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 .
Priority
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), of which papers have been placed in the file wrapper.
Double Patenting
The non-statutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A non-statutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on non-statutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claim 15 is rejected on the ground of non-statutory double patenting as being unpatentable over claim 15 of US Pat. No. 12,198,451. Although the claims at issue are not identical, they are not patentably distinct from each other because claim 15 of the present application is anticipated by claim 1 of the patent.
The table illustrates a mapping between the limitations claim 15 of the present application and claim 1 of US Pat. No. 12,198,451.
Claim 15 of present App.
Claim 1 of US Pat. No. 12,198,451
15. A method for matching a text with a design performed by an apparatus for matching a text with a design, comprising: acquiring an image from information including images and texts; learning features of the acquired image; extracting texts from the information and performing learning about a pair of an extracted text and the acquired image; extracting a trend word extracted at least a predetermined reference number of times among the extracted texts; performing learning about a pair of the trend word and the acquired image; and identifying a design feature matched with the trend word among learned features of the image.
1. A method for matching a text with a design performed by an apparatus for matching a text with a design, comprising: acquiring an image from information including images and texts; learning features of the acquired image; extracting texts from the information and performing learning about a pair of an extracted text and the acquired image; extracting a trend word extracted at least a predetermined reference number of times among the extracted texts; performing learning about a pair of the trend word and the acquired image; identifying a design feature matched with the trend word among learned features of the image;
when there is no trend word extracted at least the predetermined reference number of times, identifying a first design feature corresponding to a first text among the extracted texts based on a result of the learning about features of the acquired image and a result of the learning about the pair of an extracted text and the acquired image; and, when there is no trend word extracted at least the predetermined reference number of times, identifying a second design feature corresponding to a second text among the extracted texts based on a result of the learning about features of the acquired image and a result of the learning about the pair of an extracted text and the acquired image.
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.
Claim 15 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Song et al. (KR102045017B1) [see US Pub. No. 2020/0372193 A1 for translation]
Regarding claim 15, Song discloses, a method for matching a text with a design performed by an apparatus for matching a text with a design, comprising:
acquiring an image from information including images and texts; (See Song ¶57, “The parsing unit 202 parses an image and text data related to the image in real time from an external website, blog, or social media server, or from the storage unit 204 through a network.”)
learning features of the acquired image; extracting texts from the information (See Song ¶58, The extraction unit 203 extracts items using parsed image and text data. In order to extract items, in addition to the parsed image and text data, an item ex traction model (objection detection) is used.” Further see Song ¶59, “In addition, the extraction unit 203 extracts elements of the design for each extracted item. Elements of the design include all the external elements that constitute the item, such as color, sleeve length, fit, pattern, and print.”)
extracting a trend word extracted at least a predetermined reference number of times among the extracted texts; (See Song ¶63, “Specifically, the upper-level design or design element can be determined based on the case where the frequency of exposure of a specific design on an image exposed to social media or website is more than the reference value.”)
performing learning about a pair of an extracted text and the acquired image; (See Song ¶64, “Also, the learning unit 207 may generate and train a design generation model by reflecting image data and text data parsed using artificial intelligence, design elements/design elements for a specific extracted item.”)
and identifying a design feature matched with the trend word among learned features of the image. (See Song ¶59, “In addition, the extraction unit 203 extracts elements of the design for each extracted item. Elements of the design include all the external elements that constitute the item, such as color, sleeve length, fit, pattern, print, material, and the like.”)
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establiSongg 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 7-11, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Song et al. (KR102045017B1) [see US Pub. No. 2020/0372193 A1 for translation] in view of Danson et al. (US Pub. No. 2017/0270425 A1).
Regarding claim 1, Song discloses, a method for matching a text with a design performed by an apparatus for matching a text with a design, comprising:
acquiring an image from information including images and texts; (See Song ¶57, “The parsing unit 202 parses an image and text data related to the image in real time from an external website, blog, or social media server, or from the storage unit 204 through a network.”)
learning features of the acquired image; extracting texts from the information (See Song ¶58, The extraction unit 203 extracts items using parsed image and text data. In order to extract items, in addition to the parsed image and text data, an item ex traction model (objection detection) is used.” Further see Song ¶59, “In addition, the extraction unit 203 extracts elements of the design for each extracted item. Elements of the design include all the external elements that constitute the item, such as color, sleeve length, fit, pattern, and print.”)
and performing learning about a pair of an extracted text and the acquired image; (See Song ¶64, “Also, the learning unit 207 may generate and train a design generation model by reflecting image data and text data parsed using artificial intelligence, design elements/design elements for a specific extracted item.”)
extracting a trend word extracted at least a predetermined reference number of times among the extracted texts; (See Song ¶63, “Specifically, the upper-level design or design element can be determined based on the case where the frequency of exposure of a specific de sign on an image exposed to social media or website is more than the reference value.”)
Song discloses the above limitations, but he fails to disclose, extracting a trend word extracted at least a predetermined reference number of times among the extracted texts; adjusting a reference for extracting a trend word when there is no trend word extracted at least the predetermine d reference number of times; and according to the adjusted reference, extracting the trend word.
However, Danson discloses, extracting a trend word extracted at least a predetermined reference number of times among the extracted texts; (See Danson ¶54, “At step 350, the analysis control system 200 determines whether terms are above or below the threshold set at step 340. … Terms with a frequency above the threshold may be formulated together as a set of frequent terms in step 370.” Further see Danson ¶56, “At step 400, the analysis control system 200 compares sets of frequent terms based on the first- and second-time frames. … Terms that appear in both sets of frequent terms may also be included in the set of trending terms.”)
adjusting a reference for extracting a trend word when there is no trend word extracted at least the predetermined reference number of times; (See Danson ¶54, “At step 350, the analysis control system 200 determines whether terms are above or below the threshold set at step 340. Terms with a frequency at or below the threshold may be removed from the database 360.”
Therefore, step 350 can include a case where all the terms are below the threshold, and therefore no trend word would be detected since all the terms are removed. As shown in Danson Fig. 3 after step 360, the method returns to steps 310-340, whereby step 340 will establish a new frequency threshold.)
and according to the adjusted reference, extracting the trend word. (As shown in Danson Fig. 3, after re-establishing the frequency threshold, if the term is now above the threshold a set of trending terms will be extracted in step 480.)
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the adjusting a threshold for extracting trend words as suggested by Danson to Song’s extraction of frequent design elements from text items. This can be done using known engineering techniques, with a reasonable expectation of success. The motivation for doing so is disclosed by Danson in ¶21, “Preferably, this algorithm and the processes herein identify this trend information without reference to a library or database of pre-defined terms, which can advantageously permit identification of new trends.”
Regarding claim 2, Song and Danson disclose, the method of claim 1, further comprising: performing learning about a pair of the extracted trend word and the acquired image; (See Song ¶64, “Also, the learning unit 207 may generate and train a design generation model by reflecting image data and text data parsed using artificial intelligence, design elements/design elements for a specific extracted item.”)
and identifying a design feature matched with the trend word among learned features of the image. (See Song ¶59, “In addition, the extraction unit 203 extracts elements of the design for each extracted item. Elements of the design include all the external elements that constitute the item, such as color, sleeve length, fit, pattern, print, material, and the like.”)
Regarding claim 3, Song and Danson disclose, the method of claim 2, wherein the adjusting a reference comprises: identifying a first design feature corresponding to a first text among the extracted texts based on a result of the learning about features of the acquired image and a result of the learning about the pair of an extracted text and the acquired image, and, identifying a second design feature corresponding to a second text among the extracted texts based on a result of the learning about features of the acquired image and a result of the learning about the pair of an extracted text and the acquired image. (See Song ¶60, “In addition, the extraction unit 203 extracts elements of the design for each extracted item. Elements of the design include all the external elements that constitute the item, such as color, sleeve length, fit, pattern, print, material, and the like. For example, referring to FIG. 3, a design element of a white flare dress with sleeveless with floral prints in its entirety and a knee-length includes elements such as a flower-shaped print, sleeveless, white, knee-length, and flare dress.”)
Regarding claim 7, Song and Danson disclose, the method of claim 2, wherein the identifying a design feature includes, when the design feature relates to a pattern of clothing, identifying at least one of disposition of the pattern, a number of the pattern, a size of the pattern and a color of the pattern. (See Song ¶60, “In addition, the extraction unit 203 extracts elements of the design for each extracted item. Elements of the design include all the external elements that constitute the item, such as color, sleeve length, fit, pattern, print, material, and the like. For example, referring to FIG. 3, a design element of a white flare dress with sleeveless with floral prints in its entirety and a knee-length includes elements such as a flower-shaped print, sleeveless, white, knee-length, and flare dress.”)
Regarding claim 8, Song and Danson disclose, the method of claim 2, wherein the identifying a design feature includes learning a text expressing the design feature as a condition for generating a design. (See Song ¶88, “The design changing unit 402 generates a new design by changing the vector value of the design element using the extracted design element for each item. Specifically, if there is a floral shirt, it is possible to generate a new floral shirt design by changing the vector values of floral patterns in the floral shirt extracted from the collected image. In addition, the design changing unit 402 may generate a new design element by changing vector values for elements of a design extracted from an item and elements of a design extracted from an image without an item using the design generation model.”)
Regarding claim 9, Song and Danson disclose, the method of claim 8, wherein the learning a text expressing the design feature as a condition comprises: identifying a text expressing kinds of clothing, and by the learning a text expressing the design feature, generating the text expressing kinds of clothing as a category of the design. (See Song ¶60, “In addition, the extraction unit 203 extracts elements of the design for each extracted item. Elements of the design include all the external elements that constitute the item, such as color, sleeve length, fit, pattern, and print.” Further see Song ¶61, “In addition, the extraction unit 203 extracts elements of the design for each extracted item. Elements of the design include all the external elements that constitute the item, such as color, sleeve length, fit, pattern, print, material, and the like. For example, referring to FIG. 3, a design element of a white flare dress with sleeveless with floral prints in its entirety and a knee-length includes elements such as a flower-shaped print, sleeveless, white, knee-length, and flare dress.”)
Regarding claim 10, Song and Danson disclose, the method of claim 9, further comprising: when the category of the design is selected and the text expressing the design feature related to the pattern is input, generating a design of clothing having the input pattern, wherein a kind of the clothing corresponds to the selected category; (See Song ¶88, “The design changing unit 402 generates a new design by changing the vector value of the design element using the extracted design element for each item. Specifically, if there is a floral shirt, it is possible to generate a new floral shirt design by changing the vector values of floral patterns in the floral shirt extracted from the collected image.”
Further see Song ¶90, “In detail, referring to FIG. 6, among the trendy design elements, a knit “puff sleeve,” a shirt “ultra violet,” and a “long dress” are synthesized, so that designs with ultra-violet index long dresses with puff sleeves can be generated. It is also possible to synthesize different elements of the same item. For example, one blouse design can be generated by merging “cuff sleeves” of the blouse, “flower-shaped collar” of the blouse, and “mint color” of the blouse, which are the elements of the trendy design.”)
and outputting the generated design through a display. (See Song ¶150, “The display unit 1006 may display the generated design.”)
Regarding claim 11, Song and Danson disclose, the method of claim 10, wherein the generating a design of clothing includes, identifying a condition corresponding to the input text expressing the design feature related to the pattern, wherein the identifying a condition includes identifying vector information for each of the pattern and the category. (See Song ¶88, “In addition, the design changing unit 402 may generate a new design element by changing vector values for elements of a design extracted from an item and elements of a design extracted from an image without an item using the design generation model. The vector value can be changed automatically or by receiving a set value.”)
Regarding claim 13, Song and Danson disclose, the method of claim 1, wherein the adjusting a reference includes changing the predetermined reference number of times. (See Danson ¶53, “At step 340, the analysis control system 200 may establish a frequency threshold for terms. In some cases, the total number of terms is used to determine the frequency threshold. For example, the threshold could be established by calculating an average frequency for terms across all data sources for the entire time frame. The threshold may also be calculated by a frequency percentile across the total number of tens.”)
Allowable Subject Matter
Claims 4-6, 12, and 14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claim 4, the method of claim 3, wherein, when the first design feature and the second design feature have a similarity equal to or higher than a predetermined similarity, extracting the trend word according to the adjusted reference comprises determining the first text and the second text as trend words. (The disclosed prior art of record fails to disclose the limitations of this claim.)
Regarding claim 5, the method of claim 3, wherein, when the first design feature and the second design feature have a similarity equal to or higher than a predetermined similarity, extracting the trend word according to the adjusted reference comprises determining the first text and the second text as trend words when a sum of the number of extraction times of the first text and the number of extraction times of the second text is equal to or higher than a predetermined reference number of times. (The disclosed prior art of record fails to disclose the limitations of this claim.)
Regarding claim 6, the method of claim 1, wherein extracting texts from the information and performing learning about a pair of an extracted text and the acquired image comprises: classifying the texts into a first group and a second group, extracting texts commonly included in the first group and the second group, performing learning about a pair of the acquired image and a text based on the texts commonly included in the two groups, and generating a text for a category of the acquired image based on a result of the learning. (The disclosed prior art of record fails to disclose the limitations of this claim.)
Regarding claim 12, the method of claim 11, wherein the generating a design of clothing includes, by identifying a condition, generating a design set which is a group of design images including the generated design, wherein the outputting the generated design includes, determining disposition order of the generated design to be displayed, by reflecting the result of learning about a pair of the extracted trend word and the acquired image. (The disclosed prior art of record fails to disclose the limitations of this claim.)
Regarding claim 14, the method of claim 1, wherein the adjusting a reference includes adjusting the reference for extracting a trend word by adjusting a schedule for extracting a trend word. (The disclosed prior art of record fails to disclose the limitations of this claim.)
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant’s disclosure.
Agarwal et al. (US Pub. No. 2022/00151479 A1) Aspects of the present disclosure provide systems, methods, and computer-readable storage media facilitating automated apparel design using deep learning techniques. For example, user instructions may be received as text data (or converted to text data from audio data representing user speech), and natural language processing (NLP) may be performed on the text data to interpret the user instructions. An apparel design may be generated in real-time/substantially real-time based on the user instructions. For example, the interpreted user instructions may be provided as input to at least one machine learning (ML) model that is configured to determine one or more visual apparel elements based on the user instructions and to generate the apparel design based on the visual apparel elements. One or more operations may be initiated based on the apparel design.
Chittar et al. (US Pub. No. 2011/0238659 A1) Using a processor, receiving, a query including a query image. A database is searched for a set of images similar to the query image, using a two-pass search. The results of the searching are then provided, the results including image members of the set of images similar to the query image. The first pass may be performed using a TF-IDF algorithm and the second pass ranks a predetermined number of results from the first pass by a best match algorithm or other type of algorithm. The type of second pass algorithm may be selectable by a user. If an end signal is not detected, a further two-pass search may be made based on a subsequent query having a subsequent query image.
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(571) 273-8300.
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/DAVID PERLMAN/Primary Examiner, Art Unit 2673
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