DETAILED ACTION
The present office action is responsive to the applicant’s filling on 4/17/2024.
The application has claims 1-20 present. All present claims have been examined.
This action is Non-Final.
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 .
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claim(s) 1, 2, 11-19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wilcox (US 2023/0298273).
In regards to claims (1, 11 and 17), Wilcox discloses a device comprising: one or more processors coupled to a memory and configured to: obtain data indicative of a garment to be manufactured (see abstract and at least para 11, 18, 21-22: garment input data. Para 11 “receiving garment data related to a predefined or default garment, the garment data comprising garment piece data related to a plurality of 2D garment pieces, from which the predefined or default garment is assemble”); generate, using a machine learning model trained on the data, output data indicative of localization information, one or more properties associated with a fabric, and one or more actions associated with the fabric to be performed on the fabric in manufacturing the garment (see at least para 25-26, 188, 198: in para 25, the output instructions provide multiple types of data including position data, fabric type and trim and stitch type: “According to some embodiments, the fabrication instructions comprise: user generated
garment piece data related to the plurality of 2D garment pieces of the user generated
garment; positioning data related to the relative positions of the plurality of 2D garment pieces of the user-generated garment; and sewing instructions for sewing together the plurality of 2D garment pieces of the user-generated garment, wherein the plurality of 2D garment pieces of the user-generated garment comprise shape pieces and the finish pieces. According to some embodiments, the fabrication instructions comprise instructions for at least one of fabric type, trim type, and stitch type”. Para 188: “the trained expert system analyzes the at least one document with respect to the textual information and converts the textual information into the computer-readable instructions, which may comprise a fully digital description of the predefined or default garment with respect to the 2D pattern pieces, the preliminary 3D garment and further properties of the predefined or default garment. Such further properties may include stitches, seams, finishes, trims, or assembly instructions.” On para 198 “Once the garment adjustment process 170 is finished, fabrication instructions are automatically generated 180 that allow producing the user-generated garment. For instance, the fabrication instructions may comprise user-generated garment piece data related to shapes and sizes of 2D garment pieces of the user-generated garment, positioning data related to the relative positions of the plurality of 2D garment pieces of the user-generated garment, and sewing instructions for sewing together the plurality of 2D garment pieces of the user-generated garment, including sewing instructions for sewing together shape and finish pieces. The fabrication instructions typically may comprise further information regarding the garment, for instance including a fabric type, a trim type or a stitch type. The fabrication instructions may be generated to be computer-readable, human-readable or both”); and cause a second device to perform an action of the one or more actions associated with the fabric (see at least para 199: the instructions are sent to machines “In the case of computer-readable instructions, these may be provided to one or more garment fabrication machines, so that these machines may produce the user-generated garment based on the fabrication instructions. Providing computer-readable instructions to machines may comprise sending one or more data files via the internet to a remote factory. Alternatively, the computer system on which the method is executed may be connected directly to the machines producing the garment”).
In regards to claims (2, 12 and 18), Wilcox discloses wherein the one or more processors are further configured to obtain second data indicative of information associated with said manufacturing the garment (see at least para 12, 21-22: receiving second data e.g. user input as for modifications to garment “the garment adjustment process comprises: receiving, via the graphical user interface, user input with modification instructions to modify the preliminary 3D garment”).
In regards to claim 13, Wilcox teaches wherein the one or more processors are further configured to train the machine learning model using the synthetic data, second data, or both (see para 143: “in particular the pattern recognition algorithm, may be trainable based on machine learning using training data from previous first 3D garment model generations. Specifically, such a machine learning algorithm can “learn” from user input that is aimed at correcting the automatic choices made by the computer”).
In regards to claims (14 and 19), Wilcox discloses wherein the one or more processors are further configured to obtain metric data indicative of production metrics, quality metrics, machine performance metrics, material usage metrics, process flow metrics, or a combination thereof (see para 26, 145-148: using multiple finish garment data (metrics), finishes, fabric types, patterns, materials and attributes to properly generate and model the garment for production. Al least para 26 “the garment data comprises at least one document comprising a human-readable information describing the predefined or default garment, and the human-readable information is converted into computer-readable instructions by a trained expert system of the computer system. For instance, the human-readable information may relate to at least one of construction details, a bill of materials, a colorway, and a size chart. According to some embodiments, the human-readable information is or comprises textual information, and the trained expert system analyzes the at least one document with respect to the textual information and converts the textual information into the computer-readable instructions. According to some embodiments, the computer-readable instructions comprise a fully digital description of the predefined or default garment with respect to the 2D pattern pieces, the preliminary 3D garment and further properties of the predefined or default garment, including at least one of: stitches, seams, finishes, trims, and assembly instructions. According to some embodiments, at least one of the following method steps is based on the computer-readable instructions: identifying the piece type of each of the plurality of 2D garment pieces; 3D assembling the shape pieces based; and visualizing the preliminary 3D garment”).
In regards to claim 15. Wilcox discloses wherein the generation of the synthetic data further includes receiving user input to augment the synthetic data, historical data indicative of previously obtained data indicative of other garments to be manufactured, or both, and wherein the previously obtained data includes one or more previous fabrics and one or more previous properties for each of the one or more previous fabrics (provides user input which further modifies the garment data. See at least para 12, 17-19, 143-144. On para 12 “According to some embodiments, the garment adjustment process comprises: receiving, via the graphical user interface, user input with modification instructions to modify the preliminary 3D garment; modifying the preliminary 3D garment based on the modification instructions; and visualizing the modified preliminary 3D garment as the user-generated garment on the avatar. According to some embodiments, modifying the preliminary 3D garment comprises: adjusting a 2D shape and/or a size of one or more of the shape pieces, joining at least one finish piece to one or more shape pieces, and 3D re-assembling of the shape pieces and finish pieces”. Also, on para 143: “The first method, in particular the pattern recognition algorithm, may be trainable based on machine learning using training data from previous first 3D garment model generations. Specifically, such a machine learning algorithm can “learn” from user input that is aimed at correcting the automatic choices made by the computer.”)
In regards to claim 16, Wilcox discloses wherein the one or more properties includes one or more of: fabric color, pattern or design on the fabric, fabric weight, fabric material and characteristics associated with the fabric material, fabric density, or a combination thereof (see at least para 26, 34: “information may relate to at least one of construction details, a bill of materials, a colorway, and a size chart”).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 3-4 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wilcox (US 2023/0298273) as applied to claims above, in view of Mousavi Hondori et al. (US 11203826).
In regards to claims 3 and 20, Wilcox doesn’t specifically teach wherein the one or more processors are further configured to: obtain image data indicative of an image depicting the fabric on a fabric joining device; generate, using a second machine learning model, segmentation data indicative of pixel values associated with a location of the fabric on the fabric joining device within the image; determine a probability that the fabric is in a location on the fabric joining device that is suitable for the action; and wherein performing the action comprises performing the action in response to the probability satisfying a threshold.
Mousavi teaches wherein the one or more processors are further configured to: obtain image data indicative of an image depicting the fabric on a fabric joining device (see at least Col 1 lines 40-48: using image data to determine fabric joining locations “the system captures an image comprising the to-be-joined fabric pieces; identifies to-be-joined edges, respectively, on the fabric pieces; for each edge, computes a curve that substantially fits the edge; and determines the joinder points on the edge based on the computed curve. Variance of distance between two consecutive joinder points on the edge is within a predetermined value”. Also on Col 9 lines 13-30: teaches capturing images of fabric to determine joining locations “computer-vision system 820 can include instructions for causing camera 818 to capture images (image-capturing module 822), instructions for determining edges on fabric pieces (edge-determination module 824), instructions for tracking objects in the images”); generate, using a second machine learning model, segmentation data indicative of pixel values associated with a location of the fabric on the fabric joining device within the image; determine a probability that the fabric is in a location on the fabric joining device that is suitable for the action (see at least Col 1, lines 49-51 ”identifying the to-be-joined edges comprises inputting the captured image to a trained machine-learning model.”. On Col 4, lines 21-30 “Edge finder 204 can identify the edges of each fabric piece. Various image-processing techniques can be used. For example, using a machine-learning technique (e.g., by training a neural network), edge finder 204 can learn to determine the edge of a fabric piece. Once an edge is determined, edge finder 204 can mark the edge with a number of points along the edge. For example, edge finder 204 can determine that certain pixels in the image of the fabric pieces correspond to the edge of the fabric piece and can label a subset of the pixels as edge points”); and wherein performing the action comprises performing the action in response to the probability satisfying a threshold (see at least Col 8 lines 14-36 and claim 8 teaches determining locations and when they are within predetermined values the instructions are provided so that the machine joins the fabric pieces. On Col 8 lines 14-36 “The system can then determine, based on the length of each joinder segment, the location of each joinder point (operation 612). In some embodiments, the system may traverse the polynomial pieces on a spline, one at a time, in order to determine the locations of the joinder points such that the length of each joinder segment is set according to the previously determined length value. This operation is performed for both to-be-joined edges. The system then outputs the determined locations of the joinder points on each edge (operation 614). The location information of the joinder points can be used to guide a robotic system (e.g., a sewing robot) to place stitches (or adhesives) based on the joinder points to join the two fabric pieces together and to achieve the desired joining effect. Note that the computer-vision system can also have object- or feature-tracking capabilities, and the determined locations of the joinder points can be associated with physical locations on the fabric pieces. Therefore, even when the robotic system uses a different computer-vision system with a different viewing angle or field of view, the locations of the joinder points can remain mapped to the corresponding pixel coordinates used by the computer vision of the robotic system, thereby guiding the robotic system to perform the joining operations”. On claim 8: “a joinder-point-determination module to determine joinder points on each edge based on the computed curve, wherein variance of distance between two consecutive joinder points on the edge is within a predetermined value; and a fabric-joining module to join the to-be-joined fabric pieces by attaching the corresponding joinder points along the respective edges of the to-be-joined fabric pieces”).
As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to use these teachings of Mousavi in combination with the teachings of Wilcox, in order to use computer vision to make sure the locations of the pieces are within acceptable ranges in order to properly join them as the garment design requires, since it enhances the system to automate the steps for joining the pieces as required by the garment design (see at least Col 2 lines 54 to Col 3 line 12 and Col 6 lines 10-25).
In regards to claim 4, Wilcox doesn’t specifically teach wherein the one or more processors are further configured to, prior to generation of the segmentation data, perform one or more processing steps to the image data, wherein the one or more processing steps includes one or more of: perform an imaging processing step to the image data, or adjust one or more-pixel values within the image data based on one or more properties associated with the fabric
Mousavi teaches wherein the one or more processors are further configured to, prior to generation of the segmentation data, perform one or more processing steps to the image data, wherein the one or more processing steps includes one or more of: perform an imaging processing step to the image data, or adjust one or more-pixel values within the image data based on one or more properties associated with the fabric (see at least Col 4 lines 21-30: teaches images processing to properly find edges of the fabric “Edge finder 204 can identify the edges of each fabric piece. Various image-processing techniques can be used. For example, using a machine-learning technique (e.g., by training a neural network), edge finder 204 can learn to determine the edge of a fabric piece. Once an edge is determined, edge finder 204 can mark the edge with a number of points along the edge. For example, edge finder 204 can determine that certain pixels in the image of the fabric pieces correspond to the edge of the fabric piece and can label a subset of the pixels as edge points”).
As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to use these teachings of Mousavi in combination with the teachings of Wilcox, in order to use computer vision to make sure the locations of the pieces are within acceptable ranges in order to properly join them as the garment design requires, since it enhances the system to automate the steps for joining the pieces as required by the garment design (see at least Col 2 lines 54 to Col 3 line 12 and Col 6 lines 10-25).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wilcox (US 2023/0298273) as applied to claims above, in view of Mousavi Hondori et al. (US 11203826) and Ahmed et al. (US 11733255).
In regards to claim 5, Wilcox doesn’t specifically teach wherein the one or more processors are further configured to: obtain image data indicative of an image depicting the fabric on a fabric joining device; generate, using a second machine learning model, segmentation data indicative of pixel values associated with a location of the fabric on the fabric joining device within the image; determine a probability that the fabric is in a location on the fabric joining device that is suitable for the action.
Mousavi teaches wherein the one or more processors are further configured to: obtain image data indicative of an image depicting the fabric on a fabric joining device (see at least Col 1 lines 40-48: using image data to determine fabric joining locations “the system captures an image comprising the to-be-joined fabric pieces; identifies to-be-joined edges, respectively, on the fabric pieces; for each edge, computes a curve that substantially fits the edge; and determines the joinder points on the edge based on the computed curve. Variance of distance between two consecutive joinder points on the edge is within a predetermined value”. Also on Col 9 lines 13-30: teaches capturing images of fabric to determine joining locations “computer-vision system 820 can include instructions for causing camera 818 to capture images (image-capturing module 822), instructions for determining edges on fabric pieces (edge-determination module 824), instructions for tracking objects in the images”. On Col 8 lines 14-36 “The system can then determine, based on the length of each joinder segment, the location of each joinder point (operation 612). In some embodiments, the system may traverse the polynomial pieces on a spline, one at a time, in order to determine the locations of the joinder points such that the length of each joinder segment is set according to the previously determined length value. This operation is performed for both to-be-joined edges. The system then outputs the determined locations of the joinder points on each edge (operation 614). The location information of the joinder points can be used to guide a robotic system (e.g., a sewing robot) to place stitches (or adhesives) based on the joinder points to join the two fabric pieces together and to achieve the desired joining effect. Note that the computer-vision system can also have object- or feature-tracking capabilities, and the determined locations of the joinder points can be associated with physical locations on the fabric pieces. Therefore, even when the robotic system uses a different computer-vision system with a different viewing angle or field of view, the locations of the joinder points can remain mapped to the corresponding pixel coordinates used by the computer vision of the robotic system, thereby guiding the robotic system to perform the joining operations”); generate, using a second machine learning model, segmentation data indicative of pixel values associated with a location of the fabric on the fabric joining device within the image (see at least Col 1, lines 49-51 ”identifying the to-be-joined edges comprises inputting the captured image to a trained machine-learning model.”. On Col 4, lines 21-30 “Edge finder 204 can identify the edges of each fabric piece. Various image-processing techniques can be used. For example, using a machine-learning technique (e.g., by training a neural network), edge finder 204 can learn to determine the edge of a fabric piece. Once an edge is determined, edge finder 204 can mark the edge with a number of points along the edge. For example, edge finder 204 can determine that certain pixels in the image of the fabric pieces correspond to the edge of the fabric piece and can label a subset of the pixels as edge points.”; determine a probability that the fabric is in a location on the fabric joining device that is suitable for the action On claim 8: “a joinder-point-determination module to determine joinder points on each edge based on the computed curve, wherein variance of distance between two consecutive joinder points on the edge is within a predetermined value; and a fabric-joining module to join the to-be-joined fabric pieces by attaching the corresponding joinder points along the respective edges of the to-be-joined fabric pieces”).
As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to use these teachings of Mousavi in combination with the teachings of Wilcox, in order to use computer vision to make sure the locations of the pieces are within acceptable ranges in order to properly join them as the garment design requires, since it enhances the system to automate the steps for joining the pieces as required by the garment design (see at least Col 2 lines 54 to Col 3 line 12 and Col 6 lines 10-25).
Wilcox doesn’t specifically teach wherein performing the action comprises in response to the probability not satisfying a threshold, cause a second device to discard the fabric.
Ahmed teaches wherein performing the action comprises in response to the probability not satisfying a threshold, cause a second device to discard the fabric (see Col 13 lines 43-61: teaches setting threshold values for quality control which is use to accept or reject parts that meet the determined thresholds “In one embodiment, a quality control and monitoring software module establishes communication with smart digital gauges and other measuring instruments to receive, collect and visualize the wirelessly transmitted measurement data simultaneously from multiple wireless measurement devices through a customized user interface. The software has the following features: Adding and configuring measuring devices, establishing wireless communication, and setting the acquisition rate. The possibility of visualizing the collected data as digital, analog, and graphical, and export it to a file for further analysis. Acceptance and rejection feature. The software enables the user to define threshold measurement values to set the quality control threshold that would indicate the accepted or rejected parts while doing the measurement process and it will alert the user if the reading exceeds the predefined threshold value.”).
As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to use these teachings of Ahmed in combination with the teachings of Wilcox as modified by Mousavi, in order to set quality control measurements associated to thresholds values, since it enhances the system by including steps for quality control, thus providing steps for the process which makes sure only using parts (fabric) that would produce the desired outcome.
Claim(s) 6-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wilcox (US 2023/0298273) as applied to claims above, in view of SayyarRodsari et al. (US 20240094718).
In regards to claim 6, Wilcox doesn’t specifically teach wherein the one or more processors are further configured to: obtain image data indicative of an image depicting the fabric on a fabric joining device; based on the image data, determine, using a third machine learning model, that the fabric includes one or more wrinkles; and wherein performing the action comprises causing a second device to move along a path associated with locations of the one or more wrinkles above a third device to remove the one or more wrinkles from the fabric.
SayyarRodsari teaches wherein the one or more processors are further configured to: obtain image data indicative of an image depicting the fabric on a fabric joining device; based on the image data, determine, using a third machine learning model, that the fabric includes one or more wrinkles; and wherein performing the action comprises causing a second device to move along a path associated with locations of the one or more wrinkles above a third device to remove the one or more wrinkles from the fabric (see at least para 35, 43, 54: teaches monitoring system in automation environments. The environment allows for multiple devices to work together based on data received by cameras and sensor. Part of the controlling and monitoring system include the use of neural networks. As part of the examples, it explains that receiving data from associated devices it determines wrinkles and uses that data to control a device to eliminate the wrinkles. On para 35: “the industrial automation equipment 50 may receive data from the associated devices and use the data to perform their respective operations more efficiently. For example, a controller (e.g., the control/monitoring device 48) of a motor drive may receive data regarding a temperature of a connected motor and may adjust operations of the motor drive based on the data. As another non-limiting example, the control/monitoring device 48 of an assembly component may receive data regarding an abnormality in a product (e.g., a scratch, a wrinkle, an unpolished spot) and may adjust operations of an assembly component to remove the abnormality from the product (e.g., buffing out a scratch, applying a new coat of paint, adjusting or stretching fabric to remove a wrinkle, replacing a portion of the product)”.
As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to use these teachings of SayyarRodsari in combination with the teachings of Wilcox, in order to provide means to eliminate wrinkles with an automation equipment for garment assembly, thus enhancing the system to make corrections needed to the garment assembly process which improves the final product.
In regards to claim 7, Wilcox doesn’t specifically teach wherein the third device uses compressed air to remove the one or more wrinkles from the fabric.
SayyarRodsari teaches wherein the third device uses compressed air to remove the one or more wrinkles from the fabric (see at least para 32, 35, 43, 54: teaches monitoring system in automation environments. The environment allows for multiple devices to work together based on data received by cameras and sensor. It explains that receiving data from associated devices it determines wrinkles and uses that data to control a device to eliminate the wrinkles. On para 32 teaches “the industrial automation equipment 50 may include devices used in other applications, such as electrical equipment, hydraulic equipment, compressed air equipment, steam equipment”).
As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to use these teachings of SayyarRodsari in combination with the teachings of Wilcox, in order to provide means to communicate between equipment in an automation environment as to determine abnormality and communicate between the devices to remedy the detection as provided in one of the examples to eliminate wrinkles within garment assembly system of Wilcox, thus enhancing the system to make corrections needed to the garment assembly process which improves the final product.
Claim(s) 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wilcox (US 2023/0298273) and SayyarRodsari et al. (US 20240094718), as applied to claims above and further in view of Baker et a. (US 20210172105) and Ahmed et al. (US 11733255).
In regards to claim 8, Wilcox doesn’t specifically teach wherein the one or more processors are further configured to: determine a probability that the one or more wrinkles in the fabric has been removed.
Baker teaches wherein the one or more processors are further configured to: determine a probability that the one or more wrinkles in the fabric has been removed (see para 36, 48-50: teaches determining wrinkles using cameras and further determining if wrinkles have been removed using profile data including parameters, limits, thresholds, etc).
As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to use these teachings of Baker in combination with the teachings of Wilcox, in order to detect, eliminate wrinkles and determine if the wrinkles have been removed within garment assembly system of Wilcox, since by doing so it improves the wrinkle detection process in order to make corrections needed to the garment assembly process which improves the final product.
Wilcox teaches garment assembly process as taught above, but doesn’t specifically teach wherein performing the action comprises causing, in response to the probability satisfying a threshold, the second device to transfer the fabric to a fourth device.
Ahmed teaches wherein performing the action comprises causing, in response to the probability satisfying a threshold, the second device to transfer the fabric to a fourth device (see Col 13 lines 43-61: teaches setting threshold values for quality control which is use to accept or reject parts that meet the determined thresholds “In one embodiment, a quality control and monitoring software module establishes communication with smart digital gauges and other measuring instruments to receive, collect and visualize the wirelessly transmitted measurement data simultaneously from multiple wireless measurement devices through a customized user interface. The software has the following features: Adding and configuring measuring devices, establishing wireless communication, and setting the acquisition rate. The possibility of visualizing the collected data as digital, analog, and graphical, and export it to a file for further analysis. Acceptance and rejection feature. The software enables the user to define threshold measurement values to set the quality control threshold that would indicate the accepted or rejected parts while doing the measurement process and it will alert the user if the reading exceeds the predefined threshold value.”).
As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to use these teachings of Ahmed in combination with the teachings of Wilcox as modified by Baker, in order to set quality control measurements associated to thresholds values at a stage of the process, since it enhances the system by including steps for quality control, thus providing steps for the process which makes sure only using parts (fabric) that would produce the desired outcome to continue to a next stage or as a finish product.
In regards to claim 9, Wilcox as modified by SayyarRodsari doesn’t specifically teach wherein the one or more processors are further configured to: determine a probability that the one or more wrinkles in the fabric has been removed.
Baker teaches wherein the one or more processors are further configured to: determine a probability that the one or more wrinkles in the fabric has been removed (see para 36, 48-50: teaches determining wrinkles using cameras and further determining if wrinkles have been removed using profile data including parameters, limits, thresholds, etc).
As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to use these teachings of Baker in combination with the teachings of Wilcox, in order to detect, eliminate wrinkles and determine if the wrinkles have been removed within garment assembly system of Wilcox, since by doing so it improves the wrinkle detection process in order to make corrections needed to the garment assembly process which improves the final product.
Wilcox doesn’t specifically teach in response to the probability not satisfying a threshold, cause the second device to discard the fabric.
Ahmed teaches in response to the probability not satisfying a threshold, cause the second device to discard the fabric (see Col 13 lines 43-61: teaches setting threshold values for quality control which is use to accept or reject parts that meet the determined thresholds “In one embodiment, a quality control and monitoring software module establishes communication with smart digital gauges and other measuring instruments to receive, collect and visualize the wirelessly transmitted measurement data simultaneously from multiple wireless measurement devices through a customized user interface. The software has the following features: Adding and configuring measuring devices, establishing wireless communication, and setting the acquisition rate. The possibility of visualizing the collected data as digital, analog, and graphical, and export it to a file for further analysis. Acceptance and rejection feature. The software enables the user to define threshold measurement values to set the quality control threshold that would indicate the accepted or rejected parts while doing the measurement process and it will alert the user if the reading exceeds the predefined threshold value.”).
As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to use these teachings of Ahmed in combination with the teachings of Wilcox as modified by Baker, in order to set quality control measurements associated to thresholds values, since it enhances the system by including steps for quality control, thus providing steps for the process which makes sure only using parts (fabric) that would produce the desired outcome.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wilcox (US 2023/0298273) and SayyarRodsari et al. (US 20240094718), as applied to claims above and further in view of Baker et a. (US 20210172105).
In regards to claim 10, Wilcox teaches trained recognition algorithm and learning models (see para 2, 11, 26: trained in garment data including multiple information or a garment, types, etc), but doesn’t specifically teach wherein the third machine learning model is trained on synthetic data indicative of wrinkled fabric images.
Baker teaches wherein the third machine learning model is trained on synthetic data indicative of wrinkled fabric images (see para 36, 46, 48-50: teaches determining wrinkles using cameras and further determining if wrinkles have been removed using profile data including parameters, limits, thresholds, look-up tables, depth data associated to garment that can specify acceptable conditions or criteria for processing of the piece of the product).
As such, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to use these teachings of Baker in combination with the teachings of Wilcox, in order to include all the wrinkle data used in Baker to train the machine learning system within the garment assembly system of Wilcox, since by doing so it improves the wrinkle detection process in order to make corrections needed to the garment assembly process which improves the final product.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Schultz (US 20190125015 A1): Using Neural Networks In Creating Apparel Designs
ANGER (US 20190287150): Customized Textile Measuring, Ordering, And Manufacturing System
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIO M VELEZ-LOPEZ whose telephone number is (571)270-7971. The examiner can normally be reached on M-F 10:30am-5:30pm EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott Baderman, can be reached at telephone number 571-272-3644. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARIO M VELEZ-LOPEZ/
Examiner, Art Unit 2118
/SCOTT T BADERMAN/Supervisory Patent Examiner, Art Unit 2118
Prosecution Insights