A data annotation apparatus for machine learning is provided, which includes a stimulus generation portion, a biometrics reading portion, and a data integration portion. The stimulus generation portion is configured to generate, and present to an agent, at least one stimulus based on a first data from a first machine learning dataset. The biometrics reading portion is configured to measure at least one response of the agent to the at least one stimulus, and to generate biometrics data based on the at least one response. The data integration portion is configured to integrate the biometrics data, data of the at least one stimulus, and data of the first machine learning dataset to thereby obtain a second machine learning dataset. The data annotation apparatus can result in an improved data labeling and an enhanced machine learning.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A data annotation apparatus for machine learning, comprising: a stimulus generation portion, a biometrics reading portion, and a data integration portion, wherein: the stimulus generation portion is configured to generate, and present to an agent, at least one stimulus based on a first data from a first machine learning dataset; the biometrics reading portion is configured to measure at least one response of the agent to the at least one stimulus, and to generate biometrics data based on the at least one response; and the data integration portion is configured to integrate the biometrics data, data of the at least one stimulus, and data of the first machine learning dataset to thereby obtain a second machine learning dataset. 2. The data annotation apparatus of claim 1, wherein the stimulus generation portion comprises a stimulus presenting sub-portion, configured to present to the agent the at least one stimulus based on the first data from the first machine learning dataset. 3. The data annotation apparatus of claim 2, wherein: the stimulus generation portion further comprises a stimulus preparing sub-portion, configured to process the first data from the first machine learning dataset to thereby generate at least one stimulus data suitable for presentation by the stimulus presenting sub-portion; and the stimulus presenting sub-portion is further configured, based on the at least one stimulus data, to present to the agent the at least one stimulus, wherein each of the at least one stimulus corresponds to one of the at least one stimulus data. 4. The data annotation apparatus of claim 2, wherein the stimulus presenting sub-portion comprises at least one stimulus presenting device, wherein each of the at least one stimulus presenting device is selected from a group consisting of: a visual content displaying device configured to present a visual stimulus; an auditory content displaying device configured to present an auditory stimulus; a haptic content displaying device configured to present a haptic stimulus; an olfactory content displaying device configured to present an olfactory stimulus; and a gustatory content displaying device configured to present a gustatory stimulus. 5. The data annotation apparatus of claim 1, wherein the biometrics reading portion comprises a response measuring sub-portion and a biometrics extracting sub-portion, wherein: the response measuring sub-portion is configured to measure the at least one response of the agent to the at least one stimulus, and to send data of the at least one response to the biometrics extracting sub-portion; and the biometrics extracting sub-portion is configured to extract the biometrics data based on the data of the at least one response. 6. The data annotation apparatus of claim 5, wherein the response measuring sub-portion comprises at least one biometric response-capturing device, wherein each of the at least one biometric response-capturing device is selected from a group consisting of a brain activity measuring device, an eye tracking device, a galvanic skin response (GSR) detecting device, a temperature detecting device, an electrocardiogram (ECG) detecting device, a motion detecting device, a respiration rate detecting device, a facial code detecting device, a pupil dilation detecting device, and a blood pressure detecting device. 7. The data annotation apparatus of claim 6, wherein the at least one biometric response-capture device comprises a brain activity measuring device, selected from a group consisting of an electroencephalography (EEG) device, a magnetoencephalography (MEG) device, a functional magnetic resonance imaging (fMRI) device, a single-photon emission computed tomography (SPECT) device, a ligand-based positron emission tomography (PET) device, a near infrared spectroscopy (NIRS) device, a diffusion-tensor imaging (DTI) device, a magnetic resonance spectroscopy (MRS) device, a regional cerebral blood flow (rCBF) device, and a transcranial magnetic stimulation (TMS) device. 8. The data annotation apparatus of claim 7, wherein the at least one biometric response-capture device comprises at least one of an electroencephalography (EEG) device or an eye tracking device. 9. The data annotation apparatus of claim 1, wherein the stimulus generation portion is further configured to send a synchronization signal to the biometrics reading portion. 10. The data annotation apparatus of claim 1, wherein the first data is from at least one of a training set, a validation set, or a testing set, of the first machine learning dataset. 11. A machine learning assembly, comprising a data annotation apparatus according to claim 1. 12. The machine learning assembly according to claim 11, further comprising a machine learning apparatus, wherein the machine learning apparatus is configured to develop a machine learning algorithm based on the second machine learning dataset. 13. The machine learning assembly according to claim 12, wherein the machine learning algorithm is selected from a group consisting of a linear regression model, a logistic regression model, and an artificial neural network-based model. 14. A method for machine learning, comprising: generating a second machine learning dataset based on a first machine learning dataset, wherein the second machine learning dataset comprises biometrics data based on at least one response of an agent to at least one stimulus generated based on a first machine learning dataset; and developing a machine learning algorithm based on the second machine learning dataset. 15. The method of claim 14, wherein the generating a second machine learning dataset based on a first machine learning dataset comprises: generating, and presenting to the agent, the at least one stimulus based on a first data from the first machine learning dataset; detecting the at least one response of the agent exposed to the at least one stimulus and generating the biometrics data based on the at least one response; and integrating the biometrics data, data of the at least one stimulus, and data of the first machine learning dataset to thereby obtain a second machine learning dataset. 16. The method of claim 15, wherein the generating, and presenting to the agent, the at least one stimulus based on a first data from the first machine learning dataset comprises: preparing stimulus data based on the first data from the first machine learning dataset, wherein the stimulus data has a format suitable for presenting; and presenting the stimulus data as the at least one stimulus. 17. The method of claim 15, wherein the detecting the at least one response of the agent exposed to the at least one stimulus and generating the biometrics data based on the at least one response comprises: measuring the at least one response of the agent exposed to the at least one stimulus; and extracting the biometrics data based on the at least one response. 18. The method of claim 14, wherein the at least one stimulus comprises one, or a combination, of a visual stimulus, an auditory stimulus, a haptic stimulus, an olfactory stimulus, and a gustatory stimulus. 19. The method of claim 18, wherein the at least one stimulus comprises at least one of a visual stimulus, an auditory stimulus, or a haptic stimulus. 20. The method of claim 14, wherein the biometrics data comprises at least one of electroencephalography (EEG) data, magnetoencephalography (MEG) data, functional magnetic resonance imaging (fMRI) data, single-photon emission computed tomography (SPECT) data, ligand-based positron emission tomography (PET) data, near infrared spectroscopy (NIRS) data, diffusion-tensor imaging (DTI) data, magnetic resonance spectroscopy (MRS) data, regional cerebral blood flow (rCBF) data, transcranial magnetic stimulation (TMS) data, eye tracking data, skin sweet level data, temperature data, electrocardiogram (ECG) data, motion data, respiration rate data, facial coding data, pupil dilation data, or blood pressure data.