JP2020154180A - Information processing device, information processing method and information processing program - Google Patents

Abstract

To improve quality of a service by estimating genome information indicating a genome feature of a target user according to voice information.SOLUTION: In an information processing device, genome information and voice information acquired by an acquisition unit 30 are stored in a storage unit 4 as user information 40, a generation unit 31 generates a genome model which outputs an index about a genome feature by machine learning with genome information of a teacher user as a teacher and voice information of the teacher user as an identity, an estimation unit 32 estimates genome information indicating a genome feature of a user U on the basis of the voice information acquired by the acquisition unit 30, and more specifically, the estimation unit 32 estimates a genome feature based on the index output from the genome model as genome information.SELECTED DRAWING: Figure 3

Description

The present invention relates to an information processing device, an information processing method, and an information processing program.

Conventionally, an information processing device for identifying a speaker of input voice is known. In such an information processing device, a technology for identifying a speaker by generating a genome model by learning the characteristic parameters of a speaker's voice input in advance by machine learning and inputting voice into the genome model to estimate the voice characteristics. (See, for example, Patent Document 1).

Japanese Unexamined Patent Publication No. 2003-076390

However, the prior art has room for improvement in improving the quality of service.

The present application has been made in view of the above, and an object of the present application is to provide an information processing device, an information processing method, and an information processing program capable of improving the quality of service.

The information processing apparatus according to the present application includes an acquisition unit and an estimation unit. The acquisition unit acquires the voice information of the user. The estimation unit estimates genomic information indicating the genomic characteristics of the user based on the voice information acquired by the acquisition unit.

According to one aspect of the embodiment, there is an effect that the quality of service can be improved.

FIG. 1 is a diagram showing an example of information processing according to an embodiment. FIG. 2 is a diagram showing a configuration of an information processing system according to an embodiment. FIG. 3 is a block diagram showing a configuration example of the information processing apparatus according to the embodiment. FIG. 4 is a diagram showing an example of user information. FIG. 5 is a diagram showing an example of model information. FIG. 6 is a diagram showing group authentication by the authentication unit. FIG. 7 is a flowchart showing a procedure of model generation processing executed by the information processing apparatus according to the embodiment. FIG. 8 is a flowchart showing a procedure of authentication processing executed by the information processing apparatus according to the embodiment. FIG. 9 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device according to the embodiment.

Hereinafter, the information processing apparatus, the information processing method, and the mode for implementing the information processing program according to the present application (hereinafter, referred to as “the embodiment”) will be described in detail with reference to the drawings. The information processing device, information processing method, and information processing program according to the present application are not limited by this embodiment. Further, in each of the following embodiments, the same parts are designated by the same reference numerals, and duplicate description is omitted.

First, an example of information processing realized by the information processing program according to the embodiment will be described with reference to FIG. FIG. 1 is a diagram showing an example of information processing according to an embodiment. FIG. 1 describes, for example, a case where an information processing apparatus executes an information processing method for estimating genomic information indicating genomic characteristics of a user whose genomic information is unknown (hereinafter referred to as a target user).

In the information processing shown in FIG. 1, a model generation process for generating a genome model for estimating the genome information of the target user is executed in advance based on the genome information and the voice information of the teacher user.

First, a series of processes up to the model generation process will be described. Specifically, as shown in FIG. 1, the teacher user first requests an analysis institution to analyze genetic analysis (S1). For example, the teacher user provides saliva, blood, etc. to the analysis institution at the time of requesting analysis.

Subsequently, the analysis institution performs genetic analysis using saliva, blood, etc. collected from the teacher user (S2). Then, the information processing apparatus according to the embodiment acquires the genomic information of the teacher user as a result of the genetic analysis from the analysis institution (S3).

The genomic information includes, for example, information on the health risk of the user, information on the constitution and physical characteristics of the user, and the like. The genomic information may include, for example, information related to the genetic code such as a user's base sequence or amino acid sequence.

The information processing device is not limited to acquiring genomic information from an analysis institution, but collects saliva or blood directly from the user, performs genetic analysis, acquires genomic information, or is already possessed by the user. Genome information may be obtained directly from the user without going through an analysis institution.

Subsequently, the information processing device according to the embodiment acquires voice information indicating the voice characteristics of the teacher user (S4). The voice information is information including an analysis result obtained by analyzing a voice input by a microphone or the like, for example, an acoustic feature such as a frequency characteristic of the voice and a language feature related to an arrangement of phonemes. The voice information may be the voice itself collected by a microphone or the like.

Although FIG. 1 shows a case where genome information and voice information are acquired from one teacher user, it is preferable to acquire genome information and voice information from a plurality of teacher users. As a result, the accuracy of the model generated by the model generation process in the subsequent stage can be improved.

Subsequently, the information processing apparatus according to the embodiment executes a model generation process using the acquired genome information and voice information, and generates a model for estimating the genome information (hereinafter, referred to as a genome model) (hereinafter referred to as a genome model). S5).

Specifically, the information processing apparatus according to the embodiment generates a model that generates a genome model that outputs an index related to genome features by machine learning that uses the teacher user's genomic information as a teacher and the teacher user's voice information as a base. Execute the process.

Next, a series of processes for estimating the genomic information of the target user will be described. Specifically, first, the information processing device acquires the voice information of the target user (S6).

Subsequently, the information processing apparatus according to the embodiment executes an estimation process for estimating the genomic information of the target user based on the voice information of the target user (S7). Specifically, the information processing apparatus according to the embodiment inputs the acquired voice information into the genome model, and estimates the genome information of the target user based on the index output from the model.

The information processing apparatus according to the embodiment can perform group authentication based on the genomic information estimated by the estimation process, and this point will be described later in FIG.

As described above, the information processing apparatus according to the embodiment can provide a service based on the genomic information by estimating the user's genomic information based on the voice information, and thus can provide a useful service for the user. Therefore, according to the information processing apparatus according to the embodiment, the quality of service can be improved.

Next, the system configuration of the information processing system according to the embodiment will be described with reference to FIG. FIG. 2 is a diagram showing a configuration of an information processing system according to an embodiment.

As shown in FIG. 2, the information processing system S according to the embodiment includes an information processing device 1, a plurality of terminal devices 10-1 to 10-n, and an engine terminal 100. The information processing device 1, the plurality of terminal devices 10-1 to 10-n, and the engine terminal 100 are connected to each other via a network N so as to be able to communicate with each other by wire or wirelessly. The network N is, for example, a LAN (Local Area Network) or a WAN (Wide Area Network) such as the Internet. The terminal devices 10-1 to 10-n are operated by users U-1 to Un.

In the following, when each of the terminal devices 10-1 to 10-n is shown without distinction, it is described as the terminal device 10, and when each of the users U-1 to Un is shown without distinction, the user U It is described as. Further, the user U includes the teacher user and the target user described above.

The terminal device 10 is a terminal device of the user U, and is a smart device (communication terminal) such as a smartphone, a tablet terminal, a PDA (Personal Digital Assistant), or a personal computer. The terminal device 10 can execute a browser, various applications, and the like.

The terminal device 10 accesses the institutional terminal 100 from a browser or an application via the network N to request analysis of genetic analysis. Further, the terminal device 10 is provided with, for example, a microphone or the like, and can collect the voice emitted by the user U and transmit it as voice information to the information processing device 1.

The engine terminal 100 is a terminal device owned by an analysis institution that performs genetic analysis. The engine terminal 100 receives the analysis request of the user U from the terminal device 10 via the network N, and transmits the genomic information which is the result of the genetic analysis to the information processing device 1.

Although FIG. 2 shows a case where the terminal device 10 and the information processing device 1 are separately configured, for example, the terminal device 10 and the information processing device 1 are integrally configured, and each process of the information processing device 1 is performed. The terminal device 10 may execute.

Next, the configuration of the information processing apparatus 1 according to the embodiment will be described with reference to FIG. FIG. 3 is a block diagram showing a configuration example of the information processing device 1 according to the embodiment.

As shown in FIG. 3, the information processing device 1 includes a communication unit 2, a control unit 3, and a storage unit 4.

The communication unit 2 is realized by, for example, a NIC (Network Interface Card) or the like. The communication unit 2 is connected to the network N by wire or wirelessly, and transmits / receives information to / from the terminal device 10 and the engine terminal 100 via the network N.

The storage unit 4 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory (Flash Memory), or a storage device such as a hard disk or an optical disk. As shown in FIG. 3, the storage unit 4 stores the user information 40 and the model information 41.

The user information 40 includes user information which is information of the user U. FIG. 4 is a diagram showing an example of user information. User information is generated, for example, when genomic information or voice information is acquired.

As shown in FIG. 4, the user information includes items such as "user ID", "attribute", "genome information", and "voice information".

The "user ID" is identification information that identifies the user U. The "attribute" is information about the attribute of the user U, and includes, for example, a demographic attribute and a psychographic attribute. The "genome information" is, for example, information on the gene of the user U extracted by collecting saliva of the user U, for example, information on the health risk of the user U, and information on the constitution and physical characteristics of the user U. , Information on the genetic code such as the base sequence and amino acid sequence of user U is included. The genomic information may include genomic information generated by another laboratory. The "voice information" is voice information indicating the voice characteristics of the user U, and includes, for example, an analysis result obtained by analyzing the input voice of the user U and the input voice itself.

Next, the model information 41 is information about the genome model generated by the generation unit 31 described later. FIG. 5 is a diagram showing an example of model information. As shown in FIG. 5, the model information includes items such as "model ID" and "model information".

The "model ID" is identification information that identifies a genomic model. "Model information" indicates model information for outputting an index indicating a genomic feature. In the model information 41, the genome model identified by the model ID may be classified by, for example, gender, nationality, age, or the like, or may be one genome model. That is, the model information 41 includes one or a plurality of genome models.

The control unit 3 is a controller, and for example, various programs stored in a storage device inside the information processing device 1 by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like store RAM in a work area. It is realized by executing as. Further, the control unit 3 is, for example, a controller, and is realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

The control unit 3 includes an acquisition unit 30, a generation unit 31, an estimation unit 32, and an authentication unit 33.

The acquisition unit 30 acquires various information of the user U. The acquisition unit 30 acquires, for example, genomic information and voice information. In addition, the acquisition unit 30 acquires attribute information and the like, which are user information related to the user U who has acquired the genome information and the voice information. The acquisition unit 30 stores the acquired genome information, voice information, attribute information, and the like as user information 40 in the storage unit 4.

The generation unit 31 generates a machine learning model based on the genomic information and voice information of the user U (teacher user) acquired by the acquisition unit 30. Specifically, the generation unit 31 generates a genome model that outputs an index related to the genome characteristics by machine learning using the genome information of the teacher user as a teacher and the voice information of the teacher user as a feature.

For example, when a neural network is used as machine learning, the generation unit 31 learns each weight in the neural network based on genomic information and voice information.

More specifically, the generation unit 31 uses a regression model in which the genomic information of the teacher user is used as the objective variable (teacher) and each feature information extracted from the voice information and the user information is used as the explanatory variable (feature) as the learning model. You can ask.

The feature information extracted from the voice information includes, for example, information on the shape of the voice organ of the user U, information on the frequency characteristics of the voice of the user U, information on the arrangement of phonemes of the user U, and the like. In addition, the feature information extracted from the user information includes information on attributes such as the birthplace of the user U, the past residence, the period of residence, the age, and the gender.

In addition, the index output from the genome model includes, for example, a score indicating the genome type of the user U, a base sequence of a gene, a score related to an amino acid sequence, and the like. The genome type indicates a classification when classified by the characteristics of the genome. The generation unit 31 may generate a genome model for each of the above scores, or may generate one genome model that outputs a total score of all the scores.

The machine learning learning model used by the generation unit 31 is not limited to the neural network, and for example, another machine learning learning model such as SVM (Support Vector Machine) may be used.

In addition, the generation unit 31 can also generate a genome model by using a technique of deep learning. For example, the generation unit 31 can generate a genome model by appropriately using various deep learning techniques such as DNN (Deep Neural Network), RNN (Recurrent Neural Network), and CNN (Convolutional Neural Network).

In addition, the generation unit 31 updates the genome model after the genome model is generated. Specifically, the generation unit 31 collates the genome information of a predetermined user U estimated by the estimation unit 32 described later using the genome model with the genome information of the user U acquired by the acquisition unit 30. Update the genome model based on.

For example, the generation unit 31 updates each weight in the neural network. Specifically, the generation unit 31 sets each weight in the neural network so that the similarity between the genomic information estimated by the estimation unit 32 and the genomic information acquired by the acquisition unit 30 is equal to or higher than a predetermined threshold value. Update.

The estimation unit 32 estimates the genomic information indicating the genomic characteristics of the user U based on the voice information acquired by the acquisition unit 30. Specifically, the estimation unit 32 inputs the voice information of the user U (for example, the target user) acquired by the acquisition unit 30 into the genome model, and estimates the genome information of the user U. More specifically, the estimation unit 32 estimates the genomic features based on the index output from the genome model as genomic information. More specifically, the estimation unit 32 estimates genomic information including the genome type.

The authentication unit 33 performs an authentication process for authenticating the user U based on the genomic information estimated by the estimation unit 32. Specifically, the authentication unit 33 collates the genomic information estimated based on the voice information of the user U to be authenticated with the genomic information registered in advance, so that the user U is the person himself / herself. Authenticate that. Alternatively, the authentication unit 33 may register the genome information estimated by the estimation unit 32 in advance and perform the authentication process by collating with the genome information of the user U acquired at the time of authentication.

In addition, the authentication unit 33 authenticates the user U based on the genome type. This point will be described with reference to FIG.

FIG. 6 is a diagram showing group authentication by the authentication unit 33. In FIG. 6, it is assumed that the information processing apparatus 1 has already acquired the genome type “family # 1” of the user A by the acquisition unit 30 or estimated and registered by the estimation unit 32. It is assumed that the genome types of the user B and the user C are not registered.

The authentication unit 33 authenticates the plurality of users U as group users when the genome types of the plurality of users U are similar (or the genome types are the same). The example shown in FIG. 1 shows a case where the group user is a family member.

In such a case, first, the estimation unit 32 inputs the voice information acquired from the user B and the user C into the genome model to estimate the genome type. Here, it is assumed that the genome type of user B is "family # 1" and the genome type of user C is "family # 2".

In such a case, the authentication unit 33 authenticates the user B as a group user (family user) of the user A because the user A and the user B match in the genome type “family # 1”. On the other hand, the authentication unit 33 does not authenticate user C as a group user of user A because the genome types of user A and user C do not match.

As a result, it is possible to efficiently and effectively provide services such as collectively providing information to a group in which genetically similar users U such as a family member are gathered.

The group is not limited to a family member, and may be a group in which genetically similar users U are gathered, and may be, for example, a group having similar nationalities, genders, physical characteristics, and the like.

In the above-described embodiment, the case where the authentication unit 33 performs the authentication process using the genomic information estimated by the estimation unit 32 is shown. For example, the genomic information estimated by the estimation unit 32 is used as the terminal device 10 of the user U. You may output it as it is.

Next, the procedure of the process executed by the information processing apparatus 1 according to the embodiment will be described with reference to FIGS. 7 and 8. First, the processing procedure of the model generation processing will be described with reference to FIG. 7.

FIG. 7 is a flowchart showing a procedure of model generation processing executed by the information processing apparatus 1 according to the embodiment. As shown in FIG. 7, the acquisition unit 30 acquires the genomic information of the teacher user (S101).

Subsequently, the acquisition unit 30 acquires the voice information of the teacher user (S102). In addition, in FIG. 7, the processing procedures of S101 and S102 may be interchanged.

Subsequently, the generation unit 31 generates a genome model that outputs an index related to the genome characteristics by machine learning using the teacher user's genomic information as a teacher and the teacher user's voice information as a feature (S103).

Subsequently, the generation unit 31 stores the generated genome model as model information 41 in the storage unit 4 (S104), and ends the process.

Next, the processing procedure of the authentication process will be described with reference to FIG. FIG. 8 is a flowchart showing a procedure of the authentication process executed by the information processing apparatus 1 according to the embodiment.

As shown in FIG. 8, first, the acquisition unit 30 acquires the voice information of the target user (S201).

Subsequently, the estimation unit 32 inputs voice information into the genome model and outputs an index indicating the genome characteristics (S202).

Subsequently, the estimation unit 32 estimates the genomic information based on the output index (S203).

Subsequently, the authentication unit 33 determines whether or not the genome type in the estimated genome information is similar to the genome type of a predetermined group user (S204).

When the authentication unit 33 is similar to the genome type of the group user (S204: Yes), the authentication unit 33 authenticates as a user user (S205) and ends the process.

On the other hand, if the authentication unit 33 does not resemble the genome type of the group user (S204: No), the authentication unit 33 rejects the authentication as the group user (S206) and ends the process.

Further, the information processing apparatus 1 according to the above-described embodiment is realized by, for example, a computer 200 having a configuration as shown in FIG. FIG. 9 is a hardware configuration diagram showing an example of a computer 200 that realizes the function of the information processing device 1 according to the embodiment. The computer 200 has a CPU 201, a RAM 202, a ROM 203, an HDD 204, a communication interface (I / F) 205, an input / output interface (I / F) 206, and a media interface (I / F) 207.

The CPU 201 operates based on the program stored in the ROM 203 or the HDD 204, and controls each part. The ROM 203 stores a boot program executed by the CPU 201 when the computer 200 is started, a program that depends on the hardware of the computer 200, and the like.

The HDD 204 stores a program executed by the CPU 201, data used by the program, and the like. The communication interface 205 receives data from another device via the network N and sends it to the CPU 201, and transmits the data generated by the CPU 201 to the other device via the network N.

The CPU 201 controls an output device such as a display or a printer and an input device such as a keyboard or a mouse via the input / output interface 206. The CPU 201 acquires data from the input device via the input / output interface 206. Further, the CPU 201 outputs the generated data to the output device via the input / output interface 206.

The media interface 207 reads the program or data stored in the recording medium 208 and provides it to the CPU 201 via the RAM 202. The CPU 201 loads the program from the recording medium 208 onto the RAM 202 via the media interface 207, and executes the loaded program. The recording medium 208 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or PD (Phase change rewritable Disk), a magneto-optical recording medium such as MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. And so on.

For example, when the computer 200 functions as the information processing device 1 according to the embodiment, the CPU 201 of the computer 200 realizes the function of the control unit 3 by executing the program loaded on the RAM 202. Further, the data in the storage unit 4 is stored in the HDD 204. The CPU 201 of the computer 200 reads and executes these programs from the recording medium 208, but as another example, these programs may be acquired from another device via the network N.

As described above, the information processing apparatus 1 according to the embodiment includes an acquisition unit 30 and an estimation unit 32. The acquisition unit 30 acquires the voice information of the user U. The estimation unit 32 estimates the genomic information indicating the genomic characteristics of the user U based on the voice information acquired by the acquisition unit 30.

As a result, the quality of service can be improved.

Further, in the information processing apparatus 1 according to the above-described embodiment, the estimation unit 32 estimates genomic information including the genome type of the user U.

Thereby, for example, a user U having similar genomic characteristics can be easily identified.

Further, the information processing device 1 according to the above-described embodiment further includes an authentication unit 33. The authentication unit 33 authenticates the user U based on the genome type of the user U estimated by the estimation unit 32.

As a result, the authentication accuracy of the user U can be improved.

Further, in the information processing apparatus 1 according to the above-described embodiment, the authentication unit 33 authenticates the plurality of users U as group users when the genome types of the plurality of users U are similar.

As a result, it is possible to efficiently and effectively provide services such as collectively providing information to a group in which genetically similar users U such as a family member are gathered.

In addition, the information processing device 1 according to the above-described embodiment further includes a generation unit 31. The generation unit 31 generates a model that outputs an index related to the genomic characteristics by machine learning using the teacher user's genomic information as a teacher and the teacher user's voice information as a feature. The estimation unit 32 inputs the voice information acquired by the acquisition unit 30 into the model and estimates the genome information of the user U.

Thereby, the estimation accuracy of the genome information of the user U can be improved.

Further, in the information processing apparatus 1 according to the above-described embodiment, the acquisition unit 30 acquires the genome information of the user U. The generation unit 31 updates the model based on the collation result between the genomic information estimated by the estimation unit 32 and the genomic information acquired by the acquisition unit 30.

As a result, the accuracy of the index output from the genome model can be improved, so that the estimation accuracy of the genome information by the estimation unit 32 can be improved.

Although some of the embodiments of the present application have been described in detail with reference to the drawings, these are examples, and various modifications are made based on the knowledge of those skilled in the art, including the embodiments described in the disclosure column of the invention. It is possible to carry out the present invention in other improved forms.

Further, among the processes described in the above-described embodiment, all or a part of the processes described as being automatically performed can be manually performed, or the processes described as being manually performed can be performed. All or part of it can be done automatically by a known method. In addition, the processing procedure, specific name, and information including various data and parameters shown in the above document and drawings can be arbitrarily changed unless otherwise specified. For example, the various information shown in each figure is not limited to the illustrated information.

Further, each component of each of the illustrated devices is a functional concept, and does not necessarily have to be physically configured as shown in the figure. That is, the specific form of distribution / integration of each device is not limited to the one shown in the figure, and all or part of the device is functionally or physically distributed / physically in arbitrary units according to various loads and usage conditions. It can be integrated and configured.

In addition, the processes described in the above-described embodiments can be appropriately combined as long as the processing contents do not contradict each other.

In addition, the above-mentioned "section, module, unit" can be read as "means" or "circuit". For example, the estimation unit 32 can be read as an estimation means or an estimation circuit.

1 Information processing device 2 Communication unit 3 Control unit 4 Storage unit 10 Terminal device 30 Acquisition unit 31 Generation unit 32 Estimate unit 33 Authentication unit 100 Institutional terminal U user

Claims (8)

The acquisition unit that acquires the user's voice information,
An information processing device including an estimation unit that estimates genomic information indicating the genomic characteristics of the user based on the voice information acquired by the acquisition unit. The estimation unit
The information processing apparatus according to claim 1, further comprising estimating genomic information including the type of the user's genome. The information processing apparatus according to claim 1 or 2, further comprising an authentication unit that authenticates the user based on the type of the user's genome estimated by the estimation unit. The certification unit
The information processing apparatus according to claim 3, wherein the plurality of users are authenticated as group users when the genome types of the plurality of users are similar. It is further equipped with a generator that generates a model that outputs an index related to genomic features by machine learning that uses the teacher user's genomic information as a teacher and the teacher user's voice information as a feature.
The estimation unit
The information processing apparatus according to any one of claims 1 to 4, wherein the voice information of the user acquired by the acquisition unit is input to the model to estimate the genomic information of the user. The acquisition unit
Further acquisition of the user's genomic information
The generator
The information processing apparatus according to claim 5, wherein the model is updated based on a collation result of the genomic information estimated by the estimation unit and the genomic information acquired by the acquisition unit. Information processing method executed by a computer
The acquisition process to acquire the user's voice information,
An information processing method including an estimation step of estimating genomic information indicating the genomic characteristics of the user based on the voice information acquired by the acquisition step. The acquisition procedure to acquire the user's voice information and
An information processing program characterized by causing a computer to execute an estimation procedure for estimating genomic information indicating the user's genomic characteristics based on the voice information acquired by the acquisition procedure.

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