Big Data Expo 2026 brought together companies and industry professionals from across the data, AI, and digital economy sectors in Guiyang from August 28 to 30. This article looks at the key data-related themes from the expo and Glodom’s participation, with a focus on AI data services, high-quality datasets, multimodal data, multilingual data, and industry-specific datasets for AI training, fine-tuning, evaluation, and real-world applications.
This article explores the key stages of AI data collection, from requirements definition and long-tail scenario identification to multilingual data collection, source planning, quality control, and domain-specific dataset development. It also explains how businesses can identify data gaps and build a more targeted data development process around real-world AI requirements.
This article explores how companies can build reusable patent language assets through early terminology planning, patent-family consistency management, risk-based review, and controlled AI assistance. It also looks at how a structured language management approach can reduce repeated work as patent portfolios expand across jurisdictions.
This article looks at why terminology, claim language, and patent-family consistency should be planned from the PCT stage rather than addressed separately at each national phase. It also examines the main translation risks that can emerge during patent prosecution and explains why early language planning can make global patent management more consistent and traceable.
This article examines why data volume alone does not determine data value, how multimodal and multilingual requirements make collection more complex, and why large-scale AI data projects depend on long-term expertise, consistent standards, and continuous quality management. It also introduces Glodom's experience in AI data collection and highlights its participation in Big Data Expo 2026 in Guiyang.
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