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 .
Response to Amendment
This Final Rejection is filed in response to Applicant Arguments/Remarks Made in an Amendment filed 12/22/2025
Claims 1, 6, 15, 18 and 19 are amended.
Claims 1-2, 6-8, and 12-19 remain pending.
Response to Arguments
Argument 1, Applicant argues in Applicant Arguments/Remarks Made in an Amendment filed 12/22/2025 on pg. 11-12, that prior art fails to teach the primary claim limitation, “the tracking information being input by a salesperson of the eyeglasses using a store terminal apparatus provided in a store;
an indication that the specification information for the eyeglass lens for the user was correct is stored as the tracking information in a database in association with the attribute information, the measurement information and the use application information”
Response to Argument 1, in light of the amendments, a newly found combination of prior art (U.S. Patent Application Publication NO. 20150154679 “Fonte”, in light of U.S. Patent Application Publication NO. 20190164210 “Kornilov”, in light of U.S. Patent Application Publication NO. 20150103313 “Sarver”, in light of U.S. Patent Application Publication NO. 20200234184 “Kesarwani”, in light of U.S. Patent Application Publication NO. 20190347703 “Bleicher”, in light of U.S. Patent Application Publication NO. 20050027618 “Zucker”, and further in light of U.S. Patent Application Publication NO. 20110184829 “Kher”) is applied to updated rejections.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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, 6, 8, 12-13, 15-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication NO. 20150154679 “Fonte”, in light of U.S. Patent Application Publication NO. 20190164210 “Kornilov”, in light of U.S. Patent Application Publication NO. 20150103313 “Sarver”, in light of U.S. Patent Application Publication NO. 20200234184 “Kesarwani”, in light of U.S. Patent Application Publication NO. 20190347703 “Bleicher”, in light of U.S. Patent Application Publication NO. 20050027618 “Zucker”, and further in light of U.S. Patent Application Publication NO. 20110184829 “Kher”.
Claim 1:
Fonte teaches a learning model generation method comprising: acquiring training data in which attribute information of a user, measurement information related to eyes of the user and use application information of eyeglasses by the user are associated with specification information of an eyeglass lens (i.e. para. [0187, 0197], “ The computer system applies machine learning or predictive analysis to build a prediction of the response (preferences) based on the inputs from a new user: his new image data and anatomic model, personal information”, wherein it is noted that a user information may build a profile of “a woman in mid-30s, dark medium-length hair, a square face, very small nose, slightly blue eyes, medium skin color, trendy fashion taste, white-collar profession, prefers bold fashion, wears glasses daily, and lives in an urban area”. The BRI for attribute information encompasses a user’s age, wherein the BRI for measurement information encompasses how the computer system prompts the user to enter his optical lens prescription information, which would be required to order prescription eyewear, wherein the BRI for measurement information encompasses a user inputting optical lens prescription information, and wherein the BRI for use application encompasses the user entering a factor such as the purpose of the eyewear); using the acquired training data to generate a learning model that outputs the specification information in a case where the attribute information, the measurement information and the use application information are input (i.e. para. [0053], “The system and method include training machine learning classifiers to predict the preference of a user based on their data”, wherein the user’s data may be used to generate a recommended eyeglass specification from, attribute measurement, and user application information data input to the machine learning model)
The learning model including a classification model (i.e. para. [0053], The system and method include training machine learning classifiers to predict the preference of a user based on their data) (i.e. para. [0197], “Each of these features may be associated with various eyewear preferences, and the combined information when classified by the machine learning method is able to recommend a set of eyewear that truly matches the user's preferences”, wherein the recommended eyewear includes a suggested lens size based on the user information),
The (i.e. para. [0201], “Alternatively, the user is asked general questions about their vision, such as near or far sighted, astigmatism, vision rating, type of lenses they prefer, etc. These generic questions could be used by the computer system to associate with the most likely lens size and thickness for the user”, wherein an additional lens power for a thicker or thinner index is predicted based user information);
fabricating the eyeglass lens based on the specification information (i.e. para. [0311], Fig. 26, At 2718, the computer system transfers the final eyewear model and user information to a manufacturer's computer system via a network connection or other form of electronic communication such that the manufacturer can produce the custom eyewear. At 2719 and 2720, the manufacturer's computer system and manufacturing system preprocess the eyewear model and information and produce custom eyewear), wherein
the specification information includes the addition power of the eyeglass lens (i.e. para. [0206], “once the computer system has all the information necessary to manufacture a user's lens (… prescription information, …), the system can also realistically render the user's lens in the selected eyewear and positioned on the user's image data)”, wherein the BRI for addition power encompasses how the model outputs recommended glasses specification based on input information. Wherein the render of the recommended glasses includes a glasses prescription power which refers to the additional lens power required to correct a user’s vision) and the type of lenses (i.e. para. [0201], The computer system also automatically suggests a lens index based on the frame design and prescription to provide the best visual and aesthetic appearance. For example, a user with a very strong prescription may prefer a plastic frame due to the capability of its thicker rim to better aesthetically mask a thick lens edge, and the computer system could make that suggestion);
wherein after the user receives the eyeglasses, associating tracking information with the attribute information, the measurement information and the user application information (i.e. para. [0311-0312], “the custom eyewear is completed and shipped to the customer or is ready at the store location for pick-up… The user enables the computer system to transfer their image data, and optionally other information such as preferences, eyewear models, and settings over a network or data transfer technology to another computer system”, wherein the BRI for tracking information encompasses any metadata related to a user that is associated with a recommended pair of glasses being manufactured for a user),
in a database in association with the attribute information, the measurement information and the use application information
While Fonte teaches a machine learning model that outputs glasses specification information and taking in training data that has the attribute information, the measurement information and the use application information to generate a learning model, Fonte may not explicitly teach that the training data is
based on information relating to previously suggested eyeglasses or previously sold eyeglasses;
the learning model including … a regression model;
the regression model configured to predicts an addition power of lens
wherein the tracking information includes information on whether the eyeglasses have been returned or exchanged, the user’s impression after having used the eyeglasses, or any complaints related to the eyeglasses, the tracking information being input by a salesperson of the eyeglasses using a store terminal apparatus provided in a store; and
re-generating the learning model using re-learning trading data that includes the tracking information wherein
when there are no requests for the exchange, returns, or complaints from the user within a predetermined period after receipt of the eyeglasses, an indication that the specification information for the eyeglass lens for the user was correct is stored as the tracking information in a database in association with the attribute information, the measurement information and the use application information.
However, Kornilov also teaches
using the acquired training data to generate a learning model (i.e. para. [0067], The deep neural networks may be trained to determine combinations (relationships, correlations)).
Kornilov further teaches
based on information relating to previously suggested eyeglasses or previously sold eyeglasses (i.e. para. [0067], “products are recommended for the new user's face based on the known face purchase history”, wherein an eyeglass product is recommended by a machine learning model and is based on information related to known historical data such previously sold glasses)
It would have been obvious to one of ordinary skill in the art at the time of filing to add based on information relating to previously suggested eyeglasses or previously sold eyeglasses, to Fonte’s machine learning recommendation of glasses, with how a recommendation is based on historical data such as glasses sold to other users with the same face type as a user, as taught by Kornilov would have been motivated to combine Kornilov and Fonte in order to more accurately recommend glasses by recommending products that align more with a user’s profile information.
While Fonte-Kornilov teach training a machine learning model to suggest eyeglasses and lens addition power, Fonte-Kornilov may not explicitly that
The learning model including … a regression model;
The regression model configured to predicts an addition power of lens
However, Sarver teaches
The learning model including … a regression model (i.e. para. [0023], “Mathematical techniques, such as regression analysis, can then be used to identify mathematical relationships between various eye characteristics and the estimation error in order to improve results for future patients”, wherein higher-order regression and other techniques (e.g., neural networks, random trees, etc.) can also be used);
The regression model configured to predicts an addition power of lens (i.e. para. [0039], “the regression analysis provides coefficients which, when multiplied by the respective values for these characteristics of a patient's eye and then summed together, result in a correction value which can be added to, for example, the estimate of the postoperative optical power for that patient's eye”, wherein the regression model takes in patient data and outputs a value for corrective optical power).
It would have been obvious to one of ordinary skill in the art at the time of filing to add a regression model; the regression model configured to predicts an addition power of lens, to Fonte-Kornilov’s machine learning recommendation of glasses and lens power based on a model that takes patient attribute, measurement, and use information, with how a regression model useable for neural networks takes in patient measurement data and outputs an optical power measurement, as taught by Sarver. One would have been motivated to combine the regression models for predicting optical power based on patient information of Sarver with the machine learning models for predicting a glasses lens thickness based on patient information of Fonte-Kornilov in order to produce a more accurate corrective optical power prediction for a patient.
While Fonte-Kornilov-Sarver teach a machine learning model that recommends, manufactures, sends eyeglasses to a user based on attribute data, and associated user data with a custom manufactured pair of eyeglasses, Fonte-Kornilov-Sarver may not explicitly teach
wherein the tracking information includes information on whether the eyeglasses have been returned or exchanged, the user’s impression after having used the eyeglasses, or any complaints related to the eyeglasses, the tracking information being input by a salesperson of the eyeglasses, using a store terminal apparatus provided in a store; and
re-generating the learning model using re-learning trading data that includes the tracking information wherein
when there are no requests for the exchange, returns, or complaints from the user within a predetermined period after receipt of the eyeglasses, an indication that the specification information for the eyeglass lens for the user was correct is stored as the tracking information in a database in association with the attribute information, the measurement information and the use application information.
However, Kesarwani teaches
wherein the tracking information includes information on whether the (i.e. para. [0018], “the model is deployed in a recommendation engine, the model can be adapted to provide recommendations on many different product…since the machine learning models can be trained using input received from users, users can provide input that retrains the model to provide responses that are incorrect or inaccurate”, wherein the BRI for tracking information including complaints encompass feedback from users that the product recommendation was incorrect or inaccurate) the tracking information being input by a (i.e. para. [0018], Crowdsourcing, or receiving input from a plurality of users regarding a product, application, or service, to train a machine learning model is an effective technique for training a model) using a store terminal apparatus provided in a store; and
re-generating the learning model using re-learning trading data that includes the tracking information (i.e. para. [0018], users can provide input that retrains the model).
It would have been obvious to one of ordinary skill in the art at the time of filing to add wherein the tracking information includes information on whether the product have been returned or exchanged, the user’s impression after having used the eyeglasses, or any complaints related to the product, the tracking information being input by a [user] of the product, to Fonte-Kornilov-Sarver’s machine learning recommendation, manufacture, and information tracking of eyeglasses, with how user feedback regarding a product is tracked and used to retrain a recommendation model, as taught by Kesarwani. One would have been motivated to combine the retraining techniques of Kesarwani with the machine learning models for providing an eyeglasses product based on user information of Fonte-Kornilov-Sarver in order to continue to produce a more accurate and up to date recommendation model.
While Fonte-Kornilov-Sarver-Kesarwani teach a machine learning model that tracks user feedback regarding a return for an eyeglass product, Fonte-Kornilov-Sarver-Kesarwani may not explicitly teach
a salesperson;
using a store terminal apparatus provided in a store
However, Bleicher teaches
the tracking information being input by a salesperson of the eyeglasses (i.e. para. [0111], product fitting data from feedback of physically present personnel or remote people may be used to help modify the matchmaking of user data to product data, for example, the feedback from a salesperson or optometrist in a store may be used to update the user profile)
It would have been obvious to one of ordinary skill in the art at the time of filing to add a salesperson, to Fonte-Kornilov-Sarver-Kesarwani’s machine learning recommendation, manufacture, and information tracking of eyeglasses, with a sales person or optometrist may be the user who inputs feedback of a pair of glasses, as taught by Bleicher. One would have been motivated to combine the profile input techniques of Bleicher with the machine learning models for providing an eyeglasses product based on user information of Fonte-Kornilov-Sarver-Kesarwani in order to help create a more highly accurate and user friendly automated or semi-automated fitting solutions, both online and instore.
While Fonte-Kornilov-Sarver-Kesarwani-Bleicher-Zucker teach a machine learning model that tracks user feedback regarding a return for an eyeglass product and re-generates the learning model with new tracking information, Fonte-Kornilov-Sarver-Kesarwani- Bleicher may not explicitly teach that this information is input
using a store terminal apparatus provided in a store.
However, Zucker teaches 20050027618
using a store terminal apparatus provided in a store (i.e. para. [0086], “Buyer 120 contacts seller 110 by the phone or by an in-store visit. Seller 110 enters the return sale information along with the buyer's ID and, optionally the buyer's freight preference, into a point-of-sale terminal 116 which is connected to the seller server 118.”, wherein a seller may enter tracking information related to a product sale using an in-store terminal).
It would have been obvious to one of ordinary skill in the art at the time of filing to add using a store terminal apparatus provided in a store, to Fonte-Kornilov-Sarver-Kesarwani-Bleicher-Zucker’s machine learning recommendation, manufacture, and information tracking of eyeglasses, with how product tracking information may be entered using an instore terminal by a store employee, as taught by Zucker. One would have been motivated to combine the information input techniques of Zucker with the machine learning models for providing an eyeglasses product based on user information of Fonte-Kornilov-Sarver-Kesarwani-Bleicher-Zucker as the combination create more convenience for a user visiting a store and more flexibility as the information tracking for products may intake data from more sources.
While Fonte-Kornilov-Sarver-Kesarwani-Bleicher-Zucker teach a machine learning model that tracks user feedback regarding a return for an eyeglass product and re-generates the learning model with new tracking information, Fonte-Kornilov-Sarver-Kesarwani-Bleicher-Zucker may not explicitly teach
when there are no requests for the exchange, returns, or complaints from the user within a predetermined period after receipt of the eyeglasses, an indication that the specification information for the eyeglass lens for the user was correct is stored as the tracking information
However, Kher teaches
wherein when there are no requests for the exchange, returns, or complaints from the user within a predetermined period after receipt of the (i.e. para. [0023], “acceptance or return of the ordered product by the dental services provider is monitored by the computer unit of the dental products manufacturer. In step 230, the dental products manufacturer will debit the dental services provider's account if no indication of return of the ordered product is received from the dental services provider within the predetermined time period,”, wherein the BRI for an indication encompasses information for a case wherein there are no return requests from the user within a predetermined time period, thus the computer takes the lack of user response as an affirmation of correct product information) in a database in association with the attribute information, the measurement information and the use application information (i.e. para. [0024], “the embodiment 200 further comprises receiving user information from the dental services provider over the communication network. The user information may include shipping information, billing information, purchase history, user profile information, such as the type and nature of dental services provided, and the like. The user information may be stored on the memory component of the dental products manufacturer computer unit or otherwise at any other suitable location”, wherein information for product was correct may be stored with other customer information and the customer may be billed. Wherein storing the tracking information in a database with attribute, measurement, and use application information is analogous to how return tracking information may be stored with other user data in a same database).
It would have been obvious to one of ordinary skill in the art at the time of filing to add when there are no requests for the exchange, returns, or complaints from the user within a predetermined period after receipt of the eyeglasses, an indication that the specification information for the eyeglass lens for the user was correct is stored as the tracking information in a database in association with the attribute information, the measurement information and the use application information, to Fonte Fonte-Kornilov-Sarver-Kesarwani-Bleicher-Zucker’s eyeglass recommendation, manufacture, and feedback to a machine learning model that also stores attribute information, the measurement information and the use application information, with how a lack of a return request from a user after a predetermined time is saved in a user database as indicating information that the data options presented to a user are correct, as taught by Kher. One would have been motivated to combine the tracking and data storage methods of Kher and general machine learning apparatus specializing in glasses metadata, measurements, and use of Fonte-Kornilov-Sarver-Kesarwani-Bleicher-Zucker in order to save a user time and may be useful in improving already-existing products and in designing new products.
Claim 6:
Fonte teaches a non-transitory recording medium in which a computer program is recorded, the computer program causing a computer to execute processing comprising: acquiring attribute information of a user, measurement information related to eyes of the user and use application information of user eyeglasses by the user (i.e. para. [0187, 0197], “ The computer system applies machine learning or predictive analysis to build a prediction of the response (preferences) based on the inputs from a new user: his new image data and anatomic model, personal information”, wherein the BRI for attribute information encompasses a user , wherein the BRI for measurement information encompasses how the computer system prompts the user to enter his optical lens prescription information, which would be required to order prescription eyewear, wherein the BRI for measurement information encompasses a user inputting optical lens prescription information, and wherein the BRI for use application encompasses the user entering a factor such as the purpose of the eyewear); inputting the acquired attribute information, measurement information and use application information to a learning model that outputs specification information of an eyeglass lens in a case where the attribute information, the measurement information and the use application information are input (i.e. para. [0197], “ woman in mid-30s, dark medium-length hair, a square face, very small nose, slightly blue eyes, medium skin color, trendy fashion taste, white-collar profession, prefers bold fashion, wears glasses daily, and lives in an urban area”, wherein the information is input by a user into the machine learning or predictive analysis)
The learning model including a classification model (i.e. para. [0053], The system and method include training machine learning classifiers to predict the preference of a user based on their data) and a
The classification model being configured to predict the type of lenses for user eyeglasses according to the attribute information, the measurement information and the user application information (i.e. para. [0197], “Each of these features may be associated with various eyewear preferences, and the combined information when classified by the machine learning method is able to recommend a set of eyewear that truly matches the user's preferences”, wherein the recommended eyewear includes a suggested lens size based on the user information);
the (i.e. para. [0201], “Alternatively, the user is asked general questions about their vision, such as near or far sighted, astigmatism, vision rating, type of lenses they prefer, etc. These generic questions could be used by the computer system to associate with the most likely lens size and thickness for the user”, wherein an additional lens power for a thicker or thinner index is predicted based user information);
acquiring the specification information output by the learning model (i.e. para. [0197], Each of these features may be associated with various eyewear preferences, and the combined information when classified by the machine learning method is able to recommend a set of eyewear that truly matches the user's preferences).
wherein the specification information includes the addition power of the eyeglass lens (i.e. para. [0206], “once the computer system has all the information necessary to manufacture a user's lens (… prescription information, …), the system can also realistically render the user's lens in the selected eyewear and positioned on the user's image data)”, wherein the BRI for addition power encompasses how the model outputs recommended glasses specification based on input information. Wherein the render of the recommended glasses includes a glasses prescription power which refers to ”the additional lens power required to correct a user’s vision); and
fabricating the eyeglass lens based on the specification information.(i.e. para. [0311], Fig. 26, At 2718, the computer system transfers the final eyewear model and user information to a manufacturer's computer system via a network connection or other form of electronic communication such that the manufacturer can produce the custom eyewear. At 2719 and 2720, the manufacturer's computer system and manufacturing system preprocess the eyewear model and information and produce custom eyewear) and
wherein the attribute information, the measurement information and the use application information input to the machine learning model, the specification information output by the learning model, and tracking information related to the eyeglass lens selected by the user are stored in a database in association with each other (i.e. para. [0311-0312], “the custom eyewear is completed and shipped to the customer or is ready at the store location for pick-up… The user enables the computer system to transfer their image data, and optionally other information such as preferences, eyewear models, and settings over a network or data transfer technology to another computer system”, wherein the BRI for tracking information encompasses any metadata related to a user that is associated with a recommended pair of glasses being manufactured for a user),
While Fonte teaches a machine learning model that outputs glasses specification information and taking in training data that has the attribute information, the measurement information and the use application information to generate a learning model, Fonte may not explicitly teach that the training data is
based on information relating to previously suggested eyeglasses or previously sold eyeglasses;
the learning model including … a regression model;
the regression model configured to predicts an addition power of lens
wherein the tracking information indicates whether the eyeglasses have been returned or exchanged, the user’s impression after having used the eyeglasses, or any complaints related to the eyeglasses, the tracking information being input by a salesperson of the eyeglasses; and
re-generating the learning model using re-learning trading data that includes the tracking information wherein
when there are no requests for the exchange, returns, or complaints from the user within a predetermined period after receipt of the eyeglasses, an indication that the specification information for the eyeglass lens for the user was correct is stored as the tracking information in a database in association with the attribute information, the measurement information and the use application information.
While Fonte teaches a machine learning model that outputs glasses specification information and taking in training data that has the attribute information, the measurement information and the use application information to generate a learning model, Fonte may not explicitly teach that the training data is
based on information relating to previously suggested eyeglasses or previously sold eyeglasses;
The learning model including … a regression model;
The regression model configured to predicts an addition power of lens according to the attribute information, the measurement information, and the user application information
However, Kornilov also teaches
using the acquired training data to generate a learning model (i.e. para. [0067], The deep neural networks may be trained to determine combinations (relationships, correlations)).
Kornilov further teaches
based on information relating to previously suggested eyeglasses or previously sold eyeglasses (i.e. para. [0067], “products are recommended for the new user's face based on the known face purchase history”, wherein an eyeglass product is recommended by a machine learning model and is based on information related to known historical data such previously sold glasses)
It would have been obvious to one of ordinary skill in the art at the time of filing to add based on information relating to previously suggested eyeglasses or previously sold eyeglasses, to Fonte’s machine learning recommendation of glasses, with how a recommendation is based on historical data such as glasses sold to other users with the same face type as a user, as taught by Kornilov would have been motivated to combine Kornilov and Fonte in order to more accurately recommend glasses by recommending products that align more with a user’s profile information.
While Fonte-Kornilov teach training a machine learning model to suggest eyeglasses and lens addition power, Fonte-Kornilov may not explicitly that
The learning model including … a regression model;
The regression model configured to predicts an addition power of lens
However, Sarver teaches
The learning model including … a regression model (i.e. para. [0023], “Mathematical techniques, such as regression analysis, can then be used to identify mathematical relationships between various eye characteristics and the estimation error in order to improve results for future patients”, wherein higher-order regression and other techniques (e.g., neural networks, random trees, etc.) can also be used);
The regression model configured to predicts an addition power of lens (i.e. para. [0039], “the regression analysis provides coefficients which, when multiplied by the respective values for these characteristics of a patient's eye and then summed together, result in a correction value which can be added to, for example, the estimate of the postoperative optical power for that patient's eye”, wherein the regression model takes in patient data and outputs a value for corrective optical power).
It would have been obvious to one of ordinary skill in the art at the time of filing to add a regression model; the regression model configured to predicts an addition power of lens, to Fonte-Kornilov’s machine learning recommendation of glasses and lens power based on a model that takes patient attribute, measurement, and use information, with how a regression model useable for neural networks takes in patient measurement data and outputs an optical power measurement, as taught by Sarver. One would have been motivated to combine the regression models for predicting optical power based on patient information of Sarver with the machine learning models for predicting a glasses lens thickness based on patient information of Fonte-Kornilov in order to produce a more accurate corrective optical power prediction for a patient.
While Fonte-Kornilov-Sarver teach a machine learning model that recommends, manufactures, sends eyeglasses to a user based on attribute data, and associated user data with a custom manufactured pair of eyeglasses, Fonte-Kornilov-Sarver may not explicitly teach
wherein the tracking information indicates whether the eyeglasses have been returned or exchanged, the user’s impression after having used the eyeglasses, or any complaints related to the eyeglasses, the tracking information being input by a salesperson of the eyeglasses; and
re-generating the learning model using re-learning trading data that includes the tracking information wherein
when there are no requests for the exchange, returns, or complaints from the user within a predetermined period after receipt of the eyeglasses, an indication that the specification information for the eyeglass lens for the user was correct is stored as the tracking information in a database in association with the attribute information, the measurement information and the use application information.
However, Kesarwani teaches
wherein the tracking information includes information on whether the have been returned or exchanged, the user’s impression after having used the eyeglasses, or any complaints related to the (i.e. para. [0018], “the model is deployed in a recommendation engine, the model can be adapted to provide recommendations on many different product…since the machine learning models can be trained using input received from users, users can provide input that retrains the model to provide responses that are incorrect or inaccurate”, wherein the BRI for tracking information including complaints encompass feedback from users that the product recommendation was incorrect or inaccurate) the tracking information being input by a (i.e. para. [0018], Crowdsourcing, or receiving input from a plurality of users regarding a product, application, or service, to train a machine learning model is an effective technique for training a model), ; and
re-generating the learning model using re-learning trading data that includes the tracking information (i.e. para. [0018], users can provide input that retrains the model).
It would have been obvious to one of ordinary skill in the art at the time of filing to add wherein the tracking information includes information on whether the product have been returned or exchanged, the user’s impression after having used the eyeglasses, or any complaints related to the product, the tracking information being input by a [user] of the product, to Fonte-Kornilov-Sarver’s machine learning recommendation, manufacture, and information tracking of eyeglasses, with how user feedback regarding a product is tracked and used to retrain a recommendation model, as taught by Kesarwani. One would have been motivated to combine the retraining techniques of Kesarwani with the machine learning models for providing an eyeglasses product based on user information of Fonte-Kornilov-Sarver in order to continue to produce a more accurate and up to date recommendation model.
While Fonte-Kornilov-Sarver-Kesarwani teach a machine learning model that tracks user feedback regarding a return for an eyeglass product, Fonte-Kornilov-Sarver-Kesarwani may not explicitly teach
a salesperson;
using a store terminal apparatus provided in a store
However, Bleicher teaches
the tracking information being input by a salesperson of the eyeglasses (i.e. para. [0111], product fitting data from feedback of physically present personnel or remote people may be used to help modify the matchmaking of user data to product data, for example, the feedback from a salesperson or optometrist in a store may be used to update the user profile) using a store terminal apparatus provided in a store.
It would have been obvious to one of ordinary skill in the art at the time of filing to add a salesperson, to Fonte-Kornilov-Sarver-Kesarwani’s machine learning recommendation, manufacture, and information tracking of eyeglasses, with a sales person or optometrist may be the user who inputs feedback of a pair of glasses, as taught by Bleicher. One would have been motivated to combine the profile input techniques of Bleicher with the machine learning models for providing an eyeglasses product based on user information of Fonte-Kornilov-Sarver-Kesarwani in order to help create a more highly accurate and user friendly automated or semi-automated fitting solutions, both online and instore.
While Fonte-Kornilov-Sarver-Kesarwani-Bleicher-Zucker teach a machine learning model that tracks user feedback regarding a return for an eyeglass product and re-generates the learning model with new tracking information, Fonte-Kornilov-Sarver-Kesarwani- Bleicher may not explicitly teach
when there are no requests for the exchange, returns, or complaints from the user within a predetermined period after receipt of the eyeglasses, an indication that the specification information for the eyeglass lens for the user was correct is stored as the tracking information in a database in association with the attribute information, the measurement information and the use application information
However, Kher teaches
wherein when there are no requests for the exchange, returns, or complaints from the user within a predetermined period after receipt of the (i.e. para. [0023], “acceptance or return of the ordered product by the dental services provider is monitored by the computer unit of the dental products manufacturer. In step 230, the dental products manufacturer will debit the dental services provider's account if no indication of return of the ordered product is received from the dental services provider within the predetermined time period,”, wherein the BRI for an indication encompasses information for a case wherein there are no return requests from the user within a predetermined time period, thus the computer takes the lack of user response as an affirmation of correct product information) in a database in association with the attribute information, the measurement information and the use application information (i.e. para. [0024], “the embodiment 200 further comprises receiving user information from the dental services provider over the communication network. The user information may include shipping information, billing information, purchase history, user profile information, such as the type and nature of dental services provided, and the like. The user information may be stored on the memory component of the dental products manufacturer computer unit or otherwise at any other suitable location”, wherein information for product was correct may be stored with other customer information and the customer may be billed. Wherein storing the tracking information in a database with attribute, measurement, and use application information is analogous to how return tracking information may be stored with other user data in a same database).
It would have been obvious to one of ordinary skill in the art at the time of filing to add when there are no requests for the exchange, returns, or complaints from the user within a predetermined period after receipt of the eyeglasses, an indication that the specification information for the eyeglass lens for the user was correct is stored as the tracking information in a database in association with the attribute information, the measurement information and the use application information, to Fonte Fonte-Kornilov-Sarver-Kesarwani-Bleicher-Zucker’s eyeglass recommendation, manufacture, and feedback to a machine learning model that also stores attribute information, the measurement information and the use application information, with how a lack of a return request from a user after a predetermined time is saved in a user database as indicating information that the data options presented to a user are correct, as taught by Kher. One would have been motivated to combine the tracking and data storage methods of Kher and general machine learning apparatus specializing in glasses metadata, measurements, and use of Fonte-Kornilov-Sarver-Kesarwani-Bleicher-Zucker in order to save a user time and may be useful in improving already-existing products and in designing new products.
Claim 12:
Fonte, Kornilov, Sarver, Kesarwani, Bleicher, and Kher teach the recording medium according to claim 6.
Fonte further teaches
wherein a screen presenting a specification of eyeglasses to the user based on the specification information acquired from the learning model is displayed (i.e. para. [0111], computer system may automatically recommend shape, style, and color choices to a user. As illustrated at step 108, the computer system creates at least one new custom eyewear model with at least one component designed from scratch and automatically places the eyewear model on user image data).
Claim 13:
Fonte, Kornilov, Sarver, Kesarwani, Bleicher, and Kher teach the recording medium according to claim 12.
Fonte further teaches
wherein an image is presented on the screen schematically showing the type of the eyeglass lens and the addition power of the lens are in association with each other (i.e. para. [0111], The computer system renders a preview the custom eyewear model, which may include lenses, as illustrated at 109. The rendering may include combinations of the user image data and user anatomic model with the custom eyewear model).
Claim 15:
Claim 15 is the non-transitory recording medium claim reciting similar limitations to Claim 1 and is rejected for similar reasons.
Claim 16:
Claim 16 is the non-transitory recording medium claim reciting similar limitations to Claim 13 and is rejected for similar reasons.
Claim 17:
Claim 17 is the non-transitory recording medium claim reciting similar limitations to Claim 14 and is rejected for similar reasons.
Claim 18:
Claim 18 is the method claim reciting similar limitations to Claim 6 and is rejected for similar reasons.
Claim 19:
Claim 19 is the system claim reciting similar limitations to claim 6 and is rejected for similar reasons.
Claim(s) 2, 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication NO. 20150154679 “Fonte”, in light of U.S. Patent Application Publication NO. 20190164210 “Kornilov”, in light of U.S. Patent Application Publication NO. 20150103313 “Sarver”, in light of U.S. Patent Application Publication NO. 20200234184 “Kesarwani”, in light of U.S. Patent Application Publication NO. 20190347703 “Bleicher”, in light of U.S. Patent Application Publication NO. 20050027618 “Zucker”, and further in light of U.S. Patent Application Publication NO. 20110184829 “Kher”, as applied to Claims 1 and 6, and further in light of U.S. Patent Application Publication NO. 20170280990 “Shimizu”.
Claim 2:
Fonte, Kornilov, Sarver, Kesarwani, Bleicher, and Kher teach the learning model generation method according to claim 1.
Fonte further teaches
wherein the attribute information includes an age of the user (i.e. para. [0197], a user's image data analysis and a few basic answers to questions provides the following detailed profile of that user: a woman in mid-30s), and the measurement information includes a measurement result of a refractive error of the eyes of the user.
While Fonte-Kornilov-Sarver-Kesarwani-Bleicher-Zucker-Kher teach attribute and measurement information, Fonte may not explicitly teach wherein
the measurement information includes a measurement result of a refractive error of the eyes of the user
However, Shimizu teaches
the measurement information includes a measurement result of a refractive error of the eyes of the user (i.e. para. [0039], the present apparatus 1 acquires (stores in the memory 31) the measurement data on the examinee's eye from the wavefront sensor, that is, the data on the distribution of refractive error of the examinee's eye).
It would have been obvious to one of ordinary skill in the art at the time of filing to add wherein the measurement information includes a measurement result of a refractive error of the eyes of the user, to Fonte-Kornilov-Sarver-Kesarwani-Bleicher-Zucker-Kher’s user input measurements, with measurement information includes a refractive error of the eyes of the user, as taught by Shimizu. One would have been motivated to combine Shimizu and Fonte-Kornilov-Sarver-Kesarwani-Bleicher-Zucker-Kher in order to feed the model with more user specific information and have a more personalized eyewear recommendation.
Claim 7:
Fonte, Kornilov, Sarver, Kesarwani, Bleicher, and Kher teach the recording medium according to claim 6.
Fonte further teaches
wherein the attribute information includes an age of the user (i.e. para. [0197], a user's image data analysis and a few basic answers to questions provides the following detailed profile of that user: a woman in mid-30s), and the measurement information includes a measurement result of a refractive error of the eyes of the user.
While Fonte-Kornilov-Sarver-Kesarwani-Bleicher-Zucker-Kher teach attribute and measurement information, Fonte may not explicitly teach wherein
the measurement information includes a measurement result of a refractive error of the eyes of the user
However, Shimizu teaches
the measurement information includes a measurement result of a refractive error of the eyes of the user (i.e. para. [0039], the present apparatus 1 acquires (stores in the memory 31) the measurement data on the examinee's eye from the wavefront sensor, that is, the data on the distribution of refractive error of the examinee's eye).
It would have been obvious to one of ordinary skill in the art at the time of filing to add wherein the measurement information includes a measurement result of a refractive error of the eyes of the user, to Fonte-Kornilov-Sarver-Kesarwani-Bleicher-Zucker-Kher’s user input measurements, with measurement information includes a refractive error of the eyes of the user, as taught by Shimizu. One would have been motivated to combine Shimizu and Fonte-Kornilov-Sarver-Kesarwani-Bleicher-Zucker-Kher in order to feed the model with more user specific information and have a more personalized eyewear recommendation.
Claim 8:
Fonte, Kornilov, Sarver, Kesarwani, Bleicher, Kher, and Shimizu teach the recording medium according to claim 7.
Shimizu further teaches wherein the measurement result is acquired from a refractive error measurement apparatus that measures the refractive error of the eyes of the user (i.e. para. [0038-0039], This measurement unit 100 includes a refractive power measuring optical system 10 …The measuring optical system 10 has a detector 22… A detection signal to be output from the detector 22 is processed by the CPU 30. As a result, the present apparatus 1 acquires (stores in the memory 31) the measurement data on the examinee's eye from the wavefront sensor, that is, the data on the distribution of refractive error of the examinee's eye).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over in light of U U.S. Patent Application Publication NO. 20150154679 “Fonte”, in light of U.S. Patent Application Publication NO. 20190164210 “Kornilov”, in light of U.S. Patent Application Publication NO. 20150103313 “Sarver”, in light of U.S. Patent Application Publication NO. 20200234184 “Kesarwani”, in light of U.S. Patent Application Publication NO. 20190347703 “Bleicher”, in light of U.S. Patent Application Publication NO. 20050027618 “Zucker”, and further in light of U.S. Patent Application Publication NO. 20110184829 “Kher”, as applied to Claim 12, and further in light of U.S. Patent Application Publication NO. 20190213914 “Vallance”.
Claim 14:
Fonte, Kornilov, Sarver, Kesarwani, Bleicher, and Kher teach the recording medium according to claim 12.
While Fonte teaches an interface where a user may accept a specification of the eyeglasses presented, Fonte-Kornilov-Sarver-Kesarwani-Bleicher-Zucker-Kher may not explicitly teach
wherein an acceptance/rejection selection relating to the specification of the eyeglass presented on the screen is received, and in a case where a selection rejecting the specification is received, a screen presenting another specification is displayed.
However, Vallance teaches
wherein an acceptance/rejection selection relating to the specification of the (i.e. par. [0050], “The menu for the period will be presented to the client. By day, the client can accept the recommended recipe(s), … or reject the suggestion and have another presented”, wherein a user may accept or reject a displayed recipe specification recommended by the machine learning model) .
It would have been obvious to one of ordinary skill in the art at the time of filing to add wherein an acceptance/rejection selection relating to the specification of the eyeglass presented on the screen is received, and in a case where a selection rejecting the specification is received, a screen presenting another specification is displayed, to Fonte-Kornilov-Sarver-Kesarwani-Bleicher-Zucker-Kher’s display of recommendations to a user, with an acceptance/rejection selection of a machine learning model recommendation, with wherein a rejection selection results in the system presenting another recommended specification, as taught by Vallance. One would have been motivated to combine Vallance and Fonte-Kornilov-Sarver-Kesarwani-Bleicher-Zucker-Kher in order to save a user time by automatically presenting a user with relevant specification items.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Publication NO. 20120084391 “Patel” teaches in para. [0037], the electronic receipt system facilitates the searching of rebates available for products a user has purchased and provides a mechanism for submitting rebates via website 132. The electronic receipt system will also track return periods, display a product return countdown for each returnable product, and send reminders when a product return period is about to expire.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/D.T./Examiner, Art Unit 2145
/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145