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 .
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.
The factual inquiries 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.
Claim(s) 1-4, 6-9, 12-13, 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jung et al. (KR 10-2596977B) in view of Park et al. (KR 10-2022-0064476A).
Re claim 1, Jung teaches an electronic device comprising:
a processor comprising processing circuitry; and memory comprising one or more storage mediums storing instructions ([0082, [0203]], memory comprising medium) and ([0203], [0221], (CPU and processors)
wherein the instructions, when executed by the processor individually or
collectively, cause the electronic device to:
identify an event ([0214] [At this time, the security control server 200 inputs a plurality of features extracted from the security events collected through the communication unit 320 (120) into a classification model to obtain classification results for the plurality of features, and Enter the descriptive model to obtain description results for a plurality of features, generate first result data based on the classification result and description result, and input the first result data into the generative model to generate second result data. may be acquired, and the obtained second result data may be transmitted to the client device 300.]
generate a description representing the event ([0214] [At this time, the security control server 200 inputs a plurality of features extracted from the security events collected through the communication unit 320 (120) into a classification model to obtain classification results for the plurality of features, and Enter the descriptive model to obtain description results for a plurality of features, generate first result data based on the classification result and description result, and input the first result data into the generative model to generate second result data. may be acquired, and the obtained second result data may be transmitted to the client device 300.]
extract a prompt to generate third-person perspective content corresponding to
the event, and obtain the third-person perspective content by inputting the prompt to a
generative artificial intelligence model ([0214] [At this time, the security control server 200 inputs a plurality of features extracted from the security events collected through the communication unit 320 (120) into a classification model to obtain classification results for the plurality of features, and Enter the descriptive model to obtain description results for a plurality of features, generate first result data based on the classification result and description result, and input the first result data into the generative model to generate second result data. may be acquired, and the obtained second result data may be transmitted to the client device 300), ([0015] [In addition, the classification result includes a predicted result for the security event, the explanation result includes an explanation of the cause of the security event, and the classification model classifies the security event like the classification result. It includes a description of a cause and an importance calculated for each of the plurality of features, and the processor inputs the first result data into the generative model based on the first result data including the classification result and the description result. You can create a prompt for, and input the generated prompt into the generative model), and ([0016] [In addition, the generative model outputs second result data for the input prompt, and the second result data includes a description of the security event, characteristics of the security event, importance of the security event, and It may include why a security event is important and how to respond to the security event). Thus, the content is considered third person perspective, as it is not content observed by the user.
Jung does not explicitly teach a camera configured to generate a video; a sensor configured to obtain sensing data related to a user of the electronic device, and a microphone configured to generate audio, wherein identifying an event is based on at least one of the video or sensing data.
However, Park teaches a camera configured to generate a video a sensor configured to obtain sensing data related to a user of the electronic device (see [0004-0005] a camera collects image information about space detected and to be monitored and transmits it based on sensing data related to a user of the electronic device system)
and a microphone configured to generate audio (see [0028] audio device for collecting audio information).
wherein identifying an event is based on at least one of the video or sensing data ([0008] In order to solve the above problems, a security monitoring chatbot service providing method according to an embodiment of the present invention includes the steps of: detecting event information by analyzing monitoring information and image information collected from one or more monitoring devices; generating caption data in a text format in response to the event information and storing and managing it as security monitoring status information; providing a notification and control request security monitoring service to a user terminal and a control server using the caption data; receiving a request for information on security monitoring from the user terminal; providing a user response service using a chatbot conversation service based on caption data according to the security monitoring state information corresponding to the user request information).
Jung in view of Park teaches claim 1. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Jung’s security system of detecting events and processing the events using generative AI to explicitly include event detected by camera and sensors, as taught by Park, as the references are in the analogous art of security detection-based systems. An advantage of the modification is that it achieves the result of using captured image/video data sensed in order to process visual information for generation of event data for an end user to receive third party perspective content.
Re claim 2, Jung and Park teach claim 1. Furthermore, Park teaches wherein the instructions, when executed by the processor individually or collectively, cause the electronic device to:
identify a first event based on the video (see [0028], camera device for photographing area)
generate a first description representing a video corresponding to a first interval
in which the first event was identified (see [0029], caption sensor device manage monitoring equipment and detect and perform caption data based on detected event).
identify a second event based on the sensing data, generate a second description representing a video corresponding to a second interval in which the second event was identified (see [0028], camera devices for photographing area, wherein multiple cameras capture multiple videos/events) and (see [0029], caption sensor device manage monitoring equipment devices and detect and perform caption data based on detected events including multiple detected events).
generate a third description representing a third event, based on at least one of the first description or the second description, extract a prompt for generating third-person perspective content corresponding to the third event from the third description, wherein the third event is an event identified as an event related to the user based on at least one of the first event or the second event, [(0036] [The output data of the caption sensor device 100 may be transmitted to the user terminal 200, the relay server 300, the control service server 500, etc. according to the interface service processing. In addition, a user input through the interface service may be transmitted to the caption sensor device 100, and the caption sensor device 100 constructs and outputs an appropriate caption data response corresponding to the user input, so that the user can use the monitoring information based on the monitoring information. You can inquire and check the details of the event that has occurred) and ([0043] [Therefore, the caption sensor device 100 can not only quickly process the basic monitoring service processing according to the first monitoring event, but also perform deep learning analysis processing on the data initially analyzed and processed by the caption sensor device 100. Therefore, the occurrence of the second monitoring event, which is provided only when more accurate object classification is possible, can also be quickly detected according to the deep learning analysis processing of the data.]
and generate the third-person perspective content by inputting the prompt to the generative artificial intelligence model (see [0029] AI based caption sensor device, for performing caption-based interface service corresponding to detected event), ([0042] For example, the caption sensor device 100 configures initial analysis information according to the occurrence of the first monitoring event as caption data, and firstly transmits it to the control service server 500 or the user terminal 200 through the relay server 400 . Transmitting, according to the result of deep learning analysis of image information within a certain time range associated with the first monitoring event, a second monitoring event is detected as detailed analysis information for the first monitoring event and configured as caption data, and the The caption data generated from the second monitoring event information may be provided to the control service server 500 or the user terminal 200 through the relay server 400), and ([0043] Therefore, the caption sensor device 100 can not only quickly process the basic monitoring service processing according to the first monitoring event, but also perform deep learning analysis processing on the data initially analyzed and processed by the caption sensor device 100. Therefore, the occurrence of the second monitoring event, which is provided only when more accurate object classification is possible, can also be quickly detected according to the deep learning analysis processing of the data).
For motivation, see claim 1.
Re claim 3, Jung and Park teach claim 2. Furthermore, Park teaches extract the prompt for generating third-person perspective content corresponding to the third event from the third description, based on identifying that the third event corresponds to a valid event ([0039] In addition, the caption sensor device 100 may detect predetermined first monitoring events based on initial analysis information according to image information processing and analysis of monitoring equipment information, but more accurate detection and classification of objects in the image For the second monitoring event that requires accurate analysis, separate deep learning analysis processing may be performed) and ([0040] Here, the first monitoring event and the second monitoring event may be grouped according to whether or not deep learning analysis is processed, and the first monitoring event is an intrusion event that is quickly detected according to the initial analysis of sensor information and image information, unauthorized camera change Events and fire events may be exemplified, and the second monitoring event is a dynamic object tracking event accompanied by accurate object detection and classification information according to deep learning analysis, a trip wire event, a wandering object detection event, and a traffic direction violation detection event. , unauthorized object detection event, unauthorized moving object detection event, access counting and statistical event, crowd density detection event, unusual behavior detection event, etc.). For motivation, see claim 1.
Re claim 4, Jung and Park teach claim 1. Furthermore, Jung teaches wherein the wherein the third-person perspective content includes a thumbnail corresponding to the video ([0009] A security control device using a multiple artificial intelligence model according to an embodiment of the present disclosure to solve the above-described problem includes a communication unit; A storage unit storing one or more instructions and an artificial intelligence model including a classification model, an explanation model, and a generative model for security event monitoring; and one or more processors that execute the instructions stored in the storage unit and monitor security events collected through the communication unit using the artificial intelligence model, wherein the processor selects a plurality of features from the collected security events. (Feature) is extracted, the extracted plurality of features are input to the classification model, the classification model classifies the plurality of features (hereinafter referred to as 'classification results') to obtain a result, and the extraction A plurality of features are input into the description model, the description model obtains a result explaining the plurality of features (hereinafter referred to as 'explanation result'), and a first classification result is obtained based on the classification result and the description result. Result data can be generated, the generative model can be learned based on the generated first result data, and second result data output through the generative model can be obtained) and (see [0016-0017], wherein the second result output includes description data of security event including one of text, image, video, and audio).
Re claim 6, Jung and Park teaches claim 1. Furthermore, Park teaches wherein the instructions, when executed by the processor individually or collectively, cause the electronic device to identify the event, based on one or more objects in the video ([0046] [For example, the initial analysis processing may include at least one of image stabilization processing, background modeling processing, shape calculation processing, element connection processing, and object tracking processing, and the tracking data is, for example, from image information to the foreground. Pixels may be separated-filtered, and connected components may be labeled to include visual transformation-processed object tracking data) and ([0102] The trip wire detection unit can also process object counting that passes (intrusion) the boundary line, and detects when there is an object (person, vehicle, or other object) passing on the ground based on the trip wire, By tracking the direction (bidirectional or unidirectional) of the movement, it is possible to determine whether an event has occurred by checking whether it has passed (intruded) the trip wire while moving from the first direction to the second direction). For motivation, see claim 1.
Re claim 7, Jung and Park teaches claim 1. Furthermore, Park teach wherein the instructions, when executed by the processor individually or collectively, cause the electronic device to: estimate an action of the user, based on the sensing data; and identify the event, based on the estimated action of the user (see [0027-0039], result for the security event including object detected such as boundary sensor information) and (see [0101], virtual boundary line as an estimated line and intrusion detection of an action of a user based on the estimated detection) and (see [0102-0103], boundary-based intrusion by a detected object such as a person) based on tracking and detection within pre-determined regions of interest). For motivation, see claim 1.
Re claim 8, Jung and Park teach claim 1. Furthermore, Park teaches wherein the memory stores a first software application, wherein the first software application, when executed by the processor individually or collectively, (see [0022], storing software into memory and executing software)
cause the electronic device to store the video and the sensing data, generated in real time, identify a first event, based on the video, store a video corresponding to the first interval in which the first event was identified in the memory, identify a second event, based on the sensing data, and store sensing data corresponding to the second interval in which the second event was identified in the memory ([0042] For example, the caption sensor device 100 configures initial analysis information according to the occurrence of the first monitoring event as caption data, and firstly transmits it to the control service server 500 or the user terminal 200 through the relay server 400 . Transmitting, according to the result of deep learning analysis of image information within a certain time range associated with the first monitoring event, a second monitoring event is detected as detailed analysis information for the first monitoring event and configured as caption data, and the The caption data generated from the second monitoring event information may be provided to the control service server 500 or the user terminal 200 through the relay server 400), and ([0043] Therefore, the caption sensor device 100 can not only quickly process the basic monitoring service processing according to the first monitoring event, but also perform deep learning analysis processing on the data initially analyzed and processed by the caption sensor device 100. Therefore, the occurrence of the second monitoring event, which is provided only when more accurate object classification is possible, can also be quickly detected according to the deep learning analysis processing of the data).
For motivation, see claim 1.
Re claim 9, Jung and Park teaches claim 8. Furthermore, Park teaches wherein the memory stores a second software application, and wherein the second software application, when executed by the processor individually or collectively, cause the electronic device to: see [0022], storing software into memory and executing software)
generate a first description representing the video corresponding to the first interval based on the video corresponding to the first interval, and generate the second description representing the sensing data corresponding to the second interval based on the sensing data corresponding to the second interval 0042] For example, the caption sensor device 100 configures initial analysis information according to the occurrence of the first monitoring event as caption data, and firstly transmits it to the control service server 500 or the user terminal 200 through the relay server 400 . Transmitting, according to the result of deep learning analysis of image information within a certain time range associated with the first monitoring event, a second monitoring event is detected as detailed analysis information for the first monitoring event and configured as caption data, and the The caption data generated from the second monitoring event information may be provided to the control service server 500 or the user terminal 200 through the relay server 400), and ([0043] Therefore, the caption sensor device 100 can not only quickly process the basic monitoring service processing according to the first monitoring event, but also perform deep learning analysis processing on the data initially analyzed and processed by the caption sensor device 100. Therefore, the occurrence of the second monitoring event, which is provided only when more accurate object classification is possible, can also be quickly detected according to the deep learning analysis processing of the data).
For motivation, see claim 1.
Re claim 12, Jung and Park teach claim 1. Furthermore, Park teaches wherein sensors includes at least one of a sensor configured to obtain data related to audio (see [0028], audio device for collection audio information). For motivation, see claim 1.
Re claim 13, Jung and Park teaches claim 1. Furthermore, Park teaches wherein the instructions, when executed by the processor individually or collectively, cause the electronic device to generate third-person perspective content corresponding to first-person perspective content received from an external electronic device, using the generative artificial intelligence mode ([0098] For example, the event detection unit 180 according to an embodiment of the present invention can more accurately classify moving objects in the image into a person performing a specific action, a vehicle of a specific type, etc. by the deep learning analysis processing unit 190. As there is, it is possible to perform more various types of event detection, and the caption service processing unit 185 configures caption data suitable for each event detection, and transmits the notification service to the user terminal 200 or the relay server 400 . Service processing, control request service processing, etc.) and ([0036] The output data of the caption sensor device 100 may be transmitted to the user terminal 200, the relay server 300, the control service server 500, etc. according to the interface service processing. In addition, a user input through the interface service may be transmitted to the caption sensor device 100, and the caption sensor device 100 constructs and outputs an appropriate caption data response corresponding to the user input, so that the user can use the monitoring information based on the monitoring information. You can inquire and check the details of the event that has occurred). For motivation, see claim 1.
Claim 16 claims limitations in scope to claim 1 and is rejected for at least the reasons above.
Claim 17 claims limitations in scope to claim 4 and is rejected for at least the reasons above.
Claim 18 claims limitations in scope to claim 5 and is rejected for at least the reasons above.
Claim 19 claims limitations in scope to claim 2 and is rejected for at least the reasons above.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jung et al. (KR 10-2596977B) in view of Park et al. (KR 10-2022-0064476A) and Park et al. (US 20150241926, hereinafter “Park2”).
Re claim 5, Jung and Park teaches claim 4. Jung and Park do not explicitly teach a display configured to display visual information, wherein the instructions, when executed by the processor individually or collectively, cause the electronic device to:
receive a first user input to display a video list including the thumbnail corresponding to the video through the display, display the video list through the display, based on receiving of the first user input, receive a second user input for one thumbnail in the video list, and
play a video corresponding to the one thumbnail through the display, based on receiving of the second user input.
However, Park2 teaches explicitly teach a display configured to display visual information, wherein the instructions, when executed by the processor individually or collectively, cause the electronic device to:
receive a first user input to display a video list including the thumbnail corresponding to the video through the display, display the video list through the display, based on receiving of the first user input, receive a second user input for one thumbnail in the video list, and play a video corresponding to the one thumbnail through the display, based on receiving of the second user input (see [0192-0195), wherein a first user input displays a video list including thumbnails) and ([0209] Referring to FIG. 11A, a thumbnail list 801-2 is outputted on an auxiliary display area 151-2 and assume that a third thumbnail 801c among the thumbnail list 801-2 corresponds to a video data. If an input 10b selecting the third thumbnail is received, the controller 180 can further output a control area 1101 on the auxiliary display area 151-2 to control output of a video. The control area 1101 is outputted on the auxiliary display area 151-2 instead of the main display area 151-1 to make a user 402 more conveniently control playback of video data. This is because the auxiliary display area 151-2 corresponds to an output area provided to the user 402). Jung, Park, and Park2 teaches claim 5.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Jung and Park’s processing system of generating outputs of image/video/text data for a user to explicitly include generating a selectable list of thumbnails for display to a user, as taught by Park2, as the references are in the analogous art of display systems for user interaction/viewing. An advantage of the modification is that it achieves the result of generating thumbnails for a user to select to view further processes, such as a related video.
Claim(s) 10-11, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jung et al. (KR 10-2596977B) in view of Park et al. (KR 10-2022-0064476A) and Long et al. (US 20220083186).
Re claim 10, Jung and Park teach claim 1. Jung and Park do not explicitly teach wherein the third-person perspective content includes an avatar corresponding to the user of the electronic device.
However, Long teaches wherein the third-person perspective content includes an avatar corresponding to the user of the electronic device ([0027] For example, the virtual environment may be a picture for observing the virtual environment from a first-person perspective of a virtual object. The picture may also be referred to as a screen or a display screen. For example, a first-person shooting (FPS) game is a shooting game played in the virtual environment from the first-person perspective. The foregoing virtual environment may alternatively be a picture for observing the virtual environment from a third-person perspective of a virtual object. For example, a third-person shooting (TPS) game is a shooting game played in the virtual environment from the third-person perspective), ([0028] Virtual object: It is a movable object in a virtual environment. The movable object may be a virtual character, a virtual animal, a cartoon character, or the like, such as a character, an animal, a plant, an oil drum, a wall, a stone, or the like displayed in a 3D virtual environment. In some embodiments, the virtual object is a 3D model created based on a skeletal animation technology. Each virtual object has a shape and size in the 3D virtual environment, and occupies some space in the 3D virtual environment. Virtual objects generally refer to one or more virtual objects in a virtual environment), and (see [0034], wherein first user controls the first virtual object to perform a movement through a UI control on a virtual picture).
Jung, Park, and Long teach claim 10. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Jung and Park’s sensor system of detecting objects to explicitly include an avatar corresponding to the user of the electronic device, as taught by Long, as the references are in the analogous art of sensor detection system for display to a user. An advantage of the modification is that it achieves the result of creating a virtualized object based on user and detection of user’s inputs to modify the display of the sensor system.
Re claim 11, Jung, Park, and Long teach claim 10. Furthermore, Long teaches wherein the avatar corresponding to the user of the electronic device includes an avatar based on an object corresponding to the user included in the content ([0027] For example, the virtual environment may be a picture for observing the virtual environment from a first-person perspective of a virtual object. The picture may also be referred to as a screen or a display screen. For example, a first-person shooting (FPS) game is a shooting game played in the virtual environment from the first-person perspective. The foregoing virtual environment may alternatively be a picture for observing the virtual environment from a third-person perspective of a virtual object. For example, a third-person shooting (TPS) game is a shooting game played in the virtual environment from the third-person perspective), ([0028] Virtual object: It is a movable object in a virtual environment. The movable object may be a virtual character, a virtual animal, a cartoon character, or the like, such as a character, an animal, a plant, an oil drum, a wall, a stone, or the like displayed in a 3D virtual environment. In some embodiments, the virtual object is a 3D model created based on a skeletal animation technology. Each virtual object has a shape and size in the 3D virtual environment, and occupies some space in the 3D virtual environment. Virtual objects generally refer to one or more virtual objects in a virtual environment), and (see [0034], wherein first user controls the first virtual object to perform a movement through a UI control on a virtual picture).
For motivation, see claim 10.
Claim 20 claims limitations in scope to claim 10 and is rejected for at least the reasons above.
Claim(s) 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jung et al. (KR 10-2596977B) in view of Park et al. (KR 10-2022-0064476A) and Pardeshi et al. (US 20220215232).
Re claim 14, Jung and Park teaches claim 1. Jung and Park do not explicitly teach wherein the electronic device includes a head mounted display (HMD) device, and wherein the instructions, when executed by the processor individually or collectively, cause the HMD device to: receive a user input to play the video through the display, in a first mode providing a composite image of an external environment, change from the first mode to a second mode different from the first mode, based on receiving of the user input, and play the video, through the display, in the second mode.
However, Pardeshi teaches wherein the electronic device includes a head mounted display (HMD) device, and wherein the instructions, when executed by the processor individually or collectively, cause the HMD device to: see ([0066], VR headset AR goggles) and ([0345] In at least one embodiment, architecture and/or functionality of various previous figures are implemented in context of a general computer system, a circuit board system, a game console system dedicated for entertainment purposes, an application-specific system, and more. In at least one embodiment, computer system 1000 may take form of a desktop computer, a laptop computer, a tablet computer, servers, supercomputers, a smart-phone (e.g., a wireless, hand-held device), personal digital assistant (“PDA”), a digital camera, a vehicle, a head mounted display, a hand-held electronic device, a mobile phone device, a television, workstation, game consoles, embedded system, and/or any other type of logic).
receive a user input to play the video through the display, in a first mode providing a composite image of an external environment change from the first mode to a second mode different from the first mode, based on receiving of the user input, and play the video, through the display, in the second mode ([0045] In at least one embodiment, one or more users may engage in an electronic experience, such as may include a video game, virtual reality (AR), augmented reality (AR), mixed reality (MR), or other such experience, which may be experienced over a local or online network, or as part of a networked system. In at least one embodiment, such an experience may not be necessarily electronic in nature, but may involve video captured of multiple actors in an environment. In at least one embodiment, this may include one or more players of on an online game session, where other viewers may desire to watch this game session through one or more video clips or streams, which may be available during or after this game session. In at least one embodiment, there may be three players in this session at a given point or period in time. In at least one embodiment, each of these players may receive a player-specific view of this game session, as may be displayed through an interface, monitor, headset, goggles, or other such display or presentation mechanism. In at least one embodiment, each of these player-specific views may be generated from a point of view associated with that player, or an avatar for that player, such as for a first person or third person view. In at least one embodiment, a first player can receive a first player view 100 that shows a player avatar 102 for player 1 in third person view as illustrated in FIG. 1A. In at least one embodiment, a second player can receive a second player view 110 that shows a player avatar 112 for player 2 in third person view as illustrated in FIG. 1B. In at least one embodiment, a third player can receive a third player view 120 that shows a player avatar 122 for player 3 in third person view as illustrated in FIG. 1C. In at least one embodiment, each of these views may be optimal, or at least appropriate, for each player, as it provides a point of view or perspective that is specifically relevant for that player. In at least one embodiment, each player may also have an option to modify a view provided, such as to change between a first person view and a third person view).
Jung, Park, and Pardeshi teach claim 14. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Jung and Park’s sensor-based system to explicitly include head mounted display to display output, such as video in different modes, as taught by Pardeshi, as the references are in the analogous art of system for monitoring sensor data for output. An advantage of the modification is that it achieves the result of improving on the system to provide for viewing of video data through a display in different user selected modes.
Re claim 15, Jung, Park, and Pardeshi teach claim 14. Furthermore, Pardeshi wherein the instructions, when executed by the processor individually or collectively, cause the
receive a user input to change the video to a third-person perspective, while the
video is playing; receive a user input to change the video to a third-person perspective, while the video is playing; extract a prompt to generate a third-person perspective video, based on receiving the user input to change the video to the third-person perspective; generate the third-person perspective video, by inputting the prompt into the generative artificial intelligence model; change the second mode to the first mode; and play the third-person perspective video, in the first mode ([0045] In at least one embodiment, one or more users may engage in an electronic experience, such as may include a video game, virtual reality (AR), augmented reality (AR), mixed reality (MR), or other such experience, which may be experienced over a local or online network, or as part of a networked system. In at least one embodiment, such an experience may not be necessarily electronic in nature, but may involve video captured of multiple actors in an environment. In at least one embodiment, this may include one or more players of on an online game session, where other viewers may desire to watch this game session through one or more video clips or streams, which may be available during or after this game session. In at least one embodiment, there may be three players in this session at a given point or period in time. In at least one embodiment, each of these players may receive a player-specific view of this game session, as may be displayed through an interface, monitor, headset, goggles, or other such display or presentation mechanism. In at least one embodiment, each of these player-specific views may be generated from a point of view associated with that player, or an avatar for that player, such as for a first person or third person view. In at least one embodiment, a first player can receive a first player view 100 that shows a player avatar 102 for player 1 in third person view as illustrated in FIG. 1A. In at least one embodiment, a second player can receive a second player view 110 that shows a player avatar 112 for player 2 in third person view as illustrated in FIG. 1B. In at least one embodiment, a third player can receive a third player view 120 that shows a player avatar 122 for player 3 in third person view as illustrated in FIG. 1C. In at least one embodiment, each of these views may be optimal, or at least appropriate, for each player, as it provides a point of view or perspective that is specifically relevant for that player. In at least one embodiment, each player may also have an option to modify a view provided, such as to change between a first person view and a third person view) and ([0354] In at least one embodiment, a data processing pipeline may receive input data (e.g., imaging data 3108) in a DICOM, RIS, CIS, REST compliant, RPC, raw, and/or other format in response to an inference request (e.g., a request from a user of deployment system 3106, such as a clinician, a doctor, a radiologist, etc.). In at least one embodiment, input data may be representative of one or more images, video, and/or other data representations generated by one or more imaging devices, sequencing devices, radiology devices, genomics devices, and/or other device types. In at least one embodiment, data may undergo pre-processing as part of data processing pipeline to prepare data for processing by one or more applications. In at least one embodiment, post-processing may be performed on an output of one or more inferencing tasks or other processing tasks of a pipeline to prepare an output data for a next application and/or to prepare output data for transmission and/or use by a user (e.g., as a response to an inference request). In at least one embodiment, inferencing tasks may be performed by one or more machine learning models, such as trained or deployed neural networks, which may include output models 3116 of training system 3104).
For motivation, see claim 14.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Peter Hoang whose telephone number is (571)270-1346. The examiner can normally be reached Monday-Friday 8:00 am - 5:00 pm PST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hajnik F. Daniel can be reached at (571) 272-7642. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PETER HOANG/Primary Examiner, Art Unit 2616
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