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
Response to Amendment
This is in response to applicant’s amendment/response filed on 06/12/2026, which has been entered and made of record. Claims 1, 8, 15 and 16 have been amended. Claim 17-20 has been cancelled. Claims 21-24 have been added. Claims 1-17, 21-24 are pending in the application.
Response to Arguments
Applicant's arguments filed on 06/12/2026 regarding claims rejection under 35 U.S.C 102 have been fully considered but they are not persuasive.
Applicant submits “Ramaprakash evaluates neurological data along with video and audio vectors as inputs into a rules engine to classify a virtual reality content stream or to detect if a user is actively experiencing an ongoing seizure event. Even assuming, arguendo, that detecting a seizure event discloses determining a discomfort tolerance level, Ramaprakash's approach remains structurally deficient. Ramaprakash never utilizes a likelihood of a physiological response as a computational input alongside its neurological data (even assuming, arguendo, that neurological data discloses physiological feedback). Instead, the neurological data in Ramaprakash is utilized exclusively to classify the content stream itself or to match patterns to prior active seizure events. Because Ramaprakash lacks this required algorithmic combination to establish a personalized threshold, Ramaprakash fails to perform determining a discomfort tolerance level associated with the user based on the physiological feedback and the likelihood of the physiological response.” (Remarks, Page 7-8.)
The examiner disagrees with Applicant’s premises and conclusion. Ramaprakash teaches “seizure monitor 116 detecting more than a threshold likelihood that the VR content is likely to induce a seizure or other negative neurological and/or physiological event in the user.” “the rules engine 222 determines that the VR content stream 216 is not likely to induce a seizure or other negative neurological and/or physiological event based on the neurological/physiological data 226, the video vector(s) 218, and/or the audio vector(s) 220.” The rules engine determines “not likely to induce a seizure” is determining a discomfort tolerance level.
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 4-8, 11-15, 21-24 are rejected under 35 U.S.C. 102(A)(2) as being anticipated by Ramaprakash et al. (US Pub 2018/0088669 A1).
As to claim 1, Ramaprakash discloses a method, comprising:
executing an application to generate output data, wherein the output data is presented at a device to a user (Fig. 2, ¶0026, “The VR content 204 is presented to the user via the VR viewer 106 and/or the speaker(s) 108 communicatively coupled to the VR manager 114.”);
receiving physiological feedback associated with from the user (Fig 2, ¶0019, “he VR HMD 102 can include other number(s) and/or type(s) of sensors 110 to collect neurological and/or physiological data from the user 104, such as electrooculography (EOG) sensors, galvanic skin response sensors, and/or electrocardiography (EKG) sensors.”);
providing the output data and the physiological feedback as input to a model, wherein the model is trained to identify a classification associated with a likelihood of a physiological response when interacting with the application (Fig. 2, Fig. 3, Rule engine 222. ¶0032, “The rules engine 222 analyzes the video vector(s) 218 and/or the audio vector(s) 220 to determine whether the visual and/or audio parameter(s) of the VR content stream 216 correspond to known and/or learned seizure trigger(s). In analyzing the video vector(s) 218 and the audio vector(s) 220, the rules engine 222 of the illustrated example implements a machine learning algorithm that also considers other variables such as neurological data received from the user while viewing the VR content 204 (e.g., while viewing a video frame and/or prior video frames of the VR content 204 and/or associated therewith), user profile data such as age and gender of the user, previously analyzed VR content and/or neurological data (e.g., calibration data), and known and/or learned seizure triggers, as will be disclosed below.” ¶0060, “the rules engine 222 classifies VR content stream 216 (e.g., the video and/or audio portions). The classification can be used to identify seizure triggers in other VR content having similar visual and/or audio parameters and/or invoking similar neurological/physiological responses in users. The classification can also be used to preemptively warn VD HMD users and/or to preemptively modify the VR content across users. The prediction of the VR content stream 216 as likely to induce a seizure and/or other negative neurological/physiological response in a user and the corresponding visual and/or audio parameters of the VR content stream 216 are also used to update or refine the learning algorithm used by the predictor 316, as will be disclosed below.”):
determining an output of the model based on the input, the output indicating the classification (¶0060, “the rules engine 222 classifies VR content stream 216 (e.g., the video and/or audio portions). The classification can be used to identify seizure triggers in other VR content having similar visual and/or audio parameters and/or invoking similar neurological/physiological responses in users. The classification can also be used to preemptively warn VD HMD users and/or to preemptively modify the VR content across users. The prediction of the VR content stream 216 as likely to induce a seizure and/or other negative neurological/physiological response in a user and the corresponding visual and/or audio parameters of the VR content stream 216 are also used to update or refine the learning algorithm used by the predictor 316, as will be disclosed below.”);
determining a discomfort tolerance level associated with the user based on the physiological feedback and the likelihood of the physiological response (Fig.1, ¶0019, “the VR HMD 102 can include other number(s) and/or type(s) of sensors 110 to collect neurological and/or physiological data from the user 104, such as electrooculography (EOG) sensors, galvanic skin response sensors, and/or electrocardiography (EKG) sensors.” ¶0020, “The seizure monitor 116 of the illustrated example monitors the VR content and/or the user's reaction to the VR content to determine if a seizure and/or other negative neurological and/or physiological event are likely to be and/or are being induced by the VR content.” Fig.2, ¶0024, “seizure monitor 116 detecting more than a threshold likelihood that the VR content is likely to induce a seizure or other negative neurological and/or physiological event in the user.” ¶0034, “the neurological/physiological data collector 224 transmits the neurological/physiological data 226 to the rules engine 222 of the seizure monitor 116.” ¶0038, “The rules engine 222 analyzes the video and/or audio vector(s) 218, 220 for the modified VR content stream 232 and the corresponding neurological/physiological data 226 collected from the user. The rules engine 222 determines whether further modification(s) to the modified VR content stream 232 are required to substantially prevent a seizure (e.g., a PSE seizure) or other negative neurological and/or physiological event.” ¶0039, “the rules engine 222 determines that the VR content stream 216 is not likely to induce a seizure or other negative neurological and/or physiological event based on the neurological/physiological data 226, the video vector(s) 218, and/or the audio vector(s) 220.” The rules engine determines “not likely to induce a seizure” is determining a discomfort tolerance level. ¶0040, “the rules engine 222 determines that the user is or is likely experiencing a seizure (e.g., a PSE seizure) or other negative neurological and/or physiological event based on the neurological/physiological data 226 collected during exposure of the user to the VR content stream 216, the modified VR content stream 232, and/or other content presented before the presentation of the VR content stream 216 or the modified VR content stream 232. For example, if the rules engine 222 detects that the neurological/physiological data 226 collected during exposure of the user to the modified VR content stream 232 includes patterns indicative of a seizure (e.g., a PSE seizure) or an impending seizure (e.g., a PSE seizure), the rules engine 222 sends an instruction 228 to the VR content modifier 230 to stop transmission of the VR content to the VR viewer 106 and/or the speaker(s) 108. Thus, the example processing unit 112 dynamically responds to the user's neurological experience while the user is exposed to the VR content 204.” ¶0046, “the calibration neurological/physiological data 302 is collected from the same user to whom the VR content 204 is presented via the VR viewer 106 and/or the speaker(s) 108 of FIG. 2. For example, the user 104 of the VR HMD 102 of FIG. 1 can be shown VR content different from the VR content 204 of FIG. 2 to collect baseline neurological data from the user 104 before the user 104 is exposed to the VR content 204. The baseline neurological data can be used by the rules engine 222 to identify changes in the neurological/physiological data 226 while the user is exposed to the VR content 204. In some examples, the calibration neurological/physiological data 302 includes neurological and/or physiological data known to induce a seizure (e.g., a PSE seizure) and/or other negative neurological/physiological response.” ¶0047, “identify patterns in the neurological/physiological data 226 indicative of an onset of a seizure (e.g., a PSE seizure) an in-progress seizure (e.g., a PSE seizure), and/or other negative neurological and/or physiological event.” ¶0048-0049. ¶0066, “the predictor 316 determines that the VR content stream 216 is not likely to induce a seizure (e.g., a PSE seizure) in the user.” “the predictor 316 analyzes the neurological/physiological data 226 relative to the calibration neurological/physiological data 302 for the user to determine if the neurological data exhibits any abnormal patterns despite the absence of changes in the visual parameters between the current sequence of frames and the earlier sequence of frames. In examples where the predictor 316 determines that the VR content stream 216 is not likely to induce a seizure in the user, the rules engine 222 instructs the VR content modifier 230 to transmit the VR content stream 216 without modifications to the VR viewer 106 and/or the speaker(s) 108.”);
generating, based on the classification and the discomfort tolerance level, a notification indicating the likelihood of the physiological response (¶0060-0061, ¶0060, “the rules engine 222 classifies VR content stream 216 (e.g., the video and/or audio portions). The classification can be used to identify seizure triggers in other VR content having similar visual and/or audio parameters and/or invoking similar neurological/physiological responses in users. The classification can also be used to preemptively warn VD HMD users and/or to preemptively modify the VR content across users. The prediction of the VR content stream 216 as likely to induce a seizure and/or other negative neurological/physiological response in a user and the corresponding visual and/or audio parameters of the VR content stream 216 are also used to update or refine the learning algorithm used by the predictor 316, as will be disclosed below.”);” ¶0039, “The rules engine 222 sends an instruction 228 to the VR content modifier 230 to transmit the VR content stream 216 to the VR viewer 106 and/or the speaker(s) 108.” ¶0041, “the rules engine 222 generates one or more alerts 234 based on the analysis of the neurological/physiological data 226, the video vector(s) 218, and/or the audio vector(s) 220 while the user is exposed to the VR content stream 216 or the modified VR content stream 232. The alert(s) 234 are transmitted to one or more alert receivers 236. The alert(s) 234 can include a visual warning and/or an audio warning to the user recommending that the user stop the presentation of the VR content 204 (e.g., by turning off the VR HMD 102).”¶0042, “the rules engine 222 may transmit the alert(s) 234 to the mobile device of the third party if the neurological/physiological data 226 indicates that the user of the VR HMD 102 may be experiencing a seizure (e.g., a PSE seizure) or other negative neurological and/or physiological event. Thus, the example processing unit 112 of the illustrated example notifies the third party that the user may need medical attention.”).
As to claim 4, claim 1 is incorporated and Ramaprakash discloses wherein the output data comprises a pattern of actions taken by the user during execution of the application (Ramaprakash, ¶0027, “the VR content 204 includes special effects, such as user hand gestures, that are generated as the user interacts with the VR content 204 in real-time.” ¶0043, “provides for continued monitoring and corrective action to address the potential for seizures” ¶0065, “the predictor 316 can determine that the user is experiencing a seizure (e.g., a PSE seizure) based on similarities between the patterns in the neurological/physiological data 226 identified by the neurological/physiological data analyzer 304 and the calibration neurological/physiological data 302 collected during prior seizures (e.g., for the user 104 of the VR HMD 102 of FIG. 1 or other users).”).
As to claim 5, claim 1 is incorporated and Ramaprakash discloses wherein in the output data comprises a pattern of images in VR content of the application (Ramaprakash, Fig. 2, ¶0066, “the predictor 316 determines that the VR content stream 216 is not likely to induce a seizure (e.g., a PSE seizure) in the user. For example, if the video vector analyzer 312 does not detect any changes in the video vector(s) 218 for a current sequence of video frames of the VR content stream 216 under analysis relative to vectors for previously presented frames of the VR content 204 and the neurological/physiological data analyzer 304 does not detect any changes in the neurological/physiological data 226 collected from the user during exposure to the VR content 204, the predictor 316 may determine that the VR content stream 216 is not likely to induce a seizure (e.g., a PSE seizure) in the user.”).
As to claim 6, claim 1 is incorporated and Ramaprakash discloses generating the notification comprises
determining that the application is associated with a likelihood of a physiological response when the classification meets a threshold (Ramaprakash, ¶0024, “the seizure monitor 116 determines that the neurological data collected from the user 104 by the sensors 110 as the user 104 is exposed to the VR content generated by the VR manager 114 is indicative of one or more characteristics of an impending seizure (e.g., a PSE seizure) or an in-progress seizure (e.g., a PSE seizure).” “the seizure monitor 116 predicts that continued exposure to the VR content may induce a PSE seizure in the user 104 or continue or worsen the PSE seizure symptoms of the user 104.” “the VR manager 114 stops transmission of the VR content to the VR HMD 102 in response to the seizure monitor 116 detecting more than a threshold likelihood that the VR content is likely to induce a seizure or other negative neurological and/or physiological event in the user.”).
As to claim 7, claim 1 is incorporated and Ramaprakash discloses wherein the classification is different than a classification of the application (Ramaprakash, ¶0024, “indicative of one or more characteristics of an impending seizure (e.g., a PSE seizure) or an in-progress seizure (e.g., a PSE seizure).” ¶0060, “the rules engine 222 classifies VR content stream 216 (e.g., the video and/or audio portions). The classification can be used to identify seizure triggers in other VR content having similar visual and/or audio parameters and/or invoking similar neurological/physiological responses in users. The classification can also be used to preemptively warn VD HMD users and/or to preemptively modify the VR content across users.” ¶0067, “the alert generator 320 automatically references previous classifications of the VR content 204 (or other VR content) stored in the database 300 to determine if the alert(s) 234 should be generated.”).
As to claim 8, Ramaprakash discloses a computer system comprising: a memory configured to store computer-executable instructions; and a processor configured to access the memory and execute the computer-executable instructions to at least:
execute an application to generate output data, wherein the output data is presented at a device to a user;
receive physiological feedback associated with from the user;
provide the output data as input to a model,wherein the model is trained to identify a classification associated with a likelihood of a physiological response when interacting with the application;
determine an output of the model based on the input, the output indicating the classification; determine a discomfort tolerance level associated with the user based on the physiological feedback and the likelihood of the physiological response;
generate, based on the classification and the discomfort tolerance level, a notification indicating the likelihood of the physiological response (See claim 1 for detailed analysis.).
As to claim 11, claim 8 is incorporated and Ramaprakash discloses the output data comprises a pattern of actions taken by the user during execution of the application (See claim 4 for detailed analysis.).
As to claim 12, claim 8 is incorporated and Ramaprakash discloses the output data comprises a pattern of images in VR content of the application (See claim 5 for detailed analysis.).
As to claim 13, claim 8 is incorporated and Ramaprakash discloses generating the notification comprises determining that the application is associated with a likelihood of a physiological response when the classification meets a threshold (See claim 6 for detailed analysis.).
As to claim 14, claim 8 is incorporated and Ramaprakash discloses classification o is different than a classification of the application (See claim 7 for detailed analysis.).
As to claim 15, Ramaprakash discloses one or more non-transitory computer-readable storage media storing computer-readable instructions that, when executed by one or more processors, cause a system to perform operations comprising:
executing an application to generate output data, wherein the output data is presented at a device to a user;
receiving physiological feedback associated with from the user;
providing the output data and the phvsiological feedback as input to a model, wherein the model is trained to identify a classification associated with a likelihood of a physiological response when interacting with the application;
determining an output of the model based on the input, the output indicating the classification; determining a discomfort tolerance level associated with the user based on the physiological feedback and the likelihood of the physiological response;
generating, based on the classification and the discomfort tolerance level, a notification indicating the likelihood of the physiological response (See claim 1 for detailed analysis.).
As to claim 21, claim 15 is incorporated and Ramaprakash discloses accessing the model, wherein the model identifies a plurality of learned patterns associated with baseline virtual reality (VR) content that are likely to cause discomfort; and extracting one or more patterns from the output data, wherein determining the output of the model comprises comparing the one or more patterns extracted from the output data to the plurality of learned patterns from the baseline VR content to identify the classification (Ramaparkash, ¶0046, “The baseline neurological data can be used by the rules engine 222 to identify changes in the neurological/physiological data 226 while the user is exposed to the VR content 204. In some examples, the calibration neurological/physiological data 302 includes neurological and/or physiological data known to induce a seizure (e.g., a PSE seizure) and/or other negative neurological/physiological response.” ¶0047, “The neurological/physiological data analyzer 304 analyzes the neurological/physiological data 226 received from the neurological/physiological data collector 224 of FIG. 2 while the user is exposed to the VR content 204 to identify patterns in the neurological/physiological data 226 indicative of an onset of a seizure (e.g., a PSE seizure) an in-progress seizure (e.g., a PSE seizure), and/or other negative neurological and/or physiological event.”. ¶0047, “identify patterns in the neurological/physiological data 226 indicative of an onset of a seizure (e.g., a PSE seizure) an in-progress seizure (e.g., a PSE seizure), and/or other negative neurological and/or physiological event.” ¶0048, “the neurological/physiological data analyzer 304 can detect an onset of a seizure (e.g., a PSE seizure) while the user 104 is exposed to the VR content stream 216 based on changes in the neurological/physiological data 226 occurring within a 1-2 second time interval, such as a sudden increase in the presence of spikes in the signal data. If the increased presence of spikes in the signal data is maintained or if the frequency and/or amplitude of the spikes increases for a predetermined amount of time, such as 3 seconds from the detection of the onset of the signal data changes, the neurological/physiological data analyzer 304 determines that the user 104 may be experiencing a seizure (e.g., a PSE seizure).” ¶0049, “the neurological/physiological data analyzer 304 compares pattern(s) detected in the neurological/physiological data 226 to patterns in brain signal data collected during a seizure (e.g., a PSE seizure experienced by the user 104 of the VR HMD 102 or other users) to identify similarities between the data. In some examples, the neurological/physiological data analyzer 304 compares the neurological/physiological data 226 to the calibration neurological/physiological data 302 collected from the user (e.g., the user 104) to identify abnormalities in the neurological/physiological data 226 that may be indicative of a seizure (e.g., a PSE seizure).” ¶0059, “In view of the learned data and based on the determination that the current sequence of frames includes flashing lights and that the neurological/physiological data 226 exhibits a pattern indicative of an onset of a seizure in a user over the age of 60 with a history of PSE seizures, the predictor 316 predicts that that the current sequence of frames of the VR content stream 216 under analysis is likely to induce a seizure (e.g., a PSE seizure) in the user. In the foregoing example, the predictor 316 provides for a user-specific prediction of whether the VR content stream 216 is likely to induce a seizure in the user based on the analysis of the user's neurological/physiological data 226 and the user profile data 306 for the user.” ¶0024, “the rules engine 222 classifies VR content stream 216 (e.g., the video and/or audio portions). The classification can be used to identify seizure triggers in other VR content having similar visual and/or audio parameters and/or invoking similar neurological/physiological responses in users. The classification can also be used to preemptively warn VD HMD users and/or to preemptively modify the VR content across users. The prediction of the VR content stream 216 as likely to induce a seizure and/or other negative neurological/physiological response in a user and the corresponding visual and/or audio parameters of the VR content stream 216 are also used to update or refine the learning algorithm used by the predictor 316, as will be disclosed below.”)
As to claim 22, claim 15 is incorporated and Ramaprakash discloses correlating the physiological feedback to a pattern in the output data inducing the physiological response, wherein determining the output of the model further comprises identifying the specific pattern in the output data (¶0047-0049, ¶0055, “The rules engine 222 of the example of FIG. 3 includes a predictor 316. The predictor 316 predicts whether portions of the VR content 204 under analysis is likely to induce a seizure (e.g., a PSE seizure) and/or other negative neurological/physiological event in the user using the example VR HMD 102 of FIG. 1. The predictor 316 of this example makes the prediction based on the analysis of the neurological/physiological data 226 by the neurological/physiological data analyzer 304, the analysis of the video vector(s) 218 by the video vector analyzer 312, the analysis of the audio vector(s) 220 by the audio vector analyzer 314, and/or the user profile data 306.” ¶0056, “The predictor 316 of the illustrated example utilizes a machine learning algorithm to predict whether the VR content stream 216 is likely to induce a seizure (e.g., a PSE seizure) and/or other negative neurological/physiological event) in the user. The learning algorithm can be, for example, a supervised learning algorithm (e.g., neural networks, decision tree learning, etc.). The learning algorithm can be stored in the database 300 of the rules engine 222. The predictor 316 learns from the calibration neurological/physiological data 302 and/or the calibration video and/or audio vectors 308, 310 with respect to VR content that induced seizures (e.g., PSE seizures) in one or more users (including, in some examples, the user 104 of the VD HMD 102 of FIG. 1). Based on the learned data, the predictor 316 determines whether one or more video and/or audio portions of the VR content stream 216 is likely to induce a seizure (e.g., a PSE seizure) in the user.” ¶0057, “the predictor 316 analyzes the calibration neurological/physiological data 302 indicative of seizures (e.g., PSE seizures). The predictor 316 identifies the calibration video and/or audio vectors 308, 310 corresponding to the VR content to which the user(s) were exposed when the user(s) experienced the seizures. For example, the predictor 316 identifies the saturation and hue values of the VR content (e.g., video frame(s)) that was visually displayed during the PSE seizures. The predictor 316 also identifies the decibel level and/or patterns in the frequency spectrum of the audio stream that was playing when the user(s) experienced the seizures. Thus, the predictor 316 learns how users respond to VR content having certain visual and/or audio parameters.” ¶0065, “based on the neurological/physiological data 226 received from the user while the user is exposed to the VR content 204 (e.g., the VR content stream 216 or the modified VR content stream 232), the predictor 316 determines that the user is experiencing a seizure (e.g., a PSE seizure). For example, the predictor 316 can determine that the user is experiencing a seizure (e.g., a PSE seizure) based on similarities between the patterns in the neurological/physiological data 226 identified by the neurological/physiological data analyzer 304 and the calibration neurological/physiological data 302 collected during prior seizures (e.g., for the user 104 of the VR HMD 102 of FIG. 1 or other users). In such examples, the content modification manager 318 generates the one or more instructions 228 to stop presentation of the VR content 204.”).
As to claim 23, claim 15 is incorporated and Ramaprakash discloses the model identifies a plurality of learned patterns associated with baseline virtual reality (VR) content that are likely to cause discomfort (¶0059, “In view of the learned data and based on the determination that the current sequence of frames includes flashing lights and that the neurological/physiological data 226 exhibits a pattern indicative of an onset of a seizure in a user over the age of 60 with a history of PSE seizures, the predictor 316 predicts that that the current sequence of frames of the VR content stream 216 under analysis is likely to induce a seizure (e.g., a PSE seizure) in the user. In the foregoing example, the predictor 316 provides for a user-specific prediction of whether the VR content stream 216 is likely to induce a seizure in the user based on the analysis of the user's neurological/physiological data 226 and the user profile data 306 for the user.”).
As to claim 24, claim 23 is incorporated and Ramaprakash discloses extracting one or more patterns from the output data generated by the executing application, wherein determining the output of the model comprises comparing the one or more patterns extracted from the output data to the plurality of learned patterns from the baseline VR content to determine the likelihood of the physiological response (¶0058, “Based on the analysis of the data collected for the VR content 204 relative to the learned data, the predictor 316 predicts whether the VR content stream 216 is likely to induce a PSE seizure in the user (e.g., the user 104 of FIG. 1).” ¶0059, “In view of the learned data and based on the determination that the current sequence of frames includes flashing lights and that the neurological/physiological data 226 exhibits a pattern indicative of an onset of a seizure in a user over the age of 60 with a history of PSE seizures, the predictor 316 predicts that that the current sequence of frames of the VR content stream 216 under analysis is likely to induce a seizure (e.g., a PSE seizure) in the user. In the foregoing example, the predictor 316 provides for a user-specific prediction of whether the VR content stream 216 is likely to induce a seizure in the user based on the analysis of the user's neurological/physiological data 226 and the user profile data 306 for the user.” ¶0024, “the rules engine 222 classifies VR content stream 216 (e.g., the video and/or audio portions). The classification can be used to identify seizure triggers in other VR content having similar visual and/or audio parameters and/or invoking similar neurological/physiological responses in users. The classification can also be used to preemptively warn VD HMD users and/or to preemptively modify the VR content across users. The prediction of the VR content stream 216 as likely to induce a seizure and/or other negative neurological/physiological response in a user and the corresponding visual and/or audio parameters of the VR content stream 216 are also used to update or refine the learning algorithm used by the predictor 316, as will be disclosed below.”).
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2-3, 9-10, 16 are rejected under 35 U.S.C. 103 as being unpatentable over Ramaprakash et al. (US Pub 2018/0088669 A1) in view of Gentilin et al. (US Pub 2018/0024625 A1).
As to claim 2, claim 1 is incorporated and Ramaprakash discloses providing a recommendation of one or more video (Ramaprakash, ¶0060, “The classification can also be used to preemptively warn VD HMD users and/or to preemptively modify the VR content across users.” ¶0061, “The content modification manager 318 analyzes the video vector(s) 218 and/or the audio vector(s) 220 of the VR content stream 216 to determine one or more modifications to the visual and/or audio parameters of the VR content stream 216. The content modification manager 318 determines a factor (e.g., an amount, a percentage) by which to adjust the visual and/or the audio parameters to reduce the likelihood of an occurrence of a seizure (e.g., a PSE seizure) and/or other negative neurological/physiological event in the user.” ¶0070, “the predictor 316 and/or the feedback analyzer 322 communicate with the content modification manager 318 and/or the alert generator 320 to determine corrective actions such as modifying the upcoming VR content stream(s), generating one or more alerts 234, and/or stopping transmission of the VR content 204”. ¶0072, “The refinement of the learning algorithm improves the ability of the rules engine 222 to predict whether or not upcoming VR content 204 (or other VR content) is likely to induce a seizure (e.g., a PSE seizure) and/or other neurological/physiological event in the user (or other users).”).
Ramaprakash does not explicitly discloses games.
However, video games are obvious choice for VR content.
Gentilin teaches video game as VR content (Gentilin, ¶0003, “Through virtual reality applications, a user is capable of experiencing a fully modeled three dimensional world of a game or movie as if the user was actually in the game or movie” ¶0026, “a report may indicate the aggregated comfort level values that a large number of users have inputted at a particular level in a virtual reality game”).
Ramaprakash and Gentilin are considered to be analogous art because all pertain to virtual reality contents. It would have been obvious before the effective filing date of the claimed invention to have modified Ramaprakash with the features of “video game as VR content” as taught by Gentilin. The claim would have been obvious because the substitution of one known element for another would have yielded predictable results to one of ordinary skill in the art at the time of the invention.
As to claim 3, claim 1 is incorporated and Ramaprakash discloses generating a second notification that execution of a second application is associated with a likelihood of a physiological response based on the classification and a classification of the second application (Ramaprakash, ¶0067, “if the predictor 316 identifies the VR content stream 216 and/or portion(s) of the VR content stream 216 as including seizure trigger content, the predictor 316 sends a message to the alert generator 320 to generate one or more visual and/or audio alerts 234 warning the user (e.g., the user 104 of the VR HMD 102 of FIG. 1) that the VR content 204 may induce a PSE seizure. In some examples, the alert generator 320 automatically references previous classifications of the VR content 204 (or other VR content) stored in the database 300 to determine if the alert(s) 234 should be generated.” ¶0068, “the alert generator 320 generates the alert(s) 234 for transmission to one or more third parties designated by the user. Contact information for the one or more third parties can be received from one or more user inputs, via, for example the processing unit 112 of FIG. 2. The contact information for the one or more third parties can be stored in the database 300 of the rules engine 222 for reference by the alert generator 320.”).
Ramaprakash’ contact information can be interpreted as a classification of the second application since the term is so broad.
Gentilin also teaches a classification of the second application (Gentilin, ¶0062, “each record in database 520 has at least one key field that is associated and/or populated with identifiers of applications or VR environments, for example, particular application 120 and generated VR environment 130. Columns in these records specify comfort level values that are obtained via the first-level comfort prompt 504 and second-level comfort prompt 506, and metadata values 510, which may be received via analytics messages 512.”).
Ramaprakash and Gentilin are considered to be analogous art because all pertain to virtual reality contents. It would have been obvious before the effective filing date of the claimed invention to have modified Ramaprakash with the features of “a classification of the second application.” as taught by Gentilin. The claim would have been obvious because the substitution of one known element for another would have yielded predictable results to one of ordinary skill in the art at the time of the invention.
As to clam 9, claim 8 is incorporated and the combination of Ramaprakash and Gentilin discloses provide a recommendation of one or more video game titles suitable for the user based on the classification and one or more classifications of the one or more video game titles (See claim 2 for detailed analysis.).
As to clam 10, claim 8 is incorporated and the combination of Ramaprakash and Gentilin discloses generate a second notification that execution of a second application is associated with a likelihood of a physiological response based on the classification of the user and a classification of the second application (See claim 3 for detailed analysis.).
As to clam 16, claim 15 is incorporated and the combination of Ramaprakash and Gentilin discloses providing a recommendation of one or more video game titles suitable for the user based on the classification and one or more classifications of the one or more video game titles (See claim 2 for detailed analysis.).
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YU CHEN whose telephone number is (571)270-7951. The examiner can normally be reached on M-F 8-5 PST Mid-day flex.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xiao Wu can be reached on 571-272-7761. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/YU CHEN/
Primary Examiner, Art Unit 2613
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