Patent Analysis of Systems and Methods for Providing AI-based Cost Estimates for Services: US11270363B2

Abstract

Purpose: To understand its technological innovation, operational mechanisms, and commercial significance. The study evaluates how artificial intelligence, machine learning, computer vision, and environmental sensing technologies are integrated to automate inventory identification and generate accurate service cost estimates and interactive quotations. It further aims to assess the patent’s business value, societal impact, future opportunities, and strategic relevance through structured patent analysis frameworks such as SWOC and ABCDEF.

Methodology: This study adopts an exploratory qualitative research approach to systematically collect, organize, and analyse information related to the selected patent from sources such as Google Search, Google Patents, Google Scholar, and AI-assisted platforms. The collected data were interpreted using SWOC and ABCDEF analytical frameworks to evaluate the patent’s technological, strategic, and commercial significance.

Results & Analysis: The analysis reveals that the patent successfully integrates artificial intelligence, machine learning, computer vision, and sensor analytics to automate inventory generation and provide accurate, interactive service cost estimates. The study found that the invention offers significant technological, commercial, and operational advantages by reducing manual effort, improving estimation accuracy, enhancing customer convenience, and creating new business opportunities through digital service ecosystems. The SWOC and ABCDEF analyses further indicate that the patent possesses strong innovation potential, scalability, market value, and future commercialization prospects despite challenges related to data quality, privacy, and competitive pressures.

Originality & Values: This study is original in applying a structured patent-analysis approach using SWOC and ABCDEF frameworks to evaluate the technological, commercial, and societal significance of Patent US11270363B2. The article provides valuable insights into how artificial intelligence, machine learning, computer vision, and sensor analytics can transform traditional service-estimation processes into intelligent, automated, and scalable digital solutions. The study also offers practical value for researchers, innovators, service providers, investors, and policymakers by highlighting the patent’s innovation potential, business opportunities, and future commercialization prospects.

Type of Paper: Review-based Exploratory Research.