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One Click Search Explanation

IN-V-BAT-AI
One Click Search Breakthrough Technology Explanation

by Apolinario "Sam" Ortega, founder of IN-V-BAT-AI

IN-V-BAT-AI developed a natural language selective permutation invariant search system enabling 1 click search on regular CPU. Before diving into the discovery, a brief look at the history of search challenges sets the stage.

Building low-cost, custom 1-click web search engines is a rewarding yet challenging pursuit.

Sorting is a core computer science challenge—essential for indexing big data in inverted databases, which power lightning-fast natural language queries and full-text search, often in milliseconds.

Full-text search is a powerful technique used to find specific words or phrases within a large collection of text, such as documents or databases. Here's a simplified explanation:

How It Works

1. Indexing:
- Tokenization: The text is broken down into individual words or tokens.
- Stemming: Words are reduced to their root forms (e.g., "running" becomes "run").
- Stop Words Removal: Common words like "the" and "is" are often ignored to save space and improve search speed.
- Index Creation: An index is created, which is like a giant table of contents. It maps each word to the locations (documents and positions) where it appears.

2. Searching:
- When a user enters a search query, the algorithm looks up the words in the index. - It retrieves the documents that contain the search terms and ranks them based on relevance. Relevance can be determined by factors like word frequency, proximity of search terms, and document popularity.

Example

Imagine you have a library of books. Instead of reading every book to find a specific word, you create an index. This index tells you exactly which books and pages contain the word you're looking for. When you search for "fox," the index quickly points you to all the relevant pages across all books.

Benefits

- Speed: Searching the index is much faster than scanning the entire text.
- Accuracy: Advanced algorithms can rank results by relevance, making it easier to find what you're looking for.


Applications

Full-text search is widely used in search engines like Google, document management systems, and databases. Tools like Elasticsearch and Solr are popular for implementing full-text search in applications¹².
By understanding these basics, you can appreciate how full-text search makes finding information efficient and effective.


Source: Conversation with Copilot, 9/2/2024

(1) String Search Algorithms for Large Texts | Baeldung. https://www.baeldung.com/java-full-text-search-algorithms.
(2) Full Text Search Explained - Josh Graham. https://www.joshgraham.com/full-text-search-explained/.
(3) Full-Text Search - Glossary. https://www.devx.com/terms/full-text-search/.
(4) Full-Text Search: How It Works and Why It Matters - EMB Blogs. https://blog.emb.global/learn-about-full-text-search/.
(5) Full-text search - Wikipedia. https://en.wikipedia.org/wiki/Full-text_search.

Apache Lucene, example of open source full text search engine ⤵ 👈
Sphinx, another example of open source full text search engine ⤵ 👈
AWS DynamoDB another example of full text search engine ⤵ 👈
Azure CosmoDB another example of full text search engine ⤵ 👈
MongoDB another example of full text search engine ⤵ 👈

Do you know the search retrieval technology use by your AI provider? ⤵ 👈

Explain full text search algorithm in easy to understand in one paragraph .

ChatGPT explanation of full text search in one paragraph


Below is the author definition with utility context:

Full text search algorithm is a search solution to find and retrieve exact match or partial match of series of string text stored in inverted indexed database of record for fast retrieval in range of milliseconds.

Example of series of string text
get me quadratic equation calculator

Try copying and pasting the above series of text in input box below.

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How old is the information retrieval or search problem? Is there a known unsolved problem in information retrieval?

I quoted some information that I researched to answer the above questions.

"Since the 1940s the problem of information storage and retrieval has attracted increasing attention." ; "The problem of effective retrieval remains largely unsolved." ; "The comparatively slow progress of modern linguistics on the semantic front and the conspicuous failure of machine translation (Bar-Hillel[5]) show that these problems are largely unsolved."

source: C. J. “Keith” van Rijsbergen, often considered one of the founders of modern Information Retrieval (IR) This book was published in 1979. With ChatGPT AI technology released for public trial in November 2022, in my personal observation, the problem of effective information retrieval is now solved.

Year 2021 :
IN-V-BAT-AI is demonstrating to the world that cost effective customized information retrieval is now possible using full text search algorithm and AI natural language query (NLQ). So effective that one click search is now possible.

Problem statement:Using internet and cloud technology, search and retrieve quadratic equation calculator as an example using natural language query (NLQ) via voice or text input. Policy for search and retrieval method is one click search.

Simple accuracy testing: simply copy training data shown below (see example of training data below). and paste it into search box above. Expected result should be 100% accurately retrieve the stored information in one click search. There should be no need for model re-training as database keep on growing.

if you see error message 404 - not found. It means our inverted index database of full text does not have the exact match of text string. If you are a subscriber , we can easily add your full text string query in our database and next time you query, you can get your personal collected information any time using multiple devices.

Our parallel multiple input training label to output one click search is similar in concept of using multiple labeling to the latest paper from Amazon XMC framework , Extreme Multilabel Classification presented in Neural Information Processing Systems (NeurIPS) in Dec 14, 2021 to push the boundary on search and information retrieval. XMC framework is using 3 million training labels for 29 hours while our algorithm is using customized training label provided by our subscriber and live in production environment within 30 minutes from the time of service request.

Our one click technology search is similar in concept to Deepmind RETRO algorithm published in Dec 8, 2021 . ⤵ 👈 The concept similarity is using retrieval database in our case we are using inverted database technology. Deepmind is using about 2 trillion words database from text passages ⤵ 👈 while we are using customized text passages from our subscriber.

Illustration of parallel word clues or set of word phrases as input to output same result. I called this algorithm as selective permutation invariant because there is a human curator in the loop to select and encode the frequently ask combination of words. Once encoded and assigned a unique number then it becomes selective invariant permutation.

Few shot learning and selective permutation invariant is possible by using unique number only. This unique number is called in AI terminology as embedding number, transformer number, or vector number. Using set of word phrases transform or encoded into a unique number and stored in inverted full text database it is now possible to create a fast customize search engine using semantic search or common english language.

First advantage of our selective permutation invariant search algorithm is less memory storage because we only stored few selection of personal collected knowledge of our subscriber therefore less database to search for faster retrieval time.

The second advantage is affordable cost for $30 per year subscription, you will get on demand personal memory assistant chatbot powered by AI and cloud technology accessible via smartphone, tablet, laptop, desktop, and smart TV.

The third advantage is your personal memory assistant is available in internet within 24 hours.

The fourth advantage is guaranteed no hallucination by design because there is always a human in the loop to curate the selection of word embedding.

Few Shot Training Data

IN-V-BAT-AI discovered how to implement natural language selective permutation invariant search retrieval system using regular CPU that make 1 click search possible.

I defined selective permutation invariant search retrieval system as returning the same output search every time the same set of words input was used irrespective of word position. I think it is one of the possible solutions to AI's explainability, traceability, and hallucination problem.
The sketch above is helpful to visualize how natural language invariant retrieval system works. Imagine all the rays of line are index of natural language database. Mapped to a unique number in this example is 54.

Let me show you how our invariant retrieval system works. The ground truth is always the unique number 54. Try typing or saying any of string of text shown below in magenta color in input box above.

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Never Forget is Now Possible With
IN-V-BAT-AI. Store Your Knowledge in the Cloud.

IN-V-BAT-AI helps you recall information on demand—even when daily worries block your memory. It organizes your knowledge to make retrieval and application easier.

Source: How People Learn II: Learners, Contexts, and Cultures

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How can IN-V-BAT-AI be used in classrooms ?

IN-V-BAT-AI is a valuable classroom tool that enhances both teaching and learning experiences. Here are some ways it can be utilized:

⋆ Personalized Learning : By storing and retrieving knowledge in the cloud, students can access tailored resources and revisit concepts they struggle with, ensuring a more individualized learning journey.

⋆ Memory Support : The tool helps students recall information even when stress or distractions hinder their memory, making it easier to retain and apply knowledge during homework assignments or projects.

⋆ Bridging Learning Gaps : It addresses learning loss by providing consistent access to educational materials, ensuring that students who miss lessons can catch up effectively.

⋆ Teacher Assistance : Educators can use the tool to provide targeted interventions to support learning.

⋆ Stress Reduction : By alleviating the pressure of memorization, students can focus on understanding and applying concepts, fostering a deeper engagement with the material.

🧠 IN-V-BAT-AI vs. Traditional EdTech: Why "Never Forget" Changes Everything

📚 While most EdTech platforms focus on delivering content or automating classrooms, IN-V-BAT-AI solves a deeper problem: forgetting.

✨Unlike adaptive learning systems that personalize what you learn, IN-V-BAT-AI personalizes what you remember. With over 504 pieces of instantly retrievable knowledge, it's your cloud-based memory assistant—built for exam prep, lifelong learning, and stress-free recall.

• ✅ One-click access to formulas, calculators, and concepts
• 📧 No coding, no hosting—just email what you want to remember
• 📱 Live within 24 hours, optimized for mobile and voice search
• 💸 $30/year for 504 personalized knowledge sites (just 6¢ each)

"🧠 Forget less. Learn more. Remember on demand."
That's the IN-V-BAT-AI promise.

Personal Augmented Intelligence (AI) Explanation

🧠 Augmented Intelligence vs Artificial Intelligence

Understanding the difference between collaboration and automation

🔍 Messaging Contrast

Augmented Intelligence is like a co-pilot: it amplifies your strengths, helps you recall, analyze, and decide — but it never flies solo.

Artificial Intelligence is more like an autopilot: designed to take over the controls entirely, often without asking.

💡 Why It Matters for IN-V-BAT-AI

IN-V-BAT-AI is a textbook example of Augmented Intelligence. It empowers learners with one-click recall, traceable results, and emotionally resonant memory tools. Our “Never Forget” promise isn't about replacing human memory — it's about enhancing it.

Note: This is not real data — it is synthetic data generated using Co-Pilot to compare and contrast IN-V-BAT-AI with leading EdTech platforms.

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Year	Top 10 countries	Pages visited
2023	1. USA 2. Great Britain 3. Germany 4. Canada 5. Iran 6. Netherlands 7. India 8. China 9. Australia 10. Philippines	127,256 Pages / 27,541 Visitors
2024	1. USA 2. China 3. Canada 4. Poland 5. India 6. Philippines 7. Great Britain 8. Australia 9. Indonesia 10. Russia	164,130 Pages / 40,724 Visitors
Daily Site Visitor Ranking 10/26/2025	1. USA 2. Canada 3. Brazil 4. Vietnam 5. China 6. India 7. Argentina 8. Japan 9. Morocco 10. Indonesia	Year to Date 178,030 Pages / 60,375 Visitors

Data source: Advanced Web Statistics 7.8

Approximately between 2.3 and 2.5 million schools globally, according to the latest available data from government and education ministry reports.

🔗 The challenges schools face: 2025/2026 🔗 USA ~ Public 98,500 ~ Private 30,000 ~ Total 128,500 🔗 Canada ~ Public 15,500 ~ Private 2,000 ~ Total 17,500 🔗 Brazil ~ Public 138,000 ~ Private 40,000 ~ Total 178,000 🔗 Vietnam ~ Public 42,000 ~ Private 8,000 ~ Total 50,000 🔗 China ~ Public 217,200 ~ Private 152,800 ~ Total 470,000 🔗 India ~ Public 1,022,386 ~ Private 335,844 ~ Total 1,358,230 🔗 Japan ~ Public 30,240 ~ Private included ~ Total 30,240 🔗 Morocco ~ Public 20,600 ~ Private 6,300 ~ Total 26,900 🔗 Indonesia ~ Public 390,718 ~ Private included ~ Total 390,718 🔗 Philippines ~ Public 47,831 ~ Private 13,000 ~ Total 60,831 Great Britain ~ Public 29,202 ~ Private included ~ Total 29,202 🔗 Australia ~ Public 9,653 ~ Private included ~ Total 9,653 🔗 Russia ~ Public 39,070 ~ Private included ~ Total 39,070 🔗 Germany ~ Public 31,039 ~ Private included ~ Total 31,039 🔗 Poland ~ Public 36,291 ~ Private included ~ Total 36,291 🔗 Iran ~ Public 80,000 ~ Private included ~ Total 80,000 🔗 France ~ Public 58,100 ~ Private included ~ Total 58,100 🔗 Mexico ~ Public 132,505 ~ Private included ~ Total 132,505

Use an estimated range of 200 to 400 students per school if student enrollment is the only available data.

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