DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment was filed on 7/15/2026.
Claims 1-20 are pending.
Response to Arguments
Applicants’ arguments filed under Remarks on pages 9-16 on 3/1/2024 have been fully considered but they are not persuasive. Applicants state on page 10 that:
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On pages 11-12 applicant states:
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The Examiner respectfully disagree. Becker at paragraph [0029] discloses:
[0029] In some examples, the images include one or more additional objects from the capture environment that are not part of the three-dimensional object of interest. For instance, image 302, image 304, image 306, and image 308, each includes three-dimensional object 320 (e.g., tool table) but also includes a second three-dimensional object 322 (e.g., bicycle) different than the three-dimensional object 320. Although not shown in FIG. 3, the images may also capture other objects or aspects of the environment (e.g., floors, walls, trees, doors, sky, mountains, etc.). As described herein, in some examples, the other objects or aspects of the environment may be excluded from the reconstruction process using a bounding box. In some examples, the computing system (e.g., computing system 200) can be configured to determine which object(s) to focus on for the generation of the three-dimensional model or which object(s) to exclude from the generation of the three-dimensional model...computing system 200 may be able to determine that second three-dimensional object 322 is present in images 302-308, but not present in images 310-316, and thus, second three-dimensional object 322 is likely not the object of the reconstruction process...user interface 300 can include a user interface element (not shown) to enable or disable the feature of determining object(s) or region(s) of interest or of exclusion (e.g., a toggle button or menu item). When the feature is disabled, the reconstruction process may rely on the bounding box or other means of editing the scope of the three-dimensional object to be generated. When the feature is enabled, the reconstruction process may use an image mask to focus on the object of interest and/or exclude objects that are not of interest for the reconstruction process. [emphasis added].
Therefore, it is clear that Becker discloses “determining whether a specific point cloud is included in a second point cloud to obtain a determination result, the specific point cloud being a three-dimensional point cloud, and the second point cloud being a three-dimensional point cloud obtained by excluding the first three-dimensional point cloud from the target point cloud.
On page 13 applicant states: The
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The Examiner respectfully disagrees. Becker discloses “identify, based on labeled training data, a first three-dimensional point cloud in a target point cloud, [the first three-dimensional point cloud corresponding to the labeled training data, and the target point cloud being a three-dimensional point cloud]”, as recited in claim 1. Becker discloses generating 3D representation of a 3D object at paragraph [0015]. At paragraph [0029] Becker discloses “a second three-dimensional object 322 (e.g., bicycle) different than the three-dimensional object 320. Although not shown in FIG. 3, the images may also capture other objects or aspects of the environment (e.g., floors, walls, trees, doors, sky, mountains, etc.)”. These paragraphs are considered to have taught “identify…a first three-dimensional point cloud in a target point cloud”. The Examiner agrees that Becker might not expressly teach the amended part of claim 1, “the first three-dimensional point cloud corresponding to the labeled training data, and the target point cloud being a three-dimensional point cloud”. However, the newly cited reference, US2024/0177412 of Ranganath et al, is used in the rejection below that teaches this limitation in combination.
Applicant’s arguments with respect to claims 2, 4, 6, 9, 11, 13, 15, and 19 have been considered but are moot because the new ground of rejection using newly cited reference US2024/0177412 of Ranganath et al does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Regarding the arguments under Becker, similar analysis applies here as it did above relate to claim 1.
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 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 of this title, 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.
Claims 1, 3, 5, 7, 8, 10,12, 14, 16, 17, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0377300 to Becker et al. (hereafter, “Becker”) in combination with US 2024/0177412 to Ranganath et al. (hereafter, “Ranganath”).
With regard to claim 1 Becker discloses a point cloud processing apparatus (Figures 1-2), comprising circuitry configured to: identify, based on labeled training data, a first three-dimensional point cloud in a target point cloud [the first three-dimensional point cloud corresponding to the labeled training data, and the target point cloud] being a three-dimensional point cloud (paragraph [0015], “generating the three-dimensional representation of the three-dimensional object can include generating a first representation of the three-dimensional object (e.g., a point cloud)”); and determine whether a specific point cloud is included in a second point cloud to obtain a determining result, the specific point cloud being a three-dimensional point cloud, and the second point cloud being a three-dimensional point cloud obtained by excluding the first three-dimensional point cloud from the target point cloud (paragraph [0029], “ the images include one or more additional objects from the capture environment that are not part of the three-dimensional object of interest. For instance, image 302, image 304, image 306, and image 308, each includes three-dimensional object 320 (e.g., tool table) but also includes a second three-dimensional object 322 (e.g., bicycle) different than the three-dimensional object 320. Although not shown in FIG. 3, the images may also capture other objects or aspects of the environment (e.g., floors, walls, trees, doors, sky, mountains, etc.). As described herein, in some examples, the other objects or aspects of the environment may be excluded from the reconstruction process using a bounding box…machine learning or artificial intelligence can be implemented (e.g., as part of the processing circuitry of the computing system) to analyze the images to identify objects or regions to exclude or to identify objects or regions to include for model reconstruction”; paragraph [0039]).
However, Becker does not teach the first three-dimensional point cloud corresponding to the labeled training data, and the target point cloud [being a three-dimensional point cloud]. Ranganath teaches the first three-dimensional point cloud corresponding to the labeled training data (labeled objects 402), and the target point cloud (scene 406 with no objects 402) [being a three-dimensional point cloud] (Fig. 4A, paragraph [0086]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify Becker’s reference to have first three-dimensional point cloud corresponding to the labeled training data of Ranganath’s reference. The suggestion/motivation for doing so would have been to create a variety of scenes represented by point cloud data, and the approach of decoupling foreground objects from the map can make it easier to generate training data for new background scenes and/or new objects, suggested by Ranganath at paragraph [0087].
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Ranganath with Becker to obtain the invention as specified in claim 1.
With regard to claim 3 Becker discloses wherein the circuitry is configured to determine a position of the specific point cloud in the target point cloud (paragraphs [0039, 0052-0053, 0098, 0123, etc.]).
With regard to claim 5 Becker discloses wherein the circuitry is configured to transmit the determination result to an external apparatus (communication circuitry 120; bus 122, 123; paragraph [0020]; model gets created and then outputted, Fig. 5, paragraph [0040]; 3004-3006 in Fig. 30 and 3104 in Fig. 31).
With regard to claim 7 Becker discloses wherein the circuitry is configured to display the determination result on a display (display 106, 107, paragraphs [0020, 0022, 0028, 0034, 0036, etc.]; 3004-3006 in Fig. 30 and 3104 in Fig. 31).
With regard to claim 8 Becker discloses wherein the target point cloud is set based on an operation received on a screen, the operation being for designating one or more three-dimensional point clouds (paragraphs [0032, 0034-0038, 0041, 0044-0045, etc.).
With regard to claim 10 Becker discloses wherein the specific point cloud is set based on an operation received on a screen, the operation being for designating one or more three-dimensional point clouds (specific being repositioning the vase, for example, paragraph [0038-0041]).
With regard to claim 12 Becker discloses wherein the labeled training data includes one or more types of labeled training data, the circuitry is configured to identify the first three-dimensional point cloud corresponding to one of the one or more types of labeled training data, based on a data designation operation received on a data reception screen, the data designation operation being for designating the one or more types of labeled training data (paragraphs [0029, 0032-0045, etc.]).
With regard to claim 14 Becker discloses wherein the circuitry is configured to identify the first three-dimensional point cloud in the specific point cloud based on the labeled training data (paragraph [0029]).
With regard to claim 16 Becker discloses wherein the circuitry is configured to transmit, to an external apparatus, a result obtained by identifying the first three-dimensional point cloud in the specific point cloud (model gets created and then outputted, Fig. 5, paragraph [0040]; 3004-3006 in Fig. 30 and 3104 in Fig. 31).
With regard to claim 17 Becker discloses wherein the circuitry is configured to display, on a display, a result obtained by identifying the first three-dimensional point cloud in the specific point cloud (display 106, 107, paragraphs [0020, 0022, 0028, 0034, 0036, etc.]; 3004-3006 in Fig. 30 and 3104 in Fig. 31).
With regard to claim 18 Becker discloses the point cloud processing apparatus of claim 1; and a terminal apparatus communicably connected to the point cloud processing apparatus (Fig. 1, paragraphs [0019-0025, 0033-0035), wherein the circuitry of the point cloud processing apparatus is configured to transmit the determination result to the terminal apparatus (paragraphs [0033-0035]), and the terminal apparatus includes additional circuitry configured to, receive the determination result transmitted from the point cloud processing apparatus, and display the determination result on a display (Fig. 1, paragraphs [0019-0025]).
With regard to claim 20, claim 20 is rejected same as claim 1 and the arguments similar to that presented above for claim 1 are equally applicable to claim 20, and all of the other limitations similar to claim 1 are not repeated herein, but incorporated by reference.
Claims 2, 4, 6, 9, 11, 13, 15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0377300 to Becker et al. (hereafter, “Becker”) in combination with US 2024/0177412 to Ranganath et al. (hereafter, “Ranganath”), and further in view of US 2007/0136288 To Shimada et al. (hereafter, “Shimada”).
With regard to claim 2, Becker teaches the point cloud processing apparatus as disclosed above in claim 1 (and not repeated herein) including identifying based on labeled training data and the first three-dimensional point cloud in a target point cloud being a three-dimensional point cloud. However, Becker does not teach that the identifying is based on a data category and the first three-dimensional point cloud corresponding to the data on the category. Also, as seen in claim 1, Ranganath teaches the first three-dimensional point cloud corresponding to the data on the category (categorial data type, paragraph [0158]; and classifying objects at paragraphs [0006, 0041, 0078, 0086-0087, 0097, etc.],
Neither Becker nor Ranganath teaches [identifying is based on a] data category (Fig. 30 onwards, paragraphs [0263-0264, 0270-0271, 0299-0301, etc. throughout the reference).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify Becker’s and Ranganath’s reference to have categories of Shimada’s reference. The suggestion/motivation for doing so would have been to have document/image/objects containing multiple topics and meanings be classified into categories so that the classifications do not different from categories desired by a user, suggested by Shimada at paragraph [0019], improving usefulness of the classification categories at paragraph [0045].
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Shimada with Ranganath and Becker to obtain the invention as specified in claim 2.
With regard to claim 4 Becker discloses wherein the circuitry is configured to determine a position of the specific point cloud in the target point cloud (paragraphs [0039, 0052-0053, 0098, 0123, etc.]).
With regard to claim 6 Becker discloses wherein the circuitry is configured to transmit the determination result to an external apparatus (communication circuitry 120; bus 122, 123; paragraph [0020]; model gets created and then outputted, Fig. 5, paragraph [0040]; 3004-3006 in Fig. 30 and 3104 in Fig. 31).
With regard to claim 9 Becker discloses wherein the target point cloud is set based on an operation received on a screen, the operation being for designating one or more three-dimensional point clouds (paragraphs [0032, 0034-0038, 0041, 0044-0045, etc.).
With regard to claim 11 Becker discloses wherein the specific point cloud is set based on an operation received on a screen, the operation being for designating one or more three-dimensional point clouds (paragraphs [0032, 0034-0038, 0041, 0044-0045, etc.).
With regard to claim 13 Becker in combination with Shimada discloses wherein the category includes one or more types of categories; and the circuitry is configured to identify the first three-dimensional point cloud corresponding to the data on the one or more types of categories based on a data designation operation received on a data reception screen, the data designation operation being for designating the one or more types of categories (paragraphs [0029, 0032-0045, etc.]; Shimada having the support for categories).
With regard to claim 15 Becker discloses wherein the circuitry is configured to identify the first three-dimensional point cloud in the specific point cloud based on the data on the category (paragraph [0029]).
With regard to claim 19 Becker discloses the point cloud processing apparatus of claim 2; and a terminal apparatus communicably connected to the point cloud processing apparatus (Fig. 1, paragraphs [0019-0025, 0033-0035), wherein the circuitry of the point cloud processing apparatus is configured to transmit the determination result to the terminal apparatus (paragraphs [0033-0035]), and the terminal apparatus includes additional circuitry configured to, receive the determination result transmitted from the point cloud processing apparatus, and display the determination result on a display (Fig. 1, paragraphs [0019-0025]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2023/0146926 discloses the map generation system configured to apply machine learning to identify features for removal. For example, the map generation system may execute a machine learning algorithm or use a trained neural network to classify objects represented in sensor data according to a set of predefined categories (person, tree, road, lane, vehicle, etc.) and to identify the boundaries of each object. The machine learning algorithm or neural network may be pretrained using supervised learning on sensor data (e.g., camera images and/or LIDAR point clouds) for which the locations and boundaries of objects are known. Alternatively, unsupervised learning techniques may be used. The input to the machine learning algorithm or neural network can, for example, be a 3D representation and a corresponding 2D representation depicting the same location. The output of the machine learning or neural network can be a labeled or segmented 3D representation that depicts different classes of objects and the boundaries of such objects.
US11,399,531 discloses labeled data that may be used to train the ML models. The labeled data may include exclusion targets (e.g., objects that are to be identified as being desirable or high value objects, while everything else being considered low value or undesirable), or inclusion targets (e.g., objects that are identified as targets for elimination by treatment, while everything else is to be preserved). The labeled data may provide “ground truth” images that are known to, for sure, show weeds and/or desirable vegetation. The training data may also use raw data that is not labeled in order for ML algorithm is able to train on. In some embodiments, ML models can be models configured to detect salient points or cluster of points of a given image or point cloud. In one example, unsupervised learning can be applied to train machine learning models to analyze unlabeled data. Additionally, an ML model can be configured to detect a certain spectrum of color, or a certain shape that are of interest in an agricultural environment, particularly static objects such as tree trunks, row farm beds and troughs, etc.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHEFALI D. GORADIA whose telephone number is (571)272-8958. The examiner can normally be reached Monday-Thursday 8AM-6PM, Friday 8AM-12PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Henok Shiferaw can be reached at 571-272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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SHEFALI D. GORADIA
Primary Patent Examiner
Art Unit 2676
/SHEFALI D GORADIA/Primary Patent Examiner, Art Unit 2676
