Claims:WE CLAIM:
1. A Point of Sale (POS) system (10) comprising:
one or more processors (20) hosted on a server;
a data training subsystem (30) operable by the one or more processors (20), wherein the data training subsystem (30) is configured to train data associated with one or more items to be sold at a store for implementation of Computer vision, wherein the data to be trained comprises at least one of a plurality of images, a plurality of videos or combination thereof associated with the one or more items;
an item recognition subsystem (40) operable by the one or more processors (20), wherein the item recognition subsystem (40) is configured to:
capture at least one of one or more images, one or more videos or combination thereof associated with an item to be sold at an instant via one or more image capturing devices, and
recognize a type of the item to be sold at the store in a real time based on the Computer vision using the data trained by the data training subsystem (30), wherein the type of the item comprises a packaged item and a non-packaged item;
an item analysis subsystem (50) operable by the one or more processors (20), wherein the item analysis subsystem (50) is configured to:
weigh the item kept on a scale when the item is non-packaged and transmit the weight of the non-packaged item to an item price analysis subsystem (70);
count the item kept on a scale when the item is non-packaged and transmit the count of the non-packaged item to an item price analysis subsystem (70); and

scan one of a one dimensional barcode and a two dimensional barcode of the item kept on the scale when the item is packaged and transmit scanned response of the one of the one dimensional barcode and the two dimensional barcode of the packaged item to the item price analysis subsystem (70);
the item price analysis subsystem (70) operable by the one or more processors (20), wherein the item price analysis subsystem (70) is configured to:
fetch one or more real time price values of the non-packaged item weighed by the item analysis subsystem (50) based on one or more billing indictors associated with the non-packaged item, wherein the one or more real-time price values are a representative of one or more final selling price values; and
fetch the one or more real time price values of the packaged item scanned by the item analysis subsystem (50) based on the one or more billing indictors associated with the packaged item;
an updating subsystem (80) operable by the one or more processors (20), wherein the updating subsystem (80) is configured to update the one or more real-time price values of the packaged item and the non-packaged item fetched by the item price analysis subsystem (70) on a cloud server after a pre-defined interval of time; and
a bill generation subsystem (90) operable by the one or more processors (20), wherein the bill generation subsystem (90) is configured to automatically generate a bill for the item to be sold based on the one or more real-time price values fetched by the item price analysis subsystem (70).
2. The system (10) as claimed in claim 1, wherein the one or more billing indicators comprise quality of the one or more items, quantity of the one or more items, a category of the one or more items, a bar code, stock keeping unit, wholesale billing, identification of type of one or more customers, and one or more locations of the store.
3. The system (10) as claimed in claim 1, comprising a bill printing subsystem operable by the one or more processors, wherein the bill printing subsystem is configured to print the bill generated by the bill generation subsystem associated with the at least one of the one or more items upon receiving confirmation of payment.
4. The system (10) as claimed in claim 1, comprising an alarm generation subsystem operable by the one or more processors, wherein the alarm generation subsystem is configured to generate an alarm when one of the one dimensional barcode and the two dimensional barcode of the packaged item is not visible to the item analysis subsystem.
5. The system (10) as claimed in claim 1, wherein the bill generation subsystem (90) is configured to generate the bill for the item to be sold by adding a predefined mark-up value for one or more sellers to the one or more real-time price values fetched by the item price analysis subsystem (70).
6. A method (190) of billing one or more items at point of sale, the method comprising:
capturing, by an item recognition subsystem, at least one of one or more images, one or more videos or combination thereof associated with an item to be sold at an instant via one or more image capturing devices; (200)
recognizing, by the item recognition subsystem, a type of the item to be sold at a store in a real time based on the Computer vision using a data trained by the data training subsystem,
wherein the data trained by the data training subsystem comprises at least one of a plurality of images, a plurality of videos or combination thereof associated with the one or more items,
wherein the type of the item comprises a packaged item and a non-packaged item (210);
weighing, by an item analysis subsystem, the item kept on a scale when the item is non-packaged (220);
transmitting, by the item analysis subsystem, the weight of the non-packaged item to an item price analysis subsystem (230);
scanning, by the item analysis subsystem, one of a one dimensional barcode and a two-dimensional barcode of the item kept on the scale when the item is packaged (240);
transmitting, by the item analysis subsystem, the one of the one-dimensional barcode and the two dimensional barcode of the packaged item to the item price analysis subsystem (250);
fetching, an item price analysis subsystem, one or more real time price values of the non-packaged item weighed by the item analysis subsystem based on one or more billing indictors associated with the non-packaged item, wherein the one or more real-time price values are a representative of one or more final selling price values (260);
fetching, by the item price analysis subsystem, one or more real time price values of the packaged item scanned by the item analysis subsystem based on the one or more billing indictors associated with the packaged item (270);
updating, by an updating subsystem, the one or more real-time price values of the packaged item and the non-packaged item fetched by the item price analysis subsystem on a cloud server after a pre-defined interval of time (280); and
generating, by a bill generation subsystem, a bill automatically for the item to be sold based on the one or more real-time price values fetched by the item price analysis subsystem (290).
7. The method (190) as claimed in claim 6, comprising generating, by an alarm generation subsystem, an alarm when the one of the one dimensional barcode and the two dimensional barcode of the one or more items is not visible to the item analysis subsystem.
8. The method (190) as claimed in claim 6, wherein the generate the bill for the item to be sold by adding a predefined mark-up value for one or more sellers to the one or more real-time price values fetched by the item price analysis subsystem.
9. A weighing device for point of sale, the device comprising:
an image capturing unit operable by the one or more processors, wherein the image capturing unit is configured to:
capture at least one of one or more images and one or more videos associated with one or more items placed on a scale unit, and
send the one or more images captured to a server to identify a type of the one or more items to be sold at a store, and scan a bar code of the one or more items to be sold;
a scale unit configured to:
receive type of the item identified by the server, wherein type of the item comprises a packaged item and a non-packaged item,
fetch a price of the one or more items to be sold identified as the packaged item based on scanned bar code of the one or more items to be sold;
measure weight of the one or more items identified as the non-packaged item captured by the image capturing unit, and
fetch one or more real-time prices values associated with the one or more items kept on the scale based on one of measured weight of the one or more items and fetched price of the one or more items from the server;
a display unit operatively coupled to a bottom side of the scale unit, wherein the display unit is configured to display fetched one or more real-time prices values and one or more details associated with the one or more items kept on the scale;.
a billing unit, communicatively coupled to one or more printers and the server, configured to
generate an invoice based on the fetched one or more real-time prices values associated with the one or more items kept on the scale, and
send one of print command to the one or more printers and notification command to the server upon receiving payment confirmation for generated invoice, wherein the notification command triggers sharing of digital invoice to one or more smart device associated with a user.
10. The device as claimed in claim 9, wherein the one or more details comprise one or more price values of the one or more items, quality of the one or more items, category of the one or more items and weight of the one or more items.
Dated this 31st day of March 2021

Vidya Bhaskar Singh Nandiyal
Patent Agent (IN/PA-2912)
Agent for applicant
, Description:FIELD OF INVENTION
[0001] Embodiments of a present disclosure relate to a smart weighing machine system, and more particularly to, a point of sale system and a method for billing one or more items.
BACKGROUND
[0002] A point of sale system or POS is the place where one or more customer makes a payment for the one or more items or services at one or more stores. Simply put, every time the one or more customers make a purchase at one or more stores, the one or more customers are completing a point of sale transaction. Having an efficient point of sale (POS) system can go a long way in making sure that all the operations are running smoothly at the stores. The POS serves as the central component for superstores and malls, it’s the hub where everything merges.
[0003] Conventionally, the system, available for point of sale, uses cash registers, quick books and Excel for bookkeeping and billing persons, which increases manual work and human intervention and also gives a room for errors. The present systems use manually operated weighing machine to weigh the items to be billed, a printer to print the bar code or billing information of price over the weighed item while requiring a person to weigh the items and then either write the price/billing information or stick the printed bar code on the weighed item, POS with computing device and bar code scanners to scan the items to be billed, bill/invoice printers etc. The present systems, in the absence of seamless integration of process and devices, are marred with inaccuracy/errors. Moreover, human interventions reduce efficiency of the overall process Further, updating the data associated with the one or more items regularly becomes nearly an impossible task, which makes it very difficult for the system to keep a track on a dynamic and real-time price values of the one or more items. The system is unable to track insights about demand and supply of the one or more items. The system uses huge amount of physical storage thus making the system expensive and further, the system is unable to provide a user-friendly environment.
[0004] Hence, there is a need for a point of sale system and a method for billing of one or more items in order to address the aforementioned issues.
BRIEF DESCRIPTION
[0005] In accordance with an embodiment of the disclosure, a point of sale system for billing one or more items is disclosed. The system includes one or more processors hosted on a server. The system also includes a data training subsystem operable by the one or more processors. The data training subsystem is configured to train data associated with one or more items to be sold at a store for implementation of Computer vision, wherein the data to be trained comprises at least one of a plurality of images, a plurality of videos or combination thereof associated with the one or more items. The system also includes an item recognition subsystem operable by the one or more processors. The item recognition subsystem is configured to capture at least one of one or more images, one or more videos or combination thereof associated with an item to be sold at an instant via one or more image capturing devices. The item recognition subsystem is also configured to recognize a type of the item to be sold at the store in a real time based on the Computer vision using the data trained by the data training subsystem, wherein the type of the item comprises a packaged item and a non-packaged item.
[0006] The system also includes an item analysis subsystem operable by the one or more processors. The item analysis subsystem is configured to weigh the item kept on a scale when the item is non-packaged and transmit the weight of the non-packaged item to an item price analysis subsystem. The item analysis subsystem is also configured to scan one of a one dimensional barcode and a two dimensional barcode of the item kept on the scale when the item is packaged and transmit scanned response of the one of the one dimensional barcode and the two dimensional barcode of the packaged item to the item price analysis subsystem. The system also includes an item price analysis subsystem operable by the one or more processors. The item price analysis subsystem is configured to fetch one or more real time price values of the non-packaged item weighed by the item analysis subsystem based on one or more billing indictors associated with the non-packaged item, wherein the one or more real-time price values are a representative of one or more final selling price values. The item price analysis subsystem is also configured to fetch the one or more real time price values of the packaged item scanned by the item analysis subsystem based on the one or more billing indictors associated with the packaged item.
[0007] The system also includes an updating subsystem operable by the one or more processors. The updating subsystem is configured to update the one or more real-time price values of the packaged item and the non-packaged item fetched by the item price analysis subsystem on a cloud server after a pre-defined interval of time. The system also includes a bill generation subsystem operable by the one or more processors. The bill generation subsystem is configured to automatically generate a bill for the item to be sold based on the one or more real-time price values fetched by the item price analysis subsystem.
[0008] In accordance with another embodiment of the disclosure, a method for billing one or more items at a point of sale is disclosed. The method includes training data associated with one or more items to be sold at a store for implementation of Computer vision, wherein the data to be trained comprises at least one of a plurality of images, a plurality of videos or combination thereof associated with the one or more items. The method includes capturing at least one of one or more images, one or more videos or combination thereof associated with an item to be sold at an instant via one or more image capturing devices. The method includes recognizing a type of the item to be sold at the store in a real time based on the Computer vision using the data trained by the data training subsystem, wherein the type of the item comprises a packaged item and a non-packaged item. The method includes weighing the item kept on a scale when the item is non-packaged and transmit the weight of the non-packaged item to an item price analysis subsystem.
[0009] The method includes scanning a one of a one dimensional barcode and a two dimensional barcode of the item kept on the scale when the item is packaged and transmit the one of the one dimensional barcode and the two dimensional barcode of the packaged item to the item price analysis subsystem. The method includes fetching one or more real time price values of the non-packaged item weighed by the item analysis subsystem based on one or more billing indictors associated with the non-packaged item, wherein the one or more real-time price values are a representative of one or more final selling price values. The method includes fetching the one or more real time price values of the packaged item scanned by the item analysis subsystem based on the one or more billing indictors associated with the packaged item. The method includes updating the one or more real-time price values of the packaged item and the non-packaged item fetched by the item price analysis subsystem on a cloud server after a pre-defined interval of time. The method includes generating automatically a bill for the item to be sold based on the one or more real-time price values fetched by the item price analysis subsystem.
[0010] In accordance with yet another embodiment of the disclosure, a weighing device at a point of sale is disclosed. an image capturing unit operable by the one or more processors. The image capturing unit is configured to capture one or more images associated with one or more items placed on a scale unit and send the one or more images captured to a server to identify a type of the one or more items to be sold at a store. The weighing device includes a scale unit configured to measure weight of the one or more items captured by the image capturing unit and fetch one or more real-time prices values associated with the one or more items kept on the scale from the server. The weighing subsystem also includes a display unit operatively coupled to a bottom side of the scale unit, wherein the display unit is configured to display one or more details associated with the one or more items measured by the scale unit.
[0011] To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.
BRIEF DESCRIPTION OF DRAWINGS
The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:
[0012] FIG. 1 is a block diagram of a point of sale system for billing one or more items in accordance with an embodiment of the present disclosure;
[0013] FIG. 2 is an exemplary embodiment representing a block diagram of a point of sale system for billing one or more items of FIG. 1 in accordance with an embodiment of the present disclosure;
[0014] FIG. 3 is another exemplary embodiment representing the weighing device (10) for point of sale of FIG. 1 with an embodiment of the present disclosure;
[0015] FIG. 4 is yet another exemplary embodiment representing the weighing device (10) for point of sale of FIG. 1 with an embodiment of the present disclosure;
[0016] FIG. 5 is a block diagram of point of sale computer system or a server for billing one or more items in accordance with an embodiment of the present disclosure;
[0017] FIG. 6 is a block diagram of a weighing device for point of sale in accordance with an embodiment of the present disclosure;
[0018] FIG. 7 is an exemplary embodiment representing of the weighing device (10) for point of sale of FIG. 6 with an embodiment of the present disclosure; and
[0019] FIG. 8A and FIG. 8B are flow diagrams representing steps involved in a method for billing one or more items at point of sale in accordance with an embodiment of the present disclosure.
[0020] Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.
DETAILED DESCRIPTION
[0021] For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.
[0022] The terms "comprise", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or sub-systems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.
[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.
[0024] In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.
[0025] Embodiments of the present disclosure relate to a point of sale system and method for billing of one or more items. The system includes a data training subsystem configured to train data associated with one or more items to be sold at a store for implementation of Computer vision, wherein the data to be trained comprises at least one of a plurality of images, a plurality of videos or combination thereof associated with the one or more items. The system also includes an item recognition subsystem configured to capture at least one of one or more images, one or more videos or combination thereof associated with an item to be sold at time of billing via one or more image capturing devices. The item recognition subsystem is also configured to recognize types of the item to be sold at the store in a real time based on the Computer vision using the data trained by the data training subsystem. The types of the item include a packaged item and a non-packaged item or loose item such as, but not limited to, fruits, vegetables, grains, and pulses.
[0026] The system also includes an item analysis configured to weigh the item kept on a scale when the item is non-packaged and transmit the weight of the non-packaged item to an item price analysis subsystem. The item analysis subsystem is also configured to scan a one of a one dimensional barcode and a two dimensional barcode of the item kept on the scale when the item is packaged and transmit the scanned details of the item to the item price analysis subsystem. In such embodiment, the one-dimensional barcode may include, but not limited to, a universal product code (UPC) barcode, European article number (EAN) barcode, CODE 39 barcode, Coda bar barcode and the like. In such embodiment, the two-dimensional barcode may include, but not limited to, a quick response (QR) barcode, a data matrix barcode, Aztec barcode and the like.
[0027] The system also includes an item price analysis subsystem configured to fetch one or more real time price values of the non-packaged item weighed by the item analysis subsystem based on one or more billing indictors associated with the non-packaged item, wherein the one or more real-time price values are a representative of one or more final selling price values. The item price analysis subsystem is also configured to fetch the one or more real time price values of the packaged item scanned by the item analysis subsystem based on the one or more billing indictors associated with the packaged item.
[0028] The system also includes an updating subsystem configured to update the one or more real-time price values of the packaged item and the non-packaged item fetched by the item price analysis subsystem on a cloud server after a pre-defined interval of time. The system also includes a bill generation subsystem operable by the one or more processors. The bill generation subsystem is configured to automatically generate a bill for the item to be sold based on the one or more real-time price values fetched by the item price analysis subsystem.
[0029] FIG. 1 is a block diagram of a point of sale system (10) for billing one or more items in accordance with an embodiment of the present disclosure. The system (10) includes, in addition to other components which may not be illustrated, one or more processors (20) operatively connected to various subsystems in order to execute instructions from various subsystems. In an embodiment, the system is hosted in a server. In such an embodiment, the server may include a cloud server. The system (10) includes a data training subsystem (30) operable by the one or more processors (20). The data training subsystem (30) if fed with training data which includes plurality of images, plurality of videos or combination thereof associated with the one or more items to be billed. The training data being used to train a machine learning model for identification of the one or more items to be billed. In an embodiment, the machine learning model is Computer vision.
[0030] Further, in another embodiment, the data training subsystem (30) may use an artificial intelligence technique such as convolutional neural networks for training the data. As used herein, the term convolutional neural networks refer to a deep learning neural network designed for processing structured arrays of data such as images or videos used in computer vision.
[0031] In one embodiment, the data training subsystem (30) may sort the data in one or more categories. In such embodiment, the one or more categories may include, but not limited to, quality of the one or more items such as rotten, fresh, high quality and the like, location of the one or more items, variety of the one or more items and the like. In one exemplary embodiment, if the item to be sold is an apple then the variety of the item may include, but not limited to, a Cameo apple, a Jonagold Apple, Empire Apple, Fuji Apple and the like. In one embodiment, the data training subsystem (30) may store trained data in a database. In such embodiment, the database may include a cloud database.
[0032] Further, in one embodiment, the data training subsystem (30) may create a model of the one or more images, or the one or more videos or combination thereof. In one specific embodiment, the data training subsystem (30) may store one or more architectures in the server and by iteration chooses a best architecture for maximum accuracy. In one embodiment, the one or more architectures may include one or more convolutional neural network architectures. In such embodiment, the best architecture is stored by the data training subsystem (30) on the server to create the training data. In one embodiment, the training data further learns through supervised learning. As used herein the term ‘supervised learning’ refers to learn through trial and error, constantly trying to predict the best outcome.
[0033] Further, the system (10) includes an item recognition subsystem (40) operable by the one or more processors (20). The item recognition subsystem (40) captures at least one of one or more images, one or more videos or combination thereof associated with an item to be sold at an instant via one or more image capturing devices configured in the system. In such embodiment, the one or more image capturing devices may include, but not limited to, one or more cameras and the like. In one embodiment, the one or more cameras may include identification of the one or more items to be billed Moreover, the item recognition subsystem (40) also recognizes a type of the item to be sold at the store in a real time based on the Computer vision using the data trained by the data training subsystem (30). In such embodiment, the type of the item may include, but not limited to, a packaged item and a non-packaged item. In such embodiment, the packaged item may include a stock keeping unit (SKU) item and the like.
[0034] In another embodiment, the item recognition subsystem (40) may use convolutional neural networks (CNN) for recognizing the one or more items to be billed. In such embodiment, the CNN may include one or more layers of neural network. In one exemplary embodiment, the first layer may identify shape and colour, the second layer may be able to conclude the one or more items and the last convolution layer may classify the one or more items. Greater number of layers in the CNN may increase an accuracy of the item recognition subsystem (40).
[0035] Further, the system (10) includes an item analysis subsystem (50) operable by the one or more processors (20) and configured to receive details of the recognized item from the item recognition subsystem (40). The system follows a specific process flow depending on the type of item identified. In an event when the item is identified as the non-packaged item, then the system attempts to identify category of the item, quality of the item, quantity of the item, weight of the item and the like. In another event, when the item is identified as the packaged item, then the system attempts to read the SKU printed onto packaging of the item.
[0036] In the event of non-packaged item, the item analysis subsystem (50) also identifies whether item should be assessed in terms of weight or quantity. In one exemplary embodiment, when the item is apple, the item analysis subsystem (50) switches to weighing mode. However, when the item is bananas or lemons which are priced as per piece, then the item analysis subsystem (50) switches to counting mode. In one exemplary embodiment, if the item is a non-packaged item then the item analysis subsystem (50) may just weigh the non-packaged item and transmit the weight to the item price analysis subsystem (70).
[0037] In another embodiment, the item analysis subsystem (50) scans a one dimensional barcode or two dimensional barcode printed on the item kept on the scale when the item is packaged and transmit the SKU data associated with the one- dimensional barcode or the two- dimensional barcode of the packaged item to the item price analysis subsystem (70). In such embodiment, the item analysis subsystem (50) may use a scanner to scan the one of the one-dimensional barcode and the two dimensional barcode of the packaged item. In one embodiment, the scanner may be attached to the scale.
[0038] Further, the system (10) may include an alarm generation subsystem (60) operable by the one or more processors (20). The alarm generation subsystem (60) generates an alarm when the one dimensional barcode or the two- dimensional barcode of the packaged item is not visible to the image capturing unit (162). In one embodiment, the alarm generation subsystem (60) may notify a user to flip the packaged item so that the one- dimensional barcode or the two- dimensional barcode becomes visible and readable. In such embodiment, the user may be defined as a person using the system. In another embodiment, the alert generation subsystem (60) may notify the user to manually enter one or more details associated with the packaged item if the one of the one- dimensional barcode and the two- dimensional barcode does not work. The alert may be notified to the user via a sound tone, visually over the display unit (133), by text notification or combination thereof.
[0039] Further, the system (10) includes the item price analysis subsystem (70) operable by the one or more processors (20). The item price analysis subsystem (70) fetches one or more real time price values of the non-packaged item weighed by the item analysis subsystem (50) based on one or more billing indictors associated with the non-packaged item. The item price analysis subsystem (70) may also have an API integrated to extract price information from websites depicting wholesale price or selective price from local markets for fruits, vegetables, and grains etc. Moreover, the item price analysis subsystem (70) also fetches the price values of the packaged item scanned by the item analysis subsystem based on the one or more billing indictors associated with the packaged item.
[0040] Further, in such embodiment, the one or more real time price values may represent one or more final selling price values. In one embodiment, the one or more billing indicators may include, but not limited to, quantity of the one or more items, a category of the one or more items, a bar code, stock keeping unit, wholesale billing, identification of type of one or more customers, one or more locations of the store and the like. In one embodiment, the bill generation subsystem (90) may generate the bill for the item to be sold by adding a predefined mark-up value for one or more sellers to the one or more real-time price values fetched by the item price analysis subsystem (70).
[0041] The system (10) includes an updating subsystem (80) operable by the one or more processors (20). The updating subsystem (80) updates the one or more real-time price values of the packaged item and the non-packaged item fetched by the item price analysis subsystem (70) on a cloud server after a pre-defined interval of time. In one specific embodiment, the one or more real time price values may be updated regularly on the cloud in accordance with one or more factors. In such embodiment, the one or more factors may include, but not limited to, a demand of the one or more items and the like.
[0042] Further, the system (10) includes a bill generation subsystem (90) operable by the one or more processors (20). The bill generation subsystem (90) generates a bill automatically for the item to be sold based on the one or more real-time price values fetched by the item price analysis subsystem (70). In one embodiment, the system (10) may include a bill printing subsystem (100) operable by the one or more processors (20). The bill printing subsystem (100) prints the bill generated by the bill generation subsystem (90) associated with the at least one of the one or more items upon receiving confirmation of payment. In one embodiment, the bill printing subsystem (100) may be connected to one or more devices via one or more communication networks. In such embodiment, the one or more devices may include, but not limited to, a mobile phone, a printer and the like. In such embodiment, the one or more communication networks may include, but not limited to, an internet connection, a Bluetooth connection and the like.
[0043] Further, the system (10) may include a display subsystem (110) operable by the one or more processors (20). In one embodiment, the display subsystem (110) may be operatively coupled to a bottom side of the scale. The display subsystem (110) displays one or more details associated with the one or more items identified and measured by the item recognition subsystem (40). In such embodiment the one or more details may include, but not limited to, one or more price values of the one or more items, quality of the one or more items, category of the one or more items, weight of the one or more items.
[0044] FIG. 2 is an exemplary embodiment representing a block diagram of the point of sale system (10) for billing the one or more items of FIG. 1 in accordance with an embodiment of the present disclosure. A user ‘X’ (120) keeps an item ‘Z’ (140) associated with a customer ‘Y’ (130) on the scale. Further, the system (10) captures an image of the item ‘Z’ (140) by the item recognition subsystem (40) via a camera. After capturing the image of the item ‘Z’ (140), the system (10) recognizes the type of the item ‘Z’ (140) to be sold at the store in real-time based on a model generated using convolutional neural network technique based on the data trained by the data training subsystem (30) as non-packaged item. Further to this, the system recognizes the non-packaged item ‘Z’ (140) as Cameo apple and its quality as high quality. The item analysis subsystem (50) weighs the cameo apple to be 900gm. Further, the item price analysis subsystem (70) fetches the price value of the 900gm of the cameo apple of high quality and a location of the store associated with the item ‘Z’ (140) (the vendor’s mark-up value for final price may also vary as per the location of the store). After fetching the real time price value of the item ‘Z’ (140), the system (10) generates a bill for the item ‘Z’ (140) to be sold. After generating the bill, the system (10) prints the bill generated by the bill generation subsystem (90) associated with the item ‘Z’ (140) upon receiving the confirmation of the payment by the customer ‘Y’ (130) and prints the bill by the bill printing subsystem (100).
[0045] FIG. 3 is another exemplary embodiment representing the weighing device (10) for point of sale of FIG. 1 with an embodiment of the present disclosure. In this embodiment, the image capturing unit identifies the item as a cabbage (131) which is placed on the scale unit (132) of the weighing device (10). Further, the scale unit (132) measures the weight of the cabbage (131) as it is a non-packaged item and fetches a real-time price value of the cabbage (132) from the server. After fetching the real-time price value, the display unit (133) displays the one or more details associated with the cabbage (131). Furthermore, the billing unit generates the invoice based on the fetched real-time price value of the cabbage kept on the scale (132) and sends a print command to a printer for further billing of the cabbage.
[0046] FIG. 4 is yet another exemplary embodiment representing the weighing device (10) for point of sale of FIG. 1 with an embodiment of the present disclosure. In this embodiment, the image capturing unit identifies the item as a capsicum (134) which is placed on the scale unit (132) of the weighing device (10). Further, weighing device (10) recognizes quality, colour, and variety of the capsicum (134). Further, the scale unit measures the weight of the capsicum (134) as the capsicum (134) and fetches a real-time price value of the capsicum (134) from the server. After fetching the real-time price value, the display unit (133) displays the one or more details associated with the capsicum (134) such as quality, colour, price, weight and variety of the capsicum. Furthermore, the billing unit generates the invoice based on the fetched real-time price value of the capsicum (134) kept on the scale (132) and sends a print command to the printer for further billing of the capsicum (134).
[0047] FIG. 5 is a block diagram of point of sale computer system or a server for billing one or more items (150) in accordance with an embodiment of the present disclosure. The computer system (150) includes processor(s) (20), and memory (160) coupled to the processor(s) (20) via a bus (170). The memory (160) is stored locally on a seeker device.
[0048] The processor(s) (20), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.
[0049] The memory (160) includes multiple units stored in the form of executable program which instructs the processor (20) to perform the configuration of the system illustrated in FIG. 1. The memory (160) has following subsystems: a data training subsystem (30), an item recognition subsystem (40), an item analysis subsystem (50), an item price analysis subsystem (70), an updating subsystem (80) and a bill generation subsystem (90) of FIG. 1.
[0050] Computer memory (160) elements may include any suitable memory device(s) for storing data and executable program, such as read-only memory, random access memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, hard drive, removable media drive for handling memory cards and the like. Embodiments of the present subject matter may be implemented in conjunction with program subsystems, including functions, procedures, data structures, and application programs, for performing tasks, or defining abstract data types or low-level hardware contexts. The executable program stored on any of the above-mentioned storage media may be executable by the processor(s) (20).
[0051] The data training subsystem (30) instructs the processor(s) (20) to train data associated with one or more items to be sold at a store for implementation of Computer vision. The item recognition subsystem (40) instructs the processor(s) (20) to capture at least one of one or more images, one or more videos or combination thereof associated with an item to be sold at an instant via one or more image capturing devices and to recognize a type of the item to be sold at the store in a real time based on the Computer vision using the data trained by the data training subsystem (30). The item analysis subsystem (50) instructs the processor(s) (20) to weigh the item kept on a scale when the item is non-packaged and transmit the weight of the non-packaged item to an item price analysis subsystem (70) and to scan a one of a one dimensional barcode and a two dimensional barcode of the item kept on the scale when the item is packaged and transmit the one of the one dimensional barcode and the two dimensional barcode of the packaged item to the item price analysis subsystem (70).
[0052] The item price analysis subsystem (70) instructs the processor(s) (20) to fetch one or more real time price values of the non-packaged item weighed by the item analysis subsystem (50) based on one or more billing indictors associated with the non-packaged item and to fetch the one or more real time price values of the packaged item scanned by the item analysis subsystem (50) based on the one or more billing indictors associated with the packaged item. The updating subsystem (80) instructs the processor(s) (20) to update the one or more real-time price values of the packaged item and the non-packaged item fetched by the item price analysis subsystem (70) on a cloud server after a pre-defined interval of time. The bill generation subsystem (90) instructs the processor(s) (20) to automatically generate a bill for the item to be sold based on the one or more real-time price values fetched by the item price analysis subsystem (70).
[0053] FIG. 6 is a block diagram of a weighing device (161) for point of sale in accordance with an embodiment of the present disclosure. The weighing device (161) includes one or more processors. The weighing device (161) includes an image capturing unit (162) operable by the one or more processors. The image capturing unit (162) is configured to capture one or more images and one or more videos associated with one or more items placed on a scale unit using an image capturing device attached on top of the weighing device. The image capturing unit (162) is also configured to send the one or more images captured to a server to identify a type of the one or more items to be sold at a store. The image capturing unit (162) is also configured to scan one dimensional barcode and one or more two dimensional barcodes of the one or more items to be sold. In one embodiment, the image capturing unit (162) sends the one or more images to the server for recognition of the one or more items to be sold at the store.
[0054] Further, the weighing device (161) includes a scale unit (163) operatively coupled to the image capturing unit (162). The scale unit (163) is configured to receive type of the item identified by the server, wherein type of the item includes a packaged item and a non-packaged item. The scale unit (163) is also configured to fetch a price of the one or more items to be sold identified as the packaged item based on scanned bar code printed on the packaged item or measure weight of the one or more items identified as the non-packaged item. In one embodiment, the weighing device (161) may include a scanner to scan one or more one dimensional barcodes and one or more two dimensional barcodes associated with the one or more items which are packed items.
[0055] The scale unit (163) is also configured to calculate real-time price value associated with the one or more items kept on the scale based on one of measured weight or SKU code of the one or more items and fetched price of the one or more items from the server. In one embodiment, the one or more real-time price values may be one or more final selling price values with a margin of one or more vendors selling the one or more items.
[0056] The weighing device (161) also includes a display unit (164) operatively coupled to a bottom side of the scale unit (163). The display unit (164) is configured to display fetched real-time price values and one or more details associated with the one or more items kept on the scale unit (163). In such embodiment, the one or more details may include price values of the one or more items, quality of the one or more items, category of the one or more items and weight of the one or more items.
[0057] Further, the weighing device (161) includes a billing unit (180) communicatively coupled to one or more printers and the server. The billing unit (180) is configured to generate an invoice based on the fetched one or more real-time prices values associated with the one or more items kept on the scale. The billing unit (180) is also configured to send one of print command to the one or more printers and notification command to the server upon receiving payment confirmation for generated invoice. In such embodiment, the notification command may trigger sharing of digital invoice to one or more smart device associated with a user. In such embodiment, the one or more smart devices may include, but not limited to, a mobile phone and the like.
[0058] FIG. 7 is an exemplary embodiment representing of the weighing device (10) for point of sale of FIG. 6 with an embodiment of the present disclosure. The weighing device (10) includes a camera as the image capturing unit (162), mounted over the weighing device (10) with help of a bar, to capture at least one or more images and one or more videos of the one or more items placed on the scale unit (163). After capturing the image, the image capturing unit (162) sends the one or more images captured to the server to identify the type of the one or more items. Further, the weighing device (10) includes the scale unit (163) which measures the weight of the one or more non-packaged items or scans one or more bar codes of the one or more packaged items. Furthermore, the weighing device (10) also includes the display unit (164) integrated at the bottom side of the scale unit (163) which displays one or more price values of the one or more items, quality of the one or more items, category of the one or more items and weight of the one or more items.
[0059] FIG. 8A and FIG. 8B are flow diagrams representing steps involved in a method (190) for billing one or more items at a point of sale in accordance with an embodiment of the present disclosure. The method (190) includes training, by a data training subsystem, if fed with training data which includes plurality of images, plurality of videos or combination thereof associated with the one or more items to be sold in step (190). In such embodiment, training includes training a machine learning model using the training data for identification of the one or more items to be billed. In one embodiment, training the machine learning model may include training a computer vision.
[0060] In one embodiment, the method (190) may include using an artificial intelligence technique such as convolutional neural networks for training the data. In one embodiment, the method (190) may include sorting, by the data training subsystem, the data in one or more categories. In such embodiment, sorting the data into the one or more categories may include sorting quality of the one or more items such as rotten, fresh, high quality and the like, location of the one or more items, variety of the one or more items and the like.
[0061] In one embodiment, the method (190) may include creating a model of the one or more images, or the one or more videos or combination thereof. In one specific embodiment, the method (190) may include storing one or more architectures in the server and by iteration chooses a best architecture for maximum accuracy. In such embodiment, storing the best architecture by the data training subsystem (30) may include storing the best architecture on the server to create the training data. In one embodiment, the method (190) may include learning through supervised learning.
[0062] In one embodiment, the method (190) includes capturing, by an item recognition subsystem, at least one of one or more images, one or more videos or combination thereof associated with an item to be sold at an instant via one or more image capturing devices configured in the system (10) in step (200). In such embodiment, capturing via the one or more image capturing devices may include capturing via one or more cameras and the like. In one embodiment, capturing via the one or more cameras may include capturing via identification of the one or more items to be billed. Moreover, the method (190) may also include recognizing, by the item recognition subsystem, a type of the item to be sold at the store in a real time based on the Computer vision using the data trained by the data training subsystem in step (210). In such embodiment, recognizing the type may include recognizing a packaged item, a non-packaged item. In such embodiment, recognizing the packaged item may include recognizing a stock keeping unit (SKU) item and the like. Further, in one embodiment, the method (190) may include using convolutional neural networks (CNN) for recognizing the one or more items. In such embodiment, using the CNN may include using one or more layers.
[0063] In one embodiment, the method (190) includes weighing, by an item analysing subsystem, the item kept on a scale when the item is non-packaged and transmit the weight of the non-packaged item to an item price analysis subsystem in step (220 and 230). In one embodiment, the method (190) may include receiving, by the item analysing subsystem the one or more details of the one or more items recognized by the item recognition subsystem. Moreover, the method (190) also includes scanning, by the item analysing subsystem, a one of a one dimensional barcode and a two dimensional barcode of the item kept on the scale when the item is packaged and transmit the SKU data associated with the one of the one dimensional barcode and the two dimensional barcode of the packaged item to the item price analysis subsystem in step (240 and 250). In such embodiment, the method (190) may include using a scanner to scan the one of the one-dimensional barcodes and the two-dimensional barcode of the packaged item. In one embodiment, the method may include attaching the scanner to the scale.
[0064] In one embodiment, the method (190) may include generating, by an alarm generation subsystem, an alarm when the one of the one-dimensional barcodes and the two-dimensional barcode of the packaged item is not visible to the item analysis subsystem. In one embodiment, the method may include notifying a user to flip the packaged item so that the one of the one-dimensional barcodes and the two-dimensional barcode is visible. In such embodiment, notifying the user may include notifying a person using the system. In another embodiment, the method may include notifying the user to manually enter one or more details associated with the packaged item if the one of the one-dimensional barcodes and the two-dimensional barcode does not work.
[0065] In one embodiment, the method (190) includes fetching, by the item price analysis subsystem, one or more real time price values of the non-packaged item weighed by the item analysis subsystem based on one or more billing indictors associated with the non-packaged item in step (260). Moreover, the method (190) also includes fetching, by the item price analysis subsystem, the one or more real time price values of the packaged item scanned by the item analysis subsystem based on the one or more billing indictors associated with the packaged item in step (270).
[0066] In such embodiment, the method (190) may include representing the one or more real time price values as one or more final selling price values. In one embodiment, fetching the one or more billing indicators may include fetching quantity of the one or more items, a category of the one or more items, a bar code, stock keeping unit, wholesale billing, identification of type of one or more customers, one or more locations of the store and the like. In one embodiment, the method (190) may include generating the bill for the item to be sold by adding a predefined mark-up value for one or more sellers to the one or more real-time price values fetched by the item price analysis subsystem.
[0067] In one embodiment, the method (190) includes updating, by an updating subsystem, the one or more real-time price values of the packaged item and the non-packaged item fetched by the item price analysis subsystem on a cloud server after a pre-defined interval of time in step (280). In one specific embodiment, updating the one or more real time price values may include updating the one or more real time price values regularly on the cloud in accordance with one or more factors. In such embodiment, updating according to the one or more factors may include updating a demand of the one or more items and the like.
[0068] In one embodiment, the method (190) includes generating, by a bill generation subsystem, a bill automatically for the item to be sold based on the one or more real-time price values fetched by the item price analysis subsystem in step (290). In one embodiment, the method (190) may include printing, by a bill printing subsystem, the bill generated by the bill generation subsystem associated with the at least one of the one or more items upon receiving confirmation of payment. In one embodiment, the method (190) may include connecting the bill printing subsystem to one or more devices via one or more communication networks. In such embodiment, connecting to the one or more devices may include connecting to a mobile phone, a printer and the like. In such embodiment, connecting via the one or more communication networks may include connecting via an internet connection, a Bluetooth connection and the like.
[0069] In one embodiment, the method (190) may include displaying, by a display subsystem, one or more details associated with the one or more items measured by the item recognition subsystem. In such embodiment, displaying the one or more details may include displaying one or more price values of the one or more items, quality of the one or more items, category of the one or more items, weight of the one or more items.
[0070] From a technical effect point of view, the present disclosure reduces usage of hardware hence reducing the hardware expenses. Further, the hardware components used in the present disclosure are rechargeable by using batteries. Moreover, the current disclosure uses cloud storage hence reducing number of physical storages to a great extent.
[0071] Various embodiments of the present disclosure provide a technical solution to the problem for point of sale in a shop or a store. The present system provides an efficient system which helps vendors or the users to sell multiple items by using an end to end automated billing system, thereby reduces human intervention and manual resource requirement and improves the overall efficacy of the system. Further, the system detects and recognizes the item automatically and provides end to end automation of retail process avoiding complex stock keeping unit (SKU) bookkeeping and manual workflows. Moreover, the real time price values related to the item are updated automatically, which makes it user friendly and smart. Furthermore, the system also recognizes the quality of the item which makes sure that the customer is purchasing quality items, which makes the system in public interest and satisfies the needs of the customers.
[0072] While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.
[0073] The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, the order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependant on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.