Topcon had six patents in artificial intelligence during Q3 2023. The first patent is for an ophthalmic information processing apparatus that uses machine learning to determine the presence or absence of a disc hemorrhage in a subject’s eye. It also detects the disc hemorrhage region in the image.

The second patent is for an ophthalmic information processing apparatus that acquires multiple images with different cross-sectional orientations of a subject’s eye and uses machine learning to estimate whether the eye has glaucoma.

The third patent is for a medical system that acquires data from a patient’s eye fundus using optical methods and processes the data to generate information on the patient’s circulatory system. GlobalData’s report on Topcon gives a 360-degreee view of the company including its patenting strategy. Buy the report here.

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Topcon grant share with artificial intelligence as a theme is 33% in Q3 2023. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: Ophthalmic information processing apparatus, ophthalmic apparatus, ophthalmic information processing method, and recording medium (Patent ID: US20230263391A1)

The patent filed by Topcon Corp. describes an ophthalmic information processing apparatus that uses machine learning to determine the presence or absence of a disc hemorrhage in a fundus image of a subject's eye. The apparatus includes a determiner circuit that uses a disc hemorrhage determination model obtained through machine learning using labeled fundus images as teaching data. The determiner circuit determines the presence or absence of a disc hemorrhage in the front image of the fundus.

The apparatus also includes a detector circuit that uses a disc hemorrhage region detection model obtained through machine learning using pairs of image groups as teaching data. Each pair consists of a front image of the fundus and a disc hemorrhage region image representing the disc hemorrhage region depicted in the front image. The detector circuit detects the disc hemorrhage region in the front image that has been determined to have a disc hemorrhage by the determiner circuit.

The patent also describes additional features of the ophthalmic information processing apparatus. It can include a first learning circuit that generates the disc hemorrhage determination model through supervised machine learning using the labeled fundus images. A second learning circuit generates the disc hemorrhage region detection model through supervised machine learning using the pairs of image groups. The front image of the fundus can be a color front image.

The apparatus can further include an analyzer circuit that generates position information, shape information, or occurrence information of the disc hemorrhage region by analyzing the front image of the fundus. The position information can indicate the direction of the representative position of the disc hemorrhage region relative to a reference position, whether it is within or outside an optic disc region, or whether it is within or outside a neuroretinal rim. The shape information can include the ellipticity or area of the disc hemorrhage region. The occurrence information can include the occurrence frequency or interval of the disc hemorrhage region.

In addition, the apparatus can include a position matching circuit that performs position matching between OCT data and the front image of the subject's eye. The analyzer circuit can generate the position information, shape information, or occurrence information using the OCT data on which position matching has been performed with the front image.

Overall, the patent describes an ophthalmic information processing apparatus that uses machine learning to determine the presence or absence of a disc hemorrhage and detect the disc hemorrhage region in a fundus image, providing valuable information for ophthalmic diagnosis and treatment.

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GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.

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.