DETAILED ACTION
Remarks
This office action is issued in response to communication filed on 5/5/2026. Claims 6-8 and 10-26 are pending in this Office Action.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendments/Arguments
The 35 USC 112 rejection has been withdrawn in response to applicant’s amendment that overcomes the rejection.
Applicant’s arguments with respect to claims rejected under 35 USC 103 have been considered and are moot in view of new ground of rejection.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 6-7 and 10-18, 20-24 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al.(US Patent 12,322,068 B1, hereinafter “Kim”) and further in view of Heiser et al.(US Patent Application Publication 2021/0105339 A1, hereinafter “Heiser”) and further in view of Cui et al.(US patent Application Publication 2022/0153309 A1, hereinafter “Cui”)
As to claim 6, Kim teaches one or more non-transitory computer-readable media storing instructions that, when executed, cause one or more processors to perform operations comprising:
[controlling, by a component of a vehicle computing device, a vehicle in an environment at a first time using a first trajectory];
[determining a cost associated with the component of the vehicle computing device, the cost indicating an impact on a processor resource or a memory resource for the component to control the vehicle at the first time ; determining that the cost meets or exceeds a cost threshold];
receiving, by a diffusion model, data representing a condition of an environment;( ;(Kim col 4 ,lines 5-20 teaches a generative model takes in sequence of image, relevant camera parameter, orientation and other relevant information. Kim col 5, lines 19-20 teaches diffusion model can be used for generation);
generating, by the diffusion model and based at least in part on the condition data [and the cost meeting or exceeding the cost threshold], one of: first scene data for simulating potential interactions between the vehicle and one or more objects in the environment, or an intermediate output for input into a decoder that is configured to output second scene data including the one or more objects. (Kim col 5, line 65- col 6, line 27 teaches images can be passed to one or more encoders 204 which can each produce a set of 2D features and a density prediction. The output of volume renderer 210 can then be provided to a decoder 212 , which can generate output image 214 corresponding to this scene as viewed from this desired view )
[controlling, by the component of the vehicle computing device, the vehicle in the environment at a second time using a second trajectory, the second trajectory determined based at least in part on one of: an outcome of performing a simulation using the first scene data or a predicted state of an object of the one or more objects associated with the second scene data]
Kim fails to expressly teach determining a cost associated with the component of the vehicle computing device, the cost indicating an impact on a processor resource or a memory resource for the component to control the vehicle at the first time ; determining that the cost meets or exceeds a cost threshold.
However, Heiser teaches determining a cost associated with the component of the vehicle computing device, the cost indicating an impact on a processor resource or a memory resource for the component to control the vehicle at the first time ; determining that the cost meets or exceeds a cost threshold;
Heiser par [0039] teaches the cost module may function to determine computing resource costs to run specific application run requests. Heiser par [0040] teaches the cost modules functions to determine or calculate computing resources costs for the application run requests. Heiser par [0071] teaches the computing resource cost may be based on costs exceeding a minimum cost threshold)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kim’s diffusion model with the cost threshold of Heiser in order to determine to implement the diffusion model to achieve the claimed invention. One would have been motivated to make such combination to alleviate the perpetual strain to the computing resources and capabilities of an autonomous vehicle often caused by a combination of the normal operational requirements of the vehicle as well as by the extra operational processing of thousands to millions of routines of the applications and programs in communication with or being operated by the autonomous vehicle.(Heiser par [0029])
Kim and Heiser fail to expressly teach controlling, by a component of a vehicle computing device, a vehicle in an environment at a first time using a first trajectory];
[controlling, by the component of the vehicle computing device, the vehicle in the environment at a second time using a second trajectory, the second trajectory determined based at least in part on one of: an outcome of performing a simulation using the first scene data or a predicted state of an object of the one or more objects associated with the second scene data.
However, Cui teaches controlling, by a component of a vehicle computing device, a vehicle in an environment at a first time using a first trajectory];
[controlling, by the component of the vehicle computing device, the vehicle in the environment at a second time using a second trajectory, the second trajectory determined based at least in part on one of: an outcome of performing a simulation using the first scene data or a predicted state of an object of the one or more objects associated with the second scene data.(Cui par [0062] teaches The vehicle computing system 210 can be configured to predict a motion of the object(s) within the surrounding environment of the vehicle 205. For instance, the vehicle computing system 210 can generate prediction data 275B associated with such object(s). The prediction data 275B can be indicative of one or more predicted future locations of each respective object. For example, the prediction function 270B can determine a predicted motion trajectory along which a respective object is predicted to travel over time. Cui par [0063] teaches he motion plan can include vehicle actions (e.g., speed(s), acceleration(s), other actions, etc.) with respect to one or more of the objects within the surrounding environment of the vehicle 205 as well as the objects' predicted movements. The motion plan can include one or more vehicle motion trajectories that indicate a path for the vehicle 205 to follow)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kim and Heiser with the teaching of Cui to achieve the claimed invention. One would have been motivated to make such combination to improve motion forecast and reduce computer resource usage.(Cui par [0005])
As to claim 7,Kim , Heiser and Cui teach the one or more non-transitory computer-readable media of claim 6, wherein the first scene data or the second scene data comprises discrete data associated with the one or more objects.(Kim col 6, lines 1-10 teaches density voxels 206 and feature voxels 208)
As to claim 10, Kim , Heiser and Cui teach the one or more non-transitory computer-readable media of claim 6, wherein: the intermediate output represents an object of the one or more objects absent from the data received by the diffusion model. (Kim col 6, lines 1-10 teaches density voxels 206 and feature voxels 208)
As to claim 11, Kim , Heiser and Cui teach the one or more non-transitory computer-readable media of claim 6, wherein the data comprises text data describing an intersection type, a number of objects, or a scene characteristic to include in the first scene data. (Kim col 3 , lines 29-40 teaches encoder 106 can analyze images 102, extract representative features of those images and encode those features into a latent representation 108 of a scene represented in images 102)
As to claim 12, Kim , Heiser and Cui teach the one or more non-transitory computer-readable media of claim 6, wherein the data further represents a first action for a first object of the one or more objects and a second action for a second object of the one or more objects.(Kim col 41, lines 36-40 teaches the server 1078 may receive, from vehicles , image data representative of images showing unexpected or changed road conditions)
As to claim 13, Kim , Heiser and Cui teach the one or more non-transitory computer-readable media of claim 6, wherein the data is based at least in part on input from a user specifying the condition of the environment at a previous time.( Kim col 7 lines 50-60 teaches a user who wants to remove a car from a scene can cause corresponding density voxel values to be set to zero to effectively remove that car from consideration)
As to claim 14, Kim , Heiser and Cui teach the one or more non-transitory computer-readable media of claim 6, wherein the diffusion model is configured to apply a denoising algorithm to generate the first scene data or the second scene data. (Kim col 7, lines 20-24 teaches this process attempt to remove this random noise in order to generate images that are very similar in appearance to corresponding original input images )
As to claim 15, Kim , Heiser and Cui teach the one or more non-transitory computer-readable media of claim 6, wherein the diffusion model generates at least one object that does not exist in sensor data from a sensor associated with the vehicle.( Kim col 3, lines 32-36 teaches one or more images 102 can be input to a content generation system 104 that can generate 3D image content 112, such as may correspond to the scene or other object or representation)
As to claim 16, Kim , Heiser and Cui teach one or more non-transitory computer-readable media of claim 6, wherein the data comprises one of: a first vector representation of an object of the one or more objects or a second vector representation of the environment. (Kim col 3, lines 30-50 teaches latent representation 108 of a scene represented in images 102. Latent representation 108 may take form of a latent space or latent vector)
Claims 17-18 , 20,21 and 22 merely recite a computer method performed by the one or more non-transitory computer readable of claims 6-7,11,10 and 12 respectively. Accordingly, Kim, Heiser and Cui teach every limitation of claims 17-18 , 20,21 and 22 as indicates in the above rejection of claims 6-7,11,10 and 12 respectively.
Claims 23-24 and 26 merely recite a system to execute the instruction of the non-transitory computer readable of claims 6-7 and 14 respectively. Accordingly, Kim , Heiser and Cui teach every limitation of claims 23-24 and 26 as indicates in the above rejection of claims 6-7 and 14respectively.
Claims 8,19 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Kim, Heiser , Cui and further in view of Suo et al.(US Patent Application Publication 2022/0153314 A1, hereinafter “Suo”)
As to claim 8, Kim , Heiser and Cui teach the one or more non-transitory computer-readable media of claim 6 but fail to teach wherein the data comprises a node or a token to represent a potential action of an object of the one or more objects.
However, Suo teaches wherein the data comprises a node or a token to represent a potential action of an object of the one or more objects. ( Suo par [0103] teaches fully connected interaction graph with objects as nodes)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Kim , Heiser, Cui and Suo to achieve the claimed invention. One would have been motivated to make such combination to generate synthetic testing data which can be used to massively scale evaluation of autonomous systems enabling rapid development and deployment (Suo par [004])
As to claims 19 and 25, see the above rejection of claim 8.
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 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.
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/HIEN L DUONG/Primary Examiner, Art Unit 2147
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