Every now and again, I investigate patents recently granted using the term genealogy. Often the hits don’t relate to family history. Here are some published in the last two months that look interesting. The abstract is often complicated. I asked AI to simplify it to a grade 10 level. Here’s the result – verbatim.
ARTIFICIAL INTELLIGENCE-BASED EVENT GENERATION METHOD AND SYSTEM FOR GENERATING MEMOIR EVENTS BASED ON INFORMATION ASSOCIATED WITH USERS
Simplified Abstract
This patent describes an AI-powered method and system that automatically generates memoir events—stories about meaningful moments in a person’s life—by analyzing information collected from users.
The Process:
The AI-based event generation method works through several key steps:
- Data Collection: The system collects input data from users’ electronic devices, such as smartphones, computers, or tablets.
- Tokenization: The collected information undergoes a tokenization process, which breaks down text and data into smaller units called “first tokens.” These tokens are the basic building blocks that the AI model can analyze and understand.
- Embedding Conversion: Each token is converted into embeddings, which are numerical representations that capture the meaning and context of the information in a format suitable for processing by neural networks.
- Contextual Analysis: The system assigns weights to each token by analyzing their importance within the sequence of inputs. This weighting process helps the AI understand relationships between different tokens and determine which information is most significant. The neural network architecture processes the embeddings to perform this analysis.
- Content Generation: Using the weighted first tokens, the system generates “second tokens” (new content) by predicting subsequent tokens through probability distribution. Essentially, the AI determines what words or information should come next based on patterns it has learned from the vocabulary and the context provided by existing tokens.
- Story Assembly: The generated second tokens are concatenated (linked together) to form coherent memoir events—complete narratives about significant moments in the user’s life.
- User Interface Display: The final memoir events are presented to the user through an interface where they can view, read, and interact with their automatically generated personal stories.
This technology enables users to preserve their memories in narrative form without manually writing each story, as the AI interprets their data and transforms it into meaningful memoir content.
SYSTEMS AND METHODS FOR PERFORMING AXIOMATIC ANCESTRAL STRATIFICATION BY KINSHIP
Simplified Abstract
This invention describes a computer system that helps people understand how their DNA matches are related to them through common ancestors. When people take DNA tests, they often get hundreds or thousands of matches, but it’s hard to figure out exactly how everyone is connected. This system solves that problem.
Main Components:
1. AASK Process (Axiomatic Ancestral Stratification by Kinship)
This is the core method that organizes DNA matches into family groups based on shared ancestors. The process takes people who match your autosomal DNA (the DNA inherited from both parents) and sorts them into hierarchical groups—like a family tree structure—showing which ancestor you have in common with each match. Even when DNA matches share very little genetic material and seem unrelated at first, this system can identify their connection through common ancestral family lines.
2. Desktop Tools
The system includes automated scripts (computer programs), formulas, and organized data structures that work with common spreadsheet programs like Microsoft Excel. These tools help individual users analyze and organize their DNA matches on their personal computers. The automation makes it easier to correlate data and create tables showing family relationships without doing all the work manually.
3. Enterprise Database System
For larger-scale operations, the invention provides a complete database management system (DBMS) with specialized data tables and methods. This allows companies or organizations to use the AASK process with large amounts of data, managing thousands of users and millions of DNA matches efficiently.
How It Helps:
Instead of looking at a confusing list of DNA matches, users can see their matches organized by which ancestral family line they share. This makes genealogical research much more manageable and helps people trace their family history more accurately.
Warning: These could remind you of the saying “Laws are like sausages. It’s better not to see them being made.”
When
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The FreeBMD Database was updated on Sunday, 23 November 2025, to contain 294,585,442 unique records, up from 294,344,983 in October.
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