DETAILED ACTION
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on November 14, 2025 is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 6-9 and 15-18 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 6 depends from claim 5, which recites “visual clues including at least two or more of;” however, claim 6 does not require “the visual clues” to include “one or more knit parameters.” Therefore, considering claim 5 does not require the feature, “wherein the one or more knit parameters include” does not further limit the scope, because the feature is not required. Applicant can resolve this issue by positively reciting within the claim that the two or more of the visual clues include one or more knit parameters.
Claim 7 is rejected for the same reasons as claim 6.
Claim 8 depends from claim 5, which recites “visual clues including at least two or more of;” however, claim 8 does not require “the visual clues” to include “the shape structure on the microscale”. Therefore, considering claim 5 does not require the feature, “the shape structure on the microscale includes” does not further limit the scope, because the feature is not required. Applicant can resolve this issue by positively reciting within the claim that the two or more of the visual clues include the shape structure on a microscale.
Claim 9 depends from claim 5, which recites “visual clues including at least two or more of;” however, claim 8 does not require “the visual clues” to include “the shape structure on the macroscale”. Therefore, considering claim 5 does not require the feature, “the shape structure on the macroscale includes” does not further limit the scope, because the feature is not required. Applicant can resolve this issue by positively reciting within the claim that the two or more of the visual clues include the shape structure on a macroscale.
Claims 15-18 are rejected for substantially similar reasons as claims 6-9, respectively.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5, 10-14, 19 and 20 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Chen et al. (U.S. Publication No. 2020/0320769 A1, hereinafter referred to as “Chen”).
Regarding claim 1, Chen discloses a method of providing for search and review of an electronic database of knit fabric samples, the method comprising: (“A further advantage is improved accuracy provided in the search for garments or accessories present in one or more digital images. A further advantage is improved speed provided in the search for garments or accessories present in one or more digital images.” “The method may be one including the step of storing the predicted physics parameters into a garment database.” “This includes photographs of over 60,000 unique garment stock keeping units (SKUs) digitised and stored in our garment database “Cantor”.”)(e.g., paragraphs [0019, [0061] and [0194])
providing a plurality of digital images of a plurality of knit fabrics; (“This includes photographs of over 60,000 unique garment stock keeping units (SKUs) digitised and stored in our garment database “Cantor”.” The output of the model can be 1) a multi-class label of fabric types of the garment (e.g. “cotton”, “silk”, “polyester”) and/or associated class probabilities, or 2) an array of decimal values of fabric parameters (e.g. Young's modulus, stress and strain, or model parameters of the garment physics engine used in the virtual fitting system). These predicted physics parameters are stored into a garment database together, as shown in FIG. 6, with all the original garment photos digitised from the garment samples.”)(e.g., figure 6 and paragraphs [0194] and [0260])
organizing the plurality of digital images in the database based upon corresponding visual cues extracted using an image analysis performed on each of the plurality of digital images, wherein the visual cues are construction attributes of the plurality of knit fabrics; (“The method may be one wherein the one or more digital image datasets includes a digital image dataset based on sets of garment mannequin photos which includes metadata and multiple semantic labels associated with sets of garment mannequin photos. An advantage is that this provides a well-organized, richly-structured digital image dataset.” “Using advanced computer vision and deep learning algorithms, one or more photos are analysed and both intrinsic and extrinsic attributes of a garment or other accessories are automatically extracted (e.g. shoes, handbags, glasses), including but not limited to: style, shape, texture, colour, fabric properties. Several different deep neural network models and architectural changes have been applied to model multiple attributes simultaneously from one or more input images, and improve the prediction accuracy, as detailed in Section 2” “The “Physics Analysis Module” is a deep neural network (DNN) model for fabric attribute prediction or regression, as described in Section 2, which analyzes the captured garment images in different phases of the motion and predicts the garment fabric properties and/or model parameters for garment physics simulation. Two network architecture options can be adopted to implement the module; in the first the captured images are merged into one single multi-channel image (assuming RGB images are used it will be of 3×(K+1) channels) and fed as the input of the “Physics Analysis Module”; the second is to use an attribute prediction network based on multiple images input, as illustrated in FIGS. 2 and 3 in Section 2.2.3.” visual cues are considered to be extracted attributes)(e.g., paragraphs [0026], [0178] and [0259])
displaying one or more of the plurality of digital images for review; and (“The method may be one in which an alternative user interface (UI) for garment or accessory retrieval or search is provided, based on direct attribute inputs, in which a user is presented with a number of attribute keyword filters or drop-down lists, so that the user can reduce the search results list and find the desired item by providing a few keywords that best describe the item they are looking for. The method may be one including a step of performing a visual search from text descriptions of a garment or accessories, from an online fashion magazine, a fashion-related social network page, or on a retailer website. The method may be one in which after the initial search results are provided, if the desired item is not in the search results list, the user is then allowed to further refine the search results by clicking and selecting a few items from the initial search results which they think are visually similar to the item they are looking for.”)(e.g., figures 10 and 14 and paragraphs [0085]-[0087] and [0308])
providing for search of the database using one or more of the visual cues. (“The method may be one in which an alternative user interface (UI) for garment or accessory retrieval or search is provided, based on direct attribute inputs, in which a user is presented with a number of attribute keyword filters or drop-down lists, so that the user can reduce the search results list and find the desired item by providing a few keywords that best describe the item they are looking for. The method may be one including a step of performing a visual search from text descriptions of a garment or accessories, from an online fashion magazine, a fashion-related social network page, or on a retailer website. The method may be one in which after the initial search results are provided, if the desired item is not in the search results list, the user is then allowed to further refine the search results by clicking and selecting a few items from the initial search results which they think are visually similar to the item they are looking for.”)(e.g., figures 10 and 14 and paragraphs [0085]-[0087] and [0308]).
Regarding claim 2, Chen discloses the method of claim 1. Chen further discloses wherein the organizing the plurality of digital images utilizes an artificial intelligence (AI) system to generate and assign the visual cues to each of the plurality of digital images based on the image analysis. (“Achieving an accurate garment physics simulation is essential for rendering a photo-realistic virtual avatar image. We can first predict garment attributes (e.g. colour, pattern, material type, washing method) using a machine learning model, such as the deep neural network classifiers or regressors described in Section 2, from one or more garment images and/or garment texture samples, and then map them to a number of fabric physical properties (e.g. stiffness, elasticity, friction parameters) and/or model parameters of the 3D physics model. The garment attribute predictor can be used to initialize the model parameters of the garment physics simulator from the predicted physics attributes or material parameters so that a more accurate draping simulation can be achieved. Fig.5 shows an illustration of an example of using image-based garment attribute prediction to initialize the model parameters for precise garment physics simulation.”)(e.g., paragraph [0253]).
Regarding claim 3, Chen discloses the method of claim 1. Chen further discloses further comprising: allowing for selecting at least one of the one or more of the visual cues as search criteria; (“An alternative user interface (UI) for garment or accessory retrieval or search is based on direct attribute inputs. In such a UI, users are presented a number of attribute keyword filters or drop-down lists, as exemplified in FIG. 14, so that they can quickly reduce the candidate list and find the desired item by providing a few keywords that best describe the item they are looking for (e.g. “dress”, “black”, “no pattern”, “sleeve to the elbow”, “hem to the knee”, and “no collar”). An end-to-end system diagram of an example attributed-based garment retrieval or search system is illustrated in FIG. 15.”)(e.g., figure 15 and paragraphs [0085]-[0087] [0308])
searching the database based upon the search criteria; and (“The method may be one in which an alternative user interface (UI) for garment or accessory retrieval or search is provided, based on direct attribute inputs, in which a user is presented with a number of attribute keyword filters or drop-down lists, so that the user can reduce the search results list and find the desired item by providing a few keywords that best describe the item they are looking for. The method may be one including a step of performing a visual search from text descriptions of a garment or accessories, from an online fashion magazine, a fashion-related social network page, or on a retailer website. The method may be one in which after the initial search results are provided, if the desired item is not in the search results list, the user is then allowed to further refine the search results by clicking and selecting a few items from the initial search results which they think are visually similar to the item they are looking for.”)(e.g., paragraphs [0085]-[0087] and [0308])
updating the displaying of the one or more of the plurality of digital images for review based upon the search criteria. (“The method may be one in which an alternative user interface (UI) for garment or accessory retrieval or search is provided, based on direct attribute inputs, in which a user is presented with a number of attribute keyword filters or drop-down lists, so that the user can reduce the search results list and find the desired item by providing a few keywords that best describe the item they are looking for.” “In such a UI, users are presented a number of attribute keyword filters or drop-down lists, as exemplified in FIG. 14, so that they can quickly reduce the candidate list and find the desired item by providing a few keywords that best describe the item they are looking for (e.g. “dress”, “black”, “no pattern”, “sleeve to the elbow”, “hem to the knee”, and “no collar”).”)(e.g., paragraphs [0085]-[0087] and [0308])
Regarding claim 4, Chen discloses the method of claim 1. Chen further discloses further comprising: allowing for selecting one of the plurality of digital images; and (“The method may be one including a step of performing a visual search from text descriptions of a garment or accessories, from an online fashion magazine, a fashion-related social network page, or on a retailer website. The method may be one in which after the initial search results are provided, if the desired item is not in the search results list, the user is then allowed to further refine the search results by clicking and selecting a few items from the initial search results which they think are visually similar to the item they are looking for.”)(e.g., paragraphs [0086]-[0087]
displaying data regarding the knit fabric of the one of the plurality of digital images, wherein the data includes one or more of: colors used, fiber content, machine callout, wales per inch, thickness, grams per square meter, courses per inch, courses to wales ratio, indication of single knit or double knit, color appearance class, production history, haptic data and fiber data. (“The output of the model can be 1) a multi-class label of fabric types of the garment (e.g. “cotton”, “silk”, “polyester”) and/or associated class probabilities, or 2) an array of decimal values of fabric parameters (e.g. Young's modulus, stress and strain, or model parameters of the garment physics engine used in the virtual fitting system). These predicted physics parameters are stored into a garment database together, as shown in FIG. 6, with all the original garment photos digitised from the garment samples.”)(e.g., figure 18 and paragraphs [0260]).
Regarding claim 5, Chen discloses the method of claim 1. Chen further discloses wherein the visual cues include at least two or more of: indication of front or back, a fabric color, a manufacturer assigned tag, a fabric texture and color, a fabric structure at a macroscale that provides a pattern, a column width, a column prominence, a row prominence, a shape structure on a microscale and one or more knit parameters. (“The output of the model can be 1) a multi-class label of fabric types of the garment (e.g. “cotton”, “silk”, “polyester”) and/or associated class probabilities, or 2) an array of decimal values of fabric parameters (e.g. Young's modulus, stress and strain, or model parameters of the garment physics engine used in the virtual fitting system). These predicted physics parameters are stored into a garment database together, as shown in FIG. 6, with all the original garment photos digitised from the garment samples.”)(e.g., figure 18 and paragraphs [0260]).
Regarding claim 10, Chen discloses the method of claim 1. Chen further discloses further comprising: allowing for selecting one of the plurality of digital images; and (“The attribute-based retrieval approach can be extended to an automated system that performs visual search from text descriptions of a garment or accessories, from the online fashion magazine, fashion-related social network page, or on the retailer website. An illustration of an example of such a derived attribute-based search garment or accessory retrieval system with an input of text descriptions is given in FIG. 16.” “Commercially, this outfit search approach can be extended into an intelligent fashion-recommendation application that combines the steps of 1) applying an outfit search from an image on the internet or online fashion magazine to find all the similar garments or accessories provided by the target retailer/brand and/or are available on a specified website, and 2) display the “similar” outfit comprising of those garments or accessories and the source item on the 3D virtual avatar,”)(e.g., paragraphs [0317] and [0419])
facilitating placing a purchase order for one or more of the fabric samples associated with the one of the plurality of digital images. (“3) recommend the items by providing the links for item shopping.”)(e.g., paragraph [0419]).
Regarding claim 11, Chen discloses a system allowing for review of a digital representation of a knit fabric, the system comprising: (“A related system is also provided.” “A further advantage is improved accuracy provided in the search for garments or accessories present in one or more digital images. A further advantage is improved speed provided in the search for garments or accessories present in one or more digital images.” “The method may be one including the step of storing the predicted physics parameters into a garment database.” “This includes photographs of over 60,000 unique garment stock keeping units (SKUs) digitised and stored in our garment database “Cantor”.”)(e.g., abstract; paragraphs [0019, [0061] and [0194])
at least one processor; and (processor)(e.g., paragraph [0105]) at least one memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform actions comprising: (computer implemented)(e.g., abstract and paragraph [0128])
access an electronic database of a plurality of digital images of a plurality of knit fabrics, (“This includes photographs of over 60,000 unique garment stock keeping units (SKUs) digitised and stored in our garment database “Cantor”.” The output of the model can be 1) a multi-class label of fabric types of the garment (e.g. “cotton”, “silk”, “polyester”) and/or associated class probabilities, or 2) an array of decimal values of fabric parameters (e.g. Young's modulus, stress and strain, or model parameters of the garment physics engine used in the virtual fitting system). These predicted physics parameters are stored into a garment database together, as shown in FIG. 6, with all the original garment photos digitised from the garment samples.”)(e.g., figure 6 and paragraphs [0194] and [0260])
select, based upon a search criteria, one or more of the plurality of digital images having shared visual cues extracted using an image analysis performed on each of the plurality of digital images, wherein the visual cues are construction attributes of the plurality of knit fabrics; and (“An alternative user interface (UI) for garment or accessory retrieval or search is based on direct attribute inputs. In such a UI, users are presented a number of attribute keyword filters or drop-down lists, as exemplified in FIG. 14, so that they can quickly reduce the candidate list and find the desired item by providing a few keywords that best describe the item they are looking for (e.g. “dress”, “black”, “no pattern”, “sleeve to the elbow”, “hem to the knee”, and “no collar”). An end-to-end system diagram of an example attributed-based garment retrieval or search system is illustrated in FIG. 15.”)(e.g., figure 15 and paragraphs [0085]-[0087] [0308])
display the one or more of the plurality of digital images for review. (“The output of the model can be 1) a multi-class label of fabric types of the garment (e.g. “cotton”, “silk”, “polyester”) and/or associated class probabilities, or 2) an array of decimal values of fabric parameters (e.g. Young's modulus, stress and strain, or model parameters of the garment physics engine used in the virtual fitting system). These predicted physics parameters are stored into a garment database together, as shown in FIG. 6, with all the original garment photos digitised from the garment samples.”)(e.g., figure 18 and paragraphs [0260]).
Regarding claim 12, Chen discloses the system of claim 11. Chen further discloses wherein the search criteria correspond to one or more of the visual cues. (“An alternative user interface (UI) for garment or accessory retrieval or search is based on direct attribute inputs. In such a UI, users are presented a number of attribute keyword filters or drop-down lists, as exemplified in FIG. 14, so that they can quickly reduce the candidate list and find the desired item by providing a few keywords that best describe the item they are looking for (e.g. “dress”, “black”, “no pattern”, “sleeve to the elbow”, “hem to the knee”, and “no collar”). An end-to-end system diagram of an example attributed-based garment retrieval or search system is illustrated in FIG. 15.”)(e.g., paragraphs [0085]-[0087] and [0307]-[0309]).
Regarding claim 13, Chen discloses the system of claim 11. Chen further discloses wherein the at least one processor performs further actions comprising: allow for selection of at least one of the one or more of the plurality of digital images based upon the review; and (“The attribute-based retrieval approach can be extended to an automated system that performs visual search from text descriptions of a garment or accessories, from the online fashion magazine, fashion-related social network page, or on the retailer website. An illustration of an example of such a derived attribute-based search garment or accessory retrieval system with an input of text descriptions is given in FIG. 16.” “Commercially, this outfit search approach can be extended into an intelligent fashion-recommendation application that combines the steps of 1) applying an outfit search from an image on the internet or online fashion magazine to find all the similar garments or accessories provided by the target retailer/brand and/or are available on a specified website, and 2) display the “similar” outfit comprising of those garments or accessories and the source item on the 3D virtual avatar,”)(e.g., paragraphs [0317] and [0419])
allow for placement of a purchase order for one or more fabric samples associated with the at least one of the one or more of the plurality of digital images. (“3) recommend the items by providing the links for item shopping.”)(e.g., paragraph [0419]).
Regarding claim 14, Chen discloses the system of claim 11. Chen further discloses wherein the visual cues include at least two or more of: indication of front or back, a fabric color, a manufacturer assigned tag, a fabric texture and color, a fabric structure at a macroscale that provides a pattern, a column width, a column prominence, a row prominence, a shape structure on a microscale and one or more knit parameters. (“The output of the model can be 1) a multi-class label of fabric types of the garment (e.g. “cotton”, “silk”, “polyester”) and/or associated class probabilities, or 2) an array of decimal values of fabric parameters (e.g. Young's modulus, stress and strain, or model parameters of the garment physics engine used in the virtual fitting system). These predicted physics parameters are stored into a garment database together, as shown in FIG. 6, with all the original garment photos digitised from the garment samples.”)(e.g., figure 18 and paragraphs [0260]).
Regarding claim 19, Chen discloses the system of claim 11. Chen further discloses wherein the visual cues are extracted using an artificial intelligence (AI) system to generate and assign the visual cues to each of the digital images based on the image analysis. (“Achieving an accurate garment physics simulation is essential for rendering a photo-realistic virtual avatar image. We can first predict garment attributes (e.g. colour, pattern, material type, washing method) using a machine learning model, such as the deep neural network classifiers or regressors described in Section 2, from one or more garment images and/or garment texture samples, and then map them to a number of fabric physical properties (e.g. stiffness, elasticity, friction parameters) and/or model parameters of the 3D physics model. The garment attribute predictor can be used to initialize the model parameters of the garment physics simulator from the predicted physics attributes or material parameters so that a more accurate draping simulation can be achieved. Fig.5 shows an illustration of an example of using image-based garment attribute prediction to initialize the model parameters for precise garment physics simulation.”)(e.g., paragraph [0253]).
Regarding claim 20, Chen discloses the system of claim 11. Chen further discloses wherein the at least one processor performs further actions comprising: allow for selecting one of the plurality of digital images; and (“The attribute-based retrieval approach can be extended to an automated system that performs visual search from text descriptions of a garment or accessories, from the online fashion magazine, fashion-related social network page, or on the retailer website. An illustration of an example of such a derived attribute-based search garment or accessory retrieval system with an input of text descriptions is given in FIG. 16.” “Commercially, this outfit search approach can be extended into an intelligent fashion-recommendation application that combines the steps of 1) applying an outfit search from an image on the internet or online fashion magazine to find all the similar garments or accessories provided by the target retailer/brand and/or are available on a specified website, and 2) display the “similar” outfit comprising of those garments or accessories and the source item on the 3D virtual avatar,”)(e.g., paragraphs [0317] and [0419])
display data regarding the knit fabric of the one of the plurality of digital images, wherein the data includes one or more of: colors used, fiber content, machine callout, wales per inch, thickness, grams per square meter, courses per inch, courses to wales ratio, indication of single knit or double knit, color appearance class, production history, haptic data and fiber data. (“The output of the model can be 1) a multi-class label of fabric types of the garment (e.g. “cotton”, “silk”, “polyester”) and/or associated class probabilities, or 2) an array of decimal values of fabric parameters (e.g. Young's modulus, stress and strain, or model parameters of the garment physics engine used in the virtual fitting system). These predicted physics parameters are stored into a garment database together, as shown in FIG. 6, with all the original garment photos digitised from the garment samples.”)(e.g., figure 18 and paragraphs [0260]).
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.
The factual inquiries 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 6, 7, 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Hoover (U.S. Publication No. 2022/0147734 A1, hereinafter referred to as “Hoover”).
Regarding claim 6, Chen discloses the method of claim 5. Chen discloses fabric properties; however, Chen does not appear to specifically disclose wherein the one or more knit parameters include at least one of a complexity and a courses to wales ratio in addition to one or more of: grams per square meter, wales per inch and courses per inch.
On the other hand, Hoover, which relates to textile fabric construction (title) does disclose knit parameters include at least one of a complexity and a courses to wales ratio in addition to one or more of: grams per square meter, wales per inch and courses per inch. (“Some examples of feasibility issues may include, but are not limited to, knitting speed as a function of construction, defects/yard due to the complexity of the stitch structure, running errors, and finishing processes or chemistry interactions that impact the quality of the finished goods.” “FIG. 10A shows a loop map 1002 consistent with at least one embodiment of this disclosure. Loop map 1002 may have a first course, 1, that includes two different types of stitches, such as knit and tuck. A second course 2, may be a single stitch type, such as knit. A first wale, 1, may include two different stitch types, such as knit and tuck, and a second wale, 2, may be a single stitch type, such as knit. As shown in FIG. 10A, course and wales may be approximately the same size and are proportionally dependent on the ratio of courses per inch (CPI and wales per inch (WPI).”)(e.g., figures 10A-10B and paragraphs [0058] and [0105]).
Chen discloses a computer implemented method for predicting garment or accessory attributes using deep learning techniques. E.g., abstract. In Chen, multiple attributes are extracted to include style, shape, texture, color and fabric properties. However, Chen does not specifically disclose that the visual clues include one or more knit parameters to include complexity and a courses to wales ratio in addition to one or more of grams per square meter, wales per square inch and courses per inch. On the other hand, Hoover, which relates to maintaining data for fabric construction (title), does disclose that it is known that fabric properties can include such parameters, which is useful for understanding and selecting particular fabrics. E.g., paragraphs [0005]-[0008]. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the additional fabric properties as provided by Hoover to Chen to further enhance the information within the garment database for user’s performing search/retrieval of the garments to meet the user’s needs.
Regarding claim 7, Chen discloses in view of Hoover discloses the method of claim 6. Hoover further discloses wherein the complexity indicates if a back of a fabric is sufficiently complex that it can be used as a front of the fabric as measured by a number of knit stitches as compared to a number of tuck stitches plus a number of miss stitches. (“Feasibility metrics supply insight into manufacturing complexity, speed, and quality—from knitting to dyeing and finishing.” “As disclosed herein, circular knitted fabrics may include combinations of knit, tuck, and/or miss stitches. Table 1 shows examples of stitch type recognition. As shown in Table 1, a knit stitch may consist of a loop of yarn being formed by a needle. The knit stitch may be formed by the needle when a new loop of yarn is pulled through an old loop which is then cast off. A tuck stitch is a stitch producing a tuck or open effect by having a needle hold more than one loop of yarn at a time. A miss stitch is formed when the needle holds an old loop and does not receive a new yarn. It then connects two loops of the same course that are not in adjacent wales.” “If the image contains both knit and miss stitch types, the image may be categorized as “knit and miss.” For images that contain knit, tuck, and miss stitch types, the images may be categorized as “knit, miss, and tuck.”)(e.g., paragraphs [0058], [0070], [0076] and [0091]).
Regarding claim 15, Chen discloses the system of claim 14. Chen discloses fabric properties; however, Chen does not appear to specifically disclose wherein the one or more knit parameters include at least one of a complexity and a courses to wales ratio in addition to one or more of: grams per square meter, wales per inch and courses per inch.
On the other hand, Hoover, which relates to textile fabric construction (title) does disclose wherein the one or more knit parameters include at least one of a complexity and a courses to wales ratio in addition to one or more of: grams per square meter, wales per inch and courses per inch. (“Some examples of feasibility issues may include, but are not limited to, knitting speed as a function of construction, defects/yard due to the complexity of the stitch structure, running errors, and finishing processes or chemistry interactions that impact the quality of the finished goods.” “FIG. 10A shows a loop map 1002 consistent with at least one embodiment of this disclosure. Loop map 1002 may have a first course, 1, that includes two different types of stitches, such as knit and tuck. A second course 2, may be a single stitch type, such as knit. A first wale, 1, may include two different stitch types, such as knit and tuck, and a second wale, 2, may be a single stitch type, such as knit. As shown in FIG. 10A, course and wales may be approximately the same size and are proportionally dependent on the ratio of courses per inch (CPI and wales per inch (WPI).”)(e.g., figures 10A-10B and paragraphs [0058] and [0105]).
It would have been obvious to combine Hoover with Chen for the same reasons as provided in claim 6, above.
Regarding claim 16, Chen in view of Hoover discloses the system of claim 15. Hoover further discloses wherein the complexity indicates if a back of a fabric is sufficiently complex that it can be used as a front of the fabric as measured by a number of knit stitches as compared to a number of tuck stitches plus a number of miss stitches. (“Feasibility metrics supply insight into manufacturing complexity, speed, and quality—from knitting to dyeing and finishing.” “As disclosed herein, circular knitted fabrics may include combinations of knit, tuck, and/or miss stitches. Table 1 shows examples of stitch type recognition. As shown in Table 1, a knit stitch may consist of a loop of yarn being formed by a needle. The knit stitch may be formed by the needle when a new loop of yarn is pulled through an old loop which is then cast off. A tuck stitch is a stitch producing a tuck or open effect by having a needle hold more than one loop of yarn at a time. A miss stitch is formed when the needle holds an old loop and does not receive a new yarn. It then connects two loops of the same course that are not in adjacent wales.” “If the image contains both knit and miss stitch types, the image may be categorized as “knit and miss.” For images that contain knit, tuck, and miss stitch types, the images may be categorized as “knit, miss, and tuck.”)(e.g., paragraphs [0058], [0070], [0076] and [0091]).
Claims 8, 9, 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Zheng, Dejun (“Intelligent texture-based pattern search, classification and interpolation for woven fabric design,” Ph.D. thesis, 2012 POLYU ELECTRONIC THESIS, The Hong Kong Polytechnic University,” 210 pages, https://theses.lib.polyu.edu.hk/handle/200/6814, 2012, hereinafter referred to as “Zheng”).
Regarding claim 8, Chen discloses the method of claim 5. Chen discloses fabric properties; however, Chen does not appear to specifically disclose wherein the shape structure on the microscale includes one or more of: a dash, a smile, a rectangle, a round, a diamond, a square, a triangle, a hexagon, a polygon, a dot, a zig zag and a diagonal stitch.
On the other hand, Zheng, does disclose wherein the shape structure on the microscale includes one or more of: a dash, a smile, a rectangle, a round, a diamond, a square, a triangle, a hexagon, a polygon, a dot, a zig zag and a diagonal stitch. (“In order to capture fabric macro and micro textures, novel fabric image acquisition methods are proposed and the necessities of image acquisition conditions are discussed. Automatic and interactive fabric pattern recognition methods are developed. Fabric pattern recognition produces weave pattern images, which are key fingerprints for fabric texture search. Fabric attributes and features in OAR model are tailored to describe essential characteristics of fabric textures for fabric swatch documentation and searching.” “The texel of the weave pattern is shown on the right in Figure 39 (the small square pattern). Fabric weave patterns are characterized by the size of texel and the shape of the grid.” – square pattern is a polygon or a diamond)(e.g., figures 39 and 40 pages 108 and 111).
Chen discloses a computer implemented method for predicting garment or accessory attributes using deep learning techniques. E.g., abstract. In Chen, multiple attributes are extracted to include style, shape, texture, color and fabric properties. However, Chen does not specifically disclose that the visual clues include fabric structure on a microscale or macroscale as consideration for the visual clues. On the other hand, Zheng, which is a thesis paper, does disclose that fabric properties can include different weave patterns on a microscale and macroscale basis. Zheng further provides “(f)abric pattern recognition produces weave pattern images, which are key fingerprints for fabric texture search. Fabric attributes and features in OAR model are tailored to describe essential characteristics of fabric textures for fabric swatch documentation and searching.” E.g., page 111.Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s claimed invention to incorporate the fabric structure of both the microscale and macroscale to enhance the manner in which fabric textures are searched and documented.
Regarding claim 9, Chen discloses the method of claim 5. Chen discloses fabric properties; however, Chen does not appear to specifically disclose wherein the fabric structure at the macroscale includes one or more of: a checkerboard, a diamond board, checkers, a smooth, diamond checkers and a diagonal line.
On the other hand, Zheng, does disclose wherein the fabric structure at the macroscale includes one or more of: a checkerboard, a diamond board, checkers, a smooth, diamond checkers and a diagonal line. (“In order to capture fabric macro and micro textures, novel fabric image acquisition methods are proposed and the necessities of image acquisition conditions are discussed. Automatic and interactive fabric pattern recognition methods are developed. Fabric pattern recognition produces weave pattern images, which are key fingerprints for fabric texture search. Fabric attributes and features in OAR model are tailored to describe essential characteristics of fabric textures for fabric swatch documentation and searching.” “The texel of the weave pattern is shown on the right in Figure 39 (the small square pattern). Fabric weave patterns are characterized by the size of texel and the shape of the grid.” – square pattern is a polygon or a diamond)(e.g., figures 39 and 40 pages 108 and 111).
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine Zheng with Chen for the same reasons as claim 8.
Regarding claim 17, Chen discloses the system of claim 14. Chen discloses fabric properties; however, Chen does not appear to specifically disclose wherein the shape structure on the microscale includes one or more of: a dash, a smile, a rectangle, a round, a diamond, a square, a triangle, a hexagon, a polygon, a dot, a zig zag and a diagonal stitch.
On the other hand, Zheng, does disclose wherein the shape structure on the microscale includes one or more of: a dash, a smile, a rectangle, a round, a diamond, a square, a triangle, a hexagon, a polygon, a dot, a zig zag and a diagonal stitch. (“In order to capture fabric macro and micro textures, novel fabric image acquisition methods are proposed and the necessities of image acquisition conditions are discussed. Automatic and interactive fabric pattern recognition methods are developed. Fabric pattern recognition produces weave pattern images, which are key fingerprints for fabric texture search. Fabric attributes and features in OAR model are tailored to describe essential characteristics of fabric textures for fabric swatch documentation and searching.” “The texel of the weave pattern is shown on the right in Figure 39 (the small square pattern). Fabric weave patterns are characterized by the size of texel and the shape of the grid.” – square pattern is a polygon or a diamond)(e.g., figures 39 and 40 pages 108 and 111).
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine Zheng with Chen for the same reasons as claim 8.
Regarding claim 18, Chen discloses the system of claim 14. Chen discloses fabric properties; however, Chen does not appear to specifically disclose wherein the fabric structure at the macroscale includes one or more of: a checkerboard, a diamond board, checkers, a smooth, diamond checkers and a diagonal line.
On the other hand, Zheng, does disclose wherein the fabric structure at the macroscale includes one or more of: a checkerboard, a diamond board, checkers, a smooth, diamond checkers and a diagonal line. (“In order to capture fabric macro and micro textures, novel fabric image acquisition methods are proposed and the necessities of image acquisition conditions are discussed. Automatic and interactive fabric pattern recognition methods are developed. Fabric pattern recognition produces weave pattern images, which are key fingerprints for fabric texture search. Fabric attributes and features in OAR model are tailored to describe essential characteristics of fabric textures for fabric swatch documentation and searching.” “The texel of the weave pattern is shown on the right in Figure 39 (the small square pattern). Fabric weave patterns are characterized by the size of texel and the shape of the grid.” – square pattern is a polygon or a diamond)(e.g., figures 39 and 40 pages 108 and 111).
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine Zheng with Chen for the same reasons as claim 8.
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon is considered pertinent to applicant's disclosure.
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/RICHARD L BOWEN/ Primary Examiner, Art Unit 2165
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