CTNF 18/905,845 CTNF 64964 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 § 101 07-04-01 AIA 07-04 35 U.S.C. §101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more. The following analysis is based on the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) published on January 7, 2019 (84 Fed. Reg. 50). See Also MEPE 2106.04(a)(2)(II). With regard to claim 1: Step 1: Claim 1 meets step 1 requirement as it is directed towards a machine which is statutory subject matter. In this case, “an apparatus” satisfies a “machine” category. Step 2A, prong 1 test: Does the claim recite an abstract idea, law of nature, or natural phenomenon? Yes, claim 1 as a whole recites an apparatus facilitating steps of organizing human activity e.g., mental process as explained in details below. Claim 11 in general is about how the apparatus provides for “provide numerical values as input to a diffusion model, provide an input point cloud as a conditioning input to the diffusion model, and process the numerical values using the diffusion model based on the input point cloud to generate an output point cloud, wherein the diffusion model is trained to generate output point clouds based on input point clouds and wherein the output point clouds include more points than are included in the input point clouds”. The limitations of “provide numerical values as input to a diffusion model, provide an input point cloud as a conditioning input to the diffusion model, and process the numerical values using the diffusion model based on the input point cloud to generate an output point cloud, wherein the diffusion model is trained to generate output point clouds based on input point clouds and wherein the output point clouds include more points than are included in the input point clouds” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in a mental process/step (a mathematical relationship, formula, or calculation). That is, nothing in the claim element precludes the processing from being performed as a mental process, or merely on pencil and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of a mental step which could be performed with pen and paper, then it falls within the “mental steps” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Step 2A, prong 2 test: Does the claim recite additional elements that integrate the judicial exception into a practical application? No as explained below. The claim recites the physical elements – “a memory and a processor” for receiving and processing various tasks. As will be explained below, these various tasks can be performed as mental steps. With respect to the function of “provide numerical values as input to a diffusion model, provide an input point cloud as a conditioning input to the diffusion model, and process the numerical values using the diffusion model based on the input point cloud to generate an output point cloud, wherein the diffusion model is trained to generate output point clouds based on input point clouds and wherein the output point clouds include more points than are included in the input point clouds” the broadest reasonable interpretation would have encompassed any forms of calculating inclusive of mental calculations (a mathematical relationship, formula, or calculation). The memory and processor used in the steps are recited at a high level of generality, (i.e., as a generic memory and processor for performing a generic computer function of processing data (the “provide numerical values as input to a diffusion model, provide an input point cloud as a conditioning input to the diffusion model, and process the numerical values using the diffusion model based on the input point cloud to generate an output point cloud, wherein the diffusion model is trained to generate output point clouds based on input point clouds and wherein the output point clouds include more points than are included in the input point clouds”), such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No as explained below. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. This judicial exception does not amount to significantly more because the additional elements, i.e. a memory and a processor, amount to no more than mere instructions to apply the exception using a generic computer component, i.e. a memory and a processor. In particular, the claims recite “provide numerical values as input to a diffusion model, provide an input point cloud as a conditioning input to the diffusion model, and process the numerical values using the diffusion model based on the input point cloud to generate an output point cloud, wherein the diffusion model is trained to generate output point clouds based on input point clouds and wherein the output point clouds include more points than are included in the input point clouds” steps amounts to no more than mere instructions to apply the exception using generic computer components, i.e. a memory and a processor. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. With respect to the “provide numerical values as input to a diffusion model and provide an input point cloud as a conditioning input to the diffusion model”, the broadest reasonable interpretation (BRI) would have encompassed any forms of inputting information or mere data gathering and is considered Insignificant Extra-Solution Activity. With respect to the “provide numerical values as input to a diffusion model, provide an input point cloud as a conditioning input to the diffusion model”, this is not a practical application as such activity is routinely practiced in the field by engineers on a daily basis. By utilizing inputting data to facilitate mere data gathering does not add anything that these practitioners do routinely in the field. With regard to claims 2-14: Step 1: Claims 2-14 meet step 1 requirement as they are directed towards a machine which is statutory subject matter. In this case, “an apparatus” satisfies a “machine” category. Step 2A, prong 1 test: Does the claim recite an abstract idea, law of nature, or natural phenomenon? Yes, claims 2-14 as a whole recites an apparatus facilitating steps of organizing human activity e.g., mental process as explained in details below. Claims 2-14 in general are about how the apparatus provides for “the diffusion model is trained using training random values as input and training point clouds as conditioning inputs”, “the training point clouds are generated by a light detection and ranging system and wherein the input point cloud comprises a point cloud generated by a radio detection and ranging system”, “the training point clouds are downsampled prior to being used to train the diffusion model”, “provide time embeddings as keys and values to a cross-attention layer of the diffusion model”, “cluster points of the output point cloud”, “the points are clustered based on a spatial distance within the output point cloud”, “the points are clustered based on entropy”, “the points are clustered based on another point cloud”, “the numerical values comprise a tensor of gaussian random values”, “the numerical values comprise random values”, “adjust an operating parameter of the vehicle based on the output point cloud”, “the operating parameter is associated with at least one of a path for the vehicle to travel, a steering parameter for operating steering of the vehicle, a braking parameter for operating brakes of the vehicle, a lane-change parameter for causing the vehicle to navigate from a first lane to a second lane, or displaying information related to the output point cloud using a user interface of the vehicle”. The limitations of “the diffusion model is trained using training random values as input and training point clouds as conditioning inputs”, “the training point clouds are generated by a light detection and ranging system and wherein the input point cloud comprises a point cloud generated by a radio detection and ranging system”, “the training point clouds are downsampled prior to being used to train the diffusion model”, “provide time embeddings as keys and values to a cross-attention layer of the diffusion model”, “cluster points of the output point cloud”, “the points are clustered based on a spatial distance within the output point cloud”, “the points are clustered based on entropy”, “the points are clustered based on another point cloud”, “the numerical values comprise a tensor of gaussian random values”, “the numerical values comprise random values”, “adjust an operating parameter of the vehicle based on the output point cloud”, “the operating parameter is associated with at least one of a path for the vehicle to travel, a steering parameter for operating steering of the vehicle, a braking parameter for operating brakes of the vehicle, a lane-change parameter for causing the vehicle to navigate from a first lane to a second lane, or displaying information related to the output point cloud using a user interface of the vehicle” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in a mental process/step (a mathematical relationship, formula, or calculation). That is, nothing in the claim elements precludes the processing from being performed as a mental process, or merely on pencil and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of a mental step which could be performed with pen and paper, then it falls within the “mental steps” grouping of abstract ideas. Accordingly, the claims recite an abstract idea. Step 2A, prong 2 test: Do the claims recite additional elements that integrate the judicial exception into a practical application? No as explained below. The claims recite the physical elements – “a memory and a processor” for receiving and processing various tasks, additionally claim 12 recites “a computing system of a vehicle”. As will be explained below, these various tasks can be performed as mental steps. With respect to the function of “the diffusion model is trained using training random values as input and training point clouds as conditioning inputs”, “the training point clouds are generated by a light detection and ranging system and wherein the input point cloud comprises a point cloud generated by a radio detection and ranging system”, “the training point clouds are downsampled prior to being used to train the diffusion model”, “provide time embeddings as keys and values to a cross-attention layer of the diffusion model”, “cluster points of the output point cloud”, “the points are clustered based on a spatial distance within the output point cloud”, “the points are clustered based on entropy”, “the points are clustered based on another point cloud”, “the numerical values comprise a tensor of gaussian random values”, “the numerical values comprise random values”, “adjust an operating parameter of the vehicle based on the output point cloud”, “the operating parameter is associated with at least one of a path for the vehicle to travel, a braking parameter for operating brakes of the vehicle, a lane-change parameter for causing the vehicle to navigate from a first lane to a second lane, or displaying information related to the output point cloud using a user interface of the vehicle” the broadest reasonable interpretation would have encompassed any forms of calculating inclusive of mental calculations (a mathematical relationship, formula, or calculation). The memory, processor, and a computing system of a vehicle used in the steps are recited at a high level of generality, (i.e., as a generic memory, processor and a computing system of a vehicle for performing a generic computer function of processing data (the “the diffusion model is trained using training random values as input and training point clouds as conditioning inputs”, “the training point clouds are generated by a light detection and ranging system and wherein the input point cloud comprises a point cloud generated by a radio detection and ranging system”, “the training point clouds are downsampled prior to being used to train the diffusion model”, “provide time embeddings as keys and values to a cross-attention layer of the diffusion model”, “cluster points of the output point cloud”, “the points are clustered based on a spatial distance within the output point cloud”, “the points are clustered based on entropy”, “the points are clustered based on another point cloud”, “the numerical values comprise a tensor of gaussian random values”, “the numerical values comprise random values”, “adjust an operating parameter of the vehicle based on the output point cloud”, “the operating parameter is associated with at least one of a path for the vehicle to travel, a steering parameter for operating steering of the vehicle, a braking parameter for operating brakes of the vehicle, a lane-change parameter for causing the vehicle to navigate from a first lane to a second lane, or displaying information related to the output point cloud using a user interface of the vehicle”), such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. Step 2B: Do the claims recite additional elements that amount to significantly more than the judicial exception? No as explained below. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. This judicial exception does not amount to significantly more because the additional elements, i.e. a memory, a processor and a computing system of a vehicle, amount to no more than mere instructions to apply the exception using generic computer components, i.e. a memory, a processor and a computing system of a vehicle. In particular, the claims recite “the diffusion model is trained using training random values as input and training point clouds as conditioning inputs”, “the training point clouds are generated by a light detection and ranging system and wherein the input point cloud comprises a point cloud generated by a radio detection and ranging system”, “the training point clouds are downsampled prior to being used to train the diffusion model”, “provide time embeddings as keys and values to a cross-attention layer of the diffusion model”, “cluster points of the output point cloud”, “the points are clustered based on a spatial distance within the output point cloud”, “the points are clustered based on entropy”, “the points are clustered based on another point cloud”, “the numerical values comprise a tensor of gaussian random values”, “the numerical values comprise random values”, “adjust an operating parameter of the vehicle based on the output point cloud”, “the operating parameter is associated with at least one of a path for the vehicle to travel, a steering parameter for operating steering of the vehicle, a braking parameter for operating brakes of the vehicle, a lane-change parameter for causing the vehicle to navigate from a first lane to a second lane, or displaying information related to the output point cloud using a user interface of the vehicle” steps amounts to no more than mere instructions to apply the exception using a generic computer components, i.e. a memory, a processor and a computing system of a vehicle. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. With respect to the “provide numerical values as input to a diffusion model and provide an input point cloud as a conditioning input to the diffusion model”, “the training point clouds are generated by a light detection and ranging system and wherein the input point cloud comprises a point cloud generated by a radio detection and ranging system”, the broadest reasonable interpretation (BRI) would have encompassed any forms of inputting information or mere data gathering and is considered Insignificant Extra-Solution Activity. With respect to the “provide numerical values as input to a diffusion model”, “provide an input point cloud as a conditioning input to the diffusion model”, and “the training point clouds are generated by a light detection and ranging system and wherein the input point cloud comprises a point cloud generated by a radio detection and ranging system” this is not a practical application as such activity is routinely practiced in the field by engineers on a daily basis. By utilizing inputting data to facilitate mere data gathering does not add anything that these practitioners do routinely in the field. With regard to claim 15: Step 1: Claim 15 meets step 1 requirement as it is directed towards a process which is statutory subject matter. In this case, “a method” satisfies a “process” category. Step 2A, prong 1 test: Does the claim recite an abstract idea, law of nature, or natural phenomenon? Yes, claim 15 as a whole recites a method facilitating steps of organizing human activity e.g., mental process as explained in details below. Claim 15 in general is about how the method provides for “providing numerical values as input to a diffusion model”, “providing an input point cloud as a conditioning input to the diffusion model”, and “processing the numerical values using the diffusion model based on the input point cloud to generate an output point cloud, wherein the diffusion model is trained to generate output point clouds based on input point clouds and wherein the output point clouds include more points than are included in the input point clouds”. The limitations of “providing numerical values as input to a diffusion model”, “providing an input point cloud as a conditioning input to the diffusion model”, and “processing the numerical values using the diffusion model based on the input point cloud to generate an output point cloud, wherein the diffusion model is trained to generate output point clouds based on input point clouds and wherein the output point clouds include more points than are included in the input point clouds” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in a mental process/step (a mathematical relationship, formula, or calculation). That is, nothing in the claim element precludes the processing from being performed as a mental process, or merely on pencil and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of a mental step which could be performed with pen and paper, then it falls within the “mental steps” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Step 2A, prong 2 test: Does the claim recite additional elements that integrate the judicial exception into a practical application? No as explained below. The claim does not recite any physical elements nor does it recite additional elements that integrate the judicial exception into a practical application. As will be explained below, these various tasks can be performed as mental steps. With respect to the functions of “providing numerical values as input to a diffusion model”, “providing an input point cloud as a conditioning input to the diffusion model”, and “processing the numerical values using the diffusion model based on the input point cloud to generate an output point cloud, wherein the diffusion model is trained to generate output point clouds based on input point clouds and wherein the output point clouds include more points than are included in the input point clouds” the broadest reasonable interpretation would have encompassed any forms of calculating inclusive of mental calculations. Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No as explained below. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. This judicial exception does not amount to significantly more because it is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. In particular, the claim does not recite additional elements to integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The claim is not patent eligible. With respect to the “providing numerical values as input to a diffusion model” and “providing an input point cloud as a conditioning input to the diffusion model”, the broadest reasonable interpretation (BRI) would have encompassed any forms of inputting information or mere data gathering and is considered Insignificant Extra-Solution Activity. With respect to the “providing numerical values as input to a diffusion model” and “providing an input point cloud as a conditioning input to the diffusion model”, this is not a practical application as such activity is routinely practiced in the field by engineers on a daily basis. By utilizing “providing numerical values as input to a diffusion model” and “providing an input point cloud as a conditioning input to the diffusion model” to facilitate mere data gathering does not add anything that these practitioners do routinely in the field. With regard to claims 16-20: Step 1: Claims 16-20 meet step 1 requirement as they are directed towards a process which is statutory subject matter. In this case, “a method” satisfies a “process” category. Step 2A, prong 1 test: Do the claims recite an abstract idea, law of nature, or natural phenomenon? Yes, claims 16-20 as a whole recites a method facilitating steps of organizing human activity e.g., mental process as explained in details below. Claims 16-20 in general are about how the method provides for “the diffusion model is trained using training random values as input and training point clouds as conditioning inputs”, “the training point clouds are generated by a light detection and ranging system and wherein the input point cloud comprises a point cloud generated by a radio detection and ranging system”, “the training point clouds are downsampled prior to being used to train the diffusion model”, “providing time embeddings as keys and values to a cross-attention layer of the diffusion model”, and “adjusting an operating parameter of a vehicle based on the output point cloud, wherein the operating parameter is associated with at least one of a path for the vehicle to travel, a steering parameter for operating steering of the vehicle, a braking parameter for operating brakes of the vehicle, a lane-change parameter for causing the vehicle to navigate from a first lane to a second lane, or displaying information related to the output point cloud using a user interface of the vehicle”. The limitations of “the diffusion model is trained using training random values as input and training point clouds as conditioning inputs”, “the training point clouds are generated by a light detection and ranging system and wherein the input point cloud comprises a point cloud generated by a radio detection and ranging system”, “the training point clouds are downsampled prior to being used to train the diffusion model”, “providing time embeddings as keys and values to a cross-attention layer of the diffusion model”, and “adjusting an operating parameter of a vehicle based on the output point cloud, wherein the operating parameter is associated with at least one of a path for the vehicle to travel, a steering parameter for operating steering of the vehicle, a braking parameter for operating brakes of the vehicle, a lane-change parameter for causing the vehicle to navigate from a first lane to a second lane, or displaying information related to the output point cloud using a user interface of the vehicle” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in a mental process/step (a mathematical relationship, formula, or calculation). That is, nothing in the claim elements precludes the processing from being performed as a mental process, or merely on pencil and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of a mental step which could be performed with pen and paper, then it falls within the “mental steps” grouping of abstract ideas. Accordingly, the claims recite an abstract idea. Step 2A, prong 2 test: Do the claim recite additional elements that integrate the judicial exception into a practical application? No as explained below. The claims do not recite any physical elements nor does it recite additional elements that integrate the judicial exception into a practical application. As will be explained below, these various tasks can be performed as mental steps. With respect to the functions of “the diffusion model is trained using training random values as input and training point clouds as conditioning inputs”, “the training point clouds are generated by a light detection and ranging system and wherein the input point cloud comprises a point cloud generated by a radio detection and ranging system”, “the training point clouds are downsampled prior to being used to train the diffusion model”, “providing time embeddings as keys and values to a cross-attention layer of the diffusion model”, and “adjusting an operating parameter of a vehicle based on the output point cloud, wherein the operating parameter is associated with at least one of a path for the vehicle to travel, a steering parameter for operating steering of the vehicle, a braking parameter for operating brakes of the vehicle, a lane-change parameter for causing the vehicle to navigate from a first lane to a second lane, or displaying information related to the output point cloud using a user interface of the vehicle” the broadest reasonable interpretation would have encompassed any forms of calculating inclusive of mental calculations. Step 2B: Do the claims recite additional elements that amount to significantly more than the judicial exception? No as explained below. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea. This judicial exception does not amount to significantly more because it is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. In particular, the claims do not recite additional elements to integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The claims are not patent eligible. With respect to the “providing numerical values as input to a diffusion model” and “providing an input point cloud as a conditioning input to the diffusion model”, and “the training point clouds are generated by a light detection and ranging system and wherein the input point cloud comprises a point cloud generated by a radio detection and ranging system”, the broadest reasonable interpretation (BRI) would have encompassed any forms of inputting information or mere data gathering and is considered Insignificant Extra-Solution Activity. With respect to the “providing numerical values as input to a diffusion model” and “providing an input point cloud as a conditioning input to the diffusion model”, this is not a practical application as such activity is routinely practiced in the field by engineers on a daily basis. By utilizing “providing numerical values as input to a diffusion model”, “providing an input point cloud as a conditioning input to the diffusion model”, and “the training point clouds are generated by a light detection and ranging system and wherein the input point cloud comprises a point cloud generated by a radio detection and ranging system” to facilitate mere data gathering does not add anything that these practitioners do routinely in the field. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-07-aia AIA 07-07 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 – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention. 07-15 AIA Claim s 1-4, 6-9, 12-18 and 20 are rejected under 35 U.S.C. §102( a)(1 ) as being anticipated by Osep et al. (U.S. Patent Application Publication No. US 2025/0232557 A1) (hereafter referred to as “Osep”) . The examiner is providing applicant a copy of U.S. Patent Provisional Application No. 63/620,692 (filed on January 12, 2024) which is the priority document to U.S. Patent Application Publication No. US 2025/0232557 A1. With regard to claim 1, Osep describes at least one memory and at least one processor coupled to the at least one memory (see Figure 1 and refer for example to paragraph [0033]) and configured to provide numerical values as input to a diffusion model (refer for example to paragraph [0113] which describes providing numerical values as input, and to paragraph [0085] which discusses the diffusion model); provide an input point cloud as a conditioning input to the diffusion model (refer to paragraphs [0053], [0056], [0067], [0077], [0078], [0086], [0113], [0115] and [0131] which discusses providing an input point cloud as a conditioning input, and refer to paragraph [0085] which discusses the diffusion model); and process the numerical values using the diffusion model based on the input point cloud to generate an output point cloud, wherein the diffusion model is trained to generate output point clouds based on input point clouds and wherein the output point clouds include more points than are included in the input point clouds (refer to paragraphs [0058], [0104], [0110], [0131] and [0132]). As to claim 2, Osep describes wherein the diffusion model is trained using training random values as input and training point clouds as conditioning inputs (refer for example to paragraphs [0058], [0064] and [0079]). In regard to claim 3, Osep describes wherein the training point clouds are generated by a light detection and ranging system and wherein the input point cloud comprises a point cloud generated by a radio detection and ranging system (refer for example to paragraphs [0026] through [0030], [0034] and [0091]). With regard to claim 4, Osep describes wherein the training point clouds are downsampled prior to being used to train the diffusion model (refer to paragraphs [0053], [0056], [0067], [0077], [0078], [0086], [0113], [0115] and [0131]). In regard to claim 6, Osep describes wherein the at least one processor is configured to cluster points of the output point cloud (refer for example to paragraphs [0029], [0057] and [0080]). With regard to claim 7, Osep describes wherein the points are clustered based on a spatial distance within the output point cloud (refer for example to paragraphs [0040] and [0100]). As to claim 8, Osep describes wherein the points are clustered based on entropy (refer for example to paragraph [0063]). In regard to claim 9, Osep describes wherein the points are clustered based on another point cloud (refer for example to paragraphs [0057] and [0080]). In regard to claim 12, Osep describes wherein the apparatus comprises a computing system of a vehicle (see Figures 5A-D and refer for example to paragraphs [0086], [0094], and [0103]). With regard to claim 13, Osep describes wherein the apparatus is configured to adjust an operating parameter of the vehicle based on the output point cloud (refer for example to paragraphs [0132], [0174] and [0215]). As to claim 14, Osep describes wherein the operating parameter is associated with at least one of a path for the vehicle to travel, a steering parameter for operating steering of the vehicle, a braking parameter for operating brakes of the vehicle, a lane-change parameter for causing the vehicle to navigate from a first lane to a second lane, or displaying information related to the output point cloud using a user interface of the vehicle (refer for example to paragraphs [0087] through [0090]). In regard to claim 15, Osep describes providing numerical values as input to a diffusion model (refer for example to paragraph [0113] which describes providing numerical values as input, and to paragraph [0085] which discusses the diffusion model); providing an input point cloud as a conditioning input to the diffusion model(refer to paragraphs [0053], [0056], [0067], [0077], [0078], [0086], [0113], [0115] and [0131] which discusses providing an input point cloud as a conditioning input, and refer to paragraph [0085] which discusses the diffusion model); and processing the numerical values using the diffusion model based on the input point cloud to generate an output point cloud, wherein the diffusion model is trained to generate output point clouds based on input point clouds and wherein the output point clouds include more points than are included in the input point clouds (refer for example to paragraphs [0058], [0104], [0110], [0131] and [0132]). With regard to claim 16, Osep describes wherein the diffusion model is trained using training random values as input and training point clouds as conditioning inputs (refer for example to paragraphs [0058], [0064] and [0079]). As to claim 17, Osep describes wherein the training point clouds are generated by a light detection and ranging system and wherein the input point cloud comprises a point cloud generated by a radio detection and ranging system (refer for example to paragraphs [0026] through [0030], [0034] and [0091]). In regard to claim 18, Osep describes wherein the training point clouds are downsampled prior to being used to train the diffusion model (refer to paragraphs [0053], [0056], [0067], [0077], [0078], [0086], [0113], [0115] and [0131]). As to claim 20, Osep describes adjusting an operating parameter of a vehicle based on the output point cloud, wherein the operating parameter is associated with at least one of a path for the vehicle to travel, a steering parameter for operating steering of the vehicle, a braking parameter for operating brakes of the vehicle, a lane-change parameter for causing the vehicle to navigate from a first lane to a second lane, or displaying information related to the output point cloud using a user interface of the vehicle (refer for example to paragraphs [0087] through [0090]) . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA The following is a quotation of 35 U.S.C. §103(a) 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. 07-23-aia AIA The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21 AIA Claim s 5, 10, 11 and 19 are rejected under 35 U.S.C. §103(a) as being unpatentable over Osep et al. (U.S. Patent Application Publication No. US 2025/0232557 A1) in view of Wu et al. (U.S. Patent Application Publication No. US 2025/0252641 A1) (hereafter referred to as “Wu”) . The arguments advanced in section 6 above, as to the applicability of Osep, are incorporated herein. With regard to claims 5 and 19, although Osep does not explicitly describe providing time embeddings as keys and values to a cross-attention layer of the diffusion model, such a technique is well known and widely utilized in the prior art. Wu discloses a system for digital model generation using neural network (refer for example to the abstract) that describes using diffusion models (see Figures 3, 4 and 5, and refer for example to paragraphs [0067] and [0110]) and point clouds (refer for example to paragraph [0228]) in a computing system of a vehicle (see Figures 12A-D and refer for example to paragraphs [0179], [0189] and [0199]) wherein the training point clouds are generated by a light detection and ranging system and wherein the input point cloud comprises a point cloud generated by a radio detection and ranging system (refer for example to paragraph [0184]) which provides for time embeddings as keys and values to a cross-attention layer of the diffusion model (refer for example to paragraph [0096]). Given the teachings of the two references and the same environment of operation, namely that of vehicle systems using point clouds and diffusion models in a computing system of a vehicle wherein the training point clouds are generated by a light detection and ranging system and wherein the input point cloud comprises a point cloud generated by a radio detection and ranging system, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Osep system in the manner described by Wu according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested by Wu (refer for example to paragraph [0002]), which fails to patentably distinguish over the prior art absent some novel and unexpected result. With regard to claims 10 and 11, although Osep does not explicitly describe the numerical values comprise a tensor of gaussian random values, such a technique is well known and widely utilized in the prior art. Wu discloses a system for digital model generation using neural network (refer for example to the abstract) that describes using diffusion models (see Figures 3, 4 and 5, and refer for example to paragraphs [0067] and [0110]) and point clouds (refer for example to paragraph [0228]) in a computing system of a vehicle (see Figures 12A-D and refer for example to paragraphs [0179], [0189] and [0199]) wherein the training point clouds are generated by a light detection and ranging system and wherein the input point cloud comprises a point cloud generated by a radio detection and ranging system (refer for example to paragraph [0184]) which provides for the numerical values comprise a tensor of gaussian random values (refer for example to paragraphs [0098], [0103] and [0104]). Given the teachings of the two references and the same environment of operation, namely that of vehicle systems using point clouds and diffusion models in a computing system of a vehicle wherein the training point clouds are generated by a light detection and ranging system and wherein the input point cloud comprises a point cloud generated by a radio detection and ranging system, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Osep system in the manner described by Wu according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested by Wu (refer for example to paragraph [0002]), which fails to patentably distinguish over the prior art absent some novel and unexpected result. Relevant Prior Art 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Guo, Agia, Kreis, Weng, Nekkah, Hatamizadeh, Pronovost, Zhao, Lee, Choy and Mahdavian all disclose systems similar to applicant’s claimed invention. Contact Information Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jose L. Couso whose telephone number is (571) 272-7388. The examiner can normally be reached on Monday through Friday from 5:30am to 1:30pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella, can be reached on 571-272-7778. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300. Information regarding the status of an application may be obtained from the Patent Center information webpage on the USPTO website. For more information about the Patent Center, see https://www.uspto.gov/patents/apply/patent-center. Should you have questions about access to the Patent Center, contact the Patent Electronic Business Center (EBC) at 571-272-4100 or via email at: ebc@uspto.gov . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. /JOSE L COUSO/Primary Examiner, Art Unit 2667 May 29, 2026 Application/Control Number: 18/905,845 Page 2 Art Unit: 2667 Application/Control Number: 18/905,845 Page 3 Art Unit: 2667 Application/Control Number: 18/905,845 Page 4 Art Unit: 2667 Application/Control Number: 18/905,845 Page 5 Art Unit: 2667 Application/Control Number: 18/905,845 Page 6 Art Unit: 2667 Application/Control Number: 18/905,845 Page 7 Art Unit: 2667 Application/Control Number: 18/905,845 Page 8 Art Unit: 2667 Application/Control Number: 18/905,845 Page 9 Art Unit: 2667 Application/Control Number: 18/905,845 Page 10 Art Unit: 2667 Application/Control Number: 18/905,845 Page 11 Art Unit: 2667 Application/Control Number: 18/905,845 Page 12 Art Unit: 2667 Application/Control Number: 18/905,845 Page 13 Art Unit: 2667 Application/Control Number: 18/905,845 Page 14 Art Unit: 2667 Application/Control Number: 18/905,845 Page 15 Art Unit: 2667 Application/Control Number: 18/905,845 Page 16 Art Unit: 2667 Application/Control Number: 18/905,845 Page 17 Art Unit: 2667 Application/Control Number: 18/905,845 Page 18 Art Unit: 2667 Application/Control Number: 18/905,845 Page 19 Art Unit: 2667 Application/Control Number: 18/905,845 Page 20 Art Unit: 2667 Application/Control Number: 18/905,845 Page 21 Art Unit: 2667 Application/Control Number: 18/905,845 Page 22 Art Unit: 2667 Application/Control Number: 18/905,845 Page 23 Art Unit: 2667 Application/Control Number: 18/905,845 Page 24 Art Unit: 2667 Application/Control Number: 18/905,845 Page 25 Art Unit: 2667 Application/Control Number: 18/905,845 Page 26 Art Unit: 2667
Prosecution Insights