Patent Application
20240029291
This disclosure relates to an information processing apparatus, an information processing method and a storage medium.
Patent Literature 1 discloses an authentication system that tracks a person in a video image to extract a candidate person positioned at a distance capable of performing biometrics authentication from an authentication camera, and controls the execution of biometrics authentication based on the presence or absence of an authentication history of the candidate person.
In the authentication system as described in Patent Literature 1, even if a person has no intention of authentication, if the person exists within a predetermined distance from the authentication camera, there is a possibility that biometrics authentication would be performed on the person.
An object of this disclosure is to provide an information processing apparatus, an information processing method, and a storage medium capable of performing biometrics authentication for a person who is a target of authentication.
According to one aspect of this disclosure, there is provided an information processing apparatus including: an acquisition unit that acquires time series data representing a displacement of a position in a three-dimensional coordinate system regarding a person included in a captured image obtained by capturing a predetermined area; a determination unit that, based on the time series data, determines whether or not the person is a target person of biometrics authentication; and an authentication unit that performs the biometrics authentication to the target person.
According to another aspect of this disclosure, there is provided an information processing method including: acquiring time series data representing a displacement of a position in a three-dimensional coordinate system regarding a person included in a captured image obtained by capturing a predetermined area; based on the time series data, determining whether or not the person is a target person of biometrics authentication; and performing the biometrics authentication to the target person.
According to yet another example aspect of this disclosure, provided is a storage medium storing a program that causes a computer to perform: acquiring time series data representing a displacement of a position in a three-dimensional coordinate system regarding a person included in a captured image obtained by capturing a predetermined area; based on the time series data, determining whether or not the person is a target person of biometrics authentication; and performing the biometrics authentication to the target person.
Exemplary example embodiments of this disclosure will be described below with reference to the drawings. Throughout the drawings, similar features or corresponding features are labeled with the same references, and the description thereof may be omitted or simplified.
First, the configuration of an iris authentication system 1 according to a first example embodiment will be described.
The iris authentication system 1 performs authentication by capturing an iris of a person to be an authentication target person and performing matching of a captured image and a registered iris image. The pattern of the iris is unique and not changed throughout life. Therefore, the identity can be confirmed by matching the iris pattern acquired in the authentication processing and the iris image registered in the database in advance.
The iris authentication system 1 can be applied to, for example, identity confirmation for entry and departure at an airport or the like, identity confirmation at an administrative institution, identity confirmation for entry and exit to and from a factory or an office, identity confirmation for entry to an event site, and the like.
As illustrated in
The authentication apparatus 10 is a computer such as a server for performing biometrics authentication. Specifically, the authentication apparatus 10 executes matching processing of an iris image (or feature quantity) of an authentication target person captured by the capturing device 20 and a registered iris image (or feature quantity) of a registrant registered in advance in the database, and authenticates the authentication target person based on matching result.
The capturing device 20 captures a person who exists in a predetermined area based on the control information inputted from the authentication apparatus 10, and outputs the captured image to the authentication apparatus 10. The capturing device 20 according to first example embodiment is provided with a wide view camera 21 and three numbers of iris cameras 22a to 22c. In the first example embodiment, the term “predetermined area” refers to a three-dimensional space of a predetermined range and size located in front of the capturing device 20.
The wide view camera 21 is a capturing device that captures an image which totally takes a wide field of view of a predetermined area by visible light. A digital camera using a Complementary Metal Oxide Semiconductor (CMOS) image sensor, a Charge Coupled Device (CCD) image sensor or the like may be used as the wide view camera 21 so as to be suitable for image processing in the authentication apparatus 10. The wide view camera 21 may further include a light source for irradiating illumination light toward the front.
The iris cameras 22a to 22c are capturing devices including an infrared light irradiation device (not illustrated) and an infrared light camera (not illustrated), and images of a person's eyes are imaged by infrared light. The infrared light irradiation device includes a light emitting element emitting infrared light such as an infrared light LED. The wavelength of the infrared light emitted from the infrared light irradiation device can be, for example, a near-infrared area of about 800 nm.
The infrared light camera includes a light receiving element configured to have sensitivity to infrared light. As the infrared optical camera, a digital camera using the CMOS image sensor, the CCD image sensor or the like can be used. An image of an eye including an iris image used for iris authentication is acquired by irradiating an eye of a person with infrared light from an infrared light irradiation device and capturing the infrared light reflected by the iris with an infrared light camera. By acquiring the iris image captured by infrared light, a high contrast image can be obtained regardless of the color of the iris, and the influence of the reflection by the cornea can be reduced.
In
The motion sensor 30 is a detector which outputs a detection signal to the authentication apparatus 10 when detecting a person passing the position of the sensor. The authentication apparatus 10 can control capturing process by the wide view camera 21 and the iris cameras 22a to 22c using the detection signal from the motion sensor 30 as a trigger. As illustrated in
The notification device 40 provides various notifications and attention to persons based on the notification control information from the authentication apparatus 10. The notification device 40 includes a display 41, an LED 42 and a speaker 43.
The display 41 displays a face image of a person and a text message in a display area to notify whether or not a gate (not illustrated) can be passed. The LED 42 notifies the person of the authentication result by switching lighting/non-lighting and switching lighting colors.
The speaker 43 outputs an alarm sound and a guide sound to a person moving in a predetermined area in order to improve the accuracy of biometrics authentication.
As illustrated in
The wide view image acquisition unit 11 acquires a wide view image of a predetermined area captured by a wide view camera 21. The wide view image acquisition unit 11 continuously acquires the wide view image along a time series. The face detection unit 12 detects a face of a person included in the wide view image and outputs the face image to the time series data acquisition unit 13.
The time series data acquisition unit 13 performs image analysis processing of the wide view image and the face image, calculates the face position, the eye position, the visual line direction, the interocular distance and the like of the person in the image, and generates time series data. The time series data acquisition unit 13 stores the time series data in a storage unit 14 when acquiring the time series data representing a displacement of a position in a three-dimensional coordinate system of a person included in a captured image capturing a predetermined area. Since the displacement of the position in the three-dimensional coordinate system corresponds to the trajectory of the movement of the person, it is also referred to as the “trajectory of the movement” in the following description.
The captured image ID is a unique identifier for each captured image. The detected face image ID is a unique identifier for each face image of the person detected from the captured image. The face image link ID is an identifier for associating a plurality of detected face images regarded as the same person. The face position is a position coordinate of the face of the person in the captured image. The eye position is a position coordinate of the eye of the person in the captured image. The interocular distance is the distance in the image between the eyes of the person. The face direction is the direction of the face of the person in the captured image. Note that the data items included in the time series data are not limited to these items. The time series data may further include, for example, the visual line direction of the person or distance data to the person detected by a distance measuring sensor (not illustrated) as data items.
The information stored in the storage unit 14 is not limited to the above-described time series data. The storage unit 14 stores various data necessary for the operation of the authentication apparatus 10. More specifically, the storage unit 14 further stores the biometrics information (iris image) of the registrant referred to in the biometrics authentication of the biometrics authentication unit 18, the data of the criterion for determining whether or not the registrant is the authentication target person, and the like.
The authentication target person determination unit 15 determines whether or not the person is the authentication target person of biometrics authentication based on the time series data. The authentication target person determination unit 15 according to the first example embodiment determines a person moving toward a position for capturing a biometrics image used for biometrics authentication as the authentication target person. In other words, the authentication target person determination unit 15 determines that the person moving toward the capturing position is a person having the intention of biometrics authentication. Conversely, the authentication target person determination unit 15 determines that a person who simply passes in front of the capturing device 20, a person who is stopped in a predetermined area, a person who moves away from the capturing device 20, or the like is a person who has no intention of biometrics authentication.
The iris camera setting unit 16 selects one of the iris cameras 22a to 22c based on the eye position of the person in the three-dimensional coordinate system represented by the time series data, and sets the capturing conditions. The iris camera setting unit 16 also has a function as a control unit for controlling imaging in the iris cameras 22a to 22c.
The biometrics image acquisition unit 17 acquires a biometrics image (iris image) of a person captured by one of the iris cameras 22a to 22c. The biometrics authentication unit 18 matches a biometrics image (iris image) of the authentication target person acquired by the biometrics image acquisition unit 17 and a registered biometrics image (registered iris image) stored in the storage unit 14 to perform biometrics authentication to an authentication target person.
The processor 151 performs predetermined operations according to programs stored in the ROM 153, the storage 154, and the like, and has a function of controlling each unit of the authentication apparatus 10. The processor 151 may be a central processing unit (CPU), a graphics processing unit (GPU), a field programmable gate array (FPGA), a digital signal processor (DSP), an application specific integrated circuit (ASIC), or the like. One of the above examples may be used, or a plurality of processors may be used in parallel.
The RAM 152 is composed of a volatile storage medium and provides a temporary memory area necessary for operation of the processor 151. The ROM 153 is composed of a nonvolatile storage medium and stores necessary information such as a program used for the operation of the authentication apparatus 10.
The storage 154 is composed of a nonvolatile storage medium, and stores a database, an operation program of the authentication apparatus 10, and the like. The storage 154 is composed of, for example, a hard disk drive (HDD) or a solid state drive (SSD).
The communication I/F 155 is a communication interface based on standards such as Ethernet (Registered trademark), Wi-Fi (Registered trademark), 4G or 5G, and is a module for communicating with other devices.
The processor 151 loads the programs stored in the ROM 153, the storage 154 and the like into the RAM 152 and executes them. Thus, the processor 151 realizes the functions of the wide view image acquisition unit 11, the face detection unit 12, the time series data acquisition unit 13, the authentication target person determination unit the iris camera setting unit 16, the biometrics image acquisition unit 17, the biometrics authentication unit 18, and the like.
Note that the hardware configuration illustrated in
In step S101, the wide view image acquisition unit 11 acquires a wide view image of a predetermined area by having the wide view camera 21 capture the predetermined area.
In step S102, the face detection unit 12 analyzes the wide view image to detect a face image of a person existing in the image.
In step S103, the face detection unit 12 determines whether or not the face image of the person is detected from the wide view image. If the face detection unit 12 determines that the face image is detected (YES in step S103), the process proceeds to step S104. On the other hand, if the face detection unit 12 determines that the face image is not detected (NO in step S103), the process returns to step S101.
In step S104, the face detection unit compares the face images continuously detected in the time series each other, and if it is determined that face images are of the same person, the face detection unit 12 issues a face image link ID to associate the face images with each other. As a method for determining whether or not a person is the same person, for example, a method based on the positional relationship of faces detected in a captured image, a method for matching face images detected from each of a plurality of captured images taken continuously, and the like can be mentioned.
In addition, in order to track a person detected from a captured image, an identification image serving as a mark may be projected onto the person or around the person by a projector (not illustrated) to identify the same person among different captured images. Specific examples of the image for identification include, for example, images containing different colors, symbols or shapes for each person.
In step S105, the time series data acquisition unit 13 generates time series data relating to the captured image and the face image. More specifically, the time series data acquisition unit 13 extracts data of the face position, the eye position, the interocular distance, and the face direction of the person by analyzing the captured image. Then, the time series data acquisition unit 13 associates the captured image ID, the detected face image ID, the face image link ID, the face position, the eye position, the interocular distance, the face direction, and the capturing date and time, and stores them in the storage unit 14 as time series data.
In step S106, the authentication target person determination unit 15 specifies the trajectory of the movement of the person based on the time series data of the same person stored in the storage unit 14. The trajectory of the movement of the person in first example embodiment is specified based on the position of the eyes or face of the person and the displacement in the time series of interocular distance.
In step S107, the authentication target person determination unit 15 determines whether the trajectory of the movement of the person identified in step S106 matches the predetermined trajectory pattern for the authentication target person. The trajectory pattern is a reference displacement (reference trajectory) at the time of moving to the capturing position, which is defined in advance for a person regarded as the authentication target person.
If the authentication target person determination unit 15 determines that the trajectory of the movement of the detected person matches the predetermined trajectory pattern (YES in step S107), the process proceeds to step S108.
On the other hand, if the authentication target person determination unit 15 determines that the trajectory of the movement of the detected person does not match the predetermined trajectory pattern (NO in step S107), the process returns to step S101.
In
In step S108, the iris camera setting unit 16 selects one of the iris cameras 22a to 22c that corresponds to the position and height of the eye of the person determined to be the authentication target person, and sets capturing conditions such as focus and capturing angle. For example, when the height of the person is an average value of an adult, the iris camera setting unit 16 selects the center iris camera 22b and sets capturing conditions.
In step S109, the biometrics image acquisition unit 17 acquires an iris image of the person by controlling the imaging processing of the iris camera set in step S108. The iris image is captured by the iris camera at a higher resolution and in a narrower area than that of the wide view camera (first camera) 21, and a highly accurate iris image can be acquired by selecting and using any one of the appropriate iris cameras (second cameras) 22a to 22c.
In step S110, the biometrics authentication unit 18 matches the iris image of the person and a group of registrant's iris images previously registered in the storage unit 14 to perform iris authentication.
In step S111, the biometrics authentication unit 18 outputs the authentication result of the iris authentication and terminates the process.
According to the first example embodiment, prior to the authentication processing, the presence or absence of the intention to authenticate the person is determined based on the time series data representing the displacement (movement trajectory) of the position of the person existing in the captured image in the three-dimensional coordinate system. Thus, an iris authentication system 1 capable of executing authentication processing to an authentication target person is provided.
Further, the authentication apparatus 10 is configured to determine the detected person moving toward the capturing position as the authentication target person by comparing the displacement of the position of the detected person in the three-dimensional coordinate system obtained from the time series data with the reference displacement (reference trajectory) at the time of moving the biometrics image used for the biometrics authentication to the capturing position defined in advance for the person regarded as the authentication target person. Thus, it can easily and highly accurately determine whether the detected person is the authentication target person or not.
Hereinafter, an iris authentication system 2 according to the second example embodiment will be described below. In the following, differences with first example embodiment are mainly explained, and descriptions of common parts are omitted or simplified.
The iris authentication system 2 according to the second example embodiment is different from the iris authentication system 1 according to the first example embodiment in that a plurality of capturing device 20 are arranged apart from each other and that a structure for scoring the certainty of a detected person as the authentication target person is provided. As illustrated in
Further, as illustrated in
When acquiring a plurality of captured images acquired from a plurality of capturing devices 20 (wide view cameras 21), the wide view image acquisition unit 11 outputs the captured images to the face detection unit 12. The face detection unit 12 detects a face image of a person from captured images acquired in time series. The time series data acquisition unit 13 stores the time series data in the storage unit 14 when generating the time series data associated with the camera ID of the wide view camera 21.
When the processing of steps S101 to S106 is completed, the process proceeds to step S201.
In step S201, the authentication target person determination unit 15 calculates a score for each wide view camera 21. The score indicates the certainty of the person as the authentication target person.
In step S202, the authentication target person determination unit 15 identifies the same person among the cameras. Specific examples of a method of identifying whether or not a person is the same person among a plurality of cameras include a method of using features of clothing and gait, and a method of identifying the same color as the same person by projecting different colors around each person with a projector.
In step S203, the authentication target person determination unit 15 integrates the scores calculated for each wide view camera 21 to determine an integration score for the person. The integration score includes a mean value, a deviation value, a maximum value, and the like.
In step S204, the authentication target person determination unit 15 determines whether the integration score is equal to or greater than a predetermined threshold. If the authentication target person determination unit 15 determines that the integration score is equal to or greater than a predetermined threshold (YES in step S204), the detected person is considered to be the authentication target person, and the process proceeds to step S205.
On the other hand, if the authentication target person determination unit 15 determines that the integration score is less than a predetermined threshold (NO in step S204), the detected person is not considered to be the authentication target person, and the process returns to step S101.
In step S205, the authentication target person determination unit 15 assigns numbers to the authentication target person in the order of proximity to the authentication area. This number corresponds to the biometric priority. Thereafter, the process proceeds to step S108.
According to the second example embodiment, since it is possible to determine whether or not a person is an authentication target person based on a plurality of captured images captured from different angles by a plurality of capturing device 20, the accuracy of the determination processing can be enhanced more than in the case of first example embodiment.
Further, since the authentication apparatus 10 is configured to calculate a score indicating the certainty that the person detected from the captured image is the authentication target person. Thus, for example, the determination criteria of the authentication target person can be flexibly changed by adjusting a threshold being compared with the calculated score.
Hereinafter, an iris authentication system 3 according to the third example embodiment will be described below. In the following, differences with first example embodiment are mainly explained, and descriptions of common parts are omitted or simplified.
The training unit 19 trains a model for determining whether or not a person is the authentication target person based on the time series data regarding a first person determined to be the authentication target person and time series data regarding a second person determined not to be the authentication target person. The training unit 19 can also train a model by using time series data of one of the first person and the second person.
The node of the output layer outputs a value indicating a determination result of whether or not a person is the authentication target person by using the operation value input from each node of the hidden layer, a weight, and a bias value. When the neural network is trained, for example, an error back propagation method is used. Specifically, an output value obtained when data is input to the input layer is compared with an output value obtained from the training data, and errors of the two compared output values are fed back to the hidden layer. This is repeated until the error falls below a predetermined threshold. By such a training process, when various time series data is inputted to a neural network (trained model), a value y indicating a determination result whether or not the target person is an authentication target person can be outputted.
As the time series data used for training, it is preferable to use, for example, the following data according to the model to be generated.
When the processing of steps S101 to S105 is completed, the process proceeds to step S301.
In step S301, the authentication target person determination unit 15 inputs time series data related to the person detected from the captured image to the discriminant model. It is assumed that the discriminant model has been trained prior to the processing of
In step S302, the authentication target person determination unit 15 determines whether or not the person is an authentication target person based on the determination result output from the discriminant model. If the authentication target person determination unit 15 determines that the person is the authentication target person (YES in step S302), the process proceeds to step S108.
On the other hand, when the authentication target person determination unit 15 determines that the person is not the authentication target person (NO in step S303), the process returns to step S101.
According to the third example embodiment, it is possible to determine, at high speed and with high accuracy, whether or not the person detected from the captured image is the authentication target person on the basis of a discriminant model obtained by machine learning.
The model of machine learning in the third example embodiment is not necessarily limited to a neural network. The machine learning method that requires data and a training process in advance and can perform class classification or binary classification on high-dimensional input data can be used. For example, a classifier such as logistic regression or support vector machine (SVM) may be used in addition to a neural network.
For example, the following time series data D1 of the detection target ID1 is assumed as input data.
D1={[ID1, Time (1), Face Position (1), Interocular Distance (1)], . . . [ID1, Time (n), Face position (n), Interocular distance (n)]}
The input data of such a two-dimensional array (or time-series one-dimensional array) may be classified.
Further, the features may be extracted from the input of the time-series high-dimensional data by a neural network or the like, and the above-mentioned classification method may be combined. In this case, the time series data D1 is converted into another dimensional feature D1′ by a predetermined calculation formula (1), and the feature D1′ is classified.
D1′=f1(D1) (1)
Examples of f1 (x) are convolutional neural networks (CNN) such as ResNet, VGGNet, and GoogLeNet. In addition to a feature extractor using a Convolutional Neural Networks (CNN)-based model, a feature extractor using ResNeXt, SENet, EfficientNet, and the like can be used.
Alternatively, the above processing may be performed for each image instead of the time series, and the acquired scores may be added together in time series, or Gaussian weights may be added, so as to be integrated into one score. For example, the following data string d2_t at each time t is acquired as input data. d2_t=[ID2, Time t, Face position (t), Interocular distance (t)]
Then, the data string d2_t is substituted into a predetermined calculation formula (2) to acquire a value sc2_t indicating the certainty as the authentication target person from various classifiers.
sc2_t=f2(d2_t) (2)
Specific examples of f2 (x) include classifiers using CNN-based models represented by ResNet, VGGNet, and GoogleNet, as well as classifiers using ResNeXt, SENet, EfficientNet, and the like, and classifiers represented by logistic regression models or SVM models. Then, the value sc2_t indicating the certainty as the authentication target person is substituted into a predetermined calculation formula (3) to calculate an average (or weighted average), and the integration score Total Score is outputted as a final result.
Total Score=(1/T)Sigma sc2_t (3)
Hereinafter, an iris authentication system 4 according to a fourth example embodiment will be described below. In the following, differences with second example embodiment are mainly explained, and descriptions of common parts are omitted or simplified.
The guide unit 101 generates an image for guiding a person determined as the authentication target person to an execution place of the biometrics authentication. Since the guide unit 101 grasps the current position based on the time series data of the authentication target person, the authentication target person can be guided to the closest capturing device 20. When a plurality of authentication target persons are detected, for example, each person may be guided to the relatively uncrowded capturing device 20 based on the priority of the person determined by image analysis of the captured image.
The projector 50 projects the guide image generated by the guide unit 101 onto, for example, a floor surface or a wall surface in the vicinity of the authentication target person, or onto a part of the capturing device 20 incorporating iris cameras 22a to 22c for capturing the iris image of the authentication target person.
According to the fourth example embodiment, the image for guiding the person determined to be the authentication target person to an execution place of biometrics authentication can be presented. Thus, the efficiency of biometrics authentication can be improved. [Fifth example embodiment]
Hereinafter, a face authentication system 5 according to a fifth example embodiment will be described. In the following, differences with first example embodiment are mainly explained, and descriptions of common parts are omitted or simplified.
When the processing of steps S101 to S107 is completed, the process proceeds to step S401.
In step S401, the face detection unit 12 selects a face image suitable for face authentication from among the plurality of face images stored in the storage unit 14 for the authentication target person. For example, it is preferable that the face detection unit 12 selects a face image when the authentication target person faces the front of the camera.
In step S402, the biometrics authentication unit 18 performs face authentication by matching of the face image of the authentication target person selected in step S401 and the face image group of the registrant registered in advance in the storage unit 14. Thereafter, the process proceeds to step S111.
According to the fifth example embodiment, after it is determined that the detected person is an object to be authenticated based on the wide view image, face authentication can be performed based on the face image of the same person acquired from the wide view image. Since the iris cameras 22a to 22c do not need to separately capture the iris image of the person to be authenticated, the biometrics authentication system can be easily installed at a low cost as compared with the case of the first to fourth example embodiment described above.
Hereinafter, a biometrics authentication system according to a sixth example embodiment will be described. In the following, differences with first example embodiment are mainly explained, and descriptions of common parts are omitted or simplified.
The biometrics authentication system related to the sixth example embodiment is different from the iris authentication system 1 according to the first example embodiment in that it is determined whether or not a person is the authentication target person by taking into account not only the displacement (movement trajectory) of the position of the person in the three-dimensional coordinate system but also the direction of the body part such as the face and eyes of the person.
When the processing of steps S101 to S107 is completed, the process proceeds to step S501.
In step S501, the authentication target person determination unit 15 determines whether or not the person is oriented toward the entrance/exit gate based on the face direction or the visual line direction included in the time series data. If the authentication target person determination unit 15 determines that the person is oriented toward the entrance/exit gate (YES in step S501), the process proceeds to step S108.
On the other hand, if the authentication target person determination unit 15 determines that the person is not is oriented toward the entrance/exit gate (NO in step S501), the process returns to step S101. It is preferable to consider the duration time when the face direction or the visual line direction is within a predetermined range when determining whether the person is oriented toward the place where the biometrics authentication is performed, such as the entrance/exit gate. For example, if the person continuously faces the direction of the entrance/exit gate for a predetermined period of time or longer, the person's intention to be performed biometrics authentication may be estimated.
Therefore, in
According to the sixth example embodiment, prior to biometrics authentication, not only the displacement of the position of the person in the three-dimensional coordinate system (trajectory of movement) but also the direction of a specific body part of the person are taken into account to determine whether the person is a person to be authenticated, so that it can be determined with higher accuracy whether the person has the intention to be performed biometrics authentication.
According to the seventh example embodiment, there is provided an information processing apparatus 100 which can perform biometrics authentication for a person who is a target of authentication.
This disclosure is not limited to example embodiment described above and may be modified as appropriate without departing from the spirit of this disclosure. For example, an example in which a portion of a configuration of one example embodiment is added to another example embodiment, or an example in which a portion of a configuration of another example embodiment is replaced is also a example embodiment of this disclosure.
A processing method in which a program for operating the configuration of example embodiment is recorded in a storage medium so as to realize the function of example embodiment, the program recorded in the storage medium is read out as a code and executed in a computer is also included in each example embodiment. That is, computer-readable storage media are also included in the scope of each example embodiment. Not only the storage medium on which the above-mentioned program is recorded but also the program itself is included in each example embodiment. The one or more components included in example embodiment may be circuits such as ASICs, FPGAs, etc., configured to implement the functions of the components.
As the storage medium, for example, a floppy (Registered trademark) disk, a hard disk, an optical disk, a magneto-optical disk, a CD (Compact Disk)-ROM, a magnetic tape, a nonvolatile memory card, and a ROM can be used. Further, the scope of each of the example embodiments includes an example that operates on OS (Operating System) to perform a process in cooperation with another software or a function of an add-in board without being limited to an example that performs a process by an individual program stored in the storage medium.
The services provided by the respective example embodiment functions described above can also be provided to the user in the form of Software as a Service (SaaS).
It should be noted that example embodiment described above is merely an example of implementation of this disclosure, and the technical scope of this disclosure should not be construed as being limited thereto. That is, this disclosure may be implemented in a variety of ways without departing from its technical philosophy or its key features.
The whole or part of the example embodiments disclosed above can be described as, but not limited to, the following supplementary notes.
An information processing apparatus comprising:
The information processing apparatus according to supplementary note 1,
The information processing apparatus according to supplementary note 1 or 2,
The information processing apparatus according to supplementary note 1 or 2,
The information processing apparatus according to supplementary note 4,
The information processing apparatus according to any one of supplementary notes 1 to 5, further comprising:
The information processing apparatus according to any one of supplementary notes 1 to 6, further comprising:
An information processing method comprising:
A storage medium storing a program that causes a computer to perform:
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/JP2021/028791 | 8/3/2021 | WO |
