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 .
Claim Objections
Claim 17 is objected to because of the following informalities: Claim 17 should be amended to recite “on a fixed obstacle or on a moving object that exists …” for grammatical correctness. 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 do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “acquisition unit”, “specifying unit”, “setting unit”, “moving unit”, and “output unit” in claim 1.
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. See at least paragraphs [0038], [0039], [0085], [0110], [0112] of the as-filed specification (e.g. CPU).
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.
Claim 6-15, and 18-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the applicant regards as the invention.
Claim 6 is indefinite because the preamble recites “An obstacle proximity detection method in which a computer executes processing of: …” Claim 6 fails to recite any transitional phrase between the preamble and the body of the claim. Per MPEP, transitional phrases must be interpreted in light of the specification to determine whether open or closed claim language is intended. Applicant’s specification does not clarify such. Therefore, it is unclear, to the Examiner, whether claim 6 is intended to be open or closed. For purposes of Examination, Examiner assumes claim 6 is meant to be open-ended and suggests for Applicant to use the language “comprising”.
Claim 7 is indefinite because the preamble recites “A computer-readable non-transitory recording medium storing computer-executable program instructions that when executed by a processor cause a computer to execute an obstacle proximity detection program to execute processing of: …” Claim 7 fails to recite any transitional phrase between the preamble and the body of the claim. Per MPEP, transitional phrases must be interpreted in light of the specification to determine whether open or closed claim language is intended. Applicant’s specification does not clarify such. Therefore, it is unclear, to the Examiner, whether claim 7 is intended to be open or closed. For purposes of Examination, Examiner assumes claim 7 is meant to be open-ended and suggests for Applicant to use the language “comprising”.
Claims 9, 10, and 11 are indefinite because of the recited limitation: “the specifying unit”. There insufficient antecedent basis for the claimed limitation since independent claim 6 does not recite “a specifying unit” Examiner suggests amending claim 6 to include “a specifying unit”.
Claims 13, 14, and 15 are indefinite because of the recited limitation: “the specifying unit”. There insufficient antecedent basis for the claimed limitation since independent claim 7 does not recite “a specifying unit” Examiner suggests amending claim 7 to include “a specifying unit”.
Claims 12, 13, 14, and 15 are indefinite because of the recited limitation: “the obstacle proximity detection method further comprising: …”. There is insufficient antecedent basis for this limitation because claim 7 is directed to a computer-readable non-transitory recording medium and does not recite a method. Therefore, it is unclear, to the Examiner, what method the applicant is referring to? Examiner suggests deleting “the obstacle proximity detection method further comprising” from each of the claims.
Claim 15 is indefinite because of the recited limitation: “The computer-readable non-transitory recording medium according to claim 15.” It is unclear, to the examiner, which claim the applicant is referring back to since claim 15 cannot depend on itself.
Claim 18 is indefinite because of the recited limitations: “three-dimensional point cloud data”, “an outdoor structure”, “a three-dimensional laser scanner”, “a first point cloud data”, and “a second point cloud data.” It is unclear, to the examiner, if the applicant is referring to the same “three-dimensional point cloud data”, “outdoor structure”, “three-dimensional laser scanner”, “first point cloud data”, and “second point cloud data” in claim 1 or not?
Claim 18 is indefinite because of the recited limitations: “wherein sequentially acquires three-dimensional point cloud data representing an outdoor structure acquired by a three-dimensional laser scanner, in which the three-dimensional point cloud data is used to generate a first point cloud data and a second point cloud data.” The limitation is an incomplete sentence and examiner is unable to understand what the applicant means?
Claims 8, 19 and 20 are rejected as being dependent on a rejected claim.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claim 1 is directed to a device, claim 6 is directed to a method and claim 7 is directed to a computer-readable non-transitory recording medium. Therefore, claims 1, 6 and 7 are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. The other analogous claims 6 and 7 are rejected for the same reasons as the representative claim 1 as discussed here. Claim 1 recites:
An obstacle proximity detection device comprising:
an acquisition unit configured to sequentially acquire three-dimensional point cloud data representing an outdoor structure acquired by a three-dimensional laser scanner;
a specifying unit configured to specify first point cloud data representing an object and second point cloud data representing an obstacle from the three-dimensional point cloud data;
a setting unit configured to set a first detection area that is an area set in advance by a user and is an area around the first point cloud data;
a moving unit configured to move, on the basis of a feature point extracted from the first point cloud data, the first point cloud data and the first detection area according to movement of the feature point; and
an output unit configured to output an alert indicating proximity between the object and the obstacle in a case where a part of the first detection area overlaps with a part of a second detection area that is an area around the second point cloud data, or in a case where the number of pieces of point data of the first point cloud data existing in the second detection area is equal to or larger than a predetermined threshold.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, specifying…, setting …, and moving … in the context of this claim encompasses a person looking at data collected (received, detected, etc.) and forming a simple judgement (determination, analysis, comparison, etc.) either mentally or using a pen and paper. Accordingly, the claim recites at least one abstract idea. The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
An obstacle proximity detection device comprising:
an acquisition unit configured to sequentially acquire three-dimensional point cloud data representing an outdoor structure acquired by a three-dimensional laser scanner;
a specifying unit configured to specify first point cloud data representing an object and second point cloud data representing an obstacle from the three-dimensional point cloud data;
a setting unit configured to set a first detection area that is an area set in advance by a user and is an area around the first point cloud data;
a moving unit configured to move, on the basis of a feature point extracted from the first point cloud data, the first point cloud data and the first detection area according to movement of the feature point; and
an output unit configured to output an alert indicating proximity between the object and the obstacle in a case where a part of the first detection area overlaps with a part of a second detection area that is an area around the second point cloud data, or in a case where the number of pieces of point data of the first point cloud data existing in the second detection area is equal to or larger than a predetermined threshold.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of acquiring ... and outputting … the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (processor) to perform the process. In particular, the acquiring ... step is recited at a high level of generality (i.e. as a general means of acquiring data for use in the next steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The outputting … step is also recited at a high level of generality (i.e. as a general means of displaying information from some of the previous steps), and amounts to mere post solution action, which is a form of insignificant extra-solution activity. Lastly, claims 1, 6 and 7 further recite the "An obstacle proximity detection device comprising: an acquisition unit configured to …; a specifying unit configured to …; a setting unit configured to …; a moving unit configured to …; and an output unit configured to …" (claim 1), "An obstacle proximity detection method in which a computer executes processing of: …" (claim 6), and "A computer-readable non-transitory recording medium storing computer-executable program instructions that when executed by a processor cause a computer to execute an obstacle proximity detection program to execute processing of: …" (claim 7), which merely describes how to generally “apply” the otherwise mental judgements and/or additional limitations in a generic or general purpose vehicle control environment. See Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. at 223 (“[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”). The device(s) and processor(s) are recited at a high level of generality and merely automates the steps.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the steps amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations discussed above are insignificant extra-solution activities.
The additional limitations of acquiring … is well-understood, routine and conventional activities because the background recites that the sensors are all conventional sensors, and the specification does not provide any indication that the processor is anything other than a conventional computer. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. The additional limitation of outputting ... is a well-understood, routine, and conventional activity because the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere performances are well understood, routine, and conventional function. Hence, the claim is not patent eligible.
Dependent claims 2-5 and 8-20 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-5 and 8-20 are not patent eligible under the same rationale as provided for in the rejection of claim 1.
Therefore, claims 1-20 are ineligible under 35 USC §101.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-18 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of U.S. Patent No. 19105318. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims in the present application cover the same subject matter claimed in the reference application with only slight but obvious/implicit differences in wording, when the claims of the reference application are read in light of the reference application specification, and with the limitations of the claims in the present application corresponding to and/or obvious from the limitations in the reference application as shown in the following claim correspondence table:
Present Application
Application No. 19/105,318
1 , 19 , 20
1, 9, 10
2 , 19 , 20
2
3
3
4
4
5
5
6
7, 11, 12
7
8, 17, 18
8
13
9
14
10
15
11
5, 7
12
2, 8
13
3, 8
14
4, 8
15
5, 8
16
1
17
1, 3
18
1
This is a provisional nonstatutory double patenting because the patentably indistinct claims have not in fact been patented.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 2, 6-8, 12, 16, 18, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Endres (US 20200272816 A1) in view of Ishikawa (JP 2019201268 A – Machine translation).
Regarding claim 1, Endres discloses an obstacle proximity detection device (See at least abstract, [0029], [0032-0043], [0125-0130] The following embodiments include methods and apparatus for detecting objects such as pole points from point cloud data. One or more deep learning and/or additional algorithms segment objects from the point cloud data. ) comprising: an acquisition unit configured to sequentially acquire three-dimensional point cloud data representing an outdoor structure acquired by a three-dimensional laser scanner (See at least abstract, [0032-0040], [0042-0045] The following embodiments include methods and apparatus for detecting objects such as pole points from point cloud data. One or more deep learning and/or additional algorithms segment objects from the point cloud data. The distance detection system 104 may include a light detection and ranging (LiDAR) device, a structured light device, or one or more stereo cameras. The distance detection system 104 may generate a point cloud of data based on the surroundings of the distance detection system 104. The point cloud may include data points that represent objects in the surroundings of the mobile device 122 and/or vehicles 124. The point cloud may be collected from the roadway by a collection vehicle 124, which may be an autonomous vehicle. When the distance detection system 104 is a structured light device, structured light may include a projection of a predetermined pattern in the vicinity of the collection vehicle. The mobile device 122 may collect the point cloud data 33 through the distance detection system 104. The point cloud data 33 may include a scan of data collected at a particular position described by the location data 31. The point cloud data 33 may be generated from multiple scans from different positions described in the location data 31.); a specifying unit configured to specify first point cloud data representing an object and second point cloud data representing an obstacle from the three-dimensional point cloud data (See at least abstract, [0036-0045], [0050-0057] The object segmentation processor 126 is configured to identify the point cloud 33 for a geographic region. That is, the object segmentation processor 126 may receive location data 31 (e.g., from a probe 101) and select a portion of the point cloud 33 based on the geographic region. FIG. 4 illustrates an example scene including one or more objects 51 placed along a roadway 55. The neural network module 38 is configured to analyze the point cloud data 33 with a neural network. The output of the neural network module 38 describes detected locations of objects in the point cloud data 33. In one example, the object segmentation processor 126 and the neural network module 38 reads the point cloud data 33 from a feed of data from a distancing system that is defined according to the size and operation of the distance detection system 104, for example, LiDAR scanner. The output of the LiDAR scanner may control the width of the data provided in the feed that is processed by the neural network module 38.).
Endres does not explicitly disclose a setting unit configured to set a first detection area that is an area set in advance by a user and is an area around the first point cloud data; a moving unit configured to move, on the basis of a feature point extracted from the first point cloud data, the first point cloud data and the first detection area according to movement of the feature point; and an output unit configured to output an alert indicating proximity between the object and the obstacle in a case where a part of the first detection area overlaps with a part of a second detection area that is an area around the second point cloud data, or in a case where the number of pieces of point data of the first point cloud data existing in the second detection area is equal to or larger than a predetermined threshold. However, Ishikawa teaches a setting unit configured to set a first detection area that is an area set in advance by a user and is an area around the first point cloud data (See at least abstract, [0051-0058] The control unit 120 sets an alarm area around the specific object. If the control unit 120 detects a specific object (S14: YES), then the control unit 120 determines whether or not an alarm area already exists (S15). The presence / absence of the alarm area is determined by checking data indicating that the alarm area has been set, which is stored when the alarm area is set in S16 described later. If the warning area has already been set, the process proceeds to S20. The range in which the alarm area 500 is set is determined in advance. The alarm area 500 is three-dimensionally set within a predetermined distance from the hot metal ladle 300 in the three-dimensional coordinate system of the range scanned by the rider 110.); a moving unit configured to move, on the basis of a feature point extracted from the first point cloud data, the first point cloud data and the first detection area according to movement of the feature point (See at least abstract, [0042-0046], [0070-0075] The process of S20 will be described. In S20, the alarm area is already set up to the previous frame. Therefore, in S20, the control unit 120 moves the alarm area in accordance with the position of the specific object. For this, the coordinate value of the alarm area set up to the previous frame is moved in accordance with the movement of the coordinate value of the current position of the specific object. The control unit 120 performs moving body tracking for the clustered object (S13). In the moving object tracking, it is searched whether or not an object of the same cluster as the object clustered in the distance image of the current frame was in the previous frame. If there is an object of the same cluster in the previous frame, the position of the previous frame of the object (the position is a coordinate value, the same applies below) is compared with the position of the current frame, and the moving distance along with the current position of the object Determine the direction of travel and speed); and an output unit configured to output an alert indicating proximity between the object and the obstacle in a case where a part of the first detection area overlaps with a part of a second detection area that is an area around the second point cloud data (See at least abstract, [0060-0068], [0072-0075] The control unit 120 determines whether another object (such as a person or an object) that is different from the specific object is in the alarm area (S17). This comparison compares the range surrounded by the coordinate value of the outer shape of the cluster and the coordinate value indicating the warning area of the object clustered in S12 (that is, the object detected by the background difference method). If the coordinate value of the outer shape of the cluster of another object is within the alarm area, it is determined that the other object is within the alarm area. If it is determined that another object is in the alarm area (S17: YES), the control unit 120 outputs an alarm signal to the alarm device 140 (S18). At this time, the display 130 blinks an object (or a frame or mark surrounding the object) that is determined to be within the alarm area, changes the color of the entire screen, blinks, or even a warning message is displayed. It can be seen from these that the alarm is also issued visually. Along with the alarm device 140, various alarm operations such as turning on the rotating lamp, changing the color of the color-coded layered display lamp from blue to red, and turning on and blinking other lamps, etc. ), or in a case where the number of pieces of point data of the first point cloud data existing in the second detection area is equal to or larger than a predetermined threshold. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified Endres to incorporate the teachings of Ishikawa which teaches a setting unit configured to set a first detection area that is an area set in advance by a user and is an area around the first point cloud data; a moving unit configured to move, on the basis of a feature point extracted from the first point cloud data, the first point cloud data and the first detection area according to movement of the feature point; and an output unit configured to output an alert indicating proximity between the object and the obstacle in a case where a part of the first detection area overlaps with a part of a second detection area that is an area around the second point cloud data, and incorporation of Ishikawa would improve real-time dynamic and zone-based proximity alerts for moving objects and obstacles.
Regarding claim 2, Endres as modified by Ishikawa discloses wherein the object is a utility pole under construction (See at least Endres abstract, [0045-0050] FIG. 4 illustrates an example scene including one or more objects 51 placed along a roadway 55. The poles may be utility poles that support transmission lines such as telephone cables, electric power cables, or television cables. ), and the obstacle is a cable or a wall surface existing between existing utility poles (See at least Endres abstract, [0045-0050] The poles may be utility poles that support transmission lines such as telephone cables, electric power cables, or television cables. FIG. 4 illustrates an example scene including one or more objects 51 placed along a roadway 55. While illustrated as free-standing structures, the poles may support or otherwise be associated with a variety of other objects.).
Regarding claim 6, Endres discloses an obstacle proximity detection method in which a computer executes processing of (See at least abstract, [0003], [0029-0032]). The rest of claim 6 is commensurate in scope with claim 1. See above rejection of claim 1.
Regarding claim 7, Endres discloses a computer-readable non-transitory recording medium storing computer-executable program instructions that when executed by a processor cause a computer to execute an obstacle proximity detection program to execute processing of (See at least abstract, [0130-0134]). The rest of claim 7 is commensurate in scope with claim 1. See above rejection of claim 1.
Regarding claim 8, claim 8 is commensurate in scope with claim 2. See above rejection for claim 2.
Regarding claim 12, claim 12 is commensurate in scope with claim 2. See above rejection for claim 2.
Regarding claim 16, Endres as modified by Ishikawa discloses wherein the alert is generated on a sound output device or on a display device in a format recognizable by the user (See at least Endres abstract, [0035-0038] The alarm device 140 issues an alarm by, for example, sound, light such as a flashlight or a rotating light, and other methods that can be recognized by a person. An alarm signal is output from the control unit 120 in S18. Thereby, an alarm sound is emitted from the alarm device 140 that has received the alarm signal. At this time, the display 130 blinks an object (or a frame or mark surrounding the object) that is determined to be within the alarm area, changes the color of the entire screen, blinks, or even a warning message is displayed).
Regarding claim 18, Endres as modified by Ishikawa discloses wherein sequentially acquires three-dimensional point cloud data representing an outdoor structure acquired by a three-dimensional laser scanner (See at least Endres abstract, [0032-0040], [0042-0045] The following embodiments include methods and apparatus for detecting objects such as pole points from point cloud data. One or more deep learning and/or additional algorithms segment objects from the point cloud data. The distance detection system 104 may include a light detection and ranging (LiDAR) device, a structured light device, or one or more stereo cameras. The distance detection system 104 may generate a point cloud of data based on the surroundings of the distance detection system 104. The point cloud may include data points that represent objects in the surroundings of the mobile device 122 and/or vehicles 124. The point cloud may be collected from the roadway by a collection vehicle 124, which may be an autonomous vehicle. When the distance detection system 104 is a structured light device, structured light may include a projection of a predetermined pattern in the vicinity of the collection vehicle. The mobile device 122 may collect the point cloud data 33 through the distance detection system 104. The point cloud data 33 may include a scan of data collected at a particular position described by the location data 31. The point cloud data 33 may be generated from multiple scans from different positions described in the location data 31.), in which the three-dimensional point cloud data is used to generate a first point cloud data and a second point cloud data (See at least Endres abstract, [0036-0045], [0050-0057] The object segmentation processor 126 is configured to identify the point cloud 33 for a geographic region. That is, the object segmentation processor 126 may receive location data 31 (e.g., from a probe 101) and select a portion of the point cloud 33 based on the geographic region. FIG. 4 illustrates an example scene including one or more objects 51 placed along a roadway 55. The neural network module 38 is configured to analyze the point cloud data 33 with a neural network. The output of the neural network module 38 describes detected locations of objects in the point cloud data 33. In one example, the object segmentation processor 126 and the neural network module 38 reads the point cloud data 33 from a feed of data from a distancing system that is defined according to the size and operation of the distance detection system 104, for example, LiDAR scanner. The output of the LiDAR scanner may control the width of the data provided in the feed that is processed by the neural network module 38.).
Regarding claim 19, Endres as modified by Ishikawa discloses wherein a plurality of feature point group clusters is identified from the three-dimensional point group cloud data (See at least Endres abstract, [0012], [0056-0061] FIG. 5B illustrates example clusters associated with a pole for the point cloud data of FIG. 5A. In one example, the matrix includes a row for each point in the cluster and a column for each dimension of the space of the clustered points. For example, in a three-dimensional point cloud in (X, Y, Z), the matrix may have three columns. The clustering module 39 is configured to group a subset of the probability values based on relative locations of the assigned points in the point cloud data. FIG. 6A illustrates factorized clusters for the object of FIG. 5B. The factorization module 40 is configured to factor a matrix with the subset of the clustered probability values to assign a line 62 for a three dimensional object of the geographic region).
Claim(s) 3-5, 9-11, 13-15, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Endres (US 20200272816 A1) in view of Ishikawa (JP 2019201268 A – Machine translation), and further in view of Richards (US 20130127851 A1).
Regarding claim 3, Endres as modified by Ishikawa does not explicitly disclose wherein, in specifying the first point cloud data, the specifying unit rotates data of a predetermined region included in the three-dimensional point cloud data by a predetermined angle with respect to an xy plane, projects the rotated data of the predetermined region with respect to the xy plane, and specifies the data of the predetermined region as the first point cloud data in a case where the data of the predetermined region projected on the xy plane is circular. However, Richards teaches wherein, in specifying the first point cloud data, the specifying unit rotates data of a predetermined region included in the three-dimensional point cloud data by a predetermined angle with respect to an xy plane (See at least abstract, Fig. 2, [0007-0010], [0026-0032], [0034-0038] The extracted information can be used to identify structures and/or structural features from the point cloud. The surface discovery module is configured to rotate the 3-dimensional image data around an axis in 3-dimensional space of the 3-dimensional image data. The technology, generally, rotates the 3-dimensional image data around an axis (e.g., x-axis, y-axis, z-axis, or any in between axis) to align points of a structure (e.g., plane, cylinder, sphere, etc.) for extraction of information from the 3-dimensional image data (e.g., linear condensations, structures, etc.). The structure discovery system 110 rotates the point cloud 200 a of FIG. 2A to form the rotated point cloud 200 b. The rotated point cloud 200 b enables the structure discovery system 110 to extract the facades (e.g., rotated façade 242 b) as projected onto the x-axis and the y-axis. The structure discovery system 110 can determine a rotation angle, φ utilizing the techniques described herein via a regression line fitting through the subcloud. For example, the structure discovery system 110 can rotate a point cloud counter-clockwise through a different angle around the y-axis 220 c and then rotate the point cloud clockwise through another angle around the x-axis 210 c to discovery structures within the point cloud. The rotated point cloud 200 b enables the structure discovery system 110 to extract the facades (e.g., rotated façade 242 b) as projected onto the x-axis and the y-axis. In some examples, the structure discovery system 110 can identify where each subcloud will project onto the x and y axes as the point cloud 200 d is rotated about the origin of the specified Cartesian coordinate system since the structure discovery system 110 has depth measurements of the structures (e.g., input by a user, determined by an image device, etc.) and/or the structure discovery system 110 controls the rotation of the point cloud 200 d.), projects the rotated data of the predetermined region with respect to the xy plane (See at least abstract, Fig. 2, [0015], [0060-0065] The rotating step (b) further includes (b-1) projecting isometrically the 3-dimensional image data onto an observation plane. In some examples, the surface discovery module 312 projects (425) isometrically the 3-dimensional image data onto an observation plane (e.g., viewing plane from image acquisition system, viewing plane from camera, etc.). In other examples, the surface discovery module 312 selects (423) the axis based on a plane of interest.), and specifies the data of the predetermined region as the first point cloud data in a case where the data of the predetermined region projected on the xy plane is circular (See at least abstract., Fig. 2, [0026-0030], [0060-0065] The technology, generally, rotates the 3-dimensional image data around an axis (e.g., x-axis, y-axis, z-axis, or any in between axis) to align points of a structure (e.g., plane, cylinder, sphere, etc.) for extraction of information from the 3-dimensional image data (e.g., linear condensations, structures, etc.). The linear condensation identification module 313 identifies (430) linear condensations (e.g., lines, circles, etc.) in the rotated 3-dimensional image data. The rotation of the 3-dimensional image data enables projection of the structure onto a coordinate plane to form linear condensations of the structure (e.g., structure becomes a straight line, structure becomes a circle, etc.). The extracted information can be used to identify structures and/or structural features from the point cloud). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified Endres as modified by Ishikawa to incorporate the teachings of Richards which teaches wherein, in specifying the first point cloud data, the specifying unit rotates data of a predetermined region included in the three-dimensional point cloud data by a predetermined angle with respect to an xy plane, projects the rotated data of the predetermined region with respect to the xy plane, and specifies the data of the predetermined region as the first point cloud data in a case where the data of the predetermined region projected on the xy plane is circular since they are all directed to 3D point cloud data processing and incorporation of Richards would improve an efficient projection and shape detection techniques for identifying structures.
Regarding claim 4, Endres as modified by Ishikawa does not explicitly disclose wherein, in specifying second point cloud data representing the cable, the specifying unit rotates data of a predetermined region included in the three-dimensional point cloud data by a predetermined angle about a z axis, projects the rotated data of the predetermined region on a yz plane, and specifies the data of the predetermined region as the second point cloud data representing the cable in a case where the data of the predetermined region projected on the yz plane is circular. However, Richards teaches wherein, in specifying second point cloud data representing the cable, the specifying unit rotates data of a predetermined region included in the three-dimensional point cloud data by a predetermined angle about a z axis (See at least abstract, [0026-0032], [0034-0036] The extracted information can be used to identify structures and/or structural features from the point cloud. The structure identification can be, for example, utilized in various applications to identify specific buildings within a city, track changes within a landscape, and/or utilize specific features on a target to track the target within a landscape. FIG. 2C is a diagram illustrating an exemplary rotation of a point cloud 200 c. First, the structure discovery system 110 rotates a point cloud clockwise through angle θ around a vertical z-axis 210 c. The first rotation can orient facades of structures into a line projection. Second, the structure discovery system 110 rotates the point cloud clockwise about a y-axis 220 c through angle φ.), projects the rotated data of the predetermined region on a yz plane (See at least abstract, Fig. 2, [0015], [0031], [0036], [0050-0055], [0060-0065] The surface discovery module 312 rotates (e.g., 90° counter-clockwise, 45° clockwise, etc.) the 3-dimensional image data around an axis (e.g., z-axis, y-axis, etc.) in 3-dimensional space of the 3-dimensional image data. The structure discovery system 110 rotates a point cloud clockwise through angle θ around a vertical z-axis 210 c. The rotating step (b) further includes (b-1) projecting isometrically the 3-dimensional image data onto an observation plane. In some examples, the surface discovery module 312 projects (425) isometrically the 3-dimensional image data onto an observation plane (e.g., viewing plane from image acquisition system, viewing plane from camera, etc.). In other examples, the surface discovery module 312 selects (423) the axis based on a plane of interest. the structure discovery system 110 projects the planar condensations, for a given structure, onto the x-z plane and the y-z plane.), and specifies the data of the predetermined region as the second point cloud data representing the cable in a case where the data of the predetermined region projected on the yz plane is circular (See at least abstract., Fig. 2, [0026-0030], [0060-0065] The technology, generally, rotates the 3-dimensional image data around an axis (e.g., x-axis, y-axis, z-axis, or any in between axis) to align points of a structure (e.g., plane, cylinder, sphere, etc.) for extraction of information from the 3-dimensional image data (e.g., linear condensations, structures, etc.). The linear condensation identification module 313 identifies (430) linear condensations (e.g., lines, circles, etc.) in the rotated 3-dimensional image data. The rotation of the 3-dimensional image data enables projection of the structure onto a coordinate plane to form linear condensations of the structure (e.g., structure becomes a straight line, structure becomes a circle, etc.). The extracted information can be used to identify structures and/or structural features from the point cloud). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified Endres as modified by Ishikawa to incorporate the teachings of Richards which teaches wherein, in specifying second point cloud data representing the cable, the specifying unit rotates data of a predetermined region included in the three-dimensional point cloud data by a predetermined angle about a z axis, projects the rotated data of the predetermined region on a yz plane, and specifies the data of the predetermined region as the second point cloud data representing the cable in a case where the data of the predetermined region projected on the yz plane is circular since they are all directed to 3D point cloud data processing and incorporation of Richards would improve an efficient projection and shape detection techniques for identifying structures.
Regarding claim 5, Endres as modified by Ishikawa does not explicitly disclose wherein, in specifying second point cloud data representing the wall surface, the specifying unit projects data of a predetermined region included in the three-dimensional point cloud data on an xy plane, and specifies the data of the predetermined region as the second point cloud data representing the wall surface in a case where the data of the predetermined region projected on the xy plane has a linear shape. However, Richards teaches wherein, in specifying second point cloud data representing the wall surface, the specifying unit projects data of a predetermined region included in the three-dimensional point cloud data on an xy plane (See at least abstract, [0025-0030], [0032-0036], [0050-0055], The extracted information can be used to identify structures and/or structural features from the point cloud. The structure discovery system 110 processes the image data to generate one or more structures (e.g., building, tank, wall, etc.). Although FIG. 1 illustrates the plurality of buildings 132, 134, and 136, the structure discovery system 110 can be utilized to discovery any type of structure (e.g., wall, dome, vehicle, etc.). FIG. 2A illustrates a bird's eye view (i.e., vertical towards nadir and the x-y plane) view of an urban scene with several buildings 220 as observed by the cameras 210. The structure discovery system 110 can identify where each subcloud will project onto the x and y axes as the point cloud 200 d is rotated about the origin of the specified Cartesian coordinate system since the structure discovery system 110 has depth measurements of the structures (e.g., input by a user, determined by an image device, etc.) and/or the structure discovery system 110 controls the rotation of the point cloud 200 d. The rotated point cloud 200 b enables the structure discovery system 110 to extract the facades (e.g., rotated façade 242 b) as projected onto the x-axis and the y-axis.), and specifies the data of the predetermined region as the second point cloud data representing the wall surface in a case where the data of the predetermined region projected on the xy plane has a linear shape (See at least abstract, Fig. 2, [0025-0030], [0050-0062] The structure generation module 314 generates a structure (e.g., a building in a wireframe, a tank in a wireframe, etc.) based on the linear condensations and the rotated 3-dimensional image data. The generated structure can be utilized to identify structural elements within a scene associated with the 3-dimensional image data (e.g., streets and buildings within a city, doorways on a building, etc.). The linear condensation identification module 313 identifies (430) linear condensations (e.g., lines, circles, etc.) in the rotated 3-dimensional image data. The rotated point cloud 200 b enables the structure discovery system 110 to extract the facades (e.g., rotated façade 242 b) as projected onto the x-axis and the y-axis. The structure generation module 314 generates (440) a structure (e.g., building, car, etc.) based on the linear condensations and the rotated 3-dimensional image data. The structure discovery system 110 processes the image data to generate one or more structures (e.g., building, tank, wall, etc.)). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified Endres as modified by Ishikawa to incorporate the teachings of Richards which teaches wherein, in specifying second point cloud data representing the wall surface, the specifying unit projects data of a predetermined region included in the three-dimensional point cloud data on an xy plane, and specifies the data of the predetermined region as the second point cloud data representing the wall surface in a case where the data of the predetermined region projected on the xy plane has a linear shape since they are all directed to 3D point cloud data processing and incorporation of Richards would improve an efficient projection and shape detection techniques for identifying structures.
Regarding claim 9 and 13, claim 9 is commensurate in scope with claim 3. See above rejection for claim 3.
Regarding claim 10 and 14, claim 10 is commensurate in scope with claim 4. See above rejection for claim 4.
Regarding claim 11 and 15, claim 11 is commensurate in scope with claim 5. See above rejection for claim 5.
Regarding claim 17, Endres does not explicitly disclose wherein the alert is set to generate on a fixed obstacle or on a moving object exist within the predetermined region. However, Ishikawa discloses wherein the alert is set to generate on a fixed obstacle or on a moving object exist within the predetermined region (See at least abstract, [0036-0038], [0068-0075] Further, the display 130 and the alarm device 140 may be integrated depending on the monitoring environment. When there is an object that has entered the alarm area, the intrusion route can be determined by storing the distance image from before entering the alarm area together with the distance image at that time. Such an intrusion route can also be used, for example, for risk evaluation and analysis. If the movement of the specific object is detected, an alarm area is set in a predetermined range including the specific object. Thereafter, if there is another object different from the specific object in the alarm area in the acquired distance image, an alarm is issued. An alarm signal is output from the control unit 120 in S18. Thereby, an alarm sound is emitted from the alarm device 140 that has received the alarm signal. At this time, the display 130 blinks an object (or a frame or mark surrounding the object) that is determined to be within the alarm area, changes the color of the entire screen, blinks, or even a warning message is displayed.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified Endres to incorporate the teachings of Ishikawa which teaches wherein the alert is set to generate on a fixed obstacle or on a moving object exist within the predetermined region since they are all directed to 3D point cloud data processing and incorporation of Ishikawa would improve the system to enable an alert system when the obstacle exists in the region.
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Endres (US 20200272816 A1) in view of Ishikawa (JP 2019201268 A – Machine translation), and further in view of Wei (US 20190340447 A1).
Regarding claim 20, Endres as modified by Ishikawa does not explicitly disclose wherein the plurality of feature point group clusters further comprising an object having high reflection intensity or a unique shape among the three-dimensional point group data. However, Wei teaches wherein the plurality of feature point group clusters further comprising an object having high reflection intensity or a unique shape among the three-dimensional point group data (See at least abstract, [0018-0022], [0036-0038] In an example, the subfigure 112 shows an exemplary point-cloud generated based on the LiDAR sensor. In some embodiments, the point-cloud includes (x, y, z) coordinates in a coordinate system and the intensity of the reflection of the laser beams received at the sensor. The point-cloud may be a stored as a pixel array of four dimensions, with three dimensions corresponding to the spatial coordinates of the scanned surrounding area and one dimension representing a strength of the reflection sensed by the LiDAR sensors. The curb detection module then randomly selects some points as seeds to create clusters, and region-growth is performed from these seeds. In the region-growing process, a point cluster will check its neighboring points to see if they satisfy a criterion associated with height, the normal, and curvature. The points that satisfy the criterion will be added to the cluster, and may become new seeds that are used to expand the cluster. A cluster will be removed from the original accumulated point-cloud when it stops growing, and the curb detection module will start a new cluster from another random seed among the remaining points. The method 300 may further include determining whether any of the clusters include bounded regions of points, where each point in a bounded region does not meet the criterion. In other words, the region-growing method may have grown the largest cluster around another vehicle on the road, which results in a bounded region with feature values that different from the feature values of the largest (or road) cluster. Since the bounded region includes points that do not meet the criterion, the method is able to correctly identify that these points are not representative of the curb, but rather of another object in the road (e.g. a vehicle)). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation of success, to have modified Endres as modified by Ishikawa to incorporate the teachings of Wei which teaches wherein the plurality of feature point group clusters further comprising an object having high reflection intensity or a unique shape among the three-dimensional point group data since they are all directed to 3D point cloud data and incorporation of Wei would improve identifying objects having high reflection intensity or unique shapes within cloud point data.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LABIBAH I. ALI whose telephone number is (571)272-6738. The examiner can normally be reached M-F 8:00-5:00.
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/LABIBAH ILMA ALI/Examiner, Art Unit 3667
/SAHAR MOTAZEDI/Primary Examiner, Art Unit 3667
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