DETAILED 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 .
Status of Claims
Claims 1-20 are pending in this application.
Rejection under 35 U.S.C. §102
Applicant’s arguments, see page , line through page , line , and the amendment to claims , filed , with respect to the rejection of claims 1-4, 6-9, 12-18 and 20 under 35 U.S.C. §102(a)(1) as being anticipated by Lee (‘959) et al. (U.S. Patent Application Publication No. US 2025/0232557 A1), have been fully considered and are persuasive. The rejection of claims 1-4, 6-9, 12-18 and 20 under 35 U.S.C. §102(a)(1) as being anticipated by Lee (‘959) et al. (U.S. Patent Application Publication No. US 2025/0232557 A1) has been withdrawn.
Rejection under 35 U.S.C. §103
Applicant’s arguments, see page , line through page , line , and the amendment to claims , filed , with respect to the rejection of claims 5, 10, 11 and 19 under 35 U.S.C. §103(a) as being unpatentable over Lee (‘959) 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), have been fully considered and are persuasive. The rejection of claims 5, 10, 11 and 19 under 35 U.S.C. §103(a) as being unpatentable over Lee (‘959) 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 (U.S. Patent Application Publication No. US 2025/0252641 A1) has been withdrawn.
Rejection under 35 U.S.C. §101
Applicant’s arguments, see page , line through page , line , and the amendment to claims , filed , with respect to the rejection of claims under 35 U.S.C. §101 , have been fully considered but are not persuasive.
The recitation of “… “, do not constitute detailed and specific digital image processing nor does it improve the functioning of the claimed computer.
The rejection of claims under 35 U.S.C. §101 is both reasonable and proper and is hereby maintained.
Claim Rejections - 35 USC § 101
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.
New Grounds of Rejection
Applicant’s arguments with respect to claims 1-20 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.
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 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)(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.
Claims 1-3, 12-17 and 20 are rejected under 35 U.S.C. §102(a)(1) as being anticipated by Lee et al. (U.S. Patent Application Publication No. US 2025/0173959 A1, previously cited by the examiner on the PTO-892 form dated June 3, 2026) (hereafter referred to as “Lee (‘959)”).
With regard to claim 1, Lee (‘959) describes at least one memory and at least one processor coupled to the at least one memory (see Figure 8, elements 801 and 803, and refer for example to paragraphs [0098] and [0099]) and configured to provide numerical values as input to a diffusion model (see Figures 1 and 2A, elements 10 and 115, and refer for example to paragraphs [0045] through [0048] and to paragraphs [0052] and [0053], which describe providing numerical values as input, e.g. the input camera data 115); provide an input point cloud as a conditioning input to the diffusion model (see Figures 1 and 2A, elements 10 and 110, and refer for example to paragraphs [0045] through [0048] and to paragraphs [0052] and [0053], which describe providing the LiDAR point cloud data 110); 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 po1ints than are included in the input point clouds (see Figures 1 and 2A, elements 30 and 120, and refer for example to paragraphs [0045] through [0048] and to paragraphs [0052] and [0053], which describe 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 to claim 2, Lee (‘959) describes wherein the diffusion model is trained using training random values as input and training point clouds as conditioning inputs (refer for example to paragraph [0074]).
In regard to claim 3, Lee (‘959) 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 [0045], [0048] and [0062] through [0067]).
In regard to claim 12, Lee (‘959) describes wherein the apparatus comprises a computing system of a vehicle (see Figures 1, 2 and 3, and refer for example to paragraphs [0045], [0057] and [0062]).
With regard to claim 13, Lee (‘959) 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 [0045], [0057] and [0062] through [0064]).
As to claim 14, Lee (‘959) 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 [0045], [0057] and [0062] through [0064] the phrase "at least one of' was read disjunctively, Lee (‘959) provides for at least the operating parameter is associated with at least one of a path for the vehicle to travel and displays information related to the output point cloud using a user interface of the vehicle).
In regard to claim 15, Lee (‘959) describes providing numerical values as input to a diffusion model (see Figures 1 and 2A, elements 10 and 115, and refer for example to paragraphs [0045] through [0048] and to paragraphs [0052] and [0053], which describe providing numerical values as input, e.g. the input camera data 115); providing an input point cloud as a conditioning input to the diffusion model (see Figures 1 and 2A, elements 10 and 110, and refer for example to paragraphs [0045] through [0048] and to paragraphs [0052] and [0053], which describe providing the LiDAR point cloud data 110); 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 (see Figures 1 and 2A, elements 30 and 120, and refer for example to paragraphs [0045] through [0048] and to paragraphs [0052] and [0053], which describe 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).
With regard to claim 16, Lee (‘959) describes wherein the diffusion model is trained using training random values as input and training point clouds as conditioning inputs (refer for example to paragraph [0074]).
As to claim 17, Lee (‘959) 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 [0045], [0048] and [0062] through [0067]).
As to claim 20, Lee (‘959) 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 to paragraphs [0045], [0057] and [0062] through [0064] the phrase "at least one of' was read disjunctively, Lee (‘959) provides for at least the operating parameter is associated with at least one of a path for the vehicle to travel and displays information related to the output point cloud using a user interface of the vehicle)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. §103(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.
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.
Claims 4-5 and 18-19 are rejected under 35 U.S.C. §103(a) as being unpatentable over Lee (‘959) et al. (U.S. Patent Application Publication No. US 2025/0232557 A1) in view of Hatamizadeh (U.S. Patent Application Publication No. US 2024/0185396 A1), previously cited by the examiner on the PTO-892 form dated June 3, 2026) (hereafter referred to as “Hatamizadeh”).
The arguments advanced in section 11 above, as to the applicability of Lee (‘959), are incorporated herein.
With regard to claims 5 and 19, although Lee (‘959) 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.
Hatamizadeh discloses a system for vision transformation for image generation using neural network (see Figures 1 and 2, and refer for example to the abstract) that describes using diffusion models (see Figure 3 and refer for example to paragraph [0067] and [0070]) and LiDAR 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 [0229] and [0230]) which provides for time embeddings as keys and values to a cross-attention layer of the diffusion model (refer for example to paragraphs [0071] through [0076] and [0090]).
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 Lee (‘959) system in the manner described by Hatamizadeh 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 Hatamizadeh (refer for example to paragraphs [0002] and [0003]), which fails to patentably distinguish over the prior art absent some novel and unexpected result.
With regard to claims 4 and 18, Hatamizadeh describes wherein the training point clouds are downsampled prior to being used to train the diffusion model (refer to paragraphs [0071] and [0090]).
Claims 6-9 are rejected under 35 U.S.C. §103(a) as being unpatentable over Lee (‘959) et al. (U.S. Patent Application Publication No. US 2025/0232557 A1), previously cited by the examiner on the PTO-892 form dated June 3, 2026) in view of Hatamizadeh (U.S. Patent Application Publication No. US 2024/0185396 A1) and further in view of Agia (U.S. Patent Application Publication No. US 2023/0267615 A1), previously cited by the examiner on the PTO-892 form dated June 3, 2026) (hereafter referred to as “Agia”).
The arguments advanced in section 11 and 15 above, as to the applicability of Lee (‘959) and Hatamizadeh, are incorporated herein.
With regard to claim 6, although Lee (‘959) nor Hatamizadeh explicitly describe providing cluster points of the output point cloud, such a technique is well known and widely utilized in the prior art.
Agia discloses a system for generating a road surface semantic segmentation map from a sequence of point clouds which uses neural networks (see Figure 1 and refer for example to the abstract) that describes using a LiDAR sensor to obtain point clouds (see Figure 1 and refer for example to paragraphs [0057] through [0062]) in a computing system of a vehicle (refer for example to paragraphs [0051] through [0060]) which provides for providing cluster points of the output point cloud (refer for example to paragraphs [0062] through [0064]).
Given the teachings of references and the same environment of operation, namely that of vehicle systems using point clouds and machine learning 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 Lee (‘959) and Hatamizadeh combination in the manner described by Agia 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 Osep (refer for example to paragraph [0004]), which fails to patentably distinguish over the prior art absent some novel and unexpected result.
With regard to claim 7, Agia describes wherein the points are clustered based on a spatial distance within the output point cloud (refer for example to paragraphs [0062] through [0064]).
As to claim 8, Lee Agia describes wherein the points are clustered based on entropy (refer for example to paragraphs [0062] through [0064]).
In regard to claim 9, Agia describes wherein the points are clustered based on another point cloud (refer for example to paragraphs [0062] through [0064]).
Claims 10 and 11 are rejected under 35 U.S.C. §103(a) as being unpatentable over Lee (‘959) et al. (U.S. Patent Application Publication No. US 2025/0232557 A1) in view of Hatamizadeh (U.S. Patent Application Publication No. US 2024/0185396 A1), further in view of Agia (U.S. Patent Application Publication No. US 2023/0267615 A1), and further in view of Wu et al. (U.S. Patent Application Publication No. US 2025/0252641 A1), previously cited by the examiner on the PTO-892 form dated June 3, 2026) (hereafter referred to as “Wu”) .
The arguments advanced in section 11, 15 and 16 above, as to the applicability of Lee (‘959), Hatamizadeh and Agia, are incorporated herein.
With regard to claims 10 and 11, although Lee (‘959), Hatamizadeh nor Agia describe that 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 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 Lee, Hatamizadeh and Agia combination 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kreis, Pereira and Lee (‘758) 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.
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/JOSE L COUSO/Primary Examiner, Art Unit 2667
September 8, 2026
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