Patent Publication Number: US-2021174459-A1

Title: Method and system for a food socio-touristic media with food recognition capability using artificial intelligence lazy predictor, social media, and incentivized gamification

Description:
FIELD OF THE INVENTION 
     The present invention relates generally to a social media platform. More specifically, the present invention relates to a food recognition method applicable in a food social media platform using artificial intelligence, human intelligence, and gamification. 
     BACKGROUND ART 
     Today, artificial intelligence and machine learning have become very popular in many commercial and industrial applications such as E-commerce, chatbots, image search, customer data analytics, recommendation systems, inventory management, cybersecurity, after sales service, customer relationship management (CRM), and sales improvement. In food recognition, deep convolutional neural network (CNN) technologies have been used to identify unknown foods that have many applications in food industry and medicines. 
     However, the conventional artificial intelligence and deep learning in the CNN technologies are very limited since they can only identify 87% of a limited amount of dishes in a known region. When asked to recognize foods in different regions, artificial intelligence often fails to perform. For the vast varieties of foods around the world, the artificial intelligence cannot distinguish similar foods and beverages such as pho bo (Vietnamese beef noodle soup) with Chinese beef noddle soup, Vietnamese coffee and condensed milk and Italian cappuccino, etc. 
     No artificial intelligence (AI) is better than human intelligence, especially in the area of food image identification. This is particularly true when food experts, chefs, and food lovers around the world can participate in a social media to identify food images posted therein. As of today, no artificial intelligence system can accurately recognize the complex and rich foods from around the world. If there were one, it would have been a very large and expensive artificial intelligence system. 
     Therefore what is needed is method and system that can recognize a rich variety of foods around the world using both artificial intelligence, deep learning technology, and human intelligence from a social media. 
     What is needed is a social media that utilizes gamification to incentivize human intelligence to participate in the food recognition efforts. 
     What is needed is social media that can promote food tourism using artificial intelligence, deep learning technology, and human intelligence. 
     What is needed is an inexpensive and simple network that can recognize the complex and rich food dishes from around the world that can be used in different applications such as tourism, dietary science, etc. 
     SUMMARY OF THE INVENTION 
     Accordingly, an object of the present invention is to provide a system for a food socio-touristic media platform and a method for food recognition that include: a data center configured to store a set of N known food dishes, each having M features, where N and M are a non-zero positive integers; a web server capable of launching the food socio-touristic media platform to a plurality of users; a computing engine further configured to receive food inputs from the plurality of users and perform a lazy predictor algorithm to recognize the food inputs and related parameters; and when the computing engine fails to identify the food inputs and related parameters, the computing engine receives answers from the plurality of users from the socio-touristic media platform and updates the group of N known food dishes as a deep learning mechanism. 
     Another object of the present invention is to provide a method of identifying unknown food dishes which includes: storing a set of N known food dishes in a database, each having M features, where N and M are non-zero positive integers; building a social media platform configured to connect a plurality of users together; calculating distances for the unknown food dishes and the N known food dishes in an M coordinate space formed by the M features; selecting only distances of the N known food dishes that are closest to those of unknown food dishes; if the distances of the N known food dishes that are closest to those of the unknown food dishes are undeterminable, posting the unknown food dishes in the social media asking the plurality of users to identify the unknown food dishes; and increasing the set of N known food dishes to include the unknown food dishes that are identified by the plurality of users. 
     Another object of the present invention is to provide a socio-touristic media platform that includes: a forum where a plurality of users are enabled to post questions regarding food inputs and related parameters which include similar food dishes, a group of users who also like those food inputs and similar food dishes, and restaurants that offer the food inputs and such similar food dishes; a social network where the plurality of users are enabled to maintain and update friend lists, to receive display options, and to notify alert options; a food tourism where the plurality of users are enabled to receive recommendations and/or receive answers from either the plurality of users or a computing engines; and a gamification where the plurality of users are incentivized to provide answers to the food inputs and related parameters. 
     Yet another object of the present invention is to combine both human intelligence and artificial intelligence in food recognition using a social media and deep learning algorithm. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The patent or application file contains a least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee. 
       The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention. 
         FIG. 1  is a flow chart illustrating a food recognition method using an artificial intelligence lazy predictor, a social media, gamification, and deep learning algorithm in accordance with an exemplary embodiment of the present invention; 
         FIG. 2  is a multi-dimension vector space illustrating the food recognition method that uses the artificial intelligence lazy predictor, social media, and incentivized gamification in accordance with an exemplary embodiment of the present invention; 
         FIG. 3  is a flow chart of the Hatto™ food socio-touristic media software program that utilizes the food recognition method described in  FIG. 1  and  FIG. 2  in accordance with an exemplary embodiment of the present invention; 
         FIG. 4  is an organization of the Hatto™ food socio-touristic media in accordance with an exemplary embodiment of the present invention; 
         FIG. 5  is a perspective schematic diagram of the Hatto™ food socio-touristic network in accordance with an exemplary embodiment of the present invention; 
         FIG. 6  is a flow chart of the input and search algorithm in the Hatto™ food socio-touristic media software program that utilizes the food recognition method described in  FIG. 1  and  FIG. 2  in accordance with an exemplary embodiment of the present invention; 
         FIG. 7  is a flow chart of the food tourism algorithm in the Hatto™ food socio-touristic media software program that utilizes the food recognition method described in  FIG. 1  and  FIG. 2  in accordance with an exemplary embodiment of the present invention; 
         FIG. 8  is a flow chart of the incentivized gamification in the Hatto™ food socio-touristic media software program that utilizes the food recognition method described in  FIG. 1  and  FIG. 2  in accordance with an exemplary embodiment of the present invention; 
         FIG. 9  is a flow chart of the dis-incentivized gamification in the Hatto™ food socio-touristic media software program that utilizes the food recognition method described in  FIG. 1  and  FIG. 2  in accordance with an exemplary embodiment of the present invention; 
         FIG. 10  a flow chart of the status algorithm in the Hatto™ food socio-touristic media software program that utilizes the food recognition method described in  FIG. 1  and  FIG. 2  in accordance with an exemplary embodiment of the present invention; 
         FIG. 11  illustrates a comprehensive hardware structure of the Hatto™ food socio-touristic media in accordance with an embodiment of the present invention; 
         FIG. 12A - FIG. 12B  illustrate a log-in page and a personal page of the Hatto™ food socio-touristic media in accordance with an exemplary embodiment of the present invention; 
         FIG. 13A - FIG. 13B  illustrate a camera application and food inputs in the Hatto™ food socio-touristic media in accordance with an exemplary embodiment of the present invention; 
         FIG. 14A - FIG. 14C  illustrate the search result page and the forum page of the Hatto™ food socio-touristic media in accordance with an exemplary embodiment of the present invention; 
         FIG. 15A - FIG. 15C  illustrate the recommendation pages of the Hatto™ food socio-touristic media in accordance with an exemplary embodiment of the present invention; 
         FIG. 16A - FIG. 16C  illustrate the restaurant location recommendation pages of the Hatto™ food socio-touristic media in accordance with an exemplary embodiment of the present invention; 
         FIG. 17A - FIG. 17C  illustrate the forum pages of the Hatto™ food socio-touristic media in accordance with an exemplary embodiment of the present invention; 
         FIG. 17A - FIG. 17C  illustrate first-level prize (watermelons) rewarding and notification pages of the Hatto™ food socio-touristic media in accordance with an exemplary embodiment of the present invention; 
         FIG. 18A - FIG. 18C  illustrate first-level prize awards in the gamification section of the Hatto™ food socio-touristic media in accordance with an exemplary embodiment of the present invention; 
         FIG. 19A - FIG. 19C  illustrate prize exchange, reward status, and forum pages of the Hatto™ food socio-touristic media in accordance with an exemplary embodiment of the present invention; 
         FIG. 20A - FIG. 20C  illustrate product purchasing pages of the Hatto™ food socio-touristic media in accordance with an exemplary embodiment of the present invention; and 
         FIG. 21A - FIG. 21C  illustrate QR codes in the product purchasing pages of the Hatto™ food socio-touristic media in accordance with an exemplary embodiment of the present invention. 
     
    
    
     The figures depict various embodiments of the technology for the purposes of illustration only. A person of ordinary skill in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the technology described herein. 
     DETAILED DESCRIPTION OF THE INVENTION 
     Reference will now be made in details to the preferred embodiments of the invention, examples of which are illustrated in the accompanying drawings. While the invention will be described in conjunction with the preferred embodiments, it will be understood that they are not intended to limit the invention to these embodiments. On the contrary, the invention is intended to cover alternatives, modifications and equivalents, which may be included within the spirit and scope of the invention as defined by the appended claims. Furthermore, in the following detailed description of the present invention, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be obvious to one of ordinary skill in the art that the present invention may be practiced without these specific details. In other instances, well-known methods, procedures, components, and circuits have not been described in details so as not to unnecessarily obscure aspects of the present invention. 
     Exemplary embodiments and aspects of the present invention are now described with reference to  FIGS. 1 to 21 . The present disclosure discloses the following features of the present invention: (1) a system for rendering a socio-touristic media platform that uses the novel Hatto™ food recognition method, (2) a method for food recognition that uses artificial intelligence in combination with human intelligence in a social media (AI) lazy predictor and knowledge of the social media that provides deep learning platform supplemental to the AI lazy predictor, and (3) a socio-touristic media platform using (1) and (2) that can promote Hatto™ food tourism and social network.  FIG. 1 - FIG. 11  illustrate algorithms and system for the food socio-touristic media platform of the present invention.  FIG. 12 - FIG. 21  illustrate different display pages of the Hatto™ socio-touristic media platform as the results of (1)-(3) on a communication device. 
     Now referring to  FIG. 1 ,  FIG. 1  is a flow chart illustrating a food recognition method  100  using artificial intelligence (AI) lazy predictor, social media, incentivized gamification, and deep learning algorithm in accordance with an exemplary embodiment of the present invention. In a generalized structure of the present invention, food recognition method  100  includes 4 major components: a preparation step  110 , an artificial intelligence search step  120 , a social-media search step  130 , a deep learning step  140 , and a gamification step  150 . That is, in essence, the present invention involves food recognition using artificial intelligence combined with human expert intelligence in a social-media incentivized by gamification aspect, and a deep learning algorithm using the human intelligence to assist and update the artificial intelligence step. 
     Preparation step  110  includes a begin step  111 , a food socio-media building step  112 , a database constructing step  113 , and an input step  114 . 
     First, at step  111 , method  100  begins by preparing materials necessary to perform the subsequent steps of method  100 . Preparatory materials include gathering known food dishes to teach the artificial intelligence. Materials include newspapers articles, cook books, recipes, documents, and/or expert opinions to train the machine learning algorithsm and to check the validity of food input inquiries; hardware and software to build the food socio-media; artificial intelligence system; and the deep learning algorithms. 
     At step  112 , a food socio-touristic media is built. The food socio-touristic media is a dynamic and interactive website that allows users to make friends, submit food input inquiries, chat with friends in a forum, promote tourism, play games, etc. In an implementation of step  112 , the food socio-touristic media is built using either WordPress, C++, Java, PHP, Pearl, or Python programming languages. In various embodiments of the present invention, the food socio-touristic media includes interactive or touchscreen displays which will be presented and described in  FIG. 12 - FIG. 21 . 
     At step  113 , a set of M known food dishes and their associated N features are stored in a database, where M and N are a non-zero positive integer numbers (M, NϵI + ). In an exemplary embodiment of the present invention, M is chosen to be 327 and M be 1,792. That is, 1,792 features are selected from each dish among the 327 known food dishes. These 1,792 features are extracted from the analytical picture recognition categories such as color descriptors, texture descriptors, image segmentation, and food classification. First, in each known food dish, an image segmentation is performed by analyzing 1,000 images of a known food dish. Image segmentation is used to distinguish the components of each food dish. For example, a known cheeseburger and French fries dish shall include the buns, the burgers, cheese, salad, tomatoes, mayonnaise, onion, bacons, French fries, and other components such as ketchup. Each component has an edge region and interior region. Two components; i.e., the burgers and the buns; are segmented if the differences between the edge regions and the interior regions are large. 
     Continuing with step  113 , after image segmentation is complete, food classification is performed. In some embodiments, four color descriptors namely Scalable Color Descriptor (SCD), Color Structure Descriptor (CSD), Dominant Color Descriptor (DCD), and Color Layout Descriptor (CLD) in MPEG-7 are used. SCD is a color histogram descriptor in HSV Color Space with a uniform quantization of the HSV space. CSD expresses local color structure in HMMD color space using an I by J scanning the image. HMMD color space includes hue, shade (max.), tint (min.), and brightness of a color (differential). DCD uses color clustering to extract a small number of representing colors and their percentages from a segmented region in the perceptually uniform CIE LUV color space. CLD is used to capture the spatial distribution of color in a segmented region. The segmented region is divided into small blocks. The average color of each block in YCrCb color space is calculated to form the descriptor. 
     Continuing with step  113 , similar to color, texture is a very descriptive low-level feature for image search and matching applications. In the present invention, the following three texture descriptors for food classification are used: Gradient Orientation Spatial-Dependence Matrix (GOSDM), Entropy-Based Categorization and Fractal Dimension Estimation (EFD) and Gabor-Based Image Decomposition and Fractal Dimension Estimation (GFD). GOSDM consists of a set of gradient orientation spatial dependence matrices to describe the texture by the occurrence rate of the spatial relationship of gradient orientations for different neighborhood size. EFD is an attempt to characterize the variation of roughness of homogeneous parts of the texture in terms of complexity. In general regions of the image corresponding to high complexity (high level of detail) tend to have higher entropy, thus entropy can be seen as a measure of local signal complexity. Once the entropy is estimated for pixels in the texture image, the regions with similar entropy values are clustered to form a point categorization. The fractal dimension descriptor is, then, estimated for every point set according to this categorization. GFD is also based on fractal dimension. Instead of using entropy categorization, the image is decomposed into sub-images in its spatial frequency dimension using Gabor filter-bank which consists of a set of Gabor filters. The fractal dimension is estimated for each filtered response. 
     In the present invention, each segmented region is regarded as a stand-alone image by masking and zero padding the original image. After extracting color and texture features from a segmented region, a category label to the segment based on a majority vote rule of the nearest neighbors is assigned. The K nearest distances are calculated using the following formula: 
     
       
         
           
             
               
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     Where I i  is the known food dishes and n be the category index of the i th  known food, ϕ is the feature space, and S n  is the set the segmented objects in the known food dish, and d 0 (S n , i) is the minimum distance of the test segment s n  to all the training images and the i-th image is the best match of S n  in the feature space ϕ f . 
     As mentioned above, in the present invention, the proposed integrated image segmentation and classification method was tested on 500,000 food images with 327 unique food inputs. 1,792 features such as color quantization, segmentation, color descriptors, texture descriptors, hue, tint, shade, tone, brightness, etc. are extracted and used to calculate the nearest distances d 0 (S n , i). 
     At step  114 , food input inquiries are received. In many embodiments of the present invention, food input inquiries can be images, text descriptions, and verbal descriptions. In various aspects of the present invention, 500,000 food articles are used to teach the artificial intelligence (AI) system to recognize the validity of each food input inquiries. The Term-Frequency Inverse Document Frequency (TF-IDF) text mining algorithm is used to check the validity of a food input inquiry. 
     In artificial intelligence searching step  120 , food input inquiries are searched using artificial intelligence lazy predictor as described in step  113  above. 
     At step  121 , the food input inquiries are searched using the AI lazy predictor as described in step  113 . That is, the minimum distances d 0 (S n , i) are calculated for each food input inquiry. In many aspects of the present invention, the minimum distances are calculated using the Euclidean distance formula. In other aspects of the present invention, the minimum distances are the cosine similarity formula. 
     At step  122 , whether the food input inquiries are found in the Hatto™ database using the artificial intelligence lazy predictor is determined. In many aspects of the present invention, if the minimum distances d 0 (S n , i) can be calculated, then the food input inquiries can be determined. Otherwise, if the calculation is indeterminable, then the food input inquiries cannot be determined by the artificial intelligence (AI) lazy predictor. In the present invention, the food recognition algorithm does not stop here. If the results cannot be calculated, method  100  goes to food socio-touristic media step  130 . 
     At step  123 , if the food input inquiries are found then the results and related parameters are displayed in the food socio-touristic media. In many aspects of the present invention, related parameters include, but not limited to, similar foods, friends or users that love the same food input inquiries or similar dishes, addresses and names of restaurants or users that can offer the same food input inquiries. In other aspects of the present invention, related parameters also include dietary, medical, and physiological analyses of the food input inquiries. 
     At step  130 , if the minimum distances d 0 (S n , i) of the food input inquiries cannot be calculated, then method  100  goes to socio-media search step  130  which includes a specific step  131 . 
     At step  131 , food input inquiries are posted waiting for answers and related parameters from certified chefs and users in the food socio-tourist media. The more users register to use the food socio-touristic media, the larger variety of food dishes and related parameters can be identified. 
     At step  132 , if the solicited answers that identify the food input inquiries and related parameters are found, method  100  goes into deep learning step  140  to teach and train the artificial intelligence lazy predictor. 
     From steps  141 - 143 , the socio-touristic media platform deep learning begins. 
     At steps  141  and  142 , if the answers are found, the answers are checked for validity using the Term-Frequency Inverse Document Frequency (TF-IDF) text mining algorithm of step  114 . This is because many answers from the food socio-touristic media may contain illicit content unrelated to the food input inquiries. 
     At step  143 , if the answers are valid, then the answers are updated in the Hatto™ food database by analyzing the food input inquiry as described in step  113 . The name of the food input inquiry, its M=1,792 features, the related parameters are stored as training food dishes for the lazy predictor. For example, if the food input inquires include two dishes. The set of N known food dishes is increased to N+2 known food dishes. Finally, the valid answers are posted in the food socio-touristic media as in step  123 . 
     At step  144 , if the answers are not found by the users and chefs in the food socio-touristic media, then the validity of the input is again checked using the Term-Frequency Inverse Document Frequency (TF-IDF) text mining algorithm of step  114 . As an illustrating example, if the food input inquiries contain illicit content that has nothing to do with foods, both the TF-IDF of artificial intelligence system and the socio-media will reject and block the answers. 
     After the validity is determined, the gamification step  150  begins to incentivize users and chefs to participate in the food recognition of the Hatto™ food socio-touristic media. 
     At step  151 , if the answers are valid then rewarding gamification C begins. 
     At step  152 , if the answers are invalid then the deterring or punishing gamification D begins. The detailed description of rewarding gamification C and deterring gamification D will be described later in the present disclosure. 
     At step  153 , determine if the users post another food input inquires. 
     At step  154 , if there are no other food input inquires then method  100  ends. If there are other food input inquires then repeats steps  111 - 153 . 
     Now referring to  FIG. 2 ,  FIG. 2  is a N-dimension vector space  200  illustrating food recognition method  100  that uses the artificial intelligence (AI) lazy predictor, social media, gamification, and incentivized gamification in accordance with an exemplary embodiment of the present invention. N-dimension vector space  200  formed by the set of N features of each known food dish includes X 1  to X N  axes. The food input inquiry represented by a square symbol  201  having N features represented by coordinates (X 12 , X 22 , X 32 , . . . , X N2 ). i-th known food dishes that has the minimum Euclidean distances to square symbol  201  are represented by triangular points  210 ,  211 ,  212 , and  213 . In many aspects of the present invention, the minimum Euclidean distances are calculated using the following formula: 
     
       
         
           
             
               
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     Food dishes represented by triangular points  202  and  203  that are found by the lazy predictor using formula 1 are the implementations of step  123 . Related parameters that are same or complementary food dishes  210  are represented by circular points  211 - 213 . Food input inquiries that cannot be determined by the lazy predictor are represented by polygon points including points  220 ,  221 ,  222 , and  223 . This is when socio-media search step  130  and deep learning step  140  above take place. Such food input inquiries are posted in the food socio-touristic media platform to solicit answers from the community of users and certified chefs, which is illustrated by step  131 . If the answers are provided from the food socio-touristic media platform, food dishes  221 ,  222 , and  223  are analyzed and learned as described in step  113 . After learning and analyses, these food dishes  221 ,  222 , and  223  are added to the set of M known food dishes, which implements steps  141  to  143 . 
       FIG. 3  is a flow chart of a Hatto™ food socio-touristic media software program  300  (“software program  300 ”) that utilizes the food recognition method described in  FIG. 1  and  FIG. 2  in accordance with an exemplary embodiment of the present invention. Again, software program  300  involves artificial intelligence searching, food socio-touristic media searching, deep learning, and gamification as discussed above in  FIG. 1  and  FIG. 2 . 
     At step  301 , the software program begins. Step  301  is implemented by clicking on an icon that causes software program to be executed by a cluster of central processing units (CPU). The hardware and software implementation of step  301  will be described in details later in  FIG. 5  and  FIG. 11 . When a user is registered to use the Hatto™ food socio-touristic media, an icon (or graphic user interface or GUI) is downloaded to his or her cell phone. Please refer to  FIG. 12  for an illustration of this icon or GUI. 
     At step  302 , the Hatto™ food socio-touristic media is opened by users. Step  302  is implemented by the hardware and software discussed in  FIG. 5  and  FIG. 11 . More particularly, step  302  is implemented by a Hatto™ food socio-touristic web programs  542  written by WordPress software. The illustration of step  302  is shown in later  FIG. 13A - FIG. 21C . 
     At step  303 , an application in form of an icon or GUI is selected as shown in  FIG. 12 - FIG. 21 . As a non-limiting implementation of step  303 , a page includes a personal page that includes all personal information of a user, his/her status as a certified VIP chef or a babble user, his/her rewards as a result of gamification, her favorite food dishes, etc. Step  303  is implemented by the hardware and software discussed in  FIG. 5  and  FIG. 11 . More particularly, step  303  is implemented by the execution of an interactive icon: a blogs, posts, and forums subroutine  542 - 1 , a social network subroutine  542 - 3 , a matching/displaying subroutine  542 - 4 , a Hatto™ food tourism subroutine  542 - 5 , and a Hatto™ gamification subroutine  542 - 6 . When a user presses on an icon on his or personal page (see  FIG. 13A ), CPU and GPU cluster (or Hatto™ central brain)  541  executes the selected subroutine listed above. This way, each subroutine  541 - 1  to  541 - 6  has its own icon. Step  303  is illustrated as shown in later  FIG. 12 - FIG. 21 . Please refer forward to  FIG. 12B , examples of step  303  include icons such as a home icon  1251 , a camera icon  1253 , a thumb up icon  1254 , and a gamification icon  1255 . 
     At step  304 , food input inquiries are posted in the socio-touristic media platform. Pages in the Hatto™ food socio-touristic media are interactive. As such, users can chat, send messages, post food input inquiries, rate his or her friends posts or comments, play games, etc. An illustration of step  304  is shown in  FIG. 13A - FIG. 13B  when a user takes a picture of his or her diner as a form of the food input inquiries. 
     At step  305 , food input inquiries can be either text description in the forum page of the Hatto™ food socio-touristic media platform. Text description of a food input inquiry can be parsed using the Term-Frequency Inverse Document Frequency (TF-IDF) text mining algorithm as described in step  114 . At this stage, step  305  is implemented by the TF-IDF algorithm to understand the description of the food input inquiries. More particularly, the TF-IDF text mining algorithm is implemented by fulltext queues database  1146 , a Hatto™ fulltext parser  1156 , and an elastic search  1166  in  FIG. 11 . In some aspects of the present invention, step  305  is included for the visually impaired users and certified chefs who may use Braille displays, so that the Hatto™ food socio-touristic media platform is inclusive to all users. 
     At step  306 , food input inquiries can be images taken by the camera of the Hatto™ food socio-touristic media platform. In some aspects of the present invention, a user can use his/her cell phone to take a picture of the food dishes and post in the step  306  can only select only two input food images from each picture taken. In some other embodiments, the users may upload pictures of a food dishes that he or she has found in a magazine or other places. Step  306  is implemented as described in later  FIG. 12 - FIG. 21 . More particularly, step  306  is implemented in  FIG. 13A - FIG. 13B  when a user takes a picture of the two dishes  1311  and  1312  in the Gentle Onion restaurant. 
     At step  307 , food input inquiries can be a voice description entered into the Hatto™ food socio-touristic media platform. In some embodiments of the present invention, step  307  is included for the visually impaired users and certified chefs who may use a screen reader. A screen reader, as it implies, reads the screen using a speech synthesizer. If this is the case, a voice recognition and conversion step  309  begins. Speech synthesizers are well-known in the art and therefore will not be described in details in the present disclosure. 
     At step  308 , food input inquiries are searched in the database using artificial intelligence (AI) lazy predictor described in  FIG. 2  above. Step  308  can be implemented as shown in step  113  and  FIG. 2  above. Step  308  is implemented by an AI lazy predictor engine  551  of a Hatto™ neural network  550 . The illustrative implementations of step  311  will be discussed in  FIG. 11 - FIG. 21 . 
     At step  311 , determine if a match can be found. Step  311  can be implemented as shown in step  113  and  FIG. 2  above. Step  311  is implemented by an AI lazy predictor engine  551  of a Hatto™ neural network  550 . The illustrative implementations of step  311  will be discussed in  FIG. 11 - FIG. 21 . 
     At step  312 , if a match is found then display the results and related items in the Hatto™ food socio-touristic media platform. Step  311  is implemented by an AI lazy predictor engine  551  of Hatto™ neural network  550 . The illustrative implementations of step  312  will be discussed in  FIG. 11 - FIG. 21 . More particularly, the implementation of step  312  is illustrated in  FIG. 13B  when the names of the dishes and the addresses of the restaurants that offer the similar dishes are displayed. That is, Chao restaurant will offer the fried shrimp and stewed chicken dishes, the same as in the picture. Referring back to  FIG. 2 , the food images  1311  and  1312  are represented by square symbol  201 . The dishes that have the shortest Euclidean distance from square symbol  201  (either fried shrimp or stewed chicken) are triangular points  202  and  203 , which are fried shrimp and stewed chicken respectively. The related parameters are also found and represented by circular points  211 - 213 , which are the same dishes and the Chao restaurant. 
     At step  313 , if a match cannot be found, then post and find answers from the community of users and certified chefs in Hatto™ food socio-touristic media platform. The implementations of step  313  will be illustrated in  FIG. 11 - FIG. 21 . Step  313  can be represented by polygon points  221 - 223  when the minimum Euclidean distances cannot be determined. At this point, the system of the present invention uses human intelligence from the Hatto™ food socio-touristic media to recognize the unknown dishes. 
     At step  314 , determine whether the answers from the users in the social media are found. Then step  312  is repeated. 
     At step  315 , where deep learning as described in step  140  in  FIG. 1  begins. The food database is updated. Step  315  is implemented by steps  141 - 144  in  FIG. 1  and by polygon points  220 - 223  in  FIG. 2 . Now, polygon points  221 - 223  and their characteristics are learned. The next time when the same dishes (i.e.,  221 - 223 ) are searched, the artificial intelligence (AI) lazy predictor will find and post them as described in steps  311 - 312 . 
     At step  316 , gamification is started to provide incentives to the community of users. As alluded above, gamification step  316  can be rewarding as in subroutine C when users post proper food inquiries or answers. Otherwise, gamification step  316  can be punishing as in sub-routine D when the users post unrelated or illicit answers. The implementations of step  316  will be illustrated in  FIG. 11 - FIG. 21 . 
     At step  317 , determine whether new food input inquiries are received. 
     At step  318 , if there are no other food input inquiries are received then software program  300  ends. 
     Otherwise, steps  304  to  317  are repeated. 
     Software program  300  achieves many objects of the present invention: (1) provides a supplemental means to artificial intelligence to identify foods that have many applications in medicine, dietary science, physiology, tourism, gastronomy, etc.; this is because no AI is better than human intelligence especially in the food recognition; creates a virtual place where AI is converged with human intelligence to provide a novel method of food recognition; (2) it provides a means for promoting tourism via foods; (3) it provides a means for improving the arts of cuisine. 
     Next referring to  FIG. 4 ,  FIG. 4  is the organization of the Hatto™ food socio-touristic media in accordance with an exemplary embodiment of the present invention. A Hatto™ food socio-touristic media  401  includes a forum  410 , a social network  420 , a food tourism  430 , and a gamification  440 . In the present invention, Hatto™ &#39;s food socio-touristic media  401  achieves many objects: (1) provides means to assist artificial intelligence (AI) in food recognition method that has applications in medicine, dietary science, physiology, tourism, gastronomy, etc.; this is because no AI is better than human intelligence especially in the food recognition; Hatto™ food socio-touristic media  401  is a virtual place where AI is converged with human intelligence to provide a novel method of food recognition; (2) provides means for promoting epicure traveling and tourism; (3) provides means for improving and promoting culinary arts; and (4) provides means for food analysis in sports and health physiology. 
     Forum  420  is a social media where the community of users and certified chefs can describe and post user submissions  411 , exchange chats and messages  412 , find food locations  413 . Forum  420  also provides means for implementing step  114  in  FIG. 1  and step  304  in  FIG. 3 . In some aspects of the present invention, when users are exchanging chats and messages, they can describe or attach pictures of food input inquires to their friends in forum  420 . Hatto™ food socio-touristic media  401  can intercept these messages and provide answers to both primary users and secondary users. 
     Social network  420  can include firewalls  421 , a fan-page  422 , update friends  423 , a friend list  424 , display options  425 , and alert options  426 . Firewalls  421  can be both hardware and software which are implemented by firewalls  1112  in  FIG. 11 . Fan-page  422  includes advertising pages for business such as restaurants, hotels, retailers, certified super VIP chef to promote themselves on a personal page of a user. Fan-age  422  can be created on the profile page by calls-to-action to bring the users advertising to the forefront of Hatto™ food socio-touristic media  401 . Similarly, friend list  424  can also be created and organized from the personal page of Hatto™ food socio-touristic media  401 . In many aspects of the present invention, friend list  424  is also a smart friend list created by the analytics of the users&#39; friend lists  424  in order to suggest new friends. In display options  425 , the users can select how his or her Hatto™ food socio-touristic media  401  personal page is displayed or organized. Display options  425  are implemented by a matching and displaying subroutine  542 - 4  of Hatto™ socio-touristic platform web software programs  542  which will be discussed later in  FIG. 5 . Alert (notification) options  426  are part of the personal page. The users can choose what and how they are notified by selecting option button  1225  in  FIG. 12B . Firewalls  421 , a fan-page  422 , update friends  423 , a friend list  424 , display options  425 , and alert options  426  are integral parts of personal page  1200 B of Hatto™ food socio-touristic media  401  which is written using WordPress program. 
     Food tourism  430  includes a welcome page  431 , restaurant inquiries  432 , and local dishes and restaurants  433 . Welcome page  431  can be part of fan-page  422  designed to promote tourism. The GPS (Global Positioning System) of the communication devices of users and Hatto™ food socio-touristic media  401  always know the current location of the users. Alternatively, Hatto™ food socio-touristic media  401  learns of the users traveling plan via forum  410 . In both cases, welcome page  431  appears on a personal page  1200 B as the users arrive at a tourist destination. Restaurant inquiries  432  can be part of text food input inquires in step  305  or voice as in step  307 . That is, the traveling user can ask in either text message (e.g., step  305 ), images (e.g., step  306 ), or voice command (e.g., step  307 ) the locations of the famous local restaurants. For example, when the user travels to Berkeley, Calif., she or he can turn on Hatto™ food socio-touristic media  401  and ask the location of the famous restaurant, “Chez Panisse”. Similarly, local dishes and restaurants  433  can be either text message (e.g., step  305 ), images (e.g., step  306 ), or voice command (e.g., step  307 ). Moreover, local dishes and restaurants  433  can be old images of food input inquiries  306  uploaded from the memory of the communication device of the traveling user. 
     In the present invention, gamification  440  is designed to provide incentives to users to participate in the food recognition algorithm, to have funs, to form a network of friends, to enhance e-commerce for retailers and restauranteurs. Gamification  440  includes first-level prizes (watermelons)  441 , second-level prizes (the Tam Rice)  442 , a beginner level  443 , a VIP level  444 , super VIP levels  445 , an exchange option  446 , an upgrade option  447 , gift option  448 , purchasing option  449 , and discount/deal options  449 - 1 . First-level prizes  441  are the lowest reward given to the users when they either contribute positive posts and/or comments in Hatto™ food socio-touristic media  401 . Alternatively, the users can receive first-level prizes  441  by receiving a certain amount of “thumb up” or positive reactions from other users. Second-level prizes (the Tam Rice)  442  are given to the users after they have earned more than a predetermined amount of first-level prizes  441 . In many exemplary embodiments of Hatto™ food socio-touristic media  401 , first-level prizes  441  are symbolized as watermelons, second level-prizes  442  are symbolized as the Tam Rice which is a prestigious type brand of rice grown in Vietnam. 50 watermelons can be exchanged for 1 Tam Rice. It is noted that watermelons, the Tam Rice are only non-limiting examples of first-level prizes  441  and second level prizes  442  respectively. Any other symbols can be used and within the scope of the present invention. Beginner level  443  is a level for users who first register to use Hatto™ food socio-touristic media  401  without any experience, prizes, and certification issued by the primary artificial intelligence (PAI). VIP level  444  is a next higher level to beginner level  443 . Users at beginner level  443  can be promoted to VIP level  444  if they earn sufficient first-level prizes  441  and stay with Hatto™ food socio-touristic media  401  over a certain time. Higher than VIP level  444  is super VIP levels  445 . Super VIP levels  445  further include a super VIP specialist, a super VIP restauranteur, and a super VIP chef. Please refer to super VIP levels  1902 - 1904  in  FIG. 19  for illustrations. In many aspects of the present invention, an upgrade  447  allows beginner level  443  to become VIP level  444  to super VIP levels  445  by using second-level prizes  442 . The upgrade needs to be approved by PAI. Next, a gift feature  448  gives beginner level  443 , VIP level  444 , and super VIP levels  445  gifts such as coupons, discounts, first-level prizes  441 , and second-level prizes  442  to incentivize these members to participate in the food recognition process. In addition to gift feature  448 , a purchasing feature  449  allows users to use their earned first-level prizes (watermelons)  441 , second-level prizes (the Tam Rice)  442 , gifts  448  to purchase products from retailers who are in alliance with Hatto™ food socio-touristic media  401 . As a non-limiting illustrations,  FIG. 20  and  FIG. 21  show that members can buy smart phones by first generating a QR code using their earned prizes  441 - 442 , gift  448 , and/or paying with their own pocket money. Of course, if there is purchasing  449 , a discounts and deals  449 - 1  must follow. Discounts and deals  449 - 1  include, but not limit to, discount coupons to restaurants, to movies; discount coupons to buy smart phones, cook books, or other products such as groceries, laptops, computers, and electronic products. Discounts and deals  449 - 1  may include an invitation to dine at no costs at popular restaurants. 
     Next referring to  FIG. 5  which presents a hardware schematic diagram of a Hatto™ food socio-touristic network  500  in accordance with an exemplary embodiment of the present invention. Hatto™ food socio-touristic network  500  includes a computer network system  530 , gateway interfaces and security firewalls  511 , a network  510  which is configured to connect and serve a community of users  521 - 1  to  521 -N, and a plurality of remote leaf databases  522 - 1  to  522 -M via a first communication channel  561 . Plurality of remote leaf databases  522 - 1  to  522 -M are located outside and connected to computer network system  530  via network  510 . It is noted that plurality of remote leaf databases  522 - 1  to  522 -M can be data centers in different locations around the world and configured to provide important information to the food recognition process by the artificial intelligence and human intelligence of the present invention. It will be further noted that community of users  521 - 1  to  521 -N includes beginner  443 , VIP  444 , and super VIPs  445 . Beginner  443  has beginner symbol  1901 . VIP  444  has symbol  1902 . Super VIPs  445  have super VIP specialist symbol  1903 , super VIP restauranteur symbol  1904 , and super VIP chef  1905 . All levels are included within the community of users  521 - 1  to  521 -N. 
     Computer network system  530  includes an input/output (I/O) network interface  531 , a master aggregator  532 , a data center  533 , a neural network  550 , a Hatto™ central brain  540  (“central brain  540 ”). Master aggregator  532  combines and manages remote leaf data bases  522 - 1  to  522 -M and data center  533 . Central brain  540  further includes a cluster of central processing units and graphics processing units  541  (“cluster of CPU and GPU  521 ”) and a memory configured to store a Hatto™ socio touristic media web software programs  542  which launch Hatto™ food socio-touristic media  401  as described in  FIG. 4 . Hatto™ socio touristic media web software programs  542  includes the following subroutines: a blogs, posts, and forum subroutine  542 - 1 , a member authentication subroutine  542 - 2 , a social network subroutine  542 - 3 , a matching and displaying subroutine  542 - 4 , a Hatto™ food tourism subroutine  542 - 5 , and a Hatto™ gamification  542 - 6 . Functionally, blogs, posts, and forum subroutine  542 - 1  launches forum  410 ; member authentication subroutine  542 - 2  authenticates and verifies community of users  521 - 1  to  521 -N when they log in; social network subroutine  542 - 3  launches social network section  520 ; matching and displaying subroutine  542 - 4  displays and notifies community of users  521 - 1  to  521 -N when matches are found; Hatto™ food tourism subroutine  542 - 5  launches food tourism section  430 ; and a Hatto™ gamification  542 - 6  launches Hatto™ gamification  440 . In many embodiments of the present invention, Hatto™ socio touristic media web software programs  542  and subroutines  542 - 1  to  542 - 6  are written using the WordPress program, Pythons, Java Script, PHP, C ++ , C programming language so long as these programming languages are capable of constructing the functions as described. 
     Hatto™ neural network  550  is responsible for the artificial intelligence (AI) in the food recognition process while Hatto™ socio-touristic media  401  is responsible for the human intelligence and a medium to combine both intelligence. Hatto™ neural network  550  includes an artificial intelligence (AI) lazy predictor engine  551 , a deep learning engine  552 , an AI visionary and recommendation engine  553 , and a search engine  554 . Artificial intelligence (AI) lazy predictor engine  551  operates as described in  FIG. 1  and  FIG. 2 . Deep learning engine  552  implements deep learning algorithm  140  in order to (1) learn new food dishes and related parameters, (2) store new dishes and their features, and (3) learn to recognize new dishes next times these same food dishes are posted. That is, polygonal points  221 - 223  are learned and updated into Hato™ data center  553 . Consequentially, data center  533  stores more and more new dishes and AI lazy predictor  551  becomes smarter, capable of recognizing more and more dishes. In various embodiments of the present invention, remote leaf databases  522 - 1  to  522 -M located around the world can be connected to Hatto™ data center  533 , AI lazy predictor engine  551 , and deep learning engine  552  so that international food dishes can be recognized. In addition, this allows international users as part of the community of users  521 - 1  to  521 -N can participate and have funs in Hatto™ socio-touristic media  401 . AI visionary &amp; recommendation engine  553  performs analytics on users  521 - 1  to  521 -N behavioral patterns in order to send out appropriate advertisements, rewards, friend suggestions, foods and restaurants recommendations. Search engine  554  includes text search which can be either pure texts or texts converted from voice commands. In various embodiments of the present invention, AI lazy predictor engine  551  includes a queues database, a firewall, and a graphic processing unit (GPU). 
     Continuing with  FIG. 5  in reference with  FIG. 1  to  FIG. 4 , in operation, at the beginning at step  111  in  FIG. 1 , cluster of central processing units and graphic processing units  532  executes software program  300  to launch Hatto™ socio-touristic media  401 . As a user  521 - 1  logs in, his or her device is connected to computer network system  530  via network  510  via I/O network interface  531 . Gateway interfaces and security firewalls  511  determines that whether this user is either blocked or has been registered. In addition, firewalls  421  and security firewalls  511  determines whether this user is blocked and/or the messages are valid. VIP and members authentication program  542 - 2  is also executed to check the status of user  521 - 1 . If user  521 - 1  is not blocked and having a good standing status, cluster of central processing units and graphic processing unit (CPU and GPU)  541  executes Hatto™ socio-touristic media software programs  540 . After that, his or her personal page will be displayed by cluster central processing units and graphic processing units  541 . The personal pages of user  521 - 1  includes forum  410 , social network  420 , food tourism  430 , and gamification  440  as described in  FIG. 4 . 327 known food dishes, their 1,792 features, and related parameters are stored in Hatto™ data center  533 . Food input inquiries step  114  is implemented by blogs, posts, and forums module  542 - 1 . Artificial intelligence lazy predictor searching routine  120  is implemented by neural network  550  which includes artificial intelligence lazy predictor engine  551  as described in step  113  and  FIG. 2 . Deep learning engine  552  implements deep learning algorithm  140  of  FIG. 1 . Blogs, posts, and forums module  542 - 1  implements step  114  of  FIG. 1 . Hatto™ gamification module  542 - 6  implements rewarding gamification C  151  and penalty gamification D  152 . Matching and displaying program  542 - 4  implements step  123  in  FIG. 1 . Hatto™ food tourism software module  542 - 5  implements food tourism  430  and steps  114 ,  121  and  122 . 
       FIG. 6  is a flow chart of the input and search algorithm  600  in the Hatto™ food socio-touristic media software program that utilizes the food recognition method described in  FIG. 1  and  FIG. 2  in accordance with an exemplary embodiment of the present invention in accordance with an exemplary embodiment of the present invention is illustrated. 
     At step  601 , algorithm  600  begins at A and B. A and B represent two different methods of entering the food input inquiries. A represents the food input inquires posted by primary users,  521 - 1  to  521 -N, in forum  410 . B represents the food input inquires posted by secondary users (also among  521 - 1  to  521 -N) who are friends viewing the primary users personal page. Algorithm A and B handle both methods of food input inquires the same way. Step  601  is implemented by loading Hatto™ food socio-touristic media  401  which is rendered by the execution of Hatto™ socio-touristic media web software programs  542  by cluster of central processing unit (CPU) and graphic processing unit (GPU)  541 . In addition, VIP and members are also authenticated to allow only members with good standing status to use Hatto™ food socio-touristic media  401 . This authentication substep is implemented by member authentication subroutine  542 - 2 . 
     At step  602 , food input inquiries are posted in Hatto™ food socio-touristic media. Step  602  is implemented by blogs, posts, and forums subroutine  542 - 1 . As alluded in  FIG. 3 , food input inquiries can be in form of photo images, speech, and text messages in forum  410 . 
     At step  603 , date and time of the food input inquiries are attached to post. Step  603  is implemented by features in photo images, speech, and text messages in forum  410  and communication devices of users  521 - 1  to  521 -N that can attach the current date and time when food input inquiries are posted. Communication devices of users  521 - 1  to  521 -N include cellular phones, computers, laptops, tablets, etc. 
     At step  604 , the location of the user who posts the food input inquiries is attached. Step  604  is implemented by features in photo images, speech, and text messages in forum  410  and the communication devices that can attach the Global Positioning System (GPS) location (address) of a post. 
     At step  605 , food input inquiries and similar parameters are located by artificial intelligence lazy predictor. Step  605  is implemented by artificial intelligence lazy predictor search step  121  in  FIG. 1  and AI lazy predictor engine  551 . Food input inquiries are illustrated by square symbol  201  in  FIG. 2 . 
     At step  606 , determine if similar food dishes are found. After the search by artificial intelligence lazy predictor search step  121 , cluster of CPU and GPU  541  and AI lazy predictor engine  551  also determines if similar food dishes of the food input inquiries are found. Similar food dishes are one of the related parameters illustrated by step  113  and food dishes  210  in  FIG. 2 . 
     At step  607 , similar restaurants are located by artificial intelligence lazy predictor. As alluded before, similar restaurants are one of the related parameters kept in record by AI lazy predictor engine  551  in concert with cluster of CPU and GPU  541 . 
     At step  608 , whether similar restaurants that offer similar food dishes are determined. Cluster of PU and GPU  532  also determines if similar food dishes to food input inquiries are found. Similar restaurants are one of the related parameters illustrated by step  113  and food dishes  210  in  FIG. 2   
     At step  609 , friends among the community of users that like the same food input inquiries are searched by artificial intelligence lazy predictor. As alluded before, friends who like the same or similar food dishes are one of the related parameters kept in record by AI lazy predictor engine  551  in concert with cluster of CPU and GPU  541 . 
     At step  610 , determine if friends who like similar food dishes are found. Cluster of PU and GPU  541  also determines if similar food dishes to food input inquiries are found. Friends who like the same or similar food dishes are one of the related parameters illustrated by step  113  and food dishes  210  in  FIG. 2   
     At step  611  display all results from steps  602 - 609 . Step  611  is implemented by matching and displaying module  542  in connection to a display device of user  521 - 1 . 
     At step  612 , if no results are found, then ask certified chefs or community of users. In case neural network  550  and AI lazy predictor engine  551  cannot locate the answers, the unanswered food input inquiries are posted in Hatto™ food socio-touristic media  540  to solicit answers from the community users and certified chefs,  521 - 1  to  521 -N. 
     At step  613 , determine if answers to step  612  are found. Step  613  is implemented by inquiries and recommendations module  541  as part of Hatto™ socio-touristic media web software programs  542 . 
     At step  614 , if answers are found then store in the database and start the deep learning process as described above. Step  614  is implemented by deep learning engine  552 . The answers are analyzed by breaking down the answers from the users into features as described in step  113  and used to teach AI lazy predictor engine  551 . 
     At step  615 , if no answers are found, then store the food input inquiries for future analysis. Step  615  is handled by cluster of CPU and GPU  541  which stores the unrecognized food input inquiries to Hatto™ data center  533  for future analysis by Hatto™&#39;s group of certified chefs or by the community of users. In many aspects of the present invention, this situation identified by step  615  is very rare because the food input inquiries must be very rare dishes that neither artificial intelligence nor human intelligence can identify. 
     At step  616 , the gamification starts to incentivize users to participate in the food recognition process. Step  616  is implemented by Hatto™ gamification software module  542 - 6 . Positive gamification program C  151  as well as negative (punitive) gamification program D  152  starts. The details of the gamification program C  151  and negative (punitive) gamification program D  152  will be described in  FIG. 8 - FIG. 9  and illustrated in  FIG. 19 - FIG. 21 . 
     At step  617 , algorithm  600  ends. As the results of step  617 , new dishes, new friends, and new restaurants are found and stored in Hatto™ data center  533 . In other situations, the overall status of each user is also updated in the user personal page in Hatto™ food socio-touristic media  401 . 
       FIG. 7  is a flow chart of the food touristic algorithm  700  in the Hatto™ food socio-touristic media software program that utilizes the food recognition method described in  FIG. 1  and  FIG. 6  in accordance with an exemplary embodiment of the present invention. 
     Food touristic algorithm  700  implements methods  100 ,  200 ,  300 , and system  400  that combines artificial intelligence lazy predictor and human intelligence in a food socio-touristic media to synergistically enhance the ability to recognize a wide variety of food dishes. 
     At step  701 , food touristic algorithm  700  begins. In many aspects of the present invention, step  701  begins when (1) Hatto™ food socio-touristic media  410  opens and the personal front page of the user is loaded, or (2) deep learning engine  552  recognizes the habit of that user. 
     At step  702 , whether users,  521 - 1  to  521 -N, are traveling are determined. As mentioned in step  701 , when Hatto™ food socio-touristic media  410  learns or receives posts that a user, e.g. user  521 - 1 , is traveling, food touristic algorithm  700  starts. 
     At step  703 , displaying touristic ads on the personal page of the traveling users. The GPS module (not shown) in the communication device of a user, e.g. user  521 - 1 , in cooperation with cluster of CPU and GPU  541  recognize the location of user  521 - 1  and posts according traveling ads including taxi, Grab™, Uber™, hotels, grocery stores, entertainments, touristic sites, and nearest restaurants that offer food dishes that user  521 - 1  habitually has had. Step  703 , in many aspects, is implemented by neural network  550  and deep learning engine  542 . If user  521 - 1  finds any useful ads, he or she can touch to select the destination that the ads are offering. 
     At step  704 , food input inquiries from the traveling users are posted. In case when user  521 - 1  cannot find any useful ads, he or she may post food input inquiries on Hatto™ food socio-touristic media  410 . Step  704  is implemented by blogs, posts, and forums subroutine  542 - 1 . 
     At step  705 , the food input inquiries are searched in databases. In the present disclosure, the food input inquiries are search in the Hatto™ data center  533  and remote leaf databases  522 - 1  to  522 -M by artificial intelligence lazy predictor engine  551  as described in step  113  in  FIG. 1  and step  308  in  FIG. 3  above. The implementation of step  705  is also illustrated by the search for square symbol  201  in  FIG. 2  using AI lazy predictor engine  551  and search engine  554 . 
     At step  706 , whether the food input inquiries are found is determined. Cluster of CPU and GPU  541  in connection with neural network  550  determine whether food input inquiries represented by square symbol  201  are found. 
     At step  707 , if no answers are provided, then certified chefs and the community of users, e.g.,  521 - 1  to  521 -N, in the Hatto™ food socio-touristic media. If the closest answers represented by triangular symbols  202 - 203  and related parameters  211 - 213  are not found, the food input inquiries are sent forward by inquiries and recommendations module  541  so that the intelligence of the certified chefs and community of users can provide the answers to user  521 - 1 . The answers are represented by circular symbols  221 - 223  in  FIG. 2 . 
     At step  708 , the answers are stored in the database and deep learning procedure begins. Step  708  is implemented by Hatto™ data center  533  and deep learning algorithm  140  and deep learning engine  552 . 
     At step  709 , if answers are found by the artificial intelligence lazy predictor, then results are displayed on the personal page of the traveling users. Step  709  is implemented by matching/displaying module  542 - 4 . More particularly, square symbol  201  and related parameters triangular symbols  202 ,  203 , and circular symbols  211 - 213  are displayed on the personal page of a user, e.g., user  521 - 1 . 
     At step  710 , gamification is started to provide incentives to the community of users. Step  710  is implemented by Hatto™ gamification software subroutine  542 - 6 . Positive gamification program C  151  as well as negative (punitive) gamification program D  152  start 
     At step  711 , algorithm  700  ends. As the results of step  711 , new dishes, new friends, and new restaurants are found and stored in Hatto™ data center  533 . In other situations, the overall status of each user is also updated in that user personal page in Hatto™ food socio-touristic media  401 . 
     Referring to  FIG. 8 , a flow chart of an incentivized gamification  800  in the Hatto™ food socio-touristic media software program supplementing the food recognition method described in  FIG. 1  and  FIG. 2  in accordance with an exemplary embodiment of the present invention is illustrated. 
     At step  801 , the rewarding (or positive) gamification is started and identified as gamification C. 
     At step  802 , food input inquiries and similar parameters include friends who like the same foods, restaurants that offer the same dishes, positive comments, and recipes are determined. Referring back to  FIG. 2 , step  802  searches for square symbol  201 , triangular symbols  202 - 203 , and circular symbols  211 - 213 . This is implemented by artificial intelligence lazy predictor  551  and Hatto™ food socio-touristic media software program  540 . As described above, square symbol  201 , triangular symbols  202 - 203 , and circular symbols  211 - 213  are first searched using artificial intelligence lazy predictor  551 . If the results are not found, human intelligence is used by posting food input inquiries and similar parameters into Hatto™ food socio-touristic media  410 . The community of users and certified chefs,  521 - 1  to  521 -N, are enabled to identify the food input inquiries and recommend similar parameters. This is made possible by Hatto™ gamification subroutine  542 - 6 . 
     At step  803 , if food input inquiries and similar parameters are found, then users who post such inquiries and receive a certain number of “likes”, “hearts or loves”, positive reactions, or discussions from other members are rewarded with a first-level prize, e.g., watermelons. In many aspects of the present invention, the first-level prize is symbolized as a watermelon. Please refer to  1222  in  FIG. 12B  as an illustration of step  803 . 
     At step  804 , the total number of first-level prizes symbolized by watermelons earned by a user is displayed on the personal page. Step  804  is implemented by gamification section  440  and first-level prize  441  in  FIG. 4 , which are created by Hatto™ gamification subroutine  542 - 6  that sums up the total number of first-level prizes user, e.g.  521 - 1 , has earned and displays it on the personal page of user  521 - 1 . Please refer to  1222  in  FIG. 12B  as an illustration of step  804 . 
     At step  805 , whether the total first-level prizes which are greater than a positive threshold number K are determined. Gamification section  440  determines whether the current total number of first-level prizes is greater than a preset threshold number. In some embodiments of the present invention, this threshold number K is set to 200. That is, if the number of first-level prizes exceeds 200, a user, e.g.,  521 - 1 , is entitled to have either (1) exchange option  446 , (2) upgrade option  447 , (3) gifts  448 , (4) purchasing  449 , and (5) discounts and deals  449 - 1 . Step  804  is implemented by Hatto™ gamification subroutine  542 - 6  which is listed as gamification section  440 . 
     At step  806 , users are given a gift if the total number of first-level prizes (watermelons) earned exceeds a threshold number K. As discussed in  FIG. 4 , a gift can include a discount card for a purchase, for favorite dishes at the favorite restaurant, a promotion, a job opportunity, etc. The gift can also be a deal, a number of first-level prizes (e.g., watermelons) or second-level prizes (the Tam prizes). 
     At step  807 , the total number of first-level prizes and gifts are displayed. As a result of step  806 , the current total number of first-level, second-level prizes, and gifts earned by is updated on the personal page of a user, e.g.,  521 - 1 . Please see  1221  and  1222  in  FIG. 12B  as illustrations of step  807 . 
     At step  808 , whether users want to exchange first-level prizes (watermelons) to a higher prizes (e.g., the Tam Rice) is determined. Step  808  is implemented by the action of a user; e.g.,  521 - 1 , including pressing the exchange option  446 , upgrade option  447 , purchase option  449 . Step  808  is implemented by Hatto™ gamification subroutine  542 - 6  and illustrated by exchange action  1916  in  FIG. 19A - FIG. 19C . 
     At step  809 , if the answer is yes, then the users are asked to enter the total amount of first-level prizes (watermelons) they want to exchange. 
     At step  810 , perform the exchange and display the end results. 
     At step  811 , determine if the users want to use first-level prizes (watermelons) and Tam rice to purchase discounted merchandises. Step  811  is implemented by purchasing option  448  in gamification section  440  and Hatto™ gamification subroutine  542 - 6 . 
     At step  812 , if the answer is yes, then the purchase is performed. Users may use all of his or her prizes including first-level prizes (watermelons) or the second-level prizes (the Tam Rice) to purchase of products. Alternatively, users may use available discount cards, deals, and personal finance to complete the purchase. It is noted that, users may use these available means to go to a diner with his or her friends at his or her favorite restaurants. Step  812  is implemented by purchasing option  448  in gamification section  440 . For example, a user, e.g.,  521 - 1 , may use all or part of his 8,000 first-level prizes he has earned to pay for diner with his girlfriend at his favorite restaurant. He can also use all or parts of his 8,000 watermelons to buy an IPhone X. Alternatively, he can use additional second-level prizes (the Tam Rice) to make this purchase plus his own money via a credit card in case his first-level prizes (watermelons) are not sufficient. Step  812  is implemented by Hatto™ gamification subroutine  542 - 6  and illustrated by QR code  2101  in  FIG. 21A - FIG. 21C . 
     At step  813 , the prizes status are updated and displayed on the personal page of the users, e.g.,  521 - 1  to  521 -N. After the purchase in step  812 , the remainder prizes (first-level prizes and second-level prizes) are updated and displayed on the user personal page. Please see display  2003  in  FIG. 20A - FIG. 20C  as illustrations of step  813 . 
     At step  814 , that the users are available to a promotion is determined. In many aspects of the present invention, a user is available to a promotion if that user has been a loyal member of Hatto™ socio-touristic media  401  over a certain amount of time, i.e., 2 years, and with a good standing. In addition, in some other embodiments of the present invention, Hatto™ socio-touristic media  401  may also require that a user must have earned a significant amount of first-level prizes and second-level prizes to be available for a promotion. Please see a promotion display  1921  in  FIG. 19B  as an illustration to step  814 . 
     At step  815 , if the promotion is guaranteed, then the users are promoted and their statuses are updated accordingly. If the promotion conditions cited in step  814  are satisfied, that user is promoted to a VIP level  443  or a super VIP level  444 . The perks and privileges of VIP level or super VIP level will be described in  FIG. 10 . 
     At step  817 , if similar dishes, friends who like same dishes, and positive comments and reactions are not found by both means, i.e., artificial intelligence and human intelligence of users,  521 - 1  to  521 -N, in Hatto™ socio-touristic media  401 , then the legitimacy of the posts is determined. In the present disclosure, legitimacy includes whether the posts contain illicit contents and/or political comments. If the post is deemed illegitimate, users can enter another posts by means of algorithm  600  indicated as A and B in  FIG. 6 . Otherwise, when the posts are not legitimate, punishing or punitive gamification D begins as described in the following  FIG. 10 . Step  817  is implemented by a fulltext queues database  1146 , an elastic search  1166 , and a fulltext parser  1156  in  FIG. 11 . 
     Next referring to  FIG. 9 , a flow chart of the penalty gamification D  900  in the Hatto™ food socio-touristic media software program that utilizes the food recognition method described in  FIG. 1  and  FIG. 2  in accordance with an exemplary embodiment of the present invention is illustrated. 
     At step  901 , the penalty gamification is started and identified as gamification D. 
     At step  902 , whether input inquiries, comments, and actions of users violate Hatto™ rules and regulations are determined. Step  902  is implemented by the Term-Frequency Inverse Document Frequency (TF-IDF) text mining algorithm as described in step  114 . As an illustrating example, if the food input inquiries contain illicit content and political comments that have nothing to do with foods, both the TF-IDF of artificial intelligence system and the socio-media will reject, block, and conclude that the posting users have violated the rules and regulations of Hatto™. More specifically, step  902  is implemented by a fulltext queues database  1146 , an elastic search  1166 , and a fulltext parser  1156  in  FIG. 11 . Elastic search  1166  and fulltext search  1166  use Term-Frequency Inverse Document Frequency (TF-IDF) text mining algorithm as described in step  114 . 
     At step  903 , if the answer is yes, then the first-level prizes (watermelons) are subtracted from such users. Step  903  is implemented by Hatto™ gamification subroutine  542 - 6 . 
     At step  904 , the remaining first-level prizes (watermelons) are updated and displayed. Step  904  is implemented by Hatto™ gamification subroutine  542 - 6 . 
     At step  905 , whether the total violations are greater than a preset threshold number. Step  905  is implemented by Hatto™ gamification subroutine  542 - 6 . 
     At step  906 , if the number of violations is greater than the preset threshold number, the users, e.g.,  521 - 1  to  521 -N, are blocked permanently. Security firewalls  511  block these users from this moment on. 
     At step  907 , if the answer is no then start gamification algorithm  800  identified as C above if the posts are determined to be valid and received positive, like comments, or good reactions from the community of users, e.g.,  521 - 1  to  521 -N. 
     Referring next to  FIG. 10 , a flow chart of the status algorithm  1000  in the Hatto™ food socio-touristic media software program utilizing the food recognition method described in  FIG. 1  and  FIG. 2  in accordance with an exemplary embodiment of the present invention is illustrated. 
     At step  1001 , algorithm  1000  begins when users start to log in Hatto™ food socio-touristic media  401 . Hatto™ food socio-touristic media  401  first checks the status when a user, e.g.,  521 - 1  to  521 -N, logs in. The list of users and their up-to-date standing are stored in Hatto™ data center  533 . 
     At step  1002 , determine if a user is a super VIP which is the highest level. In many embodiments of the present invention, super VIP further divides into a super VIP specialist  1902 , a super VIP restauranteur  1903 , and a super VIP chef  1904  as illustrated in  FIG. 19C . In  FIG. 19C , the user Nguyen Phuong Anh (one of the users  521 - 1  to  521 -N) is a VIP member with a VIP title attached to her personal page and the Gentle Onion Food and Drink restaurant is a super VIP chef  1904  with the appropriate symbol  1931  attached to the restaurant picture. 
     At step  1003 , if the user is not a super VIP, then determine if user is a VIP member which is the second highest ranking. Referring again to  FIG. 19C , the user Nguyen Phuong Anh (one of the users  521 - 1  to  521 -N) is a VIP member with a VIP title attached to her personal page. 
     At step  1004 , if the log-in user is not a VIP member, then determine if the user belongs to a chef association recognized by the primary artificial intelligence (PAI). 
     At step  1005 , if the user does not belong to the recognized chef association, then determine if the user is an independent restauranteur. In many aspects of the present invention, if users own and operate restaurants that are mentioned and recommended in Hatto™ food socio-touristic media  401 , then these users are classified as restauranteurs. The knowledge and contributions of these restauranteurs, supplementary to the artificial intelligence, are very valuable since no artificial intelligence is better than human intelligence. This is especially true when the recognition of foods is needed. Yet, this is even truer when the human intelligence is an expert in foods such as restauranteurs, food critics, epicure, or chefs. 
     At step  1006 , if the log-in user is not a restauranteur, then determine if the user is a freelancer which is either a food critic for a magazine or newspapers, or an epicure. 
     At step  1007 , if none of the above are true, then determine if the log-in user is a registered user. In some aspects of the present invention, registered users may need to pay a fee. In other aspects of the present invention, only VIP members and super VIP members need to pay a fees in order to receive benefits and perks from Hatto™ food socio-touristic media  401 . It will be noted that, whether registered users, e.g.,  521 - 1  to  521 -N, pay a fee to join Hatto™ food socio-touristic media  401  are within the scope of the present invention. 
     At step  1008 , if all of the above are true, then determine if these users are current in annual fee payments. Again, the list of registered users, super VIP, VIP, association of chefs, restauranteurs, freelancers and their ranking are maintained by Hatto™ data center  533  and managed by cluster of CPU and GPU  541  and neural network  550 . 
     At step  1009 , if these users are current in annual fee payments and/or not blocked by violations of the Hatto™ rules and regulations more than the preset amount of times K, then allows them to use Hatto™ food socio-touristic media  401 . 
     At step  1010 , if the log-in users are super VIP, VIP, chefs in a recognized chef association, restauranteurs, freelancers, then determined if they are approved by the principal artificial intelligence (PAI). Step  1010  is implemented by neural network  550  in conjunction with cluster of CPU and GPU  541 . 
     At step  1011 , if the log-in users are approved by the PAI then they are stamped with a certified official stamp on the personal page. A sample of the certified official stamp will be illustrated later in  FIG. 12 - FIG. 21 . More particularly, the user Nguyen Phuong Anh (one of the users  521 - 1  to  521 -N) is a VIP member with a VIP title attached to her personal page and the Gentle Onion Food and Drink restaurant is a super VIP chef  1904  with the appropriate symbol  1931  attached to the restaurant picture. 
     Step  1012 - 1013  describe the perks and privileges of having the certified official stamp from Hatto™ food socio-touristic media  401 . 
     At step  1012 , users with certified official stamp are given the rights to modify the food database. In some aspects of the present invention, users with certified official stamp can provide answers to the unanswered food input inquiries and their answers are used to teach artificial intelligence lazy predictor engine  551  and deep learning engine  552 . In other aspects of the present invention, users with certified official stamp can provide images, written articles, any information that help deep learning engine  552  to learn and analyze new food input inquiries illustrated by circular symbols  220  in  FIG. 2 . Yet in other aspects of the present invention, users with certified official stamp are given the rights to log in and directly enter information which helps deep learning engine  552  to learn and analyze new food input inquiries. 
     At step  1013 , users with certified official stamps are also provided with job opportunities with restaurants that register with Hatto™ food socio-touristic media. More specifically, Hatto™ food socio-touristic media  401  can connect users having certified official stamsp with registered member restauranteurs for their mutual benefits: users can have a job at a good restaurant. In return, the restaurant has a good chefs since users with certified official stamp are proven via Hatto™ food socio-touristic media  401  to have good knowledge and personality to be a dependable chef. 
     Finally, at step  1014 , if any of the above users does not pay annual fees and/or blocked by violations of the Hatto™ rules and regulations more than the preset amount of times K, they may be blocked from using Hatto™ food socio-touristic media  401 . 
     From the foregoing disclosure, Hatto™ food socio-touristic media  401  and method  1000  do not only provide means to combine artificial intelligence and human intelligence in the food recognition process but also a platform to make friends, to learn, to entertain, to travel, to have funs, to network, and to find job opportunities. Method  1000  is implemented by the hardware system  500  and system  1100  as described next in  FIG. 11 . 
     Referring now to  FIG. 11 , a comprehensive hardware and software system architecture  1100  of the Hatto™ food socio-touristic media “system  1100 ” in accordance with an embodiment of the present invention is illustrated. System  1100  illustrates a more detailed hardware and software description of  FIG. 5 . In operation, system  1100  are as described that is used to execute all algorithms  100 - 1000  above. 
     System  1100  includes users (e.g.,  521 - 1  to  521 -N) communication devices  1110 , a cloud flare  1111 , a webserver  1120 , and a neural network  1150 . Communication devices  1110  can be a smart phone, a desktop computer, a tablet, or a laptop. Cloudflare  1111  provides web services and security that include Web application firewall (WAF), caching purge providing latest content to users, routing, load balancing, Distributed Denial of Service (DDoS) mitigation, WAN optimization, etc. After cloudflare  1111 , firewall  1112  is used to block malicious and/or illicit contents from being posted in Hatto™ socio-touristic media  401 . Apache 2 WSGI  1121  is a web service gateway interface used to host different web applications described in  FIG. 12 - FIG. 21 . Examples of the web applications include personal page, posts, gamification, reward statuses, etc. In various embodiments of the present invention, Hatto™ socio-touristic media  401  is constructed using Wordpress software application  1123 . MySQL application  1123  is used to manage MySQL Cluster  1128  which is Hatto™ data center  533 . In fact, Hatto™ data center  533  is a cluster of network databases which are connected to each other via network  510 . The communication between client device  1110  and network  510  is serviced by cloudflare  1111 . 
     Continuing with  FIG. 11 , since Hatto™ data center  533  is accessed by many users (i.e.,  521 - 1  to  521 -N) at the same time, it is partitioned into different nodes which are served by SQL nodes  1131 . Each node can access to data node  1135 - 1  to  1135 -K in a network database storage engine  1135 . User node or SQL node  1132  is connected to network database (NDB) storage engine  1135  by a NDB API (application program interface)  1131 . On the other hand, manager node MGM node  1133  are connected to NDB storage engine  1135  by a NDB API (application program interface)  1134 . This architecture of network database (NDB) also manages leaf remote databases  522 - 1  to  522 -M. Similarly, a load balancer  1124 , connected and controlled by firewall  1112 , to manage the load of users who want to access Hatto™ food socio-touristic media  401 . In order to achieve this goal efficiently, system  1100  partitions web servers  1125  for Hatto™ food socio-touristic media  401  into a web server group 1, web server group 2, etc. which are connected and controlled by firewalls  1112  and managed by MySQL Cluster  1128 . Load balancer  1124  regulates to achieve efficiency in distributing users to Hatto™ food socio-touristic media  401 . Referring back to  FIG. 4 , in various embodiments of the present invention, web server  1125  is partitioned into web server forum  410 , web server social network  420  for social network including forums; groups such as chat, friend lists, fan page; webserver food tourism  430 ; and web server gamification  440 . The queues into web server  1125  is managed by load balancer  1124  and their contents are controlled by firewalls  1112 . 
     Continuing with  FIG. 11 , a neural network  1180  includes a Redis open source in-memory data structure store  1157 , an AI visionary queue database  1141 , a AI lazy predictor queue database  1142 , an AI lazy predictor queue database  1143 , an AI lazy recommender and ADS queue  1144 , an APN queues database  1145 , a full text queue  1146 . Via firewall  1112 , AI visionary queue database  1141  is connected to an AI vision-farm server  1151  which is, in turn, connected to a first Nvidia graphic processing unit (GPU)  1161 . Via firewall  1112 , AI lazy predictor queue database is connected to an AI lazy predictor  1152  which is, in turn, connected to a second Nvidia GPU  1162 . Via firewall  1112 , AI lazy validator queue database  1143  is connected to a lazy validator queue database  1153  which is, in turn, connected to a third Nvidia GPU  1163 . Via firewall  1112 , AI lazy recommender &amp; ADS queue database  1144  is connected to an AI RecSys &amp; ADS server  1154  which is, in turn, connected to a recommendation system (RS)  1164 . Via firewall  1112 , AI queue database  1145  is connected to PA service  1155  which is, in turn, connected to an Apple Push Notification Service (APNS)  1165 . Via firewall  1112 , full text queue database  1146  is connected to a full text parser  1156  which is, in turn, connected to an elastic search engine  1166 . Nvidia graphic processing units (GPU)  1161 - 1163  use floating point parallel computations to perform intensive operations such as deep learning and analytics. Recommendation system (RS)  1164  is an information filtering system that seeks to predict the rating or preference a user such as user  521 - 1  would give to an item. By this, system  1100  of the present invention can match a user profile with his or her preferred dishes. Apple Push Notification Service (APNS)  1165  is a platform notification service enabled system  1100  to send notification data such as badges, sounds, updates, text alerts, etc. to users such as users  521 - 1  to  521 -N. Elastic search engine  1166  is a document-oriented database designed to manage document-oriented or semi-structured database. A database aggregator  1170  includes partitioned leaf databases  1173 - 1176  which are managed by an aggregator  1172  and a master aggregator  1171  using mem-SQL (structured query language). In many embodiments of the present invention, partitioned leaf databases  1173 - 1176  are remote leaf databases  522 - 1  to  522 -M in  FIG. 5 , which enables world-wide food recognition using leaf databases set up in different sections of the world. 
     Continuing with  FIG. 11  and referring back to  FIG. 5 , in operation, as each users  521 - 1  to  521 -N presses an icon (GUI) on their communication devices  1110  to activate Hatto™ food socio-touristic media  401 , a wireless communication channel  1101  establishing a link between Hatto™ webserver  1120  and communication devices  1110 . Cloudflare  1111  is a cloud platform operative to provide web performance services and securities among users  521 - 1  to  521 -N. Cloudflare  1111  stops malicious traffic including bad bots and crawlers, hackers and attackers; optimizes content delivery; and routes safe requests through global network such as network  510 . Apache2 WSGI module  1121  hosts various web applications and Hatto™ food socio-touristic media  401 . In the present disclosure, web applications include interactive contents such as matching and displaying page  542 - 4 , ad pages in Hatto™ food tourism  542 - 5 , and Hatto™ gamification  542 - 6  where users  521 - 1  to  521 -N interact therewith. WorldPress unit  1123  is an open source content management system (CMS) for building Hatto™ food socio-touristic media  401 . Hatto™ food socio-touristic media  401  is supported by more than one backend web servers that use multiple computing resources. In the present invention, there are servers  1125  for blogs, posts, and forums application  541 ; server  1126  for Hatto™ food tourism  542 , server  1127  for Hatto™ gamification  546 . Data storage  1128  stores the scripts or codes for these backend servers. Load balancer  1124  efficiently distributes incoming network  510  traffic across backend server  1125 - 1127  so that users  521 - 1  to  521 -N do not have to wait for his or her Hatto™  401 . 
     Continuing with  FIG. 11  and  FIG. 5 , after user  521 - 1  to  521 -N has access into Hatto™  401 , he or she can view comments from friends from previous posts, look at the first-level prizes (watermelons) and second-level prizes (the Tam Rice), view the ads, read blogs from friends, chat with friends, send text messages, post an image of food inquiry at his or her personal page, and exchanges or buy discounted products. All of these actions can take place instantaneously at the fingertip of users  521 - 1  to  521 -N without losing any contents and being exposed to illicit contents. This is implemented by methods  300 - 400  and  600 - 1000  and Hatto™ web server  1120 . The codes for methods  300 - 400  and  600 - 1000  are stored in cache memory in cluster of CPU and GPU  541  which is Hatto™ central brain. Food input inquiries are performed by AI lazy predictor queue database  1142 , lazy predictor server  1152 , and Nvidia GPU  1162  in the fashion described above in  FIG. 1  and  FIG. 2 . After a while, system  1100  learns to connect between user profile and food dishes by means of AI visionary queues database  1141 , vision farm server  1151 , and first Nvidia GPU  1161 . First Nvidia GPU  1161  is programmed to perform real-time automatic customer analysis and solution. For example, first Nvidia GPU  1161  knows which users among users  521 - 1  to  521 -N like hamburgers. Consequently, system  1100  recommends hamburgers to such users at restaurant(s) that user may not know. This is implemented by AI recommender &amp; ADS queue database  1144  via AI recSys &amp; Ads  1153  and third Nvidia GPU  1163 . If the search result is found, notification is posted via Apple Push Notification (APN) queue database  1145 , PA service server  1155 , and Apple Push Notification Service (APNS)  1165 . Text searches are parsed and understood by fulltext queue database  1146 , fulltext parser  1156 , and elastic search engine  1166 . Hatto™ data center  533  is a network database (NDB) that includes remote leaf databases  522 - 1  to  522 -M. Remote leaf databases  522 - 1  to  522 -M are represented as leaf databases  1173 - 1176  which are connected and combined together by a master aggregator  1171  (see masterand an aggregator  1172 . In some embodiments of the present invention, some remote leaf databases  522 - 1  to  522 -M belong to certified chefs or super VIP who are approved by the principal artificial intelligence (PAI) in that they can contribute and share their databases with system  1100 . In other embodiments of the present invention, remote leaf databases  522 - 1  to  522 -M are located around the world and connected to system  1100  via network  510 . It is noted that leaf databases  1173  to  1176  are either in-memory databases or remote databases. 
     Now referring to  FIG. 12A - FIG. 12B , a log-in page and a personal page of Hatto™ food socio-touristic media  401  in accordance with an exemplary embodiment of the present invention are illustrated. After users  521 - 1  to  521 -N have down loaded the Hatto™ application, the log-in page of Hatto™ food socio-touristic media  401  displaying the Hatto™ background appears on a communication device  1210 . Communication device  1210  can be either smart phones, laptops, desktops, or tablets. Hatto™ food socio-touristic media  401  begins by a sign-in section  1212 . Users  521 - 1  to  521 -N can sign in using either Facebook, Zalo, or personal email addresses. Referring back to  FIG. 3 , step  301  begins by signing in into Hatto™ food socio-touristic media  401 . 
     In  FIG. 12B , after successfully log-in, a personal page  1200 B displays a profile section  1220 , a recommendation section  1230 , current post section  1240 , and a task bar  1250 . In profile section  1230 , username (e.g., “Nguyen Phuong Anh”), profile picture, her title, a total first-level prizes (watermelon) earned  1222 , and a total second-level prizes (Tam Rice) earned  1221  are displayed. In recommendation section  1230 , pictures and the name of the dishes that are most liked, reacted to, and viewed are displayed in chronological order from left to right. In current post section  1240 , images of food dishes, descriptions, comments, and reactions from different users that are friend with the current user are chronologically displayed in top down fashion. In task bar  1250 , a home button  1251 , a restaurant search button  1252 , a camera button  1253 , a like button  1254 , and a game button  1224  are disposed for use by the primary user, e.g., user  521 - 1 . 
     Referring next to  FIG. 13A - FIG. 13B , a camera application and food inputs in the Hatto™ food socio-touristic media  401  in accordance with an exemplary embodiment of the present invention are illustrated. In  FIG. 13A , as primary user (e.g.,  521 - 1 , Nguyen Phuong Anh) presses camera button  1253 , a message box  1310  including choices of either a “take photo” button  1311  and an “upload photo” button  1312  is displayed. As message box  1310  is displayed, it becomes brighter and personal page  1200 B becomes dimmer as the background. If “take photo” button  1311  is selected, Hatto™ food socio-touristic media  401  allows the primary user to take the photo of the food image. If “upload photo” button  1312  is selected, images stored in the memory of communication device  1210  allows the primary user to select a food picture to upload.  FIG. 13A  illustrates step  303 , which is “select application” in  FIG. 3 . After the primary user choses a food picture, step  304  and step  306  are implemented. 
     In  FIG. 13B , as soon as the food image is uploaded, Hatto™ food socio-touristic media  401  searches Hatto™ data center  533  using artificial intelligence lazy predictor engine  551  operating as described in  FIG. 2 . This is the implementation of step  308 . In many aspects of the present invention, only two food images  1311  and  1312  can be selected and uploaded at a time. Step  311  and step  312  are illustrated as the name of the first dish  1311  (“Fried shrimp”) and that of second dish  1312  (“Stewed Chicken”) are displayed. The name and the address of the restaurant are also shown. Reaction menus  1314  and  1315  for each food dish including thumb up, thumb down, suggest, and love are also attached to each food image  1311  and  1312  respectively. In addition, a similar item  1320  of another restaurant “Gentle Onion” offering the same food dishes is displayed. In similar item  1320 , the primary user can either turn it off, find the exact location, or view the picture of the restaurant. At the bottom of communication device  1210 , a picture menu  1330  includes a suggest picture button  1331 , a draw box button  1332 , and an all box button  1333 , allowing a user (e.g., Nguyen Phuong Anh,  521 - 1 ) to maneuver the photo of a food input inquiry. Suggest picture button  1331  allows the user Nguyen Phuong Anh to post the fried shrimp and stewed chicken pictures as the food input inquiries. Draw box  1332  allows her to select a particular dish (e.g., fried shrimp only) as the food input inquiry. All box button  1333  allows her (Nguyen Phuong Anh) to select all dishes in the picture as the food input inquiries, equivalent to “select all” option that is well-known in the computer art. 
     Referring to  FIG. 14A - FIG. 14C , primary and secondary user comments and the like pages of the Hatto™ food socio-touristic media  401  in accordance with an exemplary embodiment of the present invention are illustrated. As alluded above, a primary user is the registered owner of his or her personal page. A secondary user is the visitor (or viewer) of the personal page of the primary user. In  FIG. 14A , comments  1401  of the primary user (“Nguyen Phuong Anh”) about the restaurant in  FIG. 13B  are posted. A reaction banner  1402  of the primary user is attached underneath comments  1401 . Secondary users comments  1403  such as “Nguyen Linh Nga” and “Hoang Anh” are also displayed. In  FIG. 14B , comments of other secondary users  1411  are shown. A picture of the favorite food dish  1412  is also attached to the picture of the secondary user. Reaction banner  1413  for each comment of secondary user is also displayed. In  FIG. 14C , a list  1400 C of all secondary users who like the primary user (Nguyen Phuong Anh) are listed with a relationship button  1422  are listed one by one. Relationship button  1422  includes “friend”, “respond to request to be friend”, or “add friends”. 
     Referring to  FIG. 15A - FIG. 15C , the answer pages of human intelligence inputs of Hatto™ food socio-touristic media  401  in accordance with an exemplary embodiment of the present invention are illustrated.  FIG. 15A - FIG. 15C  illustrate steps  313  and  314  of  FIG. 3  when the search from artificial intelligence lazy predictor engine  551  results in a “not found”. In this situation, inputs from the human intelligence contribution of secondary users are sought as discussed in step  313 . In  FIG. 15A , when the primary user (Nguyen Phuong Anh) selects an unknown dish  1501 , there are three answers as illustrated in step  314 : a first answer  1502  from secondary user “Thu Suong” declares the food input inquiry is a bowl of rice; a second answer  1503  from another secondary user “Tuan Anh” declares it is a ribs rice; and a third answer  1504  from Hatto indicates it to be a broken rice. A menu  1505  to create another new dish allows the primary user to enter the description of a new dish. A keyboard section  1506  enables the primary user to enter a new dish. In many aspects of the present invention, keyboard section  1506  enables visually impaired users to describe his or her food dishes, which is an implementation of steps  305 . In other aspects, a microphone icon is provided so that users can conveniently describe the new dish by voice. This is the implementation of step  309 . In  FIG. 15B  and  FIG. 15C , a list of dish features  1511  including meal, cook, taste, style, ingredients, etc. is included to provide the primary user (Nguyen Phuong Anh) with useful information about the input dish  1501 . If a cook menu is selected, an instruction is provided to teach the primary user how the input dish  1501  is prepared by either “boiled” or “hotpot.” In  FIG. 15C , if an ingredient menu is selected, all ingredients in input dish  1501  are listed.  FIG. 15B  and  FIG. 15C  illustrate the implementation of step  312 . 
     Referring to  FIG. 16A - FIG. 16C , the restaurant recommendation and creation pages of the Hatto™ food socio-touristic media  401  in accordance with an exemplary embodiment of the present invention are illustrated. In  FIG. 16A , a location recommendation page  1600 A is illustrated which includes restaurant name section  1601 , a restaurant address  1602 , a picture section  1603 , a text description section  1604 , a rating section  1605 , and a keyboard section  1606 . The secondary users have to complete location suggestion page  1600 A in order to move to location creation page  1600 C. The completion of location suggestion page  1600 A requires a fill-out of name section  1601 , a restaurant address  1602 , a picture section  1603 , a text description section  1604 , and a rating section  1605 . Rating section  1605  is completed by touching the pineapple symbols. In  FIG. 16B , a photo section  1611  enables the primary user to either take phot or upload photos stored in his or her phone albums. In  FIG. 16C , after all information are completed. A restaurant name  1621 , a restaurant address  1622 , and a picture of the restaurant  1623  are uploaded. Comments  1624  are described, and rating of the restaurant  1625  is selected. As the screen of communication device  1210  changes, a send button  1626  appears in place of keyboard  1606 . If the primary user presses send button  1626 , the above information will be updated in Hatto™ data storage  533  and Hatto™ neural network  1180  for deep learning analysis. This is the implementation of step  315 . 
     Referring to  FIG. 17A - FIG. 17C , forum pages  1700 A- 1700 B of the Hatto™ food socio-touristic media  401  in accordance with an exemplary embodiment of the present invention are illustrated. Forum pages  1700 A- 1700 C illustrate forum section  410  that includes user submissions  411 , chat/messages, and food location page  413  in  FIG. 4 . As discussed above, forum pages  1700 A- 1700 B are part of Hatto™ food socio-touristic media  401  written by WordPress and MySQL application  1123 . In  FIG. 17A , a personal page  1700 A of the primary user (Nguyen Phuong Anh) is displayed after log-in. Personal page  1700 A includes a status banner that includes a drop-down menu  1701 , a notification button  1702 , a first level prize button  1703 , and a second level prize button  1704 . Drop-down menu  1701  allows the primary user (Phuong Anh Nguyen) to navigate to different applications such as exiting out of Hatto™ food socio-touristic media  401  to answer an incoming phone call. Notification button  1702  informs the primary user that she has new unread messages or posts. First-level prize (watermelons) button  1703  displays the total amount of first-level prize the primary user currently has. Similarly, First-level prize (watermelons) button  1704  displays the total amount of first-level prize the primary user currently has. Personal page  1700 A also includes the name of the primary user (Nguyen Phuong Anh), her title as VIP, her skill, and her awards. A comment tab, a submission tab, and a location tab  1706  that enable the primary user to navigate to different categories of her personal page  1700 A. Please note that  FIG. 17A  shows the location tab being selected. Next, the primary can chose to let the public views her posts or search for friends on Hatto™ food socio-touristic media  401 . A restaurant location suggestion section  1708  displaying all restaurants recommended by the friends of the primary user. Next, posts  1709  by other users are displayed. The primary user can scroll up or down post  1709  to view more posts. Finally, a utility banner  1708  enables the primary user to go to “home”, find restaurants in food tourism section  430 , take a picture, like, or play game.  FIG. 17B  illustrates the detail post  1700 B of a friend of the primary user. For example, the secondary user (Mai Phuong Anh) recommends the “Amun Garden Restaurant and Lounge” on the primary user personal page  1700 A. When the primary user touches this post, the detail post page  1700 B appears, replacing the original personal page  1700 A.  FIG. 17B  illustrates the implementation of step  312  which is “display the results in Hatto™ food socio-touristic media  401 ”. Step  312  is coded in matching and displaying subroutine  542 - 2  of Hatto™ socio-touristic platform web programs  540 . Detail restaurant page  1700 B includes a share function  1711 , the background picture of the restaurant  1712 , a name and address of the restaurant  1713 , the comment of the user who posts such restaurant  1714 , and other comments from other users  1715 . In  FIG. 17C , a display  1700 C of a user comment and rating of a restaurant is displayed. Display  1700 C includes a name and address of the restaurant  1721 , a comment of other users (Nguyen Linh Nga), a chat box  1723 , a rating  1724  by touching the pineapple symbols, and a send button  1725 . Thus, if the primary user, Nguyen Phuong Anh, wants to respond to a secondary user, Nhat Anh, she touches on his comment about the Amun Garden Restaurant and Lounge to enter her comment and rating. After that, she can touch send button  1725  to post her comment. 
     Next,  FIG. 18A  to  FIG. 21C  illustrate gamification section  440  of Hatto™ food socio-touristic media  401 . Gamification section  440  is implemented by gamification subroutine  542 - 6  which uses WordPress 1213 as the building software program. 
     Referring to  FIG. 18A - FIG. 18C , first-level prize (watermelon) rewards for different activities  1800 A- 1800 B and notification page  1800 C of Hatto™ food socio-touristic media  401  in accordance with an exemplary embodiment of the present invention. In  FIG. 18A , 15 first-level prize is awarded to a user for his or her new food suggestions. In  FIG. 18B , 20 first-level prize is awarded to this user for his or her creation of a new restaurant. If this user touches on either first-level prize award in  1800 A or  1800 B, notification page  1800 C is displayed which show all rewards to other users for their activities. These are illustrations of first-level prize symbolized as watermelons component  441  of gamification  440 . As alluded above, gamification  440  is used to incentivize users to contribute their intelligence to food image recognition process, supplemental to artificial intelligence (AI) lazy predictor engine  551 . 
     Referring to  FIG. 19A - FIG. 19C , an exchange and reward notification  1900 A- 1900 C as part of the gamification of the Hatto™ food socio-touristic media  401  in accordance with an exemplary embodiment of the present invention are presented. The gamification as illustrated by  FIG. 19A - FIG. 19C  is implemented as an integral part of Hatto™ food socio-touristic media  401  which is coded using WordPress software program. More particularly, pages  1900 A- 1900 C are the execution of Hatto™ gamification subroutine  542 - 6 . In  FIG. 19A , an exchange page  1900 A from the first-level prize (watermelon) to the second-prize level (the Tam Rice) includes a title  1911 , a prize status  1912 , an exchange calculator  1913 , a confirm button  1914 , and a purchase section  1916 . A user may navigate to exchange page  1911  by touching on any first-level prize symbol on his or her personal page. Currently, prize status  1912  indicates that this user has 2,172 first-level prizes (2,172 watermelons) and 278 second-level prize (278 Tam Rice). Prize status  1912  is an illustration of step  804  in  FIG. 8 . The user enters the number of first-level prize (watermelon) to exchange for the second-level prize (Tam Rice). If the number of the first-level prize entered is 150, this user will get 10 second-level prize (Tam Rice). If this is what he or she wants, confirm button  1914  should be pressed. This is an illustration of steps  808  and  809  in  FIG. 8 . In addition, the user can buy second-level prize (Tam Rice) in purchase section  1916  by enter the amount he or she wants to buy and pay the amount of money indicated below each second-level prize (the Tam Rice). The amount of second-level prize starts with 10 second-level prize (the Tam Rice) and increased by 10. For example, 10 second-level prize (Tam Rice) costs 15.000 VND; next, 100 10 second-level prize (Tam Rice) costs 150.000 VND. The user can select the amount of second-level prize she or he wants to buy by touching each box. In  FIG. 19B , the user can also move up by exchanging the second-level prize (Tam Rice). In this situation, if the user touches the second-level prize (Tam Rice) symbol in the prize status  1912 , the VIP page  1900 B appears replacing the exchange page  1900 A. VIP exchange page  1900 B includes a title header  1921 , a second-level prize (Tam Rice) status  1922 , a VIP exchange status  1923 , a super VIP (SVIP) chef exchange status  1924 , a SVIP special exchange status  1925 , and SVIP location exchange status  1926 . Referring back to  FIG. 10 , super VIP level  1002  includes a VIP level  1901 , a super VIP specialist  1902 , a super VIP restauranteur  1903 , and a super VIP chef  1904 . VIP level  1003  is illustrated by  1901 . These VIP and super VIP levels if approved by primary artificial intelligence (PAI), will receive certified official stamps,  1901  to  1904 , next to their name. This is the illustration of steps  1010 - 1011 . In  FIG. 19C , the user (Nguyen Phuong Anh) is a VIP user. Thus, the VIP certified official stamp  1901  is stamped next to her profile picture. Her favorite restaurant, Gentle Onion Food &amp; Drink, is shown in her personal background. This restaurant is also a member of Hatto™ food socio-touristic media  401  and is a super VIP chef member with the certified official stamp  1931  which is super VIP chef  1904 . It is noted that super VIP members will have perks and privileges such as the rights to modify Hatto™ data center  533  by connecting his or her home hard drive as one of the remote leaf databases  522 - 1  to  522 -M. 
     Referring to  FIG. 20A - FIG. 20C , product purchasing and gift pages  2000 A- 2000 C as part of the gamification of the Hatto™ food socio-touristic media  401  in accordance with an exemplary embodiment of the present invention are illustrated. Product purchasing and gift pages  2000 A- 2000 C can be accessed by pressing a gamification button  2001 . This is the illustration of steps  803  to  815  in  FIG. 8 . In  FIG. 20A , a store page  2002  includes a prize status section  2003 , a selection menu  2004 , a gift selection  2005 , and a food and restaurant section  2006 . Prize status section  2003  displays total number of first-level prize (watermelons) and second-level prize (Tam Rice) that the user has earned. This is an illustration of step  804 . Selection menu  2004  includes products, food and drink, and travel categories. Gift selection  2005  displays gifts or products at discounted prices available to users. Foods and restaurants section  2006  lists foods and restaurants associated with Hatto™ that can offer discounts and free meals to users. If the user selects food and drink in selection menu, page  2000 B is displayed and replacing page  2000 A. Food and drink button is highlighted and different foods, drinks choices  2012  and restaurants  2013  are displayed for users to select. In  FIG. 20C , if the user selects gift selection  2005  and chooses to buy a smartphone, a notification  2021  announcing that this smart phone costs 150 second-level prize (Tam Rice). If the user has 278 second-level prize (Tam Rice), he or she can buy this phone without spending any out-of-pocket money. If this user does not have 150 second-level prize, he or she can exchange the first-level prize to second-level prize and/or buy second-level prize. 
     Finally referring to  FIG. 21A - FIG. 21C , QR codes in the product purchasing pages  2100 A- 2100 C of the Hatto™ food socio-touristic media  401  in accordance with an exemplary embodiment of the present invention are illustrated. The gamification as illustrated by  FIG. 21A - FIG. 21C  is implemented as an integral part of Hatto™ food socio-touristic media  401  which is coded using WordPress software program. More particularly, pages  2100 A- 2100 C are the execution of Hatto™ gamification subroutine  542 - 6 . In  FIG. 21A , a QR code generation page  2100 A from the second-level prize is displayed that includes a title section  2101 , a menu section  2102 , and a list  2103 . In title section  2101 , a QR code encoding the number of second-level prize is generated so that the user can scan this at restaurants that associates with Hatto™. This way, when the user go to these restaurants, he or she does not have to pay by money or credit cards. This QR code can be scanned at the purchase of the meal. Menu section  2102  allows the user to either generate a new QR code, receive a QR code from a friend, or retrieve an old QR code generated before to use or update. In  FIG. 21B , if the user selects to generate a new QR code for 150 second-level prize (the Tam Rice), a QR code generation page  2100 B is displayed replacing page  2100 A. QR code generation page includes a title section  2111 , a display  2112 , and a numeric touch keyboard  2103 . The user can enter the amount of second-level prize (Tam Rice) that she or he wants to generate QR code to be used. When a “done” button is pressed, QR code  2100 C is displayed in  FIG. 21C . QR code  2100 C has the value of 150 of second-level prize (Tam Rice). The user can open his or her smartphone, retrieve this QR code of 150 second-level prize and purchase meals or buys products without using credit cards or cash money. 
     The above disclosure with reference to  FIG. 1  to  FIG. 21  discloses the following features of the present invention: (1) a system for rendering a socio-touristic media platform that uses the novel Hatto™&#39;s food recognition method, (2) a method for food recognition that uses artificial intelligence (AI) lazy predictor and human intelligence from the social media that provides deep learning platform supplemental to the AI lazy predictor, and (3) a socio-touristic media platform using (1) and (2) that can promote food tourism, commerce, and social network. 
     The computer program instructions such as  100  and  1000  may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The disclosed flowchart and block diagrams illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. 
     The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, element components, and/or groups thereof. 
     The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the present invention has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated. 
     The flow diagrams depicted herein are just one example. There may be many variations to this diagram or the steps (or operations) described therein without departing from the spirit of the invention. For instance, the steps may be performed in a differing order or steps may be added, deleted or modified. All of these variations are considered a part of the claimed invention. 
     While the preferred embodiment to the invention had been described, it will be understood that those skilled in the art, both now and in the future, may make various improvements and enhancements which fall within the scope of the claims which follow. These claims should be construed to maintain the proper protection for the invention first described. 
     The foregoing description details certain embodiments of the invention. It will be appreciated, however, that no matter how detailed the foregoing appears in text, the invention can be practiced in many ways. As is also stated above, it should be noted that the use of particular terminology when describing certain features or aspects of the invention should not be taken to imply that the terminology is being re-defined herein to be restricted to including any specific characteristics of the features or aspects of the invention with which that terminology is associated. The scope of the invention should therefore be construed in accordance with the appended claims and any equivalents thereof. 
     DESCRIPTION OF NUMERALS 
     
         
         
           
               401  Hatto™ food socio-touristic media (“Hatto™”) 
               410  forum page of Hatto™ 
               411  user submissions section of Hatto™ 
               412  chat and messes section of Hatto™ 
               413  food location section of Hatto™ 
               420  social network page of Hatto™ 
               421  firewall 
               422  fan-page 
               423  update friends 
               424  friend list 
               425  display options 
               426  alert option 
               430  food tourism page 
               431  welcome page 
               432  inquiries 
               433  local dishes and restaurants 
               440  gamification page 
               441  first-level prize (watermelons) 
               442  second-level prize (Tam Rice) 
               443  VIP level 
               444  Super VIP (SVIP) level 
               445  Exchange function 
               446  upgrade function 
               447  gift function 
               449  discounts/deals function 
               500  hardware structure of Hatto™ 
               510  network 
               511  web server gateway interface and firewalls 
               521 - 1  a user of Hatto™ 
               521 - 2  a user of Hatto™ 
               521 - 3  a user of Hatto™ 
               521 -N a user of Hatto™ 
               522 - 1  a remote leaf database 
               522 - 2  a remote leaf database 
               522 - 3  a remote leaf database 
               522 -M a remote leaf database 
               530  Hatto™ hardware system 
               531  Network I/O interface managed by Cloudflare 
               532  master aggregator 
               533  Hatto™ data center 
               540  Hatto™ central brain 
               541  Cluster of CPU and GPU 
               542  Hatto™ socio-touristic media software program 
               542 - 1  blogs, posts, and forums subroutines 
               542 - 2  VIP member authentication subroutine 
               542 - 3  social network subroutine 
               542 - 4  matching/displaying subroutine 
               542 - 5  food tourism subroutine 
               542 - 6  gamification subroutine 
               550  Hatto™ neural network 
               551  AI lazy predictor 
               552  deep learning engine 
               553  visionary and recommendation 
               554  search and notification engine 
               561  wireless communication 
               1100  hardware and software structure of Hatto™ 
               1101  wireless communication channel 
               1110  user to Hatto™ as in  521 - 1  to  521 -N 
               1120  Hatto™ webserver 
               1111  cloudflare 
               1112  firewall 
               1121  Apache2 web server and gateway interface 
               1122  Hatto™ central brain 
               1123  WordPress and MySQL applications 
               1124  load balancer 
               1125  web serve group 1 
               1126  web serve group 2 
               1127  web serve group 3 
               1128  Hatto™ data center managed by MySQL Cluster 
               1131  network database (NDB) API 
               1132  SQL node 
               1133  management node (MGM-node) 
               1135  data nodes 
               1135 - 1  data node 1 
               1135 - 2  data node 2 
               1180  Hatto™ neural network 
               1141  AI visionary queues database 
               1142  AI predictor queue database 
               1143  AI lazy validator queue database 
               1144  AI lazy recommender&amp; ADS queue database 
               1145  APN queue database 
               1146  fulltext queue database 
               1151  AI vision farm server 
               1152  AI lazy predictor server 
               1153  lazy validator server 
               1154  AI RecSys &amp; Ads server 
               1155  PA service server 
               1156  fulltext parser server 
               1161  AI vision farm GPU 
               1162  Lazy predictor GPU 
               1163  AI lazy validator GPU 
               1164  Hatto™ recommendation system (RS) 
               1165  Apple push notification service (APNS) 
               1166  elastic search engine 
               1170  database aggregator 
               1171  master aggregator 
               1172  aggregator 
               1173  remote leaf database 
               1174  remote leaf database 
               1175  remote leaf database 
               1176  remote leaf database