A graduate student interrupted my class to say that GenAI was just hype. It took me 5 minutes and 4 prompts to change her mind (now she can't sleep)

By: Dr. Ivan Del Valle - Published: November 10th, 2024

The conversation started innocently enough—a graduate student in a virtual lecture raised her hand in a Google Meet session and boldly declared that Generative AI was just a passing trend, an overhyped bubble bound to burst. It wasn't the first time I'd heard such skepticism, and I welcomed the chance to engage. Instead of arguing, I decided to show, not tell. Five minutes and four prompts later, her eyes widened, her curiosity piqued, and by the end of the exercise, she couldn't stop thinking about the possibilities. Now, she tells me she can't sleep—her mind spinning with potential applications she once dismissed. This article recounts exactly how those five minutes unfolded and the prompts that led her from dismissive skepticism to restless excitement.

Me: "Let's ideate and start a business in less than 5 minutes with ChatGPT 4o. Let's design the framework for a digital product. Our initial budget is just $2,000.00 to develop an MVP and pitch the concept to potential investors."

Prompt #1: "Develop the concept of an application based on Neuromarketing principles that can run in the backend of a phone or tablet while you browse apps like Youtube, Instagram or TikTok and based on the microexpressions captured through the phone camera as you watch content. The platform content recommendations get updated based on the new app results over time. For example, if the combination of your microexpressions reflect sadness, the content recommendations focus on content to improve your mood. If it detects anger or anxiety, it recommends content to calm you down, etc. The initial budget is just $2,000"        

Output:

App Concept: "MoodSync" – Real-Time Emotion-Driven Content Recommendation System

Overview:

"MoodSync" is a backend mobile application that passively analyzes user emotions while they are browsing content on social media apps like YouTube, Instagram, or TikTok. By utilizing real-time facial recognition and microexpression analysis via the phone camera, the app identifies the user's emotional state and feeds this information into content recommendation algorithms, creating a personalized and emotionally adaptive browsing experience. The goal is to enhance user engagement by curating content that aligns with or positively shifts their emotional state.

Core Features:

1. Real-Time Emotion Detection:
2. Content Recommendation API Integration:
3. Privacy-Focused AI Processing:
4. Emotional Pattern Recognition:
5. Emotion-Based Alerts and Wellness Prompts:

Technical Architecture:

1. Emotion Detection Module:
2. API Integration with Content Platforms:
3. Privacy and Local Processing:

User Flow:

1. Setup and Calibration:
2. Real-Time Tracking:
3. Insights and Customization:

Monetization Strategy:

1. Freemium Model:
2. API Partnership Model:
3. Data-Driven Insights for Users:

Technical & Ethical Considerations:

1. Privacy Compliance:
2. Ethical Usage of Emotional Data:
3. Battery and Performance Optimization:

Feasibility and Development Timeline:

Given the estimated budget of $2000, the app's development should prioritize creating a Minimum Viable Product (MVP). Here's a three-month development plan:

Phase 1: Research and Planning (2 weeks)

• Research microexpression detection models and APIs.
• Design the app architecture and user flow.
• Create wireframes for user interaction and permission setup.

Phase 2: Core Development (6 weeks)

• Develop the emotion detection module using TensorFlow Lite or CoreML.
• Implement backend integration for real-time camera use and processing.
• Develop API interaction logic for content modification.

Phase 3: Testing and Optimization (4 weeks)

• Test microexpression recognition accuracy.
• Optimize camera usage to reduce battery drain.
• Conduct user testing with a small group to refine emotion-based recommendations.

Budget Allocation:

• Development Tools & Cloud Costs: $500 for initial testing (AWS/GCP if required).
• API and Model Integration: $500 (including using pre-built models or API costs).
• Freelancer Assistance for Core Features: $1000 for specialized help, particularly for fine-tuning emotion detection models and UI refinement.

Potential Future Features:

• Integrate with Wearables: Pair with smartwatches for more accurate stress and emotion detection through physiological signals.
• Adaptive Audio Content: Expand the recommendation to include music or podcasts that help regulate mood in real time.

The concept of MoodSync could revolutionize how users interact with content, moving towards a deeply personalized and emotionally responsive digital experience. This app would require strategic partnerships, but even the MVP would showcase your expertise in AI, neuromarketing, and emerging technologies.

Would you like more detailed assistance on any specific part of this concept, such as building the emotion detection model or integrating with a specific content platform?

Prompt #2: "Generate the framework I would need to turn this idea into a patent pending proposal."        

Output:

Certainly! To turn your innovative idea of an emotion-driven content recommendation system for mobile browsing into a patent-pending proposal, you'll need to create a set of well-prepared assets and content that will help you through the patent application process. Here is a structured guide of the assets you would need:

1. Title & Abstract of the Invention

• Title: Create a concise and descriptive title.Example: "System and Method for Emotion-Driven Content Recommendation Using Real-Time Facial Analysis on Mobile Devices"
• Abstract: A short summary of the invention, typically 150-250 words, explaining what the invention is and its purpose.Example: This invention relates to a system and method for recommending digital content based on real-time emotional analysis of the user via a mobile device's front-facing camera. The system detects microexpressions, determines the user's emotional state, and adjusts content recommendations accordingly to provide a personalized user experience that is adaptive to the user's mood in real-time. This invention aims to improve content relevance and user well-being by seamlessly integrating emotion-driven adjustments into social media and content browsing applications.

2. Technical Field of the Invention

• Describe the field of technology this invention belongs to.Example: The present invention pertains to the fields of neuromarketing, machine learning, and user content interaction, and specifically to a system that adapts content recommendations based on real-time detection of user emotions.

3. Background of the Invention

• Describe the existing problem or state of the art and the need that this invention addresses.Example: Current social media and digital content platforms offer content recommendations based largely on engagement data, which does not account for a user’s emotional state. The lack of emotional consideration in these algorithms can lead to content recommendations that are inappropriate for a user's current mood, potentially leading to user disengagement or negative emotional effects. There is a need for a system that can dynamically adapt content suggestions in response to real-time detection of a user's emotions, enhancing both content relevance and user satisfaction.

4. Summary of the Invention

• A summary that describes how your invention solves the problem stated in the background.Example: The present invention provides a system and method for real-time emotion detection using microexpression analysis performed via the front-facing camera of a mobile device. The system utilizes a combination of machine learning models for facial analysis and an API to adjust content recommendation algorithms. This emotionally adaptive approach ensures that content aligns with or positively affects the user's current mood, thereby creating a personalized and mood-aware content experience.

5. Detailed Description of the Invention

• System Architecture:
• Flow of Operation:
• Use Case Example:

6. Drawings and Diagrams

• Flowcharts: Create flowcharts illustrating the process flow of the app.Diagram showing how data flows from emotion detection, processing, to recommendation integration.
• System Architecture Diagram: Visual representation of how the different modules (emotion detection, privacy, API integration) interact with one another.
• UI/UX Sketches: Mockups or sketches to illustrate how users interact with the application, including permission setup and calibration.

7. Claims Section

• Clearly define the scope of what the invention covers.Independent Claims: Describe the broad aspects of the invention.Example: A method for recommending content, comprising: detecting a user’s emotional state using microexpression analysis via a mobile device camera; processing the emotional data to classify it into one or more emotional categories; and adjusting content recommendations based on the classified emotional state.Dependent Claims: Elaborate on specific components or details.Example: The method of claim 1, wherein the emotional state detection is performed locally on the device to ensure user privacy.

8. Key Components and Specifications

• Emotion Detection Algorithms: Description of the machine learning models used (e.g., facial action coding systems for microexpression recognition).
• Data Processing Flow: Explain the data processing pipeline, including how images are processed to derive emotional state, how data is kept anonymous, and how it integrates with content platforms.
• Privacy Measures: Detail the privacy protocols implemented (e.g., local processing, end-to-end encryption, user permissions) to mitigate risks.
• Hardware Requirements: Describe the hardware requirements, such as the need for a front-facing camera and a compatible mobile device.

9. Embodiments of the Invention

• Describe multiple variations or embodiments of the invention, including how it can be used in different settings.Example:First Embodiment: Emotion detection running locally on a mobile device and feeding into YouTube's content API.Second Embodiment: Integration with other applications, such as using Spotify to suggest mood-based playlists.Third Embodiment: Expanding use cases to work with augmented reality (AR) apps for emotional well-being.

10. Patent Drawings

• High-quality drawings that visually represent the various aspects of your invention, including:System Overview Diagram: Show all major system components and how they interact.User Interaction Flow: Show user interactions, like emotion detection and content response.Module Level Diagrams: Provide detailed depictions of modules like emotion analysis, API integration, etc.

11. Prior Art Search and Analysis

• List of existing technologies or patents that are related to your invention. Highlight how your invention differentiates itself.Conduct a preliminary patent search on databases like Google Patents or USPTO to identify related inventions.Provide analysis on how your invention solves a problem or provides a novel feature not addressed in the prior art.

12. Prototype/Proof of Concept Documentation (Optional)

• Provide evidence or documentation that demonstrates feasibility.Prototype Overview: Describe if you have developed a basic prototype to show proof of concept, such as using TensorFlow Lite for emotion analysis.Testing Data: Include initial results of emotional detection accuracy or a simple demonstration of real-time content adaptation.

13. Patentability Assessment

• Novelty: Explain what makes this idea new. Highlight that real-time emotion-based content adaptation with privacy-focused local analysis is not present in current systems.
• Inventive Step/Non-Obviousness: Describe why the use of microexpressions to modify digital content in real time is an inventive step beyond existing recommendation algorithms.

14. Legal Disclosures and Statements

• Include any statements related to data privacy, compliance with local regulations (e.g., GDPR).
• Mention ethical considerations of using emotion detection in content personalization, ensuring ethical use and preventing manipulation.

Patent Application Draft:

All the sections above should be used to draft the formal patent application. You will likely work with a patent attorney to finalize the content and ensure that the legal language is precise, protecting the unique aspects of your invention.

Budget and Timeline Considerations:

• Patent Search and Consultation: Budget $500-$1,000 for a patent search and legal consultation.
• Drafting Patent Application: Approximately $1,000-$3,000 for drafting a provisional application with an attorney.
• USPTO Filing Fees: Filing fees for a provisional patent application are usually around $75-$150 for small entities.

Patent Filing Process:

1. Conduct Prior Art Search: Use Google Patents or consult with a patent professional to verify the novelty of the invention.
2. Prepare Provisional Patent Application: Write a detailed description of the invention, including claims, drawings, and detailed embodiments.
3. File Provisional Application: Submit the provisional application to the USPTO to secure a filing date and "Patent Pending" status.
4. Develop Prototype and Iterate: During the year of "Patent Pending" status, develop a prototype to gather user feedback and strengthen the non-obviousness claims.
5. File a Non-Provisional Patent: Before the provisional patent expires (typically 12 months), file the non-provisional application that includes finalized claims based on the feedback and further development.

With these assets in place, you’ll be well-prepared to draft a strong patent application. Would you like me to expand on any specific section or help with drafting particular parts of the documentation?

Prompt #3: "Generate a high-level business plan to present the concept to potential investors."        

Output:

Business Plan: "MoodSync" – Real-Time Emotion-Driven Content Recommendation System

1. Executive Summary

Overview: MoodSync is an innovative mobile application designed to provide a real-time, emotion-driven content recommendation system. By passively analyzing user emotions while they browse social media content, MoodSync curates personalized content experiences to enhance user engagement and positively impact mood. Using the phone's front-facing camera, advanced facial recognition, and microexpression analysis, MoodSync feeds emotional insights into content recommendation algorithms for platforms like YouTube, Instagram, and TikTok.

Problem Statement: Current content recommendation systems are largely based on past user behavior metrics (likes, shares, watch time). These systems fail to consider the user’s emotional state, leading to recommendations that may not always align with how a user is feeling in real-time, potentially diminishing user satisfaction. MoodSync aims to bridge this gap by using real-time emotion analysis to curate content that positively influences users’ emotions.

Solution: MoodSync analyzes facial microexpressions via a mobile device’s front camera to determine a user's emotional state. This data is used to provide personalized content recommendations that either align with or uplift the user's mood. The processing occurs locally on the user's device, maintaining privacy and security. MoodSync aims to create emotionally adaptive and personalized digital experiences, enhancing engagement and user satisfaction.

2. Company Description

Mission: MoodSync’s mission is to provide personalized and emotionally adaptive content recommendations to improve user well-being and engagement with digital content.

Vision: To revolutionize the way users interact with digital content by integrating emotional intelligence into recommendation systems, making content consumption a more positive and enriching experience.

Business Model: MoodSync operates as a freemium application. The basic version provides real-time emotion analysis and simple content integration for free, while the premium version offers detailed emotional insights, enhanced historical analysis, and integration with additional platforms.

3. Product and Services

Core Features:

• Real-Time Emotion Detection: Utilizes the phone’s front camera to capture microexpressions, detect emotional states (e.g., happiness, sadness, anxiety), and classify these emotions using lightweight machine learning models.
• Content Recommendation Integration: Sends the detected emotional state data to partner APIs in real-time to adjust content recommendations on platforms like YouTube, Instagram, and TikTok.
• Privacy-Focused AI Processing: Processes all facial analysis locally on the user's device to protect user privacy, with no personal images stored. Only anonymized emotional state data is shared, and all information is encrypted.
• Emotional Pattern Recognition: Builds an emotional profile over time to enhance recommendation accuracy, which can be toggled off for users who prefer real-time analysis only.
• Emotion-Based Alerts and Wellness Prompts: Provides wellness prompts like breathing exercises or quick meditations if persistent negative emotions (e.g., anxiety, anger) are detected.

4. Market Analysis

Target Market: The primary target market includes individuals aged 18-45 who are active users of social media and video streaming platforms. These users are typically technology-savvy, interested in personalized digital experiences, and value mental well-being.

Market Need: With growing concerns over the negative impacts of social media on mental health, MoodSync addresses a crucial gap by providing content aligned with users' emotional states. This differentiates MoodSync from existing recommendation systems that rely purely on engagement metrics.

Market Trends: There is an increasing demand for personalized and well-being-focused tech solutions. The mental wellness app market has been expanding rapidly, showing a growing trend of integrating technology with self-care and emotional wellness.

Competitive Analysis: Current competitors include recommendation algorithms like those used by YouTube and TikTok. However, these competitors lack real-time emotional consideration. MoodSync differentiates itself by integrating emotional intelligence to provide an adaptive content experience in real-time.

5. Marketing and Sales Strategy

User Acquisition:

• Digital Advertising: Targeted ads on social media platforms to reach tech-savvy users and early adopters.
• Partnerships: Collaborate with mental wellness influencers and tech enthusiasts to promote MoodSync.
• Freemium Model: Provide a free version to attract a wide user base, encouraging users to upgrade to premium for additional insights and features.

User Engagement and Retention:

• Personalization: Users receive personalized insights on how their emotions fluctuate during content consumption.
• Wellness Prompts: Regular positive reinforcement to encourage users to engage with the app during moments of stress or anxiety.

Revenue Streams:

• Premium Subscription: Advanced features such as emotional insights, historical analysis, and integration with more platforms.
• API Partnerships: Partnering with social media platforms to provide exclusive access to emotion-driven personalization.
• White Labeling: Offer a white-label version to content platforms interested in integrating emotion-driven features into their native recommendations.

6. Technical Architecture

Emotion Detection Module: Uses OpenCV or MediaPipe for real-time facial landmark detection. TensorFlow Lite runs on-device to classify microexpressions and map them to core emotions, ensuring lightweight performance suitable for mobile devices.

API Integration: Integrates with social media platforms via public APIs. When an emotion is detected, MoodSync sends the relevant data to adjust content recommendations in real-time. Partnerships could provide deeper API integration for customized recommendations.

Privacy Compliance and Security:

• Facial analysis occurs locally, ensuring no video feed is stored or transmitted.
• Uses Apple’s CoreML or Google ML Kit for on-device model deployment.
• Full compliance with GDPR and CCPA, providing users with transparency and control over their data.

7. Development Roadmap

Phase 1: Research and Planning (2 weeks)

• Research microexpression detection models and APIs.
• Design app architecture and user flow.
• Create wireframes for user interaction and permission setup.

Phase 2: Core Development (6 weeks)

• Develop emotion detection module using TensorFlow Lite or CoreML.
• Implement backend integration for real-time processing and camera access.
• Develop API interaction for modifying content suggestions.

Phase 3: Testing and Optimization (4 weeks)

• Test microexpression recognition accuracy.
• Optimize battery usage and camera resource consumption.
• Conduct user testing to refine emotion-based recommendations.

Budget Allocation:

• Development Tools & Cloud Costs: $500 for initial testing.
• API and Model Integration: $500 for pre-built models or API costs.
• Freelancer Assistance for Core Features: $1,000 for specialized development.

8. Financial Projections

Revenue Projections (Year 1):

• Freemium Subscriptions: Aim for 100,000 downloads, with a 5% conversion to premium at $3.99/month.
• API Partnerships: Establish 2-3 partnerships, generating an estimated $10,000 in revenue.
• Total Projected Revenue: $50,000 in the first year.

Expense Projections (Year 1):

• Development Costs: $20,000 for initial development and iteration.
• Marketing and User Acquisition: $15,000.
• Operational Costs: $10,000 for server, API, and other ongoing costs.

Net Profit: Estimated at $5,000, with profitability expected to grow as user base expands and partnerships are established.

9. Team and Key Roles

• Founder/CEO: Visionary leader with expertise in AI, neuromarketing, and emerging technologies.
• CTO: Oversees technical development, focusing on machine learning models and API integrations.
• Lead Developer: Responsible for app development and optimization.
• Marketing Director: Focuses on digital marketing strategies and user acquisition.
• Privacy and Compliance Officer: Ensures the app meets all privacy standards and compliance regulations.

10. Funding Requirements

Amount Required: $50,000

Use of Funds:

• Product Development: $20,000 to complete the MVP and optimize the app.
• Marketing and User Acquisition: $15,000 for targeted ad campaigns and influencer collaborations.
• Operational Costs: $10,000 for cloud services, APIs, and team expenses.
• Legal and Compliance: $5,000 for legal consultation, privacy policy, and patent application.

11. Risks and Mitigation

Privacy Concerns: Users may be wary of emotion tracking. Mitigation involves full transparency, processing data locally, and obtaining explicit user consent.

Platform API Limitations: Integration with platforms may be limited by their API terms. To mitigate this, MoodSync will prioritize partnerships and consider alternative integration strategies.

Technological Challenges: Facial expression recognition accuracy may vary across different users and environments. MoodSync will continuously improve its models through user feedback and rigorous testing.

12. Exit Strategy

MoodSync aims to scale by establishing partnerships with major social media platforms. A potential exit strategy includes acquisition by a larger tech company (e.g., Google, Facebook) interested in integrating emotion-driven technology into their recommendation algorithms.

13. Conclusion

MoodSync represents an innovative solution to enhance digital content consumption by integrating real-time emotional intelligence into content recommendations. With a focus on user privacy, well-being, and adaptive engagement, MoodSync has the potential to redefine how users interact with digital content, making browsing a more personalized and emotionally uplifting experience. We invite investors to join us in this journey to transform the digital content landscape by putting users' emotions at the core of the experience.

Prompt #4: "Simulate and model the business-plan financials needed to turn this idea into a business in San Francisco, California."        

To generate all the financial documents required for a business plan deployment in San Francisco, California, the following documents are typically needed:

1. Income Statement (Profit and Loss Statement)

This document summarizes the revenue, expenses, and net profit over a given period. It provides insight into the profitability of MoodSync.

Projected Income Statement for Year 1:

• Revenue:
• Expenses:
• Net Profit: ($107,060) - A negative value initially, indicating the need for investment support.

2. Cash Flow Statement

The cash flow statement shows how cash is expected to flow in and out of the business, considering revenues, expenses, and financing activities.

Projected Cash Flow Statement for Year 1:

• Cash Inflows:
• Cash Outflows:
• Net Cash Flow: ($47,060)

3. Balance Sheet

The balance sheet provides a snapshot of the company’s financial position, including assets, liabilities, and equity.

Projected Balance Sheet for Year 1:

• Assets:
• Liabilities:
• Owner’s Equity:
• Total Assets = Total Liabilities + Equity: $12,940

4. Break-even Analysis

Break-even analysis helps to determine the sales volume needed to cover costs.

• Fixed Costs (Yearly):
• Variable Costs:
• Contribution Margin (Revenue per Premium User): $3.99
• Break-even Point (in subscribers):

5. Funding Requirements and Use of Funds

• Total Amount Required: $150,000Product Development and R&D: $50,000Marketing and User Acquisition: $30,000Team Salaries and Operational Costs: $50,000Rent and Utilities for SF Office: $20,000

6. Salary and Human Resources Planning

Given that the app will be launched in San Francisco, consider the cost of hiring talent in this area:

• Founder/CEO Salary: $80,000/year
• Lead Developer: $120,000/year
• Marketing Director: $90,000/year
• Privacy Officer: $75,000/year
• Total Payroll Costs (First Year): $365,000

7. Financial Assumptions and Notes

• Revenue Assumptions:
• Expense Assumptions:

8. Sensitivity Analysis

• Scenario 1 (Best Case): Conversion rate of 10%, resulting in Year 1 total revenue of $57,880 and net cash flow of ($23,120).
• Scenario 2 (Worst Case): Conversion rate of 2%, leading to Year 1 total revenue of $13,576 and net cash flow of ($67,424).

9. Investor Return and Exit Strategy

• Target ROI: Investors can expect ROI through:Subscription Revenue Growth: Reaching 500,000 active users with a 10% premium conversion by Year 3.Acquisition Opportunity: Potential acquisition by a large tech player (Google, Facebook) with a target valuation of $10-15 million in three years.
• Exit Strategy:Acquisition: Position MoodSync as a leading emotional content personalization tool to be acquired by major content platforms or wellness technology companies.

These financial documents form the foundation of a solid business plan tailored for deployment in San Francisco. They show the projected revenue, funding needs, and potential risks, making the case for why MoodSync is a worthwhile investment. If you need these details formalized into specific financial spreadsheets or want me to elaborate on any aspect, let me know!

Me: "This start-up framework was generated in less than 5 minutes. Just imagine how this could be further developed in just a few hours. Just image having access to a cross-functional team to develop this. Just imagine having much more funding to work on this. Just imagine you work in Product Design and eventually see your role disappearing or evolving at a speed you can't control or are prepared for. (Silence for a few seconds...). Do you still believe Generative AI is just hype?"

About

"Dr. Del Valle is an International Business Transformation Executive with broad experience in advisory practice building & client delivery, C-Level GTM activation campaigns, intelligent industry analytics services, and change & value levers assessments. He led the data integration for one of the largest touchless planning & fulfillment implementations in the world for a $346B health-care company. He holds a PhD in Law, a DBA, an MBA, and further postgraduate studies in Research, Data Science, Robotics, and Consumer Neuroscience." Follow him on LinkedIn: https://lnkd.in/gWCw-39g

? Author ?

With 30+ published books spanning topics from IT Law to the application of AI in various contexts, I enjoy using my writing to bring clarity to complex fields. Explore my full collection of titles on my Amazon author page: https://www.amazon.com/author/ivandelvalle

? Academia ?

As the 'Global AI Program Director & Head of Apsley Labs' at Apsley Business School London, Dr. Ivan Del Valle leads the WW development of cutting-edge applied AI curricula and certifications. At the helm of Apsley Labs, his aim is to shift the AI focus from tools to capabilities, ensuring tangible business value.

There are limited spots remaining for the upcoming cohort of the Apsley Business School, London MSc in Artificial Intelligence. This presents an unparalleled chance for those ready to be at the forefront of ethically-informed AI advancements.

Contact us for admissions inquiries at:

[email protected]

UK: +442036429121

USA: +1 (425) 256-3058