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
Acknowledge is made of Applicant’s claim of the present application claiming priority and benefit under 35 U.S.C. 119(a-d) to Japanese Patent Application No. JP2022-078898 filed 05/12/2022. Acknowledgement is also made of Applicant’s claim of the present application being a National Stage application of International Application No. PCT/JP2023/015113 filed 04/14/2023.
Information Disclosure Statement
The information disclosure statements (“IDS”) filed 10/30/2024 and 07/25/2025 have been reviewed and the listed references were noted.
Drawings
The 19-page drawings have been considered and placed in the file.
Status of Claims
Claims 1-14 are pending.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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.
Claim elements in this application that use the word "means" (or "step for") are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word "means" (or "step for") are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
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: “a three-dimensional point cloud recognition unit” and “a recognition confidence calculation unit” in claim 1, “a two-dimensional recognition unit” and “a three-dimensional point cloud recognition integration unit” in claim 4, “a three-dimensional point cloud extraction unit” in claim 7, and “a three-dimensional point cloud generation unit” in claim 10.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 7, and 13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al. (“SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation”).
Regarding claim 1, Wang teaches, “An information processing apparatus comprising: a three-dimensional point cloud recognition unit that executes recognition processing on a three-dimensional point cloud and gives a recognition result for each point of the three-dimensional point cloud;” (Wang, Page 2, “Method” discloses; “The goal of this paper is to take a 3D point cloud as in put and produce an object instance label for each point and a class label for each instance”) “and a recognition confidence calculation unit that calculates a confidence of the recognition result for each point of the three-dimensional point cloud and gives the confidence for each point of the three-dimensional point cloud.” (Wang, Page 4, “Similarity Confidence Map” discloses; “SGPN also feeds FCF through an additional PointNet layer to predict a Np × 1 confidence map CM reflecting how confidently the model believes that each grouping candidate is indeed a correct object instance” Figure 3 of Wang also shows a confidence map for each point in a three-dimensional point cloud.)
Regarding claim 7, Wang teaches, “The information processing apparatus according to claim 1, further comprising a three-dimensional point cloud extraction unit that extracts a point cloud from the three-dimensional point cloud based on the confidence of each point of the three-dimensional point cloud.” (Figure 3(b) of Wang shows extracting points based on confidence. Figure 3 caption of Wang also discloses; “(b) Confidence map. A darker color represents higher confidence.”)
Claim 13 recites a method with steps corresponding to the elements of the system recited in Claim 1. Therefore, the recited steps of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding system claim. Therefore, claim 13 is rejected in the same manner as claim 1.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claims 2-4 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (“SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation”), in view of Tchapmi et al. (“SEGCloud: Semantic Segmentation of 3D Point Clouds”).
Regarding claim 2, Wang does not explicitly teach, “The information processing apparatus according to claim 1, wherein the recognition confidence calculation unit calculates the confidence for each point of the three-dimensional point cloud from a statistic of a local region of a three-dimensional space for the recognition result for each point of the three-dimensional point cloud.” Since Wang does not explicitly disclose this limitation, Examiner relies on the teachings of Tchapmi in an analogous field of endeavor. Specifically, Tchapmi teaches, “The information processing apparatus according to claim 1, wherein the recognition confidence calculation unit calculates the confidence for each point of the three-dimensional point cloud from a statistic of a local region of a three-dimensional space for the recognition result for each point of the three-dimensional point cloud.” (Tchapmi, Page 2, Para. 2 discloses; “In detail, the 3D-FCNN provides class probabilities at the voxel level, which are transferred back to the raw 3D points using tri-linear interpolation.” Examiner interprets class probabilities to be confidence and “voxel level” to be a local region”)
Wang and Tchapmi are considered to be analogous to the claimed invention because they are in the same field of segmenting 3-dimensional point cloud data. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the teachings of Tchapmi in order to take points in a local region into consideration when calculating a confidence level. One of ordinary skill in the art would have been motivated to combine the previously described apparatus of Wang with the teachings of Tchapmi to get a more inclusive and accurate measurement of confidence. Accordingly, it would have been obvious to combine Wang and Tchapmi to obtain the invention of claim 2.
Regarding claim 3, the combination of Wang and Tchapmi teaches, “The information processing apparatus according to claim 2, wherein the recognition confidence calculation unit calculates the statistic of the local region based on a positional relationship for each point in the local region of the three-dimensional point cloud.” (Tchapmi, Page 2, Para. 2 discloses; “In detail, the 3D-FCNN provides class probabilities at the voxel level, which are transferred back to the raw 3D points using tri-linear interpolation. We then use a Fully Connected Conditional Random Field (FC-CRF) to infer 3D point labels while ensuring spatial consistency. Transferring class probabilities to points before the CRF step, allows the CRF to use point level modalities (color, intensity, etc.) to learn a fine-grained labeling over the points, which can improve the initial coarse 3D-FCNN predictions. Examiner interprets “spatial consistency” to be a positional relationship.) The proposed combination as well as the motivation for combining Wang and Tchapmi references presented in the rejection of claim 2, apply to claim 3 and are incorporated herein by reference. Thus, the apparatus recited in claim 3 is met by Wang and Tchapmi.
Regarding claim 4, the combination of Wang and Tchapmi teaches, “The information processing apparatus according to claim 2, wherein the recognition confidence calculation unit calculates the statistic of the local region based on a similarity of a color for each point in the local region of the three-dimensional point cloud.” (Tchapmi, Page 2, Para. 2 discloses; “In detail, the 3D-FCNN provides class probabilities at the voxel level, which are transferred back to the raw 3D points using tri-linear interpolation. We then use a Fully Connected Conditional Random Field (FC-CRF) to infer 3D point labels while ensuring spatial consistency. Transferring class probabilities to points before the CRF step, allows the CRF to use point level modalities (color, intensity, etc.) to learn a fine-grained labeling over the points, which can improve the initial coarse 3D-FCNN predictions.”) The proposed combination as well as the motivation for combining Wang and Tchapmi references presented in the rejection of claim 2, apply to claim 4 and are incorporated herein by reference. Thus, the apparatus recited in claim 4 is met by Wang and Tchapmi.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (“SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation”), in view of Hermans et al. (“Dense 3D Semantic Mapping of Indoor Scenes from RGB-D Images”).
Regarding claim 5, Wang does not explicitly teach, “The information processing apparatus according to claim 1, wherein the three-dimensional point cloud recognition unit includes: a two-dimensional recognition unit that executes recognition processing on a plurality of two-dimensional images; and a three-dimensional point cloud recognition integration unit that reflects a recognition result for each of the two-dimensional images in the three-dimensional point cloud.” Since Wang does not explicitly disclose these limitations, Examiner relies on the teachings of Hermans in an analogous field of endeavor. Specifically, Hermans teaches, “The information processing apparatus according to claim 1, wherein the three-dimensional point cloud recognition unit includes: a two-dimensional recognition unit that executes recognition processing on a plurality of two-dimensional images;” (Hermans, Page 2, “Approach”, Para. 2 discloses; “The 2D semantic segmentation process creates a soft classification for each pixel that corresponds to a 3D point in the cloud and accumulates this information for the points.”) “and a three-dimensional point cloud recognition integration unit that reflects a recognition result for each of the two-dimensional images in the three-dimensional point cloud.” (Hermans, Page 2, “Approach”, Para. 2 discloses; “The 2D semantic segmentation process creates a soft classification for each pixel that corresponds to a 3D point in the cloud and accumulates this information for the points.” Examiner interprets this disclosure to teach reflecting 2D classification on 3-dimensional images.)
Wang and Hermans are considered to be analogous to the claimed invention because they are in the same field of segmenting 3-dimensional point cloud data. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the teachings of Hermans in order to transfer 2-dimensional recognition data onto 3-dimensional point cloud data. One of ordinary skill in the art would have been motivated to combine the previously described apparatus of Wang with the teachings of Hermans to easily obtain 3-dimensional classification data from 2-dimensional images. Accordingly, it would have been obvious to combine Wang and Hermans to obtain the invention of claim 5.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (“SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation”), in view of Hermans et al. (“Dense 3D Semantic Mapping of Indoor Scenes from RGB-D Images”), in further view of Bhatia et al. (US 2022/0398808 A1 w/ EFD of 06/15/2021).
Regarding claim 6, the combination of Wang and Hermans does not explicitly teach, “The information processing apparatus according to claim 5, wherein, when reflecting the recognition result for each of the two-dimensional images in the three-dimensional point cloud, the recognition confidence calculation unit calculates the confidence for each point of the three-dimensional point cloud from a statistic of the recognition result for each of the two-dimensional images corresponding to the three-dimensional point cloud.” Since the combination of Wang and Hermans does not explicitly disclose this limitation, Examiner relies on the teachings of Bhatia in an analogous field of endeavor. Specifically, Bhatia teaches, “The information processing apparatus according to claim 5, wherein, when reflecting the recognition result for each of the two-dimensional images in the three-dimensional point cloud, the recognition confidence calculation unit calculates the confidence for each point of the three-dimensional point cloud from a statistic of the recognition result for each of the two-dimensional images corresponding to the three-dimensional point cloud.” (Bhatia, Para. [0009] discloses; “The element confidences per pixel from corresponding pixels of each of the images of the set of images that is known to observe the respective one of the set of 3D points may be determined using a visibility graph which indicates which images of the set of images observe each respective one of the set of 3D points. Further, the element confidences per pixel may be determined by, for each image, projecting the respective one of the set of 3D points using the known camera pose of the image to find coordinates of the respective one of the set of 3D points in an image space, and reading a confidence value from the image.”)
Wang, Hermans, and Bhatia are considered to be analogous to the claimed invention because they are in the same field of processing 3-dimensional point cloud data. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Wang and Hermans to incorporate the teachings of Bhatia in order to calculate the confidence scores of the three-dimensional point data based on the two-dimensional data. One of ordinary skill in the art would have been motivated to combine the previously described apparatus of Wang and Hermans with the teachings of Bhatia to use the two-dimensional images to create the three-dimensional data. Accordingly, it would have been obvious to combine Wang, Hermans, and Bhatia to obtain the invention of claim 6.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (“SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation”), in view of Cathrin Elich (“3D Instance Semantic Segmentation on Point Clouds”).
Regarding claim 8, Wang does not explicitly teach, “The information processing apparatus according to claim 7, wherein the three-dimensional point cloud extraction unit extracts the point cloud having the confidence in a predetermined range from the three-dimensional point cloud based on the confidence of each point of the three-dimensional point cloud.” Since Wang does not explicitly disclose this limitation, Examiner relies on the teachings of Elich in an analogous field of endeavor. Specifically, Erich discloses, “The information processing apparatus according to claim 7, wherein the three-dimensional point cloud extraction unit extracts the point cloud having the confidence in a predetermined range from the three-dimensional point cloud based on the confidence of each point of the three-dimensional point cloud.” (Elich, Page 36-37 discloses; “As a first requirement, we only consider those proposals for which the confidence score of the corresponding point is above some threshold Thc.”)
Wang and Elich are considered to be analogous to the claimed invention because they are in the same field of segmenting 3-dimensional point cloud data. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the teachings of Elich in order to only extract the points within a certain confidence range. One of ordinary skill in the art would have been motivated to combine the previously described apparatus of Wang with the teachings of Elich to only include points that have a high confidence level and exclude those that have low confidence scores. Accordingly, it would have been obvious to combine Wang and Elich to obtain the invention of claim 8.
Claims 9-12 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (“SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation”), in view of Lasang et al. (US 2025/0093515 A1 w/ EFD of 11/20/2017).
Regarding claim 9, Wang does not explicitly teach, “The information processing apparatus according to claim 7, wherein the three-dimensional point cloud extraction unit stores the confidence for each point of the three-dimensional point cloud and extracts the point cloud from the three-dimensional point cloud based on the stored confidence for each point of the three-dimensional point cloud.” Since Wang does not explicitly disclose this limitation, Examiner relies on the teachings of Lasang in an analogous field of endeavor. Specifically, Lasang teaches, “The information processing apparatus according to claim 7, wherein the three-dimensional point cloud extraction unit stores the confidence for each point of the three-dimensional point cloud and extracts the point cloud from the three-dimensional point cloud based on the stored confidence for each point of the three-dimensional point cloud.” (Lasang, Para. [0065] discloses; “External memory 140 may, for example, store information necessary for the processor, such as a computer program. External memory 140 may store data generated through processes of the processor, e.g. the three-dimensional point cloud to which the attribute value has been appended, the encoded stream, etc.” It would be obvious to combine the storage apparatus with the confidence levels of Wang to store and extract the confidence levels.)
Wang and Lasang are considered to be analogous to the claimed invention because they are in the same field of processing 3-dimensional point cloud data. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the teachings of Lasang in order to store and extract the stored confidence levels. One of ordinary skill in the art would have been motivated to combine the previously described apparatus of Wang with the teachings of Lasang to have the ability to store/save confidence levels for later use. Accordingly, it would have been obvious to combine Wang and Lasang to obtain the invention of claim 9.
Regarding claim 10, the combination of Wang and Lasang teaches, “The information processing apparatus according to claim 1, further comprising a three-dimensional point cloud generation unit that generates the three-dimensional point cloud.” (Lasang, Para. [0023] discloses; “The present disclosure has an object to provide a three-dimensional point cloud generation method and a three-dimensional point cloud generation device capable of effectively reducing the data amount of three-dimensional point cloud data, and a position estimation method and a position estimation device capable of estimating a self-location using three-dimensional point cloud data with a reduced data amount”) The proposed combination as well as the motivation for combining Wang and Lasang references presented in the rejection of claim 9, apply to claim 10 and are incorporated herein by reference. Thus, the apparatus recited in claim 10 is met by Wang and Lasang.
Regarding claim 11, the combination of Wang and Lasang teaches, “The information processing apparatus according to claim 10, wherein the three-dimensional point cloud generation unit generates the three-dimensional point cloud from three-dimensional distance information.” (Lasang, Abstract discloses; “(ii) a first three-dimensional point cloud obtained by sensing the three-dimensional object using a distance sensor” Examiner interprets this to be generating three-dimensional point cloud data using distance information.) The proposed combination as well as the motivation for combining Wang and Lasang references presented in the rejection of claim 9, apply to claim 11 and are incorporated herein by reference. Thus, the apparatus recited in claim 11 is met by Wang and Lasang.
Regarding claim 12, the combination of Wang and Lasang teaches, “The information processing apparatus according to claim 10, wherein the three-dimensional point cloud generation unit generates the three-dimensional point cloud from a plurality of two-dimensional images.” (Lasang, Para. [0026] discloses; “The three-dimensional point cloud generation method may further include: matching attribute values associated with two two-dimensional images from the plurality of two-dimensional images using the one or more attribute values detected for each of the plurality of two-dimensional images”) ”) The proposed combination as well as the motivation for combining Wang and Lasang references presented in the rejection of claim 9, apply to claim 12 and are incorporated herein by reference. Thus, the apparatus recited in claim 12 is met by Wang and Lasang.
Regarding claim 14, the combination of Wang and Lasang teaches, “An information generation method comprising: generating information including position information for each point of a three-dimensional point cloud” (Lasang, Abstract discloses; “(ii) a first three-dimensional point cloud obtained by sensing the three-dimensional object using a distance sensor” Examiner interprets this to be generating position information.) “a recognition result for each point of the three-dimensional point cloud” (Wang, Page 2, “Method” discloses; “The goal of this paper is to take a 3D point cloud as in put and produce an object instance label for each point and a class label for each instance”) “and a confidence of the recognition result for each point of the three-dimensional point cloud.” (Wang, Page 4, “Similarity Confidence Map” discloses; “SGPN also feeds FCF through an additional PointNet layer to predict a Np × 1 confidence map CM reflecting how confidently the model believes that each grouping candidate is indeed a correct object instance” Figure 3 of Wang also shows a confidence map for each point in a three-dimensional point cloud.).
Wang and Lasang are considered to be analogous to the claimed invention because they are in the same field of processing 3-dimensional point cloud data. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wang to incorporate the teachings of Lasang in order to generate position information for points in a point cloud. One of ordinary skill in the art would have been motivated to combine the previously described apparatus of Wang with the teachings of Lasang to have the ability to processing can take place based on the positional information. Accordingly, it would have been obvious to combine Wang and Lasang to obtain the invention of claim 14.
Conclusion
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/JUSTIN M OAKES/
Examiner, Art Unit 2662
/Siamak Harandi/Primary Examiner, Art Unit 2662
Prosecution Insights