DETAILED ACTION
This action is responsive to the Amendments and Remarks received 04/27/2026 in which no claims are cancelled, claims 1, 13, and 14 are amended, and claims 15–17 are added as new claims.
Claim Objections
Claim 1 recites, in part, “calculating a difference in feature amount between a first grid and the sixth grid by comparing the first grid on the sixth grid in the horizontal direction.” (emphasis added). Examiner interprets “on” to be a typographical error requiring correction. In view of the other similarly claimed features in the independent claims, it is likely the term, “on,” should instead be “and.”
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1–17 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1–7 of U.S. Patent No. 12,217,473 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the claimed subject matter represents substantially overlapping subject matter regarding defining a plurality of partial areas and determining feature amount between those areas. The Specification of the reference patent explains the same process as the instant Specification describes regarding determining a feature amount utilizing a difference in luminance between RGB components such that the skilled artisan would interpret the claimed invention to cover the exact same invention even though actual claimed elements differ slightly in scope. In other words, an identical embodiment is covered by both claim sets in view of the Specifications breathing life into claims.
Allowable Subject Matter
Claims 1–14 are allowable subject to filing a terminal disclaimer to overcome the double patenting rejection and subject to correcting the issue giving rise to the claim objection, supra.
The following is a statement of reasons for the indication of allowable subject matter: Under the constraints of examination, Examiner was unable to articulate a reason why it would have been obvious to combine the totalizing of differences in feature amount between the recited grids and the feature amount of the first partial area for a low-quality image with cutting out a part of the image in the manner claimed without the benefit of hindsight.
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 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.
Claims 15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Lewin (US 2023/0245414 A1) and Kobayashi (US 2017/0098136 A1).
Regarding claim 15, the combination of Lewin and Kobayashi teaches or suggests an object detection device comprising: a storage medium configured to store computer-readable instructions, and a processor connected to the storage medium, wherein the processor executes the computer-readable instructions to execute (Lewin, ¶ 0011: teaches a processor and memory for storing instructions) acquiring a captured image of a surface along which a mobile object is able to travel, which is captured with an inclination with respect to the surface (Lewin, ¶¶ 0042–0043: teaches various types of image sensors on a vehicle to capture the environment of the vehicle; Lewin, ¶ 0053: teaches the sensors can be configured to capture a road surface the vehicle is about to travel), generating a low-resolution image obtained by lowering image quality of the captured image (Lewin, Abstract: teaches setting a lower resolution for the field of view outside the region of interest and setting a higher resolution for the region of interest), defining a plurality of partial area sets, each of the plurality of partial area sets having a plurality of partial areas in the low-resolution image, each of the plurality of partial area sets being defined to include the plurality of partial areas for a target area of each of the plurality of partial area sets (Lewin, Fig. 4: teaches a number of regions of interest; Examiner notes Applicant’s claimed partial areas are known in the art as regions of interest; Lewin, ¶ 0060: teaches to one skilled in the art that regions of interest can be separated or combined or extended according to desired behavior), the plurality of partial area sets being defined so that the number of pixels in a partial area increases as the partial area is defined to be closer to a front side of the low-resolution image among the plurality of partial area sets and so that sizes of the plurality of partial areas are different from each other (Examiner notes the skilled artisan knows how perspective works and understands foreground vs. background and its correlation to distance in imaging; Lewin, Fig. 4: illustrates that partial area (i.e. ROIs) can be bigger when closer to the imager), the target area being obtained by cutting out a part of the low-resolution image limited in a vertical direction so that at least a part thereof does not overlap with another partial area set in the vertical direction (Lewin, ¶ 0097 and Fig. 4: teaches a lower portion of the field of view being a first region corresponding to a closer position to the vehicle and another portion of the field of view corresponding to a location much further down the road), and deriving a plurality of first total values obtained by totalizing differences in feature amount between optionally selected partial areas adjacent to each other included in each of the plurality of partial area sets, deriving second total values obtained by adding the derived plurality of first total values for the optionally selected partial areas, generating data to which the second total values are set for the plurality of partial area sets, generating extraction target data by combining the data, setting a search area corresponding to the size of the partial area, and extracting the search area in which a sum of the second total values in the extraction target data is equal to or greater than a reference value as a point of interest (Lewin, ¶ 0078: teaches adapting the region of interest based on identified features; Lewin does not teach counting features to define regions of interest or segment regions of interest within an image; However, to one skilled in the art, such a feature is obvious and is well-represented in the art; Kobayashi, ¶ 0044 and Fig. 4A: teaches that the process of tracking an object of interest within a region of interest and keeping the region of interest centered around the object of interest can be based on calculating a difference in the feature amount of a region versus a feature amount in adjacent regions (horizontally, vertically, and diagonally) to maintain a large number of feature points within the ROI versus the surrounding regions).
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to combine the elements taught by Lewin, with those of Kobayashi, because both references are drawn to tracking objects within a region of interest such that one wishing to practice in the art would have been led to their relevant teachings, and because Kobayashi is merely explaining how regions of interest can be defined by maintaining a feature point amount within the ROI compared to outside the ROI. Thus, the combination is a mere combination of prior art elements, according to known methods, to yield a predictable result. This rationale applies to all combinations of Lewin and Kobayashi used in this Office Action unless otherwise noted.
Regarding claim 17, the combination of Lewin and Kobayashi teaches or suggests the object detection device according to Claim 15, wherein the number of pixels in the partial area is set to a power of 2 (Examiner notes a number of pixels set to a power of two is, definitionally, a square; Examiner believes Applicant means an integer power of two; Even a 3x3 block, having nine pixels is a power of two although the square root of 9 is not an integer; In any event, square pixels blocks and power of two pixels blocks are the default in image processing arts and would not be deemed a distinguishing, inventive feature to a skilled artisan; Kobayashi, e.g. ¶ 0057: teaches the partial regions are blocks and that feature amount is calculated in terms of blocks of pixels, which would suggest to the skilled artisan the number of pixels being a power of two).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Lewin (US 2023/0245414 A1), Kobayashi (US 2017/0098136 A1), and Matsumura (US 2021/0398295 A1).
Regarding claim 16, the combination of Lewin, Kobayashi, and Matsumura teaches or suggests the object detection device according to Claim 15, wherein the second total values are normalized to have a value between zero and one (Matsumura, ¶ 0057: teaches a total number of feature quantities of an image frame being normalized to 1 such that the partial regions are normalized to be between zero and 1).
One of ordinary skill in the art, before the effective filing date of the claimed invention, would have been motivated to combine the elements taught by Lewin and Kobayashi, with those of Matsumura, because all three references are drawn to tracking objects within images such that one wishing to practice in the art would have been led to their relevant teachings, and because Matsumura is merely explaining how tracking objects using feature amounts in a region of interest compared to auxiliary regions can be accomplished using a histogram representing a normalized data distribution set. Thus, the combination is a mere combination of prior art elements, according to known methods, to yield a predictable result. This rationale applies to all combinations of Lewin, Kobayashi, and Matsumura used in this Office Action unless otherwise noted.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Valdmann (US 2024/0103175 A1) teaches high resolution images at a narrow field of view for highway detection of obstacles (¶ 0318).
Oblak (US 2022/0180131 A1) teaches high resolution for high-importance objects and lower resolution for unimportant objects (¶ 0063).
Chaudhuri (US 2021/0201578 A1) Fig. 1B
Bruflodt (US 2023/0001854 A1) teaches changing the aspect ratio of a crop window as vehicle speed or other operation conditions change (¶ 0026).
Seki (US 10,919,450 B2) teaches the aspect ratio of a part of the display changes according to vehicle speed (Claim 6).
Takanashi (US 2020/0326897 A1) teaches detecting a feature amount to choose from among a number of candidate regions of interest (e.g. ¶ 0063) and teaches combining regions of interest based on the proportion (ratio) of feature amounts in each region (¶ 0072).
Shin (US 2020/0307560 A1) teaches a threshold number of feature points and using it to determine the highest density of data to extract the region as the feature point (¶¶ 0064 and 0069).
Tudosie (US 2020/0239018 A1) teaches changing the aspect ratio by lengthening or shortening the map according to vehicle speed (¶ 0046).
Dwivedi (US 2019/0251372 A1) teaches a region of interest is defined by its number of features (¶ 0007) and teaches road markings and guardrail tracking using diagonally adjacent regions of interest (e.g. Figs 6A–6C and Figs. 17A–17E).
Johnson (US 2018/0241953 A1) teaches regions of interest and non-interest are defined by the number of features (¶ 0040).
Kim (US 2018/0186349 A1) teaches tracking the number of feature points in a set ROI (¶ 0009).
Yatsu (US 2017/0144591 A1) teaches aspect ratio based on vehicle speed (Claim 5).
Okumura (US 2016/0283801 A1) teaches totaling the number of feature points in ROIs (¶ 0038) and creating a feature quantity map or score map (¶ 0047).
Sano (US 2016/0247022 A1) teaches an image can be segmented into ROIs based on feature amount (¶ 0027).
Cho (US 2012/0106784 A1) teaches when the number of features falls below a threshold, the processing system determines the ROI (¶ 0032).
Huang (US 2012/0093361 A1) teaches when the number of feature points falls below a threshold, the feature point detection is executed (¶ 0032).
Koitabashi (US 2008/0199050 A1) teaches feature amount calculating units and ROI setting units (e.g. ¶ 0170) wherein when the feature amounts drop below a threshold the ROIs are considered processed (¶ 0243).
Hieida (US 2020/0079504 A1) teaches feature amount comparison in a nearest boundary area in the eight directions of a grid A comprising up, down, left, right, and the diagonals of right-up, left-down, left-up, and right-down (Fig. 8 and ¶ 0042).
Tsuji (US 2017/0374272 A1) teaches using a grid to determine feature amount (e.g. ¶¶ 0051 and 0052).
Takimoto (US 2017/0323437 A1) teaches feature amount extraction of hierarchical images segmented into grids of arbitrary size and calculating averages in the grids as a score map (¶ 0063).
Suzuki (US 2005/0213818 A1) teaches a hierarchical feature amount calculation utilizing a 3x3 direct neighborhood in a multi-resolution pyramid structure for performing feature point extraction (Suzuki, Fig. 5 and ¶¶ 0047 and 0050) and teaches feature quantity comparison between each point in the local region (Suzuki, e.g. Abstract and ¶ 0016).
Honji (US 2021/0287444 A1) teaches a feature amount calculated based on a plurality of pixels in a grid in a hierarchical manner to characterize both local feature amount and statistical feature amount at higher resolution (¶ 0050).
Wang et al., “Low-sample size remote sensing image recognition based on a multihead attention integration network,” Multimedia Tools and Applications (2020). This publication teaches multiscale convolution (Sections 3.2 and 4.4) and a 2x2, 3x3, and 4x4 convolution kernels (grids) (Figs. 1 and 2).
Thom Lane, “Multi-Channel Convolutions explained with…MS Excel!,” Medium, Oct. 18, 2018. This publication teaches 2D convolution of RGB colors using a 3x3 kernel (grid) for use in feature recognition and object classification.
THIS ACTION IS MADE FINAL. 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 extension fee 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.
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