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Google > 实例探究 > OPPO and Google Cloud Collaborate to Enhance AI Capabilities in Mobile Phones

OPPO and Google Cloud Collaborate to Enhance AI Capabilities in Mobile Phones

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公司规模
Large Corporate
地区
  • Asia
国家
  • China
产品
  • Gemini
  • Vertex AI
  • AutoML
  • Vertex AI NAS
技术栈
  • Google Cloud
  • AI Model Optimization
  • Large Language Models
实施规模
  • Enterprise-wide Deployment
影响指标
  • Productivity Improvements
  • Customer Satisfaction
  • Innovation Output
技术
  • 分析与建模 - Generative AI
  • 分析与建模 - 机器学习
  • 平台即服务 (PaaS) - 数据管理平台
适用行业
  • 电子产品
  • Software
适用功能
  • 产品研发
  • 商业运营
用例
  • AI Copilot
  • Generative AI
  • 远程协作
服务
  • 云规划/设计/实施服务
  • 软件设计与工程服务
  • 系统集成
关于客户
OPPO is a globally recognized smart device brand, known for its innovative technology and aesthetic design. Since its inception in 2008, OPPO has been committed to creating a seamless user experience through its range of smart devices, including the popular Find and Reno series. The company also offers the ColorOS operating system and various internet services. With a presence in over 70 countries and regions, OPPO employs more than 40,000 people dedicated to enhancing the lives of customers worldwide. As of August 2024, OPPO boasts approximately 700 million monthly active users, underscoring its significant impact in the mobile phone industry. The company's collaboration with Google Cloud to integrate AI functions into its mobile phones is a testament to its commitment to technological innovation and user satisfaction.
挑战
OPPO, a leading mobile phone manufacturer, faced the challenge of integrating advanced AI capabilities into its mobile phones to enhance user experience and maintain a competitive edge. The company aimed to develop AI phones that could perform efficient computing, self-learning, real-world perception, and content generation. To achieve these goals, OPPO needed to reconstruct its hardware platform, operating system, and third-party service ecosystem. Additionally, OPPO sought to leverage AI technologies to reduce manual workload and improve productivity, particularly in areas like user feedback analysis and power consumption optimization.
解决方案
To address the challenges, OPPO collaborated with Google Cloud to implement AI functions in its mobile phones, leveraging Google's advanced AI technologies. The partnership began with the OnePlus phone, where the team used Google Cloud's Vertex AI platform to develop an AI model for analyzing user feedback. This initiative reduced manual workload by 30% and allowed the product team to focus on core development tasks. OPPO also integrated hardware power consumption feedback into Google NAS, optimizing algorithms to reduce power consumption and latency, resulting in a more energy-efficient AI model. Furthermore, OPPO developed AI features like the AI Recording Summary and AI Toolbox, utilizing Google's Gemini large language model. These features automate tasks such as meeting content summarization and social media post generation, significantly improving work efficiency. The collaboration with Google Cloud enabled OPPO to integrate advanced AI capabilities into its mobile phones, providing users with a seamless and efficient experience.
运营影响
  • OPPO's collaboration with Google Cloud led to the successful integration of AI functions in its mobile phones, enhancing user experience and productivity.
  • The AI Recording Summary feature, developed using Google's Gemini large language model, automates meeting content summarization, freeing users from time-consuming tasks.
  • AI Toolbox features, such as AI Summary and AI Writer, improve work efficiency by automating content generation and editing tasks.
  • The integration of advanced AI capabilities in OPPO's mobile phones demonstrates the company's commitment to providing users with a seamless and efficient experience.
  • OPPO's partnership with Google Cloud has strengthened its position as a leader in the mobile phone industry, showcasing its ability to leverage cutting-edge AI technologies.
数量效益
  • Reduced human workload by 30% through AI model for user feedback analysis.
  • Reduced power consumption of object detection algorithms by 27%.
  • Reduced latency of object detection algorithms by 40%.

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