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 .
Applicant' s arguments, filed 3/25/2026, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed 3/25/2026, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1, 4-15, and 18 are the currently pending claims hereby under examination. Claims 2-3, 16-17 and 19-20 have been canceled.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 18 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 18 depends from canceled claim 16. A claim that depends from a canceled claim is indefinite since the metes and the bounds of the claim cannot be determined.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 18 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 18 depends from canceled claim 16, which is improper.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4-5, 7, 9, and 11-14 are rejected under 35 U.S.C. 103 as being unpatentable over Keun et al. (KR 20220170336 A), hereto referred as Keun, and further in view of Young et al. (US 20190354173 A1), hereto referred as Young, and further in view of Krukowski et al. (US 20210330185 A1), hereto referred as Krukowski.
Regarding claim 1, Keun teaches that a system comprises: a virtual-reality headset or augmented-reality system configured to measure eye-associated positions and gaze-associated direction; (Keun, ¶[0135]: “The augmented reality device (100) can obtain left eye gaze direction information based on the position of the pupil detected from the left eye, and can obtain right eye gaze direction information based on the position of the pupil detected from the right eye”, teaches measuring eye-associated positions (pupil feature points) and obtaining gaze-associated directions for both eyes; ¶[0122]: “The processor (150) can obtain pupil position information based on the position of the pupil feature point, and can obtain information about the gaze direction based on the pupil position information”, shows measuring eye-associated positions and corresponding gaze direction; FIG. 1a, 2: depict the augmented reality device as head worn glasses); an analysis system having a processor and a memory having instructions stored thereon, wherein execution of the instructions by the processor causes the processor to: (Keun, ¶[0104]: “The processor (150) can execute one or more instructions or program codes stored in the memory (160) and perform functions and/or operations corresponding to the instructions or program codes”, shows a processor executing instructions stored in memory; ¶[0108]: “Commands or program codes for performing functions or operations of the augmented reality device (100) may be stored in the memory (160) … the memory (160) may store at least one of instructions, an algorithm, a data structure, a program code, and an application program that can be read by the processor (150)”, teaches a memory storing instructions/programs for execution by the processor; ¶[0110]: “the processor (150) may be implemented by executing instructions or program codes stored in the memory (160)”, further confirms execution of stored instructions by the processor); generate a rendered scene and/or object in the virtual-reality headset or augmented-reality system; (Keun, ¶[0039]: “A typical augmented reality device has an optical engine for generating a virtual image”, shows generating a rendered image/scene for display in an AR system, where ¶[0072]: “The display engine (130) may be an optical engine"; ¶[0004]: “Augmented reality devices project virtual images onto the user's eyes through a see-through display, allowing the user to simultaneously view real-world objects and the projected virtual images”, shows rendering and presenting virtual images/objects in the headset; ¶[0088]: “The display engine (130) is configured to project a virtual image onto the wave guide (120)”, shows projecting rendered virtual images for display); receive the measured eye-associated positions and measured gaze-associated directions of a user viewing the rendered scene and/or object; (Keun, ¶[0134]: “the augmented reality device (100) can detect a left eye pupil from a left eye image acquired using a first gaze tracking sensor, and can detect a right eye pupil from a right eye image acquired using a second gaze tracking sensor”, shows receiving measurements for eye-associated positions via sensors; ¶[0135]: “The augmented reality device (100) can obtain left eye gaze direction information … and can obtain right eye gaze direction information …”, shows receiving/deriving measured gaze-associated directions); determine a gaze control point from the measured eye-associated positions and measured gaze-associated directions, wherein the gaze control point is a binocular convergence in the rendered scene and/or on the object using the measured gaze-associated directions from both eyes to reflect a combined eye movement pattern (Keun, ¶[0122]: “The processor (150) can obtain pupil position information based on the position of the pupil feature point”, shows measured eye positions; ¶[0121]: “detecting the gaze point, which is the point where the gaze directions of the user's two eyes converge”, teaches determining a binocular convergence point from both eyes’ gaze directions; ¶[0134]: “the augmented reality device (100) obtains a gaze point where the gaze directions of the user's two eyes converge”, shows binocular convergence determined using both eyes’ gaze directions; ¶[0135]: “The augmented reality device (100) can estimate the position coordinates of the gaze point by using gaze information regarding binocular disparity, the gaze direction of the left eye, and the gaze direction of the right eye”, explicitly recites binocular disparity and shows the gaze point derived from both eyes’ gaze directions, i.e., a binocular convergence in the rendered scene/object; ¶[0212]: “The augmented reality device (100) can estimate the position coordinates of the gaze point (G, see FIGS. 1A and 1B) by using gaze information about binocular disparity and the gaze direction of the left eye and the gaze direction of the right eye”, same express “binocular disparity” language tied to calculating the binocular convergence point G; ¶[0219], “the distance d between the user's eyes and the virtual screen … As a result, the vergence distance l, which is the distance to the gaze point, is given by the following mathematical formula. z represents the distance between the virtual screen and the gaze point”, ties the gaze point to coordinates with respect to the rendered virtual screen; ¶[0205], “the augmented reality device (100) can calculate the two-dimensional position coordinate value of the gaze direction of the user's eye (E) on the virtual screen (1300) using the degree of rotation (α and β) of the user's eye (E)”, places gaze coordinates on the rendered screen; ¶[0211], “The processor (150)… can determine the gaze direction of the left and right eyes using gaze information output from the gaze tracking sensor (140a, 140b). In one embodiment… can calculate a first gaze vector … and a second gaze vector …”, shows explicit per-eye vectors that are then used together, as shown in ¶[0212], to track the combined eye movement patterns and 3D gaze coordinates; see also ¶[0216]); and determine one or more statistical parameters, or associated values, from the determined gaze control point (Keun, ¶[0216]: “When calculating the focal length to the point of gaze, the visual axes of the two eyes may not meet… the coordinates of the vertical axis (y-axis) can be calculated as the average of the vertical axis (y-axis) coordinates of the two eyes”, teaches determining a statistical parameter (a mean) derived from binocular gaze geometry tied to the gaze point G; ¶[0219]: “As a result, the vergence distance l, which is the distance to the gaze point, is given by the following mathematical formula. z represents the distance between the virtual screen and the gaze point”, teaches determining associated values (e.g., distances l and z) derived from the gaze control point G; ¶[0136]: “the augmented reality device (100) can obtain the position coordinates of the center of focus based on the lens-eye distance, the convergence distance which is the distance between the gaze point and the user's eye, and the interpupillary distance”, further teaches associated values including position coordinates derived from the gaze control point G).
Also regarding claim 1, Keun does not fully teach generating a rendered gaze ray associated with the binocular convergence. Rather, Keun teaches computing binocular gaze directions and the convergence point G, and placing gaze coordinates relative to the virtual screen, but Keun does not disclose rendering a gaze ray on the display associated with that convergence. For example: (Keun, ¶[0211]: “The processor (150… ) can determine the gaze direction of the left and right eyes… In one embodiment… can calculate a first gaze vector… and a second gaze vector…”, shows per‑eye vectors computed internally for both eyes; ¶[0212]: “The augmented reality device (100) can estimate the position coordinates of the gaze point (G… ) by using gaze information about binocular disparity and the gaze direction of the left eye and the gaze direction of the right eye”, shows estimating the binocular convergence point coordinates; ¶[0219]: “the distance d between the user’s eyes and the virtual screen… z represents the distance between the virtual screen and the gaze point”, locates the gaze point relative to the rendered virtual screen but still does not disclose drawing/rendering a gaze ray; ¶[0042]: “'gaze' means an imaginary line from the user’s pupil to the gaze direction”, defines gaze as an imaginary line rather than a rendered on‑screen line).
Young explicitly teaches that the headset’s display renders gaze‑related visual indicators corresponding to eye movement vectors (Young, ¶[0094]: “The saccade path 510 is superimposed onto display 810 and shows fixation point A (e.g., direction 506 and vector XF‑o)”, teaches a rendered, on‑screen path/vector corresponding to gaze movement; ¶[0096]: “predicted landing points for fixation point B are superimposed onto display 810”, further shows rendered gaze‑related indicators superimposed on the HMD display.) These passages demonstrate that Young visualizes where the user is looking by overlaying a rendered line or point on the virtual display. This fills the gap left by Keun, which performs gaze computations internally but does not visually depict them. One of ordinary skill in the art would recognize that Young’s display technique could be integrated into Keun’s existing binocular gaze system to represent the convergence point G visually on the virtual screen, thereby creating the claimed rendered gaze ray associated with binocular convergence.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Keun in view of Young to generate a rendered gaze ray associated with the binocular convergence. The combination is feasible because Keun already computes left/right gaze vectors and the 3‑D gaze point G (including coordinates relative to the virtual screen), and Young demonstrates on‑display overlays of gaze‑related vectors/paths and landing points; adding a simple graphics overlay to depict a line (ray) from the eye or display toward G uses the same rendering pipeline that presents AR content. The benefit of the combination includes clear visual feedback for calibration and interaction, improved alignment between gaze estimation and displayed content, and enhanced usability by exposing the system’s internal gaze solution as an on‑screen indicator.
Also regarding claim 1, the modified Keun does not fully teach that the gaze-associated directions and determined one or more statistical parameters are employed to provide a clinical assessment of vision of the user. Rather, the modified Keun teaches acquiring gaze-associated directions, determining a binocular convergence point G, and deriving associated values (e.g., averages and distances) from that gaze point for purposes such as rendering, alignment, and focus determination (Keun, ¶[0121], ¶[0134], ¶[0212]: determine binocular convergence point G from left and right gaze directions; ¶[0216]: calculates an average of coordinates; ¶[0219]: calculates vergence distance to the gaze point; ¶[0136]: derives position coordinates based on convergence distance and interpupillary distance), which collectively show computation and use of gaze data for system operation. However, it does not disclose employing such gaze-derived data and statistical parameters to perform a clinical or diagnostic evaluation of a user’s vision.
Krukowski explicitly teaches employing eye movement data and associated analytics to perform clinical vision assessment and diagnostic screening. For example: (Krukowski, ¶[0008]: “A diagnostic device for use in performing refractive errors assessment and neurodegenerative disorders screening…”, teaches a diagnostic device configured for clinical assessment; ¶[0002]: “The present invention relates to vision assessment and therapy…”, shows operation in a clinical/medical context; ¶[0137]-[0139]: “collecting eye movement data… and determining neurodegenerative disease by comparing collected eye movement data…”, teaches using eye movement data to perform disease screening/diagnosis; FIG. 33: “collect eye movements data… ocular behavior analytics… provide an output”, shows a workflow of data acquisition, analysis, and diagnostic output; FIG. 42: “Eyecare / Neuro Diagnostics”, reinforces clinical diagnostic application; [0251]: "The system 10 additionally contains variation adjusted to the vision therapy and assessment provide at specialized eye care units (e.g. vision therapy clinic)", further demonstrates that the disclosed system is configured for use in clinical environments, reinforcing that the vision assessment performed constitutes a clinical assessment of a user’s vision). These passages demonstrate that Krukowski uses gaze/eye movement data and associated analytical processing to evaluate visual function and detect ocular or neurological conditions, which constitutes a clinical assessment of vision. Krukowski further teaches that the collected eye movement data and associated derived parameters (e.g., temporal and spatial parameters of eye movement) are analyzed and compared to baseline data to generate diagnostic outputs, thereby employing both the underlying gaze-associated directions and derived statistical representations of that gaze behavior in providing a clinical assessment of vision of the user (Krukowski, ¶[0002], ¶[0009]; FIG. 33).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Keun in view of Krukowski to incorporate the diagnostic vision assessment functionality so as to employ the gaze-associated directions and derived statistical parameters to provide a clinical assessment of vision of the user. The combination is feasible because Keun provides the underlying gaze-associated directions and derived statistical parameters (e.g., averages and distances) that quantify eye movement behavior relative to the gaze control point, but does not employ them for clinical assessment, while Krukowski teaches using eye movement data and associated analytics to perform clinical vision assessment and diagnostic screening. In particular, the statistical parameters derived in Keun represent processed and quantified gaze behavior that would be analyzed within Krukowski’s ocular behavior analytics, together with the underlying gaze-associated directions, to generate a diagnostic evaluation of the user’s visual function. One of ordinary skill in the art would have been motivated to combine these teachings to improve the utility of Keun from gaze tracking and rendering to include clinical evaluation, thereby enabling diagnostic assessment of visual function and potential ocular or neurological conditions using both the measured gaze directions and the derived statistical representations of that gaze behavior. The benefit of the combination includes extending a gaze-tracking AR/VR system to provide clinically relevant outputs, improving diagnostic capability without requiring separate specialized equipment, and leveraging both raw gaze data and derived statistical metrics for vision assessment and screening.
Regarding claim 4, the modified Keun teaches that the determined one or more statistical parameters includes at least one of: (i) a variance measure of the gaze control point, (ii) a mean location of the gaze control point (Keun, ¶[0216]: “the coordinates of the vertical axis (y-axis) can be calculated as the average of the vertical axis (y-axis) coordinates of the two eyes”, teaches a mean used in computing the binocular gaze point location along the y-axis (Keun, ¶[0212]: “estimate the position coordinates of the gaze point (G… ) by using gaze information about binocular disparity and the gaze direction of the left eye and the gaze direction of the right eye”, shows the 3D gaze point coordinates to which a mean can pertain);(iii) a latency measure of the gaze control point, (iv) a change in the variance of the gaze control point over time, (v) a change in the latency of the gaze control point over time, (vi) an instantaneous location of the gaze control point (Keun, ¶[0212]: “estimate the position coordinates of the gaze point (G… ) by using gaze information about binocular disparity and the gaze direction of the left eye and the gaze direction of the right eye”, teaches determining the instantaneous 3D location of the gaze control point G (Keun, ¶[0214]: “map a point (gaze point, G)… to a three-dimensional position coordinate value… or may store the three-dimensional position coordinate value of the gaze point (G) in a memory (160)”, further shows the specific 3D coordinates of G; ¶[0211]–[0212]: “The processor (150)… can calculate a first gaze vector indicating a gaze direction of the left eye and a second gaze vector indicating a gaze direction of the right eye using gaze information output from the gaze tracking sensor… [and] can estimate the position coordinates of the gaze point (G)…,” teaches determining the gaze point in real-time from live sensor data, thereby producing an instantaneous 3-D location of the gaze control point; Keun, ¶[0221]: “The convergence distance l may be adjusted depending on the movement of the gaze point or the user’s focus”, further supports instantaneous velocity and acceleration measures (items (vii) and (viii)) as changes in gaze distance over time based on user motion and focus);(vii) an instantaneous velocity measure of the gaze control point, and (viii) an instantaneous acceleration measure of the gaze control point.
Regarding claim 5, the modified Keun teaches that the determined gaze control point, or statistics derived therefrom, are subsequently employed to assess at least one of: vergence measure (Keun, ¶[0212]: “The augmented reality device (100) can estimate the position coordinates of the gaze point (G) by using gaze information about binocular disparity and the gaze direction of the left eye and the gaze direction of the right eye”, shows binocular, user-based convergence geometry from which a vergence measure is derived) (Keun, ¶[0219]: “As a result, the vergence distance l, which is the distance to the gaze point, is given by the following mathematical formula. z represents the distance between the virtual screen and the gaze point”, discloses a concrete vergence distance tied to the user’s binocular gaze that can be assessed) (Keun, ¶[0058]: “the augmented reality device (100) includes a lens-to-eye distance (ER), a convergence distance l which is a distance between a gaze point (G) and a lens, and a gaze vector of both eyes of the user toward the gaze point (G)”, further ties l to per-eye vectors and binocular geometry) (Keun, ¶[0136]: “the augmented reality device (100) can obtain the position coordinates of the center of focus based on the lens-eye distance, the convergence distance which is the distance between the gaze point and the user’s eye, and the interpupillary distance”, shows derived coordinates based on convergence distance usable for assessment; ¶[0136]: “obtain the position coordinates of the center of focus based on… the convergence distance which is the distance between the gaze point and the user’s eye”, shows an explicit vergence-related distance derived from the binocular gaze point that can be assessed; ¶[0281]: “the focal length… can be adjusted to be equal to the convergence distance”, further ties processing to a measurable convergence distance; see also FIG. 13-16 and 18), visual fixation measure, saccades measure, smooth pursuit measure, eye dominance measure, vestibular measure, optokinetic measure, nystagmus quick phase measure, or a combination thereof.
Regarding claim 7, Keun teaches that a system comprising: an analysis system integrated into a virtual-reality headset or an augmented reality system, the analysis system having a processor and a memory having instructions stored thereon, wherein execution of the instructions by the processor causes the processor to: (Keun, ¶[0135]: “The augmented reality device (100)”, teaches an augmented reality device/system; ¶[0104]: “The processor (150) can execute one or more instructions or program codes stored in the memory (160) and perform functions and/or operations corresponding to the instructions or program codes”, shows a processor executing instructions stored in memory; ¶[0108]: “Commands or program codes for performing functions or operations of the augmented reality device (100) may be stored in the memory (160) … the memory (160) may store at least one of instructions, an algorithm, a data structure, a program code, and an application program that can be read by the processor (150)”, teaches a memory storing instructions/programs for execution by the processor; ¶[0110]: “the processor (150) may be implemented by executing instructions or program codes stored in the memory (160)”, further confirms execution of stored instructions by the processor) cause the virtual-reality headset or an augmented reality system to generate a rendered scene and/or object in the virtual‑reality headset or the augmented‑reality system, wherein the virtual‑reality headset or the augmented‑reality system is configured to measure eye‑associated positions and gaze‑associated directions (Keun, ¶[0039]: “A typical augmented reality device has an optical engine for generating a virtual image”, shows generating a rendered image/scene for display in an AR system, where ¶[0072]: “The display engine (130) may be an optical engine"; ¶[0004]: “Augmented reality devices project virtual images onto the user's eyes through a see‑through display, allowing the user to simultaneously view real‑world objects and the projected virtual images”, shows rendering and presenting virtual images/objects in the headset; ¶[0088]: “The display engine (130) is configured to project a virtual image onto the wave guide (120)”, shows projecting rendered virtual images for display; ¶[0134]: “the augmented reality device (100) can detect a left eye pupil from a left eye image acquired using a first gaze tracking sensor, and can detect a right eye pupil from a right eye image acquired using a second gaze tracking sensor”, shows configuration to measure eye‑associated positions; ¶[0122]: “The processor (150) can obtain pupil position information based on the position of the pupil feature point”, shows measured positions ¶[0135]: “The augmented reality device (100) can obtain left eye gaze direction information based on the position of the pupil detected from the left eye, and can obtain right eye gaze direction information based on the position of the pupil detected from the right eye”, shows configuration to obtain gaze‑associated directions); receive the measured eye‑associated positions and measured gaze‑associated directions of a user viewing the rendered scene and/or object (Keun, ¶[0134]: “the augmented reality device (100) can detect a left eye pupil from a left eye image acquired using a first gaze tracking sensor, and can detect a right eye pupil from a right eye image acquired using a second gaze tracking sensor”, shows receiving measurements for eye‑associated positions via sensors; ¶[0135]: “The augmented reality device (100) can obtain left eye gaze direction information … and can obtain right eye gaze direction information …”, shows receiving/deriving measured gaze‑associated directions); determine a gaze control point from the measured eye‑associated positions and measured gaze‑associated directions, wherein the gaze control point is a binocular convergence in the rendered scene and/or on the object using the measured gaze‑associated directions from both eyes to reflect a combined eye movement pattern (Keun, ¶[0121]: “detecting the gaze point, which is the point where the gaze directions of the user's two eyes converge”, teaches determining a binocular convergence point from both eyes’ gaze directions; ¶[0134]: “the augmented reality device (100) obtains a gaze point where the gaze directions of the user's two eyes converge”, shows binocular convergence determined using both eyes’ gaze directions; ¶[0135]: “The augmented reality device (100) can estimate the position coordinates of the gaze point by using gaze information regarding binocular disparity, the gaze direction of the left eye, and the gaze direction of the right eye”, explicitly recites binocular disparity and shows the gaze point derived from both eyes’ gaze directions, i.e., a binocular convergence in the rendered scene/object; ¶[0212]: “The augmented reality device (100) can estimate the position coordinates of the gaze point (G, see FIGS. 1A and 1B) by using gaze information about binocular disparity and the gaze direction of the left eye and the gaze direction of the right eye”, same express “binocular disparity” language tied to calculating the binocular convergence point G; ¶[0219]: “the distance d between the user's eyes and the virtual screen … As a result, the vergence distance l, which is the distance to the gaze point, is given by the following mathematical formula. z represents the distance between the virtual screen and the gaze point”, ties the gaze point to coordinates with respect to the rendered virtual screen; ¶[0205]: “the augmented reality device (100) can calculate the two‑dimensional position coordinate value of the gaze direction of the user's eye (E) on the virtual screen (1300) using the degree of rotation (α and β) of the user's eye (E)”, places gaze coordinates on the rendered screen; ¶[0211]: “The processor (150)… can determine the gaze direction of the left and right eyes using gaze information output from the gaze tracking sensor (140a, 140b). In one embodiment… can calculate a first gaze vector … and a second gaze vector …”, shows explicit per‑eye vectors that are then used together, as shown in ¶[0212], to track the combined eye movement patterns and 3D gaze coordinates; see also ¶[0216]); and determine one or more statistical parameters, or associated values, from the determined gaze control point (Keun, ¶[0216]: “When calculating the focal length to the point of gaze, the visual axes of the two eyes may not meet… the coordinates of the vertical axis (y-axis) can be calculated as the average of the vertical axis (y-axis) coordinates of the two eyes”, teaches determining a statistical parameter (a mean) derived from binocular gaze geometry tied to the gaze point G; ¶[0219]: “As a result, the vergence distance l, which is the distance to the gaze point, is given by the following mathematical formula. z represents the distance between the virtual screen and the gaze point”, teaches determining associated values (e.g., distances l and z) derived from the gaze control point G; ¶[0136]: “the augmented reality device (100) can obtain the position coordinates of the center of focus based on the lens-eye distance, the convergence distance which is the distance between the gaze point and the user's eye, and the interpupillary distance”, further teaches associated values including position coordinates derived from the gaze control point G).
Also regarding claim 7, Keun does not fully teach generating a rendered gaze ray associated with the binocular convergence. Rather, Keun teaches computing binocular gaze directions and the convergence point G, and placing gaze coordinates relative to the virtual screen, but Keun does not disclose rendering a gaze ray on the display associated with that convergence. For example: (Keun, ¶[0211]: “The processor (150… ) can determine the gaze direction of the left and right eyes… In one embodiment… can calculate a first gaze vector… and a second gaze vector…”, shows per‑eye vectors computed internally for both eyes; ¶[0212]: “The augmented reality device (100) can estimate the position coordinates of the gaze point (G… ) by using gaze information about binocular disparity and the gaze direction of the left eye and the gaze direction of the right eye”, shows estimating the binocular convergence point coordinates; ¶[0219]: “the distance d between the user’s eyes and the virtual screen… z represents the distance between the virtual screen and the gaze point”, locates the gaze point relative to the rendered virtual screen but still does not disclose drawing/rendering a gaze ray; ¶[0042]: “'gaze' means an imaginary line from the user’s pupil to the gaze direction”, defines gaze as an imaginary line rather than a rendered on‑screen line).
Young explicitly teaches that the headset’s display renders gaze‑related visual indicators corresponding to eye movement vectors (Young, ¶[0094]: “The saccade path 510 is superimposed onto display 810 and shows fixation point A (e.g., direction 506 and vector XF‑o)”, teaches a rendered, on‑screen path/vector corresponding to gaze movement; ¶[0096]: “predicted landing points for fixation point B are superimposed onto display 810”, further shows rendered gaze‑related indicators superimposed on the HMD display.) These passages demonstrate that Young visualizes where the user is looking by overlaying a rendered line or point on the virtual display. This fills the gap left by Keun, which performs gaze computations internally but does not visually depict them. One of ordinary skill in the art would recognize that Young’s display technique could be integrated into Keun’s existing binocular gaze system to represent the convergence point G visually on the virtual screen, thereby creating the claimed rendered gaze ray associated with binocular convergence.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Keun in view of Young to generate a rendered gaze ray associated with the binocular convergence. The combination is feasible because Keun already computes left/right gaze vectors and the 3‑D gaze point G (including coordinates relative to the virtual screen), and Young demonstrates on‑display overlays of gaze‑related vectors/paths and landing points; adding a simple graphics overlay to depict a line (ray) from the eye or display toward G uses the same rendering pipeline that presents AR content. The benefit of the combination includes clear visual feedback for calibration and interaction, improved alignment between gaze estimation and displayed content, and enhanced usability by exposing the system’s internal gaze solution as an on‑screen indicator.
Also regarding claim 7, the modified Keun does not fully teach that the measured eye-associated positions and gaze-associated directions provide a clinical assessment of vision of the user. Rather, the modified Keun teaches measuring eye-associated positions, acquiring gaze-associated directions, and deriving associated values (e.g., averages and distances) from that gaze point for purposes such as rendering, alignment, and focus determination (Keun, ¶[0121], ¶[0134]-[0135], ¶[0212]: determine binocular convergence point G from left and right eye and gaze directions; ¶[0216]: calculates an average of coordinates; ¶[0219]: calculates vergence distance to the gaze point; ¶[0136]: derives position coordinates based on convergence distance and interpupillary distance), which collectively show computation and use of gaze data for system operation. However, it does not disclose employing such eye/gaze-derived data to perform a clinical or diagnostic evaluation of a user’s vision.
Krukowski explicitly teaches employing eye movement data and associated analytics to perform clinical vision assessment and diagnostic screening. For example: (Krukowski, ¶[0008]: “A diagnostic device for use in performing refractive errors assessment and neurodegenerative disorders screening…”, teaches a diagnostic device configured for clinical assessment; ¶[0002]: “The present invention relates to vision assessment and therapy…”, shows operation in a clinical/medical context; ¶[0137]-[0139]: “collecting eye movement data… and determining neurodegenerative disease by comparing collected eye movement data…”, teaches using eye movement data to perform disease screening/diagnosis; FIG. 33: “collect eye movements data… ocular behavior analytics… provide an output”, shows a workflow of data acquisition, analysis, and diagnostic output; FIG. 42: “Eyecare / Neuro Diagnostics”, reinforces clinical diagnostic application; [0251]: "The system 10 additionally contains variation adjusted to the vision therapy and assessment provide at specialized eye care units (e.g. vision therapy clinic)", further demonstrates that the disclosed system is configured for use in clinical environments, reinforcing that the vision assessment performed constitutes a clinical assessment of a user’s vision). These passages demonstrate that Krukowski uses gaze/eye movement data and associated analytical processing to evaluate visual function and detect ocular or neurological conditions, which constitutes a clinical assessment of vision. Krukowski further teaches that the collected eye movement data and associated derived parameters (e.g., temporal and spatial parameters of eye movement) are analyzed and compared to baseline data to generate diagnostic outputs, thereby employing both the underlying gaze-associated directions and derived statistical representations of that gaze behavior in providing a clinical assessment of vision of the user (Krukowski, ¶[0002], ¶[0009]; FIG. 33).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Keun in view of Krukowski to incorporate the diagnostic vision assessment functionality by employing the eye positions and gaze-associated directions to provide a clinical assessment of vision of the user. The combination is feasible because Keun provides the underlying eye positions and gaze-associated directions but does not employ them for clinical assessment, while Krukowski teaches using eye movement data and associated analytics to perform clinical vision assessment and diagnostic screening. One of ordinary skill in the art would have been motivated to combine these teachings to improve the utility of Keun from gaze tracking and rendering to include clinical evaluation, thereby enabling diagnostic assessment of visual function and potential ocular or neurological conditions using the already-available eye and gaze data. The benefit of the combination includes extending an eye and gaze-tracking AR/VR system to provide clinically relevant outputs, improving diagnostic capability without requiring separate specialized equipment, and leveraging existing eye-tracking data for vision assessment and screening.
Regarding claim 9, the modified Keun teaches that the determined gaze control point, or statistics derived therefrom, are subsequently employed to assess at least one of: vergence measure (Keun, ¶[0212]: “The augmented reality device (100) can estimate the position coordinates of the gaze point (G) by using gaze information about binocular disparity and the gaze direction of the left eye and the gaze direction of the right eye”, shows binocular, user-based convergence geometry from which a vergence measure is derived) (Keun, ¶[0219]: “As a result, the vergence distance l, which is the distance to the gaze point, is given by the following mathematical formula. z represents the distance between the virtual screen and the gaze point”, discloses a concrete vergence distance tied to the user’s binocular gaze that can be assessed) (Keun, ¶[0058]: “the augmented reality device (100) includes a lens-to-eye distance (ER), a convergence distance l which is a distance between a gaze point (G) and a lens, and a gaze vector of both eyes of the user toward the gaze point (G)”, further ties l to per-eye vectors and binocular geometry) (Keun, ¶[0136]: “the augmented reality device (100) can obtain the position coordinates of the center of focus based on the lens-eye distance, the convergence distance which is the distance between the gaze point and the user’s eye, and the interpupillary distance”, shows derived coordinates based on convergence distance usable for assessment; ¶[0136]: “obtain the position coordinates of the center of focus based on… the convergence distance which is the distance between the gaze point and the user’s eye”, shows an explicit vergence-related distance derived from the binocular gaze point that can be assessed; ¶[0281]: “the focal length… can be adjusted to be equal to the convergence distance”, further ties processing to a measurable convergence distance; see also FIG. 13-16 and 18), but does not teach: visual fixation measure, saccades measure, smooth pursuit measure, eye dominance measure, vestibular measure, optokinetic measure, or nystagmus quick phase measure.
Regarding claim 11, the modified Keun teaches that the determined one or more statistical parameters includes at least one of: (i) a variance measure of the gaze control point, (ii) a mean location of the gaze control point (Keun, ¶[0216]: “the coordinates of the vertical axis (y-axis) can be calculated as the average of the vertical axis (y-axis) coordinates of the two eyes”, teaches a mean used in computing the binocular gaze point location along the y-axis (Keun, ¶[0212]: “estimate the position coordinates of the gaze point (G… ) by using gaze information about binocular disparity and the gaze direction of the left eye and the gaze direction of the right eye”, shows the 3D gaze point coordinates to which a mean can pertain);(iii) a latency measure of the gaze control point, (iv) a change in the variance of the gaze control point over time, (v) a change in the latency of the gaze control point over time, (vi) an instantaneous location of the gaze control point (Keun, ¶[0212]: “estimate the position coordinates of the gaze point (G… ) by using gaze information about binocular disparity and the gaze direction of the left eye and the gaze direction of the right eye”, teaches determining the instantaneous 3D location of the gaze control point G (Keun, ¶[0214]: “map a point (gaze point, G)… to a three-dimensional position coordinate value… or may store the three-dimensional position coordinate value of the gaze point (G) in a memory (160)”, further shows the specific 3D coordinates of G; ¶[0211]–[0212]: “The processor (150)… can calculate a first gaze vector indicating a gaze direction of the left eye and a second gaze vector indicating a gaze direction of the right eye using gaze information output from the gaze tracking sensor… [and] can estimate the position coordinates of the gaze point (G)…,” teaches determining the gaze point in real-time from live sensor data, thereby producing an instantaneous 3-D location of the gaze control point; Keun, ¶[0221]: “The convergence distance l may be adjusted depending on the movement of the gaze point or the user’s focus”, further supports instantaneous velocity and acceleration measures (items (vii) and (viii)) as changes in gaze distance over time based on user motion and focus);(vii) an instantaneous velocity measure of the gaze control point, and (viii) an instantaneous acceleration measure of the gaze control point.
Regarding claim 12, the modified Keun teaches that the determined gaze control point, or statistics derived therefrom, are subsequently employed to assess at least one of: vergence measure (Keun, ¶[0212]: “The augmented reality device (100) can estimate the position coordinates of the gaze point (G) by using gaze information about binocular disparity and the gaze direction of the left eye and the gaze direction of the right eye”, shows binocular, user-based convergence geometry from which a vergence measure is derived) (Keun, ¶[0219]: “As a result, the vergence distance l, which is the distance to the gaze point, is given by the following mathematical formula. z represents the distance between the virtual screen and the gaze point”, discloses a concrete vergence distance tied to the user’s binocular gaze that can be assessed) (Keun, ¶[0058]: “the augmented reality device (100) includes a lens-to-eye distance (ER), a convergence distance l which is a distance between a gaze point (G) and a lens, and a gaze vector of both eyes of the user toward the gaze point (G)”, further ties l to per-eye vectors and binocular geometry) (Keun, ¶[0136]: “the augmented reality device (100) can obtain the position coordinates of the center of focus based on the lens-eye distance, the convergence distance which is the distance between the gaze point and the user’s eye, and the interpupillary distance”, shows derived coordinates based on convergence distance usable for assessment; ¶[0136]: “obtain the position coordinates of the center of focus based on… the convergence distance which is the distance between the gaze point and the user’s eye”, shows an explicit vergence-related distance derived from the binocular gaze point that can be assessed; ¶[0281]: “the focal length… can be adjusted to be equal to the convergence distance”, further ties processing to a measurable convergence distance; see also FIG. 13-16 and 18), visual fixation measure, saccades measure, smooth pursuit measure, eye dominance measure, vestibular measure, optokinetic measure, nystagmus quick phase measure, or a combination thereof.
Regarding claim 13, Keun teaches that a non-transitory computer-readable medium having instructions stored thereon, wherein execution of the instructions by a processor causes the processor to:(Keun, ¶[0108]: “Commands or program codes for performing functions or operations of the augmented reality device (100) may be stored in the memory (160)”, teaches a computer-readable memory storing program instructions; ¶[0104]: “The processor (150) can execute one or more instructions or program codes stored in the memory (160) and perform functions and/or operations corresponding to the instructions or program codes”, shows execution of stored instructions by a processor; ¶[0110]: “the processor (150) may be implemented by executing instructions or program codes stored in the memory (160)”, further confirms execution of instructions stored on a memory medium) generate a rendered scene and/or object in the virtual-reality headset or augmented-reality system(Keun, ¶[0039]: “A typical augmented reality device has an optical engine for generating a virtual image”, shows generating a rendered image/scene in a head-mounted AR/VR system, where ¶[0072]: “The display engine (130) may be an optical engine"; ¶[0088]: “The display engine (130) is configured to project a virtual image onto the wave guide (120)”, shows projecting rendered virtual images for display); receive, from a virtual-reality headset or augmented-reality system, measured eye-associated positions and measured gaze-associated directions of a user viewing the rendered scene and/or object(Keun, ¶[0134]: “the augmented reality device (100) can detect a left eye pupil from a left eye image acquired using a first gaze tracking sensor, and can detect a right eye pupil from a right eye image acquired using a second gaze tracking sensor”, teaches receiving/measuring eye-associated positions; ¶[0135]: “The augmented reality device (100) can obtain left eye gaze direction information based on the position of the pupil detected from the left eye, and can obtain right eye gaze direction information based on the position of the pupil detected from the right eye”, shows obtaining measured gaze-associated directions); determine a gaze control point from the measured eye associated positions and measured gaze-associated directions, wherein the gaze control point is a binocular convergence in the rendered scene and/or on the object using the measured gaze-associated directions from both eyes to reflect a combined eye movement pattern(Keun, ¶[0121]: “detecting the gaze point, which is the point where the gaze directions of the user's two eyes converge”, teaches determining a binocular convergence point from both eyes’ gaze directions; ¶[0134]: “the augmented reality device (100) obtains a gaze point where the gaze directions of the user's two eyes converge”, shows binocular convergence determined using both eyes’ gaze directions; ¶[0135]: “The augmented reality device (100) can estimate the position coordinates of the gaze point by using gaze information regarding binocular disparity, the gaze direction of the left eye, and the gaze direction of the right eye”, explicitly recites binocular disparity and shows the gaze point derived from both eyes’ gaze directions, i.e., a binocular convergence in the rendered scene/object; ¶[0212]: “The augmented reality device (100) can estimate the position coordinates of the gaze point (G, see FIGS. 1A and 1B) by using gaze information about binocular disparity and the gaze direction of the left eye and the gaze direction of the right eye”, same express “binocular disparity” language tied to calculating the binocular convergence point G; ¶[0219], “the distance d between the user's eyes and the virtual screen … As a result, the vergence distance l, which is the distance to the gaze point, is given by the following mathematical formula. z represents the distance between the virtual screen and the gaze point”, ties the gaze point to coordinates with respect to the rendered virtual screen; ¶[0205], “the augmented reality device (100) can calculate the two-dimensional position coordinate value of the gaze direction of the user's eye (E) on the virtual screen (1300) using the degree of rotation (α and β) of the user's eye (E)”, places gaze coordinates on the rendered screen; ¶[0211], “The processor (150)… can determine the gaze direction of the left and right eyes using gaze information output from the gaze tracking sensor (140a, 140b). In one embodiment… can calculate a first gaze vector … and a second gaze vector …”, shows explicit per-eye vectors that are then used together, as shown in ¶[0212], to track the combined eye movement patterns and 3D gaze coordinates; see also ¶[0216]); and determine one or more statistical parameters, or associated values, from the determined gaze control point (Keun, ¶[0216]: “When calculating the focal length to the point of gaze, the visual axes of the two eyes may not meet… the coordinates of the vertical axis (y-axis) can be calculated as the average of the vertical axis (y-axis) coordinates of the two eyes”, teaches determining a statistical parameter (a mean) derived from binocular gaze geometry tied to the gaze point G; ¶[0219]: “As a result, the vergence distance l, which is the distance to the gaze point, is given by the following mathematical formula. z represents the distance between the virtual screen and the gaze point”, teaches determining associated values (e.g., distances l and z) derived from the gaze control point G; ¶[0136]: “the augmented reality device (100) can obtain the position coordinates of the center of focus based on the lens-eye distance, the convergence distance which is the distance between the gaze point and the user's eye, and the interpupillary distance”, further teaches associated values including position coordinates derived from the gaze control point G).
Also regarding claim 13, Keun does not fully teach generating a rendered gaze ray associated with the binocular convergence. Rather, Keun teaches computing binocular gaze directions and the convergence point G, and placing gaze coordinates relative to the virtual screen, but Keun does not disclose rendering a gaze ray on the display associated with that convergence. For example: (Keun, ¶[0211]: “The processor (150… ) can determine the gaze direction of the left and right eyes… In one embodiment… can calculate a first gaze vector… and a second gaze vector…”, shows per-eye vectors computed internally for both eyes; ¶[0212]: “The augmented reality device (100) can estimate the position coordinates of the gaze point (G… ) by using gaze information about binocular disparity and the gaze direction of the left eye and the gaze direction of the right eye”, shows estimating the binocular convergence point coordinates; ¶[0219]: “the distance d between the user’s eyes and the virtual screen… z represents the distance between the virtual screen and the gaze point”, locates the gaze point relative to the rendered virtual screen but still does not disclose drawing/rendering a gaze ray; ¶[0042]: “'gaze' means an imaginary line from the user’s pupil to the gaze direction”, defines gaze as an imaginary line rather than a rendered on-screen line).
Young explicitly teaches that the headset’s display renders gaze-related visual indicators corresponding to eye movement vectors (Young, ¶[0094]: “The saccade path 510 is superimposed onto display 810 and shows fixation point A (e.g., direction 506 and vector XF-o)”, teaches a rendered, on-screen path/vector corresponding to gaze movement; ¶[0096]: “predicted landing points for fixation point B are superimposed onto display 810”, further shows rendered gaze-related indicators superimposed on the HMD display.) These passages demonstrate that Young visualizes where the user is looking by overlaying a rendered line or point on the virtual display. This fills the gap left by Keun, which performs gaze computations internally but does not visually depict them. One of ordinary skill in the art would recognize that Young’s display technique could be integrated into Keun’s existing binocular gaze system to represent the convergence point G visually on the virtual screen, thereby creating the claimed rendered gaze ray associated with binocular convergence.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Keun in view of Young to generate a rendered gaze ray associated with the binocular convergence. The combination is feasible because Keun already computes left/right gaze vectors and the 3-D gaze point G (including coordinates relative to the virtual screen), and Young demonstrates on-display overlays of gaze-related vectors/paths and landing points; adding a simple graphics overlay to depict a line (ray) from the eye or display toward G uses the same rendering pipeline that presents AR content. The benefit of the combination includes clear visual feedback for calibration and interaction, improved alignment between gaze estimation and displayed content, and enhanced usability by exposing the system’s internal gaze solution as an on-screen indicator.
Also regarding claim 13, the modified Keun does not fully teach that the gaze-associated directions and determined one or more statistical parameters are employed to provide a clinical assessment of vision of the user. Rather, the modified Keun teaches acquiring gaze-associated directions, determining a binocular convergence point G, and deriving associated values (e.g., averages and distances) from that gaze point for purposes such as rendering, alignment, and focus determination (Keun, ¶[0121], ¶[0134], ¶[0212]: determine binocular convergence point G from left and right gaze directions; ¶[0216]: calculates an average of coordinates; ¶[0219]: calculates vergence distance to the gaze point; ¶[0136]: derives position coordinates based on convergence distance and interpupillary distance), which collectively show computation and use of gaze data for system operation. However, it does not disclose employing such gaze-derived data and statistical parameters to perform a clinical or diagnostic evaluation of a user’s vision.
Krukowski explicitly teaches employing eye movement data and associated analytics to perform clinical vision assessment and diagnostic screening. For example: (Krukowski, ¶[0008]: “A diagnostic device for use in performing refractive errors assessment and neurodegenerative disorders screening…”, teaches a diagnostic device configured for clinical assessment; ¶[0002]: “The present invention relates to vision assessment and therapy…”, shows operation in a clinical/medical context; ¶[0137]-[0139]: “collecting eye movement data… and determining neurodegenerative disease by comparing collected eye movement data…”, teaches using eye movement data to perform disease screening/diagnosis; FIG. 33: “collect eye movements data… ocular behavior analytics… provide an output”, shows a workflow of data acquisition, analysis, and diagnostic output; FIG. 42: “Eyecare / Neuro Diagnostics”, reinforces clinical diagnostic application; [0251]: "The system 10 additionally contains variation adjusted to the vision therapy and assessment provide at specialized eye care units (e.g. vision therapy clinic)", further demonstrates that the disclosed system is configured for use in clinical environments, reinforcing that the vision assessment performed constitutes a clinical assessment of a user’s vision). These passages demonstrate that Krukowski uses gaze/eye movement data and associated analytical processing to evaluate visual function and detect ocular or neurological conditions, which constitutes a clinical assessment of vision. Krukowski further teaches that the collected eye movement data and associated derived parameters (e.g., temporal and spatial parameters of eye movement) are analyzed and compared to baseline data to generate diagnostic outputs, thereby employing both the underlying gaze-associated directions and derived statistical representations of that gaze behavior in providing a clinical assessment of vision of the user (Krukowski, ¶[0002], ¶[0009]; FIG. 33).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Keun in view of Krukowski to incorporate the diagnostic vision assessment functionality so as to employ the gaze-associated directions and derived statistical parameters to provide a clinical assessment of vision of the user. The combination is feasible because Keun provides the underlying gaze-associated directions and derived statistical parameters (e.g., averages and distances) that quantify eye movement behavior relative to the gaze control point, but does not employ them for clinical assessment, while Krukowski teaches using eye movement data and associated analytics to perform clinical vision assessment and diagnostic screening. In particular, the statistical parameters derived in Keun represent processed and quantified gaze behavior that would be analyzed within Krukowski’s ocular behavior analytics, together with the underlying gaze-associated directions, to generate a diagnostic evaluation of the user’s visual function. One of ordinary skill in the art would have been motivated to combine these teachings to improve the utility of Keun from gaze tracking and rendering to include clinical evaluation, thereby enabling diagnostic assessment of visual function and potential ocular or neurological conditions using both the measured gaze directions and the derived statistical representations of that gaze behavior. The benefit of the combination includes extending a gaze-tracking AR/VR system to provide clinically relevant outputs, improving diagnostic capability without requiring separate specialized equipment, and leveraging both raw gaze data and derived statistical metrics for vision assessment and screening.
Regarding claim 14, the modified Keun teaches that the determined gaze control point, or statistics derived therefrom, are subsequently employed to assess at least one of: vergence measure (Keun, ¶[0212]: “The augmented reality device (100) can estimate the position coordinates of the gaze point (G) by using gaze information about binocular disparity and the gaze direction of the left eye and the gaze direction of the right eye”, shows binocular, user-based convergence geometry from which a vergence measure is derived) (Keun, ¶[0219]: “As a result, the vergence distance l, which is the distance to the gaze point, is given by the following mathematical formula. z represents the distance between the virtual screen and the gaze point”, discloses a concrete vergence distance tied to the user’s binocular gaze that can be assessed) (Keun, ¶[0058]: “the augmented reality device (100) includes a lens-to-eye distance (ER), a convergence distance l which is a distance between a gaze point (G) and a lens, and a gaze vector of both eyes of the user toward the gaze point (G)”, further ties l to per-eye vectors and binocular geometry) (Keun, ¶[0136]: “the augmented reality device (100) can obtain the position coordinates of the center of focus based on the lens-eye distance, the convergence distance which is the distance between the gaze point and the user’s eye, and the interpupillary distance”, shows derived coordinates based on convergence distance usable for assessment; ¶[0136]: “obtain the position coordinates of the center of focus based on… the convergence distance which is the distance between the gaze point and the user’s eye”, shows an explicit vergence-related distance derived from the binocular gaze point that can be assessed; ¶[0281]: “the focal length… can be adjusted to be equal to the convergence distance”, further ties processing to a measurable convergence distance; see also FIG. 13-16 and 18), visual fixation measure, saccades measure, smooth pursuit measure, eye dominance measure, vestibular measure, optokinetic measure, nystagmus quick phase measure, or a combination thereof.
Claims 6, 10, 15, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Keun et al. (KR 20220170336 A), hereto referred as Keun, and further in view of Young et al. (US 20190354173 A1), hereto referred as Young, and further in view of Krukowski et al. (US 2021033018 A1), hereto referred as Krukowski, and further in view of Monti (GB 2613084 A), hereto referred as Monti.
The modified Keun teaches claim 1, claim 7, and claim 13 as described above.
Regarding claim 6, the modified Keun does not teach that the determined gaze control point, or statistics derived therefrom, are subsequently employed in a therapy to address an eye-tracking problem or a disease. Monti teaches gaze-based head-mounted displays configured to detect user eye conditions such as macular degeneration, glaucoma, cataracts, or other vision impairments and to adaptively generate images that compensate for regions of vision loss. The system tracks eye gaze and dynamically modifies displayed objects or viewpoints to redirect images toward functional regions of the retina (Monti, p. 17, ll. 28-35: “The processor 1240 can therefore generate images for display to the user according to the type of eye condition so as to compensate for a region of vision loss for the eye of the user”; p. 20-21, ll. 33-20: “In response to detecting that the object is within the predetermined distance…the object is moved in a direction so as to increase the separation distance…to allow the object to be more easily observed by the user”). Monti further discloses calibration procedures using gaze detection and saccadic eye movement tracking to determine parameters related to preferred retinal locus (PRL) and field-of-view limitations, thereby improving user adaptation and restoring visual function; p. 28-29, ll. 35-20: “The calibration circuitry 1250 is configured to calculate one or more offset parameters for the eye indicative of an offset for the detected gaze direction”; p. 30-31, ll. 30-26: “the processor 1240 is configured to calculate a modified gaze direction for the eye in dependence upon one or more of the offset parameters… the modified gaze direction being offset with respect to the detected gaze direction”, explains how the modified gaze direction is computed from offsets, “the detected gaze direction of the eye is arranged to intersect a fovea portion of the retina of the eye and the calculated modified gaze direction for the eye is arranged to intersect a different portion of the retina”, shows the modified gaze direction intentionally targets a non-foveal retinal region, “Specifically, for a user having a preferred retinal locus the different portion of the retina corresponds to the preferred retinal locus of the retina of the eye”, expressly ties that “different portion” to the PRL).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Keun in view of Monti to employ the determined gaze control point, or statistics derived therefrom, in a therapy or rehabilitation process for correcting gaze or vision deficiencies. The combination is feasible because both references employ gaze detection and convergence data in head-mounted or AR display systems to generate user-specific visual feedback. One of ordinary skill in the art would have recognized that Monti’s compensatory rendering and calibration for pathological vision loss naturally extend Keun’s gaze control data toward therapeutic use, leveraging gaze convergence information to adapt images for restoring visual function. The motivation for the combination would have been to enhance visual training and rehabilitation for patients with oculomotor or retinal deficiencies, thereby improving accuracy, adaptation, and comfort in visual interactions.
Regarding claim 10, the modified Keun does not teach that the determined gaze control point, or statistics derived therefrom, are subsequently employed in a therapy to address an eye-tracking problem or a disease. Monti teaches gaze-based head-mounted displays configured to detect user eye conditions such as macular degeneration, glaucoma, cataracts, or other vision impairments and to adaptively generate images that compensate for regions of vision loss. The system tracks eye gaze and dynamically modifies displayed objects or viewpoints to redirect images toward functional regions of the retina (Monti, p. 17, ll. 28-35: “The processor 1240 can therefore generate images for display to the user according to the type of eye condition so as to compensate for a region of vision loss for the eye of the user”; p. 20-21, ll. 33-20: “In response to detecting that the object is within the predetermined distance…the object is moved in a direction so as to increase the separation distance…to allow the object to be more easily observed by the user”). Monti further discloses calibration procedures using gaze detection and saccadic eye movement tracking to determine parameters related to preferred retinal locus (PRL) and field-of-view limitations, thereby improving user adaptation and restoring visual function; p. 28-29, ll. 35-20: “The calibration circuitry 1250 is configured to calculate one or more offset parameters for the eye indicative of an offset for the detected gaze direction”; p. 30-31, ll. 30-26: “the processor 1240 is configured to calculate a modified gaze direction for the eye in dependence upon one or more of the offset parameters… the modified gaze direction being offset with respect to the detected gaze direction”, explains how the modified gaze direction is computed from offsets, “the detected gaze direction of the eye is arranged to intersect a fovea portion of the retina of the eye and the calculated modified gaze direction for the eye is arranged to intersect a different portion of the retina”, shows the modified gaze direction intentionally targets a non-foveal retinal region, “Specifically, for a user having a preferred retinal locus the different portion of the retina corresponds to the preferred retinal locus of the retina of the eye”, expressly ties that “different portion” to the PRL).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Keun in view of Monti to employ the determined gaze control point, or statistics derived therefrom, in a therapy or rehabilitation process for correcting gaze or vision deficiencies. The combination is feasible because both references employ gaze detection and convergence data in head-mounted or AR display systems to generate user-specific visual feedback. One of ordinary skill in the art would have recognized that Monti’s compensatory rendering and calibration for pathological vision loss naturally extend Keun’s gaze control data toward therapeutic use, leveraging gaze convergence information to adapt images for restoring visual function. The motivation for the combination would have been to enhance visual training and rehabilitation for patients with oculomotor or retinal deficiencies, thereby improving accuracy, adaptation, and comfort in visual interactions.
Regarding claim 15, the modified Keun does not teach that the determined gaze control point, or statistics derived therefrom, are subsequently employed in a therapy to address an eye-tracking problem or a disease. Monti teaches gaze-based head-mounted displays configured to detect user eye conditions such as macular degeneration, glaucoma, cataracts, or other vision impairments and to adaptively generate images that compensate for regions of vision loss. The system tracks eye gaze and dynamically modifies displayed objects or viewpoints to redirect images toward functional regions of the retina (Monti, p. 17, ll. 28-35: “The processor 1240 can therefore generate images for display to the user according to the type of eye condition so as to compensate for a region of vision loss for the eye of the user”; p. 20-21, ll. 33-20: “In response to detecting that the object is within the predetermined distance…the object is moved in a direction so as to increase the separation distance…to allow the object to be more easily observed by the user”). Monti further discloses calibration procedures using gaze detection and saccadic eye movement tracking to determine parameters related to preferred retinal locus (PRL) and field-of-view limitations, thereby improving user adaptation and restoring visual function; p. 28-29, ll. 35-20: “The calibration circuitry 1250 is configured to calculate one or more offset parameters for the eye indicative of an offset for the detected gaze direction”; p. 30-31, ll. 30-26: “the processor 1240 is configured to calculate a modified gaze direction for the eye in dependence upon one or more of the offset parameters… the modified gaze direction being offset with respect to the detected gaze direction”, explains how the modified gaze direction is computed from offsets, “the detected gaze direction of the eye is arranged to intersect a fovea portion of the retina of the eye and the calculated modified gaze direction for the eye is arranged to intersect a different portion of the retina”, shows the modified gaze direction intentionally targets a non-foveal retinal region, “Specifically, for a user having a preferred retinal locus the different portion of the retina corresponds to the preferred retinal locus of the retina of the eye”, expressly ties that “different portion” to the PRL).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Keun in view of Monti to employ the determined gaze control point, or statistics derived therefrom, in a therapy or rehabilitation process for correcting gaze or vision deficiencies. The combination is feasible because both references employ gaze detection and convergence data in head-mounted or AR display systems to generate user-specific visual feedback. One of ordinary skill in the art would have recognized that Monti’s compensatory rendering and calibration for pathological vision loss naturally extend Keun’s gaze control data toward therapeutic use, leveraging gaze convergence information to adapt images for restoring visual function. The motivation for the combination would have been to enhance visual training and rehabilitation for patients with oculomotor or retinal deficiencies, thereby improving accuracy, adaptation, and comfort in visual interactions.
Regarding claim 18, the modified Keun teaches that the determined one or more statistical parameters includes at least one of: (i) a variance measure of the gaze control point, (ii) a mean location of the gaze control point (Keun, ¶[0216]: “the coordinates of the vertical axis (y-axis) can be calculated as the average of the vertical axis (y-axis) coordinates of the two eyes”, teaches a mean used in computing the binocular gaze point location along the y-axis (Keun, ¶[0212]: “estimate the position coordinates of the gaze point (G… ) by using gaze information about binocular disparity and the gaze direction of the left eye and the gaze direction of the right eye”, shows the 3D gaze point coordinates to which a mean can pertain);(iii) a latency measure of the gaze control point, (iv) a change in the variance of the gaze control point over time, (v) a change in the latency of the gaze control point over time, (vi) an instantaneous location of the gaze control point (Keun, ¶[0212]: “estimate the position coordinates of the gaze point (G… ) by using gaze information about binocular disparity and the gaze direction of the left eye and the gaze direction of the right eye”, teaches determining the instantaneous 3D location of the gaze control point G (Keun, ¶[0214]: “map a point (gaze point, G)… to a three-dimensional position coordinate value… or may store the three-dimensional position coordinate value of the gaze point (G) in a memory (160)”, further shows the specific 3D coordinates of G; ¶[0211]–[0212]: “The processor (150)… can calculate a first gaze vector indicating a gaze direction of the left eye and a second gaze vector indicating a gaze direction of the right eye using gaze information output from the gaze tracking sensor… [and] can estimate the position coordinates of the gaze point (G)…,” teaches determining the gaze point in real-time from live sensor data, thereby producing an instantaneous 3-D location of the gaze control point; Keun, ¶[0221]: “The convergence distance l may be adjusted depending on the movement of the gaze point or the user’s focus”, further supports instantaneous velocity and acceleration measures (items (vii) and (viii)) as changes in gaze distance over time based on user motion and focus); (vii) an instantaneous velocity measure of the gaze control point, and (viii) an instantaneous acceleration measure of the gaze control point.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Keun et al. (KR 20220170336 A), hereto referred as Keun, and further in view of Young et al. (US 20190354173 A1), hereto referred as Young, and further in view of Krukowski et al. (US 2021033018 A1), hereto referred as Krukowski, and further in view of Ning (US 11431955 B1), hereto referred as Ning.
The modified Keun teaches claim 7 as described above.
Regarding claim 8, with regards to the virtual-reality headset, the modified Keun primarily teaches an augmented-reality headset as shown above in claim 7, but it does not expressly teach a full virtual-reality headset.
Ning, however, explicitly discloses that its technology applies to both augmented-reality (AR) and virtual-reality (VR) systems, stating: “The present disclosure relates generally to augmented reality (AR) and/or virtual reality (VR) systems” (Ning, Col. 1, ll. 13-25). Ning demonstrates that eye tracking systems are equally compatible with both AR and VR headsets: "the AR/VR systems can use eye tracking and/or head tracking to track the user's eyes or head and accordingly present images" (Ning, Col. 1, ll. 13-25).
It would have been prima facie obvious before the effective filing date of the claimed invention to modify the modified Keun’s augmented-reality headset to function as a virtual-reality headset as taught by Ning, because both references address gaze-based visual scene rendering in head-mounted displays. A person of ordinary skill in the art would have recognized that the modified Keun’s optical and processing architecture could be implemented in the VR configuration of Ning, since both AR and VR HMDs use the same classes of sensors, processors, and gaze-based rendering pipelines, with merely the see-through optics removed, yielding predictable results. This has the benefit of providing a fully controlled non-see-through visual environment that (i) simplifies the optical stack by removing see through combiners and associated alignment tolerances, (ii) reduces stray reflections/real-world background interference to improve stability of gaze calibration and binocular convergence estimation, (iii) enables consistent full field rendered stimuli and predictable vergence-accommodation conditions that enhance rendering fidelity and user comfort, and (iv) reuses the same eye tracking sensors, processors and gaze based rendering pipeline for predictable equivalent operation without undue experimentation.
Also regarding claim 8, the modified Keun with Ning (Ning teaches AR and/or VR head-mounted systems with eye tracking (Ning, Col. 1, ll. 13–25), the modified Keun’s AR headset is implemented as a VR headset for Claim 8; the remaining Keun teachings (processor/memory, rendering, eye/gaze measurements, binocular convergence) apply in the VR HMD context) teach that the virtual-reality headset comprising a processor; and a memory having instructions stored thereon (Keun, ¶[0104]: “The processor (150) can execute one or more instructions or program codes stored in the memory (160) and perform functions and/or operations corresponding to the instructions or program codes”, shows a processor executing instructions stored in memory within the headset; ¶[0108]: “Commands or program codes for performing functions or operations of the augmented reality device (100) may be stored in the memory (160) … the memory (160) may store at least one of instructions, an algorithm, a data structure, a program code, and an application program that can be read by the processor (150)”, teaches a memory storing instructions/programs executed by the onboard processor; Ning, ¶[0002]: “The present disclosure relates generally to augmented reality (AR) and/or virtual reality (VR) systems,” confirms that such processors and memories are applicable to both AR and VR systems); wherein execution of the instructions by the processor causes the processor to: (i) display a rendered scene and/or object (Keun, ¶[0039]: “A typical augmented reality device has an optical engine for generating a virtual image”, teaches generating a rendered image/scene for display in the headset; ¶[0004]: “Augmented reality devices project virtual images onto the user's eyes through a see-through display, allowing the user to simultaneously view real-world objects and the projected virtual images”, shows rendering and presenting virtual images/objects in the headset. When implemented as in Ning’s VR context, these rendered images are fully immersive virtual scenes displayed within the VR headset); (ii) measure eye-associated positions of a user wearing the virtual-reality headset (Keun, ¶[0134]: “the augmented reality device (100) can detect a left eye pupil from a left eye image acquired using a first gaze tracking sensor, and can detect a right eye pupil from a right eye image acquired using a second gaze tracking sensor”, teaches detecting pupil feature points (eye-associated positions) from each eye using onboard sensors; ¶[0122]: “The processor (150) can obtain pupil position information based on the position of the pupil feature point”, shows measured eye positions); (iii) measure gaze-associated directions of the user (Keun, ¶[0135]: “The augmented reality device (100) can obtain left eye gaze direction information based on the position of the pupil detected from the left eye, and can obtain right eye gaze direction information based on the position of the pupil detected from the right eye”, teaches per-eye gaze direction; ¶[0122]: “The processor (150) can obtain pupil position information based on the position of the pupil feature point, and can obtain information about the gaze direction based on the pupil position information”, shows deriving gaze direction from measured positions); (iv) determine a gaze control point from the measured eye-associated positions and measured gaze-associated directions, wherein the gaze control point is a binocular convergence in the rendered scene and/or on the object using the measured gaze-associated directions from both eyes to reflect a combined eye movement pattern (Keun, ¶[0121]: “detecting the gaze point, which is the point where the gaze directions of the user's two eyes converge”, teaches determining a binocular convergence point from both eyes’ gaze directions; ¶[0134]: “the augmented reality device (100) obtains a gaze point where the gaze directions of the user's two eyes converge”, shows binocular convergence determined using both eyes’ gaze directions; ¶[0135]: “The augmented reality device (100) can estimate the position coordinates of the gaze point by using gaze information regarding binocular disparity, the gaze direction of the left eye, and the gaze direction of the right eye”, explicitly recites binocular disparity and shows the gaze point derived from both eyes’ gaze directions, i.e., a binocular convergence in the rendered scene/object; ¶[0212]: “The augmented reality device (100) can estimate the position coordinates of the gaze point (G, see FIGS. 1A and 1B) by using gaze information about binocular disparity and the gaze direction of the left eye and the gaze direction of the right eye”, same express “binocular disparity” language tied to calculating the binocular convergence point G; ¶[0219], “the distance d between the user's eyes and the virtual screen … As a result, the vergence distance l, which is the distance to the gaze point, is given by the following mathematical formula. z represents the distance between the virtual screen and the gaze point”, ties the gaze point to coordinates with respect to the rendered virtual screen; ¶[0205], “the augmented reality device (100) can calculate the two-dimensional position coordinate value of the gaze direction of the user's eye (E) on the virtual screen (1300) using the degree of rotation (α and β) of the user's eye (E)”, places gaze coordinates on the rendered screen; ¶[0211], “The processor (150)… can determine the gaze direction of the left and right eyes using gaze information output from the gaze tracking sensor (140a, 140b). In one embodiment… can calculate a first gaze vector … and a second gaze vector …”, shows explicit per-eye vectors that are then used together, as shown in ¶[0212], to track the combined eye movement patterns and 3D gaze coordinates; see also ¶[0216]).
Response to Arguments
Claim Warning
Applicant's arguments filed 3/25/2026, page 9, regarding the previous claim warning of claims 19 and 20 have been fully considered and are persuasive. The previous claim warning has been withdrawn.
Objections
Applicant's arguments filed 3/25/2026, page 10, regarding the previous Objections of claims 1 and 8 have been fully considered and are persuasive. The previous Objections have been withdrawn.
35 U.S.C. §112(b)
Applicant's arguments filed 3/25/2026, pages 10-12, regarding the previous 112(b) Rejections of claims 7-12 have been fully considered and are persuasive. The previous 112(b) rejections have been withdrawn.
35 U.S.C. §102/103
Applicant's arguments filed 3/25/2026, pages 12-14, regarding the previous 102 Rejections of claims 1, 3-5, 7, 9, 11-14, and 19 have been fully considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. That is, there are new grounds of rejection.
Applicant's arguments filed 3/25/2026, pages 14-19, regarding the previous 103 Rejections of claims 2, 6, 10, 15, 17-18, and 20 have been fully considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. That is, there are new grounds of rejection.
Applicant’s Argument: Applicant contends that the prior art does not disclose that gaze-associated directions and statistical parameters are employed to provide a clinical assessment of vision.
Examiner’s Response: The argument is not persuasive. Applicant’s argument is directed to Keun in isolation; however, the present rejection is based on Keun in view of Young and further in view of Krukowski, which are newly applied to address the disputed limitations. While Keun does not disclose performing a clinical assessment, Keun expressly teaches determining gaze-associated directions and associated derived values (e.g., averages and distances) from a gaze control point. Krukowski teaches collecting eye movement data and performing ocular behavior analytics based on temporal and spatial parameters of eye movement, which are analyzed and compared to baseline data to provide diagnostic outputs. Thus, the combination teaches employing both the gaze-associated directions and derived statistical parameters in providing a clinical assessment of vision of the user. Therefore, the rejection properly relies on the combined teachings of the references.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/AARON MERRIAM/Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791
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