WO2021120152A1

WO2021120152A1 - Computing device, method and apparatus for noninvasively diagnosing a hair

Info

Publication number: WO2021120152A1
Application number: PCT/CN2019/126891
Authority: WO
Inventors: Cindy KOH; Maxime De Boni; Amit Jayaswal; Jinyong Li
Original assignee: L'oreal
Priority date: 2019-12-20
Filing date: 2019-12-20
Publication date: 2021-06-24

Abstract

Method and apparatus for noninvasively diagnosing a hair are provided. The method comprises: emitting a near infrared light to said hair; obtaining data related to chemical damage markers of said hair; processing said data related to chemical damage markers of said hair; and outputting a virtual representation of diagnosis information of said hair. In one embodiment, said method also comprises obtaining data related to water content of said hair; processing said data related to water content of said hair. In another embodiment, said processing said data related to water content and chemical damage markers of said hair is performed by a near infrared usage model of a hair. Method and apparatus for noninvasively diagnosing color of a hair by using a visible light are also provided.

Description

[Title established by the ISA under Rule 37.2] COMPUTING DEVICE, METHOD AND APPARATUS FOR NONINVASIVELY DIAGNOSING A HAIR

TECHNICAL FIELD

The disclosure relates to the field of cosmetics. More specifically, the disclosure relates to a computing device, a method and an apparatus for noninvasively diagnosing a hair.

BACKGROUND

The appearance of hair has always been a pursue of the consumers. For this, there are a large number of the consumers whose hairs are subjected to some damaged treatments, such as a morphological damage, a permanent wave treatment, a bleaching treatment, a hair dye treatment and so on. If it becomes possible to exactly evaluate the degrees of the damages caused by those treatments, the damaged hairs can be appropriately recovered by selecting an appropriately hair cosmetic and the like or by customizing an appropriately hair cosmetic.

Conventional methods of evaluating the degree of hair damage are classified into a noninvasive and an invasive method. An invasive method of evaluating the degree of hair damage is not preferable because a hair will be chemically or physically damaged. Therefore, a noninvasive method of evaluating the degree of hair damage is preferable because a hair will be evaluated without being chemically or physically damaged. US2006/0281994A1 describes a method of noninvasively and quantitatively evaluating a degree of a hair damage. However, the disadvantage of such a method in US2006/0281994A1 is time-consuming, labor-intensive and inefficiency.

Therefore, there is a need to noninvasively diagnose a hair in a miniature, robust and cost-effective manner in order to provide finer granularity of hair classification, hair damage differentiation and color measurement.

SUMMARY

The summary is provided to introduce a selection of concepts in a simplified form that are further described below in detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

Various aspects and features of the disclosure are described in further detail below.

According to a first aspect of the disclosure, there is provided a computing device for noninvasively diagnosing a hair. Said computing device comprises a near infrared spectrometer including computational circuitry configured to emit a near infrared light to said hair and obtain data related to chemical damage markers of said hair; and a processor including computational circuitry configured to process said data related to chemical damage markers of said hair and output a virtual representation of diagnosis information of said hair.

In one embodiment of said first aspect of the disclosure, said near infrared spectrometer includes computational circuitry which is further configured to obtain data related to water content of said hair and said processor includes computational circuitry which is further configured to process said data related to water content of said hair.

In another embodiment of said first aspect of the disclosure, said processor includes computational circuitry which is further configured to process said data related to chemical damage markers of said hair by a near infrared usage model of a hair.

In yet another embodiment of said first aspect of the disclosure, said processor includes computational circuitry which is further configured to process said data related to water content of said hair by a near infrared usage model of a hair.

In yet another embodiment of said first aspect of the disclosure, said a near infrared usage model is established on basis of data related to water content and/or chemical damage markers of a large number of hair samples under a near infrared light by a machine learning algorithm.

In yet another embodiment of said first aspect of the disclosure, said near infrared spectrometer includes computational circuitry which is further configured to obtain data related to chemical damage markers of said hair by a near infrared absorption spectrum of said hair.

In yet another embodiment of said first aspect of the disclosure, said near infrared spectrometer includes computational circuitry which is further configured to obtain data related to water content of said hair by a near infrared absorption spectrum of said hair.

In one embodiment of said first aspect of the disclosure, said diagnosis information is information related to hair damage level, hair classification and hair treatment history.

In one embodiment of said first aspect of the disclosure, said chemical damage markers are cysteic acid, eumelanin, melanin, water, 18-MEA concentration change and like.

In one embodiment of said first aspect of the disclosure, said hair classification can be natural hair, colored hair, bleached hair, permed hair and like.

According to a second aspect of the disclosure, there is provided a method of noninvasively diagnosing a hair, the method comprising: emitting a near infrared light to said hair; obtaining data related to chemical damage markers of said hair; processing said data related to chemical damage markers of said hair; and outputting a virtual representation of diagnosis information of said hair.

In one embodiment of said second aspect of the disclosure, the method also comprises: obtaining data related to water content of said hair; processing said data related to water content of said hair.

In another embodiment of said second aspect of the disclosure, said processing said data related to chemical damage markers of said hair is performed by a near infrared usage model of a hair.

In another embodiment of said second aspect of the disclosure, said processing said data related to water content of said hair is performed by a near infrared usage model of a hair.

In yet another embodiment of said second aspect of the disclosure, said a near infrared usage model is established on basis of data related to water content and/or chemical damage markers of a large number of hair samples under a near infrared light by a machine learning algorithm.

In one embodiment of said second aspect of the disclosure, said obtaining data related to chemical damage markers of said hair is achieved by a near infrared absorption spectrum of said hair.

In one embodiment of said second aspect of the disclosure, said obtaining data related to water content of said hair is achieved by a near infrared absorption spectrum of said hair.

In one embodiment of said second aspect of the disclosure, said diagnosis information is information related to hair damage level, hair classification and hair treatment history.

In one embodiment of said second aspect of the disclosure, said chemical damage markers are cysteic acid, eumelanin, melanin, water, 18-MEA concentration change and like.

In one embodiment of said second aspect of the disclosure, said hair classification can be natural hair, colored hair, bleached hair, permed hair and like.

According to a third aspect of the disclosure, there is provided an apparatus of noninvasively diagnosing a hair, said apparatus comprises means for performing the method according to a second aspect of the disclosure.

According to a fourth aspect of the disclosure, there is provided a method of noninvasively diagnosing a hair, the method comprising: emitting a visible light to said hair; obtaining data related to colors of said hair; processing said data related to color of said hair; and outputting a virtual representation of diagnosis information of said hair. In one example, said diagnosis information is color information of said hair.

In an embodiment of said fourth aspect, said obtaining data related to color of said hair is achieved by a visible light absorption spectrum of said hair.

In another embodiment of said fourth aspect, said processing said data related to color of said hair is performed by a visible light usage model of a hair.

According to a fifth aspect of the disclosure, there is provided a computing device for noninvasively diagnosing a hair. Said computing device comprises a visible light spectrometer including computational circuitry configured to emit a visible light to said hair and obtain data related to colors of said hair; and a processor including computational circuitry configured to process said data related to colors of said hair and output a virtual representation of diagnosis information of said hair. In one example, said diagnosis information is color information of said hair.

In an embodiment of said fifth aspect, computational circuitry of said visible light spectrometer is also configured obtain data related to color of said hair by a visible light absorption spectrum of said hair.

In another embodiment of said fifth aspect, said computational circuitry of said processor is also configured to process said data related to colors of said hair by a visible light usage model of a hair.

According to a sixth aspect of the disclosure, there is provided a computer readable medium having stored thereon instructions that when executed cause a computing device to perform the method according to one of said second aspect and said fourth aspect of the disclosure.

According to the disclosure, a finer granularity of hair classification, hair damage differentiation and color measurement can be achieved in a non-invasive miniature, robust and cost-effective way.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features, and benefits of various embodiments of the disclosure will become more fully apparent, by way of example, from the following detailed description with reference to the accompanying drawings, in which like reference numerals or letters are used to designate like or equivalent elements. The drawings are illustrated for facilitating better understanding of the embodiments of the disclosure and not necessarily drawn to scale, in which:

Fig. 1 illustrates an example of the present disclosure;

Fig. 2 illustrates a block diagram of a computing device in accordance with a first aspect of the present disclosure;

FIG. 3 illustrates a flowchart of a method of noninvasively diagnosing a hair in accordance with a second aspect of the present disclosure;

FIG. 4 illustrates a flowchart of a method of noninvasively diagnosing a hair in accordance with a second aspect of the present disclosure;

FIG. 5 illustrates a block diagram of an apparatus of noninvasively diagnosing a hair in accordance with a third aspect of the present disclosure;

FIG. 6 illustrates a flowchart of a method of noninvasively diagnosing a hair in accordance with a fourth aspect of the present disclosure; and

Fig. 7 illustrates a block diagram of a computing device in accordance with a fifth aspect of the present disclosure.

DETAILED DESCRIPTION

Embodiments herein will be described in detail hereinafter with reference to the accompanying drawings, in which embodiments are shown. These embodiments herein may, however, be embodied in many different forms and should not be construed as being limited to the embodiments set forth herein. The elements of the drawings are not necessarily to scale relative to each other. Like numbers refer to like elements throughout.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used herein, the singular forms "a" , "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" "comprising, " "includes" and/or "including" when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meanings as commonly understood. It will be further understood that a term used herein should be interpreted as having a meaning consistent with its meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

The present technology is described below with reference to block diagrams and/or flowchart illustrations of methods, apparatus (systems) and/or computer program products according to the present embodiments. It is understood that blocks of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, may be implemented by computer program instructions. These computer program instructions may be provided to a processor, controller or controlling unit of a general purpose computer, special purpose computer, and/or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer and/or other programmable data processing apparatus, create means for implementing the functions/acts specified in the block diagrams and/or flowchart block or blocks.

Accordingly, the present technology may be embodied in hardware and/or in software (including firmware, resident software, micro-code, etc. ) . Furthermore, the present technology may take the form of a computer program product on a computer-usable or computer-readable storage medium having computer-usable or computer-readable program code embodied in the medium for use by or in connection with an instruction execution system. In the context of this document, a computer-usable or computer-readable medium may be any medium that may contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

Embodiments herein will be described below with reference to the drawings.

The inventive concept of the disclosure is to combine visible or near infrared spectroscopy with trained algorithm and modelling based on some chemical damage marks (such as cysteic acid, eumelanin, melanin, water, 18-MEA concentration change and like) , water retention capacity and color measurement.

In the disclosure, a near infrared spectroscopy NIR is used in diagnosing a hair, since the near infrared spectroscopy NIR have stronger penetration capability compared to the Medium infrared spectroscopy MIR. Thus, it is easy to get whole fiber damage information by the near infrared spectroscopy NIR. The disclosure is to first bring water retention capabilities and some chemical damage markers obtained from NIR, then input them to a model, finally output hair damage level and/or hair classification. This can be reflected by Fig. 1. Fig. 1 illustrates an example of the present disclosure. As shown in Fig. 1, a near infrared spectrometer emits a near infrared light to a hair of a customer. Some data related to a hair can be obtained from a near infrared absorption spectrum of said hair. Said data can be data related to water content and/or chemical damage markers of said hair, and such chemical damage markers can such as be cystic acid, eumelanin, melanin concentration and so on. Then, said obtained data can be uploaded to a server via wired or wireless connection. Said server can be a cloud server which has a hair database and a near-infrared usage model. Said hair database has data regarding features of hairs from a plurality of sampled users. Such a near-infrared usage model has been established on basis of data related to water content and/or chemical damage markers of a large number of hair samples under a near infrared light by a machine learning algorithm. During establishing said near-infrared usage model, in order to accelerate training speed of said model, principal component analysis PCA is used as an algorithm of the multivariate analysis. That is, when raw training data set has m-dimension and m is very large, such raw training data set is applied by principal component analysis PCA to reduce said dimension of raw training data set, then reduced dimension of raw training data set is input to said model. Finally, said reduced dimension of raw training data set is classified into a plurality of classes and is regressed. For example, in said model, value "0" can define "no damage level" , value "1" can define "low damage level" , value "2" can define "middle damage level" , value "3" can define "high damage level" and so on. Further, in said model, different colors can used to define different hair classification, such as natural hair, colored hair, bleached hair, permed hair and so on. Further, said model can classify hair treatment history in a numerical value or a visual form. Such hair treatment history can define how long the hair is treated, how often the hair is treated, when the hair is treated and son on. Finally, diagnosis information such as hair damage level, hair classification and hair treatment history is displayed to said customer. In the disclosure, said model is used to perform an automatic classification and is trained on some discriminant attributes extracted from the data acquired on a large number of hair samples under near-infrared illumination. Such data are related to water content and/or chemical damage biomarkers. The aforementioned model conflates the results of different conventional classifiers, such as Random Forest, Neural Network, Gaussian Process, Kernel SVM and so on, and gives a strong conclusion on hair damage degree, hair classification and hair treatment history using soft voting.

Fig. 2 illustrates a block diagram of a computing device 200 in accordance with a first aspect of the present disclosure.

Said computing device 200 comprises a near infrared spectrometer 201 and a processor 202. Said near infrared spectrometer 201 includes computational circuitry configured to emit a near infrared light to a hair of a customer and obtain data related to chemical damage markers of said hair. Said processor includes computational circuitry configured to process said data related to chemical damage markers of said hair and output a virtual representation of diagnosis information of said hair. In one embodiment, said computational circuitry of said near infrared spectrometer 201 is also configured to obtain data related to water content of said customer and said computational circuitry of said processor 202 is also configured to process said data related to water content of said hair. According to the first aspect of the disclosure, data related to water retention capabilities and chemical damage markers can be obtained from NIR spectrum, and then are processed by a near-infrared usage model established in Fig. 1, finally diagnosis information such as information related to hair damage level, hair classification and hair treatment history are output in a visual representation form. In one example, said chemical damage markers are cysteic acid, eumelanin, melanin, water, 18-MEA concentration change and like, but not limited to this. In one example, said hair classification can be natural hair, colored hair, bleached hair, permed hair and like, but not limited to this.

Computing device 200 can be, for example, a server of a service provider, a device associated with a client (e.g, a client device) , a system on a chip, and/or any other suitable computing device or computing system. In various implementations, computing device 200 can take a variety of different configurations. For example, computing device 200 can be implemented as a computer-like device including a personal computer, desktop computer, multi-screen computer, laptop computer, netbook, and the like. Computing device 200 can also be implemented as a mobile device-like device that includes mobile devices such as mobile phones, portable music players, portable gaming devices, tablet computers, multi-screen computers, and the like. Computing device 100 can also be implemented as a television-like device that includes a device having or connected to a generally larger screen in a casual viewing environment. These devices include televisions, set-top boxes, game consoles, and the like.

In an embodiment, computational circuitry includes, among other things, one or more computing devices such as a processor (e.g., a microprocessor, a quantum processor, qubit processor, etc. ) , a central processing unit (CPU) , a digital signal processor (DSP) , an application-specific integrated circuit (ASIC) , a field programmable gate array (FPGA) , and the like, or any combinations thereof, and can include discrete digital or analog circuit elements or electronics, or combinations thereof. In an embodiment, computational circuitry includes one or more ASICs having a plurality of predefined logic components. In an embodiment, computational circuitry includes one or more FPGAs, each having a plurality of programmable logic components.

In an embodiment, computation circuitry includes one or more electric circuits, printed circuits, flexible circuits, electrical conductors, electrodes, cavity resonators, conducting traces, ceramic patterned electrodes, electro-mechanical components, transducers, and the like.

In an embodiment, computational circuitry includes one or more components operably coupled (e.g., communicatively, electromagnetically, magnetically, ultrasonically, optically, inductively, electrically, capacitively coupled, wirelessly coupled, and the like) to each other. In an embodiment, circuitry includes one or more remotely located components. In an embodiment, remotely located components are operably coupled, for example, via wireless communication. In an embodiment, remotely located components are operably coupled, for example, via one or more communication modules, receivers, transmitters, transceivers, and the like.

In an embodiment, computation circuitry includes memory that, for example, stores instructions or information. Non-limiting examples of memory include volatile memory (e.g., Random Access Memory (RAM) , Dynamic Random Access Memory (DRAM) , and the like) , non-volatile memory (e.g., Read-Only Memory (ROM) , Electrically Erasable Programmable Read-Only Memory (EEPROM) , Compact Disc Read-Only Memory (CD-ROM) , and the like) , persistent memory, and the like. Further non-limiting examples of memory include Erasable Programmable Read-Only Memory (EPROM) , flash memory, and the like. In an embodiment, memory is coupled to, for example, one or more computing devices by one or more instructions, information, or power buses. In an embodiment, computational circuitry includes one or more databases stored in memory. In an embodiment, computational circuitry includes one or more look-up tables stored in memory.

In an embodiment, computational circuitry includes one or more computer-readable media drives, interface sockets, Universal Serial Bus (USB) ports, memory card slots, and the like, and one or more input/output components such as, for example, a graphical user interface, a display, a keyboard, a keypad, a trackball, a joystick, a touch-screen, a mouse, a switch, a dial, and the like, and any other peripheral device. In an embodiment, computational circuitry includes one or more user input/output components that are operably coupled to at least one computing device configured to control (electrical, electromechanical, software-implemented, firmware-implemented, or other control, or combinations thereof) at least one parameter associated with, for example, determining one or more tissue thermal properties responsive to detected shifts in turn-on voltage.

In an embodiment, computational circuitry includes electrical circuitry forming a general purpose computing device configured by a computer program (e.g., a general purpose computer configured by a computer program which at least partially carries out processes and/or devices described herein, or a microprocessor configured by a computer program which at least partially carries out processes and/or devices described herein) , electrical circuitry forming a memory device (e.g., forms of memory (e.g., random access, flash, read only, etc. ) ) , electrical circuitry forming a communications device (e.g., a modem, communications switch, optical-electrical equipment, etc. ) , and/or any non-electrical analog thereto, such as optical or other analogs.

The computing device 200 in accordance with a first aspect of the present disclosure can provide finer granularity of hair classification, damage level ranking and hair treatment history trace in a miniature, robust, customizable and cost-effective manner.

FIG. 3 illustrates a flowchart of a method 300 of noninvasively diagnosing a hair in accordance with a second aspect of the present disclosure.

With the method 300, the above and other potential deficiencies in the conventional approaches can be overcome. The method 300 starts at block 301. At block 301, a near infrared light is emitted to a hair of a customer. At block 302, data related to chemical damage markers of said hair is obtained. In one example, data related to chemical damage markers of said hair is obtained from a near infrared absorption spectrum of said hair. At block 303, said data related to chemical damage markers of said hair is processed. In one example, a near infrared usage model shown in Fig. 1 is used to process said data related to chemical damage markers of said hair. Finally, at block 304, a virtual representation of diagnosis information, such as hair damage level, hair classification and hair treatment history, of said hair is output. In one example, said chemical damage markers are cystic acid, eumelanin, melanin concentration and like, but not limited to this. In one example, said hair classification can be natural hair, colored hair, bleached hair, permed hair and like, but not limited to this.

The method 300 in accordance with a second aspect of the present disclosure can provide precise hair damage diagnosis, hair classification and hair treatment history in a miniature, robust, customizable and cost-effective manner.

FIG. 4 illustrates a flowchart of a method 400 of noninvasively diagnosing a hair in accordance with a second aspect of the present disclosure.

In order to obtain finer granularity of hair classification, damage level ranking and hair treatment history, a method 400 of noninvasively diagnosing a hair in accordance with a second aspect of the present disclosure is provided.

With the method 400, the above and other potential deficiencies in the conventional approaches can be overcome. The method 400 starts at block 401. At block 401, a near infrared light is emitted to a hair of a customer. At block 402, data related to water content and chemical damage markers of said hair is obtained. Compared to the Medium infrared spectroscopy MIR, NIR has stronger penetration capability, thus it is easy to get whole fiber damage information. In one example, data related to water retention capabilities and chemical damage markers of said hair is obtained from a near infrared absorption spectrum of said hair. At block 403, said data related to water retention capabilities and chemical damage markers of said hair is processed. In one example, a near infrared usage model shown in Fig. 1 is used to process said data related to water retention capabilities and chemical damage markers of said hair. Finally, at block 404, a virtual representation of diagnosis information, such as hair damage level, hair classification and hair treatment history, of said hair is output. In one example, said chemical damage markers are cysteic acid, eumelanin, melanin, water, 18-MEA concentration change and like, but not limited to this. In one example, said hair classification can be natural hair, colored hair, bleached hair, permed hair and like, but not limited to this.

The method 400 in accordance with a second aspect of the present disclosure can do very good hair classification, hair treatment history trace and hair damage level ranking based on damage chemical markers and hair water retention capabilities.

FIG. 5 illustrates a block diagram of an apparatus 500 of noninvasively diagnosing a hair in accordance with a third aspect of the present disclosure.

Said apparatus 500 comprises means 501 for emitting a near infrared light to a hair of a customer. Said apparatus 500 also comprises means 502 for obtaining data related to chemical damage markers of said hair. In one example, data related to chemical damage markers of said hair is obtained from a near infrared absorption spectrum of said hair. Said apparatus 500 also comprises means 503 for processing said data related to chemical damage markers of said hair. In one example, a near infrared usage model shown in Fig. 1 is used to process said data related to chemical damage markers of said hair. Said apparatus 500 also comprises means 504 for outputting a virtual representation of diagnosis information, such as hair damage level, hair classification and hair treatment history, of said hair. In one example, said chemical damage markers are cystic acid, eumelanin, melanin concentration and like, but not limited to this. In one example, said hair classification can be natural hair, colored hair, bleached hair, permed hair and like, but not limited to this.

In order to obtain finer granularity of hair classification, damage level ranking and hair treatment history, said apparatus 500 also comprises means for obtaining data related to water content of said hair in addition to obtaining data related to chemical damage markers of said hair and means for processing data related to water content of said hair in addition to processing data related to chemical damage markers of said hair. Said means for processing data related to water content of said hair is performed by a near infrared usage model shown in Fig. 1.

The apparatus 500 in accordance with a third aspect of the present disclosure can provide more precise hair damage diagnosis, hair classification and hair treatment history in a miniature, robust, customizable and cost-effective manner.

According to the above-mentioned aspects of the present disclosure, in noninvasively diagnosing a hair, in order to get more precise and robust results , data related to the chemical damager markers obtained from NIR is calibrated with the damage markers concentration which we got from lab analysis tools such as HPLC.

FIG. 6 illustrates a flowchart of a method 600 of noninvasively diagnosing a hair in accordance with a fourth aspect of the present disclosure.

In order to obtain color measurement in a low cost, there is provided a method 600 of noninvasively diagnosing a hair in accordance with a fourth aspect of the present disclosure. The method 600 starts at block 601. At block 601, a visible light is emitted to said hair. At block 602, data related to colors of said hair is obtained from a visible light absorption spectrum of said hair. At block 603, said data related to color of said hair is processed by a visible light usage model of a hair. Such visible light usage model of a hair is established similar to that in Fig. 1 on basis of data related to color information of a large number of hair samples under a visible light by a machine learning algorithm which is such as principal component analysis PCA, but not limited to this. Finally, at block 604, a virtual representation of diagnosis information of said hair is output. In one example, said diagnosis information is color information of said hair. In the disclosure, said model is used to perform an automatic classification and is trained on some discriminant attributes extracted from the data acquired on a large number of hair samples under visible light illumination. Such data are related to hair color. The aforementioned model conflates the results of different conventional classifiers, such as Random Forest, Neural Network, Gaussian Process, Kernel SVM and so on, and gives a strong conclusion on hair color using soft voting.

Fig. 7 illustrates a block diagram of a computing device 700 in accordance with a fifth aspect of the present disclosure.

Said computing device 700 comprises a visible light spectrometer 701 and a processor 702. Said visible light spectrometer 701 includes computational circuitry configured to emit a visible light to said hair and obtain data related to colors of said hair. Said processor 702 includes computational circuitry configured to process said data related to colors of said hair and output a virtual representation of diagnosis information of said hair. In one example, said diagnosis information is color information of said hair.

In another embodiment said computational circuitry of said processor 702 is also configured to process said data related to colors of said hair by a visible light usage model of a hair. Such visible light usage model of a hair is established similar to that in Fig. 1 on basis of data related to color information of a large number of hair samples under a visible light by a machine learning algorithm which is such as principal component analysis PCA, but not limited to this.

The computing device 700 in accordance with a fifth aspect of the present disclosure can provide color measurement in a low cost and efficient way.

According to the various aspects of the disclosure, the invention can achieve finer granularity of hair classification and damage differentiation and can do very good classification of treated hair; can do a very good correlation with real hair treatment history and hair damage ranking well aligned with hairstylist definition. Thus, for different diagnosis information, different customized products can be provided to a hair of said customer.

An embodiment of the disclosure may be an article of manufacture in which a non-transitory machine-readable medium (such as microelectronic memory) has stored thereon instructions (e.g., computer code) which program one or more data processing components (generically referred to here as a “processor” ) to perform the operations described above. In other embodiments, some of these operations might be performed by specific hardware components that contain hardwired logic (e.g., dedicated digital filter blocks and state machines) . Those operations might alternatively be performed by any combination of programmed data processing components and fixed hardwired circuit components.

While the embodiments have been illustrated and described herein, it will be understood by those skilled in the art that various changes and modifications may be made, and equivalents may be substituted for elements thereof without departing from the true scope of the present technology. In addition, many modifications may be made to adapt to a particular situation and the teaching herein without departing from its central scope. Therefore it is intended that the present embodiments not be limited to the particular embodiment disclosed as the best mode contemplated for carrying out the present technology, but that the present embodiments include all embodiments falling within the scope of the appended claims.

Claims

A computing device for noninvasively diagnosing a hair, comprising:

a near infrared spectrometer including computational circuitry configured to emit a near infrared light to said hair and obtain data related to chemical damage markers of said hair;

a processor including computational circuitry configured to process said data related to chemical damage markers of said hair and output a virtual representation of diagnosis information of said hair.
The computing device according to claim 1, wherein said near infrared spectrometer includes computational circuitry which is further configured to obtain data related to water content of said hair and said processor includes computational circuitry which is further configured to process said data related to water content of said hair.
The computing device according to claim 1, wherein said processor includes computational circuitry which is further configured to process said data related to chemical damage markers of said hair by a near infrared usage model of a hair.
The computing device according to claim 2, wherein said processor includes computational circuitry which is further configured to process said data related to water content of said hair by a near infrared usage model of a hair.
The computing device according to claim 3 or 4, wherein said a near infrared usage model is established on basis of data related to water content and/or chemical damage markers of a large number of hair samples under a near infrared light by a machine learning algorithm.
The computing device according to claim 1, wherein said near infrared spectrometer includes computational circuitry which is further configured to obtain data related to chemical damage markers of said hair by a near infrared absorption spectrum of said hair.
The computing device according to claim 2, wherein said near infrared spectrometer includes computational circuitry which is further configured to obtain data related to water content of said hair by a near infrared absorption spectrum of said hair.
The computing device according to claim 1, wherein said diagnosis information is information related to hair damage level, hair classification and hair treatment history.
The computing device according to claim 1, wherein said chemical damage markers are cysteic acid, eumelanin, melanin, water, 18-MEA concentration change and like.
The computing device according to claim 1, wherein said hair classification can be natural hair, colored hair, bleached hair, permed hair and like.
A method of noninvasively diagnosing a hair, the method comprising:

emitting a near infrared light to said hair;

obtaining data related to chemical damage markers of said hair;

processing said data related to chemical damage markers of said hair; and

outputting a virtual representation of diagnosis information of said hair.
The method according to claim 11, the method also comprising:

obtaining data related to water content of said hair;

processing said data related to water content of said hair.
The method according to claim 11, wherein said processing said data related to chemical damage markers of said hair is performed by a near infrared usage model of a hair.
The method according to claim 12, wherein said processing said data related to water content of said hair is performed by a near infrared usage model of a hair.
The method according to claim 13 or 14, wherein said near infrared usage model is established on basis of data related to water content and/or chemical damage markers of a large number of hair samples under a near infrared light by a machine learning algorithm.
The method according to claim 11, wherein said obtaining data related to chemical damage markers of said hair is achieved by a near infrared absorption spectrum of said hair.
The method according to claim 12, wherein said obtaining data related to water content of said hair is achieved by a near infrared absorption spectrum of said hair.
The method according to claim 11, wherein said diagnosis information is information related to hair damage level, hair classification and hair treatment history.
The method according to claim 11, wherein said chemical damage markers are cysteic acid, eumelanin, melanin, water concentration, 18-MEA concentration change and like.
The method according to claim 18, wherein said hair classification can be natural hair, colored hair, bleached hair, permed hair and like.
A computer readable medium having stored thereon instructions that when executed cause a computing device to perform the method according to one of claims 11-20.
An apparatus of noninvasively diagnosing a hair, said apparatus comprises means for performing the method according to one of claims 11-20.