International business machines corporation (20240119276). EXPLAINABLE PREDICTION MODELS BASED ON CONCEPTS simplified abstract

EXPLAINABLE PREDICTION MODELS BASED ON CONCEPTS

Organization Name

international business machines corporation

Inventor(s)

MATTIA Rigotti of BASEL (CH)

IOANA Giurgiu of ZURICH (CH)

THOMAS Gschwind of ZURICH (CH)

CHRISTOPH ADRIAN Miksovic Czasch of ZURICH (CH)

PAOLO Scotton of RUESCHLIKON (CH)

EXPLAINABLE PREDICTION MODELS BASED ON CONCEPTS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240119276 titled 'EXPLAINABLE PREDICTION MODELS BASED ON CONCEPTS

Simplified Explanation

The patent application describes a neural network model that generates explainable prediction outputs for input data samples by utilizing concept vectors and a cross-attention module.

• The training dataset includes data samples with prediction labels and concept vectors associated with predefined concepts.
• Input vectors are produced from each data sample, and a neural network model is trained with a cross-attention module for sample embeddings and a prediction module for prediction outputs.

Potential Applications

The technology can be applied in various fields such as healthcare for predicting patient outcomes, finance for forecasting market trends, and marketing for customer behavior analysis.

Problems Solved

1. Provides explainable prediction outputs, enhancing transparency and trust in the model's decisions. 2. Utilizes concept vectors to capture the information content of data samples, improving the model's understanding of the input data.

Benefits

1. Improved accuracy in prediction outputs. 2. Enhanced interpretability of the model's decisions. 3. Efficient utilization of concept vectors for better data representation.

Potential Commercial Applications

Optimizing supply chain management, enhancing customer relationship management systems, and improving fraud detection in financial transactions.

Possible Prior Art

Prior art may include similar neural network models with attention mechanisms for generating prediction outputs from input data samples. Research on explainable AI and concept embeddings in neural networks may also be relevant.

How does the cross-attention module improve the model's performance in generating explainable prediction outputs?

The cross-attention module allows the model to focus on relevant parts of the input data samples by incorporating concept vectors, enhancing the interpretability of the prediction outputs.

What are the potential limitations of using concept vectors in the neural network model for generating explainable predictions?

The use of concept vectors may introduce complexity in the model training process and require additional computational resources for processing large datasets. Additionally, defining accurate concept vectors for diverse data samples could be challenging.

Original Abstract Submitted

generating a neural network model for producing explainable prediction outputs for input data samples is provided. training dataset of data samples are provided, each having a prediction label indicating a desired prediction output from the model for that sample, and a set of concept vectors are defined comprising a plurality of concept vectors which are associated with respective predefined concepts characterizing information content of the data samples. a set of input vectors are produced from each data sample. a neural network model is trained that includes a cross-attention module for producing a sample embedding for a data sample and a prediction module for producing a prediction output from the sample embedding.