AI Messaging Prompt Generation for Frictionless Multimedia Sharing
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Solution Overview
Problem
Users face friction and inefficiency when using generative AI models to create and share multimedia content via text messaging, as current methods are cumbersome and time-consuming, and there are risks associated with the misuse of such technology.
Innovation Solution
Integrate a command in text messaging applications to request generative AI-generated content directly, utilizing voice assistants or text messaging applications to invoke generative AI services, and implement restrictions to mitigate risks, such as requiring prior interaction or user consent for sending AI-generated content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually interact with AI models and attach images through multiple steps, then generative AI content can be shared, but the process becomes cumbersome and discouraging
Solution Approach 1:
The patent combines the AI content generation and messaging functions into a single integrated interface. Users can generate AI images and send them through one unified command rather than separately interacting with AI models and then attaching images to messages, reducing the number of steps and simplifying the overall process
Solution Approach 2:
The system performs preliminary actions by pre-configuring the AI generation parameters and message composition based on user preferences and context. When a user initiates a request, the system has already prepared the necessary components and can execute the full workflow with minimal additional user input, effectively performing the complex steps in advance
2Productivity
If generative AI models are integrated directly into messaging applications, then content generation becomes faster and more personalized, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary layer that manages the integration between messaging applications and AI models. This intermediary handles the complex tasks of coordinating AI generation, processing results, and delivering content back to the user interface, shielding users from the underlying system complexity while enabling fast, personalized content generation
Solution Approach 2:
The integrated system is designed to perform multiple functions within a single architecture: generating AI images, composing messages, personalizing content based on user context, and managing delivery all in one unified system. This multi-functionality increases productivity by eliminating the need for separate tools while the shared infrastructure helps manage overall system complexity
3Manufacturing precision
If users specify detailed requests within messages for AI generation, then content relevance improves, but processing time and computational resources increase
Solution Approach 1:
The system implements partial action by processing only the essential elements of user requests with full detail while using predefined templates and parameters for routine aspects. For common request types, the system applies pre-configured settings that require minimal processing, reserving full detailed processing only for novel or complex requests, thus maintaining relevance while reducing average processing time
Solution Approach 2:
The system performs preliminary processing by pre-analyzing user preferences, message context, and request patterns to prepare appropriate generation parameters in advance. When a detailed request is received, much of the contextual analysis and parameter configuration has already been done, reducing the actual generation time while maintaining high content relevance through personalized pre-prepared settings
Data Source
AI summary
A method can include receiving, from an input device processor associated with an input device, a request to transmit a text message to a terminating mobile device, the request comprises an invocation key indicating a user request to generate multimedia content. A method can include parsing the request to identify the invocation key and parsing the request to identify a set of descriptors for generating the multimedia content. A method can include generating, based on the set of descriptors, a prompt for providing to a generative AI model. A method can include transmitting the prompt to the generative AI model. A method can include receiving the generated multimedia content from the generative AI model. A method can include generating the text message including the generated multimedia content. A method can include transmitting, to the terminating mobile device, the text message comprising the generated multimedia content.