Patent Publication Number: US-10783700-B2

Title: Progressive lens simulator with an axial power-distance simulator

Description:
FIELD OF INVENTION 
     This invention relates generally to methods and systems for simulating progressive lenses, more specifically to simulate progressive lenses with a guided lens design exploration system. 
     BACKGROUND 
     Prescriptions for eyeglasses, or spectacles, are generated today by devices and methods that were developed many decades ago, their foundations going back even farther in time. While these methods and technologies are mature, and served the patient population well, they did not benefit from the remarkable progress of optoelectronics and computational methods that revolutionized so many areas in telecommunications, consumer electronics, and interactive functionalities. Viewing optometry from the vantage point of modern opto-electronics and computer science, several areas of needs and opportunities can be identified. Some of the leading challenges and opportunities are listed and reviewed below. 
     (1) No trial before purchase: Patients who are asking for a progressive lens prescription are examined today only with single-power, non-progressive lenses, one for distance vision, and one for near vision. Therefore, the patients do not experience the prescribed progressive lenses in their totality before they purchase it. Since the patients do not “test-drive” the progressive lens prescriptions, they discover problems or inconveniences too late, only after the glasses have been provided to them. 
     (2) Only analog optometric devices are used: The single power lenses and other optical systems used by optometrists today have been developed long time ago. They are analog optical systems, and have not adapted and adopted the many advances of modern optoelectronic technology. This is to be contrasted by the very successful adaptation of optoelectronic technologies into other areas of ophthalmology, such as retinal imaging by Optical Coherence Tomography, aberrometric diagnostic devices, and optical diagnostic devices used in preparation for cataract surgery to determine the appropriate intraocular lenses. 
     (3) Only two distances tested: These analog lens systems test a patient&#39;s vision only at two distances, the near and the distance vision. In contrast, most patients have unique usage patterns, and often need the optimization of their spectacles for three or more distances depending on their individual habits. 
     (4) Eyes are only tested individually: Most of the diagnostic methods are applied for single eyes, blocking the other eye. Such approaches disregard the coordination between the eyes when they create the visual experiences, as well as the various effects of the vergence that are quite important for the full evaluation of the binocular visual acuity. 
     (5) Progressive lens prescriptions are under-defined: The design of progressive lenses is a complex process. Different companies have different proprietary optimization algorithms, with many parameters that use different search and optimization strategies. In contrast, the optometrists only determine 2-3 parameters about a patient&#39;s vision during an office visit. In a mathematical sense, providing only 2-3 parameters for the design of a progressive lens seriously under-defines the optimization algorithm of the lens design. When the design optimization algorithms have insufficient information, the algorithms can, and often do, stop at designs that are not truly optimal, and they are unable to identify the truly optimal design. 
     (6) Determining more parameters would increase treatment time per patient: Optometrists could run more tests to determine more parameters. However, doing so would extend the time spent with individual patients. This would have a negative effect on the economic model of the optometrists. 
     (7) Patients often need to return glasses for adjustments: Patients are unsatisfied with their progressive lenses in a statistically relevant fraction of the cases. Therefore, patients often return to the optometrist office asking for adjustments. It is not uncommon that a progressive lens has to be readjusted 3-4-5 times. The time and cost associated with these return visits seriously impacts the satisfaction of the patient, and undermines the economic model of the optometrist. 
     (8) Lens design verification groups are small: Lens design algorithms are typically optimized in interaction with a small test group, from less than a hundred to a couple hundred patients. Using only such small test groups for optimizing such a complex problem can lead to lens design algorithms that are not optimal. Subsequent complaints from the larger number of real patients yields feedback from a larger group, but this feedback is incomplete and uni-directional. 
     (9) Testing images do not reflect patient&#39;s actual visual needs: Eye testing uses standardized letters that are rarely reflective of a patient&#39;s actual needs. Testing just about never involves images that are relevant for the individual patient. 
     (10) Peripheral vision rarely tested: Optometrists rarely tests peripheral vision, whereas for some professions, peripheral vision might be a high value component of the overall visual acuity. 
     (11) Modern search algorithms are not yet utilized: Recent advances that greatly boost the efficiency of search algorithms over complex merit-landscapes, have not yet been adapted to the design of progressive lenses. 
     (12) Artificial Intelligence is not used: Recent advances in implementing Artificial Intelligence for system improvements also have not found their way yet into optometry. 
     At least the above dozen problems demonstrate that optometry could greatly benefit in a large number of ways from implementing modern optoelectronic technologies, in a patient-centric, customized manner, that also uses progress in modern computer science. 
     SUMMARY 
     To address the above described medical needs, some embodiments of the invention include a Progressive Lens Simulator, comprising: an Eye Tracker, for tracking an eye axis direction to determine a gaze distance, an Off-Axis Progressive Lens Simulator, for generating an Off-Axis progressive lens simulation (Off-Axis PLS); and an Axial Power-Distance Simulator, for simulating a progressive lens power in the eye axis direction, thereby creating a Comprehensive progressive lens simulation from the Off-Axis PLS. 
     Embodiments also include a method of operating a Progressive Lens Simulator, the method comprising: tracking an eye axis direction by an Eye Tracker to determine a gaze distance; generating an off-axis progressive lens simulation (Off-Axis PLS) by an Off-Axis Progressive Lens Simulator; and creating a Comprehensive progressive lens simulation from the Off-Axis PLS by simulating a progressive lens power in the eye axis direction by an Axial Power-Distance Simulator. 
     Embodiments further include a Progressive Lens Simulator, comprising: an Eye Tracker, for tracking an eye axis direction to determine a gaze distance; an Integrated Progressive Lens Simulator, for creating a Comprehensive Progressive Lens Simulation (PLS) by simulating a progressive lens power in the eye axis direction, in combination with generating an Off-Axis progressive lens simulation (Off-Axis PLS). 
     Embodiments also include a Head-mounted Progressive Lens Simulator, comprising: an Eye Tracker, for tracking an eye axis direction to determine a gaze distance; an Integrated Progressive Lens Simulator, for creating a Comprehensive Progressive Lens Simulation (PLS) by simulating a progressive lens power in the eye axis direction, in combination with generating an Off-Axis progressive lens simulation (Off-Axis PLS); wherein the Eye Tracker and the Integrated Progressive Lens Simulator are implemented in a head-mounted display. 
     Embodiments further include a Guided Lens Design Exploration System for Progressive Lens Simulator, comprising: a Progressive Lens Simulator, for generating a progressive lens simulation for a patient with a progressive lens design; a Feedback-Control Interface, for inputting at least one of control and feedback by the patient, in response to the progressive lens simulation; and a Progressive Lens Design processor, coupled to the Feedback-Control Interface, for receiving the at least one of control and feedback from the patient, and modifying the progressive lens design in response to the receiving, wherein the Progressive Lens Simulator is configured to generate a modified progressive lens simulation for the patient with the modified progressive lens design. 
     Embodiments also include a method of Progressive Lens Simulation, comprising: (a) activating a lens design with a Progressive Lens Design Processor; (b) generating an image by an Image Generator of a Progressive Lens Simulator; (c) generating a Comprehensive PLS, simulated from the generated image by the Progressive Lens Simulator, utilizing the lens design; (d) acquiring a visual feedback via a Feedback-Control Interface, responsive to the generating of the Comprehensive PLS with the lens design; (e) modifying the lens design by the Progressive Lens Design Processor in relation to the visual feedback; and (f) re-generating the Comprehensive PLS with the modified lens design by the Progressive Lens Simulator. 
     Embodiments further include a Deep Learning Method for an Artificial Intelligence Engine for a Progressive Lens Design Processor, comprising: activating a Visual Feedback-Design Factor Neural Network for a Progressive Lens Design Processor; receiving as input a visual feedback vector into the Visual Feedback-Design Factor Neural Network; outputting a design factor vector with the Visual Feedback-Design Factor Neural Network in response to the inputting; wherein coupling matrices of the Visual Feedback-Design Factor Neural Network of the Progressive Lens Design Processor were trained by performing a deep learning cycle. 
     Embodiments also include a Supervised Multi-station system of Progressive Lens Simulators, comprising: a set of Progressive Lens Simulators, individually including an Eye Tracker, for tracking an eye axis direction to determine a gaze distance; an Off-Axis Progressive Lens Simulator, for generating an Off-Axis progressive lens simulation (Off-Axis PLS); and an Axial Power-Distance Simulator, for simulating a progressive lens power in the eye axis direction, thereby creating a Comprehensive progressive lens simulation from the Off-Axis PLS; and a Central Supervision Station, in communication with the Progressive Lens Simulators, for providing supervision for an operation of the individual Progressive Lens Simulators. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates a Guided Lens Design Exploration System of Simulated Progressive Lenses (GPS). 
         FIG. 2  illustrates the Guided Lens Design Exploration System of Simulated Progressive Lenses (GPS) in some detail. 
         FIG. 3  illustrates a Multistage embodiment of a Progressive Lens Simulator. 
         FIGS. 4A-B  illustrate an Off-Axis Progressive Lens Simulator OPS. 
         FIG. 5  illustrates a Progressive Lens Simulator with a Vergence-Distance Simulator VDS and a Zoom-Distance Simulator ZDS. 
         FIGS. 6A-B  illustrate embodiments of an Axial Power-Distance Simulator ADS. 
         FIG. 7  illustrates a Multistage embodiment of a Progressive Lens Simulator. 
         FIG. 8  illustrates a Method to operate a Multistage embodiment of a Progressive Lens Simulator. 
         FIG. 9  illustrates an Integrated Progressive Lens Simulator. 
         FIG. 10  illustrates a MEMS Laser Scanner. 
         FIGS. 11A-B  illustrate a MEMS Deformable Mirror, and a MEMS Actuated Mirror Array. 
         FIGS. 12A-D  illustrate a Microlens Array, a MEMS Curved Mirror Array, a LED projector Array, and a Deformable display embodiment of the IPLS. 
         FIG. 13  illustrates a method of operating an Integrated Progressive Lens Simulator. 
         FIG. 14  illustrates a head-mounted Integrated Progressive Lens Simulator. 
         FIGS. 15A-B  illustrate embodiments of Head-mounted Integrated Progressive Lens Simulators. 
         FIG. 16  illustrates a Progressive Lens Simulator with a Lens Design Exploration System. 
         FIGS. 17A-F  illustrate embodiments of patient controllers. 
         FIGS. 18A-B  illustrate methods of Progressive Lens Simulation in some detail. 
         FIGS. 19A-B  illustrate methods of Progressive Lens Simulation in some detail. 
         FIGS. 20A-B  illustrate Design Factors. 
         FIGS. 20C-D  illustrate Visual Feedbacks. 
         FIG. 21  illustrates a modifying of a Design Factor based on Visual Feedback in the Design Factor space. 
         FIGS. 22A-B  illustrate a Visual Feedback-to-Design Factor matrix of a Visual Feedback-to-Lens Design Transfer Engine. 
         FIGS. 23A-B  illustrate a Search Management method, with an interactive aspect. 
         FIGS. 24A-B  illustrate Lens Merit factors. 
         FIG. 25  illustrates a modifying of a Design Factor based on Visual Feedback and Lens Merit factors in the Design Factor space. 
         FIG. 26  illustrates a Visual Feedback+Lens Merit-to-Lens Design matrix of a Visual Feedback-to-Lens Design Transfer Engine. 
         FIG. 27  illustrates a method for modifying the Design Factor locally. 
         FIGS. 28A-B  illustrate a method for modifying the Design Factor non-locally. 
         FIGS. 29A-B  illustrate performing a Search Management step, in some cases interactively. 
         FIG. 30  illustrates a Progressive Lens Simulator with a Guided Lens Design Exploration System and with Artificial Intelligence Engines. 
         FIG. 31  illustrates a Visual Feedback-Design Factor Neural Network. 
         FIG. 32  illustrates an Artificial Intelligence Engine for Progressive Lens Design Processor. 
         FIG. 33  illustrates a Backpropagation with Gradient Descent. 
         FIG. 34  illustrates a Deep Learning method for an AI Engine for a Progressive Lens Design Processor. 
         FIG. 35  illustrates a Deep Learning method for an AI Engine for a Search Guidance Engine. 
         FIG. 36  illustrates a Supervised Multi-station System of Progressive Lens Simulators. 
     
    
    
     DETAILED DESCRIPTION 
     The systems and methods described in the present patent document address the above articulated medical needs at least in the following aspects. These aspects are organized in a contrasting format to the previously described challenges of the state of the art. 
     (1) No trial before purchase: In embodiments, the visual experience of progressive lenses is simulated by a Progressive Lens Simulator. This system empowers the patient to actively and interactively explore and experience progressive lenses with different designs in real time. The patient can explore as many simulated progressive lens designs as she or he wishes before settling on a particular design. In short, the patient can explore, “test drive”, and “try on” the progressive lens before purchasing it. 
     (2) Only analog optometric devices are used: Embodiments use modern digital optoelectronic technology instead of analog optical technologies. 
     (3) Only two distances tested: The patient can explore the performance of the various progressive lens designs at as many distances as he or she wishes by virtue of modern optoelectronic designs. 
     (4) Eyes are tested individually: The patient can explore the visual experience with the simulated progressive lenses with both eyes simultaneously. This approach allows the lens design selection process to include and optimize for the patient&#39;s experience of the effects of vergence. 
     (5) Progressive lens prescriptions are under-defined: The patient can explore the many possible progressive lens designs exhaustively. The search can concentrate on many particular aspects of the performance of the lens. Monitoring the search process in detail provides an extensive amount of data about the patient&#39;s vision for the advanced lens design software. The acquisition of this larger amount of data turns the lens design process from under-defined to appropriately defined in a mathematical sense. The exploration process can be continued until the lens design software concludes that it has sufficient data to zoom in on the most optimal lens design. 
     (6) Determining more parameters would increase treatment time per patient: The selection process of the best progressive lens design with the here-described embodiments may take longer, or even substantially longer than the duration of a typical office visit today. This may be perceived as an economic “excessively high costs” argument against the described system. However, much of the search is guided by smart software and thus does not require the active presence of the optometrist. Rather, the optometrists play a supervisory role and thus can supervise even a greater number of patients per day with these Progressive Lens Simulators than with the traditional methods. 
     (7) Patients often need to return glasses for adjustments: Since the patients explore all relevant and possible progressive lens designs in real time, the here-described Progressive Lens Simulator system minimizes the patient complaints and returns. This dramatically improves patient satisfaction and greatly boosts the economic model of the optometrists. 
     (8) Lens design verification groups are small: The search data are collected from all participating optometrist offices. Therefore, the progressive lens design software will have access to the recorded search patterns, patient behaviors, and eventual patient choices from the test group of millions of patients. Access to such a huge and rapidly growing database will be used to rapidly and efficiently improve the progressive lens design algorithms. 
     (9) Testing images do not reflect patient&#39;s actual visual needs: The Progressive Lens Simulators offer to patients the testing of their vision on any images they choose. A promising implementation is presenting use-relevant images that are relevant for and simulate the patient&#39;s actual activities. 
     (10) Peripheral vision rarely tested: The digital image-projection system can present both central and peripheral images for a full characterization of a patient&#39;s vision. 
     (11) Modern search algorithms are not yet utilized: The Guidance System for Patient Exploration in some embodiments uses modern search techniques that were developed to explore complex, multiply interdependent and constrained merit-landscapes. 
     (12) Artificial Intelligence is not used: Artificial Intelligence Engines are used for constantly improving and upgrading the system&#39;s software block-by-block. 
     The here-described systems can only yield positive outcomes, because they can be first used to determine the traditional progressive lens prescription with the traditional procedure. Subsequently, the Progressive Lens Simulator can perform various advanced searches by here-described embodiments, and guide the patient to the advanced progressive lens prescription best suited for him/her. Finally, the Progressive Lens Simulator can simulate the traditional progressive lens experience, followed by the advanced progressive lens experience, and alternate back-and-forth so that the patient can compare the two progressive lens experiences to make her/his final choice. Since the patient can always return to and select the traditional progressive lens design, the overall outcome of the process cannot be worse than the traditional process, only better. 
     This patent document describes many embodiments, techniques and methods. It also describes more than a dozen advantages over existing traditional systems. Thus, the described advantages are not limiting for all embodiments. Indeed, embodiments which possess only one or a few of the advantages are already novel over existing systems. Also, several other, not-yet-listed advantages exist that make the system novel. Moreover, several of the described aspects can be combined in various embodiments for additional synergetic advantages. 
       FIG. 1  and  FIG. 2  illustrate the Guided Lens Design Exploration System of Simulated Progressive Lenses (GPS)  10  on a high, system level. Embodiments of the GPS  10  possess one or more of the above described features and advantages, as follows. 
     (1) Patients can explore and “try on” many different progressive lens designs before selecting one for purchase. 
     (2) Embodiments use modern digital optoelectronic technology. 
     (3) Patients can test their vision at as many distances as they wish. 
     (4) The two eyes of the patients can be tested synchronously, thus factoring their vergence into the overall visual experience. 
     (5) The eventually selected progressive lens designs and prescriptions are well-defined because a sufficient number of parameters are determined. 
     (6) Since patients are exploring the progressive lens designs on their own, guided by a smart software, the demand on the time of the optometrists actually decreases relative to present systems, as the optometrists are expected only to supervise the patients&#39; exploration. 
     (7) Since patients select their own design, glasses are returned for adjustments much less frequently. 
     (8) Lens design verification groups are large and ever expanding. 
     (9) Testing images reflect patient&#39;s actual visual needs. 
     (10) Patients&#39; peripheral vision is tested as extensively as the patients desire. 
     (11) Cutting edge search algorithms are utilized to guide the patient exploration. 
     (12) Artificial Intelligence is used to continuously upgrade and improve both the lens design and the patient guidance systems. 
     These system level concepts of the GPS  10  are described in general in  FIGS. 1-2 , and subsequently in detail in  FIGS. 3-36 . In particular,  FIGS. 3-15  describe many embodiments of Progressive Lens Simulators that generate life-like Comprehensive Progressive Lens Simulations of as many progressive lens designs as the patient wishes to explore.  FIGS. 16-29  describe guidance systems and methods that guide the patients in their exploration of the progressive lens designs.  FIGS. 30-35  describe Artificial Intelligence systems and methods to train and improve the Progressive Lens Simulators. Finally,  FIG. 36  describes a supervised Multi-station GPS system. 
       FIG. 1  illustrates that the Guided Lens Design Exploration System of Simulated Progressive Lenses (GPS)  10  can include a Progressive Lens Simulator (PLS)  100 , for simulating various progressive lens designs; a Lens Design Exploration System for Progressive Lens Simulator (LDES)  300 , for intelligently guiding the exploration of the many possible progressive lens designs by the patient; and an Artificial Intelligence Engine for GPS (AI-GPS)  500 , for monitoring the lens design exploration process by the patients, in order to discover and extract possible improvements of the GPS  10  system, followed by actually implementing of the discovered improvements. These three major building blocks of the fully integrated GPS  10  system can be all coupled to each other for efficient communication. [In the rest of the specification, sometimes only the abbreviating acronyms will be used for reference and brevity.] 
       FIG. 2  illustrates elements of these three main building blocks PLS  100 , LDES  300  and AI-GPS  500  in some detail. The Progressive Lens Simulator PLS  100  can include an Eye Tracker (ET)  110 , for tracking the direction of an axis, or gaze, of the patient&#39;s eyes, as well as the eye movements. The Eye Tracker  110  can determine the distance of the target the patient is looking at from the vergence of the axes of the two eyes. Several Eye Tracker designs are known in the art and can be adapted and implemented in this PLS  100 . The PLS  100  can further include an Off-Axis Progressive Lens Simulator (OPS)  120 , for simulating the off-axis visual experience of a selected Progressive Lens design. This experience is quite complex, as the effective optical power of progressive lenses changes with the angle relative to the optical axis. 
     The PLS  100  can further include an Axial Power-Distance Simulator (ADS)  130 , that simulates a combination of the distance of the viewed image and the axial power of the progressive lens. Since the PLS  100  simulates progressive lenses, the optical power varies substantially over the lens surface. In multistage embodiments, the PLS  100  simulates this with a combination of simulating the most important axial power with the ADS  130 , and the off-axis power with the separate OPS  120 . In integrated embodiments, the PLS  100  simulates the spatially varying optical power with an Integrated Progressive Lens Simulator IPLS  200 . 
     The PLS  100  can further include a Vergence-Distance Simulator (VDS)  140  that simulates the viewing distance in a different manner. The VDS  140  can present the images for the two eyes not dead ahead, but moved closer to each other, in order to create the visual experience of the target image being at a closer viewing distance. Finally, a Zoom-Distance Simulator (ZDS)  150  can simulate a change of the viewing distance (changed by the PLS  100  from a first distance to a second distance) by zooming the image in or out. Doing so can further increase the sense of reality of the visual experiences generated by the PLS  100  for the patients. 
     The GPS  10  can include the Guided Lens Design Exploration System for Progressive Lens Simulator (LDES)  300 , to guide the exploration of the large number of possible progressive lens designs by the patient with an efficient and informed strategy. The LDES  300  can include a Feedback-Control Interface (FCI)  310 . This FCI  310  can be used by the patient to enter feedback and control signals for the PLS  100 , in order to express preferences and provide feedback on the simulated progressive lens designs  123 . In some embodiments, the LDES  300  can include a Progressive Lens Design Processor (PLD)  320 . The PLD  320  can create the specific progressive lens design based on measurements of the patient&#39;s eyes; based on the patient&#39;s input, feedback, and control signals, and on lens design algorithms. The created specific progressive lens designs can be communicated by the PLD  320  to the PLS  100  to create the corresponding progressive lens visual experience for the patient. 
     The LDES  300  can further include a Search Guidance Engine (SGE)  330 . The patient often, or even typically may not know how to change the design of the progressive lens to improve its optical performance. The patient typically only senses that the last change of the design made the visual experience better or worse. Or, the patient can articulate what improvements she/he is looking for. But since the progressive lens design can be modified in many different ways to bring about such changes, a guidance system to affect the desired change in an informed and strategic manned can be useful and in fact necessary. Providing such guidance is one of the functions of the SGE  330 . The SGE  330  can receive a desired improvement or preference from a patient, and then suggest in return to the patient how to translate the requested improvement into a change of the lens design. 
     Some embodiments of the LDES  300  can further include a Synchronous Eye Exploration Controller (SEC)  340 , that oversees and controls the visual experience of the two eyes synchronously, and plays an important role in integrating desired design improvements from the two eyes. Finally, the LDES  300  can also include a Peripheral Vision Explorer (PVE)  350 , that evaluates the patient&#39;s vision in the peripheral zones, and feeds back this information into the simulation of the progressive lenses by the PLS  100 . 
     Finally, the Artificial Intelligence Engine for GPS (AI-GPS)  500  can be included into some embodiments of the GPS  10 , for monitoring the performance of the components of the PLS  100  and the LDES  300 , and for developing suggested adjustments to improve the performance of the managed components of the GPS system  10 . In some detail, the GPS  10  can include an Artificial Intelligence (AI) Engine for the Progressive Lens Design Processor (AI-PLD)  510 , to monitor and improve the performance of the PLD  320 . Other embodiments can include an AI engine for the Search Guidance Engine (AI-SGE)  520 . Finally, some embodiments of the GPS  10  can include an AI Engine for the Progressive Lens Simulator (AI-PLS)  530 . Each of the three AI engines  510 / 520 / 530  can be configured to monitor the functioning of the corresponding system blocks PLD  320 , SGE  330 , and PLS  100 , and then perform AI-based training cycles to improve the performance of these blocks. In some embodiments, these AI engines are implemented by neural networks. 
     The above, system-level description of the GPS  10  is now expanded with the detailed description of a number of specific embodiments in more detail. For clarity, the presentation of these embodiments is organized into titled sections. 
     1. Progressive Lens Simulator with an Axial Power-Distance Simulator 
       FIG. 3  illustrates a Multistage embodiment of a Progressive Lens Simulator PLS  100 , comprising: an Eye Tracker (ET)  110 , for tracking an eye axis direction to determine a gaze distance, an Off-Axis Progressive Lens Simulator (OPS)  120 , for generating an Off-Axis progressive lens simulation (Off-Axis PLS)  50  according to a progressive lens design  123 ; and an Axial Power-Distance Simulator (ADS)  130 , for simulating a progressive lens power in the eye axis direction, thereby creating a Comprehensive progressive lens simulation  30  from the Off-Axis PLS  50 . The eye axis direction is sometimes referred to as a visual axis. 
     In some embodiments, the Off-Axis Progressive Lens Simulator OPS  120  can include an Image Generator  121 , for generating an image  21 ; an Off-Axis Progressive Lens Simulator Processor, or OPS processor,  122 , for transforming the generated image  21  into Off-Axis PLS signals  20 - 1  and  20 - 2  according to according to the progressive lens design  123 ; and Off-Axis Progressive Lens Simulator Displays  124 - 1 / 124 - 2 , for displaying the Off-Axis Progressive Lens Simulation (Off-Axis PLS)  50  according to the Off-Axis PLS signal  20 . Here and in the following, many items X are included in the GPS  10  pairwise, one for each eye. They will be typically labeled as items X- 1  and X- 2 . Sometimes, for brevity, the collection of items X- 1  and X- 2  will be simply referred to as X. 
     In some PLS  100 , the Off-Axis Progressive Lens Simulator Display  124  includes a pair of Off-Axis Progressive Lens Simulator Screens  124 - 1  and  124 - 2 , each displaying an Off-Axis PLS  50 - 1  and  50 - 2 , the two PLS together providing a stereoscopic Off-Axis PLS  50  for a first/left eye  1  and a second/right eye  2 . 
     In some PLS  100 , the Off-Axis Progressive Lens Simulator Display  124  includes a single stereoscopic alternating Off-Axis Progressive Lens Simulator Screen  124 , controlled by an image-alternator, for alternating the displaying the Off-Axis PLS  50 - 1  for the first eye  1 , and subsequently PLS  50 - 2  for the second eye  2 , with suitable stereoscopic adjustments. This rapidly alternating display on left eye/right eye images, with synchronized image-alternation, i.e. blocking the image for the non-targeted eye, allows the use of a single screen to generate stereoscopic images and viewing experience. This image-alternating technology has mechanical embodiments involving shuttering or rotating wheels, opto-electronic embodiments involving rapid polarization changes, and liquid crystal embodiments to block the images. Any one of these embodiments can be utilized in the stereoscopic alternating Off-Axis Progressive Lens Simulator Screen  124 . 
       FIG. 4A  illustrates that the progressive lens design  123  includes characteristic regions of a typical progressive lens. These include: a distance vision region  123   d  in the upper portion of the progressive lens with a distance vision optical power OPd, a near vision region  123   n  in the lower portion of the progressive lens, typically nasally shifted, having a stronger, near vision optical power OPn, and a progression region  123   p , sometimes also called a channel, where the progressive optical power OPp progressively and smoothly interpolates between OPd and OPn. The progressive lens design  123  also includes a transition region  123   t , typically on both sides of the channel/progression region  123   p , where the front and back surfaces of the lens are shaped to minimize optical distortions arising from the optical power progressing in the channel  123   p.    
       FIG. 4A  illustrates that in the Progressive Lens Simulator PLS  100 , the Off-Axis Progressive Lens Simulator Processor  122  can be configured (1) to receive the generated image  21  from the Image Generator  121 ; and (2) to transform the generated image  21  into the Off-Axis PLS signal  20  by introducing a locally varying blur  126 , representative of the progressive lens design  123 . This blur  126  is caused by the optical power of the progressive lens design locally varying in the transition region  123   t  and in the channel, or progressive region  123   p , causing the light rays from an object point not getting focused into a single, well-defined image point. 
     Analogously, in some embodiments of the PLS  100 , the OPS Processor  122  can be configured (1) to receive the generated image  21  from the Image Generator  121 ; and (2) to transform the generated image  21  into the Off-Axis PLS signal  20  by introducing a locally varying curvature, or swim  127 , representative of the progressive lens design  123 . 
       FIG. 4B  illustrates the swim  127  with a square grid as the imaged object. A typical progressive lens design  123  bends and curves the originally rectilinear lines of the square grid into a curved swim grid  128 . These two effects are demonstrated in  FIG. 4A : the transition regions of a regular image develop the blur  126  by the typical progressive lens design  123 , and the straight lines get bent by the swim  127 . 
     The OPS Processor  122  can perform a detailed ray tracing computation of light emanating from the generated image  21  through the progressive lens design  123  to quantitatively produce this blur  126  and swim  127 . In alternate embodiments, wavefront propagation methods can be used by the OPS Processor  122 . Generating the correct blur  126  and swim  127  are key functions of the OPS Processor  122  to generate the life-like Off-Axis Progressive Lens Simulation (PLS)  50  at least because of the followings. When a patient evaluates the visual experience of the particular progressive lens design  123 , the primary positive experience is the customized increase of the optical power from OPd in the distance region to OPn in the near region, while the primary negative experience is “the price of the positives”, the concomitant blur  126  and swim  127 , induced by the power progression. The GPS  10  simulates different progressive lens designs  123  for a patient. The search for the optimal progressive lens design  123  is performed by the patient evaluating the balance of the positives against the negatives of the Comprehensive Progressive Lens Simulations  30  of the individual simulated designs  123 , eventually identifying his/her most preferred design  123 . The OPS Processor  122  crucially helps this search process by the most life-like simulation of the blur  126  and swim  127  of the designs  123 . In some PLS  100 , at least two of the Image Generator  121 , the Off-Axis Progressive Lens Simulator Processor  122 , and the Off-Axis Progressive Lens Simulator Display  124  can be integrated. 
       FIG. 3  and  FIG. 5  further illustrate that the PLS  100  can include a Vergence-Distance Simulator VDS  140 , for simulating a vergence for the displayed Off-Axis PLS  50  at the gaze distance, as determined by either the Eye Tracker  110 , or intended by an operator. The utility of the VDS  140  was outlined earlier. The life-like visual experience of the Comprehensive PLS  30  can be further enhanced by moving the Off-Axis Progressive Lens Simulations PLS  50 - 1  and  50 - 2 , and thereby the Comprehensive PLS  30 - 1  and  30 - 2  closer to each other when the eyes focus on a closer gaze distance. This can happen when the patient decides to lower and inwardly rotate her/his visual axis, intending to look through the near vision region  123   n  of the simulated progressive lens design  123 . Another situation is when an operator, or a computer controller of GPS  10 , decides to present a Comprehensive PLS  30  that corresponds to a closer gaze distance, to test the near vision of a patient. Simulating the vergence corresponding to a gaze distance correctly enhances the life-like visual experience to a remarkable degree. 
     The Vergence-Distance Simulator VDS  140  can be configured to simulate the vergence for the displayed Off-Axis PLS  50  at the gaze distance by (1) moving a screen of the Off-Axis Progressive Lens Simulator Display  124  dominantly laterally, or by (2) shifting the displayed Off-Axis PLS  50  on the Off-Axis Progressive Lens Simulator Display  124  dominantly laterally. In the latter embodiment, the Off-Axis PLS  50  is typically displayed only on a portion of the OPS display  124 , thus leaving room to electronically moving the image of the Off-Axis PLS  50  laterally. Some PLS  100  includes the combination of (1) and (2). Other solutions can be used as well, such as rotating mirrors in the optical path of the Off-Axis PLS  50 . 
       FIG. 5  illustrates that in some embodiments of the PLS  100 , the VDS  140  can include a VDS processor  142 , optionally coupled to the OPS Processor  122  to receive a vergence signal  40  and to control the Vergence Simulation. The VDS processor  142  can be coupled to vergence VDS actuators  144 - 1  and  144 - 2 . In some embodiments, these VDS actuators  144 - 1  and  144 - 2  can mechanically move the OPS displays  124 - 1  and  124 - 2  laterally. 
       FIG. 3  and  FIG. 5  also illustrate that some PLS  100  can include a Zoom-Distance Simulator ZDS  150 , to further increase the life-like visual experience of the Comprehensive PLS  30  by zooming the Comprehensive PLS  30  in accord with the changes of the gaze distance. This ZDS  150  can be activated when a patient decides to move his/her gaze relative to the progressive lens design  123 . For example, the patient moves his/her gaze from the distance vision region  123   d  to the near visions region  123   n  of the progressive lens design, in order to look at a near object. The ZDS  150  can increase the life-like experience of this move by zooming in on the near object. As shown in  FIG. 5 , the PLS  100  can include a ZDS processor  152 , coupled to the OPS Processor  122  to receive or send a zoom signal  50 . In some cases, the ZDS processor  152  can be notified by the Eye Tracker  110  that the patient turned his/her gaze direction lower and inward, as part of a process of switching to looking at a nearby portion of the overall generated image  21 , for example to look at a foreground object. In response, the ZDS processor  152  can notify the OPS Processor  122  via the zoom signal  50  to zoom in on the nearby portion of the generated image  21 , for example, on the foreground object. 
     With modern opto-electronic techniques, the above described simulators can be integrated to various degrees. In some PLS  100 , at least one of the Off-Axis Progressive Lens Simulator Processor  122 , the Off-Axis Progressive Lens Simulator Display  124 , and the Axial Power-Distance Simulator ADS  130  can include at least one of the Vergence-Distance Simulator  140  and the Zoom-Distance Simulator  150 . In some cases, only the VDS processor  142  or the ZDS processor  152  can be included. 
     Next, the description turns to various embodiments of the Axial Power-Distance Simulator ADS  130 . In general, the ADS  130  can be an adjustable optical system that has an adjustable optical refractive power. This adjustable optical refractive power of the ADS  130  can be adjustable to be consistent with the gaze distance, determined by the Eye Tracker  110 . In other embodiments, the adjustable optical power of the ADS  130  can be adjusted to an intended gaze distance, such as when the patient, or an operator of the GPS  10  decides to explore and test vision at a different distance. 
     In some embodiments, the ADS  130  uses optical elements such as lenses and mirrors whose optical power is adjustable, but whose position is fixed. In other embodiments, the ADS  130  can use optical elements whose position is also adjustable to simulate a vergence corresponding to the eye axis direction, or visual axis. A simple embodiment of the ADS can include a pair of adjustable lenses or mirrors that are laterally translatable, or rotatable, to increase the life-likeness of the simulation of the vergence. 
       FIGS. 6A-B  illustrate specific embodiments of ADS  130 .  FIG. 6A  illustrates an ADS  130  that includes an Alvarez lens system  132 . The Alvarez lens system  132  can include at least two (sliding) lenses  134 - 1  and  134 - 2  for each eye, at least one of the two lenses  134  having laterally varying curvature; and one or more actuators  135 , for sliding at least one of the lenses  134  laterally relative to the other lens, thereby changing an optical refractive power of the Alvarez lens system  132  in a central region. The actuator  135  is only shown once to avoid clutter. In embodiments of the Alvarez lens system  132  the optical (refractive) power in a central region can be changed by 2 Diopters (2D) or more, without introducing substantial aberrations. The diameter of the central region can be 2, 2.5, 3 cm, or more than 3 cm. Adding 2D optical power to the ADS  130  changes the perceived image distance from far away to 1/2D=50 cm. Therefore, the ability to change the optical power by 2D is typically sufficient to change the axial optical power from OPd of the distance vision region  123   d  to OPn of the near vision region  123   n , thereby simulating the entire range of interest. As described before, one function of the ADS  130  is to simulate the gaze distance to the object, the other function is to simulate the axial optical power of the progressive lens design  123 . Different ADS  130  can integrate and implement these two functions differently. 
       FIG. 6B  illustrates another embodiment of the ADS  130 : an adjustable fluid lens system  136  that includes a pair of adjustable fluid lenses  138 - 1 , with optical refractive power that is controlled by an amount of fluid in the fluid lenses  138 - 1  (only one lens shown); a fluid-management system  138 - 2 , for adjusting the amount of fluid in the fluid lenses  138 - 1 ; and lens-adjusting electronics  138 - 3 , for controlling the pair of adjustable fluid lenses  138 - 1  and the fluid-management system  138 - 2  to adjust the optical refractive power of the ADS  130 . As shown, the adjustable fluid lens  138 - 1  can include a deformable rounded polymer skin that contains a liquid. The fluid management system  138 - 2  can inject or drain fluid from the lens  138 - 1 . Doing so changes a center height from h 1  to h 2 . By the basic law of lenses, this height change changes the focal length of the lens  138 - 1  from f 1  to f 2 , as shown, adjusting its optical refractive power. 
     Many other embodiments of the ADS  130  exist, including a shape-changing lens, an index-of-refraction-changing lens, a variable mirror, a deformable optic, an aspheric lens with adjustable aperture, a liquid crystal optical element, an adjustable reflective optical element, an adjustable opto-electronic element, and an optical system with an optical component with adjustable relative position. 
       FIG. 7  illustrates an embodiment of the PLS  100  in more detail. Most of the elements of the PLS  100  of  FIG. 7  are specific embodiments of the general PLS  100  of  FIG. 3 , and will not be repeated here. Next, additional features of  FIG. 7  are called out as follows. 
     In the PLS  100  of  FIG. 7 , the Eye Tracker  110  can include infrared light emitting diodes, or IR LEDs,  112 - 1  and  112 - 2 , positioned close to a front of the PLS  100 , to project infrared eye-tracking beams on the first eye  1  and the second eye  2 ; as well as infrared light sources  111 - 1  and  111 - 2 , to illuminate the first eye  1  and the second eye  2  with an infrared imaging light. The infrared eye-tracking beams and the infrared imaging light get both reflected from the eyes  1  and  2 , as reflected IR beam and IR imaging light  11 - 1  and  11 - 2 . 
     The Eye Tracker  110  can further include infrared (IR) telescopes  113 - 1  and  113 - 2 , with infrared (IR) cameras  114 - 1  and  114 - 2 , to detect the infrared eye-tracking beams and the infrared imaging light  11 - 1  and  11 - 2 , reflected from the eyes  1  and  2 . The IR cameras  114 - 1  and  114 - 2  then generate the eye-tracking images  14 - 1  and  14 - 2 , and send them to the eye-tracking processor  115 . The eye-tracking processor  115  can process and analyze these eye-tracking images to generate eye-tracking image/data  15 - 1  and  15 - 2 , or jointly  15 . In some detail, the IR beams of the IR LEDs  112  are reflected as Purkinje reflections, or Purkinje spots, that reflect from the various surfaces of the eye, starting with the cornea. Tracking these Purkinje spots delivers pin-point information to track the eye position and orientation. The IR light source  111 , on the other hand, generates a wide-angle IR light that can be used to image the entire frontal region of the cornea. The Eye Tracker  110  can use both the pin-point information from the reflected Purkinje spots, and the wide-angle image from the reflected imaging light (together referenced as  11 ) to develop the comprehensive eye-tracking image/data  15 . 
     In the embodiment of  FIG. 3  the Eye Tracker  110  directly sends eye-tracking image/data  15  into the OPS Processor  122 . In the embodiment of  FIG. 7 , there is an intervening eye-tracking processor  115 , separate from the OPS Processor  122 . Several analogous variations have been contemplated for the various embodiments of PLS  100 . 
     In operation, the OPS Processor  122  can receive the generated image  21  from the image generator  121 , adjust it to generate the Off-Axis Progressive Lens Simulation signals  20 - 1  and  20 - 2 , and send these Off-Axis PLS signals  20  to the OPS Displays  124 - 1  and  124 - 2 , so that the OPS Displays  124  generate the Off-Axis PLS  50 - 1  and  50 - 2 . 
     As shown, in some embodiments, the VDS processor  142  and the ZDS processor  152  can be integrated in the OPS Processor  122 . In these embodiments, the Off-Axis PLS signal  20  also includes vergence and zoom components. The vergence component can instruct the VDS actuators  144 - 1  and  144 - 2  to laterally move, or rotate, the OPS Displays  124 - 1  and  124 - 2 , in order to simulate the needed vergence. In these embodiments, the Off-Axis PLS  50  includes Vergence and Zoom, as indicated. 
       FIG. 7  illustrates that the PLS  100  can further include infrared-transmissive visible mirrors  146 - 1  and  146 - 2 , one for each eye, to redirect the Off-Axis PLS  50 - 1  and  50 - 2 , from the OPS display  124 - 1  and  124 - 2  to the eyes  1  and  2 . With this reflection, the Off-Axis PLS  50 - 1  and  50 - 2  are redirected into the main optical pathway of the PLS  100 , in the direction of the eyes  1  and  2 . The Off-Axis PLS  50 - 1  and  50 - 2  are finally going through the Axial Power-Distance Simulator ADS  130 - 1  and  130 - 2 . In this PLS, the ADS  130 - 1  and  130 - 2  include adjustable optical power systems  131 - 1  and  131 - 2 , that can be an Alvarez lens system  132 , an adjustable fluid lens system  136 , or any of the other adjustable optical elements, described earlier. The ADS  130  transform the Off-Axis PLS  50  into the Comprehensive Progressive Lens Simulation PLS  30 , for the patient&#39;s eyes. 
     It is noted that the infrared-transmissive visible mirrors  146 - 1  and  146 - 2  reflect visible light, while transmitting infrared light. Therefore, mirrors  146  are configured to reflect the Off-Axis PLS  50  towards the eyes, while transmitting the reflected infrared eye tracking beam and the infrared imaging light  11 - 1  and  11 - 2 , from the eyes. 
     In the described embodiment of the PLS  100 , the OPS Display screens  124 - 1  and  124 - 2  can be positioned peripheral to the main optical pathway of the PLS  100 , while the infrared telescopes  113 - 1  and  113 - 2  of the Eye Tracker  110  can be positioned in the main optical pathway, as shown. In other embodiments, the positioned can be reversed. The mirrors  146  can be IR reflective and visible transmissive, in which case the IR telescopes  113  can be positioned peripherally, while the OPS Displays  124  can be positioned in the main optical pathway, in effect trading places. 
     2. Method of Operating a Progressive Lens Simulator with an Axial Power-Distance Simulator 
       FIG. 8  illustrates a method  101   m  of operating a multistage embodiment of the Progressive Lens Simulator PLS  100 . Here the label “m” refers to the multistage embodiment of the PLS  100 . The method  101   m  can include the following steps. 
     (a) tracking  102   m  of an eye axis direction by an Eye Tracker  110  to determine a gaze distance of the eye; 
     (b) generating  103   m  an Off-Axis Progressive Lens Simulation (Off-Axis PLS)  50  by an Off-Axis Progressive Lens Simulator OPS  120 , including blur and swim, according to a progressive lens design  123 ; 
     (c) creating  104   m  a Comprehensive Progressive Lens Simulation (Comprehensive PLS)  30  from the Off-Axis PLS  50  by simulating a progressive lens power in the eye axis direction by an Axial Power-Distance Simulator ADS  130 ; 
     (d) shifting  105   m  the Off-Axis PLS  50  by a Vergence-Distance Simulator VDS  140  to vergence appropriate for the gaze distance; and 
     (e) zooming the Off-Axis PLS  50  by a Zoom-Distance Simulator ZDS  150  to simulate transitions of gaze distance. 
     Various aspects of these steps have been described before in relation to the PLS  100  embodiments of  FIGS. 1-7 . 
     The generating  103   m  of an Off-Axis PLS  50  can include the following. 
     (a) generating an image  21  by an Image Generator  121 ; 
     (b) transforming the generated image  21  into an Off-Axis PLS signal  20  by an Off-Axis Progressive Lens Simulator Processor  122  according to the progressive lens design  123 ; and 
     (c) displaying an Off-Axis PLS  50  according to the Off-Axis PLS signal  20  by an Off-Axis Progressive Lens Simulator Display  124 . 
     Various aspects of these steps have been described before in relation to the PLS  100  embodiments of  FIGS. 1-7 . 
     As described earlier, the displaying can include providing a stereoscopic Off-Axis PLS  50 - 1  and  50 - 2  for the first eye  1  and the second eye  2  by a pair of Off-Axis Progressive Lens Simulator Displays, or Screens  124 - 1  and  124 - 2 . 
     In other embodiments, the displaying can include alternating the displaying the Off-Axis PLSs  50 - 1  and  50 - 2  for the first eye  1 , and subsequently for the second eye  2 , with suitable stereoscopic adjustments, by a stereoscopic alternating Off-Axis Progressive Lens Simulator Screen  124 , that is controlled by an image-alternator. 
     In some embodiments, as shown in  FIGS. 4A-B , the transforming can include receiving the generated image  21  from the Image Generator  121  by the Off-Axis Progressive Lens Simulator Processor  122 ; and transforming the generated image  21  into the Off-Axis PLS signal  20  by introducing a locally varying blur  126 , representative of the progressive lens design  123 . 
     In some embodiments, as shown in  FIGS. 4A-B , the transforming can include receiving the generated image  21  from the Image Generator  121  by the Off-Axis Progressive Lens Simulator Processor  122 ; and transforming the generated image  21  into the Off-Axis PLS signal  20  by introducing a locally varying curvature, or swim,  127 , representative of the progressive lens design  123 . 
     In some embodiments, at least two of the Image Generator  121 , the Off-Axis Progressive Lens Simulator Processor  122 , and the Off-Axis Progressive Lens Simulator Display  124  can be integrated. 
     In some embodiments, as shown in  FIG. 5 , the method  101   m  can further include shifting  105   m  the Off-Axis PLS  50  by a Vergence-Distance Simulator VDS  140  to a vergence appropriate for the gaze distance. 
     In some embodiments, the simulating a vergence can include moving a screen of the Off-Axis Progressive Lens Simulator Display  124  dominantly laterally; and shifting the displayed Off-Axis PLS  50  on the Off-Axis Progressive Lens Simulator Display  124  dominantly laterally. 
     In some embodiments, as shown in  FIG. 5 , the method  101   m  can further include zooming  106   m  the Off-Axis PLS  50  by a Zoom-Distance Simulator  150  to simulate transitions of the gaze distance. 
     In some embodiments, at least one of the Off-Axis Progressive Lens Simulator Processor  122 , the Off-Axis Progressive Lens Simulator Display  124 , and the Axial Power-Distance Simulator ADS  130  can include at least one of the Vergence-Distance Simulator VDS  140  and the Zoom-Distance Simulator ZDS  150 . 
     In some embodiments, the simulating a progressive lens power (within the creating  104   m ) can include adjusting an optical refractive power of an adjustable optical power system  131  of the Axial Power-Distance Simulator ADS  130 . 
     In some embodiments, the adjusting can include adjusting the optical refractive power of the Axial Power-Distance Simulator ADS  130  to be consistent with the determined gaze distance. In some embodiments, the adjusting can include adjusting the Axial Power-Distance Simulator  130  to simulate a vergence corresponding to the eye axis direction. 
     In some embodiments, as shown in  FIG. 6A , the adjustable optical power system  131  of the Axial Power-Distance Simulator ADS  130  can include an Alvarez lens system  132  that includes at least two lenses for an eye  134 - 1  and  134 - 2 , at least one of the two lenses having laterally varying curvature, and one or more actuators  135 . I these embodiment, the adjusting can include sliding at least one of the lenses  134 - 1  laterally relative to the other lens  134 - 2  by the one or more actuators  135 , thereby changing an optical refractive power of the Alvarez lens system  132  in a central region. 
     In some embodiments, as shown in  FIG. 6B , the adjustable optical power system  131  of the Axial Power-Distance Simulator ADS  130  can include an adjustable fluid lens system  136  that includes a pair of adjustable fluid lenses  138 - 1 , with refractive power that is controlled by an amount of fluid in the fluid lenses; a fluid management system  138 - 2 , for adjusting the amount of fluid in the fluid lenses; and lens adjusting electronics  138 - 3 , for controlling the pair of adjustable fluid lenses  138 - 1  and the fluid management system  138 - 2 . In these embodiments, the adjusting can include adjusting the amount of fluid in the fluid lenses  138 - 1  by the fluid management system  138 - 2  under the control of the lens adjusting electronics  138 - 3 , thereby changing the optical refractive power of the adjustable optical power system  131 . In some embodiments, the adjustable optical power system  131  can include a shape-changing lens, an index-of-refraction-changing lens, a variable mirror, a deformable optic, an aspheric lens with adjustable aperture, a liquid crystal optical element, an adjustable reflective optical element, an adjustable opto-electronic element, and an optical system with an optical component with adjustable relative position. As before, aspects and elements of this method  101   m  have been described earlier in relation to  FIGS. 1-7 . 
     3. Integrated Progressive Lens Simulator 
     In this section, another embodiment of the Progressive Lens Simulator  100  will be described. Numerous elements of this embodiment have been already described in relation to  FIGS. 1-8  and will not be repeated, only referred to where needed. 
       FIG. 9  illustrates a Progressive Lens Simulator  100  that includes an Eye Tracker  110 , for tracking an eye axis direction to determine a gaze distance; an Integrated Progressive Lens Simulator (IPLS)  200 , for creating a Comprehensive Progressive Lens Simulation (Comprehensive PLS)  30  according to a progressive lens design  123  by simulating a progressive lens power in the eye axis direction, in combination with generating an Off-Axis progressive lens simulation (Off-Axis PLS)  50 . On a general level, embodiments of the IPLS  200  can perform some, or all, of the functions of some, or all, of the OPS  120 , the ADS  130 , the VDS  140  and the ZDS  150  of the previously described multistage PLS  100 , each referenced with a representative symbol. 
     In some embodiments, the PLS  100  can include an Image Generator  121 , for generating an image  21 ; and a Progressive Lens Simulator Processor  122 , for transforming the generated image  21  into a Comprehensive PLS signal  20 - 1  and  20 - 2  according to the progressive lens design  123 , and for coupling the generated PLS signal  20 - 1  and  20 - 2  into the Integrated Progressive Lens Simulator  200  for creating the Comprehensive PLS  30 - 1  and  30 - 2 . In some embodiments, the Image Generator  121  and the Progressive Lens Simulator Processor  122  can be integrated into the IPLS  200 . 
     Just as the multistage PLS  100  could include a single OPS display  124 , or a pair of OPS displays  124 - 1  and  124 - 2 , the PLS  100  of  FIG. 9  can also include a single IPLS  200 , or a pair of IPLS  200 - 1  and  200 - 2 , as shown, for providing a stereoscopic Comprehensive PLS  30 - 1  and  30 - 2  for the first eye  1  and for the second eye  2 . 
     In the single IPLS  200  embodiments, the IPLS  200  can be a stereoscopic alternating Integrated Progressive Lens Simulator  200 , controlled by an image-alternator, for alternating the generating the Comprehensive PLS  30 - 1  for the first eye  1 , and subsequently  30 - 2  for the second eye  2 , with suitable stereoscopic adjustments. 
     In some embodiments, similarly to  FIGS. 4A-B , the Progressive Lens Simulator Processor  122  can be configured to receive the generated image  21  from the Image Generator  121 ; and to create an Off-Axis PLS signal-component of the Comprehensive PLS signal  20  by introducing a locally varying blur  126  into the generated image  21 , representative of the progressive lens design  123 . 
     In some embodiments, similarly to  FIGS. 4A-B , the Progressive Lens Simulator Processor  122  can be configured to receive the generated image  21  from the Image Generator  121 ; and to create an Off-Axis PLS signal-component of the Comprehensive PLS signal  20  by introducing a locally varying curvature, or swim,  127  into the generated image  21 , representative of the progressive lens design  123 . 
     In some embodiments, similarly to  FIG. 5 , the Progressive Lens Simulator PLS  100  can include a Vergence-Distance Simulator VDS  140 , for simulating a vergence for the displayed Comprehensive PLS  30  at the gaze distance. In some embodiments, the VDS  140  is integrated into the IPLS  200 . In some cases, the Vergence-Distance Simulator VDS  140  can be configured to simulate a vergence for the Comprehensive PLS  30  at the gaze distance by at least one of moving the Integrated Progressive Lens Simulator  200 - 1  and  200 - 2  dominantly laterally, and shifting the created Comprehensive PLS  30  on the Integrated Progressive Lens Simulator  200 - 1  and  200 - 2  dominantly laterally. 
     In some embodiments, similarly to  FIG. 5 , the Progressive Lens Simulator PLS  100  can include a Zoom-Distance Simulator  150 , for zooming the Comprehensive PLS  30  to represent a change in the gaze distance. In some embodiments, the Integrated Progressive Lens Simulator PLS  200  can include at least one of the Vergence-Distance Simulator  140  and the Zoom-Distance Simulator  150 . 
     The Integrated Progressive Lens Simulator  200  can be configured to simulate a primary function of the ADS  130 : the optical power of the progressive lens design  123  in the eye axis direction by creating the Comprehensive PLS  30  with light rays having a vergence related to the gaze distance. As described before, the simulated optical power can be selected by combining the simulation of the distance of the viewed object with the simulation of the axial power of the progressive lens design  123 . 
     Various embodiments of the IPLS  200  will be described next, in relation to  FIGS. 10-13 . A shared aspect of these embodiments is that they simulate a further aspect of the Comprehensive PLS  30 : the (di)vergence of the light rays, emanating from the viewed object according to the gaze distance. The (di)vergence is often thought of as an important component of the overall visual experience, used by our brain to analyze and perceive the viewed images. An aspect of the embodiments in  FIGS. 1-8  was that the Comprehensive PLS  30  of the PLS  100  was generated by flat OPS Displays  124 , thus generating flat wavefronts. These flat wavefronts do not fully represent the true viewing, or gaze, distance. In contrast,  FIGS. 10-13  illustrate embodiments of the IPLS  200  that at least have the capacity to generate the Comprehensive PLS  30  with a non-flat wavefront, the light rays diverging from every image point, thereby representing the viewing, or gaze distance more faithfully, more life-like. 
       FIG. 10  illustrates that the IPLS  200  can include a Micro Electro Mechanical System (MEMS) Laser Scanner  201  that includes a light, or laser source  208 , for generating and projecting a light; and a XY scanning mirror  206 , for reflecting and scanning the projected light as an XY-scanned light  209 . In this IPLS  200  the light source  208  can be an LED, a collection of different color LEDs, a laser, a collection of different color lasers, and a digital light projector. The MEMS Laser Scanner  201  can include a base  202 , such as a frame, and a first/Y-scanner hinge system  203   h , to rotate a Y-rotating frame  203 . The Y-rotating frame  203  can support a drive coil  204  that is energized through a control line  207 . When a current is flowing from the control line  207  to the Y-rotating frame  203 , it induces a magnetic field in the drive coil  204 . A magnet  205 , positioned under the Y-rotating frame  203  exerts a torque on the energized drive coil  204 , thereby rotating the Y-rotating frame  203 . 
     The IPLS  200  can further include a second/X-scanner hinge system  206   h , optionally embedded in the first/Y-scanner hinge system  203 , for reflecting and scanning the projected light by the XY-scanning mirror  206  in two spatial dimensions as the XY-scanned light  209 . This X-scanner hinge system  206   h  can be driven by various embodiments of electro-mechanical actuators. The scanning speed of the MEMS Laser Scanner  201  can be high enough so that it projects the Comprehensive PLS  30  with a high enough refresh rate that the patient perceives it as realistic. Helpfully, the images used in evaluating vision, are typically static, or only slowly moving, thus the demands on the refresh rates and therefore scanning rates are lower than, e.g. in fast-action video games or live TV. 
       FIG. 11A  illustrates another embodiment of the IPLS  200 . This IPLS  200  includes a Micro Electro Mechanical System (MEMS) Deformable Mirror  210  that includes a light/laser source  215 , for generating and projecting a light; and a deformable mirror  214 , for reflecting and scanning the projected light into an XY-scanned light  216 . The light/laser source  215  can be an LED, a collection of different color LEDs, a laser, a collection of different color lasers, or a digital light projector. The deformable mirror  214  can include a base  211 , actuator electrodes  212 , and an array of actuators  213 , for deforming the deformable mirror  214  in a segmented manner. In the shown IPLS  200 , the actuators  213  are deformable as well. Each segment of the deformable mirror  214  can redirect the XY-scanned light/laser  216 , as the light is scanned across the deformable mirror  214 . 
       FIG. 11B  illustrates another embodiment of the IPLS  200  that includes a Micro Electro Mechanical System (MEMS) Actuated Mirror Array  220 , with a light/laser/digital light source  225 , for generating and projecting a light. The light source  225  can include at least one of a LED, an LED group, a laser, a laser group, a scanning light source, and a digital light projector. The MEMS Actuated Mirror Array  220  can include a base  221 , supporting an actuator array  222 , actuator electrodes  223 , to carry control signals for the actuators  222 , and an array of actuable mirrors  224 , for actuably reflecting the light from the laser/light source  225  into a XY-scanned light/laser  226 . 
     In the embodiments of  FIGS. 11A and 11B  the light can be provided in a scanned manner, or in a simultaneous manner, illuminating all mirror segments in  FIG. 11A , or all actuable mirrors in  FIG. 11B  essentially simultaneously. The latter embodiments can use a digital light projector  225 , for example. 
       FIG. 12A  shows an IPLS  200  that includes a Microlens Array Light Field System  230  that includes an Off-Axis Progressive Lens Simulator  231 , for the generating the Off-Axis PLS  50 . This Off-Axis Progressive Lens Simulator  231  can be the Off-Axis PLS  120  from  FIGS. 1-7 . The Microlens Array Light Field System  230  can also include a microlens array  232 , for receiving the generated Off-Axis PLS  50 , and for transmitting it as a divergently propagating light field  233 , to simulate a progressive lens power in the eye axis direction, a vergence related to the gaze distance, or a combination of these two, as described earlier. Microlens arrays are favored in related opto-electronic systems, such as virtual reality goggles, to create very life-like visual experiences by generating light fields with non-flat wavefronts. 
       FIG. 12B  illustrates another embodiment of the IPLS  200 . This IPLS  200  is analogous to the IPLS  200  in  FIG. 11B  with one difference: it utilizes curved mirrors in place of the flat mirrors of the IPLS  200  of  FIG. 11B . As such, this IPLS  200  includes a Micro Electro Mechanical System (MEMS) Curved Mirror Array  240 , including a digital light projector, or light source  245 , for generating and projecting a light, the light source  245  including at least one of a LED, an LED group, a laser, a laser group, a scanning light source, and a digital light projector. The IPLS  200  further includes a base  241 , an actuator array  242 , controlled by actuator electrodes, and an array of actuable curved mirrors  244 , for reflecting the light to generate a vergence related to the gaze distance; wherein the curved mirrors  244  include at least one of fixed mirrors and actuable mirrors. The light, reflected from the curved mirrors forms a divergently propagating light field  246 . In some embodiments, the curvature of the actuable curved mirrors  244  can be modified according to the gaze distance, to further increase the life-like divergence of the wavefront. 
       FIG. 12C  illustrates yet another embodiment of the IPLS  200  that includes a LED Projector Array  250  that includes a base  251 , a LED array  252 , controlled by control electrodes  253 , for creating the Comprehensive PLS  30  with a divergently propagating curved wavefront  254 , for the simulating the progressive lens power in the eye axis direction, in combination with generating the Off-Axis progressive lens simulation. 
       FIG. 12D  illustrates yet another embodiment of the IPLS  200  that includes an actuated deformable display  259 . As with other embodiments, this one can also be formed on a base, or substrate  256 . A set of control electrodes  257  may carry control signals to control actuators of an actuator array  258 . The deformable display  259  can be deformably disposed on top of the actuator array  258 . The deformable display  259  can be an OLED display, and any soft, flexible, or deformable equivalents. The actuators  258  can deform the display  259  by expanding and contracting in a vertical, normal direction. An aspect of this Deformable display  255  embodiment is that it is capable of emitting a non-flat wavefront, thus improving the life-like divergence of the emitted wavefront. 
       FIG. 13  illustrates a method  101   i  of operating the Progressive Lens Simulator  100 . Here the label “i” refers to the PLS  100  being integrated, such as the IPLS  200 . The method  101   i  can include the following steps. 
     (a) tracking  102   i  an eye axis direction by Eye Tracker ET  110  to determine a gaze distance; 
     (b) creating  103   i  a Comprehensive Progressive Lens Simulation (PLS) by the Integrated Progressive Lens Simulator IPLS  200 , by simulating an effect of an Off-Axis Progressive Lens Simulator OPS  120 , and an effect of an Axial Power-Distance Simulator ADS  130 ; 
     (c) shifting  104   i  the Comprehensive PLS by a Vergence-Distance Simulator VDS  140  to vergence appropriate for the gaze distance; and 
     (d) zooming  105   i  the comprehensive PLS by a Zoom-Distance Simulator ZDS  150  to simulate transitions of the gaze distance. 
     4. Head-Mounted Progressive Lens Simulator 
       FIGS. 14-15  illustrate a head-mounted PLS  260 , secured to a patient&#39;s head by a head-mount  262 . The head-mounted PLS  260  can include an Integrated Progressive Lens Simulator (IPLS)  200  and XYZ position or motion sensors  263 . The IPLS  200  can be any of the IPLS  200  embodiments described in relation to  FIGS. 9-13 . Some embodiments of the head-mounted PLS  260  can include any embodiment of the PLS  100 . 
     In some detail, embodiments of the PLS  100  can include an Eye Tracker  110 , for tracking an eye axis direction to determine a gaze distance; and the Integrated Progressive Lens Simulator  200 , for creating a Comprehensive Progressive Lens Simulation (PLS)  30  by simulating a progressive lens power in the eye axis direction, in combination with generating an Off-Axis progressive lens simulation (Off-Axis PLS)  50 . In these embodiments, the Eye Tracker  110  and the Integrated Progressive Lens Simulator  200  can be implemented in a head-mounted display, virtual reality viewer, or goggles. Next, two specific embodiments of the head-mounted PLS  260  are described in more detail. 
       FIG. 15A  illustrates a double LCD head-mounted PLS  270 , as an embodiment of the head-mounted PLS  260 . The double LCD head-mounted PLS  270  can include a backlight  271 ; a first liquid crystal display  272 , positioned in front of the backlight  271 ; a second liquid crystal display  273 , spaced apart from the first liquid crystal display  271  by a spacer  274 , for together simulating a progressive lens design  123  by creating a light field effect, and thereby a Comprehensive PLS  30 . The double LCD head-mounted PLS  270  can further include binocular viewing lenses  275 , and eye tracker  277  that can be an embodiment of the Eye Tracker  110 . Because of the extreme spatial constraints, the eye tracker  277  can be configured to track the eye movements from high angles. In other embodiments, an IR reflective visible transmissive mirror can be used as part of the eye tracker  277 , in analogy with the similar mirrors  146  in the embodiment of  FIG. 7 . Finally, the above elements can be energized and controlled by a driver electronics  276 . The elements  271 - 276  together can be thought of as forming the IPLS  200 . 
     The field of virtual reality goggles is rapidly expanding. These goggles are more and more capable of generating life-like visual experiences, and therefore their technology can be promisingly implemented and adapted for use in embodiments of the head-mounted PLS  260  to create the Comprehensive PLS  30 . 
     In particular, the light field effect generated by the head-mounted PLS  260  can be three dimensional, or four dimensional. The latter ( 4 D) technology also represents the depth of focus perception, making objects that are in front or behind the object plane blurrier. Doing so further enhances the life-likeness of the visual perception. 
     Some embodiments of the PLS  100  can use not only XYZ position sensors but XYZ motion sensors  263 . Picking up not only the position and direction of the head-mounted PLS  260 / 270 , but also sensing a motion of a wearer of the head-mounted PLS  260 / 270  can be integrated into the control software that runs on the driver electronics  276 , or in a separate computer. The XYZ motion sensor  263 , or motion sensors can include at least one of an accelerometer, a gyroscope, and a magnetometer. These can all contribute to sensing the motion, position and direction of the user&#39;s gaze. 
       FIG. 15B  illustrates another, related embodiment of the head-mounted PLS  260 : a microlens array head-mounted PLS  280 . The microlens array head-mounted PLS  280  can include a backlight  281 ; a liquid crystal display  282 , positioned in front of the backlight  281 ; and a microlens array  283 , spaced apart from the liquid crystal display  282  by a spacer  284 , for together simulating a progressive lens design  123  by creating a light field effect, and thereby creating a Comprehensive PLS  30 . As discussed earlier in the context of  FIG. 12A , microlens arrays can create light field effects and non-flat wavefronts particularly effectively, thereby increasing the life-likeness of the visual experience of the Comprehensive PLS  30 . The light field effect can be three dimensional or four dimensional. 
     The microlens array head-mounted PLS  280  can further include binocular viewing lenses  285 ; and an eye tracker  287 . As before, the eye tracker  287  can be an embodiment of the Eye Tracker  110 . In some cases, the eye tracker  287  has to be able to work at high angles, or with the help of an IR reflective, visible transmissive mirror, similar to the mirror  146  in  FIG. 7 . Finally, the above elements can be energized and controlled by a driver electronics  286 . The elements  281 - 286  together can be thought of as forming the IPLS  200 . 
     This embodiment can further include at least one XYZ position, direction, or motion sensor  263 , for sensing a position, direction, or motion of a wearer of the head-mounted PLS  280 . This sensor can sense the position, the direction, or the motion of the head-mounted PLS  280 , thereby aiding the generation of the Comprehensive PLS  30 . 
     All embodiments of  FIGS. 14 and 15A -B can further include a housing  278 / 288  for accommodating the Eye Tracker  277 / 287 , and the Integrated Progressive Lens Simulator  200 . Also, some of the computers used in the PLS  100  and IPLS  200 , such as the Progressive Lens Simulator Processor  122 , can be implemented in a self-standing computer, separate from the head-mount. The self-standing computer can communicate with the head-mounted PLS  260 / 270 / 280  via a wired connection, or via a Bluetooth connection. 
     Finally, in some embodiments, head-mounted PLS  260 / 270 / 280  can involve augmented reality glasses, wherein the Comprehensive Progressive Lens Simulation  30  is generated from an image viewed via the augmented reality glasses. 
     5-6. Guided Lens Design Exploration System and Method for a Progressive Lens Simulator 
     As mentioned in the introductory part,  FIGS. 3-15  describe Progressive Lens Simulators  100  that generate Comprehensive Progressive Lens Simulations  30 , in order to enable the patients to explore many progressive lens designs  123  via high quality, life-like visual experiences. A class of these PLS  100  systems can be operated by the optometrists in the traditional manner, using only verbal feedbacks from the patients. In this section, additional classes of systems are described, that empower the patient to control and to manage the exploration of as many progressive lens designs as they desire under their own control. 
       FIGS. 16-29  illustrate that important additional systems can be employed in some embodiments of GPS  10  to control, to manage and to accelerate the exploration of a substantial number of progressive lens designs. These GPS  10  systems can be managed and controlled by the patient, by an optometrist, or a vision technician. In these embodiments, the patient can provide feedback and optionally control signals in response to the Comprehensive Progressive Lens Simulation  30  of a progressive lens design  123 . As described in the early sections, this feature is a powerful departure from existing optometry systems, at least for the listed dozen reasons. 
       FIG. 16  illustrates that in embodiments, a PLS  100  can be combined with a Lens Design Exploration System for Progressive Lens Simulator (LDES)  300 . This combined system includes the Progressive Lens Simulator  100 , for generating a Comprehensive Progressive Lens Simulation  30  utilizing a progressive lens design  123  with Design Factors  420  for a patient, and for receiving a Visual Feedback  430  in response to the Comprehensive Progressive Lens Simulation (PLS)  30 . Several embodiments of the PLS  100  have been described in relation to  FIGS. 3-15 . Any one of those embodiments, and any of their combinations can be used in the description below. 
     The LDES  300  can further include a Progressive Lens Design processor  320 , coupled to the Progressive Lens Simulator  100 , for modifying the progressive lens design  123  in response to the Visual Feedback  430 , and transmitting the modified progressive lens design  123  to the Progressive Lens Simulator  100  to generate a modified Comprehensive Progressive Lens Simulation  30  for the patient with the modified progressive lens design  123 . The progressive Lens Design Processor  320  can be part of, or even integrated into, the OPS  120 . 
     The LDES  300  can further include a Feedback-Control Interface  310 , coupled to the Progressive Lens Design processor  320 , for receiving the Visual Feedback  430  from an operator, selected from the group consisting of a joystick, a touchpad, a mouse, an audio interface, an external tablet GUI, and an internal visual-interface GUI, and forwarding the received Visual Feedback in the form of a feedback-control signal  311  to the Progressive Lens Design processor  320 . Other embodiments may include the Eye tracker  110 , coupled to the Progressive Lens Design processor  320 , for receiving a Visual Feedback  430  in a form of an objective patient vision measurement. In other embodiments, other systems can provide objective feedbacks, including Purkinje-spot based imagers, Scheimpflug systems, and OCT systems. 
     Also, the Progressive Lens Design Processor  320  can base some of its calculations of eye modeling. This may involve imaging some of the ophthalmic layers in the eye, and then building an eye model like the widely used Holladay model. 
     Several modes of operation of the embodiments of the LDES  300  have been contemplated regarding the feedback. (1) Some LDES  300  are operated by the patient himself/herself. The PLS  100  generates the Comprehensive PLS  30  for the patient, who evaluates the visual experience and directly enters a subjective feedback into the FCI  310 . 
     (2) In other embodiments of LDES  300 , the Visual Feedback may only be indirect. The patient may only express a verbal feedback, such as the last modification made the visual experience of the Comprehensive PLS  30  better or worse, and a trained operator, technician, or the optometrist herself/himself may enter a control signal  311  via the FCI  310 . 
     (3) Other LDES  300  can be based on objective patient feedback and do not require an active, or subjective, patient feedback. For example, the Eye Tracker  110  can monitor the patient&#39;s eye movements and draw conclusions from the monitoring. E.g. if the jitter of the patient&#39;s eye increases, or the patient struggles to focus in response to a modification of the Comprehensive PLS  30 , then the Eye Tracker  110  may report this to the Progressive Lens Design Processor  320  of the LDES  300 . In response, a software of the Progressive Lens Design Processor  320  may conclude that the modification was undesirable, and undo the modification, or try a different one. 
     (4) Finally, in some embodiments of LDES  300 , the objective patient feedback, or objective patient vision measurement, such as the rapidity of the patient&#39;s eye movement, or inability to focus, may be monitored not by a computer software but by the operator of the LDES  300 , such as the optometrist herself/himself without an express, or subjective cooperation of the patient. In such embodiments, the monitoring operator can enter the feedback or control into the FCI  310  that transforms it into a feedback-control signal  311 . 
     The combination of the PLS  100  and LDES  300 , described next, can be operated in any of the above four modes, or in some combination of these modes. Sometimes the patient, technician, optometrist, or some combination of more than one of these possible sources providing a feedback or a control input together will be referred to as “the operator”. Also for this reason, the inputs into the FCI  310 , and the signal  311  outputted by FCI  310  can be feedback, control, and any combination of feedback and control input and feedback and control signals. These possibilities and combinations will be inclusively referred to as feedback-control input and feedback-control signal  311 . 
       FIGS. 17A-F  illustrate that the Feedback-Control Interface FCI  310 , where an operator can enter a feedback or control input, can have numerous embodiments. These include a (twin) joystick FCI  310 - 1  in  FIG. 17A , a touchpad-mouse FCI  310 - 2  in  FIG. 17B , an audio interface FCI  310 - 3  in  FIG. 17C , an external tablet GUI (Graphical User Interface) FCI  310 - 4  in  FIG. 17D , and an internal visual-interface GUI FCI  310 - 5 , possibly overlaid with the visual experience of the Comprehensive PLS  30  in  FIG. 17E . As mentioned in the second embodiment of the LDES  300 , in some cases the patient can only provide a subjective feedback which then can be used by an operator, or technician to actually enter an input into an indirect FCI  310 - 6 .  FIG. 17F  symbolically illustrates such combined operator modes. In analogous embodiments, the optometrist can enter an input into a tablet, an iPad, a fixed terminal or a GUI of the LDES  300 . 
     It is a non-obvious, challenging task to “translate” the feedback into an actionable command on how to modify the progressive lens design  123  in response to the feedback. Several embodiments will be described next to carry out this translation, in response to the feedback, or “Visual Feedback”. The method of exploring and changing the progressive lens design in response to a Visual Feedback in general will be referred to as a method  400  of Progressive Lens Simulation. Several embodiments of this method will be described next. 
       FIG. 18A  illustrates the method  400  of Progressive Lens Simulation, comprising: 
     (a) activating  401  a progressive lens design  123  with Design Factors  420  by a Progressive Lens Design Processor  320 ; 
     (b) generating  402  an image  21  by an Image Generator  121  of a Progressive Lens Simulator  100 ; 
     (c) generating  403  a Comprehensive Progressive Lens Simulation (PLS)  30 , simulated from the generated image  21  by the Progressive Lens Simulator  100 , utilizing the progressive lens design  123 ; 
     (d) acquiring  404  a Visual Feedback  430 , responsive to the generating of the Comprehensive PLS  30  with the progressive lens design  123 ; 
     (e) modifying  405  the progressive lens design  123  by the Progressive Lens Design Processor  320  in relation to the Visual Feedback  430 ; and 
     (f) re-generating  406  the Comprehensive PLS  30  with the modified progressive lens design  123  by the Progressive Lens Simulator  100 . 
     The method  400  can typically involve repeating steps (d)-(e)-(f) until the Visual Feedback  430  indicates a satisfactory outcome of the method. 
     The activating  401  the progressive lens design  123  with a Progressive Lens Design Processor  320  can be in response to a progressive lens design selection based on a preparatory measurement. The generating the image  21  by an Image Generator  121  can be in response to an image selection. Embodiments of the activating  401  are broadly understood. The activating  401  of the progressive lens design  123  can include recalling the progressive lens design  123  from a memory, or from a storage medium. The activating  401  can also include that the Progressive Lens Design Processor  320  computes or models the progressive lens design  123  from some model parameters. Whichever way the progressive lens design  123  is activated, the Progressive Lens Simulator  100  can be generating  403  a Comprehensive Progressive Lens Simulation PLS  30  from the generated image  21  by utilizing the activated progressive lens design  123 . 
     The above steps of the method  400  are typically performed by the PLS  100  and the LDES  300 .  FIG. 18B  illustrates steps of a method  410  of an operator interacting with the LDES  300  to carry out the method  400 . The method  410  can include the following steps. 
     (a) optionally selecting  411  a progressive lens design  123  with a Progressive Lens Design Processor  320 , based on a preparatory measurement; 
     (b) optionally selecting  412  an image  21  with an Image Generator  121 ; 
     (c) evaluating  413  a visual experience of a generated Comprehensive PLS  30 , simulated by a Progressive Lens Simulator PLS  100  from a selected image  21  with a progressive lens design  123 ; 
     (d) the evaluating optionally including inspecting  414  an image region of the generating a Comprehensive PLS  30  in an inspection direction; and 
     (e) providing  415  a Visual Feedback  430  to a Progressive Lens Design Processor  320  based on the evaluating via a Feedback-Control Interface  310 . 
       FIGS. 19A-B  illustrate the method steps that were described in  FIGS. 18A-B .  FIG. 19A  illustrates the steps of the method  400  in some detail that were described in relation to  FIG. 18A . For the activating step  401 , the progressive lens design  123  is illustrated with a progressive lens design  123  defined by contour lines. For the generating an image step  402 , a generated image  21  is shown. For the generating the comprehensive PLS step  403 , it is shown that generating the Comprehensive PLS  30  from the generated image  21  can involve introducing a blur  126  into the peripheral regions of the image  21 , and introducing a swim  127  by bending the straight lines of the image  21 , both based on detailed optical calculations. The acquiring step  404  can involve a measurement of a jitter of an angle α of the visual axis of the eye as shown: excessive jitter may indicate excessive discomfort with the Comprehensive PLS  30  of the progressive lens design  123 . 
       FIG. 19B  illustrates the steps of the method  410 , executed by an operator of the PLS  100 −LDES  300  system. The steps of the method  410  are shown in relation to the steps of the method  400  that are executed by the combined PLS  100 −LDES  300  system itself. These steps once again closely correspond to the steps described in  FIG. 18B . 
       FIGS. 20A-B  illustrate Designs Factors  420 . A large number of design factors can be used, and different lens manufacturers often have their own specialized set of Design Factors. By way of example, Designs Factors  420  may include contour lines  421 , pupil height or optical center  422 , corridor, or channel width  423 , corridor length  424 , near-vision nasal offset  425 , progression pitch  426 , prism, or prism angle  427 , cylinder orientation  428 , progressive prism, Zernike coefficients, and many-many others. With the ever-strengthening influence of the Schneider free-form lens manufacturing technology, any encoding of the lens topographic, contour or height map can be used as design factors. 
     Some design factors can be supplemented by preparatory measurements by the optometrist. One example is that the optometrist can observe the glass-positioning height, where the patient is wearing the glasses on her/his nose. The optometrist can shift the height of the optical center  422  Design Factor to account for the patient&#39;s individual glass-positioning height. 
       FIG. 20B  illustrates that the collection of these individual design factors DF 1 , DF 2 , . . . , DF k  together can be thought of as defining a Design Factor Space. In this Design Factor Space, the specific progressive lens design can be represented with a Design Factor vector DF. The exploration of the progressive lens designs  123  can then be thought of as a guided wandering of the Design Factor vector DF through a series of iterations DF( 1 ), DF( 2 ), . . . , until the optimal Design Factor vector DF(final) is reached. (For clarity, the subscripts 1, 2, . . . k, refer to the individual Design Factors as components of the DF vector, whereas the indices 1, 2, . . . n in brackets refer to the number of iterative steps during the course of the iterative exploration of the progressive lens designs  123 .) 
       FIGS. 20C-D  illustrate the analogous organization of the Visual Feedback  430 . These Visual Feedbacks  430  can be of different types or classes, including the followings. 
     (a) A subjective patient feedback via a Feedback-Control Interface; 
     (b) an objective patient vision measurement; 
     (c) an eye tracker image/data from an Eye Tracker; 
     (d) a direct patient feedback; 
     (e) an indirect patient feedback; 
     (f) an operator control input; 
     (g) an operator command; 
     (h) an operator response to a proposition; and 
     (i) an operator selection. 
     The term “Visual Feedback” is used broadly in this document. It can include subjective feedbacks, like the patient expressing a subjective evaluation of the visual experience. It can be an objective feedback, like a measurement of a jitteriness of the patient&#39;s visual axis. It can be a direct feedback directly entered by the patient into the FCI  310 . It can be an indirect feedback, the patient verbally stating an experience or preference and an operator entering the corresponding feedback into the FCI  310 . The feedback can come from a single operator, such as the patient, or from more than one operator, such as the patient entering a partial visual feedback and the operator entering another complementary feedback. Also, the Visual Feedback  430  can be simply a feedback, or a control, or command input, where the operator translated the visual experience into a control, command, selection, or response. 
     In each of the above classes, there can be a large number of specific Visual Feedbacks  430 . To expand by examples, the subjective Visual Feedbacks can include the patient subjectively perceiving the lower nasal quadrant blurry, the lower temporal quadrant blurry, the nasal progression region has too much swim, progression corridor too long, progression corridor too wide. The subjective Visual Feedback  430  can include requests, commands, or other control input, such as the patient requesting rotate cylinder orientation angle, increase prism, and decrease prism progression. The objective Visual Feedback  430  can include the optometrist observing that the patient&#39;s visual inspection direction, or visual axis, being too jittery, or the patient having difficulty focusing on the presented object, and inputting a corresponding feedback-control input into the FCI  310 . All of these possibilities are included in the scope of “Visual Feedback  430 ”. 
       FIG. 20D  illustrates that the individual Visual Feedbacks VF 1 , VF 2 , . . . , VF l  can be thought of as forming a Visual Feedback vector VF  430 , in analogy to the Design Factor vector DF. In various embodiments, the length of the DF vector may not be equal to the length of the VF vector. In this approach, in each iteration of the progressive lens exploration by the method  400 , a new Visual Feedback vector VF(n) is received from an operator, patient, or measurement system, such as the Eye tracker  110 , and in response, the combined PLS  100 −LDES  300  system modifies the Design Factor vector DF. 
       FIG. 16  shows that in several embodiments of the Lens Design Exploration System  300  for a Progressive Lens Simulator  100 , the Progressive Lens Design Processor  320  includes a Visual Feedback-to-Lens Design Transfer Engine FLE  325 , that plays an important role in modifying the progressive lens design  123  in response to the Visual Feedback  430 . 
     An important role of this Visual Feedback-to-Lens Design Transfer Engine FLE  325  is to “translate” the received Visual Feedback  430  into a modification of the Design Factors  420 . In an example, if the patient inputs the Visual Feedback  430  that “lower nasal region too blurry” into the Feedback Control Interface  310 , it is a non-obvious task to translate this specific Visual Feedback  430  into which Design Factors to change to what degree to reduce the blurriness in the lower nasal region. A large amount of optical modelling, ray-tracing, patient-testing, and refinement are needed to establish that which set of Design Factors  420  need to be modified to what degree to respond to the various Visual Feedbacks  430 . This complex knowledge is embodied, managed and carried out in the Visual Feedback-to-Lens Design Transfer Engine FLE  325 . 
       FIG. 21  illustrates the usefulness of the above-introduced vector concepts to visualize the exploration of progressive lens designs. The Design Factors DF 1 , DF 2 , . . . , DF n  define a multi-dimensional Design Factor space. A specific progressive lens design  30  is represented by a specific Design Factor vector DF=(DF 1 , DF 2 , . . . , DF k ) in this space. The PLS  100  generates an initial Comprehensive PLS  30  for the patient, using a specific progressive lens design  123 , now represented by a Design Factor vector DF( 1 ). The PLS  100  then acquires a Visual Feedback vector VF( 1 )=(VF 1 , VF 2 , . . . , VF l ), responsive to the Comprehensive simulation of the Progressive Lens DF( 1 ). The LDES  300  system modifies the Design Factor vector DF( 1 ) into DF( 2 ) in response to the Visual Feedback vector VF( 1 ).  FIG. 21  illustrates this process via the n-th iteration. The LDES  300  modifies the Design Factor vector DF(n) into DF(n+1) in response to the Visual Feedback vector VF(n) by adding:
 
ΔDF( n )=DF( n+ 1)−DF( n )  (1)
 
     Visibly, the ΔDF(n) increment vectors form a search path in the Design Factor space. As the combined PLS  100  and LDES  300  system performs the method  400 , in interaction with an operator, patient, or optometrist according to the method  410 , this search path converges to a customized optimal Design Factor vector DF  420  that best suits the patient&#39;s needs. 
       FIGS. 22A-B  illustrate the case, when the relationship is approximately locally linear between the Visual Feedback vector VF  430  and the Design Factor vector DF  420 . In such cases, the Visual Feedback-to-Lens Design Transfer Engine  325  can utilize a Visual Feedback-to-Design Factor matrix VFDF  326  for the modifying the progressive lens design by a Matrix Method. (Matrices and vectors are referenced with boldface.) 
       FIG. 22A  illustrates a 4×4 representation of the VFDF matrix  326 . The equation below is the extended form of the above Eq. (1). As discussed earlier, in general the length “k” of the DF vector  420  is not equal to the length “l” of the VF vector  430 , so often the VFDF matrix  326  is a non-square, rectangular matrix. 
     The elements of the VFDF matrix  326  represent how to translate the elements of the Visual Feedback vector  430  into a change of the Design Factor vector  420 . Typically, more than one element of the DF vector  420  need to be changed in a correlated manner. These correlations that translate the inputted Visual Feedback vector VF  430  into a design factor vector DF  420  constitute the elements of the VFDF matrix  326 . 
       FIG. 22B  illustrates this translation by an example. The VFDF matrix  326  is a 7×9 matrix in this embodiment. Visibly, the Visual Feedback vector VF  430  includes subjective patient feedbacks, like the patient indicating that the optical center too high, as well as objective feedbacks, like the Eye tracker  110  measuring that the visual inspection direction, or visual axis of the patient is too jittery. As an example, if the patient&#39;s Visual Feedback is “lower temporal quadrant blurry”, that can be represented with a binary Visual Feedback vector  430  of VF=(0,1,0,0,0,0,0), or with a quantitative expression x, representing how blurry the image is VF=(0,x,0,0,0,0,0), e.g. determined by an observing optometrist. If the non-zero elements in the second column of the VFDF matrix  326  are VFDF 32  and VFDF 72 , that means that the best response to the above Visual Feedback is to modify the Design Factor vector DF  420  with ΔDF=VFDF*VF=(0,0,VFDF 32 ,0,0,0,VFDF 72 ,0,0), i.e. to increase or decrease the progression corridor width by VFDF 32 , depending on the sign of VFDF 32 ; and to rotate the cylinder clockwise or counter-clockwise VFDF 72 , depending on the sign of VFDF 72 . A wide variation of related and analogous embodiments exists, including the size of the VFDF matrix  326 , and the choice of factors both in the DF vector  420  and the VF vector  430 . 
       FIG. 23A  illustrates that in many instances, the exploration of the progressive lens designs  123  may require non-local and non-linear steps, which therefore are not well represented by a VFDF matrix  326 . For example, the patient&#39;s exploration may end up in a region of the Design Factor space, where the patient keeps giving the Visual Feedback  430  that no change is improving the visual experience. In simple terms, that the search got stuck in a dead-end, and likely the optimal solution is in a far away, distinct region of the Design Factor space that cannot be easily reached. In such cases, the patient&#39;s search may be best assisted by a large jump to another region in the Design Factor space, or by some other robust move. In general, such moves will be referred to as performing Search Management steps  450 . To facilitate such non-local and/or non-linear moves, the Lens Design Exploration System  300  can include a Search Guidance Engine SGE  330 , coupled to the Progressive Lens Design Processor  320 , for performing a Search Management step  450 , including at least one of 
     (a) reversing a search path in a Design Factor space; 
     (b) reverting to a preceding bifurcation in a search path in a Design Factor space; 
     (c) jumping to another Design Factor vector; 
     (d) changing a number of the Design Factors; 
     (e) fixing a design factor; 
     (f) changing a speed of performing the method; and 
     (g) evaluating whether search has been successful. 
       FIG. 23B  illustrates that these Search Management steps  450  can be carried out in an interactive manner in some embodiments. In such embodiments, the Search Guidance Engine  330  may be coupled to the Feedback-Controller interface  310 , for performing the Search Management step interactively  455  by 
     (a) proposing to an operator to select a Search Management step  450 ; 
     (b) receiving a selection of a Search Management step  450  from the operator; and 
     (c) initiating an execution of the selected Search Management step  450 . The initiating  455 ( c ) may involve the Search Guidance Engine  330  instructing the Progressive Lens Design Processor  320  to carry out the selected Search Management step  450 . 
     In these embodiments, the Search Guidance Engine SGE  330  may not simply carry out a Search Management step  450  on its own directive, such as fixing a Design Factor. Instead, it may interactively propose a contemplated Search Management step  450  to the patient and act only upon receipt of a response. By way of an example, the SGE  300  may prompt the patient via the FCI  310 : “Should we fix the location of the optical center, and restrict the search to the remaining Design Factors?” in the step  455 ( a ), then receive the selection from an operator, e.g. “Yes” in step  455 ( b ), and initiate carrying the selection, such as reduce the number of Design Factors by one, thereby reducing the dimensions of the Design Factor space by one in step  455 ( c ). In some embodiments, the Search Guidance Engine  330  may offer alternatives: “should we go back to a preceding bifurcation in a search path in a Design Factor space, or should we reverse the search path?”, and carry out the responsive wish of the patient or the operator. 
     In some embodiments, the Search Management method  455  can involve 
     (d) the patient storing a selected first lens design  123 ; 
     (e) continuing the Lens Design Exploration by any embodiment of the method  400 ; for example, by reversing the search path by step  450 ( a ), or reverting to a preceding bifurcation by step  450 ( b ); 
     (f) selecting a subsequent second lens design  123 ; and 
     (g) comparing the stored first lens design with the second lens design. 
     In some embodiments, the Search Guidance Engine SGE  330  may be integrated with the Progressive Lens Design Processor  320 . 
     An aspect of the described embodiments that rely on subjective Visual Feedbacks  430  is that the exploration is not guided by objective merits and measures. In practice, this can, and does, reduce the repeatability of the exploration. When the same patient repeats the same lens design exploration at a later time, it has been observed that she/he sometimes arrives at different lens designs: a potentially unsatisfying outcome. 
     Repeatability and the soundness of the lens selection can be enhanced by PLS  100  systems acquiring and prioritizing objective Visual Feedbacks  430 . One such objective Visual Feedback  430  has been already described by the Eye tracker  110  measuring a jitter of the visual axis. Another class of objective Visual Feedbacks  430  can be generated by integrating diagnostic systems into the PLS  100 . Such systems may include Purkinje-spot-based imaging systems, OCT systems, Scheimpflug imaging systems, wavefront analyzers, corneal topographers, slit lamps, and related other systems. 
     Another class of improvements can be introduced by using eye models to evaluate, organize and analyze the diagnostic measurements. Several eye models are known in ophthalmology, often used in preparation for cataract surgery, such as the Holladay and Hoffer models. 
     The next class of embodiments provides an improvement in this direction. These embodiments define a quantitative metric that can prove quite useful to make the lens design exploration more effective, and lead to repeatable outcomes. 
       FIGS. 24A-B  introduce the concept of objective, quantitative measures and metrics to assist the exploration of the progressive lens designs  123 . Such objective measures are already used by the designers of progressive lenses. However, presently these measures are used by computer programs separately from the patient&#39;s exploration, much after the patient visits the optometrist&#39;s office. Typically, the computer code performs the lens design search separated from the patient by hundreds, or thousands, of miles and hundreds, or thousands, of hours. In typical cases today, an optometrist determines a couple vision correction parameters in her office, for example, the optical powers for the near and distance vision regions, and then sends these parameters to a progressive lens designer. Weeks later, the progressive lens designer runs a computer code that computes a contour map that optimizes the lens performance with the sent optical powers. This optimization uses some quantitative measures to characterize the lens&#39; performance, typically developed by the lens design company. 
     However, in today&#39;s practice, this optimization is not interactive; the patient is simply given the end product. There is no visual feedback from the patient during the design process, and no chance for the patient to indicate that the computer-designed progressive lens is far from optimal for the patient&#39;s specific needs. Thus, if the patient decides that the visual experience with the progressive lens is unsatisfactory, then he returns the lens and a time consuming, inefficient back-and-forth process starts between the lens designing company and the patient, typically shipping the glasses back-and-forth, and involving additional grinding of the lens, often taking many weeks, and wasting valuable time from all involved. 
     The here-described embodiments in general offer a substantive improvement over this state of the art by simulating a progressive lens design  123  in real time in steps  401 - 403 , then acquiring a Visual Feedback  430  from the patient in real time in step  404 , and modifying the progressive lens design  123  in real time in step  405 , performing all these steps iteratively. The now-described embodiments offer a further improvement by introducing objective lens merit factors  460  and weave these lens merit factors  460  into the modifying the progressive lens design step  405  of the method  400 . 
     These lens merit factors  460  can include: 
     (a) an improved visual acuity in one of a near and a far vision region; 
     (b) a reduced astigmatism in one of a near and a far vision region; 
     (c) a reduced swim in one of a near and a far vision region; 
     (d) a reduced blur in one of a near and a far vision region; 
     (e) a suitable progressive region; 
     (f) an alignment of a cylinder; 
     (g) a suitable prism; and 
     (h) a suitable progressive prism. 
     Many-many more Lens Merit factors can be developed, including more technical ones, such as the integral of a measure of an astigmatism over a vision region, or the coefficient of a Zernike polynomial of the lens, possibly narrowed to a region. 
       FIG. 24B  shows that, as before, these Lens Merit Factor  460  can be thought of as a Lens Merit vector LM  460 , with components LM=(LM 1 , LM 2 , . . . , L m ). In some cases, these individual Lens Merit factors can be combined into a single global Lens Merit with suitable weight factors. For example, this single combined Lens Metric can be |LM|, the overall length, or magnitude of the LM vector, combined from the sum of the squares of the individual components: |LM|=[Σ i LM i   2 ] 1/2 . To simplify the notation in the below complex discussion, the labels  420 ,  430  and  460  are sometimes suppressed for the DF, VF and LM vectors. 
       FIG. 25  illustrates the impact of introducing the Lens Merit factors into the progressive lens designs as a quantitative metric to assist the exploration of lens designs. In embodiments, the modifying step  440  can be expanded into the modifying the one or more Design Factors step  465 , the modifying step  465  being based on the Visual Feedback  430  and on one or more Lens Merit factors  460 . In the vector notation, the n th  iteration of the Design Factor vector DF(n) gets modified by ΔDF(n)=DF(n+1)−DF(n), where ΔDF(n) is computed based on the Visual Feedback vector VF(n)  430  and the Lens Merit factor vector LM(n). 
       FIG. 26  illustrates that when VF(n) and LM(n) determine ΔDF(n) approximately linearly, then the modifying step  465  can specifically involve a modifying step  470  that includes performing a Merit-Matrix Method step by modifying the one or more Design Factors DF(n)  420  in relation to the Visual Feedback VF(n)  430  and on one or more Lens Merit factors  460  with a Visual Feedback+Lens Merit-to-Design Factor matrix  329 . As illustrated, this method  470  is driven not only by the Visual Feedback VF  430 , but also by the objective Lens Merit LM  460 . These two vectors VF and LM can be combined into a composite VF+LM vector whose length equals the length of the two vectors separately. Next, the VFDF matrix  326  can be extended by a Lens Merit-Design Factor matrix LMDF  328 , the juxtaposition of the two matrices together forming the Visual Feedback+Lens Merit-to-Design Factor matrix  329 . Multiplying the extended VF+LM vector with the extended VFDF+LMDF matrix determines ΔDF(n) in these embodiments. 
       FIG. 27  illustrates some of the benefits of such Lens Merit-computing embodiments. For a specific progressive lens design  123 , represented by a Design Factor vector DF, the components of the Lens Merit vector LM can be calculated.  FIG. 27  shows only one component of the LM vector. In certain cases, this can be the global Lens Merit |LM|, mentioned earlier. In other embodiments, the method  475  described below can be practiced for several of the Lens Merit components simultaneously. By way of example, the shown component of the LM vector, LM j , can be the integrated astigmatism over the near vision region. The lower LM j , the integrated astigmatism, the better the progressive lens design  123 . In the context of  FIG. 27 , this means that the optimal progressive lens design  123  corresponds to the DF=(DF 1 , DF 2 ) vector  420  that points to the extremum of the Lens Merit function. For a multicomponent Lens Merit vector LM, the individual Lens Merit factors LM i  can be minimized in a coupled, interconnected manner. The issue of the Lens Merit function having more than one extrema (as illustrated in  FIG. 27 ) will be addressed below. 
     In some existing systems, where the progressive lens design  123  is determined by a lens design code without a Visual Feedback  430 , the lens design code optimizes the lens design only by modifying the Design Factors DF 1  and DF 2  so that the Design Factor vector DF points to the location of the local minimum of the Lens Merit function, shown in  FIG. 27 . Such local minima indicate that the corresponding combination of the Design Factors DF 1  and DF 2  optimizes the visual experience according to ray tracing, optical measurements and large number of collected patient experiences, but without a feedback of the specific patient whose progressive lens is being designed. Such optima reflect a compromise between the different design goals. 
     The methods  465 ,  470  and  475  transcend previously described systems by modifying the one or more Design Factors  420  based on the Visual Feedback  430  in combination with one or more Lens Merit factors  460 . Therefore, the methods  465 / 470 / 475  transcend the method  440  that primarily relies on the Visual Feedbacks  430  but does not use the Lens Merit  460 . These methods also transcend the present progressive lens design codes that rely only on computing Lens Merit  460 , but do not use the Visual Feedback  430 . Combining the two design-drivers VF  430  and LM  460  is challenging, for several reasons. First, to be able to combine these design-drivers requires the Visual Feedbacks  430  to be quantified, but the quantification of the various subjective Visual Feedbacks  430  is far from obvious. Second, a lot of thoughtful decisions are needed to select the weight factors that combine the VF and LM factors and components. Further, communicating the contemplated lens design updates to the patient in a relatable manner is also a non-trivial task, among others. 
     Typically, the patient&#39;s Visual Feedback VF  430  may request, or prompt, a modification of the Design Factor vector DF  420  that is different from the DF  420  pointing to the local minimum of LM. This can happen for a variety of reasons including the following. (a) The local minimum of LM in the lens design code was determined by averaging feedback from a large number of patients, and thus may not be optimal for any specific patient. (b) The search may involve optimizing several LM components simultaneously. Compromising between these coupled searches can make a DF  420  optimal for a vector other than the minimum for the coupled search. (c) The exploration uses a combination of the VF  430  and the LM vectors  460  to modify the DF vector  420 . The subjective VF  430  inputs may nudge the exploration towards new design goal compromises. Also, in some embodiments, the patient may be offered to shift the mixing and weighing parameters, again moving the local minimum. 
     In some embodiments, the modifying step  475  may include modifying the Design Factors locally in a Design Factor space by utilizing at least one of a gradient descent method, a conjugate descent method, and a hill-climbing method. In  FIG. 27 , this can be implemented at any DF(n) vector by, e.g., searching out the locally steepest downhill gradient that promises to move the exploration towards the local minimum in the fastest manner. Since these methods build on local information about how the LM i  components depend on their DF j  coordinates, the method  475  is characterized as a local modification. 
     However,  FIG. 27  also illustrates that such local methods may not reach the global minimum of the Lens Merit function LM  460 , if the global minimum is at some distance away from the local minimum in the Design Factor space. In this typical case, the local steepest descent gradient methods often guide the search only into the local minimum, where the methods may get stuck and never reach the global minimum, located a distance apart. 
       FIGS. 28A-B  illustrate that in such cases a modifying  480  of the one or more Design Factors  420  based on the Visual Feedback  430  and on one or more Lens Merit factors  460  may include modifying  480  the Design Factor  420  non-locally in the Design Factor space. This non-local modification  480  may utilize at least one of a simulated annealing, a genetic algorithm, a parallel tempering, a stochastic gradient descent, stochastic jumps, and a sequential Monte Carlo. 
     In the shown example, the lens design exploration may be getting temporarily stuck around a Local Optimum  482  when only local search methods  475  are utilized. It is noted that the optimum can be either a minimum or a maximum. In  FIG. 27 , the desired design corresponded to a minimum of the LM function, in  FIGS. 28A-B , it corresponds to a maximum. The local modification methods  475  can get stuck in the Local Optimum  482  that is distinctly away from the Global Optimum  484 . Embodiments of the method  480  can free the stuck search by employing the above listed non-local methods. A large number of additional non-local modifications are also known in the arts. One such method is to implement large, stochastic jumps, to get a stuck search “unstuck”.  FIG. 28A  illustrates that a search that uses only local techniques may have gotten stuck around the Local Optimum  482 , but performing a stochastic jump embodiment of the non-local method  480  can get the search unstuck, and enable it to find the Global Optimum  484 .  FIG. 28B  illustrates the same stochastic jump embodiment of the method  480  from a perspective view. Visibly, the stochastic jump is taking the stuck search from the jump-start to the well-separated jump-end point in the Design Factor space, enabling it to reach the Global Optimum  484 . 
       FIG. 29A  illustrates that—in analogy to method  450  in  FIG. 23A —any one of the  465 - 480  embodiments of the modifying the one or more Design Factors step based on the Visual Feedback  430  in combination with on one or more Lens Merit factors  460  can comprise a Search Management step  490 , performed by the Search Guidance Engine  330 , that can include at least one of the followings. 
     (a) Reversing a search path in a Design Factor space; 
     (b) reverting to a preceding bifurcation in a search path in a Design Factor space; 
     (c) jumping to another Design Factor vector; 
     (d) changing a number of the Design Factors; 
     (e) fixing a Design Factor; 
     (f) changing a speed of performing the method; and 
     (g) evaluating whether search has been successful. 
     As discussed in relation to the analogous method  450 , such Search Management methods, or steps  490  can be very helpful when the patient&#39;s exploration of lens designs becomes disoriented, or stuck. In other embodiments, their primary value can be that they accelerate the progressive lens design exploration, making it much more efficient. In this method  490  the added factor relative to the method  450  is that a quantitative metric is involved in the form of the Lens Merit  460 . The introduction of such a quantitative metric greatly narrows down the search. For example, the large number of possible ways of modifying the Design Factors  420  that need to be evaluated when practicing the methods  440  and  450  can be reduced substantially by narrowing the search to paths that the Lens Merit  460  identifies as locally optimal. In the example of  FIG. 29A  this is demonstrated by a non-merit guided searches  440 - 455  needing to evaluate all possible Design Factor modifications, whereas the Lens Merit factor guided searches  465 - 490  needing to evaluate only moves along the narrow ridge, preferred by the Lens Merit  460 , or in some vicinity of this ridge. 
     As before,  FIG. 29A  shows the search path on the ridge for only one LM component for clarity. As the Search Guidance Engine  330  is monitoring the search and senses that the ridge is getting lower (as shown), i.e. the search by the local modifying method  475  is getting farther from possibly finding a local maximum of the Lens Merit  460 , the Search Guidance Engine  330  may activate the method  490 ( a ) and perform a Search Management step to reverse the search path. Here the availability of the quantitative metric of the Lens Merit  460  is quite useful, as the reverse search path in the Design Factor space is a well-defined ridge of the Lens Merit function  460 . The availability of a well-defined reverse path can greatly increase the efficiency of the exploration and search. 
       FIG. 29A  also illustrates an embodiment of the method  490 ( b ), reverting to a preceding bifurcation in a search path in the Design Factor space. During the search, or exploration, at some points two choices may appear comparably meritorious. In such instances, the patient, or the Progressive Lens Design Processor  320 , needs to choose one of the possibilities.  FIG. 29A  illustrates such a case, when the search path encountered the locally optimal ridge splitting, or bifurcating, into two, comparable ridges. The search method  465 , possibly with its local embodiment  475 , chose the left branch and pursued the exploration for a while. However, after monitoring the exploration on the left ridge for a while, the SGE  330  decided that is was time to practice the method  490 ( a ) and reversed the search. Advantageously, the quantitative Lens Merit function  460  provided clear guidance how to pursue the reversed path: by retracing the ridge of the LM  460 . Retracing the ridge leads the search back to the bifurcation, where previously the search selected the left ridge. At this junction, the Search Guidance Engine  330  now may choose the right ridge instead and then re-engage the local modifying method  475  to guide the search along the other ridge. Throughout these steps of the methods  475  and  490 , the quantitative Lens Merit function  460  provided clear guidance and greatly narrowed the searches to be pursued (from exploring surfaces to exploring ridges), thereby enhancing the efficiency and speed of the overall exploration. 
       FIG. 29B  illustrates that in some embodiments, the method  490  can be expanded to include method  495  that includes performing the Search Management step with a Search Guidance Engine  330  interactively by 
     (a) proposing to an operator to select a Search Management step; 
     (b) receiving a selection of a Search Management step from the operator; and 
     (c) initiating the execution of the selected Search Management step. 
     As with the interactive method  455  earlier, the method  490  can also be improved by performing it interactively. Instead of the Progressive Lens Design Processor  320  and the Search Guidance Engine  330  of the LDES  300  making choices by some algorithm, the method  495  prompts the patient, or operator, to provide Visual Feedback  420 , command or control signal, and to select a Search Management step, thereby accelerating the lens design exploration via executing steps  495 ( a )-( b )-( c ). 
     In some further embodiments, the modifying the progressive lens design can include modifying the progressive lens design by the Progressive Lens Design Processor  320  utilizing non-linear Visual Feedback-to-Design Factor functional relations, or non-linear Visual Feedback+Merit-to-Design Factor functional relations. These embodiments can be alternatives and complementary of the Matrix methods  445  and  470 , where these relations are dominantly linear. 
     Some embodiments of the PLS  100 +LDES  300  system and the method  400  of its operation can include additional elements and steps that make it essentially certain that the overall method  400  will not deliver inferior results for the patient relative to traditional practices. These additional steps can include determining a second, “traditional” progressive lens design that is based on traditional measurements that do not involve simulating the second progressive lens design for the patient and asking for his/her Visual Feedback. This can be followed by generating a Comprehensive PLS  30  with this second, traditional progressive lens design. In such embodiments, the patient gets to experience the progressive lens design the traditional approach delivers, as well as the lens design identified by method  400  with the help of PLS  100 +LDES  300 . Generating the two differently designed progressive lenses, one with simulation and feedback, the other with traditional measurements, the patient can compare the Comprehensive PLS of these two designs, and select his/her preference. With this extension of the method  400 , the patient cannot end up with a lens worse than the traditional progressive lens, as he/she can elect the traditional progressive lens anytime, even at the very end of the office visit, if he/she is unsatisfied with the lens design determined by the method  400 . 
     In some cases, the generating  402  an image  21  by an Image Generator  121  can be in response to an image selection. This additional step can again improve patient satisfaction, as the patients can select images that are relevant for their activities, instead of the well-known rows of letters which do not capture life-like applications. For example, a team sports person may need extra emphasis on peripheral vision, and thus can select an image  21  that has notable features located peripherally. A long-distance truck driver may select moving images, as she/he may prioritize optimizing her/his vision for moving images. A night guard may select images with low light conditions, as she/he may need to optimize his vision for low light circumstances. Offering to test a patient&#39;s vision on use-relevant images can further improve the customization and thereby the satisfactory outcome of the method  400 . 
     7. Deep Learning Method for a Progressive Lens Simulator with an Artificial Intelligence Engine 
     Translating the Visual Feedbacks  430  into the Design Factors  420  most effectively is a substantial challenge. The various methods  400 - 495  offer promising embodiments. To make these embodiments the most efficient, their many parameters need to be optimized. These include the elements of the Visual Feedback-to-Design Factor matrix  326  and the elements of the Visual Feedback+Lens Merit-to-Lens Design matrix  329 . For other embodiments, the possibly non-linear connections between the Visual Feedbacks  430  and the Design Factors  420  need to be tabulated and parameterized. Also, the Search Guidance Engines  330  are best operated on the basis of past experiences. The Search Management methods  450 / 455  and  490 / 495  can also be improved by learning from the lessons of the preceding explorations. Finally, performing the local modification steps  475  and non-local modification steps  480  can be done most efficiently by utilizing the lessons of past searches. 
     Recently, entirely new ways of learning from past experiences have been automated in remarkably effective new ways. In various technical treatises, these ways are called Artificial Intelligence, Deep Learning, Machine Learning, or Neural Networks. Other names are also used to capture roughly the same learning methods. The embodiments in this section integrate Artificial Intelligence-based learning methods and systems into the GPS  10  so as to improve and optimize the many parameters of the matrices  326  and  329 , the operations of the SGE  330  and the methods of  450 / 455  and  490 / 495 , as well as the other areas where learning from past experiences is beneficial. 
       FIG. 30  illustrates such a Guided Lens Design Exploration System of Simulated Progressive Lenses (GPS)  10 . This GPS  10  can include any embodiment of the Multistage PLS  100 , Integrated PLS  200 , or head-mounted PLS  260 .  FIG. 30  illustrates these possibilities via the multistage PLS  100  embodiment as an example. The PLS  100  can be combined with any, previously described embodiment of the Lens Design Exploration System for the PLS LDES  300 . The combination of the PLS  100  and the LDES  300  can be integrated with an Artificial Intelligence (AI) Engine for the GPS (AI-GPS)  500 . In this section, the GPS  10  will reference the combination of any embodiment of the PLS  100  with the LDES  300  and the AI-GPS  500 . 
     The AI-GPS  500  can include an AI Engine for the Progressive Lens Design Processor (AI-PLD)  510 , integrated into, or at least coupled to the Progressive Lens Design Processor  320 . The AI-PLD  510  can carry out most of the functions of the Visual Feedback-to-Lens Design Transfer Engine FLE  325 , and thus can be viewed as an embodiment of the FLE  325 . A function of the AI-PLD  510  can be to perform any one of the many known Deep Learning methods in order to make translating the Visual Feedbacks  430  into modifications of the Design Factors  420  more efficient. As indicated, these Deep Learning methods can repeatedly update and improve the matrix elements of the Visual Feedback-to-Design Factor matrix  326 , and the matrix elements of the Visual Feedback+Lens Merit-to-Lens Design matrix  329 . 
     In some embodiments, the GPS  10  can also include an AI Engine for the Search Guidance Engine (AI-SGE)  530 , integrated into, or at least coupled to the Search Guidance Engine SGE  330 . A function of the AI-SGE  530  can be to perform any one of the many known Deep Learning methods with the SGE  330  in order to make it more efficient in guiding the exploration of the progressive lens designs  123  by any of the described methods. These methods include method step  450 : performing a Search Management step with the SGE  330 ; method step  455 : performing the method step  450  interactively with a patient; performing the method step  490 : performing a Search Management step  330  with the SGE  330  that involves a Lens Merit  460 ; and method step  495 —performing the method step  490  interactively with a patient, among others. 
     Finally, the AI-GPS  500  can also include an AI Engine for the Progressive Lens Simulator (AI-PLS)  550 , integrated into, or at least coupled to PLS  100 , in some case integrated into its OPS processor  122 . Embodiments of the AI-PLS  550  can perform any of the known AI Deep Learning methods with the OPS Processor  122  to simulate the progressive lens designs  123  better, learning from the Visual Feedbacks  430  of the patients. 
       FIGS. 31-33  illustrate an embodiment of the AI-PLD  510 . The AI-SGE  530  and the AI-PLS  550  can have closely analogous designs and will not be expressly described to avoid triplication. 
     The AI-PLD  510  can include an input layer  515 , configured to receive the Visual Feedbacks VF i (n) as inputs. (In what follows, the label  430  will not always be expressly shown for the Visual Feedbacks  430 , and the label  420  for the Design Factors  420 , in order to avoid clutter). The patient, the operator or an objective feedback generating system, such as the Eye tracker  110 , or any other eye imaging system can input the VF i  elements of the Visual Feedback vector VF(n). As before, “n” references the n th  iteration of the lens design exploration method  400 . A Visual Feedback-Design Factor VFDF Neural Network  520  can receive the VF i (n) Visual Feedbacks as inputs. This VFDF Neural Network  520  can be configured to output a proposed update of the Design Factor vector ΔDF(n)=DF(n+1)−DF(n) at an output layer  525 . In this sense, the VFDF Neural Network  520  plays an analogous function as the Visual Feedback-to-Lens Design Factor Engine FLE  325 . 
     One of the differences is that the input VF vector  430  is not connected to the output DF vector  420  by a linear VFDF matrix  326 . Instead, it is generated by a series of transformations, performed by a series of hidden layers  521 ,  522 ,  523 , as shown. Each hidden layer can include a set of neurons. In the shown example, the first hidden layer  521  includes neurons N 11 , N 12 , . . . , N 16 . In this description, the specific number of hidden layers, and the number of neurons in each layer is for illustrational purposes only. Other embodiments may contain any other number of hidden layers and neurons per layer. 
     The neurons N 1i  in the first layer can be coupled to the neurons of the second layer N 2j , with coupling strength C( 1   i - 2   j ). 
       FIG. 32  illustrates the operation of the VFDF Neural Network  520 . The effect of the Visual Feedback vector elements VF i  getting processed by the neurons of a hidden layer is to multiply the VF vector with a Coupling Matrix CM. Here the notation is used that the (ij) element of the CM(m) matrix is the coupling constant between the neuron N mi  and N (m+1)j . With this notation, the above referenced coupling constant C( 1   i - 2   j ) is CM( 1 ) ij , the (ij) element of the CM( 1 ) matrix. Thus, the output of a VFDF Neural Network  520  with K hidden layers can be written as:
 
VF( n ) T *CM(1)*CM(2)* . . . *CM( K )=ΔDF( n ) T ,  (2)
 
     where the superscripts T in VF(n) T  and ΔDF(n) T  indicate vector transposition. This is needed, as in  FIG. 22A  the vectors were multiplied with the matrices from the left, while in  FIG. 32  from the right, to represent the left-to-right layer-to-layer processing of the Visual Factor vector VF  430  in  FIG. 31 . Obviously, these two descriptions are equivalent. 
     In words, the output vector ΔDF  420  is related to the input vector VF  430  not by a single linear relation, as in the case of the VFDF matrix  326 , but by a product of several bilinear relations. It is noted that the dimensions of the individual coupling matrices can vary in this product, or chain, of coupling matrices. Selecting these dimensions wisely can enhance the efficiency of the lens design exploration. For example, there are AI methods that advocate initially shrinking the dimensions of the CM(i) matrices, then increasing them back up, forming a sort of tunnel, or spool. 
     The neurons in each layer can be set up to react to their input in a variety of ways. A simple, widely used algorithm is a sharp threshold input-output relation output=ƒ(input). The inputs from the preceding layer i to a particular neuron N (i+1)l  in the (i+1) th  layer can be added up, and if this sum exceeds a threshold T, then the neuron N (i+1)l  outputs a value, whereas if the sum does not exceed the threshold T, then the output remains a different, lower number, typically zero. In formula,
 
 N   (i+1)l =ƒ(Σ j   N   ij CM( i ) jl   −T ),  (3)
 
     where ƒ(x) is a sharp switching function of its argument x, typically a step function centered at 0, or a somewhat smoothed step function such a tan h(x). A motivation for this switching function ƒ(x) emerged from the operations of neurons: the inputs from several dendrites that bring in the inputs in the form of stimuli are often added up by the electric charging processes of the neuron. As carried out by the ion pumps across the cell membrane. If the sum of these stimuli, often in the form of a total charge, exceeds a threshold, then the output axon of the neuron fires. If the charging does not reach the threshold, the neuron does not produce an output. 
     One way to set up such a VFDF Neural Network  520  is Google&#39;s TensorFlow machine learning framework. This is a convenient sophisticated framework that can be set up with limited preparation. A designer of the VFDF Neural Network  520  can simply enter the number of hidden layers, the number of neurons in each layer, and the switching functions ƒ(x). Then, the initial coupling constants of the VFDF Neural Network  520  can be entered or defined. 
     After this initialization, the VFDF Neural Network  520  is driven through many teaching, or learning cycles so that the VFDF Neural Network  520  learns from incorrect, or low fidelity results, and the initial coupling constants “learn” and evolve to reduce the incorrect outputs and produce the correct output in a high percentage of the cases. 
       FIG. 33  illustrates one such teaching, or learning approach, called Back-propagation with Gradient Descent (BGD)  510 T. In such a supervised learning method, a Visual Feedback vector VF  430  is inputted where the supervisor knows what output is expected. As an example, if the Visual Feedback  430  is that the image is blurry in the lower nasal quadrant after the refractive power has been increased in this quadrant in the few preceding cycles, then the expected output ΔDF is to keep increasing the refractive power in this quadrant. With this known expectation, this VF is inputted into the input layer  515  and then processed with the VFDF Neural Network  520 , resulting in an output at the output layer  525 . An output evaluator  526  then can compare the output with the targeted output and send the difference signals Δ i (n+1) back through the same VFDF Neural Network  520  as an error-back-feed  527 . In the given example, whether the VFDF Neural Network  520  outputted a “Increase the refractive power further in the lower nasal quadrant” ΔDF(n) modification of the Design Factor vector DF  420 , and what power increase was outputted specifically. As the difference signal is propagating backwards, a coupling trainer  528  can modify the individual couplings CM(i) kl  to reduce the difference. Performing these training cycles a large number of times with a large number of inputted visual feedback vectors VF  430  trains the elements of the Coupling Matrices CM( 1 ), CM( 2 ), . . . , CM(K) so that after the training, when new Visual Feedbacks VFs are inputted for which the outputs are not known, the VFDF Neural Network  520  will still output the most appropriate ΔDF with high reliability. 
     Training the AI Engine AI-GPS  500  with this Back-propagation with Gradient Descent (BGD)  510 T is just one of the many possible methods to train this VFDF Neural Network  520 . Also, using this particular Neural Network implementation of the AI-GPS  500  is just one of the many embodiments of the basic idea to utilize Artificial Intelligence systems and methods to improve the GPS  10 . All these combinations and alternatives share the following two basic design principles, and can be viewed as embodiments of the GPS  10  system, as long as they do so. These design principles include the followings. 
     (1) To fine tune the large number of parameters needed to operate the GPS  10  system, such as the elements of the VFDF matrix  326  or the Visual Feedback+Lens Merit-to-Design Factor matrix  329 , utilizing the remarkable power of Artificial Intelligence in any suitable AI-GPS engine  500 . 
     (2) To continue developing and improving these GPS  10  systems as a rapidly increasing number of GPS  10   s  are installed worldwide. It is envisioned that a centralized system may collect the exponentially increasing amount of data on the many-many lens design explorations performed worldwide daily by a rapidly increasing number of patients. This exponentially increasing “big data” can be analyzed by central AI-GPS engines  500 . The results of this analysis can be pushed out from the central system to the individual GPS  10  systems in the form of updates for the on-board search software, including the training of the elements of the matrices  326  and  329 , the training of the various steps that can be involved in the Search Management  450 / 455  and  490 / 495 , and the training of the simulation of the progressive lens designs  123  by the PLS  100   s . Any embodiment that shares these two basic drivers is an embodiment of the GPS  10 . 
       FIGS. 34-35  illustrate methods of operation of the AI-GPS engines  510  and  530 .  FIG. 34  illustrates a Method  610  of operating a Progressive Lens Simulator  100  with an Artificial Intelligence Engine  500 , comprising: 
     (a) generating  611  a Comprehensive Progressive Lens Simulation (Comprehensive PLS)  30  for a patient with a Progressive Lens Simulator  100 , based on a progressive lens design  123  with a Design Factor vector DF  420 , generated by a Progressive Lens Design Processor  320 ; 
     (b) receiving  612  a Visual Feedback vector VF  430  into a Visual Feedback-Design Factor Neural Network  520  of an Artificial Intelligence Engine for the Progressive Lens Design Processor AI-PLD  510 , in response to the Comprehensive PLS  30 ; and 
     (c) outputting  613  a modification ΔDF of the Design Factor vector DF  420  with the Visual Feedback-Design Factor Neural Network  520 , in response to the receiving  612 ; wherein 
     (d) coupling matrices CM(i) of the Visual Feedback-Design Factor Neural Network  520  were trained by performing  614  a deep learning cycle. 
     The method  610 , further comprising: 
     (e) modifying the progressive lens design  123  by the Progressive Lens Design Processor  320  using the modified Design Factor vector DF  420 ; and 
     (f) generating a modified Comprehensive PLS  30  by the Progressive Lens Simulator  100 , using the modified progressive lens design  123 . 
     Repeating steps (b)-(f) iteratively can serve as the backbone of the exploration of the progressive lens designs, as described in relation to method  400 . 
     The Visual Feedback-Design Factor Neural Network  520  can include layers of neurons N ij , including an input layer  515 , one or more hidden layers  521 / 522 / 523 , and an output layer  525 . The neurons N ij  can having switching functions ƒ(x), and the neurons N ij  can be coupled by the coupling matrices CM(i). 
     The performing a learning cycle step  614  may include performing the deep learning cycle by Backpropagation with Gradient Descent (BGD)  510 T. Performing the deep learning cycle step with BGD  510 T can fine tune the elements of the Coupling matrices CM(i) kl  to provide the most reliable translation of the Visual Feedbacks  430  into changes of the Design Factor vector  420 . 
     The performing a learning cycle step  614  may also include using  615  a Lens Merit function LM  460 . Using a Lens Merit function LM  460  can enable the AI-GPS engine  500  and specifically the AI-PLD  510  to train and improve the Lens Merit based methods  465 - 495 . 
     The performing a deep learning cycle  614  can also include evaluating  616  the outputted Design Factor vector DF  420  with an output evaluator  526  in relation to a target Design Factor vector DF  420  corresponding to the inputted Visual Feedback vector VF  430 ; and training the coupling matrices CM(i) according to the evaluating with a coupling trainer  528 . 
     In some embodiments, the performing a deep learning cycle step  614  can include modifying  617  a software of the Progressive Lens Design Processor  320 . 
       FIG. 35  illustrates a Method  620  of operating a Progressive Lens Simulator  100  with an Artificial Intelligence Engine  500 , comprising: 
     (a) generating  621  a Comprehensive Progressive Lens Simulation (Comprehensive PLS)  30  for a patient with a Progressive Lens Simulator  100 , based on a progressive lens design  123  generated by a Progressive Lens Design Processor  320 ; 
     (b) receiving  622  a Visual Feedback vector VF  430  into a Visual Feedback-Search Management Neural Network  520 -SGE of an Artificial Intelligence Engine for a Search Guidance Engine AI-SGE  530 , in response to the Comprehensive PLS  30 ; and 
     (c) outputting  623  a Search Management step  450 / 455 , or  490 / 495 , with the Visual Feedback-Search Management Neural Network  520 -SGE to the Progressive Lens Design Processor  320 , in response to the receiving  622 ; wherein 
     (d) coupling matrices CM(i) of the Visual Feedback-Search Management Neural Network  520 -SGE were trained by performing a deep learning cycle  624 . 
     Here, and in what follows, the Visual Feedback-Search Management Neural Network  520 -SGE will be referenced with the same numerical labels as the Visual Feedback-Design Factor Neural Network  520 , only with the sub-label “SGE” appended to it. This is to avoid needless repetition, as the embodiments are largely analogous. 
     In some embodiments, the method  620  can also include the followings. 
     (e) modifying the progressive lens design  123  by the Progressive Lens Design Processor  320  prompted by the Search Management step  450 / 490 ; and 
     (f) generating a modified Comprehensive PLS  30  by the Progressive Lens Simulator  100 , using the modified progressive lens design  123 . 
     Repeating steps (b)-(f) iteratively can serve as the backbone of the exploration of the progressive lens designs, as described in relation to method  400 . 
     The Visual Feedback-Search Management Neural Network  520 -SGE of the AI-SGE  530  can include layers of neurons N ij , including an input layer  515 -SGE, one or more hidden layers  521 / 522 / 523 -SGE, and an output layer  525 -SGE; the neurons N ij , having switching functions ƒ(x), and the neurons N ij  being coupled by the coupling matrices CM(i). 
     In some embodiments, the performing the deep learning cycle  624  can include performing the deep learning cycle by Backpropagation with Gradient Descent BGD  510 T-SGE. 
     In some embodiments, the performing a deep learning cycle  624  can include using  625  a Lens Merit function LM  460 . 
     In some embodiments, the performing a deep learning cycle  624  can include evaluating  626  the outputted Search Management step  450 / 455  or  490 / 495  with an output evaluator  526  in relation to a target Search Management step corresponding to the inputted Visual Feedback vector VF  430 ; and training the coupling matrices CM(i) according to the evaluating with a coupling trainer  528 . 
     By way of an example, the Search Management step can be  490 ( a ), the reversing the search path. The decision that at which Visual Feedback VF  430  to reverse the path can be very much a complex decision. For example, referring back to  FIG. 29A , it needs to be determined how much does the height of the ridge of the Lens Merit function LM  460  has to drop from its previous maximum for the AI-SGE  530  to activate the Search Management step  490 ( a ). The AI-SGE  530  can be driven through many deep learning cycles to find and learn the optimal value to reverse the search path. 
     Referring to  FIGS. 28A-B  is another example: when a search starts to show less and less progress, the AI-SGE  530  needs to decide when to execute a non-local jump, and whether the jump should be entirely random or be driven by some consideration, e.g., the stored memory of an earlier portion of the search remembering the value of the Lens Merit function LM  460 . As before, the AI-SGE  530  can be driven through many deep learning cycles to find and learn when to initiate a jump, how big a jump to initiate, and how to relate the jump of data stored from the earlier search path. 
     In some embodiments, the performing a deep learning cycle  624  can include modifying  627  a software of the Search Guidance Engine  330 . 
     In some embodiments, the AI-GPS  500  system may include a deep learning unit that uses an eye-model, such as the Holladay or Hofer eye model, wherein the AI-GPS  500  can train the parameters of the GPS  10  based of the eye model. 
     Finally, very analogous methods can be practiced to train and then operate the AI Engine for the Progressive Lens Simulator (AI-PLS)  550 . This AI-PLS  550  can include a Visual Feedback-Lens Simulation Neural Network  520 -PLS, with components and elements very analogous shown in  FIGS. 31-33 , and operated analogously to  FIGS. 34-35 . 
     As mentioned earlier, the Artificial Intelligence Engine  500  can use any of the known AI methods, and is not narrowed down to neural networks alone. Other AI methods include Supervised learning methods, Non-supervised learning, Regression analysis-based methods, Clustering, Dimensionality reduction, Structured predictions, Anomaly detection and Reinforcement training. Any one of these AI methods can be implemented in the AI-GPS  500 . 
     8. Central Supervision Station System for Progressive Lens Simulators 
     One of the substantial benefits of the various embodiments of the GPS system  10 , for example, the PLS  100 , or the PLS  100  combined with the LDES  300 , or the PLS  100  and the LDES  300  combined with the Artificial Intelligence Engine for GPS, AI-GPS  500 , together referenced as the  100 / 300 / 500  embodiments of GPS  10 , is that many of the functions previously executed by an optometrist are now carried out by the automated systems of the GPS  10 . Therefore, an optometrist does not have to be continuously involved in the exploration of progressive lens designs  123 . The patient himself/herself can engage with the  100 / 300 / 500  embodiments of the GPS  10  to execute any version of the exploration method  400 , and take all the time he/she needs. The patient can go back to previously identified lens designs  123  and compare them with new ones; can chose different images  21  with the Image generator  121 ; keep performing Search Management steps  450 / 455  or  490 / 495  to retrace search paths, explore other choices, fix a Design Factor  420  to narrow the search, slow down the search in some region of the Design Factor space, and so on. All of these can be performed without active involvement by an optometrist. 
     From a time-management point of view, this aspect of the GPS  10  systems frees up an optometrist to such a degree that she/he can supervise more than one individual GPS systems  10  simultaneously, thereby eliminating the need of manning each optometry station individually with an optometrist. This aspect can substantially reduce the number of personnel required to service a given number of patients, and thus can be greatly helpful for the business model of the overall optometrist office. 
       FIG. 36  illustrates this concept in some detail.  FIG. 36  illustrates a Supervised Multi-station system  700  of Progressive Lens Simulators  100  that comprises a Central Supervision Station  710 ; coupled to a set of Progressive Lens Simulators  720 - 1 ,  720 - 2 , . . .  720 - n , (together referenced as  720 - i ) by two-way communication-supervision channels  730 - 1 ,  730 - 2 , . . .  730 - n , together referenced as  730 - i .  FIG. 36  illustrates a three station (n=3) embodiment. 
     The individual stations can include the Progressive Lens Simulators  720 - i  that can be any embodiment described earlier in this application, including the multistage PLS  100 , the integrated IPLS  200 , a table-top embodiment of a PLS  100 , and the head-mounted PLS  260 , this latter embodiment being shown in  FIG. 36 . In the shown embodiment, the PLS  720 - i  can individually include an Eye Tracker  110 , for tracking an eye axis direction to determine a gaze distance; an Off-Axis Progressive Lens Simulator  120 , for generating an Off-Axis progressive lens simulation (Off-Axis PLS  20 ) of a progressive lens design  123 ; and an Axial Power-Distance Simulator ADS  130 , for simulating a progressive lens power in the eye axis direction, thereby creating a Comprehensive Progressive Lens Simulation (PLS)  30  of a progressive lens design  123  from the Off-Axis PLS  20 . 
     Some elements of the PLS  720 - i  can be implemented in the Central Supervision station  710 . The Central Supervision Station  710  can be in communication with the Progressive Lens Simulators  720 - i , for providing supervision for an operation of the individual Progressive Lens Simulators  720 - i.    
     This communication can take place via the two-way communication channels  730 - i  between the individual Progressive Lens Simulators  720 - i  and the Central Supervision Station  710 : the Progressive Lens Simulators  720 - i  can inform the Central Supervision Station  710  about simulations of progressive lens designs  123 ; and the Central Supervision Station  710  supervising the simulation by the Progressive Lens Simulators  720 - i . The communication channels  730 - i  can be wired communication channels or wireless communication channels. 
     In some embodiments, the Progressive Lens Simulators  720 - i  can individually comprise Lens Design Exploration Systems LDES  300 , for guiding an exploration of progressive lens designs  123 . 
     In other embodiments, the Central Supervision Station  710  can comprise a centralized Lens Design Exploration System LDES  300 , for guiding an exploration of progressive lens designs, and communicating corresponding guidance signals to the individual Progressive Lens Simulators  720 - i.    
     In some embodiments, the individual Progressive Lens Simulators  720 - i  can include dedicated individual Artificial Intelligence (AI) Engines for executing a deep learning method for Progressive Lens Design Processors  320 - i  of the Progressive Lens Simulators  720 - i . These AI Engines can be embodiments of the AI Engines for the Progressive Lens Design Processors (AI-PLD)  510 . 
     In other embodiments, the Central Supervision Station  710  can comprise a centralized Artificial Intelligence Engine  500 , for executing a deep learning method for the Progressive Lens Design Processors  320  of the Progressive Lens Simulators  720 - i , and communicating corresponding training signals to the individual Progressive Lens Simulators  720 - i.    
     In some embodiments, the Progressive Lens Simulators  720 - i  can individually comprise Artificial Intelligence Engines, for executing a deep learning method for Search Guidance Engines  330  of the Progressive Lens Simulators  720 - i . These AI Engines can be embodiments of the AI Engines for the Search Guidance Engines (AI-SGE)  520 . 
     In other embodiments, the Central Supervision Station  720 - i  can comprise a centralized Artificial Intelligence Engine  500 , for executing a deep learning method for a centralized Search Guidance Engine, and communicating corresponding guiding signals to the individual Progressive Lens Simulators  720 - i . The Search Guidance Engine can be also centralized, or can reside in the individual PLS  720 - i  as individual SGEs  330 - i.    
     Similarly, an AI Engine can be included for executing a deep learning method for the Off-Axis PLS  120 . As before, this AI Engine can be centralized, or can reside with the individual PLS  720 - i.    
     While this document contains many specifics, these should not be construed as limitations on the scope of an invention or of what may be claimed, but rather as descriptions of features specific to particular embodiments of the invention. Certain features that are described in this document in the context of separate embodiments can also be implemented in combination in a single embodiment. For example, to structure the presentation more clearly, the description of the embodiments was organized into eight sections. However, the features of the embodiments in any one of these sections can be combined with features and limitations of any embodiments from the other seven sections. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or a variation of a subcombination.