Description:Field of the Invention
This invention relates to IOT and AI assisted automated feedback system for hospitality industry.
Background of the Invention
Digitalization helps in achieving sustainable development through resilient infrastructure in all applications. In the hospitality industry, for instance, resilient infrastructure based on digital technologies plays a key role in providing quality service which will lead to positive customer feedbacks. Hospitality services have been enhanced by digital technology that uses real-time data to make rational choices. Several theoretical and empirical studies have broadened the importance of digital technologies in the hotel industry. However, there is still a lack of research concerning feedback systems in hospitality with regards to applications of digital technologies. With that motivation in mind, this study aims at laying out an explanation of the significance and application of Internet of Things (IoT), artificial intelligence (AI), cloud computing, and big data approach for customer quality and satisfaction. Additionally, we discussed how important each technology is and its application towards achieving e-customer quality and satisfaction. The AI-based system has been identified as collecting input data from various popular sites and then comparing it with different algorithm using neural network. It has been found out by this research that artificial intelligence and personnel quality of service affect customers’ satisfaction and loyalty. Finally, the study makes several recommendations for such as creating dedicated hardware to receive actual feedback from the customer on a wide scale in order to increase accuracy in future.
US11651412B2 In selected embodiments a recommendation generator builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venue are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.
RESEARCH GAP: Enhanced Customer Satisfaction: Utilizing real-time data and AI algorithms, this system offers personalized recommendations and services, thereby significantly enhancing customer satisfaction and loyalty.
US11151617B2 A recommendation generator builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venues are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.
RESEARCH GAP: Improved Operational Efficiency: Through automating feedback collection as well as analysis, operations are streamlined with minimal human intervention while employees concentrate on availing high quality services to the customers.
US9208443B2 In selected embodiments a recommendation generator builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venue are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.
RESEARCH GAP: Real-Time Decision Making: IoT sensors have been integrated into an artificial intelligence for real time monitoring that enables immediate response to customer needs as well as problems hence improving the overall guest experience.
US8732101B1 In certain implementations, a system may receive attribute data corresponding to attributes of a plurality of users and to one or more venues for which the plurality of users has an affinity. A user personality matrix may be calculated for one or more of the plurality of users based on interrelation nodal link strengths between the one or more users and the venues. The user personality matrices may be merged to calculate a combined personality matrix representing a unified taste profile for the one or more users. A candidate list of venues having the highest link strength with the combined personality matrix may be determined. One or more recommended venues from the candidate list of venues that have the strongest links to the combined personality matrix may be determined, and recommendation data corresponding to the recommended venues may be output.
RESEARCH GAP: Data-Driven Insights: The system gathers vast amounts of information that is analyzed and provides actionable insights about what customers prefer and how they behave, which can be used to personalize services and marketing strategies.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. This invention relates to IOT and AI assisted automated feedback system for hospitality industry.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
This given architecture is for an IoT and AIML-assisted automated feedback system. It shows an example of how these technologies can be applied to create a robust infrastructure that enables real-time data processing and intelligent decision making.
The main unit of this system is Nvidia Nano Jetson – a powerful AI computing platform which acts as the central processing unit. With touch screen interface, customers can give their feedback directly through which onboard Convolutional Neural Networks (CNNs) filters it down before processing any message, thus making sure what comes out represents customer experience accurately. The CNNs process visual data captured by the Full HD Camera enabling it to identify and respond to visual cues thereby enhancing the precision of feedback retrieval.
Combining peripheral devices such as keyboards and mice allows for manual data input, while the speaker gives auditory feedback making it a multi-dimensional interactive experience to employees and visitors, alike. The Wi-Fi module maintains the network connection of the system thus enhancing seamless transfer of information to cloud-based storage and analytics platforms. This real-time processing is vital for analysis so that the system can make smart choices based on most current data. It is this microcontroller which assumes critical roles in managing various sensors connected to this system. For example, exhaust fan is controlled at an optimum temperature thus preventing overheating therefore ensuring durability of a device. Lastly, smoke/fire sensors and motion sensors are integral safety and automation devices associated with any system that provides warnings as well as actuating necessary responses to guarantee secure environment.

The device has a battery with a 12v 3amp Lithium Polymer Battery, and charger which belongs to the AC outlet, thus the device remains functional even after power failures making it reliable and stable in critical hospitality settings. The AI component is built to gather input data from diverse sources such as common websites and direct customers’ interactions, and then process this data through neural network algorithms. A typical example is that of comparing against benchmark, so as to understand about customer’s general satisfaction and service quality. This approach provides room for improvement in service delivery hence increased customer satisfaction and loyalty. The hospitality industry stands able to analyze feedback on a grand scale, in real time so as to respond promptly to every guest’s desires towards enhanced service quality and happy clients.
The diagram indicates the flow of developing and refining a recommendation system through machine learning. The journey starts with Data Collection stage, which is all about obtaining relevant data. This is important since the quality and quantity of data collected influence how well the model performs. The gathered information then goes through Preprocessing where it is cleaned, transformed and shaped for analysis purpose. It will make sure that data consistency is in place, without any interference by anomalies, which could hinder performance. This is performed before statistical analysis takes place to help improve on datasets properties like skewness and outliers.
Techniques done during pre-processing include normalization, missing values handling and categorical variables encoding aimed at improving the quality of data as well as making it ready for effective analysis. The data is Split into training and testing sets after preprocessing. The model is Initialized and Trained with the usage of a training set, where the machine learning algorithm learns patterns and relationships in the data. During training, appropriate algorithms are selected and hyperparameters are tuned for optimum model performance. In the Evaluation phase, the testing set is used to assess how well the model has performed. This step helps in determining how good it can generalize to unseen instances using metrics such as accuracy, precision, recall, and F1 score. If results are satisfactory, then we proceed. On the contrary, parameters need to be adjusted so as to retrain the model thus creating an iterative loop for evaluation and adjustment until desired performance is achieved. Thus this cycle ensures that our model is robust enough to make accurate predictions.
After the acceptable model is found, the User Input of a system begins. Personalization of recommendations cannot be achieved without this input. To accomplish this, the system performs Feature Extraction on user input to obtain relevant characteristics that are used to Compute Similarity between items. It is through this similarity computation that recommendations that are suitable to the user’s preferences are developed by the system. In addition, collaborative filtering, content-based filtering or hybrid techniques can be employed in order to improve recommendation accuracy. These recommended items go to users who in turn give feedback on them forming Feedback Loop. Every time real world interactions between users take place and as a result it helps refine and adjust model hence a system must have Feedback Loop thereby enabling it always effective over time and accurate in terms of predictions with regards to its clients’ desires as made by software like MS Office Excel, Java etc…Incorporating user feedback enables the system to dynamically adapt to changing user preferences and emerging trends; thus improving overall user experience and satisfaction levels.
In essence, the flowchart for this method helps to show that machine learning model development is cyclical, starting with collecting and preprocessing data and ending with training, evaluating and improving models. It is about constantly getting better by taking feedback from users as well as iterative improvement in order to develop a complete recommendation system. All these steps aim at giving personalized recommendations that are relevant because of combining data power, machine learning algorithms and so on all this serves to make user experience better The goal here is to utilize data and machine learning algorithms and thereby providing personalized, suitable recommendations that will enhance overall user satisfaction. This technique ensures that the recommendation platform keeps changing and becoming better resulting into increased user interest and happiness in various avenues such as e-commerce platforms, streaming applications or personalized content delivery systems.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
Figure 1: Basic Structure of the working of each component
Figure 2: Detailed structure of the device with power management
Figure 3: Flowchart of the working of the model
Figure 4: Flowchart of the working of the model
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
This given architecture is for an IoT and AIML-assisted automated feedback system. It shows an example of how these technologies can be applied to create a robust infrastructure that enables real-time data processing and intelligent decision making.
The main unit of this system is Nvidia Nano Jetson – a powerful AI computing platform which acts as the central processing unit. With touch screen interface, customers can give their feedback directly through which onboard Convolutional Neural Networks (CNNs) filters it down before processing any message, thus making sure what comes out represents customer experience accurately. The CNNs process visual data captured by the Full HD Camera enabling it to identify and respond to visual cues thereby enhancing the precision of feedback retrieval.
Combining peripheral devices such as keyboards and mice allows for manual data input, while the speaker gives auditory feedback making it a multi-dimensional interactive experience to employees and visitors, alike. The Wi-Fi module maintains the network connection of the system thus enhancing seamless transfer of information to cloud-based storage and analytics platforms. This real-time processing is vital for analysis so that the system can make smart choices based on most current data. It is this microcontroller which assumes critical roles in managing various sensors connected to this system. For example, exhaust fan is controlled at an optimum temperature thus preventing overheating therefore ensuring durability of a device. Lastly, smoke/fire sensors and motion sensors are integral safety and automation devices associated with any system that provides warnings as well as actuating necessary responses to guarantee secure environment.
The device has a battery with a 12v 3amp Lithium Polymer Battery, and charger which belongs to the AC outlet, thus the device remains functional even after power failures making it reliable and stable in critical hospitality settings. The AI component is built to gather input data from diverse sources such as common websites and direct customers’ interactions, and then process this data through neural network algorithms. A typical example is that of comparing against benchmark, so as to understand about customer’s general satisfaction and service quality. This approach provides room for improvement in service delivery hence increased customer satisfaction and loyalty. The hospitality industry stands able to analyze feedback on a grand scale, in real time so as to respond promptly to every guest’s desires towards enhanced service quality and happy clients.
The diagram indicates the flow of developing and refining a recommendation system through machine learning. The journey starts with Data Collection stage, which is all about obtaining relevant data. This is important since the quality and quantity of data collected influence how well the model performs. The gathered information then goes through Preprocessing where it is cleaned, transformed and shaped for analysis purpose. It will make sure that data consistency is in place, without any interference by anomalies, which could hinder performance. This is performed before statistical analysis takes place to help improve on datasets properties like skewness and outliers.
Techniques done during pre-processing include normalization, missing values handling and categorical variables encoding aimed at improving the quality of data as well as making it ready for effective analysis. The data is Split into training and testing sets after preprocessing. The model is Initialized and Trained with the usage of a training set, where the machine learning algorithm learns patterns and relationships in the data. During training, appropriate algorithms are selected and hyperparameters are tuned for optimum model performance. In the Evaluation phase, the testing set is used to assess how well the model has performed. This step helps in determining how good it can generalize to unseen instances using metrics such as accuracy, precision, recall, and F1 score. If results are satisfactory, then we proceed. On the contrary, parameters need to be adjusted so as to retrain the model thus creating an iterative loop for evaluation and adjustment until desired performance is achieved. Thus this cycle ensures that our model is robust enough to make accurate predictions.
After the acceptable model is found, the User Input of a system begins. Personalization of recommendations cannot be achieved without this input. To accomplish this, the system performs Feature Extraction on user input to obtain relevant characteristics that are used to Compute Similarity between items. It is through this similarity computation that recommendations that are suitable to the user’s preferences are developed by the system. In addition, collaborative filtering, content-based filtering or hybrid techniques can be employed in order to improve recommendation accuracy. These recommended items go to users who in turn give feedback on them forming Feedback Loop. Every time real world interactions between users take place and as a result it helps refine and adjust model hence a system must have Feedback Loop thereby enabling it always effective over time and accurate in terms of predictions with regards to its clients’ desires as made by software like MS Office Excel, Java etc…Incorporating user feedback enables the system to dynamically adapt to changing user preferences and emerging trends; thus improving overall user experience and satisfaction levels.
In essence, the flowchart for this method helps to show that machine learning model development is cyclical, starting with collecting and preprocessing data and ending with training, evaluating and improving models. It is about constantly getting better by taking feedback from users as well as iterative improvement in order to develop a complete recommendation system. All these steps aim at giving personalized recommendations that are relevant because of combining data power, machine learning algorithms and so on all this serves to make user experience better The goal here is to utilize data and machine learning algorithms and thereby providing personalized, suitable recommendations that will enhance overall user satisfaction. This technique ensures that the recommendation platform keeps changing and becoming better resulting into increased user interest and happiness in various avenues such as e-commerce platforms, streaming applications or personalized content delivery systems.
An IoT and AI assisted automated feedback system for hospitality industry comprises a plurality of Edge Devices (1.1, 1.2, 1.N), Wifi Module (2), Information Collector (3), Cloud Server (4), LCD Screen Web App/ Mobile App (5), Content-based filtering (6), Conventional neural networks (CNNs) (7), FHD camera (1280 x 1080p) (8), Nvidia Nano Jetson (9), Keyboard (10), Mouse (11), Speaker (12), Exhaust fan (13), Smoke/Fire Sensors (MQ-2 gas sensor) (14), Microcontroller (15), Motion Sensor (16), 12v 3amp Lithium Polymer (battery) (17), Charger (18), AC outlet (19) and Changing Current (20) wherein the data collection module collects information from IoT sensors, cameras and user interactions within the hospitality environment so as to provide comprehensive acquisition of all required data.
In another embodiment the central processing unit enhances quality assurance through application of normalization techniques, handling missing values, encoding categorical variables among other methods.
In another embodiment With touch screen interface, customers can give their feedback directly through which onboard Convolutional Neural Networks (CNNs) filters it down before processing any message, thus making sure what comes out represents customer experience accurately.
In another embodiment the keyboards and the mice allows for manual data input, while the speaker gives auditory feedback making it a multi-dimensional interactive experience to employees and visitors, alike.
In another embodiment the Wi-Fi module maintains the network connection of the system thus enhancing seamless transfer of information to cloud-based storage and analytics platforms.
In another embodiment the device has a battery with a 12v 3amp Lithium Polymer Battery, and charger which belongs to the AC outlet, thus the device remains functional even after power failures making it reliable and stable in critical hospitality settings.
ADVANTAGES OF THE INVENTION
Scalability: It is a scalable system where the amount of data and user interactions can be controlled for small luxury hotels and large hotel chains.
Cost Reduction: The automation of repetitive tasks as well as better resource management in the system go a long way to reducing operating expenses such as manpower, energy, and maintenance.
Predictive Maintenance: By doing so, IoT sensors will enable monitoring the conditions of hotel facilities and equipment thereby predicting when to perform repairs before failures occur. Therefore, downtimes are reduced while repair costs decrease.
Enhanced Security: Such continuous monitoring and analysis of data help identify any possible threats or even abnormality that could result to guests’ or staff’s insecurity.
Environmental Sustainability: The eco-friendly initiatives require efficient resource management and predictive maintenance which involve waste reduction alongside optimized use of energy.
Competitive Advantage: Implementing advanced technologies like AI and IoT distinguishes the hospitality business from competitors, attracting tech-savvy customers and positioning the business as a leader in innovation.
, Claims:1. An IoT and AI assisted automated feedback system for hospitality industry comprises a plurality of Edge Devices (1.1, 1.2, 1.N), Wifi Module (2), Information Collector (3), Cloud Server (4), LCD Screen Web App/ Mobile App (5), Content-based filtering (6), Conventional neural networks (CNNs) (7), FHD camera (1280 x 1080p) (8), Nvidia Nano Jetson (9), Keyboard (10), Mouse (11), Speaker (12), Exhaust fan (13), Smoke/Fire Sensors (MQ-2 gas sensor) (14), Microcontroller (15), Motion Sensor (16), 12v 3amp Lithium Polymer (battery) (17), Charger (18), AC outlet (19) and Changing Current (20) wherein the data collection module collects information from IoT sensors, cameras and user interactions within the hospitality environment so as to provide comprehensive acquisition of all required data.
2. The system as claimed in claim 1, wherein the central processing unit enhances quality assurance through application of normalization techniques, handling missing values, encoding categorical variables among other methods.
3. The system as claimed in claim 1, wherein with touch screen interface, customers can give their feedback directly through which onboard Convolutional Neural Networks (CNNs) filters it down before processing any message, thus making sure what comes out represents customer experience accurately.
4. The system as claimed in claim 1, wherein the keyboards and the mouse allow for manual data input, while the speaker gives auditory feedback making it a multi-dimensional interactive experience to employees and visitors, alike.
5. The system as claimed in claim 1, wherein the Wi-Fi module maintains the network connection of the system thus enhancing seamless transfer of information to cloud-based storage and analytics platforms.
6. The system as claimed in claim 1, wherein the device has a battery with a 12v 3amp Lithium Polymer Battery, and charger which belongs to the AC outlet, thus the device remains functional even after power failures making it reliable and stable in critical hospitality settings.