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 .
Drawings
The drawings are objected to because fig. 1 states, "class reflection characteristic extracto" but should read, "class reflection characteristic extractor". Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities: The equations listed throughout the disclosure are blurry enough so as to render them unreadable, as are many of the special characters used in description of the equations. For example, para. 0088, rendered below, contains an equation for which the superscripts are unreadable.
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Other equations cited in the specification contain similar legibility issues. Appropriate correction is required.
Claim Objections
Claim 3 is objected to because of the following informalities: claim 3 recites "on relative distance and angle plane...based on Fast Fourier Transform" but should recite "on a relative distance and angle plane...based on a Fast Fourier Transform". Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitations "each of classes" and “each of objects” in lines 2-3. There is insufficient antecedent basis for this limitation in the claim.
Claim 6 recites the limitation “to each of predetermined object sizes” in lines 2-3. There is insufficient antecedent basis for this limitation in the claim.
Claim 12 recites the limitation, “normalizing the similarity based on a number of the classes.” The metes and bounds of this limitation are unclear because the meaning of “a number of the classes” is not clear from the claims. A person of ordinary skill in the art would not know whether “a number of the classes” refers to a reference number, a measurement that is associated with each class, or simply the total number of classes being taken into account.
Claims 2-13 are rejected because they depend upon rejected claim 1.
Claim 14 is rejected for the same reasons as claim 1.
Claims 15-20 are rejected because they depend upon rejected claim 14.
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.
Step 1: Claims 1-13 are directed to a process, claims 14-20 are directed to an apparatus.
Step 2a, Prong 1: Claim 1 recites the abstract ideas of
…determining, among the classes, a class of a reference reflection characteristic of a high similarity with a reflection characteristic of received signal data transmitted from a radar; and identifying a target object of the received signal data based on the determined class…
These are abstract ideas because their broadest reasonable interpretation includes someone simply visually comparing a spectrogram of received radar data with spectrograms of radar data for each class, by, e.g., comparing two images on a computer screen. See, e.g., section 4.3 of I. Bilik, J. Tabrikian and A. Cohen, "Target classification using Gaussian mixture model for ground surveillance Doppler radar," IEEE International Radar Conference, 2005., Arlington, VA, USA, 2005, pp. 910-915, doi: 10.1109/RADAR.2005.1435957.
Step 2a, Prong 2: The judicial exceptions are not integrated into a practical application because the additional elements (storing a reference reflection characteristic, outputting information of the target object) are nothing more than insignificant extra-solution activity. Storing a reference reflection characteristic for each of classes based on modeling radar reflection signal data for each of objects corresponding to each of the classes is no more than selecting a particular data source to be used (see MPEP 2106.05(g)). Similarly, outputting data is also considered by existing case law to be no more than insignificant extra-solution activity (see MPEP 2106.05(g)).
Step 2b: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because both selecting a particular data source to be used and outputting data are understood by the courts to be well-understood, routine, and conventional in the art (see MPEP 2106.05(d)).
Regarding claim 2,
Step 2a, Prong 1: The limitation,
…wherein the modeling the radar reflection signal data for each of objects comprises modeling an intensity of the radar reflection signal data into a mixed normal distribution.
Modeling data into a mixed normal distribution recites an abstract idea because modeling data into a mixed normal distribution is a mathematical concept.
Step 2a, Prong 2: The judicial exceptions are not integrated into a practical application because the additional elements (using the intensity of the radar reflection signal data in the model) are nothing more than insignificant extra-solution activity. Modeling radar reflection signal data using intensity is no more than selecting a particular data source to be used (see MPEP 2106.05(g)).
Step 2b: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because both selecting a particular data source to be used is understood by the courts to be well-understood, routine, and conventional in the art (see MPEP 2106.05(d)).
Regarding claim 3,
Step 2a, Prong 1: claim 3 recites no additional judicial exceptions, but includes the exceptions recited in claims 1 and 2 due to its dependency.
Step 2a, Prong 2: claim 3 recites the additional element,
…an intensity of the radar reflection signal on a relative distance and angle plane extracted from a radar data cube generated based on a Fast Fourier Transform of the radar reflection signal data.
The judicial exceptions are not integrated into a practical application because the additional elements are nothing more than insignificant extra-solution activity. Modeling radar reflection signal data by extracting relative distance and angle plane data from a radar data cube based on a Fast Fourier Transform is no more than selecting a particular data source to be used (see MPEP 2106.05(g)).
Step 2b: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because both selecting a particular data source to be used is understood by the courts to be well-understood, routine, and conventional in the art (see MPEP 2106.05(d)).
Regarding claim 4,
Step 2a, Prong 1: claim 4 recites no additional judicial exceptions, but includes the exceptions recited in claim 1 due to its dependency.
Step 2a, Prong 2: claim 4 recites the additional element,
…wherein the radar reflection signal data is generated by a radar simulation signal generator
The judicial exceptions are not integrated into a practical application because the additional elements are nothing more than insignificant extra-solution activity. Modeling radar reflection using a simulation is no more than selecting a particular data source to be used (see MPEP 2106.05(g)).
Step 2b: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because both selecting a particular data source to be used is understood by the courts to be well-understood, routine, and conventional in the art (see MPEP 2106.05(d)).
Regarding claim 5,
Step 2a, Prong 1: claim 5 recites the limitation,
…wherein the classes include one or more classes selected from a class corresponding to a two-wheeled vehicle, a class corresponding to a passenger vehicle, and a class corresponding to a commercial vehicle.
This limitation merely modifies the judicial exception recited in claim 1 to further specify the vehicle classes that are used in the determining and identifying steps. Therefore, this limitation is being understood by the examiner to merely modify the judicial limitations of claim 1 without reciting any additional elements beyond the specific categories between which a person might be differentiating.
Step 2a, Prong 2: The judicial exceptions are not integrated into a practical application because claim 5 recites no new additional elements.
Step 2b: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because claim 5 recites no new additional elements.
The same analysis is used for claim 6, because classes being determined based on object size does no more than modify the judicial exceptions recited in claim 1.
Regarding claim 7,
Step 2a, Prong 1: claim 7 recites the limitation,
…determining a similarity between the reference reflection characteristic for each of the classes and the reflection characteristic of the received signal data
This limitation is directed to an abstract idea because determining a similarity between two different radar reflection characteristics can be done entirely within the human mind by, e.g., someone looking at images of reflection characteristics and reference reflection characteristics.
Step 2a, Prong 2: claim 7 recites the additional element,
…applying the received signal data to a radar reflection characteristic model and obtaining the reflection characteristic with respect to a predetermined reference distance
The judicial exceptions are not integrated into a practical application because the additional elements are nothing more than insignificant extra-solution activity. Obtaining reflection characteristics is no more than selecting a particular data source to be used (see MPEP 2106.05(g)).
Step 2b: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because both selecting a particular data source to be used is understood by the courts to be well-understood, routine, and conventional in the art (see MPEP 2106.05(d)).
Regarding claim 8,
Step 2a, Prong 1: claim 8 does not recite any additional judicial exceptions.
Step 2a, Prong 2: The judicial exceptions are not integrated into a practical application because the additional elements recited in claim 8, including obtaining information regarding radar distance and observation angle data and applying said data to the model, are nothing more than insignificant extra-solution activity. These additional elements amount to no more than selecting a particular data source to be used (see MPEP 2106.05(g)).
Step 2b: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because both selecting a particular data source to be used is understood by the courts to be well-understood, routine, and conventional in the art (see MPEP 2106.05(d)).
Regarding claim 9,
Step 2a, Prong 1: claim 9 does not recite any additional judicial exceptions.
Step 2a, Prong 2: The judicial exceptions are not integrated into a practical application because the additional elements recited in claim 9, including obtaining information regarding radar distance and angular resolution and applying said data to the model, are nothing more than insignificant extra-solution activity. These additional elements amount to no more than selecting a particular data source to be used (see MPEP 2106.05(g)).
Step 2b: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because both selecting a particular data source to be used is understood by the courts to be well-understood, routine, and conventional in the art (see MPEP 2106.05(d)).
Regarding claim 10,
Step 2a, Prong 1: claim 10 does not recite any additional judicial exceptions.
Step 2a, Prong 2: The judicial exceptions are not integrated into a practical application because the additional elements recited in claim 10, including obtaining detection information the location of each of the objects and applying said data to the model, are nothing more than insignificant extra-solution activity. These additional elements amount to no more than selecting a particular data source to be used (see MPEP 2106.05(g)).
Step 2b: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because both selecting a particular data source to be used is understood by the courts to be well-understood, routine, and conventional in the art (see MPEP 2106.05(d)).
Regarding claim 11,
Step 2a: Prong 1: claim 11 recites the abstract ideas of,
…applying a weight, an average, and a variance of the reflection characteristic of the received signal data and the detection information to a mixed normal distribution model to determine the similarity between the reference reflection characteristic for each of the classes and the reflection characteristic of the received signal data.
This limitation is directed to a mathematical concept.
Step 2a, Prong 2: claim 11 does not recite any additional elements.
Step 2b: claim 11 does not recite any additional elements.
Regarding claim 12,
Step 2a: Prong 1: claim 12 recites the abstract ideas of,
…determining a reference similarity for each of the classes by normalizing the similarity based on a number of the classes.
This limitation is directed to a mathematical concept.
Step 2a, Prong 2: claim 12 does not recite any additional elements.
Step 2b: claim 12 does not recite any additional elements.
Regarding claim 13,
Step 2a: Prong 1: claim 13 recites the abstract ideas of,
…identifying one or more classes having the reference similarity exceeding a threshold value among the classes, and identifying a class having a highest similarity among the identified one or more classes as the class of the reference reflection characteristic of the high similarity with the reflection characteristic of the received signal data transmitted from the radar.
This limitation is directed to a mental process, namely, identifying from a set of calculated similarities which similarity is the highest, a process that can be done entirely in the human mind.
Step 2a, Prong 2: claim 13 does not recite any additional elements.
Step 2b: claim 13 does not recite any additional elements.
Claim 14 is rejected for the same reasons as claim 1.
Claim 15 is rejected for the same reasons as claim 2.
Claim 16 is rejected for the same reasons as claim 3.
Claim 17 is rejected for the same reasons as claim 4.
Claim 18 is rejected for the same reasons as claim 5.
Claim 19 is rejected for the same reasons as claim 7.
Claim 20 is rejected for the same reasons as claim 9.
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 5-8, 10, 14, and 18-19 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Zhu et al. (U.S. Pub. No. 2022/0222922 A1), hereinafter Zhu.
Regarding claim 1, Zhu teaches,
A method for recognizing an object (para. 0002, “The present application relates to the field of computer vision technology, and in particular to an object recognition method and device, and a storage medium.”), the method comprising: storing a reference reflection characteristic for each of classes based on modeling radar reflection signal data for each of the objects corresponding to each of the classes (para. 0043, “Thus, it is possible to determine the category to which the object to be recognized belongs by comparing the similarity between the head shape of the object to be recognized and the head shape corresponding to each of the plurality of categories”); determining, among the classes, a class of a reference reflection characteristic of a high similarity with a reflection characteristic of received signal data transmitted from a radar; and identifying a target object of the received signal data based on the determined class and outputting information of the target object (para. 0042, “Therefore, in some embodiments, objects belonging to different categories have different head shapes; and determining, according to the target feature, the target category to which the object to be recognized belongs among the plurality of categories may include: determining, according to the target feature, a similarity between a head shape of the object to be recognized and a head shape corresponding to each of the plurality of categories to obtain a plurality of similarities; and determining a category corresponding to the maximum similarity among the plurality of similarities as the target category.” The examiner notes that para. 0029 specifies that the data is from a radar).
Regarding claim 5, Zhu teaches,
The method of claim 1, wherein the classes include one or more classes selected from a class corresponding to a two-wheeled vehicle, a class corresponding to a passenger vehicle, and class corresponding to a commercial vehicle (paras. 0032 and 0091 both indicate “car” (a passenger vehicle) and “truck” (a commercial vehicle) as potential object categories).
Regarding claim 6, Zhu teaches,
The method of claim 5, wherein the class corresponding to the passenger vehicle and the class corresponding to the commercial vehicle each includes a class corresponding to each of predetermined object sizes (para. 0032, “For example, the target category includes Large vehicles, Medium vehicles, and Small vehicles. If the object to be recognized is a truck, then the target category corresponding to the truck is Large vehicle; if the object to be recognized is a car, then the target category corresponding to the car is Small vehicle; and if the object to be recognized is a van, then the target category corresponding to the van is Medium vehicle.”).
Regarding claim 7, Zhu teaches,
The method of claim 1, wherein the determining the class includes: applying the received signal data to a radar reflection characteristic model and obtaining the reflection characteristic with respect to a predetermined reference distance (para. 0040, “Assuming that the to-be-processed point cloud data includes M points, and coordinates of each point are expressed as (X, Y, Z), the points in the target point cloud data may be expressed in the form of W×H×N×(Xi, Yi, Zi), where N indicates there are N points in each target geometry and may generally be set or adjusted according to requirements such as accuracy requirements, and W×H indicates a preset range of the point cloud.”), and determining a similarity between the reference reflection characteristic for each of the classes and the reflection characteristic of the received signal data (para. 0042, “determining, according to the target feature, a similarity between a head shape of the object to be recognized and a head shape corresponding to each of the plurality of categories to obtain a plurality of similarities”).
Regarding claim 8, Zhu teaches,
The method of claim 7, further comprising: obtaining, from the radar, information indicating a relative distance and an observation angel between the radar and the target object, wherein the obtaining the reflection characteristic with respect to the predetermined reference distance comprises: when applying the received data to the radar reflection characteristic model, applying the information indicating the relative distance and the observation angle to the radar reflection characteristic model (para. 0038, “In some implementations, e.g., in driving environments where objects are moving substantially within a specific plane (e.g., parallel to ground), the radar intensity map and the Doppler map can be defined using two-dimensional coordinates, such as the radial distance and azimuthal angle: (R, ϕ), Δf (R, ϕ).”).
Regarding claim 10, Zhu teaches,
The method of claim 7, further comprising: obtaining detection information for determining location information of each of the objects from the radar (para. 0041, “In this way, compared with the independent scattered points in the to-be-processed point cloud data, the present application traverses the to-be-processed point cloud data by the target geometry at the target step length to obtain the target point cloud data containing location information, such that the to-be-processed point cloud data without structured information may be represented as the target point cloud data containing the structured information, which helps to obtain more accurate semantic features, thereby improving the accuracy of object recognition.”), wherein the similarity is based on the detection information (para. 0042, “…determining, according to the target feature, a similarity between a head shape of the object to be recognized and a head shape corresponding to each of the plurality of categories to obtain a plurality of similarities”).
Claim 14 is rejected for the same reasons and using the same references as claim 1. The examiner notes that Zhu further teaches an apparatus, a memory, and a processor (para. 0015, “A third aspect of the present application features an object recognition device. The device includes at least one processor; and one or more memories coupled to the at least one processor and storing programming instructions for execution by the at least one processor to perform operations”).
Claim 18 is rejected for the same reasons and using the same citations as claim 5.
Claim 19 is rejected for the same reasons and using the same citations as claim 7.
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.
The factual inquiries 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 2, 11-13, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu in view of Bilik et al. (I. Bilik, J. Tabrikian and A. Cohen, "Target classification using Gaussian mixture model for ground surveillance Doppler radar," IEEE International Radar Conference, 2005., Arlington, VA, USA, 2005, pp. 910-915, doi: 10.1109/RADAR.2005.1435957.), hereinafter Bilik.
Regarding claim 2, Zhu teaches the method of claim 1. Zhu further teaches (note: what Zhu does not teach is struck through,
…wherein the modeling the radar reflection signal data for each of objects comprises modeling an intensity of the radar reflection signal data (paras. 0028-0029, “In this embodiment, the to-be-processed point cloud data is a data set of at least part of points collected from an exterior surface of the object. The method of acquiring the to-be-processed point cloud data is not limited in this embodiment. For example, the to-be-processed point cloud data may be collected by radar.” The examiner notes that radar point cloud data includes signal intensity)
Bilik teaches,
…wherein the modeling the radar reflection signal data for each of objects comprises modeling an intensity of the radar reflection signal data (fig. 3, spectrograms of radar data for a variety of objects) into a mixed normal distribution (section 2.3, para. 1, “The GMMs are estimated from the training database in an offline training stage. The detected radar target is classified into one of possible classes using models estimated in the training stage.” The examiner notes that GMM as used in this reference refers to Gaussian mixture modeling and further notes that a Gaussian distribution is a normal distribution).
Zhu and Bilik are analogous to the claimed invention because they teach methods of classifying objects using radar data. It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Zhu with the Gaussian mixture modeling (i.e., mixed normal distribution) of Bilik because the GMM of Bilik has a maximum accurate classification rate of 96%, which outperforms human operators.
Regarding claim 11, Zhu teaches the method of claim 10. Zhu does not teach,
…wherein determining the similarity comprises: applying a weight, an average and a variance of the reflection characteristic of the received signal data and the detection information to a mixed normal distribution model to determine the similarity between the reference reflection characteristic for each of the classes and the reflection characteristic of the received signal data
Bilik teaches,
…wherein determining the similarity comprises: applying a weight, an average and a variance of the reflection characteristic of the received signal data and the detection information to a mixed normal distribution model to determine the similarity between the reference reflection characteristic for each of the classes and the reflection characteristic of the received signal data (eqs. 6, noting that the pdf of a classification feature is understood to be its probability density function, which takes into account a mixing weight and the Gaussian for a particular component, which includes its mean and variance. See, e.g., the documentation for the pdf function in MATLAB included with this office action. The examiner further notes that both equations labeled with the number 6 include the probability density function fK--).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Zhu with the similarity determination of Bilik that uses weight, average, and variance to classify data because weighing data based on average and variance is a well-known technique in the art to measure the similarity between different datasets.
Regarding claim 12, Zhu in view of Bilik teaches the method of claim 11. Zhu does not teach,
…further comprising: determining a reference similarity for each of the classes by normalizing the similarity based on a number of classes
Bilik teaches,
…further comprising: determining a reference similarity for each of the classes by normalizing the similarity based on a number of classes (section 2.3.2, para. 2, “Thus, the LRT is performed between each pair of hypotheses, and the corresponding threshold is optimized independently of all other tests. The optimal threshold, γm, is determined to minimize the classification error for the pair of target classes, (m,n). The pair-wise decisions are combined by voting, that is the class with the most pair-wise wins is selected.” The examiner notes that the majority voting concept is a method of normalizing the similarity based on the number of classes, since similarity is compared between each pair of classes to determine “winners,” with the class with the most votes being the identified class. That is, the number of classes affects the number of votes, thus affecting how much each similarity comparison matters).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Zhu with the pairwise majority voting technique of Bilik because the technique of Bilik enables adjusting estimation thresholds for each pair, thus outperforming ML-based decision-making in the presence of modeling errors (see Bilik, section 2.3.2).
Regarding claim 13, Zhu in view of Bilik teaches the method of claim 12. Zhu does not teach,
…wherein the determining the class comprises: identifying one or more classes having the reference similarity exceeding a threshold value among the classes, and identifying a class having a highest similarity among the identified one or more classes as the class of the reference reflection characteristic of the high similarity with the reflection characteristic of the received signal data transmitted from the radar
Bilik teaches,
…wherein the determining the class comprises: identifying one or more classes having the reference similarity exceeding a threshold value among the classes, and identifying a class having a highest similarity among the identified one or more classes as the class of the reference reflection characteristic of the high similarity with the reflection characteristic of the received signal data transmitted from the radar (section 2.3.3, para. 2, “The “majority voting” decision rule enables to adjust the thresholds for each pair in order to minimize the cost function evaluated using the training database. Thus, the LRT is performed between each pair of hypotheses, and the corresponding threshold is optimized independently of all other tests. The optimal threshold, γm, is determined to minimize the classification error for the pair of target classes, (m,n). The pair-wise decisions are combined by voting, that is the class with the most pair-wise wins is selected.” The examiner notes that any class that has a similarity that optimizes the pairwise threshold γm is identified as exceeding said threshold by its reception of a vote, and the class with the highest similarity is identified by receiving the majority of these votes).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Zhu with the thresholding of Bilik because the thresholding of Bilik, as used in the AVA majority voting model, reduces the effect of modeling errors on the final classification decision (see Bilik, section 2.3.2).
Claim 15 is rejected for the same reasons and using the same citations as claim 2.
Claims 3 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu in view of Bilik as applied to claims 2 and 15, respectively, above, and further in view of Xia et al. (U.S. Pub. No. 2023/0351243 A1), hereinafter Xia.
Regarding claim 3, Zhu in view of Bilik teaches the method of claim 2. Zhu does not teach,
…wherein the intensity of the radar reflection signal data includes and intensity of the radar reflection signal on relative distance and angle plane extracted from a radar data cube generated based on Fast Fourier Transform of the radar reflection signal data
Xia teaches,
…wherein the intensity of the radar reflection signal data includes and intensity of the radar reflection signal on relative distance and angle plane extracted from a radar data cube generated based on Fast Fourier Transform of the radar reflection signal data (para. 0038, “the radar intensity map and the Doppler map can be defined using two-dimensional coordinates, such as the radial distance and azimuthal angle: (R, ϕ), Δf (R, ϕ).” The examiner notes that FFTs are used to generate radar intensity maps).
Xia is analogous to the claimed invention because it teaches radar-based object identification. It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhu in view of Bilik with the radial distance-azimuthal angle plane extraction of Xia because objects that are moving along the ground have relatively consistent elevation angles. Therefore, extracting a 2D coordinate graph for the purposes of classification reduces processing needs without significantly affecting data accuracy.
Claim 16 is rejected for the same reasons and using the same citations as claim 3.
Claims 4 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu in view of Matlab (Radar Signal Simulation and Processing for Automated Driving (2020, Oct. 22). MathWorks. https://www.mathworks.com/help/driving/ug/radar-signal-simulation-and-processing-for-automated-driving.html.). The examiner notes that a copy of the webpage as it appeared on 10/22/2020, retrieved from the Wayback Machine, has been included with this office action.
Regarding claim 4, Zhu teaches the method of claim 1. Zhu does not teach,
…wherein the radar reflection signal data is generated by a radar simulation signal generator
Matlab teaches,
…wherein the radar reflection signal data is generated by a radar simulation signal generator (p. 8, Range-Angle Image generated via MATLAB simulation).
Matlab is analogous to the claimed invention because it teaches radar data modeling for various vehicles. It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to use the radar simulation signal generator of Matlab to train the classification model of Zhu because simulated data is significantly less expensive to generate than real data, thus allowing for vehicle class data to be stored by the model without significant time being spent generating radar data using real vehicles.
Claim 17 is rejected for the same reasons and using the same citations as claim 4.
Claims 9 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu in view of Wodrich et al. (U.S. Pub. No. 2018/0067194 A1), hereinafter Wodrich.
Regarding claim 9, Zhu teaches the method of claim 8. Zhu further teaches (note: what Zhu does not teach is struck through),
…wherein obtaining the reflection characteristic with respect to the predetermined reference distance further comprises: when applying the received data to the radar reflection characteristic model, applying a predetermined radar distance (para. 0040, “Assuming that the to-be-processed point cloud data includes M points, and coordinates of each point are expressed as (X, Y, Z), the points in the target point cloud data may be expressed in the form of W×H×N×(Xi, Yi, Zi), where N indicates there are N points in each target geometry and may generally be set or adjusted according to requirements such as accuracy requirements, and W×H indicates a preset range of the point cloud.”).
Wodrich teaches
…wherein obtaining the reflection characteristic with respect to the predetermined reference distance further comprises: when applying the received data to the radar reflection characteristic model, applying a predetermined radar distance and a predetermined angular resolution of the radar to the radar reflection characteristic model (para. 0016, “For both systems, the FOV of the sensor controls what can be seen, and at what location relative to the source or equipped vehicle. For radar systems, this is further effected by the effective range and angular resolution of the radar, controlled respectively by the available signal bandwidth and the beam shape defined by the antenna design.”).
Wodrich is analogous to the claimed invention because it teaches object classification using radar sensors. It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Zhu with the known angular resolution of Wodrich because taking angular resolution into account increases the accuracy of classification.
Claim 20 is rejected for the same reasons and using the same citations as claim 9.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Anna K Gosling whose telephone number is (571)272-0401. The examiner can normally be reached Monday - Thursday, 7:30-4:30 Eastern, Friday, 10:00-2:00 Eastern.
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.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vladimir Magloire can be reached at (571) 270-5144. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Anna K. Gosling/Examiner, Art Unit 3648
/VLADIMIR MAGLOIRE/Supervisory Patent Examiner, Art Unit 3648
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