DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 5, 6, 8, 10, 14-16, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mills US 2023/0035668 in view of Jensen et al. US 2023/0055308 and Fonte et al. US 2015/0154678.
Regarding 1, Mills teaches a method for making eyewear (see paras. 0023, 0025 and Figs. 8A-8B: teaches method of providing prescription glasses to a user according to the user’s vision prescription, including performing an eye test, determining a user’s vision prescription, displaying/selecting glasses frames, and transmitting the user’s vision prescription and selected-frame identifier to one or more vendors of optical prescription services), comprising:
at a computer system comprising one or more processors and memory (paras. 0031-0032, Figs. 1 and 4: teaches the method being executed by one or more processors and also teaches a computer system including processors and memory storing instructions for providing prescription glasses):
collecting information of a vision test including information of a sequence of visual stimuli and user responses of a user associated with an electronic device having a head-mounted display (HMD) (see paras. 0022, 0036, 0087-0088, 0108-0109, Figs. 3, 7, 8A-8B: teaches a wearable/head-word device, such as AR glasses, that displays a virtual AR eye chart to the user, including rows of letters of diminishing size, captures the user’s responses to prompts, compares the user’s responses with expected responses, and scores the user’s performance);
applying a vision assessment model to process and the information of the vision test and generate a personalized vision plan (paras. 0022, 0099 and Fig. 9: teaches comparing the user’s responses to expected responses, scoring user performance, generating an overall visual score, and generating prescription results including sphere, cylinder, and axis parameters).
However, Mills fails to teach: obtaining personal information and medical history of a user; and a vision assessment model to process the personal information, the medical history.
sending an instruction to a machine for making an eyewear of the user based on the personalized vision plan.
In the same field of endeavor, Jensen teaches obtaining personal information and medical history of a user, and applying a vision assessment model to process the personal information, the medical history, and the information of the vision test and generate a personalized vision plan (see para. 0031 and Figs. 7-10: teaches receiving user/patient information through GUI screens, including age, approximate date of last comprehensive eye exam, gender, whether the user has had an eye infection, eye surgery, prescription eye drops, and listed conditions experienced by the user), storing medical-history information, vision-test results, processed data from remote analysis, screening comments/determinations, and physician review comments/determination (see para 0026 and Fig. 1). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Mills to included Jensen’s patient information and medical history intake and processing because Mills and Jensen are both directed to computerized or remote vison assessment for providing or renewing prescription eyewear, and Jensen teaches that collecting such medical history information supports more reliable assessment, physician review, prescription renewal and identification of users who may require further in person evaluation.
In para 0104 Mills teaches transmitting prescription/frame information to vendors, including a lens grinding vendor. The combination of Mills and Jensen fails to teach: sending an instruction to a machine for making an eyewear of the user based on the personalized vision plan.
In the same field of endeavor, Fonte teaches sending an instruction to a machine for making an eyewear of the user based on the personalized vision plan (see para 0107-0108: teaches preparing the custom product for manufacturing using specification, CNC instructions, 2D or 3D model files, and computer-controlled instruction, and then providing instructions to a manufacturing system. See also para 0300-0301: manufacturing instructions based on prescription information, lens material, and user information). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the Mills/Jensen system to include Fonte’s machine instruction manufacturing process to automate fulfillment of the electronically generated prescription/vision plan, reduce manual conversion of prescription/order date, and reliably manufacture user specific eyewear based on the user’s prescription and selected eyewear information.
Regarding claim 2, the combination of Mills teaches the method of claim 1, and Mills further teaches wherein the personalized vision plan includes eyewear prescription of the user (see para 0099: teaches upon completion of the eye test, the results include prescription parameters such as sphere, cylinder and axis and that the results may be stored, displayed, saved or forwarded).
Regarding claim 3, the combination of Mills teaches the method of claim 2, and Jensen further teaches wherein the personalized vision plan further includes one or more of: a time of usage, a usage pattern, a lifestyle change, and further professional evaluation (para 0007: further professional evaluation, including physician review of eye-examination data and recommending an in-person eye examination for further assessment).
Regarding claims 5 and 19, the combination of Mills teaches the method of claims 1 and 18, Mills further teaches further comprising implementing the vision test for the user at the electronic device having the HMD (see para 0025: teaches performing an eye test using a display device of a head-worn device, such as AR glasses to determine the user’s vision prescription), including: rendering the sequence of visual stimuli on a user interface (para 0022, 0087-0088: teaches displaying a virtual/AR eye chart to the wearer, including rows of letters of diminishing size for visual acuity testing); and obtaining the user responses to the sequence of visual stimuli (para 0022: teaches capturing or recognizing the user’s responses to prompts, comparing the responses with expected responses, and scoring the user’s performance.).
Regarding claims 6 and 20, the combination of Mills teaches the method of claims 5 and 19, and Mills further teaches wherein the vision assessment model is applied after the vision test is completed para 0099 and Fig. 9: teaches that upon completing of the eye test, the results, including prescription parameters such as sphere, cylinder, and axis are stored, displayed, saved, or forwarded).
Regarding claims 8 and 16, the combination of Mills teaches the method of claims 1 and 15, and Mills further teaches wherein the user responses include active user inputs captured by one or more first sensors of the electronic device, the active user inputs associated with the sequence of visual stimuli (para 0022: teaches that the user’s responses to prompts may be captured and recognized by the wearable device using voice recognition techniques and hand gestures), and the one or more first sensors include a forward facing camera for detecting a hand gesture (para 0061 and Fig. 3: teaches AR glasses having outward-facing or front-facing video sensors or digital imaging modules, such as cameras) and a microphone for collecting an audio response (para 0022, 0064: teaches that the AR glasses include peripheral elements such as microphones, and Mills also teaches voice-recognition responses to the eye-test prompts).
Regarding claim 10, the combination of Mills teaches the method of claim 1, and Jensen further teaches wherein the information of the vision test further includes a user survey filled by the user before the vision test (see para 0031: receiving patient information through GUI screens, including an eye health survey).
Regarding claim 14, the combination of Mills teaches the method of claim 1, and Jensen further teaches wherein the personal information of the user includes one or more of: age, sex, education, nationality, ethnicity, religion, and address (see para 0031: The GUI screen shown in FIG. 7 includes an instruction portion 424 asking for the user's age, the approximate date of the last comprehensive eye exam, and gender (optional).).
Regarding claims 15 and 18, recites respectively, a computer-system implementation and a non-transitory computer readable storage medium implementation of substantially the same operations recited in claim 1. Mills teaches one or more processors, memory, and stored program instructions for performing the disclosed computerized vision testing and prescription eyewear process (see Mills para 0031-0032), Figs. 1, 4. Accordingly, the combination of Mills, Jensen, and Fonte renders claims 15 and 18 obvious for the same reasons discussed above with respect to claim 1.
Claim(s) 4 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mills, Jensen and Fonte as applied to claim 3 above, and further in view of Young UK GB 2546517.
Regarding claim 4, the combination of Mills teaches the method of claim 3, and but fails to further teaches further comprising: automatically generating a message including the personalized vision plan to request a follow-up meeting with an optician.
In the same field of endeavor, Young teaches providing information on the examinee’s user device to contact a health professional when the examinee’s visual acuity is below normal, including displaying contact details or a hyperlink for an optometrist or optician, and arranging an appointment with the optician/optometrist (see page 21 lines 1-2 of Young). Accordingly, it would have been obvious to modify Mills/Jensen/Fonte to generate a message/request for a follow-up meeting with an optician, because Mills/Jensen already generate the user’s vision plan/prescription, and Young teaches using an eye-test app to arrange an optician appointment when the test result indicates below normal visual acuity. The motivation would be to automatically connect the user with an eyewear professional for follow-up after the vision assessment.
Regarding claim 7, the combination of Mills teaches the method of claim 5, but fails to teach wherein implementing the vision test further comprises: while displaying a first visual stimulus, dynamically adjusting one or more visual stimuli to be displayed after the first visual stimulus based on a first user response to the first visual stimulus.
In the same field of endeavor, Young teaches modifying the graphical element from a first type to a second type when the user input is determined to be a valid selectin, and also modifying the size and/or animating the graphical element based on the user input (see page. 2 lines 10-17). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify Mills’s HMD/AR vision test to include young’s dynamic adjustment of later visual stimuli based on a user response, because both references are directed to computerized eye testing using displayed visual stimuli and user responses, and Young’s adjustment provided an adaptive eye-test workflow.
Claim(s) 9 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mills, Jensen and Fonte as applied to claims 1 and 15 above, and further in view of Blaha et al. US Patent No. 9,706,910.
Regarding claims 9 and 17, the combination of Mills teaches the method of claims 1 and 15, but fails to teach wherein the user responses include spontaneous user response monitored by one or more second sensors of the electronic device, and the one or more second sensors include one or more of: an eye tracking camera, a heart rate sensor, a body temperature sensor, a blood oxygen level, a Galvanic skin response sensor, a hand gesture camera, a body gesture camera, a microphone, a motion sensor, and a set of one or more brain activity electrodes.
In the same field of endeavor, Blaha teaches head-mounted VR vision assessment system in which user input may be acquired from passively acquired sensor data (see col. 8 lines 7-23), and further teaches measuring a user’s reaction to displayed visual information using sensors including eye tracking, voice recognition, heart rate, skin capacitance, EKG, brain activity sensors such as EEG, hand and body tracking, temperature, and pupil tracking (see col. 3 lines 49-61). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify Mills’s HMD vision test to include Blaha’s passive sensor monitoring to provide additional physiological and behavioral information regarding the user’s reaction to displayed visual stimuli.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mills, Jensen and Fonte as applied to claim 1 above, and further in view of Abou US 2025/0040800
Regarding claim 12, the combination of Mills teaches the method of claim 1, and Jensen further teaches further comprising: collecting training data including personal information, medical history, vision test information, and personalized vision plans of a plurality of historical users (see para 0031: collects user information and medical information and para. 0044: analyzed test results obtained from multiple prior tests, para 0055 generates individualized determinations concerning prescriptions renewal and additional vision care recommendation). However, the combination of Mills fails to teach: collecting user feedback on the personalized vision plans; generating ground truth information based on the user feedback; and training the vision assessment model based on the training data and the ground truth information.
Abou teaches collecting user feedback on the personalized vision plans; generating ground truth information based on the user feedback; and training the vision assessment model based on the training data and the ground truth information (see para 0083-0085: Abou teaches prediction models to generate personalized modification or correction profiles and collects feedback concerning the model outputs including user indications of output accuracy, and para 0084: the user feedback reference labels and reference feedback constitute the claimed ground-truth information and are fed back to the model to adjust its weights and improves its predictions and para 0085, 0109-0110: further trains the model using feedback from the individual user and other users and stores multi-user feedback and modification profiles for training prediction models). It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate Abou’s feedback-based model training into Jensen’s computerized vision-assessment method so that Jensen’s stored historical user data and individualized vision recommendations could be used with verified user feedback to train and improve the vision assessment model, thereby predictably improving the accuracy and personalization of future vision assessments.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mills, Jensen and Fonte as applied to claim 1 above, and further in view of Bhoi et al. Premier: Personalized Recommendation for Medical prescriptions from Electronic Records NPL Dated: 2020
Regarding claim 13, the combination of Mills teaches the method of claim 1, wherein the vision assessment model further includes a plurality of feature extraction models and a classifier, the method further comprising: applying the plurality of feature extraction models to process the personal information, the medical history, and the information of the vision test and generate a plurality of feature vectors; and applying the classifier to process the plurality of feature vectors and select one of a plurality of predefined vision plans as the personalized vision plan.
Jensen teaches the personal information, the medical history, and the information of the vison test (see para 0026, 0031 and 0044), and Jensen also teaches providing the personalized vision plane (see para 0055). Therefore, it would have been obvious to modify Mills to use Jensen’s persona information, medical history and vision test information because such information provides a more complete representation of the user’s visual condition and would predictably improve the accuracy and personalization of Mills’s vision assessment and resulting vision plan.
The combination of Mills and Jensen fails to expressly teach: wherein the vision assessment model further includes a plurality of feature extraction models and a classifier applying the plurality of feature extraction models to process the personal information, the medical history, and the information of the vision test and generate a plurality of feature vectors; and applying the classifier to process the plurality of feature vectors and select one of a plurality of predefined vision plans as the personalized vision plan.
Fonte teaches that the model includes a classifier (see para 0052-0053: teaches training machine learning classifiers using stored user information and applying the trained classifier to a new user to provide a personalized eyewear design suited to the user’s anatomy and preferences, and also Fonte teaches select one of a plurality of predefined vision plans as the personalized vision plane (see para 0202: teaches selecting or recommending among predefined vision related alternatives based on user’s information). Accordingly, it would have been obvious to modify the combination of Mills and Jensen to include Fonte’s trained classifier for selecting an appropriate predefined vision plan for automatically matching user characteristics with predefined personalized eyewear and lens options thereby improving consistency and reducing the need for manual selection.
The combination of Mills, Jensen, Fonte fails to expressly teach: a plurality of feature extraction models, applying the plurality of feature extraction models to process the personal information, the medical history, and the information of the vision test and generate a plurality of feature vectors and applying the plurality of feature extraction models to process the personal information, the medical history, and the information of the vision test and generate a plurality of feature vectors.
Bhoi teaches that model includes a plurality of feature extraction (see section 3.1 and Fig. 2: uses separate neural attention models for diagnosis information and procedure information and separately embeds diagnosis, procedure, and prior-medication into respective numerical representations), and applying the plurality of feature extraction models … and generate a plurality of feature vectors (see Sec. 3.1: neural attention model for diagnosis data and another neural attention model for procedure data i.e., it separately embed diagnosis, procedure, and prior-medication data into 64-dimensional representations, and the model generate a diagnosis response vector, procedure response vector visit history vector and query vector), and applying the classifier to process the plurality of feature vectors (see sec. 3.2: teaches combines vectors into a final output vector and passes the output vector through a linear transformation and sigmoid function, and medication is selected when its corresponding output value exceeds 0.5). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date to further modify the combination of Mills to incorporate Bhoi plurality of feature extraction models and classification architecture to Jensen’s personal information, medical history, and vision test information and would have predictably improved the accuracy of Fonte’s classifier in selecting the personalized vision plan.
Allowable Subject Matter
Claim 11 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claim 11, the method of claim 1, wherein the information of the vision test further includes a response rate, a success rate of the vision test, and a plurality of confidence scores, the method further comprising: determining the response rate, the success rate, and the plurality of confidence scores of the vision test based on the user responses to the sequence of visual stimuli, each confidence score corresponding to a respective visual stimuli.
Conclusion
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/EPHREM Z MEBRAHTU/Primary Examiner, Art Unit 2872
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