Tableside AI waiter for personalized guest experience

1 . A tableside artificially intelligent (AI) customer engagement system, comprising:

a processing subsystem including one or more tablet processors configured to perform data processing, one or more memory units, one or more input devices, and one or more input/output devices, each configured to communicate with the others;

a high-definition multi-touch display for interactive user interface;

a Wi-Fi module supporting low-latency, multi-node connectivity across mesh devices;

a microphone array configured to capture audio data in a full radius;

a set of high-definition cameras providing 360-degree panoramic coverage for capturing video data;

an RFID reader for device monitoring and security;

an NFC payment card reader for detecting and processing payment transactions from compatible payment instruments;

a customer recognition module configured to process a plurality of customer recognition datasets collected from a plurality of sensors and wherein the module is further configured to analyze the said datasets using one or more recognition models to identify a returning customer;

a speech recognition and response unit configured to transcribe speech to text to determine intent and entities and generate context aware responses;

a personalized recommendation module configured to analyze a plurality of datasets using AI models to generate personalized menu items recommendations;

a backend database configured to store and manage a plurality of datasets with a caching module that selectively retrieves either cached or freshly updated menu data based on availability;

a fulfillment terminal communication interface configured to relay order and payment information to and from a connected fulfillment terminal or kitchen display system in real-time;

a secure communication interface to facilitate data exchange between the backend and the tableside AI device;

a central cloud processor and storage system configured to host and execute AI models remotely and store relevant data; and

a detachable wireless charging pad supporting up to 15 Watts of wireless charging for compatible devices.

2 . The system of claim 1 , wherein the customer recognition models of the customer recognition module include facial feature extraction from captured video data, voice feature extraction from captured audio data, and hardware identifier detection via QR code, Wi-Fi, or Bluetooth, integrated with a geolocation module to improve customer identification accuracy.

3 . The system of claim 1 , wherein the natural language processing and response unit further comprises of a transcription module configured to convert speech into text and a chatbot module integrated with a large language model (LLM) to generate context-aware responses.

4 . The system of claim 3 , wherein the transcription module further comprises:

an audio preprocessing unit configured to trim and remove noise from the captured audio data;

a voice feature extraction module configured to extract voice features from the said audio data;

an encoder neural network to convert the said extracted features into fixed-sized vectors or encoded representations of the most important aspect of the said extracted features; and

a decoder neural network to transform the said fixed-sized vectors or encoded representations into transcribed text.

5 . The system of claim 3 , wherein the chatbot module further comprises:

an intent and entity recognition module configured to analyze the transcribed text to determine user intent and identify relevant entities;

a chat context unit configured to store and integrate a plurality of datasets including data from the backend database, data in transit at recommendation engine, items in the cart, customer data, restaurant establishment data and real-time input data; and

a response generation module to integrate the identified intent and entities and context from the plurality of datasets in the chat context unit using AI models to produce context-aware responses wherein the AI models include large language models (LLM), natural language processing (NLP), sequence-to-sequence models, or other machine learning architectures.

6 . The system of claim 5 , wherein the intent and entity recognition module further comprising:

a text pre-processing unit configured to streamline text generated by the transcription module;

an intent recognition model trained on text-intent pairs and text classification data and fine-tuned to determine customer intent; and

an entity recognition model trained on labeled entity data, token classification, and word classification, and fine-tuned to identify entities within the audio data.

7 . The system of claim 1 , wherein the advanced AI personalized recommendation System, further comprises:

a specialized database configured to store and retrieve a plurality of datasets collected from one or more sensors;

a data analysis and filtering module for processing and filtering the said stored datasets against real-time data inputs;

a ranking module integrated with predictive AI models to rank items based on real-time input data and the datasets stored in the specialized database; and

a personalization engine configured to generate personalized recommendations based on the said analysis, menu filtering and item ranking and wherein the personalization engine is further configured to verify the customer-selected items for allergens, dietary preferences, and user-defined modifiers by querying associated metadata against stored customer data via a secure communication interface.

8 . The system of claim 7 , wherein the plurality of datasets stored in the specialized database comprises:

restaurant establishment data including menu items, promotional items, and restaurant specialties;

contextual data including geolocation, weather, time, day, date, calendar events, and seasonal data retrieved via APIs; and

customer data, including customer order history, preferences, allergies, and personal information collected from one or more connected systems.

9 . A method for artificially intelligent tableside customer engagement, comprising:

detecting a wake word using deep learning-based wake word recognition or keyword spotting, wherein audio input is continuously monitored through a microphone array for wake word detection and thereby transitioning the system to a listening and processing state upon detecting the said wake word;

triggering a plurality of sensors to gather input data the said sensors including microphone, camera, hardware identifier of customer's devices and geotagging and wherein the said input data includes video data, audio data, hardware identity data of customer's devices and location history;

extracting facial features and vocal features of a customer from the captured video and audio data respectively using face detection and voice detection models and comparing the extracted face and voice features with customer data stored in the database to identify a returning customer;

comparing the collected unique hardware identifiers data of the customer's devices and geolocation data with the data stored in the database under the customer's profile for enhanced customer recognition;

retrieving profile of a returning customer stored in the database, and greeting the customer;

signing up a new customer by creating a unique customer profile using predeterminate templates and said input data;

displaying available menu items from restaurant establishment data on a display device;

processing customer's natural language input via natural language processing and transcribing the said input into text by a transcription module thereby classifying the intent and entity through an intent and entity recognition of the chatbot module to generate context-aware responses;

providing personalized menu recommendations to the customer based on AI models, the said AI models processing a plurality of data sets including customer profile data, restaurant establishment data, contextual data, and real-time input data for generating personalized recommendations;

transmitting the said personalized order information to a fulfillment terminal or kitchen display system in real-time;

re-transmitting the real-time updates from the fulfillment terminal or Kitchen Display System to the connected tableside system; and

relaying the said order information and payment details from the centrally integrated point-of-sale system to the connected tableside system.

10 . The method of claim 9 , wherein the available menu items are retrieved from the backend database and displayed on the user interface in response to a customer-initiated selection and wherein the said selection could be via an audio input or touch input.

11 . The method of claim 10 , wherein retrieving available menu items from the backend database further comprises of selecting either cached menu data or freshly updated menu data wherein the freshly updated menu data includes current menu items, pricing, availability, and dietary information and wherein the said retrieved menu items are parsed using a structured format and rendered on a user interface for interaction.

12 . The method of claim 9 , further comprising:

generating personalized customer engagement content using large language models (LLMs), wherein the LLMs process a plurality of datasets to personalize the said customer engagement content and wherein the said datasets include customer data, historical interaction data, and contextual data, and wherein the content is further displayed on the user interface for interaction.

13 . The method of claim 9 , wherein processing customer's natural language input further comprising:

determining whether the device is in a “listening for wake word” state using device state detection, and, if the device is not in the “listening for wake word” state, transferring the audio data to a transcription module that processes the audio in chunks at set intervals and generates partial transcriptions.

14 . The method of claim 13 , further comprising detecting the end of speech using a silence detection code, wherein the silence detection code utilizes an AI model, voice activity detection model, or logic to identify the end of speech within raw audio data or extracted features, and upon detecting the end of speech, transferring the audio data to the transcription module for processing.

15 . The method of claim 9 , wherein transcribing natural language voice input into text, further comprises:

extracting audio features from the natural language input through Natural Language Processing (NLP);

transforming the most relevant extracted features of the said input into fixed-sized vectors or encoded representation through an encoder neural network;

transcribing the transformed fixed-sized vectors or encoded representations into text through a decoder neural network; and

applying text normalization and cleaning to the transcribed text for usage by the chatbot module.

16 . The method of claim 15 , wherein partial transcriptions are generated from the audio snippets collected at set intervals to determine if the said collected audio contains domain-relevant content, actionable instructions, or irrelevant information wherein the audio data transmission continues if relevant, or the device transitions to an idle state to stop listening if irrelevant.

17 . The method of claim 9 , wherein generating context-aware responses by the chatbot module, further comprises:

ingesting the transcribed text to determine the intent and entity from the natural language input using an intent and entity recognition module;

aggregating contextual data from a plurality of sources wherein the sources include the backend database, data in transit at the recommendation engine, items in the cart, contextual data, customer data, restaurant establishment data, and real time input data; and

generating a response based on the analysis of said aggregated contextual data using AI models wherein the AI models may include large language models (LLM), natural language processing (NLP), sequence-to-sequence models, or other machine learning architectures.

18 . The method of claim 17 , wherein the intent and entity recognition of the chatbot module further comprises:

pre-processing the text generated by the transcription module through a pre-processing unit;

determining customer intent from the transcribed text using intent recognition models trained on text-intent pairs and fine-tuned for text classification; and

determining entities within the transcribed text using entity recognition models trained on labeled entity data including token and word classification and fine-tuned for recognition through voice data.

19 . The method of claim 17 , wherein the restaurant establishment data includes, the menu items of the restaurant, the promotional items, and the specialties of the restaurant collected by, injected through, or ingested by a point-of-sale system at the restaurant establishment or backend database.

20 . The method of claim 17 , wherein the contextual data comprises data on geolocation, weather, time, day, date, calendar events, and seasons fetched through APIs.

21 . The method of claim 17 , wherein customer data comprises the customer's order history, preferences, allergies, and personal information collected through a point-of-sale system at a restaurant establishment or stored in the backend database.

22 . The method of claim 17 , wherein the generated response may be executed as an action including modifying a user interface, updating an order, triggering a service request or performing another system action and displaying the result on the user interface of connected output device.

23 . The method of claim 9 , further comprising:

collecting encrypted payment data through a plurality of wireless payment technologies;

transmitting the said encrypted data via a secure communication protocol; and

processing payment using a secure element to perform cryptographic operations, tokenization, and key management, in compliance with applicable security standards.

24 . The method of claim 9 , wherein generating AI based personalized menu recommendations, further comprises:

filtering and ranking menu items by analyzing a plurality of datasets including real-time input data, customer data, restaurant establishment data, and contextual data, using a filtering and ranking module integrated with predictive AI models to generate personalized menu items recommendations;

generating group offers for a group of customers by analyzing the size of the customer group from the input data gathered from a plurality of sensors along with the analysis of said datasets to recommend tailored offers for the group of customers; and

displaying the personalized recommendations and group offers on the user interface.

25 . The method of claim 24 , wherein personalization of menu items for recommendation further comprises of utilizing AI models to pair existing menu items with the most likely choice of the customer based on predictive analysis of a plurality of datasets including customer data, restaurant establishment data, contextual data, and real-time input data.

26 . The method of claim 24 , wherein the customer-selected items are transmitted to a personalization engine to verify the said selected items for allergens, dietary preferences and user-defined modifiers by querying metadata associated with the said selected items against the customer data stored in the database via a secure communication interface.

27 . The method of claim 24 , wherein personalization of menu items for recommendation further comprises of calculating menu items relevance for a customer based on the customer's transaction history stored as part of the customer's profile data in the backend database wherein the menu items with frequent purchases or interactions exceeding a predefined threshold are ranked according to a configurable scale defined by the restaurant establishment.

28 . The method of claim 24 , wherein personalization of menu items for recommendation further comprises of estimating the probability of menu items selection using a category-based preference algorithm, the said algorithm applying weighted preferences derived from the customer data stored in the database and wherein the said customer data includes customer's order history, preferences, contextual factors, and real-time inputs.

29 . The method of claim 24 , wherein personalization of menu items for recommendation further comprises of generating a personalized recommendation score for menu items associated with multiple categories by performing a dot product calculation between the customer's category preference vector and the item's category vector.

30 . The method of claim 24 , wherein personalization of menu items for recommendation further comprises of extracting menu item data from the backend database via a query interface and generating item pairings in response to updates or additions of categories to the said menu and wherein large language models (LLMs) compare menu item categories by analyzing semantic relationships between attributes.

31 . The method of claim 30 , wherein menu item pairings are labeled as compatible or incompatible based on the evaluation of the semantic relationships between category attributes and their compatibility.

32 . The method of claim 30 , wherein compatible menu items are combined to form AI-generated menu item pairings and wherein the said AI generated menu item pairings re stored in the backend database in a structured format for subsequent retrieval and recommendation purposes.