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 .
Information Disclosure Statement
Acknowledgement is made of receipt of Information Disclosure Statement (PTO-1449) filed 06/05/2025. An initialed copy is attached to this Office Action.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 6 and 8-11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gonzalez et al. (US 20220013228 A1).
Regarding claim 1, Gonzalez discloses in at least figure 2, a method of dynamically administering ocular exams (an implementation of the described systems and methods to assist patients to self-perform visual field tests and other ophthalmological tests paragraph [0051]), the method comprising:
conducting a first ocular exam (visual acuity test paragraph [0122]) on a screen (testing platform 103 includes a head mounted or other display device paragraph [0051]) of a virtual reality (VR) headset (a virtual reality-based user testing platform paragraph [0051]) to evaluate a patient wearing (the display can be head mounded paragraph [0051]) the VR headset (a virtual reality-based user testing platform paragraph [0051]) for a first ocular condition (visual acuity paragraph [0122]);
receiving a first input from the patient (receive from the patient at least one response wherein the response comprises selection of a position of the at least one optotype or location of an arrow associated with the at least one optotype paragraph [0122]);
comparing (the percentage of response is compared to a historical value for the patient or a probably score paragraph [0122], when the processor accesses a historical database of patient data in order to make comparisons paragraph [0050]) the first input (receive from the patient at least one response wherein the response comprises selection of a position of the at least one optotype or location of an arrow associated with the at least one optotype paragraph [0122]) with a database (a training database, wherein the training database includes, for each member of a training population comprised of visual acuity tests taken by users, an assessment dataset that includes at least data relating to a respective user response to the visual acuity set and or a sensor input and or a system state paragraph [0141] and a historical database of patient data is used to make comparisons paragraph [0050]) to determine whether to diagnose the patient (calculate a visual acuity score paragraph [0122]) with the first ocular condition (if a percentage of responses in step (iii) labeled as correct is less than the percentage expected to be correct based on a historical value for the patient's visual acuity score or an estimated percentage of correct choices based on a probability score of visual acuity paragraph [0122])
after comparing (the percentage of response is compared to a historical value for the patient or a probably score paragraph [0122]) the first input (receive from the patient at least one response wherein the response comprises selection of a position of the at least one optotype or location of an arrow associated with the at least one optotype paragraph [0122]) with the database (a training database, wherein the training database includes, for each member of a training population comprised of visual acuity tests taken by users, an assessment dataset that includes at least data relating to a respective user response to the visual acuity set and or a sensor input and or a system state paragraph [0141] and a historical database of patient data is used to make comparisons paragraph [0050]), continuing to conduct (repeat steps (i) to (iv) paragraph [0122]) the first ocular exam (visual acuity test paragraph [0122]) and receive the first input (receive from the patient at least one response wherein the response comprises selection of a position of the at least one optotype or location of an arrow associated with the at least one optotype paragraph [0122]);
conducting a first ocular sub-exam on the screen to evaluate the patient for a first ocular sub-condition and receiving a first sub-input from the patient (not required by claim); or
conducting a second ocular exam (a contrast sensitivity test paragraph [0176]) on the screen (testing platform 103 includes a head mounted or other display device paragraph [0051]) to evaluate the patient for a second ocular condition (a contrast sensitivity score paragraph [0182]) and receiving a second input from the patient (receive from the patient at least one response when the patient views at least one optotype, wherein the response comprises selection of a position of the at least one optotype or location of an arrow associated with the at least one optotype paragraph [0180]).
Regarding claim 6, Gonzalez discloses all the limitations of claim 1 and further discloses, further comprising diagnosing the patient (calculate a visual acuity score paragraph [0122]) with one or more of the first ocular condition (if a percentage of responses in step (iii) labeled as correct is less than the percentage expected to be correct based on a historical value for the patient's visual acuity score or an estimated percentage of correct choices based on a probability score of visual acuity paragraph [0122]), the first ocular sub-condition (not required by claim), or the second ocular condition (not required by claim).
Regarding claim 8, Gonzalez discloses in at least figure 2, a system (system for implementing visual field and other eye tests paragraph [0012]) for dynamically administering ocular exams (an implementation of the described systems and methods to assist patients to self-perform visual field tests and other ophthalmological tests paragraph [0051]), the system (system for implementing visual field and other eye tests paragraph [0012]) comprising:
a virtual reality (VR) headset (a virtual reality-based user testing platform paragraph [0051]) having a screen (testing platform 103 includes a head mounted or other display device paragraph [0051]) and at least one sensor (sensors 90 fig. 11) configured to collect input from a patient (sensors 901, that are utilized to capture user input as well as the position and movement of a user or wearer of the user testing platform 103 paragraph [0033]) wearing (the display can be head mounded paragraph [0051]) the VR headset (a virtual reality-based user testing platform paragraph [0051]); and
a computing device (analysis platform 105 fig. 1) in electronic communication with (the analysis system 105 configures a processor generate a virtual assistant 203 that is displayed or otherwise projected into the field of view presented to the user by the user testing platform 103 paragraph [0051]) the VR headset (a virtual reality-based user testing platform paragraph [0051]) and being configured to process (a virtual assistant 203 is configured to use any kind of artificial intelligence, machine learning or statistical methodology to evaluate data obtained from the sensors 901 [0095]) the input from the patient (sensors 901, that are utilized to capture user input as well as the position and movement of a user or wearer of the user testing platform 103 paragraph [0133]) and control an environment displayed (the virtual assistant 203 utilizes a predictive model to provide users with audiovisual guidance on how to self-administer psychophysical tests paragraph [0096]) on the screen (testing platform 103 includes a head mounted or other display device paragraph [0051]) based on the input (sensors 901, that are utilized to capture user input as well as the position and movement of a user or wearer of the user testing platform 103 paragraph [0133]),
wherein the computing device (analysis platform 105 fig. 1) is configured to (the virtual assistant presents to a patient a set of instructions for the visual acuity test paragraph [0122]) conduct a first ocular exam (visual acuity test paragraph [0122]) on the screen (testing platform 103 includes a head mounted or other display device paragraph [0051]) to evaluate the patient (system for a visual acuity test paragraph [0122]) for a first ocular condition (visual acuity paragraph [0122]) and receive a first input (receive from the patient at least one response wherein the response comprises selection of a position of the at least one optotype or location of an arrow associated with the at least one optotype paragraph [0122]) from the VR headset (a virtual reality-based user testing platform paragraph [0051]);
(b) compare (the percentage of response is compared to a historical value for the patient or a probably score paragraph [0122]) the first input (receive from the patient at least one response wherein the response comprises selection of a position of the at least one optotype or location of an arrow associated with the at least one optotype paragraph [0122]) with a database (a training database, wherein the training database includes, for each member of a training population comprised of visual acuity tests taken by users, an assessment dataset that includes at least data relating to a respective user response to the visual acuity set and or a sensor input and or a system state paragraph [0141] and a historical database of patient data is used to make comparisons paragraph [0050]) for diagnosing the patient (calculate a visual acuity score paragraph [0122]) with the first ocular condition (if a percentage of responses in step (iii) labeled as correct is less than the percentage expected to be correct based on a historical value for the patient's visual acuity score or an estimated percentage of correct choices based on a probability score of visual acuity paragraph [0122], when the processor accesses a historical database of patient data in order to make comparisons paragraph [0050]); and
(c)(i) continue to conduct the first ocular exam and receive the first input (not required by claim),
(ii) conduct a first ocular sub-exam on the screen to evaluate the patient for a first ocular sub-condition and receive a first sub-input from the VR headset (not required by claim), or
(iii) conduct a second ocular exam (a contrast sensitivity test paragraph [0176]) on the screen (testing platform 103 includes a head mounted or other display device paragraph [0051]) to evaluate the patient (a contrast sensitivity test paragraph [0176]) for a second ocular condition (a contrast sensitivity score paragraph [0182]) and receive a second input (receive from the patient at least one response when the patient views at least one optotype, wherein the response comprises selection of a position of the at least one optotype or location of an arrow associated with the at least one optotype paragraph [0180]) from the VR headset (a virtual reality-based user testing platform paragraph [0051]).
Regarding claim 9, Gonzalez discloses all the limitations of claim 8 and further discloses, wherein the VR headset (a virtual reality-based user testing platform paragraph [0051]) further comprises cameras (eye-tracking cameras paragraph [0412]), speakers (speakers paragraph [0095]), and microphones (microphone paragraph [0095]).
Regarding claim 10, Gonzalez discloses all the limitations of claim 9 and further discloses, wherein the first input (receive from the patient at least one response wherein the response comprises selection of a position of the at least one optotype or location of an arrow associated with the at least one optotype paragraph [0122]) comprises an audio input (receive audio data from a microphone paragraph [0095]) received at the microphones (microphone paragraph [0095]).
Regarding claim 11, Gonzalez discloses all the limitations of claim 9 and further discloses, wherein the first input (receive from the patient at least one response wherein the response comprises selection of a position of the at least one optotype or location of an arrow associated with the at least one optotype paragraph [0122]) comprises a visual input (are utilized to capture user input as well as the position and movement of a user or wearer of the user testing platform paragraph [0033]) received at the sensors (sensors 901 fig. 9) or the cameras (not required by claim).
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 2 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Gonzalez et al. (US 20220013228 A1) as applied to claims 1 and 8 above and in further view of Burns et al. (US 20170112373 A1).
Regarding claim 2, Gonzalez discloses all the limitations of claim 1 and further discloses, wherein the method (an implementation of the described systems and methods to assist patients to self-perform visual field tests and other ophthalmological tests paragraph [0051]) further comprises, before conducting the first ocular sub-exam (not required by claim) or conducting the second ocular exam (a contrast sensitivity test paragraph [0176]),
Gonzalez does not disclose, ending the first ocular exam before the patient completes the first ocular exam.
Burns discloses in at least figure 16, ending (the application 50 conducts a determination of whether additional steps should be conducted including a determination that, a determination that the near vision of the subject should be tested or a determination that binocular vision of the subject should be tested paragraph [0079) the first ocular exam (visual acuity assessment 110 beginning in step 302 fig. 16) before the patient completes the first ocular exam (Visual acuity assessment 110 ends at the N branch of additional steps 314 fig. 16).
Therefore it would be obvious for one skilled in the art before the effective filling date of the claimed invention to end the first test before conducting the second test as taught by Burns in the method of Gonzalez . If the application 50 determines that the aspects of the visual acuity assessment 110 specified in the test information are satisfied it can perform analysis on the visual acuity information generated in step 312 paragraph [0079]).
Regarding claim 20, Gonzalez discloses all the limitations of claim 8.
Gonzalez does not disclose, wherein the computing device ends the first ocular exam and conducts the first ocular sub-exam or the second ocular exam before the patient completes the first ocular exam.
However Burns discloses in at least figure 16, wherein the computing device ends (the application 50 conducts a determination of whether additional steps should be conducted including a determination that, a determination that the near vision of the subject should be tested or a determination that binocular vision of the subject should be tested paragraph [0079]) the first ocular exam (visual acuity assessment 110 beginning in step 302 fig. 16) and conducts the first ocular sub-exam (not required by claim) or the second ocular exam (the near vision of the subject should be tested or a determination that binocular vision of the subject should be tested paragraph [0079]) before the patient completes the first ocular exam (visual acuity assessment 110 ends at the N branch of additional steps 314 fig. 16).
Therefore it would be obvious for one skilled in the art before the effective filling date of the claimed invention to end the first test before conducting the second test as taught by Burns in the method of Gonzalez . If the application 50 determines that the aspects of the visual acuity assessment 110 specified in the test information are satisfied it can perform analysis on the visual acuity information generated in step 312 paragraph [0079]).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Gonzalez et al. (US 20220013228 A1) as applied to claim 1 above and in further view of Borsody (US 20240038390 A1).
Regarding claim 3, Gonzalez discloses all the limitations of claim 1.
Gonzalez does not disclose, wherein comparing the first input comprises implementing a machine-learning algorithm to compare the first input with the database.
However Borsody discloses in at least figure 9, wherein comparing the first input (extraction of data inputs 903 fig. 9) comprises implementing a machine-learning algorithm (MLP 105 fig. 1) to compare (the machine learning process module (MLP) analyzes the data inputs extracted from the user or from third party platforms by mapping to pre-established diagnoses present in one or more database paragraph [0011]) the first input (extraction of data inputs 903 fig. 9) with the database (database 125 fig. 1).
Therefore it would be obvious for one skilled in the art before the effective filling date of the claimed invention to use machine learning to compare the input to the database as taught by Borsody in the method of Gonzalez . The extracted data/data inputs are evaluated by the SA 107 to identify matching classic syndromes, some of which may be associated with a serious health condition paragraph [0088]).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Gonzalez et al. (US 20220013228 A1) as applied to claim 1 above and in further view of Batta et al. (US 20210015357 A1) (Batta 1).
Regarding claim 4, Gonzalez discloses all the limitations of claim 1.
Gonzalez does not disclose, further comprising implementing a machine-learning algorithm to process the first input, the first sub-input, and the second input to determine the nature of a next ocular exam or sub-exam.
However Batta 1 discloses in at least figure 5, further comprising implementing a machine-learning algorithm (artificial intelligence component 502 fig. 5) to process the first input (the artificial intelligence component 502 can analyze the crowdsourced data collected from all users that have used the system 500), the first sub-input (not required by claim 1), or the second input (not required by claim) to determine the nature of a next ocular exam (the test component 112 can be adjusted by the artificial intelligence component 502 to supplement tests delivered to this subset of users in order to more thoroughly test for that potential deficiency paragraph [0049]) or sub-exam (not required by claim).
Therefore it would be obvious for one skilled in the art before the effective filling date of the claimed invention to use machine learning to choose a second test as taught by Batta in the method of Gonzalez . If the assessment component 114 consistently places less emphasis on certain tests associated with subsets of users due to age, language skills or cultural background, the artificial intelligence component 502 can delete or modify such tests going forward for this subset of users (paragraph [0049]).
Claims 5 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Gonzalez et al. (US 20220013228 A1) as applied to claims 1 and 8 above and in further view of Batta et al. (US 20210315499 A1) (Batta 2).
Regarding claim 5, Gonzalez discloses all the limitations of claim 1.
Gonzalez does not disclose, further comprising implementing a machine-learning algorithm to process the first input, the first sub-input, and the second input to diagnose the patient with one or more of a first ocular condition, a first ocular sub-condition, or a second ocular condition.
However Batta 2 discloses in at least figure 10, further comprising implementing a machine-learning algorithm (machine learning classifier paragraph [0025]) to process (the invention can include a trained machine learning classifier that receives as input the recorded images and/or videos of the eyes, face, and/or body of the entity and produces as output a classification and/or label of the eye health of the entity paragraph [0025]) the first input (the recorded images and/or videos of the eyes, face, and/or body of the entity paragraph [0025]), the first sub-input (not required by claim 1), or the second input (not required by claim) to diagnose the patient (the trained machine learning classifier estimates, infers, determines, and/or diagnoses a medical health condition of the eye of the entity paragraph [0025]) with one or more of a first ocular condition (medical health condition of the eye paragraph [0025]), a first ocular sub-condition (not required by claim), or a second ocular condition (not required by claim).
Therefore it would be obvious for one skilled in the art before the effective filling date of the claimed invention to use machine learning to compare the input to make a diagnosis as taught by Batta in the method of Gonzalez . Systems and/or techniques for automatically monitoring and/or diagnosing eye health of a user in the absence of medical professionals, in continuous and/or continual fashion, and/or without interrupting the user's daily activities can be beneficial paragraph [0023]).
Regarding claim 5, Gonzalez discloses all the limitations of claim 8.
Gonzalez does not disclose, wherein the computing device is further configured to implement a machine-learning algorithm that processes the input in real-time.
However Batta 2 discloses in at least figure 10, wherein the computing device (smart device paragraph [0025]) is further configured to implement (software on the smart device that, upon execution, causes the camera of the smart device to record images and/or videos of the eyes, face, and/or body of the entity. In various aspects, embodiments of the invention can include a trained machine learning classifier paragraph [0025]) a machine-learning algorithm (machine learning classifier paragraph [0025] that processes (the invention can include a trained machine learning classifier that receives as input the recorded images and/or videos of the eyes, face, and/or body of the entity and produces as output a classification and/or label of the eye health of the entity paragraph [0025]) the input (the recorded images and/or videos of the eyes, face, and/or body of the entity paragraph [0025]) in real-time (perform continual background monitoring of eye health of the entity paragraph [0025]).
Therefore it would be obvious for one skilled in the art before the effective filling date of the claimed invention to use machine learning to compare the input to make a diagnosis as taught by Batta in the method of Gonzalez . Systems and/or techniques for automatically monitoring and/or diagnosing eye health of a user in the absence of medical professionals, in continuous and/or continual fashion, and/or without interrupting the user's daily activities can be beneficial paragraph [0023]).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Gonzalez et al. (US 20220013228 A1) as applied to claim 6 above and in further view of Singh et al. (US 20220313077 A1).
Regarding claim 7, Gonzalez discloses all the limitations of claim 6.
Gonzalez does not disclose, further comprising recommending a plan to treat the first ocular condition, the first ocular sub-condition, or the second ocular condition with which the patient is diagnosed.
However Singh discloses in at least figure 36TT, further comprising recommending a plan to treat (treatment logic for occupational dry eye fig. 36TT) the first ocular condition (occupational dry eye fig. 36TT), the first ocular sub-condition (not required by claim), or the second ocular condition (not required by claim) with which the patient is diagnosed (diagnostic logic for occupational dry eye fig. 36TT).
Therefore it would be obvious for one skilled in the art before the effective filling date of the claimed invention to recommend treatment as taught by Borsody in the method of Gonzalez . The logic package can recognize the ocular disease and provide treatment and management rules for it paragraph [0269]).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Gonzalez et al. (US 20220013228 A1) as applied to claim 11 above and in further view of Dawson et al. (US 20250169734 A1).
Regarding claim 12, Gonzalez discloses all the limitations of claim 11.
Gonzalez does not disclose, wherein the visual input comprises a blink rate.
However Dawson discloses in at least figure 4, wherein the visual input (capturing user interaction data (e.g., recording of user face, eye paragraph [0117]) comprises a blink rate (blink rate paragraph [0117]).
Therefore it would be obvious for one skilled in the art before the effective filling date of the claimed invention to use blink rate as an input as taught by Dawson in the method of Gonzalez . Blink rate and facial orientation reveal distinctive patterns of attentional engagement in autistic toddlers (paragraph [0279]).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Gonzalez et al. (US 20220013228 A1) as applied to claim 11 above and in further view of Pasley et al. (US 20240164672 A1).
Regarding claim 13, Gonzalez discloses all the limitations of claim 11.
Gonzalez does not disclose, wherein the visual input comprises a pupil dilation.
However Pasley discloses in at least figure 2, wherein the visual input comprises a pupil dilation (the pupillary response may include a dilation response (mydriasis) which the device 10 may detect paragraph [0043]).
Therefore it would be obvious for one skilled in the art before the effective filling date of the claimed invention to use pupil dilation as an input as taught by Pasley in the method of Gonzalez . Pupil diameter tracking may be potentially indicative of a physiological state of a user (paragraph [0045]).
Claims 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Gonzalez et al. (US 20220013228 A1) as applied to claim 11 above and in further view of Ooi et al. (US 20230337911 A1).
Regarding claim 14, Gonzalez discloses all the limitations of claim 11.
Gonzalez does not disclose, wherein the visual input comprises saccades.
However Ooi discloses in at least figure 2A, wherein the visual input comprises saccades (the user input for system 100 can include saccades assessment paragraph [0047]).
Therefore it would be obvious for one skilled in the art before the effective filling date of the claimed invention to use saccades as an input as taught by Ooi in the method of Gonzalez . The saccades assessment analysis outputs a score or value that is associated with the ability of the subject to bring a 3D object of interest onto the fovea (paragraph [0051]).
Regarding claim 15, Gonzalez discloses all the limitations of claim 11.
Gonzalez does not disclose, wherein the visual input comprises smooth pursuit movements.
However Ooi discloses in at least figure 2A, wherein the visual input comprises smooth pursuit movements (the user input for system 100 can include smooth pursuit assessment paragraph [0047]).
Therefore it would be obvious for one skilled in the art before the effective filling date of the claimed invention to use smooth pursuit movements as an input as taught by Ooi in the method of Gonzalez . The smooth pursuit assessment analysis outputs a score or value that is associated with the ability of the subject to maintain gaze on a small moving 3D object steady on the fovea (paragraph [0050]).
Regarding claim 16, Gonzalez discloses all the limitations of claim 11.
Gonzalez does not disclose, wherein the visual input comprises vergence movements.
However Ooi discloses in at least figure 2A, wherein the visual input comprises vergence movements (the user input for system 100 can include vergence assessment paragraph [0047]).
Therefore it would be obvious for one skilled in the art before the effective filling date of the claimed invention to use vergence movements as an input as taught by Ooi in the method of Gonzalez . The vergence assessment analysis outputs a score or value that is associated with the ability of the subject to move the eyes in opposite directions so that images of a single object are placed or held simultaneously on the fovea of each eye (paragraph [0055]).
Regarding claim 17, Gonzalez discloses all the limitations of claim 11.
Gonzalez does not disclose, wherein the visual input comprises vestibulo-ocular movements.
However Ooi discloses in at least figure 2A, wherein the visual input comprises vestibulo-ocular movements (the user input for system 100 can include vestibular assessment paragraph [0047]).
Therefore it would be obvious for one skilled in the art before the effective filling date of the claimed invention to use vestibulo-ocular movements as an input as taught by Ooi in the method of Gonzalez . The vestibular assessment analysis outputs a score or value that is associated with the ability of the subject to maintain the image of the 3D world on the retina during a brief head rotation or translation of the subject's head (paragraph [0052]).
Claims 18 is rejected under 35 U.S.C. 103 as being unpatentable over Gonzalez et al. (US 20220013228 A1) as applied to claim 8 above and in further view of Bradley et al. (US 20210271318 A1).
Regarding claim 18, Gonzalez discloses all the limitations of claim 8.
Gonzalez does not disclose, further comprising a handheld device in electronic communication with the VR headset and computing device, and wherein the first input comprises a tactile input received at the handheld device.
However Bradley discloses in at least figure 1D, further comprising a handheld device (Bluetooth controller 146 fig. 1D) in electronic communication (Bluetooth controller 146 to communicate via Bluetooth with smartphone 110 paragraph [0097]) with the VR headset (vision-enhancement system 100 includes smartphone 110 fig. 1A which can execute different operating systems and a variety of different virtual reality systems paragraph [0081]) and computing device (processor is positioned in the virtual reality headset paragraph [0081]), and wherein the first input (patient input step 752 fig. 7B) comprises a tactile input received at (receives one or more user inputs, using the above-described hardware paragraph [0123] and the input device may be a Bluetooth game controller paragraph [0087]) the handheld device (Bluetooth controller 146 fig. 1D).
Therefore it would be obvious for one skilled in the art before the effective filling date of the claimed invention to use a handheld device for user input as taught by Bradley in the method of Gonzalez . If the patient finds a specific row/region/point 801 to be too light to see effectively the patient uses the input device to select it (paragraph [0123]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Miseikis (US 20230284902 A1) discloses an information processing device with a virtual reality glasses and a mobile phone.
Tang et al. (US 20210290051 A1) discloses a system and method for vision testing where the test can stop a test and move to the next test.
Pradhan et al. (US 11869386 B2) discloses an oculo-cognitive test using tactile input.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW R WRIGHT whose telephone number is (703)756-5822. The examiner can normally be reached Mon-Thurs 7:30-5 Friday 8-12.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pinping Sun can be reached at 1-571-270-1284. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW R WRIGHT/Examiner, Art Unit 2872 /PINPING SUN/Supervisory Patent Examiner, Art Unit 2872
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