Aifanfan IM የደንበኛ አገልግሎት መተግበሪያ by Baidu Online Network Technology Beijing is a highly esteemed winner of the very prestigious 2023 A' Design Award የብረት ሽልማት in የኢንፎርሜሽን ቴክኖሎጂ ዲዛይን Competition Category.
ብረትየኢንፎርሜሽን ቴክኖሎጂ· 2023
Aifanfan IM የደንበኛ አገልግሎት መተግበሪያ
by Baidu Online Network Technology Beijing
Client: Baidu Online Network Technology. Beijing
A' Design Award የብረት ሽልማት Winner in የኢንፎርሜሽን ቴክኖሎጂ Category
- 1,088,241
- Verified Award Winner
- Verify Award Status for #144072
The Design
About Aifanfan IM የደንበኛ አገልግሎት መተግበሪያ
አይፋንፋን አይኤም ለመስመር ላይ ከሽያጭ በኋላ ድጋፍ የተዘጋጀ ብልህ የደንበኛ አገልግሎት መተግበሪያ ነው። መተግበሪያው በአገልግሎት ወኪሎችና በደንበኞች መካከል ያለውን ሁለት አቅጣጫዊ ግንኙነት ለማቅለል የግብዓት ድጋፍ፣ የሂደት ማሻሻያ እና የአርቴፊሻል ኢንተለጀንስ ረዳት ያቀርባል። ካሜራንና ድምጽን ጨምሮ ባለብዙ ዘዴ ግብዓት የውጭ ቋንቋዎችንና ዘዬዎችን በመለየት የግብዓት ደረጃን ይቀንሳል። የአርቴፊሻል ኢንተለጀንስ ረዳቱ በንግግር ጽሑፍ ውስጥ ያሉ ስሜታዊ ነገሮችን በቅጽበት በመተንተን ፈጣን ምላሽ ይዘቶችን ይመክራል። የንግግር ለይቶ ማወቅ፣ የምስል ለይቶ ማወቅ እና የተፈጥሮ ቋንቋ ማቀናበሪያ ጥቅም ላይ ውለዋል።
The Recognition
About the A' Design Award የብረት ሽልማት
Aifanfan IM የደንበኛ አገልግሎት መተግበሪያ የA' Design Award የብረት ሽልማት አሸናፊ ነው፤ ይህም በጥሩ ሁኔታ ከተነደፉ ፈጠራዎች የሚጠበቁትን ሙያዊና ኢንዱስትሪያዊ መስፈርቶች ለሚያሟሉ ጥሩ ዲዛይኖች የሚሰጥ ከፍተኛ ክብር ያለው እውቅና ነው። የብረት አሸናፊዎች የፈጣሪዎቻቸውን ታላቅ ችሎታ፣ ከፍተኛ ልዩ ሙያ፣ የባለሙያ ግንዛቤ እና የፈጠራ አቅም ያሳያሉ። Aifanfan IM የደንበኛ አገልግሎት መተግበሪያ ከፍ ያለ ግምት የሚሰጠው፣ የሚደነቅና የሚወደድ፣ ተግባራዊና ፈጠራ የተሞላበት ሥራ እንደሚሆን ይጠበቃል። ይህ የA' Design Award የብረት ሽልማትን ያሸነፈ ዲዛይን የኢንዱስትሪ ምርጥ ልምዶችን ያዋህዳል፤ ከእውቀትና ከተሰጥኦ ጋር የተሳሰሩ ብቁ ቴክኒካዊ ባህሪያትም አሉት። Aifanfan IM የደንበኛ አገልግሎት መተግበሪያ በጥልቅነቱ የተከበረ ሲሆን ዓለምን የተሻለ ቦታ ለማድረግ የሚረዱ የሕይወት ጥራት ማሻሻያዎችን ለማቅረብ የተነደፈ ነው።
Behind the Design
Press Kit
Credits, team members and technical details provided by the designer.
- Unique Properties
- A smart customer service system with a series of optimization tools for customer service and consumers, such as input assistance, process optimization, and AI assistant, aiming to simplify the two-way communication process of both ends, provides more reliable security and protection, and supports multi-device and multi-scenario input switching, making the communication process easier and more convenient.
- Tags
- Customer service system, Program, Online customer service, After-sales service, AI technology
- Production Technology
- It uses the leading AI technologies of Baidu, such as speech recognition, image recognition, and natural language processing.
- Design Challenge
- The challenge in this project research is to optimize the two-end process. In the design process, it is necessary to balance the two-way needs of customer service and users, so that the process optimization of one end does not impose burden on the other. Therefore, the application is developed based on the pain points of users and customer service through various investigation methods.
- Research
- The application is developed through conducting questionnaire survey by the design team based on a multi software customer service system, collecting objective problems with the current intelligent customer service system, and analyzing user sentiment, usage scenarios and current design trends.
- Inspiration
- As online service platforms develop, the growth of online customer service has been achieved. However, due to the digitalization, many users who are not familiar with smart devices face problems in online communication, making it hard to obtain good after sales service. The complex flow of data and information and text descriptions cause problems to customer service. The application aims to meet the needs of both ends, lower the threshold, and improve service efficiency and user satisfaction.
- Project Duration
- The project was designed and developed in Beijing in April 2021 and went live in China in October 2021
- Operation Flow
- Multi modal input methods, including camera and voice input, can recognize foreign languages and dialects to lower the input threshold. Users can search for corresponding keywords or key words through communication aids to better understand the meaning of sentences while talking. The equipped AI assistant can analyze the emotional factors in the dialogue text in real time, generate response statements, and recommend quick response content to users.
- Team Members
- Yuan Lu
Fengyang Wang
Yujie Shi
Yi Liao
Nuo Xu
Liying Li
Li Liu
Xue Luan
Tian Xie
Lu Gan
Yanyan Wei
Jingjing Luo
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