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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “first calculation unit configured to execute”, “second calculation unit configured to execute” and “processing device is configured to: provide” in claim 10.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim(s) 1-4 and 6-10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tatarchenko et al. (“Histogram-based Deep Learning for Automotive Radar”).
Regarding claim 1, Tatarchenko et al. discloses a method for sensor data processing of sensor data of a surroundings sensor, the method comprising the following steps:
providing the sensor data of the surroundings sensor acquiring at least one target object in surroundings of the sensor (“A car with a front-mounted automotive radar sensor operating at 77 GHz was used to collect the measurements. We use the recordings from both test-track scenarios (with different moving and stationary objects) and real-world drives in cities, on rural roads and on highways. An example frame from a recording is shown in Fig. 5. A radar signal processing pipeline is used to detect and track objects” at section IVA, line 1);
calculating a point cloud including a spatial distribution of a plurality of points assigned to respective reflections of the target object from the sensor data (“Our method receives a set of radar reflections that are associated to a single object in a single measurement cycle. We further refer to such a reflection set as a point cloud” at section III, line 1);
inputting the points and information sections of the sensor data respectively assigned to the points to a trained processing model (“Our method expects an input point cloud with N points, each being a tuple of size M, where M is the number of associated features. For every feature, we split its effective value range (determined by the normalization step, see below) into K bins and count the number of points that fall within each bin. This yields M histogram vectors each of length K, which we then flatten and pass through an MLP to produce the point cloud encoding” at section IIIA, line 1);
calculating feature vectors assigned to the points by the processing model depending on the input (the resulting encoding corresponds to the histogram vectors for the point cloud); and
outputting the points and the assigned feature vectors for further processing by a further processing model (“The resulting encoding becomes an input for the downstream application. An overview of our pipeline is shown in Fig. 2. In this work, we use classification as our main application” at section IIIA, line 8).
Regarding claim 2, Tatarchenko et al. discloses a method wherein the sensor data are divided into data bins as discretization elements and at least one of the data bins is assigned to each of the points as a respective point bin (“For every feature, we split its effective value range (determined by the normalization step, see below) into K bins and count the number of points that fall within each bin” at section IIIA, line 3).
Regarding claim 3, Tatarchenko et al. discloses a method wherein the information sections assigned to the points include a plurality of data bins adjacent to the respective point bins (“Therefore, each object contains several radar reflections associated to it, see Fig. 3. Here we use a simple approach, where all radar reflections which are located inside a rectangular box around the object are associated to it” at section IVA, line 16; therefore the feature bins associated with each point of the object will be relatively similar and therefore be contained in adjacent bins).
Regarding claim 4, Tatarchenko et al. discloses a method wherein the information sections assigned to the points include a plurality of data bins surrounding the respective point bins (“Therefore, each object contains several radar reflections associated to it, see Fig. 3. Here we use a simple approach, where all radar reflections which are located inside a rectangular box around the object are associated to it” at section IVA, line 16; therefore the feature bins associated with each point of the object will be relatively similar and therefore be contained in surrounding bins).
Regarding claim 6, Tatarchenko et al. discloses a method wherein the further processing model processes the points and the feature vectors and from the points and the feature vectors calculates at least one object property of the target object (“Those are used as input to a neural network with 3 fully-connected (fc) layers, which outputs the final class predictions” at Figure 2 description, line 2; the object class is indicative of a property of that object).
Regarding claim 7, Tatarchenko et al. discloses a method wherein the sensor data are in the form of a spectrum (“Digital radar signal processing (DSP) is used to convert the sampled base-band signal into radar spectra, detect the radar reflections using a constant false alarm rate (CFAR) detector and to estimate the angles” at section IVA, line 11).
Regarding claim 8, Tatarchenko et al. discloses a method wherein the spectrum is at least two-dimensional (“In additional experiments, we use point cloud data combined with spectral radar data around the object reflections as an input to the neural network” at section IVA, last paragraph, line 1).
Regarding claim 9, Tatarchenko et al. discloses a method wherein, as a result of processing in the processing model, the feature vectors respectively contain information derived from the information sections (“The features of the radar reflections we use are the radial distance, ego-motion-compensated radial Doppler velocity (subtracting the radial projection of the ego velocity), RCS, and the Cartesian coordinates x,y,z. The Cartesian coordinates are computed w. r. t. the center of the tracked object, similar to [3]. We use Cartesian coordinates in the object coordinate system instead of the spherical (range, azimuth, elevation) representation, since the object shape in Cartesian coordinates is independent of the distance between the object and the radar sensor” at section IVA, line 20).
Regarding claim 10, Tatarchenko et al. discloses a processing device configured to process sensor data, the processing device comprising:
a surroundings sensor configured to acquire at least one target object in surroundings of the sensor (“A car with a front-mounted automotive radar sensor operating at 77 GHz was used to collect the measurements. We use the recordings from both test-track scenarios (with different moving and stationary objects) and real-world drives in cities, on rural roads and on highways. An example frame from a recording is shown in Fig. 5. A radar signal processing pipeline is used to detect and track objects” at section IVA, line 1);
a first calculation unit configured to execute a trained processing model (portion that executes the point cloud encoding in Figure 2); and
a second calculation unit configured to execute a further processing model (portion that performs the classification in Figure 2);
wherein the processing device is configured to:
provide sensor data of the surroundings sensor acquiring the at least one target object in the surroundings of the sensor (see sensor above);
calculate a point cloud including a spatial distribution of a plurality of points assigned to respective reflections of the target object from the sensor data (“Our method receives a set of radar reflections that are associated to a single object in a single measurement cycle. We further refer to such a reflection set as a point cloud” at section III, line 1);
input the points and information sections of the sensor data respectively assigned to the points to a trained processing model (“Our method expects an input point cloud with N points, each being a tuple of size M, where M is the number of associated features. For every feature, we split its effective value range (determined by the normalization step, see below) into K bins and count the number of points that fall within each bin. This yields M histogram vectors each of length K, which we then flatten and pass through an MLP to produce the point cloud encoding” at section IIIA, line 1);
calculate feature vectors assigned to the points by the processing model depending on the input (the resulting encoding corresponds to the histogram vectors for the point cloud); and
output the points and the assigned feature vectors for further processing by a further processing model (“The resulting encoding becomes an input for the downstream application. An overview of our pipeline is shown in Fig. 2. In this work, we use classification as our main application” at section IIIA, line 8).
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.
Claim(s) 5 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Tatarchenko et al. and Mao et al. (US 2021/0150199).
Tatarchenko et al. discloses the elements of claim 1 as described above.
Tatarchenko et al. does not explicitly disclose that the calculation of the feature vectors by the processing model and the further processing by the further processing model take place on calculation units which are different from one another.
However, Mao et al., in the same field of endeavor of neural network based vehicle point cloud processing, teaches that the calculation of the feature vectors by the processing model and the further processing by the further processing model take place on calculation units which are different from one another (“The described techniques, on the other hand, employ an end-to-end two-stage neural network, referred to as a spatio-temporal-interactive network” at paragraph 0014, line 1; “A program can be stored in a portion of a file that holds other programs or data, e.g., one or more scripts stored in a markup language document, in a single file dedicated to the program in question, or in multiple coordinated files, e.g., files that store one or more modules, sub-programs, or portions of code. A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a data communication network” at paragraph 0104, line 10).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize a distributed sub-module processing as taught by Mao et al. as separate calculation units of Tatarchenko et al. for purposes of load distribution and efficiency.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATRINA R FUJITA whose telephone number is (571)270-1574. The examiner can normally be reached Monday - Friday 9:30-5:30 pm ET.
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/KATRINA R FUJITA/Primary Examiner, Art Unit 2672
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