Patent Publication Number: US-2022222757-A1

Title: Personalized food item design and culinary fulfillment system

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     Priority is claimed in the application data sheet to the following patents or patent applications, the entire written description of each of which is expressly incorporated herein by reference in its entirety: 
     Ser. No. 17/005,012 
     62/984,237 
     Ser. No. 16/993,488 
     62/956,289 
    
    
     BACKGROUND 
     Field of the Art 
     The disclosure relates to the field of computerized comparative and artificial intelligent systems, and more particularly to the field of computerized systems for food item personalization, optimization, business selection, food ordering, for retail business establishments and its patrons. 
     Discussion of the State of the Art 
     People frequently wishing to dine at a retail business establishment are limited to ordering and consuming a limited set of food items based on a restaurant&#39;s long-standing menu with limited manual customization that take into account a patron&#39;s dietary preferences or desired long-term outcomes. Similarly, restaurants are not free to dynamically change menu items based on ingredients on hand and/or culinary skills available that maximizes their business outcomes and impact on a particular patron and/or prospective patron dining experience. The result is often a suboptimal dining experience for restaurant consumers and reduced long-term viability for the restaurant. 
     There is currently no automated system that personalizes and optimizes food item recipe generation and fulfilment to address these shortcomings. 
     What is needed is a system and method for automated personalized food item design and culinary fulfilment to optimize the dining experience for both the patron and the dining establishment. 
     SUMMARY 
     Accordingly, the inventor has conceived, and reduced to practice, a system and method for automated personalized food item design and culinary fulfillment. The system is a cloud-based network containing a food item design engine, portals for restaurants and patrons, to enter their information, and a recipe generator which creates a unique dietary experience for patrons based on a multitude of variables associated with the business enterprises, patrons&#39; historic culinary transactions, dietary needs and preferences both explicit and inferred. The system may be accessed through web browsers or purpose-built computer and mobile phone applications. 
     According to a preferred aspect, a system for automated personalized food item design and culinary fulfillment, comprising: a business enterprise database comprising a plurality of business enterprise locations, business enterprise information comprising a food item provided by a business enterprise location, and a plurality of ingredients for each food item; and a food item design engine comprising a first plurality of programming instructions stored in a memory of, and operable on a processor of, a computing device, wherein the first plurality of programming instructions, when operating on the processor, cause the computing device to: receive consumer food item preference information from a plurality of consumer computing devices for one or more food item requests, the food item information for each food item request comprising desired food type, food amount and timeframe descriptors; retrieve the consumer profile from the consumer profile database; retrieve the consumer culinary transactions from the culinary transaction database; receive recipe information from one or more business enterprise devices each associated with a business, the recipe availability information comprising business location, skill set of chefs, availability of ingredients; analyze the ingredients and compare with culinary transactions, consumer profile, recipe data and consumer food item preference request information; generate a consumer-specific food item based on real-time data from consumer food item preference, historical data from consumer profile, culinary transaction, recipe data, ingredient availability, preparation options, skill set of on-duty chefs; send a food item recommendation to consumers compute device, the recommendation comprising a food item description, name and location of business and estimate of availability for pick up or in-establishment dining, send a food item order request to the business enterprise computing device and specification for pick up or in-establishment dining. 
     According to another preferred aspect, a method for automated personalized food item design and culinary fulfillment, comprising: a business enterprise database comprising a plurality of business enterprise locations, business enterprise information comprising a food item provided by a business enterprise location, and a plurality of ingredients for each food item; and a food item design engine comprising a first plurality of programming instructions stored in a memory of, and operable on a processor of, a computing device, wherein the first plurality of programming instructions, when operating on the processor, cause the computing device to: receive consumer food item preference information from a plurality of consumer computing devices for one or more food item requests, the food item information for each food item request comprising desired food type, food amount and timeframe descriptors; retrieve the consumer profile from the consumer profile database; retrieve the consumer culinary transactions from the culinary transaction database; receive recipe information from one or more business enterprise devices each associated with a business, the recipe availability information comprising business location, skill set of chefs, availability of ingredients; analyze the ingredients and compare with culinary transactions, consumer profile, recipe data and consumer food item preference request information; generate a consumer-specific food item based on real-time data from consumer food item preference, historical data from consumer profile, culinary transaction, recipe data, ingredient availability, preparation options, skill set of on-duty chefs; send a food item recommendation to consumers compute device, the recommendation comprising a food item description, name and location of business and estimate of availability for pick up or in-establishment dining, send a food item order request to the business enterprise computing device and specification for pick up or in-establishment dining. 
     According to an aspect of an embodiment, the food item design engine optimization is based on artificial intelligence to create a unique food item for a particular patron. 
     According to an aspect of an embodiment, the food item design engine optimization is based on artificial intelligence to create a unique food item for a typical patron. 
     According to an aspect of an embodiment, the business enterprise information further comprises hours of availability of culinary chefs with required food preparation skills. 
     According to an aspect of an embodiment, the patron provided real-time food item information is vague and therefore inferred by system. 
     According to an aspect of an embodiment, the patron provided real-time food item information is updated with feedback information rating their experience. 
     According to an aspect of an embodiment, the external data resources includes rating data from social media sites. 
     According to an aspect of an embodiment, the external data resources includes rating data from restaurant review sites. 
     According to an aspect of an embodiment, the external data resources includes health care service provider data. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWING FIGURES 
       The accompanying drawings illustrate several aspects and, together with the description, serve to explain the principles of the invention according to the aspects. It will be appreciated by one skilled in the art that the particular arrangements illustrated in the drawings are merely exemplary, and are not to be considered as limiting of the scope of the invention or the claims herein in any way. 
         FIG. 1  is a block diagram illustrating an exemplary system architecture for an automated personalized food item design and culinary fulfillment system. 
         FIG. 2  is a block diagram illustrating an exemplary architecture for an aspect of an automated food item design engine. 
         FIG. 3  is a block diagram illustrating an exemplary architecture for an aspect of an automated culinary fulfilment engine. 
         FIG. 4  is a flow diagram showing the steps of an exemplary method for personalized food item design, selection, restaurant selection, order fulfilment and receipt by a restaurant patron. 
         FIG. 5  is a flow diagram showing the steps of an exemplary method for an optimized food item recipe generation process based on a particular patron current food preferences, historical culinary transactions, current geographic location, and the restaurant&#39;s ingredients on hand and culinary skills. 
         FIG. 6  is a flow diagram showing the steps of an exemplary method for an optimized food item recipe generation process based on the restaurants&#39; food ingredients on hand, culinary skills and a predicted patron preference. 
         FIG. 7  is a block diagram illustrating an exemplary hardware architecture of a computing device. 
         FIG. 8  is a block diagram illustrating an exemplary logical architecture for a client device. 
         FIG. 9  is a block diagram showing an exemplary architectural arrangement of clients, servers, and external services. 
         FIG. 10  is block diagram illustrating another aspect of an exemplary hardware architecture of a computing device. 
         FIG. 11  is a message diagram showing exemplary messaging between patron device and recipe generation system with output to the recipe optimization system. 
         FIG. 12  is a message diagram showing exemplary messaging within the recipe optimization system taking inputs from a recipe generation system and a recipe validation system and providing an optimized personalized recipe information as an output to restaurant recommendation system. 
         FIG. 13  is a message diagram showing exemplary messaging within a restaurant recommendation system with various inputs and providing culinary preparation and personalized food item output information. 
     
    
    
     DETAILED DESCRIPTION 
     The inventor has conceived, and reduced to practice, a system and method for personalized food item designer and culinary fulfillment. The system is a cloud-based network containing a food item design server, portals for restaurants and patrons, to enter their information, and a recipe generator which creates a unique dietary experience for patrons based on a multitude of variables associated with the business enterprises, patrons historic culinary transactions, dietary needs and preferences both explicit and inferred. The system may be accessed through web browsers or purpose-built computer and mobile phone applications. 
     It is frequently the case that a person wishes to order food from a restaurant that meets a set of explicit requirements (e.g. healthy, fast, good price value, etc.) as well as an implicit requirement (reputable establishment, high quality ingredients, etc.). Additionally, the person has longer term nutritional goals (e.g. maintain healthy weight, blood pressure, energy level, etc.) that should be taken in consideration as they decide where and what to dine on. However, achieving the desired outcome using currently available tools is difficult and in doing so often results in a suboptimal experience for the patron and loss of viability for the business. The person could select a restaurant by chance, usually by seeing a sign for a restaurant while driving. Alternately, they could try to find a restaurant by searching using a mobile device. In this case, the person first has to open an application, search for nearby restaurants, and select a restaurant by clicking on it. However, in doing so, the decision is, again, based largely on chance, as the driver is forced to make a restaurant selection from restaurants shown in the nearby area and based only on the restaurant name, which may or may not indicate a type of cuisine (e.g., Italian food, American food, Mexican food, Japanese food, etc.). If the person wishes to get additional information, such as menu options, pricing, etc., the person is forced to take additional steps and time to researching restaurant websites, opening up menus, or calling the restaurant for more information. All of these methods are inefficient and none of them takes into account a myriad of factors that may affect the decision such as the person&#39;s current food preferences, historical culinary transactions, restaurants ingredients on hand and culinary skills available. 
     The invention is particularly useful to both restaurants and their patrons in personalizing and optimizing the dining experience. Personalized food item design enables restaurants to differentiate themselves by offering a unique menu that caters to their patron&#39;s needs while optimizing the food ingredients and culinary skills on hand. Patrons can select food items based on their current and past dietary requirements and preferences. As will be further disclosed herein, the invention makes a multivariate analysis of a large variety of factors (patron preferences; restaurant location, ingredient on-hand, culinary skill; social validation; etc.) to allow a patron to gain access to personalized food items fulfilled by convenient restaurant selection which optimize their dining experience and longer-term dietary goals. 
     While the use case of patrons searching for food at a dining establishment is a primary example used herein, it is important to note that the invention is not so limited, and may be used by any person (e.g., person preparing food from home) seeking to purchase food items or ingredients at any retail business establishment (i.e., the invention is not limited to restaurants, and can be applied to any retail goods, such as grocery stores, on-line and/or brick and mortar; home food inventory). 
     One or more different aspects may be described in the present application. Further, for one or more of the aspects described herein, numerous alternative arrangements may be described; it should be appreciated that these are presented for illustrative purposes only and are not limiting of the aspects contained herein or the claims presented herein in any way. One or more of the arrangements may be widely applicable to numerous aspects, as may be readily apparent from the disclosure. In general, arrangements are described in sufficient detail to enable those skilled in the art to practice one or more of the aspects, and it should be appreciated that other arrangements may be utilized and that structural, logical, software, electrical and other changes may be made without departing from the scope of the particular aspects. Particular features of one or more of the aspects described herein may be described with reference to one or more particular aspects or figures that form a part of the present disclosure, and in which are shown, by way of illustration, specific arrangements of one or more of the aspects. It should be appreciated, however, that such features are not limited to usage in the one or more particular aspects or figures with reference to which they are described. The present disclosure is neither a literal description of all arrangements of one or more of the aspects nor a listing of features of one or more of the aspects that must be present in all arrangements. 
     Headings of sections provided in this patent application and the title of this patent application are for convenience only, and are not to be taken as limiting the disclosure in any way. 
     Devices that are in communication with each other need not be in continuous communication with each other, unless expressly specified otherwise. In addition, devices that are in communication with each other may communicate directly or indirectly through one or more communication means or intermediaries, logical or physical. 
     A description of an aspect with several components in communication with each other does not imply that all such components are required. To the contrary, a variety of optional components may be described to illustrate a wide variety of possible aspects and in order to more fully illustrate one or more aspects. Similarly, although process steps, method steps, algorithms or the like may be described in a sequential order, such processes, methods and algorithms may generally be configured to work in alternate orders, unless specifically stated to the contrary. In other words, any sequence or order of steps that may be described in this patent application does not, in and of itself, indicate a requirement that the steps be performed in that order. The steps of described processes may be performed in any order practical. Further, some steps may be performed simultaneously despite being described or implied as occurring non-simultaneously (e.g., because one step is described after the other step). Moreover, the illustration of a process by its depiction in a drawing does not imply that the illustrated process is exclusive of other variations and modifications thereto, does not imply that the illustrated process or any of its steps are necessary to one or more of the aspects, and does not imply that the illustrated process is preferred. Also, steps are generally described once per aspect, but this does not mean they must occur once, or that they may only occur once each time a process, method, or algorithm is carried out or executed. Some steps may be omitted in some aspects or some occurrences, or some steps may be executed more than once in a given aspect or occurrence. 
     When a single device or article is described herein, it will be readily apparent that more than one device or article may be used in place of a single device or article. Similarly, where more than one device or article is described herein, it will be readily apparent that a single device or article may be used in place of the more than one device or article. 
     The functionality or the features of a device may be alternatively embodied by one or more other devices that are not explicitly described as having such functionality or features. Thus, other aspects need not include the device itself. 
     Techniques and mechanisms described or referenced herein will sometimes be described in singular form for clarity. However, it should be appreciated that particular aspects may include multiple iterations of a technique or multiple instantiations of a mechanism unless noted otherwise. Process descriptions or blocks in figures should be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process. Alternate implementations are included within the scope of various aspects in which, for example, functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those having ordinary skill in the art. 
     Definitions 
     “Business establishment” or “place of business” as used herein mean the location of any business entity with which customers may transact business. Typically, this will be a physical location where customers may enter the location and transact business directly with employees of the business, but may also be a delivery-based business. Many examples herein use a restaurant as the business establishment, but the invention is not limited to use in restaurants, and is applicable to any business establishment. “Patron” is used to reference the customer or prospective customer of the business establishment. 
     Conceptual Architecture 
       FIG. 1  is a block diagram illustrating an exemplary system architecture  110  for a personalized food item design and culinary fulfilment system, according to a preferred aspect. According to an aspect, and using a restaurant as an exemplary business establishment, system  110  comprises a food item design engine  200 , a patron portal  120 , a restaurant portal  140 , databases  150 , and a culinary fulfillment engine  300 . Patron mobile devices  121  may connect to patron portal  120 , typically via a cellular phone network  160 , although connections may be made through other means, as well, such as through Internet  170  (e.g., through a Wi-Fi router). Restaurant computers  141  and/or restaurant mobile devices  131  may connect to restaurant portal  140 , typically through an Internet  170  connection, although other network connections may be used. 
     According to an aspect, a patron may be enroute to a destination, such as her home. The patron may connect to patron portal  120  to pre-enter a variety of preferences and other information that may be stored in a database  150 , and used by food item design engine  200  to suggest personalized food items that meet the patron&#39;s preferences. Examples of the types of preferences that a patron may enter include, but are not limited to: food preferences such as types of food (e.g. ethnicity such as Chinese, American, Greek, as well as for example style such as spicy or soup and salad or steakhouse fare, etc.), frequency with which preferred foods are eaten, ranking of particular foods relative to other foods, patrons inconvenience preferences such as time delays and distance/time required of detour, food attributes such as price, calories, ingredients, and side dishes. In some aspects, certain of these preferences may be determined by system  110 . For example, the types of food preferred by the patron and the frequency with which preferred foods are eaten may be determined based on the culinary transaction history of usage or stored in a database  150  in the system. Other such preferences and factors may also be determined by system through access to one or more external resources  180  such as a health service provider that may include known food allergies, blood pressure history, diabetic information and so forth. Other exemplary external resources may comprise research organizations such as National Library of Medicine, government data sources such as data.gov, corporate sources such as Registry of Open Data (RODA) on Amazon Web Services 
     Likewise, restaurants may connect to restaurant portal  140  to enter information about the restaurant and its menu. Examples of the types of information that a restaurant may enter include, but are not limited to: restaurant name, location, types of food offered, hours of operation, phone number, specific menu offerings, food preparation times for certain dishes (including adjustments to food preparation times during busy periods for the restaurant), prices, calorie counts, ingredients, side dishes, drinks, and special pricing options like daily “happy hour” specials or seasonal offerings. In some aspects, the system may be able to determine certain restaurant information by accessing external resources  180  such as mapping websites and applications. For example, system may access a publicly-available mapping website such as Google maps, which may contain information about the restaurant&#39;s name, location, types of food offered, hours of operation, phone number, etc. Thus, in some aspects, it is not necessary for the restaurant to enter certain information through portal, as the information may be automatically obtained from external resources  180 . 
     When a patron mobile device  121  connects to personalized food item design and culinary fulfilment system  110  and the patron requests en-route food item assistance, food item design engine  200  retrieves the patron preferences from a database  150 . The patron may further enter additional food item preferences and a destination or select a pre-entered destination presented from the patron&#39;s preferences through patron real-time update engine  211 , which will allow the system to better customize its restaurant suggestions. A culinary fulfilment engine  300  then determines the patron&#39;s location by querying the patron&#39;s mobile device for location information (e.g., provided by the mobile device&#39;s GPS hardware, Wi-Fi location applications, etc.) and gathers information from external resources  180  about restaurant options located nearby and along the route from the patron&#39;s currently location to the patron&#39;s destination, as well as traffic information related to the patron&#39;s location, intended route, and identified restaurant options. A culinary fulfilment engine  300  retrieves additional information from a database about identified restaurant options, if such information is available. Based on the patron preferences, restaurant information, and traffic information, culinary fulfilment engine  300  identifies one or more restaurants and one or more food options available at those restaurants that are compatible with the patron&#39;s preferences, and presents the identified restaurants and their corresponding food options to the patron on the patron&#39;s mobile device  121  as suggestions along with indications of the additional delay that will be caused by choosing each suggestion. 
     In some aspects, an application on patron&#39;s mobile device  121  may dial the phone number of the chosen restaurant for the patron to place the order via voice and combination of text message. In an aspect, culinary fulfilment server  300  will contact the restaurant through restaurant portal  140  to automatically enter an order into the restaurant&#39;s computer  141 , or to direct an employee of the restaurant to call the patron&#39;s mobile device  121 , or to establish a voice connection between the restaurant and the patron&#39;s mobile device  121  through another means (e.g., voice over internet protocol, or VOIP). 
     In some aspects, culinary fulfilment engine  300 , through restaurant portal  140 , may also provide information to the restaurant to schedule the restaurant&#39;s food preparation activities to coordinate with the patron&#39;s arrival. If the restaurant has entered information such as food preparation times, culinary fulfilment engine  300  may use that information to instruct the restaurant&#39;s kitchen staff when to start preparation of the patron&#39;s order, such that the order will be ready just prior to arrival of the patron. Such food preparation times and scheduling may be adjusted for busy periods at the restaurant (typically around lunch and dinner) either automatically based on the restaurant&#39;s history as stored in a database  150 , or by retrieving information stored in a database  150  that has been manually entered by the restaurant through restaurant portal  140 . 
       FIG. 2  is a block diagram illustrating an exemplary architecture for an aspect of an automated food item design engine  200 . According to an aspect, a food item design engine  200  comprises several subsystems, a recipe generation subsystem  210 , a recipe optimization subsystem  220 , and a recipe validation subsystem  230 . A recipe generation subsystem comprises a patron real-time update engine  211 , a patron profile  212 , patron culinary transaction  213 , recipe generator engine  214 , restaurant ingredient data  215 , and restaurant recipe data  216 . A patron real-time update engine  211  enables the patron to provide up-to-date food item input by the patron using an application on his or her mobile device  131 . A patron profile  212 , patron culinary transactions  213 , restaurant ingredient data  215 , and restaurant recipe data  216  may be retrieved from a database  150  or, in some aspects, obtained from external resources  180 . 
     A recipe optimization subsystem comprises a recipe optimizer  222 , a health data retriever  221 , and a cost data retriever  223 . A health data retriever  221  obtains health data from external sources  180 , that may include a health provider system, while a cost data retriever  223  may either obtain cost data from a database  150  or from external resources  180 . 
     A recipe validation subsystem  230  comprises patron review data  231 , a validation engine  232 , and wearable data  233 . A validation engine may take as input patron review data, wearable data and personal food item information; and provides as output updates to a food item recipe to a recipe optimizer  222 . 
     In operation, when a patron is desiring food item assistance a recipe generator engine  214  receives the patron&#39;s current food item requirements from a patron real time update engine  211  along with a patron profile  213 . A recipe generation engine  214  obtains restaurant ingredient data  215  and restaurant recipe data  216  for one or more restaurants either from a database  150  or from external resources  180 . A recipe generation engine  214  then uses machine learning algorithms to create a personalized food item optimized to meet the patron preferences and outcomes. 
     A recipe generator engine  214  presents recommendations to the patron about food items meeting the patron&#39;s preferences and allows the patron to select an option on his or her mobile device  121  by simply selecting an option (on a touch-based mobile device interface, for example). A recipe generator engine  214  then sends the information about the selected recipe to a recipe optimizer  222 , which obtains health data from health data retriever  221  and cost from cost data retriever  223  and optimizes the recipe. Optimization may occur around one or more parameters including health, cost, restaurant dining experience, etc. depending on patrons near and long-range goals and stated outcomes. Once complete, recipe optimizer engine  222  sends personalized recipe information  241  to a culinary fulfillment engine  300 . 
     In some aspects, food item design  200  engine may have a recipe validation subsystem  230 , in which a validation engine  232  receives feedback from the patron&#39;s experience from patron review data  231  and patrons wearable information  233  and associates with a personalized food item  242 . The feedback in the form of subjective text comments and/or objective measurements (e.g. blood pressure, glucose levels) may then be updated in the patron&#39;s culinary transactions  213  for use in future food item optimization. 
     Note that this example is simplified for clarity, and that food item design engine  200  will address a much broader set of factors and variables, as described elsewhere herein. The food item design engine may use any number of optimization algorithms, including machine learning algorithms or others known in the art, to find optimal solutions to the large number of variables presented. 
       FIG. 3  is a block diagram illustrating an exemplary architecture for an aspect of an automated culinary fulfilment engine. According to an aspect, culinary fulfilment engine  300  comprises, a restaurant recommendation system  310 , comprising a personalized recipe information  241 , patron location data  312 , traffic data  313 , a recommendation engine  314 , restaurant location data  315 , restaurant skill data  316 , restaurant review data  317 , culinary preparation information  318 , and patron personalized food item  242 . 
     In operation, recommendation engine  314  will take as inputs a personalized recipe information  241 , patron location data  312 , traffic data  313 , restaurant location data  315 , restaurant skill data  316 , restaurant review data  317 . Using semantic vector space methods familiar to those skilled in the art, the input data is represented as word vector and compared using cosine similarity techniques with the optimized target vector to provide as outputs a culinary preparation information  318  that is used by the restaurant and a patron personalized food item  242  that is displayed to the patron. 
     Detailed Description of Exemplary Aspects 
       FIG. 4  is a flow diagram showing the steps of an exemplary method for personalized food item design, selection, restaurant selection, order fulfilment by selected restaurant. A patron portal is provided for the patron to pre-enter preferences such as food types, food attributes, diet restrictions, health goals, and other preferences  401  this information is subsequently stored in a historical database  403  for future use. During mealtime and/or when patron is mobile, the patron is presented with an interface on mobile app to make real-time preferences on meal interests or desires for food ingestion, the app may ask “for dining, what are you in the mood for?”  402 . An analysis (as further exemplified in  FIG. 5 ) is performed on patrons historical and real-time food item requirements and compared to menu options and culinary capabilities of restaurants in proximity of patron  404  from which a consumer specific food item is generated  405 . The food item options  406  are displayed to the patron, along with a recommended restaurant, with details such as type of food, food cost, additional drive time  407 . A choice is made from the patron  408  for one or more food item displayed with its recommended restaurant. The patron&#39;s food item information is sent to the restaurant, confirmation to patron and food item fulfilment  409 . Display food item confirmation along with restaurant details including restaurant address, driving, estimated travel time and estimated food item availability  410 . Notify and update patron on order status and restaurant fulfilment  411 . 
       FIG. 5  is a flow diagram showing the steps of an exemplary method for an optimized food item recommendation to a particular restaurant patron based upon their preferences and patron profile. Convert patron food item text documents to corresponding word vector  501 . Convert restaurant recipe, restaurant ingredient data and culinary preparation skill text documents to corresponding word vectors  502 . Using a matrix dimension reduction technique such as principal dimension analysis or others known to those skilled in the art, reduce the input matrix for more effective processing. Compare resultant vectors using semantic term vector space techniques known to one skilled in the art  503 . Select restaurant word vector that is most similar to the patron food item requirement  504 . Modify restaurant recipe items based on restaurant ingredients, culinary capabilities to most closely align to patron&#39;s requirements  505 . Output food item description and recipe to patron and restaurant  506 . 
       FIG. 6  is a flow diagram showing the steps of an exemplary method for an optimized food item based on the restaurants&#39; food ingredients on hand, culinary skills and a predicted preference of a patron. Convert aggregate historical patron food item text documents to corresponding word vectors to represent generalized patron food profile  601 . Convert restaurant recipe and culinary preparation text documents to corresponding word vectors  602 . Compare resultant vectors using term vector space techniques  603 . Select restaurant word vector that is most similar to the generalized patron food item requirement  604 . Modify restaurant recipe items based on restaurant ingredients, culinary capabilities to most closely align to generic patron&#39;s requirements  605 . Output food item menu to patron  606 . 
     An exemplary semantic comparison method may include term vector space analysis technique to those familiar in the art. Term vector modeling is an algebraic model for representing text and text documents as vectors. Each term or word in a text document typically corresponds to a dimension in that vector. Once a text document is described as a word vector, comparisons between two vectors may be made using vector calculus. One useful technique to determine similarities between documents is by comparing the deviation of angles between each document vector and the original query vector where the query is represented as a vector with same dimension as the vectors that represent the other documents. 
     An exemplary dimensional reduction technique familiar to those skilled in the art is Principal Component Analysis (“PCA”), which may be used to optimize the variables prior to vectorization to reduce dimensionality of resulting vectors prior to feeding into a machine learning algorithm. 
     An exemplary recipe optimization method may include deep learning techniques familiar to those skilled in the art. One such form of deep learning that is particularly useful when generating text is Recurrent Neural Networks (“RNN”) using long short-term memory (“LSTMs”) units or cells. A single LSTM is comprised of a memory-containing cell, an input gate, an output gate and a forget gate. The input and forget gate determine how much of incoming values transit to the output gate and the activation function of the gates is usually a logistic function. The initial input data will cause the model to learn the weights of connections that influence the activity of these gates which will impact the resultant output. To generate unique personalized recipes for a given patron, standard recipes along with the patron profile data are fed into the input gate of the RNN, in turn the RNN will learn what&#39;s important to the patron and create unique recipe outputs. 
       FIG. 11  is a message diagram showing exemplary messaging between patron device  110  and recipe generation system with output to the recipe optimizer  222 . Initially, a patron device  110  connects to a patron portal  120  to submit a food item request. The request may then be relayed by the patron portal  120  to a patron realtime update engine  211 , which then relays the request to a recipe generator engine  214  and updates the patron&#39;s profile in a database  150 . Recipe generator engine  214  acknowledges the request and retrieves stored patron profile and previous culinary transactions from the database  150 , and uses this information to generate personalized recipe data for the specific patron that is then sent to the recipe optimizer  222 . 
       FIG. 12  is a message diagram showing exemplary messaging within the recipe optimization system taking inputs from a recipe generation system and a recipe validation system and providing an optimized personalized recipe information as an output to restaurant recommendation system. Patron review data  231 , submitted by patrons, and patron wearable data  233 , transmitted by wearable devices patrons may be wearing, are received at a validation engine  232 . Validation engine  232  uses this information to produce a validated recipe rating that is sent to a recipe optimizer  222 , which then retrieves patron health data and cost data associated with the recipe (for example, ingredient costs and prep times) using health data retriever  221  and cost data retriever  223 , respectively. This information is used to further adjust the recipe and produce personalized recipe information  241 , which is then sent as output to a recommendation engine  314 . 
       FIG. 13  is a message diagram showing exemplary messaging within a restaurant recommendation system with various inputs and providing culinary preparation and personalized food item output information. Personalized recipe information  241  is received at a recommendation engine  314  from a recipe optimizer  222 , as described above (with reference to  FIG. 12 ). Recommendation engine  314  also receives information from a number of sources to assist with producing a specific recipe recommendation, including (but not limited to) patron location data  312 , traffic data  313 , restaurant location data  315 , restaurant skill data  316  (such as the skills of individual chefs that are working at the time), and restaurant review data  317 . This aggregated information may then be used to produce a patron-specific personalized food item  242 , along with a set of culinary instructions for preparing the patron-specific item that may be sent as culinary preparation information  318 . 
     Hardware Architecture 
     Generally, the techniques disclosed herein may be implemented on hardware or a combination of software and hardware. For example, they may be implemented in an operating system kernel, in a separate user process, in a library package bound into network applications, on a specially constructed machine, on an application-specific integrated circuit (ASIC), or on a network interface card. 
     Software/hardware hybrid implementations of at least some of the aspects disclosed herein may be implemented on a programmable network-resident machine (which should be understood to include intermittently connected network-aware machines) selectively activated or reconfigured by a computer program stored in memory. Such network devices may have multiple network interfaces that may be configured or designed to utilize different types of network communication protocols. A general architecture for some of these machines may be described herein in order to illustrate one or more exemplary means by which a given unit of functionality may be implemented. According to specific aspects, at least some of the features or functionalities of the various aspects disclosed herein may be implemented on one or more general-purpose computers associated with one or more networks, such as for example an end-user computer system, a client computer, a network server or other server system, a mobile computing device (e.g., tablet computing device, mobile phone, smartphone, laptop, or other appropriate computing device), a consumer electronic device, a music player, or any other suitable electronic device, router, switch, or other suitable device, or any combination thereof. In at least some aspects, at least some of the features or functionalities of the various aspects disclosed herein may be implemented in one or more virtualized computing environments (e.g., network computing clouds, virtual machines hosted on one or more physical computing machines, or other appropriate virtual environments). 
     Referring now to  FIG. 7 , there is shown a block diagram depicting an exemplary computing device  10  suitable for implementing at least a portion of the features or functionalities disclosed herein. Computing device  10  may be, for example, any one of the computing machines listed in the previous paragraph, or indeed any other electronic device capable of executing software- or hardware-based instructions according to one or more programs stored in memory. Computing device  10  may be configured to communicate with a plurality of other computing devices, such as clients or servers, over communications networks such as a wide area network a metropolitan area network, a local area network, a wireless network, the Internet, or any other network, using known protocols for such communication, whether wireless or wired. 
     In one aspect, computing device  10  includes one or more central processing units (CPU)  12 , one or more interfaces  15 , and one or more busses  14  (such as a peripheral component interconnect (PCI) bus). When acting under the control of appropriate software or firmware, CPU  12  may be responsible for implementing specific functions associated with the functions of a specifically configured computing device or machine. For example, in at least one aspect, a computing device  10  may be configured or designed to function as a server system utilizing CPU  12 , local memory  11  and/or remote memory  16 , and interface(s)  15 . In at least one aspect, CPU  12  may be caused to perform one or more of the different types of functions and/or operations under the control of software modules or components, which for example, may include an operating system and any appropriate applications software, drivers, and the like. 
     CPU  12  may include one or more processors  13  such as, for example, a processor from one of the Intel, ARM, Qualcomm, and AMD families of microprocessors. In some aspects, processors  13  may include specially designed hardware such as application-specific integrated circuits (ASICs), electrically erasable programmable read-only memories (EEPROMs), field-programmable gate arrays (FPGAs), and so forth, for controlling operations of computing device  10 . In a particular aspect, a local memory  11  (such as non-volatile random access memory (RAM) and/or read-only memory (ROM), including for example one or more levels of cached memory) may also form part of CPU  12 . However, there are many different ways in which memory may be coupled to system  10 . Memory  11  may be used for a variety of purposes such as, for example, caching and/or storing data, programming instructions, and the like. It should be further appreciated that CPU  12  may be one of a variety of system-on-a-chip (SOC) type hardware that may include additional hardware such as memory or graphics processing chips, such as a QUALCOMM SNAPDRAGON™ or SAMSUNG EXYNOS™ CPU as are becoming increasingly common in the art, such as for use in mobile devices or integrated devices. 
     As used herein, the term “processor” is not limited merely to those integrated circuits referred to in the art as a processor, a mobile processor, or a microprocessor, but broadly refers to a microcontroller, a microcomputer, a programmable logic controller, an application-specific integrated circuit, and any other programmable circuit. 
     In one aspect, interfaces  15  are provided as network interface cards (NICs). Generally, NICs control the sending and receiving of data packets over a computer network; other types of interfaces  15  may for example support other peripherals used with computing device  10 . Among the interfaces that may be provided are Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, graphics interfaces, and the like. In addition, various types of interfaces may be provided such as, for example, universal serial bus (USB), Serial, Ethernet, FIREWIRE™, THUNDERBOLT™, PCI, parallel, radio frequency (RF), BLUETOOTH™, near-field communications (e.g., using near-field magnetics), 802.11 (Wi-Fi), frame relay, TCP/IP, ISDN, fast Ethernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) or external SATA (ESATA) interfaces, high-definition multimedia interface (HDMI), digital visual interface (DVI), analog or digital audio interfaces, asynchronous transfer mode (ATM) interfaces, high-speed serial interface (HSSI) interfaces, Point of Sale (POS) interfaces, fiber data distributed interfaces (FDDIs), and the like. Generally, such interfaces  15  may include physical ports appropriate for communication with appropriate media. In some cases, they may also include an independent processor (such as a dedicated audio or video processor, as is common in the art for high-fidelity A/V hardware interfaces) and, in some instances, volatile and/or non-volatile memory (e.g., RAM). 
     Although the system shown in  FIG. 7  illustrates one specific architecture for a computing device  10  for implementing one or more of the aspects described herein, it is by no means the only device architecture on which at least a portion of the features and techniques described herein may be implemented. For example, architectures having one or any number of processors  13  may be used, and such processors  13  may be present in a single device or distributed among any number of devices. In one aspect, a single processor  13  handles communications as well as routing computations, while in other aspects a separate dedicated communications processor may be provided. In various aspects, different types of features or functionalities may be implemented in a system according to the aspect that includes a client device (such as a tablet device or smartphone running client software) and server systems (such as a server system described in more detail below). 
     Regardless of network device configuration, the system of an aspect may employ one or more memories or memory modules (such as, for example, remote memory block  16  and local memory  11 ) configured to store data, program instructions for the general-purpose network operations, or other information relating to the functionality of the aspects described herein (or any combinations of the above). Program instructions may control execution of or comprise an operating system and/or one or more applications, for example. Memory  16  or memories  11 ,  16  may also be configured to store data structures, configuration data, encryption data, historical system operations information, or any other specific or generic non-program information described herein. 
     Because such information and program instructions may be employed to implement one or more systems or methods described herein, at least some network device aspects may include nontransitory machine-readable storage media, which, for example, may be configured or designed to store program instructions, state information, and the like for performing various operations described herein. Examples of such nontransitory machine-readable storage media include, but are not limited to, magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROM disks; magneto-optical media such as optical disks, and hardware devices that are specially configured to store and perform program instructions, such as read-only memory devices (ROM), flash memory (as is common in mobile devices and integrated systems), solid state drives (SSD) and “hybrid SSD” storage drives that may combine physical components of solid state and hard disk drives in a single hardware device (as are becoming increasingly common in the art with regard to personal computers), memristor memory, random access memory (RAM), and the like. It should be appreciated that such storage means may be integral and non-removable (such as RAM hardware modules that may be soldered onto a motherboard or otherwise integrated into an electronic device), or they may be removable such as swappable flash memory modules (such as “thumb drives” or other removable media designed for rapidly exchanging physical storage devices), “hot-swappable” hard disk drives or solid state drives, removable optical storage discs, or other such removable media, and that such integral and removable storage media may be utilized interchangeably. Examples of program instructions include both object code, such as may be produced by a compiler, machine code, such as may be produced by an assembler or a linker, byte code, such as may be generated by for example a JAVA™ compiler and may be executed using a Java virtual machine or equivalent, or files containing higher level code that may be executed by the computer using an interpreter (for example, scripts written in Python, Perl, Ruby, Groovy, or any other scripting language). 
     In some aspects, systems may be implemented on a standalone computing system. Referring now to  FIG. 8 , there is shown a block diagram depicting a typical exemplary architecture of one or more aspects or components thereof on a standalone computing system. Computing device  20  includes processors  21  that may run software that carry out one or more functions or applications of aspects, such as for example a client application  24 . Processors  21  may carry out computing instructions under control of an operating system  22  such as, for example, a version of MICROSOFT WINDOWS™ operating system, APPLE macOS™ or iOS™ operating systems, some variety of the Linux operating system, ANDROID™ operating system, or the like. In many cases, one or more shared services  23  may be operable in system  20  and may be useful for providing common services to client applications  24 . Services  23  may for example be WINDOWS™ services, user-space common services in a Linux environment, or any other type of common service architecture used with operating system  21 . Input devices  28  may be of any type suitable for receiving user input, including for example a keyboard, touchscreen, microphone (for example, for voice input), mouse, touchpad, trackball, or any combination thereof. Output devices  27  may be of any type suitable for providing output to one or more users, whether remote or local to system  20 , and may include for example one or more screens for visual output, speakers, printers, or any combination thereof. Memory  25  may be random-access memory having any structure and architecture known in the art, for use by processors  21 , for example to run software. Storage devices  26  may be any magnetic, optical, mechanical, memristor, or electrical storage device for storage of data in digital form (such as those described above, referring to  FIG. 8 ). Examples of storage devices  26  include flash memory, magnetic hard drive, CD-ROM, and/or the like. 
     In some aspects, systems may be implemented on a distributed computing network, such as one having any number of clients and/or servers. Referring now to  FIG. 9 , there is shown a block diagram depicting an exemplary architecture  30  for implementing at least a portion of a system according to one aspect on a distributed computing network. According to the aspect, any number of clients  33  may be provided. Each client  33  may run software for implementing client-side portions of a system; clients may comprise a system  20  such as that illustrated in  FIG. 8 . In addition, any number of servers  32  may be provided for handling requests received from one or more clients  33 . Clients  33  and servers  32  may communicate with one another via one or more electronic networks  31 , which may be in various aspects any of the Internet, a wide area network, a mobile telephony network (such as CDMA or GSM cellular networks), a wireless network (such as Wi-Fi, WiMAX, LTE, and so forth), or a local area network (or indeed any network topology known in the art; the aspect does not prefer any one network topology over any other). Networks  31  may be implemented using any known network protocols, including for example wired and/or wireless protocols. 
     In addition, in some aspects, servers  32  may call external services  37  when needed to obtain additional information, or to refer to additional data concerning a particular call. Communications with external services  37  may take place, for example, via one or more networks  31 . In various aspects, external services  37  may comprise web-enabled services or functionality related to or installed on the hardware device itself. For example, in one aspect where client applications  24  are implemented on a smartphone or other electronic device, client applications  24  may obtain information stored in a server system  32  in the cloud or on an external service  37  deployed on one or more of a particular enterprise&#39;s or user&#39;s premises. In addition to local storage on servers  32 , remote storage  38  may be accessible through the network(s)  31 . 
     In some aspects, clients  33  or servers  32  (or both) may make use of one or more specialized services or appliances that may be deployed locally or remotely across one or more networks  31 . For example, one or more databases  34  in either local or remote storage  38  may be used or referred to by one or more aspects. It should be understood by one having ordinary skill in the art that databases in storage  34  may be arranged in a wide variety of architectures and using a wide variety of data access and manipulation means. For example, in various aspects one or more databases in storage  34  may comprise a relational database system using a structured query language (SQL), while others may comprise an alternative data storage technology such as those referred to in the art as “NoSQL” (for example, HADOOP CASSANDRA™, GOOGLE BIGTABLE™, and so forth). In some aspects, variant database architectures such as column-oriented databases, in-memory databases, clustered databases, distributed databases, or even flat file data repositories may be used according to the aspect. It will be appreciated by one having ordinary skill in the art that any combination of known or future database technologies may be used as appropriate, unless a specific database technology or a specific arrangement of components is specified for a particular aspect described herein. Moreover, it should be appreciated that the term “database” as used herein may refer to a physical database machine, a cluster of machines acting as a single database system, or a logical database within an overall database management system. Unless a specific meaning is specified for a given use of the term “database”, it should be construed to mean any of these senses of the word, all of which are understood as a plain meaning of the term “database” by those having ordinary skill in the art. 
     Similarly, some aspects may make use of one or more security systems  36  and configuration systems  35 . Security and configuration management are common information technology (IT) and web functions, and some amount of each are generally associated with any IT or web systems. It should be understood by one having ordinary skill in the art that any configuration or security subsystems known in the art now or in the future may be used in conjunction with aspects without limitation, unless a specific security  36  or configuration system  35  or approach is specifically required by the description of any specific aspect. 
       FIG. 10  shows an exemplary overview of a computer system  40  as may be used in any of the various locations throughout the system. It is exemplary of any computer that may execute code to process data. Various modifications and changes may be made to computer system  40  without departing from the broader scope of the system and method disclosed herein. Central processor unit (CPU)  41  is connected to bus  42 , to which bus is also connected memory  43 , nonvolatile memory  44 , display  47 , input/output (I/O) unit  48 , and network interface card (NIC)  53 . I/O unit  48  may, typically, be connected to peripherals such as a keyboard  49 , pointing device  50 , hard disk  52 , real-time clock  51 , a camera  57 , and other peripheral devices. NIC  53  connects to network  54 , which may be the Internet or a local network, which local network may or may not have connections to the Internet. The system may be connected to other computing devices through the network via a router  55 , wireless local area network  56 , or any other network connection. Also shown as part of system  40  is power supply unit  45  connected, in this example, to a main alternating current (AC) supply  46 . Not shown are batteries that could be present, and many other devices and modifications that are well known but are not applicable to the specific novel functions of the current system and method disclosed herein. It should be appreciated that some or all components illustrated may be combined, such as in various integrated applications, for example Qualcomm or Samsung system-on-a-chip (SOC) devices, or whenever it may be appropriate to combine multiple capabilities or functions into a single hardware device (for instance, in mobile devices such as smartphones, video game consoles, in-vehicle computer systems such as navigation or multimedia systems in automobiles, or other integrated hardware devices). 
     In various aspects, functionality for implementing systems or methods of various aspects may be distributed among any number of client and/or server components. For example, various software modules may be implemented for performing various functions in connection with a system of any particular aspect, and such modules may be variously implemented to run on server and/or client components. 
     According to an aspect, restaurant menu optimization and experimentation may be performed with a patron who enters a restaurant with a known patron profile. The system may predict and offer highly desirable “chefs specials” that satisfies the patron preferences by making variations of known dishes on the restaurant menu. The “chef&#39;s special” are automatically designed by system and may include Artificial Intelligent methods familiar to those skilled in the art. 
     According to another aspect, restaurant menu optimization and experimentation may be performed with a patron who enters a restaurant with an unknown patron profile. The system may predict and offer highly desirable “chefs specials” that provide A/B experimentation by making variations of known dishes on the restaurant menu and then by tuning the menu to provide an optimal patron menu. The “chef&#39;s special” are automatically designed by system and may include Artificial Intelligent methods familiar to those skilled in the art. 
     According to another aspect, recipe optimization may be performed on multiple patrons at the same time as may be the case for dining parties of two or more at a restaurant. For example, in a party of four seated at the same table, of whom three have profile information available to system, and one with a raspberry allergy and one is gluten intolerant. The system may predict and offer highly desirable “chefs specials” that satisfy each persons preferences amongst those whom food preferences are known while avoiding allergic ingredients for the whole table. The “chef&#39;s special” may include Artificial Intelligent methods familiar to those skilled in the art. 
     According to another aspect, patron wearable devices may provide real-time feedback directly into the food design system. For example, a Continuous Glucose Monitor (GCM) may provide input into the recipe device, and based on patron current glucose level offer additional/different options for a choice of dessert and/or menu options for future meals. 
     According to another aspect, a home food inventory system may be used as input into a recipe generator to provide food preparation options based on current in home food inventory. 
     The skilled person will be aware of a range of possible modifications of the various aspects described above. Accordingly, the present invention is defined by the claims and their equivalents.