DETAILED ACTION
This action is filed in response to the application filed on 4//18/2024.
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
Acknowledgement is made of Applicant’s Information Disclosure Statements (IDS) form PTO-1149 filed on 4/18/2024 and 8/08/2025. These IDS have been considered.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claim 1, the second limitation teaches “wherein different fibers of interest are represented in varying blend ratios across the plurality of textiles.” Examiner notes the cited limitation is so broad that is does not meaningfully limit the scope of the invention. It is unclear what fibers can be classified as fibers of interest or how many blend ratios should be present in the sample. In its current form this limitation could pertain to any fiber and any ratios, therefore failing to define the metes and bounds of the invention and rendering the scope of the claim indefinite.
Claims 2-17 are dependent claims and therefore inherit the deficiencies of Claim 1 and therefore are likewise rejected.
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 1-2, 4-5, 7-12, 14-16, 18, and 19 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Chae (US20200042822 A1).
Regarding Claim 1, Chae teaches A method for training a machine learning model to determine composition of textiles (e.g. see [0011] “Specifically, a fabric identifying method may include obtaining image information on a fabric structure of clothing, applying data on the fabric structure of the image information to a learned artificial intelligence model in order to identify the type of the fabric of the clothing, outputting the information on the type of the fabric from the learned artificial intelligence model”), the method comprising:
for each of a plurality of textiles, obtaining a blend ratio that indicates a ratio of fibers included in that textile (e.g. see [0066] “The NIR Spectrometer is based on Texas Instruments' NIRscan™ Nano design and may operate in the wavelength range from 900 nm to 1,700 nm. The NIR Spectrometer may determine the composition of the fiber (for example, blend of 60% cotton and 40% polyester, or 100% wool) by using the near infrared wave. In an embodiment, the NIR Spectrometer may identify the type of the fabric through AI model learning by using the near infrared wave”),
wherein different fibers of interest are represented in varying blend ratios across the plurality of textiles (e.g. see [0074] “The type of the fabric identified by the fabric identifying apparatus from the data on the fabric structure may be configured to have a main classification category and a sub classification category for each category as in FIG. 6A. The main classification may have a synthetic fiber category such as nylon, or polyester in addition to jean, fur, silk, and wool. In the present disclosure, the term ‘type of fabric’ includes expressing the type of the fabric as a percentage, such as 50% cotton and 50% polyester, or 40% cotton and 60% ramie, for the blend. In another embodiment of the present disclosure, the main classification (sub classification) may have cotton, hemp (linen, ramie, burlap, etc.), wool (surge, muslin, etc.), silk (chiffon, etc.), synthetic fibers (nylon, polyester, rayon, poly-rayon, acrylic, etc.)”),
for each of the plurality of textiles, obtaining a spectrum that is measured through analysis of that textile (e.g. see [0064] “In addition, the fabric identifying apparatus 100 may include a sensor 108 including a millimeter wave sensor or a near infrared spectrometer (NIR Spectrometer) capable of sensing the fabric structure. The controller 130 may detect the type (material) information of the fabric and generate a control signal having protocol information on the clothing-related home appliance 200 to be communicated with respect to the detected result. The communicator 134 serves to transfer the generated control signal to the interlocked clothing-related home appliance 200,” and [0081] “In another embodiment, the fabric identifying apparatus may obtain waveform information from the clothing by the millimeter wave sensor, or obtain information of the near infrared wavelength from the clothing by the near infrared (NIR) spectrometer”);
creating a textile dataset (e.g. see [0085] “The fabric identifier 120 may store the data matching the obtained image information and the identified type of the fabric in the memory 132 (operation S1500).”) by associating each of the plurality of textiles with (i) the corresponding blend ratio (e.g. see [0074] and (ii) the corresponding spectrum (e.g. see [0065] “The millimeter wave sensor sends a signal by using the wavelength in the millimeter (mm) range, which are regarded as a short wavelength in the electromagnetic spectrum. Actually, a system component such as an antenna necessary for processing a mmWave signal is small in size, and the short wavelength have high resolution. The mmWave system that confirms the distance between the wavelengths may have accuracy of the mm range in 76 to 81 GHz. In an embodiment, the mmWave system may identify the type of the fabric through the AI model learning by using the millimeter wave (mmWave) sensor”);
providing the textile dataset to a machine learning model as training data, so as to produce a trained machine learning model (e.g. see [0056] “The server 300 may include an AI model learner for generating the learned AI model that has learned the type of the fabric of the collected clothing through a deep neural network (DNN). The AI model learner of the server may be configured to extract the learning data necessary for learning through the deep neural network from the database storing the data necessary for identifying the fabric of the clothing necessary for machine learning or deep learning, to preprocess the learning data in order to increase the accuracy of the learning data, to learn the learning data through the deep neural network (DNN), and to generate the learned AI model” and [0093] “The AI model learning data including fabric data on a plurality of the fabric structure and the data matching a label of the type of the fabric to the plurality of data”);
appending metadata that specifies the varying blend ratios of the different fibers to the trained machine learning model (e.g. see [0128-0129] “Throughout the present specification, assigning one or more labels to training data in order to train an artificial neural network may be referred to as labeling the training data with labeling data. Training data and labels corresponding to the training data together may form a single training set, and as such, they may be inputted to an artificial neural network as a training set”); and
storing the trained machine learning model in a storage medium (e.g. see [0082]).
Regarding Claim 2, Chae teaches the limitations of Claim 1. Chae further discloses wherein the different fibers include cotton, linen, rayon, Tencel lyocell, modal, viscose, polyester, acrylic, elastane, nylon, wool, cashmere, silk, or any combination thereof (e.g. see [0074] “The main classification may have a synthetic fiber category such as nylon, or polyester in addition to jean, fur, silk, and wool. In the present disclosure, the term ‘type of fabric’ includes expressing the type of the fabric as a percentage, such as 50% cotton and 50% polyester, or 40% cotton and 60% ramie, for the blend. In another embodiment of the present disclosure, the main classification (sub classification) may have cotton, hemp (linen, ramie, burlap, etc.), wool (surge, muslin, etc.), silk (chiffon, etc.), synthetic fibers (nylon, polyester, rayon, poly-rayon, acrylic, etc.)”).
Regarding Claim 4, Chae teaches the limitations of Claim 1. Chae further discloses performing spectral analysis by a spectroscopy instrument that produces, as output, the spectrum (e.g. see [0081] “In another embodiment, the fabric identifying apparatus may obtain waveform information from the clothing by the millimeter wave sensor, or obtain information of the near infrared wavelength from the clothing by the near infrared (NIR) spectrometer”).
Regarding Claim 5, Chae teaches the limitations of Claim 1. Chae further discloses wherein the spectrum is one of multiple spectra (e.g. see [0064] and [0066]) obtained for each of the plurality of textiles (e.g. see [0069] “The fabric identifier 120 may learn the AI model based on the data received from the image camera 110 and the sensor 108. For this purpose, the fabric identifier 120 may include a data collector 122 for collecting fabric data on a plurality of fabric structures of the clothing from the image camera 110, the AI model learner 124 for learning by the learning data including data on the plurality of fabric structures and the data that has matched a label of the type of the fabric to data on the plurality of fabric structures”).
Regarding Claim 7, Chae teaches the limitations of Claim 1. Chae further discloses wherein the plurality of textiles are selected so as to cover a predetermined range of blend ratios of the different fibers (e.g. see [0066] and [0074]).
Regarding Claim 8, Chae teaches the limitations of Claim 1. Chae further discloses wherein the trained machine learning model is a regression model trained on the textile dataset using supervised learning (e.g. see [0104] “SVM may include a supervised learning model for pattern detection and data analysis, heavily used in classification and regression analysis,” and [0124] “Then, among the thus inferred functions, outputting consecutive values is referred to as regression, and predicting and outputting a class of an input vector is referred to as classification”).
Regarding Claim 9, Chae teaches the limitations of Claim 1. Chae further discloses wherein the trained machine learning model is a classification model trained on the textile dataset using supervised learning (e.g. see [0104] “SVM may include a supervised learning model for pattern detection and data analysis, heavily used in classification and regression analysis,” and [0124] “Then, among the thus inferred functions, outputting consecutive values is referred to as regression, and predicting and outputting a class of an input vector is referred to as classification”).
Regarding Claim 10, Chae teaches the limitations of Claim 1. Chae further discloses a method for implementing the trained machine learning model (e.g. see [0011] “Specifically, a fabric identifying method may include obtaining image information on a fabric structure of clothing, applying data on the fabric structure of the image information to a learned artificial intelligence model in order to identify the type of the fabric of the clothing, outputting the information on the type of the fabric from the learned artificial intelligence model”), the method comprising:
obtaining a textile having an unknown blend ratio; measuring a spectrum through spectral analysis by a spectroscopy instrument (e.g. see [0066] “The NIR Spectrometer is based on Texas Instruments' NIRscan™ Nano design and may operate in the wavelength range from 900 nm to 1,700 nm. The NIR Spectrometer may determine the composition of the fiber (for example, blend of 60% cotton and 40% polyester, or 100% wool) by using the near infrared wave.”); and
applying the trained machine learning model to the spectrum, so as to produce an output that is representative of a predicted blend ratio (e.g. see [0066] “In an embodiment, the NIR Spectrometer may identify the type of the fabric through AI model learning by using the near infrared wave”).
Regarding Claim 11, Chae teaches the limitations of Claim 10. Chae further discloses wherein the spectrum is one of multiple spectra measured through spectral analysis by the spectroscopy instrument, and wherein upon applying the trained machine learning model to the multiple spectra, the trained machine learning model produces multiple outputs (e.g. see [0064 and [0066]).
Regarding Claim 12, Chae teaches the limitations of Claim 11. Chae further discloses wherein each of the multiple outputs is representative of a blend ratio predicted by the trained machine learning model based on a corresponding one of the multiple spectra (e.g. see [0066] and [0074] “The type of the fabric identified by the fabric identifying apparatus from the data on the fabric structure may be configured to have a main classification category and a sub classification category for each category as in FIG. 6A. The main classification may have a synthetic fiber category such as nylon, or polyester in addition to jean, fur, silk, and wool. In the present disclosure, the term ‘type of fabric’ includes expressing the type of the fabric as a percentage, such as 50% cotton and 50% polyester, or 40% cotton and 60% ramie, for the blend. In another embodiment of the present disclosure, the main classification (sub classification) may have cotton, hemp (linen, ramie, burlap, etc.), wool (surge, muslin, etc.), silk (chiffon, etc.), synthetic fibers (nylon, polyester, rayon, poly-rayon, acrylic, etc)”).
Regarding Claim 14, Chae teaches the limitations of Claim 10. Chae further discloses fragmenting the textile into fragments; and compacting the fragments into a clump; wherein said fragmenting and said compacting are performed prior to said measuring (e.g. see [0118-0119] “the Artificial Neural Network may be trained by using training data. Here, the training may refer to the process of determining parameters of the artificial neural network by using the training data, to perform tasks such as classification, regression analysis, and clustering of inputted data. Such parameters of the artificial neural network may include synaptic weights and biases applied to neurons”).
Regarding Claim 15, Chae teaches the limitations of Claim 10. Chae further discloses sorting, based on the output, the textile amongst various collections of textiles having different blend ratios (e.g. see [0069] “learning a fabric type identifying engine so as to identify and output the type of the fabric of the clothing, and the fabric type classifier 126 for identifying and outputting the type of the fabric of the clothing through the fabric type identifying engine based on the data on the fabric structure obtained from the image camera 110. The fabric type information of the clothing output from the fabric type classifier 126 may be matched with the data on the fabric structure to be stored in the memory 132, and sent to the clothing-related home appliances 200 through the communicator 134”), such that the textile is collocated with other textiles having a comparable blend ratio (e.g. see [0119] “An artificial neural network trained using training data may classify or cluster inputted data according to a pattern within the inputted data”).
Regarding Claim 16, Chae teaches the limitations of Claim 15. Chae further discloses indicating that the textile was sorted into a particular collection of textiles in a data structure (e.g. see [0118-0119] “The Artificial Neural Network may be trained by using training data. Here, the training may refer to the process of determining parameters of the artificial neural network by using the training data, to perform tasks such as classification, regression analysis, and clustering of inputted data. Such parameters of the artificial neural network may include synaptic weights and biases applied to neurons. An artificial neural network trained using training data may classify or cluster inputted data according to a pattern within the inputted data,” and [0124] “among the thus inferred functions, outputting consecutive values is referred to as regression, and predicting and outputting a class of an input vector is referred to as classification”).
Regarding Claim 18, Chae teaches A non-transitory medium with instructions stored thereon that, when executed by a processor of a computing device, cause the computing device to perform operations (e.g. see [0177] “Embodiments according to the present disclosure described above may be implemented in the form of a computer program that may be executed through various components on a computer, and such a computer program may be recorded in a computer readable medium. At this time, the media may be magnetic media such as a hard disk, a floppy disk, and a magnetic tape, optical recording media such as a CD-ROM and a DVD, magneto-optical media such as a floptical disk, and hardware devices specifically configured to store and execute program instructions, such as a ROM, a RAM, and a flash memory”) comprising:
receiving input indicative of a request to train a machine learning model to predict a characteristic of a textile via spectral analysis (e.g. see [0081] “The fabric identifying apparatus 100 obtains image information on the fabric structure of the clothing by the image camera 110 (operation S1100). The fabric identifying apparatus 100 may collect data on a full screen by collecting the captured image as it is, resizing the full screen of the image, or cropping a portion of the full screen. When the image of a specific part, for example, a sleeve part, a button part, a neck part, or a lower end part of the clothing having a feature that is distinguished from other clothing is captured through the image camera 110, it may be matched with the image of the fabric structure, thereby identifying more accurate and faster the type of the fabric. In another embodiment, the fabric identifying apparatus may obtain waveform information from the clothing by the millimeter wave sensor, or obtain information of the near infrared wavelength from the clothing by the near infrared (NIR) spectrometer”);
identifying a plurality of textiles that have different values for the characteristic (e.g. see [0074]);
obtaining a plurality of spectra that are measured through analysis of the plurality of textiles (e.g. see [0066]);
creating a training dataset (e.g. see [0085] “The fabric identifier 120 may store the data matching the obtained image information and the identified type of the fabric in the memory 132 (operation S1500).”) by associating each of the plurality of spectra with a label that is indicative of the corresponding value for the characteristic (e.g. see [0128-0129] “Throughout the present specification, assigning one or more labels to training data in order to train an artificial neural network may be referred to as labeling the training data with labeling data. Training data and labels corresponding to the training data together may form a single training set, and as such, they may be inputted to an artificial neural network as a training set”);
providing the training data to the machine learning model, so as to produce a trained machine learning model (e.g. see [0119-0120] “An artificial neural network trained using training data may classify or cluster inputted data according to a pattern within the inputted data. Throughout the present specification, an artificial neural network trained using training data may be referred to as a trained model”); and
storing the trained machine learning model (e.g. see [0090] “the applying to the learned AI model (operation S1300), the outputting the information on the type of the fabric (operation S1400), and the storing in the memory (operation S1500) may be performed through a fabric identifying app of the mobile terminal”).
Regarding Claim 19, Chae teaches the limitations of Claim 18. Chae further discloses wherein the characteristic is fiber composition, blend ratio, treatment, color, age, wear-and-tear level, thickness, or layer count (e.g. see [0070] “The server 300 may receive, from the fabric identifying apparatus 100, at least one of the color, the pattern, or the contour of the specific part of the clothing obtained by the fabric identifying apparatus 100, the tag related data, or the data on the fabric structure of the specific part”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 3, 6, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Chae (US20200042822 A1) in view of Vandeputte (WO2020234466A1).
Regarding Claim 3, Chae teaches the limitations of Claim 1. Chae further discloses determining the blend ratio through visual analysis of a label that accompanies that textile (e.g. see [0067] “Accordingly, when the tag information is readable to be captured by the image camera, the type of the fabric may be determined by the information on the fabric structure of the clothing, or the information on the fabric structure of the clothing by matching the color, the pattern, or the contour of the specific part of the clothing to store it in the memory, or one of the color, the pattern, or the contour of the specific part of the clothing. In addition, the tag information may be used as a label value of the AI model that learns the type of the fabric of the learning data upon supervised learning of the AI model”)
Chae does not explicitly disclose confirming the blend ratio determined through visual analysis with through chemical analysis of that textile.
In the same field of endeavor, Vandeputte teaches confirming the blend ratio determined through visual analysis with through chemical analysis of that textile (e.g. see [pg. 23 lines 4-11] “Table 6 show composition percentages by weight for blended textile samples, i.e. comprising multiple fiber material types, predicted (pred.) according to the present invention (columns 2 and 3) and obtained via chemical (chem.) analysis (columns 4 and 5). The predicted composition according to the present invention matches the results obtained by chemical analysis to a high degree of accuracy. This renders the present invention suitable for the envisaged sorting application”).
It would have been obvious to one of ordinary skill in the art before the effective filling date, to combine the ratio determinations of Chae with the chemical analysis of Vandeputte for the purpose of detecting fiber compositions with the advantage of confirming the detected composition is accurate.
Regarding Claim 6, Chae teaches the limitations of Claim 1. Chae further discloses applying a filter to the normalized spectrum to reduce noise (e.g. see [0057] “Data preprocessing refers to removing or modifying learning data to maximally increase the accuracy of source data. In addition, if it contains excessively data whose importance is significantly low, they may also be properly scaled down to change into a form that is easy to manage and use. The data preprocessing includes data refinement, data integration, data transformation, data reduction, etc. The data refinement is to fill missing values, to smooth noisy data, to identify outliers, and to calibrate data inconsistency”).
Chae does not explicitly disclose for each of the plurality of textiles, normalizing the spectrum measured for that textile to ensure that each amplitude is between a fixed range of values.
In the same field of endeavor, Vandeputte teaches for each of the plurality of textiles, normalizing the spectrum measured for that textile to ensure that each amplitude is between a fixed range of values (e.g. see [pg. 19 lines 11-13] “A vector of spectral values (31) is obtained for a textile sample. The vector may optionally be preprocessed (32). The preprocessing may comprise normalizing the vector based on calibration vectors”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the noise reduction of Chae with the spectrum normalization of Vandeputte for the purpose of detecting fiber composition with the advantage of ensuring the accuracy of any determination through only inputting clean useful data.
Regarding Claim 13, Chae teaches the limitations of Claim 11. Chae does not explicitly disclose determining fiber composition of the textile by deterministically averaging the multiple outputs produced by the trained machine learning model.
In the same field of endeavor, Vandeputte teaches determining fiber composition of the textile by deterministically averaging the multiple outputs produced by the trained machine learning model (e.g. see [pg. 10 lines 1-6] “In a preferred embodiment, the vector of spectral values comprises sampled spectral values; or spectral values averaged or summed over a wavelength interval. In the latter case, the wavelength interval is also referred to as a band, and the size of the wavelength interval as a band width. Particularly preferred are spectral values corresponding to captured reflected light intensities over a band, or a function thereof,” and [pg. 12 line 35- pg. 13 line 14] “In a preferred embodiment, the apparatus comprises multiple spatially separated output locations. The apparatus may be configured for determining an output location based on the output of the artificial neural network and transporting the textile sample to the determined output location. Each output location may comprise a bin. In a preferred embodiment, a composition category from a group of composition categories is selected for the textile sample based on the numerical relative composition amounts of the output. A non-limitative exemplary list of composition categories may comprise: 0% cotton and 100% polyester; 5% cotton and 95% polyester; 10% cotton and 90% polyester; 15% cotton and 85% polyester; 20% cotton and 80% polyester; 25% cotton and 75% polyester; and the like. For a textile sample with determined numerical relative composition amounts 19.8% cotton, 79.4% polyester and 0.8% remainder, for example, the category 20% cotton and 80% polyester may be selected, for example. Preferably, each output location corresponds with one or more composition categories.”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the multiple outputs of Chae with the averaging method of Vandeputte for the purpose of detecting a fiber composition with the advantage of removing unwanted signals that could incorrectly influence a composition determination.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Chae (US20200042822 A1) in view of Schreiber (US20170320103 A1).
Regarding Claim 17, Chae teaches the limitations of Claim 16. Chae does not explicitly disclose wherein the data structure is used, as input, by a recycling system to determine whether to initiate a recycling operation, and wherein the recycling operation is initiated for a given collection of textiles when a predetermined number of textiles having the corresponding blend ratio are available for recycling.
In the same field of endeavor, Schreiber teaches wherein the data structure is used, as input, by a recycling system to determine whether to initiate a recycling operation (e.g. see [0032] “Once classified using a system and/or method such as described herein, a textile sample may be sorted into like groups and processed for material-specific or material-appropriate reuse, repurposing, or recycling”), and
wherein the recycling operation is initiated for a given collection of textiles when a predetermined number of textiles having the corresponding blend ratio are available for recycling (e.g. see [0082] “Once the textile type classifier 206 has determined one or more textile types of the textile sample, the textile type may be communicated to a textile sorter 210. The textile sorter 210 may be implemented in any number of suitable ways. In some cases, the textile sorter 210 is a conveyor sorter or a pick and place machine. The textile sorter 210 is configured to sort the textile sample, and any other samples that are known to be related to or associated with the textile sample, into groups based on the one or more textile types. Once sorted, groups of like materials may be packaged, prepared, or otherwise processed for reuse, repurposing, or recycling by a packaging or storage system 212”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the fiber composition detection method of Chae with the recycling embodiment of Schreiber for the purpose of grouping materials by fiber composition with the advantage of recycling those grouped materials for environment conservation interests.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NYLA GAVIA whose telephone number is (703)756-1592. The examiner can normally be reached M-F 8:30-5:30pm.
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/NYLA GAVIA/Examiner, Art Unit 2857
/Catherine T. Rastovski/ Supervisory Primary Examiner, Art Unit 2857
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