Patent Publication Number: US-2020279280-A1

Title: Algorithmic generation, qualification, and ranking of potential sales leads for human consumable nondurable goods

Description:
BACKGROUND OF THE INVENTION 
     The present invention is in the technical field of computer software. More particularly, the present invention is in the technical field of computer software as a service. More particularly, the present invention is in the technical field of computer software as a service for the purpose of algorithmic generation, qualification, and ranking of potential sales leads for human consumable nondurable goods based on a wide range of input data sources and systemic feedback signals. 
     Traditional approaches to lead generation and product recommendation have many significant drawbacks. While some attempts have been made to design algorithmic systems to address sales lead generation and recommendation systems for specific product markets, in most cases non algorithmic approaches are still heavily utilized. Existing algorithmic recommendation systems are generally focused on optimizing consumer purchasing behavior, and are used extensively to select and rank sets of products for individual consumers. Comparable algorithmic recommendation systems designed to generate ranked sets of potential buyers for individual products are not generally utilized. It is clear that existing recommendation and lead generation systems have an inherent directional bias, as they are mostly designed to find n many products to recommend to a single consumer rather than to find n many potential buyers for a single product. On the other hand, algorithmic sales lead generation and ranking methods can be used quite effectively to match individual products to sets of one or more potential buyers, but only for fairly specific product types. The products for which algorithmic methods are used are mostly limited to those with high revenue and profit generating potential such as financial instruments, insurance, real estate, and vehicles, among others. Algorithmic sales lead generation and ranking approaches are not generally utilized for products that do not share these characteristics in whole or in part, and are specifically not often utilized for products which fall under the category of human consumable nondurable goods. Even if algorithmic methods are used for the generation and ranking of sales leads for various human consumable nondurable goods, they do not make use of a sufficiently broad set of input data sources or systemic feedback signals. Such methods usually focus on product data, purchaser data, or sales data but generally not a combination of qualitative and quantitative data on all three. Systemic feedback signals are also often not utilized, as purchasing patterns for durable goods do not have the same recursive predictive utility as they do with respect to nondurable goods, and existing algorithmic sales lead generation and ranking methods have focused for the most part on durable goods as durable goods are much more likely to have high revenue and profit generating potential. 
     SUMMARY OF THE INVENTION 
     The present invention is a software as a service platform employing novel means and methods for the purpose of algorithmic generation, qualification, and ranking of potential sales leads for human consumable nondurable goods based on a wide range of input data sources and systemic feedback signals. By using a comprehensive set of qualitative and quantitative data sets including producer, product, sales, and purchaser information in conjunction with a range of manual, hybrid, and algorithmic based processing, training, analysis, feedback, and ranking methods, the present invention addresses some of the drawbacks associated with existing algorithmic sales lead generation and ranking methods and provides a novel framework for dynamic generation and ranking of potential sales leads for a wide range of human consumable nondurable goods. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  provides an overview of the data flow for classifier training in the present invention; 
         FIG. 2  shows an overview of the data flow for classification in the present invention; and 
         FIG. 3  provides an overview of the user flow for lead generation in the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     Referring now to the invention in more detail, in  FIG. 1  there is shown an overview of the data flow model for classifier training in the present invention. A variety of data sources  100  generally including at least product data  101 , sales data  102 , and purchaser data  103  is accessed, standardized, and normalized by the data standardization  104  component, and the standardized data thus extracted is then stored by the system in the primary datastore  105 . Product data  101  may include but is not limited to data such as tasting notes, wholesale pricing, retail pricing, recipes, flavor pairings, cuisine matching, expert reviews including but not limited to (date, source, details, etc), reviews including but not limited to (date, source, details, etc), ratings including but not limited to (date, source, details, etc), chemical analysis, ingredients, product name, product type, product subtype, producer name, producer location, producer notes, production notes, production date, and sell by date among others. Sales data  102  may include but is not limited to data such as a link to a product identifier, a link to a purchaser identifier, a sales date, number of units sold, unit definition, per unit sale price, and total sales among others. Purchaser data  103  may include but is not limited to data such as location data including but not limited to (address, square footage, etc), demographic data including but not limited to (race, gender, age, income, etc), purchasing history (based on aggregate sales data), product listing including but not limited to (current wine/beer/cocktail menu, food menu, product listings, etc), expert reviews, reviews, ratings, pricing data (based on aggregate sales data), purchaser name, purchaser type, purchaser subtype, purchaser details including but not limited to (license types, health and safety rating, age of business, etc), purchaser inventory history including but not limited to (products in inventory and for how long etc), purchaser event calendar, purchaser preferences, and purchaser systemic metadata including but not limited to (last referenced, last sale, etc) among others. The types of product for which product data  101  is sourced and utilized in the present invention are, in a preferred embodiment, human consumable nondurable goods for which qualitative tasting data can be generated. These include but are not limited to wine, beer, liquor, fresh vegetables, fruits, chocolate, coffee, mushrooms, caviar, cigars, electronic cigarette fluids, cannabis, cannabis extracts, medicinal extracts, condiments, desserts, snacks, chips, sodas, energy drinks, frozen foods, prepared meals, candy, baked goods, meat, fish, protein, supplements, oils, grains, flavored powders, sauces, tea, drinks, kombucha, ciders, herbs, and spices among many others. 
     In more detail, still referring to the invention of  FIG. 1 , data sources  100  for product data  101 , sales data  102 , and purchaser data  103  can include but are not limited to data accessed from local or remote static data files, local databases, remote databases, third party API&#39;s, both real and non real time signals, systemic feedback signals, websites, human interaction, or any other sources via programmatic or non programmatic methods. 
     In more detail, still referring to the invention of  FIG. 1 , once standardized data is stored in the primary datastore  105  the feature extraction  106  component extracts features from various entities in the primary datastore  105  which are then stored in the feature datastore  107 . Features extracted by the feature extraction  106  component may be directly related to entities in the primary datastore  105  or indirectly related to entities in the primary datastore  105 . An example of a direct primary relationship between an entity in the primary datastore  105  and an extracted feature stored in the feature datastore  107  could be a production date, while an example of an indirect secondary relationship could be a feature consisting of the top five most frequently occurring words sorted by total word count extracted from an entity in the primary datastore  105  such as an expert review or a menu listing. In general the feature extraction  106  component may employ manual, hybrid, or algorithmic methods to extract features which may include but are not limited to human interactions, rule engines, statistical methods, and machine learning algorithms (i.e. linear regression, logistic regression, cluster analysis, neural networks, etc.) among others. 
     In yet more detail, still referring to the invention of  FIG. 1 , once features are extracted  106  and stored in the feature datastore  107  feature analysis  108  is conducted to determine the relative and absolute utility associated with the various primary and secondary features extracted  106  based on a wide variety of potential quantitative and qualitative metrics. These evaluation metrics can include but are not limited to statistical metrics such as mean, median, mode, standard deviation, skew or kurtosis, variance, range, L-statistics, coefficient of variation, covariance, and various correlation measures among many others. In some cases when feature analysis  108  is conducted if one of the analysis metrics falls below or above a desired threshold value for one of the analyzed features, a signal may be sent back to the feature extraction  106  component resulting in the extraction of a different replacement feature or the use of alternative feature extraction methods for the feature in question. In general the feature analysis  108  component may employ manual, hybrid, or algorithmic methods to analyze extracted features which may include but are not limited to human interactions, rule engines, statistical methods, and machine learning algorithms (i.e. linear regression, logistic regression, cluster analysis, neural networks, etc.) among others. 
     In more detail, still referring to the invention as shown in  FIG. 1 , the results of feature analysis  108  are used in conjunction with other criteria by the model selection  109  component to select a predictive model from the model datastore  110  to train in the classifier training  113  component. Types of parametric, semi-parametric, and non-parametric predictive models in the model datastore  110  in the preferred embodiment of the current invention may include but are not limited to statistical models, rulesets, clustering models, neural networks, bayesian models, support vector machines, decision trees, graphs, regression models, and many others. Model selection  109  may use a variety of manual, hybrid, or algorithmic methods to select appropriate models from the model datastore  110  based on a broad range of criteria including the results of feature analysis  108 . Once a model has been selected  109 , feature selection  111  for classifier training  113  can take place. Based on the model selected  109  and the results of feature analysis  108 , a set of specific features to use in classifier training  113  is selected using manual, hybrid, or algorithmic methods from the feature datastore  107  and a corresponding set of feature vectors are constructed and stored in the feature vector datastore  112 . Classifier training  113  then uses a subset of these feature vectors from the feature vector datastore  112  to train and evaluate a classifier  113  based on the model selected  109 . The resulting classifier, classifier metadata, and classifier evaluation results are then stored in the classifier datastore  114  for future use during run time classification  116 . 
     In more detail, still referring to the invention as shown in  FIG. 1  to  FIG. 3 , the functional system components (identified in  FIG. 1  to  FIG. 3  by solid rectangular borders), namely data standardization  104 , feature extraction  106 , feature analysis  108 , model selection  109 , feature selection  111 , classifier training  113 , classifier selection  115 , classification  116 , random classification  121 , human classification  122 , reclassification  123 , classification handling  119 , feature vector extraction  127 , API  124 , user interface  125 , product entry  126 , product selection  128 , lead generation  129 , lead ranking  130 , lead output  131 , export functions  132 , lead resolution  133 , lead conversion analysis  134 , and lead data deconstruction  135 , in a preferred embodiment of the present invention consist of one or more independent software programs controlled by a single organization or individual running on dedicated compute devices and interacting with each other, their data sources, data stores, and any external users or applications via APIs over LAN, WAN, or wireless network connections. 
     In more detail, still referring to the invention of  FIG. 1  to  FIG. 3 , in a preferred embodiment of the present invention, all input and output data elements and datastores (identified in  FIG. 1  to  FIG. 3  by solid ellipsoid borders) associated with each functional system component, namely each primary datastore  105 , feature datastore  107 , model datastore  110 , feature vector datastore  112 , classifier datastore  114 , and result datastore  120 , are stored in independent data stores by type. All data stores are, in a preferred embodiment, controlled by a single organization or individual running on dedicated compute devices each controlling one or more storage devices which provide storage capacity sufficient to redundantly store all data elements associated with each independent data store and interacting with the functional system components, each other, and any external users or applications via APIs over LAN, WAN, or wireless network connections. In alternative embodiments, one, several, or all of the system functional components and data stores together comprising the present invention may be implemented as one or more software programs and data stores controlled by one or more organizations or individuals, and one, several, or all functional components and data stores may be implemented in such a way as to run on a single or several compute and storage devices. 
     Referring now to the invention in more detail, in  FIG. 2  there is shown an overview of the data flow for classification in the present invention. The first step in classification  116  is classifier selection  115  where a classifier is selected from the classifier datastore  114  based on the feature vector obtained from the feature vector datastore  112  to be classified in addition to other selection metrics. The selection metrics utilized in classifier selection  115  can include but are not limited to various direct, indirect, and derived statistical and performance metrics related to the classifier metadata available such as total samples classified, classifier age, true positive, true negative, false positive, and false negative rates as well as various derived measures such as sensitivity, specificity, precision, negative predictive value, accuracy, confusion matrices, logarithmic loss, AUC or area under curve, F1 score or harmonic mean, uncertainty coefficient, mean absolute error, and mean squared error among many others. The classifier selection  115  component can utilize manual, hybrid, and algorithmic methods which may include but are not limited to human interactions, rule engines, statistical methods, and machine learning algorithms (i.e. linear regression, logistic regression, cluster analysis, neural networks, etc.) among others to select a classifier based on the input feature vector and relevant metrics. 
     In more detail, referring to the invention as shown in  FIG. 1  to  FIG. 2 , once a classifier is selected  115  from the classifier datastore  114  and an input feature vector is acquired from the feature vector datastore  112 , the input feature vector is classified utilizing the selected classifier in the classification  116  component. Feature vector classification  116  can result in success  117  or failure  118  which is determined based on a number of potential metrics for each classifier, including but not limited to confidence thresholds and the size and aggregate quality of the result set generated. If classification  116  results in a success  117  the result set generated by classification  116  is sent to classification handling  119  where the members of the result set are entered into the result datastore  120  for the purposes of outcome tracking and future classifier training  113  as well as added to metadata in the classifier datastore  114  for the purposes of classifier evaluation and future classifier selection  115 . Classification handling  119  then qualifies and ranks the members of the result dataset before being sending the result dataset on to the end user. Methods utilized by the classification handling  119  component for qualification and ranking of generated result sets can include manual, hybrid, or algorithmic methods such as white and black lists, various ordering signals and rules, preference settings, and confidence values among many others. If classification  116  results in a failure  118  there are several possible outcomes, random classification  121 , human classification  122 , and reclassification  123 . Reclassification  123  is the first preference, in which the sample feature vector is simply reclassified by an alternative choice of classifier in classifier selection  115  assuming there are classifiers available in the classifier datastore  114  which have not previously been used to attempt classification  116  of the sample feature vector. Human classification  122  can also be used when one or all of the classifiers in the classifier datastore  114  have failed to identify the sample feature vector or random classification  121  can be used thereby generating a systemic feedback signal upon the evaluation of result dataset outcomes which can contribute to future classifier training  113 . Both human classification  122  and random classification  121  generate a result dataset that is sent to the classification handling  119  component for processing just as the result dataset generated by a classification  116  success  117  is. 
     Referring now to the invention in more detail, in  FIG. 3  there is shown an overview of the user flow for lead generation of the present invention. The first stage in the lead generation  129  process involves an end user interacting with a user interface  125  or a program interacting via an API  124  (Application Programming Interface) with the system in order to enter a new product  126  or select  128  an existing product from the primary datastore  105  for which to generate a set of potential leads  129 . If it is a product new to the system, product data is input via user interface  125  or API  124 , standardized  104  and entered into the primary datastore  105  as well as being sent to feature vector extraction  127 . The feature vector extracted  127  is stored in the feature vector datastore  112  and sent to lead generation  129  for processing along with the product data from the primary datastore  112 . If the product is already stored in the primary datastore  105  the existing product is selected  128  via user interface  125  or API  124  and product data from the primary datastore  105  as well as the associated feature vector from the feature vector datastore  112  are sent to lead generation  129  for processing. 
     In more detail, still referring to the invention of  FIG. 3 , in the lead generation  129  component a classifier is selected and classification of the input product feature vector is conducted as previously discussed in the detailed description of classification in  FIG. 2  above. The lead dataset generated as a result of running classification on a product feature vector consists of a set of purchasers and classifier generated metadata for each, as well as detailed data for each purchaser which is loaded from the primary datastore  105 . Classifier generated metadata for each lead can include match percentage, decision reason, and confidence rating among others. The lead dataset is then sent to the lead ranking  130  component for qualification and ranking. Qualification criteria can vary on a dynamic basis, but frequently includes user defined white lists, black lists, preferences, filters, and many others. Ranking criteria can also be dynamic, but frequently match percentage and confidence rating classifier produced metadata are used. In general, qualification and ranking may employ manual, hybrid, or algorithmic methods which may include but are not limited to human interactions, rule engines, statistical methods, and machine learning algorithms (i.e. linear regression, logistic regression, cluster analysis, neural networks, etc.) among others. 
     In yet more detail, still referring to the invention of  FIG. 3 , the qualified and ranked lead dataset generated by the lead ranking  130  component is then sent to the lead output  131  component. Lead output  131  sends the lead dataset directly to end user via user interface  125 , to a program via API  124 , or to one or more output processing export functions  132 . Once the lead dataset makes it to the end user via one or more of these methods, the lead resolution  133  component tracks the outcome associated with each potential sales lead generated. The lead resolution  133  data is used in lead conversion analysis  134  where various success metrics and criteria are generated or evaluated via manual, hybrid, or algorithmic methods. Lead resolution  133  and lead conversion analysis  134  data are then processed by the lead data deconstruction  135  component for storage back in the primary datastore  105  where it will be added to classifier metadata and used in future classifier training  113 . 
     The advantages of the present invention include, without limitation, the ability to standardize, normalize, and extract features from a wide variety of data sources related to human consumable nondurable goods utilizing manual, hybrid, or algorithmic methods. Further, to select, train, and refine classifiers based on those extracted features using a range of predictive models and, most importantly, to generally provide novel means and methods for utilizing those classifiers to generate, qualify, and rank potential sales leads for one or more selected human consumable nondurable goods to the state of the art which are comparable or better than those means and methods currently existing for the generation, qualification, and ranking of potential sales leads for human consumable nondurable goods. 
     In broad embodiment, the present invention is a software as a service platform which provides the capability to collect, integrate, normalize, and standardize a wide range of qualitative and quantitative data regarding product, sales, and purchaser information via human, hybrid, and algorithmic methods and uses this data to train a wide variety of classifiers based on a number of predictive models in order to use these trained classifiers to generate, qualify, and rank potential sales leads for human consumable nondurable goods. 
     While the foregoing written description of the invention enables one of ordinary skill to make and use what is considered presently to be the best mode thereof, those of ordinary skill will understand and appreciate the existence of variations, combinations, and equivalents of the specific embodiment, method, and examples herein. The invention should therefore not be limited by the above described embodiment, method, and examples, but by all embodiments and methods within the scope and spirit of the invention.