DETAILED ACTION
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.
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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 07/08/25 comply with provisions of 37 CFR 1.97. Accordingly, the examiner considered the information disclosure statements.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The claim(s) recite(s) determining an eye endurance level of at least one eye of a user based on a sequence of eye images, a mental process because evaluating eye images and forming a judgment about the eye’s condition is the type of observation and evaluation clinicians have long performed without a computer, and also a mathematical concept because, per claim 4 and the specification, the determination is made by applying an ye endurance model disclosed as a neural network that computes a linear weighted combination of inputs, processes that combination through a non-linear activation function, and is trained by adjusting weights to reduce a loss function. This judicial exception is not integrated into a practical application because the additional elements, an electronic device including an HMD and an infrared camera, executing a visual assessment application including displaying a user interface to create a 3D virtual environment, displaying a body of text for an extended duration of time, and obtaining a sequence of eye images, merely recite generic computer, camera, and display components performing their ordinary functions, together with insignificant data-gathering activity, without reciting any improvement to the HMD, the camera, the computer, or any other technology, or any technical means for performing the determination beyond the abstract process itself. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because HMDs with an infrared camera directed at the eye to capture and crop a sequence of eye images, and the display of text on an HMD interface within a 3D virtual environment for an extended duration as part of a vision test, were well-understood, routine, and conventional, and applying a generically-described machine-learning model, weighted linear combinations processed through an activation function, trained by backpropagation to reduce a loss function, to produce an output value from collected data is itself well-understood, routine, conventional computer functionality, with no improvement to the model's architecture, training method, or operation disclosed or claimed.
Step 2A, Prong One:
Claim 1 recites “based on the sequence of eye images, determining an eye endurance level of
at least one eye of a user associated with the electronic device” The limitation recites an abstract idea falling within two groupings. A mental process, evaluating a sequence of eye images and forming a judgment about the eye’s condition (how fatigued, irritated, or dry it appears) is the type of observation and evaluation clinicians have long performed without a computer. A mathematical concept, in claim 4 and the specification, this determination is carried out by applying an “eye endurance model,” disclosed as a neural network in that combination through a non-linear activation function, and is trained by adjusting weights to reduce a loss function (¶¶0072-0077, fig. 5A). That is a mathematical calculation, and claim 1’s “determining step is broad enough to be practiced by it.
The remaining limitations are additional elements, not part of the abstract idea: “obtaining a sequence of eye images, each eye image including a respective infrared image of a region of interest (ROI) corresponding to at least one eye” is data gathering performed as input to determination; “executing a visual assessment application, including displaying a user interface to create a 3D virtual environment” and “displaying a body of text on the user interface for an extended duration of time” recite generic display output; “an electronic device including an HMD and an infrared camera” recites a the generic hardware.
Step 2A, Prong Two:
These additional elements do not integrate the abstract idea into a practical application. The HMD and infrared camera are recited generically and used for their ordinary functions, the camera captures images, the HMD display content. Obtaining the eye images is insignificant data-gathering activity. Displaying the user interface and the body of text is generic output/setup activity. No additional element reflects an improvement to the HMD, the camera, the computer, or to any other technology; the claim uses generic components to gather data and reach a conclusion, without a recited technical means for the “determining” step beyond the abstract process itself.
Step 2B
Considered individually and as a combination, the additional elements amount to no more than well-understood, routine, conventional activity. HMDs with an infrared camera directed at the eye, capturing and cropping a sequence of eye images to a region of interest, were well-understood, routine, and conventional. Displaying text on an HMD interface within a 3D virtual environment for an extended duration, as part of a vision test, was likewise well understood, routine and conventional. Applying a machine-learning model of the generic type as disclosed, weighted linear combinations processed through an activation function, trained by adjusting weights to reduce a loss function, to produce an output value from collected data is itself well-understood, routine, conventional computer functionality, no improvement to the model’s architecture, training method, or operation is disclosed or claimed, and applying his generic model’s architecture, training method, or operation is disclosed or claimed, and applying this generic model does not supply an inventive concept. Claim 1 is not eligible under 35 USC 101.
Claims 14 and 17 recite the same abstract idea (mental process and mathematical concept) and the same additional elements as claim 1, merely reformatted as a non-transitory storage medium storing instructions (claim 14) or an apparatus comprising a HMD, infrared camera, processor and memory (claim 17). Reciting the same generic hardware and software elements in a different statutory format does not integrate the exception into a practical application or add significantly more, for the reasons given above. Claims 14 and 17 are not eligible under 35 USC 101.
Claims 2 and 15 states predefined brightness level an font size, an additional element selecting generic display parameters for the displaying text. Selecting a brightness level and font size for on-screen text is well-understood, routing, and conventional. Does not integrate the exception or add significantly more.
Claims 3 and 16 states directing the infrared camera; capturing a sequence of camera images cropping to the ROI is a data-gathering and pre-processing performed generically by the camera and processor, well-understood, routine, and conventional for the same reasons as claim 1’s “obtaining” limitation. Does not cure the deficiency.
Claims 4 and 18 states applying an eye endurance model to generate a model output, and claim 6 and 20 states the model comprising a feature extraction model and an endurance assessment model, these make explicit the mathematical concept already implicated by claim 1. As per the specification, the feature extraction model and endurance assessment model are each an instance of the same generically disclosed neural network. Dividing the calculation into an “extraction” stage and an ”assessment” stage, without disclosing any improvement to how either stage is architected or trained, remains a generic computational technique and does not add significantly more.
Claims 5 and 19 states a model output includes a diagnosis indicator identifying a dry eye severity level, that further specifies the output of the abstract idea rather than adding a technical means of reaching it. A dry-eye severity determination from observed eye characteristics is itself an evaluative clinical conclusion reached by observation, reinforcing rather than curing the mental process characterization.
Claim 7 states an eye endurance level determined with respect to a predefined temporal length greater than the extended duration of time, that relates the abstract determination to a longer time horizon than the displays session. No additional technical means is recited, does not integrate the exception.
Claim 8 states receiving the model from a server; training it at the server using a sequence of eye images and a ground truth eye endurance level, is a generic data-transfer/data-gathering activity combined with application of a generic, unimproved training methodology related to claims 4, 6, 18, 20, performed on generic server infrastructure. Does not add significantly more.
Claims 10, 11, and 12 states detecting blink events/times; determining sequences of eyelid positions and pupil sizes; determining the eye endurance level from these, tracked relative to a start time, is further inputs to, and temporal bookkeeping around, the same abstract determination. Manually observing blink rate, eyelid position, and pupil size is itself routine to a clinical eye exam, reinforcing the mental-process characterization; combining multiple tracked signals into one model output is the same mathematical concept discussed for claims 4, 6, 18, 20, applied to additional variables. Does not integrate the exception or add significantly more.
Claim 13 states extracting a sclera feature; determining an eye dryness feature and the eye endurance level from sclera features, visually assessing scleral redness as a sign of ocular dryness is a particularly clear example of the mental-process grouping, and adds no technical means beyond the abstract determination. Claim 9 states executing a media play application, controlling its execution based on the eye endurance level, adds a further generic computer function (executing a media application) and a generic, high-level instruction to adjust that function based on the abstract determination, i.e., pausing playback and displaying a reminder once a computed fatigue threshold is reached per the specification. Without a more specific, non-generic technical mechanism for how execution is controlled, this does not integrate the exception into a practical application or add significantly more; automatically pausing playback and prompting a break in response to a calculated value is a generic response, and arguably a further abstract idea (managing personal behavior) rather than a technical improvement.
Claims 1-20 do not recite additional elements that integrate the recited judicial exceptions into a practical application, and do not recite additional elements that amount to significantly more than the judicial exceptions. Claims 1-20 are rejected under 35 USC 101.
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, 3, 4, 6, 7, 13, 14, 16-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ong et al. (US 11,800,975) in view of Torkos et al. (US 11,749,018).
Regarding claim 1, Ong teaches a method of implementing a vision test (the system is related to
a diagnostic of the eye’s condition (predict eye fatigue, abstract and claim 1) and the teaches the “computer vision syndrome (CVS)” prevention, a recognized eye-health assessment context), executing a visual assessment application, including displaying a user interface to create a 3D virtual environment (col. 3, lines 1-20, as shown in FIG. 1, a user is using or operating the VR device 102. For example, the user may be playing a VR video game or using a VR application;…fatigue predictor 110; note: the eye-fatigue prediction operates while “the user may be playing a VR video game or using a VR application”; the “eye fatigue predictor 110” is itself the visual assessment functionality layered onto that VR application), displaying a body of text on the user interface for an extended duration of time (col. 4, lines 25-35, the alert may be a visual alert, an audible alert, or both. The alert 30 presenter 120 may present the alert to a user. For example, the alert presenter 120 may display a visual alert to a user via
a display of the VR device 102, note: a generally is to provide a message of an alert which would be in text (visual alert)), based on the sequence of eye images, determining an eye endurance level of the at least one eye of a user associated with the electronic device (claim 1, “predict eye fatigue based on the blood vessel density score” computed from the image sequence. Note: “Eye fatigue” and “eye endurance level” are inverse framings of the same underlying eye condition metric, a person of ordinary skill in the (POSITA) would predict one thereby would determine the other. The fatigue predictor 116/734 performs this determination per user which is associated with the VR device 102/ 700 the user is wearing.). Ong does not specifically teach at an electronic device including an HMD and an infrared camera: obtaining a sequence of eye images, each eye image including a respective infrared image of a region of interest (ROI) corresponding to at least one eye. However, in a similar field of endeavor, Torkos teaches a method, at an electronic device including an HMD and an infrared camera (col. 7, lines 55-68 and col. 8, lines 35-50, For example, the one or more image sensors 330 may include an infrared camera and a visible light camera. Note: the “image sensor” which is “an infrared image” sensor is used in the “head mounted enclosure 120”, the image sensors 330 include an infrared camera and a visible light camera), obtaining a sequence of eye images, each eye image including a respective infrared image of a region of interest (ROI) corresponding to at least one eye (claim 1, determine a region of interest in the image … wherein the image depicts an eye of the user in the region of interest; claim 13, the image is an infrared image; col. 1, lines 30-68, capturing “a set of images” such as a sequence). It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the method of Ong with at an electronic device including an HMD and an infrared camera: obtaining a sequence of eye images, each eye image including a respective infrared image of a region of interest (ROI) corresponding to at least one eye of Torkos, for the purpose of reducing the amount of image data that must be processed (col. 19, lines 45-60).
Regarding claim 3, Ong in view of Torkos teaches the invention as set forth above and Torkos further teaches directing the infrared camera towards the at least one eye (col. 7, lines 25- 20, one or more image sensors 232 (e.g., sensing infrared … light) that are directed at a face of the user, note: while wearing the head-mounted enclosure shown in fig. 2); capturing by the infrared camera a sequence of camera images including the ROI corresponding to the at least one eye (claim 1, access a set of images, captured using the image sensor … the image depicts an eye of the user in the region of interest; Process 900 from fig. 9, steps 910-930 detect the marker and determine the ROI across the captured image set.) and for each camera image, cropping a respective one of the sequence of camera images based on the ROI to generate the respective eye image (claim 2 and 16; process 940 from fig. 9;col. 19, lines 50-68 states cropping 940 the first image to the region of interest to obtain a cropped image … accomplished by copying a subset of the pixel values … one cropped image for each eye of a user.). Motivation to combine same as in claim 1.
Regarding claim 4, Ong in view of Torkos teaches the invention as set forth above and Ong further teaches determining the eye endurance level further comprising: applying an eye endurance model to process the sequence of eye images and generate a model output including the eye endurance level (col. 2, lines 60-65 and col. 7 lines 1-10, the fatigue predictor 116 also 734/810 processes the extracted sclera image sequence and col. 9, lines 5-15, can predict eye fatigue in the eye based on a calculated blood vessel density score, note: the model output is the blood vessel density/fatigue score).
Regarding claim 6, Ong in view of Torkos teaches the invention as set forth above and Ong further teaches wherein the eye endurance model includes a feature extraction model and an endurance assessment model (image segmenter 114(extraction) which feeding fatigue predictor 116 (assessment)), applying the eye endurance model further comprising, applying the feature extraction model to extract a respective eye feature vector from each of the sequence of eye images (image segmenter 114 extracts/segments the sclera region (a feature) from each received image; fig. 6, step 604); applying the endurance assessment model to process respective eye feature vectors of the sequence of eye images and generate the model output (fatigue predictor 116 processes the segmented sclera features across the sequence and can predict eye fatigue based on a calculated blood vessel density score (fig. 6, step 606).
Regarding claim 7, Ong in view of Torkos teaches the invention as set forth above and Ong further teaches wherein the eye endurance level is determined with respect to a predefined temporal length that is greater than the extended duration of time (col. 3, lines 35-45, the “baseline score” β is “calculated during profile creation during a first use” and then “adjusted automatically .. over time. Note example 5/17/27/35/45, i.e., tracked over a longer horizon than any one measurement session).
Regarding claim 13, Ong in view of Torkos teaches the invention as set forth above and Ong further teaches determining the eye endurance level further comprising: extracting a sclera feature from each of the sequence of eye images (claim 1, identify a sclera based on the first curved line and the second curved line” (eyelid curve and iris curve) for each image; col. 3, lines 5-20, image segmenter 114 performs this extraction per image in the sequence.) applying an eye endurance model to determine an eye dryness feature based on the respective sclera features of the sequence of eye images, the eye endurance level is determined based on respective sclera features (claim 1, calculate a blood vessel density score based on blood vessels in the sclera; and predict eye fatigue based on the blood vessel density score. Note: col. 1, lines 45-68; increased visible scleral blood-vessel density (hyperemia/redness) is a recognized clinical sign of ocular surface dryness. The eye fatigue is tied to dry eyes among CVS symptoms, so the blood vessel density score is properly read as (or rendered obvious as) the claimed “eye dryness feature,” from which “fatigue predictor 116 determines the eye fatigue/endurance output.)
Regarding claim 14, Ong teaches a non-transitory computer readable storage medium, storing one or more programs for execution by one or more processors , the one or more programs including instructions (claim 11, An article of manufacture comprising instructions … and col. 9, line 66 to col. 10, line 5, the computer readable media 800 may be non-transitory computer readable media, Example 21, at least one computer readable medium …) for executing a visual assessment application, including displaying a user interface to create a 3D virtual environment (col. 3, lines 1-20, as shown in FIG. 1, a user is using or operating the VR device 102. For example, the user may be playing a VR video game or using a VR application;…fatigue predictor 110; note: the eye-fatigue prediction operates while “the user may be playing a VR video game or using a VR application”; the “eye fatigue predictor 110” is itself the visual assessment functionality layered onto that VR application); displaying a body of text on the user interface for an extended duration of time (col. 4, lines 25-35, the alert may be a visual alert, an audible alert, or both. The alert 30 presenter 120 may present the alert to a user. For example, the alert presenter 120 may display a visual alert to a user via a display of the VR device 102, note: a generally is to provide a message of an alert which would be in text (visual alert)); based on the sequence of eye images, determining an eye endurance level of the at least one eye of a user associated with the electronic device (claim 1, “predict eye fatigue based on the blood vessel density score” computed from the image sequence. Note: “Eye fatigue” and “eye endurance level” are inverse framings of the same underlying eye condition metric, a person of ordinary skill in the (POSITA) would predict one thereby would determine the other. The fatigue predictor 116/734 performs this determination per user which is associated with the VR device 102/ 700 the user is wearing.). Ong does not specifically teach an electronic device having an HMD and an infrared camera; obtaining a sequence of eye images, each eye image including a respective infrared image of a region of interest (ROI) corresponding to at least one eye. However, in a similar field of endeavor, Torkos teaches a medium, an electronic device having an HMD and an infrared camera (col. 7, lines 55-68 and col. 8, lines 35-50, For example, the one or more image sensors 330 may include an infrared camera and a visible light camera. Note: the “image sensor” which is “an infrared image” sensor is used in the “head mounted enclosure 120”, the image sensors 330 include an infrared camera and a visible light camera); obtaining a sequence of eye images, each eye image including a respective infrared image of a region of interest (ROI) corresponding to at least one eye (claim 1, determine a region of interest in the image … wherein the image depicts an eye of the user in the region of interest; claim 13, the image is an infrared image; col. 1, lines 30-68, capturing “a set of images” such as a sequence). It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the medium of Ong with an electronic device having an HMD and an infrared camera; obtaining a sequence of eye images, each eye image including a respective infrared image of a region of interest (ROI) corresponding to at least one eye of Torkos, for the purpose of reducing the amount of image data that must be processed (col. 19, lines 45-60).
Regarding claim 16, Ong in view of Torkos teaches the invention as set forth above and Torkos further teaches the one or more programs including instructions for directing the infrared camera towards the at least one eye (col. 7, lines 25- 20, one or more image sensors 232 (e.g., sensing infrared … light) that are directed at a face of the user, note: while wearing the head-mounted enclosure shown in fig. 2); capturing by the infrared camera a sequence of camera images including the ROI corresponding to the at least one eye (claim 1, access a set of images, captured using the image sensor … the image depicts an eye of the user in the region of interest; Process 900 from fig. 9, steps 910-930 detect the marker and determine the ROI across the captured image set.); and for each camera image, cropping a respective one of the sequence of camera images based on the ROI to generate the respective eye image (claim 2 and 16; process 940 from fig. 9;col. 19, lines 50-68 states cropping 940 the first image to the region of interest to obtain a cropped image … accomplished by copying a subset of the pixel values … one cropped image for each eye of a user.). Motivation to combine same as in claim 1.
Regarding claim 17, Ong teaches an electronic device, comprising: one or more processors; and memory for storing one or more programs for execution by the one or more processors, the one or more programs including instructions (fig. 7, computing device 700 with CPU 702, memory device 704, and camera 726 for a virtual reality (VR) system) for executing a visual assessment application, including displaying a user interface to create a 3D virtual environment (col. 3, lines 1-20, as shown in FIG. 1, a user is using or operating the VR device 102. For example, the user may be playing a VR video game or using a VR application;…fatigue predictor 110; note: the eye-fatigue prediction operates while “the user may be playing a VR video game or using a VR application”; the “eye fatigue predictor 110” is itself the visual assessment functionality layered onto that VR application); displaying a body of text on the user interface for an extended duration of time (col. 4, lines 25-35, the alert may be a visual alert, an audible alert, or both. The alert 30 presenter 120 may present the alert to a user. For example, the alert presenter 120 may display a visual alert to a user via a display of the VR device 102, note: a generally is to provide a message of an alert which would be in text (visual alert)); and based on the sequence of eye images, determining an eye endurance level of the at least one eye of a user associated with the electronic device (claim 1, “predict eye fatigue based on the blood vessel density score” computed from the image sequence. Note: “Eye fatigue” and “eye endurance level” are inverse framings of the same underlying eye condition metric, a person of ordinary skill in the (POSITA) would predict one thereby would determine the other. The fatigue predictor 116/734 performs this determination per user which is associated with the VR device 102/ 700 the user is wearing.) and based on the sequence of eye images, determining an eye endurance level of the at least one eye of a user associated with the electronic device (claim 1, “predict eye fatigue based on the blood vessel density score” computed from the image sequence. Note: “Eye fatigue” and “eye endurance level” are inverse framings of the same underlying eye condition metric, a person of ordinary skill in the (POSITA) would predict one thereby would determine the other. The fatigue predictor 116/734 performs this determination per user which is associated with the VR device 102/ 700 the user is wearing.). Ong does not specifically teach an HMD; an infrared camera; obtaining a sequence of eye images, each eye image including a respective infrared image of a region of interest (ROI) corresponding to at least one eye; and based on the sequence of eye images, determining an eye endurance level of the at least one eye of a user associated with the electronic device. However, in a similar field of endeavor, Torkos teaches a device comprising, an HMD; an infrared camera (col. 7, lines 55-68 and col. 8, lines 35-50, For example, the one or more image sensors 330 may include an infrared camera and a visible light camera. Note: the “image sensor” which is “an infrared image” sensor is used in the “head mounted enclosure 120”, the image sensors 330 include an infrared camera and a visible light camera); obtaining a sequence of eye images, each eye image including a respective infrared image of a region of interest (ROI) corresponding to at least one eye (claim 1, determine a region of interest in the image … wherein the image depicts an eye of the user in the region of interest; claim13, the image is an infrared image; col. 1, lines 30-68, capturing “a set of images” such as a sequence). It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the device of Ong with an HMD; an infrared camera; obtaining a sequence of eye images, each eye image including a respective infrared image of a region of interest (ROI) corresponding to at least one eye; and based on the sequence of eye images, determining an eye endurance level of the at least one eye of a user associated with the electronic device of Torkos, for the purpose of reducing the amount of image data that must be processed (col. 19, lines 45-60).
Regarding claim 18, Ong in view of Torkos teaches the invention as set forth above and Ong further teaches determining the eye endurance level further comprising: applying an eye endurance model to process the sequence of eye images and generate a model output including the eye endurance level (col. 2, lines 60-65 and col. 7 lines 1-10, the fatigue predictor 116 also 734/810 processes the extracted sclera image sequence and col. 9, lines 5-15, can predict eye fatigue in the eye based on a calculated blood vessel density score, note: the model output is the blood vessel density/fatigue score).
Regarding claim 20, Ong in view of Torkos teaches the invention as set forth above and Ong further teaches wherein the eye endurance model includes a feature extraction model and an endurance assessment model (image segmenter 114 feeding fatigue predictor 116), applying the eye endurance model further comprising, applying the feature extraction model to extract a respective eye feature vector from each of the sequence of eye images (image segmenter 114 extracts/segments the sclera region (a feature) from each received image; fig. 6, step 604); applying the endurance assessment model to process respective eye feature vectors of the sequence of eye images and generate the model output (fatigue predictor 116 processes the segmented sclera features across the sequence and can predict eye fatigue based on a calculated blood vessel density score (fig. 6, step 606).
Claims 2 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Ong et al. (US 11,800,975) in view of Torkos et al. (US 11,749,018) as applied to claims 1 and 14 above, and further in view of Roe et al. (US 10,097,809).
Regarding claim 2, Ong in view of Torkos teaches the invention as set forth above but does not teach selecting a predefined brightness level and a predefined font size, wherein the body of text is displayed with the predefined brightness level and the predefined font size. However, in a similar field of endeavor, Roe teaches the method, further comprising, selecting a predefined brightness level and a predefined font size, wherein the body of text is displayed with the predefined brightness level and the predefined font size (col. 5, lines 39-45, the media guidance application selects a second display setting that is within the third subset. For example, if the third subset of display settings includes font sizes 20 and 24 and brightness levels 1 and 2). It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the method of Ong in view of Torkos with selecting a predefined brightness level and a predefined font size, wherein the body of text is displayed with the predefined brightness level and the predefined font size of Row, for the purpose of selecting a brightness/font-size combination specifically to keep a viewer’s eye-strain metric within an acceptable range (abstract).
Regarding claim 15, Ong in view of Torkos teaches the invention as set forth above but does not teach selecting a predefined brightness level and a predefined font size, wherein the body of text is displayed with the predefined brightness level and the predefined font size. However, in a similar field of endeavor, Roe teaches the medium for, selecting a predefined brightness level and a predefined font size, wherein the body of text is displayed with the predefined brightness level and the predefined font size (col. 5, lines 39-45, the media guidance application selects a second display setting that is within the third subset. For example, if the third subset of display settings includes font sizes 20 and 24 and brightness levels 1 and 2). It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the medium of Ong with selecting a predefined brightness level and a predefined font size, wherein the body of text is displayed with the predefined brightness level and the predefined font size of Row, for the purpose of selecting a brightness/font-size combination specifically to keep a viewer’s eye-strain metric within an acceptable range (abstract).
Claims 5 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ong et al. (US 11,800,975) in view of Torkos et al. (US 11,749,018) as applied to claims 1 and 17 above, and further in view of Freeman et al. (WO 2016126556).
Regarding claim 5, Ong in view of Torkos teaches the invention as set forth above but does not specifically teach the model output includes a diagnosis indicator identifying a dry eye severity level associated with the eye endurance level. However, in a similar field of endeavor, Freeman teaches the method, wherein the model output includes a diagnosis indicator identifying a dry eye severity level associated with the eye endurance level (page 4, last full paragraph, Both qualitative and quantitative assessment of the severity of a patient's DES condition may be achieved by estimating or counting the number of lines and branches within the reticular pattern. A score may be assigned based on the numbers of lines and branches, by the area covered by the total pattern, or by some combination thereof. In one embodiment, automated detection and scoring of the patient's DES severity, as 25 well as the efficacy of treatment, may be performed by using computer-aided image analysis)(claim 1, calculate a blood vessel density score based on blood vessels in the sclera; and predict eye fatigue based on the blood vessel density score. Note: col. 1, lines 45-68; increased visible scleral blood-vessel density (hyperemia/redness) is a recognized clinical sign of ocular surface dryness. The eye fatigue is tied to dry eyes among CVS symptoms, so the blood vessel density score is properly read as (or rendered obvious as) the claimed “eye dryness feature,” from which “fatigue predictor 116 determines the eye fatigue/endurance output as in combination with Ong). It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the method of Ong in view of Torkos with the model output includes a diagnosis indicator identifying a dry eye severity level associated with the eye endurance level of Freeman, for the purpose of generating an automated, image-based score correlated to the severity of a patient’s dry eye symptoms (page 4, last full paragraph).
Regarding claim 19, Ong in view of Torkos teaches the invention as set forth above but does not specifically teach the model output includes a diagnosis indicator identifying a dry eye severity level associated with the eye endurance level. However, in a similar field of endeavor, Freeman teaches the device, wherein the model output includes a diagnosis indicator identifying a dry eye severity level associated with the eye endurance level (page 4, last full paragraph, Both qualitative and quantitative assessment of the severity of a patient's DES condition may be achieved by estimating or counting the number of lines and branches within the reticular pattern. A score may be assigned based on the numbers of lines and branches, by the area covered by the total pattern, or by some combination thereof. In one embodiment, automated detection and scoring of the patient's DES severity, as 25 well as the efficacy of treatment, may be performed by using computer-aided image analysis)(claim 1, calculate a blood vessel density score based on blood vessels in the sclera; and predict eye fatigue based on the blood vessel density score. Note: col. 1, lines 45-68; increased visible scleral blood-vessel density (hyperemia/redness) is a recognized clinical sign of ocular surface dryness. The eye fatigue is tied to dry eyes among CVS symptoms, so the blood vessel density score is properly read as (or rendered obvious as) the claimed “eye dryness feature,” from which “fatigue predictor 116 determines the eye fatigue/endurance output as in combination with Ong). It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the device of Ong in view of Torkos with the model output includes a diagnosis indicator identifying a dry eye severity level associated with the eye endurance level of Freeman, for the purpose of generating an automated, image-based score correlated to the severity of a patient’s dry eye symptoms (page 4, last full paragraph).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Ong et al. (US 11,800,975) in view of Torkos et al. (US 11,749,018) as applied to claim 7 above, and further in view of Al-Ghoul (US 12,437,870).
Regarding claim 8, Ong further teaches receiving an eye endurance model from a server communicatively coupled to the electronic device (col. 4, lines 30- 40, The VR device 102 may also collect current user profile 35 data … upload … to the server 104 may be a node in a cloud storage solution. Server 104 is communicatively coupled (col.2, lines 45-50) to the VR device via network 106, and profile specific baseline threshold data (the model’s operative parameters) is loaded from it on login, example in col. 4, lines 30-60). Ong in view of Torkos does not specifically teach at the server, training the eye endurance model using training data including a sequence of eye images and a ground truth eye endurance level corresponding to the predefined temporal length. However, in a similar field of endeavor, Al-Ghoul teaches the method comprising, at the server, training the eye endurance model using training data including a sequence of eye images and a ground truth eye endurance level corresponding to the predefined temporal length (col. 7, lines 5-15, All the information obtained from process 100 … can be saved for future machine-learning optimizations and/or training processes. Including camera derived blink rate data collected by the dry eye analysis system.). It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the method of Ong in view of Torkos with the server, training the eye endurance model using training data including a sequence of eye images and a ground truth eye endurance level corresponding to the predefined temporal length of Al-Ghoul, for the purpose of using the block data as training date to build and refine ML-based ocular diagnostic models (¶52, ¶57, ¶60).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Ong et al. (US 11,800,975) in view of Torkos et al. (US 11,749,018) as applied to claim 7 above, and further in view of Ryuma et al. (US 20240143073).
Regarding claim 9, Ong further teaches controlling execution of the media play application based on the eye endurance level (col. 2, lines 55-65, alert generator 118 … generate an alert in response to predicting the eye fatigue and col. 2, lines 60-68, alert presenter 120 … present the alert to a user of the VR device). Ong in view of Torkos does not specifically teach executing a media play application to display multimedia content on the electronic device. However, in a similar field of endeavor, Ryuma teaches the method comprising: executing a media play application to display multimedia content on the electronic device (¶16, wearable apparatus used to generate and transmit media content (e.g., pictures, videos, etc.) on a head wearable device). It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the method of Ong in view of Torkos with executing a media play application to display multimedia content on the electronic device of Ryuma, for the purpose of generating and transmitting media content (e.g., picture, videos etc.)(¶16).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yokoi et al. (US 8,996,097) teaches ophthalmic measuring apparatus used to screen dry eye and evaluate dry eye severity in a subject’s eye.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HENRY DUONG whose telephone number is (571)270-0534. The examiner can normally be reached Monday-Friday from 9:00 AM to 5:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pinping Sun can be reached at (571)270-1284. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HENRY DUONG/Primary Patent Examiner, Art Unit 2872 07/28/26
Prosecution Insights