A new patent has been published, addressing a critical problem in the field of artificial intelligence (AI) communication. The patent, titled "SYSTEMS AND METHODS FOR COMMUNICATION MODIFICATION TO REDUCE INACCURATE MACHINE-LEARNING-BASED COMMUNICATION SUPPRESSIONS" and bearing the number US20240039798A1, aims to optimize the messages sent by computers to people and minimize communication mistakes.
In recent years, the use of artificial intelligence models, including machine learning and deep learning, has seen rapid growth. These models have the potential to perform tasks that typically require human intelligence, such as processing data, identifying patterns, and making real-time determinations. However, practical implementation of AI has been hindered by several technical challenges.
One such challenge is the reliance on large amounts of high-quality data. Acquiring and ensuring the quality of this data is a complex and time-consuming process. Additionally, the design, programming, and integration of AI-based solutions require specialized knowledge, limiting the number of people and resources available for their creation. Moreover, reviewing AI results is often challenging, as the process by which these results are generated may be unclear or unknown, hindering error identification and model improvement.
One specific area where these challenges manifest is in communication suppression. Machine learning predictions to suppress communications may be inaccurate, resulting in the loss of crucial information. Conversely, predictions that fail to suppress communications may allow malicious or unwanted messages to target accounts. Thus, there is a pressing need for systems and methods to reduce these inaccurate machine-learning-based communication suppressions.
The newly published patent proposes a solution to this problem by introducing systems and methods for communication modification. By accurately predicting potential communication suppressions, this invention can automatically modify messages to make them more effective and prevent them from being incorrectly suppressed.
Figures accompanying the patent illustrate the workings of the system. Figure 1 shows an illustrative system for generating communication modifications, while Figure 2 presents a data structure for input into a machine learning model. Figure 3 depicts an exemplary machine learning model, and Figure 4 displays a table that could store training data. Figure 5 represents a data structure that represents communication predictions, and Figure 6 showcases a computing device.
In practice, this patent envisions a world where AI messages are enhanced, resulting in improved communication experiences for users. For instance, AI-powered customer service chatbots could better understand user queries and provide accurate responses promptly, eliminating frustration caused by misinterpreted messages. This would benefit businesses by boosting customer satisfaction and reducing customer support costs.
It is important to note that while this patent offers a promising solution to the problem of communication suppression, there is no guarantee that it will be successfully implemented on a large scale or reach the market. However, Capital One Services, the company behind this invention, is actively working toward leveraging this patent to improve AI-driven communication systems, potentially revolutionizing the way humans interact with computers.
In conclusion, the newly published patent, US20240039798A1, presents an innovative solution to the problem of inaccurate machine-learning-based communication suppressions. By introducing communication modifications, this patent aims to enhance AI messages and reduce communication mistakes. Although the path to implementation remains uncertain, this patent holds promise for improving AI-driven communication systems and enhancing user experiences in the future.
P.S. Please note that, as a patent, the implementation and appearance of this invention in the market are not guaranteed.