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 .
Response to Arguments
Applicant’s arguments with respect to claims 1-8 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.
Applicant argues, page 9 of the field remarks, that “forming, by the trained spatial estimation model, a spatial distribution in which a teaching region spatial distribution is reproduced” is a concrete, tangible result regarding a physical teaching space observed by the sensor and therefore eligible at prong two of step 2A MPEP 2106.04(d)(I). The current application’s originally filed specification pages 10 and 11 are argued by applicant to provide more efficient training of the model due to the larger number of difference amounts (i.e. a case where a difference between a pixel value of teaching data and an estimated pixel value is acquired).
MPEP 2106.4(a)(2) Abstract Idea Groupings I. Mathematical concepts: The Court’s rationale for identifying these "mathematical concepts" as judicial exceptions is that a ‘‘mathematical formula as such is not accorded the protection of our patent laws,’’ Diehr, 450 U.S. at 191, 209 USPQ at 15 (citing Benson, 409 U.S. 63, 175 USPQ 673), and thus ‘‘the discovery of [a mathematical formula] cannot support a patent unless there is some other inventive concept in its application.’’ Flook, 437 U.S. at 594, 198 USPQ at 199.
As shown above, a claim employing a mathematical algorithm does direct the claims to an abstract idea unless “there is some other inventive concept in its application”. Currently, the claims newly amended subject matter states to form a teaching region spatial distribution.
To identify the inventive concept, in view of the claims, we look at the updated subject matter analysis provided by Ex Parte Desjardins (December 5, 2025), herein after referred to as Desjardins, and 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence published July 17, 2024 (89 FR 58128) (AI-SME Update). Desjardins, page 1 “2)”, states “improvements in computational performance, learning, storage, data set and structures, for example, can constitute patent-eligible technological advancements under the Alice framework.” In order to identify the improvement as being an eligible improvement in technology “ when the specification provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing a technological improvement and the claims reflect the disclosed improvement. The specification need not explicitly set forth the improvement, so long as the specification describes the invention such that the improvement would be apparent to one of ordinary skill in the art.” (Desjardin page 3)(MPEP2106.05(a)).
Therefore, establishing whether the claimed mathematical concepts are directed to an abstract idea is tied to whether the inventive concept is claimed or reflected by the claims. Applicant’s argument provides pages 10-11 of the specification, as recited above, as the improvement to the technology. However, the specification does not define or imply the range of acceptable values to be considered a “large number” of difference amounts as compared to the prior art. Further, the claims only state a singular difference amount itself and cannot be considered reflective of the implied “large number” of generated difference amounts which performs the improvement due to the vast number of values produced or training compared with the prior art method. It is suggested to further clarify in the claims how the updated model based on the calculated difference amount relates to the improvement. 101 rejection is upheld with the new amendments. See rejection below.
In regards to 103 rejection arguments new rejection necessitated by amendments is added below.
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims 1, 5 and 7 recite acquiring a spatial distribution signal observed by a sensor using a emission wave for a spatial structure along a region of interest, the region of interest being a curved line region or a curved surface region representing a wavefront of the emission wave at a distance from the sensor, the curved line region or the curved surface region, performing training of a spatial estimation model using the signal where performing the training includes estimated density related to a probability that an object emitting the emission wave is present, calculating an estimated signal, calculating the difference amount and updating the spatial estimation model based on the difference amount, forming, by the trained spatial estimation model, a spatial distribution in which a teaching region spatial distribution is reproduced. This limitations specifically the calculating limitations recite mathematical concept, (organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging).
This judicial exception is not integrated into a practical application because while the claims acquire information for training a spatial estimation model the limitations use the acquired information to perform calculations, updating the spatial estimation model and forming a spatial distribution in which a teaching region spatial distribution is reproduced amounts to data-gathering steps which is considered to be insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claim recite the abstract idea of mathematical concepts and the additional elements do not integrate the judicial exception into a practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea. The claim is not eligible.
Claims 2, 6 and 8 are dependent on claims 1, 5 and 7 and includes all the limitations of claims 1, 5 and 7. Therefore, claim 2, 6 and 8 recites the same abstract idea of claim 1. The claim recites the additional limitation of “function representing each of a plurality of regions of interest at different distances from the sensor and including a first parameter is connected to a calculation graph of the training, and the processes include updating the first parameter based on the difference amount”, which is merely elaborating on the abstract idea, by further specifying an additional mathematical calculation, therefore, does not amount to significantly more than the abstract idea.
Claim 3 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 3 recites the same abstract idea of claim 1. The claim recites the additional limitation of “estimating, by using the function in which the first parameter is optimized, a refractive index distribution for emission waves in a space based on a shape of the region of interest represented by the function”, which is merely elaborating on the abstract idea, by further specifying an additional mathematical calculation, therefore, does not amount to significantly more than the abstract idea.
Claim 4 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 4 recites the same abstract idea of claim 1. The claim recites the additional limitation of “wherein the sensor is a LiDAR”, which is merely elaborating on the abstract idea, by further specifying an additional element recited at a high-level of generality, therefore, does not amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4-8 are rejected under 35 U.S.C. 103 as being unpatentable over Lopez et al. (US 2022/0245841 A1) in view of Gohl et al. (US 2022/0334264 A1).
With respect to Claim 1, Lopez’841 shows an arithmetic operation system (figure 15 paragraphs [0095]-[0102]) comprising: at least one memory configured to store instructions; and at least one processor configured to execute, according to the instructions (Figure 15 entity 1111 described in paragraph [0097] to train (a process) artificial intelligence models (detailed in figure 14). Paragraph [0096] describes entity 1111 to be physical devices implemented by a processor and memory that stores non-transient way code executed by said processor to implement the process of training.), a process (Figure 13 and paragraph [0089] generalizes teacher-student models of training and figure 14 details the teacher-student model of training particular to depth estimation ),
a process comprising: acquiring, as a teaching signal (Figure 14 real input to teacher described in paragraph [0091] to include RGB input image and LiDAR), a spatial distribution signal observed by a sensor (Figure 1 examples the spatial distribution signal 102 of LiDAR as described in paragraph [0004]. 103 depicts the projection of LiDAR data onto the RGB image) using an emission wave for a spatial structure along a region of interest (Figure 1 and paragraphs [000]-[0004] describes LiDAR as a time of flight technique that utilizes a wave with known properties to be emitted and reflected back to determine the time the wave took to travel and determine the distance of objects within a specified range (region of interest)), [ ]; and
performing training of a spatial estimation model using the teaching signal (Paragraph [0089] generalizes the teacher model for training. Figure 14 and paragraph describes the teach model to be utilized for depth estimation only for training purposes. Said figure depicts the input of the Stereo RGB camera and LiDAR to the teacher model. Figures (a) and 3(b) and paragraphs [0056] and [0066] describes the stereo RGB images and LiDAR data are both utilized for model training), wherein the performing of the training the spatial estimation model includes performing processes including:
inputting information about a position of each of a plurality of sample points on the region of interest to the spatial estimation model (Figure 14 reference sample and project of ground truths GT to the synthetic input to the depth estimation model as described in paragraph [0091]), and acquiring, from the spatial estimation model, estimated density related to a probability that an object emitting the emission wave to the plurality of sample points is present (Figure 14 reference output of depth estimation model to real prediction and synthetic prediction for depth estimation as described in paragraphs [0091]-[0092]);
calculating an estimated signal by integrating a plurality of pieces of estimated density corresponding to the plurality of sample points, respectively (Figure 14 depicts synthetic input receives sample and projected ground truths GT utilized by the depth estimation model to output the estimation/predictions as described in paragraph [0091]);
calculating a difference amount based on the teaching signal and the estimated signal (Figure 14 reference estimated/prediction in the real space utilized in combination with the teach supervision loss function to determine a difference as described in paragraphs [0091], [0088], [0073], and [0070]);
updating the spatial estimation model based on the difference amount (Paragraph [0091] describes the loss function can be applied to identify errors in prediction and help updating weights (updating the estimation model as a whole)); and
forming, by the trained spatial estimation model, a spatial distribution in which a teaching region spatial distribution is reproduced (Figure 13 and paragraph [0087] feeding input, spatial distribution 102 described in paragraph [0004], to the teacher model 402 which outputs the teacher’s prediction 403 for the next training iteration (including the next spatial distribution input interpreted as the reproduced teaching region spatial distribution).).
Lopez’841 does not specifically show the region of interest being a curved line region or a curved surface region representing a wavefront of the emission wave at a distance from the sensor, the curved line region or the curved surface region intersecting a plurality of emission reference directions in an emission wave region in which emission waves that are emitted from the plurality of emission reference directions and reach the sensor spread.
Gohl’264 shows the region of interest being a curved line region or a curved surface region representing a wavefront of the emission wave at a distance from the sensor, the curved line region or the curved surface region intersecting a plurality of emission reference directions in an emission wave region in which emission waves that are emitted from the plurality of emission reference directions and reach the sensor spread (Figure 1 depicts laser sensor 3 (described in paragraph [0002] to be LiDar) emitting distance measuring pulses along direction 13 (paragraph [0064]). Figure 2 depicts the reception signals over time reflected back from objects that receive the emission signal (paragraphs [0064]-[0065]). Figure 3 depicts curved line region or curved surface region 7 representing a wavefront at a distance (depicted in figure 1) from the sensor 3, paragraph [0066]. Figure 4 depicts the distribution of the curved region 7 intersecting the emitted waves, of the emission spread along direction 13 (figure 1), represented by the dot, paragraphs [0067]-[0068]).
At the time of the invention, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claim invention to modify Lopez’841 to include the region of interest being a curved line region or a curved surface region representing a wavefront of the emission wave at a distance from the sensor, the curved line region or the curved surface region intersecting a plurality of emission reference directions in an emission wave region in which emission waves that are emitted from the plurality of emission reference directions and reach the sensor spread method taught by Gohl’264. The suggestion/motivation for doing so would have been to improve the system’s ability to be able to determining and identifying transparent and mirroring planes within the sensor emission region (paragraph [0046]).
With respect to Claim 2, the combination of Lopez’841 and Gohl’264 shows the arithmetic operation system according to claim 1, wherein a function representing each of a plurality of regions of interest at different distances from the sensor and including a first parameter is connected to a calculation graph of the training, and the processes include updating the first parameter based on the difference amount (in Lopez’841: Paragraph [0091] describes the loss function can be applied to identify errors in prediction and help updating weights (updating the estimation model as a whole)).
With respect to Claim 4, the combination of Lopez’841 and Gohl’264 shows the arithmetic operation system according to claim 1, wherein the sensor is a LiDAR (Light Detection and Ranging) (in Lopez’841: Paragraph [0052] describes the sensor as LiDAR).
With respect to Claims 5 and 7, rejection analogous to those presented for claim 1, are applicable.
With respect to Claims 6 and 8, rejection analogous to those presented for claim 2, are applicable.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Lopez et al. (US 2022/0245841 A1) in view of Gohl et al. (US 2022/0334264 A1) and further in view of Mohseni et al. (US 2022/0034824 A1).
With respect to Claim 3, the combination of Lopez’841 and Gohl’264 does not specifically shows the arithmetic operation system according to claim 2, wherein the process further comprises estimating, by using the function in which the first parameter is optimized, a refractive index distribution for emission waves in a space based on a shape of the region of interest represented by the function.
Mohseni’824 shows wherein the process further comprises estimating, by using the function in which the first parameter is optimized, a refractive index distribution for emission waves in a space based on a shape of the region of interest represented by the function (Paragraph [0050] describes the weights for processing the optical properties directly relate to the refractive index. Paragraph [0066] describes this to be applied for LiDAR).
At the time of the invention, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claim invention to modify Lopez’841 and Gohl’264 to include estimating, by using the function in which the first parameter is optimized, a refractive index distribution for emission waves in a space based on a shape of the region of interest represented by the function method taught by Mohseni’824. The suggestion/motivation for doing so would have been to improve the system’s ability to be able to enabling the model to learn the physics (paragraph [0050]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Juette et al. (US 2025/0224732): Figures 3-4 and paragraphs [0057]-[0069] describes converting LiDAR data onto a camera image via utilizing a spherical coordinate system for the LiDAR data. This includes curved cells/region of interests 308, comprise points of interest such as P1, respective to the center of emission 302.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/IRIANA CRUZ/Primary Examiner, Art Unit 2681
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