Messaging system utilizing generative ai system images
An artificial intelligence (AI) system for processing and validating editable text, including tracked changes, comments, and version metadata. The system determines whether edits are approved, rejected, or pending and applies AI-driven text verification, fraud detection, and hallucination prevention accordingly. AI validation occurs dynamically to prevent misinterpretation of unapproved changes while ensuring text security and integrity. The invention includes automated fraud detection, cryptographic authentication, and real-time validation to enhance AI-assisted document workflows. The system may integrate with document collaboration platforms, apply blockchain-based verification, and generate validation logs tracking document modifications. AI-driven text formatting correction, security watermarking, and structured validation techniques further improve accuracy and trustworthiness. The system applies across content creation, messaging, legal document processing, and collaborative environments, ensuring AI-generated responses reflect finalized and authenticated content.
1 . A method for communication, the method comprising:
receiving, by a processor, one or more first manual inputs into a generative Artificial Intelligence (GenAI) model from a messaging application;
analyzing, by the GenAI model, the one or more first manual inputs to determine if the one or more first manual inputs contain information that can be accurately conveyed using an image;
on a determination that the one or more first manual inputs contain information that can be accurately conveyed using an image, generating an image that conveys the information, wherein the generated image conveys the information using text present in the image;
analyzing the text within the generated image to determine the text content;
comparing the text content from the generated image to the one or more first manual inputs; if the text content in the generated image does not match the one or more first manual inputs, updating the text in the image to match the one or more first manual inputs; and
outputting the image to the messaging application.
2 . The method of claim 1 , wherein the GenAI model is a generic GenAI model.
3 . The method of claim 2 , wherein the GenAI model is a diffusion model.
4 . The method of claim 3 , wherein the one or more first manual inputs are associated with a first profile.
5 . The method of claim 4 , wherein the one or more first manual inputs and the first profile are associated with a first user equipment.
6 . The method of claim 3 , wherein the one or more first manual inputs are associated with a first user equipment.
7 . The method of claim 1 , further comprising sending the image via the messaging application associated with a first user equipment to a messaging application associated with a second user equipment.
8 . The method of claim 1 , wherein the determination of whether the one or more first manual inputs contain information that can be accurately conveyed using an image is done in real time as the one or more first manual inputs are being entered into the messaging application.
9 . The method of claim 1 , wherein generating the image further comprises:
determining the sentiment of the one or more first manual inputs; and
based on the determined sentiment, matching the sentiment of the generated image.
10 . The method of claim 9 , further comprising:
after the image is generated, receiving, by the processor, one or more second manual inputs from the messaging application; and
updating the generated image based on the one or more second manual inputs.
11 . The method of claim 10 , wherein updating the generated image comprises updating one or more stylistic elements of the text provided in the generated image.
12 . The method of claim 11 , wherein the stylistic elements of the text provided in the generated image include sizing, font, styling, and spacing.
13 . The method of claim 12 , further comprising matching the stylistic elements of the text provided in the generated image to the sentiment of the one or more first manual inputs.
14 . The method of claim 1 , further comprising:
determining whether the one or more first manual inputs contain one or more typographical errors; and
based on the determination that the one or more first manual inputs contain one or more typographical errors, correcting the typographical errors in the text generated in the image.
15 . The method of claim 1 , further comprising:
determining whether the one or more first manual inputs are missing one or more punctuation marks; and
based on the determination that the one or more first manual inputs are missing one or more punctuation marks, adding the missing punctuation marks in the text generated in the image.
16 . The method of claim 1 , wherein the messaging application is a peer-to-peer messaging application or a person-to-person messaging application.
17 . The method of claim 1 , wherein the GenAI system is integrated into the messaging application.
18 . The method of claim 1 , wherein generating the image further comprises:
dynamically modifying a positioning of the text before finalizing the generated image to optimize readability.
19 . A method comprising:
receiving one or more first manual inputs;
generating an image using an AI model, wherein the generated image conveys the information using text present in the image, and wherein generating the image comprises:
determining the sentiment of the one or more first manual inputs; and
based on the determined sentiment, matching the sentiment of the generated image;
receiving, after the image is generated, one or more second manual inputs from the messaging application; and
updating the generated image based on the one or more second manual inputs, wherein the updating the generated image comprises:
updating one or more stylistic elements of the text provided in the generated image, wherein the stylistic elements of the text provided in the generated image include sizing, font, styling, and spacing, and
matching the stylistic elements of the text provided in the generated image to the sentiment of the one or more first manual inputs.
20 . The method of claim 19 , wherein the generating the image further comprises:
dynamically modifying a positioning of the text before finalizing the generated image to optimize readability.