DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
2. The amendment filed January 13, 2026 has been entered. Claims 1, 10, 16, 26-27, 31-32, and 43-55 remain pending in the application.
Response to Arguments
3. Applicant’s arguments with respect to claim(s) 1, 10, 16, 26-27, 31-32, and 43-55 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
4. Conclusion: The rejections set in the previous Office Action are shown to have been proper, and the claims are rejected below. New citations and parenthetical remarks can be considered new grounds of rejection and such new grounds of rejection are necessitated by the Applicant’s amendments to the claims. Therefore, the present Office Action is made final.
Claim Rejections - 35 USC § 103
5. 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.
6. The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
7. Claim(s) 1, 10, 16, 31-32, 43-44, and 46-55 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ju (U.S. Patent Application Publication No. 2024/0135686 A1) in view of Morrison et al. (U.S. Patent Application Publication No. 2014/0365891 A1), hereinafter referred to as Morrison, Cen et al. (“Segment Anything in 3D with NeRFs”), hereinafter referred to as Cen, and Yamamoto (U.S. Patent Application Publication No. 2013/0136341 A1). (Note: The Ju reference is a valid reference through its foreign priority date of December 6, 2022 which was checked for support.)
8. Regarding claim 1, Ju teaches a device, comprising: a processor system comprising one or more processors (Paragraph 7 teaches a cloud server including a processor); and storage accessible to the processor system and comprising instructions executable by the processor system to (Paragraph 7 teaches a memory storing at least one instruction that the processor is configured to execute): access a semantic map (Paragraph 42 teaches a master or server device can receive, store, and manage spatial maps obtained from other IoT devices. Paragraph 50-54, Figure 2A mentions these spatial maps consists of a semantic map layer which means we can consider these spatial maps as semantic maps);
present a graphical user interface (GUI) on a display (Paragraph 171 teaches a user interface displayed on the display unit which receives user input. The user interface can be considered a GUI), t
receive input from at least a first camera indicated in the semantic map (Paragraph 40 mentions the electronic device 100, robots 300-1 and 300-2, and smart home camera 300-3 can generate and transmit object information and a spatial map, which includes the semantic map, of the space. These can be considered an input received from the camera. The first camera could be the smart home camera 300-3; Paragraph 74 mentions information on the device, position, and viewpoint from where the image has been captured is also collected. Also, it mentions that the spatial map can be labeled or tagged with information regarding the position and viewpoint from which the object recognition result was obtained from. This means the first camera can be indicated in the spatial map; Paragraph 76, Figure 5 mentions a second camera in electronic device 100 can takes images of the space from different viewpoints);
(Paragraph 74 as cited above mentions tagging the location of the device where images were captured from), the input (Paragraph 40 as cited above mentions transmitting object information and a spatial map), (Paragraph 52-54 mention assigning semantic information to regions in the map and also providing information of at least one object in the map. Also mentions processing 3D point cloud data which can provide 3D information of an object; Paragraph 158 teaches using LIDAR to model objects into three-dimensional images),
However, Ju fails to teach the GUI showing one or more images of an area indicated in the semantic map; receive, via the GUI, a user selection of a first object indicated in the one or more images; responsive to receipt of the user selection, use a neural radiance field (NeRF) neural network to generate a three-dimensional (3D) model of the first object, the 3D model being different from the semantic map, the 3D model being a model of a single object from the semantic map, the single object established by the first object, the 3D model also being generated based on enough coverage data being available for the first object to generate the 3D model; and responsive to determining that not enough coverage data is available for the first object to generate the 3D model, one or more of: control a motor to change a field of view of a second camera to show the first object; and/or present a prompt for a user to move the second camera to a different location to gather additional coverage data for the first object.
Morrison teaches the GUI showing one or more images of an area indicated in the semantic map (Paragraph 54 teaches a video surveillance system which provides access to live video data from cameras in the facility. The video data can be considered one or more images; Paragraph 66 teaches there is a map layer that represents a floorplan or semantic map. GUI components are overlaid on the map layer to provide access to a live video feed from a camera; Paragraph 144 and Figure 4 teach live video data is displayed in a GUI. The live video data displays the scene of an area indicated in the map).
Ju and Morrison are considered analogous to the claimed invention because both are in the same field of surveying a space with a camera. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the device with instructions to create a 3D model of an object from a semantic map taught by Ju with the GUI displaying one or more images of an area taught by Morrison in order to allow users to conveniently access lots of information about a facility (Morrison Abstract).
However, Ju and Morrison fail to teach receiving, via the GUI, a user selection of a first object indicated in the one or more images; responsive to receipt of the user selection, use a neural radiance field (NeRF) neural network to generate a three-dimensional (3D) model of the first object, the 3D model being different from the semantic map, the 3D model being a model of a single object from the semantic map, the single object established by the first object, the 3D model also being generated based on enough coverage data being available for the first object to generate the 3D model; and responsive to determining that not enough coverage data is available for the first object to generate the 3D model, one or more of: control a motor to change a field of view of a second camera to show the first object; and/or present a prompt for a user to move the second camera to a different location to gather additional coverage data for the first object.
Cen teaches receiving, via the GUI, a user selection of a first object indicated in the one or more images; and responsive to receipt of the user selection to use a neural radiance field (NeRF) neural network to generate a three-dimensional (3D) model of the first object, the 3D model being different from the semantic map, the 3D model being a model of a single object from the semantic map, the single object established by the first object (Figure 1 teaches a user can select a first object, shown through a red dot, which creates the 3D model in the 3rd step. The GUI is taught by Morrison and would be obvious to combine since the GUI displays images of the area or scene and Cen teaches selecting an object from a scene. Thus, the object selected taught by Cen can be done through a GUI; Section 3.2 and Figure 2 teaches using the NeRF model to create the 3D mask or 3D model of just one selected object in a scene which can be from a semantic map as taught in Ju. This single object exists in a scene and is different from a semantic map).
Ju and Morrison are considered analogous to the claimed invention because both are in the same field of surveying a space with a camera. Cen is considered analogous to the claimed invention because it is in the same field of using neural radiance fields to create 3D objects from a scene. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the device with instructions to create a 3D model of an object from a semantic map taught by Ju in view of Morrison with the user selection of an object to generate a 3D model taught by Cen in order to provide a cheap and efficient solution that quickly generates 3D objects from various scenes (Cen Abstract).
However, Ju, Morrison, and Cen are not relied upon for the below claim language: the 3D model also being generated based on enough coverage data being available for the first object to generate the 3D model; and responsive to determining that not enough coverage data is available for the first object to generate the 3D model, one or more of: control a motor to change a field of view of a second camera to show the first object; and/or present a prompt for a user to move the second camera to a different location to gather additional coverage data for the first object.
Yamamoto teaches the 3D model also being generated based on enough coverage data being available for the first object to generate the 3D model (Paragraph 66-67 and Figure 8 teaches determining if capturing or coverage of the object is enough to execute a 3D model based on the 3D model data. If enough coverage data is available, then the 3D model generation process is terminated with the 3D model generated);
and responsive to determining that not enough coverage data is available for the first object to generate the 3D model, one or more of: control a motor to change a field of view of a second camera to show the first object; and/or present a prompt for a user to move the second camera to a different location to gather additional coverage data for the first object (Paragraph 54 teaches detecting missing areas and determining a position and direction for the camera to move along at which the missing area can be captured. Paragraph 57 teaches the missing-area direction for the camera to move along can be shown on a screen which teaches a prompt for the user to move the second camera to a different location to gather additional coverage data for the first object. The Applicant uses and/or which is interpreted as an ‘or’. Thus, only one of the limitations need to be taught; Paragraph 66-69 and Figure 5 teaches checking in B16 if there is enough coverage data. Else, it will detect missing areas and prompt the user to move the camera to get missing regions for 3D modeling;).
Ju and Morrison are considered analogous to the claimed invention because both are in the same field of surveying a space with a camera. Cen is considered analogous to the claimed invention because it is in the same field of using neural radiance fields to create 3D objects from a scene. Yamamoto is considered analogous to the claimed invention because it is in the same field of generating 3D models from a camera. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the device with instructions to create a 3D model of an object from a semantic map taught by Ju in view of Morrison and Cen with moving the camera to get more coverage data taught by Yamamoto in order to prevent the failure of not obtaining enough images to create a proper 3D model (Yamamoto Paragraph 4).
9. Regarding claim 10, Ju teaches a method, comprising: accessing a semantic map (Paragraph 42 mentions a master or server device can receive, store, and manage spatial maps obtained from other IoT devices. Paragraph 50-54, Figure 2A mentions these spatial maps consists of a semantic map layer which means we can consider these spatial maps as semantic maps);
presenting a graphical user interface (GUI) on a display (Paragraph 171 teaches a user interface displayed on the display unit which receives user input. The user interface can be considered a GUI),
receiving input from at least a first camera indicated in the semantic map (Paragraph 40 mentions the electronic device 100, robots 300-1 and 300-2, and smart home camera 300-3 can generate and transmit object information and a spatial map, which includes the semantic map, of the space. These can be considered an input received from the camera. The first camera could be the smart home camera 300-3; Paragraph 74 mentions information on the device, position, and viewpoint from where the image has been captured is also collected. Also, it mentions that the spatial map can be labeled or tagged with information regarding the position and viewpoint from which the object recognition result was obtained from. This means the first camera can be indicated in the spatial map; Paragraph 76, Figure 5 mentions a second camera in electronic device 100 can takes images of the space from different viewpoints);
(Paragraph 74 as cited above mentions tagging the location of the device where images were captured from), the input (Paragraph 40 as cited above mentions transmitting object information and a spatial map), and (Paragraph 52-54 mention assigning semantic information to regions in the map and also providing information of at least one object in the map. Also mentions processing 3D point cloud data which can provide 3D information of an object),
However, Ju fails to teach the GUI showing one or more images of an area indicated in the semantic map; receiving, via the GUI, a user selection of a first object indicated on the GUI; responsive to receipt of the user selection to use a neural radiance field (NeRF) neural network to generate a three-dimensional (3D) model of the first object, the 3D model being different from the semantic map, the 3D model being a model of a single object from the semantic map and not other objects from the semantic map, the single object established by the first object, the 3D model also being generated based on enough coverage data being available for the first object to generate the 3D model; and responsive to determining that not enough coverage data is available for the first object to generate the 3D model, one or more of: control a motor to change a field of view of a second camera to show the first object; and/or present a prompt for a user to move the second camera to a different location to gather additional coverage data for the first object.
Morrison teaches the GUI showing one or more images of an area indicated in the semantic map (Paragraph 54 teaches a video surveillance system which provides access to live video data from cameras in the facility. The frames in the live video data can be considered one or more images of the area; Paragraph 66 teaches there is a map layer that represents a floorplan or semantic map. GUI components are overlaid on the map layer to provide access to a live video feed from a camera; Paragraph 144 and Figure 4 teach live video data is displayed in a GUI. The live video data displays the scene of an area indicated in the map).
Ju and Morrison are considered analogous to the claimed invention because both are in the same field of surveying a space with a camera. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method to create a 3D model of an object from a semantic map taught by Ju with the images displayed on the GUI taught by Morrison in order to allow users to conveniently access lots of information about a facility (Morrison Abstract).
However, Ju and Morrison fail to teach receiving, via the GUI, a user selection of a first object indicated on the GUI; responsive to receipt of the user selection to use a neural radiance field (NeRF) neural network to generate a three-dimensional (3D) model of the first object, the 3D model being different from the semantic map, the 3D model being a model of a single object from the semantic map and not other objects from the semantic map, the single object established by the first object, the 3D model also being generated based on enough coverage data being available for the first object to generate the 3D model; and responsive to determining that not enough coverage data is available for the first object to generate the 3D model, one or more of: control a motor to change a field of view of a second camera to show the first object; and/or present a prompt for a user to move the second camera to a different location to gather additional coverage data for the first object.
Cen teaches receiving, via the GUI, a user selection of a first object indicated in the one or more images; and responsive to receipt of the user selection to use a neural radiance field (NeRF) neural network to generate a three-dimensional (3D) model of the first object, the 3D model being different from the semantic map, the 3D model being a model of a single object from the semantic map, the single object established by the first object (Figure 1 teaches a user can select a first object, shown through a red dot, which creates the 3D model in the 3rd step. The GUI is taught by Morrison and would be obvious to combine since the GUI displays images of the area or scene and Cen teaches selecting an object from a scene. Thus, the object selected taught by Cen can be done through a GUI; Section 3.2 and Figure 2 teaches using the NeRF model to create the 3D mask or 3D model of just one selected object in a scene which can be from a semantic map as taught in Ju. This single object exists in a scene and is different from a semantic map).
Ju and Morrison are considered analogous to the claimed invention because both are in the same field of surveying a space with a camera. Cen is considered analogous to the claimed invention because it is in the same field of using neural radiance fields to create 3D objects from a scene. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method to create a 3D model of an object from a semantic map taught by Ju in view of Morrison with the user selection of an object to generate a 3D model taught by Cen in order to provide a cheap and efficient solution that quickly generates 3D objects from various scenes (Cen Abstract).
However, Ju, Morrison, and Cen are not relied upon for the below claim language: the 3D model also being generated based on enough coverage data being available for the first object to generate the 3D model; and responsive to determining that not enough coverage data is available for the first object to generate the 3D model, one or more of: control a motor to change a field of view of a second camera to show the first object; and/or present a prompt for a user to move the second camera to a different location to gather additional coverage data for the first object.
Yamamoto teaches the 3D model also being generated based on enough coverage data being available for the first object to generate the 3D model (Paragraph 66-67 and Figure 8 teaches determining if capturing or coverage of the object is enough to execute a 3D model based on the 3D model data. If enough coverage data is available, then the 3D model generation process is terminated with the 3D model generated);
and responsive to determining that not enough coverage data is available for the first object to generate the 3D model, one or more of: control a motor to change a field of view of a second camera to show the first object; and/or present a prompt for a user to move the second camera to a different location to gather additional coverage data for the first object (Paragraph 54 teaches detecting missing areas and determining a position and direction for the camera to move along at which the missing area can be captured. Paragraph 57 teaches the missing-area direction for the camera to move along can be shown on a screen which teaches a prompt for the user to move the second camera to a different location to gather additional coverage data for the first object. The Applicant uses and/or which is interpreted as an ‘or’. Thus, only one of the limitations need to be taught; Paragraph 66-69 and Figure 5 teaches checking in B16 if there is enough coverage data. Else, it will detect missing areas and prompt the user to move the camera to get missing regions for 3D modeling;).
Ju and Morrison are considered analogous to the claimed invention because both are in the same field of surveying a space with a camera. Cen is considered analogous to the claimed invention because it is in the same field of using neural radiance fields to create 3D objects from a scene. Yamamoto is considered analogous to the claimed invention because it is in the same field of generating 3D models from a camera. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method to create a 3D model of an object from a semantic map taught by Ju in view of Morrison and Cen with moving the camera to get more coverage data taught by Yamamoto in order to prevent the failure of not obtaining enough images to create a proper 3D model (Yamamoto Paragraph 4).
10. Regarding claim 16, claim 16 is the CRSM that is not a transitory signal (Ju Paragraph 7 mentions a memory storing at least one instruction that the processor is configured to execute) claim of claim 10 and is accordingly rejected using substantially similar rationale as to that which is set for with respect to claim 10.
11. Regarding claim 31, Ju in view of Morrison, Cen, and Yamamoto teaches the limitations of claim 1. Ju further teaches the device wherein the instructions are executable to: identify a change in location of a device; and responsive to identification of the change in the location of the device, update the semantic map (Paragraph 49 teaches detecting a change in an objects position. The object can be a device. When the position is changed, it teaches updating the spatial map which is the semantic map).
12. Regarding claim 32, Ju in view of Morrison, Cen, and Yamamoto teaches the limitations of claim 1. Ju further teaches the device comprising the display (Paragraph 171 teaches a user interface is displayed on a display unit. Thus, the device comprises of a display).
13. Regarding claim 43, Ju in view of Morrison, Cen, and Yamamoto teach the limitations of claim 1. However, Ju, Morrison, and Cen are not relied upon for the below claim language: responsive to determining that not enough coverage data is available for the first object to generate the 3D model, control the motor to change the field of view of the second camera to show the first object.
Yamamoto teaches responsive to determining that not enough coverage data is available for the first object to generate the 3D model, control the motor to change the field of view of the second camera to show the first object (Paragraph 44-46 teaches controlling the vibration of a vibrator to change the view of the second camera to show the object. The vibrations are controlled to notify the user on how to move the camera which teaches changings its field of view. Controlling the vibrations teaches controlling a motor).
Ju and Morrison are considered analogous to the claimed invention because both are in the same field of surveying a space with a camera. Cen is considered analogous to the claimed invention because it is in the same field of using neural radiance fields to create 3D objects from a scene. Yamamoto is considered analogous to the claimed invention because it is in the same field of generating 3D models from a camera. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the device to create a 3D model of an object from a semantic map taught by Ju in view of Morrison and Cen with using a motor to control the camera to get more coverage data taught by Yamamoto in order to prevent the failure of not obtaining enough images to create a proper 3D model (Yamamoto Paragraph 4).
14. Regarding claim 44, Ju in view of Morrison, Cen, and Yamamoto teach the limitations of claim 1. However, Ju, Morrison, and Cen are not relied upon for the below claim language: responsive to determining that not enough coverage data is available for the first object to generate the 3D model, present the prompt for the user to move the second camera to a different location to gather additional coverage data for the first object.
Yamamoto teaches responsive to determining that not enough coverage data is available for the first object to generate the 3D model, present the prompt for the user to move the second camera to a different location to gather additional coverage data for the first object (Paragraph 54 teaches detecting missing areas and determining a position and direction for the camera to move along at which the missing area can be captured. Paragraph 57 teaches the missing-area direction for the camera to move along can be shown on a screen which teaches a prompt for the user to move the second camera to a different location to gather additional coverage data for the first object. The Applicant uses and/or which is interpreted as an ‘or’. Thus, only one of the limitations need to be taught; Paragraph 66-69 and Figure 5 teaches checking in B16 if there is enough coverage data. Else, it will detect missing areas and prompt the user to move the camera to get missing regions for 3D modeling;).
Ju and Morrison are considered analogous to the claimed invention because both are in the same field of surveying a space with a camera. Cen is considered analogous to the claimed invention because it is in the same field of using neural radiance fields to create 3D objects from a scene. Yamamoto is considered analogous to the claimed invention because it is in the same field of generating 3D models from a camera. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method to create a 3D model of an object from a semantic map taught by Ju in view of Morrison and Cen with moving the camera to get more coverage data taught by Yamamoto in order to prevent the failure of not obtaining enough images to create a proper 3D model (Yamamoto Paragraph 4).
15. Regarding claim 46, Ju in view of Morrison, Cen, and Yamamoto teach the limitations of claim 44. Ju further teaches wherein the prompt is presented as part of a graphical user interface (GUI), the GUI presented on a display accessible to the device, the GUI comprising a graphic indicating a current location of the second camera (Paragraph 108 teaches the user can mark a position on the spatial map and Paragraph 171 teaches a user interface displayed on the display unit which receives user input and displays information processed by the electronic device. Thus, the user interface can be considered a GUI which displays a spatial map for user interaction; Paragraph 74 teaches information on the device, position, and viewpoint from where the image has been captured is also collected. Also, it teaches that the spatial map can be labeled or tagged with information regarding the position and viewpoint from which the object recognition result was obtained from. This means the second camera can be indicated in the spatial map).
16. Regarding claim 47, Ju in view of Morrison, Cen, and Yamamoto teach the limitations of claim 46. Ju further teaches wherein the graphic is a first graphic, and wherein the GUI comprises a second graphic different from the first graphic, the second graphic indicating the different location to which to move the second camera (Paragraph 108 teaches the user can mark a position on the spatial map and Paragraph 184 teaches that the second camera 100 can move to a position specified in the spatial map. The first graphic can be the spatial map before the user input and the second graphic the map after the user input which signifies a new location to move the second camera; Paragraph 171 teaches a user interface displayed on the display unit which receives user input and displays information processed by the electronic device. Thus, the user interface can be considered a GUI which displays a spatial map for user interaction).
17. Regarding claim 48, Ju in view of Morrison, Cen, and Yamamoto teach the limitations of claim 46. However, Ju, Morrison, and Cen are not relied upon for the below claim language: responsive to the second camera being placed at the different location, update the GUI to instruct the user to rotate the second camera to a particular orientation in which the second camera's field of view shows the first object.
Yamamoto teaches responsive to the second camera being placed at the different location, update the GUI to instruct the user to rotate the second camera to a particular orientation in which the second camera's field of view shows the first object (Paragraph 46 teaches the user moves the camera to capture the missing or occluded areas. It teaches the instructions can be displayed on the screen of the LCD which teaches the GUI can instruct the user to move the camera; Paragraph 51 teaches the missing-area direction can be a rotation at the present position. The missing-area direction is the direction in which to move the camera to capture the missing coverage data. Figure 5 teaches the missing areas like 44A, 44B, and 44C are areas on an object. Rotating an object to those missing areas teaches rotating the camera to an orientation in which the field of view shows the object. Thus, Yamamoto teaches updating a GUI to instruct the user to rotate the second camera to show the first object).
Ju and Morrison are considered analogous to the claimed invention because both are in the same field of surveying a space with a camera. Cen is considered analogous to the claimed invention because it is in the same field of using neural radiance fields to create 3D objects from a scene. Yamamoto is considered analogous to the claimed invention because it is in the same field of generating 3D models from a camera. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method to create a 3D model of an object from a semantic map taught by Ju in view of Morrison and Cen with rotating the camera taught by Yamamoto in order to prevent the failure of not obtaining enough images to create a proper 3D model (Yamamoto Paragraph 4).
18. Regarding claim 49, Ju in view of Morrison, Cen, and Yamamoto teach the limitations of claim 46. However, Ju, Morrison, and Cen are not relied upon for the below claim language: responsive to the second camera being placed at the different location, control the motor to reorient the second camera toward the first object.
Yamamoto teaches responsive to the second camera being placed at the different location, control the motor to reorient the second camera toward the first object. (Paragraph 44-46 teaches controlling the vibration of a vibrator to change the view of the second camera to show the object. The vibrations are controlled to notify the user on how to move the camera which teaches changings its field of view. Controlling the vibrations teaches controlling a motor; Paragraph 46 teaches the user moves the camera to capture the missing or occluded areas. It teaches the instructions can be done through vibrations which instruct the user to move the camera to another location; Paragraph 51 teaches the missing-area direction can be a direction of parallel movement and a rotation at the present position. The missing-area direction is the direction in which to move the camera to capture the missing coverage data. Figure 5 teaches the missing areas like 44A, 44B, and 44C are areas on an object. Rotating an object to those missing areas teaches rotating the camera to an orientation in which the field of view shows the object. Thus, Yamamoto teaches controlling a motor to reorient the camera toward the first object at any location which can be the location the second camera is placed at).
Ju and Morrison are considered analogous to the claimed invention because both are in the same field of surveying a space with a camera. Cen is considered analogous to the claimed invention because it is in the same field of using neural radiance fields to create 3D objects from a scene. Yamamoto is considered analogous to the claimed invention because it is in the same field of generating 3D models from a camera. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method to create a 3D model of an object from a semantic map taught by Ju in view of Morrison and Cen with rotating the camera taught by Yamamoto in order to prevent the failure of not obtaining enough images to create a proper 3D model (Yamamoto Paragraph 4).
19. Regarding claim 50, Ju in view of Morrison, Cen, and Yamamoto teach the limitations of claim 1. Ju further teaches the device wherein the second camera is different from the first camera (Paragraph 40 mentions the electronic device 100, robots 300-1 and 300-2, and smart home camera 300-3 can generate and transmit object information and a spatial map, which includes the semantic map, of the space. These can be considered an input received from the camera. The first camera could be the smart home camera 300-3 and the second camera could be the electronic device 100 which is different from the first camera).
20. Regarding claim 51, Ju in view of Morrison, Cen, and Yamamoto teach the limitations of claim 10. Claim 51 is similar in scope to claim 43. Therefore, similar rationale as applied in the rejection of claim 43 applies herein.
21. Regarding claim 52, Ju in view of Morrison, Cen, and Yamamoto teach the limitations of claim 10. Claim 52 is similar in scope to claim 44. Therefore, similar rationale as applied in the rejection of claim 44 applies herein.
22. Regarding claim 53, Ju in view of Morrison, Cen, and Yamamoto teach the limitations of claim 16. Claim 53 is similar in scope to claim 43. Therefore, similar rationale as applied in the rejection of claim 43 applies herein.
23. Regarding claim 54, Ju in view of Morrison, Cen, and Yamamoto teach the limitations of claim 16. Claim 54 is similar in scope to claim 44. Therefore, similar rationale as applied in the rejection of claim 44 applies herein.
24. Regarding claim 55, Ju in view of Morrison, Cen, and Yamamoto teach the limitations of claim 54. Ju further teaches wherein the prompt is presented as part of a graphical user interface (GUI), the GUI presented on a display, the GUI comprising a graphic indicating the different location to which to move the second camera. (Paragraph 108 teaches the user can mark a position on the spatial map and Paragraph 184 teaches that the second camera 100 can move to a position specified in the spatial map; Paragraph 171 teaches a user interface displayed on the display unit which receives user input and displays information processed by the electronic device. Thus, the user interface can be considered a GUI which displays a spatial map for user interaction).
25. Claim(s) 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ju (U.S. Patent Application Publication No. 2024/0135686 A1) in view of Morrison et al. (U.S. Patent Application Publication No. 2014/0365891 A1), hereinafter referred to as Morrison, Cen et al. (“Segment Anything in 3D with NeRFs”), hereinafter referred to as Cen, and Yamamoto (U.S. Patent Application Publication No. 2013/0136341 A1), as applied to claim 1, and further in view of Lee et al. (U.S. Patent Application Publication No. 2024/0242502 A1).
Regarding claim 26, Ju in view of Morrison, Cen, and Yamamoto teaches the limitations of claim 1. However, Ju, Morrison, Cen, and Yamamoto fail to teach the device wherein the instructions are executable to: wherein the instructions are executable to: assign a first name to the 3D model, the first name corresponding to a second name of the first object as indicated in the semantic map.
Lee teaches the device wherein the instructions are executable to: wherein the instructions are executable to: assign a first name to the 3D model (Paragraph 100 teaches storing the identification information and model name of each object. This can be considered assigning the object a first name. The object is associated with a 3D model as taught by Ju, Morrison, and Cen in Claim 16. Thus, naming the object will also name the 3D model), the first name corresponding to a second name of the first object as indicated in the semantic map (Paragraph 105 teaches the objects are tagged on the spatial map with object information. Figure 7 teaches the object information can correspond to a second name which is the category type).
Ju, Morrison, and Lee are considered analogous to the claimed invention because both are in the same field of surveying a space with a camera. Cen is considered analogous to the claimed invention because it is in the same field of using neural radiance fields to create 3D objects from a scene. Yamamoto is considered analogous to the claimed invention because it is in the same field of generating 3D models from a camera. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the device with instructions to create a 3D model of an object from a semantic map taught by Ju in view of Morrison, Cen, and Yamamoto with the naming of objects taught by Lee in order to recognize and provide information for objects belonging to the space (Lee Paragraph 6).
26. Claim(s) 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ju (U.S. Patent Application Publication No. 2024/0135686 A1) in view of Morrison et al. (U.S. Patent Application Publication No. 2014/0365891 A1), hereinafter referred to as Morrison, Cen et al. (“Segment Anything in 3D with NeRFs”), hereinafter referred to as Cen, and Yamamoto (U.S. Patent Application Publication No. 2013/0136341 A1), as applied to claim 1 and further in view of Huang et al. (Chinese Patent Publication No. 114119838 A), hereinafter referred to as Huang.
Regarding claim 27, Ju in view of Morrison, Cen, and Yamamoto teaches the limitations of claim 1. However, Ju fails to teach the device wherein the 3D model indicates, for the first object, both texture data and color data for the first object.
Huang teaches the device wherein the 3D model indicates, for the first object, both texture data and color data for the first object (Paragraph n0020 teaches the 3d model of the object preserves texture information. Thus, the 3D model indicates texture data; Paragraph n0024 teaches the 3D model expressions the texture information of the object; Paragraph n0025 teaches the 3d model of the object includes color information).
Ju and Cen are considered analogous to the claimed invention because both are in the same field of recognizing objects in a view. Morrison is considered analogous to the claimed invention because it is in the same field of surveying a space with a camera. Huang is considered analogous to the claimed invention because it is in the same field of capturing images and obtaining 3D information of a scene. Yamamoto is considered analogous to the claimed invention because it is in the same field of generating 3D models from a camera. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to combine the device with the semantic map taught by Ju in view of Morrison, Cen, and Yamamoto with the 3D model generation with texture and color data taught by Huang in order to obtain a more precise geometric structure through a simple method (Huang Paragraph n0002-n0003).
27. Claim(s) 45 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ju (U.S. Patent Application Publication No. 2024/0135686 A1) in view of Morrison et al. (U.S. Patent Application Publication No. 2014/0365891 A1), hereinafter referred to as Morrison, Cen et al. (“Segment Anything in 3D with NeRFs”), hereinafter referred to as Cen, and Yamamoto (U.S. Patent Application Publication No. 2013/0136341 A1), as applied to claim 44 and further in view of Tytgat et al. (U.S. Patent Application Publication No. 2023/0196655 A1), hereinafter referred to as Tytgat.
Regarding claim 45, Ju in view of Morrison, Cen, and Yamamoto teach the limitations of claim 44. However, Ju, Morrison, Cen, and Yamamoto are not relied upon for the below claim language: wherein the prompt also indicates that there exists an occlusion for the first object.
Tytgat teaches wherein the prompt also indicates that there exists an occlusion for the first object (Paragraph 53 teaches issuing an alert to a user when there are occlusions on an object).
Ju, Morrison, and Tytgat are considered analogous to the claimed invention because both are in the same field of surveying a space with a camera. Cen is considered analogous to the claimed invention because it is in the same field of using neural radiance fields to create 3D objects from a scene. Yamamoto is considered analogous to the claimed invention because it is in the same field of generating 3D models from a camera. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the device creating a 3D model of an object taught by Ju in view of Morrison, Cen, and Yamamoto with an occlusion prompt taught by Tytgat in order to provide let the user know further analysis or actuation of one or more devices is needed (Tytgat Paragraph 38).
Conclusion
28. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
29. Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTINE Y AHN whose telephone number is (571)272-0672. The examiner can normally be reached M-F 8-5pm.
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/CHRISTINE YERA AHN/Examiner, Art Unit 2615
/ALICIA M HARRINGTON/Supervisory Patent Examiner, Art Unit 2615