This study investigates people's use of objects and the things they do in their daily lives. This is because interaction with objects can give us valuable insights with regard to behavior. The problem is that in an environment where multiple users interact with the same object, it is difficult to obtain data relating to each individual. To this end, we propose a model that extracts individual data from a data set. We can recognize individual users in environments where multiple users interact with an object. The model consists of 3 policies. As a result of our experiment, we proved that the system could extract individual data that fits an individual examinee's consciousness an average of 85.3% of the time. In addition, by letting each examinee look back on the system results, we could make the examinee conscious of behavior of which they had previously been unaware.