Artificial intelligence device and method for executing an operation based on predicted biometric state of a user

A computer-implemented method for controlling a device based on an ensemble model can include receiving sensing information associated with a user's biometric state; inputting first sensing information to a first model, determining a first uncertainty of the first model, and generating a first weight value for weighting a first result value; inputting second sensing information into a second model, determining a second uncertainty of the second model, and generating a second weight value for weighting a second result value; generating a final result value based on combining the first result value weighted by the first weight value and the second result value weighted by the second weight value; generating a predicted biometric state of the user based on the final result value; and executing an operation of the device based on the predicted biometric state.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Korean Patent Application No. 10-2018-0112480 filed on Sep. 19, 2018 in Korea, the entire contents of which is hereby incorporated by reference in its entirety.

BACKGROUND

Field of the Invention

The present invention relates to an artificial intelligence device which controls weight values of result values of a plurality of models in an ensemble model which combines the result values of the plurality of models to output a final result value.

Discussion of the Related Art

Artificial intelligence (AI) is in the field of information technology and computer engineering for researching a method of allowing a computer to perform thinking, learning, and self-development based on intelligence of humans, and denotes that computers imitate intelligent behaviors of humans.

Moreover, AI is indirectly and directly much associated with the field of computer engineering without existing itself. Particularly, an AI component is applied to various fields of information technology recently, and an attempt to solve problems in the fields is being very actively made.

An ensemble learning method is a method which uses a number of learning algorithms for obtaining prediction performance better than a case where a learning algorithm is separately used in machine learning.

Moreover, an ensemble model uses the ensemble learning method and denotes a final prediction model which is obtained by combining a plurality of prediction models differently learned based on various learning algorithms and various data.

When data is input, each of the plurality of prediction models outputs a result value, and the ensemble model combines the result values output from the plurality of prediction models to output a final result value.

A related art ensemble model assigns the same weight value to result values output from a plurality of prediction models to output a final result value.

When noise or previously unlearned data is input to a specific prediction model of the plurality of prediction models, the uncertainty of a result value of the specific prediction model increases. That is, the uncertainties of result values output from the plurality of prediction models may differ.

However, in the related art ensemble model, despite an uncertainty difference, since the same weight value is assigned to the result values output from the plurality of prediction models, the uncertainty of the final result value is reduced.

SUMMARY

An aspect of the present invention is directed to providing an artificial intelligence (AI) device which controls weight values of result values of a plurality of models in an ensemble model which combines the result values of the plurality of models to output a final result value.

To achieve these and other advantages and in accordance with the purpose of the disclosure, as embodied and broadly described herein, there is provided computer-implemented method for inputting sensing information to an ensemble model to obtain a final result value, the computer-implemented method including obtaining pieces of sensing information associated with a biometric state, inputting the pieces of sensing information to an ensemble model which includes a plurality of models and combines result values output from the plurality of models to output the final result value, inputting first sensing information of the pieces of sensing information to a first model of the plurality of models, obtaining a first uncertainty of the first model by using at least one of an input value and an output value of the first model, and determining a first weight value of a first result value of the first model by using the first uncertainty, inputting second sensing information of the pieces of sensing information to a second model of the plurality of models, obtaining a second uncertainty of the second model by using at least one of an input value and an output value of the second model, and determining a second weight value of a second result value of the second model by using the second uncertainty, combining, by using the ensemble model, the first result value to which the first weight value is applied and a second result value to which the second weight value is applied to obtain the final result value, and performing an operation corresponding to the biometric state, based on the final result value.

DETAILED DESCRIPTION OF THE DISCLOSURE

FIG. 1is a diagram for describing a problem which occurs when the same weight value is assigned to result values of a plurality of models in a case of generating an ensemble model for obtaining information about a biometric state, according to an embodiment of the present invention.

The ensemble model may include a first model which receives first data to output a first result value, a second model which receives second data to output a second result value, and a third model which receives third data to output a third result value.

Here, the ensemble model may be a model for predicting a biometric state of a user.

In a case where various features such as a motion, a physiological signal, and a sound of a user are combined and used, a biometric state of the user may be more accurately predicted.

Therefore, the ensemble model may combine result values of a plurality of models to output a final result value, thereby more accurately predicting the biometric state of the user.

The first data may be motion data obtained by sensing a motion of the user, and the first model may be a model for predicting a motion state of the user.

The second data may be biometric signal data obtained by sensing the physiological signal, such as a heart rate or a temperature, of the user, and the second model may be a model for predicting the biometric state of the user.

The third data may be sound data obtained by sensing a sound signal, such as snoring, of the user, and the third model may be a model for predicting a sound state of the user.

Each of the first model, the second model, and the third model may output a result value, based on data input thereto. Also, the ensemble model may apply the same weight value to the result values output from the first to third models to output a final result value.

Noise or unlearned data may be input to the ensemble model.

For example, when the first model is a model which predicts a motion state of a sleeping user on the basis of a motion of the user, data unassociated with a motion, performed in sleeping, of the user and data corresponding to a motion, such as waking up and going to a restroom, moving of a person next to the user, or moving of a surrounding object, of another person may be input to the first model.

As another example, when the second model is a model which predicts a physiological state of the user on the basis of the physiological signal of the user, data corresponding to a case where breathing or pulsation is abnormally measured due to a motion of the user may be input to the second model, and data corresponding to a case where a signal of a person next to the user is measured may be input to the second model.

As another example, when the third model is a model which predicts a sound state of the user on the basis of a sound of the user, noise of an ambient environment may be measured and may be input to the third model, and a changed sound when the user has a cold may be input to the third model.

An example, where a heart rate is abnormally measured due to a motion of the user and thus noise is included in the second data, will be described below.

When the second data including noise is received, the second model may output a right answer as high reliability. For example, an actual heart rate of the user may be 60 but may be abnormally measured as 90, and when a signal measured as 90 is input, the second model may output a right answer corresponding to 90. However, since the second model does not know the uncertainty of 90, the second model may output a right answer corresponding to 90 as high reliability.

Since a result value of the second model is inaccurate, the ensemble model, in a case where the ensemble model applies the same weight value to the result values of the first to third models to output a final result value, the final result value may be inaccurate.

That is, each of the first to third models may output a result value representing a case where a right answer is ensured or may output a result value representing a case where the right answer is not ensured, based on data input thereto. Here, a degree to which a result value output from each model is ensured as a right answer may be referred to as uncertainty.

When the ensemble model assigns the same weight value to an output value of the first model, an output value of the second model, and an output value of the third model regardless of uncertainty, an output value which is high in uncertainty and an output value which is low in uncertainty may be identically applied to a final result value. In this case, the reliability of the final result value of the ensemble model may be reduced.

FIG. 2is a block diagram for describing an artificial intelligence (AI) device10according to an embodiment of the present invention.

The AI device10according to an embodiment of the present invention may include a sensing unit100and an AI unit200.

The sensing unit100may obtain pieces of sensing information associated with a biometric state, for predicting the biometric state of a user.

In detail, a first sensing unit110of the sensing unit100may obtain first sensing information. Here, the first sensing information may be information obtained by sensing a motion of the user.

A second sensing unit120of the sensing unit100may obtain second sensing information. Here, the second sensing information may be information obtained by sensing a physiological signal of the user. Here, the physiological signal of the user may include at least one of a heart rate, a respiration rate, a respiration flow, and a temperature.

A third sensing unit130of the sensing unit100may obtain third sensing information. Here, the third sensing information may be information obtained by sensing a sound of the user. Here, the sound of the user may include at least one of snoring, a breathing sound, and a heartbeat sound of the user.

The AI unit200may include an ensemble model300.

Here, the ensemble model may include a first model310, a second model320, and a third model330.

Here, the first model310may be a model for predicting a motion state of the user by using the first sensing information and may be referred to as a motion prediction model.

The second model320may be a model for predicting a physiological state of the user by using the second sensing information and may be referred to as a physiological state prediction model.

The third model330may be a model for predicting a sound state of the user by using the third sensing information and may be referred to as a sound state prediction model.

The sensing unit100may transmit pieces of sensing information to the AI unit200.

The AI unit200may input each of the pieces of sensing information to the ensemble model300as an input value.

In detail, the AI unit200may input the first sensing information to the first model310. Also, the AI unit200may input the second sensing information to the second model320. Also, the AI unit200may input the third sensing information to the third model330.

The first model may output a first result value corresponding to the first sensing information input thereto. Here, the first result value output from the first model may denote a motion state of the user which is predicted based on the first sensing information by the first model.

The first model may be a machine learning model which is previously learned so as to output a result value corresponding to the first sensing information.

In this case, the first model may be a model which has trained an artificial neural network through supervised learning. For example, the first model may be a model which has been trained by inputting motion data of the user and a label (a motion state) corresponding to the motion data.

The second model may output a second result value corresponding to the second sensing information input thereto. Here, the second result value output from the second model may denote a motion state of the user which is predicted based on the second sensing information by the second model.

The second model may be a machine learning model which is previously learned so as to output a result value corresponding to the second sensing information.

In this case, the second model may be a model which has trained the artificial neural network through supervised learning. For example, the second model may be a model which is trained by inputting physiological data of the user and a label (a physiological state) corresponding to the physiological data.

The third model may output a third result value corresponding to the third sensing information input thereto. Here, the third result value output from the third model may denote a motion state of the user which is predicted based on the third sensing information by the third model.

The third model may be a machine learning model which is previously learned so as to output a result value corresponding to the third sensing information.

In this case, the third model may be a model which has trained the artificial neural network through supervised learning. For example, the third model may be a model which is trained by inputting sound data of the user and a label (a sound state) corresponding to the sound data.

The ensemble model300may combine the first result value output from the first model, the second result value output from the second model, and the third result value output from the third model to output a final result value.

For example, the ensemble model300may combine the motion state output from the first model, the physiological state output from the second model, and the sound state output from the third model to output information about a sleeping stage of the user.

In this case, the AI unit200may perform an operation corresponding to a biometric state of the user, based on the final result value.

For example, when the AI device10is an AI speaker, the AI unit200may control volume or may turn off the AI device10, based on the sleeping stage of the user.

As another example, when the AI device10is an AI lighting device, the AI unit200may control illuminance, based on the sleeping stage of the user.

FIG. 3is a flowchart for describing an operating method of an AI device according to an embodiment of the present invention.

The operating method of the AI device according to an embodiment of the present invention may include step S410of obtaining pieces of sensing information, step S430of inputting, as input values, the pieces of sensing information to an ensemble model including a plurality of models, step S450of determining weight values of result values of the plurality of models, based on at least one of the input values input to the plurality of models and result values output from the plurality of models, and step S470of applying the weight values to the result values of the plurality of models to obtain a final result value and performing an operation corresponding to a biometric state, based on the final result value.

The above-described method may be for inputting the sensing information to the ensemble model to obtain a final result value and may be executed in a computer.

The AI device may obtain uncertainty by using at least one of an input value and a result value and may control weight values of result values of a plurality of models by using the uncertainty.

FIGS. 4 and 5are diagrams for describing a weight value determining method according to a first embodiment of the present invention.

An AI unit may obtain weight values applied to result values output from a plurality of models (for example, first to third models)310,320, and330, based on the result values output from the plurality of models310,320, and330.

In detail, the first model310may output a plurality of probability values respectively corresponding to a plurality of classes. In this case, the AI unit may obtain an uncertainty of a first result value of the first model310, based on a variance between the plurality of probability values.

In detail, referring toFIG. 5, when first sensing information is input, the first model310may obtain a plurality of scores “1.79, 1.8, and 1.7” respectively corresponding to a plurality of classes (for example, first to third classes) S1 to S3. Here, the plurality of classes S1 to S3 may be right answers which are to be predicted by the first model310, and may respectively represent a plurality of motion states.

Moreover, the first model310may obtain a plurality of probability values “0.34, 0.35, and 0.31” respectively corresponding to the plurality of scores “1.79, 1.8, and 1.7”.

The AI unit may include a first uncertainty determination model311. Also, the first uncertainty determination model311may obtain a weight value W1 of a result value S2 of the first model310by using a variance between the plurality of probability values “0.34, 0.35, and 0.31”.

In detail, it may be assumed that the first class S1 is a motion of a breast when a user breathes, the second class S2 is a motion where the user tosses and turns to the left, and the third class S3 is a motion where the user turns a body. Also, when first sensing information is information obtained by sensing a motion where the user tosses and turns to the left, a probability corresponding to the second class S2 may be output as a very high value, and for example, may be output as a probability value close to 1.

In this case, each of a probability value corresponding to the first class S1 and a probability value corresponding to the third class S3 may be output as a very low value, and for example, may be output as a probability value close to 0.

In this case, a variance between a plurality of probability values may be large. Also, when the variance between the plurality of probability values is large, the first uncertainty determination model311may determine an uncertainty of a first result value S2 of the first model310as a low level.

As another example, it may be assumed that the first class S1 is a motion of the breast when the user breathes, the second class S2 is a motion where the user tosses and turns to the left, and the third class S3 is a motion where the user turns the body. Also, it may be assumed that the first sensing information is noise (a motion where the user goes to a restroom).

When the first sensing information is the noise, data differing from a right answer which is to be predicted by the first model may be input, and thus, the variance between the plurality of probability values “0.34, 0.35, and 0.31” may be reduced. Also, when the variance between the plurality of probability values “0.34, 0.35, and 0.31” is small, the first uncertainty determination model311may determine the uncertainty of the first result value S2 of the first model310as a high level.

The first uncertainty determination model311may determine a first weight value W1 of the first result value S2, based on the uncertainty of the first result value S2.

The first model may output a result value corresponding to the first sensing information. In detail, the first model may output, as a result value, the class S2 where a score is largest or a probability value is largest.

An ensemble model300may apply (S2*W1) the first weight value W1 to the result value S2 of the first model.

The same process may be performed on the second model and the third model.

In detail, the AI unit may include a second uncertainty determination model321. Also, the second uncertainty determination model321may obtain an uncertainty of a result value of the second model320, based on a variance between a plurality of probability values output from the second model320and may determine a second weight value W2 of the result value of the second model.

Moreover, the AI unit may include a third uncertainty determination model331. Also, the third uncertainty determination model331may obtain an uncertainty of a result value of the third model330, based on a variance between a plurality of probability values output from the third model330and may determine a third weight value W3 of the result value of the third model.

The ensemble model300may apply the first weight value W1 to the first result value of the first model, the second weight value W2 to the second result value of the second model, and the third weight value W3 to the third result value of the third model.

Moreover, the ensemble model300may output a final output value by using the first result value with the first weight value applied thereto, the second result value with the second weight value applied thereto, and the third result value with the third weight value applied thereto.

FIG. 6is a diagram for describing a weight value determining method according to a second embodiment of the present invention.

Hereinafter, the weight value determining method according to a second embodiment of the present invention will be described with reference toFIGS. 4 and 6.

A first model310may include one ensemble model including a plurality of single models310ato310d.

An AI unit may obtain a plurality of probability value sets (for example, first to fourth probability value sets)621to624which are obtained by randomly combining the plurality of single models310ato310dwith respect to one input value.

In detail, when first sensing information is input, the first model310may randomly combine the plurality of single models310ato310dto output a plurality of score sets611to614. Also, the first model310may output the plurality of probability value sets621to624respectively corresponding to the plurality of score sets611to614.

In this case, the AI unit may obtain an uncertainty of a first result value of the first model, based on a variance between the plurality of probability value sets621to624which are output by randomly combining the plurality of single models310ato310d.

Moreover, the AI unit may obtain a weight value W1 of a result value S2 of the first model, based on the uncertainty of the first result value.

For example, the first probability value set621may be a probability value set corresponding to the score set611which is output by combining result values of a first single model, a second single model, and an nthsingle model.

As another example, the second probability value set622may be a probability value set corresponding to the score set612which is output by combining result values of the first single model, a third single model, and the nthsingle model.

When the first sensing information is data which is previously learned in the plurality of single models310ato310d, a variance between the plurality of probability value sets621to624.

When the variance between the plurality of probability value sets621to624is small, the first uncertainty determination model311may determine an uncertainty of a first result value S2 of the first model as a low level.

On the other hand, when the first sensing information is noise instead of the data which is previously learned in the plurality of single models310ato310d, the variance between the plurality of probability value sets621to624may be large.

Moreover, when the variance between the plurality of probability value sets621to624is large, the first uncertainty determination model311may determine the uncertainty of the first result value S2 of the first model as a high level.

The first uncertainty determination model311may determine a first weight value W1 of the first result value S2, based on the uncertainty of the first result value S2.

In detail, when the uncertainty of the first result value S2 is a low level, the first uncertainty determination model311may determine a weight value W1 corresponding to a high level.

Moreover, when the uncertainty of the first result value S2 is a high level, the first uncertainty determination model311may determine a weight value W1 corresponding to a low level.

The first model may output a result value corresponding to the first sensing information. In detail, the first model may output, as a result value, the class S2 where a score is largest or a probability value is largest.

An ensemble model300may apply (S2*W1) the first weight value W1 to the result value S2 of the first model.

The same process may be performed on the second model and the third model.

For example, the second model320may be an ensemble model including a plurality of single models. Also, when second sensing information is input, the second model320may randomly combine the plurality of single models to output a plurality of probability value sets.

In this case, the AI unit may obtain an uncertainty of a second result value of the second model, based on a variance between a plurality of probability value sets which are output by randomly combining a plurality of single models.

Moreover, the AI unit may obtain a weight value of a result value of the second model, based on the uncertainty of the second result value.

The ensemble model300may apply the first weight value W1 to the first result value of the first model, the second weight value W2 to the second result value of the second model, and the third weight value W3 to a third result value of the third model.

Moreover, the ensemble model300may output a final output value by using the first result value with the first weight value applied thereto, the second result value with the second weight value applied thereto, and the third result value with the third weight value applied thereto.

FIGS. 7 and 8are diagrams for describing a weight value determining method according to a third embodiment of the present invention.

An AI unit300may obtain a weight value applied to result values output from a plurality of models (for example, first to third models)310,320, and330, based on sensing information input to the plurality of models310,320, and330.

In detail, first sensing information may be input to the first model. In this case, the first sensing information may be input to a first uncertainty determination model311. Here, the first uncertainty determination model311may be a machine learning model820pre-learning noise of the first sensing information.

In detail, referring toFIG. 8, the first uncertainty determination model311may be the machine learning model820which has been trained by inputting noise data N and an uncertainty corresponding to the noise data N.

For example, the first uncertainty determination model311may be a machine learning model which has been trained by inputting sensing information810, sensed when a user goes to a restroom, and an uncertainty corresponding to the sensing information.

When first sensing information is input to the first uncertainty determination model311, the first uncertainty determination model311may output an uncertainty corresponding to the first sensing information.

Moreover, the AI unit300may determine a weight value of a result value of a first model, based on an uncertainty output from the first uncertainty determination model311.

The same process may be performed in the second model and the third model.

For example, when second sensing information is input to a second uncertainty determination model321, the second uncertainty determination model321may output an uncertainty corresponding to the second sensing information. In this case, the AI unit300may determine a weight value of a result value of the second model, based on an uncertainty output from the second uncertainty determination model321.

Moreover, when third sensing information is input to a third uncertainty determination model331, the third uncertainty determination model331may output an uncertainty corresponding to the third sensing information. In this case, the AI unit300may determine a weight value of a result value of the third model, based on an uncertainty output from the third uncertainty determination model331.

The ensemble model300may apply a first weight value W1 to a first result value of the first model, a second weight value W2 to a second result value of the second model, and a third weight value W3 to a third result value of the third model.

Moreover, the ensemble model300may output a final output value by using the first result value with the first weight value applied thereto, the second result value with the second weight value applied thereto, and the third result value with the third weight value applied thereto.

FIG. 9is a diagram for describing a weight value determining method according to a fourth embodiment of the present invention.

An AI unit may obtain weight values applied to result values output from a plurality of models (for example, first to third models)310,320, and330, based on sensing information input to the plurality of models310,320, and330.

First sensing information may be input to the first model. In this case, the first sensing information may be input to a first uncertainty determination model311. Here, the first uncertainty determination model311may be an auto encoder pre-learning a plurality of classes of the first model.

In detail, when a first class S1 is a motion of a breast when a user breathes, a second class S2 is a motion where the user tosses and turns to the left, and a third class S3 is a motion where the user turns a body, sensing information corresponding to the first class S1, sensing information corresponding to the second class S2, and sensing information corresponding to the third class S3 may be provided as learning data and output data to an auto encoder930. Also, the auto encoder930may be trained to minimize a loss of each of the learning data and the output data.

When first sensing information910is input to the auto encoder930, the auto encoder may output a result value920corresponding to the first sensing information910input thereto.

In this case, a first uncertainty determination model311may obtain an uncertainty of a result value of the first model, based on a loss of each of the first sensing information input to the auto encoder and the result value output from the auto encoder.

In terms of a characteristic of the auto encoder, the auto encoder may output a result value which enables an input value to be almost identically restored, with respect to data similar to learned data, but when data differing from learned data is input, the auto encoder cannot normally perform a restoration operation.

For example, when the first sensing information910is data obtained by sensing the first class S1, the loss of each of the first sensing information input to the auto encoder and the result value output from the auto encoder may be small. Also, when the loss is small, an uncertainty of the result value of the first model may be a low level.

Moreover, when the uncertainty of the result value of the first model is a low level, the AI unit may output a weight value corresponding to a high level.

As another example, when the first sensing information910is noise, the loss of each of the first sensing information input to the auto encoder and the result value output from the auto encoder may be large. Also, when the loss is large, the uncertainty of the result value of the first model may be a high level.

Moreover, when the uncertainty of the result value of the first model is a high level, the AI unit may output a weight value corresponding to a low level.

The same process may be performed in the second model and the third model.

The ensemble model300may apply a first weight value W1 to a first result value of the first model, a second weight value W2 to a second result value of the second model, and a third weight value W3 to a third result value of the third model.

Moreover, the ensemble model300may output a final output value by using the first result value with the first weight value applied thereto, the second result value with the second weight value applied thereto, and the third result value with the third weight value applied thereto.

FIG. 10is a diagram for describing a weight value determining method according to a fifth embodiment of the present invention.

An AI unit may obtain weight values applied to result values output from a plurality of models (for example, first to third models)310,320, and330, based on sensing information input to the plurality of models310,320, and330and the result values output from the plurality of models310,320, and330.

In detail, a first uncertainty determination model may obtain a 1-1thuncertainty Ux by using first sensing information input to the first model. Also, the first uncertainty determination model may obtain a 1-2thuncertainty Uy′ by using an output value output from the first model.

In this case, the first uncertainty determination model may obtain an uncertainty of a result value of the first model by using the 1-1thuncertainty Ux and the 1-2thuncertainty Uy′ and may obtain a first weight value W1 applied to a result value of the first model.

The same process may be performed in the second model and the third model.

The ensemble model300may apply a first weight value W1 to a first result value of the first model, a second weight value W2 to a second result value of the second model, and a third weight value W3 to a third result value of the third model.

Moreover, the ensemble model300may output a final output value by using the first result value with the first weight value applied thereto, the second result value with the second weight value applied thereto, and the third result value with the third weight value applied thereto.

The AI unit may include a weight determination model (not shown). Also, the weight determination model (not shown) may output weight values W1 to W3 corresponding to first sensing information, second sensing information, and third sensing information.

In detail, the weight determination model (not shown) may be a learning model pre-learning a weight value based on an uncertainty.

A learning process of the weight determination model will be described below with reference toFIG. 11.

Sensing data may be input as learning data. In this case, a plurality of models may output a result value corresponding to input data.

The sensing information may be input to an input-based uncertainty determination model, and the input-based uncertainty determination model may output an uncertainty Ux corresponding to sensing information.

Moreover, a result value output from each of the plurality of models may be input to a result value-based uncertainty determination model. In this case, the result value-based uncertainty determination model may output an uncertainty Uy′ corresponding to a result value output from each of the plurality of models.

The ensemble model may combine the uncertainty Ux corresponding to the sensing information and the uncertainty Uy′ corresponding to the result value to obtain a weight value (π(Ux, Uy′)) and may obtain a final result value (π(Ux, Uy′)F(x)) which is obtained by applying the obtained weight value to the result value.

The weight determination model may search for a weight value for minimizing a loss (L(π(Ux, Uy′)Fx, y)) between learning data and the final result value (π(Ux, Uy′)F(x)) or minimizing an uncertainty.

This may be represented by the following Equation (1):
ensemble loss:L(vF(x),y)=π(Ux,Uy′)F(x)  (1)

The weight determination model may search for a, b, and c for minimizing a loss in the following Equation (2), or may search for a for minimizing a loss in the following Equation (3):
v=π(Ux,Uy′)=aUx+bUy′+c(2)
v=π(Ux,Uy′)=a(Ux+Uy′)  (3)

Moreover, by repeating such a process, the weight determination model may be learned to predict a weight value corresponding to the sensing information.

FIG. 12is a diagram for describing a method of determining, by a weight determination model, a weight value corresponding to sensing information.

Sensing information may be input. In this case, each of a plurality of models may output a result value corresponding to input data.

The sensing information may be input to an input-based uncertainty determination model, and the input-based uncertainty determination model may output an uncertainty Ux corresponding to the sensing information.

Moreover, result values output from the plurality of models may be input to a result value-based uncertainty determination model. In this case, the result value-based uncertainty determination model may output an uncertainty Uy′ corresponding to the result value output from each of the plurality of models.

A learned weight determination model may output a weight value (π(Ux, Uy′)) by using the uncertainty Ux corresponding to the sensing information and the uncertainty Uy′ corresponding to the result value.

In this case, an ensemble model may apply weight values to the result values of the plurality of models to output a final output value.

The ensemble model may output or may not output the final result value, based on the uncertainty.

For example, when the uncertainty is less than a threshold value, the ensemble model may output the final result value.

On the other hand, when the uncertainty is greater than the threshold value, the ensemble model may not output the final result value, or may output information representing that it is unable to search for a right answer.

FIG. 13is a diagram for describing a weight value determining method according to a sixth embodiment of the present invention.

An AI unit may obtain weight values applied to result values output from a plurality of models (for example, first to third models)310,320, and330, based on sensing information input to the plurality of models310,320, and330and the result values output from the plurality of models310,320, and330.

In detail, a 1-1thuncertainty determination model313may obtain a 1-1thuncertainty by using first sensing information input to the first model.

The 1-1thuncertainty determination model313may determine whether the 1-1thuncertainty is lower than a predetermined value. Also, when the 1-1thuncertainty is lower than the predetermined value, the 1-1thuncertainty determination model313may input the first sensing information to the first model. On the other hand, when the 1-1thuncertainty is higher than the predetermined value, the 1-1thuncertainty determination model313may block an input of the first sensing information to the first model.

A 1-2thuncertainty determination model314may obtain a 1-2thuncertainty by using a result value output from the first model. Also, the 1-2thuncertainty determination model314may determine a first weight value W1 of a first result value S2, based on the 1-2thuncertainty.

The same process may be performed in the second model and the third model (e.g.c323,324,333and334).

The present invention may obtain biometric information about a user by using an ensemble model configured by a combination of a motion model, a sound model, and a physiological model, thereby preventing performance from being reduced when low-quality sensing information is received.

Moreover, the present invention may calculate an uncertainty of a result value of each model to determine a quality of sensing information input to each model. Also, the present invention may reduce a weight value corresponding to an output value of a model to which low-quality sensing information is input and may increase a weight value corresponding to an output value of a model to which high-quality sensing information is input, thereby outputting a final result value having high reliability.

Moreover, when reliability is low, the present invention may not output a final result value, thereby preventing a risk caused by an output of an abnormal final result value.

Moreover, it has been described that the present invention is applied for determining a sleeping state a user, but the present invention is not limited thereto.

For example, the present invention may be applied to an apparatus which determines an optimal operation by using information collected through various sensors like self-driving vehicles.

The foregoing embodiments are merely exemplary and are not to be considered as limiting the present disclosure. This description is intended to be illustrative, and not to limit the scope of the claims. Many alternatives, modifications, and variations will be apparent to those skilled in the art. The features, structures, methods, and other characteristics of the exemplary embodiments described herein may be combined in various ways to obtain additional and/or alternative exemplary embodiments.