Sales Advertisement Generation Using ML Product Data Alignment
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Solution Overview
Problem
The manual creation of product sales advertisements for online shops is labor-intensive, time-consuming, and prone to errors, especially when dealing with a wide variety of products from different manufacturers and multiple sales channels, leading to incorrect links and increased costs.
Innovation Solution
A computer-implemented method using machine learning and artificial intelligence to automate the retrieval, recording, and generation of product advertisements, ensuring accurate alignment of text and image data, and tailoring to user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual compilation and formatting of product information is performed, then the sales advertisement can be created with proper structure and layout, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The system enables self-service by automatically retrieving product information from manufacturer data, performing validation, and generating sales advertisements without manual intervention. The automated workflow includes fetching data, validating product details, generating ad content, and publishing to multiple channels, eliminating the need for manual compilation and formatting while maintaining structured output
Solution Approach 2:
The patent replaces the mechanical manual process of information gathering and ad creation with an automated computer-based system. The system uses algorithms to retrieve data from manufacturer sources, validate product information, generate advertisement content, and publish to multiple sales channels, substituting human labor with automated computational processes
2Reliability
If manual information gathering from multiple sources is performed, then comprehensive product data can be collected, but the process becomes complex and error-prone
Solution Approach 1:
The system implements feedback through automated validation mechanisms that verify retrieved manufacturer data against predefined criteria. The validation process checks for data completeness, consistency, and accuracy, providing feedback loops that ensure only verified information is used in generating sales advertisements, thereby maintaining high reliability
Solution Approach 2:
The patent creates a universal system that handles multiple product types, manufacturers, and sales channels through a single automated workflow. The system is designed to work with diverse data sources and generate advertisements in various formats, reducing process complexity by providing a unified approach rather than separate manual procedures for each scenario
3Reliability
If manual verification of product information is performed, then errors can be detected and corrected, but the process remains time-consuming and costly
Solution Approach 1:
The system ensures continuity of useful action by performing validation and verification as continuous automated processes throughout the advertisement generation workflow. Rather than separate manual verification steps, the system continuously validates data during retrieval, generation, and publishing phases, maintaining high correctness while enabling rapid parallel processing that improves overall productivity
Solution Approach 2:
The patent replaces manual verification with automated computational validation mechanisms. The system uses algorithms to verify product information accuracy, check data consistency, and detect errors automatically, substituting human verification with faster computer-based processes that maintain reliability while significantly improving production speed
4Productivity
If automated machine learning model is used for generating sales advertisement, then the production speed and accuracy are improved, but the initial system complexity increases
Solution Approach 1:
The system applies preliminary action by pre-configuring validation rules, data retrieval parameters, and advertisement templates before the actual generation process. The machine learning model is pre-trained with manufacturer data patterns, and the system establishes automated workflows in advance, reducing the complexity of real-time decision-making while maintaining high productivity during execution
Solution Approach 2:
The patent introduces an intermediary layer that manages the complexity of the machine learning system. This intermediary includes automated validation mechanisms, data preprocessing routines, and template-based generation systems that mediate between raw manufacturer data and final advertisements, simplifying the overall system architecture while enabling sophisticated automated production
Data Source
Figure 1
AI summary
Computer-implemented method (100) for automatically producing a sales advertisement for a product, comprising the following steps: electronic retrieval (1) and capture (2) of at least one product from a list of products relating to manufacturer data provided by a manufacturer of the product via an online platform; generation and/or adaptation (5) of at least one advertisement text and arrangement of electronic images and/or photographs by analyzing the captured product data and/or product images using a machine learning model, using the retrieved manufacturer data and, in particular, depending on a predefined arrangement of information and attribute fields to form a product sales advertisement.