阅读说明：本技术 镜片订购系统、镜片订购方法、程序以及数据结构 (Lens ordering system, lens ordering method, program, and data structure ) 是由 伊藤伸介 于 2020-03-10 设计创作，主要内容包括：镜片订购系统具备计算机,所述计算机具备CPU、存储部和由所述CPU执行的选定部。在所述存储部中存储有能够基于用户信息来确定镜片信息的数据,所述用户信息包含用途信息及身心信息中的至少一方、和与用户相关的基本信息,所述用途信息与用户所期望的镜片用途相关,所述身心信息包含包括用户眼睛在内的身心的症状和状态,所述镜片信息包含镜片的材料所用的树脂材料的种类、色素的种类、以及涂层的种类。所述选定部基于针对用户所取得的所述用户信息、和存储于所述存储部中的数据,确定并输出所述镜片信息。(The lens ordering system is provided with a computer having a CPU, a storage unit, and a selection unit executed by the CPU. The storage unit stores data capable of specifying lens information based on user information including basic information related to a user and at least one of usage information and mind and body information related to a usage of a lens desired by the user, the mind and body information including symptoms and states of the mind and body including eyes of the user, and the lens information including a type of a resin material used for a material of the lens, a type of a pigment, and a type of a coating. The selection unit determines and outputs the lens information based on the user information acquired for the user and the data stored in the storage unit.)

1. A lens ordering system is provided with a computer,

the computer includes a CPU, a storage unit, and a selection unit executed by the CPU,

the storage unit stores data capable of specifying lens information based on user information including basic information on a user and at least one of usage information and mind and body information, the usage information being related to a usage of the lens desired by the user, the mind and body information including symptoms and states of the mind and body including eyes of the user, the lens information including a type of a resin material used for a material of the lens, a type of a pigment, and a type of a coating,

the selection unit determines and outputs the lens information based on the user information acquired for the user and the data stored in the storage unit.

2. The lens ordering system of claim 1,

the computer further comprises an acquisition section for acquiring the image data,

the acquisition unit acquires at least one of the usage information and the mind and body information in the user information in a predetermined order by inputting the user information.

3. The lens ordering system according to claim 1 or 2,

the data is a learned model that is learned in advance so that the lens information is output from the user information including at least one of the usage information and the mind and body information, and is set as follows: using the respective weights of the usage information and the mind and body information and correct data of the kind of the material, the kind of the pigment and the kind of the coating of the lens as learning data, learning the weight parameters of a neural network model by a deep learning method,

the selection unit specifies the lens information output from the learned model, using the user information acquired for the user as an input to the learned model.

4. The lens ordering system according to any one of claims 1 to 3,

the computer further comprises a collection portion for collecting the data,

the collecting unit collects the user information of the user input by the terminal via the network and records the user information in the storage unit,

the selecting unit compares the user information of the user recorded in the storage unit with the data recorded in the storage unit to determine the lens information of the user.

5. The lens ordering system according to any one of claims 1 to 4,

the lens information further includes information relating to the dyeing property of the lens, and information relating to the solubility of the resin indicating the compatibility of the specific pigment with the specific resin.

6. A lens ordering method for causing a computer to execute:

the lens information is determined and outputted based on data stored in a storage unit of a computer, the data being capable of determining lens information based on user information including at least one of use information and physical and mental information including physical and mental symptoms and states including eyes of a user, and basic information related to the user, and the user information acquired for the user, the lens information including a type of resin material used for a material of the lens, a type of pigment, and a type of coating.

7. A program for causing a computer to function as the following selection unit in a lens ordering system including a storage unit and the selection unit:

the lens information is determined and outputted based on data stored in a storage unit of the computer, the data being capable of determining lens information based on user information including at least one of use information and physical and mental information including physical and mental symptoms and states including eyes of a user, and basic information related to the user, and the user information acquired for the user, the lens information including a type of resin material used for a material of the lens, a type of pigment, and a type of coating.

8. A data structure including user information for use in a process of specifying information relating to processing of a lens by a lens ordering system having a storage unit, a selection unit, and a display unit,

the data structure includes user information and lens information, the user information includes at least one of usage information and mind and body information, and basic information related to a user, the usage information is related to usage of a lens desired by the user, the mind and body information includes symptoms and states of mind and body including eyes of the user, the lens information includes a kind of resin material used for a material of the lens, a kind of pigment, and a kind of coating,

the storage unit stores data capable of specifying the lens information from the user information,

causing the selection unit to determine the lens information based on the data stored in the storage unit when the user information acquired for the user is input,

causing the display unit to output the lens information determined by the selection unit.

Technical Field

The present disclosure relates to a lens ordering system, a lens ordering method, a program, and a data structure.

Background

Conventionally, there is a technique of japanese patent application laid-open No. 2000-325840 relating to a system for coloring a lens based on coloring information on coloring of the lens.

Disclosure of Invention

Problems to be solved by the invention

In the lens, it is preferable to select an appropriate material, pigment, or coating layer and perform lens processing according to the use and physical and mental state of a user wearing glasses with lenses. For example, it has been found that light emitted from a display of a digital device (e.g., light having a wavelength near 460 nm) causes eye fatigue and the like, which is considered to be light not good for the human body. It is desirable to perform lens processing for masking such light that is not good for the human body. Lens processing according to the needs of users suffering from eye fatigue and the like can also be said to be a social issue necessary for improving so-called quality life.

In lens processing, there is a technique of making a lens a specific pigment and thickness and cutting a specific wavelength.

For example, in international publication No. WO2014-133111, lens processing that performs so-called blue light cut has been proposed for lens processing in consideration of the physical and mental state such as the occurrence of eye fatigue and pain associated with the use of a display or the like. In addition, lens processing in consideration of prevention of eye diseases (age-related macular degeneration and the like) has been proposed.

In addition, international publication WO2015-37628 proposes lens processing for keeping the circadian rhythm normal. Regarding sleep disorders, lens processing has been proposed to suppress insufficient melatonin secretion due to light exposure at night.

In addition, in International publication WO2015-37627, lens processing for preventing and suppressing migraine is proposed. Lens processing for suppressing the reflection of pupil light has been proposed, which can be used for applications such as nighttime driving.

As described above, there are many factors to be considered in lens processing, and it is difficult to completely consider these factors to determine how to perform lens processing. Therefore, systems are sought that support lens processing or work advice on lenses.

An object of the present disclosure is to provide a lens ordering system, a lens ordering method, a program, and a data structure capable of presenting lens information recommended to a user in consideration of a plurality of items.

Means for solving the problems

The lens ordering system of the present disclosure includes a computer having a CPU, a storage unit, and a selection unit executed by the CPU, wherein data capable of specifying lens information based on user information is stored in the storage unit, the user information includes basic information related to a user and at least one of usage information and mind and body information, the usage information is related to a lens usage desired by the user, the mind and body information includes symptoms and states of mind and body (including eyes of the user), and the lens information includes a type of resin material used for a material of a lens, a type of pigment, and a type of coating. The selection unit determines and outputs the lens information based on the user information acquired for the user and the data stored in the storage unit.

In the lens ordering system according to the present disclosure, the computer may further include an acquisition unit configured to acquire at least one of the usage information and the mind and body information in the user information in a predetermined order by inputting the usage information and the body and body information.

In the lens ordering system according to the present disclosure, the data may be a learned model that is learned in advance so that the lens information is output from the user information including at least one of the usage information and the mind and body information, and the learned model may be a learned model that: the weight parameter of a neural network model is learned by a deep learning method using, as learning data, the weight of each of the usage information and the mind and body information and the correct data of the material type, the pigment type, and the coating type of the lens, and the selecting unit determines the lens information output from the learned model using the user information acquired for the user as input to the learned model.

In the lens ordering system according to the present disclosure, the computer may further include a collecting unit that collects the user information of the user input at the terminal via the network and records the user information in the storage unit, and the selecting unit may compare the user information of the user recorded in the storage unit with the data recorded in the storage unit and determine the lens information of the user.

In addition, the lens ordering method of the present disclosure causes a computer to execute: the lens information is determined and outputted based on data stored in a storage unit of a computer, the data being capable of determining lens information based on user information including at least one of use information and physical and mental information including physical and mental symptoms and states including eyes of a user, and basic information related to the user, and the user information acquired for the user, the lens information including a type of resin material used for a material of the lens, a type of pigment, and a type of coating.

Further, a program of the present disclosure causes a computer to function as the following selection unit in a lens ordering system including a storage unit and the selection unit: the lens information is determined and outputted based on data stored in a storage unit of the computer, the data being capable of determining lens information based on user information including at least one of use information and physical and mental information including physical and mental symptoms and states including eyes of a user, and basic information related to the user, and the user information acquired for the user, the lens information including a type of resin material used for a material of the lens, a type of pigment, and a type of coating.

The data structure of the present disclosure is a data structure including user information used when a lens ordering system having a storage unit, a selection unit, and a display unit performs processing for specifying information relating to processing of a lens, the data structure including user information and lens information, the user information including basic information relating to a user and at least one of usage information and mind and body information, the usage information relating to usage of a lens desired by the user, the mind and body information including symptoms and states of the mind and body including eyes of the user, the lens information including a type of a resin material used for a material of the lens, a type of a pigment, and a type of a coating, the storage unit having stored therein data enabling the lens information to be specified from the user information in advance, and the selection unit, when the user information acquired for the user is input, the lens information is determined based on the data stored in the storage unit, and the lens information determined by the selection unit is caused to be output by the display unit.

ADVANTAGEOUS EFFECTS OF INVENTION

According to the biological information acquisition method, the lens ordering system, the lens ordering method, the program, and the data structure of the present disclosure, it is possible to provide an effect that lens information recommended to a user can be presented in consideration of a plurality of items.

Drawings

Fig. 1 is a block diagram showing an example of a system configuration of the lens ordering apparatus according to the first embodiment.

Fig. 2 is a schematic block diagram of a computer that functions as the lens ordering apparatus according to the present embodiment.

Fig. 3 is a diagram showing an example of input/output of a model.

Fig. 4 is a diagram showing an example of a screen of an input interface of the acquisition unit.

Fig. 5 is a diagram showing an example of a screen of an input interface of the acquisition unit.

Fig. 6 is a diagram showing an example of a screen of an input interface of the acquisition unit.

Fig. 7 is a diagram showing an example of a screen of an input interface in a case where the acquisition unit receives a symptom input.

Fig. 8 is a diagram showing an example of a screen of the input interface when the acquisition unit receives a symptom input.

Fig. 9 is a diagram showing an example of a screen of an input interface in a case where the acquisition unit receives a symptom input.

Fig. 10 is a diagram showing an example of a screen of an input interface in a case where the acquisition unit receives a symptom input.

Fig. 11 is a diagram showing an example of a screen of an input interface in a case where the acquisition unit receives a symptom input.

Fig. 12 is a diagram showing an example of a processing routine executed by the lens ordering apparatus of the first embodiment.

Fig. 13 is a block diagram showing a system configuration of the lens ordering system of the second embodiment.

Fig. 14 is a diagram showing an example of a processing routine executed by the lens ordering apparatus of the second embodiment.

Detailed Description

The present embodiment will be described in detail below. The lens ordering apparatus of the present embodiment outputs recommended lens information based on input user information for a user. The user of the present embodiment is, for example, a customer who wants to purchase a customized lens in a spectacle shop.

< System configuration of the first embodiment >

Fig. 1 is a block diagram showing an example of a system configuration of a lens ordering apparatus 10 according to a first embodiment. The lens ordering apparatus 10 having the configuration shown in fig. 1 may be configured by a computer including a CPU, a RAM, and a ROM in which a program for executing each processing routine described later and various data are stored. The lens ordering apparatus 10 is an example of a lens ordering system.

For example, the lens ordering apparatus 10 can be implemented by the computer 50 shown in fig. 2. The computer 50 includes a CPU51, a memory 52 as a temporary storage area, and a nonvolatile storage unit 53. The computer 50 includes an input/output interface (I/F)54 to which an input/output device and the like (not shown) are connected, and a read/write (R/W) unit 55 that controls reading and writing of data to and from a recording medium. The computer 50 is also provided with a network I/F56 connected to a network such as the internet. The CPU51, the memory 52, the storage section 53, the input/output I/F54, the R/W section 55, and the network I/F56 are connected to each other via a bus 57.

The storage section 53 may be realized by a Hard Disk Drive (HDD), a Solid State Drive (SSD), a flash memory, or the like. The storage unit 53 as a storage medium stores a program for causing the computer 50 to function. The CPU51 reads out the program from the storage unit 53, expands the program in the memory 52, and sequentially executes the processes of the program.

The above is a description of an example of the electrical configuration of the computer in fig. 2.

The following describes each processing unit in the lens ordering apparatus 10 of fig. 1. The lens ordering apparatus 10 functionally includes a data storage unit 22, an acquisition unit 28, a selection unit 30, and a display unit 32, as shown in fig. 1.

The data storage unit 22 stores a learned model for outputting lens information with user information as input. The user information includes basic information, usage information, and physical and mental information. The basic information is information such as power, astigmatism, age, sex, prescription, frame type, and the like corresponding to the vision of the user. The usage information is information related to the type of usage of the lens desired by the user, for example, "driving", "sport", "business work (PC work)", and the like. The physical and mental information includes physical and mental symptoms and states including the eyes of the user, and is information such as "cataract", "migraine", "eyestrain", and the like. The lens information includes the type of resin material used for the lens material, the type of pigment, the type of coating, and the like. Examples of the kind of the resin material include polyurethane, acrylate, and polyolefin. Examples of the type of the pigment include Tinuvin326 (manufactured by Basff Japan K.K.), FDB series such as FDB-001 (manufactured by Shanda chemical Co., Ltd.), FDG series such as FDG-001 (manufactured by Shanda chemical Co., Ltd.), and FDR series such as FDR-001 (manufactured by Shanda chemical Co., Ltd.). Examples of the type of the coating layer include an undercoat layer, a hard coat layer, an antireflection layer, an antifogging layer, an antifouling layer, and a hydrophobic layer as the coating layer. By combining the types of these materials, the types of pigments, and the types of coatings, lens information corresponding to the usage information and the physical and mental information can be output. For example, as shown in patent document 2 and the like, the information is lens information for cutting blue light wavelength, preventing eye diseases such as age-related macular degeneration, and suppressing processes such as sleep disorder and pupil reflex. The lens information further includes information on the dyeing property of the lens and information on the resin solubility indicating the compatibility of the specific dye with the specific resin. The information relating to the dyeing properties of the lenses is: for example, information indicating the color tone, density, hue, and the like of the lens corresponding to the color sample of the colored lens corresponding to the resin type of the lens. By including such information on the dyeing property, feedback can be achieved for the purpose of dyeing the lens, when an option is designated, or the like. The information on the resin solubility indicating the compatibility of the specific dye with the specific resin is: for example, information indicating a relationship that a certain color element has a property of being insoluble in a monomer of another resin. By including such information on solubility, feedback can be achieved in the case where a specific pigment or the like is required according to symptoms. Note that these lens information are merely examples.

For learning the model, the weight parameters of the neural network model may be learned in advance by a deep learning method using, as learning data, the weights of the usage information and the mind and body information, and the correct data such as the type of the lens material, the type of the pigment, and the type of the coating. The deep learning method may be a method such as GAN (generic adaptive Network), LSTM (Long Short-Term Memory), or the like.

Fig. 3 is a diagram showing an example of input/output of a model. As shown in fig. 3, the input user information can be divided into information elements that are accessible from a study at an ophthalmic hospital, and information elements that are accessible at a store by a query or the like to the user. As shown in fig. 3, each input element may be an adjustment parameter for a plurality of output elements. For example, when there is a diagnosis of "migraine" due to the influence of the eyes in the information of the ophthalmic hospital, the element of "migraine" as the input physical and mental information affects the parameters of the elements of "type and function of pigment" and "amount of pigment" as the type of pigment. The "type/function of pigment" and "amount of pigment" elements learn the weight of the model in advance to adjust to the parameter for alleviating "migraine". By using the learned model thus learned, when an input of "migraine" including the mind and body information as the user information is accepted, the learned model can output the kind of the pigment corresponding to the "migraine". Since the model has a plurality of input elements, learning is performed in advance to adjust the influence of each input element. For example, the input elements of the data for learning are determined with priority and weight parameters, and then the model is learned. The priority and weight parameters are adjusted based on the lens information that the user ultimately selects. Further, for example, it is sometimes determined that the "headache" symptom of the user is highly likely to be a photoallergic headache based on information obtained from an inquiry or the like for the user at a store. In this case, in the output of the learned model, the diagnosis by the ophthalmologist is facilitated, and the same processing as in the case where the learned model outputs the kind of pigment corresponding to "migraine" based on the above-described information of the ophthalmic hospital can be performed.

Note that, instead of learning the model, the data storage unit 22 may store data in a table format in which combinations of the user information and the lens information are recorded in advance. In this case, a rule of output elements corresponding to a combination of input elements may be stored in advance as an item for providing adjustment to the combination.

The data storage unit 22 has a customer database in which a history of basic information in the acquired user information is recorded. In addition, the customer database also records the finally determined lens information in advance.

The acquisition unit 28 acquires at least one of the usage information and the mind and body information of the user information in a predetermined order by inputting the information. When the user information of the user does not include the basic information, the acquiring unit 28 acquires the user basic information as a premise and records the user basic information in the customer database of the data storage unit 22.

Fig. 4 to 6 are diagrams showing an example of a screen of the input interface of the acquisition unit 28. As shown in fig. 4 to 6, the interface of the acquisition unit 28 is an interface such as a touch panel that allows the user to input each item. In the interface screen shown in fig. 4, any one of "use", "symptom, etc", "use, symptom, etc" is selected. When the user selects, for example, "use", then a transition is made to the interface screen shown on the left side of fig. 5. On the interface screen shown in fig. 5, selection of application types such as large items "driving", "sport", "business work (PC work)" and the like is accepted as the application. When the user touches "next" after selecting, for example, "motion", transition is made to the next interface screen. In the next interface screen, detailed selection of small and medium items of the "sports" use category is accepted. When "next" is touched after the details of the usage type are selected in the next interface screen on the right side of fig. 5, the screen is switched to the interface screen shown in fig. 6. In the interface screen shown in fig. 6, an option of the lens specification is selected. As shown in fig. 6, selection of "light transmittance", "color contrast", "other", and the like, for example, is accepted as an option. In addition, the annotation is displayed according to the selection. For example, when "polarization" is selected, it is displayed that "if a polarizing film is added, it is difficult to see water accumulated on the road surface. "this annotation. It should be noted that, as the coating specification, blue light interception, hard coating, hydrophobic, photosensitive, and the like are also acceptable. In addition, as shown in fig. 6, when "specification has determined simulation of viewing performance (viewing performance)" is touched, the simulation of viewing performance is executed after the processing of the selection section 30 is executed. Examples of the simulation include appearance and viewing performance. In terms of appearance, for example, an appearance showing a state where a user or a network avatar carries glasses provided with lenses based on lens information is displayed. When the user inputs the frame information, the appearance of a combination of the lens and the frame based on the lens information, and the appearance of the user or the network avatar wearing the glasses in which the lens and the frame are combined are displayed. In view of viewing performance, for example, an image or video is displayed on a display operated by a user, and a difference in viewing performance of the user between a case where a lens based on lens information is used and a case where the lens is not used is displayed. The image or video is stored in advance or shot in advance. In this case, the user may perform an operation of comparing the viewing performance by switching between the case of using the lens based on the lens information and the case of not using the lens on the operation screen, or the user may perform an operation of comparing the viewing performance by aligning the both on the same screen.

Fig. 7 to 11 are diagrams showing an example of a screen of an input interface in a case where the acquisition unit 28 receives an input of a symptom. For example, in fig. 4, when the user selects, for example, "symptom or the like", selection items of the symptom are displayed as shown in fig. 7 next. On the interface screen shown on the left side of fig. 7, selection of symptom types such as "easily feel dizzy", "often headache", and "easily tired eyes" is accepted as symptoms. When the user touches "next" after selecting, for example, "frequent headache", a transition is made to the next interface screen shown on the right side of fig. 7. Since this selection corresponds to "headache" which is a symptom of the user, the following description will be given by taking a case where the symptom is headache as an example. On the interface screen shown on the right side of fig. 7, selection of a part such as "one side or both sides of the head", "both sides of the head", or "the entire head" is accepted as a part of a symptom. When the user touches "next" after selecting, for example, "frequent headache", a transition is made to the next interface screen shown on the left side of fig. 8. On the interface screen shown on the left side of fig. 8, selection of a state such as "strong pain of one beat and one hop according to pulse or blood flow" or "relatively light pain of heavy tightening" is received as a symptom state. When the user touches "next" after selecting, for example, "strong pain of one hop and one hop coinciding with pulse and blood flow", the interface screen shown on the right side of fig. 8 is switched to next. On the interface screen shown on the right side of fig. 8, a selection of a relevant state such as "nausea and vomiting, or" photosensitivity "is accepted as a relevant symptom. The related symptoms are sometimes explained in detail, and a "detail" key is provided in parallel to the selection item "light-sensitive" on the right side of fig. 8. As shown in fig. 9, when the "details" key is pressed, the details of the associated symptom are displayed on the top (or another screen). When the user touches "next" after selecting, for example, "frequent headache", transition is made to the next interface screen. In the next interface screen, a selection item for selecting a symptom state is displayed. When the user selects an arbitrary selection item and then touches "next", transition is made to the next interface screen shown on the left side of fig. 10. On the interface screen shown on the left side of fig. 10, selection of a symptom cause that is perceived with ease, such as "none" and "when the user feels light (dazzling)" is accepted as a question regarding the symptom cause. When the user touches "next" after selecting, for example, "when it is lighted (dazzled)", the transition is made to the following interface screen shown on the right side of fig. 10. In the interface screen shown on the left side of fig. 10, the selection of subjective symptoms related to causes such as "always feel", "often feel", and the like is accepted as subjective symptoms related to the selected cause. When the user touches "next" after selecting an arbitrary subjective symptom, the transition is made to the following interface screen shown in fig. 11. In the interface screen shown on the left side of fig. 10, the judgment result related to the symptom is displayed, and the lens information and the inducement of the order to the lens are displayed. Fig. 11 is an example of a case where the presence of the possibility of the photo-allergic migraine is indicated as a result of the determination in accordance with the above selection.

As described above, the order of receiving data by the acquisition unit 28 is set such that the levels of the large items and the small and medium items are set for use, and the upper level is selected first and the lower level is selected next. Further, the order of reception of the stepwise data selection is set according to the symptoms, the diagnosis results of the symptoms are displayed, and the ophthalmic diagnosis is prompted in the diagnosis results according to the selection. The same applies to the case of acquiring physical and mental information such as basic information and symptoms. The acquisition unit 28 receives the lens specification as an option. The output is fixed for the lens specification that has been selected as an option. The hierarchy may be appropriately designed in consideration of the priority, the weight, and the like.

The selection unit 30 determines the lens information output from the learned model by using the user information acquired by the acquisition unit 28 as an input to the learned model stored in the data storage unit 22. The selection section 30 performs simulation based on the determined lens information. The selection unit 30 displays the simulation result on the display unit 32. The selection unit 30 outputs the final lens information to the display unit 32 if the simulation result is OK, and returns to the acquisition of the user information by the acquisition unit 28 if NG. When basic information, usage information, and physical and mental information, which are user information, are output to the learned model, recommended lens information is output according to the weight parameters of the neural network of the learned model.

The display unit 32 outputs the simulation result of the lens information selected by the selection unit 30. The display unit 32 outputs the lens information finally specified by the selection unit 30, the acquired user information, and the simulation result.

< Effect of the first embodiment >

Fig. 12 is a diagram showing an example of a processing routine executed by the lens ordering apparatus 10. The operation of the lens ordering apparatus 10 will be described with reference to fig. 12.

In step S100, the acquisition unit 28 receives a request from a user to order a lens, and determines whether the order is a new order. The determination as to whether or not the order is newly placed is made with reference to the customer database of the data storage unit 22. In the case of a new subscription, the process shifts to step S102. In the case of no new subscription, the process shifts to step S104.

In step S102, the acquisition unit 28 refers to the basic information of the user from the customer database of the data storage unit 22.

In step S104, the acquisition unit 28 receives input and selection through the interface screen for acquiring basic information, and acquires the basic information of the user.

In step S106, the acquisition unit 28 receives selection of a lens according to the usage information, the physical and mental information, and the type of any user information of the usage information and the physical and mental information. The category is selected through the interface screen described with respect to fig. 4.

In step S108, the acquisition unit 28 acquires a category selection of the large item for the selected category. The large item is acquired through the interface screen described with reference to fig. 5. When the types of the usage information and the physical and mental information are selected, the usage information and the physical and mental information are acquired, respectively. The same is true with respect to the following steps.

In step S110, the acquisition unit 28 acquires the selection of the details of the small item or the medium item, which is the lower item of the large item, for the selected category.

In step S112, the acquisition unit 28 acquires the selection of the option. The options are obtained through the interface screen described with reference to fig. 6.

In step S114, the selection unit 30 determines the lens information output from the learned model as the lens information to be simulated, using the user information acquired by the acquisition unit 28 as an input to the learned model stored in the data storage unit 22.

In step S116, the selection section 30 performs simulation based on the determined lens information.

In step S118, the display unit 32 displays the simulation result.

In step S120, the acquisition unit 28 acquires that the simulation result is OK or NG, and if OK, the process proceeds to step S122, and if NG, the process returns to step S106 to repeat the process. Note that, in the repetition, the previous selection history may be reflected.

In step S122, the display unit 32 outputs the lens information finally specified by the selection unit 30, the acquired user information, and the simulation result.

As explained above, with the lens ordering apparatus 10 of the first embodiment, lens information recommended to the user can be prompted in consideration of a plurality of items.

< System configuration of the second embodiment >

A second embodiment will be explained. Fig. 13 is a block diagram showing a system configuration of the lens ordering system of the second embodiment. As shown in fig. 13, in the lens ordering system 200, a plurality of terminals 210 and a lens ordering apparatus 220 are connected via a network N. The second embodiment is different from the lens ordering apparatus 10 of the first embodiment in that the collecting unit 228 collects user information for each user and presents the lens information to the terminal 210.

The terminal 210 is a smart terminal owned by the user, a tablet terminal assigned to a glasses shop, or the like. The terminal 210 has the same interface as the acquisition unit 28 and the display unit 32 of the first embodiment. At least one of the use information and the mind and body information and the basic information are acquired as the user information through the interface. In addition, through the interface, lens information received from the lens ordering apparatus 220 is displayed. The terminal 210 transmits the user information and the identification information assigned to the terminal 210 each time it is acquired, to the lens ordering apparatus 220. The identification information is identification information of the terminal 210 and identification information of the user who has acquired the information.

The collecting unit 228 of the lens ordering apparatus 220 collects the identification information of the terminal 210, and at least one of the usage information and the mind and body information and the basic information as the user information from each terminal 210, and stores various information such as the usage information, the mind and body information and the basic information as the user information for each identification information in the data storage unit 22. The selection unit 30 identifies the lens information recommended to the user by the same processing as that of the first embodiment for each piece of identification information stored in the data storage unit 22. The transmitting unit 232 transmits the lens information determined for the identification information to the terminal 210 to which the identification information is assigned.

< Effect of the second embodiment >

Fig. 14 is a diagram showing an example of a processing routine executed by the lens ordering apparatus according to the second embodiment. The operation of the lens ordering apparatus 220 will be described with reference to fig. 14. The terminal 210 performs the same processing as in steps S102 to 112 described above. When the simulation result is received and the user information is newly input, the same processing may be performed and the user information may be transmitted.

In step S200, the collection unit 228 collects, from each terminal 210, identification information of the terminal 210 and each piece of information of basic information, usage information, and physical and mental information, which is user information.

In step S202, the collection unit 228 stores, for each piece of identification information, each piece of basic information, usage information, and physical and mental information, which is user information, in the data storage unit 22.

In step S204, the selection unit 30 determines, for each piece of identification information stored in the data storage unit 22, at least one of the usage information and the mind and body information and the basic information, which are user information, as input to the learned model stored in the data storage unit 22, and specifies the lens information output from the learned model as the lens information of the user of the identification information. The processing in step S204 may be performed at predetermined intervals (5 minutes, 10 minutes, 20 minutes, or the like).

In step S206, the selection unit 30 executes simulation based on the determined lens information for each piece of identification information stored in the data storage unit 22.

In step S208, the transmission unit 232 transmits the lens information specified by the user and the simulation result with respect to the identification information to the terminal 210 to which the identification information is assigned.

As described above, with the lens ordering system 200 of the second embodiment, lens information recommended to each user can be presented in consideration of a plurality of items.

The present invention is not limited to the above-described embodiments, and various modifications and applications can be made without departing from the spirit of the present invention.

For example, the simulation result obtained by the selection unit in the above-described embodiment is described by taking as an example a case where the simulation result is displayed on the lens ordering apparatus or the terminal, but the present invention is not limited thereto. For example, the simulation result may be displayed by the following method: drawing a three-dimensional model of glasses based on lens information in an imaginary space realized by VR (Virtual Reality); alternatively, glasses based on lens information are drawn in real space as virtual glasses by an Augmented Reality method implemented by AR (Augmented Reality).

For example, the determined lens information may be subjected to display control and access control so that information related to a part of data of the lens information is partially disclosed to an external lens processing company.

In the present specification, the description has been given as an embodiment in which a program is installed in advance, but the program may be stored in a computer-readable recording medium and provided.

The disclosure of japanese laid-open application 2019-047275 filed on 3, 14, 2019 is incorporated by reference in its entirety.

All documents, patent applications, and technical specifications described in the present specification are incorporated by reference in the present specification to the same extent as if each document, patent application, and technical specification was specifically and individually described.