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
In the response to this office action, the Examiner respectfully requests that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line numbers in the specification and/or drawing figure(s). This will assist the Examiner in prosecuting this application.
INFORMATION DISCLOSURE STATEMENT
The information disclosure statements filed 04/25/2025 and 01/10/2025, have been acknowledged and considered by the examiner. Initialed copies of the PTO-1449 forms are included in this correspondence.
CLAIM REJECTIONS - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 , if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
1. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mallinson US 20190236836 in view of Son et al. US 20190171280.
Consider claim 1. Mallinson discloses an information processing apparatus comprising one or more processors and/or circuity fig. 1A HMD 102 [0035] client device 100 may be an HMD configured to:
execute an HMD information obtainment processing that obtains HMD configuration information indicating a relationship between components of an HMD and a visually induced motion sickness fig. 1A see data from the client 100 being sent to the discomfort classification model [0046] data collected from VR content engine 111 including metadata. Baseline VR content is associated with predetermined an expected discomfort and or sickness reactions;
execute an estimation processing [0050] discomfort and/or sickness state a user is predicted to experience as determined by the deep learning engine 190. [0117][0133] present invention is embodied in the form of hardware and software and the hardware maybe a general processor that estimates whether or not a user using the HMD experiences the visually induced motion sickness based on an induction degree of the visually induced motion sickness obtained by using the HMD configuration information [0041-0042] physiological data (active or passive) is collected and monitored to produce reliable correlations between discomfort and sickness patterns of the VR content; and
execute an output processing that outputs an estimation result obtained in the estimation processing [0050] Given a set of inputs the output nodes indicate the level of discomfort and/or sickness state a user is predicted to experience as determined by the deep learning engine 190.
Mallinson however does not explicitly disclose components of an HMD.
Son however disclose components of an HMD fig. 4 human factor parameters or VR sickness-inducing factors[Wingdings font/0xE0] device factor. Also see fig. 5 which shows HMD component related human factor parameters and their associated cyber sickness symptoms. also see fig. 6 resolution and movement of HMD camera.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the information processing apparatus of Mallinson to include components of an HMD, as taught by Son, to help build machine learning based VR motion sickness prediction model for virtual reality content [0002].
Consider claim 2. Mallinson as modified by Son disclose the information processing apparatus according to claim 1, wherein the one or more processors and/or circuitry is further configured to execute a measurement obtainment processing that obtains biological effect information in which biological effects of the visually induced motion sickness on the user have been indicated as results of psychological or physiological measurements Mallinson fig. 1A [0042] passive sensors 125 measure heart rate brain activity etc. Son [0032] detect EEG ECG PPG or galvanic skin response; and
execute a model generation processing that generates a trained model by using the HMD configuration information and the biological effect information as learning data to perform training, and in the estimation processing Mallinson fig. 1A biometric data from passive sensors are sent to the deep learning engine to create a model along with HMD related data. also see Son, fig. 4 device factor and personal factor are both used in the machine learning model, the induction degree of the visually induced motion sickness is obtained by inputting the HMD configuration information into the trained model Son fig. 5 [0053] . Referring to FIG. 5, it is confirmed that the correspondence of each item in the list of VR sickness symptoms to each VR sickness-inducing factor and the magnitude of each item in the list of VR sickness symptoms are acquired, as a result of performing the machine learning.
Motivation to combine is similar to motivation in claim 1.
Consider claim 3. Mallinson as modified by Son disclose the information processing apparatus according to claim 2, wherein the one or more processors and/or circuitry is further configured to execute an operation obtainment processing that obtains input information indicating whether or not the user is experiencing the visually induced motion sickness based on an operation input performed by the user Mallinson [0040-0041] active or passive monitoring of motion sickness. when a user is feeling discomfort and or sickness the user may actively engage the actuator. the user may also be prompted to enter the degree of discomfort and or sickness. ; and
execute a model update processing that updates the trained model by using the input information Mallinson [0050] the deep learning engine’s parameters are modified and refined to iteratively determine which VR content or patterns of VR content induce discomfort by comparing predetermined and true discomfort and sickness reactions.
Consider claim 4. Mallinson as modified by Son disclose the information processing apparatus according to claim 2, wherein the biological effect information includes information indicating the result of the measurement of at least one of an SSQ score, a body temperature, a heart rate, the number of times of eye closures, a skin moisture content, brain waves, and a mismatch between an eye movement and a video image Mallinson [0042] heart rate, brain activity, among many others.
Consider claim 5. Mallinson as modified by Son disclose the information processing apparatus according to claim 1, wherein the one or more processors and/or circuitry is further configured to execute a content information obtainment processing Mallinson fig. 1a 115 VR content is send to the classification modeler that obtains content information indicating a relationship between an XR content displayed on the HMD and the visually induced motion sickness Mallinson [0038] the VR content 115, various data is monitored, wherein the data is associated with the VR content 115 (e.g., rendered images, etc.) and the user 105. The data may be monitored and collected by client device 100, or directly by the discomfort classification modeler 120, and in the estimation processing, the induction degree of the visually induced motion sickness is obtained by also using the content information. Mallinson [0045-0046] VR content patterns. Also see Son [0045-0047] machine learning is used to predict correlation between VR sickness inducing factors and the list of VR sickness symptoms for any VR content fig. 5.
Motivation to combine is similar to motivation in claim 1.
Consider claim 6. Mallinson as modified by Son disclose the information processing apparatus according to claim 5, wherein the one or more processors and/or circuitry is further configured to execute a measurement obtainment processing that obtains biological effect information in which biological effects of the visually induced motion sickness on the user have been indicated as results of psychological or physiological measurements Mallinson fig. 1A [0042] passive sensors 125 measure changes in physiological data e.g. heart rate brain activity etc. which indicate discomfort and or sickness. Son [0032] detect EEG ECG PPG or galvanic skin response; and execute a model generation processing that generates a trained model by using the HMD configuration information, the content information, and the biological effect information as learning data to perform training, and in the estimation processing Mallinson fig. 1A 120 receives biometric data from 125 metadata 117 and VR content data. Also see Son fig. 4 device factor content factor and personal factor, the induction degree of the visually induced motion sickness is obtained by inputting the HMD configuration information and the content information into the trained model see Son fig. 4 device factor content factor and personal factor [0045].
Motivation to combine is similar to motivation in claim 1.
Consider claim 7. Mallinson as modified by Son disclose the information processing apparatus according to claim 6, wherein the one or more processors and/or circuitry is further configured to execute an operation obtainment processing that obtains input information indicating whether or not the user is experiencing the visually induced motion sickness based on an operation input performed by the user Mallinson [0040-0041] active or passive monitoring of motion sickness. when a user is feeling discomfort and or sickness the user may actively engage the actuator. the user may also be prompted to enter the degree of discomfort and or sickness ; and execute a model update processing that updates the trained model by using the input information Mallinson [0050] the deep learning engine’s parameters are modified and refined to iteratively determine which VR content or patterns of VR content induce discomfort by comparing predetermined and true discomfort and sickness reactions.
Consider claim 8. Mallinson as modified by Son disclose the information processing apparatus according to claim 6, wherein the biological effect information includes information indicating the result of the measurement of at least one of an SSQ score, a body temperature, a heart rate, the number of times of eye closures, a skin moisture content, brain waves, and a mismatch between an eye movement and a video image Mallinson [0042] heart rate, brain activity, among many others.
Consider claim 9. Mallinson as modified by Son disclose the information processing apparatus according to claim 5, wherein the content information includes information indicating a relationship between the visually induced motion sickness, and at least one of a frame rate, a degree of a video image shake, a viewpoint type, a movement method of an avatar, a moving speed of the avatar, a moving acceleration of the avatar, and presence or absence of a visually induced motion sickness reduction function when the avatar moves Son [0035] VR content which causes visually induced motion sickness (VIMS) a change in intrinsic characteristics of the content or extrinsic characteristics of the content (for example, movement of an object, movement of a camera). Mallinson [0039] if the same pattern of VR content (e.g., moving horizon, avatar linear and rotational accelerations, etc.), or patterns of actions, consistently induces discomfort and/or sickness in the test users, then those patterns can be labeled as producing discomfort and/or sickness reactions in consumer users who interact with that VR content.
Motivation to combine is similar to motivation in claim 1.
Consider claim 10. Mallinson as modified by Son disclose the information processing apparatus according to claim 1, wherein the one or more processors and/or circuitry is further configured to execute a content information obtainment processing Mallinson fig. 1a 115 VR content is send to the classification modeler that obtains content information indicating a relationship between an XR content displayed on the HMD and the visually induced motion sickness Mallinson [0038] the VR content 115, various data is monitored, wherein the data is associated with the VR content 115 (e.g., rendered images, etc.) and the user 105. The data may be monitored and collected by client device 100, or directly by the discomfort classification modeler 120,; and execute a user information obtainment processing that obtains user information indicating a relationship between the user and the visually induced motion sickness Mallinson [0042] head movement. Son fig. 4 Task factor, and in the estimation processing, the induction degree of the visually induced motion sickness is obtained by also using the content information and the user information Mallinson [0075] fig. 3B predicting discomfort as the VR content including FOV throttling as the head of the user is moving in time.
Consider claim 11. Mallinson as modified by Son disclose the information processing apparatus according to claim 10, wherein the one or more processors and/or circuitry is further configured to execute a measurement obtainment processing that obtains biological effect information in which biological effects of the visually induced motion sickness on the user have been indicated as results of psychological or physiological measurements Mallinson fig. 1A [0042] passive sensors 125 measure changes in physiological data e.g. heart rate brain activity etc. which indicate discomfort and or sickness. Son [0032] detect EEG ECG PPG or galvanic skin response; and execute a model generation processing that generates a trained model by using the HMD configuration information, the content information, the user information, and the biological effect information as learning data to perform training Mallinson fig. 1A 120 receives biometric data from 125 metadata 117 and VR content data. Also see Son fig. 4 device factor content factor and personal factor and task factor, and in the estimation processing, the induction degree of the visually induced motion sickness is obtained by inputting the HMD configuration information, the content information, and the user information into the trained model see Son fig. 4 device factor content factor and personal factor task factor [0045]. Also see Son fig. 5 [0053]
Motivation to combine is similar to motivation in claim 1.
Consider claim 12. Mallinson as modified by Son disclose the information processing apparatus according to claim 11, wherein the one or more processors and/or circuitry is further configured to execute an operation obtainment processing that obtains input information indicating whether or not the user is experiencing the visually induced motion sickness based on an operation input performed by the user Mallinson [0040-0041] active or passive monitoring of motion sickness. when a user is feeling discomfort and or sickness the user may actively engage the actuator. the user may also be prompted to enter the degree of discomfort and or sickness. ; and
execute a model update processing that updates the trained model by using the input information Mallinson [0050] the deep learning engine’s parameters are modified and refined to iteratively determine which VR content or patterns of VR content induce discomfort by comparing predetermined and true discomfort and sickness reactions.
Consider claim 13. Mallinson as modified by Son disclose the information processing apparatus according to claim 11, wherein the biological effect information includes information indicating the result of the measurement of at least one of an SSQ score, a body temperature, a heart rate, the number of times of eye closures, a skin moisture content, brain waves, and a mismatch between an eye movement and a video image Mallinson [0042] heart rate, brain activity, among many others.
Consider claim 14. Mallinson as modified by Son disclose the information processing apparatus according to claim 10, wherein the content information includes information indicating a relationship between the visually induced motion sickness, and at least one of a frame rate, a degree of a video image shake, a viewpoint type, a movement method of an avatar, a moving speed of the avatar, a moving acceleration of the avatar, and presence or absence of a visually induced motion sickness reduction function when the avatar moves Son [0035] VR content which causes visually induced motion sickness (VIMS) a change in intrinsic characteristics of the content or extrinsic characteristics of the content (for example, movement of an object, movement of a camera). Mallinson [0039] if the same pattern of VR content (e.g., moving horizon, avatar linear and rotational accelerations, etc.), or patterns of actions, consistently induces discomfort and/or sickness in the test users, then those patterns can be labeled as producing discomfort and/or sickness reactions in consumer users who interact with that VR content.
Motivation to combine is similar to motivation in claim 1.
Consider claim 15. Mallinson as modified by Son disclose the information processing apparatus according to claim 10, wherein the user information includes information indicating a relationship between the visually induced motion sickness, and at least one of a head movement amount, a head rotation amount, an age, a race, a health condition, a use frequency of the HMD, and a use time of the HMD Mallinson [0042] head movement. Son fig. 4 Task factor.
Consider claim 16. Mallinson as modified by Son disclose the information processing apparatus according to claim 1, wherein the one or more processors and/or circuitry is further configured to execute a user information obtainment processing that obtains user information indicating a relationship between the user and the visually induced motion sickness Mallinson [0042] head movement. Son fig. 4 Task factor, and in the estimation processing, the induction degree of the visually induced motion sickness is obtained by also using the user information Mallinson [0075] fig. 3B predicting discomfort as the VR content including FOV throttling as the head of the user is moving in time.
Consider claim 17. Mallinson as modified by Son disclose the information processing apparatus according to claim 16, wherein the one or more processors and/or circuitry is further configured to execute a measurement obtainment processing that obtains biological effect information in which biological effects of the visually induced motion sickness on the user have been indicated as results of psychological or physiological measurements Mallinson fig. 1A [0042] passive sensors 125 measure changes in physiological data e.g. heart rate brain activity etc. which indicate discomfort and or sickness. Son [0032] detect EEG ECG PPG or galvanic skin response; and execute a model generation processing that generates a trained model by using the HMD configuration information, the user information, and the biological effect information as learning data to perform training Mallinson fig. 1A 120 receives biometric data from 125 metadata 117 and VR content data. Also see Son fig. 4 device factor, content factor, personal factor, and task factor are used in building the machine learning model, and in the estimation processing, the induction degree of the visually induced motion sickness is obtained by inputting the HMD configuration information and the user information into the trained model see Son fig. 4 device factor content factor and personal factor task factor [0045]. Also see Son fig. 5 [0053].
Motivation to combine is similar to motivation in claim 1.
Consider claim 18. Mallinson as modified by Son disclose the information processing apparatus according to claim 16, wherein the user information includes information indicating a relationship between the visually induced motion sickness, and at least one of a head movement amount, a head rotation amount, an age, a race, a health condition, a use frequency of the HMD, and a use time of the HMD Mallinson [0042] head movement. Son fig. 4 Task factor.
Claim 19 is rejected mutatis mutandis for the reasons set forth in claim 1.
Claim 20 is rejected mutatis mutandis for the reasons set forth in claim 1. [0117][0133] present invention is embodied in the form of hardware and software and the hardware maybe a general processor.
CONCLUSION
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20240256042 Saito et al. discloses using user history/biometric data/and VR content to determine a degree of VIMS.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IBRAHIM A KHAN whose telephone number is (571)270-7998. The examiner can normally be reached on 10am-6pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nitin Patel can be reached on 571-272-7677. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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IBRAHIM A. KHAN
Primary Examiner
Art Unit 2628
/IBRAHIM A KHAN/ 03/03/2026Primary Examiner, Art Unit 2628