Abstract: Systems, apparatuses, devices, applications, and methods are provided herein useful to provide retrieval of products within a retail store and control a point of sale system to execute purchase of a product list in conjunction with a mobile device. A device control circuit of the mobile device is configured to receive a proposed product list from a respective customer; determine a suggested product list based on the proposed product list and a weighted average of a plurality of variables; determine a shopping route for the suggested product list, reduce the duration of the shopping route if possible, acquire payment from the respective customer for products on a final product list, and control the point of sale system to execute purchase of the products on the final product list on behalf of the respective customer.

Abstract: Some embodiments provide retail inventory identification systems, comprising: a mobile product mapping system comprising: a transport system, a tower housing, and a cluster processing system comprising: a plurality of product identifying (PI) systems each in different orientations and directed along different fields of view, and a mobile control circuit; wherein the PI systems each comprise an image capture system, and a location determination system, wherein each of the plurality of PI systems is configured to: capture images of products supported on product support systems; and locally process the images comprising applying a plurality of different machine learning modeling techniques, to infer identification of multiple products and generate product identifying information in association with location information; and the mobile control circuit is configured to generate product identifying mapping of products as the product mapping system moves; and communicate the generated product identifying mapping to

Abstract: In some embodiments, systems and methods are provided to recognize retail products, comprising: a model training system configured to: identify a customer; access an associated customer profile; access and apply a set of filtering rules to a product database based on customer data; generate a listing of products specific to the customer; access and apply a model training set of rules to train a machine learning model based on the listing of products and corresponding image data for each of the products in the listing of products; and communicate the trained model to the portable user device associated with first customer.

Abstract: In some embodiments, systems and methods are provided to recognize products in a physical facility through a portable device that comprises a decision control circuit configured to: process each frame of the subset of frames by multiple modeling techniques each relative to a corresponding image attribute and obtain a corresponding product identification probability; determine corresponding aggregated identification probabilities of the first product based on the product identification probabilities; collectively evaluate the aggregated identification probabilities and identify when a predefined relationship with a collective threshold probability exists; and cause an image of the first product to be displayed in response to identifying that one or more of the aggregated identification probabilities having the predefined relationship with the collective threshold probability.

Abstract: In some embodiments, systems and methods are provided to recognize retail products, comprising: a model training system configured to: identify a customer; access an associated customer profile; access and apply a set of filtering rules to a product database based on customer data; generate a listing of products specific to the customer; access and apply a model training set of rules to train a machine learning model based on the listing of products and corresponding image data for each of the products in the listing of products; and communicate the trained model to the portable user device associated with first customer.

Abstract: In some embodiments, systems and methods are provided to recognize retail products, comprising: a model training system configured to: identify a customer; access an associated customer profile; access and apply a set of filtering rules to a product database based on customer data; generate a listing of products specific to the customer; access and apply a model training set of rules to train a machine learning model based on the listing of products and corresponding image data for each of the products in the listing of products; and communicate the trained model to the portable user device associated with first customer.

Abstract: Some embodiments provide retail inventory identification systems, comprising: a mobile product mapping system comprising: a transport system, a tower housing, and a cluster processing system comprising: a plurality of product identifying (PI) systems each in different orientations and directed along different fields of view, and a mobile control circuit; wherein the PI systems each comprise an image capture system, and a location determination system, wherein each of the plurality of PI systems is configured to: capture images of products supported on product support systems; and locally process the images comprising applying a plurality of different machine learning modeling techniques, to infer identification of multiple products and generate product identifying information in association with location information; and the mobile control circuit is configured to generate product identifying mapping of products as the product mapping system moves; and communicate the generated product identifying mapping to

Abstract: In some embodiments, systems and methods are provided to recognize products in a physical facility through a portable device that comprises a decision control circuit configured to: process each frame of the subset of frames by multiple modeling techniques each relative to a corresponding image attribute and obtain a corresponding product identification probability; determine corresponding aggregated identification probabilities of the first product based on the product identification probabilities; collectively evaluate the aggregated identification probabilities and identify when a predefined relationship with a collective threshold probability exists; and cause an image of the first product to be displayed in response to identifying that one or more of the aggregated identification probabilities having the predefined relationship with the collective threshold probability.

Abstract: In some embodiments, systems and methods are provided to recognize retail products in a physical retail store through a portable device that comprises a decision control circuit configured to: process each frame of the subset of frames by multiple modeling techniques each relative to a corresponding image attribute and obtain a corresponding product identification probability; determine corresponding aggregated identification probabilities of the first product based on the product identification probabilities; collectively evaluate the aggregated identification probabilities and identify when a predefined relationship with a collective threshold probability exists; and cause an image of the first product to be displayed in response to identifying that one or more of the aggregated identification probabilities having the predefined relationship with the collective threshold probability.

Abstract: In some embodiments, systems and methods are provided to recognize retail products, comprising: a model training system configured to: identify a customer; access an associated customer profile; access and apply a set of filtering rules to a product database based on customer data; generate a listing of products specific to the customer; access and apply a model training set of rules to train a machine learning model based on the listing of products and corresponding image data for each of the products in the listing of products; and communicate the trained model to the portable user device associated with first customer.

Abstract: In some embodiments, systems and methods are provided to recognize retail products, comprising: a model training system configured to: identify a customer; access an associated customer profile; access and apply a set of filtering rules to a product database based on customer data; generate a listing of products specific to the customer; access and apply a model training set of rules to train a machine learning model based on the listing of products and corresponding image data for each of the products in the listing of products; and communicate the trained model to the portable user device associated with first customer.

Abstract: Systems, apparatuses, devices, applications, and methods are provided herein useful to provide retrieval of products within a retail store and control a point of sale system to execute purchase of a product list in conjunction with a mobile device. A device control circuit of the mobile device is configured to receive a proposed product list from a respective customer; determine a suggested product list based on the proposed product list and a weighted average of a plurality of variables; determine a shopping route for the suggested product list, reduce the duration of the shopping route if possible, acquire payment from the respective customer for products on a final product list, and control the point of sale system to execute purchase of the products on the final product list on behalf of the respective customer.

Abstract: In some embodiments, systems and methods are provided to recognize retail products, comprising: a model training system configured to: identify a customer; access an associated customer profile; access and apply a set of filtering rules to a product database based on customer data; generate a listing of products specific to the customer; access and apply a model training set of rules to train a machine learning model based on the listing of products and corresponding image data for each of the products in the listing of products; and communicate the trained model to the portable user device associated with first customer.

Abstract: In some embodiments, systems and methods are provided to recognize retail products, comprising: a model training system configured to: identify a customer; access an associated customer profile; access and apply a set of filtering rules to a product database based on customer data; generate a listing of products specific to the customer; access and apply a model training set of rules to train a machine learning model based on the listing of products and corresponding image data for each of the products in the listing of products; and communicate the trained model to the portable user device associated with first customer.

Abstract: In some embodiments, systems and methods are provided to recognize retail products in a physical retail store through a portable device that comprises a decision control circuit configured to: process each frame of the subset of frames by multiple modeling techniques each relative to a corresponding image attribute and obtain a corresponding product identification probability; determine corresponding aggregated identification probabilities of the first product based on the product identification probabilities; collectively evaluate the aggregated identification probabilities and identify when a predefined relationship with a collective threshold probability exists; and cause an image of the first product to be displayed in response to identifying that one or more of the aggregated identification probabilities having the predefined relationship with the collective threshold probability.

Abstract: In some embodiments, systems and methods are provided to recognize retail products in a physical retail store through a portable device that comprises a decision control circuit configured to: process each frame of the subset of frames by multiple modeling techniques each relative to a corresponding image attribute and obtain a corresponding product identification probability; determine corresponding aggregated identification probabilities of the first product based on the product identification probabilities; collectively evaluate the aggregated identification probabilities and identify when a predefined relationship with a collective threshold probability exists; and cause an image of the first product to be displayed in response to identifying that one or more of the aggregated identification probabilities having the predefined relationship with the collective threshold probability.

Abstract: In some embodiments, systems and methods are provided to recognize retail products, comprising: a model training system configured to: identify a customer; access an associated customer profile; access and apply a set of filtering rules to a product database based on customer data; generate a listing of products specific to the customer; access and apply a model training set of rules to train a machine learning model based on the listing of products and corresponding image data for each of the products in the listing of products; and communicate the trained model to the portable user device associated with first customer.

Abstract: In some embodiments, systems and methods are provided to recognize retail products in a physical retail store through a portable device that comprises a decision control circuit configured to: process each frame of the subset of frames by multiple modeling techniques each relative to a corresponding image attribute and obtain a corresponding product identification probability; determine corresponding aggregated identification probabilities of the first product based on the product identification probabilities; collectively evaluate the aggregated identification probabilities and identify when a predefined relationship with a collective threshold probability exists; and cause an image of the first product to be displayed in response to identifying that one or more of the aggregated identification probabilities having the predefined relationship with the collective threshold probability.