Technical Field
[0001] The embodiments herein generally relate to a system and method for optimizing
room and task allocation in a hospitality establishment, and, more particularly, to a system and
method for automated room and employee location-based task allocation in the hospitality
establishment.

Description of the Related Art
[0002] Every day, it is cumbersome for a hospitality establishment to manage several
tasks such as room allotment, room maintenance, front office maintenance, task allotment, task
monitoring, resource monitoring, etc. Over the years, many automated systems are developed
for managing tasks of the hospitality establishment. However, still, the hospitality
establishment finds difficulty in allocating rooms and tasks. Existing automated systems may
allocate the rooms to a customer by simply allocating vacant rooms according to the customer
preferences without checking whether the rooms are in good condition to occupy or not.
Sometimes, the vacant rooms which are not in good condition may be allocated to the
customers. In that case, during the check-in time, the customer may have a bad impression on
the brand of the hospitality establishment if the rooms which are allocated to the customer is
not in good condition. Further, the existing automated systems may not allocate rooms on floor
wise and may not allocate the different rooms of a single reservation closest for a group of
customers. Further, keeping the customers closest to each other and keeping the customers on
the lowest floors may improve customer satisfaction.
[0003] In addition to room allocation, task management and resource management are
other big challenges to the hospitality establishment. The existing systems are simply

allocating tasks to the available employees. However, the existing system does not consider whether the available employee has the efficiency to complete the task and whether the location of the employee is nearest to the task. Further, the existing systems may fail to track the status of the task and location of the employees in real-time. Further, in the existing systems, various departments of the hospitality establishment such as housekeeping, maintenance, Food and Beverage (F&B) department, etc. are not interlinked with each other for efficient functioning. For example, if a housekeeping employee finds some of the elements (e.g. TV, furniture, etc.) are not working in the hospitality establishment, the employee has to go manually and inform the maintenance department. In this situation, more time may be consumed by the maintenance department to repair the elements which are not working, as the maintenance department has to go directly to analyze the fault when the housekeeping employee informs about the faults. Sometimes, the fault may not be rectified as the housekeeping employee may tend to forget to inform about the fault to the maintenance department. Further, due to improper allocation of tasks, lack of communication between employee and management and lack of tracking status of tasks and employees, the existing systems are ineffective for allotting and managing tasks to the employees. Further, the existing systems are consuming more time and labor-intensive.
[0004] Accordingly, there remains a need for an automated system for room and employee location-based task allocation in the hospitality establishment efficiently to improve customer satisfaction as well as to improve the revenue of the hospitality establishments.
SUMMARY
[0005] The main objective of the present invention is to provide a system and method for automated room and employee location-based task allocation in a hospitality establishment.
[0006] In one embodiment, the present invention relates to a system for automated room and employee location-based task allocation in a hospitality establishment using a server and an employee device. The system includes (i) a server that includes a first memory to store

a first set of modules and a first processor that is configured to execute the first set of modules and (ii) an employee device associated with each employee that includes a second memory to store a second set of modules and a second processor that is configured to execute the second set of modules to perform the desired functions of the employee device. The server is connected with the employee device through a communication network. The first set modules includes (a) a room information database updation module, executed by said first processor, configured to update in real time, a room information of the hospitality establishment in a room information database (b) a task information database updation module, executed by said first processor, configured to update in real time, a task information associated with the hospitality establishment in a task information database, the task information is mapped with each employee (c) a room availability determination module, executed by said first processor, configured to determine availability of rooms upon receiving a customer room booking request by interrogating the room information database, the customer room booking request includes at least one of a new room booking request or a room booking extension request (d) a room demand prediction module, executed by said first processor, configured to predict demand for rooms in the hospitality establishment using a machine learning model by analyzing at least one of (i) booking feature of previous and current bookings (ii) events in the city (iii) a location of hospitality establishment, (iv) weather or (v) other demographic factors from a first database in the server (e) a floor list generation module, executed by said first processor, configured to generate, by analyzing previous bookings, current bookings and predicted demand for rooms from the room demand prediction module, at least one of (i) an active floor list with at least one of desired vacant room, (ii) a meta active floor list with at least one of desired vacant room or (iii) an inactive floor list with at least one of desired vacant room when the number of rooms available are greater than or equal to number of rooms required in the customer room booking request (f) a room allotment module, executed by said first processor, configured to (i)

determine room(s) in the lowest floor according to the layout of the hospitality establishment by iterating through the at least one of the active floor list, the meta active floor list or the inactive floor list and (ii) allot the determined room(s) to the customer (g) an automatic task generation module, executed by said first processor, configured to generate a task based on at least one of (i) customer's location proximity to the hospitality establishments at the day of check in (ii) a customer input from a guest device at the time of stay in the hospitality establishment or (iii) an employee input from the employee device and (h) a task allotment module, executed by said first processor, configured to (i) determine an employee to allot the generated task by analyzing at least one of (a) employee's work department (b) employee's incomplete task counts for the day (c) employee's real time task's location (d) employee's turnaround time or (e) completion time of the incomplete tasks by interrogating the task information database and (ii) allot the generated task to the employee device associated with the determined employee.
[0007] In another embodiment, (i) the active floor list includes a list of floors that includes occupied rooms/actual bookings and at least one desired vacant room, (ii) the meta active floor list includes a list of floors that includes no actual bookings/non-occupied rooms but expected to be booked during the staying period of the customer or list of floors that includes no vacant rooms but expected to be vacant during the staying period of the customer and (iii) the inactive floor list includes a list of floors that includes either no actual bookings/non-occupied rooms and not to be booked during the stay period of the customer or list of floors that includes no vacant rooms that are not expected to be vacant during the stay period of the customer.
[0008] In another embodiment, the second set of modules of the employee device comprises at least one of (i) an employee login module, executed by said second processor, configured to receive login details of an employee account and login to the employee account

upon receiving the login details, (ii) a task receiving module executed by said second processor, configured to receive a task from the server or a manager device, (iii) a task accepting or rejecting module, executed by said second processor, configured to allow to accept or reject the task allotted from the server or the manager device by the employee, (iv) a task status updating module, executed by said second processor, configured to allow to update the status of the task accepted by the employee, (v) an update on task receiving module, executed by said second processor, configured to receive an update on task from the server or the manager device, (vi) a work timings and break timings entering module, executed by said second processor, configured to receive the work timings and break timings of the employee and transmit the work timings and break timings of the employee to the server and the manager device, (vii) a delay reporting module, executed by said second processor, configured to receive and report a reason for delay of the task by the employee, if so to the server or the manger device, (viii) a display module, executed by said second processor, configured to display list of tasks accepted by the employee in predetermined color according to the status of the task, (ix) an entity status marking module, executed by said second processor, configured to allow to mark and report an entity of room as working or as not working to the server or the manger device, (x) a signal receiving module, executed by said second processor, configured to receive at least one of (a) a Bluetooth signal from Bluetooth beacons (b) a Wi-Fi/Li-Fi signal from Wi-Fi/Li-Fi access points or (c) a magnetic field data of the hotel establishment, (xi) a signal transmitting module, executed by said second processor, configured to transmit at least one of (a) a Bluetooth signal from Bluetooth beacons (b) a Wi-Fi/Li-Fi signal from Wi-Fi/Li-Fi access points or (c) a magnetic field data of the hotel establishment to the server or the manger device; or or (xii) an employee logout module, executed by said second processor, configured to allow to logout from the employee account by the employee.
[0009] In yet another embodiment, the room allotment module is executed by the first

processor, further configured to determine rooms nearest to each other in the lowest floor according to the layout of the hospitality establishment by iterating through the at least one of the active floor list, the meta active floor list or the inactive floor list when the new room booking request is a group room booking request.
[0010] In yet another embodiment, the first set of modules further comprises an employee location tracking module, executed by the first processor, configured to determine real-time task's location of the employee by (a) receiving at least one of (a) a Bluetooth signal from Bluetooth beacons (b) a Wi-Fi/Li-Fi signal from Wi-Fi/Li-Fi access points or (c) a magnetic field data of the hotel establishments from the signal transmitting module of the employee device and (b) determining the real-time task's location of the employee by processing at least one of (a) the Bluetooth signal from Bluetooth beacons (b) the Wi-Fi/Li-Fi signal from Wi-Fi/Li-Fi access points or (c) the magnetic field data of the hotel establishments.
[0011] In another embodiment, the customer's location proximity to the hospitality establishment is determined by tracking the Global Positioning System (GPS) of the guest device (110) associated with the customer.
[0012] In another embodiment, the first set of modules further includes a cancellation prediction module, executed by said first processor, configured to predict cancellation probability of each booking includes at least one of (i) current booking or (ii) expected future booking, by analyzing at least one of (i) booking feature (ii) customer profile or (iii) historical data of customers.
[0013] In another embodiment, the manager device includes a third memory to store a third set of modules and a third processor that is configured to execute the third set of modules to perform desired functions of the manager device.
[0014] In another aspect, the present invention relates to a method for automated room and employee location-based task allocation in a hospitality establishment using a server and

an employee device. The method includes (a) updating, in real time, a room information database with a room information of the hospitality establishment (b) updating, in real time, a task information database with a task information associated with the hospitality establishment, the task information are mapped with each other (c) determining availability of rooms upon receiving a customer room booking request by interrogating the room information database, the customer room booking request includes at least one of a new room booking request or a room booking extension request (d) predicting demand for rooms in the hospitality establishment using a machine learning model by analyzing at least one of (i) booking feature of previous and current bookings (ii) events in the city (iii) a location of hospitality establishment, (iv) weather or (v) other demographic factors (e) generating, by analyzing previous bookings, current bookings and predicted demand for rooms, at least one of (i) an active floor list with at least one of desired vacant room, (ii) a meta active floor list with at least one of desired vacant room or (iii) an inactive floor list with at least one of desired vacant room when the number of rooms available are greater than or equal to number of rooms required in the customer room booking request (f) determining room(s) in the lowest floor according to the layout of the hospitality establishment by iterating through the at least one of the active floor list, the meta active floor list or the inactive floor list and allotting the determined room(s) to the customer (g) generating a task based on at least one of (i) customer's location proximity to the hospitality establishments at the day of check in (ii) a customer input from a guest device at the time of stay in the hospitality establishment or (iii) an employee input from the employee device and (h) determining an employee to allot the generated task by analyzing least one of (a) employee's work department (b) employee's incomplete task counts for the day (c) employee's real time task's location (d) employee's turnaround time or (e) completion time of the incomplete tasks by interrogating the task information database and allotting the generated task to the employee device associated with the determined employee.

[0015] In another embodiment, the method further includes determining rooms nearest to each other in the lowest floor according to the layout of the hospitality establishment by iterating through the at least one of the active floor list, the meta active floor list or the inactive floor list when the new room booking request is a group room booking request. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:
[0017] FIG. 1 is a system 100 view that illustrates an interaction between a server 104, an employee device 106, a guest device 110, and a manager device 108 according to an embodiment herein;
[0018] FIG. 2 is an exploded view of the server 104 of FIG. 1 according to an embodiment herein;
[0019] FIG. 3 is an exploded view of the employee device 106 of FIG. 1 according to an embodiment herein;
[0020] FIG. 4 is an exploded view of the manager device 108 of FIG. 1 according to an embodiment herein;
[0021] FIG. 5 is a flow chart that illustrates a process of room booking according to an embodiment herein;
[0022] FIG. 6A and 6B are a flow chart that illustrates a process of room allotment according to an embodiment herein;
[0023] FIG. 7 is a flow chart that illustrates a process of task allotment to an employee according to an embodiment herein;
[0024] FIG. 8 is a flow chart that illustrates a process of time management of the allocated tasks according to an embodiment herein;
[0025] FIG. 9 is a flow chart that illustrates a process of table reservation in a restaurant

for a customer according to an embodiment herein;
[0026] FIG. 10 is a flow chart that illustrates a process of allotment of tasks to the Food and Beverage (F&B) employees when order received from a customer according to an embodiment herein;
[0027] FIG. 11A and 1 IB is a flow chart that illustrates a method for automated room and employee location-based task allocation in a hospitality establishment, according to an embodiment herein; and
[0028] FIG. 12 is a schematic drawing illustrates a hardware configuration of computer architecture in accordance with the embodiments herein.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0029] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
[0030] As mentioned, there remains a need for an automatic system for room allocation and employee location-based task allocation in a hospitality establishment in an efficient way to improve customer satisfaction along with increasing revenue of the hospitality establishment. Referring now to the drawings, and more particularly to FIGS. 1 through 12, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments.
[0031] FIG. 1 is a system 100 view that illustrates an interaction between a server 104,

an employee device 106, a guest device 110, and a manager device 108 according to an embodiment herein. The system 100 includes a computing device 102, the server 104, the employee device 106 associated with each employee, the manager device 108 associated with each managing personnel, the guest device 110, a communication network 112, Bluetooth beacons 114 and WIFI/LIF1 access points 116. The Bluetooth beacons 114 and the WIFI/LIF1 access points 116 are connected to the server 104, the employee device 106, the manager device 108 and the guest device 110 by a method known in the art. The Bluetooth beacons 114 and the WIFI/LIFI access points 116 are located at a plurality of locations of the hospitality establishment. The server 104 includes a first memory to store a first set of modules and a first processor that is configured to execute the first set of modules. The first memory further includes a first database 222, a room information database 218 and a task information database 220 (not shown in FIG. 1). The employee device 106 associated with each employee includes a second memory to store a second set of modules and a second database and a second processor that is configured to execute the second set of modules to perform the desired functions of the employee device 106 and the employee device 106 is connected to the server 104 through the communication network 112. The guest device 110 is associated with each customer. The manager device 108 includes a third memory to store a third set of modules and a third database and a third processor configured to execute the third set of modules to perform desired functions of the manager device 108.
[0032] The system 100 updates in real-time, a room information of the hospitality establishment in the room information database 218. In one embodiment, the room information of the hospitality establishment includes but not limited to (i) a room type, (ii) a bed type, (iii) a room amenity information, (iv) a floor information (v) a room occupancy status, (vi) a status of the tasks associated with the rooms, (vii) a status of the rooms and (viii) reservation status of the rooms. In one embodiment, the hospitality establishment includes but not limited to a

hotel, a resort, a lodge, an inn, service apartment, etc. In one embodiment, the tasks associated with the rooms include but not limited to at least one of a maintenance task or a housekeeping task. In an embodiment, the system 100 receives the status of the tasks from an employee performing at least one task through the employee device 106 associated with the employee. In an embodiment, the status of the tasks includes at least one of a task starting time, the progress of the task, a task completion time or a reason for the delay to complete the task, if so. In an embodiment, the status of rooms includes in order and out of order. In one embodiment, the room occupancy status includes occupied and non-occupied. In one embodiment, the reservation status includes reserved and unreserved. In an embodiment, the employee device 106 is but not limited to a smartphone, personal digital assistant (PDA), tablet computer, notebook computer, or any other suitable computing device.
[0033] The system 100 updates in real-time, a task information in the task information database 220. The task information is mapped with each employee. The task information includes but not limited to (i) employee's work department (ii) employee's incomplete task counts for the day (ii) employee's real-time task's location (iii) employee's turnaround time or (iv) completion time of the incomplete tasks. In one embodiment, the employee's work department includes but not limited to a maintenance department, a housekeeping department, a front desk department, a Food and Beverage (F&B) department, a laundry department, and a security department.
[0034] The system 100 receives a customer room booking request from a customer through the at least one of the guest device 110 associated with the customer or any computing device 102 suitable to initiate the customer room booking request. In one embodiment, the guest device 110 or the computing device 102 executes a client-side application to generate the customer room booking request.
[0035] In one embodiment, the customer room booking request is at least one of a new

room booking request or a room booking extension request. In one embodiment, the customer room booking request includes at least one customer preferences selected from a group includes but not limited to a room type, a bed type, a floor type, a staying period, a check-in time, a check-out time, or a number of rooms required. In one embodiment, the new room booking request includes at least one of an individual room booking request or a group room booking request. The customer room booking request is initiated by at least one of customer or a booking agent through the guest device 110 associated with the customer or through the computing device 102 suitable for initiating the customer room booking request. In one embodiment, the guest device 110 and the computing device 102 are but not limited to a smartphone, personal digital assistant (PDA), tablet computer, notebook computer, or any other suitable computing device. The employee device 106, and manager device 108 are communicatively coupled with the server 104 through the communication network 112. In one embodiment, the communication network 110 is a wired network. In one embodiment, the communication network 110 is a wireless network. In one embodiment, server 104 is a cloud service. In one embodiment, server 104 is but not limited to a smartphone, personal digital assistant (PDA), tablet computer, notebook computer, or any other suitable computing device. [0036] The system 100 determines the availability of rooms upon receiving the customer room booking request by interrogating the room information database 218. In one embodiment, the system 100 determines the availability of rooms by analyzing at least one of (i) room occupancy status, (ii) status of the tasks associated with the rooms, (iii) a status of the rooms or (iv) reservation status of the rooms. In one embodiment, the customer room booking request includes a new room booking request. In one embodiment, the customer room booking request includes a room booking extension request. In an embodiment, the system 100 directs the customer room booking request to an overbooking list when the number of rooms available is less than the number of rooms required in the customer room booking request.

[0037] The system 100 predicts demand for rooms in the hospitality establishment using a machine learning model by analyzing at least one of (i) booking feature of previous and current bookings (ii) events in the city (iii) a location of the hospitality establishment, (iv) weather data or (v) other demographic factors from the first database 222 (not shown in FIG. 1) in the server 104.
[0038] The system 100 generates, by analyzing previous bookings, current bookings and predicted demand for rooms, at least one of (i) an active floor list with at least one of desired vacant room, (ii) a meta active floor list with at least one of desired vacant room or (iii) an inactive floor list with at least one of desired vacant room when the number of rooms available is greater than or equal to the number of rooms required in the customer room booking request.
[0039] In one embodiment, the system 100 predicts cancellation probability of each booking includes at least one of (i) current booking or (ii) expected future booking, by analyzing at least one of (i) booking feature (ii) customer profile or (iii) historical data of customers. In one embodiment, the system 100 generates, based on cancellation probability of each booking, at least one of (i) an active floor list with at least one of desired vacant room, (ii) a meta active floor list with at least one of desired vacant room or (iii) an inactive floor list with at least one of desired vacant room when the number of rooms available is greater than or equal to the number of rooms required in the customer room booking request.
[0040] In one embodiment, the active floor list including a list of floors that includes occupied rooms/actual bookings and at least one desired vacant room or where the actual booking(s) has a very high possibility of canceled that can be assigned to the future bookings. The meta active floor list including a list of floors that includes no actual bookings/non-occupied rooms but expected to be booked during the staying period of the customer or list of floors that includes no vacant rooms but expected to be vacant or high possibility of cancelled

during the staying period of the customer and booked with less possibility of cancelled during the staying period of the customer. The inactive floor list including a list of floors that includes either no actual bookings/non-occupied rooms and not to be booked during the stay period of the customer or list of floors that includes no vacant rooms that are not expected to be vacant during the stay period of the customer.
[0041] In one embodiment, the machine learning model for room demand prediction includes a Decision forest algorithm (DF). In one embodiment, the ASUM-DM process is used. In one embodiment, regression is used to project the demand for rooms and the classification method is used for each booking to categorize whether the room booking is having a high possibility of getting canceled or not.
[0042] The system 100 determines, using a k-nearest neighbor algorithm, room(s) in the lowest floor according to the layout of the hospitality establishments by iterating through the at least one of the active floor list, the meta active floor list or the inactive floor list. In one embodiment, the system 100, using a k-nearest neighbor algorithm, determines rooms nearest to each other in the lowest floor according to the layout of the hospitality establishments by iterating through the at least one of the active floor list, the meta active floor list or the inactive floor list when the new room booking request is the group room booking request. The system 100 allows the determined room(s) to the customer. In one embodiment, the system 100 allots rooms to the overbooking list when a booking is canceled. The system 100 (i) selects advance paid bookings from the overbooking list and (ii) allots the rooms among the advance paid bookings on first come first serve basis.
[0043] The system 100 tracks a real-time location of the customer at the day of check-in by tracking the guest device 110 associated with the customer and determines location proximity of the customer to the hospitality establishment based on the real-time location of the customer. In one embodiment, the guest device 110 runs a guest application related to the

room and task allocation system 100. The customer provided access to track the guest device 110 using GPS of the guest device 110 when the customer happens to login to the guest application related to the room and task allocation system 100.
[0044] The system 100 generates a task automatically based on at least one of (i) customer's location proximity to the hospitality establishment (ii) a customer input from the guest device (110) at the time of stay in the hospitality establishment or (iii) an employee input from the employee device 106. In one embodiment, the system 100 generates a task sheet automatically.
[0045] In one embodiment, the task associated with the rooms which are allotted to the customer is generated based on the location proximity of the customer to the hospitality establishment. In one embodiment, the task is generated based on the customer input from the guest device 110 at the time of stay in the hospitality establishment. In one embodiment, the customer input includes at least one of (i) room service request (ii) room customization request (iii) table reservation request (iv) occupied / non-occupied details (v) customer service request (vi) check-out request or (vii) maintenance request. In one embodiment, the task is generated based on the employee input from the employee device 106. In one embodiment, the employee input includes at least one of but not limited to a maintenance request. In one embodiment, the guest device 110 is integrated with Alexa model and converse with the guest, can handle any facilities related to hotel or any other events/facilities available in the city during the stay of the guest.
[0046] The system 100 determines an employee to allot the generated task by, (i) fetching a list of employees by interrogating the task information database 220 (ii) filtering the employee's who comprises work department same as the work department of the task to be allocated from the list (ii) categorizing the employees with work department same as the work department of the task to be allocated in an ascending order on the basis of at least one of (i)

incomplete task counts for the day (ii) distance between a real-time task's location and the location of the task to be allotted (iii) turnaround time of the employees or (iv) task completion time of the incomplete task. The system 100 allots the task to the employee in the categorized list by selecting a first employee in the categorized list and allotting the task with predetermined task completion time to the first employee in the categorized list. In one embodiment, the work department of the employee includes but not limited to a housekeeping department, a maintenance department, a food and beverage production, and service department, a front desk department, a car parking maintenance department, a laundry department, security department, etc.
[0047] In one embodiment, the guest device 110 is integrated with smart switches in the room of the hospitality establishment. The guest device 110 allows the customer to control electronic appliances in the rooms. In one embodiment, the electronic appliances are but not limited to TV, fan, Air conditioner (AC), Air cooler, lights, refrigerator, water heaters, coffee machine, etc. In one embodiment, the smart switches are but not limited to PERT switches. In one embodiment, controlling the electronic appliances include but not limited to switch on/off the electronic appliances, increasing/decreasing temperature of the AC, increasing/decreasing the speed of the fan, schedule tasks for a water heater or coffee machine, etc.
[0048] In one embodiment, the system 100 determines the real-time task's location of the employee by tracking the employee device 106 associated with the employee using an Indoor Positioning Method (IPS). The indoor positioning method (IPS) includes at least one of magnetic positioning method, Wi-fi/Li-fi access point based positioning, Bluetooth beacons based positioning or dead reckoning.
[0049] In a preferred embodiment, the magnetic positioning method is used to identify the real-time task's location of the employee. In one embodiment, the magnetic positioning method includes (i) detecting magnetic field data of the hospitality establishment where the

employee currently located by measuring the magnetic field data of the hospitality establishment using magnetic sensor of the employee device 106 associated with the employee (ii) receiving magnetic field data of the hospitality establishment from the employee device 106 associated with the employee and (ii) identifying the real-time task's location of the employee by comparing the magnetic field data from the employee device 106 associated with the employee with magnetic field map of the hospitality establishment. The employee device 106 includes at least one of magnetometer or magnetic sensors to detect the magnetic field data. The server 104 is loaded with the magnetic field map of the hospitality establishment. The magnetic field map of the hospitality establishment in the server 104 is automatically updated in near real-time.
[0050] In another preferred embodiment, the Bluetooth beacons based positioning method is used to identify the real-time task's location of the employee. In one embodiment, the Bluetooth beacons based positioning method includes (i) receives Bluetooth signal from the Bluetooth beacons 114 located nearby to the employee using the employee device 106 associated with the employee (ii) transfer the Bluetooth signal from the employee device 106 to the server 104 (iii) identifying the real-time location of the employee by processing the Bluetooth signal from the employee device 106. In one embodiment, the hospitality establishment is equipped with a plurality of Bluetooth beacons 114 at a plurality of locations.
[0051] In another preferred embodiment, the Wi-Fi/Li-Fi access point based positioning method is used to identify the real-time task's location of the employee. In one embodiment, the Wi-Fi/Li-Fi access point based positioning method includes (i) receives Wi-Fi/Li-Fi signal from the Wi-Fi/Li-Fi access point 116 located nearby to the employee using the employee device 106 associated with the employee (ii) transfer the Wi-Fi/Li-Fi signal from the employee device 106 to the server 104 (iii) identifying the current location of the employee by processing the Wi-Fi/Li-Fi signal from the employee device 106. In one embodiment, the

hospitality establishment is equipped with a plurality of Wi-Fi/Li-Fi access point 116 at a plurality of locations.
[0052] In one embodiment, the employee device 106 and the manager device 108 associated with each employee and each managing personnel executes an employee application and managing personnel application respectively related to the room and task allocation system 100. When the employee and the managing personnel happen to log in to the employee device 106 and the manager device 108, the system 100 starts to track the employee device 106 and the manager device 108. In one embodiment, the identification code of each employee device 106 and the manager device 108 are stored in the first memory of server 104. Based on the identification code, the system 100 differentiates between a signal from each employee device 106 and the manager device 108.
[0053] In one embodiment, the machine learning model calculates the turnaround time of the employee based on the historical data of the employee. The machine learning model is trained with the historical data of the employee. The historical data of the employee includes the employee's at least one of (i) a task response time (ii) a task completion time or (iii) a delay report for the task.
[0054] . In one embodiment, the predetermined task completion time for the task to be allotted is predicted by the machine learning model based on historical task completion time data from the task information database 220 in the server 104. In one embodiment, if the first employee in the second list unavailable to receive the task, the system 100 selects a second employee in the second list and allocate the task to the second employee with predetermined task completion time.
[0055] In one embodiment, the system 100 generates a housekeeping task automatically for each room in the hospitality establishment daily and allots the housekeeping task to the housekeeping employees by identifying the housekeeping employee according to

the embodiment herein.
[0056] . In one embodiment, the system 100 generates a maintenance task automatically when the housekeeping employee marks an entity of the room as not working in the employee device 106 and allots the maintenance task to the maintenance employees by identifying the maintenance employee according to the embodiment herein. In one embodiment, if the housekeeping employee finds some elements (E.g. TV, AC, fan, etc.) are not working, the housekeeping employee can transfer a maintenance task to the server 104 or the manager device 108 through the employee device 106 associated with the housekeeping employee. Then, the system 100 allots the maintenance task according to an embodiment herein.
[0057] In one embodiment, the system 100 monitors the tasks allocated to the employees using at least one sensor mounted in the rooms.
[0058] FIG. 2 is an exploded view of the server 104 of FIG. 1 according to an embodiment herein. The server 104 includes a first memory that stores a first set of modules, a plurality of database and a first processor is configured to execute the first set of modules. The plurality of database includes a room information database 218, a task information database 220, and a first database 222. The room information database 218 includes a room information. The room information includes but not limited to a room type, a bed type, a room amenity information, a floor information, a room occupancy status, a status of the tasks associated with the rooms, a status of the rooms and reservation status of the rooms. The task information database 220 includes a task information that the task information is mapped with each employee. The task information includes but not limited to employee's work department, employee's incomplete task counts for the day, employee's real-time task's location, employee's turnaround time and completion time of the incomplete tasks. The first database 222 stores one or more machine learning model. In one embodiment, the first database 222

stores but not limited to a customer profile, a floor plan, price details of rooms, cancellation details of the rooms, and historical data of the customer, booking feature, demographic factors, location information of the hospitality establishment and weather condition. In one embodiment, parameters of customer profile including but not limited to adults, babies, children, region of the customer and customer type. In one embodiment, the region of the customer includes but not limited to country of the customer, state of the customer, city of the customer and area of the customer. In one embodiment, the customer type includes but not limited to repeated customer, VIP customer and one-time customer. In one embodiment, parameters of the room booking feature include but not limited to average daily rate, customer arrival date/day of week, customer arrival date month, arrival date week number, customer age at booking date, customer arrival date/day of month, booking changes, room cancellation time by customer, cash deposit time by customer, customer age at room booking time, cancellation details, reserved room types, room quantity, previous stays of the customer, length of stay, required car parking space, stays in weekend night, stays in weeks night, total of special request, waiting list details of customer.
[0059] The first set of modules includes (i) a room information database updation module 202, executed by said first processor, configured to update in real time, a room information in a room information database 218 (ii) a task information database updation module 204, executed by said first processor, configured to update in real time a task information in the task information database 220 (iii) a room availability determination module 206, executed by said first processor, configured to determine availability of rooms upon receiving a customer room booking request by interrogating the room information database 218, the customer room booking request includes at least one of a new room booking request or a room booking extension request (iv) a room demand prediction module 208, executed by said first processor, configured to predict demand for rooms using the machine learning model

by analyzing at least one of (i) booking feature of previous bookings (ii) events in the city (iii) a location of hospitality establishment, (iv) weather or (v) other demographic factors (v) a floor list generation module 210, executed by said first processor, configured to generate, by analyzing previous bookings, current bookings and predicted demand for rooms, at least one of (i) an active floor list with at least one of desired vacant room, (ii) a meta active floor list with at least one of desired vacant room or (iii) an inactive floor list with at least one of desired vacant room when the number of rooms available are greater than or equal to number of rooms required in the customer room booking request (vi) a room allotment module 212, executed by said first processor, configured to (i) determine room(s) in the lowest floor according to the layout of the hospitality establishment by iterating through the at least one of the active floor list, the meta active floor list or the inactive floor list and (ii) allot the determined room(s) to the customer (vii) an automatic task generation module 214, executed by said first processor, configured to generate a task based on at least one of (i) customer's location proximity to the hospitality establishment at the day of check in (ii) a customer input from the guest device 110 at the time of stay in the hospitality establishment or (iii) an employee input received from the employee device 106 and (viii) a task allotment module 216, executed by said first processor, configured to (i) determine an employee to allot the generated task by analyzing least one of (a) employee's work department (b) employee's incomplete task counts for the day (c) employee's real time task's location (d) employee's turnaround time or (e) completion time of the incomplete tasks by interrogating the task and employee information database and (ii) allot the generated task to the employee device 106 associated with the determined employee.
[0060] In one embodiment, the active floor list including a list of floors that includes occupied rooms/actual bookings and at least one desired vacant room or where the actual booking(s) has a very high possibility of canceled that can be assigned to the future bookings. The meta active floor list including a list of floors that includes no actual bookings/non-

occupied rooms but expected to be booked during the staying period of the customer or list of floors that includes no vacant rooms but expected to be vacant or high possibility of cancelled during the staying period of the customer and booked with less possibility of cancelled during the staying period of the customer. The inactive floor list including a list of floors that includes either no actual bookings/non-occupied rooms and not to be booked during the stay period of the customer or list of floors that includes no vacant rooms that are not expected to be vacant during the stay period of the customer.
[0061] In an embodiment, the room allotment module 212 is executed by the first processor, further configured to determine rooms nearest to each other in the lowest floor according to the layout of the hospitality establishment by iterating through the at least one of the active floor list, the meta active floor list or the inactive floor list when the new room booking request is a group room booking request.
[0062] In an embodiment, the first set of modules further comprises an employee location tracking module, executed by the first processor, configured to determine the real-time task's location of the employee by (a) receiving at least one of (a) a Bluetooth signal from Bluetooth beacons 114 (b) a Wi-Fi/Li-Fi signal from Wi-Fi/Li-Fi access points 116 or (c) a magnetic field data of the hotel establishment from the signal transmitting module 324 of the employee device 106 and (b) determining the real-time task's location of the employee by processing at least one of (a) the Bluetooth signal from Bluetooth beacons 114 (b) the Wi-Fi/Li-Fi signal from Wi-Fi/Li-Fi access points 116 or (c) the magnetic field data of the hotel establishment.
[0063] FIG. 3 A is an exploded view of an employee device 106 of FIG. 1 according to an embodiment herein. The employee device 106 includes a second memory that stores a second set of modules, a second database 302 and a second processor configured to execute the second set of modules. The second set of modules includes (i) an employee login module

304, executed by the second processor, configured to receive login details of an employee account and login to the employee account upon receiving the login details of an employee account and login to the employee account upon receiving the login details (ii) a task receiving module 306, executed by the second processor, configured to receive a task from the server 104 or the manager device 108 (iii) a task accepting or rejecting module 308, executed by the second processor, configured to allow to accept or reject the task allotted from the server 104 or the manager device 108 by the employee (iv) a task status updating module 310, executed by the second processor, configured to allow to update the status of the task accepted by the employee (v) an update on task receiving module 312, executed by the second processor, configured to receive an update on task from the server 104 or the manager device 108 (vi) a work timings and break timings entering and transmitting module 314, executed by the second processor, configured to allow to receive work timings and break timings of the employee and transmit the work timings and break timings of the employee to the server 104 and the manager device 108 (vii) a delay reporting module 316, executed by the second processor, configured to receive and report a reason for delay of the task by the employee, if so to the server 104 or the manger device 108 (viii) a display module 318, executed by the second processor, configured to display list of tasks accepted by the employee in predetermined color according to the status of the task (ix) an entity status marking module 320, executed by the second processor, configured to allow to mark and report an entity of room as working or as not working to the server 104 or the manger device 108 (x) a signal receiving module 322, executed by the second processor, configured to receive at least one of (a) a Bluetooth signal from Bluetooth beacons 114 (b) a Wi-Fi/Li-Fi signal from Wi-Fi/Li-Fi access points 116 or (c) a magnetic field data of the hotel establishment (xi) a signal transmitting module 324, executed by the second processor, configured to transmit at least one of (a) a Bluetooth signal from Bluetooth beacons 114 (b) a Wi-Fi/Li-Fi signal from Wi-Fi/Li-Fi access points 116 or (c) a

magnetic field data of the hotel establishment to the server 104 or the manger device 108 or (xii) an employee logout module 326, executed by the second processor, configured to allow to logout from the employee account by the employee. In an embodiment, the employee device 106 includes a color-coded interface.
[0064] FIG. 4 is an exploded view of the manager device 108 of FIG. 1 according to an embodiment herein. The manager device 108 that includes a third memory to store a third set of modules, a third database 402 and a third processor that is configured to execute the third set of modules to perform desired functions of the manager device. The third set of modules includes at least one of (i) a manager login module 404, executed by the third processor, configured to receive login details of the managing personnel account and login to the managing personnel account upon receiving login details from the managing personnel (ii) a manual task generation module 406, executed by the third processor, configured to generate a task manually by the managing personnel (iii) a manual task allotment module 408, executed by the third processor, configured to allot the manual task to the concerned employee according to an embodiment herein (iv) a monitoring module 410, executed by the third processor 402, configured to monitor the tasks allotted to the employee based on the status of the tasks received from the employee device 106 or (v) a manager logout module 412, executed by the third processor, configured to logout from the managing personnel account by the managing personnel. In one embodiment, the managing personnel generates at least one meeting task using the manual task allotment module 408 of the manager device 108. In an embodiment, the manager device 108 includes a color-coded interface.
[0065] FIG. 5 is a flow chart that illustrates a process of room booking according to an embodiment herein. At step 502, a reservation request or a room booking request is received from a customer through at least one of guest device 110 or any computing device 102. In one embodiment, the reservation request or room booking request is at least one of a new room

booking request or a room booking extension request. At step 504, customer details are obtained from the customer through at least one of guest device 110 or any computing device 102. In one embodiment, the customer details include but not limited to name of customer, age, address, at least one customer preference includes a room type, a bed type, a floor type, a staying period, a check-in time, a check-out time, or a number of rooms, number of the persons, gender of the persons, category of the persons such as adult, child, baby etc. At step 506, is the rooms available or not is checked based on the customer details. If YES, at step 508, the rooms are allotted to the customer. If NO, at step 510, the reservation request/room booking request is directed to an overbooking list. At step 512, the rooms are booked for the customer by receiving the advance payment. At step 514, the customer check-in the booked room. At 516, whether any room booking is canceled or not, is checked. If YES, the room is allotted to the room booking request in the overbooking list based on first come first serve.
[0066] FIG. 6A and 6B are a flow chart that illustrates a process of room allotment according to an embodiment herein. At step 602, the room allotment process is started. At step 604, a list of rooms that includes at least one of a number of vacant rooms or number of occupied rooms in each floor based on the at least 1) room type 2) bed type 3) status of tasks associated with the rooms 4) status of the room 5) reservation status or 6) occupancy status is prepared. In one embodiment, the task associated with rooms includes at least one of housekeeping task or a maintenance task. In one embodiment, the status of the tasks includes a task starting time, progress of the task, a task completion time and a reason for the delay to complete the task, if the task is delayed. In one embodiment, the status of the room includes out of order and in order. In one embodiment, the reservation status includes reserved or unreserved. In one embodiment, the occupancy status includes occupied and non-occupied. At step 606, it is checked, whether the number of rooms available is greater than or equal to the number of rooms required. If NO, at step 608, the overbooking is initiated. If YES, at step

610, it is checked whether the number of rooms available is greater than one. If NO, at step 612, the room is selected on the lowest floor. If YES, at step 614, a floor list is prepared that includes (i) active floors list (ii) meta active floors list and (iii) inactive floors list. In one embodiment, (i) the active floor list including a list of floors that includes occupied rooms/actual bookings and at least one desired vacant room or where the actual booking(s) has very high possibility of cancelled that can be assigned to the future bookings (ii) the meta active floor list including a list of floors that includes no actual bookings/non-occupied rooms but expected to be booked during the staying period of the customer or list of floors that includes no vacant rooms but expected to be vacant or high possibility of cancelled during the staying period of the customer and booked with less possibility of cancelled during the staying period of the customer and (iii) the inactive floor list including a list of floors that includes either no actual bookings/non-occupied rooms and not to be booked during the stay period of the customer or list of floors that includes no vacant rooms that is not expected to be vacant during the stay period of the customer. At step 616, the active floor lists are taken from the floor list. At step 618, it is checked whether the rooms are available in the active floor list. If YES, at step 620, the rooms are determined in the lowest floor according to the layout of the hospitality establishment by iterating through the active floor list using the k-nearest neighbor algorithm. At step 622, the determined rooms are allotted to the customer. If NO, at step 624, the meta active floor lists are taken from the floor list. At step 626, it is checked whether the rooms are available in the meta active floor list. If YES, at step 628, it is checked whether the rooms are available on the same floor or not. If YES, step 622 is executed. If NO, at step 630, the inactive floor list is taken from the floor list. At step 632, it is checked whether the number of required rooms are available in the inactive floor list. If YES, step 622 is executed. If NO, at step 634, the number of rooms available on the lowest floor is checked and allotted the rooms available on the lowest floor and the number of rooms needs to be allotted is calculated by subtracting

the number of allotted rooms from the total number of rooms needs to be allotted. At step 636, it is checked whether the allotted rooms are less than the number of rooms needs to be allotted. If YES, from step 616 to step 636 are repeated.
[0067] FIG. 7 is a flow chart that illustrates a process of task allotment to an employee according to an embodiment herein. At step 702, a list of employees logged in for the day is retrieved. At step 704, the list of logged-in employees including work department same as the work department of the task to be allocated is filtered. At step 706, a list that includes employees with work department as same as the work department of the task to be allocated is prepared. At step 708, employees in the list are arranged in an ascending order based on the employees (i) incomplete task count for the day (ii) distance between the real-time working floor and new task's working floor (iii) turnaround time of the employees and (iv) completion time of an incomplete task. At step 710, a first employee from the arranged list is selected and it is checked whether the first employee's today's incomplete work floor is equal to the work floor of the task to be allocated. If YES, at step 712, the task is allocated to the first employee. If NO, at step 714, it is checked whether the first employee's meta active work floor is equal to the new task's work floor. In one embodiment, the meta active work floor of the employee includes employees with work department as same as the work department of the task to be allocated and no active work, but work can be assigned for required work floor as predicted using ML model. If YES, at step 716, if the task's work floor and the first employee's work floor is same, the real-time location of the first employee is located using an indoor positioning method. In one embodiment, the indoor positioning method includes at least one of magnetic positioning method, Wi-fi/Li-fi access point based positioning, Bluetooth beacons based positioning or dead reckoning. If the real-time location of the selected employee is tracked, step 712 is executed. If NO, at step 718, it is checked whether the first employee's preferred work floor distance matches the work floor of the task to be allocated. If YES, step 716 is

executed and then step 712 is executed. If NO, at step 720, it is checked whether the employee count in the prepared list is greater than zero or not. If YES, step 708 to 720 are repeated. If NO, at step 722, an employee from the list having a minimum number of incomplete works and minimum floor distance from the floor of allocated tasks is selected and step 716 is executed then step 712 is executed.
[0068] FIG. 8 is a flow chart that illustrates a process of time management of the allocated tasks according to an embodiment herein. At step 802, the task completion time for each task is received from a machine learning model. In one embodiment, the machine learning model predicts task completion time for the task based on the historical task completion time for the various task by various employees. At step 804, tasks with predetermined task completion time are allotted to the different employee as per task allotment process according to an embodiment herein. At step 806, it is checked whether the distribution of task to the employees is equal or not. If YES, at step 810, the tasks are distributed to different employees. If NO, at step 808, the tasks are balanced using the load balancing method and execute step 810. At step 812, the countdown for tasks is started, if the employee accepted the task. At step 814, it is checked whether the task is completed or not in predetermined task completion time. If NO, at step 816, the time starts in red color and a delay report for the assigned task is created. If YES, at step 818, the status of the task is marked as completed. At step 820, the value of task completion time is changed by the K-NN algorithm based on the actual task completion time.
[0069] FIG. 9 is a flow chart that illustrates a process of table reservation in a restaurant for a customer according to an embodiment herein. At step 902, a table reservation request is received from the guest device 110. At step 904, it is checked whether the empty table is available or not. If YES, at step 906, the table is reserved or booked. If NO, at step 908, the table reservation request is auto queued. At step 910, the table is reserved for the auto queued

list when the table is available. At step 912, an order is placed from the reserved table from at least one of (i) the guest device 110 (ii) a table device or (iii) a waiter/server device. At step 914, the order is queued as per first come first serve basis, in case of concurrency of orders, orders with a greater number of covers will be having higher priority. At step 916, the order is displayed in a digital display of a kitchen. At step 918, the order is prepared as per the queue. At step 920, the notification is issued to the server/waiter to serve the order to the table. At step 922, the order is served to the table.
[0070] FIG. 10 is a flow chart that illustrates a process of allotment of tasks to the Food and Beverage (F&B) department employees when an order is received from a customer according to an embodiment herein. At step 1002, a table is reserved for a customer through the guest device 110. At step 1004, an order is placed from the customer from the reserved table through at least one of (i) the guest device 110, (ii) a server/waiter device or (iii) a table device. At step 1006, a list of the chef in ascending order on the basis of pendency of work, turnaround time and completion time of incomplete task is prepared using the ML model. At step 1008, it is checked whether the first chef on the list is available or not. If YES, at step 1010, the order preparation task is allotted to the first chef in the list. If NO, at step 1012, select the second chef in the list and repeat the steps 1008 and 1010. At step 1014, the order is prepared by the allotted chef. At step 1016, a list of waiters is prepared in ascending order based on the distance between the real-time task's location and table location, the turnaround time of the waiters and completion time of an incomplete task. At step 1018, it is checked whether the first waiter is available or not to serve the order. If YES, at step 1020, the order is served for the table by the first waiter. If NO, the second waiter from the list is selected and steps 1018 and 1020 is repeated. In an embodiment, the turnaround time of the chef and waiter is predicted by the ML model. In one embodiment, the real-time location of a waiter is identified using the IPS method.

[0071] FIG. 11A and 1 IB is a flow chart that illustrates a method for automated room and employee location-based task allocation in a hospitality establishment using a server 104 and an employee device 106, according to an embodiment herein. At step 1102, a room information of the hospitality establishment is updated in a room information database 218 in real-time. At step 1104, the task information is updated in a task information database 220. At step 1106, availability of rooms is determined upon receiving a customer room booking request by interrogating the room information database 218, the customer room booking request includes at least one of a new room booking request or a room booking extension request. At step 1108, demand for rooms is predicted using a machine learning model by analyzing at least one of (i) booking feature of previous and current bookings (ii) events in the city (iii) a location of hospitality establishment, (iv) weather data or (v) other demographic factors. At step 1110, a floor list is generated by analyzing on previous bookings, current bookings and predicted demand for rooms when the number of rooms available is greater than or equal to the number of rooms required, the floor list includes at least one of (i) an active floor list with at least one of desired vacant room, (ii) a meta active floor list with at least one of desired vacant room or (iii) an inactive floor list with at least one of desired vacant room is generated. At step 1110, room(s) in the lowest floor is determined according to the layout of the hospitality establishment by iterating through the at least one of the active floor list, the meta active floor list or the inactive floor list and the determined rooms are allotted to the customer. At step 1112, a task is generated based on at least one of (i) customer's location proximity to the hospitality establishments at the day of check-in (ii) a customer input from the guest device 110 at the time of stay in the hospitality establishment or (iii) employee input from an employee device 106. At step 1114, an employee is determined to allot the generated by analyzing least one of (a) employee's work department (b) employee's incomplete task counts for the day (c) employee's real-time task's location (d) employee's turnaround time or (e) completion time of

the incomplete tasks by interrogating the task and employee information database 220 and the task is allotted to the employee device 106 associated with the determined employee.
[0072] In one embodiment, the employee is determined to allot the task generated by, fetching a list of employees who comprises work department same as the work department of the task to be allocated from the task and employee information database 220, categorizing the list of employees in an ascending order on the basis of employees (i) incomplete task counts for the day (ii) distance between a real time current task's location and location of task to be allotted (iii) turnaround time of the employees and (iv) completion time of the incomplete task, and allotting the task to the employee in the list by selecting a first employee in the list and allotting the task with predetermined task completion time to the first employee in the list.
[0073] In one embodiment, method further comprises determining rooms nearest to each other in the lowest floor according to the layout of the hospitality establishment by iterating through the at least one of the active floor list, the meta active floor list or the inactive floor list when the new room booking request is a group room booking request.
[0074] FIG. 12 is a schematic drawing illustrates a hardware configuration of computer architecture in accordance with the embodiments herein. The computer architecture includes at least one processor or central processing unit (CPU) 10. The CPUs 10 are interconnected via system bus 12 to various devices such as a random access memory (RAM) 14, read-only memory (ROM) 16, and an input/output (I/O) adapter 18. The I/O adapter 18 can connect to peripheral devices, such as disk units 11 and tape drives 13, or other program storage devices that are readable by the system. The system can read the inventive instructions on the program storage devices and follow these instructions to execute the methodology of the embodiments herein. The system further includes a user interface adapter 19 that connects a keyboard 15, mouse 17, speaker 24, microphone 22, and/or other user interface devices such as a touch screen device (not shown) or remote control to the bus 12 to gather user input. Additionally, a

communication adapter 20 connects the bus 12 to a data processing network 25, and a display adapter 21 connects the bus 12 to a display device 23 which may be embodied as an output device such as a monitor, printer, or transmitter, for example.
[0075] According to an embodiment herein, the system and method fill the rooms in the hospitality establishment floor wise to preserve energy and time of employees performing the task. The allotment of rooms floor wise and rooms nearest to each other saves energy and time of employees performing the task. The system and method always keep in mind to allocate different rooms of a single reservation closest in the lowest floor. Further, the system and method allots rooms at the time of reservation hence it minimizes manual allotment of the rooms by the front desk at the day of check-in which is a time-consuming and labor-intensive process. Further, the system provides a sense of surety to the customers about which rooms they are going to stay once they check-in. Further, the system and method generate tasks automatically based on booking events, customer input and employee input and allocates the task to the employee based on real-time task's location, no. of incomplete task counts, a turnaround time of the employee, that will save time and energy of the employee. The automatic task allotment according to an embodiment herein reduces the time to complete the work hence the customer will be satisfied.
[0076] It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments.

CLAIMS
1. A system (100) for automated room and employee location-based task allocation in a hospitality establishment using a server (104) and an employee device (106) wherein,
the server (104) comprises a first memory to store a first set of modules and a first processor that is configured to execute the first set of modules, wherein the server (104) is connected to the employee device (106) through a communication network (112);
wherein the employee device (106) associated with each employee comprises a second memory to store a second set of modules and a second processor that is configured to execute the second set of modules to perform the desired functions of the employee device (106),
characterized in that the first set modules comprise,
a room information database updation module (202), executed by said first
processor, configured to update in real-time, a room information of the hospitality
establishment in a room information database (218);
a task information database updation module (204), executed by said first
processor, configured to update in real-time, a task information associated with the
hospitality establishment in a task information database (220), wherein the task
information is mapped with each employee.
a room availability determination module (206), executed by said first
processor, configured to determine availability of rooms upon receiving a customer
room booking request by interrogating the room information database (218), wherein
the customer room booking request comprises at least one of a new room booking
request or a room booking extension request;

a room demand prediction module (208), executed by said first processor, configured to predict demand for rooms in the hospitality establishment, using a machine learning model, by analyzing at least one of (i) a booking feature of previous and current bookings (ii) events in the city (iii) a location of the hospitality establishment, (iv) weather data or (v) other demographic factors from a first database (222) in the server (104);
a floor list generation module (210), executed by said first processor, configured to generate, by analyzing previous bookings, current bookings and predicted demand for rooms from the room demand prediction module (208), at least one of (i) an active floor list with at least one of desired vacant room, (ii) a meta active floor list with at least one of desired vacant room or (iii) an inactive floor list with at least one of desired vacant room when the number of rooms available is greater than or equal to number of rooms required in the customer room booking request, wherein the details of the previous bookings and current bookings are stored in the first database (222) in the server (104);
a room allotment module (212), executed by said first processor, configured to (i) determine room(s) in the lowest floor according to the layout of the hospitality establishment by iterating through the at least one of the active floor list, the meta active floor list or the inactive floor list and (ii) allot the determined room(s) to the customer;
an automatic task generation module (214), executed by said first processor, configured to generate a task based on at least one of (i) customer's location proximity to the hospitality establishments at the day of check-in (ii) a customer input from a guest device (110) at the time of stay in the hospitality establishment or (iii) an employee input from the employee device (106); and

a task allotment module (216), executed by said first processor, configured to (i) determine an employee to allot the generated task by analyzing at least one of (a) employee's work department (b) employee's incomplete task counts for the day (c) employee's real-time task's location (d) employee's turnaround time or (e) completion time of the incomplete tasks by interrogating the task information database (220) and (ii) allot the generated task to the employee device (106) associated with the determined employee.
2. The system (100) as claimed in claim 1, wherein,
(i) the active floor list comprises a list of floors that comprises occupied rooms/actual bookings and at least one desired vacant room;
(ii) the meta active floor list comprises a list of floors that comprises no actual bookings/non-occupied rooms but expected to be booked during the staying period of the customer or list of floors that comprises no vacant rooms but expected to be vacant during the staying period of the customer; and
(iii) the inactive floor list comprises a list of floors that comprises either no actual bookings/non-occupied rooms but not expected to be booked during the stay period of the customer or list of floors that comprises no vacant rooms that are not expected to be vacant during the stay period of the customer.
3. The system (100) as claimed in claim 1, wherein the second set of modules of the employee
device (106) comprises at least one of
(i) an employee login module (304), executed by said second processor, configured to receive login details of an employee account and login to the employee account upon receiving the login details;

a task receiving module (306), executed by said second processor, configured
to receive a task from the server (104) or a manager device (108);
a task accepting or rejecting module (308), executed by said second processor,
configured to allow to accept or reject the task allotted from the server (104) or
the manager device (108) by the employee;
a task status updating module (310) executed by said second processor,
configured to allow to update the status of the task accepted by the employee;
an update on task receiving module (312), executed by said second processor,
configured to, receive an update on task from the server (104) or the manager
device (108);
a work timing and break timing entering and transmitting module (314),
executed by said second processor, configured to receive the work timings and
break timings of the employee and transmit the work timings and break timings
of the employee to the server (104) and the manager device (108);
a delay reporting module (316), executed by said second processor, configured
to receive and report a reason for the delay of the task by the employee, if so to
the server (104) or the manager device (108);
a display module (318), executed by said second processor, configured to
display a list of tasks accepted by the employee in predetermined color
according to the status of the task;
an entity status marking module (320), executed by said second processor,
configured to allow to mark and report an entity of room as working or as not
working to the server (104) or the manager device (108);
a signal receiving module (322), executed by said second processor, configured
to receive at least one of (a) a Bluetooth signal from Bluetooth beacons (114)

(b) a Wi-Fi/Li-Fi signal from Wi-Fi/Li-Fi access points (116) or (c) a magnetic
field data of the hotel establishment;
(xi) a signal transmitting module (324), executed by said second processor, configured to transmit at least one of (a) a Bluetooth signal from Bluetooth beacons (114) (b) a Wi-Fi/Li-Fi signal from Wi-Fi/Li-Fi access points (116) or
(c) a magnetic field data of the hotel establishment to the server (104) or the
manager device (108); or
(xii) an employee logout module (326), executed by said second processor, configured to allow to logout from the employee account by the employee.
4. The system (100) as claimed in claim 1, wherein the room allotment module (212) is executed by said first processor, further configured to determine rooms that are nearest to each other in the lowest floor according to the layout of the hospitality establishment by iterating through the at least one of the active floor list, the meta active floor list or the inactive floor list when the new room booking request is a group room booking request.
5. The system (100) as claimed in claim 1, wherein the first set of modules further comprises an employee location tracking module, executed by the first processor, configured to determine real-time task's location of the employee by
receiving at least one of (a) a Bluetooth signal from Bluetooth beacons (114) (b) a Wi-Fi/Li-Fi signal from Wi-Fi/Li-Fi access points (116) or (c) a magnetic field data of the hospitality establishments from the signal transmitting module (324) of the employee device (106); and
determining the real-time task's location of the employee by processing at least one of (a) the Bluetooth signal from Bluetooth beacons (114) (b) the Wi-Fi/Li-Fi signal from

Wi-fi/Li-fi access points (116) or (c) the magnetic field data of the hospitality establishments.
6. The system (100) as claimed in claim 1, wherein the customer's location proximity to the hospitality establishment is determined by tracking the Global Positioning System (GPS) of the guest device (110) associated with the customer.
7. The system (100) as claimed in claim 1, wherein the first set of modules further comprises a cancellation prediction module, executed by said first processor, configured to predict cancellation probability of each booking comprising at least one of (i) current booking or (ii) expected future booking, by analyzing at least one of (i) booking feature (ii) customer profile or (iii) historical data of customers.
8. The system (100) as claimed in claim 3, wherein the manager device (108) comprises a third memory (404) to store a third set of modules and a third processor that is configured to execute the third set of modules to perform desired functions of the manager device.
9. A method for automated room and employee location-based task allocation in a hospitality establishment using a server (104) and an employee device (106) comprising:
updating, in real-time, a room information database (218) with a room information of the hospitality establishment;
updating, in real-time, a task information database (220) with a task information associated with the hospitality establishment, wherein the task information is mapped with each employee;

determining availability of rooms upon receiving a customer room booking request by interrogating the room information database (218), wherein the customer room booking request comprises at least one of a new room booking request or a room booking extension request;
predicting demand for rooms in the hospitality establishment using a machine learning model by analyzing on at least one of (i) booking feature of previous and current bookings (ii) events in the city (iii) a location of hospitality establishment, (iv) weather data or (v) other demographic factors from a first database (222) in the server (104);
generating, by analyzing previous bookings, current bookings and predicted demand for rooms, at least one of (i) an active floor list with at least one of desired vacant room, (ii) a meta active floor list with at least one of desired vacant room or (iii) an inactive floor list with at least one of desired vacant room when the number of rooms available is greater than or equal to number of rooms required in the customer room booking request, wherein the details of the previous bookings and current bookings are stored in the first database (222) in the server (104);
determining room(s) in the lowest floor according to the layout of the hospitality establishment by iterating through the at least one of the active floor list, the meta active floor list or the inactive floor list and allotting the determined room(s) to the customer;
generating a task based on at least one of (i) customer's location proximity to the hospitality establishments at the day of check-in (ii) a customer input from a guest device (110) at the time of stay in the hospitality establishment or (iii) an employee input from the employee device (106); and
determining an employee to allot the generated task by analyzing at least one of (a) employee's work department (b) employee's incomplete task counts for the day (c)

employee's real-time task's location (d) employee's turnaround time or (e) completion time of the incomplete tasks by interrogating the task and employee information database (220) and allotting the generated task to the employee device (106) associated with the determined employee.
10. The method as claimed in claim 9, wherein in the method further comprises determining rooms nearest to each other in the lowest floor according to the layout of the hospitality establishment by iterating through the at least one of the active floor list, the meta active floor list or the inactive floor list when the new room booking request is a group room booking request.