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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 22nd June, 2026 has been entered.
Response to Amendment
This action is in response to the amendment filed on 22nd June, 2026. Claims 1, 8, and 15 have been amended. Claim 21 has been cancelled. Claims 1-20 remain rejected in the application.
Response to Arguments
Applicant's arguments with respect to Claims 1 and 15 filed on 22nd June, 2026, with respect to the rejection under 35 U.S.C. § 103, regarding that the prior art does not teach the limitation(s): "the estimated real-world value comprises a statistical average measurement of said at least two facial features derived from a population" and "the estimated facial feature is an average measurement, in a population, of at least one of…" have been fully considered, but are moot because of new grounds for rejection. It has now been taught by the combination of Kornilov-37 and Fu.
Applicant's arguments with respect to Claim 8 filed on 22nd June, 2026, with respect to the rejection under 35 U.S.C. § 103, regarding that the prior art does not teach the limitation(s): "the estimated measurement is an average measurement, in a population, of at least one of an iris diameter, an ear junction distance, and a temple distance" has been fully considered, but are moot because of new grounds for rejection. It has now been taught by the combination of Kornilov-37, Fu, and Morrell.
Regarding arguments to Claims 2-7, 9-14, and 16-20, they directly/indirectly depend on independent Claims 1, 8, and 15 respectively. Applicant does not argue anything other than independent Claims 1, 8, and 15. The limitations in those claims, in conjunction with combination, was previously established as explained.
Claim Objections
Claim 2 is objected to because of the following informalities:
Claim 2 recites the limitation(s): "the estimated facial features comprise" on PG(s). 1, Line(s) 19; it is unclear as to where "estimated facial features" was introduced in Claim 1; the examiner suggests clarifying what this term is; and
Claim 2 recites the limitation(s): "determining the scaling information" on PG(s). 1, Line(s) 23; the examiner suggests amending this to "determining scaling information" and clarifying what this term is.Appropriate correction is required.
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.
Claims 1 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Kornilov et al. (US 20180096537 A1), hereinafter referenced as Kornilov-37, in view of Fu et al. (US 20210299384 A1), hereinafter referenced as Fu.
Regarding Claim 1, Kornilov-37 discloses a system (Kornilov-37, [0028]: teaches a system), comprising:
a processor (Kornilov-37, [0028]: teaches a processor of the system); and
a memory coupled to the processor and configured to provide the processor with instructions, which instructions, when executed by the processor, cause the processor to (Kornilov-37, [0028]: teaches the processor of the system being configured to "execute instructions stored on and/or provided by a memory coupled to the processor"):
obtain a set of images of a user’s head (Kornilov-37, [0053]: teaches receiving images and/or video frames <read on set of images> of the user's face and head as shown in FIG. 4);
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generate a three-dimensional model of the user’s head based on the set of images, without using any external reference object (Kornilov-37, [0054]: teaches generating a 3D model of the user's face based on the defined reference points, which are locations of various facial features from a set of images of the user's face; [0044]: teaches obtaining a set of intrinsic reference data, which is used for camera calibration for model scaling, not requiring a scaling reference object <read on external reference object>);
detect, from the set of images, a plurality of facial features, including landmarks usable in three dimensions (Kornilov-37, [0055]: teaches combining the obtained reference points to create a 3D model of the user's face, where "the corresponding reference points are combined to make a set of (x, y, z) coordinates <read on 3D landmarks> representing the position of each of the reference points (e.g., locations of facial features) on the user's face/head"; Note: it is noted that the reference points are based on the set of 2D images of the user's face);
[[determine a scaling ratio as a ratio between a distance computed between at least two facial features represented by three-dimensional coordinates within the three-dimensional model of the user's head, and an estimated real-world value associated with said at least two facial features, wherein]]
[[the estimated real-world value comprises a statistical average measurement of said at least two facial features derived from a population; and]]
[[apply the scaling ratio to the three-dimensional model of the user’s head to obtain a scaled user’s head model.]]
However, Kornilov-37 does not expressly disclose
determine a scaling ratio as a ratio between a distance computed between at least two facial features represented by three-dimensional coordinates within the three-dimensional model of the user's head, and an estimated real-world value associated with said at least two facial features, wherein
the estimated real-world value comprises a statistical average measurement of said at least two facial features derived from a population; and
apply the scaling ratio to the three-dimensional model of the user’s head to obtain a scaled user’s head model.
Fu discloses
determine a scaling ratio as a ratio between a distance computed between at least two facial features represented by three-dimensional coordinates within the three-dimensional model of the user's head, and an estimated real-world value associated with said at least two facial features (Fu, [0053]: teaches "calculating a value of the measured aspect based on a scaling factor <read on determined scaling ratio> derived from the reference feature," where the reference feature has a known dimension, such as a coin; Note: it should be noted that although the cited portion uses a real-world reference, such as a coin, Paragraph [0201] discloses an alternative method for determining scale without the use of a reference feature, such as a laser and detector combination, or a laser that detects the distance to the target, or a stereoscopic camera; [0166]: teaches measuring the distance between facial features 630, which involves identifying the pixel coordinates <read on 3D coordinates> of certain facial features; [0172]: teaches processor 310 averaging the corrected and scaled measurements for a set of images to obtain final measurements <read on determined estimated real-world value> of the patient's facial anatomy; [0197]: teaches a facial feature measurement step 850, which measures the pixel distance between two selected landmarks <read on two facial features>), wherein
the estimated real-world value comprises a statistical average measurement of said at least two facial features derived from a population (Fu, [0193]: teaches an active shape model using identified landmarks within a set of training images to develop a deformable model for the relative positions of the landmarks (the shape) and a statistical model of the pixel values <read on statistical average measurement> provided by the images at those positions; [0194]: teaches the statistical model of pixel values is determined by sampling a range of pixels normal to each landmark <read on facial features> within each image in the training set <read on population>); and
apply the scaling ratio to the three-dimensional model of the user’s head to obtain a scaled user’s head model (Fu, [0195]: teaches updating the scale <read on scaling ratio>, translation, and rotation of the shape estimate x to best match the new estimated landmark positions of the deformable model <read on scaled user's head model>).
Fu is analogous art with respect to Kornilov-37 because they are from the same field of endeavor, namely measuring and collecting data of the user's face. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement an active shape model that is trained to identify facial landmarks from training image datasets as taught by Fu into the teaching of Kornilov-37. The suggestion for doing so would allow the neural network to approximate the proportions of the user's face using measurement data that is a constant without the use of external measuring tools, such as coins or cards. Therefore, it would have been obvious to combine Fu with Kornilov-37.
Regarding Claim 15, Kornilov-37 discloses a computer product (Kornilov-37, [0028]: teaches a computer program product),
the computer program product being embodied in a non-transitory computer readable storage medium and comprising computer instructions for (Kornilov-37, [0028]: teaches the computer program product being embodied on a computer readable storage medium <read on non-transitory computer readable storage medium>, which also includes computer program instructions):
receiving a set of images of a user’s head (Kornilov-37, [0053]: teaches receiving images and/or video frames <read on set of images> of the user's face and head as shown in FIG. 4);
generating an initial three-dimensional (3D) model of the user’s head based on the set of images, without using any external reference object (Kornilov-37, [0054]: teaches generating a 3D model <read on initial 3D model> of the user's face based on the defined reference points, which are locations of various facial features from a set of images of the user's face; [0044]: teaches obtaining a set of intrinsic reference data, which is used for camera calibration for model scaling, not requiring a scaling reference object <read on external reference object>);
analyzing the set of images to detect a facial feature on the user’s head (Kornilov-37, [0055]: teaches combining the obtained reference points to create a 3D model of the user's face, where "the corresponding reference points are combined to make a set of
(
x
,
y
,
z
)
coordinates representing the position of each of the reference points (e.g., locations of facial features) on the user's face/head");
[[comparing the detected facial feature with an estimated facial feature to determine a scaling ratio, wherein]]
[[the estimated facial feature is an average measurement, in a population, of at least]]
[[one of an iris diameter,]]
an ear junction distance (Kornilov-37, [0099]: teaches the scale being computed such that the glasses' width will be equal to a weighted average of the distance between the ear junctures <read on ear junction distance> and the distance between the external eye corners of the 3D model of the user's face), and
a temple distance (Kornilov-37, [0099]: teaches the scale being computed such that the glasses' width will be equal to a weighted average of the distance between the ear junctures and the distance between the external eye corners <read on temple distance> of the 3D model of the user's face); and
[[scaling the initial 3D model to generate a scaled 3D model based on the scaling ratio.]]
However, Kornilov-37 does not expressly disclose
comparing the detected facial feature with an estimated facial feature to determine a scaling ratio, wherein
the estimated facial feature is an average measurement, in a population, of at least
one of an iris diameter; and
scaling the initial 3D model to generate a scaled 3D model based on the scaling ratio.
Fu discloses
comparing the detected facial feature with an estimated facial feature to determine a scaling ratio (Fu, [0053]: teaches "calculating a value of the measured aspect based on a scaling factor <read on determined scaling ratio> derived from the reference feature," where the reference feature has a known dimension, such as a coin; Note: it should be noted that although the cited portion uses a real-world reference, such as a coin, Paragraph [0201] discloses an alternative method for determining scale without the use of a reference feature, such as a laser and detector combination, or a laser that detects the distance to the target, or a stereoscopic camera; [0194]: teaches a statistical model of pixel values is determined by sampling a range of pixels normal to each landmark <read on estimated facial feature> within each image in the training set), wherein
the estimated facial feature is an average measurement, in a population (Fu, [0193]: teaches an active shape model using identified landmarks within a set of training images to develop a deformable model for the relative positions of the landmarks (the shape) and a statistical model of the pixel values <read on average measurement> provided by the images at those positions; [0194]: teaches the statistical model of pixel values is determined by sampling a range of pixels normal to each landmark <read on estimated facial feature> within each image in the training set <read on population>), of at least
one of an iris diameter (Fu, [0198]: teaches the reference feature being a feature of the eyes, such as the iris or cornea of the user since "the iris diameter is remarkably similar across all races, and does not grow after the early teens"); and
scaling the initial 3D model to generate a scaled 3D model based on the scaling ratio (Fu, [0195]: teaches updating the scale <read on scaling ratio>, translation, and rotation of the shape estimate x to best match the new estimated landmark positions of the deformable model <read on scaled 3D model>).
Fu is analogous art with respect to Kornilov-37 because they are from the same field of endeavor, namely measuring and collecting data of the user's face. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement an active shape model that is trained to identify facial landmarks from training image datasets as taught by Fu into the teaching of Kornilov-37. The suggestion for doing so would allow the neural network to approximate the proportions of the user's face using measurement data that is a constant without the use of external measuring tools, such as coins or cards. Therefore, it would have been obvious to combine Fu with Kornilov-37.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Kornilov et al. (US 20180096537 A1), hereinafter referenced as Kornilov-37, in view of Fu et al. (US 20210299384 A1), hereinafter referenced as Fu as applied to Claim 1 above respectively, and further in view of Son et al. (US 20160125228 A1, previously cited), hereinafter referenced as Son.
Regarding Claim 2, the combination of Kornilov-37 and Fu discloses the system of Claim 1. Kornilov-37 does not expressly disclose the limitations of Claim 2; however, Fu discloses wherein the estimated facial features comprise
[[historical facial features, and wherein]]
determining the scaling ratio comprises:determining a measured facial feature from an image of the set of images (Fu, [0055]: teaches calculating an average of a measured aspect of a facial feature from a plurality of captured images of the one or more facial features);
updating the model of the user’s head based on the measured facial feature (Fu, [0195]: teaches the ASM model being iterative, where pixels of landmark positions (i.e., facial features) are selected and processed, which then updates the face shape model); and
determining the scaling information based on the measured facial feature and at least a portion of the estimated facial features (Fu, [0195]: teaches calculating for mean shape x <read on scaling information> to iteratively update the face model by updating the scale, translation, and rotation of shape estimate x using new estimated landmark positions <read on portion of estimated facial features>).
Fu is analogous art with respect to Kornilov-37 because they are from the same field of endeavor, namely measuring and collecting data of the user's face. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement an active shape model that is trained to identify facial landmarks from training image datasets as taught by Fu into the teaching of Kornilov-37. The suggestion for doing so would allow the neural network to approximate the proportions of the user's face using measurement data that is a constant without the use of external measuring tools, such as coins or cards. Therefore, it would have been obvious to combine Fu with Kornilov-37.
However, the combination of Kornilov-37 and Fu does not expressly disclose
historical facial features.
Son discloses
historical facial features (Son, [0069]: teaches skin analysis information 151 obtaining historical information <read on historical facial features> of the user's face in each face region, where the user can compare past results with other measured results).
Son is analogous art with respect to Kornilov-37, in view of Fu because they are from the same field of endeavor, namely image analysis of human faces. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a skin analysis information system to obtain historical information of the user's face as taught by Son into the teaching of Kornilov-37, in view of Fu. The suggestion for doing so would allow the user to check and compare their face with a past analyzed result. Therefore, it would have been obvious to combine Son with Kornilov-37, in view of Fu.
Claims 3, 17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kornilov et al. (US 20180096537 A1), hereinafter referenced as Kornilov-37, in view of Fu et al. (US 20210299384 A1), hereinafter referenced as Fu as applied to Claim 1 above respectively, and further in view of Morrell et al. (US 20230103129 A1, previously cited), hereinafter referenced as Morrell.
Regarding Claim 3, the combination of Kornilov-37 and Fu discloses the system of Claim 1. The combination of Kornilov-37 and Fu does not expressly disclose the limitations of Claim 3; however, Morrell discloses wherein determining the scaling ratio comprises:
determining a head width classification corresponding to the user’s head using a machine learning model based on the set of images (Morrell, [0040]: teaches using a machine learning model that identifies, for each image, and extracts coordinate locations of various facial landmarks, such as the top and bottom of the head, center, top, bottom, and edges of the eyes and mouth (i.e., facial geometry <read on head width classification> of the identified face));
obtaining a set of proportions corresponding to the head width classification (Morrell, [0078]: teaches a landmark detection model 315 being a trained machine learning model that identifies facial geometry <read on head width classification> of the face, where it determines or estimates a variety of facial landmarks <read on set of proportions> based on a single camera input without the need for a dedicated depth sensor), wherein
the estimated facial features comprise the set of proportions (Morrell, [0078]: teaches a landmark detection model 315 being a trained machine learning model that identifies facial geometry of the face, where it determines or estimates a variety of facial landmarks <read on set of proportions of estimated facial features> based on a single camera input without the need for a dedicated depth sensor; [0079]: teaches "the landmark detection model 315 outputs, for each landmark, a set of landmark coordinates");
determining a measured facial feature from the model of the user’s head (Morrell, [0099]: teaches determining facial measurements <read on measured facial feature>); and
determining the scaling ratio based on the measured facial feature and the estimated facial features (Morrell, [0101]: teaches a geometry analyzer 320 determining a scaling factor <read on scaling ratio> 507 of the user's face based on the user's face measurements <read on measured facial feature>, such as the iris diameter, which "can be used to estimate various measurements <read on estimated facial features> of the user's face, such as facial height and nose width").
Morrell is analogous art with respect to Kornilov-37, in view of Fu because they are from the same field of endeavor, namely image analysis of human faces. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a landmark detection model to identify facial regions and geometry of the user's face as taught by Morrell into the teaching of Kornilov-37, in view of Fu. The suggestion for doing so would allow for automatic labelling and classification of facial regions, thereby speeding up 3D model generation. Therefore, it would have been obvious to combine Morrell with Kornilov-37, in view of Fu.
Regarding Claim 17, the combination of Kornilov-37 and Fu discloses the computer product of Claim 15. The combination of Kornilov-37 and Fu does not expressly disclose the limitations of Claim 17; however, Morrell discloses wherein:
the estimated facial feature comprises the iris diameter (Morrell, [0043]: teaches an "iris width 130A and/or 130B (also referred to in some embodiments as iris diameter)"); and
the iris diameter is from 11 mm to 13 mm (Morrell, [0043]: teaches "the average iris diameter or width of an adult human is roughly 11.7 millimeters across a wide population (plus or minus some standard deviation, such as 0.5 millimeters)").
Morrell is analogous art with respect to Kornilov-37, in view of Fu because they are from the same field of endeavor, namely facial analysis from different angles. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to define the average iris diameter as taught by Morrell into the teaching of Kornilov-37, in view of Fu. The suggestion for doing so would allow the system to compare a user's iris to a control, thereby offering eye size information with relation to the average. Therefore, it would have been obvious to combine Morrell with Kornilov-37, in view of Fu.
Regarding Claim 19, the combination of Kornilov-37 and Fu discloses the computer product of Claim 15. The combination of Kornilov-37 and Fu does not expressly disclose the limitations of Claim 19; however, Morrell discloses wherein the computer instructions further comprise:
determining a head width classification of the user’s head (Morrell, [0078]: teaches a landmark detection model 315 being a trained machine learning model that identifies facial geometry <read on head width classification> of the face); and
determining the estimated facial feature based on the head width classification of the user’s head (Morrell, [0078]: teaches a landmark detection model 315 being a trained machine learning model that identifies facial geometry <read on head width classification> of the face, where it determines or estimates a variety of facial landmarks <read on estimated facial feature> based on a single camera input without the need for a dedicated depth sensor).
Morrell is analogous art with respect to Kornilov-37, in view of Fu because they are from the same field of endeavor, namely image analysis of human faces. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a landmark detection model to identify facial regions and geometry of the user's face as taught by Morrell into the teaching of Kornilov-37, in view of Fu. The suggestion for doing so would allow for automatic labelling and classification of facial regions, thereby speeding up 3D model generation. Therefore, it would have been obvious to combine Morrell with Kornilov-37, in view of Fu.
Regarding Claim 20, the combination of Kornilov-37, Fu, and Morrell discloses the computer product of Claim 19. The combination of Kornilov-37 and Fu does not expressly disclose the limitations of Claim 20; however, Morrell discloses wherein the computer instructions further comprise
associating head width classifications of a set of head width classifications with respective estimated facial features of a set of estimated facial features using a machine learning model that comprises an input of a set of images (Morrell, [0052]: teaches "using machine learning to determine biometric (e.g., facial) measurements <read on set of head width classifications> based on captured images <read on input of set of images>"), wherein
each image of the set of images comprises a head width classification and a facial feature measurement (Morrell, [0052]: teaches "using machine learning to determine biometric (e.g., facial) measurements <read on head width classification> based on captured images <read on input image>"; [0071]: teaches an example of the measurement system determining the user's facial height and nose width in the input image).
Morrell is analogous art with respect to Kornilov-37, in view of Fu because they are from the same field of endeavor, namely image analysis of human faces. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to a machine learning model to determine biometric measurements of the user's face as taught by Morrell into the teaching of Kornilov-37, in view of Fu. The suggestion for doing so would result in more accurate landmark measurements of the user's face. Therefore, it would have been obvious to combine Morrell with Kornilov-37, in view of Fu.
Claims 8-9 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kornilov et al. (US 20180096537 A1), hereinafter referenced as Kornilov-37, in view of Fu et al. (US 20210299384 A1), hereinafter referenced as Fu, and further in view of Morrell et al. (US 20230103129 A1, previously cited), hereinafter referenced as Morrell.
Regarding Claim 8, Kornilov-37 discloses a method for generating a three-dimensional (3D) model (Kornilov-37, [0040]: teaches a process <read on method> of a model generator 206 that is configured to "determine a mathematical 3D model for a user's face associated with each set of images"), comprising:
receiving a set of images of an object (Kornilov-37, [0053]: teaches receiving images and/or video frames <read on set of images> of the user's face <read on object> and head as shown in FIG. 4);
generating an initial model of the object based on the set of images (Kornilov-37, [0054]: teaches generating a 3D model <read on initial model> of the user's face based on the defined reference points, which are locations of various facial features from a set of images of the user's face);
determining a first measurement of a first feature of the object (Kornilov-37, [0055]: teaches combining the obtained reference points to create a 3D model of the user's face <read on object>, where "the corresponding reference points are combined to make a set of
(
x
,
y
,
z
)
coordinates <read on first measurement> representing the position of each of the reference points (e.g., locations of facial features <read on first feature>) on the user's face/head");
[[classifying the object with a head width classification using a machine learning model based on the set of images;]]
[[obtaining, from a storage, a set of proportions corresponding to the head width classification, wherein]]
[[the set of proportions comprises an estimated measurement of the first feature of the object associated with the head width classification, wherein]]
[[the estimated measurement is an average measurement, in a population, of at least one of]]
[[an iris diameter,]]
an ear junction distance (Kornilov-37, [0099]: teaches the scale being computed such that the glasses' width will be equal to a weighted average of the distance between the ear junctures <read on ear junction distance> and the distance between the external eye corners of the 3D model of the user's face), and
a temple distance (Kornilov-37, [0099]: teaches the scale being computed such that the glasses' width will be equal to a weighted average of the distance between the ear junctures and the distance between the external eye corners <read on temple distance> of the 3D model of the user's face);
[[determining a scaling ratio for the initial model based on the first measurement and the estimated measurement; and]]
[[scaling the initial model to generate a scaled model based on the scaling ratio.]]
However, Kornilov-37 does not expressly disclose
classifying the object with a head width classification using a machine learning model based on the set of images;
obtaining, from a storage, a set of proportions corresponding to the head width classification, wherein
the set of proportions comprises an estimated measurement of the first feature of the object associated with the head width classification, wherein
the estimated measurement is an average measurement, in a population, of at least one of
an iris diameter,
determining a scaling ratio for the initial model based on the first measurement and the estimated measurement; and
scaling the initial model to generate a scaled model based on the scaling ratio.
Fu discloses
[[classifying the object with a head width classification using a machine learning model based on the set of images;]]
[[obtaining, from a storage, a set of proportions corresponding to the head width classification, wherein]]
the set of proportions comprises an estimated measurement of the first feature of the object associated with the head width classification (Fu, [0172]: teaches processor 310 averaging the corrected and scaled measurements for a set of images to obtain final measurements <read on estimated real-world value> of the patient's facial anatomy; [0197]: teaches a facial feature measurement step 850, which measures the pixel distance between two selected landmarks <read on first feature of object>), wherein
the estimated measurement is an average measurement, in a population (Fu, [0193]: teaches an active shape model using identified landmarks within a set of training images to develop a deformable model for the relative positions of the landmarks (the shape) and a statistical model of the pixel values <read on average measurement> provided by the images at those positions; [0194]: teaches the statistical model of pixel values is determined by sampling a range of pixels normal to each landmark <read on estimated facial feature> within each image in the training set <read on population>), of at least one of
an iris diameter (Fu, [0198]: teaches the reference feature being a feature of the eyes, such as the iris or cornea of the user since "the iris diameter is remarkably similar across all races, and does not grow after the early teens"),
determining a scaling ratio for the initial model based on the first measurement and the estimated measurement (Fu, [0053]: teaches "calculating a value of the measured aspect based on a scaling factor <read on determined scaling ratio> derived from the reference feature," where the reference feature has a known dimension, such as a coin; Note: it should be noted that although the cited portion uses a real-world reference, such as a coin, Paragraph [0201] discloses an alternative method for determining scale without the use of a reference feature, such as a laser and detector combination, or a laser that detects the distance to the target, or a stereoscopic camera; additionally, the reference feature is defined as a known value; since the training datasets comprise of iris diameter data, which has very little deviation, the reference feature is interpreted to be using this; [0194]: teaches a statistical model of pixel values is determined by sampling a range of pixels normal to each landmark <read on estimated facial feature> within each image in the training set); and
scaling the initial model to generate a scaled model based on the scaling ratio (Fu, [0195]: teaches updating the scale <read on scaling ratio>, translation, and rotation of the shape estimate x to best match the new estimated landmark positions of the deformable model <read on scaled user's head model>).
Fu is analogous art with respect to Kornilov-37 because they are from the same field of endeavor, namely measuring and collecting data of the user's face. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement an active shape model that is trained to identify facial landmarks from training image datasets as taught by Fu into the teaching of Kornilov-37. The suggestion for doing so would allow the neural network to approximate the proportions of the user's face using measurement data that is a constant without the use of external measuring tools, such as coins or cards. Therefore, it would have been obvious to combine Fu with Kornilov-37.
However, the combination of Kornilov-37 and Fu does not expressly disclose
classifying the object with a head width classification using a machine learning model based on the set of images; and
obtaining, from a storage, a set of proportions corresponding to the head width classification.
Morrell discloses
classifying the object with a head width classification using a machine learning model based on the set of images (Morrell, [0040]: teaches using a machine learning model that identifies, for each image, and extracts coordinate locations of various facial landmarks, such as the top and bottom of the head, center, top, bottom, and edges of the eyes and mouth (i.e., facial geometry <read on head width classification> of the identified face)); and
obtaining, from a storage, a set of proportions corresponding to the head width classification (Morrell, [0078]: teaches a landmark detection model 315 being a trained machine learning model that identifies facial geometry <read on head width classification> of the face, where it determines or estimates a variety of facial landmarks <read on set of proportions> based on a single camera input without the need for a dedicated depth sensor; [0078]: further teaches the landmark detection model 615 being part of a set of machine learning models that are used to generate model parameters 1325, where the machine learning models are interpreted to be from storage).
Morrell is analogous art with respect to Kornilov-37, in view of Fu because they are from the same field of endeavor, namely image analysis of human faces. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a landmark detection model to identify facial regions and geometry of the user's face as taught by Morrell into the teaching of Kornilov-37, in view of Fu. The suggestion for doing so would allow for automatic labelling and classification of facial regions, thereby speeding up 3D model generation. Therefore, it would have been obvious to combine Morrell with Kornilov-37, in view of Fu.
Regarding Claim 9, the combination of Kornilov-37, Fu, and Morrell discloses the method of Claim 8. Additionally, Kornilov-37 further discloses wherein:
the object comprises a user’s head (Kornilov-37, [0053]: teaches received images of the user's face and head); and
the first feature comprises a face width (Kornilov-37, [0048]: teaches the facial features of the user's 3D face model, where the 3D model comprises various facial features, such as the cheekbones; Note: it should be noted that although a "face width" is not expressly stated, it would be obvious for one of ordinary skill in the art to deduce that images of a user's face would include a face width as distances between facial points are disclosed).
Regarding Claim 14, the combination of Kornilov-37, Fu, and Morrell discloses the method of Claim 8. The combination of Kornilov-37 and Fu does not expressly disclose the limitations of Claim 14; however, Morrell discloses
receiving a second set of images (Morrell, [0081]: teaches the measurement system selecting a set of reference images <read on second set of images> that satisfy an orientation criteria), wherein
each image of the second set of images comprises a learning object including a learning feature associated with a second measurement (Morrell, [0048]: teaches the measurement system determining a second facial height measurement, where "these two measurements can then be compared to determine the relative scale between the images <read on second set of images>"; [0078]: teaches the landmark detection model 315 identifying facial geometry of the face, and determining or estimating a variety of facial landmarks <read on learning feature>, where "the landmark detection model 315 may correspond to or include a MediaPipe Face Mesh model <read on learning object>") and
a respective measurement classification (Morrell, [0040]: teaches the measurement system 105 using "a machine learning model to identify and extract, for each image in a stream of images (e.g., in a video), coordinate locations of various facial landmarks (such as the top and bottom of the head, center, top, bottom, and edges of the eyes and mouth, and the like)" in order to evaluate these coordinate locations based on the facial measurements <read on measurement classification>); and
analyzing the second set of images with a machine learning model to associate each respective measurement classification of a set of measurement classifications with a respective second measurement (Morrell, [0052]: teaches "using machine learning to determine biometric (e.g., facial) measurements <read on set of measurement classifications> based on captured images <read on second set of images>"), wherein
the measurement classification is selected from the set of measurement classification to classify the object (Morrell, [0052]: teaches "using machine learning to determine biometric (e.g., facial) measurements <read on measurement classification> based on captured images"; [0071]: teaches an example of the measurement system determining the user's facial height and nose width <read on classify object>).
Morrell is analogous art with respect to Kornilov-37, in view of Fu because they are from the same field of endeavor, namely image analysis of human faces. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to a machine learning model to determine biometric measurements of the user's face as taught by Morrell into the teaching of Kornilov-37, in view of Fu. The suggestion for doing so would result in more accurate landmark measurements of the user's face. Therefore, it would have been obvious to combine Morrell with Kornilov-37, in view of Fu.
Claims 4-7, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kornilov et al. (US 20180096537 A1), hereinafter referenced as Kornilov-37, in view of Fu et al. (US 20210299384 A1), hereinafter referenced as Fu as applied to Claim 1 above respectively, and further in view of Kornilov et al. (US 20140293220 A1, previously cited), hereinafter referenced as Kornilov-20.
Regarding Claim 4, the combination of Kornilov-37 and Fu discloses the system of Claim 1. The combination of Kornilov-37 and Fu does not expressly disclose the limitations of Claim 4; however, Kornilov-20 discloses wherein the processor is further configured to:
position a glasses frame model on the scaled user’s head model (Kornilov-20, [0032]: teaches "making an initial 3D model of the user's head and determining user head measurements", where "the initial 3D model is used in the process of making an adjusted 3D model <read on scaled user's head>, from which the user's head measurements are determined"; [0027]: teaches "a 3D model of each glasses frame is stored in the database"; [0043]: teaches "the selected glasses are sent to a display to be rendered <read on glasses frame model> on a 3D model of the user's head", where "the 3D model of the user's head is also interactive and the user can interact with the model and see how the glasses may look on the user"); and
determine a set of facial measurements associated with the user’s head based on stored measurement information associated with the glasses frame model (Kornilov-20, [0027]: teaches "a 3D model of each glasses frame is stored in the database", where "other glasses frame information <read on stored measurement information> is stored in the database, including one or more of the following: glasses frame measurements, identifier, name, picture, manufacturer, model number, description, category, type, glasses frame material, brand, part number, and price"; [0028]: teaches using a set of reference points on a user's head 300 to determine user head measurements <read on set of facial measurements>, which are portions of the 3D model of the user's face) and
the position of the glasses frame model on the scaled user’s head model (Kornilov-20, [0024]: teaches rendering selected glasses on a 3D interactive model of the user; [0021]: teaches the initial 3D model of the user's head being adjusted into an adjusted 3D model <read on scaled user's head model>; Note: it should be noted that it would be obvious for one skilled in the art to understand that the rendering of the selected glasses on a 3D model of the user's head would be around the eye area).
Kornilov-20 is analogous art with respect to Kornilov-37, in view of Fu because they are from the same field of endeavor, namely analyzing images of user's faces for virtual glasses-fitting. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a neural network that evaluates the fit of glasses on a 3D face model of the user as taught by Kornilov-20 into the teaching of Kornilov-37, in view of Fu. The suggestion for doing so would allow the system to train based on weights and penalty functions regardless of facial proportions, thereby improving the accuracy and quality of the glasses-fitting experience. Therefore, it would have been obvious to combine Kornilov-20 with Kornilov-37, in view of Fu.
Regarding Claim 5, the combination of Kornilov-37, Fu, and Kornilov-20 discloses the system of Claim 4. The combination of Kornilov-37 and Fu does not expressly disclose the limitations of Claim 5; however, Kornilov-20 discloses wherein the processor is further configured to
determine a confidence level corresponding to a facial measurement of the set of facial measurements (Kornilov-20, [0023]: teaches the comparison engine 142 comparing "the user head measurements <read on facial measurements> from the 3D model to a database of glasses frame information 144" using a penalty function, where "one or more glasses frames are selected based on a score <read on confidence level> computed from the penalty function and set thresholds of the score that comprise different levels of fit").
Kornilov-20 is analogous art with respect to Kornilov-37, in view of Fu because they are from the same field of endeavor, namely analyzing images of user's faces for virtual glasses-fitting. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a neural network that evaluates the fit of glasses on a 3D face model of the user as taught by Kornilov-20 into the teaching of Kornilov-37, in view of Fu. The suggestion for doing so would allow the system to train based on weights and penalty functions regardless of facial proportions, thereby improving the accuracy and quality of the glasses-fitting experience. Therefore, it would have been obvious to combine Kornilov-20 with Kornilov-37, in view of Fu.
Regarding Claim 6, the combination of Kornilov-37, Fu, and Kornilov-20 discloses the system of Claim 4. The combination of Kornilov-37 and Fu does not expressly disclose the limitations of Claim 6; however, Kornilov-20 discloses wherein the processor is further configured to:
compare the set of facial measurements to stored dimensions of a set of glasses frames (Kornilov-20, [0027]: teaches "the measurements of the glasses frames comprise a portion of the glasses frame information stored in a database <read on set of glasses frames>"; [0023]: teaches a comparison engine 142, which "compares the user head measurements from the 3D model to a database of glasses frame information 144"); and
output a recommended glasses frame at a user interface based at least in part on the comparison (Kornilov-20, [0042]: teaches a results list that comprises "all glasses in the data that fit the user (i.e., all glasses above the "does not fit" threshold <read on comparison>)", where the user can select one of the resulting glasses <read on recommended glasses frame> from the list; [0043]: teaches displaying <read on user interface> the results list of glasses frames).
Kornilov-20 is analogous art with respect to Kornilov-37, in view of Fu because they are from the same field of endeavor, namely analyzing images of user's faces for virtual glasses-fitting. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a neural network that evaluates the fit of glasses on a 3D face model of the user as taught by Kornilov-20 into the teaching of Kornilov-37, in view of Fu. The suggestion for doing so would allow the system to train based on weights and penalty functions regardless of facial proportions, thereby improving the accuracy and quality of the glasses-fitting experience. Therefore, it would have been obvious to combine Kornilov-20 with Kornilov-37, in view of Fu.
Regarding Claim 7, the combination of Kornilov-37, Fu, and Kornilov-20 discloses the system of Claim 4. The combination of Kornilov-37 and Fu does not expressly disclose the limitations of Claim 7; however, Kornilov-20 discloses wherein the processor is further configured to:
Regarding Claim 16, the combination of Kornilov-37 and Fu discloses the computer product of Claim 15. The combination of Kornilov-37 and Fu does not expressly disclose the limitations of Claim 16; however, Kornilov-20 discloses wherein:
the estimated facial feature comprises an average measurement of a facial feature in a population (Kornilov-20, [0042]: teaches a results list <read on set of recommended glasses frame> that comprises "all glasses in the data that fit the user (i.e., all glasses above the "does not fit" threshold)"); and
the computer instructions further comprise determining the estimated facial feature (Kornilov-20, [0042]: teaches a results list <read on recommended glasses frame> that comprises "all glasses in the data that fit the user (i.e., all glasses above the "does not fit" threshold)"; [0043]: teaches displaying <read on user interface> the results list of glasses frames).
Kornilov-20 is analogous art with respect to Kornilov-37, in view of Fu because they are from the same field of endeavor, namely analyzing images of user's faces for virtual glasses-fitting. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a neural network that evaluates the fit of glasses on a 3D face model of the user as taught by Kornilov-20 into the teaching of Kornilov-37, in view of Fu. The suggestion for doing so would allow the system to train based on weights and penalty functions regardless of facial proportions, thereby improving the accuracy and quality of the glasses-fitting experience. Therefore, it would have been obvious to combine Kornilov-20 with Kornilov-37, in view of Fu.
Regarding Claim 18, the combination of Kornilov-37 and Fu discloses the computer product of Claim 15. The combination of Kornilov-37 and Fu does not expressly disclose the limitations of Claim 18; however, Kornilov-20 discloses wherein the computer instructions further comprise:
positioning a 3D model of a glasses frame on the scaled 3D model (Kornilov-20, [0032]: teaches "making an initial 3D model of the user's head and determining user head measurements", where "the initial 3D model is used in the process of making an adjusted 3D model <read on scaled user's head>, from which the user's head measurements are determined"; [0027]: teaches "a 3D model of each glasses frame is stored in the database"; [0043]: teaches "the selected glasses are sent to a display to be rendered <read on glasses frame model> on a 3D model of the user's head", where "the 3D model of the user's head is also interactive and the user can interact with the model and see how the glasses may look on the user"); and
determining facial measurements of the user based on measurements associated with the 3D model of the glasses frame (Kornilov-20, [0027]: teaches "a 3D model of each glasses frame is stored in the database", where "other glasses frame information <read on stored measurement information> is stored in the database, including one or more of the following: glasses frame measurements, identifier, name, picture, manufacturer, model number, description, category, type, glasses frame material, brand, part number, and price"; [0028]: teaches using a set of reference points on a user's head 300 to determine user head measurements <read on set of facial measurements>, which are portions of the 3D model of the user's face) and
the position of the glasses frame on the scaled 3D model (Kornilov-20, [0024]: teaches rendering selected glasses on a 3D interactive model of the user; [0021]: teaches the initial 3D model of the user's head being adjusted into an adjusted 3D model <read on scaled user's head model>; Note: it should be noted that it would be obvious for one skilled in the art to understand that the rendering of the selected glasses on a 3D model of the user's head would be around the eye area).
Kornilov-20 is analogous art with respect to Kornilov-37, in view of Fu because they are from the same field of endeavor, namely analyzing images of user's faces for virtual glasses-fitting. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a neural network that evaluates the fit of glasses on a 3D face model of the user as taught by Kornilov-20 into the teaching of Kornilov-37, in view of Fu. The suggestion for doing so would allow the system to train based on weights and penalty functions regardless of facial proportions, thereby improving the accuracy and quality of the glasses-fitting experience. Therefore, it would have been obvious to combine Kornilov-20 with Kornilov-37, in view of Fu.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Kornilov et al. (US 20180096537 A1), hereinafter referenced as Kornilov-37, in view of Fu et al. (US 20210299384 A1), hereinafter referenced as Fu, and further in view of Morrell et al. (US 20230103129 A1, previously cited), hereinafter referenced as Morrell as applied to Claim 9 above respectively, and further in view of Takano et al. (US 20100220933 A1, previously cited), hereinafter referenced as Takano.
Regarding Claim 10, the combination of Kornilov-37, Fu, and Morrell discloses the method of Claim 9. The combination of Kornilov-37, Fu, and Morrell does not expressly disclose the limitations of Claim 10; however, Takano discloses wherein
the head width classification is selected from a list comprising narrow, medium, and wide (Takano, [0164]: teaches a system analyzing a user's face, where it categorizes it into the "short and plump" face type; [0165]: teaches the system analyzing a user's face, where it categorizes it into the "long and refined" face type; FIG. 21 teaches a categorization map, where a user's face type can be determined <read on narrow, medium, and wide>).
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Takano is analogous art with respect to the combination of Kornilov-37, Fu, and Morrell because they are from the same field of endeavor, namely image analysis of human faces. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a categorization map to categorize face types as taught by Takano into the combined teaching of Kornilov-37, Fu, and Morrell. The suggestion for doing so would allow for more possible classifications than preset options, thereby offering more classification options for the user. Therefore, it would have been obvious to combine Takano with the combination of Kornilov-37, Fu, and Morrell.
Claims 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Kornilov et al. (US 20180096537 A1), hereinafter referenced as Kornilov-37, in view of Fu et al. (US 20210299384 A1), hereinafter referenced as Fu, and further in view of Morrell et al. (US 20230103129 A1, previously cited), hereinafter referenced as Morrell as applied to Claim 8 above respectively, and further in view of Kornilov et al. (US 20140293220 A1, previously cited), hereinafter referenced as Kornilov-20.
Regarding Claim 11, the combination of Kornilov-37, Fu, and Morrell discloses the method of Claim 8. The combination of Kornilov-37, Fu, and Morrell does not expressly disclose the limitations of Claim 11; however, Kornilov-20 discloses
positioning a 3D model on the scaled model (Kornilov-20, [0032]: teaches "making an initial 3D model of the user's head and determining user head measurements", where "the initial 3D model is used in the process of making an adjusted 3D model <read on scaled model>, from which the user's head measurements are determined"; [0027]: teaches "a 3D model of each glasses frame is stored in the database"; [0043]: teaches "the selected glasses are sent to a display to be rendered <read on 3D model> on a 3D model of the user's head", where "the 3D model of the user's head is also interactive and the user can interact with the model and see how the glasses may look on the user"; Note: it should be noted that "3D model" in this instance is being interpreted as a 3D model representation of glasses), wherein
the 3D model is associated with real-world dimensions (Kornilov-20, [0027]: teaches "measurements of the glasses frames comprise a portion of the glasses frame information stored in a database", where "the glasses frames are scanned with a 3D imager <read on real-world dimensions> and are stored in the database"); and
generating measurements of the object based on the position of the 3D model on the scaled model (Kornilov-20, [0024]: teaches rendering selected glasses <read on 3D model> on a 3D interactive model of the user; [0021]: teaches the initial 3D model of the user's head being adjusted into an adjusted 3D model <read on scaled model>; [0041]: teaches comparing glasses frames <read on position of 3D model> to the user's head measurements <read on object measurements> to generate a fit score using a penalty function) and
a comparison of the 3D model with the scaled model (Kornilov-20, [0027]: teaches "the measurements of the glasses frames comprise a portion of the glasses frame information stored in a database <read on set of glasses frames>"; [0023]: teaches a comparison engine 142, which "compares the user head measurements from the 3D model to a database of glasses frame information 144", where the 3D model of the user's head has already been adjusted).
Kornilov-20 is analogous art with respect to the combination of Kornilov-37, Fu, and Morrell because they are from the same field of endeavor, namely analyzing images of user's faces for virtual glasses-fitting. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a neural network that evaluates the fit of glasses on a 3D face model of the user as taught by Kornilov-20 into the combined teaching of Kornilov-37, Fu, and Morrell. The suggestion for doing so would allow the system to train based on weights and penalty functions regardless of facial proportions, thereby improving the accuracy and quality of the glasses-fitting experience. Therefore, it would have been obvious to combine Kornilov-20 with the combination of Kornilov-37, Fu, and Morrell.
Regarding Claim 12, the combination of Kornilov-37, Fu, and Morrell discloses the method of Claim 8. The combination of Kornilov-37, Fu, and Morrell does not expressly disclose the limitations of Claim 12; however, Kornilov-20 discloses
determining measurements of the object based on the scaled model (Kornilov-20, [0032]: teaches adjusting the initial 3D model to obtain an adjusted 3D model <read on scaled model>, "from which the user's head measurements are determined").
Kornilov-20 is analogous art with respect to the combination of Kornilov-37, Fu, and Morrell because they are from the same field of endeavor, namely analyzing images of user's faces for virtual glasses-fitting. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a neural network that evaluates the fit of glasses on a 3D face model of the user as taught by Kornilov-20 into the combined teaching of Kornilov-37, Fu, and Morrell. The suggestion for doing so would allow the system to train based on weights and penalty functions regardless of facial proportions, thereby improving the accuracy and quality of the glasses-fitting experience. Therefore, it would have been obvious to combine Kornilov-20 with the combination of Kornilov-37, Fu, and Morrell.
Regarding Claim 13, the combination of Kornilov-37, Fu, Morrell, and Kornilov-20 discloses the method of Claim 12. The combination of Kornilov-37, Fu, and Morrell does not expressly disclose the limitations of Claim 13; however, Kornilov-20 discloses
determining a confidence level corresponding to each measurement of the measurements (Kornilov-20, [0023]: teaches the comparison engine 142 comparing "the user head measurements <read on measurements> from the 3D model to a database of glasses frame information 144" using a penalty function, where "one or more glasses frames are selected based on a score <read on confidence level> computed from the penalty function and set thresholds of the score that comprise different levels of fit").
Kornilov-20 is analogous art with respect to the combination of Kornilov-37, Fu, and Morrell because they are from the same field of endeavor, namely analyzing images of user's faces for virtual glasses-fitting. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a neural network that evaluates the fit of glasses on a 3D face model of the user as taught by Kornilov-20 into the combined teaching of Kornilov-37, Fu, and Morrell. The suggestion for doing so would allow the system to train based on weights and penalty functions regardless of facial proportions, thereby improving the accuracy and quality of the glasses-fitting experience. Therefore, it would have been obvious to combine Kornilov-20 with the combination of Kornilov-37, Fu, and Morrell.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kornilov et al. (US 20180374261 A1) discloses generating a face image that corresponds to a set of images based at least in part on a face model;
McVey (US 20130259333 A1) discloses predicting one or more characteristics of an individual by applying computational methods to images of the individual to generate one or more metrics indicative of the characteristics;
Wang (US 20180197330 A1) discloses modeling a user's face based on an input image; and
Foley (US 6535223 B1) discloses determining pupillary distance and multi-focal element height for prescription eyeglasses.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KARL TRUONG whose telephone number is (703)756-5915. The examiner can normally be reached 10:30 AM - 7:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kent Chang can be reached at (571) 272-7667. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.D.T./Examiner, Art Unit 2614
/KENT W CHANG/Supervisory Patent Examiner, Art Unit 2614
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