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 the entry of the following papers:
Amended claims filed 12/22/2025.
Applicant’s arguments/remarks made in amendment submitted 12/22/2025.
Claims 1, 16, and 20 are amended. Claims 3 and 17 are canceled.
Claims 1-2, 4-16, and 18-20 are presented for examination.
Response to Arguments
Applicant presents arguments. Each is addressed.
Applicant argues “Independent claims have been amended in a manner that is believed to overcome the rejection under 35 U.S.C. § 101.” (Remarks, page 6, paragraph 2, line 1.) Examiner agrees. The rejections under 35 U.S.C. §101 have been withdrawn.
Applicant argues “The references do not teach …(applicant lists several limitations) as amended in the independent claims. (Remarks, page 9, paragraph 3, line 1.) The argument is moot in view of new grounds of rejection necessitated by amendment.
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.
Claims 1-2, 4-10, 16, and 18-20 are rejected under 35 U.S.C. § 103 as being unpatentable over Bright et al (System and Method for Providing Customers with Personalized Information About Products (US2014/0244431 A1), herein Bright), Sturlaugson et al (Advanced Analytical Infrastructure for Machine Learning (US 2016/0358099 A1), herein Sturlaugson), and Wang, et al (Interactive 3D garment design with constrained contour curves and style curves, herein Wang).
Regarding claim 1,
Bright teaches a method, comprising:
identifying one or more target audiences for a new item to be manufactured, wherein the new item is based on a base garment (Bright, paragraph [0094], line 1 “Various aspects of the systems and methods for practicing features of the present invention may be implemented on one or more computer systems, such as the exemplary computer system 800 shown in FIG. 8. Computer system 800 includes input device(s) 802, output device(s) 801, processor 803, memory system 804 and storage 806, all of which are coupled, directly or indirectly, via interconnection mechanism 805, which may comprise one or more buses, Switches, networks and/or any other Suitable interconnection.” And, paragraph [0005], line 1 “Accordingly, some embodiments of the invention provide information regarding products that is personalized to each consumer. Data about consumers may include such information as age, demographics, location, height, weight, body measurements…” and, paragraph [0008], line 4 “…may provide consumer with personalized information about an item of apparel using data about the consumer…”
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In other words, method is method, personalized to consumer…consumer demographic is identifying one or more target audiences, products is new item to be manufactured, and apparel is based on a base garment.), wherein
the base garment is associated with one or more fixed size components and one or more variable size components (Bright, paragraph [0035] “FIG.3 depicts in greater detail data collected for an item in act 102 (FIG. 1). Data describing an item may comprise a de facto “spec sheet” (e.g., created by an expert through measurement of the item), as indicated at 301 and 303-305, a spec sheet or technical package (e.g., provided by a garment manufacturer or retailer), as indicated at 302-305. Expert samples and measurements of the item (e.g., in stretched and relaxed states, which may be determined using a device to stretch and/or relax the item), as indicated at 306-307, may be merged with the nominal item measurement data indicated at 305, as indicated at 308, and then analyzed to determine mean values and variances, as indicated at 309. This information may then be stored in a database of information about items, as indicated at 318.”
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FIG. 1
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In other words, de facto spec sheet is a base garment associated with one or more fixed size components and analyzed to determine… variances is one or more variable size components.), wherein
the one or more target audiences are identified based on one or more of a time frame, a purchase history, a likelihood of purchase, a length as a customer, or sizing information of the customer (Bright, abstract, line 1 “Embodiments of the invention may provide a personalized fit prediction to a particular consumer about a particular product. Personalization of the fit prediction may be accomplished by processing various types of data, including data about consumers, products, and previous purchases by consumers.” Examiner notes the specification of the instant application recites “Target audiences may be selected based on one or more characteristics including time frame, purchase history, likelihood of purchase, length as a customer, sizing information of the customer, etc. For example, the target audience may be customers that are likely to purchase a product in the next week. Another target audience may be customers sharing a subset of size measurements.” (Specification, paragraph [0026], line 1.) Based on this, Examiner is interpreting “audience” as a set of customers with some type of similar characteristics, such as age, zip code, sex, etc. In other words, particular consumer is one or more audiences, and previous purchases by consumers is purchase history which is one or more of a time frame, a purchase history, a likelihood of purchase, a length as a customer, or sizing information of the customer.);
[determining a specific learning model configuration from a plurality of potential model configurations]
based on an analysis of data corresponding to a first target audience or a second target audience, and wherein the first target audience is different form the second target audience (Bright, FIG. 6
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In other words, (601) user self-reports or modifies demographic information, (602) user self-reports or modifies style/lifestyle preferences, and (603) user self-reports or modifies body heuristics, all of which cause (606) system immediately re-scores user in Fit Prediction Engine and updates fit prediction scores and user interface in near-real time is based on an analysis of data of a first target audience or a second target audience where the first target audience is different from the second target audience.);
training a machine learning model using data for the specific machine learning model configuration corresponding to a corpus of garment sizing profile data and user feedback data to predict a size fit satisfaction for a variable size component of the one or more variable size components (Bright, FIG. 4, and, paragraph [0038], line 1 “In some embodiments, developing a set of rules used to make a fit prediction involves training the machine learning engine on historical data. FIG. 4 depicts example processing performed by, and data used to train, a machine learning engine implemented in accordance with embodiments of the invention.” and, paragraph [0033], line 1 “Other measurement data may include current preferred brand, style, and/or size for a specified garment category (e.g., Levi’s, 501, 34" waist, 32" inseam) and symbolic descriptions of fit and style preferences (e.g., fashion forward, conservative, form-fitting), as indicated at 218. This data may be merged with information gleaned from the consumer's web browsing history, as indicated at 219, to identify the consumer's preferences, as indicated at 220-221.”
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In other words, training the machine learning engine on historical data is training a machine learning model using data, and measurement data is corresponding to garment sizing data, and consumer’s preference is user feedback data.); wherein
training the machine learning model (Bright, FIG. 4, and, paragraph [0014], line 1 “ FIG. 4 is a block diagram depicting example processing and data used to train and/or bootstrap a machine learning engine, in accordance with some embodiments of the invention.” In other words, train…a machine learning engine is training the machine learning model.) comprises
[generating a base to create a non-linear function using spline functions that maps a feature space into multiple dimensions];
using the machine learning model to determine a value for at least one of the one or more variable size components [specific to a silhouette category] of the base garment that correspond to an optimal predicted size fit satisfaction (Bright, FIG. 3, and, paragraph [0006] “Some embodiments of the invention provide a fit prediction engine that employs output of the machine learning engine to provide information about products that is personalized to a particular consumer. For example, the fit prediction engine may employ a decision tree, neural network and/or other output produced by a machine learning engine to personalize information about a product for a consumer. The fit prediction engine may generate information relating to any kind of fit, including but not limited to whether a product is the appropriate size and shape for a consumer. For example, a determination whether a product is likely to fit a consumer may consider the consumer's aesthetic preferences, age, style preferences, sensibilities, and/or any of numerous other factors.” In other words, machine learning engine is machine learning model, generate …appropriate size is value of at least one or more size components, and likely to fit a consumer is optimal predicted size fit satisfaction.) by
[applying the non-linear function]; and
using the determined value of the variable size component in creating the new item with a sizing variation based on the determined value (Bright, paragraph [0037] “Any or all of the data described with reference to FIGS. 2 and 3 may be used by a machine learning engine to define the criteria whereby a fit prediction is made, and adjust those criteria over time. In some embodiments, machine learning engine may analyze these and/or data from other sources to define generalized rules which allow a fit prediction to be produced for a given consumer and item. The machine learning engine may be routinely re-run, whether in real-time or at prescheduled intervals, to identify trends, correlations, and relationships in the data and incorporate new findings into a newly defined set of rules, algorithms, and/or models. Thus, fit predictions employing these rules, algorithms, and/or models may become more accurate over time, as data is augmented, changes, or is supplemented.” In other words, fit prediction to be produced for a given consumer and item is using the determined value in creating the new item, and augmented, changes, supplemented, is with a sizing variation based on the determined value.), including by
initiating physical manufacturing of the new item is based on the one or more fixed sized components and at least the determined value of the variable size component thereby
producing a tangible garment having the machine learning model determined value for a physical size of the tangible garment (Bright, paragraph [0091], line 4 “For example, embodiments of the invention may be employed to generate garment pattern outputs optimized for a specific consumer, such as which may be used by a manufacturer to produce custom apparel items or custom footwear which suits the specific consumers unique body profile and style preferences.” Examiner notes that the specification of the instant application recites “The determined variable size component values include the measurements necessary for the garment to be manufactured in the newly created size.” (Specification, paragraph [0019], line 8.) There is no description for selecting fabric, cutting fabric, placing fabric, sewing fabric, controlling actuators, etc. in either the specification or claims. There is no description of manufacturing that one of ordinary skill in the art would recognize. There is no specific definition of manufacturing found in the specification. Therefore, Examiner is interpreting “initiating physical manufacturing” as prepare a list of measurements that describe the garment and allow for it to be manufactured. In other words, generate garment pattern outputs… which may be used by a manufacturer to produce custom apparel items is initiating physical manufacturing of the new item based on the one or more fixed size components and the variable size component.).
Thus far, Bright does not explicitly teach determining a specific learning model configuration from a plurality of potential model configurations.
Sturlaugson teaches determining a specific learning model configuration from a plurality of potential model configurations (Sturlaugson, Fig. 3, and, paragraph [0006], line 8 “The data input module is configured to receive a dataset and a selection of machine learning models. Each machine learning model includes a machine learning algorithm from the machine learning algorithm library and one or more associated parameter values. The experiment module is configured to train and evaluate each machine learning model to produce a performance result for each machine learning model. The aggregation module is configured to aggregate the performance results for all of the machine learning models to form performance comparison statistics.”
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Examiner notes there is no description in the specification for “determining a specific learning model configuration.” The only mention of “configuration” is in relation to computer system architectures. The specification recites “As will be apparent, other computer system architectures and configurations can be used to perform the described product generation technique.” (Specification, paragraph [0031], line 2.) Examiner notes that the specification recites “At 107, applicable machine learning models are identified. For example, one or more machine learning models are identified for predicting a size fit satisfaction.” (Specification, paragraph [0027], line 1.) Therefore, Examiner is interpreting that “determining a specific learning model configuration” is selecting a machine learning model from a plurality of machine learning models. In other words, from Fig. 3, train and evaluate machine learning models, etc., and then build deployable machine learning model is determining a specific learning model configuration from a plurality of potential model configurations.)
Both Bright and Sturlaugson are directed to using machine learning, among other things. Bright teaches a method for identifying one or more target audiences for a new item to be manufactured, using machine learning to determine a value for at least one of the one or more variable size components, and providing the determined value for use in creating the new item with a sizing variation, but does not explicitly teach determining a specific learning model configuration. Sturlaugson teaches determining a specific learning model configuration.
In view of the teaching of Bright, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Sturlaugson into Bright. This would result in a method for identifying one or more target audiences for a new item to be manufactured, using machine learning to determine a value for at least one of the one or more variable size components, providing the determined value for use in creating the new item with a sizing variation, and determining a specific learning model configuration.
One of ordinary skill in the art would be motivated to do this because different models can be tailored for different purposes and based on the task at hand, one model may be more effective than another. (Sturlaugson, paragraph [0004] “The large number of machine learning options available to address a problem makes it difficult to choose the best option or even a well-performing option. The amount, type, and quality of data affect the accuracy and
stability of training and the resultant trained models. Further, problem-specific considerations, such as tolerance of errors (e.g., false positives, false negatives) scalability, and execution speed, limit the acceptable choices.”)
Thus far, the combination of Bright and Sturlaugson does not explicitly teach generating a base to create a non-linear function using spline functions that maps a feature space into multiple dimensions; applying the non-linear function; or specific to a silhouette category.
Wang teaches generating a base to create a non-linear function using spline functions that maps a feature space into multiple dimensions (Wang, Fig. 5, and page 616, column 1, paragraph 4, line 1 “Human feature points are the base of automatic 3D garment generation. After a series of cross sections are acquired by intersecting the body with horizontal cutting planes from head to foot, feature points can be searched by judging the number and geometric shape information of cross sections [13].” And, page 616, paragraph 5, line 1 “ Free-form curves are used to represent the extracted line segments for convenient editing, as shown in Fig. 5(b). Silhouette curves can be represented as B-Spline curves, as listed in Eq. (1). However, the Ferguson curve is hard for acquiring a conical shape precisely, and the C-Spline has a similar property to the B-Spline except that it can represent the conical shape precisely [28_30], as listed in Eq. (2). So for cross section curves, we can adopt C-Splines to represent them.”
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In other words, human feature points are the base is base, and C-splines are non-linear spline functions used to map a feature space into multiple dimensions.)
Want teaches applying the non-linear function (Wang, Eq. 2, page 617, column 1, paragraph 1, line 1 “…the C-Spline has a similar property to the B-Spline except that it can represent the conical shape precisely [28_30], as listed in Eq. (2). So for cross section curves, we can adopt C-Splines to represent them.”
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In other words, is adopt C-splines to represent them is applying the non-linear functions.)
Wang teaches specific to a silhouette category (Wang, Fig. 10, and, page 619, column 2, paragraph 1, line 5 “Figs. 10 and 11 show the result of the garment surface modeled by silhouette and cross section curves. Both Wang and the combination of Bright and Sturlaugson are directed to garment design, among other things.”
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In other words, modeled by silhouette is specific to a silhouette category.)
Both Wang and the combination of Bright and Sturlaugson are directed to garment design, among other things. The combination of Bright and Sturlaugson teaches a method for identifying one or more target audiences for a new item to be manufactured, using machine learning to determine a value for at least one of the one or more variable size components, providing the determined value for use in creating the new item with a sizing variation, and determining a specific learning model configuration, but does not explicitly teach generating a base to create a non-linear function using spline functions that maps a feature space into multiple dimensions, applying the non-linear function, or specific to a silhouette category. Wang teaches generating a base to create a non-linear function using spline functions that maps a feature space into multiple dimensions, applying the non-linear function, and specific to a silhouette category.
In view of the teaching of the combination of Bright and Sturlaugson, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Wang into the combination of Bright and Sturlaugson. This would result in a method for identifying one or more target audiences for a new item to be manufactured, using machine learning to determine a value for at least one of the one or more variable size components, providing the determined value for use in creating the new item with a sizing variation, determining a specific learning model configuration, generating a base to create a non-linear function using spline functions that maps a feature space into multiple dimensions, applying the non-linear function, and using a silhouette category.
One of ordinary skill in the art would be motivated to do this in order to provide 3D interactive tools for apparel design to designers. (Wang, page 614, column 1, paragraph 1, line 1 “The purpose of this paper is to provide interactive techniques of 3D apparel design through multiple parametric curves, and it enables the stylist design surface shapes and patterns directly on a 3D human model more freely. Up to now, there has been three typical garment design methods, namely the A, B, C approaches shown in Fig. 1. The traditional 2D CAD systems provide such 2D grading tools to generate patterns of different sizes from the basic pattern set, and then use them to make garments, as path A shown in Fig. 1. Along with the development of garment simulation techniques [1_3], some commercial CAD systems test the garment design results by assembling 2D patterns and draping them on a virtual human body [4_10], as path B shown in Fig. 1. Such solutions are not intuitive enough and need the designers to have rich experiences and accomplished skills.”)
Regarding claim 2,
The combination of Bright, Sturlaugson, and Wang teaches the method of claim 1, further comprising
manufacturing the new item (Bright, paragraph [0091], line 4. See mapping of claim 1, office action page 11. In other words, manufacturer to produce custom apparel items is manufacturing the new item.).
Regarding claim 4,
The combination of Bright, Sturlaugson, and Wang teaches the method of claim 1, wherein
the machine learning model is identified for predicting the size fit satisfaction (Sturlaugson, Fig. 3, and, paragraph [0006], line 8. See mapping of claim 1, office action page 12.).
Regarding claim 5,
The combination of Bright, Sturlaugson, and Wang teaches the method of claim 4, wherein
a size fit satisfaction prediction is optimized by applying a cost function analysis (Bright, paragraph [0093], line 1 “Further, embodiments of the invention may be employed to generate specific garment specifications for an apparel category, based on inputs supplied about a target consumer and desired garment category attributes, filtering the garment category by specific style heuristics and attributes. For example, a series of example specifications of “best selling items (e.g., those having highest actual sales volume, lowest return rate, etc.) may be profiled in a digital garment library. A clothing designer could use this information to expedite the process of defining the fit of a new garment or shoe (as examples).” In other words, generate specific garment specifications…based on actual sales volume, lowest return rate… is optimized by applying a cost function analysis.).
Regarding claim 6,
The combination of Bright, Sturlaugson, and Wang teaches the method of claim 1, wherein
the machine learning model is further determined based on one or more of an optimization goal, an identified silhouette, and/or one or more identified features (Sturlaugson, paragraph [0006], line 12 “The experiment module is configured to train and evaluate each machine learning model to produce a performance result for each machine learning model.” See mapping of claim 1. In other words, performance result is optimization goal, and evaluate each machine learning model to produce a performance result is machine learning model is further determined based on one or more of an optimization goal, an identified silhouette, one or more identified features, and/or one or more identified features.).
Regarding claim 7,
The combination of Bright, Sturlaugson, and Wang teaches the method of claim 1, wherein
the machine learning model was trained using feedback from subjects regarding a size of one or more items (Bright, FIG. 1, Steps 101-105. In other words, receive item feedback and enable changes/additions to consumer data is trained using feedback from subjects.) .
Regarding claim 8,
The combination of Bright, Sturlaugson, and Wang teaches the method of claim 1, wherein
the one or more variable size components include at least one of a sleeve length, a distance from a top of a shirt to a first button, a neck length, a bicep size, and/or a body length (Bright, paragraph [0057], line 4 “For example, the item ID in each row of the training data may be used to look up various information on an item. Some examples of data for an item are style, color/wash, fabric, size, measurement data (e.g., neck, sleeve length, arm, waist (relaxed), waist (extended), high hip, high hip (extended), low hip, low hip (extended), front rise, back rise, thigh, thigh (extended), knee, inseam, outseam, leg opening, shoe last, shaft circumference and/or other measurements), heuristic data (e.g., waist type, thigh type, leg type, leg length, torso length, hip shape min, seat shape, stomach shape, thigh shape, age appropriate range, and/or other heuristics).” In other words, sleeve length is at least one of a sleeve length, a distance from a top of a shirt to a first button, a neck length, a bicep size, and/or a body length.).
Regarding claim 9,
The combination of Bright, Sturlaugson, and Wang teaches the method of claim 1, further comprising
generating garment measurements for the new item (Bright, Fig. 3, step 309. In other words, item measurements with mean values and variances is generate garment measurements for the new item.) .
Regarding claim 10,
The combination of Bright, Sturlaugson, and Wang teaches the method of claim 1, wherein
corresponding garment measurements for the new item are generated for the first target audience and the second target audience, wherein the corresponding garment
measurements for the first target audience are different than the corresponding garment measurements for the second target audience (Bright, Fig. 1, step 101, and Fig. 3. In other words, collect consumer data is the target audience, and item measurements is garment measurements. Examiner notes that Bright teaches gathering consumer demographics. Consumer demographics have more than one demographic which is the first target audience and the second target audience. It is implicit that doing so allows for automatic measurement for different types of people. For example, designing pants for a woman is different that designing pants for a man, or a child. Therefore, Examiner is interpreting the intent of the claim language is to point out that the model can be used for different categories of people.).
Claims 16, 18, and 19 are system claims corresponding to method claims 1, 6, and 9, respectively. Otherwise, they are the same. Bright teaches a system (Bright, paragraph [0095], line 1 “The processor 803 typically executes a computer program called an operating system (e.g., a Microsoft Windows-family operating system, or any other Suitable operating system) which controls the execution of other computer programs, and provides Scheduling, input/output and other device control, accounting, compilation, storage assignment, data management, memory management, communication and dataflow control. Collectively, the processor and operating system define the computer platform for which application programs and other computer program languages are written.” In other words, computer platform is a system.) Therefore, claims 16, 18, and 19 are rejected for the same reasons as claims 1, 6, and 9, respectively.
Claim 20 is a computer program product embodied in a non-transitory computer readable medium claim corresponding to method claim 1. Otherwise, they are the same. Bright teaches a computer program product embodied in a non-transitory computer readable medium (Bright, paragraph [0096], line 6 “These computer programs may be stored in storage system 806. Storage system 806 may hold information on a Volatile or non-volatile medium, and may be fixed or removable. Storage system 806 is shown in greater detail in FIG. 9.” In other words, programs are program products, and non-volatile storage system is non-transitory, computer-readable medium.) Therefore, claim 20 is rejected for the same reasons as claim 1.
Claims 11-15 are rejected under 35 U.S.C. § 103 as being unpatentable over Bright, Sturlaugson, Wang, and Guan et al (Apparel Recommendation System Evolution: An empirical review, herein Guan).
Regarding claim 11,
The combination of Bright, Sturlaugson, and Wang teaches the method of claim 1, further comprising
Thus far, the combination of Bright, Sturlaugson, and Wang does not explicitly teach identifying a target garment for the one or more target audiences.
Guan teaches identifying a target garment for the one or more target audiences (Guan, Figure 4, and page 2, paragraph 5, line 1 “In apparel retailing, a new trend of smart shopping is highlighted aiming to improve customer experiences and increase sales, with several new technologies support, such as virtual try-on for clothing display, smart recommendation for clothes searching and selection.” And, page 3, paragraph 2, line 3 “The main function of a general recommendation system is to predict products that potential consumers might want to buy based on their stated preferences, online shopping choices, and purchases of people with similar tastes or demographics (Leavitt, 2006).” And, page 9, paragraph 2, line 8 “Interactive Genetic Algorithm has been applied in his model with three sets (levels) of parameters:
silhouette, key style elements, and design details.” And, page 10, paragraph 1 “Apparel recommendation systems refer to feature matching between apparel and users under certain matching criteria. As shown in figure 4, from the apparel side, current research addresses apparel features from its formulation of colours, lines and shapes, pattern/prints and textures. The descriptive methods of above features were studied through the process of feature recognition, extraction and encoding. On the other side, user features are recognised as facial features, body features, personal preference (taste) and wearing occasions.”
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In other words, predict products that potential customers might want to buy based on…demographics is identifying a garment for one or more target audiences.)
Both Guan and the combination of Bright, Sturlaugson, and Wang are directed to machine learning in the apparel industry, among other things. The combination of Bright, Sturlaugson, and Wang teaches a method for identifying one or more target audiences for a new item to be manufactured, determining a machine learning model, using machine learning to determine a value for at least one of the one or more variable size components, and providing the determined value for use in creating the new item with a sizing variation, but does not explicitly teach identifying target garments for one or more target audiences. Guan teaches identifying target garments for one or more target audiences.
In view of the teaching of the combination of Bright, Sturlaugson, and Wang it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Guan into the combination of Bright, Sturlaugson, and Wang. This would result in a method for identifying one or more target audiences for a new item to be manufactured, determining a machine learning model, using machine learning to determine a value for at least one of the one or more variable size components, providing a determined value for use in creating the new item with a sizing variation, and identifying target garments for one or more target audiences.
One of ordinary skill in the art would be motivated to do this because developments with e-commerce markets have driven a need to automate the apparel industry. (Guan, page 1, paragraph 1, line 1 “With the developments of e-commerce markets, novel recommendation technologies are becoming an essential part of many online retailers’ economic models to help drive online sales. Initially, this paper undertakes an investigation of apparel recommendations in the commercial market in order to verify the research value and significance. Then, this article reviews apparel recommendation techniques and systems through academic research, aiming to acquaint apparel recommendation context, summarize the pros and cons of various research methods, identify research gaps, and eventually propose new research solutions to benefit apparel retailing market.”)
Regarding claim 12,
The combination of Bright, Sturlaugson, Wang, and Guan teaches the method of claim 11, wherein
the target garment is identified based on one or more metrics (Guan, Figure 4. In other word, matching criteria such as style similarity and fashion coordination is target garment is identified by one or more metrics.) .
Regarding claim 13,
The combination of Bright, Sturlaugson, and Guan teaches the method of claim 11, wherein
the target garment is identified based on one or more features (Guan, Figure 4. In other words, colour, lines and shapes, pattern, and textures are one or more features.).
Regarding claim 14,
The combination of Bright, Sturlaugson, and Guan teaches the method of claim 11, further comprising
determining a target silhouette category for the identified target garment (Guan, page 13, paragraph 4, line 3 “Apparel lines and shapes were also classified based on style components of silhouette, collar, shoulder, waist, sleeve, hem, pocket and front by Li and Li (2012). In other words, based on components of silhouette is determining a target silhouette.)
Regarding claim 15,
The combination of Bright, Sturlaugson, Wang and Guan teaches the method of claim 14, wherein
the target silhouette category is determined based on one or more of inventory levels, popularity associated with the target silhouette category, and/or a return rate (Bright, paragraph [0026] “In act 103, a set of rules is produced that can be used to predict whether and/or how well a particular item will fit a particular consumer, even if the consumer has no previous interaction with that item. The set of rules may be defined in any of numerous ways. For example, in some embodiments a machine learning engine may employ the consumer data received in act 101, item data received in act 102, and/or other data (information regarding the consumer's previous interaction with other items (e.g., orders, returns, Surveys, etc.), and construct a model using the data that may be used to make a fit prediction.” In other words, consumer’s previous interaction…orders, returns… is determined based on one or more of inventory levels, popularity, and/or return rate. Examiner notes that silhouette is previously mapped.)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, 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 BART RYLANDER whose telephone number is (571)272-8359. The examiner can normally be reached Monday - Thursday 8:00 to 5:30.
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, Miranda Huang can be reached at 571-270-7092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/B.I.R./Examiner, Art Unit 2124
/MIRANDA M HUANG/Supervisory Patent Examiner, Art Unit 2124