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1、 2024 Cognizant 2024 Cognizant How Edge Computing,Artificial Intelligence,and Generative AI are changing the future of restaurant technology 2|2024 Cognizant Abstract Frequent challenges faced by the restaurant industry can be categorized under three broad level segments,including low customer reten

2、tion,inefficient operations and inventory management and high labor cost.Low customer retention:The restaurant industry is highly competitive and fragmented,with customers having a wide range of choices and preferences.The percentage of customers who are loyal to a specific restaurant brand is decli

3、ning,whereas those who switch brands more than once a month is on the rise.To retain and attract customers,restaurants need to offer personalized and engaging experiences,such as customized menus,recommendations,rewards,and feedback.Inefficient operations and inventory management:The restaurant indu

4、stry faces various operational challenges,such as optimizing food quality and safety,reducing food waste and spoilage,managing supply chain and inventory,and complying with health and safety regulations.According to a report,the average food wastage in restaurants is 11%of food purchases,which amoun

5、ts to significant losses annually.To improve operational efficiency and profitability,restaurants need to leverage real-time data and analytics,automate processes,and optimize resources.High labor cost and turnover rates:The restaurant industry is one of the most labor-intensive sectors.According to

6、 a survey,98%of operators say higher labor costs are an issue for their restaurant.Moreover,the industry suffers from a high turnover rate which impacts the quality and consistency of service and increases training and hiring costs.This paper discusses how Edge Computing,AI,and Generative AI can hel

7、p address these challenges by bringing computing power closer to where the data is generated,reducing latency,and enabling faster decision-making.It explores the advantages of Edge Computing,the use of in-restaurant cloud technology,and the benefits of using Large Language Models(LLMs)on Edge device

8、s.The paper also discusses the technical challenges that are needed to be resolved,leveraging the methods for model quantization.Introduction The restaurant industry is undergoing a digital transformation,driven by the need to enhance customer experiences,optimize operations,and increase revenue.Edg

9、e Computing,AI,and Generative AI are some of the key technologies that are enabling this transformation.According to a report by Grand View Research,the global Edge Computing market size is expected to reach$155.90 billion by 3030,growing at a compound annual growth rate(CAGR)of 36.9%.The report als

10、o states that,“Artificial Intelligence(AI)integration into the Edge environment is projected to drive market growth.An Edge AI system is estimated to help businesses make decisions in real time in milliseconds.The need to minimize privacy concerns associated while transmitting large amounts of data,

11、as well as latency and bandwidth issues that limit an organizations data transmission capabilities,are factors projected to fuel market growth in the coming years.”Generative AI:Generative AI is a branch of AI that can generate novel and realistic content,such as images,text,music,or video,based on

12、existing data.Edge Computing:Edge Computing is the idea of doing computing activities near where the data comes from,to reduce the delay between the data and the decisions.One of the main differences between Edge and Cloud Computing is the location of data processing.While Cloud Computing relies on

13、centralized servers to store and process data,Edge Computing distributes the data processing across local devices or servers that are closer to the data source.This reduces the latency,bandwidth,and privacy issues that are associated with Cloud Computing.Edge Computing can also enable more efficient

14、 and reliable AI applications that allow real-time or near real-time decision making.Restaurants use in-restaurant Cloud technology with Edge Computing to speed up and ensure data processing and system uptime for in-store applications.The comparison between Edge and Cloud deployments is shown in the

15、 picture below.QoSCloudEdgeDiagram 1:Edge vs Cloud3|2024 Cognizant Memory Data Storage Latency Power Requirement Concurrency Low Level task Low Latency GenAI/LLM Applications Lightweight Models Fewer Computing Resources High Level task Asynchronous offline tasks Large Models More Computing Resources

16、4|2024 Cognizant In-restaurant cloud technology driven by Edge Computing can help restaurants process data faster and more reliably and enhance system uptime.Data is synchronized with the central cloud data store and the in-store applications can switch between the in-store cloud and the public clou

17、d as needed.The hybrid Edge infrastructure blends the public cloud and the in-store cloud and forms the basis of the new Cloud Computing for restaurants to ensure business continuity.By analyzing data at the Edge in real-time or near real-time,businesses can train AI models and improve the performan

18、ce of AI driven applications.Some of the decisions that can happen at the store level are:Computer vision technology,Generative AI,machine learning and deep learning frameworks for AI-driven personalization,in-restaurant housekeeping,dynamic pricing,promotion,inventory,and production optimization an

19、d various other IoT driven operations that use huge amount of data for predictive analysis.A small and powerful in-store device,an example of Edge Computing,brings computing power to the data required to run the restaurant operations.Also,the APIs in the public cloud that are needed for restaurant o

20、perations are copied at the in-store cloud to enable faster order management and payment processing.These APIs will keep the restaurant running even when there is no connectivity.This kind of resilient and redundant architecture helps restaurants maintain business continuity,reliability in payment p

21、rocessing,which reduces financial losses and increases customer satisfaction due to faster speed and uptime.Edge Computing is essential for the rapid development of Generative AI as it solves the problems of real-time processing,lower latency,and effective data management.As the companies deploy Gen

22、erative AI solutions,they will have to deal with the issues of long-term cost,data privacy and security.Edge Computing helps to overcome these challenges and clean up the raw data before moving the data to the public cloud for more costly AI training operations.Large scale datasetPre-trained modelPo

23、werful computationLightweight modelGenerated contentPromptFine tuningEnd user devicesWeb based appsUser dataCloudEdge serverUser deviceDiagram 2:Logical representation of LLM Model deployment on Edge and Cloud5|2024 Cognizant Diagram 3:Restaurant Business Map with GenAI opportunities highlighted.How

24、 Edge Computing and GenAI can improve restaurant operationsBased on this research,the following business map depicts the modules(blue highlighted),in which the combination of Edge,GenAI and Traditional AI can have considerable impact on restaurant operations.Above restaurantData Polling&DeliveryData

25、 Aggregation&ReportingIT Service ManagementIdentity Management&User ProvisioningOrder managementIn-restaurantBack of house operationsFront of house operationsKitchen Display SystemsDrive-ThruChannel/Third PartyService ProviderIntegrationInventory ManagementLabor SchedulingFood Safety&Waste Managemen

26、tPaymentDelivery Management SystemOrder Queueing&ConfirmationSocial Profile ManagementTime Keeping&PayrollBOH ReportingDemand&ForecastingUpsell/Cross sellReceipt ManagementKioskMulti Channel Ordering&MobilityCash and Sales ReconciliationStore Asset Management ReconciliationEmployee management offer&

27、coupon managementPricing/taxCustomer RelationshipManagementEnd of Day ProcessPurchase OrderPOS Configuration ManagementDigital SignageEnd of Day ProcessLoyaltyPOS ReportingEmployee Clock In/OutCorporate Menu and Restaurant Data ManagementSupply Chain ManagementFranchisee ManagementVendor Management/

28、Supplier SpecificationManagement SystemCustomer Relationship ManagementCustomer compliant ManagementCoupon and Offer ManagementRestaurant Design&Development/Visual MerchandisingLearning ManagementMenu engineeringFinanceStore Asset ManagementInsight and AnalyticsHuman ResourceFacilities ManagementCus

29、tomer 360Public RelationsEmployee ServicesBranding and Marketing StrategyLegalInfo Security ManagementCorporate ProcurementBusiness Process Management6|2024 Cognizant The reference architecture can enable the following example use cases:A customer walks up to a kiosk and is identified by the face re

30、cognition model.The kiosk shows a customized menu created by the Generative AI model based on the customers preferences,history,and context.The customer orders using voice recognition and pays using biometric authentication.A tablet on a table senses a customer and turns on the speaker.The speaker w

31、elcomes the customer and offers a suggestion created by the Generative AI model based on the customers profile,mood,and time of day.The customer can talk with the speaker using natural language and order.A camera tracks the crowd size and behavior in the restaurant and sends the data to the Generati

32、ve AI model.The Generative AI model estimates the demand and supply of food items and changes the inventory and production accordingly.The Generative AI model also improves the staffing and scheduling of the restaurant based on the data.A sensor records the temperature and humidity in the kitchen an

33、d sends the data to the Generative AI model.The Generative AI model manages the heating and ventilation system to keep the optimal conditions for food preparation and safety.The Generative AI model also warns the staff if any abnormality or hazard is detected.Reference ArchitectureThe following are

34、the key components of the reference architecture:A GenAI cloud server that runs the models and applications for tasks like menu generation,order prediction,customer segmentation,etc.The cloud server also keeps and processes the data from the Edge devices and sends them feedback and updates.A local n

35、etwork of Edge devices that operate the GenAI models and applications at the restaurant level,such as kiosks,tablets,cameras,speakers,etc.The Edge devices use compact AI models to do tasks like face recognition,voice recognition,sentiment analysis,etc.The Edge devices also talk to each other and to

36、the cloud server via Wi-Fi or cellular connection.A set of sensors and actuators that gather data from the physical environment,such as temperature,humidity,noise,motion,etc.The sensors and actuators also regulate the physical aspects of the restaurant,such as lighting,heating,ventilation,etc.7|2024

37、 Cognizant Technical ArchitectureLarge Language Models(LLMs)on Edge Devices:LLMs on Edge Devices can provide more speed,better privacy and security,and online and offline functionality.However,there are some challenges that need to be resolved,including hardware limits,energy use,maintenance,and eth

38、ics issues.Quantization:Quantization is a method to shrink the model size and make it more efficient for use on Edge devices.It uses a technique that lowers the precision of numerical values to lower the computational and memory requirements of AI models.Quantization can be applied at different leve

39、ls,such as weights,activations,or outputs.Quantization can also be performed at different stages,such as during training,after training,or during inference.Quantization can impact the accuracy,speed,and size of AI models.Hybird cloud managementDiagram 4:Technical Reference ViewBusiness DriversProvid

40、es realtime insights from edge to centralized sitesReducing maintenance costSecure DevOps management across restaurant sitesEdgeManagementLocal DashboardEdge AlApplicationSensor DataRestaurantAbove storeSecretsEdgeManagementDevOpsManagementSensor DataStreamAlOpsManagementContainer ImagesML Model Tra

41、iningData LakeThe Generative AI cloud server is the central component of the architecture,as it hosts the main models and applications for restaurant management and optimization.The cloud server uses a variety of AI techniques,such as natural language processing,computer vision,machine learning,and

42、Generative AI,to create and improve the solutions for the restaurant.The cloud server also communicates with the Edge devices via APIs or MQTT messages,sending them feedback,updates,and commands.The Edge devices are the peripheral components of the architecture,as they run the Generative AI models a

43、nd applications at the restaurant level.The Edge devices use quantized AI models to perform tasks that require low latency,high privacy,or offline availability,such as face recognition,voice recognition,sentiment analysis,etc.The Edge devices also communicate with each other and with the sensors and

44、 actuators via Bluetooth,Zigbee,or Wi-Fi,exchanging data and information.The sensors and actuators are the physical components of the architecture,as they collect and control data from the environment.The sensors and actuators use simple protocols,such as GPIO,I2C,or SPI,to connect with the Edge dev

45、ices,sending them signals and receiving instructions.The sensors and actuators also enable the GenAI models and applications to interact with the physical aspects of the restaurant,such as lighting,heating,ventilation,etc.8|2024 Cognizant 9|2024 Cognizant Technology optionsGoogle Coral:This is a pla

46、tform that offers a range of products,such as a development board,a USB accelerator,and a system-on-module,which can run TensorFlow Lite models at the Edge.It can be used as an Edge device to enable Generative AI capabilities such as face detection,object recognition,and sentiment analysis for QSR k

47、iosks and other restaurant devices.Some advantages of Google Coral are its ease of use,scalability,and integration with Google Cloud services.Some disadvantages are its limited support for other frameworks and languages,its dependency on Googles ecosystem,and its new and evolving status.NVIDIA Jetso

48、n Nano:This is a potent and energy-efficient platform that can run multiple neural networks in parallel and process high-resolution data from multiple sensors.It can be used as an Edge device to boost Generative AI tasks such as computer vision,natural language processing,and speech recognition for

49、QSR kiosks and other restaurant devices.Some advantages of NVIDIA Jetson Nano are its high performance,low power consumption,and compatibility with popular frameworks and tools.Some disadvantages are its higher cost,complexity,and learning curve,as well as its potential overheating and instability i

50、ssues.Raspberry Pi:This is a low-cost,small,and adaptable single-board computer that can run Linux-based operating systems and support various programming languages.It can be used as an Edge device to host GenAI models and applications for QSR kiosks and other restaurant devices.Some advantages of R

51、aspberry Pi are its cost-effectiveness,mobility,versatility,and large community support.Some disadvantages are its limited processing power,memory,and storage,as well as its reliance on external peripherals and power sources.Google Anthos:This is a platform that enables the deployment and management

52、 of cloud-native applications across different environments,such as on-premises,public cloud,or Edge devices.It can be used as an Edge device to run Generative AI models and applications for QSR kiosks and other restaurant devices with consistent policies and security.Some advantages of Google Antho

53、s are its portability,scalability,and integration with Google Cloud services.Some disadvantages are its high cost,complexity,and dependency on Googles ecosystem.Google Anthos supports Kubernetes,which is a framework that offers various options for quantization,such as operator-level quantization,mod

54、el-level quantization,and graph-level quantization.10|2024 Cognizant The technology choices described above have different implications for quantization:Google Coral:This device has a dedicated TPU,which means that it can run TensorFlow Lite models at the Edge with high speed and low latency.Therefo

55、re,quantization is required for Google Coral,as it can enable the device to run the models on the TPU.However,quantization can also limit the flexibility and compatibility of Google Coral,as it can restrict the choice of frameworks and languages.Google Coral supports TensorFlow Lite,which is a frame

56、work that offers various options for quantization,such as full integer quantization,float fallback quantization,and hybrid quantization.NVIDIA Jetson Nano:This device has a powerful GPU,which means that it can run high-resolution and parallel AI models effectively.Therefore,quantization may not be n

57、eeded for NVIDIA Jetson Nano,as it can handle the computational and memory demands of large models.However,quantization can still be beneficial for NVIDIA Jetson Nano,as it can reduce the power consumption and increase the battery life of the device.NVIDIA Jetson Nano supports TensorFlow,PyTorch,and

58、 ONNX,which are frameworks that offer various options for quantization,such as quantization-aware training,quantization emulation,and quantization export.Raspberry Pi:This device has a limited CPU and GPU,which means that it cannot run complicated or large AI models efficiently.Therefore,quantizatio

59、n can be helpful for Raspberry Pi,as it can reduce the model size and improve the inference speed.However,quantization can also cause accuracy loss,which can affect the performance of Generative AI tasks.Raspberry Pi supports TensorFlow Lite,which is a framework that offers various options for quant

60、ization,such as post-training quantization,dynamic range quantization,and integer-only quantization.Google Anthos:This is a platform that allows the deployment and management of AI applications across different cloud providers and on-premise environments.Therefore,quantization can be useful for Goog

61、le Anthos,as it can enable the portability and scalability of AI models across heterogeneous hardware and software platforms.However,quantization can also introduce some challenges for Google Anthos,such as ensuring the consistency and compatibility of quantized models across different frameworks an

62、d languages.Google Anthos supports TensorFlow,PyTorch,and Scikit-learn,which are frameworks that offer various options for quantization,such as mixed precision training,quantization-aware fine-tuning,and model optimization tools.Application of Edge and Generative AI use cases in types of restaurants

63、Fine dining Generative AI can make new and different recipes,menus,and pairings with the ingredients,cuisines,seasons,and occasions.For example,a Generative AI model can create a dish with unusual tastes and textures,or a wine that matches a dessert.Generative AI can also make the food look better b

64、y making artistic and attractive designs,colors,and arrangements.For example,a Generative AI model can use edible flowers,sauces,and garnishes to make a more visually appealing effect.Edge Computing can speed up and improve the data processing and communication between the restaurants front-end and

65、back end,and the cloud.For example,an Edge device can handle the customers reservation,order,feedback,and payment,and send them to the kitchen,the management,and the loyalty program right away.Edge Computing can also protect the customers data and follow data rules.For example,an Edge device can hid

66、e and change the customers personal information,like their name,email,phone number,and payment details,before sending them to the cloud.Edge Computing can also let the restaurant work offline or with low connection,which can make the service more available and reliable.For example,an Edge device can

67、 keep the important data and functions locally and synchronize them with the cloud when the connection is back.With Generative AI and Edge Computing,fine dining restaurants can give more new,fine,and custom experiences for the customers,and faster,correct,and lower-cost operations for the restaurant

68、s.Quick serve restaurant Using computer vision and natural language processing to identify the customers face,voice,and order,and suggest customized recommendations and offers.Using machine learning and reinforcement learning to change the menu,the prices,and the promotions based on the demand,the s

69、eason,and the competition.Using sensors and actuators to check and manage the temperature,the humidity,and the hygiene of the food and the equipment,and to notify the staff of any issues or anomalies.Using chatbots and virtual assistants to help the customers and the employees with their questions,c

70、omplaints,and suggestions,and provide feedback and guidance.Using data analytics and dashboarding to measure and show the performance,the trends,and the outcomes of the restaurant,and spot areas for improvement and innovation.11|2024 Cognizant 12|2024 Cognizant Application of Edge and GenAI Use Case

71、s in key restaurant functions QSR kiosks Generative AI can create tailor-made menus,deals,and suggestions based on the customers prefrences,behavior,location,and time of the day.For instance,a Generative AI model can recommend a low-calorie salad for a customer who cares about their health or a comb

72、o meal for a family with children.Generative AI can also improve the user interface and interaction of the kiosks by creating natural language responses,voice synthesis,facial expressions,and gestures.For example,a Generative AI model can welcome the customer,take their order,verify their payment,an

73、d express their gratitude for their visit.Edge Computing can enable quicker and more dependable data processing and communication between the kiosks and the kitchen,as well as the cloud.For instance,an Edge device can process the customers order,transmit it to the kitchen,and update the inventory an

74、d sales data in real-time.Edge Computing can also provide more privacy and security for the customers data,as well as compliance with data regulations.For instance,an Edge device can encrypt and anonymize the customers personal information,such as their name,email,phone number,and payment details,be

75、fore sending it to the cloud.Edge Computing can also allow the kiosks to work offline or in low connectivity scenarios,which can enhance the availability and resilience of the service.For instance,an Edge device can store the essential data and functions locally and synchronize them with the cloud w

76、hen the connection is restored.By using Generative AI and Edge Computing,QSR kiosks can offer more personalized,interactive,and convenient experiences for the customers,as well as more efficient,precise,and cost-effective operations for the restaurants.Restaurant POSThe point-of-sale(POS)system is a

77、 key element of any restaurant,as it handles the payments,orders,inventory,and customer data.However,traditional POS systems are often old-fashioned,slow,and prone to mistakes and breaches.By using Edge Computing and Generative AI,restaurants can upgrade their POS systems into smart,fast,and secure

78、platforms that can improve the customer experience and business efficiency.Quicker and more dependable data processing and communication:Edge Computing can solve the latency and bandwidth problems that often impact cloud-based POS systems,especially during busy times or network outages.By processing

79、 the data locally on the Edge devices,the POS system can work quicker and more dependably,ensuring smooth payments and orders.Better privacy and security of customer data:Edge Computing can also safeguard customer data from unauthorized access or leakage,as it reduces the exposure of sensitive infor

80、mation to the cloud or the internet.By encrypting and anonymizing the data on the Edge devices,the POS system can follow data regulations and avoid identity theft,fraud,or cyberattacks.Improved customization and interaction of customer service:Generative AI can enable the POS system to provide more

81、personalized and engaging service for customers,by using natural language processing,computer vision,and speech recognition to understand and respond to customer needs and preferences.For example,a Generative AI model can welcome the customer by their name,offer them relevant discounts or loyalty re

82、wards,recommend items based on their order history or dietary limitations,and generate natural and human-like conversations.Enhanced efficiency and accuracy of restaurant operations:Generative AI can also help the POS system optimize restaurant operations,by using data analytics,machine learning,and

83、 reinforcement learning to monitor and improve the performance,trends,and outcomes.For example,a Generative AI model can track and manage the inventory,supply chain,and waste,predict the demand,adjust the prices and promotions,and provide feedback and suggestions for the staff.13|2024 Cognizant One

84、example of how Generative AI,AI and Edge Computing can help improve restaurant POS is as follows:A customer walks into a fast-food restaurant and scans a QR code on the table with their smartphone.The QR code directs them to a web app that serves as a POS system for the restaurant.The web app runs o

85、n the Edge device,which is a small server located in the restaurant.The Edge device processes the data locally and communicates with the cloud only when necessary,ensuring fast and reliable service.The web app greets the customer by their name and shows them a menu that is customized based on their

86、previous orders,preferences,and allergies.The customer can use voice or text to place their order,and the web app uses natural language processing and speech recognition to understand and confirm their order.The web app also offers them a discount coupon that they can apply to their order and sugges

87、ts some upsell items that they might like.The customer pays for their order using their credit card or digital wallet,and the web app securely encrypts and verifies their payment information on the Edge device,without sending it to the cloud or the internet.The web app also updates the customers loy

88、alty points and rewards,and thanks them for their purchase.The web app sends the order details to the kitchen staff,who prepare the food and deliver it to the customers table.The web app also monitors the inventory and supply chain of the restaurant and alerts the manager if any item is running low

89、or needs to be reordered.The web app also analyzes the sales data and customer feedback and provides insights and recommendations to the manager on how to optimize the menu,pricing,and promotions.The customer enjoys their meal and leaves the restaurant satisfied.The web app asks them to rate their e

90、xperience and provide any suggestions or complaints.The web app uses natural language generation and sentiment analysis to generate a friendly and personalized response to the customer,and to address any issues or concerns they might have.The web app also learns from the customers feedback and prefe

91、rences and improves its service for the next time.14|2024 Cognizant 15|2024 Cognizant Examples of how Generative AI,AI and Edge Computing can help improve restaurant contact centers include:A customer who has ordered food online from a restaurant may receive a phone call or a text message from the c

92、ontact center,which uses Generative AI to start a natural and friendly conversation.The contact center can use speech recognition and natural language understanding to recognize the customers voice,name,and order details,and to confirm that the order has been delivered on time and as expected.The co

93、ntact center can also use sentiment analysis to detect the customers mood and satisfaction level,and to offer appropriate apologies,compliments,or incentives.The contact center can use Edge Computing to run the Generative AI models locally on the device,without relying on the internet connection or

94、the cloud server,which can reduce latency,cost,and security risks.The contact center can also use Edge Computing to store and process the customers data and feedback,and to update and improve the Generative AI models over time.By using Generative AI,AI and Edge Computing,the restaurant contact cente

95、r can enhance the customer experience,increase customer loyalty,and reduce the human workload.Restaurant contact centersEdge Computing combined with Generative AI can enhance the efficiency of restaurant contact centers that handle customer inquiries,feedback,and problems.Through Edge Computing,data

96、 processing becomes faster,less costly,more secure,and accessible even without an internet connection.Generative AI can use natural language,speech,and vision to give more accurate,personal,and friendly responses to customers,and to handle repetitive tasks.Generative AI can also use data to understa

97、nd and satisfy customers better,and to suggest suitable offers and solutions.ConclusionEdge Computing,AI,and Generative AI are revolutionizing the future of restaurant technology by providing solutions to the challenges faced by the industry.By bringing computing power closer to where data is genera

98、ted,Edge Computing reduces latency and enables faster decision-making.In-restaurant cloud technology,driven by Edge Computing,can help restaurants process data faster and more reliably,enhancing system uptime.Large Language Models(LLMs)on Edge devices provide more speed,better privacy and security,a

99、nd online and offline functionality.However,there are challenges that need to be resolved,including hardware limits,energy use,update and maintenance issues,and ethics issues.Quantization is a method to shrink the model size and make it more efficient for use on Edge devices.Overall,the integration

100、of Edge Computing,AI,and Generative AI in the restaurant industry has the potential to enhance profitability by reducing costs and improving processes.16|2024 Cognizant 1.https:/coral.ai/2.https:/www.tensorflow.org/lite3.https:/ Language ModelGenAIGenerative AIAPIApplication Programming InterfaceQSR

101、Quick Serve RestaurantPOSPoint of SaleTPUTensor Processing UnitCPUCentral Processing UnitGPUGraphics Processing UnitIoTInternet of ThingsMQTTMessage Queuing Telemetry TransportGPIOGeneral Purpose Input/OutputI2CInter Integrated CircuitiSPISerial Peripheral Interface17|2024 Cognizant AuthorAditya has

102、 over 24 years of experience working as an Enterprise/Solution Architect for Retail,Consumer Goods and T&H industry.His primary focus area is around modernization of Restaurant Technology for both QSRs and Fine Dining Restaurants.He has keen interest in the evolution of AI and GenAI technologies and

103、 has been involved in extensive research work on how to leverage these new technologies for improved Retaurant oprations and customer experience.Aditya Sankar Sarkar(Principal Architect,Travel&Hospitality)WF2792255 Copyright 2024,Cognizant.All rights reserved.No part of this document may be reproduc

104、ed,stored in a retrieval system,transmitted in any form or by any means,electronic,mechanical,photocopying,recording,or otherwise,without the express written permission of Cognizant.The information contained herein is subject to change without notice.All other trademarks mentioned here in are the pr

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