A system of hands-free intelligent point of sale

Abstract

The present invention relates to a system of hands-free intelligent point of sale. The system (100) includes a computing device(102), a shopkeeper touch interface(108), and a customer touch interface(140), a camera(110), a customer microphone(112), a shopkeeper microphone(142), a first speaker(114), a second speaker(116), a networking device(118), a cloud server computer(120). The system(100) is being used as a point of sale that provides the shopkeeper with data analytics for sales, customers and inventory management. The camera(110) captures customer image and sends to the cloud server computer(120) through the computing device(102). The cloud server processor(124) recognize the customer in real time using machine learning models provides the personalized services, promotions and entertainment options to customer as per previous purchase. Simultaneously, the cloud server processor(124) provides the inventory data to the shopkeeper, so that shopkeeper is able to track inventory of particular items and order items from distributor through voice command using the shopkeeper microphone(142).

Description

[0001] A SYSTEM OF HANDS-FREE INTELLIGENT POINT OF SALE

[0002] FIELD OF THE INVENTION

[0003] The present invention relates to a system of Point of Sale. Most specifically the present invention relates to a system of voice-controlled Point of Sale.

[0004] BACKGROUND OF THE INVENTION

[0005] Traditional retail systems often struggle with inefficiencies in customer engagement and inventory control, leading to suboptimal customer experiences and operational bottlenecks. These shortcomings have prompted the development of various solutions aimed at automating processes and improving overall efficiency.

[0006] Previous attempts to address these challenges have primarily focused on separate components for customer interaction, sales management, and inventory tracking. However, these solutions often lack cohesive integration, resulting in disjointed operations and limited scalability. Moreover, existing systems have typically relied on manual inputs and outdated technology, which can lead to inaccuracies and delays in decision-making.

[0007] US2021366586A1 discloses A data management system is disclosed wherein at least a portion of the data is generated at a point of sale (POS) and / or retail store server and the data is stored, at least in part, on a blockchain database or another secure database. A digital replica of a point-of-sale device is configured to receive a product unique identifier, and to receive customer unique identifying data. An adapter is configured to facilitate communication between incompatible entities. A repository is configured to store the product unique identifier and to store the customer unique identifying data associated with a purchase of the product. A query module configured to execute a query of the repository to identify customer unique identifying data for the customers who purchased the recalled product. A communication module is configured to receive communication regarding a product recall and to send communications regarding a product recall. The existing inventions are less effective in providing voice-based assistants. The existing inventions are unable to provide personalized assistance to customers. The existing inventions are unable to process language properly. Hence there is a need for the present invention to overcome the drawbacks of existing inventions.

[0008] OBJECTIVE OF THE INVENTION

[0009] The main objective of the present invention is to provide a hands-free intelligent point of sale system that enables natural and interactive communication between the shopkeeper and the customer using touch interfaces, microphones, and speakers.

[0010] The objective of the present invention is to utilize machine learning models and cloud computing to recognize customers, manage sales and inventory data, and offer personalized services and recommendations based on previous purchase behaviour.

[0011] The objective of the present invention is to automate inventory management by providing real-time updates, analysing sales and inventory data, and automatically placing orders to distributors based on predicted customer demand and stock levels.

[0012] The objectives, advantages, and features of the present invention will become apparent from the detailed description provided herein below, in which various embodiments of the disclosed invention are illustrated by way of example.

[0013] SUMMARY OF THE PRESENT INVENTION

[0014] The present invention relates to a system of hands-free intelligent point of sale. The present invention includes a computing device, the shopkeeper touch interface, the customer touch interface, camera, PIR sensor, retractable contact interface, the customer microphone, the shopkeeper microphone, a first speaker, a second speaker, the networking device, a button board, a third microphone, a fourth microphone, cooling fan, LED indicator, charging port, data transfer port, RGB LED strip, add-on card board, power board, wireless board, cube box and a cloud server computer. The computing device includes a computing device database and a microprocessor. The computing device database stores data and computer-readable instructions. The microprocessor executes computer-readable instructions for controlling the hardware of the system and communicating with the cloud server for using machine-learning model and extracting data of sales, customers, and inventor. The shopkeeper touch interface is connected to the microprocessor, thus the microprocessor controls and display sales, customer and inventory data on the shopkeeper touch interface to a shopkeeper and also helps computing device to interact with the shopkeeper through touch display. The customer touch interface is connected to the microprocessor of the computing device, thus the microprocessor controls and display data on the customer touch interface to the customer and also helps computing device to interact with the customer through touch display. The camera which is also connected to the microprocessor of the computing device detects the presence of the customer, and the computing device recognizes the identity of the customer through a machine learning model by using customer image. The PIR sensor which is connected to the microprocessor of the computing device is used to detect the presence of customer through the PIR sensor optimizing responsiveness of system and enabling intelligent interaction control based on user proximity and engagement. The retractable contact interface comprises spring-loaded electrical contacts that establish electrical and data connectivity between the cube box and a docking station when engaged, thereby enabling power transfer and system expansion without requiring traditional cable connections. The customer microphone is also connected to the microprocessor of the computing device for receiving a voice command from the customer and sending the voice to the computing device for executing the command and the computing device also recognize customer from the voice as well. The shopkeeper microphone is also connected to the microprocessor of the computing device for receiving a voice command from the shopkeepers and sending the voice to the computing device for executing command of the shopkeeper. The first speaker is also connected to the microprocessor of the computing device for the system to interact with the customer and shopkeeper to provide information through the system generated voice. The second speaker is also connected to the microprocessor of the computing device for the system to interact with the customer and shopkeeper and provide information through the system generated voice. The computing device is connected to the networking device. The battery is used to provide power to the system. The button board includes RGB LED, mute LED and user control buttons. The third microphone and the fourth microphone are used for wide-field voice input. The cooling fan is controlled through pulse-width modulation (PWM) signals by the microprocessor of the computing device and is configured to exhaust warm air from within the system thereby maintaining optimal temperature for internal components. The LED indicator is also connected to the microprocessor of the computing device, the LED indicator is configured to display the charging status of the system and may illuminate in different colors or patterns to indicate conditions such as charging in progress, fully charged, or charging error. The system is being used as a point of sale that provides the shopkeeper with data analytics for sales, customers, and inventory management, the system also interacts with customers through voice from the first speaker and touch display of the customer touch interface.

[0015] The main advantage of the present invention is that the present invention provides a hands-free intelligent point of sale system enabling natural and interactive communication between the shopkeeper and the customer through touch interfaces, microphones, and speakers.

[0016] Yet another advantage of the present invention is that the present invention utilizes machine learning models and cloud computing to recognize customers, manage sales and inventory data, and offer personalized services and recommendations based on previous purchase behavior. The objectives, advantages, and features of the present invention will become apparent from the detailed description provided herein below, in which various embodiments of the disclosed invention are illustrated by way of example.

[0017] BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are incorporated in and constitute a part of this specification to provide a better understanding of the invention. The drawings illustrate one embodiment of the invention and together with the description, serve to explain the principles of the invention.

[0019] Fig.l. illustrates the block diagram a system of hands-free intelligent point of sale.

[0020] Fig. 2 illustrates the system architecture of the hands-free intelligent point-of-sale system.

[0021] Fig. 3 illustrates the top view of the cube box.

[0022] Fig. 4 illustrates the front view of the cube box.

[0023] Fig. 5 illustrates the back view of the cube box.

[0024] Fig. 6 illustrates the right-side view of the cube box.

[0025] DETAILED DESCRIPTION OF THE INVENTION

[0026] Definition

[0027] The terms “a” or “an”, as used herein, are defined as one or as more than one. The term “plurality”, as used herein, is defined as two as or more than two. The term “another”, as used herein, is defined as at least a second or more. The terms “including” and / or “having”, as used herein, are defined as comprising (i.e., open language). The term “coupled”, as used herein, is defined as connected, although not necessarily directly, and not necessarily mechanically.

[0028] The term “comprising” is not intended to limit inventions to only claiming the present invention with such comprising language. Any invention using the term comprising could be separated into one or more claims using “consisting” or “consisting of’ claim language and is so intended. The term “comprising” is used interchangeably used by the terms “having” or “containing”.

[0029] Reference throughout this document to “one embodiment”, “certain embodiments”, “an embodiment”, “another embodiment”, and “yet another embodiment” or similar terms means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of such phrases or in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics are combined in any suitable manner in one or more embodiments without limitation.

[0030] The term “or” as used herein is to be interpreted as an inclusive or meaning any one or any combination. Therefore, “A, B or C” means any of the following: “A; B; C; A and B; A and C; B and C; A, B and C”. An exception to this definition will occur only when a combination of elements, functions, steps or acts are in some way inherently mutually exclusive.

[0031] As used herein, the term "one or more" generally refers to, but not limited to, singular as well as the plural form of the term.

[0032] The drawings featured in the figures are to illustrate certain convenient embodiments of the present invention and are not to be considered as a limitation. Term "means" preceding a present participle of an operation indicates a desired function for which there is one or more embodiments, i.e., one or more methods, devices, or apparatuses for achieving the desired function and that one skilled in the art could select from these or their equivalent in view of the disclosure herein and use of the term "means" is not intended to be limiting.

[0033] Fig.l. illustrates the block diagram a system of hands free intelligent point of sale. The system (100) includes a computing device(102), a shopkeeper touch interface(108), and a customer touch interface(140), a camera(HO), a customer microphone(112), a shopkeeper microphone(142), a first speaker(l 14), a second speaker(116), a networking device(118), a cloud server computer(120) and a cube box(126). The computing device(102) includes a computing device database(104), and a microprocessor(106). The shopkeeper touch interface(108) is connected to the microprocessor(106). The customer touch interface(140) is connected to the microprocessor 106) of the computing device(102). The camera (110) is also connected to the microprocessor(106) of the computing device(102). The customer microphone(l 12) is also connected to the microprocessor(106) of the computing device(102). The shopkeeper microphone(142) is also connected to the microprocessor 106) of the computing device(102). The first speaker(114) is also connected to the microprocessor(106). The second speaker(116) is also connected to the microprocessor 106) of the computing device(102). The computing device(102) is connected to the networking device(118). The cloud server computer( 120) is connected to the computing device(102) through the networking device(118). The cloud server computer(120) includes a cloud database(122), and a cloud server processor 124).

[0034] Fig. 2 illustrates the system architecture of the hands-free intelligent point-of-sale system, comprising a computing device (102), a button board (144), a power board (166), a wireless board (168), an add-on card board (164), and associated peripheral interfaces, all integrated within a cube-shaped enclosure (126). The components are interconnected using board-to-board stack connectors, flexible printed circuit cables, ribbon cables, and wired connections.

[0035] Fig. 3 illustrates the top view of the cube box (126), wherein the top panel (134) includes the button board (144), a cooling fan (146), a third microphone (170), and a fourth microphone (172).

[0036] Fig. 4 illustrates the front view of the cube box (126), wherein the front panel (128) includes the camera (110), the PIR sensor (148), the customer touch interface (140), and a customer microphone (112).

[0037] Fig. 5 illustrates the back view of the cube box (126), the back panel (138) includes the shopkeeper touch interface (108) and a shopkeeper microphone (142). The shopkeeper touch interface (108) is mounted on the upper portion of the back panel (138).

[0038] Fig. 6 illustrates the right-side view of the cube box (126), the first side panel (130) includes the first speaker (114), an LED indicator (156), a charging port (158), a data transfer port (160) and air inlets.

[0039] The present invention relates to a system of hands-free intelligent point of sale. The present invention includes a computing device, the shopkeeper touch interface, the customer touch interface, camera, PIR sensor, retractable contact interface, the customer microphone, the shopkeeper microphone, a first speaker, a second speaker, the networking device, a button board, a third microphone, a fourth microphone, cooling fan, LED indicator, charging port, data transfer port, RGB LED strip, add-on card board, power board, wireless board, cube box and a cloud server computer. The computing device includes a computing device database and a microprocessor. The computing device database stores data and computer-readable instructions. The microprocessor executes computer-readable instructions for controlling the hardware of the system and communicating with the cloud server for using machine-learning model and extracting data of sales, customers, and inventor. The shopkeeper touch interface is connected to the microprocessor, thus the microprocessor controls and display sales, customer and inventory data on the shopkeeper touch interface to a shopkeeper and also helps computing device to interact with the shopkeeper through touch display. The customer touch interface is connected to the microprocessor of the computing device, thus the microprocessor controls and display data on the customer touch interface to the customer and also helps computing device to interact with the customer through touch display. The camera which is also connected to the microprocessor of the computing device detects the presence of the customer, and the computing device recognizes the identity of the customer through a machine learning model by using customer image. The PIR sensor which is connected to the microprocessor of the computing device is used to detect the presence of customer through the PIR sensor optimizing responsiveness of system and enabling intelligent interaction control based on user proximity and engagement. The retractable contact interface comprises spring-loaded electrical contacts that establish electrical and data connectivity between the cube box and a docking station when engaged, thereby enabling power transfer and system expansion without requiring traditional cable connections. The customer microphone is also connected to the microprocessor of the computing device for receiving a voice command from the customer and sending the voice to the computing device for executing the command and the computing device also recognize customer from the voice as well. The shopkeeper microphone is also connected to the microprocessor of the computing device for receiving a voice command from the shopkeepers and sending the voice to the computing device for executing command of the shopkeeper. The first speaker is also connected to the microprocessor of the computing device for the system to interact with the customer and shopkeeper to provide information through the system generated voice. The second speaker is also connected to the microprocessor of the computing device for the system to interact with the customer and shopkeeper and provide information through the system generated voice. The computing device is connected to the networking device. The battery is used to provide power to the system. The button board includes RGB LED, mute LED and user control buttons. The third microphone and the fourth microphone are used for wide-field voice input. The cooling fan is controlled through pulse-width modulation (PWM) signals by the microprocessor of the computing device and is configured to exhaust warm air from within the system thereby maintaining optimal temperature for internal components. The LED indicator is also connected to the microprocessor of the computing device, the LED indicator is configured to display the charging status of the system and may illuminate in different colors or patterns to indicate conditions such as charging in progress, fully charged, or charging error. The charging port is configured to receive electrical power from an external power source or docking station, enabling the recharging of the battery of the system. The data transfer port is configured to facilitate communication between the cube box and external devices for functions such as diagnostics, firmware updates, or peripheral interfacing and supports USB, serial, or other standard data transfer protocols. The RGB LED strip is configured to emit colored illumination patterns based on system events or states such as startup, idle, error, transaction success, or customer interaction. The add-on card board provides hardware debugging and diagnostic functions and includes a serial communication bridge and a hardware debugging interface, and is connected to the computing device. The power board comprises a charger IC, power delivery controller, fuel gauge, audio amplifier, universal data and power interface, and connectors for external devices and is connected to the computing device. The wireless board comprises multi-mode communication and positioning module configured to facilitate wireless data exchange and geolocation services. The cube box having a a front panel, first side panel, second side panel, top panel, bottom panel and a back panel. The customer touch interface, the camera, the customer microphone, the camera and the PIR sensor are assembled on the front panel. The front panel faces towards the customer. The first speaker, the LED indicator, the charging port, the data transfer port is attached to the first side panel. The second speaker is attached to the second side panel. The button board, the cooling fan, the third microphone and the fourth microphone are assembled on the top panel. The shopkeeper touch interface, the shopkeeper microphone are assembled on the back panel. The back panel faces towards the shopkeeper. The cloud server computer which is connected to the computing device through the networking device includes a cloud database and a cloud server processor. The cloud database stores machine-learning model, sales data, inventory data of products, customer data, shopkeeper data that are being sent by the computing device. The cloud server processor executes computer-readable instructions for analyzing sales data, inventory data of products, customer data, and shopkeeper data that provides deeper insights into sales and inventory management ensuring real-time updates and efficient stock management, and also provides insight of customer purchase behavior. The first side panel is configured with two air inlets designed to allow the intake of ambient air into the cube box and which form part of the active and passive cooling system, allowing airflow across the internal components. The system is being used as a point of sale that provides the shopkeeper with data analytics for sales, customers, and inventory management, the system also interacts with customers through voice from the first speaker and touch display of the customer touch interface. The microprocessor of the computing device sends voice to the cloud server processor that converts the voice into text and sends back a suitable reply through the speaker to the user from the predefined database of conversations that is being programmed and stored in the cloud database. The computing device can converse with the customer and the shopkeeper in multiple languages and provide a human-like conversation experience, and natural interactions. The camera captures customer image and sends to the cloud server computer through the computing device, the cloud server processor recognizes the customer in real-time using machine-learning model, greets the customer, and provides personalized services, promotions, and entertainment options to the customer as per previous purchases, simultaneously, the cloud server processor provides the inventory data to the shopkeeper so that shopkeeper can track inventory of particular items and order items from distributor through voice command using the shopkeeper microphone. The computing device is also able to automatically place orders before the distributor through the machine-learning model of the cloud server computer based on the sales data, inventory data and customer demand, the computing device and the cloud server computer intelligently manages inventory and sales without need of large workforce and complicated machinery. In an embodiment, the cloud server computer utilizes the Haar Cascade Classifier of Open CV to initially identify customer’s face and again uses custom Deep learning model code to increase the accuracy of recognition in real-time. In an embodiment, the computing device is a custom embedded System-on-Chip (SoC) architecture integrated with the microprocessor, memory, and connectivity modules to enable real-time on-device Al inference, local voice processing, and sensor integration within the cube box. In an embodiment, the camera is of a different type selected from a microwave sensor, a PIR sensor, and a tomographic sensor. In an embodiment, the computing device database has SQLite for local database management and product lookup from the cloud server computer and the computing device database is linked with inventory and supply chain management platforms, ensuring real-time updates and efficient inventory management. In an embodiment, the cloud server computer utilizes Large Language Models that are enabled with advanced conversational abilities and thus provides intelligent personal assistance to shopkeepers and customers with offerings, business insights, inventory data, and personalized recommendations. In an embodiment, the networking device comprises a multimode communication module including at least a Wi-Fi module, a Bluetooth module, a 4G LTE communication module, and a SIM card slot configured to support wireless connectivity for data transmission, cloud synchronization, and emergency communication functionalities. In an embodiment, the computing device also comprises an FM stereo module for playing customer-centric music, an NFC RFID Read and Write Module for assisting payments, and Bluetooth Interface. In an embodiment, the display of the shopkeeper touch interface and the customer touch interface is mounted via tiltable hinge mechanism allowing the shopkeeper to adjust the screen angle for optimal visibility and ergonomic comfort.

[0040] In an embodiment, the present invention relates to a system of hands-free intelligent point of sale. The present invention includes a computing device, the one or more shopkeeper touch interfaces, the one or more customer touch interfaces, camera, PIR sensor, retractable contact interface, the one or more customer microphones, the one or more shopkeeper microphones, a first speaker, a second speaker, the one or more networking devices, a button board, a third microphone, a fourth microphone, cooling fan, LED indicator, charging port, data transfer port, RGB LED strip, add-on card board, power board, wireless board, cube box and a cloud server computer. The computing device includes a computing device database and a microprocessor. The computing device database stores data and computer-readable instructions. The microprocessor executes computer-readable instructions for controlling the hardware of the system and communicating with the cloud server for using machine-learning model and extracting data of sales, customers, and inventor. The one or more shopkeeper touch interfaces are connected to the microprocessor, thus the microprocessor controls and display sales, customer and inventory data on the one or more shopkeeper touch interfaces to a shopkeeper and also helps computing device to interact with the shopkeeper through touch display. The one or more customer touch interfaces are connected to the microprocessor of the computing device, thus the microprocessor controls and display data on the one or more customer touch interfaces to the customer and also helps computing device to interact with the customer through touch display. The camera which is also connected to the microprocessor of the computing device detects the presence of the customer, and the computing device recognizes the identity of the customer through a machine learning model by using customer image. The PIR sensor which is connected to the microprocessor of the computing device is used to detect the presence of customer through the PIR sensor optimizing responsiveness of system and enabling intelligent interaction control based on user proximity and engagement. The retractable contact interface comprises spring-loaded electrical contacts that establish electrical and data connectivity between the cube box and a docking station when engaged, thereby enabling power transfer and system expansion without requiring traditional cable connections. The one or more customer microphones are also connected to the microprocessor of the computing device for receiving a voice command from the customer and sending the voice to the computing device for executing the command and the computing device also recognize customer from the voice as well. The one or more shopkeeper microphones are also connected to the microprocessor of the computing device for receiving a voice command from the shopkeepers and sending the voice to the computing device for executing command of the shopkeeper. The first speaker is also connected to the microprocessor of the computing device for the system to interact with the customer and shopkeeper to provide information through the system generated voice. The second speaker is also connected to the microprocessor of the computing device for the system to interact with the customer and shopkeeper and provide information through the system generated voice. The computing device is connected to the one or more networking devices. The battery is used to provide power to the system. The button board includes RGB LED, mute LED and user control buttons. The third microphone and the fourth microphone are used for wide-field voice input. The cooling fan is controlled through pulse-width modulation (PWM) signals by the microprocessor of the computing device and is configured to exhaust warm air from within the system thereby maintaining optimal temperature for internal components. The LED indicator is also connected to the microprocessor of the computing device, the LED indicator is configured to display the charging status of the system and may illuminate in different colors or patterns to indicate conditions such as charging in progress, fully charged, or charging error. The charging port is configured to receive electrical power from an external power source or docking station, enabling the recharging of the battery of the system. The data transfer port is configured to facilitate communication between the cube box and external devices for functions such as diagnostics, firmware updates, or peripheral interfacing and supports USB, serial, or other standard data transfer protocols. The RGB LED strip is configured to emit colored illumination patterns based on system events or states such as startup, idle, error, transaction success, or customer interaction. The add-on card board provides hardware debugging and diagnostic functions and includes a serial communication bridge and a hardware debugging interface, and is connected to the computing device. The power board comprises a charger IC, power delivery controller, fuel gauge, audio amplifier, universal data and power interface, and connectors for external devices and is connected to the computing device. The wireless board comprises multi-mode communication and positioning module configured to facilitate wireless data exchange and geolocation services. The cube box having a front panel, first side panel, second side panel, top panel, bottom panel and a back panel. The one or more customer touch interfaces, the camera, the one or more customer microphones, the camera and the PIR sensor are assembled on the front panel. The front panel faces towards the customer. The first speaker, the LED indicator, the charging port, the data transfer port is attached to the first side panel. The second speaker is attached to the second side panel. The button board, the cooling fan, the third microphone and the fourth microphone are assembled on the top panel. The one or more shopkeeper touch interfaces, the one or more shopkeeper microphones are assembled on the back panel. The back panel faces towards the shopkeeper. The cloud server computer which is connected to the computing device through the one or more networking devices include a cloud database and a cloud server processor. The cloud database stores machine-learning model, sales data, inventory data of products, customer data, shopkeeper data that are being sent by the computing device. The cloud server processor executes computer- readable instructions for analyzing sales data, inventory data of products, customer data, and shopkeeper data that provides deeper insights into sales and inventory management ensuring real-time updates and efficient stock management, and also provides insight of customer purchase behavior. The first side panel is configured with two air inlets designed to allow the intake of ambient air into the cube box and which form part of the active and passive cooling system, allowing airflow across the internal components. The system is being used as a point of sale that provides the shopkeeper with data analytics for sales, customers, and inventory management, the system also interacts with customers through voice from the first speaker and touch display of the one or more customer touch interfaces. The microprocessor of the computing device sends voice to the cloud server processor that converts the voice into text and sends back a suitable reply through the speaker to the user from the predefined database of conversations that is being programmed and stored in the cloud database. The computing device can converse with the customer and the shopkeeper in multiple languages and provide a human-like conversation experience, and natural interactions. The camera captures customer image and sends to the cloud server computer through the computing device, the cloud server processor recognizes the customer in real-time using machine-learning model, greets the customer, and provides personalized services, promotions, and entertainment options to the customer as per previous purchases, simultaneously, the cloud server processor provides the inventory data to the shopkeeper so that shopkeeper can track inventory of particular items and order items from distributor through voice command using the one or more shopkeeper microphones. The computing device is also able to automatically place orders before the distributor through the machine-learning model of the cloud server computer based on the sales data, inventory data and customer demand, the computing device and the cloud server computer intelligently manages inventory and sales without need of large workforce and complicated machinery. In an embodiment, the cloud server computer utilizes the Haar Cascade Classifier of Open CV to initially identify customer’s face and again uses custom Deep learning model code to increase the accuracy of recognition in real-time. In an embodiment, the computing device is a custom embedded System-on-Chip (SoC) architecture integrated with the microprocessor, memory, and connectivity modules to enable real-time on-device Al inference, local voice processing, and sensor integration within the cube box. In an embodiment, the camera is of a different type selected from a microwave sensor, a PIR sensor, and a tomographic sensor. In an embodiment, the computing device database has SQLite for local database management and product lookup from the cloud server computer and the computing device database is linked with inventory and supply chain management platforms, ensuring real-time updates and efficient inventory management. In an embodiment, the cloud server computer utilizes Large Language Models that are enabled with advanced conversational abilities and thus provides intelligent personal assistance to shopkeepers and customers with offerings, business insights, inventory data, and personalized recommendations In an embodiment, the one or more networking devices comprise a multi-mode communication module including at least a Wi-Fi module, a Bluetooth module, a 4G LTE communication module, and a SIM card slot configured to support wireless connectivity for data transmission, cloud synchronization, and emergency communication functionalities. In an embodiment, the computing device also comprises an FM stereo module for playing customer-centric music, an NFC RFID Read and Write Module for assisting payments, and Bluetooth Interface. In an embodiment, the display of the one or more shopkeeper touch interfaces and the one or more customer touch interfaces are mounted via tiltable hinge mechanism allowing the shopkeeper to adjust the screen angle for optimal visibility and ergonomic comfort.

[0041] In an embodiment, the present invention relates to a method of using hands free intelligent point of sale system, the method includes: a method of voice interaction of the customer with the system, the method includes capturing the customer image using the camera and sending it to the cloud server computer for recognition via a machine learning model, activating the customer touch interface to display instructions for activating the system via a hot-word, receiving the hot-word through the customer microphone to activate the system, transmitting the customer voice from the microphone to the computing device, executing computer-readable instructions in the microprocessor and initially processing the voice input locally using an on-device Small Language Model (SLM) for intent recognition and basic response generation; if the query exceeds the capability of the local model, the voice is sent to the cloud server computer for advanced analysis, analyzing the voice via the cloud server processor when required, recognizing the customer and responding with personalized services, promotions, and entertainment, allowing the customer to place product orders via voice, converting the voice to text and processing the request through the cloud server processor, then sending the response as voice to the computing device, conversing with the customer via the first speaker and the second speaker; a method of interacting with shopkeepers and managing sales, payment, and inventory, the method includes activating the system using a hot-word via the shopkeeper microphone, sending voice commands from the microphone to the computing device, which uses the on-device SLM in the microprocessor to interpret the request and extract relevant intent, retrieving sales, customer, and inventory statistics either locally or by forwarding the request to the cloud server computer for complex analytics, executing machine learning models in the cloud server processor as needed to generate relevant statistics and convert them into voice, displaying the statistics on the shopkeeper touch interface, and announcing them via the first speaker and second speaker, enabling the shopkeeper to place orders to the distributor based on analyzed sales and inventory data, wherein, the computing device, through its on-device SLM and Al engine, can autonomously place distributor orders using predictive analytics models reducing manual intervention, wherein, the computing device is also able to automatically place order before distributor through machine learning model of the cloud server computer based on the sales data, inventory data and customer demand, the computing device and the cloud server computer intelligently manages inventory and sales without need of large workforce and complicated machinery, a method for a backend software process flow for voice-based interaction, intelligent task execution, and adaptive response generation in the hands-free intelligent point-of-sale system, the method having the process begins when the system enters a passive listening state and upon detecting an audio signal, the system first performs speaker identification to determine whether the input is from a known user; the detected speech is converted into text using a speech-to-text (STT) engine; at decision point the system evaluates whether the transcribed input contains a predefined wake-up phrase, which acts as a trigger for interaction and if the wake-up phrase is not detected, the system returns to the listening state; if the wake-up phrase is confirmed, the transcribed text is processed by a local Small Language Model (SLM) which extracts sentence structure and predicts intent with low latency; the processed sentence is passed to a Natural Language Understanding (NLU) engine, which classifies the overall intent; if the intent is determined to be an emergency the system immediately triggers SOS mode which activates all audio and visual input devices; the captured data is encrypted and uploaded to a secure cloud server and predefined emergency contacts are notified ensuring rapid response and evidence preservation; if the intent is not an emergency, the system evaluates whether the input represents an actionable task such as generating a report, placing an order, or retrieving analytics; if the intent is emergency the system routes the request to the appropriate Al engine for execution and upon completion, the system checks the result; if successful, the system generates a task completion message which is passed through a text-to-speech (TTS) engine for auditory output. if unsuccessful, an error message is displayed and similarly converted into speech. a method for software-based backend flow for intelligent voice interaction, intent processing, and multi-channel response generation in the hands-free point-of-sale system, the method having the process begins when the shopkeeper speaks a command into one of the onboard microphones which is processed locally; the voice signal is interpreted using an on-device Small Language Model (SLM) engine; the output of the SLM engine is passed to an intent dispatcher layer which acts as a logic controller that routes the interpreted command to the most appropriate backend engine or service depending on the type of intent such as requests related to inventory, sales insights, marketing, product categorization, or customer engagement; the intent dispatcher communicates with a suite of Al Labs Core Engines which form the intelligence backbone of the system and may include, but are not limited to recommendation engines for upsell, cross-sell, or restocking suggestions; market intelligence modules for generating insights based on pricing trends, customer preferences, or sales performance, smart categorization modules for auto-classifying newly added inventory items; once a response or result is produced, it is passed to the output feedback engine which determines the appropriate mode of delivery. a method for the emergency response protocol integrated into the intelligent hands-free point-of-sale system, the method having the process begins with detection of an emergency trigger command which may consist of a predetermined verbal phrase configured by the shopkeeper or system provider (e.g., “Emergency,” “Help me,” or a custom keyword) and is recognized using onboard voice and intent recognition subsystems, which classify the request as a high-priority safety event; once the emergency command is identified, the system proceeds to trigger SOS mode in which the system activates predefined emergency routines without requiring manual intervention; the next step is to start camera and recording of all microphones to simultaneously capture a multi-modal, time-stamped record of the event and provide both audio and visual context for post-event review or evidence generation; following data capture, the system executes an encryption and upload process to cloud storage using secured protocols; upon successful upload, the system initiates contact with emergency contacts which involves sending alerts to a predefined list of recipients, such as store managers, law enforcement authorities, family members, or private security services, wherein the alert message may include event metadata, live status updates, or secure links to the uploaded content, wherein the communication methods may be configured to include SMS, voice calls, messaging APIs, or other wireless protocols, depending on available connectivity and system setup, a method for internal cooling mechanism of the system, the method having ambient air is drawn into the cube box through the air inlets provided on both the first side panel and the second side panel; the incoming air flows across and between the internal components, including the computing device microprocessor and other heat-generating modules, thereby absorbing the heat dissipated during system operation; this directed airflow enables efficient heat transfer and maintains optimal operating temperatures within the enclosure; the heated air is then expelled through the top panel by the action of the fan which is configured to push air outward; wherein, the described airflow pathway ensures continuous ventilation and stable thermal performance, supporting the longevity and reliability of the internal electronic components housed within the cube box. In an embodiment, the present invention relates to a method of using hands free intelligent point of sale system, the method includes: a method of voice interaction of the customer with the system, the method includes capturing the customer image using the camera and sending it to the cloud server computer for recognition via a machine learning model, activating the customer touch interface to display instructions for activating the system via a hot-word, receiving the hot-word through the customer microphone to activate the system, transmitting the customer voice from the microphone to the computing device, executing computer-readable instructions in the microprocessor and initially processing the voice input locally using an on-device Small Language Model (SLM) for intent recognition and basic response generation; if the query exceeds the capability of the local model, the voice is sent to the cloud server computer for advanced analysis, analyzing the voice via the cloud server processor when required, recognizing the customer and responding with personalized services, promotions, and entertainment, allowing the customer to place product orders via voice, converting the voice to text and processing the request through the cloud server processor, then sending the response as voice to the computing device, conversing with the customer via the first speaker and the second speaker; a method of interacting with shopkeepers and managing sales, payment, and inventory, the method includes activating the system using a hot-word via the shopkeeper microphone, sending voice commands from the microphone to the computing device, which uses the on-device SLM in the microprocessor to interpret the request and extract relevant intent, retrieving sales, customer, and inventory statistics either locally or by forwarding the request to the cloud server computer for complex analytics, executing machine learning models in the cloud server processor as needed to generate relevant statistics and convert them into voice, displaying the statistics on the shopkeeper touch interface, and announcing them via the first speaker and second speaker, enabling the shopkeeper to place orders to the distributor based on analyzed sales and inventory data, wherein, the computing device, through its on-device SLM and Al engine, can autonomously place distributor orders using predictive analytics models reducing manual intervention, wherein, the computing device is also able to automatically place order before distributor through machine learning model of the cloud server computer based on the sales data, inventory data and customer demand, the computing device and the cloud server computer intelligently manages inventory and sales without need of large workforce and complicated machinery, a method for a backend software process flow for voice-based interaction, intelligent task execution, and adaptive response generation in the hands-free intelligent point-of-sale system, the method having the process begins when the system enters a passive listening state and upon detecting an audio signal, the system first performs speaker identification to determine whether the input is from a known user; the detected speech is converted into text using a speech-to-text (STT) engine; at decision point the system evaluates whether the transcribed input contains a predefined wake-up phrase, which acts as a trigger for interaction and if the wake-up phrase is not detected, the system returns to the listening state; if the wake-up phrase is confirmed, the transcribed text is processed by a local Small Language Model (SLM) which extracts sentence structure and predicts intent with low latency; the processed sentence is passed to a Natural Language Understanding (NLU) engine, which classifies the overall intent; if the intent is determined to be an emergency the system immediately triggers SOS mode which activates all audio and visual input devices; the captured data is encrypted and uploaded to a secure cloud server and predefined emergency contacts are notified ensuring rapid response and evidence preservation; if the intent is not an emergency, the system evaluates whether the input represents an actionable task such as generating a report, placing an order, or retrieving analytics; if the intent is emergency the system routes the request to the appropriate Al engine for execution and upon completion, the system checks the result; if successful, the system generates a task completion message which is passed through a text-to-speech (TTS) engine for auditory output. if unsuccessful, an error message is displayed and similarly converted into speech. a method for software-based backend flow for intelligent voice interaction, intent processing, and multi-channel response generation in the hands-free point-of-sale system, the method having the process begins when the shopkeeper speaks a command into one of the onboard microphones which is processed locally; the voice signal is interpreted using an on-device Small Language Model (SLM) engine; the output of the SLM engine is passed to an intent dispatcher layer which acts as a logic controller that routes the interpreted command to the most appropriate backend engine or service depending on the type of intent such as requests related to inventory, sales insights, marketing, product categorization, or customer engagement; the intent dispatcher communicates with a suite of Al Labs Core Engines which form the intelligence backbone of the system and may include, but are not limited to recommendation engines for upsell, cross-sell, or restocking suggestions; market intelligence modules for generating insights based on pricing trends, customer preferences, or sales performance, smart categorization modules for auto-classifying newly added inventory items; once a response or result is produced, it is passed to the output feedback engine which determines the appropriate mode of delivery. a method for the emergency response protocol integrated into the intelligent hands-free point-of-sale system, the method having the process begins with detection of an emergency trigger command which may consist of a predetermined verbal phrase configured by the shopkeeper or system provider (e.g., “Emergency,” “Help me,” or a custom keyword) and is recognized using onboard voice and intent recognition subsystems, which classify the request as a high-priority safety event; once the emergency command is identified, the system proceeds to trigger SOS mode in which the system activates predefined emergency routines without requiring manual intervention; the next step is to start camera and recording of all microphones to simultaneously capture a multi-modal, time-stamped record of the event and provide both audio and visual context for post-event review or evidence generation; following data capture, the system executes an encryption and upload process to cloud storage using secured protocols; upon successful upload, the system initiates contact with emergency contacts which involves sending alerts to a predefined list of recipients, such as store managers, law enforcement authorities, family members, or private security services, wherein the alert message may include event metadata, live status updates, or secure links to the uploaded content, wherein the communication methods may be configured to include SMS, voice calls, messaging APIs, or other wireless protocols, depending on available connectivity and system setup, a method for internal cooling mechanism of the system, the method having ambient air is drawn into the cube box through the air inlets provided on both the first side panel and the second side panel; the incoming air flows across and between the internal components, including the computing device microprocessor and other heat-generating modules, thereby absorbing the heat dissipated during system operation; this directed airflow enables efficient heat transfer and maintains optimal operating temperatures within the enclosure; the heated air is then expelled through the top panel by the action of the fan which is configured to push air outward; wherein, the described airflow pathway ensures continuous ventilation and stable thermal performance, supporting the longevity and reliability of the internal electronic components housed within the cube box.

Claims

I / WE CLAIM1. A system(lOO) of hands-free intelligent point of sale, the system (100) comprising: a computing device(102), the computing device(102) having a computing device database(104), the computing device database(104) stores data and computer-readable instructions, and a microprocessor 106), the microprocessor 106) executes computer- readable instructions for controlling the hardware of the system(lOO) and communicating with the cloud server for using machine-learning model and extracting data of sales, customers, and inventory; an at least one shopkeeper touch interface(108), the at least one shopkeeper touch interface(108) is connected to the microprocessor 106), thus the microprocessor 106) controls and display sales, customer and inventory data on the at least one shopkeeper touch interface(108) to a shopkeeper and also helps computing device(102) to interact with the shopkeeper through touch display; an at least one customer touch interface(140), the at least one customer touch interface(140) is connected to the microprocessor(106) of the computing device(102), thus the microprocessor(106) controls and display data on the at least one customer touch interface(140) to the customer and also helps computing device(102) to interact with the customer through touch display; a camera(HO), the camera (110) is also connected to the microprocessor 106) of the computing device(102), the camera(HO) detects the presence of the customer, and the computing device(102) recognizes the identity of the customer through a machine learning model by using customer image; a PIR sensor (148), the PIR sensor (148) is also connected to the microprocessor 106) of the computing device(102), the PIR sensor (148) is used to detect the presence of customer through the PIR sensor (148) optimizing responsiveness of system (100) and enabling intelligent interaction control based on user proximity and engagement;a retractable contact interface (150), the retractable contact interface (150) comprises spring-loaded electrical contacts that establish electrical and data connectivity between the cube box (126) and a docking station when engaged, thereby enabling power transfer and system expansion without requiring traditional cable connections; an at least one customer microphone(112), the at least one customer microphone(112) is also connected to the microprocessor 106) of the computing device(102) for receiving a voice command from the customer and sending the voice to the computing device(102) for executing the command, the computing device(102) also recognize customer from the voice as well; an at least one shopkeeper microphone(142), the at least one shopkeeper microphone(142) is also connected to the microprocessor 106) of the computing device(102) for receiving a voice command from the shopkeepers and sending the voice to the computing device(102) for executing command of the shopkeeper; a first speaker( 114), the first speaker(114) is also connected to the microprocessor 106) of the computing device(102) for the system(lOO) to interact with the customer and shopkeeper to provide information through the system (100) generated voice; a second speaker(116), the second speaker(116) is also connected to the microprocessor 106) of the computing device(102) for the system(lOO) to interact with the customer and shopkeeper and provide information through the system (100) generated voice; an at least one networking device(l 18), the computing device(102) is connected to the at least one networking device(l 18); a battery(174), the battery(174) is used to provide power to the system (100); a button board (144), the button board (144) includes RGB LED, mute LED and user control buttons; a third microphone (170);a fourth microphone (172); wherein, the third microphone (170) and the fourth microphone (172) are used for wide-field voice input, a cooling fan (146), the cooling fan (146) is controlled through pulse-width modulation (PWM) signals by the microprocessor(106) of the computing device(102) and is configured to exhaust warm air from within the system (100) thereby maintaining optimal temperature for internal components; an LED indicator (156), the LED indicator (156) is also connected to the microprocessor 106) of the computing device(102), the LED indicator (156) is configured to display the charging status of the system (100) and may illuminate in different colors or patterns to indicate conditions such as charging in progress, fully charged, or charging error; a charging port (158), the charging port (158) is configured to receive electrical power from an external power source or docking station, enabling the recharging of the battery(174) of the system (100); a data transfer port (160), the data transfer port (160) is configured to facilitate communication between the cube box (126) and external devices for functions such as diagnostics, firmware updates, or peripheral interfacing and supports USB, serial, or other standard data transfer protocols; a RGB LED strip (162), the RGB LED strip (162) is configured to emit colored illumination patterns based on system events or states such as startup, idle, error, transaction success, or customer interaction; an add-on card board (164), the add-on card board (164) provides hardware debugging and diagnostic functions and includes a serial communication bridge and a hardware debugging interface, and is connected to the computing device (102); a power board (166), the power board (166) comprises a charger IC, power delivery controller, fuel gauge, audio amplifier, universal data and powerinterface, and connectors for external devices and is connected to the computing device(102); a wireless board (168), the wireless board (168) comprises multi-mode communication and positioning module configured to facilitate wireless data exchange and geolocation services; a cube box(126), the cube box(126) having a front panel(128), the at least one customer touch interface(140), the camera(HO), the at least one customer microphone(112), the camera(HO) and the PIR sensor (148) are assembled on the front panel(128), the front panel(128) faces towards the customer, a first side panel(130), the first speaker(114), the LED indicator (156), the charging port (158), the data transfer port (160) is attached to the first side panel(130), a second side panel(132), the second speaker(116) is attached to the second side panel(132), a top panel(134), the button board (144), the cooling fan (146), the third microphone (170) and the fourth microphone (172) are assembled on the top panel(134), a bottom panel(136), and a back panel(138), the at least one shopkeeper touch interface(108), the at least one shopkeeper microphone(142) are assembled on the back panel(138), the back panel(138) faces towards the shopkeeper; and a cloud server computer(120), the cloud server computer(120) is connected to the computing device(102) through the at least one networking device(l 18), the cloud server computer(120) having a cloud database(122), the cloud database(122) stores machine-learning model, sales data, inventory data of products, customer data, shopkeeper data that are being sent by the computing device(102), anda cloud server processor(124), the cloud server processor(124) executes computer-readable instructions for analyzing sales data, inventory data of products, customer data, and shopkeeper data that provides deeper insights into sales and inventory management ensuring real-time updates and efficient stock management, and also provides insight of customer purchase behavior; wherein, the first side panel (130) is configured with two air inlets designed to allow the intake of ambient air into the cube box (126) and which form part of the active and passive cooling system, allowing airflow across the internal components, wherein, the system(lOO) is being used as a point of sale that provides the shopkeeper with data analytics for sales, customers, and inventory management, the system(lOO) also interacts with customers through voice from the first speaker(l 14) and touch display of the at least one customer touch interface(140), wherein, the microprocessor 106) of the computing device(102) sends voice to the cloud server processor(124) that converts the voice into text and sends back a suitable reply through the speaker to the user from the predefined database of conversations that is being programmed and stored in the cloud database(122), characterize in that, the computing device(102) can converse with the customer and the shopkeeper in multiple languages and provide a human-like conversation experience, and natural interactions, characterize in that, the camera(HO) captures customer image and sends to the cloud server computer(120) through the computing device(102), the cloud server processor(124) recognizes the customer in real-time using machine-learning model, greets the customer, and provides personalized services, promotions, and entertainment options to the customer as per previous purchases, simultaneously, the cloud server processor(124) provides the inventory data to the shopkeeper so that shopkeeper can track inventory of particular items and order items from distributor through voice command using the at least one shopkeeper microphone(142),characterize in that, the computing device(102) is also able to automatically place orders before the distributor through the machine-learning model of the cloud server computer(120) based on the sales data, inventory data and customer demand, the computing device(102) and the cloud server computer(120) intelligently manages inventory and sales without need of large workforce and complicated machinery.

2. The system (100) as claimed in claim 1, wherein the cloud server computer(120) utilizes the Haar Cascade Classifier of Open CV to initially identify customer’s face and again uses custom Deep learning model code to increase the accuracy of recognition in real-time.

3. The system (100) as claimed in claim 1, wherein the computing device (102) is a custom embedded System-on-Chip (SoC) architecture integrated with the microprocessor (106), memory, and connectivity modules to enable real-time on- device Al inference, local voice processing, and sensor integration within the cube box (126).

4. The system (100) as claimed in claiml, wherein the camera(HO) is of a different type selected from a microwave sensor, a PIR sensor, and a tomographic sensor.

5. The system (100) as claimed in claiml, wherein the computing device database(104) has SQLite for local database management and product lookup from the cloud server computer(120) and the computing device database(104) is linked with inventory and supply chain management platforms, ensuring real-time updates and efficient inventory management.

6. The system (100) as claimed in claiml, wherein, the cloud server computer(120) utilizes Large Language Models that are enabled with advanced conversational abilities and thus provides intelligent personal assistance to shopkeepers and customers with offerings, business insights, inventory data, and personalized recommendations7. The system (100) as claimed in claim 1, wherein the at least one networking device (118) comprises a multi-mode communication module including at least aWi-Fi module, a Bluetooth module, a 4G LTE communication module, and a SIM card slot configured to support wireless connectivity for data transmission, cloud synchronization, and emergency communication functionalities.

8. The system (100) as claimed in claim 1, wherein the computing device(102) also comprises an FM stereo module for playing customer-centric music, an NFC RFID Read and Write Module for assisting payments, and Bluetooth Interface.

9. The system (100) as claimed in claiml, wherein the display of the at least one shopkeeper touch interface(108) and the at least one customer touch interface(140) is mounted via tiltable hinge mechanism allowing the shopkeeper to adjust the screen angle for optimal visibility and ergonomic comfort.

10. A method of using hands free intelligent point of sale system(lOO) as claimed in claim 1, wherein the method comprises: a method of voice interaction of the customer with the system(lOO), the method having capturing the customer image using the camera (110) and sending it to the cloud server computer (120) for recognition via a machine learning model, activating the customer touch interface (140) to display instructions for activating the system (100) via a hot-word, receiving the hot-word through the customer microphone (112) to activate the system (100), transmitting the customer voice from the microphone (112) to the computing device (102), executing computer-readable instructions in the microprocessor (106) and initially processing the voice input locally using an on-device Small Language Model (SLM) for intent recognition and basic response generation; if the query exceeds the capability of the local model, the voice is sent to the cloud server computer (120) for advanced analysis,analyzing the voice via the cloud server processor (124) when required, recognizing the customer and responding with personalized services, promotions, and entertainment, allowing the customer to place product orders via voice, converting the voice to text and processing the request through the cloud server processor (124), then sending the response as voice to the computing device (102), conversing with the customer via the first speaker (114) and the second speaker (116); a method of interacting with shopkeepers and managing sales, payment, and inventory, the method of having activating the system (100) using a hot- word via the shopkeeper microphone (142), sending voice commands from the microphone (142) to the computing device (102), which uses the on-device SLM in the microprocessor (106) to interpret the request and extract relevant intent, retrieving sales, customer, and inventory statistics either locally or by forwarding the request to the cloud server computer (120) for complex analytics, executing machine learning models in the cloud server processor (124) as needed to generate relevant statistics and convert them into voice, displaying the statistics on the shopkeeper touch interface (108), and announcing them via the first speaker (114) and second speaker (116), enabling the shopkeeper to place orders to the distributor based on analyzed sales and inventory data, wherein, the computing device (102), through its on-device SLM and Al engine, can autonomously place distributor orders using predictive analytics models reducing manual intervention, wherein, the computing device(102) is also able to automatically place order before distributor through machine learning model of the cloud server computer(120) based on the sales data, inventory data and customer demand, the computing device(102) and the cloud server computer(120)intelligently manages inventory and sales without need of large workforce and complicated machinery, a method for a backend software process flow for voice-based interaction, intelligent task execution, and adaptive response generation in the hands-free intelligent point-of-sale system (100), the method having the process begins when the system enters a passive listening state and upon detecting an audio signal, the system first performs speaker identification to determine whether the input is from a known user; the detected speech is converted into text using a speech-to-text (STT) engine; at decision point the system evaluates whether the transcribed input contains a predefined wake-up phrase, which acts as a trigger for interaction and if the wake-up phrase is not detected, the system returns to the listening state; if the wake-up phrase is confirmed, the transcribed text is processed by a local Small Language Model (SLM) which extracts sentence structure and predicts intent with low latency; the processed sentence is passed to a Natural Language Understanding (NLU) engine, which classifies the overall intent; if the intent is determined to be an emergency the system immediately triggers SOS mode which activates all audio and visual input devices; the captured data is encrypted and uploaded to a secure cloud server and predefined emergency contacts are notified ensuring rapid response and evidence preservation; if the intent is not an emergency, the system evaluates whether the input represents an actionable task such as generating a report, placing an order, or retrieving analytics;if the intent is emergency the system routes the request to the appropriate Al engine for execution and upon completion, the system checks the result; if successful, the system (100) generates a task completion message which is passed through a text-to-speech (TTS) engine for auditory output. if unsuccessful, an error message is displayed and similarly converted into speech. a method for software-based backend flow for intelligent voice interaction, intent processing, and multi-channel response generation in the hands-free point-of-sale system (100), the method having the process begins when the shopkeeper speaks a command into one of the onboard microphones which is processed locally; the voice signal is interpreted using an on-device Small Language Model (SLM) engine; the output of the SLM engine is passed to an intent dispatcher layer which acts as a logic controller that routes the interpreted command to the most appropriate backend engine or service depending on the type of intent such as requests related to inventory, sales insights, marketing, product categorization, or customer engagement; the intent dispatcher communicates with a suite of Al Labs Core Engines which form the intelligence backbone of the system and may include, but are not limited to recommendation engines for upsell, cross-sell, or restocking suggestions; market intelligence modules for generating insights based on pricing trends, customer preferences, or sales performance, smart categorization modules for auto-classifying newly added inventory items; once a response or result is produced, it is passed to the output feedback engine which determines the appropriate mode of delivery.a method for the emergency response protocol integrated into the intelligent hands-free point-of-sale system (100), the method having the process begins with detection of an emergency trigger command which may consist of a predetermined verbal phrase configured by the shopkeeper or system provider (e.g., “Emergency,” “Help me,” or a custom keyword) and is recognized using onboard voice and intent recognition subsystems, which classify the request as a high-priority safety event; once the emergency command is identified, the system (100) proceeds to trigger SOS mode in which the system (100) activates predefined emergency routines without requiring manual intervention; the next step is to start camera(l 10) and recording of all microphones to simultaneously capture a multi-modal, time-stamped record of the event and provide both audio and visual context for post-event review or evidence generation; following data capture, the system(lOO) executes an encryption and upload process to cloud storage (240) using secured protocols; upon successful upload, the system(lOO) initiates contact with emergency contacts which involves sending alerts to a predefined list of recipients, such as store managers, law enforcement authorities, family members, or private security services, wherein the alert message may include event metadata, live status updates, or secure links to the uploaded content, wherein the communication methods may be configured to include SMS, voice calls, messaging APIs, or other wireless protocols, depending on available connectivity and system setup. a method for internal cooling mechanism of the system(lOO), the method having ambient air is drawn into the cube box (126) through the air inlets provided on both the first side panel (130) and the second side panel (132);the incoming air flows across and between the internal components, including the computing device (102), microprocessor (106), and other heat-generating modules, thereby absorbing the heat dissipated during system operation; this directed airflow enables efficient heat transfer and maintains optimal operating temperatures within the enclosure; the heated air is then expelled through the top panel (134) by the action of the fan (146), which is configured to push air outward; wherein, the described airflow pathway ensures continuous ventilation and stable thermal performance, supporting the longevity and reliability of the internal electronic components housed within the cube box (126).

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