DETAILED ACTION
Status of Claims
Claims 1-20 are currently pending and have been examined in this application. This NON-FINAL communication is the first action on the merits.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Applicant’s claim for the benefit of a prior-filed application filed in IN 202411065282 on 08/29/2024 under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 1-20, are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Naujoks (US 20250349074).
Regarding Claims 1, 7, Naujoks discloses the following limitations:
A method for generating synthetic three dimensional weather data, the method comprising: (Naujoks - [0013] The processors, systems, and/or methods described herein can be implemented by or included in at least one of a system associated with an autonomous or semi-autonomous machine (e.g., an in-vehicle infotainment system); a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system for generating or presenting virtual reality (VR) content, augmented reality (AR) content, and/or mixed reality (MR) content; a system for performing conversational AI operations; a system for performing generative AI operations, a system implemented using at least one language model—such as one or more large language models (LLMs) and/or one or more vision language models (VLMs), a system for generating synthetic data; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources. [0031] The data processing system 102 can execute a point cloud attenuation 110 process, machine learning models 112 (including at least a first model 114 and a second model 116), and a point filtering process 118 to simulate weather conditions in input point clouds 104, according to the techniques described herein. The input point clouds 104 may be any type of point cloud captured using any type of (e.g., emissive or non-visual) sensor, such as a LiDAR sensor, a RADAR sensor, an ultrasonic sensor, among others. The input point cloud 104 may be received or retrieved from one or external computing systems, provided in a request to process the input point cloud 104, or maintained in storage of the data processing system 102 for processing. The input point cloud 104 may include any number of points in a three-dimensional (3D) space.)
(Claim 7) A non-transitory computer readable medium storing a program causing at least one processor to execute a process to generating synthetic three dimensional weather data, the process comprising: (Naujoks - [0013], [0031], [0093] The computer-storage media may include both volatile and nonvolatile media and/or removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, and/or other data types.)
receiving, at a trained artificial intelligence (AI), descriptive information of at least one weather element; and (Naujoks - [0046] To generate and determine the properties of such additional points using the input weather condition 106 and the input weather parameter(s) 108, the data processing system 102 can execute the machine-learning models 112. The machine-learning models 112 may be stored, maintained, or in some implementations retrieved/received by the data processing system 102 to simulate specified input weather conditions in the input point cloud 104. The data processing system 102 can execute the model updater 120 to train/update the machine-learning models 112, as described in further detail herein. In some implementations, the machine-learning models 112 may be trained/updated for particular input weather conditions 106. For example, a first set of machine-learning models 112 may be trained/updated to generate and determine attributes for additional points 124 to simulate rain in the input point cloud 104, while a second set of machine-learning models 112 may be trained/updated to generate and determine attributes for additional points 124 to simulate fog.)
using the descriptive information of the at least one weather element, generating, with the AI, the synthetic three dimensional weather data. (Naujoks – [0031], [0046])
Regarding Claims 2, 8, 15, Naujoks further discloses:
wherein the descriptive information of the at least one weather element includes at least one two dimensional weather image, (Naujoks – [0046], [0050] each position in the 2D output data structure corresponds to a respective emitter and scan direction of the sensor. [0088] The rendering component 512 may render the application session (e.g., representative of the result of the input data) and the render capture component 514 may capture the rendering of the application session as display data (e.g., as image data capturing the rendered frame of the application session). The rendering of the application session may include ray or path-traced lighting and/or shadow effects, computed using one or more parallel processing units-such as GPUs, which may further employ the use of one or more dedicated hardware accelerators or processing cores to perform ray or path-tracing techniques—of the application server(s) 502. In some embodiments, one or more virtual machines (VMs)—e.g., including one or more virtual components, such as vGPUs, vCPUs, etc.—may be used by the application server(s) 502 to support the application sessions. The encoder 516 may then encode the display data to generate encoded display data and the encoded display data may be transmitted to the client device 504 over the network(s) 506 via the communication interface 518. The client device 504 may receive the encoded display data via the communication interface 520 and the decoder 522 may decode the encoded display data to generate the display data. The client device 504 may then display the display data via the display 524.)
text describing one or more of the at least one two dimensional weather image, and/or one or more other weather elements, and/or at least one image illustrating position of the at least one weather element. (Naujoks – [0046], [0050], [0088])
Regarding Claims 3, 10, 17, Naujoks further discloses:
(Claim 17) wherein the trained AI includes a trained synthetic two dimensional weather data generator AI and a trained two dimensional weather data to three dimensional weather data converter AI; (Naujoks – [0031], [0046], [0050])
wherein receiving, at the trained AI, the descriptive information of the at least one weather element comprises receiving, at a trained synthetic two dimensional weather data generator AI, the descriptive information of the at least one weather element; (Naujoks – [0031], [0046], [0050])
wherein using the descriptive information of the at least one weather element, generating, with the AI, the synthetic three dimensional weather data includes: (Naujoks – [0031], [0046], [0050])
using the descriptive information of the at least one weather element, generating, with the trained synthetic two dimensional weather data generator AI, a synthetic two dimensional weather image; and (Naujoks – [0031], [0046], [0050], [0088])
using the synthetic two dimensional weather image, generating, with a trained two dimensional weather data to three dimensional weather data converter AI, the synthetic three dimensional weather data. (Naujoks – [0031], [0046], [0050], [0088])
Regarding Claims 4, 11, 18, Naujoks further discloses:
(Claim 18) wherein the trained synthetic two dimensional weather data generator AI includes a trained two dimensional weather image encoder, a trained other weather data encoder, and/or a trained positional encoder; (Naujoks – [0031], [0046], [0050], [0088])
(Claim 18) wherein the trained synthetic two dimensional weather data generator AI further includes a trained three dimensional weather data decoder; (Naujoks – [0031], [0046], [0050], [0088])
(Claim 18) wherein the trained two dimensional weather data to three dimensional weather data converter AI includes the trained two dimensional weather image encoder and the trained three dimensional weather data decoder (Naujoks – [0031], [0046], [0050], [0088])
wherein receiving, at the trained synthetic two dimensional weather data generator AI, the descriptive information of the at least one weather element comprises receiving, by a trained two dimensional weather image encoder, at least one two dimensional weather image; (Naujoks – [0031], [0046], [0050], [0088])
receiving, by a trained other weather data encoder, text describing one or more of two dimensional weather images; and/or receiving, by a trained positional encoder, at least one image illustrating position of the at least one weather element; and (Naujoks – [0031], [0046], [0050], [0088])
wherein using the descriptive information of the at least one weather element, generating, with the trained synthetic two dimensional weather data generator AI, the synthetic two dimensional weather image comprises: at least one of: (Naujoks – [0031], [0046], [0050], [0088])
using the at least one two dimensional weather image, generating, with the trained two dimensional weather image encoder, first embedded data; (Naujoks – [0031], [0046], [0050], [0088], [0118] an embedded system controller)
using the text describing one or more of the two dimensional weather images, generating, with the trained other weather data encoder, second embedded data; and using the at least one image illustrating position of the at least one weather element, generating, with the trained positional encoder, third embedded data; (Naujoks – [0031], [0046], [0050], [0088], [0118])
using the first embedded data, the second embedded data, and/or the third embedded data, generating, with a trained two dimensional weather image decoder, the synthetic two dimensional weather image; (Naujoks – [0031], [0046], [0050], [0088], [0118], [0060] In a non-limiting example, the accuracy of the first model 114 may be tested periodically (e.g., after predetermined numbers of training/updating examples have been used to train/update the first model 114, etc.). This process can be repeated until a training termination condition is reached, such as an accuracy threshold being met or upon using a predetermined number of training/updating examples to train/update the first model 114. The model updater 120 can therefore update/train the first model 114 using the training dataset to generate predictions of whether additional points are to be added for corresponding emitters of input point clouds.)
using the synthetic two dimensional weather image, generating, with the trained two dimensional weather image encoder, fourth embedded data; and using the fourth embedded data, generating, with a trained three dimensional weather data decoder, the synthetic three dimensional weather data. (Naujoks – [0031], [0046], [0050], [0060], [0088], [0118])
Regarding Claims 5, 12, 19, Naujoks further discloses:
wherein an untrained two dimensional weather image encoder in a synthetic two dimensional weather data generator AI is first trained, and then the trained two dimensional weather image encoder is used to train an untrained three dimensional weather data decoder. (Naujoks – [0031], [0046], [0050], [0088])
Regarding Claims 6, 13, 20, Naujoks further discloses:
further comprising: using the synthetic three dimensional weather data, generating synthetic two dimensional weather data; (Naujoks – [0031], [0046], [0050], [0088])
using the synthetic two dimensional weather data, generating a synthetic two dimensional weather image; and verifying that the synthetic two dimensional weather image represents the descriptive information. (Naujoks – [0031], [0046], [0050], [0088], [0003] The machine learning models can be used to process the point clouds to generate data to automatically add false positive measurements, and in some implementations remove measurements that fall below a sensor-specific intensity threshold. Additional transformations can be applied to transform the point clouds to conform to specified atmospheric conditions.)
Regarding Claims 14, Naujoks discloses the following limitations:
An apparatus for generating synthetic three dimensional weather data, the apparatus comprising: (Naujoks – [0013], [0031])
input circuitry configured to receive descriptive information of at least one weather element; and processing circuitry communicatively coupled to the input circuitry, including a trained artificial intelligence (AI), and configured to: (Naujoks – [0013], [0031], [0046], [0004] At least one aspect relates to a processor. The processor can include one or more circuits.)
receive, at the trained AI, the descriptive information of at least one weather element; and using the descriptive information of the at least one weather element, generate, with the AI, the synthetic three dimensional weather data. (Naujoks – [0013], [0031], [0046])
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's
disclosure or directed to the state of art is listed on the enclosed PTO-892.
The following is a brief description for relevant prior art that was cited but not applied:
Mazed (US 11892746) teaches A real time image reconstruction algorithm can generally model how harsh weather/environmental conditions—rain/fog/snow affect the scattering of a laser light in a wavelength range, then the real time image reconstruction algorithm can eliminate such scattering to create a clear picture/image of what is actually ahead in harsh weather/environmental conditions.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON JAMES HENSON whose telephone number is (703)756-1841. The examiner can normally be reached Monday-Friday 9:00 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Resha H. Desai can be reached at (571) 270-7792. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRANDON JAMES HENSON/Examiner, Art Unit 3648
/BERNARR E GREGORY/Primary Examiner, Art Unit 3648
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