US 12,334,223 B2
Learning apparatus, mental state sequence prediction apparatus, learning method, mental state sequence prediction method and program
Shuhei Yamamoto, Tokyo (JP); Hiroyuki Toda, Tokyo (JP); Takeshi Kurashima, Tokyo (JP); and Tomu Tominaga, Tokyo (JP)
Assigned to NIPPON TELEGRAPH AND TELEPHONE CORPORATION, Tokyo (JP)
Appl. No. 18/245,965
Filed by NIPPON TELEGRAPH AND TELEPHONE CORPORATION, Tokyo (JP)
PCT Filed Nov. 12, 2020, PCT No. PCT/JP2020/042295
§ 371(c)(1), (2) Date Mar. 20, 2023,
PCT Pub. No. WO2022/102060, PCT Pub. Date May 19, 2022.
Prior Publication US 2023/0368920 A1, Nov. 16, 2023
Int. Cl. G16H 50/20 (2018.01); G16H 20/70 (2018.01)
CPC G16H 50/20 (2018.01) [G16H 20/70 (2018.01)] 8 Claims
OG exemplary drawing
 
1. A learning apparatus comprising:
a memory; and
a processor configured to execute:
scanning each of behavior data columns that are extracted from a behavior sequence database,
replacing a missing value or an unexpected value with another value in a case where there is the missing value or the unexpected value, thereby generating past behavior sequence data;
calculating a duration of a mental state from mental state sequence data, and generating preprocessed mental state sequence data including the mental state and the duration; and
learning a mental state sequence prediction model, using input sequence data including the behavior sequence data and the preprocessed mental state sequence data, and correct sequence data that is preprocessed mental state sequence data at a time later than the input sequence data,
wherein the mental state is defined by mood data including valence and arousal, together with information about dates and times when a user makes self-reports, said valence indicating degree of positiveness and negativity of emotion of the user at that time, and said arousal indicating degree of excitement in the emotion of the user, and
wherein the processor is further configured to execute:
calculating the duration of the mental state by calculating a difference of an entry of the mood data and next entry of the mood data.