Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's amendments filed on 11/24/2025 overcome the following set forth in the previous Office Action:
The claims 17-25 being rejected under 35 USC §101.
The claims 1-24 being rejected under 35 USC §112 (b) or 35 USC §112 (pre-AIA ), second paragraph.
Applicant's arguments filed 11/24/2025 have been fully considered but they are not persuasive. The Office has thoroughly reviewed applicants' arguments but firmly believes that the cited references reasonably and properly met the claim limitations as previously filed. Furthermore, the amendments necessitate new grounds of rejections as to be detailed below.
On pages 10-12 of the Remarks, applicant argues that “Hotson does not disclose a neural network with specific feedback outputs for actively selecting pixels and color channels from the input image” and “when starting from Hotson, the skilled person would find little suggestion, much less specific teaching to modify the system to include feedback outputs specific for active selection of pixels and color channels at each iteration of the neural network.” Applicant further argues that “the skilled person would not find any suggestion or motivation in Hotson to implement the claimed feedback outputs for active input selection, as this would require a fundamental alteration of the approach taught by Hotson” and “would be neither drawn nor motivated to look to Toyama to further enhance such selection via further feedback output.” The requirements for a proper response to a rejection may be found in 37 CFR 1.111(b) and MPEP § 714.02; see also 707.07(a). The remarks do not provide any specific reasons as to why either the findings of fact or the legal conclusion of obviousness is allegedly in error. Thus, the remarks in response to the obviousness rejection do not comply with 37 CFR 1.111(b) and MPEP § 714.02. However, Applicant’s reply is considered to be a bona fide attempt at a response and is being accepted as a complete response.
The claims 1-16 as amended are interpreted under BRI as to have a similar scope to or a broader scope than the original claims 1-16. As a result, the provisional rejection of claims 1-16 on the ground of nonstatutory double patenting as presented in the previous Office action is maintained.
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.
Claims 8 and 25 are 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 pre-AIA the applicant regards as the invention.
Claim 8 recites in two places “the patch of the plurality to which the portion belongs”. However, it is unclear what this claim limitation means as it is unclear what “the plurality” is referring to.
The dependent claim 25 recites “the vehicle”. There is insufficient antecedent basis for “the vehicle” since it is unclear which of “a vehicle” in claims 1 and 25 “the vehicle” is referring to.
References Cited in Prior Art Rejections
The following references are cited in the prior art rejections set forth below and are referred to as noted:
Hotson et al., US 20180211128 A1, published on 2018-07-26, hereinafter Hotson, and
Toyama, US 20050008193 A1, published on 2005-01-13, hereinafter Toyama.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-25 are rejected under 35 U.S.C. 103 as being unpatentable over Hotson in view of Toyama.
Regarding claim 1, Hotson discloses a computer-implemented method for use in a vehicle for identifying a feature of the environment of the vehicle, (Hotson: Figs. 1-3, 5 and 7, [0016]) the method comprising:
pre-processing an original image from a sensor or camera to produce an input image; (Hotson: [0017-0020, 0029]. For example, processing point cloud data from radar or LIDAR to obtain a depth map and registering the depth map with an RGB camera image.)
presenting the input image to a neural network; (Hotson: Fig. 2, [0017-0021, 0031])
wherein the neural network is trained to classify a feature in an image presented to it, the neural network having an input layer, a hidden layer and an output layer, the output layer including three outputs: (Hotson: 202-210 in Fig. 2, [0031].)
a first feedback output for iteratively selecting pixels from the input image to input at the input layer at each iteration of the neural network; (Hotson: Fig. 2, [0031-0032, 0035, 0037, 0040], Input nodes 202 represent input information for each pixel or pixels in an image [0031] and receive feedback for each pixel or the pixels from output nodes 210 [0035] based on processing results of previous images, thus effectively selecting pixels from the current input image, as illustrated in Figs. 4 and 6 for processing stages along time line. The newly added “iteratively” does not in any way further limiting the claimed invention since the “selecting” operation is done at each iteration as originally claimed.) and
post-processing an output value from the neural network to identify a feature of the environment of the vehicle. (Hotson: Fig. 2, [0031-0032, 0035, 0040, 0044], i.e., values corresponding to classes and location for each sub-region. Also a vehicle, a bicycle, a pedestrian, a curb or barrier, etc., is identified as a result. “[0031] … At the end of the computation, the output nodes 210 yield values that correspond to the class inferred by the neural network.”)
Hotson does not disclose explicitly a second feedback output for selecting a colour channel of the selected pixels to input at the input layer at each iteration.
But Toyama teaches, in an analogous art of video image processing involving object identification and tracking, a second feedback output for selecting a colour channel of the selected pixels to input at the input layer at each iteration. (Toyama: [0015, 0017, 0022, 0028-0031, 0056, 0067-0068, 0089, 0100, 0108], color based object tracking for image pixels through iterative feedback learning process.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hotson’s disclosure with Toyama’s teachings by combining the method for use in a vehicle for identifying a feature of the environment of the vehicle (from Hotson) with the technique of selecting a colour channel of pixels through iterative feedback learning process (from Toyama) to yield no more than predictable use of prior art elements according to their established functions since all the claimed elements, which are taught by prior art references, would continue to operate in the same manner, particularly, the method for use in a vehicle for identifying a feature of the environment of the vehicle would still work in the way according to Hotson and the technique of selecting a colour channel of pixels through iterative feedback learning process would continue to function as taught by Toyama. In fact, the inclusion of Toyama's technique would provide a practical and/or alternative implementation of the method from Hotson and would also broaden the application of Hotson’s method into the color-based tracking using a color-based object model. As a result, the combination would enable a better and more flexible method for use in a vehicle for identifying a feature of the environment of the vehicle due to the alternative implementation made available by Toyama’s technique as well as for use in a broader range of applications such as color-based applications also made available by Toyama’s technique.
Therefore, it would have been obvious to combine Hotson with Toyama to obtain the invention as specified in claim 1.
Regarding claim 2, Hotson {modified by Toyama} discloses the method of claim 1 wherein the feature is one of an object for object detection, or is a road or driving surface for road segmentation or is present in the input image for image classification. (Hotson: [0024, 0032, 0035, 0040, 0044], a vehicle, a bicycle, a pedestrian, and/or a curb or barrier is detected.)
Regarding claim 3, Hotson {modified by Toyama} discloses the method of claim 1 wherein the neural network has a continuous time recurrent neural network architecture. (Hotson: Figs. 4-5, [0025, 0028-0029, 0031, 0043-0049, 0051]. “Note that, by the Shannon sampling theorem, discrete time recurrent neural networks can be viewed as continuous-time recurrent neural networks”, according to Wikipedia (see page 6 of the attached reference of Wikipedia on RNN with this office action.) Furthermore, since Hotson’s disclosure is on real-time object detection using real-time sensor data for such applications as an autonomous vehicle, its RNN is interpreted as a recurrent active vision neural network.)
Regarding claim 4, Hotson {modified by Toyama} discloses the method of claim 1 wherein the pre-processing includes splitting the original image into a plurality of smaller-sized patches, and presenting the input image to the neural network includes consecutively presenting the smaller-sized patches to the neural network. (Hotson: “[0038] … Thus, the image may be processed one sub-region at a time. For example, the window 302 represents a portion of the image 302 that may be fed to a neural network for object or feature detection. The window 302 may be slid to different locations to effectively process the whole image 302. For example, the window 302 may start in a corner and then be subsequently moved from point to point to detect features.”)
Regarding claim 5, Hotson {modified by Toyama} discloses the method of claim 4, wherein obtaining the output value from the neural network comprises obtaining an output value for each of the smaller-sized patches; (Hotson: Figs. 2-4, [0038, 0043-0047])
wherein post-processing the output value comprises post-processing the output value of each of the smaller-sized patches to produce a heat-map image, (Hotson: Figs. 2-4, [0031-0032, 0038, 0043-0044]. “[0043] … For each sub-region 410 (such as a location of the window 302 of FIG. 3), an object prediction is generated.” “[0044] The object predictions may indicate an object type, and/or an object location. For example, a ‘0’ value for the object prediction may indicate that there is no object, a ‘1’ may indicate that the object is a car, a ‘2’ may indicate that the object is a pedestrian, and so forth.” The claimed “heat-map image” is interpreted as the image in Fig. 3 with each window 302 assigned a value (i.e., 0, 1, 2, etc.) indicating an object type (no object, car, pedestrian, etc.)) wherein the heat-map image is formed by:
generating a second plurality of patches, wherein each of the second plurality of patches is paired with an individual patch in the plurality of the smaller-sized patches; (Hotson: Figs. 2-4, [0038, 0043-0044].)
filling each of the second plurality of patches with a singular pixel value based on the output value for each of the paired smaller-sized patches; (Hotson: Figs. 2-4, [0031-0032, 0035, 0038, 0043-0044]. Discussions regarding Fig. 2 applies to each sub-region since the neural network in Fig. 2 is used to processing one sub-region at a time. The claimed “singular pixel value” is the output value of 0, 1, 2, etc., indicating an object type of no object, car, pedestrian, etc. [0044].) and
positioning each of the second plurality of patches in a heat-map image plane in the same relative position as each of the paired smaller-sized patches with respect to the image plane of the original image; (Hotson: Figs. 2-4, [0031-0032, 0035, 0038, 0043-0044]. This is implied since the sub-region 302 in Fig. 2 is processed one at a time using the neural network in Fig. 2 which produces an output value for each sub-region 302 in Fig. 2 with a value 0, 1, 2, etc., indicating an object type of no object, car, pedestrian, etc. [0044].)
and wherein post-processing further comprises applying Hotson: Figs. 3-5, [0038, 0049])
Hotson {modified by Toyama} does not disclose explicitly a segmentation or fitting algorithm for feature identification in an image, which is, however, well known and commonly practiced in the image processing art for object detection. Examiner takes an official notice to this fact.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hotson {modified by Toyama}’s disclosure with the Official Notice’s teachings by combining the method for use in a vehicle for identifying a feature of the environment of the vehicle (from Hotson {modified by Toyama}) with the segmentation or fitting algorithm for feature identification in an image (from the Official Notice) to yield no more than predictable use of prior art elements according to their established functions since all the claimed elements, which are taught by prior art references, would continue to operate in the same manner, particularly, the method for use in a vehicle for identifying a feature of the environment of the vehicle would still work in the way according to Hotson {modified by Toyama} and the segmentation or fitting algorithm for feature identification in an image would continue to function as taught by the Official Notice. In fact, the inclusion of the Official Notice's technique would provide a practical and/or alternative implementation of the method from Hotson {modified by Toyama} and thus would enable a better and more flexible method for use in a vehicle for identifying a feature of the environment of the vehicle due to the practical implementation made available by the Official Notice’s technique.
Therefore, it would have been obvious to combine Hotson {modified by Toyama} with the Official Notice to obtain the invention as specified in claim 5.
Regarding claim 6, Hotson {modified by Toyama} discloses the method of claim 5 wherein each of the second plurality of patches is reduced to one pixel or a singular array entry before forming the heat-map image, such that the resolution of the heat-map image is less than the resolution of the original image. (Hotson: Figs. 2-4, [0031-0032, 0038, 0043-0044]. Each sub-region is effectively reduced to “one pixel” in the sense that “[0043] … For each sub-region 410 (such as a location of the window 302 of FIG. 3), an object prediction is generated.”)
Regarding claim 7, Hotson {modified by Toyama} discloses the method of claim 5 wherein during pre-processing the original image is split such that each of the plurality of smaller-sized patches has an Hotson: Figs. 3-4, [0038-0040, 0043-0045].)
Hotson {modified by Toyama} does not disclose explicitly splitting an image into overlapping patches each having an region overlapping with neighbouring patches, which is, however, well known and commonly practiced in the image processing art. Examiner takes an official notice to this fact.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hotson {modified by Toyama}’s disclosure with the Official Notice’s teachings by combining the method for use in a vehicle for identifying a feature of the environment of the vehicle (from Hotson {modified by Toyama}) with the technique of splitting an image into overlapping patches each having an region overlapping with neighbouring patches (from the Official Notice) to yield no more than predictable use of prior art elements according to their established functions since all the claimed elements, which are taught by prior art references, would continue to operate in the same manner, particularly, the method for use in a vehicle for identifying a feature of the environment of the vehicle would still work in the way according to Hotson {modified by Toyama} and the technique of splitting an image into overlapping patches each having an region overlapping with neighbouring patches would continue to function as taught by the Official Notice. In fact, the inclusion of the Official Notice's technique would provide a practical and/or alternative implementation of the method from Hotson {modified by Toyama} and thus would enable a better and more flexible method for use in a vehicle for identifying a feature of the environment of the vehicle due to the practical implementation made available by the Official Notice’s technique.
Therefore, it would have been obvious to combine Hotson {modified by Toyama} with the Official Notice to obtain the invention as specified in claim 8.
Regarding claim 8, Hotson {modified by Toyama} discloses the method of claim 7, wherein, when generating the heat-map image, the second plurality of patches are formed as sub-patches that are Hotson: Figs. 2-4, [0038, 0043-0044]. Each sub-patch is the same as its paired patch from the (first) plurality of patches and thus is paired with the entire portion of its paired patch.) and wherein:
if said one of the sub-patches is paired to said portion of a patch of the plurality of smaller-sized patches that is an overlapping region, the method further comprises filling the one sub-patch with a singular pixel value based on the output values for the patch of the plurality to which the portion belongs and the neighbouring patches that share the overlapping region; or
if said one of the sub-patches is paired to said portion of a patch of the plurality of smaller-sized patches that is not an overlapping region, the method further comprises filling the one sub-patch with a singular pixel value based on the output value for the patch of the plurality to which the portion belongs. (Hotson: Figs. 2-4, [0031-0032, 0038, 0043-0044].)
Hotson {modified by Toyama} does not disclose explicitly further splitting a patch into smaller sub-patches and determining the value of a sub-patch in an overlapping region based on the output values of patches sharing the overlapping region, which is, however, well known and commonly practiced in the image processing art for object detection. Examiner takes an official notice to this fact.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hotson {modified by Toyama}’s disclosure with the Official Notice’s teachings by combining the method for use in a vehicle for identifying a feature of the environment of the vehicle (from Hotson {modified by Toyama}) with the technique of further splitting a patch into sub-patches and determining the value of a sub-patch in an overlapping region based on the output values of patches sharing the overlapping region (from the Official Notice) to yield no more than predictable use of prior art elements according to their established functions since all the claimed elements, which are taught by prior art references, would continue to operate in the same manner, particularly, the method for use in a vehicle for identifying a feature of the environment of the vehicle would still work in the way according to Hotson {modified by Toyama} and the technique of further splitting a patch into sub-patches and determining the value of a sub-patch in an overlapping region based on the output values of patches sharing the overlapping region would continue to function as taught by the Official Notice. In fact, the inclusion of the Official Notice's technique would provide a practical and/or alternative implementation of the method from Hotson {modified by Toyama} and thus would enable a better and more flexible method for use in a vehicle for identifying a feature of the environment of the vehicle due to the practical implementation made available by the Official Notice’s technique.
Therefore, it would have been obvious to combine Hotson {modified by Toyama} with the Official Notice to obtain the invention as specified in claim 8.
Regarding claim 9, Hotson {modified by Toyama} discloses the method of claim 1 wherein the pre-processing includes performing a Hotson: [0017-0020]. For example, processing point cloud data to obtain a depth map and registering the depth map with an RGB camera image.)
Regarding claim 10, Hotson {modified by Toyama} discloses the method of claim 9 wherein the colour Toyama: HSV or HSI in [0015, 0067].)
Although Hotson {modified by Toyama} teaches RGB images as obtained by a camera (Hotson: [0019-0021]) and HSV or HSI color channels (Toyama: [0015, 0067]), Hotson {modified by Toyama} does not disclose explicitly wherein the pre-processing includes the colour transformation into hue, saturation and green/magenta colour channels (interpreted as HSI or HSV color channels), which is, however, well known and commonly practiced in image processing in the pre-processing stage, for example when acquired image is in RGB color channels. Examiner takes an official notice to this fact.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hotson {modified by Toyama}’s disclosure with the Official Notice’s teachings by combining the method for use in a vehicle for identifying a feature of the environment of the vehicle (from Hotson {modified by Toyama}) with the technique of transforming an obtained image from its original color channels into HSV or HSI color channels during pre-processing of the obtained image (from the Official Notice) to yield no more than predictable use of prior art elements according to their established functions since all the claimed elements, which are taught by prior art references, would continue to operate in the same manner, particularly, the method for use in a vehicle for identifying a feature of the environment of the vehicle would still work in the way according to Hotson {modified by Toyama} and the technique of transforming an obtained image from its original color channels into HSV or HSI color channels during pre-processing of the obtained image would continue to function as taught by the Official Notice. In fact, the inclusion of the Official Notice's technique would provide a practical and/or alternative implementation of the method from Hotson {modified by Toyama} and thus would enable a better and more flexible method for use in a vehicle for identifying a feature of the environment of the vehicle due to the alternative implementation made available by the Official Notice’s technique.
Therefore, it would have been obvious to combine Hotson {modified by Toyama} with the Official Notice to obtain the invention as specified in claims 9-10.
Regarding claims 11-12, which depends on claim 1, Hotson {modified by Toyama} discloses processing the output value from the neural network over the plurality of iterations (Hotson: Figs. 2-5.), but Hotson {modified by Toyama} does not disclose explicitly averaging the output value from the neural network over the plurality of iterations with first n iterations excluded, which is, however, well known and commonly practiced in the image processing art involving neural networks which take a few iterations to converge or stabilize. Examiner takes an official notice to this fact.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hotson {modified by Toyama}’s disclosure with the Official Notice’s teachings by combining the method for use in a vehicle for identifying a feature of the environment of the vehicle (from Hotson {modified by Toyama}) with the technique of averaging the output value from the neural network over the plurality of iterations with first n iterations excluded (from the Official Notice) to yield no more than predictable use of prior art elements according to their established functions since all the claimed elements, which are taught by prior art references, would continue to operate in the same manner, particularly, the method for use in a vehicle for identifying a feature of the environment of the vehicle would still work in the way according to Hotson {modified by Toyama} and the technique of averaging the output value from the neural network over the plurality of iterations with first n iterations excluded would continue to function as taught by the Official Notice. In fact, the inclusion of the Official Notice's technique would provide a practical and/or alternative implementation of the method from Hotson {modified by Toyama} and thus would enable a better and more reliable method for use in a vehicle for identifying a feature of the environment of the vehicle due to the practical implementation made available by the Official Notice’s technique to average the output results over multiple iterations while excluding first few iterations.
Therefore, it would have been obvious to combine Hotson {modified by Toyama} with the Official Notice to obtain the invention as specified in claims 11-12.
Regarding claim 13, Hotson {modified by Toyama} discloses the method of claim 1 wherein pre-processing includes at least one of: scaling the original image; reducing the resolution of the original image; and reducing the dimensions of the original image to a one-dimensional array. (Hotson: Fig. 3. “[0039] … the image 300 may be down sampled to process the full image 300 or a larger portion or different scale window 302 of the image 300.”)
Regarding claim 14, Hotson {modified by Toyama} discloses the method of claim 1 wherein: presenting the input image to the neural network includes presenting the input image to multiple neural networks simultaneously or consecutively, wherein each of the multiple neural networks are trained differently; obtaining the output value from the neural network includes obtaining the output values from each of the multiple neural networks; and post-processing the output value from the neural network to identify a feature of the environment of a vehicle includes post-processing each of the output values from the multiple neural networks and combining or comparing the post-processed output values to identify a feature of the environment of the vehicle. (Hotson: [0031-0032, 0037, 0041-0047, 0050]. “[0041] … a plurality of different recurrent neural networks may be used to generate each feature map. For example, a feature map for pedestrian detection may be generated using a neural network trained for pedestrian detection while a feature map for vehicle detection may be generated using a neural network trained for vehicle detection. Thus, a plurality of different feature maps may be generated for the single image 300 shown in FIG. 3.” “[0045] … Thus, recurrent neural networks may be used to generate the feature maps as well as the object predictions.” “[0047] … In one embodiment, a single neural network, or set of neural networks is used during each stage such that the recurrent connections 420, 422 simply feedback outputs from previous frames as input into a current frame.” The claimed combining or comparing is implied in order to classify the contents of an image (“[0032] According to one embodiment, a deep neural network 200 of FIG. 2 may be used to classify the content(s) of an image into four different classes: a first class, a second class, a third class, and a fourth class.”) These neural networks are trained differently due to different tasks or different types of objects that they need to classify. ([0031-0032, 0037, 0050]))
Regarding claim 15, Hotson {modified by Toyama} discloses the method of claim 14 wherein the combining or comparing of the post-processed output values includes combining and/or averaging the output values from each of the multiple neural networks, and/or applying a swarm optimization algorithm to the post-processed output values to identify the feature of the environment the vehicle. (Hotson: [0031-0032, 0041-0047]. At least, the claimed combining is implied as discussed above. )
Regarding claim 16, Hotson {modified by Toyama} discloses the method of claim 1,further including controlling the speed and/or direction of the vehicle based on the identified feature. (Hotson: Figs. 1-2, 5 and 7, [0024-0028, 0033, 0048, 0055]. .)
Claims 17 and 25 are computer-readable medium claims (Hotson: Fig. 8) similarly rejected, respectively, as the method claims 1 and 16 (the claimed 4th and 5th layers are interpreted as part of the disclosed 210 in Fig. 2 of Hotson).
Claims 18 and 23-24 are computer-readable medium claims (Hotson: Fig. 8) similarly rejected, respectively, as the method claims 10, 14 and 2.
Claims 19 is a computer-readable medium claim (Hotson: Fig. 8) similarly rejected as the method claim 4 or 13.
Regarding claim 20, Hotson {modified by Toyama} discloses the non-transitory computer-readable medium of claim 17, wherein the input layer of the neural network comprises fewer input nodes than the number of pixels in the input image. (Hotson: implied by [0031, 0038-0039]. For example, “[0032] … For example, larger networks may include an input node 202 for each pixel of an image, and thus may have hundreds, thousands, or other number of input nodes.” This implies that regular networks include an input node for more than one pixel. “[0038] … In one embodiment, the image 300 is too large to be processed at full resolution by an available neural network. Thus, the image may be processed one sub-region at a time.” This implies that the number of input nodes is much smaller than the number of pixels.)
Regarding claim 21, Hotson {modified by Toyama} discloses the non-transitory computer-readable medium of claim 20 wherein the neural network comprises 150 input nodes or fewer in the input layer. (Hotson: Fig. 3. [0031-0032, 0038-0039]. As shown in Fig. 3, the sub-region 302 is less than 1/100 of the image 300. Even if we assume that the image 300 include hundreds or thousands of pixels [0031], the sub-region 302 may include a few pixels or a few tens of pixels. Since only “larger networks may include an input node 202 for each pixel of an image” [0031], the claimed “150 input nodes or fewer in the input layer” are implied.)
Regarding claim 22, Hotson {modified by Toyama} discloses the non-transitory computer-readable medium of claim 17 wherein the first feedback output comprises two feedback output nodes, wherein the two feedback output nodes are configured to output a first and a second value respectively, the first and second values indicating a starting point in the input image from which to select a next iteration of pixels in the input image to process by the neural network. (Hotson: Figs. 2-4. “”[0038] … For example, the window 302 may start in a corner and then be subsequently moved from point to point to detect features.” The claimed “first and second values” are interpreted as the coordinate values of the “corner” where the window 302 may start.)
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FENG NIU whose telephone number is (571)272-9592. The examiner can normally be reached on Monday - Friday, 8am-5pm PT.
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/FENG NIU/Primary Examiner, Art Unit 2669
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