Systems and methods for contact lens selection

Abstract

Disclosed herein are methods for selecting a contact lens. An example method comprises capturing, using one or more sensors, an image of at least a portion of a face of a user. The example method comprises determining, based at least on the image, one or more user features indicative of a biometric or physiologic characteristic of the user. The example method comprises determining, based at least on the one or more user features and using an optimization model, one or more target lens parameters. The example method comprises outputting, based on at least the one or more target contact lens parameters, an indication of a target contact lens.

Description

SYSTEMS AND METHODS FOR CONTACT LENS SELECTIONCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Applications63 / 516,870, filed on August 1, 2023. The contents of these applications are incorporated by reference herein.BACKGROUND

[0001] Contact lenses have long been an alternative to glasses for people in need of visual correction. Advances in contact lens technology have made contact lenses an appropriate option for people with a variety of visual impairments. However, the rate at which new contact lens wearers drop out of contact lenses within their first year of wear remains high.SUMMARY

[0002] The comfort of the contact lens on the eye is an important consideration in enabling new wearers to adapt successfully, and comfort is dependent in part on how well the lens is fit to the eye. Achieving a proper lens fit requires evaluation of various parameters that are specific to a particular user’s eyes. Improvements and tools that aid in the process of ensuring lenses fit properly could enable more new wearers to adapt successfully to lenses and avoid contact lens intolerance.

[0003] Disclosed herein are methods and systems for selecting a contact lens. An example method may comprise producing, using one or more sensors, an image of at least a portion of a face of a user. The method may comprise determining, based at least on the image, one or more user features indicative of a biometric or physiologic characteristic of the user. The method may comprise determining, based at least on the one or more user features, one or more target contact lens parameters. Determining the one or more target contact lens parameters may be based on an optimization model, which may include but is not limited to, a decision algorithm, software routine, machine learning model, or other processing tool. The method may comprise outputting, based on at least the one or more target contact lens parameters, an indication of a target contact lens.

[0004] The one or more sensors may comprise one or more of an optical sensor, a LIDAR (Light Detection and Ranging) sensor, or combinations of both. The image may comprise one or more of an optical image, a LIDAR image, or combinations of both. The image may include any representation of an eye of the user, a portion of the eye, or particularinformation about the eye of a user. The one or more user features may relate to any aspect of a user’s eye, such as the eye aperture, pupil, cornea, iris, eye lids, tear film, or any combination of these. The one or more target lens parameters may comprise a lens type, lens diameter, base curve measurement, cylinder value, axis, add power, or any combination of these. The optimization model may be based on one or more target comfort metrics, target insertion or removal metrics, target visual acuities, or any combination of these. Target comfort metrics may include, for example, whether the eyes feel dry or strained. Target insertion or removal metrics may include, for example, how long it took the user to insert or remove the lens or how many attempts were required.

[0005] The method may comprise determining one or more available lenses. The outputting an indication of a target contact lens may comprise selecting at least one of the available lenses and outputting an indication of the at least one of the available lenses.

[0006] A system may be configured to implement the method. The system may comprise: one or more sensors and a processor configured to analyze an image and determine one or more user features indicative of a biometric or physiologic characteristic of the user.

[0007] Another example method may comprise capturing, using one or more sensors, an image of at least a portion of a face of a user. The method may comprise determining, based at least on the image, a biometric or physiologic characteristic of the user. The second example method may comprise outputting, based at least on the biometric or physiologic characteristic of the user and using an optimization model, an indication of a target contact lens.

[0008] The one or more sensors may comprise one or more of an optical sensor, a LIDAR (Light Detection and Ranging) sensor, or combinations of both. The image may comprise one or more of an optical image, a LIDAR image, or combinations of both. The biometric or physiologic characteristic may relate to a user’s eye aperture, pupil, cornea, iris, eye lids, tear film, or combinations of these. The optimization model may be based on a target comfort metric. The optimization model may be based on a target insertion or removal metric. The optimization model may be based on a target visual acuity.

[0009] The second example method may comprise determining available lenses. The outputting an indication of a target contact lens may comprise selecting at least one of the available lenses and outputting an indication of at least one of the available lenses.

[0010] A system may be configured to implement the second example method. The system may comprise : one or more sensors and a processor configured to analyze an image and determine a biometric or physiologic characteristic of the user.

[0011] A third example method may comprise capturing, using one or more sensors, an image of at least a portion of a face of a user. The third example method may comprise determining, based at least on the image, one or more user features indicative of a biometric or physiologic characteristic of the user. The third example method may comprise determining, based at least on the one or more user features, one or more target lens parameters. The third example method may comprise outputting, based on at least the one or more target contact lens parameters, an indication of a target contact lens.

[0012] A system may be configured to implement the third example method. The system may comprise: one or more sensors and a processor configured to analyze an image, determine a plurality of available lenses, and output an indication of a target contact lens. The output of an indication of a target contact lens may comprise selecting at least one of the plurality of available lenses and outputting an indication of the at least one of the plurality of available lenses.BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The following drawings show generally, by way of example, but not by way of limitation, various examples discussed in the present disclosure. In the drawings:

[0014] FIG. 1 depicts an exemplary flowchart illustrating an exemplary method for selecting a contact lens.

[0015] FIG. 2 depicts an exemplary flowchart illustrating an exemplary method for selecting a contact lens.

[0016] FIG. 3 depicts an exemplary flowchart illustrating an exemplary method for selecting a contact lens.DETAILED DESCRIPTION

[0017] The present disclosure relates to improvements in contact lens selection. It would be beneficial to provide a system that would guide the initial contact lens fitting process, reduce the number of new contact lens wearers who may struggle with contact lenses during the initial fitting period, and therefore prevent unnecessary contact lens dropout.

[0018] The systems and methods described herein may comprise a consumer facing- mobile application executing on a computing device, such as a smart phone, tablet, or other device or system. The application may cause or otherwise initialize camera systems and / or sensors associated with the computing device to scan a user’s face to create one or more scanned images of the user’s face or eye. Other sensors associated with the computing device(e.g., smart phone) may be present and assist in execution of the methods, such as motion sensors, position sensors, proximity sensors, or voice recognition sensors.

[0019] The scanned images of the face may be processed by a processor associated with the computing device to identify and characterize one or more eye anatomies associated with the face based on specific measurements. The specific measurement may include, for example, eye aperture and comeal measurements. User eye anatomy data may be analyzed against different lens offerings to find a best match between the eye anatomy and lens parameters that would optimize eye performance. As an example, associations between aggregate anatomy data and user outcomes may be determined and may be referenced based on real-time analysis of user anatomy data to make a recommendation for the specific user. Methods may incorporate machine learning models, software programs, database routines, and the like to provide a recommendation engine to propose a recommended contact lens of a plurality of available lenses. The recommendation engine may be optimized for various conditions and outcomes such as comfort, ease of removal or insertion, eye performance, and / or other metrics / factors.

[0020] The systems and methods described herein may comprise an example system. The example system may comprise a contact lens fitting set encompassing lenses with two or more diameter sizes across at least one base curve. The example system may comprise an electronic device comprising a central processing unit and array of sensors, (e.g., such as optical sensors and LIDAR sensors, etc.) configured to generate data to define an anatomy of an eye, based on measurements (e.g., a user’s eye aperture, a user’s corneal measurement, etc.). The example system may comprise a consumer-facing application executing on a computing device, such as a smart phone. The application may use libraries (e.g., iOS Augmented Reality kit (ARkit), etc.).

[0021] An example system may comprise a central management system configured to access the eye anatomy data. The central management system may be configured to determine an optimal lens size based upon available lens parameters. An optimal size may be dependent on factors such as a desired outcome, balancing needs based on unique anatomy from person to person, easier insertion and / or removal, maximizing comfort, etc. The example system may comprise a client device, such as a contact fitter’s computing device, configured to receive data from the central management system. The data received from the central management system may direct the fitter of the specified lens size.

[0022] Described herein are systems and methods for lens selection. An example method may comprise identification of one or more eye features. The example method maycomprise measurement of the one or more identified eye features. The example method may comprise reception of selected outcomes. Selected outcomes may comprise preferences, such as how much weight factors such as corrective ability, comfort, fit, ease of insertion, ease of removal, etc., should be given in selecting a contact lens. The example method may comprise a determination of one or more optimal lens parameters based on the measured one or more features and the selected outcomes. The example method may comprise selection of a best available lens (i.e., a lens that includes the highest number of matches or correlations with the measured features) based on the one or more determined lens parameters.

[0023] Turning to FIG. 1, an example method 100 for selecting a contact lens is illustrated. At 102, an image of at least a portion of a face of a user may be captured using one or more sensors. For example, a computing device may use one or more sensors to capture an image of at least a portion of a face of a user (e.g., an eye of a user). The one or more sensors may comprise an optical sensor, a LIDAR (Light Detection and Ranging) sensor, or both. An optical sensor is an electronic device that converts light into an electronic signal. Examples of optical sensors include photoresistors, photodiodes, phototransistors, diode arrays, PSDs (Position Sensitive Diodes), and cameras containing CCD (Charged-Coupled Devices) or CMOS (Complementary Metal Oxide Semiconductor) sensors. LIDAR is a sensing method that uses light in the form of a pulsed laser to measure variable distances. A laser is targeted at an object and measurements are made of the time it takes for the reflected light to return to a receiver. The measurements can be used to generate precise three-dimensional information about the shape and dimensions of the object.

[0024] The image may comprise an optical image, a LIDAR image, or both. The image may include a representation of an eye of the user. The computing device may be a handheld device such as a cell phone or table. For instance, the device could be a smart device leveraging an app library (e.g., iOS ARKit). The image may be stored on a storage are (or a separate device) such as a memory on the computing device or at a remote location (either another device on a local network and / or in the cloud so as to be accessible via one or more servers). Alternately or additionally, the image may be stored in a secured storage location that may be configured for the storage of personally identifiable and / or other patient data, e.g., a system configured to confirm with regulatory or other standards for the storage of sensitive data.

[0025] At 104, one or more user features indicative of a biometric or physiologic characteristic of the user may be determined based at least on the image. For example, a computing device may determine one or more user features indicative of a biometric orphysiologic characteristic of the user based at least on the image . The one or more user features may related to a user’s eye aperture, pupil, cornea, iris, eye lids, tear fdm, or any combination of these

[0026] At 106, one or more target lens parameters may be determined based at least on the one or more user features and using an optimization model. The optimization model may include, but is not limited to, a decision algorithm, software routine, machine learning model, or other processing tool. For example, a computing device may determine one or more target lens parameters based at least on the one or more user features and using an optimization model. The one or more target lens parameters may comprise a lens type, lens diameter, base curve measurement, cylinder value, axis, add power, or any combination of these. The optimization model may be based on a target comfort metric. The optimization model may be based on a target insertion or removal metric. The optimization model may be based on a target visual acuity.

[0027] At 108, an indication of a target contact lens may be outputted based on at least the one or more target contact lens parameters. For example, a computing device may output an indication of a target contact lens based on at least the one or more target contact lens parameters.

[0028] A plurality of available lenses may be determined. For example, a computing device may determine a plurality of available lenses. The outputting an indication of a target contact lens may comprise selecting at least one of the plurality of available lenses and outputting an indication of the at least one of the plurality of available lenses.

[0029] A system may be configured to implement the method 100. The system may comprise one or more sensors and a processor configured to analyze an image and determine one or more user features indicative of a biometric or physiologic characteristic of the user.

[0030] A contact wearer (or another person or device on behalf of the contact wearer) may use an application executing on a smart phone to take (or instruct a camera application to take) an image (whether photographic, LIDAR or other imaging standard) that includes the contact wearer’s eyes (or in some cases, a single eye). The application may implement one or more programmatic rules or algorithms to detect the size and shape of the contact wearer’s eye lid(s). The application may use the detected eye lid sizes and shapes and an optimization model to determine an appropriate contact lens type and size for the contact wearer. The application may use the determined contact lens type and size to determine a contact lens recommendation for the contact wearer. The application may output the determined contact lens recommendation to the contact wearer.

[0031] Turning to FIG. 2, an example method 200 for selecting a contact lens is illustrated. At 202, an image of at least a portion of a face of a user may be captured using one or more sensors. For example, a computing device may use one or more sensors to capture an image of at least a portion of a face of a user. The one or more sensors may comprise an optical sensor, a LIDAR (Light Detection and Ranging) sensor, or both. The image may comprise an optical image, a LIDAR image, or both.

[0032] At 204, a biometric or physiologic characteristic of the user may be determined based at least on the image. For example, a computing device may determine a biometric or physiologic characteristic of the user based at least on the image. The biometric or physiologic characteristic may comprise a user’s eye aperture, pupil, cornea, iris, eye lids, tear film, or any combination of these.

[0033] At 206, an indication of a target contact lens may be outputted based at least on the biometric or physiologic characteristic of the user and using an optimization model. For example, a computing device may output an indication of a target contact lens based at least on the physiologic characteristic of the user and using an optimization model. The optimization model may be based on a target comfort metric. The optimization model may be based on a target insertion or removal metric. The optimization model may be based on a target visual acuity.

[0034] A plurality of available lenses may be determined. For example a computing device may determine a plurality of available lenses. The outputting an indication of a target contact lens may comprise selecting at least one of the plurality of available lenses and outputting an indication of the at least one of the plurality of available lenses.

[0035] A system may be configured to implement the method 200. The system may comprise: the one or more sensors and a processor configured to analyze the image and determine the physiologic characteristic of the user.

[0036] A contact wearer may use an application executing on a smart phone to capture an image of their face, including one or more of their eyes. The application may detect the size and shape of the contact wearer’s eye lids. The application may use the detected eye lid sizes and shapes and an optimization model to determine an appropriate contact lens recommendation for the contact wearer. The application may output the determined contact lens recommendation to the contact wearer.

[0037] Turning to FIG. 3, an example method 300 for selecting a contact lens is illustrated. At 302, an image of at least a portion of a face of a user may be captured using one or more sensors. For example, a computing device may use one or more sensors to capture animage of at least a portion of a face of a user. The one or more sensors may comprise an optical sensor, a LIDAR (Light Detection and Ranging) sensor, or both. The image may comprise an optical image, a LIDAR image, or both. The image may include a representation of an eye of the user.

[0038] At 304, one or more user features indicative of a biometric or physiologic characteristic of the user may be determined based at least on the image. For example, a computing device may determine one or more user features indicative of a biometric or physiologic characteristic of the user based at least on the image . The one or more user features may comprise a user’s eye aperture, pupil, cornea, iris, eye lids, tear fdm, or any combination of these.

[0039] At 306, one or more target lens parameters may be determined based at least on the one or more user features. For example, a computing device may determine one or more target lens parameters based at least on the one or more user features. The one or more target lens parameters may comprise a lens type, lens diameter, base curve measurement, cylinder value, axis, add power, or any combination of these.

[0040] At 308, an indication of a target contact lens may be outputted based on at least the one or more target contact lens parameters. For example, a computing device may output an indication of a target contact lens based on at least the one or more target contact lens parameters.

[0041] A plurality of available lenses may be determined. For example a computing device may determine a plurality of available lenses. The outputting an indication of a target contact lens may comprise selecting at least one of the plurality of available lenses and outputting an indication of the at least one of the plurality of available lenses.

[0042] A system may be configured to implement the method 300. The system may comprise: the one or more sensors and a processor configured to analyze the image and determine the one or more user features indicative of a biometric or physiologic characteristic of the user.

[0043] A contact wearer may use an application executing on a smart phone to take a picture of their eyes. The application may detect the size and shape of the contact wearer’s eye lids. The application may use the detected eye lid sizes and shapes to determine an appropriate contact lens type and size for the contact wearer. The application may use the determined contact lens type and size to determine a contact lens recommendation for the contact wearer. The application may output the determined contact lens recommendation to the contact wearer.EXAMPLES

[0044] Example 1: A method for selecting a contact lens, the method comprising: capturing, using one or more sensors, an image of at least a portion of a face of a user; determining, based at least on the image, one or more user features indicative of a biometric or physiologic characteristic of the user; determining, based at least on the one or more user features and using an optimization model, one or more target lens parameters; and outputting, based on at least the one or more target contact lens parameters, an indication of a target contact lens.

[0045] Example 2: The method of example 1, wherein the one or more sensors comprises an optical sensor, a LIDAR (Light Detection and Ranging) sensor, or both.

[0046] Example 3 : The method of any of examples 1 -2, wherein the image comprises an optical image, a LIDAR image, or both.

[0047] Example 4: The method of any of examples 1-3, wherein the image includes a representation of an eye of the user.

[0048] Example 5 : The method of any of examples 1 -4, wherein the one or more user features comprises a user’s eye aperture, pupil, cornea, iris, eye lids, tear fdm, or any combination of these.

[0049] Example 6: The method of any of examples 1-5, wherein the one or more target lens parameters comprises a lens type, lens diameter, base curve measurement, cylinder value, axis, add power, or any combination of these.

[0050] Example 7: The method of any of examples 1-6, wherein the optimization model is based on a target comfort metric.

[0051] Example 8: The method of any of examples 1-7, wherein the optimization model is based on a target insertion or removal metric.

[0052] Example 9: The method of any of examples 1-8, wherein the optimization model is based on a target visual acuity.

[0053] Example 10: The method of any of examples 1-9, further comprising determining a plurality of available lenses, wherein the outputting an indication of a target contact lens comprises selecting at least one of the plurality of available lenses and outputting an indication of the at least one of the plurality of available lenses.

[0054] Example 11: A system configured to implement the method of any of examples 1-10, the system comprising: the one or more sensors and a processor configured to analyze the image and determine the one or more user features indicative of a biometric or physiologic characteristic of the user.

[0055] Example 12: A method for selecting a contact lens, the method comprising: capturing, using one or more sensors, an image of at least a portion of a face of a user; determining, based at least on the image, a biometric or physiologic characteristic of the user; and outputting, based at least on the biometric or physiologic characteristic of the user and using an optimization model, an indication of a target contact lens.

[0056] Example 13: The method of example 12, wherein the one or more sensors comprises an optical sensor, a LIDAR (Light Detection and Ranging) sensor, or both.

[0057] Example 14: The method of any of examples 12-13, wherein the image comprises an optical image, a LIDAR image, or both.

[0058] Example 15: The method of any one of examples 12-14, wherein the biometric or physiologic characteristic comprises a user’s eye aperture, pupil, cornea, iris, eye lids, tear fdm, or any combination of these.

[0059] Example 16: The method of any one of examples 12-15, wherein the optimization model is based on a target comfort metric.

[0060] Example 17: The method of any one of examples 12-16, wherein the optimization model is based on a target insertion or removal metric.

[0061] Example 18: The method of any one of examples 12-17, wherein the optimization model is based on a target visual acuity.

[0062] Example 19: The method of any one of examples 12-18, further comprising determining a plurality of available lenses, wherein the outputting an indication of a target contact lens comprises selecting at least one of the plurality of available lenses and outputting an indication of the at least one of the plurality of available lenses.

[0063] Example 20: A system configured to implement the method of examples 12- 19, the system comprising: the one or more sensors and a processor configured to analyze the image and determine the biometric or physiologic characteristic of the user.

[0064] Example 21: A method for selecting a contact lens, the method comprising: capturing, using one or more sensors, an image of at least a portion of a face of a user; determining, based at least on the image, one or more user features indicative of a biometric or physiologic characteristic of the user; determining, based at least on the one or more user features, one or more target lens parameters; and outputting, based on at least the one or more target contact lens parameters, an indication of a target contact lens.

[0065] Example 22: The method of example 21, wherein the one or more sensors comprises an optical sensor, a LIDAR (Light Detection and Ranging) sensor, or both.

[0066] Example 23: The method of any one of examples 21-22, wherein the image comprises an optical image, a LIDAR image, or both.

[0067] Example 24: The method of any of examples 21-23, wherein the image includes a representation of an eye of the user.

[0068] Example 25: The method of any one of examples 21-24, wherein the one or more user features comprises a user’s eye aperture, pupil, cornea, iris, eye lids, tear fdm, or any combination of these.

[0069] Example 26: The method of any one of examples 21-25, wherein the one or more target lens parameters comprises a lens type, lens diameter, base curve measurement, cylinder value, axis, add power, or any combination of these.

[0070] Example 27: The method of any one of examples 21-26, further comprising determining a plurality of available lenses, wherein the outputting an indication of a target contact lens comprises selecting at least one of the plurality of available lenses and outputting an indication of the at least one of the plurality of available lenses.

[0071] Example 28: A system configured to implement the method of any of examples 21-27, the system comprising: the one or more sensors and a processor configured to analyze the image and determine the one or more user features indicative of a biometric or physiologic characteristic of the user

[0072] Many operating systems, including Linux, UNIX®, OS / 2®, and Windows®, are capable of running many tasks at the same time and are called multitasking operating systems. Multi-tasking is the ability of an operating system to execute more than one executable at the same time. Each executable is running in its own address space, meaning that the executables have no way to share any of their memory. Thus, it is impossible for any program to damage the execution of any of the other programs running on the system. However, the programs have no way to exchange any information except through the operating system (or by reading files stored on the file system).

[0073] Multi-process computing is similar to multi-tasking computing, as the terms task and process are often used interchangeably, although some operating systems make a distinction between the two. The present invention may be or comprise a system, a method, and / or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device.

[0074] The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing.

[0075] A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (for example, light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0076] Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In thelater scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0077] In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.

[0078] Aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer readable program instructions. These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the fimctions / acts specified in the flowchart and / or block diagram block or blocks.

[0079] These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and / or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the fimction / act specified in the flowchart and / or block diagram block or blocks. The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the fimctions / acts specified in the flowchart and / or block diagram block or blocks. The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s).

[0080] In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustration, and combinations of blocks in the block diagrams and / or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or that carry out combinations of special purpose hardware and computer instructions. Although specific embodiments of the present invention have been described, it will be understood by those of skill in the art that there are other embodiments that are equivalent to the described embodiments. Accordingly, it is to be understood that the invention is not to be limited by the specific illustrated embodiments, but only by the scope of the appended claims.

[0081] From the above description, it can be seen that the present invention provides a system, computer program product, and method for the efficient execution of the described techniques. References in the claims to an element in the singular is not intended to mean “one and only” unless explicitly so stated, but rather “one or more.” All structural and functional equivalents to the elements of the above-described example embodiment that are currently known or later come to be known to those of ordinary skill in the art are intended to be encompassed by the present claims. No claim element herein is to be construed under the provisions of 35 U.S.C. section 112, sixth paragraph, unless the element is expressly recited using the phrase “means for” or “step for.”

[0082] While the foregoing written description of the invention enables one of ordinary skill to make and use what is considered presently to be the best mode thereof, those of ordinary skill will understand and appreciate the existence of alternatives, adaptations, variations, combinations, and equivalents of the specific embodiment, method, and examples herein. Those skilled in the art will appreciate that the within disclosures are example only and that various modifications may be made within the scope of the present invention. In addition, while a particular feature of the teachings may have been disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular function. Furthermore, to the extent that the terms “including”, “includes”, “having”, “has”, “with”, orvariants thereof are used in either the detailed description and the claims, such terms are intended to be inclusive in a manner similar to the term “comprising.”

[0083] Other embodiments of the teachings will be apparent to those skilled in the art from consideration of the specification and practice of the teachings disclosed herein. The invention should therefore not be limited by the described embodiment, method, and examples, but by all embodiments and methods within the scope and spirit of the invention. Accordingly, the present invention is not limited to the specific embodiments as illustrated herein, but is only limited by the following claims.

Claims

CLAIMSWhat is claimed is:

1. A method for selecting a contact lens, the method comprising: capturing, using one or more sensors, an image of at least a portion of a face of a user; determining, based at least on the image, one or more user features indicative of a biometric or physiologic characteristic of the user; determining, based at least on the one or more user features and using an optimization model, one or more target lens parameters; and outputting, based on at least the one or more target contact lens parameters, an indication of a target contact lens.

2. The method of claim 1, wherein the one or more sensors comprises an optical sensor, a LIDAR (Light Detection and Ranging) sensor, or both.

3. The method of claim 1, wherein the image comprises an optical image, a LIDAR image, or both.

4. The method of claim 1, wherein the image includes a representation of an eye of the user.

5. The method of claim 1, wherein the one or more user features relates to a user’s eye aperture, pupil, cornea, iris, eye lids, tear film, or any combination of these.

6. The method of claim 1, wherein the one or more target lens parameters comprise a lens type, lens diameter, base curve measurement, cylinder value, axis, add power, or any combination of these.

7. The method of claim 1, wherein the optimization model is based on a target comfort metric.

8. The method of claim 1, wherein the optimization model is based on a target insertion or removal metric.

9. The method of claim 1, wherein the optimization model is based on a target visual acuity.

10. The method of claim 1, further comprising determining a plurality of available lenses, wherein the outputting an indication of a target contact lens comprises selecting at least one of the plurality of available lenses and outputting an indication of the at least one of the plurality of available lenses.

11. A system configured to implement the method of claim 1, the system comprising: the one or more sensors and a processor configured to analyze the image and determine the one or more user features indicative of a biometric or physiologic characteristic of the user.

12. A method for selecting a contact lens, the method comprising: capturing, using one or more sensors, an image of at least a portion of a face of a user; determining, based at least on the image, a biometric or physiologic characteristic of the user; and outputting, based at least on the biometric or physiologic characteristic of the user and using an optimization model, an indication of a target contact lens.

13. The method of claim 12, wherein the one or more sensors comprises an optical sensor, a LIDAR (Light Detection and Ranging) sensor, or both.

14. The method of claim 12, wherein the image comprises an optical image, a LIDAR image, or both.

15. The method of claim 12, wherein the biometric or physiologic characteristic relates to a user’s eye aperture, pupil, cornea, iris, eye lids, tear film, or any combination of these.

16. The method of claim 12, wherein the optimization model is based on a target comfort metric.

17. The method of claim 12, wherein the optimization model is based on a target insertion or removal metric.

18. The method of claim 12, wherein the optimization model is based on a target visual acuity.

19. The method of claim 12, further comprising determining a plurality of available lenses, wherein the outputting an indication of a target contact lens comprises selecting at least one of the plurality of available lenses and outputting an indication of the at least one of the plurality of available lenses.

20. A system configured to implement the method of claim 12, the system comprising: the one or more sensors and a processor configured to analyze the image and determine the biometric or physiologic characteristic of the user.

21. A method for selecting a contact lens, the method comprising: capturing, using one or more sensors, an image of at least a portion of a face of a user; determining, based at least on the image, one or more user features indicative of a biometric or physiologic characteristic of the user; determining, based at least on the one or more user features, one or more target lens parameters; and outputting, based on at least the one or more target contact lens parameters, an indication of a target contact lens.

22. The method of claim 21, wherein the one or more sensors comprises an optical sensor, a LIDAR (Light Detection and Ranging) sensor, or both.

23. The method of claim 21, wherein the image comprises an optical image, a LIDAR image, or both.

24. The method of claim 21, wherein the image includes a representation of an eye of the user.

25. The method of claim 21, wherein the one or more user features relates to a user’s eye aperture, pupil, cornea, iris, eye lids, tear fdm, or any combination of these.

26. The method of claim 21, wherein the one or more target lens parameters comprises a lens type, lens diameter, base curve measurement, cylinder value, axis, add power, or any combination of these.

27. The method of claim 21, further comprising determining a plurality of available lenses, wherein the outputting an indication of a target contact lens comprises selecting at least one of the plurality of available lenses and outputting an indication of the at least one of the plurality of available lenses.

28. A system configured to implement the method of claim 21, the system comprising: the one or more sensors and a processor configured to analyze the image and determine the one or more user features indicative of a biometric or physiologic characteristic of the user.

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