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 submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 03/25/2026 and 06/01/2026 are being considered by the examiner.
Response to Amendment
Applicant’s remarks filed 06/03/2026 regarding the objections made to the specification and some of the objections made to the claims submitted in the non-final office action dated 03/06/2026 are withdrawn due to the amendments made to the specification and claims. It is also noted that additional claim objections have been made to the claims and some claim objections have not been obviated, as noted in the claim objections section of this office action.
Response to Arguments
Applicant’s arguments see remarks, filed 06/03/2026, with respect to the claims 1-14 have been fully considered but are moot because the arguments do not apply to the current combinations of references being used in the current rejection.
The applicant argues on page 13, “The Office asserted that the features in Li at paragraph [0060] and Fig. 1A allegedly correspond to the old language "cumulatively storing a reflected signal intensity of the point in a voxel ID of the searched voxel to create a dictionary" and the features at paragraph [0032] correspond to the old language "a reference point position determination step of determining a position of a reference point for calibration based on the separately stored position coordinates of the point." (see Office Action pp. 12-13). Li cannot be relied upon in the manner the Office suggested.”
In response, the Office does not find this argument to be persuasive. Based on the breadth of the claim language, the prior art LI et al. (US 20220214448 A1) explicitly teaches and cumulatively storing a reflected signal intensity of the point in a voxel ID of the searched voxel to create a dictionary (Fig. 1A. Paragraph [0060]-LI discloses then at least one 3D voxel grid corresponding to each reflectivity of each scanning line is determined based on position information of the multiple target scanning points, i.e. at least one 3D voxel grid corresponding to each box in the reflectivity calibration table is determined. Then target reflectivity information in each box may be determined based on an average reflectivity value corresponding to at least one 3D voxel grid corresponding to each box, and the reflectivity calibration table is generated. For clarity on the record, the reflectivity calibration table is a dictionary and a box is a voxel ID.);
Further, based on the breadth of the claim language, the prior art LI et al. (US 20220214448 A1) explicitly teaches a reference point position determination step of determining a position of a reference point for calibration based on the separately stored position coordinates of the point (Fig. 1A. Paragraph [0032]-LI discloses in the point cloud data collected by the primary radar, the data corresponding to each scanning point includes position information and reflectivity of the scanning point in a rectangular coordinate system corresponding to the primary radar For clarity on the record, a scanning point is a reference point, each point is collected by the radar, and points are separately stored in the point cloud.).
The applicant argues on pages 14-15, “Based on what is discussed above, Li discusses that point cloud data collected by the primary radar and at least one secondary radar respectively may be acquired, and the point cloud data collected by the primary radar and the adjusted point cloud data corresponding to the secondary radar are fused to obtain fused point cloud data. In other words, Li discloses a method of determining the position of the reflector using two or more radars to address difficulty in accurately distinguishing a reference object due to diffusely reflected signal, as described in page 8, lines 19-22 of the instant application. On the contrary, in a voxelization step of the present invention, the point data, e.g., position coordinates and a reflected signal intensity of a point, collected by one radar is stored in a radar sensing file; a voxel corresponding to the position coordinates of the point is searched; a voxel ID of the searched voxel and the reflected signal intensity of the point are stored in a dictionary. Here, the reflected signal intensity of the point is accumulatively stored in the dictionary, in other words, the voxel ID of the voxel corresponding to a newly collected point is present in the dictionary, the reflected signal intensity of the newly collected point is stored under name of the same voxel ID. (see the instant application, page 12, line 21 to page. 13, line 7). In the point extraction step of the present invention, a process of reading points stored in the radar sensing file F, determining whether the points are included in the top voxel stored in the dictionary, and extracting only the point included in the top voxel. Here, the position coordinates of the extracted point is separately stored. By separately performing the voxelization step (S200), which creates the dictionary, and the point extraction step (S300), which extracts the point included in the top voxel, it is possible to increase data processing speed and to minimize the size of the dictionary. (see the instant application, p. 13, lines 8-15).”
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., one radar) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Despite LI et al. (US 20220214448 A1) disclosing the use of multiple radars, the scope and breadth of the claim language in the instant application encompasses the use of the any number of radars, as the claims are not restricted to the use of one, singular radar.
The applicant argues on page 15, “As such, Li fails to teach or suggest the features, "a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is a reflected signal returned as a result of a radio wave signal transmitted by the radar and reflected by the object, searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file, and storing a voxel ID of the searched voxel and the reflected signal intensity of the point to a dictionary, wherein the reflected signal intensity of the point is cumulatively stored in the dictionary; a point extraction step of loading the radar sensing file, searching for the voxel corresponding to the position coordinates of the point stored in the radar sensing file, and in response to determining whether the voxel ID of the searched voxel is the same as a voxel having a largest accumulated value of the reflected signal intensity in the dictionary, extracting the point and storing the position coordinates of the extracted point; and a reference point position determination step of determining a position of a reference point for calibration based on the position coordinates of the extracted point," as recited in amended claim 1.”
In response to applicant's arguments against the references individually, one cannot show non-obviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). The prior arts HAYAKAWA et al. (US 6573855 B1), in view of LI et al. (US 20220214448 A1), and further in view of ISHIKAWA et al. (US 20200160526 A1) were used in combination to reject claims 1 and 8.
The office will like to bring to the applicant attention that claims 1 and 8 are now rejected under 35 U.S.C. 103 as being unpatentable over HAYAKAWA et al. (US 6573855 B1), hereinafter referenced as HAYAKAWA, in view of LI et al. (US 20220214448 A1), hereinafter referenced as LI, and further in view of HOYOSA et al. (US 5724493 A), hereinafter referenced as HOYOSA.
In response, the office do-not find this argument to be persuasive. Based on the breadth of the claim language the prior art by HAYAKAWA explicitly teaches a method of determining a position of an object sensed by a radar using spatial voxelization (Fig. 3. Col. 15. Lines [58-61]-HAYAKAWA discloses a three-dimensional voxel data generating means 31 for editing and processing the received signals input from the receiving circuit 14 in terms of their relationship relative to the position (x, y) on the medium surface and the time (t).), the method comprising:
a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is a reflected signal returned as a result of a radio wave signal transmitted by the radar and reflected by the object (Figs. 1 and 3. Col. 17, Lines [55-61]-HAYAKAWA discloses the digitized received signal is stored in a predetermined area of a memory 21a inside the data processing unit as the multiple scales source three-dimensional voxel data s (x, y, t) such that the coordinates (x, y, t) determined by the position (x, y) on the medium surface and the reflection time (t) of the reflected wave 5 from the object 2 are encoded (wherein the received signal is a reflected signal).),
searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file (Figs. 1 and 3. Col. 17, Lines [15-19]-HAYAKAWA discloses a section coordinate designating means 33b for designating a coordinate point on the displayed section in response to a manual operation on the input unit 22 such as a mouse, thereby to select the voxel at this coordinate point as an object voxel.), and
a point extraction step of loading the radar sensing file (Figs. 1 and 3. Col. 17, Lines [55-61]-HAYAKAWA discloses the digitized received signal is stored in a predetermined area of a memory 21a inside the data processing unit as the multiple scales source three-dimensional voxel data s (x, y, t) such that the coordinates (x, y, t) determined by the position (x, y) on the medium surface and the reflection time (t) of the reflected wave 5 from the object 2 are encoded (wherein the received signal is a reflected signal).), searching for the voxel corresponding to the position coordinates of the point stored in the radar sensing file (Figs. 1 and 3. Col. 17, Lines [15-19]-HAYAKAWA discloses a section coordinate designating means 33b for designating a coordinate point on the displayed section in response to a manual operation on the input unit 22 such as a mouse, thereby to select the voxel at this coordinate point as an object voxel.), and
HAYAKAWA fails to explicitly teach storing a voxel ID of the searched voxel and the reflected signal intensity of the point to a dictionary, wherein the reflected signal intensity of the point is cumulatively stored in the dictionary; a reference point position determination step of determining a position of a reference point for calibration based on the position coordinates of the extracted point.
However, LI explicitly teaches storing a voxel ID of the searched voxel and the reflected signal intensity of the point to a dictionary (Fig. 1A. Paragraph [0060]-LI discloses then at least one 3D voxel grid corresponding to each reflectivity of each scanning line is determined based on position information of the multiple target scanning points, i.e. at least one 3D voxel grid corresponding to each box in the reflectivity calibration table is determined. Then target reflectivity information in each box may be determined based on an average reflectivity value corresponding to at least one 3D voxel grid corresponding to each box, and the reflectivity calibration table is generated.), wherein the reflected signal intensity of the point is cumulatively stored in the dictionary (Fig. 1A. Paragraph [0060]-LI discloses then at least one 3D voxel grid corresponding to each reflectivity of each scanning line is determined based on position information of the multiple target scanning points, i.e. at least one 3D voxel grid corresponding to each box in the reflectivity calibration table is determined. Then target reflectivity information in each box may be determined based on an average reflectivity value corresponding to at least one 3D voxel grid corresponding to each box, and the reflectivity calibration table is generated.);
a reference point position determination step of determining a position of a reference point for calibration based on the position coordinates of the extracted point (Fig. 1A. Paragraph [0032]-LI discloses in the point cloud data collected by the primary radar, the data corresponding to each scanning point includes position information and reflectivity of the scanning point in a rectangular coordinate system corresponding to the primary radar (wherein the position of the primary radar is the reference point).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of HAYAKAWA of a method of determining a position of an object sensed by a radar using spatial voxelization, the method comprising: a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is a reflected signal returned as a result of a radio wave signal transmitted by the radar and reflected by the object, searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file, with the teachings of LI of storing a voxel ID of the searched voxel and the reflected signal intensity of the point to a dictionary, wherein the reflected signal intensity of the point is cumulatively stored in the dictionary; a reference point position determination step of determining a position of a reference point for calibration based on the position coordinates of the extracted point.
Wherein having HAYAKAWA’s point cloud and voxel data processing method having storing a voxel ID of the searched voxel and the reflected signal intensity of the point to a dictionary, wherein the reflected signal intensity of the point is cumulatively stored in the dictionary; a reference point position determination step of determining a position of a reference point for calibration based on the position coordinates of the extracted point.
The motivation behind the modification would have been to obtain a point cloud and voxel data processing method that enhances the accuracy in detecting objects based on received signals. Since both HAYAKAWA and LI relate to processing and analyzing data received through a radar system, wherein HAYAKAWA to provide a method or means which affords easy interpolation of a deficient voxel when such voxel deficient in data is present in three-dimensional voxel data so as to enable high-efficiency and high-precision detection of location of the underground buried object, while LI can more easily calibrate the reflectivity of the radar. Please see HAYAKAWA et al. (US 6573855 B1), Col. 4, Lines [19-34], and LI et al. (US 20220214448 A1), Paragraph [0044].
HAYAKAWA in view of LI fails to explicitly teach in response to determining whether the voxel ID of the searched voxel is the same as a voxel having a largest accumulated value of the reflected signal intensity in the dictionary, extracting the point and storing the position coordinates of the extracted point.
However, new prior art HOSOYA discloses in response to determining whether the voxel ID of the searched voxel is the same as a voxel having a largest accumulated value of the reflected signal intensity in the dictionary (Fig. 5. Col. 9. Lines [49-57]-HOSOYA discloses the compare/update processing part 33 reads out of the basic back-projection line information storage part 34 the maximum voting score V.sub.M of voxels selected until then, in correspondence with the voting scores V counted by the voting processing part 32 for a series of voxels selected in an arbitrary sequence on each basic back-projection line, and the compare/update processing part 33 compares the readout maximum voting score V.sub.M with each of the voting scores V of the currently selected voxels.), extracting the point and storing the position coordinates of the extracted point (Fig. 5. Col. 9-10. Lines [57-67] and [1-4]-HOYOSA discloses if V.sub.M >V, then the voting score V of the currently seleced voxel and its position (X.sub.t,Y.sub.t,Z.sub.t) are used as a new maximum voting score V.sub.M and a new maximum point position (X.sub.M,Y.sub.M,Z.sub.M) to update information about the corresponding basic back-projection line in the basic back-projection line information storage part 34. After execution of the voting and the compare/update processing has been completed for all the voxels on one basic back-projection line (L.sub.00, for instance), the maximum voting score in the storage area for the back-projection line L.sub.00 and the position (X.sub.M,Y.sub.M,Z.sub.M) of the voxel given the maximum voting score V.sub.M are obtained in the basic back-projection line information storage part 34. This position represents the position of one 3D feature point.); and
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of HAYAKAWA in view of LI of a method of determining a position of an object sensed by a radar using spatial voxelization, the method comprising: a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is a reflected signal returned as a result of a radio wave signal transmitted by the radar and reflected by the object, searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file, with the teachings of HOYOSA of in response to determining whether the voxel ID of the searched voxel is the same as a voxel having a largest accumulated value of the reflected signal intensity in the dictionary, extracting the point and storing the position coordinates of the extracted point.
Wherein having HAYAKAWA’s point cloud and voxel data processing method having in response to determining whether the voxel ID of the searched voxel is the same as a voxel having a largest accumulated value of the reflected signal intensity in the dictionary, extracting the point and storing the position coordinates of the extracted point.
The motivation behind the modification would have been to obtain a point cloud and voxel data processing method that enhances the accuracy in detecting objects based on received signals. Since both HAYAKAWA and HOYOSA relate to processing and analyzing 3D data, wherein HAYAKAWA to provide a method or means which affords easy interpolation of a deficient voxel when such voxel deficient in data is present in three-dimensional voxel data so as to enable high-efficiency and high-precision detection of location of the underground buried object, while HOYOSA provides a method of decreasing the processing time required. Please see HAYAKAWA et al. (US 6573855 B1), Col. 4, Lines [19-34], and HOYOSA et al. (US 5724493 A), Col. 3, Lines [35-59].
The applicant argues on page 17, “The Office rejected claims 3-7 and 10-14 under 35 U.S.C. § 103 as being unpatentable over Hayakawa in view of Li and Ishikawa, and further in view of U.S. Patent Application Publication No. 2018/0364717 to Douillard et al. (hereinafter "Douillard"). Applicant respectfully traverses this rejection. Claims 3-7 and 10-14 depend from claim 1 or 8 and recites additional features. The deficiencies of Hayakawa, Li, and Ishikawa with respect to claim 1 were discussed above. Douillard was not applied in a manner that attempted to make up for the above-identified deficiencies. Claims 3-7 and 10-14, therefore, distinguish over the applied references for at least the same reasons as those discussed with respect to claims 1 and 8, and/or for the additionally recited features. Accordingly, reconsideration and withdrawal of the rejection of claims 3-7 and 10-14 under 35 U.S.C. § 103as being unpatentable over the applied references are respectfully requested.”
In response, the Office does not find this argument to be persuasive, as DOUILLARD et al. (US 20180364717 A1) was applied to teach claims 3-7 and 10-14 rather than claim 1 and 8. The office respectfully encourages the applicant to amend the independent claims to overcome the prior arts of record.
Claim Objections
Claims 1, 3, 8, and 10 are objected to because of the following informalities:
In claim 1, line 6, the term “the position coordinates of the point stored in the radar sensing file” should be changed to “the position coordinates of a point stored in the radar sensing file” in order to avoid an insufficient antecedent issue and prevent a rejection under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
In claim 3, line 11, the term “the point in a voxel ID of the searched voxel” should be changed to “the point in the voxel ID of the searched voxel” in order to maintain consistency through the claims and avoid an insufficient antecedent issue and prevent a rejection under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
In claim 8, line 20, the term “the position coordinates of the point stored in the radar sensing file” should be changed to “the position coordinates of a point stored in the radar sensing file” in order to avoid an insufficient antecedent issue and prevent a rejection under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
In claim 10, line 7, the term “creating a voxel grid based on the voxel size” should be changed to “creating the voxel grid based on the voxel size” in order to maintain consistency through the claims and avoid an insufficient antecedent issue and prevent a rejection under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
Appropriate correction is required.
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 use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function.
Claim 8, recites limitations that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f):
Claim 8; recites the limitation, “a determination module configured to...” [Line 5].
Claim 8; recites the limitation, “storage unit configured to…” [Line 10].
Claim 8; recites the limitation, “input/output unit configured to…” [Line 13].
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.
After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claim 8;
(i) “determination module” (Fig. 2, #30. Page 10, Lines [11-23] and Page 11, Lines [11-13 and 19-21]-The determination module 30 may include a computer device. For example, the determination module 30 may include a desktop computer, a laptop computer, a server, a tablet computer, and other devices capable of performing information processing functions. The determination module 30 may be connected to the radar 20 to receive the position coordinates and the reflected signal intensity of the point output by the radar 20. The determination module 30 may include a processor 310 configured to execute program code, a storage unit 320 connected to the processor 310 so as to transmit and receive data to and from the processor, the storage unit being configured to store the program code, the radar sensing file F storing the reflected signal received by the radar 20, and a dictionary, and an input/output unit 340 connected to the processor 310 so as to transmit and receive data to and from the processor, the input/output unit being configured to receive a parameter required for voxelization. The determination module 30 may further include a communication unit 330 connected to the processor 310 so as to transmit and receive data to and from the processor. The storage unit 320 may store data required to perform the method of determining the position of the object sensed by the radar using spatial voxelization. The storage unit 320 may include RAM, ROM, memory, a hard disk, or cloud storage. The input/output unit 340 may include a keyboard, a mouse, a touchpad, a touchscreen, or a pen configured to receive input by a user. The input/output unit 340 may include a display or a speaker configured to provide information to the user. The determination module is illustrated in fig. 2, as #30 thus having sufficient structure or material wherein is a desktop computer, a laptop computer, a server, a tablet computer processor and memory.).
(ii) “storage unit” (Fig. 2, #320. Page 11, Lines [11-18]- The storage unit 320 may store data required to perform the method of determining the position of the object sensed by the radar using spatial voxelization. The storage unit 320 may include RAM, ROM, memory, a hard disk, or cloud storage. The storage unit 320 may store program code written to perform each step of the method of determining the position of the object sensed by the radar using spatial voxelization. The program code may be executed by the processor 310. The storage unit 320 may store a radar sensing file F, a voxel dictionary, position coordinates of separately stored points, parameters, and other data. The storage unit is illustrated in Fig. 2, as black box #320 thus having sufficient structure or material wherein is a memory.).
(iii) “input/output unit” (Fig. 2, #320. Page 11, Lines [11-18]-the input/output unit 340 may include a keyboard, a mouse, a touchpad, a touchscreen, or a pen configured to receive input by a user. The input/output unit 340 may include a display or a speaker configured to provide information to the user. The input/output unit 340 may provide a screen configured to allow the user to input a parameter, and may visually provide analysis results. The input/output unit is illustrated in Fig. 2, as black box #340 thus having sufficient structure or material wherein is a display, keyboard, a mouse, a touchpad, a touchscreen, or a pen.).
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 § 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, 2-8, and 10-14 and associated dependent claims 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 pre-AIA the applicant regards as the invention.
Claims 1 and 8 recites the limitation “storing a voxel ID of the searched voxel and the reflected signal intensity of the point to a dictionary,” in Line 7 and Line 21 respectively. The office finds the term “the reflected signal intensity of the point” rendering the claim indefinite. It is not clear what the applicant refers to as “the point”, whether or not it is a new point, “a point that is a reflected signal returned as a result of a radio wave signal transmitted by the radar and reflected by the object”, or “the point stored in the radar sensing file” the applicant is referring to since claims 1 and 8 recite “point” in different contexts. For purpose of examination the examiner is interpreting the limitation as “storing a voxel ID of the searched voxel and the reflected signal intensity of the point stored in the radar sensing file to a dictionary”. The office respectfully requests the Applicant to amend claims 1 and 8 in order to clarify the claimed invention.
Claims 1 and 8 recites the limitations “extracting the point and storing the position coordinates of the extracted point,” in Line 14 and Line 28 respectively. The office finds the term “extracting the point” rendering the claim indefinite. It is not clear what the applicant refers to as “the point”, whether or not it is a new point, “a point that is a reflected signal returned as a result of a radio wave signal transmitted by the radar and reflected by the object”, or “the point stored in the radar sensing file” the applicant is referring to since claims 1 and 8 recite “point” in different contexts. For purpose of examination the examiner is interpreting the limitation as “extracting the point stored in the radar sensing file and storing the position coordinates of the extracted point”. The office respectfully requests the Applicant to amend claims 1 and 8 in order to clarify the claimed invention.
Claims 3 and 10 recites the limitations “cumulatively storing the reflected signal intensity of the point in a voxel ID of the searched voxel to create a dictionary” in Line 10-11 and Line 10-11. The office finds the term “cumulatively storing the reflected signal intensity of the point” rendering the claim indefinite. It is not clear what the applicant refers to as “the point”, whether or not it is a new point, “a point that is a reflected signal returned as a result of a radio wave signal transmitted by the radar and reflected by the object”, “the point stored in the radar sensing file”, “the extracted point”, or “a reference point” the applicant is referring to since claims 1 and 8 recite “point” in different contexts. For purpose of examination the examiner is interpreting the limitation as “cumulatively storing the reflected signal intensity of the point stored in the radar sensing file in a voxel ID of the searched voxel to create a dictionary”. The office respectfully requests the Applicant to amend claims 3 and 10 in order to clarify the claimed invention.
Claims 4 and 11 recite the limitations “storing a count of the point recorded in the voxel ID,” and “cumulatively storing a count of the point recorded in the voxel ID” in Line 9 and Line 12 and Line 9 and Line 12 respectively. The office finds the term “the point recorded in the voxel ID” rendering the claim indefinite. It is not clear what the applicant refers to as “the point”, whether or not it is a new point, “a point that is a reflected signal returned as a result of a radio wave signal transmitted by the radar and reflected by the object”, “the point stored in the radar sensing file”, “the extracted point”, or “a reference point” the applicant is referring to since claims 1 and 8 recite “point” in different contexts. For purpose of examination the examiner is interpreting the limitation as “storing a count of the point stored in the radar sensing file” and “cumulatively storing a count of the point stored in the radar sensing file”. The office respectfully requests the Applicant to amend claims 4 and 11 in order to clarify the claimed invention.
Claims 5 and 12 recites the limitations “separately storing position coordinates of the point when the voxel ID of the searched voxel is the same as a voxel ID of a top voxel having a largest accumulated value of the reflected signal intensity in the dictionary” in Line 10-12 and Line 10-12 respectively. The office finds the term “the point” rendering the claim indefinite. It is not clear what the applicant refers to as “the point”, whether or not it is a new point, “a point that is a reflected signal returned as a result of a radio wave signal transmitted by the radar and reflected by the object”, “the point stored in the radar sensing file”, “the extracted point”, or “a reference point” the applicant is referring to since claim 1 recites “point” in different contexts. For purpose of examination the examiner is interpreting the limitation as “separately storing position coordinates of the point stored in the radar sensing file when the voxel ID of the searched voxel is the same as a voxel ID of a top voxel having a largest accumulated value of the reflected signal intensity in the dictionary”. The office respectfully requests the Applicant to amend claims 5 and 12 in order to clarify the claimed invention.
Claims 6 and 13 recites the limitations “a point position storage step of, when the voxel ID of the searched voxel matches the voxel ID of the top voxel, storing the position of the point in the voxel ID of the top voxel” in Line 7-8 and Line 7-8 respectively. The office find the term “position of the point in the voxel ID of the top voxel” rendering the claim indefinite. It is not clear what the applicant refers to as “the point”, whether or not it is a new point, “a point that is a reflected signal returned as a result of a radio wave signal transmitted by the radar and reflected by the object”, “the point stored in the radar sensing file”, “the extracted point”, “a reference point”, or “a point of the top voxel” the applicant is referring to since claims 1, 5, 8, and 12 recite “point” in different contexts. For purpose of examination the examiner is interpreting the limitation as “a point position storage step of, when the voxel ID of the searched voxel matches the voxel ID of the top voxel, storing the position of the point stored in the radar sensing file in the voxel ID of the top voxel”. The office respectfully requests the Applicant to amend claims 6 and 13 in order to clarify the claimed invention.
Claims 7 and 14 recites the limitations “wherein the top voxel point extraction step further comprises determining whether a current file comprising the point matches a target file when the voxel ID of the searched voxel matches the voxel ID of the top voxel in the dictionary” in Line 2-4 and Line 2-4 respectively. The office find the term “the point” rendering the claim indefinite. It is not clear what the applicant refers to as “the point”, whether or not it is a new point, “a point that is a reflected signal returned as a result of a radio wave signal transmitted by the radar and reflected by the object”, “the point stored in the radar sensing file”, “the extracted point”, or “a reference point” the applicant is referring to since claims 1, 5, 8, and 12 recite “point” in different contexts. For purpose of examination the examiner is interpreting the limitation as “wherein the top voxel point extraction step further comprises determining whether a current file comprising the point stored in the radar sensing file matches a target file when the voxel ID of the searched voxel matches the voxel ID of the top voxel in the dictionary”. The office respectfully requests the Applicant to amend claims 7 and 14 in order to clarify the claimed invention.
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 (i.e., changing from AIA to pre-AIA ) 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 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 and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over HAYAKAWA et al. (US 6573855 B1), hereinafter referenced as HAYAKAWA, in view of LI et al. (US 20220214448 A1), hereinafter referenced as LI, and further in view of HOYOSA et al. (US 5724493 A), hereinafter referenced as HOYOSA.
Regarding claim 1, HAYAKAWA explicitly teaches a method of determining a position of an object sensed by a radar using spatial voxelization (Fig. 3. Col. 15. Lines [58-61]-HAYAKAWA discloses a three-dimensional voxel data generating means 31 for editing and processing the received signals input from the receiving circuit 14 in terms of their relationship relative to the position (x, y) on the medium surface and the time (t).), the method comprising:
a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is a reflected signal returned as a result of a radio wave signal transmitted by the radar and reflected by the object (Figs. 1 and 3. Col. 17, Lines [55-61]-HAYAKAWA discloses the digitized received signal is stored in a predetermined area of a memory 21a inside the data processing unit as the multiple scales source three-dimensional voxel data s (x, y, t) such that the coordinates (x, y, t) determined by the position (x, y) on the medium surface and the reflection time (t) of the reflected wave 5 from the object 2 are encoded (wherein the received signal is a reflected signal).),
searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file (Figs. 1 and 3. Col. 17, Lines [15-19]-HAYAKAWA discloses a section coordinate designating means 33b for designating a coordinate point on the displayed section in response to a manual operation on the input unit 22 such as a mouse, thereby to select the voxel at this coordinate point as an object voxel.), and
a point extraction step of loading the radar sensing file (Figs. 1 and 3. Col. 17, Lines [55-61]-HAYAKAWA discloses the digitized received signal is stored in a predetermined area of a memory 21a inside the data processing unit as the multiple scales source three-dimensional voxel data s (x, y, t) such that the coordinates (x, y, t) determined by the position (x, y) on the medium surface and the reflection time (t) of the reflected wave 5 from the object 2 are encoded (wherein the received signal is a reflected signal).), searching for the voxel corresponding to the position coordinates of the point stored in the radar sensing file (Figs. 1 and 3. Col. 17, Lines [15-19]-HAYAKAWA discloses a section coordinate designating means 33b for designating a coordinate point on the displayed section in response to a manual operation on the input unit 22 such as a mouse, thereby to select the voxel at this coordinate point as an object voxel.), and
HAYAKAWA fails to explicitly teach storing a voxel ID of the searched voxel and the reflected signal intensity of the point to a dictionary, wherein the reflected signal intensity of the point is cumulatively stored in the dictionary; a reference point position determination step of determining a position of a reference point for calibration based on the position coordinates of the extracted point.
However, LI explicitly teaches storing a voxel ID of the searched voxel and the reflected signal intensity of the point to a dictionary (Fig. 1A. Paragraph [0060]-LI discloses then at least one 3D voxel grid corresponding to each reflectivity of each scanning line is determined based on position information of the multiple target scanning points, i.e. at least one 3D voxel grid corresponding to each box in the reflectivity calibration table is determined. Then target reflectivity information in each box may be determined based on an average reflectivity value corresponding to at least one 3D voxel grid corresponding to each box, and the reflectivity calibration table is generated.), wherein the reflected signal intensity of the point is cumulatively stored in the dictionary (Fig. 1A. Paragraph [0060]-LI discloses then at least one 3D voxel grid corresponding to each reflectivity of each scanning line is determined based on position information of the multiple target scanning points, i.e. at least one 3D voxel grid corresponding to each box in the reflectivity calibration table is determined. Then target reflectivity information in each box may be determined based on an average reflectivity value corresponding to at least one 3D voxel grid corresponding to each box, and the reflectivity calibration table is generated.);
a reference point position determination step of determining a position of a reference point for calibration based on the position coordinates of the extracted point (Fig. 1A. Paragraph [0032]-LI discloses in the point cloud data collected by the primary radar, the data corresponding to each scanning point includes position information and reflectivity of the scanning point in a rectangular coordinate system corresponding to the primary radar (wherein the position of the primary radar is the reference point).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of HAYAKAWA of a method of determining a position of an object sensed by a radar using spatial voxelization, the method comprising: a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is a reflected signal returned as a result of a radio wave signal transmitted by the radar and reflected by the object, searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file, with the teachings of LI of storing a voxel ID of the searched voxel and the reflected signal intensity of the point to a dictionary, wherein the reflected signal intensity of the point is cumulatively stored in the dictionary; a reference point position determination step of determining a position of a reference point for calibration based on the position coordinates of the extracted point.
Wherein having HAYAKAWA’s point cloud and voxel data processing method having storing a voxel ID of the searched voxel and the reflected signal intensity of the point to a dictionary, wherein the reflected signal intensity of the point is cumulatively stored in the dictionary; a reference point position determination step of determining a position of a reference point for calibration based on the position coordinates of the extracted point.
The motivation behind the modification would have been to obtain a point cloud and voxel data processing method that enhances the accuracy in detecting objects based on received signals. Since both HAYAKAWA and LI relate to processing and analyzing data received through a radar system, wherein HAYAKAWA to provide a method or means which affords easy interpolation of a deficient voxel when such voxel deficient in data is present in three-dimensional voxel data so as to enable high-efficiency and high-precision detection of location of the underground buried object, while LI can more easily calibrate the reflectivity of the radar. Please see HAYAKAWA et al. (US 6573855 B1), Col. 4, Lines [19-34], and LI et al. (US 20220214448 A1), Paragraph [0044].
HAYAKAWA in view of LI fails to explicitly teach in response to determining whether the voxel ID of the searched voxel is the same as a voxel having a largest accumulated value of the reflected signal intensity in the dictionary, extracting the point and storing the position coordinates of the extracted point.
However, HOSOYA discloses in response to determining whether the voxel ID of the searched voxel is the same as a voxel having a largest accumulated value of the reflected signal intensity in the dictionary (Fig. 5. Col. 9. Lines [49-57]-HOSOYA discloses the compare/update processing part 33 reads out of the basic back-projection line information storage part 34 the maximum voting score V.sub.M of voxels selected until then, in correspondence with the voting scores V counted by the voting processing part 32 for a series of voxels selected in an arbitrary sequence on each basic back-projection line, and the compare/update processing part 33 compares the readout maximum voting score V.sub.M with each of the voting scores V of the currently selected voxels.), extracting the point and storing the position coordinates of the extracted point (Fig. 5. Col. 9-10. Lines [57-67] and [1-4]-HOYOSA discloses if V.sub.M >V, then the voting score V of the currently seleced voxel and its position (X.sub.t,Y.sub.t,Z.sub.t) are used as a new maximum voting score V.sub.M and a new maximum point position (X.sub.M,Y.sub.M,Z.sub.M) to update information about the corresponding basic back-projection line in the basic back-projection line information storage part 34. After execution of the voting and the compare/update processing has been completed for all the voxels on one basic back-projection line (L.sub.00, for instance), the maximum voting score in the storage area for the back-projection line L.sub.00 and the position (X.sub.M,Y.sub.M,Z.sub.M) of the voxel given the maximum voting score V.sub.M are obtained in the basic back-projection line information storage part 34. This position represents the position of one 3D feature point.); and
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of HAYAKAWA in view of LI of a method of determining a position of an object sensed by a radar using spatial voxelization, the method comprising: a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is a reflected signal returned as a result of a radio wave signal transmitted by the radar and reflected by the object, searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file, with the teachings of HOYOSA of in response to determining whether the voxel ID of the searched voxel is the same as a voxel having a largest accumulated value of the reflected signal intensity in the dictionary, extracting the point and storing the position coordinates of the extracted point.
Wherein having HAYAKAWA’s point cloud and voxel data processing method having in response to determining whether the voxel ID of the searched voxel is the same as a voxel having a largest accumulated value of the reflected signal intensity in the dictionary, extracting the point and storing the position coordinates of the extracted point.
The motivation behind the modification would have been to obtain a point cloud and voxel data processing method that enhances the accuracy in detecting objects based on received signals. Since both HAYAKAWA and HOYOSA relate to processing and analyzing 3D data, wherein HAYAKAWA to provide a method or means which affords easy interpolation of a deficient voxel when such voxel deficient in data is present in three-dimensional voxel data so as to enable high-efficiency and high-precision detection of location of the underground buried object, while HOYOSA provides a method of decreasing the processing time required. Please see HAYAKAWA et al. (US 6573855 B1), Col. 4, Lines [19-34], and HOYOSA et al. (US 5724493 A), Col. 3, Lines [35-59].
Regarding claim 2, HAYAKAWA in view of LI and further in view of HOYOSA explicitly teach the method according to claim 1,
HAYAKAWA fails to explicitly teach wherein the radar sensing file is created by the radar receiving the reflected signal and storing position coordinates and reflected signal intensity of a point in a form of point cloud data.
However, LI explicitly teaches wherein the radar sensing file is created by the radar receiving the reflected signal (Figs. 1A-B. Paragraph [0050]-LI discloses the radar acquires the point cloud data by scanning the environment periodically) and storing position coordinates and reflected signal intensity of a point in a form of point cloud data (Fig. 1A. Paragraph [0032]-LI discloses in the point cloud data collected by the primary radar, the data corresponding to each scanning point includes position information and reflectivity of the scanning point in a rectangular coordinate system corresponding to the primary radar.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of HAYAKAWA of a method of determining a position of an object sensed by a radar using spatial voxelization, the method comprising: a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is a reflected signal returned as a result of a radio wave signal transmitted by the radar and reflected by the object, searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file, with the teachings of LI of wherein the radar sensing file is created by the radar receiving the reflected signal and storing position coordinates and reflected signal intensity of a point in a form of point cloud data.
Wherein having HAYAKAWA’s point cloud and voxel data processing method wherein the radar sensing file is created by the radar receiving the reflected signal and storing position coordinates and reflected signal intensity of a point in a form of point cloud data.
The motivation behind the modification would have been to obtain a point cloud and voxel data processing method that enhances the accuracy in detecting objects based on received signals. Since both HAYAKAWA and LI relate to processing and analyzing data received through a radar system, wherein HAYAKAWA to provide a method or means which affords easy interpolation of a deficient voxel when such voxel deficient in data is present in three-dimensional voxel data so as to enable high-efficiency and high-precision detection of location of the underground buried object, while LI can more easily calibrate the reflectivity of the radar. Please see HAYAKAWA et al. (US 6573855 B1), Col. 4, Lines [19-34], and LI et al. (US 20220214448 A1), Paragraph [0044].
Regarding claim 8, HAYAKAWA explicitly teaches an apparatus for determining a position of an object sensed by a radar using spatial voxelization (Fig. 3. Col. 15. Lines [58-61]-HAYAKAWA discloses a three-dimensional voxel data generating means 31 for editing and processing the received signals input from the receiving circuit 14 in terms of their relationship relative to the position (x, y) on the medium surface and the time (t).), the apparatus comprising:
an input/output unit (Fig. 1, #20 called a data analyze includes #23 called a display unit and #22 called an input unit. Col. 1, Lines [45-47]-HAYAKAWA discloses a display unit 23 comprising a CRT monitor, a liquid crystal display or the like for displaying image data or output results at each stage of the processing.) configured to transmit and receive data to and from the processor (Fig. 1. Col. 15, Lines [41-50]-HAYAKAWA discloses this data analyzer 20 includes a data processing unit 21 comprising a microcomputer, a semiconductor memory or the like, an input unit 22 comprising a mouse, a keyboard or the like for receiving an instruction from the outside and a display unit 23 comprising a CRT monitor, a liquid crystal display or the like for displaying image data or output results at each stage of the processing. The data analyzer 20 further includes an external auxiliary storage 24 comprising a magnetic disc or the like for storing the data or output results at each stage of the processing.), and receive a parameter required for voxelization (Col. 9, Lines [9-15]-HAYAKAWA discloses a section displaying means 33a for selecting a desired section of the three-dimensional voxel data S (x, y, t) generated by the three-dimensional voxel data generating means 31 in response to a manual operation from the input unit 22 such as a mouse and then displaying this section on the display unit 23.), and
a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is the reflected signal (Figs. 1 and 3. Col. 17, Lines [55-61]-HAYAKAWA discloses the digitized received signal is stored in a predetermined area of a memory 21a inside the data processing unit as the multiple scales source three-dimensional voxel data s (x, y, t) such that the coordinates (x, y, t) determined by the position (x, y) on the medium surface and the reflection time (t) of the reflected wave 5 from the object 2 are encoded (wherein the received signal is a reflected signal).), searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file (Figs. 1 and 3. Col. 17, Lines [15-19]-HAYAKAWA discloses a section coordinate designating means 33b for designating a coordinate point on the displayed section in response to a manual operation on the input unit 22 such as a mouse, thereby to select the voxel at this coordinate point as an object voxel.), and
a point extraction step of loading the radar sensing file (Figs. 1 and 3. Col. 17, Lines [55-61]-HAYAKAWA discloses the digitized received signal is stored in a predetermined area of a memory 21a inside the data processing unit as the multiple scales source three-dimensional voxel data s (x, y, t) such that the coordinates (x, y, t) determined by the position (x, y) on the medium surface and the reflection time (t) of the reflected wave 5 from the object 2 are encoded (wherein the received signal is a reflected signal).), searching for the voxel corresponding to the position coordinates of the point stored in the radar sensing file (Figs. 1 and 3. Col. 17, Lines [15-19]-HAYAKAWA discloses a section coordinate designating means 33b for designating a coordinate point on the displayed section in response to a manual operation on the input unit 22 such as a mouse, thereby to select the voxel at this coordinate point as an object voxel.),
HAYAKAWA fails to explicitly teach a radar configured to transmit a radio wave signal and to receive a reflected signal that is the radio wave signal transmitted by the radar and reflected by the object; and a determination module configured to analyze a radar sensing file storing the reflected signal received by the radar using spatial voxelization and to determine a position of a reflector based on which calibration is performed, wherein the determination module comprises: a processor configured to execute program code; a storage unit configured to transmit and receive data to and from the processor, and store the program code, the radar sensing file storing the reflected signal received by the radar, and a dictionary; and the program code is written to perform: storing a voxel ID of the searched voxel and the reflected signal intensity of the point to a dictionary, wherein the reflected signal intensity of the point is cumulatively stored in the dictionary; a reference point position determination step of determining a position of a reference point for calibration based on the position coordinates of the extracted point.
However, LI explicitly teaches a radar (Fig. 1B, #11 called a first radar. Paragraph [0030]) configured to transmit a radio wave signal and to receive a reflected signal that is the radio wave signal transmitted by the radar and reflected by the object (Figs. 1A-B. Paragraph [0049]-LI discloses multiple pieces of pose data may be calculated according to time when the primary radar or the secondary radar transmits and receives a radio beam); and
a determination module (Fig. 4, illustrates electronic device #400 with a processor $401 and memory #402. Paragraph [0130]-LI discloses when the electronic device 400 operates, the processor 401 communicates with the memory 402 through the bus 403, so that the processor 401 performs any point cloud data fusion method as described above.) configured to analyze a radar sensing file storing the reflected signal received by the radar using spatial voxelization and to determine a position of a reflector based on which calibration is performed (Figs. 1A-B. Paragraph [0032]-LI discloses in the point cloud data collected by the primary radar, the data corresponding to each scanning point includes position information and reflectivity of the scanning point in a rectangular coordinate system corresponding to the primary radar. The point cloud data collected by the secondary radar may include data respectively corresponding to multiple scanning points. In the point cloud data collected by the secondary radar, the data corresponding to each scanning point includes position information and reflectivity of the scanning point in a rectangular coordinate system corresponding to the secondary radar.),
wherein the determination module comprises (Fig. 4, illustrates electronic device #400 with a processor $401 and memory #402. Paragraph [0130]):
a processor (Fig. 4, #401 called a processor. Paragraph [0130].) configured to execute program code (Fig. 4. Paragraph [0131]-LI discloses further provide a computer-readable storage medium, which has a computer program stored thereon which, when executed by a processor, performs the point cloud data fusion method described in any of the above method embodiments.);
a storage unit (Fig. 4, #402 called a memory. Paragraph [0130]) configured to transmit and receive data to and from the processor, and store the program code (Fig. 4. Paragraph [0130]-LI discloses the processor 401 exchanges data with the external memory 4022 through the memory 4021. When the electronic device 400 operates, the processor 401 communicates with the memory 402 through the bus 403, so that the processor 401 performs any point cloud data fusion method as described above.), the radar sensing file storing the reflected signal received by the radar, and a dictionary (Fig. 4. Paragraph [0130]-LI discloses the memory 4021 here is also referred to as an internal memory, and is configured to temporarily store operation data in the processor 401 and data exchanged with the external memory 4022 such as a hard disk (wherein the operational data is signal received by the radar and a point cloud data helps form part of a dictionary).); and
the program code is written to perform (Fig. 4. Paragraph [0131]-LI discloses further provide a computer-readable storage medium, which has a computer program stored thereon which, when executed by a processor, performs the point cloud data fusion method described in any of the above method embodiments.):
storing a voxel ID of the searched voxel and the reflected signal intensity of the point to a dictionary (Fig. 1A. Paragraph [0060]-LI discloses then at least one 3D voxel grid corresponding to each reflectivity of each scanning line is determined based on position information of the multiple target scanning points, i.e. at least one 3D voxel grid corresponding to each box in the reflectivity calibration table is determined. Then target reflectivity information in each box may be determined based on an average reflectivity value corresponding to at least one 3D voxel grid corresponding to each box, and the reflectivity calibration table is generated.), wherein the reflected signal intensity of the point is cumulatively stored in the dictionary (Fig. 1A. Paragraph [0060]-LI discloses then at least one 3D voxel grid corresponding to each reflectivity of each scanning line is determined based on position information of the multiple target scanning points, i.e. at least one 3D voxel grid corresponding to each box in the reflectivity calibration table is determined. Then target reflectivity information in each box may be determined based on an average reflectivity value corresponding to at least one 3D voxel grid corresponding to each box, and the reflectivity calibration table is generated.);
a reference point position determination step of determining a position of a reference point for calibration based on the position coordinates of the extracted point (Fig. 1A. Paragraph [0032]-LI discloses in the point cloud data collected by the primary radar, the data corresponding to each scanning point includes position information and reflectivity of the scanning point in a rectangular coordinate system corresponding to the primary radar (wherein the position of the primary radar is the reference point).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of HAYAKAWA of an apparatus for determining a position of an object sensed by a radar using spatial voxelization, the apparatus comprising: an input/output unit configured to transmit and receive data to and from the processor, and receive a parameter required for voxelization, and a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is the reflected signal, searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file, and a point extraction step of loading the radar sensing file, searching for the voxel corresponding to the position coordinates of the point stored in the radar sensing, with the teachings of LI of a radar configured to transmit a radio wave signal and to receive a reflected signal that is the radio wave signal transmitted by the radar and reflected by the object; and a determination module configured to analyze a radar sensing file storing the reflected signal received by the radar using spatial voxelization and to determine a position of a reflector based on which calibration is performed, wherein the determination module comprises: a processor configured to execute program code; a storage unit configured to transmit and receive data to and from the processor, and store the program code, the radar sensing file storing the reflected signal received by the radar, and a dictionary; and the program code is written to perform: storing a voxel ID of the searched voxel and the reflected signal intensity of the point to a dictionary, wherein the reflected signal intensity of the point is cumulatively stored in the dictionary; a reference point position determination step of determining a position of a reference point for calibration based on the position coordinates of the extracted point.
Wherein having HAYAKAWA’s point cloud and voxel data processing method having a radar configured to transmit a radio wave signal and to receive a reflected signal that is the radio wave signal transmitted by the radar and reflected by the object; and a determination module configured to analyze a radar sensing file storing the reflected signal received by the radar using spatial voxelization and to determine a position of a reflector based on which calibration is performed, wherein the determination module comprises: a processor configured to execute program code; a storage unit configured to transmit and receive data to and from the processor, and store the program code, the radar sensing file storing the reflected signal received by the radar, and a dictionary; and the program code is written to perform: storing a voxel ID of the searched voxel and the reflected signal intensity of the point to a dictionary, wherein the reflected signal intensity of the point is cumulatively stored in the dictionary; a reference point position determination step of determining a position of a reference point for calibration based on the position coordinates of the extracted point.
The motivation behind the modification would have been to obtain a point cloud and voxel data processing method that enhances the accuracy in detecting objects based on received signals. Since both HAYAKAWA and LI relate to processing and analyzing data received through a radar system, wherein HAYAKAWA to provide a method or means which affords easy interpolation of a deficient voxel when such voxel deficient in data is present in three-dimensional voxel data so as to enable high-efficiency and high-precision detection of location of the underground buried object, while LI can more easily calibrate the reflectivity of the radar. Please see HAYAKAWA et al. (US 6573855 B1), Col. 4, Lines [19-34], and LI et al. (US 20220214448 A1), Paragraph [0044].
HAYAKAWA in view of LI fail to explicitly teach and in response to determining whether the voxel ID of the searched voxel is the same as a voxel having a largest accumulated value of the reflected signal intensity in the dictionary, extracting the point and storing the position coordinates of the extracted point; and.
However, HOYOSA explicitly teaches and in response to determining whether the voxel ID of the searched voxel is the same as a voxel having a largest accumulated value of the reflected signal intensity in the dictionary (Fig. 5. Col. 9. Lines [49-57]-HOSOYA discloses the compare/update processing part 33 reads out of the basic back-projection line information storage part 34 the maximum voting score V.sub.M of voxels selected until then, in correspondence with the voting scores V counted by the voting processing part 32 for a series of voxels selected in an arbitrary sequence on each basic back-projection line, and the compare/update processing part 33 compares the readout maximum voting score V.sub.M with each of the voting scores V of the currently selected voxels.), extracting the point and storing the position coordinates of the extracted point (Fig. 5. Col. 9-10. Lines [57-67] and [1-4]-HOYOSA discloses if V.sub.M >V, then the voting score V of the currently seleced voxel and its position (X.sub.t,Y.sub.t,Z.sub.t) are used as a new maximum voting score V.sub.M and a new maximum point position (X.sub.M,Y.sub.M,Z.sub.M) to update information about the corresponding basic back-projection line in the basic back-projection line information storage part 34. After execution of the voting and the compare/update processing has been completed for all the voxels on one basic back-projection line (L.sub.00, for instance), the maximum voting score in the storage area for the back-projection line L.sub.00 and the position (X.sub.M,Y.sub.M,Z.sub.M) of the voxel given the maximum voting score V.sub.M are obtained in the basic back-projection line information storage part 34. This position represents the position of one 3D feature point.); and
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of HAYAKAWA in view of LI of an apparatus for determining a position of an object sensed by a radar using spatial voxelization, the apparatus comprising: an input/output unit configured to transmit and receive data to and from the processor, and receive a parameter required for voxelization, and a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is the reflected signal, searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file, and a point extraction step of loading the radar sensing file, searching for the voxel corresponding to the position coordinates of the point stored in the radar sensing, with the teachings of HOYOSA of and in response to determining whether the voxel ID of the searched voxel is the same as a voxel having a largest accumulated value of the reflected signal intensity in the dictionary, extracting the point and storing the position coordinates of the extracted point; and.
Wherein having HAYAKAWA’s point cloud and voxel data processing method having and in response to determining whether the voxel ID of the searched voxel is the same as a voxel having a largest accumulated value of the reflected signal intensity in the dictionary, extracting the point and storing the position coordinates of the extracted point; and.
The motivation behind the modification would have been to obtain a point cloud and voxel data processing method that enhances the accuracy in detecting objects based on received signals. Since both HAYAKAWA and HOYOSA relate to processing and analyzing 3D data, wherein HAYAKAWA to provide a method or means which affords easy interpolation of a deficient voxel when such voxel deficient in data is present in three-dimensional voxel data so as to enable high-efficiency and high-precision detection of location of the underground buried object, while HOYOSA provides a method of decreasing the processing time required. Please see HAYAKAWA et al. (US 6573855 B1), Col. 4, Lines [19-34], and HOYOSA et al. (US 5724493 A), Col. 3, Lines [35-59].
Regarding claim 9, HAYAKAWA in view of LI and further in view of HOYOSA explicitly teach the apparatus according to claim 8,
HAYAKAWA fails to explicitly teach wherein the radar sensing file is created by the radar receiving the reflected signal and storing position coordinates and reflected signal intensity of a point in a form of point cloud data.
However, LI explicitly teaches wherein the radar sensing file is created by the radar receiving the reflected signal (Figs. 1A-B. Paragraph [0050]-LI discloses the radar acquires the point cloud data by scanning the environment periodically) and storing position coordinates and reflected signal intensity of a point in a form of point cloud data (Fig. 1A. Paragraph [0032]-LI discloses in the point cloud data collected by the primary radar, the data corresponding to each scanning point includes position information and reflectivity of the scanning point in a rectangular coordinate system corresponding to the primary radar.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of HAYAKAWA an apparatus for determining a position of an object sensed by a radar using spatial voxelization, the apparatus comprising: an input/output unit configured to transmit and receive data to and from the processor, and receive a parameter required for voxelization, and a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is the reflected signal, searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file, and a point extraction step of loading the radar sensing file, searching for the voxel corresponding to the position coordinates of the point stored in the radar sensing, with the teachings of LI of wherein the radar sensing file is created by the radar receiving the reflected signal and storing position coordinates and reflected signal intensity of a point in a form of point cloud data.
Wherein having HAYAKAWA’s point cloud and voxel data processing method wherein the radar sensing file is created by the radar receiving the reflected signal and storing position coordinates and reflected signal intensity of a point in a form of point cloud data.
The motivation behind the modification would have been to obtain a point cloud and voxel data processing method that enhances the accuracy in detecting objects based on received signals. Since both HAYAKAWA and LI relate to processing and analyzing data received through a radar system, wherein HAYAKAWA to provide a method or means which affords easy interpolation of a deficient voxel when such voxel deficient in data is present in three-dimensional voxel data so as to enable high-efficiency and high-precision detection of location of the underground buried object, while LI can more easily calibrate the reflectivity of the radar. Please see HAYAKAWA et al. (US 6573855 B1), Col. 4, Lines [19-34], and LI et al. (US 20220214448 A1), Paragraph [0044].
Claims 3-7 and 10-14 are rejected under 35 U.S.C. 103 as being unpatentable over HAYAKAWA et al. (US 6573855 B1), hereinafter referenced as HAYAKAWA, in view of LI et al. (US 20220214448 A1), hereinafter referenced as LI, and further in view HOYOSA et al. (US 5724493 A), hereinafter referenced as HOYOSA, and further in view of DOUILLARD et al. (US 20180364717 A1), hereinafter referenced as DOUILLARD.
Regarding claim 3, HAYAKAWA in view of LI and further in view of HOYOSA explicitly teach the method according to claim 2,
HAYAKAWA further explicitly teaches wherein the voxelization step comprises (Figs. 1 and 3. Col. 17, Lines [55-61]-HAYAKAWA discloses the digitized received signal is stored in a predetermined area of a memory 21a inside the data processing unit as the multiple scales source three-dimensional voxel data s (x, y, t) such that the coordinates (x, y, t) determined by the position (x, y) on the medium surface and the reflection time (t) of the reflected wave 5 from the object 2 are encoded (wherein the received signal is a reflected signal).):
a dictionary creation step of searching, in the voxel grid, for the voxel corresponding to the position of the point stored in the radar sensing file (Figs. 1 and 3. Col. 17, Lines [15-19]-HAYAKAWA discloses a section coordinate designating means 33b for designating a coordinate point on the displayed section in response to a manual operation on the input unit 22 such as a mouse, thereby to select the voxel at this coordinate point as an object voxel.) and
HAYAKAWA fails to explicitly teach a space range in which a voxel grid is to be created; a voxel grid creation step of creating the voxel grid based on the voxel size and the space range; and cumulatively storing the reflected signal intensity of the point in a voxel ID of the searched voxel to create a dictionary.
However, LI explicitly teaches a space range in which a voxel grid is to be created (Fig. 2. Paragraph [0042]-LI discloses if the first sample point cloud data is sample point cloud data within a first distance range, a second distance range corresponding to the voxel map data may be determined from the first distance range. The second distance range corresponding to the voxel map data is located within the first distance range.);
a voxel grid creation step of creating the voxel grid based on the voxel size and the space range (Fig. 2. Paragraph [0042]-LI discloses if the first sample point cloud data is sample point cloud data within a first distance range, a second distance range corresponding to the voxel map data may be determined from the first distance range. The second distance range corresponding to the voxel map data is located within the first distance range. And then the voxel map data in the second distance range is divided to obtain multiple 3D voxel grids within the second distance range, and initial data of each 3D voxel grid is determined, i.e. the initial data of each 3D voxel grid is set as a preset initial value);
and cumulatively storing the reflected signal intensity of the point in a voxel ID of the searched voxel to create a dictionary (Figs. 1A and 2. Paragraph [0060]-LI discloses then at least one 3D voxel grid corresponding to each reflectivity of each scanning line is determined based on position information of the multiple target scanning points, i.e. at least one 3D voxel grid corresponding to each box in the reflectivity calibration table is determined. Then target reflectivity information in each box may be determined based on an average reflectivity value corresponding to at least one 3D voxel grid corresponding to each box, and the reflectivity calibration table is generated.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of HAYAKAWA of a method of determining a position of an object sensed by a radar using spatial voxelization, the method comprising: a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is a reflected signal returned as a result of a radio wave signal transmitted by the radar and reflected by the object, searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file, with the teachings of LI of a space range in which a voxel grid is to be created; a voxel grid creation step of creating the voxel grid based on the voxel size and the space range; and cumulatively storing the reflected signal intensity of the point in a voxel ID of the searched voxel to create a dictionary.
Wherein having HAYAKAWA’s point cloud and voxel data processing method having a space range in which a voxel grid is to be created; a voxel grid creation step of creating the voxel grid based on the voxel size and the space range; and cumulatively storing the reflected signal intensity of the point in a voxel ID of the searched voxel to create a dictionary.
The motivation behind the modification would have been to obtain a point cloud and voxel data processing method that enhances the accuracy in detecting objects based on received signals. Since both HAYAKAWA and LI relate to processing and analyzing data received through a radar system, wherein HAYAKAWA to provide a method or means which affords easy interpolation of a deficient voxel when such voxel deficient in data is present in three-dimensional voxel data so as to enable high-efficiency and high-precision detection of location of the underground buried object, while LI can more easily calibrate the reflectivity of the radar. Please see HAYAKAWA et al. (US 6573855 B1), Col. 4, Lines [19-34], and LI et al. (US 20220214448 A1), Paragraph [0044].
HAYAKAWA in view of LI and further in view of HOYOSA fail to explicitly teach a parameter setting step of setting parameters comprising a voxel size, lower and upper thresholds of the reflected signal intensity, and a filtering step of excluding points having reflected signal intensities deviating from a range between the lower and upper thresholds;
However, DOUILLARD explicitly teaches a parameter setting step of setting parameters comprising a voxel size (Fig. 2. Paragraph [0045]- DOUILLARD discloses the voxel space module 212 can define dimensions of a voxel space, including a length, width, and height of the voxel space. Further, the voxel space module 212 may determine a size of individual voxels.), lower and upper thresholds of the reflected signal intensity (Fig. 2. Paragraph [0045]-DOUILLARD discloses filtering may include removing data below a threshold amount of data per voxel (e.g., a number of LIDAR data points associated with a voxel) or over a predetermined number of voxels (e.g., a number of LIDAR data points associated with a number of proximate voxels). Further in paragraph [0015]- DOUILLARD discloses LIDAR data may be accumulated in the voxel space, with an individual voxel including processed data, such as: a number of data points, an average intensity, average x-value of LIDAR data associated with the individual voxel; average-y value of the LIDAR data associated with the individual voxel; average z-value of the LIDAR data associated with the individual voxel; and a covariance matrix based on the LIDAR data associated with the voxel.),
a filtering step of excluding points having reflected signal intensities deviating from a range between the lower and upper thresholds (Fig. 2. Paragraph [0045]-DOUILLARD discloses filtering may include removing data below a threshold amount of data per voxel (e.g., a number of LIDAR data points associated with a voxel) or over a predetermined number of voxels (e.g., a number of LIDAR data points associated with a number of proximate voxels) Further in paragraph [0015]- DOUILLARD discloses LIDAR data may be accumulated in the voxel space, with an individual voxel including processed data, such as: a number of data points, an average intensity, average x-value of LIDAR data associated with the individual voxel; average-y value of the LIDAR data associated with the individual voxel; average z-value of the LIDAR data associated with the individual voxel; and a covariance matrix based on the LIDAR data associated with the voxel.);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of HAYAKAWA in view of LI and further in view of HOYOSA of a method of determining a position of an object sensed by a radar using spatial voxelization, the method comprising: a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is a reflected signal returned as a result of a radio wave signal transmitted by the radar and reflected by the object, searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file, with the teachings of DOUILLARD of a parameter setting step of setting parameters comprising a voxel size, lower and upper thresholds of the reflected signal intensity, and a filtering step of excluding points having reflected signal intensities deviating from a range between the lower and upper thresholds.
Wherein having HAYAKAWA’s point cloud and voxel data processing method having a parameter setting step of setting parameters comprising a voxel size, lower and upper thresholds of the reflected signal intensity, and a filtering step of excluding points having reflected signal intensities deviating from a range between the lower and upper thresholds.
The motivation behind the modification would have been to obtain a point cloud and voxel data processing method that enhances the accuracy in detecting objects based on received signals. Since both HAYAKAWA and DOUILLARD relate to processing and analyzing data in voxels, wherein HAYAKAWA to provide a method or means which affords easy interpolation of a deficient voxel when such voxel deficient in data is present in three-dimensional voxel data so as to enable high-efficiency and high-precision detection of location of the underground buried object, while DOUILLARD complex multi-dimensional data, such as LIDAR data, can be represented in a voxel space, which can partition the data, allowing for efficient evaluation and processing of the data. Please see HAYAKAWA et al. (US 6573855 B1), Col. 4, Lines [19-34], and DOUILLARD et al. (US 20180364717 A1), Paragraph [0020].
Regarding claim 4, HAYAKAWA in view of LI and further in view of HOYOSA and further in view of DOUILLARD explicitly teach the method according to claim 3,
HAYAKAWA further explicitly teaches wherein the dictionary creation step comprises (Figs. 1 and 3. Col. 17, Lines [15-19]-HAYAKAWA discloses a section coordinate designating means 33b for designating a coordinate point on the displayed section in response to a manual operation on the input unit 22 such as a mouse, thereby to select the voxel at this coordinate point as an object voxel.):
a voxel search step of searching for the voxel corresponding to the position of the point stored in the radar sensing file in the voxel grid (Figs. 1 and 3. Col. 17, Lines [15-19]-HAYAKAWA discloses a section coordinate designating means 33b for designating a coordinate point on the displayed section in response to a manual operation on the input unit 22 such as a mouse, thereby to select the voxel at this coordinate point as an object voxel.);
a determination step of determining whether the voxel ID of the searched voxel is present in the dictionary (Fig. 14. Col. 22, Lines [33-44]-HAYAKAWA discloses the three-dimensional voxel data corresponding to the reception position has a received signal intensity as a data value, but the other three-dimensional voxels have no substantive data values and are deficient in the data. Here, for the sake of convenience, the former three-dimensional voxels is defined as a source voxel and the latter three-dimensional voxels are defined as deficient voxels, respectively.);
an addition step of, when the voxel ID of the searched voxel is not present in the dictionary, adding the voxel ID of the searched voxel to the dictionary (Fig. 14. Col. 22, Lines [33-44]-HAYAKAWA discloses the three-dimensional voxel data corresponding to the reception position has a received signal intensity as a data value, but the other three-dimensional voxels have no substantive data values and are deficient in the data. Here, for the sake of convenience, the former three-dimensional voxels is defined as a source voxel and the latter three-dimensional voxels are defined as deficient voxels, respectively. Because deficient voxels may be generated depending on the moving pathway, the data analyzer 21 includes a linear interpolating means 26 for interpolating such deficient voxels by a one-dimensional linear interpolation (wherein deficient voxels are not present voxels).),
an accumulation step of, when the voxel ID of the searched voxel is present in the dictionary (Fig. 14. Col. 22, Lines [33-44]-HAYAKAWA discloses the three-dimensional voxel data corresponding to the reception position has a received signal intensity as a data value, but the other three-dimensional voxels have no substantive data values and are deficient in the data. Here, for the sake of convenience, the former three-dimensional voxels is defined as a source voxel and the latter three-dimensional voxels are defined as deficient voxels, respectively. Because deficient voxels may be generated depending on the moving pathway, the data analyzer 21 includes a linear interpolating means 26 for interpolating such deficient voxels by a one-dimensional linear interpolation (wherein source voxels are present voxels).).
HAYAKAWA fails to explicitly teach storing the reflected signal intensity in the voxel ID, storing a count of the point recorded in the voxel ID, dividing the reflected signal intensity by the count, and storing an average reflected signal intensity; and cumulatively storing the reflected signal intensity of the voxel ID of the searched voxel in the dictionary, cumulatively storing a count of the point recorded in the voxel ID, dividing the reflected signal intensity by the count, and storing an average reflected signal intensity.
However, LI explicitly teaches storing the reflected signal intensity in the voxel ID (Fig. 1A. Paragraph [0060]-LI discloses then at least one 3D voxel grid corresponding to each reflectivity of each scanning line is determined based on position information of the multiple target scanning points, i.e. at least one 3D voxel grid corresponding to each box in the reflectivity calibration table is determined. Then target reflectivity information in each box may be determined based on an average reflectivity value corresponding to at least one 3D voxel grid corresponding to each box, and the reflectivity calibration table is generated.), storing a count of the point recorded in the voxel ID, dividing the reflected signal intensity by the count, and storing an average reflected signal intensity (Fig. 1A. Paragraph [0042]-LI discloses when the data of the 3D voxel grid includes an average reflectivity value, a reflectivity variance and a number of scanning points, the initial data of each 3D voxel grid may be that the average reflectivity value is 0, the reflectivity variance is 0 and the number of scanning points is 0. And then the initial data of each 3D voxel grid is updated using the point cloud data of the multiple scanning points in the first sample point cloud data to obtain updated data of each 3D voxel grid (wherein the count is the number of scanning points).); and
cumulatively storing the reflected signal intensity of the voxel ID of the searched voxel in the dictionary (Fig. 1A. Paragraph [0060]-LI discloses then at least one 3D voxel grid corresponding to each reflectivity of each scanning line is determined based on position information of the multiple target scanning points, i.e. at least one 3D voxel grid corresponding to each box in the reflectivity calibration table is determined. Then target reflectivity information in each box may be determined based on an average reflectivity value corresponding to at least one 3D voxel grid corresponding to each box, and the reflectivity calibration table is generated.), cumulatively storing a count of the point recorded in the voxel ID, dividing the reflected signal intensity by the count, and storing an average reflected signal intensity (Fig. 1A. Paragraph [0042]-LI discloses when the data of the 3D voxel grid includes an average reflectivity value, a reflectivity variance and a number of scanning points, the initial data of each 3D voxel grid may be that the average reflectivity value is 0, the reflectivity variance is 0 and the number of scanning points is 0. And then the initial data of each 3D voxel grid is updated using the point cloud data of the multiple scanning points in the first sample point cloud data to obtain updated data of each 3D voxel grid (wherein the count is the number of scanning points).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of HAYAKAWA of a method of determining a position of an object sensed by a radar using spatial voxelization, the method comprising: a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is a reflected signal returned as a result of a radio wave signal transmitted by the radar and reflected by the object, searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file, with the teachings of LI of storing the reflected signal intensity in the voxel ID, storing a count of the point recorded in the voxel ID, dividing the reflected signal intensity by the count, and storing an average reflected signal intensity; and cumulatively storing the reflected signal intensity of the voxel ID of the searched voxel in the dictionary, cumulatively storing a count of the point recorded in the voxel ID, dividing the reflected signal intensity by the count, and storing an average reflected signal intensity.
Wherein having HAYAKAWA’s point cloud and voxel data processing method having storing the reflected signal intensity in the voxel ID, storing a count of the point recorded in the voxel ID, dividing the reflected signal intensity by the count, and storing an average reflected signal intensity; and cumulatively storing the reflected signal intensity of the voxel ID of the searched voxel in the dictionary, cumulatively storing a count of the point recorded in the voxel ID, dividing the reflected signal intensity by the count, and storing an average reflected signal intensity.
The motivation behind the modification would have been to obtain a point cloud and voxel data processing method that enhances the accuracy in detecting objects based on received signals. Since both HAYAKAWA and LI relate to processing and analyzing data received through a radar system, wherein HAYAKAWA to provide a method or means which affords easy interpolation of a deficient voxel when such voxel deficient in data is present in three-dimensional voxel data so as to enable high-efficiency and high-precision detection of location of the underground buried object, while LI can more easily calibrate the reflectivity of the radar. Please see HAYAKAWA et al. (US 6573855 B1), Col. 4, Lines [19-34], and LI et al. (US 20220214448 A1), Paragraph [0044].
Regarding claim 5, HAYAKAWA in view of LI and further in view of HOYOSA explicitly teach the method according to claim 2,
HAYAKAWA further explicitly teaches wherein the point extraction step comprises (Figs. 1 and 3. Col. 17, Lines [55-61]-HAYAKAWA discloses the digitized received signal is stored in a predetermined area of a memory 21a inside the data processing unit as the multiple scales source three-dimensional voxel data s (x, y, t) such that the coordinates (x, y, t) determined by the position (x, y) on the medium surface and the reflection time (t) of the reflected wave 5 from the object 2 are encoded (wherein the received signal is a reflected signal).):
a top voxel point extraction step of loading the radar sensing file (Figs. 1 and 3. Col. 17, Lines [55-61]-HAYAKAWA discloses the digitized received signal is stored in a predetermined area of a memory 21a inside the data processing unit as the multiple scales source three-dimensional voxel data s (x, y, t) such that the coordinates (x, y, t) determined by the position (x, y) on the medium surface and the reflection time (t) of the reflected wave 5 from the object 2 are encoded (wherein the received signal is a reflected signal).), searching for the voxel corresponding to the position of the point stored in the radar sensing file (Figs. 1 and 3. Col. 17, Lines [15-19]-HAYAKAWA discloses a section coordinate designating means 33b for designating a coordinate point on the displayed section in response to a manual operation on the input unit 22 such as a mouse, thereby to select the voxel at this coordinate point as an object voxel.),
HAYAKAWA fails to explicitly teach a space range in which a voxel grid is to be created; a voxel grid creation step of creating the voxel grid based on the voxel size and the space range.
However, LI explicitly teaches a space range in which a voxel grid is to be created (Fig. 2. Paragraph [0042]-LI discloses if the first sample point cloud data is sample point cloud data within a first distance range, a second distance range corresponding to the voxel map data may be determined from the first distance range. The second distance range corresponding to the voxel map data is located within the first distance range.);
a voxel grid creation step of creating the voxel grid based on the voxel size and the space range (Fig. 2. Paragraph [0042]-LI discloses if the first sample point cloud data is sample point cloud data within a first distance range, a second distance range corresponding to the voxel map data may be determined from the first distance range. The second distance range corresponding to the voxel map data is located within the first distance range. And then the voxel map data in the second distance range is divided to obtain multiple 3D voxel grids within the second distance range, and initial data of each 3D voxel grid is determined, i.e. the initial data of each 3D voxel grid is set as a preset initial value); and
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of HAYAKAWA of a method of determining a position of an object sensed by a radar using spatial voxelization, the method comprising: a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is a reflected signal returned as a result of a radio wave signal transmitted by the radar and reflected by the object, searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file, with the teachings of LI of a space range in which a voxel grid is to be created; a voxel grid creation step of creating the voxel grid based on the voxel size and the space range.
Wherein having HAYAKAWA’s point cloud and voxel data processing method having a space range in which a voxel grid is to be created; a voxel grid creation step of creating the voxel grid based on the voxel size and the space range.
The motivation behind the modification would have been to obtain a point cloud and voxel data processing method that enhances the accuracy in detecting objects based on received signals. Since both HAYAKAWA and LI relate to processing and analyzing data received through a radar system, wherein HAYAKAWA to provide a method or means which affords easy interpolation of a deficient voxel when such voxel deficient in data is present in three-dimensional voxel data so as to enable high-efficiency and high-precision detection of location of the underground buried object, while LI can more easily calibrate the reflectivity of the radar. Please see HAYAKAWA et al. (US 6573855 B1), Col. 4, Lines [19-34], and LI et al. (US 20220214448 A1), Paragraph [0044].
HAYAKAWA in view of LI fails to explicitly teach separately storing position coordinates of the point when the voxel ID of the searched voxel is the same as a voxel ID of a top voxel having a largest accumulated value of the reflected signal intensity in the dictionary, and extracting a point of the top voxel.
However, HOSOYA explicitly teaches separately storing position coordinates of the point when the voxel ID of the searched voxel is the same as a voxel ID of a top voxel having a largest accumulated value of the reflected signal intensity in the dictionary (Fig. 5. Col. 9. Lines [49-57]-HOSOYA discloses the compare/update processing part 33 reads out of the basic back-projection line information storage part 34 the maximum voting score V.sub.M of voxels selected until then, in correspondence with the voting scores V counted by the voting processing part 32 for a series of voxels selected in an arbitrary sequence on each basic back-projection line, and the compare/update processing part 33 compares the readout maximum voting score V.sub.M with each of the voting scores V of the currently selected voxels.), and extracting a point of the top voxel (Fig. 5. Col. 9-10. Lines [57-67] and [1-4]-HOYOSA discloses if V.sub.M >V, then the voting score V of the currently seleced voxel and its position (X.sub.t,Y.sub.t,Z.sub.t) are used as a new maximum voting score V.sub.M and a new maximum point position (X.sub.M,Y.sub.M,Z.sub.M) to update information about the corresponding basic back-projection line in the basic back-projection line information storage part 34. After execution of the voting and the compare/update processing has been completed for all the voxels on one basic back-projection line (L.sub.00, for instance), the maximum voting score in the storage area for the back-projection line L.sub.00 and the position (X.sub.M,Y.sub.M,Z.sub.M) of the voxel given the maximum voting score V.sub.M are obtained in the basic back-projection line information storage part 34. This position represents the position of one 3D feature point.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of HAYAKAWA in view of LI of a method of determining a position of an object sensed by a radar using spatial voxelization, the method comprising: a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is a reflected signal returned as a result of a radio wave signal transmitted by the radar and reflected by the object, searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file, with the teachings of HOYOSA of separately storing position coordinates of the point when the voxel ID of the searched voxel is the same as a voxel ID of a top voxel having a largest accumulated value of the reflected signal intensity in the dictionary, and extracting a point of the top voxel.
Wherein having HAYAKAWA’s point cloud and voxel data processing method having separately storing position coordinates of the point when the voxel ID of the searched voxel is the same as a voxel ID of a top voxel having a largest accumulated value of the reflected signal intensity in the dictionary, and extracting a point of the top voxel.
The motivation behind the modification would have been to obtain a point cloud and voxel data processing method that enhances the accuracy in detecting objects based on received signals. Since both HAYAKAWA and HOYOSA relate to processing and analyzing 3D data, wherein HAYAKAWA to provide a method or means which affords easy interpolation of a deficient voxel when such voxel deficient in data is present in three-dimensional voxel data so as to enable high-efficiency and high-precision detection of location of the underground buried object, while HOYOSA provides a method of decreasing the processing time required. Please see HAYAKAWA et al. (US 6573855 B1), Col. 4, Lines [19-34], and HOYOSA et al. (US 5724493 A), Col. 3, Lines [35-59].
HAYAKAWA in view of LI and further in view of HOYOSA fail to explicitly teach a parameter setting step of setting parameters comprising a voxel size, lower and upper thresholds of the reflected signal intensity, and a filtering step of excluding points having reflected signal intensities deviating from a range between the lower and upper thresholds;
However, DOUILLARD explicitly teaches a parameter setting step of setting parameters comprising a voxel size (Fig. 2. Paragraph [0045]- DOUILLARD discloses the voxel space module 212 can define dimensions of a voxel space, including a length, width, and height of the voxel space. Further, the voxel space module 212 may determine a size of individual voxels.), lower and upper thresholds of the reflected signal intensity (Fig. 2. Paragraph [0045]-DOUILLARD discloses filtering may include removing data below a threshold amount of data per voxel (e.g., a number of LIDAR data points associated with a voxel) or over a predetermined number of voxels (e.g., a number of LIDAR data points associated with a number of proximate voxels) Further in paragraph [0015]- DOUILLARD discloses LIDAR data may be accumulated in the voxel space, with an individual voxel including processed data, such as: a number of data points, an average intensity, average x-value of LIDAR data associated with the individual voxel; average-y value of the LIDAR data associated with the individual voxel; average z-value of the LIDAR data associated with the individual voxel; and a covariance matrix based on the LIDAR data associated with the voxel.), and
a filtering step of excluding points having reflected signal intensities deviating from a range between the lower and upper thresholds (Fig. 2. Paragraph [0045]-DOUILLARD discloses filtering may include removing data below a threshold amount of data per voxel (e.g., a number of LIDAR data points associated with a voxel) or over a predetermined number of voxels (e.g., a number of LIDAR data points associated with a number of proximate voxels) Further in paragraph [0015]- DOUILLARD discloses LIDAR data may be accumulated in the voxel space, with an individual voxel including processed data, such as: a number of data points, an average intensity, average x-value of LIDAR data associated with the individual voxel; average-y value of the LIDAR data associated with the individual voxel; average z-value of the LIDAR data associated with the individual voxel; and a covariance matrix based on the LIDAR data associated with the voxel.);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of HAYAKAWA in view of LI and further in view of HOYOSA of a method of determining a position of an object sensed by a radar using spatial voxelization, the method comprising: a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is a reflected signal returned as a result of a radio wave signal transmitted by the radar and reflected by the object, searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file, with the teachings of DOUILLARD of a parameter setting step of setting parameters comprising a voxel size, lower and upper thresholds of the reflected signal intensity, and a filtering step of excluding points having reflected signal intensities deviating from a range between the lower and upper thresholds.
Wherein having HAYAKAWA’s point cloud and voxel data processing method having a parameter setting step of setting parameters comprising a voxel size, lower and upper thresholds of the reflected signal intensity, and a filtering step of excluding points having reflected signal intensities deviating from a range between the lower and upper thresholds.
The motivation behind the modification would have been to obtain a point cloud and voxel data processing method that enhances the accuracy in detecting objects based on received signals. Since both HAYAKAWA and DOUILLARD relate to processing and analyzing data in voxels, wherein HAYAKAWA to provide a method or means which affords easy interpolation of a deficient voxel when such voxel deficient in data is present in three-dimensional voxel data so as to enable high-efficiency and high-precision detection of location of the underground buried object, while DOUILLARD complex multi-dimensional data, such as LIDAR data, can be represented in a voxel space, which can partition the data, allowing for efficient evaluation and processing of the data. Please see HAYAKAWA et al. (US 6573855 B1), Col. 4, Lines [19-34], and DOUILLARD et al. (US 20180364717 A1), Paragraph [0020].
Regarding claim 6, HAYAKAWA in view of LI and further in view of HOYOSA and further in view of DOUILLARD explicitly teach the method according to claim 5,
HAYAKAWA further explicitly teaches wherein the top voxel point extraction step comprises (Figs. 1 and 3. Col. 17, Lines [55-61]-HAYAKAWA discloses the digitized received signal is stored in a predetermined area of a memory 21a inside the data processing unit as the multiple scales source three-dimensional voxel data s (x, y, t) such that the coordinates (x, y, t) determined by the position (x, y) on the medium surface and the reflection time (t) of the reflected wave 5 from the object 2 are encoded (wherein the received signal is a reflected signal).):
a voxel search step of searching for the voxel corresponding to the position of the point stored in the radar sensing file in the voxel grid (Figs. 1 and 3. Col. 17, Lines [15-19]-HAYAKAWA discloses a section coordinate designating means 33b for designating a coordinate point on the displayed section in response to a manual operation on the input unit 22 such as a mouse, thereby to select the voxel at this coordinate point as an object voxel.);
HAYAKAWA in view of LI fail to explicitly teach a determination step of determining whether the voxel ID of the searched voxel matches the voxel ID of the top voxel in the dictionary; and a point position storage step of, when the voxel ID of the searched voxel matches the voxel ID of the top voxel, storing the position of the point in the voxel ID of the top voxel.
However, HOYOSA explicitly teaches a determination step of determining whether the voxel ID of the searched voxel matches the voxel ID of the top voxel in the dictionary (Fig. 5. Col. 9. Lines [49-57]-HOSOYA discloses the compare/update processing part 33 reads out of the basic back-projection line information storage part 34 the maximum voting score V.sub.M of voxels selected until then, in correspondence with the voting scores V counted by the voting processing part 32 for a series of voxels selected in an arbitrary sequence on each basic back-projection line, and the compare/update processing part 33 compares the readout maximum voting score V.sub.M with each of the voting scores V of the currently selected voxels.); and
a point position storage step of, when the voxel ID of the searched voxel matches the voxel ID of the top voxel, storing the position of the point in the voxel ID of the top voxel (Fig. 5. Col. 9-10. Lines [57-67] and [1-4]-HOYOSA discloses if V.sub.M >V, then the voting score V of the currently seleced voxel and its position (X.sub.t,Y.sub.t,Z.sub.t) are used as a new maximum voting score V.sub.M and a new maximum point position (X.sub.M,Y.sub.M,Z.sub.M) to update information about the corresponding basic back-projection line in the basic back-projection line information storage part 34. After execution of the voting and the compare/update processing has been completed for all the voxels on one basic back-projection line (L.sub.00, for instance), the maximum voting score in the storage area for the back-projection line L.sub.00 and the position (X.sub.M,Y.sub.M,Z.sub.M) of the voxel given the maximum voting score V.sub.M are obtained in the basic back-projection line information storage part 34. This position represents the position of one 3D feature point.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of HAYAKAWA in view of LI of a method of determining a position of an object sensed by a radar using spatial voxelization, the method comprising: a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is a reflected signal returned as a result of a radio wave signal transmitted by the radar and reflected by the object, searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file, with the teachings of HOYOSA of a determination step of determining whether the voxel ID of the searched voxel matches the voxel ID of the top voxel in the dictionary; and a point position storage step of, when the voxel ID of the searched voxel matches the voxel ID of the top voxel, storing the position of the point in the voxel ID of the top voxel.
Wherein having HAYAKAWA’s point cloud and voxel data processing method having a determination step of determining whether the voxel ID of the searched voxel matches the voxel ID of the top voxel in the dictionary; and a point position storage step of, when the voxel ID of the searched voxel matches the voxel ID of the top voxel, storing the position of the point in the voxel ID of the top voxel.
The motivation behind the modification would have been to obtain a point cloud and voxel data processing method that enhances the accuracy in detecting objects based on received signals. Since both HAYAKAWA and HOYOSA relate to processing and analyzing 3D data, wherein HAYAKAWA to provide a method or means which affords easy interpolation of a deficient voxel when such voxel deficient in data is present in three-dimensional voxel data so as to enable high-efficiency and high-precision detection of location of the underground buried object, while HOYOSA provides a method of decreasing the processing time required. Please see HAYAKAWA et al. (US 6573855 B1), Col. 4, Lines [19-34], and HOYOSA et al. (US 5724493 A), Col. 3, Lines [35-59].
Regarding claim 7, HAYAKAWA in view of LI and further in view of HOYOSA and further in view of DOUILLARD explicitly teach the method according to claim 6,
HAYAKAWA in view of LI fail to explicitly teach wherein the top voxel point extraction step further comprises determining whether a current file comprising the point matches a target file when the voxel ID of the searched voxel matches the voxel ID of the top voxel in the dictionary, when the current file matches the target file, the point position storage step is performed, and when the current file does not match the target file, the point position storage step is not performed.
However, HOSOYA explicitly teaches wherein the top voxel point extraction step further comprises determining whether a current file comprising the point matches a target file when the voxel ID of the searched voxel matches the voxel ID of the top voxel in the dictionary (Fig. 5. Col. 9. Lines [49-57]-HOSOYA discloses the compare/update processing part 33 reads out of the basic back-projection line information storage part 34 the maximum voting score V.sub.M of voxels selected until then, in correspondence with the voting scores V counted by the voting processing part 32 for a series of voxels selected in an arbitrary sequence on each basic back-projection line, and the compare/update processing part 33 compares the readout maximum voting score V.sub.M with each of the voting scores V of the currently selected voxels.),
when the current file matches the target file, the point position storage step is performed (Fig. 6. Col. 11, Lines [7-16]-HOYOSA discloses in the next step S4, the maximum voting score V.sub.M obtained on each basic back-projection line until then and the voting scores V obtained in step S3 are compared. If V>V.sub.M, then the process goes to step S5, wherein the current voting score V and its voxel position (X.sub.t,Y.sub.t,Z.sub.t) are used to update the corresponding previous pieces of information to provide a new maximum voting score (hereinafter referred to simply as the maximum value) V.sub.M and its voxel position (X.sub.M,Y.sub.M,Z.sub.M) in step S5, after which the process goes to step S6.), and
when the current file does not match the target file, the point position storage step is not performed (Fig. 6. Col. 11, Lines [7-16]-HOYOSA discloses if not V>V.sub.M in step S4, the process proceeds directly to step S6. It is checked in step S2 whether the processing for all the voxel slices is completed, and if not, the process goes back to step S2, wherein another voxel slice is selected and the same processing as described above is repeated.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of HAYAKAWA in view of LI of a method of determining a position of an object sensed by a radar using spatial voxelization, the method comprising: a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is a reflected signal returned as a result of a radio wave signal transmitted by the radar and reflected by the object, searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file, with the teachings of HOYOSA of wherein the top voxel point extraction step further comprises determining whether a current file comprising the point matches a target file when the voxel ID of the searched voxel matches the voxel ID of the top voxel in the dictionary, when the current file matches the target file, the point position storage step is performed, and when the current file does not match the target file, the point position storage step is not performed.
Wherein having HAYAKAWA’s point cloud and voxel data processing method wherein the top voxel point extraction step further comprises determining whether a current file comprising the point matches a target file when the voxel ID of the searched voxel matches the voxel ID of the top voxel in the dictionary, when the current file matches the target file, the point position storage step is performed, and when the current file does not match the target file, the point position storage step is not performed.
The motivation behind the modification would have been to obtain a point cloud and voxel data processing method that enhances the accuracy in detecting objects based on received signals. Since both HAYAKAWA and HOYOSA relate to processing and analyzing 3D data, wherein HAYAKAWA to provide a method or means which affords easy interpolation of a deficient voxel when such voxel deficient in data is present in three-dimensional voxel data so as to enable high-efficiency and high-precision detection of location of the underground buried object, while HOYOSA provides a method of decreasing the processing time required. Please see HAYAKAWA et al. (US 6573855 B1), Col. 4, Lines [19-34], and HOYOSA et al. (US 5724493 A), Col. 3, Lines [35-59].
Regarding claim 10, HAYAKAWA in view of LI and further in view of HOYOSA explicitly teach the method according to claim 9,
HAYAKAWA further explicitly teaches wherein the voxelization step comprises (Figs. 1 and 3. Col. 17, Lines [55-61]-HAYAKAWA discloses the digitized received signal is stored in a predetermined area of a memory 21a inside the data processing unit as the multiple scales source three-dimensional voxel data s (x, y, t) such that the coordinates (x, y, t) determined by the position (x, y) on the medium surface and the reflection time (t) of the reflected wave 5 from the object 2 are encoded (wherein the received signal is a reflected signal).):
a dictionary creation step of searching, in the voxel grid, for the voxel corresponding to the position of the point stored in the radar sensing file (Figs. 1 and 3. Col. 17, Lines [15-19]-HAYAKAWA discloses a section coordinate designating means 33b for designating a coordinate point on the displayed section in response to a manual operation on the input unit 22 such as a mouse, thereby to select the voxel at this coordinate point as an object voxel.) and
HAYAKAWA fails to explicitly teach a space range in which a voxel grid is to be created; a voxel grid creation step of creating a voxel grid based on the voxel size and the space range; and cumulatively storing the reflected signal intensity of the point in the voxel ID of the searched voxel to create a dictionary.
However, LI explicitly teaches a space range in which a voxel grid is to be created (Fig. 2. Paragraph [0042]-LI discloses if the first sample point cloud data is sample point cloud data within a first distance range, a second distance range corresponding to the voxel map data may be determined from the first distance range. The second distance range corresponding to the voxel map data is located within the first distance range.);
a voxel grid creation step of creating a voxel grid based on the voxel size and the space range (Fig. 2. Paragraph [0042]-LI discloses if the first sample point cloud data is sample point cloud data within a first distance range, a second distance range corresponding to the voxel map data may be determined from the first distance range. The second distance range corresponding to the voxel map data is located within the first distance range. And then the voxel map data in the second distance range is divided to obtain multiple 3D voxel grids within the second distance range, and initial data of each 3D voxel grid is determined, i.e. the initial data of each 3D voxel grid is set as a preset initial value); and
cumulatively storing the reflected signal intensity of the point in the voxel ID of the searched voxel to create a dictionary (Figs. 1A and 2. Paragraph [0060]-LI discloses then at least one 3D voxel grid corresponding to each reflectivity of each scanning line is determined based on position information of the multiple target scanning points, i.e. at least one 3D voxel grid corresponding to each box in the reflectivity calibration table is determined. Then target reflectivity information in each box may be determined based on an average reflectivity value corresponding to at least one 3D voxel grid corresponding to each box, and the reflectivity calibration table is generated.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of HAYAKAWA of an apparatus for determining a position of an object sensed by a radar using spatial voxelization, the apparatus comprising: an input/output unit configured to transmit and receive data to and from the processor, and receive a parameter required for voxelization, and a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is the reflected signal, searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file, and a point extraction step of loading the radar sensing file, searching for the voxel corresponding to the position coordinates of the point stored in the radar sensing, with the teachings of LI of a space range in which a voxel grid is to be created; a voxel grid creation step of creating a voxel grid based on the voxel size and the space range; and cumulatively storing the reflected signal intensity of the point in the voxel ID of the searched voxel to create a dictionary.
Wherein having HAYAKAWA’s point cloud and voxel data processing method having a space range in which a voxel grid is to be created; a voxel grid creation step of creating a voxel grid based on the voxel size and the space range; and cumulatively storing the reflected signal intensity of the point in the voxel ID of the searched voxel to create a dictionary.
The motivation behind the modification would have been to obtain a point cloud and voxel data processing method that enhances the accuracy in detecting objects based on received signals. Since both HAYAKAWA and LI relate to processing and analyzing data received through a radar system, wherein HAYAKAWA to provide a method or means which affords easy interpolation of a deficient voxel when such voxel deficient in data is present in three-dimensional voxel data so as to enable high-efficiency and high-precision detection of location of the underground buried object, while LI can more easily calibrate the reflectivity of the radar. Please see HAYAKAWA et al. (US 6573855 B1), Col. 4, Lines [19-34], and LI et al. (US 20220214448 A1), Paragraph [0044].
HAYAKAWA in view of LI and further in view of HOYOSA fail to explicitly teach a parameter setting step of setting parameters comprising a voxel size, lower and upper thresholds of the reflected signal intensity, a filtering step of excluding points having reflected signal intensities deviating from a range between the lower and upper thresholds;
However, DOUILLARD explicitly teaches a parameter setting step of setting parameters comprising a voxel size (Fig. 2. Paragraph [0045]- DOUILLARD discloses the voxel space module 212 can define dimensions of a voxel space, including a length, width, and height of the voxel space. Further, the voxel space module 212 may determine a size of individual voxels.), lower and upper thresholds of the reflected signal intensity (Fig. 2. Paragraph [0045]-DOUILLARD discloses filtering may include removing data below a threshold amount of data per voxel (e.g., a number of LIDAR data points associated with a voxel) or over a predetermined number of voxels (e.g., a number of LIDAR data points associated with a number of proximate voxels). Further in paragraph [0015]- DOUILLARD discloses LIDAR data may be accumulated in the voxel space, with an individual voxel including processed data, such as: a number of data points, an average intensity, average x-value of LIDAR data associated with the individual voxel; average-y value of the LIDAR data associated with the individual voxel; average z-value of the LIDAR data associated with the individual voxel; and a covariance matrix based on the LIDAR data associated with the voxel.),
a filtering step of excluding points having reflected signal intensities deviating from a range between the lower and upper thresholds (Fig. 2. Paragraph [0045]-DOUILLARD discloses filtering may include removing data below a threshold amount of data per voxel (e.g., a number of LIDAR data points associated with a voxel) or over a predetermined number of voxels (e.g., a number of LIDAR data points associated with a number of proximate voxels) Further in paragraph [0015]- DOUILLARD discloses LIDAR data may be accumulated in the voxel space, with an individual voxel including processed data, such as: a number of data points, an average intensity, average x-value of LIDAR data associated with the individual voxel; average-y value of the LIDAR data associated with the individual voxel; average z-value of the LIDAR data associated with the individual voxel; and a covariance matrix based on the LIDAR data associated with the voxel.);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of HAYAKAWA in view of LI and further in view of HOYOSA of an apparatus for determining a position of an object sensed by a radar using spatial voxelization, the apparatus comprising: an input/output unit configured to transmit and receive data to and from the processor, and receive a parameter required for voxelization, and a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is the reflected signal, searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file, and a point extraction step of loading the radar sensing file, searching for the voxel corresponding to the position coordinates of the point stored in the radar sensing, with the teachings of DOUILLARD of a parameter setting step of setting parameters comprising a voxel size, lower and upper thresholds of the reflected signal intensity, a filtering step of excluding points having reflected signal intensities deviating from a range between the lower and upper thresholds.
Wherein having HAYAKAWA’s point cloud and voxel data processing method having a parameter setting step of setting parameters comprising a voxel size, lower and upper thresholds of the reflected signal intensity, a filtering step of excluding points having reflected signal intensities deviating from a range between the lower and upper thresholds.
The motivation behind the modification would have been to obtain a point cloud and voxel data processing method that enhances the accuracy in detecting objects based on received signals. Since both HAYAKAWA and DOUILLARD relate to processing and analyzing data in voxels, wherein HAYAKAWA to provide a method or means which affords easy interpolation of a deficient voxel when such voxel deficient in data is present in three-dimensional voxel data so as to enable high-efficiency and high-precision detection of location of the underground buried object, while DOUILLARD complex multi-dimensional data, such as LIDAR data, can be represented in a voxel space, which can partition the data, allowing for efficient evaluation and processing of the data. Please see HAYAKAWA et al. (US 6573855 B1), Col. 4, Lines [19-34], and DOUILLARD et al. (US 20180364717 A1), Paragraph [0020].
Regarding claim 11, HAYAKAWA in view of LI and further in view of HOYOSA and further in view of DOUILLARD explicitly teach the apparatus according to claim 10,
HAYAKAWA further explicitly teaches wherein the dictionary creation step comprises (Figs. 1 and 3. Col. 17, Lines [15-19]-HAYAKAWA discloses a section coordinate designating means 33b for designating a coordinate point on the displayed section in response to a manual operation on the input unit 22 such as a mouse, thereby to select the voxel at this coordinate point as an object voxel.):
a voxel search step of searching for the voxel corresponding to the position of the point stored in the radar sensing file in the voxel grid (Figs. 1 and 3. Col. 17, Lines [15-19]-HAYAKAWA discloses a section coordinate designating means 33b for designating a coordinate point on the displayed section in response to a manual operation on the input unit 22 such as a mouse, thereby to select the voxel at this coordinate point as an object voxel.);
a determination step of determining whether the voxel ID of the searched voxel is present in the dictionary (Fig. 14. Col. 22, Lines [33-44]-HAYAKAWA discloses the three-dimensional voxel data corresponding to the reception position has a received signal intensity as a data value, but the other three-dimensional voxels have no substantive data values and are deficient in the data. Here, for the sake of convenience, the former three-dimensional voxels is defined as a source voxel and the latter three-dimensional voxels are defined as deficient voxels, respectively.);
an addition step of, when the voxel ID of the searched voxel is not present in the dictionary, adding the voxel ID of the searched voxel to the dictionary (Fig. 14. Col. 22, Lines [33-44]-HAYAKAWA discloses the three-dimensional voxel data corresponding to the reception position has a received signal intensity as a data value, but the other three-dimensional voxels have no substantive data values and are deficient in the data. Here, for the sake of convenience, the former three-dimensional voxels is defined as a source voxel and the latter three-dimensional voxels are defined as deficient voxels, respectively. Because deficient voxels may be generated depending on the moving pathway, the data analyzer 21 includes a linear interpolating means 26 for interpolating such deficient voxels by a one-dimensional linear interpolation (wherein deficient voxels are not present voxels).),
an accumulation step of, when the voxel ID of the searched voxel is present in the dictionary (Fig. 14. Col. 22, Lines [33-44]-HAYAKAWA discloses the three-dimensional voxel data corresponding to the reception position has a received signal intensity as a data value, but the other three-dimensional voxels have no substantive data values and are deficient in the data. Here, for the sake of convenience, the former three-dimensional voxels is defined as a source voxel and the latter three-dimensional voxels are defined as deficient voxels, respectively. Because deficient voxels may be generated depending on the moving pathway, the data analyzer 21 includes a linear interpolating means 26 for interpolating such deficient voxels by a one-dimensional linear interpolation (wherein source voxels are present voxels).).
HAYAKAWA fails to explicitly teach storing the reflected signal intensity in the voxel ID, storing a count of the point recorded in the voxel ID, dividing the reflected signal intensity by the count, and storing an average reflected signal intensity; and cumulatively storing the reflected signal intensity of the voxel ID of the searched voxel in the dictionary, cumulatively storing a count of the point recorded in the voxel ID, dividing the reflected signal intensity by the count, and storing an average reflected signal intensity.
However, LI explicitly teaches storing the reflected signal intensity in the voxel ID (Fig. 1A. Paragraph [0060]-LI discloses then at least one 3D voxel grid corresponding to each reflectivity of each scanning line is determined based on position information of the multiple target scanning points, i.e. at least one 3D voxel grid corresponding to each box in the reflectivity calibration table is determined. Then target reflectivity information in each box may be determined based on an average reflectivity value corresponding to at least one 3D voxel grid corresponding to each box, and the reflectivity calibration table is generated.), storing a count of the point recorded in the voxel ID, dividing the reflected signal intensity by the count, and storing an average reflected signal intensity (Fig. 1A. Paragraph [0042]-LI discloses when the data of the 3D voxel grid includes an average reflectivity value, a reflectivity variance and a number of scanning points, the initial data of each 3D voxel grid may be that the average reflectivity value is 0, the reflectivity variance is 0 and the number of scanning points is 0. And then the initial data of each 3D voxel grid is updated using the point cloud data of the multiple scanning points in the first sample point cloud data to obtain updated data of each 3D voxel grid (wherein the count is the number of scanning points).); and
cumulatively storing the reflected signal intensity of the voxel ID of the searched voxel in the dictionary (Fig. 1A. Paragraph [0060]-LI discloses then at least one 3D voxel grid corresponding to each reflectivity of each scanning line is determined based on position information of the multiple target scanning points, i.e. at least one 3D voxel grid corresponding to each box in the reflectivity calibration table is determined. Then target reflectivity information in each box may be determined based on an average reflectivity value corresponding to at least one 3D voxel grid corresponding to each box, and the reflectivity calibration table is generated.), cumulatively storing a count of the point recorded in the voxel ID, dividing the reflected signal intensity by the count, and storing an average reflected signal intensity (Fig. 1A. Paragraph [0042]-LI discloses when the data of the 3D voxel grid includes an average reflectivity value, a reflectivity variance and a number of scanning points, the initial data of each 3D voxel grid may be that the average reflectivity value is 0, the reflectivity variance is 0 and the number of scanning points is 0. And then the initial data of each 3D voxel grid is updated using the point cloud data of the multiple scanning points in the first sample point cloud data to obtain updated data of each 3D voxel grid (wherein the count is the number of scanning points).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of HAYAKAWA of an apparatus for determining a position of an object sensed by a radar using spatial voxelization, the apparatus comprising: an input/output unit configured to transmit and receive data to and from the processor, and receive a parameter required for voxelization, and a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is the reflected signal, searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file, and a point extraction step of loading the radar sensing file, searching for the voxel corresponding to the position coordinates of the point stored in the radar sensing, with the teachings of LI of storing the reflected signal intensity in the voxel ID, storing a count of the point recorded in the voxel ID, dividing the reflected signal intensity by the count, and storing an average reflected signal intensity; and cumulatively storing the reflected signal intensity of the voxel ID of the searched voxel in the dictionary, cumulatively storing a count of the point recorded in the voxel ID, dividing the reflected signal intensity by the count, and storing an average reflected signal intensity.
Wherein having HAYAKAWA’s point cloud and voxel data processing method having storing the reflected signal intensity in the voxel ID, storing a count of the point recorded in the voxel ID, dividing the reflected signal intensity by the count, and storing an average reflected signal intensity; and cumulatively storing the reflected signal intensity of the voxel ID of the searched voxel in the dictionary, cumulatively storing a count of the point recorded in the voxel ID, dividing the reflected signal intensity by the count, and storing an average reflected signal intensity.
The motivation behind the modification would have been to obtain a point cloud and voxel data processing method that enhances the accuracy in detecting objects based on received signals. Since both HAYAKAWA and LI relate to processing and analyzing data received through a radar system, wherein HAYAKAWA to provide a method or means which affords easy interpolation of a deficient voxel when such voxel deficient in data is present in three-dimensional voxel data so as to enable high-efficiency and high-precision detection of location of the underground buried object, while LI can more easily calibrate the reflectivity of the radar. Please see HAYAKAWA et al. (US 6573855 B1), Col. 4, Lines [19-34], and LI et al. (US 20220214448 A1), Paragraph [0044].
Regarding claim 12, HAYAKAWA in view of LI and further in view of HOYOSA explicitly teach the apparatus according to claim 9,
HAYAKAWA further explicitly teaches wherein the point extraction step comprises (Figs. 1 and 3. Col. 17, Lines [55-61]-HAYAKAWA discloses the digitized received signal is stored in a predetermined area of a memory 21a inside the data processing unit as the multiple scales source three-dimensional voxel data s (x, y, t) such that the coordinates (x, y, t) determined by the position (x, y) on the medium surface and the reflection time (t) of the reflected wave 5 from the object 2 are encoded (wherein the received signal is a reflected signal).):
a top voxel point extraction step of loading the radar sensing file (Figs. 1 and 3. Col. 17, Lines [55-61]-HAYAKAWA discloses the digitized received signal is stored in a predetermined area of a memory 21a inside the data processing unit as the multiple scales source three-dimensional voxel data s (x, y, t) such that the coordinates (x, y, t) determined by the position (x, y) on the medium surface and the reflection time (t) of the reflected wave 5 from the object 2 are encoded (wherein the received signal is a reflected signal).), searching for the voxel corresponding to the position of the point stored in the radar sensing file (Figs. 1 and 3. Col. 17, Lines [15-19]-HAYAKAWA discloses a section coordinate designating means 33b for designating a coordinate point on the displayed section in response to a manual operation on the input unit 22 such as a mouse, thereby to select the voxel at this coordinate point as an object voxel.),
HAYAKAWA fails to explicitly teach a space range in which a voxel grid is to be created; a voxel grid creation step of creating the voxel grid based on the voxel size and the space range.
However, LI explicitly teaches a space range in which a voxel grid is to be created (Fig. 2. Paragraph [0042]-LI discloses if the first sample point cloud data is sample point cloud data within a first distance range, a second distance range corresponding to the voxel map data may be determined from the first distance range. The second distance range corresponding to the voxel map data is located within the first distance range.);
a voxel grid creation step of creating the voxel grid based on the voxel size and the space range (Fig. 2. Paragraph [0042]-LI discloses if the first sample point cloud data is sample point cloud data within a first distance range, a second distance range corresponding to the voxel map data may be determined from the first distance range. The second distance range corresponding to the voxel map data is located within the first distance range. And then the voxel map data in the second distance range is divided to obtain multiple 3D voxel grids within the second distance range, and initial data of each 3D voxel grid is determined, i.e. the initial data of each 3D voxel grid is set as a preset initial value); and
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of HAYAKAWA an apparatus for determining a position of an object sensed by a radar using spatial voxelization, the apparatus comprising: an input/output unit configured to transmit and receive data to and from the processor, and receive a parameter required for voxelization, and a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is the reflected signal, searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file, and a point extraction step of loading the radar sensing file, searching for the voxel corresponding to the position coordinates of the point stored in the radar sensing, with the teachings of LI of a space range in which a voxel grid is to be created; a voxel grid creation step of creating the voxel grid based on the voxel size and the space range.
Wherein having HAYAKAWA’s point cloud and voxel data processing method having a space range in which a voxel grid is to be created; a voxel grid creation step of creating the voxel grid based on the voxel size and the space range.
The motivation behind the modification would have been to obtain a point cloud and voxel data processing method that enhances the accuracy in detecting objects based on received signals. Since both HAYAKAWA and LI relate to processing and analyzing data received through a radar system, wherein HAYAKAWA to provide a method or means which affords easy interpolation of a deficient voxel when such voxel deficient in data is present in three-dimensional voxel data so as to enable high-efficiency and high-precision detection of location of the underground buried object, while LI can more easily calibrate the reflectivity of the radar. Please see HAYAKAWA et al. (US 6573855 B1), Col. 4, Lines [19-34], and LI et al. (US 20220214448 A1), Paragraph [0044].
HAYAKAWA in view of LI fails to explicitly teach separately storing position coordinates of the point when the voxel ID of the searched voxel is the same as a voxel ID of a top voxel having a largest accumulated value of the reflected signal intensity in the dictionary, and extracting a point of the top voxel.
However, HOSOYA explicitly teaches separately storing position coordinates of the point when the voxel ID of the searched voxel is the same as a voxel ID of a top voxel having a largest accumulated value of the reflected signal intensity in the dictionary (Fig. 5. Col. 9. Lines [49-57]-HOSOYA discloses the compare/update processing part 33 reads out of the basic back-projection line information storage part 34 the maximum voting score V.sub.M of voxels selected until then, in correspondence with the voting scores V counted by the voting processing part 32 for a series of voxels selected in an arbitrary sequence on each basic back-projection line, and the compare/update processing part 33 compares the readout maximum voting score V.sub.M with each of the voting scores V of the currently selected voxels.), and extracting a point of the top voxel (Fig. 5. Col. 9-10. Lines [57-67] and [1-4]-HOYOSA discloses if V.sub.M >V, then the voting score V of the currently seleced voxel and its position (X.sub.t,Y.sub.t,Z.sub.t) are used as a new maximum voting score V.sub.M and a new maximum point position (X.sub.M,Y.sub.M,Z.sub.M) to update information about the corresponding basic back-projection line in the basic back-projection line information storage part 34. After execution of the voting and the compare/update processing has been completed for all the voxels on one basic back-projection line (L.sub.00, for instance), the maximum voting score in the storage area for the back-projection line L.sub.00 and the position (X.sub.M,Y.sub.M,Z.sub.M) of the voxel given the maximum voting score V.sub.M are obtained in the basic back-projection line information storage part 34. This position represents the position of one 3D feature point.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of HAYAKAWA in view of LI of an apparatus for determining a position of an object sensed by a radar using spatial voxelization, the apparatus comprising: an input/output unit configured to transmit and receive data to and from the processor, and receive a parameter required for voxelization, and a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is the reflected signal, searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file, and a point extraction step of loading the radar sensing file, searching for the voxel corresponding to the position coordinates of the point stored in the radar sensing, with the teachings of HOYOSA of separately storing position coordinates of the point when the voxel ID of the searched voxel is the same as a voxel ID of a top voxel having a largest accumulated value of the reflected signal intensity in the dictionary, and extracting a point of the top voxel.
Wherein having HAYAKAWA’s point cloud and voxel data processing method having separately storing position coordinates of the point when the voxel ID of the searched voxel is the same as a voxel ID of a top voxel having a largest accumulated value of the reflected signal intensity in the dictionary, and extracting a point of the top voxel.
The motivation behind the modification would have been to obtain a point cloud and voxel data processing method that enhances the accuracy in detecting objects based on received signals. Since both HAYAKAWA and HOYOSA relate to processing and analyzing 3D data, wherein HAYAKAWA to provide a method or means which affords easy interpolation of a deficient voxel when such voxel deficient in data is present in three-dimensional voxel data so as to enable high-efficiency and high-precision detection of location of the underground buried object, while HOYOSA provides a method of decreasing the processing time required. Please see HAYAKAWA et al. (US 6573855 B1), Col. 4, Lines [19-34], and HOYOSA et al. (US 5724493 A), Col. 3, Lines [35-59].
HAYAKAWA in view of LI and further in view of HOYOSA fail to explicitly teach a parameter setting step of setting parameters comprising a voxel size, lower and upper thresholds of the reflected signal intensity, and a filtering step of excluding points having reflected signal intensities deviating from a range between the lower and upper thresholds.
However, DOUILLARD explicitly teaches a parameter setting step of setting parameters comprising a voxel size (Fig. 2. Paragraph [0045]- DOUILLARD discloses the voxel space module 212 can define dimensions of a voxel space, including a length, width, and height of the voxel space. Further, the voxel space module 212 may determine a size of individual voxels.), lower and upper thresholds of the reflected signal intensity (Fig. 2. Paragraph [0045]-DOUILLARD discloses filtering may include removing data below a threshold amount of data per voxel (e.g., a number of LIDAR data points associated with a voxel) or over a predetermined number of voxels (e.g., a number of LIDAR data points associated with a number of proximate voxels) Further in paragraph [0015]- DOUILLARD discloses LIDAR data may be accumulated in the voxel space, with an individual voxel including processed data, such as: a number of data points, an average intensity, average x-value of LIDAR data associated with the individual voxel; average-y value of the LIDAR data associated with the individual voxel; average z-value of the LIDAR data associated with the individual voxel; and a covariance matrix based on the LIDAR data associated with the voxel.), and
a filtering step of excluding points having reflected signal intensities deviating from a range between the lower and upper thresholds (Fig. 2. Paragraph [0045]-DOUILLARD discloses filtering may include removing data below a threshold amount of data per voxel (e.g., a number of LIDAR data points associated with a voxel) or over a predetermined number of voxels (e.g., a number of LIDAR data points associated with a number of proximate voxels) Further in paragraph [0015]- DOUILLARD discloses LIDAR data may be accumulated in the voxel space, with an individual voxel including processed data, such as: a number of data points, an average intensity, average x-value of LIDAR data associated with the individual voxel; average-y value of the LIDAR data associated with the individual voxel; average z-value of the LIDAR data associated with the individual voxel; and a covariance matrix based on the LIDAR data associated with the voxel.);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of HAYAKAWA in view of LI and further in view of HOYOSA of an apparatus for determining a position of an object sensed by a radar using spatial voxelization, the apparatus comprising: an input/output unit configured to transmit and receive data to and from the processor, and receive a parameter required for voxelization, and a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is the reflected signal, searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file, and a point extraction step of loading the radar sensing file, searching for the voxel corresponding to the position coordinates of the point stored in the radar sensing, with the teachings of DOUILLARD of a parameter setting step of setting parameters comprising a voxel size, lower and upper thresholds of the reflected signal intensity, and a filtering step of excluding points having reflected signal intensities deviating from a range between the lower and upper thresholds.
Wherein having HAYAKAWA’s point cloud and voxel data processing method having a parameter setting step of setting parameters comprising a voxel size, lower and upper thresholds of the reflected signal intensity, and a filtering step of excluding points having reflected signal intensities deviating from a range between the lower and upper thresholds.
The motivation behind the modification would have been to obtain a point cloud and voxel data processing method that enhances the accuracy in detecting objects based on received signals. Since both HAYAKAWA and DOUILLARD relate to processing and analyzing data in voxels, wherein HAYAKAWA to provide a method or means which affords easy interpolation of a deficient voxel when such voxel deficient in data is present in three-dimensional voxel data so as to enable high-efficiency and high-precision detection of location of the underground buried object, while DOUILLARD complex multi-dimensional data, such as LIDAR data, can be represented in a voxel space, which can partition the data, allowing for efficient evaluation and processing of the data. Please see HAYAKAWA et al. (US 6573855 B1), Col. 4, Lines [19-34], and DOUILLARD et al. (US 20180364717 A1), Paragraph [0020].
Regarding claim 13, HAYAKAWA in view of LI and further in view of HOYOSA and further in view of DOUILLARD explicitly teach the apparatus according to claim 12,
HAYAKAWA further explicitly teaches wherein the top voxel point extraction step comprises (Figs. 1 and 3. Col. 17, Lines [55-61]-HAYAKAWA discloses the digitized received signal is stored in a predetermined area of a memory 21a inside the data processing unit as the multiple scales source three-dimensional voxel data s (x, y, t) such that the coordinates (x, y, t) determined by the position (x, y) on the medium surface and the reflection time (t) of the reflected wave 5 from the object 2 are encoded (wherein the received signal is a reflected signal).):
a voxel search step of searching for the voxel corresponding to the position of the point stored in the radar sensing file in the voxel grid (Figs. 1 and 3. Col. 17, Lines [15-19]-HAYAKAWA discloses a section coordinate designating means 33b for designating a coordinate point on the displayed section in response to a manual operation on the input unit 22 such as a mouse, thereby to select the voxel at this coordinate point as an object voxel.);
HAYAKAWA in view of LI fail to explicitly teach a determination step of determining whether the voxel ID of the searched voxel matches the voxel ID of the top voxel in the dictionary; and a point position storage step of, when the voxel ID of the searched voxel matches the voxel ID of the top voxel, storing the position of the point in the voxel ID of the top voxel.
However, HOYOSA explicitly teaches a determination step of determining whether the voxel ID of the searched voxel matches the voxel ID of the top voxel in the dictionary (Fig. 5. Col. 9. Lines [49-57]-HOSOYA discloses the compare/update processing part 33 reads out of the basic back-projection line information storage part 34 the maximum voting score V.sub.M of voxels selected until then, in correspondence with the voting scores V counted by the voting processing part 32 for a series of voxels selected in an arbitrary sequence on each basic back-projection line, and the compare/update processing part 33 compares the readout maximum voting score V.sub.M with each of the voting scores V of the currently selected voxels.); and
a point position storage step of, when the voxel ID of the searched voxel matches the voxel ID of the top voxel, storing the position of the point in the voxel ID of the top voxel (Fig. 5. Col. 9-10. Lines [57-67] and [1-4]-HOYOSA discloses if V.sub.M >V, then the voting score V of the currently seleced voxel and its position (X.sub.t,Y.sub.t,Z.sub.t) are used as a new maximum voting score V.sub.M and a new maximum point position (X.sub.M,Y.sub.M,Z.sub.M) to update information about the corresponding basic back-projection line in the basic back-projection line information storage part 34. After execution of the voting and the compare/update processing has been completed for all the voxels on one basic back-projection line (L.sub.00, for instance), the maximum voting score in the storage area for the back-projection line L.sub.00 and the position (X.sub.M,Y.sub.M,Z.sub.M) of the voxel given the maximum voting score V.sub.M are obtained in the basic back-projection line information storage part 34. This position represents the position of one 3D feature point.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of HAYAKAWA in view of LI an apparatus for determining a position of an object sensed by a radar using spatial voxelization, the apparatus comprising: an input/output unit configured to transmit and receive data to and from the processor, and receive a parameter required for voxelization, and a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is the reflected signal, searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file, and a point extraction step of loading the radar sensing file, searching for the voxel corresponding to the position coordinates of the point stored in the radar sensing, with the teachings of HOYOSA of a determination step of determining whether the voxel ID of the searched voxel matches the voxel ID of the top voxel in the dictionary; and a point position storage step of, when the voxel ID of the searched voxel matches the voxel ID of the top voxel, storing the position of the point in the voxel ID of the top voxel.
Wherein having HAYAKAWA’s point cloud and voxel data processing method having a determination step of determining whether the voxel ID of the searched voxel matches the voxel ID of the top voxel in the dictionary; and a point position storage step of, when the voxel ID of the searched voxel matches the voxel ID of the top voxel, storing the position of the point in the voxel ID of the top voxel.
The motivation behind the modification would have been to obtain a point cloud and voxel data processing method that enhances the accuracy in detecting objects based on received signals. Since both HAYAKAWA and HOYOSA relate to processing and analyzing 3D data, wherein HAYAKAWA to provide a method or means which affords easy interpolation of a deficient voxel when such voxel deficient in data is present in three-dimensional voxel data so as to enable high-efficiency and high-precision detection of location of the underground buried object, while HOYOSA provides a method of decreasing the processing time required. Please see HAYAKAWA et al. (US 6573855 B1), Col. 4, Lines [19-34], and HOYOSA et al. (US 5724493 A), Col. 3, Lines [35-59].
Regarding claim 14, HAYAKAWA in view of LI and further in view of HOYOSA and further in view of DOUILLARD explicitly teach the apparatus according to claim 13,
HAYAKAWA in view of LI fail to explicitly teach wherein the top voxel point extraction step further comprises determining whether a current file comprising the point matches a target file when the voxel ID of the searched voxel matches the voxel ID of the top voxel in the dictionary, when the current file matches the target file, the point position storage step is performed, and when the current file does not match the target file, the point position storage step is not performed.
However, HOSOYA explicitly teaches wherein the top voxel point extraction step further comprises determining whether a current file comprising the point matches a target file when the voxel ID of the searched voxel matches the voxel ID of the top voxel in the dictionary (Fig. 5. Col. 9. Lines [49-57]-HOSOYA discloses the compare/update processing part 33 reads out of the basic back-projection line information storage part 34 the maximum voting score V.sub.M of voxels selected until then, in correspondence with the voting scores V counted by the voting processing part 32 for a series of voxels selected in an arbitrary sequence on each basic back-projection line, and the compare/update processing part 33 compares the readout maximum voting score V.sub.M with each of the voting scores V of the currently selected voxels.),
when the current file matches the target file, the point position storage step is performed (Fig. 6. Col. 11, Lines [7-16]-HOYOSA discloses in the next step S4, the maximum voting score V.sub.M obtained on each basic back-projection line until then and the voting scores V obtained in step S3 are compared. If V>V.sub.M, then the process goes to step S5, wherein the current voting score V and its voxel position (X.sub.t,Y.sub.t,Z.sub.t) are used to update the corresponding previous pieces of information to provide a new maximum voting score (hereinafter referred to simply as the maximum value) V.sub.M and its voxel position (X.sub.M,Y.sub.M,Z.sub.M) in step S5, after which the process goes to step S6.), and
when the current file does not match the target file, the point position storage step is not performed (Fig. 6. Col. 11, Lines [7-16]-HOYOSA discloses if not V>V.sub.M in step S4, the process proceeds directly to step S6. It is checked in step S2 whether the processing for all the voxel slices is completed, and if not, the process goes back to step S2, wherein another voxel slice is selected and the same processing as described above is repeated.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of HAYAKAWA in view of LI of an apparatus for determining a position of an object sensed by a radar using spatial voxelization, the apparatus comprising: an input/output unit configured to transmit and receive data to and from the processor, and receive a parameter required for voxelization, and a voxelization step of loading a radar sensing file storing position coordinates and a reflected signal intensity of a point that is the reflected signal, searching for a voxel corresponding to the position coordinates of the point stored in the radar sensing file, and a point extraction step of loading the radar sensing file, searching for the voxel corresponding to the position coordinates of the point stored in the radar sensing, with the teachings of HOYOSA of wherein the top voxel point extraction step further comprises determining whether a current file comprising the point matches a target file when the voxel ID of the searched voxel matches the voxel ID of the top voxel in the dictionary, when the current file matches the target file, the point position storage step is performed, and when the current file does not match the target file, the point position storage step is not performed.
Wherein having HAYAKAWA’s point cloud and voxel data processing method wherein the top voxel point extraction step further comprises determining whether a current file comprising the point matches a target file when the voxel ID of the searched voxel matches the voxel ID of the top voxel in the dictionary, when the current file matches the target file, the point position storage step is performed, and when the current file does not match the target file, the point position storage step is not performed.
The motivation behind the modification would have been to obtain a point cloud and voxel data processing method that enhances the accuracy in detecting objects based on received signals. Since both HAYAKAWA and HOYOSA relate to processing and analyzing 3D data, wherein HAYAKAWA to provide a method or means which affords easy interpolation of a deficient voxel when such voxel deficient in data is present in three-dimensional voxel data so as to enable high-efficiency and high-precision detection of location of the underground buried object, while HOYOSA provides a method of decreasing the processing time required. Please see HAYAKAWA et al. (US 6573855 B1), Col. 4, Lines [19-34], and HOYOSA et al. (US 5724493 A), Col. 3, Lines [35-59].
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant’s disclosure.
CHAYAT et al. (US 20190254544 A1) - Method and MIMO radar apparatus using correlated motion decomposed from reflectance data of multiple time frames to enhance discriminatory capacity in imaging. Such imaging has application in tracking temporal patterns of respiratory and cardiac activities in addition to recognition of targets within non-stationary environments…Abstract, Fig. 4A-4C.
AKAHOSHI et al. (US 20220018959 A1) - There is provided a distance measurement system in which a plurality of distance measurement sensors are installed to detect an object in a measurement area, the system including: a detection intensity distribution display device that performs quantification according to light intensities of light which reaches the object after being emitted from the distance measurement sensors, or point cloud numbers, and displays colors or lights and shades according to magnitudes of numerical values to perform visualization and a display. The detection intensity distribution display device regards a space in front of the distance measurement sensors as one cube, divides the cube into a plurality of small cubes (voxels), and quantifies a detection intensity according to the light intensity of the light that reaches each of the voxels after being emitted from the distance measurement sensors, or the point cloud number of each of the voxels...Abstract, Fig. 1.
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).
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/ETHAN N WOLFSON/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
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