Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This action is in response to the amendment filed on 12/22/2025. Claims 1, 10, and 15 have been amended while claims 3 and 17 have been cancelled. The amended claims have been fully considered but are not persuasive. Claims 1-2, 4-16, and 18-20 remain rejected in the application.
Response to Arguments
In response to applicant’s argument regarding selecting a set of variation icons, arguments fully considered but is not persuasive. Limitations are explicitly disclosed (Barbosa: Col. 2, Lines 57-62 “the synthetic training data generator may overlay one or multiple ones of the object-depicting images-which may have been modified, such as via one or more of resizing, rotating, filtering ( e.g., via "softness" or "blur" filters, color filters, etc.)-over ones of the background images”)) and [Feinleib: 0044 “templates may come in multiple sizes”][Feinleib: 0049 “The software algorithm may first identify a largest image of the one or more images to be placed on the output image canvas . This image may be resized down to a maxi mum size specified for the given output template”](explicit teaching for defined dimensions and identifying images based on their size to be placed on the canvas).
In response to applicant’s argument regarding positioning the set of variation icon, arguments fully considered but is not persuasive. Limitations are explicitly disclosed (Barbosa: Col. 2, Lines 57-62 “the synthetic training data generator may overlay one or multiple ones of the object-depicting images-which may have been modified, such as via one or more of resizing, rotating, filtering ( e.g., via "softness" or "blur" filters, color filters, etc.)-over ones of the background images”)(explicitly teaches overlaying objects that could have been modified over the background).
In response to applicant’s argument regarding annotating, arguments fully considered but is not persuasive. Limitations are explicitly disclosed (Wu: Col. 3, Lines 64-66 “the training datasets may be annotated with quadrilateral annotations representing bounding box regions including one or more text strings”)(Wu: Col. 4, Lines 2-3 “the synthetic data of the training dataset may be annotated with the quadrilateral markings” and Lines 52-55 “identifies the regions of interest corresponding to a text string and provides the information associated with the regions of interest to the character detector 126 for character annotation”)(teaches annotating datasets (corresponding to icons).
In response to applicant’s arguments regarding the dependent claims being allowable, since the rejection for independent claims are maintained, rejections for these dependent claims are maintained. Claims 1-2, 4-16, and 18-20 remain rejected in the application.
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 1, 8, 10, 14, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Barbosa et al. (U.S. Patent No. 11,341,367), in view of Wu et al. (U.S. Patent No. 10,423,827), in further view of Feinleib (U.S. Patent Publication No. 2021/0406961).
Regarding claim 1, Barbosa discloses a method of generating sample labels, the method comprising: receiving, by a sample label generating device, from a user, a selection of: one or more icons from a plurality of icons; and one or more backgrounds from a plurality of backgrounds (interpreted as a computing device gets user choices of graphic items and of backgrounds)(Barbosa: Abstract “A user provides a synthetic training data generator with first set of images and a corresponding set of class identifiers each indicating a type of object depicted in a corresponding image . Each image depicts an object with a substantially or completely mono chromatic or transparent background . A user also provides or identifies a second set of images to be used as backgrounds.”)(teaches user provides images which correspond to icons and backgrounds); applying, by the sample label generating device, a background of the one or more backgrounds to the sample label template (interpreted as putting the chosen background into the template)(Barbosa: Col. 2, Line 55 “merging images from a set of background images”)(teaches merging selected backgrounds into the generated image which corresponds to applying backgrounds to the template); and positioning, by the sample label generating device, the set of variation-icons in the sample label template over the background of the one or more backgrounds, to generate a sample label (interpreted as placing the chosen icon variants at positions in the template over the background to produce the label image)(Barbosa: Col. 2, Lines 57-62 “the synthetic training data generator may overlay one or multiple ones of the object-depicting images-which may have been modified, such as via one or more of resizing, rotating, filtering ( e.g., via "softness" or "blur" filters, color filters, etc.)-over ones of the background images”)(explicitly teaches overlaying objects that could have been modified over the background), but fails to explicitly disclose creating, by the sample label generating device, a first plurality of variation-icons corresponding to each of the one or more icons, by applying one or more pre-augmentation operations to each of the one or more icons; selecting, by the sample label generating device, a set of variation-icons from a second plurality of variation-icons corresponding to the one or more icons, based on dimensions of each variation-icon of the set of variation-icons and predefined dimensions of a sample label template, and annotating, by the sample label generating device, each of the set of variation- icons based on a location associated with the respective variation-icons of the set of variation-icons.
However, Wu discloses creating, by the sample label generating device, a first plurality of variation-icons corresponding to each of the one or more icons, by applying one or more pre-augmentation operations to each of the one or more icons (interpreted as the device makes multiple variants of each icon by applying pre-augmentation before later steps)(Wu: Col. 4, Lines 18-19 “background image, rotation (e.g., perspective transformations)” and Lines 21-25 “adds data augmentation "noise" filters, such as, for example, static blurring, contour embossing, smoothing, sharpening, motion blurring, random levels of compression (e.g., JPEG com-25 pression ), etc.”)(teaches performing operations that create additional versions of source items such as rotation, blurring, and compression which are before generation which correspond to before augmentation), and annotating, by the sample label generating device, each of the set of variation- icons based on a location associated with the respective variation-icons of the set of variation-icons (interpreted as create annotations for each placed item that are tied to that items position coordinates in the image)(Wu: Col. 3, Lines 64-66 “the training datasets may be annotated with quadrilateral annotations representing bounding box regions including one or more text strings”)(Wu: Col. 4, Lines 2-3 “the synthetic data of the training dataset may be annotated with the quadrilateral markings” and Lines 52-55 “identifies the regions of interest corresponding to a text string and provides the information associated with the regions of interest to the character detector 126 for character annotation”)(teaches annotating datasets (corresponding to icons).
However, Feinleib discloses selecting, by the sample label generating device, a set of variation-icons from a second plurality of variation-icons corresponding to the one or more icons, based on dimensions of each variation-icon of the set of variation-icons and predefined dimensions of a sample label template (interpreted as the device chooses from the many variations a subset those size fit a predefined template size which is a layout with fixed dimensions)[Feinleib: 0044 “templates may come in multiple sizes”][Feinleib: 0049 “The software algorithm may first identify a largest image of the one or more images to be placed on the output image canvas . This image may be resized down to a maxi mum size specified for the given output template”](explicit teaching for defined dimensions and identifying images based on their size to be placed on the canvas).
Barbosa, Wu, and Feinleib are considered analogous to the claimed invention because they are in the same field of automated generation of composite training images. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Barbosa to incorporate Wu and Feinleib’s teachings of overlaying icons on the background and selecting and sizing content based on predefined template dimensions. The motivation for such a combination would provide the benefit of ensuring consistent and correctly sized labels across template sizes.
Regarding claim 8, Barbosa discloses the method of claim 1 further comprising: creating a training data set for training a machine leaning (ML) model for identifying labels, the training data set comprising the third plurality of variation-sample labels (interpreted as making a dataset from the previously generated label images and using that to train the machine learning model)(Barbosa: Col. 3, Lines 1-3 “the generated synthetic images, along with the corresponding labels, may then be used to train a machine learning model”)(explicit teaching of using the created dataset (corresponding to images) to train the machine learning model).
Claim 14 is a system claim corresponding to claim 8 without any additional limitations. Thus, claim 14 is rejected for the same reasons as claim 8 above.
Claims 10 and 15 are system and non-transitory computer readable medium (Barbosa: Col. 2, Line 7 “non-transitory computer-readable storage”) claims corresponding to claim 1 without any additional limitations. Thus, claims 10 and 15 are rejected for the same reasons as claim 1 above.
Claims 2, 11, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Barbosa et al. (U.S. Patent No. 11,341,367), in view of Wu et al. (U.S. Patent No. 10,423,827), in view of Feinleib (U.S. Patent Publication No. 2021/0406961), in further view of Love et al. (U.S. Patent No. 9,740,368).
Regarding claim 2, Barbosa, Wu, and Feinleib disclose the method of claim 1 further comprising: but fail to explicitly disclose identifying a text associated with each of one or more icons; determining a position of the text associated with each icon of one or more icons with respect to the respective icon of the one or more icons; and positioning the text associated with each icon of one or more icons at the associated position with respect to the respective icon of the one or more icons.
However, Love discloses identifying a text associated with each of one or more icons (Love: Col. 2, Lines 65-66 “graphical visualizations of graph data structures often include text labels”)(teaches the visualization system includes text so inherently it can identify texts); determining a position of the text associated with each icon of one or more icons with respect to the respective icon of the one or more icons (Love: Col. 30, Lines 59-62 “determining positions of the text labels in the visual representation comprises: positioning a text label in an upper left quadrant upward and to the left of a corresponding icon”)(explicitly and directly teaches claimed limitation); and positioning the text associated with each icon of one or more icons at the associated position with respect to the respective icon of the one or more icons (interpreted as positioning the text relative to the icons positioning) (Love: Col. 34, Lines 1-4 “icons that represent nodes of the clusters and are positioned in the visual representation adjacent a cluster icon of a cluster in which the corresponding node is disposed”)(Love: Col. 10, Lines 2-4 “the text labels are dispose radially away from a center of the field of- view 502 relative to their respective associated icon”)(Love: Col. 10, Lines 45-48 “the icons representing nodes 562 that are similar to one another in some sense, or otherwise exhibit a relatively strong relationship, may be positioned relatively close to one another within the visual representation”)(Love: Col. 11, Lines 14-17 “the text labels 566 may be positioned with in the visual representation 560 relative to the collection of nodes 570 that are represented by those items”)(the final quote is the most explicit disclosure teaching positioning the text relative to the icons).
Barbosa, Wu, Feinleib, and Love are considered analogous to the claimed invention because they are in the same field of automated generation of composite images. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Barbosa, Wu, and Feinleib to incorporate Love’s teachings of identifying text associated with each icon and determining its position relative to its respective icon. The motivation for such a combination would provide the benefit of consistent, readable labels, with reduced manual layout effort.
Claims 11 and 16 are system and non-transitory computer readable medium (Barbosa: Col. 2, Line 7 “non-transitory computer-readable storage”) claims corresponding to claim 2 without any additional limitations. Thus, claims 11 and 16 are rejected for the same reasons as claim 2 above.
Claims 4, 5, 12, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Barbosa et al. (U.S. Patent No. 11,341,367), in view of Wu et al. (U.S. Patent No. 10,423,827), in view of Feinleib (U.S. Patent Publication No. 2021/0406961), in further view of Shlens et al. (U.S. Patent No. 11,301,733).
Regarding claim 4, Barbosa and Feinleib disclose the method of claim 3 further comprising: but fail to explicitly disclose creating a third plurality of variation-sample labels corresponding to each of a fourth plurality of sample labels, by applying one or more post-augmentation operations to each of the fourth plurality of sample labels, wherein the fourth plurality of sample labels is generated using a plurality of unique combinations of sets of variation-icons from the second plurality of variation-icons corresponding to the one or more icons and the one or more backgrounds, and wherein the one or more post-augmentation operations comprise: a rotation, a vertical flipping, and a horizontal flipping of each of the fourth plurality of sample labels.
However, Wu discloses wherein the fourth plurality of sample labels is generated using a plurality of unique combinations of sets of variation-icons from the second plurality of variation-icons corresponding to the one or more icons and the one or more backgrounds (interpreted as the earlier label images are produced by combining sets of icon variants with backgrounds)(Wu: Col. 4, Lines 13-19 “for text string localization, multiple "words" or text strings ( e.g., thousands of text strings, such as "%1rd29", "house" johndoe@email.com, "144-41-009312") are placed in an image. In one embodiment, the image text generator is configured to vary various attributes of the image and text strings, such as, for example, font color, font size, background image, rotation”)(teaches composing many images by placing multiple items and varying the background image and attributes, these unique combinations of sets with backgrounds correspond to the claimed limitation).
However, Shlens discloses creating a third plurality of variation-sample labels corresponding to each of a fourth plurality of sample labels, by applying one or more post-augmentation operations to each of the fourth plurality of sample labels (interpreted as after making a set of label images, make additional label images by applying post augmentation operations to each prior image)(Shlens: Col. 4, Lines 36-38 “augmentation operations 14 to be applied to one or more training images 12 to respectively generate one or more augmented images 16.”)(teaches applying augmentation operations to each image in a set to generate corresponding images which is a post augmentation operation), wherein the one or more post-augmentation operations comprise: a rotation, a vertical flipping, and a horizontal flipping of each of the fourth plurality of sample labels (interpreted as post augmentation includes rotation and vertical/horizontal flipping in each label image)(Shlens: Col. 6, Lines 20-23 “A rotate operation that rotates the image portion (e.g., including the bounding box) by magnitude degrees; A flipping operation that flips the image portion about a horizontal or vertical axis”).
Barbosa, Wu, Feinleib, and Shlens are considered analogous to the claimed invention because they are in the same field of automated generation of composite training images. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Barbosa and Feinleib’s to incorporate Wu and Shlen’s teachings of combining variations of the images and backgrounds and applying post augmentation operations that includes flipping and rotating the image. The motivation for such a combination would provide the benefit of numerous variations of the generated icons.
Regarding claim 5, Barbosa, Wu, and Feinleib disclose the method of claim 4 but fail to explicitly disclose further comprising: upon applying the one or more post-augmentation operations to each of the fourth plurality of sample labels, updating annotation of each of the set of variation-icons based on an updated location associated with the respective variation-icons of the set of variation-icons.
However, Shlens discloses further comprising: upon applying the one or more post-augmentation operations to each of the fourth plurality of sample labels, updating annotation of each of the set of variation-icons based on an updated location associated with the respective variation-icons of the set of variation-icons (interpreted as after augmentation operations are applied to each label image, update each items annotation to reflect its new position)(Shlens: Col. 2, Lines 4-6 “performing the series of one or more augmentation operations on each of one or more training images to generate one or more augmented images”)(Shlens: Col. 3, Lines 5-12 “the controller model can select the augmentation operations from a defined search space of available operations which can, for example, include operations that augment the training image without modification of the locations of a target object and corresponding bounding shape within the image and/or operations that do modify the locations of the target object and bounding shape within the training image”)(clearly teaches the ability to modify the location with or without (before or after) augmentation).
Barbosa, Wu, Feinleib, and Shlens are considered analogous to the claimed invention because they are in the same field of automated generation of composite training images. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Barbosa, Feinleib, and Wu to incorporate Shlen’s teachings of modifying the location of the icon. The motivation for such a combination would provide the benefit of ensuring accuracy of the icons location post augmentation.
Claims 12 and 18 are system and non-transitory computer readable medium (Barbosa: Col. 2, Line 7 “non-transitory computer-readable storage”) claims corresponding to claim 4 without any additional limitations. Thus, claims 12 and 18 are rejected for the same reasons as claim 4 above.
Claim 19 is a non-transitory computer readable medium (Barbosa: Col. 2, Line 7 “non-transitory computer-readable storage”) claim corresponding to claim 5 without any additional limitations. Thus, claim 19 is rejected for the same reasons as claim 5 above.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Barbosa et al. (U.S. Patent No. 11,341,367), in view of Wu et al. (U.S. Patent No. 10,423,827), in view of Feinleib (U.S. Patent Publication No. 2021/0406961), in further view of Hinterstoisser et al. (U.S. Patent No. 11,741,666).
Regarding claim 6, Barbosa, Wu, and Feinleib disclose the method of claim 1, but fail to explicitly disclose wherein positioning the set of variation-icons in the sample label template comprises: determining an optimized position of each icon of the set of icons in the sample label template, based on an occupancy region-based map.
However, Hinterstoisser discloses wherein positioning the set of variation-icons in the sample label template comprises: determining an optimized position of each icon of the set of icons in the sample label template (interpreted as compute where to place each icon in the template and optimized is interpreted as position per a rule or criterion, not necessarily a numeric solver)(Hinterstoisser: Col. 2, Lines 16-20 “rendering the foreground 3D object model at a foreground location in the foreground layer. The rendering of the foreground 3D object model is at the size and being at a given rotation of the foreground 3D object model”)(teaches that foreground locations are chosen from an allowed set of locations which is a rule-based optimization for placement), based on an occupancy region-based map (Hinterstoisser: Col. 3, Lines 9-17 “selecting the background location based on no other background 3D object having yet been rendered at the background location. In some of those implementations, the rendering the selected background 3D object models is iteratively performed, each time for an additional of the selected background 3D object models. The iterative rendering of the selected background 3D object models can be performed until it is determined that one or more coverage conditions are satisfied.”)(Hinterstoisser: Col. 7, Lines 4-7 “A background layer can be generated by successively selecting regions in the background where no other background 3D object model has been rendered (a "bare region")”)(Hinterstoisser: Col. 15, Lines 10-19 “in determining whether a region is a bare region, the system determines whether the region is of at least a threshold size. For example, the system can determine a region is a bare region only if there are at least a threshold quantity of contiguous bare pixels (in one or more directions) in that region. For instance, a region can be determined bare if a quantity of bare pixels in that region is greater than a threshold quantity of pixels, and considered not bare otherwise.”)(teaches bare regions and regions where no other object is rendered which corresponds to occupancy region-based map).
Barbosa, Wu, Feinleib, and Hinterstoisser are considered analogous to the claimed invention because they are in the same field of automated generation of composite images. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Barbosa, Wu, and Feinleib to incorporate Hinterstoisser’s teachings of utilizing unoccupied positions. The motivation for such a combination would provide the benefit of ensuring even placement of objects and placing them only in unoccupied positions.
Claims 7, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Barbosa et al. (U.S. Patent No. 11,341,367), in view of Wu et al. (U.S. Patent No. 10,423,827), in view of Feinleib (U.S. Patent Publication No. 2021/0406961), in view of Hinterstoisser et al. (U.S. Patent No. 11,741,666), in further view of Andersson et al. (U.S. Patent No. 11,481,083).
Regarding claim 7, Barbosa and Wu disclose the method of claim 6, but fail to explicitly disclose wherein determining the optimized position of each icon of the set of icons in the sample label template comprises: randomly positioning an icon of the set of icons at a first location in the sample label template, wherein each icon of the set of icons and the sample label template is configured in a rectangular shape; and positioning remaining icons of the set of icons in a vacant region within the sample label template, wherein the set of icons are equally spaced from each other, and wherein the set of icons are spaced by a predetermined gap.
However, Feinleib discloses wherein each icon of the set of icons and the sample label template is configured in a rectangular shape [Feinleib: 0035 “Templates may be specific to each e - commerce retailer and the pre - configuration settings and may include settings such as specific colors , font sizes , fonts , image placements , and shapes , such as circles or rectangles”](teaches templates can be rectangles and although doesn’t explicitly specify objects are rectangles, that is an obvious limitation).
However, Hinterstoisser discloses wherein determining the optimized position of each icon of the set of icons in the sample label template comprises: randomly positioning an icon of the set of icons at a first location in the sample label template (interpreted as place the first icon at a random location in the template)(Hinterstoisser: Col. 8, Lines 59-61 “For each object, it can be placed in a random location, with additional attempts at placing if the random location(s) of the initial attempt(s) fail”)(teaches random placement of the objects at locations), and positioning remaining icons of the set of icons in a vacant region within the sample label template (interpreted as place the other icons only in empty unoccupied regions of the template) (Hinterstoisser: Col. 7, 4-8 “A background layer can be generated by successively selecting regions in the background where no other background 3D object model has been rendered (a "bare region"), and rendering a random background 3D object model onto each selected region”)(explicit teaching of placing objects (icons) in vacant regions).
However, Andersson discloses wherein the set of icons are equally spaced from each other (Andersson: Col. 8, Lines 36-37 “the objects overlap along the horizontal x-axis, and the spacing between adjacent objects is uniform”)(teaches uniform spacing between the objects which corresponds to equal spacing between icons), and wherein the set of icons are spaced by a predetermined gap (Andersson: Col. 5, Lines 54-56 “The computing device may adjust the arrangement according to predetermined parameters or according to variable parameters”)(teaches predetermined parameters which corresponds to predetermined gap).
Barbosa, Wu, Feinleib, Hinterstoisser, and Andersson are considered analogous to the claimed invention because they are in the same field of automated generation of composite images. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Barbosa and Wu to incorporate Hinterstoisser, Feinleib, and Andersson’s teachings of randomly placing icons at unoccupied positions, having templates in the shape of rectangles, and ensuring uniform predefined spacing between icons. The motivation for such a combination would provide the benefit of ensuring even placement of objects and placing them only in unoccupied positions.
Regarding claim 13, Barbosa and Wu disclose the system of claim 10, but fail to explicitly disclose wherein positioning the set of variation-icons in the sample label template comprises: determining an optimized position of each icon of the set of icons in the sample label template, based on an occupancy region-based map, and wherein determining the optimized position of each icon of the set of icons in the sample label template comprises: randomly positioning an icon of the set of icons at a first location in the sample label template, wherein each icon of the set of icons and the sample label template is configured in a rectangular shape; and positioning remaining icons of the set of icons in a vacant region within the sample label template, wherein the set of icons are equally spaced from each other, and wherein the set of icons are spaced by a predetermined gap.
However, Feinleib discloses wherein each icon of the set of icons and the sample label template is configured in a rectangular shape [Feinleib: 0035 “Templates may be specific to each e - commerce retailer and the pre - configuration settings and may include settings such as specific colors , font sizes , fonts , image placements , and shapes , such as circles or rectangles”](teaches templates can be rectangles and although doesn’t explicitly specify objects are rectangles, that is an obvious limitation).
However, Hinterstoisser discloses wherein positioning the set of variation-icons in the sample label template comprises: determining an optimized position of each icon of the set of icons in the sample label template, based on an occupancy region-based map (interpreted as choosing the best position based on the occupancy of the location)(Hinterstoisser: 208-210; Fig. 2)(explicitly teaches selecting the next location if it is bare which corresponds to occupancy), and wherein determining the optimized position of each icon of the set of icons in the sample label template comprises: randomly positioning an icon of the set of icons at a first location in the sample label template (interpreted as first place one icon at a random location)(Hinterstoisser: Col. 9, Lines 46-49 “are generated for all foreground objects, all foreground objects can be iterated through during generation of foreground layers, and each of them rendered with the given rotation at a random location”)(teaches placing the objects (corresponding to icons) at random locations); and positioning remaining icons of the set of icons in a vacant region within the sample label template (Hinterstoisser: Col. 14, Lines 47-49 “location based on it being a region that is currently bare (i.e., currently lacks any rendered objects).”) (explicitly teaches placing objects in bare regions which corresponds to vacant region).
However, Andersson discloses wherein the set of icons are equally spaced from each other (Andersson: Col. 8, Lines 36-37 “the objects overlap along the horizontal x-axis, and the spacing between adjacent objects is uniform”)(teaches uniform spacing between the objects which corresponds to equal spacing between icons), and wherein the set of icons are spaced by a predetermined gap (Andersson: Col. 5, Lines 54-56 “The computing device may adjust the arrangement according to predetermined parameters or according to variable parameters”)(teaches predetermined parameters which corresponds to predetermined gap).
Barbosa, Wu, Feinleib, Hinterstoisser, and Andersson are considered analogous to the claimed invention because they are in the same field of automated generation of composite images. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Barbosa and Wu to incorporate Hinterstoisser, Feinleib, and Andersson’s teachings of randomly placing icons at unoccupied positions, having templates in the shape of rectangles, and ensuring uniform predefined spacing between icons. The motivation for such a combination would provide the benefit of ensuring even placement of objects and placing them only in unoccupied positions.
Claim 20 is a non-transitory computer readable medium (Barbosa: Col. 2, Line 7 “non-transitory computer-readable storage”) claim corresponding to claim 13 without any additional limitations. Thus, claim 20 is rejected for the same reasons as claim 13 above.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Barbosa et al. (U.S. Patent No. 11,341,367), in view of Wu et al. (U.S. Patent No. 10,423,827), in view of Feinleib (U.S. Patent Publication No. 2021/0406961), in further view of Rowell (U.S. Patent Publication No. 2020/0342652).
Regarding claim 9, Barbosa and Feinleib disclose the method of claim 1, wherein the one or more pre-augmentation operations comprise: at least one geometric distortion (Barbosa: Col. 2, Lines 59-64 “which may have been modified, such as via one or more of resizing, rotating, filtering ( e.g., via "softness" or "blur" filters, color filters, etc.)-over ones of the background images (which similarly may have been modified, e.g., via applying filters) to create the new synthetic image set”)(teaches resizing and rotating which are geometric distortions), but fail to explicitly disclose a noise addition, and at least one lens distortion.
However, Wu discloses a noise addition (Wu: Col. 4, Lines 20-22 “In one embodiment, the image text generator adds data augmentation "noise" filters”).
However, Rowell discloses at least one lens distortion [Rowell: 0021 “Introducing lens distortion”].
Barbosa, Wu, Feinleib, and Rowell are considered analogous to the claimed invention because they are in the same field of automated generation of composite training images. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Barbosa and Feinleib to incorporate Wu and Rowell’s teachings of utilizing geometric and lens distortions. The motivation for such a combination would provide the benefit of simulating real capture defects to increase dataset realism and model robustness.
Conclusion
THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to AHMED TAHA whose telephone number is (571)272-6805. The examiner can normally be reached 8:30 am - 5 pm, Mon - Fri. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, XIAO WU can be reached at (571)272-7761. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786 9199 (IN USA OR CANADA) or 571-272-1000.
/AHMED TAHA/Examiner, Art Unit 2613
/XIAO M WU/Supervisory Patent Examiner, Art Unit 2613
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