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Technical Description of AI Interaction Personalization Technology

Field of Technology

This technology relates to information technologies, specifically to personalizing interaction with AI assistants through structured data with an instruction management mechanism. The solution aims to eliminate a fundamental limitation of modern AI systems — the lack of long-term memory between communication sessions.

Current Technology Level

Modern AI chats (Grok, ChatGPT, Claude, etc.) do not have long-term memory about the user between sessions, losing context after the dialogue ends. Our preliminary testing indicates that most information provided to AI in one session becomes unavailable when starting a new session. Existing solutions have drawbacks:

  1. Cloud storage requires transferring sensitive data to third parties, creating security and privacy risks (many users express concern about this approach).

  2. Built-in memory of AI systems has technical limitations (usually limited to previous messages) and can be deleted by the service provider at any time.

  3. Manual re-entry of context requires significant time investment with each new session, reducing productivity with regular use.

The market lacks solutions that allow users to structure, store, and transfer personalized context to AI systems without relying on cloud storage or internal AI memory, with a key management mechanism through instructions.

Essence of the Technology

The proposed method allows users to transfer structured data to AI with a mandatory instruction block. Instructions define how AI interprets, processes, and updates data, providing personalization and long-term memory between sessions. The method is applicable to any data formats that AI can process (JSON, YAML, XML, graph structures, relational data, etc.).

Structured data can be stored:

  • Locally on the user's device
  • In secure cloud storage with controlled access
  • In corporate storage with a multi-level permission system
  • Temporarily (only for the session period)

Data transfer to AI is carried out:

  • By pasting into chat
  • By attaching a file
  • Via API
  • By link to storage with authorized access

A key feature and integral part of the technology is the inclusion of an instruction block in the data, which acts as a personalization management mechanism and contains rules for processing, updating, and accessing information.

Technical Result

The technology provides the following benefits:

  1. Addressing the lack of long-term memory in AI chats:

    • Comprehensive preservation of relevant context between sessions
    • Substantial time savings compared to manual context re-entry

  2. Flexibility in using various data formats:

    • Support for multiple structured data formats that AI can process
    • Compatibility with various AI systems

  3. Increased user control over data:

    • Choice of storage location (local/cloud)
    • Ability to selectively provide information to AI
    • Management through instruction blocks

  4. Improved recommendation relevance:

    • More targeted answers through contextual personalization
    • Reduction in the need for repeated clarifications

  5. Time efficiency:

    • Significant time savings for active AI users
    • More concise dialogues while maintaining response quality

Process Description

The method includes the following steps:

  1. Forming structured data with instructions

    The user creates structured data in any format that AI can process, with a mandatory instruction block. The data may contain:

    • Basic information (name, age, location)
    • Professional information (skills, experience, projects)
    • Preferences (communication, formats)
    • Interests and goals (professional, personal)
    • Schedule and plans
    • Metadata (creation date, version, change history)

    The mandatory instruction block contains:

    • Primary instructions (how to use the data)
    • Update rules (when and how to modify data)
    • Access parameters (which fields are private/public)
    • Interaction protocol (specific communication rules)

  2. Choosing data storage

    The user chooses how to store structured data with instructions:

    • Local storage on the user's device
    • Secure cloud storage with controlled access
    • Corporate storage with a multi-level permission system
    • Temporary storage (only for the duration of interaction)

    When choosing cloud or corporate storage, the following can be configured:

    • Encryption level
    • Access rights for other users
    • Authentication and authorization parameters
    • Backup and synchronization policies

  3. Transferring data with instructions to AI

    The user transfers structured data with instructions in one of the following ways:

    • Inserts text/code into the chat
    • Attaches a file to the chat
    • Sends through API
    • Provides a link to storage with configured access

    This approach significantly reduces context transfer time compared to manually recreating context. Data can be transferred in full or selectively based on access instructions.

  4. Processing data and instructions by artificial intelligence

    AI analyzes the received structured data, guided by instructions:

    • Reads the data structure according to its format
    • Applies rules defined in the instructions
    • Preserves context from past sessions
    • Recognizes data versioning (if applicable)
    • Complies with private field access restrictions
    • Forms responses based on available data and instructions
    • Suggests updates according to rules in the instructions

  5. Updating structured data

    The user or authorized system makes changes to structured data:

    • Applies updates recommended by AI
    • Adds new data manually
    • Includes metadata about the update (time, source)
    • Can version changes
    • Updates access rights if necessary
    • Saves the version in the selected storage

  6. Repeating the interaction cycle

    The process continues in new sessions:

    • Updated version of data is transferred during a new session
    • AI receives the full context from previous interactions
    • Volume of repeated explanations is substantially reduced
    • Relevance and personalization of responses are increased
    • Continuity of interaction between sessions is ensured

Advantages Over Existing Approaches

  1. Compared to cloud storage:

    • Flexible storage choice: local or cloud
    • Complete control over the choice and volume of data
    • Possibility of local encryption before transfer
    • Independence from a specific AI provider

  2. Compared to built-in AI memory:

    • Extended data capacity compared to message-based history
    • Independence from the AI provider (compatibility with any AI)
    • Resilience to history clearing or system failures
    • Ability to structure and categorize data

  3. Compared to manual context:

    • Significant time savings per session
    • Structured and accurate information
    • Consistency of data between sessions
    • Ability to update data based on conversation results

  4. Unique advantages:

    • Instructions as the key management mechanism
    • Support for any data formats processed by AI
    • Multi-format and platform independence
    • Possibility of corporate application with access control

Implementation Examples

Example 1: Individual use with JSON format and local storage

A user creates a personal_context.json file:

{
  "basic_info": {
    "name": "Alexander",
    "age": 32,
    "location": "Moscow, Russia"
  },
  "professional_info": {
    "occupation": "programmer",
    "level": "middle",
    "skills": ["JavaScript", "React", "MongoDB"]
  },
  "health": {
    "physical_activity": "running, 3 times a week"
  },
  "goals": {
    "career": "developing ML/AI skills",
    "personal": "learning English"
  },
  "instruction": {
    "primary": "Use this context when answering my questions",
    "context_update": "If you learn new information, suggest adding it to the appropriate section",
    "privacy": "Professional information is public, health is private"
  },
  "metadata": {
    "version": "1.0",
    "last_updated": "2025-03-16",
    "storage": "local"
  }
}

The user pastes this JSON into a chat with AI. The AI analyzes the data according to instructions and responds:

"Hello, Alexander! Considering your goal of developing ML/AI skills and your current stack (JavaScript, React), I recommend adding a Python course to your learning plan, as it is the primary language for ML/AI. According to research, a combined learning approach increases effectiveness by 20-30%.

I suggest updating your context:

"goals": {
  "career": "developing ML/AI skills, learning Python",
  "personal": "learning English"
  "update_info": "added Python learning (updated by Claude 3.7, 2025-03-16)"
}

In the next session, the user transfers the updated JSON, and the AI "remembers" the previous recommendation, continuing work from the same point without needing to explain the context again.

Example 2: Corporate use with YAML format and cloud storage

A company implements a system of personal contexts for a development team with a multi-level access system:

# team_context.yaml
basic_info:
  company: "InnoSoft"
  department: "R&D"
  team_lead: "Dmitry Nikolaev"

team_members:
  - name: "Pavel Smirnov"
    position: "Senior Developer"
    projects: ["Project Beta", "Cloud Infrastructure"]
    access_level: 2
  - name: "Elena Sokolova"
    position: "Middle Developer"
    projects: ["Project Beta"]
    access_level: 1
  - name: "Andrey Volkov"
    position: "Junior Developer"
    projects: ["Testing", "Documentation"]
    access_level: 1

projects:
  Project Beta:
    deadline: "2025-06-30"
    status: "in development"
    critical_tasks:
      - "Microservices architecture"
      - "Cluster optimization"
    notes: "Priority project, investor demonstration in July"
  
  Cloud Infrastructure:
    deadline: "2025-05-15"
    status: "finalizing"
    remaining_tasks:
      - "Monitoring"
      - "Documentation"

instruction:
  primary: "Use this context for answers about team projects"
  context_update: "Add new data to the appropriate sections with source indication"
  access_control:
    level_1: ["basic_info", "team_members", "projects.*.deadline", "projects.*.status"]
    level_2: ["projects.*.critical_tasks", "projects.*.remaining_tasks"]
    level_3: ["projects.*.notes", "instruction.access_control"]

storage:
  type: "encrypted_cloud"
  provider: "EnterpriseSecureCloud"
  encryption: "AES-256"
  backup_frequency: "daily"

metadata:
  version: "2.1"
  last_updated: "2025-03-15"
  updated_by: "D.Nikolaev"
  change_history:
    - date: "2025-03-15"
      source: "Team Meeting"
      changes: "Added new team member: Andrey"

The corporate AI system receives the YAML via API with authorization and checks the user's access level. The team leader with access level 3 receives complete information, including private notes, while junior developers see only basic information and public project data.

Example 3: Using graph data structure

A user creates a graph structure in DOT format to visualize connections between goals and tasks:

digraph PersonalContext {
  // Basic information
  User [label="Irina\nDesigner, 28 years old", shape=box];
  
  // Goals
  Goal1 [label="Master UX Research", shape=ellipse, color=blue];
  Goal2 [label="Freelance projects", shape=ellipse, color=blue];
  Goal3 [label="Healthy lifestyle", shape=ellipse, color=green];
  
  // Tasks
  Task1 [label="Complete UX course", shape=rectangle];
  Task2 [label="Create portfolio", shape=rectangle];
  Task3 [label="Register on freelance platforms", shape=rectangle];
  Task4 [label="Yoga twice a week", shape=rectangle];
  
  // Connections
  User -> Goal1;
  User -> Goal2;
  User -> Goal3;
  Goal1 -> Task1;
  Goal2 -> Task2;
  Goal2 -> Task3;
  Goal3 -> Task4;
  
  // Instruction subgraph
  subgraph cluster_instructions {
    label="Instructions";
    Instructions [label="1. Use for planning\n2. Add connections with progress indication\n3. Track deadlines", shape=note];
  }
}

The AI processes this graph structure, visualizes it, and recommends an action plan to achieve goals according to the instructions. After discussion, the graph structure is updated:

// Adding progress and new connections
Task1 -> Progress1 [label="Started, 35%\nUpdated: Claude 3.7, 2025-03-16"];
Task4 -> Progress2 [label="Found yoga studio\nStart: 2025-03-22"];

This example demonstrates the flexibility of the method when working with graph data, which can be more visual for planning interconnected goals and tasks.

Example 4: Multi-format use with synchronization between devices

A user creates the main context in JSON, but uses specialized formats for individual parts:

  • Basic information: context.json
  • Charts and diagrams: DOT files
  • Tabular data: CSV files
  • Instructions: a unified YAML file
# instructions.yaml
primary: "Use data from all associated files"
files:
  - path: "context.json"
    type: "basic_info"
    update_rules: "manual_only"
  - path: "project_plan.dot"
    type: "planning"
    update_rules: "suggest_new_nodes"
  - path: "learning_progress.csv"
    type: "education"
    update_rules: "append_only"

privacy:
  public: ["basic_info.name", "basic_info.professional"]
  private: ["education.grades", "planning.personal"]

sync:
  devices: ["desktop", "mobile", "tablet"]
  method: "encrypted_cloud"
  last_sync: "2025-03-16T15:30:00Z"

When interacting with AI, the user transfers instructions.yaml and links to the other files. The AI follows instructions for accessing, processing, and updating each file according to its format and update rules, while maintaining the integrity of the entire context.

Future Benchmarking

We are developing a comprehensive benchmarking framework to quantify the performance improvements provided by PCM technology. Our preliminary testing indicates substantial benefits in several key areas:

  • Context preservation between sessions compared to traditional approaches
  • Recommendation accuracy enhancement through contextual personalization
  • Response time optimization through efficient context transfer

Detailed benchmarking methodology and results will be published as our testing program progresses. The benchmarking will include controlled comparisons with baseline approaches, documented test cases, and reproducible methodologies.

Social Impact

This technology has the potential to:

  1. Transform daily AI interaction

    • Shift from general, impersonal AI responses to deeply personalized interactions
    • Form individual "digital assistants" that know the user at a previously unavailable level
    • Make personalization a standard expectation rather than a premium feature

  2. Change education and work

    • Enable personalized educational trajectories based on the learner's full context
    • Reduce adaptation time for new tasks and projects through AI contextual memory
    • Create new positions like "context curators" and "personal data architects" in large organizations

  3. Improve healthcare and personal well-being

    • Enhance healthcare efficiency through improved medical context of the patient
    • Support disease prevention through continuous analysis of personal data
    • Develop new telemedicine models based on contextual information

  4. New understanding of privacy

    • Rethinking the concept of privacy: from binary (private/public) to a multi-level system with access gradations
    • Formation of new social norms regarding which aspects of personal context are appropriate to make available to different AI systems
    • Increasing importance of "information hygiene" and regular personal data audits

Market Potential

Implementation of this technology may impact several key areas:

  1. AI assistant market

    • Increased interaction efficiency due to continuous context
    • Growth in AI assistant usage due to personalization
    • New category of "personal AI" with long-term memory

  2. Corporate sector

    • Reduction in loss of corporate knowledge when employees change
    • Improved training efficiency for new employees
    • Potential productivity improvements for knowledge workers due to more accurate recommendations

  3. Personal data market and privacy

    • Shift from cloud data storage to local/hybrid approaches
    • Development of tools for encrypting and managing personal data
    • Potential reduction in risk of data leaks through storage decentralization

  4. Technology startups

    • Creation of startups around the personal context ecosystem
    • Opportunity for venture capital investment in this emerging field
    • Potential for innovation in personalized AI applications

  5. Education and human capital

    • Improvements in education efficiency through personalized context
    • Potential reduction in continuing education costs
    • Development of new specialized roles in context management

Technology Diagram

┌─────────────────┐        ┌───────────────────────────────────┐        ┌───────────────────┐
│                 │        │                                   │        │                   │
│      User       │───────▶│  Structured data                  │───────▶│        AI         │
│                 │        │  with mandatory instruction block │        │                   │
│                 │◀───────│                                   │◀───────│                  │
└─────────────────┘        └───────────────────────────────────┘        └───────────────────┘
        │                                    ▲                                    │
        │                                    │                                    │
        │                                    │                                    │
        │                                    │                                    │
        ▼                                    │                                    ▼
┌─────────────────┐                          │                          ┌───────────────────┐
│                 │                          │                          │                   │
│    Storage      │                          │                          │    Processing     │
│    (local/      │                          │                          │    according to   │
│    cloud/       │                          │                          │    instructions   │
│    corporate)   │                         │                           │                   │
│                 │                          │                          └───────────────────┘
└─────────────────┘                          │                                    │
        ▲                                    │                                    │
        │                                    │                                    │
        │                                    │                                    │
        │                                    │                                    ▼
        │                          ┌───────────────────┐                 ┌───────────────────┐
        │                          │                   │                 │                   │
        └──────────────────────────│     Update        │◀────────────────│  AI               │
                                   │     data          │                 │  recommendations  │
                                   │                   │                 │                   │
                                   └───────────────────┘                 └───────────────────┘

schema.png

This diagram demonstrates the key components of the technology and their interaction:

  1. User creates structured data with instructions
  2. Data is stored in the selected storage
  3. Data is transferred to AI
  4. AI processes data according to instructions
  5. AI provides recommendations
  6. Data is updated based on recommendations
  7. The cycle repeats with updated data

Open Standards and Interoperability

To ensure the widest possible adoption and prevent monopolization, this technology encourages:

  1. Open data format standards

    • Common schemas for personal context data
    • Standardized instruction blocks across different AI systems
    • Interoperable update mechanisms

  2. Vendor-neutral implementations

    • Cross-platform support
    • Independence from specific AI providers
    • Community-developed extensions and tools

  3. Privacy-enhancing features

    • Local-first approach to data storage
    • Selective sharing mechanisms
    • User-controlled encryption

This openness ensures that the technology remains accessible to everyone and can evolve through community contributions rather than being controlled by a single entity.

Comparison with Other Approaches

Personal Context Technology (PCT) is not the only approach to enhancing AI interactions, and it's useful to understand how it differs from other methods. Below is a comparison with several notable technologies and frameworks.

Retrieval-Augmented Generation (RAG)

RAG, as implemented in systems like ChatGPT (RAG for GPTs), enhances AI responses by retrieving relevant information from external documents in real-time. When a user asks a question, RAG uses semantic search to find pertinent data and incorporates it into the AI's response.

  • Difference: RAG focuses on retrieving external knowledge to improve factual accuracy, whereas PCT focuses on long-term personalization by providing a structured user profile. RAG treats a context file as a document to search through, not as an instruction for tailoring responses. For example, if a PCT file specifies a preference for visual explanations, an AI using PCT will consistently provide diagrams, while RAG might only mention this preference if explicitly asked.
  • Use Case: RAG is ideal for fact-based queries, while PCT is better for personalized, user-centric interactions like education.

Memory in ChatGPT

ChatGPT introduced a memory feature (Memory and New Controls for ChatGPT) that allows the AI to automatically remember details from user conversations. For instance, if a user mentions a preference for visual learning, ChatGPT can recall this in future interactions.

  • Difference: ChatGPT's memory is an automatic, server-side feature that collects data during conversations and stores it on OpenAI's servers. PCT, in contrast, is a user-controlled, structured file that the user creates and maintains locally or in a chosen storage location. PCT provides deeper personalization by allowing users to specify complex details (e.g., cognitive profiles, learning schedules), while ChatGPT's memory is more surface-level and depends on what the user mentions in dialogue.
  • Privacy: PCT prioritizes user control and privacy, as data is not automatically shared with the AI provider, unlike ChatGPT's memory, which requires trust in OpenAI's data handling practices.

DeepMind's Gemini Personalization

DeepMind's approach to personalization, as seen in Gemini (Gemini Personalization), involves collecting user data (e.g., search history, interaction patterns) to tailor AI responses. This is often done through machine learning models that adapt to user behavior over time.

  • Difference: DeepMind's personalization is an automated, server-side process that relies on continuous data collection. PCT, however, is a manual, user-driven framework where the user explicitly defines their context in a structured file. This gives users more control over what data is shared and ensures transparency, as opposed to DeepMind's more opaque, algorithm-driven approach.
  • Flexibility: PCT allows users to update their context file at any time, while DeepMind's personalization depends on the system's ability to infer user preferences, which may not always be accurate.

DeepSeek

DeepSeek is an open-source AI model focused on research and development, often used for tasks requiring high accuracy and reasoning (DeepSeek GitHub). It excels in natural language understanding and can be fine-tuned for specific applications, but it does not have a built-in mechanism for long-term user personalization.

  • Difference: DeepSeek primarily focuses on improving response quality through advanced training and fine-tuning, rather than maintaining user context across sessions. PCT, on the other hand, is specifically designed to provide a persistent user context, enabling the AI to adapt responses based on user-defined preferences and progress. DeepSeek could potentially use a PCT file if integrated, but it lacks a native framework for such personalization.
  • Use Case: DeepSeek is better suited for research or technical queries, while PCT enhances user-specific applications like education.

Grok by xAI

Grok, developed by xAI, is an AI designed to assist users in understanding complex topics, with a focus on truthfulness and helpfulness (xAI Grok). It supports long context windows and can process structured data, making it compatible with PCT.

  • Difference: Grok does not have a native personalization mechanism like PCT. Without a context file, Grok relies on the user to provide context in each session, which can lead to repetitive interactions. PCT complements Grok by providing a structured, reusable context that ensures consistency across sessions. For example, a PCT file can instruct Grok to always provide visual explanations, a feature Grok would not inherently prioritize without such guidance.
  • Compatibility: Grok's ability to handle long contexts makes it well-suited for PCT, as it can process the entire context file and follow its instructions effectively.

Ethical Considerations

PCT is designed with ethical principles in mind, as outlined in the Ethical Guidelines. Key considerations include:

  • User Control: Users maintain full control over their data, with no automatic collection by the AI system.
  • Transparency: The framework ensures that data usage is clear and understandable to users.
  • Prohibited Uses: PCT must not be used for military applications, mass surveillance, or manipulation of public opinion.

Implementation Recommendations

To implement PCT effectively, consider the following:

  1. Data Structure: Use a structured format like JSON for the context file, ensuring it includes sections for user goals, preferences, and progress.
  2. Instruction Block: Clearly define how the AI should use the data, such as prioritizing certain response styles or learning strategies.
  3. Privacy: Store context files locally or in a secure cloud environment, and avoid sharing sensitive information unless necessary.
  4. Updates: Regularly update the context file to reflect changes in user progress or preferences.

For a practical example, see the Quick Start Guide.

License

This project is released under the MIT License. See LICENSE for details.