Personalized assistance system for vision correction device users

Abstract

A personalized assistance system for a user of a vision correction device includes a remote computing unit including a controller having a processor and a tangible, non-transitory memory having instructions recorded thereon. The controller is configured to selectively execute one or more machine learning models. The user device is operable by the user and includes an electronic diary module configured to prompt the user to answer one or more preselected questions at specified intervals. The electronic diary module is configured to store each answer entered by the user in response to the one or more preselected questions as self-reported data. The controller is configured to obtain the self-reported data from the electronic diary module and generate an analysis of the self-reported data via the one or more machine learning models. The controller is configured to provide assistance to the user based in part on the analysis.

Description

[Technical Field]

[0001] FIELD OF THE DISCLOSURE The present disclosure relates generally to a personalized assistance system for users of vision correction devices and methods. [Background technology]

[0002] Humans have five basic senses: sight, hearing, smell, taste, and touch. Vision gives us the ability to visualize the world around us and connects us to our surroundings. Several scientific reports have shown that the brain devotes more space to processing and storing visual information than the other four senses combined, highlighting the importance of vision. Many people around the world suffer from various problems with the quality of their vision, for example, due to refractive errors. At least some of these problems can be addressed with vision correction devices such as eyeglasses and contact lenses. Summary of the Invention [Means for solving the problem]

[0003] Disclosed herein is a personalized assistance system for users of vision correction devices and methods. The personalized assistance system includes a remote computing unit including a controller having a processor and a tangible, non-transitory memory having instructions recorded thereon. The controller is configured to selectively execute one or more machine learning models. A user device is operable by a user and configured to communicate with the remote computing unit. The user device includes an electronic diary module configured to prompt the user to answer one or more preselected questions at specified intervals.

[0004] The electronic diary module is configured to store as self-reported data respective responses entered by the user in response to one or more preselected questions. The one or more preselected questions may include a query regarding the user's comfort level, including at least one of dryness and irritation. The one or more preselected questions may include a query regarding when the user last cleaned the vision correction device.

[0005] The controller is configured to retrieve the self-reported data from the electronic diary module and generate an analysis of the self-reported data via one or more machine learning models. The controller is configured to provide user assistance based in part on the analysis. The vision correction device may include, but is not limited to, a contact lens. For example, the contact lens may be a multifocal lens having a first zone for distance vision, a second zone for near vision, and a third zone for intermediate vision.

[0006] The remote computing unit may include a first cloud unit and a central server, and the controller may be incorporated in at least one of the first cloud unit and the central server. The user device may include a query module configured to receive at least one question generated by a user. The controller may be configured to receive the question from the query module, construct an answer based in part on a first of the one or more machine learning models, and write the answer for consumption by the user via the query module.

[0007] The provider device is configured to communicate with a remote computing unit, the provider device being operable by an eye care provider associated with the user. The user device and the provider device include respective message modules. The remote computing unit may be configured to provide two-way communication between the eye care provider and the user via the respective message modules.

[0008] The remote computing unit may include a first database storing information about each user, including a type of vision correction device. The remote computing unit may include a second database storing group data about a group of users, the group data including self-reported data for each of the group of users. The user device includes a comparison tracking module configured to enable the user to compare their self-reported data with the group data. Assisting the user may include at least one of providing guidance regarding caring for the vision correction device and / or the user's eyes, suggesting a follow-up visit with an eye care provider, and suggesting an alternative vision correction product.

[0009] The above and other features and advantages of the present disclosure will become readily apparent from the following detailed description of the best mode for carrying out the disclosure, when read in conjunction with the accompanying drawings. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a schematic diagram of a personalized assistance system having a remote computing unit with a controller. [Figure 2] FIG. 2 is a simplified flow diagram for a method executable by the controller of FIG. [Figure 3] FIG. 3 is a diagram of an example machine learning model that can be executed by the controller of FIG. DETAILED DESCRIPTION OF THE INVENTION

[0011] Referring to the drawings, in which like reference numerals refer to like components, FIG. 1 illustrates a schematic diagram of a personalized assistance system 10 for assisting a user 12 of a vision correction device 14. The personalized assistance system 10 may include interfacing the user 12 with an eye care provider 16 associated with the user 12. The personalized assistance system 10 is configured to address issues that lead to the user 12 no longer wearing the vision correction device 14, i.e., to mitigate underuse of the vision correction device 14 by the user 12. In one embodiment, the vision correction device 14 is a contact lens having multiple zones with different respective refractive powers, such as a first zone 22 for distance vision, a second zone 24 for near vision, and a third zone 26 for intermediate vision. It should be understood that contact lenses may take many different forms and include multiple and / or alternative components. Additionally, any type of vision correction device available to one of ordinary skill in the art may be employed.

[0012] After being fitted with vision correction devices 14 by eye care provider 16, user 12 may use personalization assistance system 10 to accomplish several goals, including, but not limited to, reporting results over a period of time so that progress can be tracked and monitored, asking and receiving questions in real time, and receiving personalized suggestions based on previously reported results and past queries. Additionally, personalization assistance system 10 may be configured to respond to specific actions requested by user 12. For example, user 12 may request to set a reminder to remove their vision correction devices 14. As described below, personalization assistance system 10 utilizes both self-reported and comparative data to optimize the user's 12 experience.

[0013] 1, a personalization assistance system 10 includes a remote computing unit 30 having a controller C. The controller C has at least one processor P and at least one memory M (or non-transitory tangible computer-readable storage medium) having instructions recorded thereon for performing a method 100. The method 100 is illustrated in and described below with reference to FIG.

[0014] 1 , the remote computing unit 30 may include one or more cloud units, such as a first cloud unit 32, a second cloud unit 34, and a central server 36. The controller C may be incorporated in at least one of the cloud units and the central server 36. The central server 36 may be a private or public information source maintained by an organization, such as, for example, a research institute, a company, a university, and / or a hospital. The first cloud unit 32 and the second cloud unit 34 may include one or more servers hosted on the Internet to store, manage, and process data.

[0015] The controller C has access to and is specifically programmed to selectively execute one or more machine learning models 40, such as a first machine learning model 42 and a second machine learning model 44. The machine learning models 40 may be configured to find parameters, weights, or structures that minimize respective cost functions. Each of the machine learning models 40 may be a respective regression model. In one embodiment, the first machine learning model 42 and the second machine learning model 44 are embedded in the first cloud unit 32 and the second cloud unit 34, respectively. The remote computing unit 30 may include a first database 46 for storing respective information about the users 12, including the type of vision correction device. The remote computing unit 30 may include a second database 48 for storing group data about groups of users.

[0016] 1 , user devices 50 are operable by user 12 and configured to communicate, i.e., send and receive wireless communications, with remote computing units 30 via a first network 52. User devices 50 may include respective processors 54 and respective memories 56. User devices 50 may execute a first application 58, which may be a mobile application or "app." Server, network, and mobile application ("app") circuits and components available to those skilled in the art may be employed.

[0017] The user device 50 may be a smartphone, laptop, tablet, desktop, or other electronic device that the user 12 may operate, for example, via a touchscreen interface or an I / O device such as a keyboard or mouse. Multiple modules 60 may execute in conjunction with the remote computing unit 30. In one embodiment, the multiple modules 60 include an electronic diary module 62, a query module 64, a first messaging module 66, and a suggestion module 68. The multiple modules 60 may consume the output of a common or different machine learning models 40.

[0018] The electronic diary module 62 is configured to prompt the user 12 to answer one or more preselected questions at specific intervals, for example, daily. The electronic diary module 62 is configured to store each answer entered by the user 12 in response to the one or more preselected questions as self-reported data. The one or more preselected questions may include a query regarding the user 12's comfort level, including at least one of dryness and irritation. The one or more preselected questions may include a query regarding when the user 12 last cleaned the vision correction device 14. The controller C may be configured to obtain the self-reported data from the electronic diary module 62 and generate an analysis of the self-reported data via one or more machine learning models 40. The user 12 may compare the self-reported data via the electronic diary module 62 with group data (second database 48) generated by other users of the same type of vision correction device 14.

[0019] The controller C may be configured to assist the user 12 based in part on the analysis. Assisting the user 12 based in part on the analysis may include at least one of the following: providing guidance regarding care of the vision correction device 14 (e.g., cleaning procedures) and / or eye care of the user 12, comparing comfort scores and other metrics of the user 12 over a specific period of time (e.g., after one week of wearing the vision correction device 14) to a group of users of the same product, suggesting a follow-up visit with the eye care provider 16, and suggesting an alternative vision correction product.

[0020] 1 , a query module 64 in the user device 50 may be configured to receive at least one question entered by the user 12. The controller C may be configured to receive the at least one question from the query module 64 and construct an answer based in part on one or more machine learning models 40. The answer may be written via the query module 64 for consumption by the user 12. The personalized assistance system 10 may be configured to be “adaptive” and may be updated periodically after collection of additional data. In other words, the machine learning model 40 may be configured to be an “adaptive machine learning” algorithm that is not static but improves after collection of additional user data.

[0021] 1 , provider devices 70 are operable by an eye care provider 16 associated with a user. The provider devices 70 are configured to communicate with a remote computing unit 30 via a second network 72. The provider devices 70 include respective processors 74 and respective memories 76. Similar to the user devices 50, the provider devices 70 may execute a second application 78 (embodying a plurality of modules 80) that executes in conjunction with the remote computing unit 30. The plurality of modules 80 may include a patient database 82 (organized by type of vision correction device 14), a user progress tracking module 84 configured to track the progress of the user 12 and other users associated with the eye care provider 16, a second message module 86, and a comparison tracking module 88 configured to provide trend and comparative analysis.

[0022] Remote computing unit 30 may be configured to provide two-way communication between eye care provider 16 and user 12 via first message module 66 and second message module 86. Referring to FIG. 1 , personalization assistance system 10 may include an intermediary module 90 for routing messages from user 12 to eye care provider 16 and vice versa. Intermediary module 90 may be configured in different ways. While FIG. 1 illustrates an exemplary implementation of personalization assistance system 10, it should be understood that other implementations may be made.

[0023] Referring to FIG. 1 , the first network 52 and the second network 72 may be wireless or may include physical components, and may be short-range or long-range networks. For example, the first network 52 and the second network 72 may be implemented in the form of a local area network. Local area networks include, but are not limited to, Controller Area Network (CAN), Controller Area Network with Flexible Data Rate (CAN-FD), Ethernet, Bluetooth, Wi-Fi, and other data connection topologies. The local area network may also be a Bluetooth connection, which is defined as a short-range wireless technology (or radio technology) intended to simplify communication between Internet devices and between devices and the Internet. Bluetooth is an open wireless technology standard for transmitting data over short distances between fixed and mobile electronic devices and for creating personal networks operating in the 2.4 GHz band. The local area network may be a wireless local area network (LAN) that connects multiple devices using a wireless distribution method, a wireless metropolitan area network (MAN) that connects several wireless LANs, or a wireless wide area network (WAN) that covers a large geographic area such as a nearby city or town. Other types of connections may also be employed.

[0024] The machine learning model 40 of FIG. 1 may include a neural network algorithm. While a neural network is illustrated herein, it should be understood that the machine learning model 40 may be based on different types or algorithms, including, but not limited to, neural networks, support vector regression, linear or logistic regression, k-means clustering, random forests, and others. As will be appreciated by those skilled in the art, neural networks are designed to recognize patterns from real-world data (e.g., images, sounds, text, time series, etc.), translate or convert them into numerical form, and incorporate them into vectors or matrices. The neural network may employ a deep learning map to connect an input vector x to an output vector y. In other words, each of the multiple machine learning models 40 learns an activation function f such that f(x) corresponds to y. Through a training process, the neural network can associate an appropriate activation function f(x) to convert the input vector x to the output vector y. In the case of a linear regression model, two parameters, a bias and a slope, are learned. The bias is the level of the output vector y when the input vector x is set to 0, and the slope is the predicted rate of increase or decrease of the output vector y for each unit increase in the input vector x. Once multiple machine learning models 40 are each trained, an estimate of the output vector y can be calculated for a given new value of the input vector x.

[0025] Referring to Figure 3, an exemplary network 200 for the machine learning model 40 of Figure 1 is shown. The exemplary network 200 is a feedforward artificial neural network with at least three layers of nodes N, including an input layer 202, one or more hidden layers, such as a first hidden layer 204 and a second hidden layer 206, and an output layer 208. Each of these layers includes nodes N configured to perform an affine transformation of a linear sum of inputs. The nodes N are neurons characterized by respective biases and respective weighted links. The nodes N of the input layer 202 receive, normalize, and forward the inputs to the nodes N of the first hidden layer 204. Each node N in a subsequent layer computes a linear combination of the outputs of the previous layer. A network with three layers forms an activation function f(x) = f(3)(f(2)(f(1)(x))). The activation function f may be linear for each node N in the output layer 210. The activation function f may be sigmoid for the first hidden layer 204 and the second hidden layer 206. A linear combination of sigmoids is used to approximate a continuous function that characterizes the output vector y.

[0026] The exemplary network 200 may generate multiple outputs, such as a first output factor 212 and a second output factor 214, and the controller C is configured to use a weighted average of the multiple outputs to arrive at the final output 210. For example, if the inputs to the input layer 202 are various factors (e.g., comfort level, visual acuity score) related to a particular type of vision correction device 14, the first output factor 212 and the second output factor 214 may be an objective satisfaction score and a subjective satisfaction score, respectively, for that particular type of vision correction device 14. Other machine learning models available to one skilled in the art may also be employed.

[0027] Referring now to Figure 2, there is shown a flow diagram of a method 100 executable by the controller C of Figure 1. Although the start and end of the method 100 are indicated in Figure 2 by "S" and "E", respectively, the method does not have to be applied in the particular order listed herein. It will be appreciated that some blocks may be omitted. The memory M may store a controller-executable instruction set, and the processor P may execute the controller-executable instruction set stored in the memory M.

[0028] 2, the controller C is configured to determine whether the electronic diary module 62 has been triggered to determine whether the user 12 has answered any of the preselected questions. If so, the method 100 proceeds to block 115, where the controller C is configured to record each answer entered by the user 12. If not, the method 100 loops back to start S.

[0029] 2, the controller C is configured to determine whether the query module 64 has been triggered and the user 12 has asked a question. If so, then, per block 125, the controller C is configured to construct an answer based in part on one or more machine learning models 40 and write the answer via the query module 64 for consumption by the user 12. For example, the controller C may extract keywords from the question and input them as input to the input layer 202. The answer may be extracted based on the output layer 208 of the example network 200. If not, the method 100 loops back to start S.

[0030] 2, the controller C is configured to determine whether one or more enabling conditions are met. The enabling conditions may include irritation and / or discomfort factors exceeding a predetermined threshold. If so, per block 135, the controller C may perform one or more actions, which may include interfacing with the eye care provider 16 via a message sent to the second message module 86. The actions may include posting a reminder, such as "Don't forget to clean your contact lenses daily," via the suggestion module 68 in the user device 50.

[0031] In summary, the personalization assistance system 10 employs a multi-pronged approach that utilizes one or more machine learning models 40. The personalization assistance system 10 may be configured to recognize non-compliance with suggested guidelines, recognize when a follow-up visit to the eye care provider 16 is warranted, or suggest alternative contact lenses. The personalization assistance system 10 provides effective two-way communication between the user 12 and the eye care provider 16.

[0032] The controller C of FIG. 1 includes computer-readable media (also referred to as processor-readable media), including non-transitory (e.g., tangible) media that participate in providing data (e.g., instructions) that may be read by a computer (e.g., by a computer processor). Such media may take many forms, including, but not limited to, non-volatile and volatile media. Non-volatile media include, for example, optical or magnetic disks and other persistent memory. Volatile media include, for example, dynamic random access memory (DRAM), which may constitute primary storage. Such instructions may be transmitted over one or more transmission media, including coaxial cables, copper wire, and fiber optics, including the wires that comprise a system bus coupled to the computer's processor. Some forms of computer-readable media include, for example, floppy disks, flexible disks, hard disks, magnetic tape, or other magnetic media; CD-ROMs, DVDs, or other optical media; punch cards, paper tape, or other physical media with patterns of holes; RAM, PROMs, EPROMs, Flash EEPROMs, or other memory chips or cartridges; or other computer-readable media.

[0033] The lookup tables, databases, data repositories, or other data stores described herein may include various types of mechanisms for storing, accessing, and retrieving various types of data, including a hierarchical database, a set of files in a file system, a proprietary application database, a relational database management system (RDBMS), etc. Each such data store may be contained within a computing device employing a computer operating system such as those described above, or may be accessed over a network in one or more of a variety of ways. The file system may be accessible from the computer operating system and may include files stored in various formats. The RDBMS may employ a Structured Query Language (SQL) in addition to a language for creating, saving, editing, and executing stored procedures, such as the PL / SQL language described above.

[0034] While the detailed description and drawings or figures support and explain the present disclosure, the scope of the present disclosure is defined solely by the claims. While the best mode and some alternative embodiments for carrying out the claimed disclosure have been described in detail, various alternative designs and embodiments exist for carrying out the disclosure defined in the appended claims. Furthermore, the features of the embodiments shown in the drawings or described herein should not necessarily be understood as independent embodiments. Rather, each of the characteristics described in one of the example embodiments can be combined with one or more other desirable characteristics from other embodiments, resulting in other embodiments not described in words or with reference to the drawings. Accordingly, such other embodiments are encompassed within the scope of the appended claims. According to aspect (1), there is provided a personalization support system for a user of a vision correction device, comprising: a remote computing unit including a controller having a processor and a tangible, non-transitory memory having instructions stored thereon, the controller configured to selectively execute one or more machine learning models; a user device operable by the user and configured to communicate with the remote computing unit; the user device includes an electronic diary module configured to prompt the user to answer one or more preselected questions at specified intervals; the electronic diary module is configured to store each response entered by the user in response to the one or more pre-selected questions as self-reported data; The controller obtaining the self-report data from the electronic diary module; generating an analysis of the self-reported data via the one or more machine learning models; configured to assist the user based in part on the analysis. It is a personalization support system. According to aspect (2), the remote computing unit includes a first cloud unit and a central server, and the controller is incorporated in at least one of the first cloud unit and the central server. According to aspect (3), the user device includes a query module configured to receive at least one question generated by the user; The controller receiving the at least one question from the query module; constructing an answer based in part on the one or more machine learning models; The answer is configured to be posted for consumption by the user via the query module. According to aspect (4), the method further comprises: a provider device configured to communicate with the remote computing unit and operable by an eye care provider associated with the user; the user device and the provider device each include a respective message module; The remote computing unit is configured to provide two-way communication between the eye care provider and the user via the respective message modules. According to aspect (5), the remote computing unit includes a first database that stores information about each of the users, including the type of the vision correction device; the remote computing unit includes a second database storing group data relating to a group of users, the group data including at least in part self-reported data of each of the group of users; The user device includes a comparison tracking module configured to allow the user to compare the self-reported data with the group data. According to aspect (6), assisting the user based in part on the analysis includes: providing instruction regarding caring for at least one of the vision correction device and the user's eye; Suggesting a follow-up visit to the eye care provider; and and suggesting an alternative vision correction product. According to aspect (7), the vision correction device is a contact lens. According to aspect (8), the contact lens is a multifocal lens having a first zone for distance vision, a second zone for near vision, and a third zone for intermediate vision. According to aspect (9), the one or more preselected questions include: The method includes querying the user's comfort level, including at least one of dryness and irritation. According to aspect (10), the one or more preselected questions include: This includes a query as to when the user last cleaned the vision correction device. According to aspect (11), there is provided a personalization assistance system for selectively interfacing a user of a vision correction device with an eye care provider associated with the user, the system comprising: a remote computing unit including a controller having a processor and a tangible, non-transitory memory having instructions stored thereon, the controller configured to selectively execute one or more machine learning models; a user device operable by the user and configured to communicate with the remote computing unit; a provider device configured to communicate with the remote computing unit and operable by the eye care provider; the user device includes an electronic diary module configured to prompt the user to answer one or more preselected questions at specified intervals; the electronic diary module is configured to store each response entered by the user in response to the one or more pre-selected questions as self-reported data; the controller is configured to obtain the self-reported data from the electronic diary module and generate an analysis of the self-reported data via the one or more machine learning models; the user device and the provider device include respective message modules, and the remote computing unit is configured to provide two-way communication between the eye care provider and the user based in part on the analysis via the respective message modules. It is a personalization support system. According to aspect (12), the remote computing unit includes a first cloud unit and a central server, and the controller is incorporated in at least one of the first cloud unit and the central server. According to aspect (13), the user device includes a query module configured to receive at least one question generated by the user; The controller receiving the at least one question from the query module; constructing an answer based in part on the one or more machine learning models; The answer is configured to be posted for consumption by the user via the query module. According to aspect (14), the remote computing unit includes a first database that stores information about each of the users, including the type of the vision correction device; the remote computing unit includes a second database storing group data relating to a group of users, the group data including at least in part self-reported data of each of the group of users; The user device includes a comparison tracking module configured to allow the user to compare the self-reported data with the group data. According to aspect (15), assisting the user based in part on the analysis includes: providing instruction regarding caring for at least one of the vision correction device and the user's eye; Suggesting a follow-up visit to the eye care provider; and and suggesting an alternative vision correction product. According to an aspect (16), the vision correction device is a contact lens. According to aspect (17), the contact lens is a multifocal lens having a first zone for distance vision, a second zone for near vision, and a third zone for intermediate vision. According to aspect (18), the one or more preselected questions include: The method includes querying the user's comfort level, including at least one of dryness and irritation. According to aspect (19), the one or more preselected questions include: This includes a query as to when the user last cleaned the vision correction device. According to aspect (20), there is provided a method of operating a personalized assistance system for a user of a vision correction device, the method comprising: a remote computing unit including a controller having a processor and a tangible non-transitory memory; configuring the controller to selectively execute one or more machine learning models; configuring a user device operable by the user to communicate with the remote computing unit; configuring the user device with an electronic diary module configured to prompt the user to answer one or more preselected questions at specified intervals; storing, via the electronic diary module, each response entered by the user in response to the one or more pre-selected questions as self-reported data; obtaining the self-reported data from the electronic diary module via the controller and generating an analysis of the self-reported data via the one or more machine learning models; and providing assistance to the user via the controller based in part on the analysis. It is a method.

Claims

1. A personalization support system for a user of a vision correction device, comprising: a remote computing unit including a controller having a processor and a tangible, non-transitory memory having instructions stored thereon, the controller configured to selectively execute one or more machine learning models; a user device operable by the user and configured to communicate with the remote computing unit; the user device includes an electronic diary module configured to prompt the user to answer one or more preselected questions at specified intervals; the electronic diary module is configured to store each response entered by the user in response to the one or more pre-selected questions as self-reported data; The controller obtaining the self-report data from the electronic diary module; generating an analysis of the self-reported data via the one or more machine learning models; configured to assist the user based in part on the analysis; Assisting the user based in part on the analysis includes: providing instruction regarding caring for at least one of the vision correction device and the user's eye; Suggesting a follow-up visit to the eye care provider; and and suggesting an alternative vision correction product. Personalization support system.

2. The remote computing units include a first cloud unit and a central server, and the controller is embedded in at least one of the first cloud unit and the central server; The personalization support system according to claim 1 .

3. the user device includes a query module configured to receive at least one question generated by the user; The controller receiving the at least one question from the query module; constructing an answer based in part on the one or more machine learning models; configured to write the answer for consumption by the user via the query module; The personalization support system according to claim 1 .

4. a provider device configured to communicate with the remote computing unit and operable by an eye care provider associated with the user; the user device and the provider device each include a respective message module; the remote computing unit is configured to provide two-way communication between the eye care provider and the user via the respective message modules. The personalization support system according to claim 1 .

5. the remote computing unit includes a first database that stores respective information about the users, including the type of the vision correction device; the remote computing unit includes a second database storing group data relating to a group of users, the group data including at least in part self-reported data of each of the group of users; the user device includes a comparison tracking module configured to enable the user to compare the self-reported data to the group data; The personalization support system according to claim 1 .

6. The vision correction device is a contact lens. The personalization support system according to claim 1 .

7. the contact lens is a multifocal lens having a first zone for distance vision, a second zone for near vision, and a third zone for intermediate vision; The personalization support system according to claim 6.

8. The one or more preselected questions include: including a query regarding the user's comfort level, the comfort level including at least one of dryness and irritation; The personalization support system according to claim 7.

9. The one or more preselected questions include: including querying when the user last cleaned the vision correction device; The personalization support system according to claim 7.

10. 1. A personalization assistance system for selectively interfacing a user of a vision correction device with an eye care provider associated with the user, comprising: a remote computing unit including a controller having a processor and a tangible, non-transitory memory having instructions stored thereon, the controller configured to selectively execute one or more machine learning models; a user device operable by the user and configured to communicate with the remote computing unit; a provider device configured to communicate with the remote computing unit and operable by the eye care provider; the user device includes an electronic diary module configured to prompt the user to answer one or more preselected questions at specified intervals; the electronic diary module is configured to store each response entered by the user in response to the one or more pre-selected questions as self-reported data; the controller is configured to obtain the self-reported data from the electronic diary module and generate an analysis of the self-reported data via the one or more machine learning models; the user device and the provider device include respective message modules, and the remote computing unit is configured to provide two-way communication between the eye care provider and the user based in part on the analysis via the respective message modules; Assisting the user based in part on the analysis includes: providing instruction regarding caring for at least one of the vision correction device and the user's eye; suggesting a follow-up visit to the eye care provider; and and suggesting an alternative vision correction product. Personalization support system.

11. The remote computing units include a first cloud unit and a central server, and the controller is embedded in at least one of the first cloud unit and the central server; The personalization support system according to claim 10.

12. the user device includes a query module configured to receive at least one question generated by the user; The controller receiving the at least one question from the query module; constructing an answer based in part on the one or more machine learning models; configured to write the answer for consumption by the user via the query module; The personalization support system according to claim 10.

13. the remote computing unit includes a first database that stores respective information about the users, including the type of the vision correction device; the remote computing unit includes a second database storing group data relating to a group of users, the group data including at least in part self-reported data of each of the group of users; the user device includes a comparison tracking module configured to enable the user to compare the self-reported data to the group data; The personalization support system according to claim 10.

14. The vision correction device is a contact lens. The personalization support system according to claim 10.

15. the contact lens is a multifocal lens having a first zone for distance vision, a second zone for near vision, and a third zone for intermediate vision; The personalization support system according to claim 14.

16. The one or more preselected questions include: including a query regarding the user's comfort level, the comfort level including at least one of dryness and irritation; The personalization support system according to claim 14.

17. The one or more preselected questions include: including querying when the user last cleaned the vision correction device; The personalization support system according to claim 14.

18. 1. A method of operating a personalized assistance system for a user of a vision correction device, the method comprising: a remote computing unit comprising a controller having a processor and a tangible non-transitory memory, the method comprising: configuring the controller to selectively execute one or more machine learning models; configuring a user device operable by the user to communicate with the remote computing unit; configuring the user device with an electronic diary module configured to prompt the user to answer one or more preselected questions at specified intervals; storing, via the electronic diary module, each response entered by the user in response to the one or more pre-selected questions as self-reported data; obtaining the self-reported data from the electronic diary module via the controller and generating an analysis of the self-reported data via the one or more machine learning models; and providing assistance to the user via the controller based in part on the analysis; Assisting the user based in part on the analysis includes: providing instruction regarding caring for at least one of the vision correction device and the user's eye; Suggesting a follow-up visit to the eye care provider; and and suggesting an alternative vision correction product. method.

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