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 .
DETAILED ACTION
Priority
No foreign or domestic priority is claimed. The effective filing date of U.S. Application No. 18/912,067 is 10/10/2024.
Status of Claims
Claims 1–20 are pending in the application. Claims 1-4, 6, 8, 10-20 are rejected.
Claims 5, 7, 9 are objected to.
Allowable Subject Matter
Claims 5, 7, 9 are objected to as being dependent upon a rejected base claim(s), but would be allowable if rewritten in independent form including all of the limitations of the base claim(s) and any intervening claim(s).
Overview of Grounds of Rejection
Ground of Rejection
Claim(s)
Statute(s)
Reference(s)
Ground 1
1, 6, 10, 11, 13, 16
§ 103
Glynn et al. (US20180190019A1) in view of Legendre et al. (US20210166437A1)
Ground 2
2
§ 103
Glynn et al. (US20180190019A1) in view of Legendre et al. (US20210166437A1), and further in view of Michailidis et al. (US20240149167A1)
Ground 3
3, 4
§ 103
Glynn et al. (US20180190019A1) in view of Legendre et al. (US20210166437A1), and further in view of Chang et al. (US20210158010A1)
Ground 4
8
§ 103
Glynn et al. (US20180190019A1) in view of Legendre et al. (US20210166437A1), and further in view of Scott et al. (US20210118232A1)
Ground 5
12, 15
§ 103
Glynn et al. (US20180190019A1) in view of Legendre et al. (US20210166437A1), and further in view of Zielkowski (US20190206132A1)
Ground 6
14
§ 103
Glynn et al. (US20180190019A1) in view of Legendre et al. (US20210166437A1), and further in view of Zielkowski (US20190206132A1) and Scott et al. (US20210118232A1)
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 of this title, 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.
(Please see the cited paragraphs, sections, pages, or surrounding text in the references for the paraphrased content.)
Ground of Rejection 1
Claims 1, 6, 10, 11, 13, 16, 17 are rejected under 35 U.S.C. § 103 as being unpatentable over Glynn et al. (US20180190019A1) in view of Legendre et al. (US20210166437A1).
As per Claim 1, Glynn teaches the following portion of Claim 1, which recites: “A method comprising: determining current lighting conditions for a real-world location at a current time;”
Glynn et al. teaches a process for improving visibility of AR interfaces in changing environments, including sensing ambient light and adjusting the AR interface. Glynn et al., ¶ [0017].
Glynn et al. teaches an ambient light sensor that “determines an ambient luminosity” and “measures the ambient light in a room where the HMD 101 is located.” Glynn et al., ¶ [0035]. Thus, Glynn et al. teaches determining the current lighting conditions of the real-world environment.
Glynn alone does not explicitly teach all the limitation(s) of the claim. However, when combined with Legendre, they collectively teach all of the limitation(s).
Legendre teaches the following portion of Claim 1, which recites: “retrieving historical lighting data for the real-world location;”
Legendre et al. teaches that the device may “obtain a scene lighting estimation from an earlier time in the AR session,” including an “ambient light intensity measurement for the scene.” Legendre et al., ¶ [0149].
Thus, Legendre et al. teaches obtaining earlier, or historical, lighting data for the same AR scene.
Legendre teaches the following portion of Claim 1, which recites:
“determining, based at least in part on the historical lighting data, predicted lighting conditions over a time period after the current time;”
Legendre et al. teaches neural networks that “model predicted lighting for a scene” and “compute HDR lighting estimations several timesteps into the future.” Legendre et al., ¶ [0069].
Legendre et al. further teaches an HDR “lighting estimate for the scene for an upcoming time period.” Legendre et al., ¶ [0155].
Thus, Legendre et al. teaches predicting future lighting conditions over a period after the current time using prior lighting-related information.
Glynn and Legendre teach the following portion of Claim 1, which recites: “based at least in part on the current lighting conditions for the real-world location at the current time and the predicted lighting conditions over the time period, selecting a text style for text to be displayed within an augmented reality (AR) environment over the time period, wherein the AR environment comprises the text overlaid on the real-world location; and”
Glynn et al. teaches that an AR device “senses ambient light levels and adjusts the font or theme setting of an AR interface.” Glynn et al., ¶ [0017]. Glynn et al. also teaches “adjusting a font and a color of the AR content in response to the measured ambient light.” Glynn et al., ¶ [0091].
Glynn et al. therefore teaches selecting a text style based on current lighting. Legendre et al. supplies the predicted future lighting discussed above. The proposed combination uses both Glynn et al.'s current-lighting information and Legendre et al.'s predicted-lighting information when selecting the AR text style. Glynn et al. further teaches that virtual content is “overlaid on top of real-world objects.” Glynn et al., ¶ [0016].
Glynn teaches the following portion of Claim 1, which recites: “generating for display the text, in the selected text style, within the AR environment over the time period.”
Glynn et al. teaches generating AR information “in the form of text or graphics” and displaying the GUI as a “layer on the objects.” Glynn et al., ¶ [0042]. Glynn et al. further teaches selecting “white fonts for text” and dynamically adjusting the “color, font, shape, size, brightness” of the displayed AR GUI. Glynn et al., ¶ [0055].
Thus, Glynn et al. teaches generating and displaying the AR text in the selected text style. Before the effective filing date of the claimed invention, a person of ordinary skill in the art (POSITA) would have been motivated to combine Glynn et al.'s lighting-responsive AR text-style selection with Legendre et al.'s prediction of future AR lighting conditions to improve readability of AR text as lighting changes over time. Using Legendre et al.'s predicted lighting as an additional input to Glynn et al.'s known font and color adjustment technique would enhance the AR interface by allowing the text style to account for both present and anticipated lighting conditions, yielding the predictable result of improved text visibility over the display period.
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As per Claim 6, Glynn alone does not explicitly teach all the limitation(s) of the claim. However, when combined with Legendre, they collectively teach all of the limitation(s).
Glynn and Legendre teach Claim 6, which recites: “The method of claim 1, further comprising: based at least in part on the current lighting conditions for the real-world location at the current time and the predicted lighting conditions over the time period, selecting a position within the AR environment to display the text, wherein the text is generated for display, in the selected text style, at the position within the AR environment.”
Glynn teaches determining contrast based partly on “ambient light” and “chang[ing] the location” of the GUI so that it is displayed “in an area or region where the background region provides the most contrast.” Glynn et al., ¶¶ [0047]-[0048]. Legendre further teaches “predicted lighting for a scene” and HDR lighting estimates “several timesteps into the future.” Legendre et al., ¶ [0069].
Thus, the combined teachings render obvious selecting the AR text position based on current and predicted lighting conditions and displaying the styled text at that selected position.
Before the effective filing date, a POSITA would have been motivated to use Legendre et al.’s predicted lighting with Glynn et al.’s contrast-based AR text positioning to place text where visibility would remain improved as lighting changes, yielding predictable results.
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As per Claim 10, Glynn alone does not explicitly teach all the limitation(s) of the claim. However, when combined with Legendre, they collectively teach all of the limitation(s).
Glynn and Legendre teach Claim 10, which recites: “The method of claim 1, wherein the selecting the text style comprises: based at least in part on the current lighting conditions for the real-world location at the current time and the predicted lighting conditions over the time period, selecting at least one of a color or a texture for the text to be displayed within the AR environment.”
Glynn et al. teaches “measuring an ambient light outside the device” and “adjusting a font and a color of the AR content in response to the measured ambient light.” Glynn et al., ¶ [0091]. Legendre et al. further teaches “predicted lighting for a scene” and “HDR lighting estimations several timesteps into the future.” Legendre et al., ¶ [0069].
Thus, the combined teachings render obvious selecting a color for the AR text based on current and predicted lighting conditions.
Before the effective filing date, a POSITA would have been motivated to use Legendre et al.’s current and predicted lighting information with Glynn et al.’s lighting-responsive text color selection to maintain AR text visibility as lighting changes over time, yielding predictable results.
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As per Claim 11, Glynn alone does not explicitly teach all the limitation(s) of the claim. However, when combined with Legendre, they collectively teach all of the limitation(s).
Legendre teaches Claim 11, which recites: “The method of claim 1, wherein the historical lighting data comprises an average luminance for the real-world location over at least one time period before the current time.”
Legendre et al. teaches obtaining a “scene lighting estimation from an earlier time in the AR session” and teaches that the corresponding image measurement may represent “luminance.” Legendre et al., ¶ [0149]. Legendre further teaches that the first and second ambient-light measurements are “averaged linear ambient intensity values.” Legendre et al., ¶ [0010].
Thus, Legendre et al. teaches historical lighting data comprising an average luminance/intensity value from a time period before the current time.
Before the effective filing date, a POSITA would have been motivated to use Legendre et al.’s averaged prior luminance/intensity data as historical lighting information to improve the reliability of future AR lighting predictions, yielding predictable results.
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As per Claim 13, Glynn alone does not explicitly teach all the limitation(s) of the claim. However, when combined with Legendre, they collectively teach all of the limitation(s).
Glynn teaches the following portion of Claim 13, which recites: “The method of claim 1, wherein the selected text style is a first selected text style displayed at a first time during the time period”
Glynn teaches displaying AR text using a selected font/color and dynamically adjusting the color, font, shape, size, and brightness as conditions change. Glynn et al., ¶ [0055].
Legendre and Glynn teach the following portion of Claim 13, which recites: “during the time period, selecting a second text style for the text, based at least in part on the predicted lighting conditions over the time period; and generating for display the text, in the second selected text style ... at a second time ... later than the first time.”
Legendre teaches neural networks that “model predicted lighting for a scene” and “compute HDR lighting estimations several timesteps into the future.” Legendre et al., ¶ [0069]. Legendre further teaches multiple light predictions D1-D4 occurring at respective timestamps, with corresponding lighting representations generated at those successive timestamps. Legendre et al., ¶¶ [0131]-[0133].
Glynn teaches that the AR interface controller “dynamically adjusts the color, font, shape, size, brightness” of displayed AR text as conditions change. Glynn et al., ¶ [0055].
Thus, applying Legendre’s predicted lighting at successive future times to Glynn’s dynamic text-style adjustment renders obvious displaying a second text style at a later time based on the predicted lighting for that later time.
Before the effective filing date, a POSITA would have been motivated to use Legendre et al.’s predicted lighting at successive future times with Glynn et al.’s dynamic AR text styling so that the text style could be updated as predicted lighting changes, thereby maintaining readability with predictable results.
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As per Claim 16, Glynn alone does not explicitly teach all the limitation(s) of the claim. However, when combined with Legendre, they collectively teach all of the limitation(s).
Legendre teaches Claim 16, which recites: “The method of claim 1, wherein the predicted lighting conditions comprise at least one of an average luminance for the real-world location over the time period, a light color, a light color temperature, a light hardness, or shadow positioning.”
Legendre teaches computing “HDR lighting estimations several timesteps into the future” and predicting multiple lighting environments. Legendre et al., ¶¶ [0069]-[0072]. The predicted light representations are further processed to “determine an ambient intensity,” and Legendre expressly teaches computing “linear average ambient intensity.” Legendre et al., ¶ [0146].
Thus, Legendre teaches predicted lighting conditions comprising an average luminance/ambient-light intensity over the relevant period.
Before the effective filing date, a POSITA would have been motivated to use Legendre et al.’s predicted average ambient-light intensity in the Glynn-Legendre AR system to improve future lighting-aware text presentation, yielding predictable results.
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Claim 17 does not include any additional limitations that would significantly distinguish it from claim 1. Therefore, it is likewise rejected under 35 U.S.C. § 103 in view of the same references and for the same reasons set forth above.
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Ground of Rejection 2
Claims 2, 18 are rejected under 35 U.S.C. § 103 as being unpatentable over Glynn et al. (US20180190019A1) in view of Legendre et al. (US20210166437A1), and further in view of Michailidis et al. (US20240149167A1).
As per Claim 2, Glynn alone does not explicitly teach all the limitation(s) of the claim. However, when combined with Legendre and Michailidis, they collectively teach all of the limitation(s).
Legendre and Michailidis teach the following portion of Claim 2, which recites: “The method of claim 1, further comprising: determining the time period based at least in part on a predicted AR session length.”
Legendre et al. teaches operation of the claimed lighting-prediction techniques during an “AR session.” Legendre et al., ¶ [0010].
Michailidis et al. teaches predicting session length using historical session data: “the system uses a statistical characterisation of historical playtime data to predict the duration of the current playtime session.” Michailidis et al., ¶ [0052]. Michailidis further uses the predicted session duration to establish the available time period for activities, selecting tasks “having respective durations, that fit within the estimated timing of the subsequent session.” Michailidis et al., ¶ [0082].
Thus, Michailidis teaches determining an available time period based on a predicted session length, while Legendre provides the AR-session context.
Before the effective filing date of the claimed invention, a POSITA would have been motivated to apply Michailidis et al.’s known session-duration prediction to Legendre et al.’s AR session so that the future lighting-prediction period corresponds to the expected duration of the AR session, thereby avoiding unnecessary prediction beyond the expected session and yielding the predictable result of more efficient AR processing.
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Claim 18 does not include any additional limitations that would significantly distinguish it from claim 2. Therefore, it is likewise rejected under 35 U.S.C. § 103 in view of the same references and for the same reasons set forth above.
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Ground of Rejection 3
Claims 3, 4, 19, 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Glynn et al. in view of Legendre et al., and further in view of Chang et al. (US20210158010A1).
As per Claim 3, Glynn alone does not explicitly teach all the limitation(s) of the claim. However, when combined with Chang, they collectively teach all of the limitation(s).
Chang teaches the following portion of Claim 3, which recites: “The method of claim 1, further comprising: based at least in part on the historical lighting data for the real-world location and the current lighting conditions for the real-world location, generating a lighting condition model;”
Chang et al. teaches historical training data collected over “one or more years of data collection,” including “ground truth solar irradiation” and associated images/metadata, and machine-training “an artificial neural network ... to predict solar irradiation.” Chang et al., ¶¶ [0018], [0024]. Chang further teaches supplying the model with “past measures of irradiation” and a “current measure of irradiation.” Chang et al., ¶ [0057].
Chang teaches the following portion of Claim 3, which recites: “using the lighting condition model to determine the predicted lighting conditions over the time period,”
Chang et al. teaches that the machine-learned network outputs “a list of values for different future times for a future period.” Chang et al., ¶ [0060].
Chang teaches the following portion of Claim 3, which recites: “wherein the lighting condition model comprises at least one neural network.”
Chang et al. teaches “an artificial neural network [that] is machine trained to predict solar irradiation.” Chang et al., ¶ [0024].
Before the effective filing date, a POSITA would have been motivated to use Chang et al.’s neural-network prediction technique with Legendre et al.’s AR lighting system to improve prediction of future lighting from historical and current lighting information, yielding the predictable result of more accurate future AR lighting estimates.
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As per Claim 4, Glynn alone does not explicitly teach all the limitation(s) of the claim. However, when combined with Chang, they collectively teach all of the limitation(s).
Chang teaches the following portion of Claim 4, which recites: “The method of claim 3, further comprising: prior to the determining the predicted lighting conditions: training the at least one neural network using the historical lighting data for the real-world location,”
Chang et al. teaches obtaining training data for “one or more years of data collection,” including “ground truth solar irradiation” and associated images/metadata, for “a given geographical region,” and machine-training the neural network to predict solar irradiation. Chang et al., ¶¶ [0018], [0022], [0024].
Chang teaches the following portion of Claim 4, which recites: “wherein the historical lighting data comprises a plurality of lighting characteristics for a plurality of previous times, respectively, wherein each lighting characteristic is associated with at least one of a time of day or weather conditions at the corresponding previous time.”
Chang et al. teaches “past solar irradiation measures,” “time of day and/or date,” and “weather information, such as humidity, cloud coverage, weather pattern, pressure, and/or solar information,” with irradiation measurements obtained “for each of the times at which an image is captured.” Chang et al., ¶¶ [0020]-[0021].
Thus, Chang et al. teaches training the neural-network lighting prediction model using historical location-specific lighting measurements associated with corresponding time and/or weather information before using the trained model to make future predictions.
Before the effective filing date, a POSITA would have been motivated to train the neural-network lighting model using historical location-specific lighting, time, and weather data, as taught by Chang et al., to improve future lighting predictions with predictable results.
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Claim 19 does not include any additional limitations that would significantly distinguish it from claim 3. Therefore, it is likewise rejected under 35 U.S.C. § 103 in view of the same references and for the same reasons set forth above.
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Claim 20 does not include any additional limitations that would significantly distinguish it from claim 4. Therefore, it is likewise rejected under 35 U.S.C. § 103 in view of the same references and for the same reasons set forth above.
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Ground of Rejection 4
Claim 8 is rejected under 35 U.S.C. § 103 as being unpatentable over Glynn et al. (US20180190019A1) in view of Legendre et al. (US20210166437A1), and further in view of Scott et al. (US20210118232A1).
As per Claim 8, Glynn alone does not explicitly teach all the limitation(s) of the claim. However, when combined with Scott, they collectively teach all of the limitation(s).
Scott teaches the following portion of Claim 8, which recites: “The method of claim 1, wherein the AR environment is displayed at an AR device, and wherein the AR device is associated with a user profile, the method further comprising: retrieving user preference data from the user profile,”
Scott et al. teaches a “first user 300 of first AR device 112” associated with “a profile 302,” wherein the profile includes settings for displaying AR content, including “display fonts (i.e., size, color, type, etc.)” Scott et al., ¶¶ [0030]-[0031]. Scott further teaches that “user profiles ... may be provided to the air writing system to automatically adjust the AR visualizations based on the user's profile.” Scott et al., ¶ [0035].
Scott teaches the following portion of Claim 8, which recites: “wherein the selecting the text style for the text is based at least in part on the user preference data.”
Scott et al. teaches selecting user-profile-based text styles, including “larger size font,” “italicized or bolded font,” and displaying different gesture display styles “based on that user's profile.” Scott et al., ¶ [0033].
Thus, Scott et al. teaches retrieving/accessing user-profile preferences and selecting the AR text style based on those preferences.
Before the effective filing date, a POSITA would have been motivated to apply Scott et al.’s user-profile-based display preferences to the AR text styling of Glynn et al. to personalize font and other text characteristics for the user, thereby improving readability and user experience with predictable results.
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Ground of Rejection 5
Claims 12, 15 are rejected under 35 U.S.C. § 103 as being unpatentable over Glynn et al. (US20180190019A1) in view of Legendre et al. (US20210166437A1), and further in view of Zielkowski (US20190206132A1).
As per Claim 12, Glynn alone does not explicitly teach all the limitation(s) of the claim. However, when combined with Zielkowski, they collectively teach all of the limitation(s).
Zielkowski teaches Claim 12, which recites: “The method of claim 1, wherein the selected text style is maintained in the AR environment throughout the time period.”
Zielkowski teaches determining “textual characteristics (e.g., the content of the text 25, the color of the text 25, the size of the text 25, the font of the text 25, the text duration)” and displaying the text with those characteristics “until an end time, t1,” where the duration from t0 to t1 may be predetermined. Zielkowski, ¶¶ [0071]-[0072]. Zielkowski further teaches that the displayed text “may remain ... for the duration of the display (e.g., starting at time, t0, and ending at the end time, t1).” Zielkowski, ¶ [0073].
Thus, Zielkowski teaches maintaining the selected textual characteristics in the AR environment throughout the defined display period.
Before the effective filing date, a POSITA would have been motivated to apply Zielkowski’s fixed-duration AR text display to the Glynn-Legendre system so that the selected text style remains consistent throughout the intended display period, providing stable and readable AR text with predictable results.
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As per Claim 15, Glynn alone does not explicitly teach all the limitation(s) of the claim. However, when combined with Zielkowski, they collectively teach all of the limitation(s).
Zielkowski teaches Claim 15, which recites: “The method of claim 1, further comprising modifying a color of a portion of the AR environment on which the selected text is placed.”
Zielkowski teaches displaying AR text within a “text box 124” forming the background portion on which the text is placed, where “the text box 124 may be one color and the text 25 may be a different color,” for example, “the text box 124 may be black and the text 25 may be white.” Zielkowski, ¶ [0069].
Thus, Zielkowski teaches providing/modifying a colored portion of the AR environment behind the displayed text to improve text visibility.
Before the effective filing date, a POSITA would have been motivated to use Zielkowski’s colored text-background region with the Glynn-Legendre AR display to improve text contrast and readability, yielding predictable results.
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Ground of Rejection 6
Claim 14 is rejected under 35 U.S.C. § 103 as being unpatentable over Glynn et al. (US20180190019A1) in view of Legendre et al. (US20210166437A1), further in view of Zielkowski (US20190206132A1) and Scott et al. (US20210118232A1).
As per Claim 14, Glynn alone does not explicitly teach all the limitation(s) of the claim. However, when combined with Zielkowski and Scott, they collectively teach all of the limitation(s).
Zielkowski and Scott teach Claim 14, which recites: “The method of claim 1, wherein selecting the text style is further based on whether the text is on a same depth plane as at least one other object in the AR environment.”
Zielkowski teaches determining a “plane 114 to include the text 25” and using the relationship between the user's gaze and a virtual object to determine “textual characteristics” for the displayed text. Zielkowski, ¶¶ [0050]-[0051]. Scott further teaches using relative depth in the Z-dimension to determine AR visualization modifications of displayed text. Scott et al., ¶¶ [0024]-[0027].
Thus, the combined teachings suggest selecting the AR text style based on the text’s depth relationship to other AR content, including whether they occupy the same depth plane.
Before the effective filing date, a POSITA would have been motivated to combine Zielkowski’s plane-based AR text placement with Scott et al.’s depth-responsive AR visualization modifications so that text appearance could account for its depth relationship to nearby AR content, improving legibility and spatial distinction with predictable results.
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Conclusion
The prior art made of record and relied upon in this action is as follows:
Patent Literature:
Glynn et al. (US20180190019A1) — “Augmented reality user interface visibility.”
Legendre et al. (US20210166437A1) — “Compute amortization heuristics for lighting estimation for augmented reality.”
Chang et al. (US20210158010A1) — “Solar irradiation prediction using deep learning with end-to-end training.”
Michailidis et al. (US20240149167A1) — “System and method of operational control.”
Scott et al. (US20210118232A1) — “Method and System for Translating Air Writing To An Augmented Reality Device.”
Zielkowski (US20190206132A1) — “Systems and methods for textual overlay in an amusement park environment.”
Non-Patent Literature (NPL):
(none)
Note: A PDF copy of each NPL reference is attached with this Office Action. URLs are included for applicant convenience. If a link becomes unavailable in the future, the citation information may be used to locate the reference or access archived versions via the Wayback Machine.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and is listed as follows:
Patent Literature:
Ganu (US20160112520A1) — “Method and system for client association management based on estimated session duration.”
Non-Patent Literature (NPL):
(none)
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/ADEEL BASHIR/
Examiner, Art Unit 2616
/DANIEL F HAJNIK/Supervisory Patent Examiner, Art Unit 2616
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