KDDI had 13 patents in artificial intelligence during Q1 2024. The patent filed by KDDI Corp in Q1 2024 describes a neural network with layers containing units with weight coefficients and importance parameters. The method involves adjusting weight coefficients during learning tasks based on importance parameters and gradients, and calculating importance parameters for future tasks based on gradients from the training set. GlobalData’s report on KDDI gives a 360-degree view of the company including its patenting strategy. Buy the report here.

KDDI grant share with artificial intelligence as a theme is 38% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: Method and information processing apparatus for performing transfer learning while suppressing occurrence of catastrophic forgetting (Patent ID: US20240086678A1)

The patent filed by KDDI Corp. describes a method for training a neural network with multiple tasks by adjusting weight coefficients based on importance parameters. The method involves adjusting weight coefficients during learning of a task and calculating importance parameters for future tasks based on gradients of the weight coefficients. The importance parameter is determined by normalizing gradients of the weight coefficients and adjusting them based on their importance in the learning process.

The method outlined in the patent involves adjusting weight coefficients in a neural network based on importance parameters and gradients during the learning process of multiple tasks. By calculating importance parameters for each weight coefficient, the neural network can efficiently adapt to new tasks while retaining knowledge from previous tasks. The system described in the patent includes an information processing apparatus with processors and storage devices to execute the learning process, ensuring effective training of the neural network for sequential tasks.

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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.