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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 11/27/2023 and 01/23/2025 were filed after the mailing date of the Non-final Rejection on December 1st, 2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
In paragraph 0031, there is a missing reference number for the example 2D image (402).
Appropriate correction is required.
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 2, 6-9, 11, 15-18 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 2, the applicant recites the phrase “the selected handling action” in line 7. It is unclear from the specification and claim language whether the claimed “selected handling action” is referring to the previous limitations recited in “(i) suppressing delivery of the three-dimensional image…” or step “(ii) delivering the three-dimensional image…”, or to some other handling action. Therefore, the applicant has failed to particularly point out and distinctly claim the subject matter which the inventor regards as the invention. Further clarification must be provided to explain what constitutes “the selected handling action”.
Claim 6 recites the limitation "suppressing delivery" in line 2. There is insufficient antecedent basis for this limitation in the claim. “Suppressing delivery” of the three-dimensional image to the dimensioning module is first recited in claim 2. It appears as though claim 6 may have been intended to depend from claim 2 that first recites the limitation of “suppressing delivery”.
The term “substantially simultaneously” in claim 7, on line 2, is a relative term which renders the claim indefinite. The term “substantially simultaneously” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The term of degree for this phrase is defined by the specification but is only presented as a range of options, and renders this claim indefinite.
Claim 8 recites the limitation "the handling action" in line 5. There is insufficient antecedent basis for this limitation in the claim. It is unclear what definitively constitutes this handling action. Here, “the handling action” is first recited in claim 2. It appears as though claim 8 may have been intended to depend from claim 2 that first recites the limitation of “the handling action”.
Claim 9 recites the limitation "the handling action" in line 1. There is insufficient antecedent basis for this limitation in the claim. It is unclear what definitively constitutes this handling action. Here, “the handling action” is first recited in claim 2. It appears as though claim 9 may have been intended to depend from claim 2 that first recites the limitation of “the handling action”.
Regarding claim 11, the applicant recites the phrase “the selected handling action” in line 7. It is unclear from the specification and claim language whether the claimed “selected handling action” is referring to the previous limitations recited in “(i) suppressing delivery of the three-dimensional image…” or step “(ii) delivering the three-dimensional image…”, or to some other handling action. Therefore, the applicant has failed to particularly point out and distinctly claim the subject matter which the inventor regards as the invention.
Claim 15 recites the limitation "suppressing delivery" in line 2. There is insufficient antecedent basis for this limitation in the claim. “Suppressing delivery” of the three-dimensional image to the dimensioning module is first recited in claim 11. It appears as though claim 15 may have been intended to depend from claim 11 that first recites the limitation of “suppressing delivery”.
The term “substantially simultaneously” in claim 16, on line 2, is a relative term which renders the claim indefinite. The term “substantially simultaneously” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The term of degree for this phrase is defined by the specification but is only presented as a range of options, and renders this claim indefinite.
Claim 17 recites the limitation "the handling action" in line 5. There is insufficient antecedent basis for this limitation in the claim. It is unclear what definitively constitutes this handling action. Here, “the handling action” is first recited in claim 11. It appears as though claim 17 may have been intended to depend from claim 11 that first recites the limitation of “the handling action”.
Claim 18 recites the limitation "the handling action" in line 1. There is insufficient antecedent basis for this limitation in the claim. It is unclear what definitively constitutes this handling action. Here, “the handling action” is first recited in claim 11. It appears as though claim 18 may have been intended to depend from claim 11 that first recites the limitation of “the handling action”.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 is reproduced below with additional annotations:
A method, comprising:
capturing, via a sensor of a computing device, a three-dimensional image corresponding to an object;
detecting a region of interest in the three-dimensional image, the region of interest corresponding to an obstruction;
determining a depth from the sensor to the region of interest;
comparing the determined depth to a threshold;
determining whether to deliver the three-dimensional image to a dimensioning module, based on the comparison of the depth of the obstruction with the threshold.
Analysis of claim 1 for subject matter eligibility under the Alice/Mayo framework is as follows:
Step 1: Evaluating whether the claim belongs to one of the statutory categories.
Claim 1 recites a plurality of actions directed to a process, which falls under one of the statutory categories of invention. (Step 1: Yes).
Step 2A Prong One: Evaluating whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon). If no exception is recited, the claim is eligible. This concludes the eligibility analysis. If the claim recites an exception, go to Step 2A Prong Two.
Independent claim 1 recites a plurality of mental processes, which fall under the category of abstract ideas. Steps c-f of claim 1, under broadest reasonable interpretation, recites processes that could be practically performed in the human mind. (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.).
Step c describes observing a region of interest corresponding to an obstruction in a three-dimensional image; the human mind is capable of recognizing an obstruction in a three-dimensional mental image observed through a pair of eyes (analogous to a sensor).
Step d describes determining a depth from a sensor to a region of interest; the human mind is capable of approximating the distance of an object from the eyes.
Step e describes comparing the observed depth to a threshold; the human mind can readily perform the same comparison, either to an exact degree with pen and paper, or to an approximate degree mentally.
Step f describes determining whether to deliver a three-dimensional image to a dimensioning module for dimensioning depending on the depth comparison; a human mind can readily make that judgement.
These steps would fall into the “mental processes” group of abstract ideas – specifically, observation, evaluation, and/or judgement. The limitations, interpreted under their broadest reasonable interpretation in consistence with the specification, cover performance of the limitations in the mind or by generic computer components. See MPEP 2106.04 and the 2019 PEG. (Step 2A Prong One: Yes).
Step 2A Prong Two: Evaluating whether the claim recites additional elements that integrate the exception into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
In claim 1, step b recites an additional element – capturing via a sensor of a computer device, a three-dimensional image corresponding to an object. This additional element does not integrate the exception into a practical application. Note, even if the specification discloses that the invention pertains to an improvement in technology, the claim must be evaluated to ensure the claim itself reflects the improvement in technology. It is also important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. Capturing a three-dimensional image belongs to data collection and acquisition. The recited “sensor” is merely a generic image capturing device. This additional element merely adds insignificant extra-solution activities to the judicial exception, and does not integrate the abstract idea into a practical application. See MPEP 2106.05(g). (Step 2A Prong Two: No).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim.
In claim 1, step b is regarded as an additional element – capturing via a sensor of a computer device, a three-dimensional image corresponding to an object – under the broadest reasonable interpretation, this is recognized as an insignificant extra-solution activity that is well-understood, routine, and conventional (WURC) activity in the field. This additional element, does not result in the claim, as a whole, amounting to significantly more than an abstract idea. See MPEP 2106.05(d). (Step 2B: No). This claim is not eligible.
Independent claim 10, which recites a computing device, is directed to a machine, which is a statutory category of invention. Similar analysis is applicable to claim 10 since the recited function is substantially identical.
Claim 10 further recites additional elements – a sensor; and a processor. The sensor and the processor are recited at a high level of generality such that they do not amount to more than generic computer system elements. The claim does not direct to a specific improvement in computers in their communication role or provides a specific improvement in the way computers operate. These limitations generally link the use of the abstract idea to the environment of digital image processing. These additional elements neither integrate the abstract idea into a practical application of the abstract idea, nor result in the claim as a whole amounting to significantly more than the abstract idea. Therefore, claim 10 is not eligible.
Claims 2 and 11 recite, “determining whether to deliver the three-dimensional image to the dimensioning module includes:
suppressing delivery of the three-dimensional image to a dimensioning module when the depth is below the threshold, and
delivering the three-dimensional image to the dimensioning module, to obtain dimensions for the object, when the depth exceeds the threshold; and
executing the selected handling action.”
These claims recite contingent limitations. Examiner considers path (i), which recites mental processes. Further claim 2 does not recite additional elements. Similar analysis is applicable to claim 11 which recites substantially identical function. Claims 2 and 11 are not eligible.
Claims 3 and 12 recite “detecting the region of interest includes performing a segmentation operation on the three-dimensional image”. Under the broadest reasonable interpretation, this act can be performed in human mind and therefore recites mental processes. Further claim 3 does not recite additional elements. Similar analysis is applicable to claim 12 which recites substantially identical function. Claims 3 and 12 are not eligible.
Claims 4 and 13 recite the following: “determining a size of the region of interest; and determining that the size exceeds a size threshold prior to determining the depth”. Both acts are directed to abstract ideas with no additional elements, specifically the mental processes of evaluations and judgements. Similar analysis is applicable to claim 13 which recites substantially identical function. Claims 4 and 13 are not eligible.
Claims 5 and 14 recite “wherein the threshold corresponds to a minimum depth setting of the sensor”. These additional elements provide supplement information about the threshold. Under the broadest reasonable interpretation, a person can obtain this value from the manual of the sensor, or can figure it out by trial and error since a depth below the minimum depth setting of the sensor may result in a blurred image. These additional elements, neither integrate the judicial exception into a practical application, nor amount to significantly more than the judicial exception. Similar analysis is applicable to claim 14 which recites substantially identical function. Claims 5 and 14 are not eligible.
Claims 6 and 15 recite “in response to suppressing delivery of the three-dimensional image, determining whether delivery to the dimensioning module has been suppressed for a predetermined number of consecutive three-dimensional images; and when delivery to the dimensioning module has been suppressed for the predetermined number of consecutive three-dimensional images, generating a notification via an output of the computing device”. The “determining” act recites mental processes. The “generating” act recites additional elements of outputting data, an insignificant extra-solution activity that is well-understood, routine, and conventional (WURC) in the field. These additional elements, neither integrate the judicial exception into a practical application, nor amount to significantly more than the judicial exception. Similar analysis is applicable to claim 15 which recites substantially identical function. Claims 6 and 15 are not eligible.
Claims 7 and 16 recite the following: “capturing a two-dimensional image via a sensor”, under the broadest reasonable interpretation, can be understood as an insignificant extra-solution activity that is well-understood, routine, and conventional (WURC) activity in the field. Claim 7 further recites: “selecting the notification based on whether the two-dimensional image depicts at least a portion of the region of interest”. These elements are directed to the mental processes of evaluations and judgements, which are abstract ideas. These additional elements, neither integrate the judicial exception into a practical application, nor amount to significantly more than the judicial exception. Similar analysis is applicable to claim 16 which recites substantially identical function. Claims 7 and 16 are not eligible.
Claim 8 and 17 recite the following: “capturing, via a sensor, with the three-dimensional image, an intensity value associated with the region of interest”. Under the broadest reasonable interpretation, this act recites data acquisition, which is an insignificant extra-solution activity that is well-understood, routine, and conventional (WURC) activity in the field.
Claims 8 and 17 further recite: “comparing the intensity value to a second threshold; and selecting the handling action based on the comparison of the depth with the threshold, and the comparison of the intensity with the second threshold”. These elements are directed to abstract ideas, specifically the mental processes of evaluations and judgements. These additional elements, neither integrate the judicial exception into a practical application, nor amount to significantly more than the judicial exception. Similar analysis is applicable to claim 17 which recites substantially identical function. Claims 8 and 17 are not eligible.
Claims 9 and 18 recite “when the depth exceeds the threshold and the intensity is below the second threshold, delivering the three-dimensional image to the dimensioning module”. The phrase “when the depth exceeds the threshold and the intensity is below the second threshold” involves mental processes of evaluation and judgement. The “delivering” act belongs to data output, an insignificant extra-solution activity that is well-understood, routine, and conventional (WURC) in the field. These additional elements, neither integrate the judicial exception into a practical application, nor amount to significantly more than the judicial exception. Similar analysis is applicable to claim 18 which recites substantially identical function. Claims 9 and 18 are not eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 6, 7, 10-12, 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma et al (US 2012/0087573 A1) in view of Ajamian et al (US 2023/0308746 A1).
Regarding claim 1, Sharma teaches a method of identifying obstructions in three-dimensional depth images. More specifically as it relates to the applicants claims, Sharma discloses a method, comprising: capturing, via a sensor of a computing device, a three-dimensional image corresponding to an object (depth camera 400 in Fig. 4 and ¶ 0031, and 3D imaging system 402 in Fig. 4; and ¶ 0031), detecting a region of interest in the three-dimensional image, the region of interest corresponding to an obstruction (¶ 0052: detection of foreground blobs (an obstruction) in the depth image (step 600 in Fig. 6)), determining a depth from the sensor to the region of interest (¶ 0053: a computed depth indicative of the distance between the camera and the blob (step 602 in Fig. 6), comparing the determined depth to a threshold (¶0053: comparing the depth of the blob to a threshold (step 604 in Fig. 6)), based on the comparison of the depth of the obstruction with the threshold (¶ 0053: and if the depth of the blob is not greater than a depth threshold (step 604), marking it as clutter (step 606 in Fig. 6)).
Sharma et al fails to disclose determining whether to deliver the three-dimensional image to a dimensioning module. Ajamian et al is analogous art relevant to the technical field and problems addressed in this application and teaches a method for validating time-of flight images for performing object dimensioning (¶ 0040; step 400 in Fig. 4) and determining whether the images are sufficient for object dimensioning (¶ 0049; step 414 in Fig. 4). The obstruction detection method described in Sharma could be utilized to determine image sufficiency for object dimensioning. Ajamian further discloses that this feature serves to reduce the total time for and improve the accuracy of measuring the sizes of multiple items across shipping, storing, or moving industries (¶ 0002). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of this application to modify the teachings of Sharma for the application of dimensioning three-dimensional objects.
Regarding claim 2, mentioned above in the discussion of claim 1, Sharma et al in view of Ajamian et al teach all of the limitations of the parent claim. Sharma further teaches a binary decision based on the comparison to a depth threshold. More specifically, Sharma discloses (i) … when the depth is below the threshold; and (ii) … when the depth exceeds the threshold (¶ 0053: when the blob is not greater than a threshold (step 604), the blob is marked as clutter (step 606 in Fig. 6)).
Ajamian et al further teaches wherein determining whether to deliver the three-dimensional image to the dimensioning module (¶ 0049: determining whether images are sufficient for object dimensioning (step 414 in Fig. 4)), (i) suppressing delivery of the three-dimensional image to a dimensioning module when the depth is below the threshold (¶ 0050: a capture verifying component (element 158 of Fig. 1) that determines a given image is insufficient for dimensioning (step 414 in Fig. 4), (ii) delivering the three-dimensional image to the dimensioning module, to obtain dimensions for the object (¶ 0049: the method 400 can include, at step 414, determining the images are sufficient for object dimensioning (in Fig. 4)), and executing the selected handling action (¶ 0050-51: and proceeding with either object dimensioning (step 416) or not (step 412; in Fig. 4)). Ajamian further discloses that this feature serves to reduce the total time for and improve the accuracy of measuring the sizes of multiple items across shipping, storing, or moving industries (¶ 0002). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of this application to modify the teachings of Sharma for the application of dimensioning three-dimensional objects.
Regarding claim 3, mentioned above in the discussion of claim 1, Sharma et al in view of Ajamian et al teach all of the limitations of the parent claim. Sharma further teaches a method of segmenting a three-dimensional image. More specifically, Sharma discloses wherein detecting the region of interest includes performing a segmentation operation on the three-dimensional image (¶ 0052: to detect foreground blobs, background subtraction is performed between the depth image, and a depth background model of an observed scene to generate a binary mask image. The generation of a binary mask is a type of image segmentation.
Regarding claim 6, Sharma et al in view of Ajamian et al teach the obstruction detection method based on a comparison to a depth threshold for the use of dimensioning an object in a three-dimensional image according to claim 1 (as described previously). Additionally, Ajamian et al further teaches a system for collecting multiple images of an environment having one or more objects. More specifically as it relates to the applicants claims, Ajamian discloses in response to suppressing delivery of the three-dimensional image, determining whether delivery to the dimensioning module has been suppressed for a predetermined number of consecutive three-dimensional images (collecting pairs of images in back-to-back TOF frames can provide improved signal-to-noise ratio (SNR) for the pair of images (¶ 0035) and determining whether enough images are sufficient for object dimensioning (¶0049; element 414 in Fig. 4)), when delivery to the dimensioning module has been suppressed for the predetermined number of consecutive three-dimensional images, generating a notification via an output of the computing device (and if it is determined that the images are not sufficient for dimensioning at 414, method 400 can process back to step 402 to display the prompt (¶0050; elements of Fig. 4)). Ajamian further discloses that this feature serves to reduce the total time for and improve the accuracy of measuring the sizes of multiple items across shipping, storing, or moving industries (¶ 0002). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of this application to modify the teachings of Sharma for the application of dimensioning three-dimensional objects.
Regarding claim 7, Sharma et al in view of Ajamian et al teach the obstruction detection method based on a comparison to a depth threshold for the use of dimensioning an object in a three-dimensional image according to claim 1 (as described previously).
Sharma et al additionally teaches capturing, via a second sensor, a two-dimensional image substantially simultaneously with the three-dimensional image (a depth camera that also captures human-viewable two-dimensional images of the scene in addition to the depth image (¶ 0056), with foreground blobs detected in the two-dimensional image (¶ 0057; element 700 in Fig. 7)), and based on whether the two-dimensional image depicts at least a portion of the region of interest (and that the extracted features [of the two-dimensional image], are then used to determine if the blob is sufficiently similar to an expected object (¶ 0057; element 704 in Fig. 7)).
Sharma does not teach the selection of a notification based on the aforementioned similarity comparison. Ajamian et al further teaches selecting the notification (that when the images are not sufficient for dimensioning at 414, method 400 can process back to 402 to display the prompt (¶ 0050; Fig. 4)). Ajamian further discloses that this feature serves to reduce the total time for and improve the accuracy of measuring the sizes of multiple items across shipping, storing, or moving industries (¶ 0002). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of this application to modify the teachings of Sharma for the application of dimensioning three-dimensional objects.
Regarding claim 10, Sharma teaches a method of identifying obstructions in three-dimensional depth images. More specifically as it relates to the applicants claims, Sharma discloses a computing device (the computer 310 ins configured to receive video sequence(s) from one or more video surveillance cameras (¶ 0029; elements in Fig. 3), a sensor (depth camera (¶ 0031; element 400 in Fig. 4)), a processor configured to: capture via the sensor (the components of the depth camera 400 may be implemented in any suitable combination of software, firmware, and hardware, such as, for example, one or more digital signal processors… stored in the memory component 410 (¶0030; elements in Fig. 4), a three-dimensional image corresponding to an object (3D imaging system 402 in Fig. 4; and ¶ 0031), detect a region of interest in the three-dimensional image, the region of interest corresponding to an obstruction (¶ 0052: detection of foreground blobs (an obstruction) in the depth image (step 600 in Fig. 6)), determine a depth from the sensor to the region of interest (¶ 0053: a computed depth indicative of the distance between the camera and the blob (step 602 in Fig. 6), compare the determined depth to a threshold (¶0053: comparing the depth of the blob to a threshold (step 604 in Fig. 6)), based on the comparison of the depth of the obstruction with the threshold (¶ 0053: and if the depth of the blob is not greater than a depth threshold (step 604), marking it as clutter (step 606 in Fig. 6)).
Sharma et al fails to disclose determining whether to deliver the three-dimensional image to a dimensioning module. Ajamian et al is analogous art relevant to the technical field and problems addressed in this application and teaches a method for validating time-of flight images for performing object dimensioning (¶ 0040; step 400 in Fig. 4) and determining whether the images are sufficient for object dimensioning (¶ 0049; step 414 in Fig. 4). The obstruction detection method described in Sharma could be utilized to determine image sufficiency for object dimensioning. Ajamian further discloses that this feature serves to reduce the total time for and improve the accuracy of measuring the sizes of multiple items across shipping, storing, or moving industries (¶ 0002). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of this application to modify the teachings of Sharma for the application of dimensioning three-dimensional objects.
Regarding claim 11, mentioned above in the discussion of claim 1, Sharma et al in view of Ajamian et al teach all of the limitations of the parent claim. Sharma further teaches a binary decision based on the comparison to a depth threshold. More specifically, Sharma discloses (i) … when the depth is below the threshold; and (ii) … when the depth exceeds the threshold (¶ 0053: when the blob is not greater than a threshold (step 604), the blob is marked as clutter (step 606 in Fig. 6)).
Ajamian et al further teaches to determine whether to deliver the three-dimensional image to the dimensioning module (¶ 0049: determining whether images are sufficient for object dimensioning (step 414 in Fig. 4)), (i) suppressing delivery of the three-dimensional image to a dimensioning module when the depth is below the threshold (¶ 0050: a capture verifying component (element 158 of Fig. 1) that determines a given image is insufficient for dimensioning (step 414 in Fig. 4), (ii) delivering the three-dimensional image to the dimensioning module, to obtain dimensions for the object (¶ 0049: the method 400 can include, at step 414, determining the images are sufficient for object dimensioning (in Fig. 4)), and executing the selected handling action (¶ 0050-51: and proceeding with either object dimensioning (step 416) or not (step 412; in Fig. 4)). Ajamian further discloses that this feature serves to reduce the total time for and improve the accuracy of measuring the sizes of multiple items across shipping, storing, or moving industries (¶ 0002). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of this application to modify the teachings of Sharma for the application of dimensioning three-dimensional objects.
Regarding claim 12, mentioned above in the discussion of claim 1, Sharma et al in view of Ajamian et al teach all of the limitations of the parent claim. Sharma further teaches a method of segmenting a three-dimensional image. More specifically, Sharma discloses detect the region of interest includes performing a segmentation operation on the three-dimensional image (¶ 0052: to detect foreground blobs, background subtraction is performed between the depth image, and a depth background model of an observed scene to generate a binary mask image. The generation of a binary mask is a type of image segmentation.
Regarding claim 15, Sharma et al in view of Ajamian et al teach the obstruction detection method based on a comparison to a depth threshold for the use of dimensioning an object in a three-dimensional image according to claim 1 (as described previously). Additionally, Ajamian et al further teaches a system for collecting multiple images of an environment having one or more objects. More specifically as it relates to the applicants claims, Ajamian discloses in response to suppressing delivery of the three-dimensional image, determine whether delivery to the dimensioning module has been suppressed for a predetermined number of consecutive three-dimensional images (collecting pairs of images in back-to-back TOF frames can provide improved signal-to-noise ratio (SNR) for the pair of images (¶ 0035) and determining whether enough images are sufficient for object dimensioning (¶0049; element 414 in Fig. 4)), when delivery to the dimensioning module has been suppressed for the predetermined number of consecutive three-dimensional images, generate a notification via an output of the computing device (and if it is determined that the images are not sufficient for dimensioning at 414, method 400 can process back to step 402 to display the prompt (¶0050; elements of Fig. 4)). Ajamian further discloses that this feature serves to reduce the total time for and improve the accuracy of measuring the sizes of multiple items across shipping, storing, or moving industries (¶ 0002). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of this application to modify the teachings of Sharma for the application of dimensioning three-dimensional objects.
Regarding claim 16, Sharma et al in view of Ajamian et al teach the obstruction detection method based on a comparison to a depth threshold for the use of dimensioning an object in a three-dimensional image according to claim 1 (as described previously).
Sharma et al additionally teaches capture, via a second sensor, a two-dimensional image substantially simultaneously with the three-dimensional image (a depth camera that also captures human-viewable two-dimensional images of the scene in addition to the depth image (¶ 0056), with foreground blobs detected in the two-dimensional image (¶ 0057; element 700 in Fig. 7)), and based on whether the two-dimensional image depicts at least a portion of the region of interest (and that the extracted features [of the two-dimensional image], are then used to determine if the blob is sufficiently similar to an expected object (¶ 0057; element 704 in Fig. 7)).
Sharma does not teach the selection of a notification based on the aforementioned similarity comparison. Ajamian et al further teaches select the notification (that when the images are not sufficient for dimensioning at 414, method 400 can process back to 402 to display the prompt (¶ 0050; Fig. 4)). Ajamian further discloses that this feature serves to reduce the total time for and improve the accuracy of measuring the sizes of multiple items across shipping, storing, or moving industries (¶ 0002). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of this application to modify the teachings of Sharma for the application of dimensioning three-dimensional objects.
Claims 4, 8, 9, 13, 17, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma et al (US 2012/0087573 A1) in view of Ajamian et al (US 2023/0308746 A1) further in view of Eldar et al (US 2013/0206608 A1).
Regarding claim 4, Sharma et al in view of Ajamian et al teach the obstruction detection method based on a comparison to a depth threshold for the use of dimensioning an object in a three-dimensional image according to claim 1 (as described previously).
Sharma et al in view of Ajamian et al does not teach an additional step in identifying obstructions by comparing the size of a region of interest (corresponding to an obstruction), to a size threshold. Eldar et al is analogous art relevant to the technical field and problems addressed in this application and teaches a method of obstruction detection. More specifically, Eldar discloses wherein detecting the region of interest further comprises: determining a size of the region of interest; and determining that the size exceeds a size threshold prior to determining the depth (predicting whether a presented image includes a representation of a blockage may include determining whether a presented image includes a region of pixels associated with respective indicators that the respective pixels are associated with a representation of a blockage and that the region is at least a threshold size ((¶ 0373; process 2700 in Fig. 27)).
Eldar further recites that an image region with a blockage may be a portion of an image that the model predicted to have pixels indicative of a blockage (¶ 0373) and establishes a need for systems to accurately, consistently, and efficiently identify and respond to image blockages (¶ 0357). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Sharma and Ajamian to incorporate the teachings of Eldar to include a step to determine the size of an obstruction and compare that to a threshold before proceeding with object dimensioning. Doing so would further validate the efficiency and accuracy of the detection of an obstruction. Adding an extra comparison to minimize false positives that could arrive from obstructions that may be too close to the sensor (in relation to the depth threshold), but not large enough (in relation to the size threshold) to not block the field of view for dimensioning the object of interest.
Regarding claim 8, Sharma et al in view of Ajamian et al teach the obstruction detection method based on a comparison to a depth threshold for the use of dimensioning an object in a three-dimensional image according to claim 1 (as described previously).
Sharma et al again teaches based on comparison of the depth with the threshold (comparing the depth of the blob to a threshold (¶ 0053; element 604)). Ajamian et al teaches selecting the handling action (proceeding with either object dimensioning (416) or not (412) (¶ 0050-51; elements in Fig. 4)). Sharma et al in view of Ajamian et al does not teach capturing an associated intensity value corresponding to the region of interest, and comparing that intensity to a second threshold and determining an appropriate handling action as a result of that comparison.
Eldar et al teaches capturing, via the sensor, with the three-dimensional image, an intensity value associated with the region of interest (a blockage indicator associated with each of the plurality of training images, each blockage indicator representing a presence of a blockage or an absence of a blockage; or an analysis of intensities of pixels located at corresponding pixel coordinates of the plurality of training images (¶ 0380; step 3120 in Fig. 31)), and comparing the intensity value to a second threshold (Determining the at least one output value may include computing at least one feature based on using information determined as part of the analysis of the intensities of the pixels (¶ 0368), and model may be further configured to compare the at least one output value to a threshold (¶ 0369)).
Eldar further recites that an image region with a blockage may be a portion of an image that the model predicted to have pixels indicative of a blockage (¶ 0373) and establishes a need for systems to accurately, consistently, and efficiently identify and respond to image blockages (¶ 0357). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Sharma and Ajamian to incorporate the teachings of Eldar to include a step to determine the pixel intensity of an obstruction and compare that to a threshold before proceeding with object dimensioning. Doing so would further validate the efficiency and accuracy of the detection of an obstruction. Adding an extra comparison to minimize false positives that could arrive from obstructions that may be too close to the sensor (in relation to the depth threshold), but not intense or bright enough (in relation to the intensity threshold) to not distort the field of view for dimensioning the object of interest.
Regarding claim 9, Sharma et al in view of Ajamian et al teaches the obstruction detection method based on a comparison to a depth threshold for the use of dimensioning an object in a three-dimensional image according to claim 1 (as described previously). Sharma et al again teaches when the depth exceeds the threshold (that if the depth of the blob is not greater than a depth threshold (604), the blob is marked as clutter (¶ 0053; element 606 in Fig. 6)), Ajamian et al again teaches wherein selecting the handling action further comprises… delivering the three-dimensional image to the dimensioning module (a method (400), including, at 414, determining the images are sufficient for object dimensioning and proceeding with object dimensioning (¶0050; element 416 in Fig. 4)).
Sharma et al and Ajamian et al do not teach delivering the three-dimensional image to the dimensioning module when the intensity is below the second threshold. Eldar et al discloses and the intensity is below the second threshold (model may be further configured to compare the at least one output value to a threshold (¶ 0369)).
Eldar further recites that an image region with a blockage may be a portion of an image that the model predicted to have pixels indicative of a blockage (¶ 0373) and establishes a need for systems to accurately, consistently, and efficiently identify and respond to image blockages (¶ 0357). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Sharma and Ajamian to incorporate the teachings of Eldar to include a step to determine the pixel intensity of an obstruction and compare that to a threshold before proceeding with object dimensioning. Doing so would further validate the efficiency and accuracy of the detection of an obstruction. Adding an extra comparison to minimize false positives that could arrive from obstructions that may be too close to the sensor (in relation to the depth threshold), but not intense or bright enough (in relation to the intensity threshold) to not distort the field of view for dimensioning the object of interest.
Regarding claim 13, Sharma et al in view of Ajamian et al teach the obstruction detection method based on a comparison to a depth threshold for the use of dimensioning an object in a three-dimensional image according to claim 1 (as described previously).
Sharma et al in view of Ajamian et al does not teach an additional step in identifying obstructions by comparing the size of a region of interest (corresponding to an obstruction), to a size threshold. Eldar et al is analogous art relevant to the technical field and problems addressed in this application and teaches a method of image correction. More specifically, Eldar discloses wherein the processor is configured to detect the region of interest by: determining a size of the region of interest; and determining that the size exceeds a size threshold prior to determining the depth (predicting whether a presented image includes a representation of a blockage may include determining whether a presented image includes a region of pixels associated with respective indicators that the respective pixels are associated with a representation of a blockage and that the region is at least a threshold size ((¶ 0373; process 2700 in Fig. 27)).
Eldar further recites that an image region with a blockage may be a portion of an image that the model predicted to have pixels indicative of a blockage (¶ 0373) and establishes a need for systems to accurately, consistently, and efficiently identify and respond to image blockages (¶ 0357). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Sharma and Ajamian to incorporate the teachings of Eldar to include a step to determine the size of an obstruction and compare that to a threshold before proceeding with object dimensioning. Doing so would further validate the efficiency and accuracy of the detection of an obstruction. Adding an extra comparison to minimize false positives that could arrive from obstructions that may be too close to the sensor (in relation to the depth threshold), but not large enough (in relation to the size threshold) to not block the field of view for dimensioning the object of interest.
Regarding claim 17, Sharma et al in view of Ajamian et al teach the obstruction detection method based on a comparison to a depth threshold for the use of dimensioning an object in a three-dimensional image according to claim 1 (as described previously).
Sharma et al again teaches based on comparison of the determined depth with the threshold (comparing the depth of the blob to a threshold (¶ 0053; element 604)). Ajamian et al teaches selecting the handling action (proceeding with either object dimensioning (416) or not (412) (¶0050-51; elements in Fig. 4)). Sharma et al in view of Ajamian et al does not teach capturing an associated intensity value corresponding to the region of interest, and comparing that intensity to a second threshold and determining an appropriate handling action as a result of that comparison.
Eldar et al teaches capture, via the sensor, with the three-dimensional image, an intensity value associated with the region of interest (a blockage indicator associated with each of the plurality of training images, each blockage indicator representing a presence of a blockage or an absence of a blockage; or an analysis of intensities of pixels located at corresponding pixel coordinates of the plurality of training images (¶ 0380; step 3120 in Fig. 31)), and compare the intensity value to a second threshold (Determining the at least one output value may include computing at least one feature based on using information determined as part of the analysis of the intensities of the pixels (¶ 0368), and model may be further configured to compare the at least one output value to a threshold (¶ 0369)).
Eldar further recites that an image region with a blockage may be a portion of an image that the model predicted to have pixels indicative of a blockage (¶ 0373) and establishes a need for systems to accurately, consistently, and efficiently identify and respond to image blockages (¶ 0357). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Sharma and Ajamian to incorporate the teachings of Eldar to include a step to determine the pixel intensity of an obstruction and compare that to a threshold before proceeding with object dimensioning. Doing so would further validate the efficiency and accuracy of the detection of an obstruction. Adding an extra comparison to minimize false positives that could arrive from obstructions that may be too close to the sensor (in relation to the depth threshold), but not intense or bright enough (in relation to the intensity threshold) to not distort the field of view for dimensioning the object of interest.
Regarding claim 18, Sharma et al in view of Ajamian et al teaches the obstruction detection method based on a comparison to a depth threshold for the use of dimensioning an object in a three-dimensional image according to claim 1 (as described previously). Sharma et al again teaches when the depth exceeds the threshold (that if the depth of the blob is not greater than a depth threshold (604), the blob is marked as clutter (¶ 0053; element 606 in Fig. 6)), Ajamian et al again teaches wherein selecting the handling action further comprises… delivering the three-dimensional image to the dimensioning module (a method (400), including, at 414, determining the images are sufficient for object dimensioning and proceeding with object dimensioning (¶0050; element 416 in Fig. 4)).
Sharma et al and Ajamian et al do not teach delivering the three-dimensional image to the dimensioning module when the intensity is below the second threshold. Eldar et al discloses and the intensity is below the second threshold (model may be further configured to compare the at least one output value to a threshold (¶ 0369)).
Eldar further recites that an image region with a blockage may be a portion of an image that the model predicted to have pixels indicative of a blockage (¶ 0373) and establishes a need for systems to accurately, consistently, and efficiently identify and respond to image blockages (¶ 0357). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Sharma and Ajamian to incorporate the teachings of Eldar to include a step to determine the pixel intensity of an obstruction and compare that to a threshold before proceeding with object dimensioning. Doing so would further validate the efficiency and accuracy of the detection of an obstruction. Adding an extra comparison to minimize false positives that could arrive from obstructions that may be too close to the sensor (in relation to the depth threshold), but not intense or bright enough (in relation to the intensity threshold) to not distort the field of view for dimensioning the object of interest.
Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma et al (US 2012/0087573 A1) in view of Ajamian et al (US 2023/0308746 A1) further in view of Liu et al (US 2017/0039731 A1).
Regarding claim 5, Sharma et al in view of Ajamian et al teaches the obstruction detection method based on a comparison to a depth threshold for the use of dimensioning an object in a three-dimensional image according to claim 1 (as described previously).
Sharma et al in view of Ajamian et al does not teach the added limitation of a minimum depth threshold. Liu et al is analogous art relevant to the technical field and problems addressed in this application and discloses wherein the threshold corresponds to a minimum depth setting of the sensor (a minimum depth a camera providing the image can sense (¶ 0029)). Liu further discloses that the spacing in sample points of the depth image (304) depending on the minimum depth a camera can sense can be selected based on the needs of a user’s application – with larger spacing applicable for far range photography, while smaller spacing is more applicable to close range viewing (¶ 0029; Fig. 3). It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to modify the teachings of Sharma et al in view of Ajamian et al for identifying an obstruction via a depth comparison and utilizing that comparison for determining whether or not to dimension an object in a three-dimensional image, with the added specification of minimum depth provided by Liu et al.
Regarding claim 14, Sharma et al in view of Ajamian et al teaches the obstruction detection method based on a comparison to a depth threshold for the use of dimensioning an object in a three-dimensional image according to claim 1 (as described previously).
Sharma et al in view of Ajamian et al does not teach the added limitation of a minimum depth threshold. Liu et al is analogous art relevant to the technical field and problems addressed in this application and discloses wherein the threshold corresponds to a minimum depth setting of the sensor (a minimum depth a camera providing the image can sense (¶ 0029)). Liu further discloses that the spacing in sample points of the depth image (304) depending on the minimum depth a camera can sense can be selected based on the needs of a user’s application – with larger spacing applicable for far range photography, while smaller spacing is more applicable to close range viewing (¶ 0029; Fig. 3). It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to modify the teachings of Sharma et al in view of Ajamian et al for identifying an obstruction via a depth comparison and utilizing that comparison for determining whether or not to dimension an object in a three-dimensional image, with the added specification of minimum depth provided by Liu et al.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Ackley et al (Handheld Dimensioning System with Measurement-Conformance Feedback; US 2016/0109219 A1) teaches a system and method for obtaining dimension measurements that conforms to disclosed conformance criteria, with a method to provide feedback on criteria compliance and adjustments made to improve criteria compliance.
Boardman et al (Determining Object Structure Using Physically Mounted Devices with Only Partial View of Object; US 2022/0254045 A1) teaches a method of automated analysis of data acquired about an object of interest from physically mounted cameras with only partial coverage of the object exterior.
Gorodetsky et al (Mixed Depth Object Detection; US 2022/0020170 A1) teaches a method of detecting an obstruction and obtaining 3D position data of an object of interest.
Thrimawithana (System and Method for Dimensioning Target Objects; US 2023/0025659 A1) teaches a method of obtaining depth data of a target object and comparing it to a threshold and further dimensioning the target object.
Laffargue et al (Handheld Dimensioner with Data-Quality Indication; US 2016/0112643 A1) teaches a handheld dimensioner system with a user interphase for capturing 3D data and assessing the data’s quality, which is then is then indicated to a use to prompt the user to find the optimal position for a particular dimension measurement.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael M. Sofroniou whose telephone number is (571)272-0287. The examiner can normally be reached M-F: 7:30 AM - 5:00 PM.
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/MICHAEL M SOFRONIOU/Examiner, Art Unit 2661
/JOHN VILLECCO/Supervisory Patent Examiner, Art Unit 2661
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