# How a Smart System Verifies and Updates Customer Data > This patent describes an automated system that uses artificial intelligence to pick the best ways to check and update information about people or businesses, choosing from methods like web searches, phone calls, or direct mail. - **Patent:** US 8285656 - **Original title:** Systems and methods for data verification - **Owner:** Consumerinfo com Inc - **Granted:** 2012 - **Status:** Active - **Times cited:** 102 - **Field:** software, telecommunications, ai_ml, finance, ecommerce ## What it does This system automatically verifies and updates data, such as customer contact information. It includes a "data selection module" (Claim 1) that picks data to be checked. An "artificial intelligence module" (Claim 1) then decides which verification methods to use, like web-crawling, phone calls (tele-verification), or emails, based on how well those methods worked in the past (Claim 1). For example, if the system needs to verify a person's address, the AI might first try a low-cost web search. If that's not enough, it might then use a more expensive phone call for a smaller group of records, using the results to see how good the web search was (Claim 2, 3). The AI can also adapt its choices over time by monitoring how effective each method is (Claim 4). ## What it does NOT cover - Does not cover systems that verify data using only one fixed method, without an AI module choosing between options. - Does not cover manual data verification processes where a human decides the method for each record. - Does not cover systems that select verification methods without considering prior results or cost data (Claim 1, 2). - Does not cover systems that verify data without the ability to update or append information as a result of the verification. - Does not cover systems that only segment data without an AI module selecting methods based on those segments (Claim 9). ## The clever bit The truly novel aspect is the "artificial intelligence module" that dynamically selects and adapts data verification methods based on their past effectiveness, cost, or characteristics of the data itself. This moves beyond simple rule-based systems to a more intelligent, self-optimizing approach. ## Real-world examples 1. Credit reporting agencies updating consumer profiles 2. Marketing companies cleaning customer lists 3. Identity verification services checking user details 4. Business directories ensuring contact information is current 5. Fraud detection systems validating suspicious data points ## Why it matters Accurate customer and business data is crucial for many industries, from marketing to fraud prevention. This patent outlines a systematic, intelligent approach to keeping that data fresh and reliable, which helps companies save money by using the most efficient verification methods. Consumerinfo.com Inc., the assignee, operates in the credit reporting and identity protection space, where data accuracy is paramount. ## Frequently asked questions ### What does How a Smart System Verifies and Updates Customer Data cover? This patent describes an automated system that uses artificial intelligence to pick the best ways to check and update information about people or businesses, choosing from methods like web searches, phone calls, or direct mail. ### Who owns patent US 8285656? Consumerinfo com Inc owns this patent, granted in 2012. ### When does this patent expire? This patent is expected to expire on October 9, 2032, when the invention enters the public domain. ### What is patent US 8285656 cited by? This patent has been cited by 102 later patents that build on its ideas. ### What problem does this patent solve? Accurate customer and business data is crucial for many industries, from marketing to fraud prevention. This patent outlines a systematic, intelligent approach to keeping that data fresh and reliable, which helps companies save money by using the most efficient verification methods. Consumerinfo.com Inc., the assignee, operates in the credit reporting and identity protection space, where data accuracy is paramount. ### What does this patent NOT cover? Does not cover systems that verify data using only one fixed method, without an AI module choosing between options. **Full plain-English explainer:** https://patentbrief.org/patent/us/8285656/cortana-virtual-assistant **Original patent:** https://patents.google.com/patent/US8285656 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [How to Automatically Detect and Fix Changes in AI Model Data](https://patentbrief.org/patent/us/10599957/systems-and-methods-for-detecting-data-drift-for-data-used-in-machine-learning-m) — This patent describes a system that automatically notices when the real-world data an AI model sees changes, causing its predictions to become less accurate, and then fixes the model. - [How Online Systems Verify Your Identity Using External Data](https://patentbrief.org/patent/us/10212148/touch-id) — A system for verifying user identity and payment details in an online environment by pulling data from external sources and generating virtual payment tokens. - [How Cloud Systems Automatically Create and Train AI Data Models](https://patentbrief.org/patent/us/11615208/dall-e-text-to-image-generation) — A cloud-based system that generates fake, privacy-safe data to train AI models, ensuring they remain accurate while protecting sensitive personal information. - [Using Non-AI Systems to Improve AI Text Generation](https://patentbrief.org/patent/us/12353827/computer-implemented-methods-for-the-automated-analysis-or-use-of-data-including) — This patent describes a method where a traditional, rule-based computer system helps a Large Language Model (LLM) generate more accurate and reliable text by providing it with better context and fact-checking. - [How Computers Match and Join Messy Data from Different Sources](https://patentbrief.org/patent/us/9607103/amazon-athena) — A method for merging datasets by identifying related but non-identical items using flexible matching rules rather than strict equality.