RELATIONSHIP ESTIMATION SYSTEM

• Main Authors: NONOGAMI HITOSHI, ARAKUMA RYOICHI
• Format: Patent
• Language: English; Japanese
• Published: 18.01.2022
• Subjects: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS; PHYSICS

To provide a relationship estimation system that estimates a relationship between meaningful attributes based on time series data of attributes related to the body or activity of a person.SOLUTION: In a relationship estimation system, an information terminal device 1 acquires time series data of a plurality of types of attributes from an IoT device 2 outside the terminal, an external web service 3, and an application of the terminal, calculates the similarity between the pieces of time series data of the attributes, creates a non-directional relationship graph obtained by connecting respective nodes with the link between the nodes with the similarity as the weight on the link, and calculates a path with a large weight on the link for every pair of nodes. Based on the plurality of paths, the relationship estimation system extracts the pair of nodes with a large comprehensive weight on the link. The relationship estimation system calculates the correlation between the pieces of time series data of the attributes, calculates the causal relationship between the nodes in the relationship graph according to the pair of attributes, updates the relationship graph to a directional relationship graph based on the causal relationship between the nodes, and estimates the relationship between the attributes corresponding to the extracted pair of nodes.SELECTED DRAWING: Figure 1 【課題】人の身体または活動に関する属性の時系列データに基づき、意味のある属性の関係性を推定する関係性推定システムを提供する。【解決手段】関連性推定システムにおいて、情報端末装置１は、端末外部のＩｏＴデバイス２、外部Ｗｅｂサービス３及び端末のアプリケーションから、複数の種類の属性の時系列データを取得し、各属性の時系列データ間の類似度を計算し、この類似度を各ノード間のリンクの重みとして各ノードをリンクで接続した無向性関係性グラフを生成し、ノード間毎にリンクの重みが大きい経路を算出する。この複数の経路に基づき総合的なリンクの重みが大きいノード間を抽出する。また、各属性の時系列データ間の相関を計算し、当該各属性間に応じた関係性グラフの各ノード間の因果関係を算定し、各ノード間の因果関係に基づいて、有向性の関係性グラフにアップデートし、抽出されたノード間に対応する属性間の関係性を推定する。【選択図】図１