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 .
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.
The following title is suggested: “REGION OF INTEREST DETERMINATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND PROGRAM”
Drawings
The drawings are objected to because figure 3 states “MAGE REGION ESTIMATION UNIT” and should read as “IMAGE REGION ESTIMATION UNIT”.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter, for the reasons as follows:
In re to claim 1, the claim is directed to an apparatus, which falls within one of the four statutory categories.
Claim 1 recites: “An information processing apparatus comprising: one or more processors; and
one or more storage devices in which an instruction executed by the one or more processors is stored, wherein the one or more processors are configured to: acquire an image, related information related to the image, and one or more first region-of-interest candidates included in the image;
estimate one or more image regions indicated by the related information from the image and from the related information; and
determine a second region-of-interest candidate from among the first region-of-interest candidates based on the estimated image region.”
The limitations of claim 1, as drafted, are considered to fall under the category of “mental process,” and are thus abstract ideas.
For example, an individual may see an image and determine areas of interest based on some additional information that the individual may have with respect to the image (such as knowledge of the objects in the image). They may further determine a particular region of interest from the areas of interest.
Thus, the claim recites an abstract idea. Additionally, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional element “…one or more processors …” as well as “…one or more storage devices…”
The additional elements do not recite an improvement in the functioning of a computer or
other technology or technical field, the claimed steps are not performed using a particular machine, the
claimed steps do not effect a transformation, and the additional element does not apply the judicial
exception in any meaningful way beyond generically linking the use of the judicial exception to a particular technological environment (See MPEP 2106.04(d)). Therefore, the analysis under prong two of
step 2A of the Subject Matter Eligibility Test does not result in a conclusion of eligibility (See flowchart in MPEP 2106).
Furthermore, the additional elements do not add significantly more to the judicial exception.
A processor may be implemented by a generic computer that performs functions that are well-understood, routine and conventional. A processor is a generic computer element which performs generic computer functions/computation. Thus, this element does not amount to more than implementing the abstract idea with a computerized system.
Furthermore, the additional elements do not add significantly more to the judicial exception.
A storage device may be implemented by a generic computer that performs functions that are well-understood, routine and conventional. A storage device is a generic computer element which performs the generic function of storing data. Thus, this element does not amount to more than implementing the abstract idea with a computerized system.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation, and mere implementation on a generic computer does not add significantly more to the claims. Accordingly, the analysis under step 2B of the Subject Matter Eligibility Test does not result in a conclusion of eligibility (See flowchart in MPEP 2106).
As to claim 20, it is the method executed by the apparatus of claim 1. Further, it does not provide additional limitations that go beyond a mental process. As such it recites similar limitations to claim 1 and is rejected for the same reasons as provided above.
As to claim 21, it is the non-transitory computer readable tangible recording medium that performs the processes of the apparatus of claim 1. Further, it does not provide additional limitations that go beyond a mental process. As such it recites similar limitations to claim 1 and is rejected for the same reasons as provided above.
Dependent claims
Dependent claims 2-10, 14, and 17 are dependent on claim 1 and disclose information in relation to processing the medical image taken in claim 1, and further information in relation to said image. They do not add significantly more than the abstract idea nor integrate into a practical application.
Dependent claims 11-13 are dependent on claim 9 and disclose information in relation to processing the medical image data with respect to machine learning/object detection models. The models only serve to perform the abstract idea and add significantly more than the abstract idea nor integrate into a practical application.
Dependent claims 16, 18, and 19 are dependent on claim 1 and disclose information in relation to processing the medical image data with respect to calculations performed. The calculations do not add significantly more than the abstract idea nor integrate into a practical application. See MPEP 2106.05 with regard to the use of math not resulting in making a claim “non-abstract”
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6, 8, 9, 14, 20, and 21 are rejected under 35 U.S.C. 102 (a)(1)/(a)(2) as being anticipated by Sakamoto et al. (US patent 11075003 B2; hereinafter “Sakamoto”).
In re to claim 1, Sakamoto teaches wherein: an information processing apparatus (Fig. 2 (100) shows the interpretation report creation assistance apparatus utilized to perform interpretation of image and text data (see also the abstract/title which further corroborates this)) comprising: one or more processors (CPU; col. 5 line 60- col. 6 line 6 and Fig. 2 indicate use of the CPU to execute system software to perform its operations); and
one or more storage devices in which an instruction executed by the one or more processors is stored (CPU, main memory, and display memory; Fig. 2 shows the memory components of the system, understood to be the storage devices used to execute instructions by the processor (correspondent to the claims). See also col. 5 line 67- col. 6 line 18, which describes the usage of memory in the system), wherein the one or more processors are configured to: acquire an image (medical image; col. 3 lines 26-30 discloses the system obtaining a medical image. Col. 8 lines 3-19 disclose use of a medical image by the system for interpretation), related information related to the image (description data; Col. 9 lines 47-57 disclose use of descriptions of data obtained by the system to locate corresponding image data), and one or more first region-of-interest candidates included in the image (obtained region of interest; Col. 9 lines 47-57 disclose use of descriptions data obtained by the system that to locate corresponding image data. This is done in comparison to regions of interest found by the system prior to comparison to description data. It is understood that these regions of interest are candidates due to first undergoing comparison to the described region in order to be determined as presentable or needing correction (as performed by Fig. 3 (S306-S309)));
estimate one or more image regions indicated by the related information from the image and from the related information (described region; col. 9 lines 34-46 discloses the system determining regions that match the report sentence, thus disclosing an estimation of an image region based on related information); and
determine a second region-of-interest candidate from among the first region-of-interest candidates based on the estimated image region (region of interest following consistency determination; col. 9 lines 47-67 discloses the determination of a region to coincide with a described region. It is understood that the output region of interest modified by a notification or lack thereof (as a lack of notification means that the image contains the described region, per col. 47-67) is the estimated image region (as it is indicated by the related information, correspondent to the claims)).
In re to claim 2 [dependent on claim 1], Sakamoto teaches wherein: the related information includes a text related to a content of the image (Col. 9 lines 47-57 disclose use of descriptions of data obtained by the system to locate corresponding image data. Thus, disclosing text related to content within the medical image. See also Fig. 5, which shows the input text data of the report sentence).
In re to 3 [dependent on claim 1], Sakamoto teaches wherein: the related information includes a text described with respect to a region of interest included in the image (Fig. 5 and Col. 9 lines 47-57 disclose use of descriptions to locate corresponding image data within a medical image. Further, as the report sentence is made with respect to the medical image and is used to compare the region of interest candidate (correspondent to the claims) to a resulting described region, it is understood to contain text that is with respect to the described region and obtained region of interest (which are understood to collectively be the region of interest included in the image)).
In re to claim 4 [dependent on claim 1], Sakamoto teaches wherein: the related information includes information about a structured text including at least one of a size, a position, or a property of a region of interest included in the image (Col. 9 lines 47-57 disclose use of descriptions of data obtained by the system to locate corresponding image data. It is understood that the description includes a position of a region of interest by virtue of said description being used to determine the position of the described region, as is suggested to be the case in relation to anatomical structures (see col. 5 lines 31-34, which corroborates determination of location based on structures)).
In re to claim 5 [dependent on claim 2], Sakamoto teaches wherein: the one or more processors are configured to estimate at least one of a position, a size, or a property indicated by the text (Col. 9 lines 47-57 disclose use of descriptions of data obtained by the system to locate corresponding image data. It is understood that the description includes a position of a region of interest by virtue of said description being used to determine the position of the described region, as is suggested to be the case in relation to anatomical structures (see col. 5 lines 31-34, which corroborates determination of location based on structures)).
In re to claim 6 [dependent on claim 2], Sakamoto teaches wherein: the image is a medical image (col. 3 lines 26-30 discloses the system obtaining a medical image. Col. 8 lines 3-19 disclose use of a medical image by the system for interpretation) the one or more processors are configured to: recognize an organ included in the image (col. 4 lines 32-43 disclose that the system may use segmentation in order to extract the position of organs. This extraction of the positions of organs is understood as a recognition of organs within the image); and
Estimate the region from the text and from a recognition result of the organ (col. 9 lines 34-46 discloses the system determining regions that match the report sentence) and a positioning anatomical structures (col. 4 lines 25-53 discloses that the system performs determinations of regions based on the determining that an anatomical structure is present. This is understood to be a recognition of said anatomical structure. Further, according to Fig. 3 and col. 4 lines 25-53, the anatomical structure recognition is utilized to determine image regions as descriptions are analyzed by the system to determine described region based on region information (such as the recognized anatomical structures)).
In re to 8 [dependent on claim 1], Sakamoto teaches wherein: the one or more processors are configured to receive input of the image, the related information, and the one or more first region-of-interest candidates (col. 3 lines 26-30 discloses the system obtaining a medical image. Fig. 3 further shows acquisition of the described region, and thus, the report sentence text (exemplified in Fig. 5). Fig. 3 also shows the system obtaining the region of interest candidate (correspondent to the claims)).
In re to claim 9 [dependent on claim 1], Sakamoto teaches wherein: the one or more processors are configured to: receive input of the image and the related information (Fig. 3 shows acquisition of the described region, and thus, the report sentence text (exemplified in Fig. 5)); and
acquire the one or more first region-of-interest candidates by generating the one or more first region-of-interest candidates based on the received image (col. 3 lines 26-30 discloses the system obtaining a medical image. Further, Fig. 3 shows that the region of interest candidate (correspondent to the claims) is obtained based on the medical image. See col. 8 lines 10-17, which discloses the use of the input image data for region of intertest obtainment (thus disclosing basis on the received image, correspondent to the claims)).
In re to claim 14 [dependent on claim 1], Sakamoto teaches wherein: the one or more processors are configured to determine the first region-of-interest candidate included in the estimated image region among the one or more first region-of-interest candidates as the second region-of-interest candidate (col. 9 lines 47-67 discloses the determination of a region to coincide with a described region. It is understood that the output region of interest modified by a lack of notification (as a lack of notification means that the image contains the described region, per col. 47-67) is indicative of said region included in the estimated image region. Thus, the region of interest following consistency determination (being understood as the second region-of-interest candidate) is a first region of interest candidate).
As to claim 20, it is the method executed by the apparatus of claim 1. As such, it recites similar limitations to claim 1 and is rejected for the same reasons as provided above.
As to claim 21, it is the non-transitory computer readable tangible recording medium that performs the processes of the apparatus of claim 1. As such, it recites similar limitations to claim 1 and is rejected for the same reasons as provided above.
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.
Claims 7, 10, 11-13, and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Sakamoto, in further view of Mansoor et al. (non-patent literature titled “REGION PROPOSAL NETWORKS WITH CONTEXTUAL SELECTIVE ATTENTION FOR REAL-TIME ORGAN DETECTION”; hereinafter “Mansoor”).
In re to claim 7 [dependent on claim 1], Sakamoto does not explicitly teach wherein: the one or more first region-of-interest candidates include at least one of a bounding box, a heatmap, or a mask.
However, in a similar field of endeavor, Mansoor teaches wherein: the one or more first region-of-interest candidates include at least one of a bounding box, a heatmap, or a mask (Fig. 1 shows the use of bounding boxes in its generation of region proposals. Additionally, it is understood that the generated proposals are first region-of-interest candidates).
Mansoor, like Sakamoto is a medical focused image processing system that performs actions with respect to anatomical structures in image data.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sakamoto to utilize bounding boxes, as taught by Mansoor. The motivation for the proposed modification would have been to leverage increased clarity to a user during intermediate processes as to what the system is recognizing as viable proposals.
In re to claim 10 [dependent on claim 9], Sakamoto does not explicitly teach wherein: the one or more processors (Fig. 1 description and the abstract discloses the system utilizes neural networks to perform proposals/detection, thus being indicative of using a processor) are configured to perform processing of disposing a plurality of bounding boxes as the first region-of-interest candidates at a constant interval on the image in a rule-based manner.
However, in a similar field of endeavor, Mansoor teaches wherein: the one or more processors are configured to perform processing of disposing a plurality of bounding boxes as the first region-of-interest candidates at a constant interval on the image in a rule-based manner (section 3.2 indicates that the system processes proposal disposal using a stride of 16 pixels. It is understood that the implementation of a stride to when producing proposals constitutes disposing bounding boxes (as is done in Fig. 1, which is an example of the system’s method) according to a constant interval in a rule-based manner (being said stride)).
Mansoor, like Sakamoto is a medical focused image processing system that performs actions with respect to anatomical structures in image data.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sakamoto to utilize bounding boxes, as taught by Mansoor. The motivation for the proposed modification would have been to leverage increased clarity to a user during intermediate processes as to what the system is recognizing as viable proposals.
In re to claim 11 [dependent on claim 9], Sakamoto does not explicitly teach wherein: the one or more processors are configured to generate the one or more first region-of-interest candidates from the image using a machine learning model that is trained to receive input of the image and estimate the one or more first region-of-interest candidates from the image.
However, in a similar field of endeavor, Mansoor teaches wherein: the one or more processors are configured to generate the one or more first region-of-interest candidates from the image using a machine learning model that is trained to receive input of the image and estimate the one or more first region-of-interest candidates from the image (section 2 discloses the utilized networks leveraged by the system to perform its methodology. Additionally, as the system comprises a neural network architecture, it is understood to be a machine learning model. Further, sections 2.2.2 and 2.2.3 disclose training being performed for the networks that comprise the system (which per Fig. 1 and section 2 is used to process input image data for the generation of first region-of-interest candidates, correspondent to the claims)).
Mansoor, like Sakamoto is a medical focused image processing system that performs actions with respect to anatomical structures in image data.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sakamoto use a trained learning model, as taught by Mansoor. The motivation for the proposed modification would have been to reduce reliance on the user to provide gaze point information in order to generate initial regions of interest, as is currently needed according to Sakamoto Fig. 3.
In re to claim 12 [dependent on claim 9], Sakamoto does not explicitly teach wherein: the one or more processors are configured to generate the one or more first region-of-interest candidates from the image using an object detection model
However, in a similar field of endeavor, Mansoor teaches wherein: the one or more processors are configured to generate the one or more first region-of-interest candidates from the image using an object detection model (section 2.2 discloses the use of object detection as part of the system methodology. Further, section 2.2.1 lines 1-25 disclose the determination of likely areas of organs of interest based on lung shape. It is thus understood that these proposals are performed based on an object detection model (due to said proposals being selectively done based on an object of interest’s known parameters. This is further corroborated based on the loss function of 2.2.3 being based on an object being present within a proposal, used to train the system)).
Mansoor, like Sakamoto is a medical focused image processing system that performs actions with respect to anatomical structures in image data.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sakamoto use an object detection model, as taught by Mansoor. The motivation for the proposed modification would have been to reduce reliance on the user to provide gaze point information in order to generate initial regions of interest, as is currently needed according to Sakamoto Fig. 3.
In re to claim 13 [dependent on claim 12], Sakamoto does not explicitly teach wherein: the object detection model is a model that is trained by machine learning using training data including the determined second region-of-interest candidate.
However, in a similar field of endeavor, Mansoor teaches wherein: the object detection model is a model that is trained by machine learning using training data (section 2.2.3 discloses the training of the object detection module (correspondent to the claims) based its attached machine learning training data resultant from object detection of the contextual R-CNN model) including the determined second region-of-interest candidate (Per section 2.2.2, the system produces a detection output of the R-CNN as a result of the region selection of the previous portions of the system. This is understood as the second region of interest candidate).
Mansoor, like Sakamoto is a medical focused image processing system that performs actions with respect to anatomical structures in image data.
The reason for combination is the same as provided above.
In re to claim 15 [dependent on claim 1], Sakamoto teaches wherein: the one or more processors are configured to: acquire a plurality of the first region-of-interest candidates; and
determine the second region-of-interest candidate from among the plurality of first region-of-interest candidates
However, in a similar field of endeavor, Mansoor teaches wherein: the one or more processors are configured to: acquire a plurality of the first region-of-interest candidates (Fig. 1 shows the generation of a plurality of proposals); and
determine the second region-of-interest candidate from among the plurality of first region-of-interest candidates (Per section 2.2.2, the system produces a detection output of the R-CNN as a result of the region selection of the previous portions of the system. This is understood as the second region of interest candidate).
Mansoor, like Sakamoto is a medical focused image processing system that performs actions with respect to anatomical structures in image data.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sakamoto to process the second region of interest candidate according to a plurality of first region of interest candidates, as taught by Mansoor. The motivation for the proposed modification would have been to reduce processing iterations and expand the system’s ability to output a presentation of a second region-of-interest candidate not needing correction within the first iteration of processing that coincides by having more regions to check consistency against (as is performed in Sakamoto Fig. 3).
In re to claim 16 [dependent on claim 1], Sakamoto does not explicitly teach wherein: the one or more processors are configured to calculate a probability for the image region indicated by the related information in pixel units of the image
However, in a similar field of endeavor, Mansoor teaches wherein: the one or more processors are configured to calculate a probability for the image region indicated by the related information in pixel units of the image (section 2.2.3 discloses calculation of a loss function based on determined region of image data, and thus, of the pixel units within the image).
Mansoor, like Sakamoto is a medical focused image processing system that performs actions with respect to anatomical structures in image data.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sakamoto to calculate probabilities, as taught by Mansoor. The motivation for the proposed modification would have been to enable the system to self-correct according to a loss function, as is performed in section 2.2.3 of Mansoor.
In re to claim 17 [dependent on claim 1], Sakamoto does not explicitly teach wherein: the image region estimated by the one or more processors includes at least one of a bounding box, a heatmap, or a mask
However, in a similar field of endeavor, Mansoor teaches wherein: the image region estimated by the one or more processors (reduced search area; section 2.2.1 denotes an area that proposal is performed within that is likely to have organs of interest) includes at least one of a bounding box, a heatmap, or a mask (Fig. 1 shows the generation of a plurality of proposals in the form of bounding boxes).
Mansoor, like Sakamoto is a medical focused image processing system that performs actions with respect to anatomical structures in image data.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sakamoto to utilize bounding boxes, as taught by Mansoor. The motivation for the proposed modification would have been to leverage increased clarity to a user during intermediate processes as to what the system is recognizing as viable proposals.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Sakamoto, in view of Mansoor, in further view of Piao (US publication 20210073564 A1; hereinafter “Piao”) and Kitamura et al. (US publication 20220004797 A1; hereinafter “Kitamura”).
In re to claim 18 [dependent on claim 1], Sakamoto in view of Mansoor, does not explicitly teach wherein: the one or more processors are configured to: calculate a confidence degree of the first region-of-interest candidate; and
However, in a similar field of endeavor, Piao teaches wherein: the one or more processors are configured to: calculate a confidence degree of the first region-of-interest candidate ([0027] discloses the determination of a probability that the target object is represented within a region (understood as a confidence degree)).
Piao, like Sakamoto is an image processing system that performs segmentation operations on the input image data.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sakamoto, in view of Mansoor, to calculate a confidence degree, as taught by Piao. The motivation for the proposed modification would have been to decrease the likelihood that an area does not include objects of interest by requiring that they meet a threshold value (as is performed in Piao [0027]).
Sakamoto in view of Mansoor and Piao, does not explicitly teach wherein: delete the first region-of-interest candidate not corresponding to the estimated image region among the one or more first region-of-interest candidates
However, in a similar field of endeavor, Kitamura teaches wherein: to delete the first region-of-interest candidate not corresponding to the estimated image region among the one or more first region-of-interest candidates ([0141] and [142] disclose the deletion of candidate regions with respect to an area that constitutes a feature map (understood to be an estimated region, being a determined region output from another network, per [0062])).
Kitamura, like Sakamoto, discloses a medically focused image processing system that performs region based analysis of medical image data.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sakamoto, in view of Mansoor and Piao, to include deletion of regions, as taught by Kitamura. The motivation for the proposed modification would have been to reduce the number of region proposals that the system may process while still considering a plurality of candidates.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Sakamoto, in view of Mansoor, in further view of Piao.
In re to claim 19 [dependent on claim 1], Sakamoto in view of Mansoor, does not explicitly teach wherein: the one or more processors are configured to: calculate an evaluation value of the one or more first region-of-interest candidates from the estimated image region; and
determine the second region-of-interest candidate based on the evaluation value.
However, in a similar field of endeavor, Piao teaches wherein: the one or more processors are configured to: calculate an evaluation value of the one or more first region-of-interest candidates from the estimated image region ([0027] discloses the candidate regions use a probability determination, understood as an evaluation value, to determine if they represent the target object); and
determine the second region-of-interest candidate based on the evaluation value (Fig. 1 shows that the system, following estimation of the candidate containing the target object, determines an output that region (understood to be the second region-of-interest candidate)).
Piao, like Sakamoto is an image processing system that performs segmentation operations on the input image data.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sakamoto, in view of Mansoor, to an consider an evaluation value, as taught by Piao. The motivation for the proposed modification would have been to decrease the likelihood that an area does not include objects of interest by requiring that they meet a threshold value (as is performed in Piao [0027]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEVIN M COOMBER whose telephone number is (571)270-0950. The examiner can normally be reached Monday - Friday 8:00am-5:00pm.
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/KEVIN M COOMBER/Examiner, Art Unit 2663
/GREGORY A MORSE/Supervisory Patent Examiner, Art Unit 2698