State estimation device, program, state estimation system, and state estimation method

Abstract

The state estimation device (100) is characterized by comprising an addition unit (141) that adds personal optimization information to a parent model by inputting detection data detected from a person, so as to make estimation using the parent model for estimating the state of that person suitable for the user, and a state estimation unit (142) that estimates the state of the user by inputting the detection data detected from the user to the parent model to which the personal optimization information has been added.

Description

[Technical field]

[0001] The present disclosure relates to a state estimation device, a program, a state estimation system, and a state estimation method. [Background technology]

[0002] Conventionally, there are technologies that aim to estimate a user's condition using detection data such as biosignals obtained from wearable devices, etc. Generally, bioinformation varies from person to person, so it is difficult to prepare a general-purpose model that can be used by all users in advance.

[0003] Therefore, for example, the situation estimation device described in Patent Document 1 receives a signal from a wearable device worn by a user, selects a model from pre-trained models based on the received signal, and re-trains the selected model every time learning data is acquired. This enables the situation estimation device described in Patent Document 1 to improve the estimation accuracy of the user's situation, and enables each user to estimate their state using a model optimized for them. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2022-55736 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, in the conventional technology, the state estimation model is retrained using data obtained from each user, thereby achieving personal optimization of the inference model for each user. Therefore, it is necessary to retrain the model for each user who uses the system, and it is necessary to store the models in the system for the number of users.

[0006] In recent years, estimation models using neural networks etc. have become more complex, and as a result, the size of the models (in other words, capacity) has become larger. When such models are adopted in a system, it takes a very long time to retrain the models.

[0007] Therefore, one or more aspects of the present disclosure aim to make it possible to perform appropriate estimation for each user without re-learning a model for estimating a state. [Means for solving the problem]

[0008] According to an embodiment of the present disclosure, an information estimation device As multiple types of data An addition unit that adds personal optimization information to a parent model for estimating a state of the person by inputting the detected detection data, so that estimation using the parent model for estimating the state of the person is suitable for a user; and a state estimation unit that estimates a state of the user by inputting the detection data detected from the user to the parent model to which the personal optimization information has been added. the parent model includes a plurality of sub-models that estimate the state of the person from each of the plurality of types of data, and the personal optimization information is information that estimates the state of the user by weighting and evaluating the outputs of the plurality of sub-models. It is characterized by:

[0009] A program according to an embodiment of the present disclosure is a program for causing a computer to As multiple types of data an adding unit that adds personal optimization information to a parent model by inputting the detected detection data, the personal optimization information being used to make estimation using a parent model for estimating the state of the person suitable for the user; and a state estimating unit that inputs the detection data detected from the user to the parent model to which the personal optimization information has been added, to estimate the state of the user. the parent model includes a plurality of sub-models that estimate the state of the person from each of the plurality of types of data, and the personal optimization information is information that estimates the state of the user by weighting and evaluating the outputs of the plurality of sub-models. It is characterized by:

[0010] An information estimation system according to an embodiment of the present disclosure As multiple types of data An addition unit that adds personal optimization information to a parent model for estimating a state of the person by inputting the detected detection data, so that estimation using the parent model for estimating the state of the person is suitable for a user; and a state estimation unit that estimates a state of the user by inputting the detection data detected from the user to the parent model to which the personal optimization information has been added. the parent model includes a plurality of sub-models that estimate the state of the person from each of the plurality of types of data, and the personal optimization information is information that estimates the state of the user by weighting and evaluating the outputs of the plurality of sub-models. It is characterized by:

[0011] An information estimation method according to an embodiment of the present disclosure includes: The additional portion is From people As multiple types of data Adding personal optimization information to the parent model to make estimation using the parent model for estimating the state of the person suitable for a user by inputting the detected detection data; The state estimation unit: The state of the user is estimated by inputting detection data detected from the user to the parent model to which the personal optimization information is added. The parent model includes a plurality of sub-models that estimate the state of the person from each of the plurality of types of data, and the personal optimization information is information that estimates the state of the user by weighting and evaluating the outputs of the plurality of sub-models. It is characterized by: Effect of the Invention

[0012] According to one or more aspects of the present disclosure, appropriate estimation can be performed for each user without re-learning a model for estimating a state. [Brief description of the drawings]

[0013] [Figure 1] 1 is a block diagram illustrating a schematic configuration of a state estimating device according to a first embodiment. [Diagram 2] 13 is a schematic illustrating an example of adding personal optimization information to the output of a parent model. [Diagram 3] 13A and 13B are schematic diagrams for explaining an example of adding personal optimization information to some layers of a parent model. [Figure 4] FIG. 2 is a block diagram illustrating a schematic configuration of a PC. [Diagram 5] FIG. 11 is a block diagram illustrating a schematic configuration of a state estimating device according to a second embodiment. [Figure 6] FIG. 11 is a block diagram illustrating a schematic configuration of a state estimating device according to a third embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] Embodiment 1 FIG. 1 is a block diagram illustrating a schematic configuration of a state estimating device 100 according to the first embodiment. The state estimation device 100 includes a model management unit 110, an optimization information management unit 120, an input unit 130, and a detection unit 140.

[0015] The model management unit 110 includes a data storage unit 111 , a model generation unit 112 , and a parent model storage unit 113 .

[0016] The data storage unit 111 stores teacher data for learning a parent model, which will be described later. The condition estimation device 100 is a device that estimates the condition of a user based on detection data detected from the user. Therefore, the teacher data here includes detection data detected from a person and the person's condition estimated from the detection data. In addition to the detection data, the teacher data may also include clinical data of the person collected at a hospital or the like and the person's condition diagnosed from the clinical data.

[0017] The model generation unit 112 uses the teacher data stored in the data storage unit 111 to learn a parent model, which is a model for estimating the state of a person, based on detection data detected from the person.

[0018] The parent model storage unit 113 is a storage unit that stores the parent model generated by the model generation unit 112. In the above example, the parent model is generated by the model generation unit 112, but the first embodiment is not limited to such an example. For example, the parent model may be generated by a device other than the state estimation device 100 and stored in the parent model storage unit 113. In this case, the data storage unit 111 and the model generation unit 112 are unnecessary.

[0019] The optimization information management unit 120 includes a personal optimization information generation unit 121 and a personal optimization information storage unit 122 .

[0020] The personal optimization information generation unit 121 generates personal optimization information for correcting a part or the output of the parent model so that estimation using the parent model is suitable for the user who uses the state estimation device 100. For example, the personal optimization information generating unit 121 may generate personal optimization information for a user of the state estimation device 100, using data (also called personal optimization data) in which detection data is previously associated with a state when the detection data is detected, so that the state of the user is estimated from an output when the detection data is input to a parent model. Such personal optimization data may be input via the input unit 130, or may be stored in the data storage unit 111.

[0021] The personal optimization information storage unit 122 stores the personal optimization information generated by the personal optimization information generation unit 121. Here, when there are multiple users using the state estimation device 100, the personal optimization information is stored in association with each of the multiple users. For example, the multiple users are assigned a user ID (IDentification) as user identification information that is identification information for identifying each of the multiple users, and the personal optimization information may be associated with the user ID.

[0022] In the first embodiment, it is assumed that personal optimization information of the user has already been generated and stored in the personal optimization information storage unit 122 before the state estimation device 100 estimates the state of the user. In the above example, the personal optimization information is generated by the personal optimization information generation unit 121, but the first embodiment is not limited to such an example. For example, the personal optimization information may be generated by a device other than the state estimation device 100 and stored in the personal optimization information storage unit 122. In this case, the personal optimization information generation unit 121 is not necessary.

[0023] The input unit 130 functions as an input receiving unit that receives input of various data. For example, the input unit 130 accepts an input of the user ID of the user of the state estimation device 100. The input unit 130 also functions as a detection data receiving unit that receives an input of detection data detected by a user of the state estimation device 100. Here, the detection data is preferably vital data detected by a sensor (not shown), for example. The vital data may be detected by a medical device (not shown), or may be detected by a user terminal (not shown), such as a smart watch, used by the user. The input user ID and detection data are provided to the detection unit 140.

[0024] The detection unit 140 includes an addition unit 141 and a state estimation unit 142 .

[0025] The adding unit 141 reads out the personal optimization information corresponding to the user ID from the input unit 130 from the personal optimization information storage unit 122 . Then, the addition unit 141 adds the read personal optimization information to the parent model stored in the parent model storage unit 113. The parent model to which the personal optimization information is added has an estimation suitable for the user. Specifically, when the parent model includes a plurality of sub-models that estimate a person's state from each of a plurality of types of data included in the detection data, the addition unit 141 adds the personal optimization information, which is information for estimating the state of the user, to the output of the parent model by weighting and evaluating the outputs of the plurality of sub-models. Furthermore, the addition unit 141 may add information for changing the output of a part of layers of the parent model to be suitable for the user, to the parent model as the personal optimization information.

[0026] FIG. 2 is a schematic diagram illustrating one example of adding personal optimization information to the output of a parent model. For example, as shown in FIG. 2, when parent model 113a is composed of multiple sub-models such as a first model, a second model, ... that perform estimation for each of multiple types of data included in the detection data, the personal optimization information can be information for estimating the user's state from the synthesis result by weighting the outputs from the multiple sub-models with weights (e.g., w1, w2, w3, ...) determined for each user.

[0027] 3A and 3B are schematic diagrams for explaining an example of adding personal optimization information to some layers of a parent model. As shown in FIG. 3(A), when the normal space CA1 assumed by the parent model differs from the normal space CA2, which is the data among the detected data of the user when the user's condition is normal, for example, the user's normal biometric information may not be mapped to the normal space assumed by the parent model and may be determined to be abnormal. For this reason, the adding unit 141 inserts a projection function for correcting the output of a layer for each user into a part of the layers of the parent model, so that the normal space CA1 assumed by the parent model coincides with the normal space CA3, which is data of the user's detection data when the user's condition is normal, as shown in Fig. 3(B). In such a case, the projection function inserted into a part of the layers of the parent model becomes personal optimization information.

[0028] The state estimation unit 142 estimates the state of the user by inputting detection data detected from the user to the parent model to which the personal optimization information has been added. The estimated user state is, for example, displayed on a display unit (not shown) or transmitted to a terminal used by the user via a communication unit (not shown). In other words, the estimated user state is output from an output unit (not shown).

[0029] The above-described state estimation device 100 can be realized by a computer such as the PC 10 shown in FIG. The PC 10 includes an auxiliary storage device 11 such as an HDD (Hard Disk Drive) or SSD (Solid State Drive), a memory 12, a processor 13 such as a CPU (Central Processing Unit), a communication I / F (Interface) 14 such as a NIC (Network Interface Card), an input I / F 15 such as a mouse or keyboard, and a display 16.

[0030] The data storage unit 111, the parent model storage unit 113, and the personal optimization information storage unit 122 of the state estimation device 100 can be realized by the auxiliary storage device 11 or the memory 12. The model generation unit 112, the personal optimization information generation unit 121, the addition unit 141 and the state estimation unit 142 can be realized by the processor 13 reading a program stored in the auxiliary storage device 11 into the memory 12 and executing the program. The input unit 130 can be realized by the communication I / F 14 or the input I / F 15 . An output unit (not shown) can be realized by the communication I / F 14 or the display 16.

[0031] As described above, according to the first embodiment, estimation suitable for a user can be performed using the parent model without re-learning the parent model itself for each user.

[0032] Embodiment 2 FIG. 5 is a block diagram illustrating a schematic configuration of a state estimating device 200 according to the second embodiment. The state estimation device 200 includes a model management unit 110 , an optimization information management unit 120 , an input unit 130 , a detection unit 140 , and an update unit 250 .

[0033] The model management unit 110, the optimization information management unit 120, the input unit 130, and the detection unit 140 of the state estimation device 200 according to the second embodiment are similar to the model management unit 110, the optimization information management unit 120, the input unit 130, and the detection unit 140 of the state estimation device 100 according to the first embodiment. However, the input unit 130 also provides the input user ID and detection data to the update unit 250 .

[0034] The update unit 250 includes a storage unit 251 and a personal optimization information update unit 252 . The storage unit 251 stores the user ID and the detection data from the input unit 130 in association with each other.

[0035] The personal optimization information update unit 252 reads out the personal optimization information associated with the user ID stored in the accumulation unit 251 from the personal optimization information storage unit 122, and updates the personal optimization information using the detection data associated with the user ID. Here, the personal optimization information update unit 252 may update the personal optimization information of the user when the condition of the user in the detection data becomes clear. The condition of the user in the detection data can be clarified, for example, by the user or a third party such as a doctor inputting the condition to the input unit 130. In other words, the personal optimization information update unit 252 updates the personal optimization information so that when a state of a user corresponding to detection data detected from the user is acquired, the acquired state is estimated from the detection data.

[0036] Specifically, as shown in FIG. 2, when the personal optimization information is information for estimating a user's state by weighting and combining the outputs from multiple sub-models with weights determined for each user, the personal optimization information update unit 252 updates the weights so that the user's state estimated by the output of the detection data input to the parent model becomes the clarified user's state. Also, as explained using Figures 3(A) and (B), when the personal optimization information is a projection function to be inserted into a part of a layer of the parent model, the personal optimization information update unit 252 updates the projection function so that the output of inputting the detection data into the parent model becomes the clarified state of the user.

[0037] The above-described state estimation device 200 can also be realized by a computer such as the PC 10 shown in FIG. For example, the storage unit 251 can be realized by the auxiliary storage device 11 or the memory 12. Furthermore, the personal optimization information update unit 252 can be realized by the processor 13 reading a program stored in the auxiliary storage device 11 into the memory 12 and executing the program.

[0038] As described above, according to the second embodiment, the personal optimization information can be updated to be more optimal by using the state estimation device 200. Therefore, by using the state estimation device 200, the estimation accuracy for each user is improved.

[0039] Embodiment 3 FIG. 6 is a block diagram showing a schematic configuration of a state estimating device 300 according to the third embodiment. The state estimation device 300 includes a model management unit 110 , an optimization information management unit 320 , an input unit 330 , a detection unit 140 , an update unit 250 , and a cluster identification unit 360 .

[0040] The model management unit 110 and the detection unit 140 of the state estimation device 300 according to the third embodiment are similar to the model management unit 110 and the detection unit 140 of the state estimation device 100 according to the first embodiment. Moreover, the update unit 250 of the state estimating device 300 according to the third embodiment is similar to the update unit 250 of the state estimating device 200 according to the second embodiment.

[0041] The optimization information management unit 320 includes a personal optimization information generation unit 121 , a personal optimization information storage unit 122 , a clustering unit 323 , a cluster optimization information generation unit 324 , and a cluster optimization information storage unit 325 .

[0042] The optimization information management unit 320 includes a personal optimization information generation unit 121 , a personal optimization information storage unit 122 , a clustering unit 323 , a cluster optimization information generation unit 324 , and a cluster optimization information storage unit 325 .

[0043] The personal optimization information generating unit 121 and the personal optimization information storage unit 122 of the optimization information managing unit 320 in the third embodiment are similar to the personal optimization information generating unit 121 and the personal optimization information storage unit 122 of the optimization information managing unit 120 in the first embodiment.

[0044] The clustering unit 323 generates clusters that are groups of similar users by performing clustering using the teacher data stored in the data storage unit 111. The clustering performed here may be a known method of dividing data into groups (clusters) based on the similarity between the data.

[0045] For example, if the teacher data includes clinical data of a user, the clustering unit 323 can perform clustering using attributes of the subject, such as age, medical history, and lifestyle, included in the clinical data.

[0046] Furthermore, the clustering unit 323 may perform clustering using detection data included in the teacher data. Furthermore, the clustering unit 323 may perform clustering using both the attributes of the subjects and the detection data. For example, the clustering unit 323 can perform clustering on the feature space after including the attributes of the subjects in the feature amount calculated from the detection data.

[0047] The cluster optimization information generating unit 324 generates cluster optimization information for modifying a part or an output of the parent model so that the output from the parent model is suitable for the clusters generated by the clustering unit 323 . For example, the cluster optimization information generation unit 324 may generate cluster optimization information for each cluster such that the state of a user included in the cluster generated by the clustering unit 323 is estimated based on the output when the detection data of the user is input to a parent model.

[0048] The cluster optimization information storage unit 325 stores the cluster optimization information generated by the cluster optimization information generation unit 324. For example, a cluster ID is assigned to each of the multiple clusters as cluster identification information that is identification information for identifying each of the multiple clusters, and the cluster optimization information may be associated with the cluster ID. In the above example, the cluster optimization information is generated by the cluster optimization information generation unit 324, but the third embodiment is not limited to such an example. For example, the cluster optimization information may be generated by a device other than the state estimation device 300 and stored in the cluster optimization information storage unit 325. In such a case, the clustering unit 323 and the cluster optimization information generation unit 324 are not necessary.

[0049] The input unit 330 accepts input of various data. For example, the input unit 330 accepts an input of the user ID of the user of the state estimation device 300, as in the first embodiment. Moreover, the input unit 330 receives an input of detected data from a user of the state estimation device 300, as in the first embodiment. The input user ID and detection data are provided to the detection unit 140 and the update unit 250 .

[0050] In the third embodiment, the input unit 330 also functions as a clustering data input receiving unit that receives an input of clustering data that is data necessary for clustering users of the state estimation device 300. For example, when the cluster optimization information generating unit 324 generates clusters by performing clustering using the attributes of the subjects, attribute data indicating the attributes of the users becomes the clustering data. Furthermore, when the cluster optimization information generating unit 324 generates clusters by performing clustering using detection data, the detection data of the user becomes the clustering data. Furthermore, when the cluster optimization information generating unit 324 performs clustering using the attributes and detection data of the subject to generate clusters, the attribute data indicating the attributes of the user and the detection data become the clustering data. The input unit 330 provides the user ID and the clustering data to the cluster specifying unit 360 .

[0051] The cluster identification unit 360 performs clustering using the clustering data from the input unit 330, thereby identifying a cluster of a user indicated by the user ID from the input unit 330. The clustering here may be performed in the same manner as the clustering performed by the clustering unit 323. In other words, the cluster identifying unit 360 identifies the cluster to which the user belongs among a plurality of clusters, based on at least one of the user's attributes and the detection data detected from the user.

[0052] Then, the cluster identification unit 360 reads out the cluster optimization information associated with the cluster ID of the identified cluster from the cluster optimization information storage unit 325, and stores the cluster optimization information as personal optimization information in the personal optimization information storage unit 122 in association with the user ID from the input unit 330. The cluster optimization information stored as personal optimization information is updated by the personal optimization information update unit 252, as in the second embodiment.

[0053] The above-described state estimation device 300 can also be realized by a computer such as the PC 10 shown in FIG. For example, the cluster optimization information storage unit 325 can be realized by the auxiliary storage device 11 or the memory 12. Furthermore, the clustering unit 323, the cluster optimization information generation unit 324, and the cluster identification unit 360 can be realized by the processor 13 reading a program stored in the auxiliary storage device 11 into the memory 12 and executing the program.

[0054] As described above, according to the third embodiment, even if personal optimization information of a user of the state estimation device 300 is not stored in the state estimation device 300, estimation is performed using the cluster optimization information of a cluster into which similar users are classified as an initial value. Then, by the user using the state estimation device 300, the personal optimization information is updated to become more optimal. Therefore, by using the state estimation device 300, the estimation accuracy for each user is improved.

[0055] In the above-described first to third embodiments, an example has been shown in which processing is performed by one state estimation device 100 to 300, but the first to third embodiments are not limited to such an example. For example, the processing performed by each of the state estimation devices 100 to 300 according to the first to third embodiments may be distributed and performed by a plurality of computers, such as a server and a PC, connected to a network such as the Internet. In other words, the processing performed by each of the state estimation devices 100 to 300 according to the first to third embodiments may be performed by a state estimation system.

[0056] For example, in the first embodiment, the state estimation system may be configured by a server (not shown) including the model management unit 110 and the optimization information management unit 120, and a PC (state estimation device) including the input unit 130 and the detection unit 140. Also, in the second embodiment, the state estimation system may be configured by a server (not shown) including the model management unit 110 and the optimization information management unit 120, and a PC (state estimation device) including the input unit 130, the detection unit 140, and the update unit 250. Furthermore, in the third embodiment, the state estimation system may be configured by a server (not shown) including the model management unit 110 and the optimization information management unit 320, and a PC (state estimation device) including the input unit 330, the detection unit 140, the update unit 250, and the cluster identification unit 360. [Explanation of symbols]

[0057] 100,200,300 state estimation device, 110 model management unit, 111 data storage unit, 112 model generation unit, 113 parent model storage unit, 120 optimization information management unit, 121 personal optimization information generation unit, 122 personal optimization information storage unit, 323 clustering unit, 324 cluster optimization information generation unit, 325 cluster optimization information storage unit, 130 input unit, 140 detection unit, 141 addition unit, 142 state estimation unit, 250 update unit, 251 accumulation unit, 252 personal optimization information update unit, 360 cluster identification unit.

Claims

1. an adding unit that adds personal optimization information to a parent model by inputting detection data detected as a plurality of types of data from a person, so that estimation using the parent model for estimating a state of the person is suitable for a user; a state estimation unit that estimates a state of the user by inputting detection data detected from the user to the parent model to which the personal optimization information is added, The parent model includes a plurality of sub-models that estimate a state of the person from each of the plurality of types of data, The personal optimization information is information for estimating the state of the user by weighting and evaluating outputs of the plurality of sub-models. A state estimation device comprising:

2. and a personal optimization information update unit that updates the personal optimization information when a state of the user corresponding to detection data detected from the user is acquired, so that the acquired state can be estimated from the detection data corresponding to the acquired state. The state estimation device according to claim 1 .

3. A cluster identification unit that identifies a cluster to which the user belongs among a plurality of clusters based on an attribute of the user or detection data detected from the user, the adding unit identifies cluster optimization information corresponding to the identified cluster from among a plurality of cluster optimization information that makes estimation using the parent model suitable for people classified into each of the plurality of clusters, and adds the identified cluster optimization information to the parent model as the personal optimization information; and a personal optimization information update unit that updates the specified cluster optimization information when a state of the user corresponding to detection data detected from the user is acquired, so that the acquired state can be estimated from the detection data corresponding to the acquired state. The state estimation device according to claim 1 .

4. an adding unit that adds personal optimization information to a parent model by inputting detection data detected from a person, so that estimation using the parent model for estimating a state of the person is suitable for a user; a state estimation unit that estimates a state of the user by inputting detection data detected from the user to the parent model to which the personal optimization information is added, The personal optimization information is information for changing an output of a part of layers of the parent model to be suitable for the user. A state estimation device comprising:

5. and a personal optimization information update unit that updates the personal optimization information when a state of the user corresponding to detection data detected from the user is acquired, so that the acquired state can be estimated from the detection data corresponding to the acquired state. The state estimating device according to claim 4 .

6. A cluster identification unit that identifies a cluster to which the user belongs among a plurality of clusters based on an attribute of the user or detection data detected from the user, the adding unit identifies cluster optimization information corresponding to the identified cluster from among a plurality of cluster optimization information that makes estimation using the parent model suitable for people classified into each of the plurality of clusters, and adds the identified cluster optimization information to the parent model as the personal optimization information; and a personal optimization information update unit that updates the specified cluster optimization information when a state of the user corresponding to detection data detected from the user is acquired, so that the acquired state can be estimated from the detection data corresponding to the acquired state. The state estimating device according to claim 4 .

7. an adding unit that adds personal optimization information to a parent model by inputting detection data detected from a person, so that estimation using the parent model for estimating a state of the person is suitable for a user; a state estimation unit that estimates a state of the user by inputting detection data detected from the user to the parent model to which the personal optimization information is added; and a personal optimization information update unit that updates the personal optimization information when a state of the user corresponding to detection data detected from the user becomes clear, so that the cleared state can be estimated from the detection data corresponding to the cleared state. A state estimation device comprising:

8. an adding unit that adds personal optimization information to a parent model by inputting detection data detected from a person, so that estimation using the parent model for estimating a state of the person is suitable for a user; a state estimation unit that estimates a state of the user by inputting detection data detected from the user to the parent model to which the personal optimization information is added; a cluster identification unit that identifies a cluster to which the user belongs among a plurality of clusters based on an attribute of the user or detection data detected from the user, the adding unit identifies cluster optimization information corresponding to the identified cluster from among a plurality of cluster optimization information that makes estimation using the parent model suitable for people classified into each of the plurality of clusters, and adds the identified cluster optimization information to the parent model as the personal optimization information; and a personal optimization information update unit that updates the specified cluster optimization information when a state of the user corresponding to detection data detected from the user is acquired, so that the acquired state can be estimated from the detection data corresponding to the acquired state. A state estimation device comprising:

9. Computer, an adding unit that adds personal optimization information to a parent model by inputting detection data detected as a plurality of types of data from a person, so that estimation using the parent model for estimating a state of the person is suitable for a user; and by inputting detection data detected from the user into the parent model to which the personal optimization information has been added, the parent model functions as a state estimation unit that estimates a state of the user; The parent model includes a plurality of sub-models that estimate a state of the person from each of the plurality of types of data, The personal optimization information is information for estimating the state of the user by weighting and evaluating outputs of the plurality of sub-models. A program characterized by.

10. Computer, an adding unit that adds personal optimization information to a parent model by inputting detection data detected from a person, so that estimation using the parent model for estimating a state of the person is suitable for a user; and by inputting detection data detected from the user into the parent model to which the personal optimization information has been added, the parent model functions as a state estimation unit that estimates a state of the user; The personal optimization information is information for changing an output of a part of layers of the parent model to be suitable for the user. A program characterized by.

11. Computer, an adding unit that adds personal optimization information to a parent model by inputting detection data detected from a person, so that estimation using the parent model for estimating a state of the person is suitable for a user; a state estimation unit that estimates a state of the user by inputting detection data detected from the user to the parent model to which the personal optimization information is added; and and functioning as an individual optimization information update unit that updates the individual optimization information when a state of the user corresponding to detection data detected from the user becomes clear, so that the cleared state can be estimated from the detection data corresponding to the cleared state. A program characterized by.

12. Computer, an adding unit that adds personal optimization information to a parent model by inputting detection data detected from a person, so that estimation using the parent model for estimating a state of the person is suitable for a user; a state estimation unit that estimates a state of the user by inputting detection data detected from the user to the parent model to which the personal optimization information is added; and a cluster identification unit that identifies a cluster to which the user belongs among a plurality of clusters based on the attributes of the user or detection data detected from the user; the adding unit identifies cluster optimization information corresponding to the identified cluster from among a plurality of cluster optimization information that makes estimation using the parent model suitable for people classified into each of the plurality of clusters, and adds the identified cluster optimization information to the parent model as the personal optimization information; The computer further comprises: and functioning as a personal optimization information update unit that updates the specified cluster optimization information when a state of the user corresponding to detection data detected from the user is acquired, so that the acquired state can be estimated from the detection data corresponding to the acquired state. A program characterized by.

13. an adding unit that adds personal optimization information to a parent model by inputting detection data detected as a plurality of types of data from a person, so that estimation using the parent model for estimating a state of the person is suitable for a user; a state estimation unit that estimates a state of the user by inputting detection data detected from the user to the parent model to which the personal optimization information is added, The parent model includes a plurality of sub-models that estimate a state of the person from each of the plurality of types of data, The personal optimization information is information for estimating the state of the user by weighting and evaluating outputs of the plurality of sub-models. A state estimation system comprising:

14. an adding unit that adds personal optimization information to a parent model by inputting detection data detected from a person, so that estimation using the parent model for estimating a state of the person is suitable for a user; a state estimation unit that estimates a state of the user by inputting detection data detected from the user to the parent model to which the personal optimization information is added, The personal optimization information is information for changing an output of a part of layers of the parent model to be suitable for the user. A state estimation system comprising:

15. an adding unit that adds personal optimization information to a parent model by inputting detection data detected from a person, so that estimation using the parent model for estimating a state of the person is suitable for a user; a state estimation unit that estimates a state of the user by inputting detection data detected from the user to the parent model to which the personal optimization information is added; and a personal optimization information update unit that updates the personal optimization information when a state of the user corresponding to detection data detected from the user becomes clear, so that the cleared state can be estimated from the detection data corresponding to the cleared state. A state estimation system comprising:

16. an adding unit that adds personal optimization information to a parent model by inputting detection data detected from a person, so that estimation using the parent model for estimating a state of the person is suitable for a user; a state estimation unit that estimates a state of the user by inputting detection data detected from the user to the parent model to which the personal optimization information is added; a cluster identification unit that identifies a cluster to which the user belongs among a plurality of clusters based on an attribute of the user or detection data detected from the user, the adding unit identifies cluster optimization information corresponding to the identified cluster from among a plurality of cluster optimization information that makes estimation using the parent model suitable for people classified into each of the plurality of clusters, and adds the identified cluster optimization information to the parent model as the personal optimization information; and a personal optimization information update unit that updates the specified cluster optimization information when a state of the user corresponding to detection data detected from the user is acquired, so that the acquired state can be estimated from the detection data corresponding to the acquired state. A state estimation system comprising:

17. An adding unit inputs detection data detected as multiple types of data from a person, and adds personal optimization information to the parent model to make an estimation using the parent model for estimating a state of the person more suitable for a user; a state estimation unit estimates a state of the user by inputting detection data detected from the user to the parent model to which the personal optimization information is added; The parent model includes a plurality of sub-models that estimate a state of the person from each of the plurality of types of data, The personal optimization information is information for estimating the state of the user by weighting and evaluating outputs of the plurality of sub-models. A state estimation method comprising:

18. An adding unit adds personal optimization information to a parent model by inputting detection data detected from a person, the personal optimization information being used to make an estimation using the parent model for estimating a state of the person more suitable for a user; a state estimation unit estimates a state of the user by inputting detection data detected from the user to the parent model to which the personal optimization information is added; The personal optimization information is information for changing an output of a part of layers of the parent model to be suitable for the user. A state estimation method comprising:

19. An adding unit adds personal optimization information to a parent model by inputting detection data detected from a person, the personal optimization information being used to make an estimation using the parent model for estimating a state of the person more suitable for a user; a state estimation unit estimates a state of the user by inputting detection data detected from the user to the parent model to which the personal optimization information is added; a personal optimization information update unit updates the personal optimization information when a state of the user corresponding to detection data detected from the user becomes clear, so that the cleared state is estimated from the detection data corresponding to the cleared state. A state estimation method comprising:

20. An adding unit adds personal optimization information to a parent model by inputting detection data detected from a person, the personal optimization information being used to make an estimation using the parent model for estimating a state of the person more suitable for a user; a state estimation unit estimates a state of the user by inputting detection data detected from the user to the parent model to which the personal optimization information is added; a cluster identification unit that identifies a cluster to which the user belongs among a plurality of clusters based on an attribute of the user or detection data detected from the user; The adding unit identifies cluster optimization information corresponding to the identified cluster from among a plurality of cluster optimization information that makes estimation using the parent model suitable for people classified into each of the plurality of clusters, and adds the identified cluster optimization information to the parent model as the personal optimization information; a personal optimization information update unit updates the identified cluster optimization information so that, when a state of the user corresponding to detection data detected from the user is acquired, the acquired state is estimated from the detection data corresponding to the acquired state. A state estimation method comprising:

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