or one or more other standards, e.g., BLUETOOTH, cellular-telephony standards such as code division multiple access (CDMA), global system for mobile communication (GSM), 3 rd  Generation Partnership Project (3GPP) standards, such as long-term evolution (LTE) and/or new radio (NR), voice over internet protocols (VOIP), worldwide interoperability for microwave access (WiMAX), etc. 
     Different networks have different characteristics, e.g., bandwidth, latency, accessibility (open, announced but secured, and/or not announced), and/or coverage area. The type of network  108  used for any given connection between, e.g., a computing device  104  and distributed computing resources  106  can be selected based on these characteristics and on the type of interaction. 
     Still referring to the example of  FIG. 1 , details of an example computing device  102 ( 3 ) are illustrated at inset  110 . The details of example computing device  102 ( 3 ) can be representative of others of computing device(s)  102 . However, each of the computing device(s)  102  can include additional or alternative hardware and/or software components. 
     Illustrated computing device  102  can include one or more processing unit(s)  112 , e.g., integrated electronic circuit(s) operably connected to one or more computer-readable media  114 , e.g., memories, such as via a bus  116 . In some examples, a plurality of processing unit(s)  112  can exchange data through an internal interface bus (e.g., PCIe), rather than and/or in addition to network  108 . While the processing unit(s)  112  are described as residing on the computing device  102 ( 3 ), in this example, the processing unit(s)  112  can also reside on different computing device(s)  102  and/or  104  in some examples. In some examples, at least two of the processing unit(s)  112  can reside on different computing device(s)  102  and/or  104 . In such examples, multiple processing unit(s)  112  on the same computing device  102  and/or  104  can use a bus  116  of the computing device  102  and/or  104  to exchange data, while processing unit(s)  112  on different computing device(s)  102  and/or  104  can exchange data via network(s)  108 . 
     Processing unit(s)  112  can include one or more microprocessors, single-core processors, multi-core processors, CPUs, GPUs, GPGPUs, and/or hardware logic components configured, e.g., via specialized programming from modules and/or APIs, to perform functions described herein. For example, and without limitation, illustrative types of hardware logic components that can be used in and/or as processing unit(s)  112  include Field-Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application-Specific Standard Products (ASSPs), System-on-a-Ship systems (SOCs), Complex Programmable Logic Devices (CPLDs), Digital Signal Processors (DSPs), and other types of customizable processors. For example, a processing unit  114  can represent a hybrid device, such as a device from ALTERA and/or XILINX that includes a CPU core embedded in an FPGA fabric. These and/or other hardware logic components can operate independently and/or, in some instances, can be driven by a CPU. In some examples, at least some of computing device(s)  102  and/or  104  can include a plurality of processing unit(s)  112  of multiple types. For example, the processing unit(s)  112  shown in computing device  102 ( 3 ) can be a combination of one or more CPUs, GPGPUs, FPGAs, etc. Different processing unit(s)  112  can have different execution models, e.g., as is the case for graphics processing units (GPUs) and central processing unit (CPUs). 
     Computer-readable media described herein, e.g., computer-readable media  114 , includes digital storage media also termed non-transitory computer-readable media, and/or communication media. Digital storage media includes tangible storage units such as volatile memory, nonvolatile memory, and/or other persistent and/or auxiliary computer storage media, removable and non-removable digital storage media implemented in any method and/or technology for storage of information such as computer-readable instructions, data structures, program modules, and/or other data. Digital storage media includes tangible and/or physical forms of media included in a device and/or hardware component that is part of a device and/or external to a device, including but not limited to RAM, static RAM (SRAM), dynamic RAM (DRAM), phase change memory (PRAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory, compact disc read-only memory (CD-ROM), digital versatile disks (DVDs), optical cards and/or other optical storage media, magnetic cassettes, magnetic tape, magnetic disk storage, magnetic cards and/or other magnetic storage devices and/or media, solid-state memory devices, storage arrays, network attached storage, storage area networks, hosted computer storage and/or memories, storage, devices, and/or storage media that can be used to store and maintain information for access by a computing device  102  and/or  104 . 
     In contrast to digital storage media also termed non-transitory computer-readable media, communication media can embody computer-readable instructions, data structures, program modules, and/or other data in a modulated data signal, such as a carrier wave, and/or other transitory transmission mechanism. As defined herein, digital storage media does not include communication media. 
     In some examples, computer-readable media  114  can store instructions executable by the processing unit(s)  112  that, as discussed above, can represent a processing unit incorporated in computing device  102 . Computer-readable media  114  can additionally and/or alternatively store instructions executable by external processing units such as by an external central processing unit (CPU) and/or external processor of any type discussed herein. In some examples at least one processing unit  112 , e.g., a CPU, graphics processing unit (GPU), and/or hardware logic device, can be incorporated in computing device  102 , while in some examples at least one processing unit  112 , e.g., one or more of a CPU, GPU, and/or hardware logic device, can be external to computing device  102 . 
     Computer-readable media  114  can store, for example, computer-executable instructions of an operating system  118 , module(s) of a nutrient optimization engine  122 , and/or other modules, programs, and/or applications that are loadable and executable by processing unit(s)  112 . In various examples, computer-readable media  114  can store, computer-executable instructions of a nutritional training engine  120  and/or nutritional model(s)  124 , etc. In some examples not shown, one or more of the processing unit(s)  112  in one of the computing device(s)  102  and/or  104  can be operably connected to computer-readable media  114  in a different one of the computing device(s)  102  and/or  104 , e.g., via communications interface and network  108 . For example, program code to perform steps of flow diagrams herein, e.g., as described herein with reference to nutrient optimization engine  122 , can be downloaded from a computing device  102  operating as a server to a computing device  104  operating as a client, e.g., via the network  108 , and executed by one or more processing unit(s) in computing device  104 . For example, the computer-executable instructions stored on the computer-readable media  114  can upon execution configure a computer such as a computing device  102  and/or  104  to perform operations described herein with reference to the operating system  118 , the nutrient optimization engine  122 , and in some instances the nutritional training engine  120 . 
     Computer-readable media  114  can also store, for example, one or more nutritional model(s)  124 , individually and/or collectively referred to herein with reference  124 . When included, nutritional model(s)  124  can include nutritional guidelines and can accommodate varied criteria e.g., one or more specialized diets such as vegetarian, vegan, pescatarian, Mediterranean, ketogenic (keto), kosher, halal, etc., and/or nutritional guidelines tailored to avoid food allergies, e.g., gluten free, peanut free, dairy free, etc., and can be associated with one or more datastore(s)  126 . Nutritional models can also be customized, e.g., a vegan diet with allowance for eggs but not dairy. As noted above, the nutritional models  124  can also be associated with a variety of characteristics associated with a user, e.g., family size and/or makeup (infants, children, teens, adults), user age or age range, sex, activity level (sedentary, moderately active, active, athlete), specialty diet(s), food purchase history, customized nutrient guideline, etc. (collectively “parameters”) to tailor grocery recommendation(s). In some examples without nutritional model(s)  124 , nutritional guidelines can be stored in datastore  126 . In at least one example, nutrient optimization engine  122  can perform data analysis and/or processing. In examples including nutritional training engine  120  and nutritional model(s)  124 , nutritional training engine  120  and/or the nutrient optimization engine  122  can determine values of parameters compared to nutritional model(s)  124  to perform data analysis and/or processing. 
     Bus  116 , which in some instances can include one or more of a system bus, a data bus, an address bus, a Peripheral Component Interconnect (PCI) Express (PCIe) bus, a PCI bus, a Mini-PCI bus, and any variety of local, peripheral, and/or independent buses, and/or any combination thereof can operably connect one or more processing unit(s)  112  to one or more computer-readable media  114 . 
     Computing device  102  can also include one or more communications interfaces  128  connected via the bus  116  to processing unit(s)  112  to enable wired and/or wireless communications between computing device(s)  102  and other networked computing devices  102  and/or  104  involved in AI assisted technology for grocery recommendations and nutrient optimization, and/or other computing device(s), e.g., over network(s)  108 . Such communications interface(s)  128  can include one or more transceiver devices, e.g., network interface controllers (NICs) such as Ethernet NICs and/or other types of transceiver devices, to send and receive communications over a network. The processing unit(s)  112  can exchange data through respective communications interface(s)  128 . In some examples, the communications interface  128  can include a PCI Express (PCIe) transceiver, and the network  108  can include a PCIe bus. In some examples, the communications interface  128  can include, but is not limited to, a transceiver for cellular (3G, 4G, 5G, and/or other), WI-FI, Ultra-wideband (UWB), BLUETOOTH, and/or satellite transmissions. The communications interface  128  can include a wired I/O interface, such as an Ethernet interface, a serial interface, a Universal Serial Bus (USB) interface, an INFINIBAND interface, and/or other wired interfaces. The communications interface  128  can additionally and/or alternatively include one or more user-interface devices, buses such as memory buses and/or local buses, memory interfaces, and/or hardwired interfaces such as 0-20 mA control lines. For simplicity, these and other components are omitted from the illustrated computing device  102 ( 3 ). 
     As noted above, computer-readable media  114  of the computing device  102  can store an operating system  118 . In some examples, an operating system  118  is not used (commonly referred to as a “bare metal” configuration). In some examples, the operating system  118  can include components that enable and/or direct the computing device  102  to receive data via various inputs (e.g., user controls, network and/or communications interfaces, memory devices, and/or sensors), and process the data using the processing unit(s)  112  to generate output. The operating system  118  can further include one or more components that present the output (e.g., display an image on an electronic display, store data in memory, and/or transmit data to another computing device). The operating system  118  can enable a developer or an engineer, to interact with the computing device  102  using a user interface. User interface(s) (UI)s described herein can include one or more of a graphical user interface (GUI), and audio user interface (AUI), and/or various other input/output interfaces generally referenced as UI including touch-input interfaces and the like. Additionally, the operating system  118  can include components that perform various functions generally associated with an operating system  118 , e.g., storage management and internal-device management. 
     In some examples, computing device  102  can include a user interface  130  configured to permit a developer or an engineer responsible for computing device  102 ( 3 ), distributed computing resources  106 , environment  100  and/or an application administrator, to operate the nutrient optimization engine  122  and/or to access the datastore(s)  126 . In examples including nutritional training engine  120  computing device  102  can include a user interface  130  configured to permit a developer or an engineer responsible for computing device  102 ( 3 ), distributed computing resources  106 , environment  100  and/or an application administrator, to operate, the nutritional training engine  120  and the model(s)  124 , the nutrient optimization engine  122  and/or to access the datastore(s)  126 . 
     Details of an example computing device  104 ( 1 ) are illustrated at inset  132 . The details of example computing device  104 ( 1 ) can be representative of others of computing device(s)  104 . However, each of the computing device(s)  104  can include additional and/or alternative hardware and/or software components. Computing device  104 ( 1 ) can include one or more processing unit(s)  134  operably connected to one or more computer-readable media  136 , e.g., via a bus  138 . Some examples of processing unit(s)  134  are discussed above with reference to processing unit(s)  112 . Some examples of computer-readable media  136  are discussed above with reference to computer-readable media  114 . For example, computer-readable media  136  can include one or more digital storage media or communications media. Some examples of bus  138  are discussed above with reference to bus  116 . 
     Computer-readable media  136  can store, for example, computer-executable instructions of an operating system  140 , and/or other modules, programs, and/or applications  142  that are loadable and executable by processing unit(s)  134 . Other applications in applications  142  can be operable with nutrient optimization application  144 . Some examples of operating system  140  are discussed above with reference to inset  110 . 
     In some examples, the computing device  104  can be configured to communicate with distributed computing resources  106  and/or computing device(s)  102  to query or send information to datastore  126  and/or to operate nutrient optimization engine  122  and/or nutrition model  124 . For example, the computing device  104  can transmit a request to distributed computing resources  106  and/or computing device(s)  102  for an output from the nutrient optimization engine  122  based on the nutritional training engine  120  and/or nutritional model  124  in accordance with information from datastore  126 , receive a response, and provide a recommendation to address a nutritional opportunity via a user interface  148  based on that response. In some examples, functions described herein can be shared between one or more computing device(s)  102  and one or more computing device(s)  104 . For example, the computing device(s)  104  can operate an input layer of one or more neural network(s) and the distributed computing resources  106  and/or computing device(s)  102  can operate one or more hidden layers and/or output layers of one or more neural network(s). In some examples, the computing device(s)  104  can obtain cart parameters and the distributed computing resources  106  and/or computing device(s)  102  can perform optimization on those parameters via an optimization algorithm associated with nutrient optimization engine  122 . 
     Computing device  104  can also include one or more communications interfaces  146  connected via the bus  138  to processing unit(s)  134  to enable wired and/or wireless communications between computing device(s)  104  distributed computing resources  106  and/or and other networked computing devices  102  and/or  104  involved in end-to-end RNN for joint language understanding and dialogue management, and/or other computing device(s), over network(s)  108 . Some examples are discussed above with reference to communications interface(s)  128 . 
     Computing device  104  can include a user interface  148 . For example, computing device  104 ( 4 ) can provide user interface  148  to control and/or otherwise interact with nutrient optimization application  144 , distributed computing resources  106 , and/or computing devices  102 . For example, processing unit(s)  134  can receive inputs e.g., typed and/or spoken queries and/or user utterances and/or other input actions associated with grocery shopping such as one or more of online grocery shopping, grocery membership clubs that tie purchases to membership accounts, and/or other shopping experiences, such as smart phone scanning functionalities via user interface  148  and transmit corresponding data via communications interface(s)  146  to computing device(s)  102 . 
     User interfaces  130  and/or  148  can include one or more input devices, integral and/or peripheral to computing device  102  and/or  104 . The input devices can be user-operable, and/or can be configured for input from other computing device  102  and/or  104 . Examples of input devices can include, e.g., a keyboard, keypad, a mouse, a trackball, a pen sensor and/or smart pen, a light pen and/or light gun, a game controller such as a joystick and/or game pad, a voice input device such as a microphone, voice-recognition device, and/or speech-recognition device, a touch input device such as a touchscreen, a gestural and/or motion input device such as a depth camera, a grip sensor, an accelerometer, another haptic input, a visual input device such as one or more cameras and/or image sensors, a QR code or bar code scanner, and the like. User interfaces  130  and/or  148  can include one or more output devices configured for communication to a user and/or to another computing device  102  and/or  104 . Output devices can be integral and/or peripheral to computing device  102  and/or  104 . Examples of output devices can include a display, a printer, audio speakers, beepers, and/or other audio output devices, a vibration motor, linear vibrator, and/or other haptic output device, and the like. 
     Illustrative Processes 
     The order in which the operations are described in each example flow diagram and/or process is not intended to be construed as a limitation, and any number of the described operations can be combined in any order and/or in parallel to implement each process. Moreover, the operations in each of  FIGS. 2 and 3  can be implemented in hardware, software, and/or a combination thereof. In the context of software, the operations represent computer-executable instructions that, when executed by one or more processors, cause one or more processors to perform the recited operations. In the context of hardware, the operations represent logic functions implemented in circuitry, e.g., datapath-control and finite-state-machine sequencing functions. 
       FIG. 2  is a flow diagram that illustrates an example process  200  of interactions for an intuitive application (app) that can use AI to improve grocery recommendations during grocery shopping to optimize nutrition and encourage users to purchase foods that can help the user receive complete nutrition. The app can capture nutritional information while a user is doing grocery shopping including one or more of: online grocery shopping, using scanning functionality on their smartphone while they shop at a grocery store, and/or from a record of their purchases associated with a membership club, which can help users seeking to improve their health because it can alleviate the need for the users to separately record their consumption whether manually or through an app. In addition, the app can enable users to plan food consumption and optimize their nutrition in advance of purchasing the food, with reference to food that is in stock at a grocer or other food provider, which is not possible with existing food tracking applications. In some examples, the app can include an option for a user to report meals and/or food items acquired without activating the app, e.g., one or more of a restaurant meal, a meal at which the user is a guest, food items acquired from a grocery store without interaction with the app, etc. In some examples, the app can include an option for a user to exclude certain food items from recommendations, e.g., food purchased for someone else, food purchased in bulk, etc. 
     Example functions shown in  FIG. 2  can be implemented by consumer device(s)  104 , e.g., using software such as nutrient optimization app  144  running on such device(s). For the sake of illustration, the example process  200  of configuring a device according to an app that can use AI to improve grocery recommendations during grocery shopping to optimize nutrition, e.g., nutrient optimization app  144  is described below with reference to processing unit  134  and other components of consumer device(s)  104  as shown in inset  132 ,  FIG. 1 . Consumer device(s)  104  can carry out and/or participate in the steps of the example method. However, other processing unit(s) and/or other components of computing device(s)  102  and/or  104  can carry out step(s) of described example processes such as process  200 . Similarly, example method(s) shown in  FIG. 2  are also not limited to being carried out by any specifically-identified components. 
     At block  202 , a consumer device  104  with nutrient optimization app  144  installed can receive parameters associated with a user for grocery shopping, including online grocery shopping, using scanning functionality on their smartphone while they shop at a grocery store, and/or from a record of their purchases associated with a grocery membership club. For example, parameters associated with users can include one or more of a user identifier, device identifier, family size and/or makeup (infants, children, teens, adults), user age or age range, sex, activity level (sedentary, moderately active, active, athlete), specialty diet(s), food purchase history, storage authorization, registration information, customized nutrition guideline, etc. In various examples, a food item is a source of energy to a consumer, wherein the energy is measured in kilocalories (referred to herein as “Calories”). A kilocalorie is the amount of heat needed to raise the temperature of one kilogram of water by one degree Kelvin or Celsius. In examples, a food item can include various nutrients, such as vitamins, minerals, fiber, macronutrients, probiotics, and/or bacteria as set forth above. 
     At block  204 , consumer device  104  with nutrient optimization app  144  installed can present an output UI, e.g., a GUI or AUI associated with grocery shopping via an output interface associated with the consumer device, e.g., UI  148  of device  104 . 
     At block  206  a consumer device  104  can receive designation of a food item being added, for example to an online shopping cart, smart phone scanning functionalities, and or the like, via an input interface such as a touch interface, a keypad, a microphone, etc. 
     At block  208  a consumer device  104  can present a recommendation for a nutritionally complementary food item via an output UI. In at least one example, the nutritionally complementary food item can be identified based on nutritional information associated with the designated food item and nutritional information associated with other food items in a shopping cart such as one or more of an online grocery shopping cart, grocery membership clubs&#39; records, and/or smart phone scanning functionalities. In at least one example, the recommendation can include to reduce a nutritional deficit for a first nutrient relative to the nutritional guideline for the first nutrient while minimizing a nutritional surplus for a second nutrient relative to the nutritional guideline. 
     In various examples, the recommendation can be presented responsive to designation of the food item being added to the cart at block  206  and before another food item is designated for addition to the cart. In some examples, to cause less interruption of the user&#39;s shopping experience, the recommendation can be presented responsive to a discrete number of food items being designated for addition to the cart. In various example, the recommendation can be presented responsive to receiving an indication that the shopping session is concluding such as receiving a request to check out. The latter examples can provide the least interruption to the user&#39;s shopping experience, but they may also not be as effective in encouraging purchase of complementary food items. Settings related to recommendation timing and other aspects of nutrient optimization app  144  can be controlled by one or more of an application administrator, licensor, a food item sponsor or promoter, a grocery shopping partner, and/or the user in various examples. 
     In some examples nutrient optimization app  144  can offer an opportunity for a user to register the app including authorization to store parameters associated with the user like family size and/or makeup (infants, children, teens, adults), user age or age range, sex, activity level (sedentary, moderately active, active, athlete), specialty diet(s), food purchase history, and/or dietary restrictions/allergies, customized nutrition guideline, etc. In such examples, the user may be able to opt in or opt out of: registration, storing parameters, and/or sharing any personally identifiable information. In situations where a user opts out of registering or authorizing the nutrient optimization app  144  to store parameters, the nutrient optimization app  144  can provide recommendations for complementary food item(s) based on general nutritional guidelines, which may not be as beneficial or relevant to the user. In situations where a user opts in to registering and authorizing the nutrient optimization app  144  to store parameters, the nutrient optimization app  144  can anonymize personally identifiable information associated with the user that will be maintained as part of a user record. 
     In various examples in which a user has opted in to registering and authorizing the nutrient optimization app  144  to store parameters, the nutrient optimization app  144  can receive an indication of a food purchase and can cause an indication of a food purchase to be added to (or omitted from) the user record. In examples, nutrient optimization app  144  can use such previous purchase information to improve recommendations of complementary food items for the associated user. 
     Example functions shown in  FIG. 3  and example processes described herein can be implemented by distributed computing resources  106  on and/or otherwise embodied in one or more computing device(s)  102  and/or  104 , e.g., using software running on such device(s). For the sake of illustration, the example process  300  is described below with reference to processing unit  112  and other components of computing device  102  as shown in inset  110 ,  FIG. 1 , which can carry out and/or participate in the steps of the example method. However, other processing unit(s) such as processing unit  112  and/or other components of computing device(s)  102  and/or  104  can carry out step(s) of described example processes such as process  300 . Similarly, example method(s) shown in  FIG. 3  are also not limited to being carried out by any specifically identified components. 
       FIG. 3  is a flow diagram that illustrates example processes for AI assisted nutritional optimization according to various examples described herein. 
     At block  302 , one or more parts of a system including a computing device  102  configured to implement nutritional optimization as described herein can create a user record associated with a user for grocery shopping. In some examples, the user record can include stored parameters associated with a user and can include one or more of a user identifier, device identifier, family size and/or makeup (infants, children, teens, adults), user age or age range, sex, activity level (sedentary, moderately active, active, athlete), specialty diet(s), food purchase history, storage authorization, registration information, customized nutrition guideline, etc. 
     At block  304 , one or more parts of a system including a computing device  102  configured to implement nutritional optimization as described herein can receive a designation corresponding to addition of a food item to a grocery shopping cart such as one or more of an online grocery shopping cart, smart phone scanning functionalities, and the like. In some examples, the designation can take place via a standalone app. In various examples, the designation can be received via an API for one or more other apps such as grocery shopping apps and/or delivery apps. In some examples, the designation can be received via a browser extension and can be activated when the browser navigates to an online grocery portal or meal subscription service. In examples, designations associated with a particular user can occur through any combination of these examples, such as both from another app and a browser, etc. In various examples, the system can make these designations available to nutrient optimization engine  122  to apply nutritional model(s)  124 . In some examples, the system can store these designations in datastore(s)  126 . 
     At block  306 , one or more parts of a system including a computing device  102  configured to implement nutritional optimization as described herein can add to the user record or update the user record in datastore(s)  126  with an indication of the food item designated for addition to the shopping cart, such as one or more of an online grocery shopping cart, via a smart phone scanning functionalities to scan items while shopping, and the like. In various examples, the user record, which can include grocery membership club account information, can be updated with this information at a variety of times such as when the consumer device is connected to a particular type of network, periodically, e.g., once a week, each night, etc., responsive to the user completing a grocery shopping session, and/or immediately upon addition of the food item to the shopping cart, e.g., online shopping cart, via smart phone scanning functionality while shopping, etc. or a combination of any of these times. One of more of these time settings can be controlled by one or more of an application administrator, licensor, a food item sponsor or promoter, grocery shopping partner, and/or the user in various examples. 
     At block  308 , one or more parts of a system including a computing device  102  configured to implement nutritional optimization as described herein can identify nutritional information associated with the food item. In examples the system can identify a record from a food datastore, e.g., in datastore(s)  126 , associated with the food item designated for addition to the shopping cart. The record associated with the food item can include nutritional information associated with the food item such as serving size and/or nutrient content including one or more of calcium, cholesterol, carbohydrates, vitamin A, vitamin C, vitamin D, fiber, iron, potassium, protein, saturated fat, sugar, sodium, etc. 
     At block  310 , one or more parts of a system including a computing device  102  configured to implement nutritional optimization as described herein can compare nutritional information associated with the food item designated for addition to the shopping cart to one or more nutritional guideline(s), which can be stored in datastore(s)  126 . In some examples, the one or more nutritional guideline(s) can include a specialized nutritional guideline and/or a nutritional guideline tailored to avoid food allergies. Based on the comparison, nutrient optimization engine  122  can identify one or more nutritional opportunities related to the food item or food items in the shopping cart. In various examples, a nutritional opportunity can include a nutritional deficit relative to the referenced nutritional guideline(s) for at least one nutrient and/or a nutritional surplus relative to the reference nutritional guideline(s) for at least one nutrient. 
     At block  312 , one or more parts of a system including a computing device  102  configured to implement nutritional optimization as described herein can generate a recommendation including, relative to the referenced nutritional guideline(s), the nutritional opportunity to improve the deficit for a nutrient while minimizing a surplus for another nutrient. In the example illustrated in  FIGS. 4A and 4B  and discussed below, the nutritional opportunity is to improve a deficit in potassium and fiber while minimizing surpluses in vitamin A, vitamin C, and sugar. 
     At block  314 , one or more parts of a system including a computing device  102  configured to implement nutritional optimization as described herein can send an indication of a recommendation for presentation via a user interface  148 , e.g., a GUI associated with the nutrient optimization app  144  and/or the shopping cart. Several examples are illustrated in  FIGS. 4A and 4B  and discussed below. 
     Illustrative Graphical User Interface 
       FIG. 4A  is an illustrative diagram that shows parts of one or more example graphical user interface(s) (GUIs)  400 ,  400 ( 1 ) that can be presented by consumer device(s)  104 . The illustration includes a shopping cart  402 , which in the illustrated example includes the food items apples, chicken breast, lettuce, broccoli, brown rice, steak, carrots, cheddar cheese, and almond milk, though many other options are possible and to be expected. In the illustrated example, the food items presented, i.e., almond milk and steak, can represent what the user actually selected for addition to their cart and/or a more general representation of what the user selected. That is, in some examples, the general representations almond milk and steak can represent selections made by the user, and the particular food items added to the cart can be based on the user&#39;s previous purchases. Alternatively, in some examples, the general representations almond milk and steak can represent more specific and/or branded selections of vanilla almond milk and steak strips. 
     The illustration includes a presentation of recommended items  404 , which in the illustrated example include salt-free seasoning, organic young jackfruit, and potatoes. The recommended items are based at least in part on the contents of the shopping cart  402 . In some examples, the particular products recommended can be based on one or more of a user&#39;s previous purchases, inventory of the particular grocery store(s), food item(s) that are sponsored or promoted by one or more advertiser(s)/sponsor(s) or the grocery store partner(s), etc. In various examples, the recommended items can be presented via text, image, audio, a link to purchase the item, etc., or any combination thereof. 
     The illustration includes a graphical representation of total nutrients  406 , which in the illustrated example include a plurality of bar graphs corresponding to nutritional information and a plurality of nutrients from the food items in the shopping cart  402 ; in some examples, a pie chart presentation may be selected or shown in place of the bar graphs. As shown, the bar graphs reference a percentage (%) of recommended daily value for the respective nutritional information and nutrients and the bar graphs show the change that would take place with the addition of the recommended items to the shopping cart. For example, the bar graphs show that the number of Calories in the cart are less than 100% of the recommended daily value, which would be met with the addition of the recommended items while the amount of vitamin A present in the foods in the cart represents a surplus relative to the other amount of the other items—almost 300% of the recommended daily value, or almost 3 days&#39; worth of vitamin A. Thus, nutritional optimization app  144  can highlight to users when the food items in the cart represent a nutritional opportunity indicating a surplus, in the illustrated instance particularly of vitamin A, protein, and saturated fat. In some examples nutritional optimization app  144  can highlight to users when the food items in the cart represent a nutritional opportunity indicating a surplus of Calories, fat, sodium, or any of the tracked nutrients. In various examples, elements  402 ,  404 , and/or  406  can be presented in separate locations, or separate screens, and/or via pop-up windows, etc. In some examples, element  402  and/or  406  can be omitted and/or element  404  may indicate that no recommendations are available. In some examples, element  406  (or another interface) can include a GUI that can enable the user to customize parameters such as food items included in the summary, which nutrient contents to display, etc. 
       FIG. 4B  is an illustrative diagram that shows another part of an example graphical user interface (GUI)  400 ,  400 ( 2 ) that can be presented by consumer device(s)  104 . The illustration includes a table  408  of detailed nutrient values in percentage of recommended daily value for the food items from shopping cart  402  and recommended items  404 ,  FIG. 4A . In various examples, table  408  can be presented in separate location or on a separate screen from none, any, or all of elements  402 ,  404 , and/or  406 , and/or via a pop-up window, etc. In the illustrated example the food items are brown rice, vanilla almond milk (which was simply shown as “almond milk” in shopping cart  402 ), broccoli, steak strips (which was simply shown as “steak” in shopping cart  402 ), apples, carrots, cheddar cheese, lettuce, chicken breast, salt free seasoning, organic young jack fruit, and potatoes, though many other options are possible and to be expected. In the illustration, the food items are presented across the top of the table, while nutrients: Calories, total fat, saturated fat, trans fat, cholesterol, sodium, carbohydrate, fiber, sugars, protein, vitamin D, calcium, iron, potassium, vitamin A, and vitamin C are presented on the left side of the table. Of course, alternative presentations are possible. In the illustrated example, the more detailed food items presented, i.e., vanilla almond milk and steak strips, can represent what the user actually selected for addition to their cart and/or the user may have selected the more general representations almond milk and steak, and the food items shown may be based on the user&#39;s previous purchases. 
     Corresponding to the bar graphs,  406 , the table  408  provides the underlying values to show how the addition of the recommended items salt free seasoning, organic young jack fruit, and potatoes address the nutritional opportunities of deficits in potassium. Vitamin C, and fiber while also minimizing the nutritional opportunities of surpluses of vitamin A, protein, saturated fat, or Calories. In various examples, the recommended daily value percentage(s) for table  408  can be optimized according to user parameters. In some examples, table  408  can be omitted from GUI  400 . 
     Illustrative Components 
       FIG. 5  is an illustrative diagram that shows an example configuration of components of a computing device  500 , which can represent a computing device(s)  102 , and which can be and/or implement a training and/or operation system, device, and/or apparatus, according to various examples described herein. Example computing device  500  includes one or more processing unit(s)  502 , network interface(s)  504 , input interface(s)  506 , output interface(s)  508 , and computer-readable media  510 . The components of computing device  500  are operatively connected, for example, via a bus  512 . These components can represent corresponding components from device(s)  102   a , e.g., processing unit(s)  502  can represent processing unit(s)  112 , bus  512  can represent bus  116 , etc. 
     In example computing device  500 , processing unit(s)  502  can correspond to processing unit(s)  112 , and can represent, for example, a CPU-type processing unit, a GPU-type processing unit, a field-programmable gate array (FPGA), another class of digital signal processor (DSP), or other hardware logic components that may, in some instances, be driven by a CPU. For example, and without limitation, illustrative types of hardware logic components that can be used include Application-Specific Integrated Circuits (ASICs), Application-Specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc. In some examples, processing unit(s)  502  can include an on-board memory, e.g., a RAM and/or cache, not shown. 
     Network interface(s)  504 , which can represent communications interface(s)  128 , can represent, for example, network interface controllers (NICs) or other types of transceiver devices to send and receive communications over a network. 
     Input/output (I/O) interfaces  506  and  508  allow computing device  500  to communicate with input/output devices such as user input devices including peripheral input devices (e.g., a keyboard, a mouse, a pen, a game controller, a voice input device, a touch input device, a gestural input device, and the like) and/or output devices including peripheral output devices (e.g., a display, a printer, audio speakers, a haptic output, and the like). 
     Computer-readable media  510  can correspond to computer-readable media  124  and can store instructions executable by the processing unit(s)  502 . Computer-readable media  510  can also store instructions executable by external processing units such as by an external CPU, an external GPU, and/or executable by an external accelerator, such as an FPGA type accelerator, a DSP type accelerator, or any other internal or external accelerator. In various examples, at least one CPU, GPU, and/or accelerator is incorporated in computing device  200 , while in some examples one or more of a CPU, GPU, and/or accelerator is external to computing device  500 . 
     In the illustrated example, computer-readable media  510  includes two data stores, food datastore  514  and user datastore  516 , which can also be represented by datastore  126 ,  FIG. 1 . In various examples, food datastore  514  and/or user datastore  516  can include data storage such as a database, data warehouse, or other type of structured or unstructured data storage (e.g., a Structured Query Language, SQL, and/or NoSQL database), etc. In some examples, food datastore  514  and/or user datastore  516  can include a corpus and/or a relational database with one or more tables, indices, stored procedures, and so forth to enable data access including one or more of hypertext markup language (HTML) tables, resource description framework (RDF) tables, web ontology language (OWL) tables, and/or extensible markup language (XML) tables, for example. Food datastore  514  and/or user datastore  516  can store data for the operations of processes, applications, components, and/or modules stored in computer-readable media  510  and/or executed by processing unit(s)  502  and/or accelerator(s). 
     Computing device  500  can implement a nutritional optimization engine  520 , which generally operates online and can represent nutritional optimization engine  122 ,  FIG. 1 . In examples including a nutritional training engine, computing device  500  can implement a nutritional training engine  518 , which generally performs training offline and can represent nutritional training engine  120 ,  FIG. 1 . In some examples, nutritional training engine  518  operates on initial input associated with a grocery shopping experience, e.g., selections, typed and/or spoken queries, scans, etc. In some examples, nutritional training engine  518  can operate on initial input, and nutritional optimization engine  520  can operate on ongoing input. Computing device  500  can also implement a nutritional model  522 , which is generally updated incrementally and can represent nutritional model  124 ,  FIG. 1 . Computing device  500  can include and/or be included in a system and/or device for training and/or operating a neural network and/or other computational model as described herein. 
     Nutritional optimization engine  520  and/or other modules, can in various examples, scrape nutritional information from a variety of sources like food manufacturer websites, online grocery sites, and the like. Nutritional optimization engine  520  can, in various examples, aggregate nutritional information for food item(s) in a grocery cart. In at least one example, the nutritional optimization engine  520  can represent the nutritional information associated with each food item (nutrient(s) and amount of the nutrient(s) associated with the food item) in the cart in percentage of recommended daily value or convert the nutritional information associated with each food item in the cart to percentage of recommended daily value, and then aggregate the percentages of recommended daily values for the nutrients from the food item(s) represented in the cart. In some examples, the nutritional optimization engine  520  can represent the nutritional information associated with each food item in the cart in identified units of measure, aggregate the units associated with each food item in the cart, and then convert the aggregated values to determine the percentage of recommended daily values for the nutrients from the food items represented in the cart. In some examples, the nutritional information for different food items can be represented in different serving sizes as selected by a user, e.g., by serving, by package size, in bulk, etc. 
     Nutritional optimization engine  520 , in various examples, can calculate a value to represent a nutritional deviation associated with food items in the cart, the deviation being from a perfect balance of nutrients relative to a nutritional guideline. In some examples, the perfect balance of nutrients can be dependent on a user&#39;s recommended nutritional intake. In some examples, the deviation value calculation can incorporate a user&#39;s previous purchase history in addition to the current cart items. This deviation value, in various examples, can be calculated as a sum of the squared difference between a goal nutrient amount and cart nutrient amount, over all nutrients (equation 1). In some examples, the goal nutrient amount can be the daily recommended value (such as 100% DV), maximum nutrient amount among all nutrient values in a cart, the mean nutrient amount of all nutrients in a cart, etc. In some examples, the deviation value can be calculated based on select nutrients and not all possible nutrients, which can be customized by the user. 
       Deviation value=Σ n=1, 2, . . . N (goal n   −x   n ) 2   (Equation 1)
 
     Where n is a nutrient 1 to N (e.g., Calories, fat, sodium, etc.), x is the amount of nutrient in the cart, g is amount of goal nutrient. 
     In some examples, nutrition optimization engine  520  can use an optimizer to search through a food datastore relevant to the user&#39;s parameters and cart contents, e,g, food datastore  514 , to find a food item or food category for recommendation that, if added to current cart items, would decrease and minimize the cart deviation. In some examples, the optimizer used can include greedy search, embedded conic solvers, commercial optimization solvers such as GUROBI, etc. In some examples, multiple food items can be interchangeable and similarly minimize cart deviation—in this case, the recommended food item or category found by the nutrition optimization engine can be input into the sponsor manager  524  and/or integration manager  526  (and/or nutritional model  522 ) for further processing. In some examples, nutrition optimizer engine  520  can perform multiple levels of optimization, which can improve efficiency. For example, nutrition optimizer engine  520  can first recommend a food category that best improves overall nutrition for the food items represented in the cart, and second recommend a specific food item, e.g., nutrition optimizer engine  520  can first recommend that the food category that best improves overall nutrition for food items represented in the cart is a category of food items that are high in both vitamin A and vitamin K, and second recommend a specific food item kale. 
     In some examples, computer-readable media  510  of the computing device  500  can represent computer-readable media  114 ,  FIG. 1 , and can store a plurality of modules associated with the nutritional optimization engine  520 , (and/or the nutritional training engine  518  and the nutritional model  522 ), to enable a nutritional optimization app like nutritional optimization app  144 . Processing unit(s)  502  can be configured to execute modules of the plurality of modules. For example, the computer-executable instructions stored on the computer-readable media  510  can, upon execution, configure a computer such as a computing device  500  to perform operations described herein with reference to modules such as sponsor manager module  524 , integration manager module  526 , and/or food item recommender module  528 , which are discussed below. 
     Nutritional model  522  and/or a nutrional guideline and/or user record can, in various examples, include a seasonal model that can take temperature or weather for a geographical location associated with the user into account either from historic information or by accessing weather forecast data. For example, the nutritional model  522 , nutrional guideline, and/or user record incorporating a seasonal model can cause changes in the types of food items recommended as complementary food items such as biasing to recommend seasonal produce or hot foods, e.g., soups (or ingredients to make soup), when temperatures are relatively cold for the geographical location and cold foods, e.g., salads (or ingredients to make salads), when temperatures are relatively hot for the geographical location. In some examples, nutritional model  522 , nutrional guideline, and/or user record can include a personalization model that can take a particular user&#39;s purchase history into account. For example, the nutritional model  522 , nutrional guideline, and/or user record incorporating a personalization model can cause changes in the types of food items, brands of food items, and/or amounts of food items recommended as complementary food items such as biasing to recommend food items with a later use-by date and/or more food items for users that shop relatively infrequently and/or biasing to previously purchased brands and/or alternatives of similar cost to previously purchased brands, e.g., recommend premium substitutions for premium brands that are not available and low-cost substitutions for low-cost brands that are not available. In some examples, nutritional model  522 , nutrional guideline, and/or user record can employ a convolutional neural network, a recursive neural network, a recurrent neural network, etc. to learn and optimize nutritional information. 
     Sponsor manager module  524  can, in various examples, enable nutrition optimization engine  520  (and/or nutritional model  522 ) to accept information related to one or more sponsors or promoters and associated product(s) as parameters. Sponsor manager module  524  can cause particular brands of products to be presented or to be presented first by food item recommender  528 , such as in the recommended items  404 . 
     Integration manager module  526  can, in various examples, enable nutrition optimization engine  520  (nutritional model  522 ) to integrate information related to one or more of a food item sponsor or promoter, a grocery shopping partner, such as one or more of grocery stores, grocery membership clubs, and/or other shopping experiences with nutritional information from nutritional guidelines, and/or user parameters, etc. to produce optimized nutritional opportunities that can help the user receive complete nutrition. In at least one example, the app can participate in or broker payments associated with grocery shopping including one or more of charging the user a fee to customize grocery recommendations based on dietary preferences and purchase history, charging grocery providers a fee to access the API so their shoppers can have access to recommendations that improve nutrition, charging sponsors a fee to have their product prioritized in recommendations for a specific food category, charging a grocery retailer a percentage of their increased sales revenue due to the app recommendations, etc. In some examples such fees can be on-demand fees, periodic fees, subscription fees, etc. 
     Food item recommender module  528  can, in examples, produce one or more recommended food items according to the operations of the nutrition optimization engine  520  and/or the integration manager  526 . The food item recommender module can generate the list of food items to be presented via a UI associated with the nutrient optimization app  144 . In some examples, the particular products recommended can be based on one or more of a user&#39;s previous purchases, inventory of the particular grocery store(s), food item(s) that are sponsored or promoted by one or more advertiser(s) or the grocery store partner(s), etc. 
     Computing device  500  can exchange data with computing devices  102  and/or  104  (e.g., servers, desktop computers, laptop computers, hybrid computing devices, tablet computers, smart phones, etc.) via one or more network(s)  108 , such as the Internet. In some examples, computing device  500  can receive data from one or more data source(s) (not shown) via one or more network(s)  108 . Example data source(s) can include computing devices  102  and/or  104 , sensors, data aggregators, and/or data feeds, e.g., via application programming interfaces (APIs). The processing units  502  can retrieve data from the data source(s), e.g., via an HTTP request such as a GET to a Web Services and/or Representational State Transfer (REST) API endpoint. 
     CONCLUSION 
     Although the techniques have been described in language specific to structural features and/or methodological acts, it is to be understood that the appended claims are not necessarily limited to the features and/or acts described. Rather, the features and acts are described as example implementations of such techniques. For example, network  108 , processing unit(s)  112 / 134 / 502 , and other structures described herein for which multiple types of implementing devices or structures are listed can include any of the listed types, and/or multiples and/or combinations thereof. 
     The operations of the example processes are illustrated in individual blocks and summarized with reference to those blocks. The processes are illustrated as logical flows of blocks, each block of which can represent one or more operations that can be implemented in hardware, software, and/or a combination thereof. In the context of software, the operations represent computer-executable instructions stored on one or more computer-readable media that, when executed by one or more processors, enable the one or more processors to perform the recited operations. Generally, computer-executable instructions include routines, programs, objects, modules, components, data structures, and the like that can configure a processor to perform particular functions and/or implement particular abstract data types. The order in which the operations are described is not intended to be construed as a limitation, and any number of the described operations can be executed in any order, combined in any order, subdivided into multiple sub-operations, and/or executed in parallel to implement the described processes. The described processes can be performed by resources associated with one or more computing device(s)  102 ,  104 , and/or  500  such as one or more internal and/or external CPUs and/or GPUs, and/or one or more pieces of hardware logic such as FPGAs, DSPs, and/or other types described above. 
     All of the methods and processes described above can be embodied in, and fully automated via, software code modules executed by one or more general-purpose computer(s) and/or processor(s) thereby reconfiguring the general-purpose computer(s) and/or processor(s) as special purpose computer(s) and/or processor(s) during their execution. The code modules can be stored in any type of computer-readable storage medium and/or other computer storage device. Some and/or all of the methods can be embodied in specialized computer hardware. 
     Conditional language such as, among others, “can,” “could,” “might” and/or “may,” unless specifically stated otherwise, are understood within the context to present that certain examples include, while other examples need not include, certain features, elements and/or steps. Thus, such conditional language is not generally intended to imply that certain features, elements and/or steps are in any way required for one or more examples and/or that one or more examples necessarily include logic for deciding, with and/or without user input and/or prompting, whether certain features, elements and/or steps are included and/or are to be performed in any particular example. The word “or” is used herein in an inclusive sense unless specifically stated otherwise. Accordingly, conjunctive language such as the phrases “X, Y, or Z” or “at least one of X, Y or Z,” unless specifically stated otherwise, is to be understood as signifying that an item, term, etc., can be either X, Y, or Z, or any combination thereof. 
     Any routine descriptions, elements and/or blocks in the flow diagrams described herein and/or depicted in the attached figures should be understood as potentially representing modules, segments, and/or portions of code that include one or more executable instructions for implementing specific logical functions and/or elements in the routine. Alternative implementations are included within the scope of the examples described herein in which elements and/or functions can be deleted and/or executed out of order from any order shown or discussed, including substantially synchronously and/or in reverse order, depending on the functionality involved as would be understood by those skilled in the art. It should be emphasized that many variations and modifications can be made to the above-described examples, the elements of which are to be understood as being among other acceptable examples. All such modifications and variations are intended to be included herein within the scope of this disclosure and protected by the following claims. Moreover, in the claims, any reference to a group of items provided by a preceding claim or clause is a reference to at least some and not necessarily all of the items in the group of items, unless specifically stated otherwise.