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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after allowance or after an Office action under Ex Parte Quayle, 25 USPQ 74, 453 O.G. 213 (Comm'r Pat. 1935). Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, prosecution in this application has been reopened pursuant to 37 CFR 1.114. Applicant's submission filed on 2/17/26 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2/17/26 is being considered by the examiner.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
(a)(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 1-19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by PCT/EP2020/000106, which corresponds to Szeli et al. (USPGPUB 20220188897) “Szeli”.
Regarding claim 1, Szeli discloses a method implemented by a computing system ([0012]), the method comprising: receiving, by the computing system ([0012]) and from a user device (1), subject profile information (S60) that specifies a plurality of body measurements (S60) associated with one or more images (S10) of a subject (2) that are captured by the user device (1); receiving, by the computing system ([0012]) and from the user device (1), a selection of a garment (S210), wherein the garment (S210) is associated with a category (Table 1; “product category”), a brand (Table 1; “brand”), a style (Table 1; “product fit”), and a plurality of sizes (Table 1; “Size 1,2,3,4…”); making a determination that member training data ([0150]-[0152]) corresponding to at least a threshold number ([0151]; “τ”) of members (2) selecting a first size (“size”) of the garment and at least the threshold number ([0151]; “τ”) of members selecting a second size (“size”) of the garment has become available for training a sizing recommendation engine (“clothing size recommendation model”) of the computing system ([0012]); training the sizing recommendation engine (“clothing size recommendation model”) with the member training data (“LSM”) in response to making the determination; thereafter determining, by the sizing recommendation engine of the computing system and based on the plurality of body measurements, a particular size (“size”) of the garment that is associated with the subject profile information (“LSM” and [0141]-[0143]); and communicating, by the computing system ([0012]), the particular size to the user device (1).
Regarding claim 2, Szeli discloses the method according to claim 1, further comprising: training the sizing recommendation engine (“clothing size recommendation model”) with calibrated member training data ([0150]-[0152], and S60) that specifies a plurality of profiles (S60) associated with different subjects (2), and for each subject (2), garment information (Table 1) that specifies one or more garments selected by the subject (2), wherein for each garment, the garment information specifies a category (Table 1; “product category”), a brand (Table 1; “brand”), a style (Table 1; “product fit”), and a size (Table 1; “Size 1,2,3,4…”).
Regarding claim 3, Szeli discloses the method according to claim 2, further comprising: receiving, from the user device (1), an indication (Fig. 6; “RI”) of whether the particular size of garment communicated to the user device fits the subject; and when the particular size of garment fits the subject, updating the calibrated member training data ("LSM") to include the subject profile information, the selected garment, and the particular size of garment (Table 1).
Regarding claim 4, Szeli discloses the method according to claim 2, wherein the sizing recommendation engine (“clothing size recommendation model”) includes style-specific recommendation logic ([0138]) for each of a plurality of different styles (Table 1; “product fit”), brand-specific recommendation logic ([0138]) for each of a plurality of different brands (Table 1; “brand”), and category-specific recommendation logic ([0138]) for each of a plurality of different categories (Table 1; “product category”), wherein the method comprises: training the style-specific recommendation logic ([0138]) with a subset of calibrated member training data associated with a corresponding style to determine a particular size of a selected garment of that style associated with the subject profile information ("LSM"); training brand-specific recommendation logic ([0138]) with a subset of calibrated member training data associated with a corresponding brand to determine a particular size of a selected garment of that brand associated with the subject profile information ("LSM"); and training category-specific recommendation logic ([0138]) with a subset of calibrated member training data associated with a corresponding category to determine a particular size of a selected garment of that category associated with the subject profile information ("LSM").
Regarding claim 5, Szeli discloses the method according to claim 4, wherein determining the particular size (“size”) of the garment that is associated with the subject profile information (S60) further comprises: responsive to determining that the subset of calibrated member training data ([0150]-[0152]) associated with a style of the selected garment includes a threshold amount ([0151]; “τ”) of calibrated member training data for each of the plurality of sizes (Table 1; “Size 1,2,3,4…”) associated with the garment, determining, by the style-specific recommendation logic ([0138]) and based on the plurality of body measurements (S60), a particular size (Table 1; “Size 1,2,3,4…”) of the garment that is associated with the subject profile information (S60); otherwise, responsive to determining that the subset of calibrated member training data ([0150]-[0152]) associated with a brand (Table 1; “brand”) of the selected garment includes a threshold amount ([0151]; “τ”) of calibrated member training data ([0150]-[0152]) for each of a plurality of sizes (Table 1; “Size 1,2,3,4…”) associated with the brand (Table 1; “brand”), determining, by the brand-specific recommendation logic recommendation logic ([0138]) and based on the plurality of body measurements (S60), a particular size (Table 1; “Size 1,2,3,4…”) of the garment that is associated with the subject profile information (S60); and otherwise, determining, by the category-specific recommendation logic ([0138]) and based on the plurality of body measurements (S60), a particular size (Table 1; “Size 1,2,3,4…”) of the garment that is associated with the subject profile information (S60).
Regarding claim 6, Szeli discloses the method according to claim 1, wherein each body measurement (S60) specifies a circumferential distance (Table 1; “waist circumference”, “hips circumference”) around a region of the subject (2) and a distance between a particular anchor point (S44; “key points”) of the subject and the region.
Regarding claim 7, Szeli discloses the method according to claim 1, wherein the one or more images (S10) of the subject (2) include a front view ([0083] and Fig. 3) of the subject and a side-view ([0083] and Fig. 3) of the subject.
Regarding claim 8, Szeli discloses a computing system ([0012]) comprising: one or more processors ([0060]); and a memory in communication with the one or more processors ([0060]), wherein the memory ([0060]) stores instruction code ([0065]) that, when executed by the one or more processors, causes the computing system ([0012]) to perform operations comprising: receiving from a user device (1), subject profile information (S60) that specifies a plurality of body measurements (S60) associated with one or more images (S10) of a subject (2) that are captured by the user device (1 and Fig. 3); receiving a selection of a garment (S210) from the user device, wherein the garment is associated with a category (Table 1; “product category”), a brand (Table 1; “brand”), a style (Table 1; “product fit”), and a plurality of sizes (Table 1; “Size 1,2,3,4…”); making a determination that member training data ([0150]-[0152]) corresponding to at least a threshold number ([0151]; “τ”) of members selecting a first size (“size”) of the garment and at least the threshold number of members selecting a second size (“size”) of the garment has become available for training a sizing recommendation engine (“clothing size recommendation model”) of the computing system; training (“LSM”) the sizing recommendation engine with the member training data in response to making the determination; thereafter determining, by the sizing recommendation engine of the computing system and based on the plurality of body measurements, a particular size (Fig. 6; “REC”) of the garment that is associated with the subject profile information; and communicating the particular size (Fig. 6; “REC”) to the user device (1).
Regarding claim 9, Szeli discloses the computing system according to claim 8, wherein the instruction code ([0065]) is executable to cause the computing system ([0012]) to perform operations comprising: training the sizing recommendation engine (“clothing size recommendation model”) with calibrated member training data ([0150]-[0152], and S60) that specifies a plurality of profiles (S60) associated with different subjects (2), and for each subject (2), garment information (Table 1) that specifies one or more garments selected by the subject (2), wherein for each garment, the garment information specifies a category (Table 1; “product category”), a brand (Table 1; “brand”), a style (Table 1; “product fit”), and a size (Table 1; “Size 1,2,3,4…”).
Regarding claim 10, Szeli discloses the computing system according to claim 9, wherein the instruction code ([0065]) is executable to cause the computing system ([0012]) to perform operations comprising: receiving, from the user device (1), an indication (Fig. 6; “RI”) of whether the particular size of garment communicated to the user device fits the subject; and when the particular size of garment fits the subject, updating the calibrated member training data ("LSM") to include the subject profile information, the selected garment, and the particular size of garment (Table 1).
Regarding claim 11, Szeli discloses the computing system according to claim 9, wherein the sizing recommendation engine (“clothing size recommendation model”) includes style-specific recommendation logic ([0138]) for each of a plurality of different styles (Table 1; “product fit”), brand-specific recommendation logic ([0138]) for each of a plurality of different brands (Table 1; “brand”), and category-specific recommendation logic ([0138]) for each of a plurality of different categories (Table 1; “product category”), wherein the instruction code ([0065]) is executable to cause the computing system ([0012]) to perform operations comprising: training the style-specific recommendation logic ([0138]) with a subset of calibrated member training data associated with a corresponding style to determine a particular size of a selected garment of that style associated with the subject profile information ("LSM"); training brand-specific recommendation logic ([0138]) with a subset of calibrated member training data associated with a corresponding brand to determine a particular size of a selected garment of that brand associated with the subject profile information ("LSM"); and training category-specific recommendation logic ([0138]) with a subset of calibrated member training data associated with a corresponding category to determine a particular size of a selected garment of that category associated with the subject profile information ("LSM").
Regarding claim 12, Szeli discloses the computing system according to claim 11, wherein determining the particular size (“size”) of the garment that is associated with the subject profile information (S60), the instruction code ([0065]) is executable to cause the computing system ([0012]) to perform operations comprising: responsive to determining that the subset of calibrated member training data ([0150]-[0152]) associated with a style of the selected garment includes a threshold amount ([0151]; “τ”) of calibrated member training data for each of the plurality of sizes (Table 1; “Size 1,2,3,4…”) associated with the garment, determining, by the style-specific recommendation logic ([0138]) and based on the plurality of body measurements (S60), a particular size (Table 1; “Size 1,2,3,4…”) of the garment that is associated with the subject profile information (S60); otherwise, responsive to determining that the subset of calibrated member training data ([0150]-[0152]) associated with a brand (Table 1; “brand”) of the selected garment includes a threshold amount ([0151]; “τ”) of calibrated member training data ([0150]-[0152]) for each of a plurality of sizes (Table 1; “Size 1,2,3,4…”) associated with the brand (Table 1; “brand”), determining, by the brand-specific recommendation logic recommendation logic ([0138]) and based on the plurality of body measurements (S60), a particular size (Table 1; “Size 1,2,3,4…”) of the garment that is associated with the subject profile information (S60); and otherwise, determining, by the category-specific recommendation logic ([0138]) and based on the plurality of body measurements (S60), a particular size (Table 1; “Size 1,2,3,4…”) of the garment that is associated with the subject profile information (S60).
Regarding claim 13, Szeli discloses the computing system according to claim 8, wherein each body measurement (S60) specifies a circumferential distance (Table 1; “waist circumference”, “hips circumference”) around a region of the subject (2) and a distance between a particular anchor point (S44; “key points”) of the subject and the region.
Regarding claim 14, Szeli discloses the computing system according to claim 8, wherein the one or more images (S10) of the subject (2) include a front view ([0083] and Fig. 3) of the subject and a side-view ([0083] and Fig. 3) of the subject.
Regarding claim 15, Szeli discloses a non-transitory computer-readable medium ([0066]) having stored thereon instruction code ([0065]) that, when executed by one or more processors ([0060]) of a computing system ([0012]), causes the computing system ([0012]) to perform operations comprising: receiving from a user device (1), subject profile information (S60) that specifies a plurality of body measurements (S60) associated with one or more images (S10) of a subject (2) that are captured by the user device (1); receiving, by the computing system ([0012]) and from the user device (1), a selection of a garment (S210), wherein the garment (S210) is associated with a category (Table 1; “product category”), a brand (Table 1; “brand”), a style (Table 1; “product fit”), and a plurality of sizes (Table 1; “Size 1,2,3,4…”); making a determination that member training data ([0150]-[0152]) corresponding to at least a threshold number ([0151]; “τ”) of members (2) selecting a first size (“size”) of the garment and at least the threshold number ([0151]; “τ”) of members selecting a second size (“size”) of the garment has become available for training a sizing recommendation engine (“clothing size recommendation model”) of the computing system ([0012]); training the sizing recommendation engine (“clothing size recommendation model”) with the member training data (“LSM”) in response to making the determination; thereafter determining, by the sizing recommendation engine of the computing system and based on the plurality of body measurements, a particular size (“size”) of the garment that is associated with the subject profile information (“LSM” and [0141]-[0143]); and communicating, by the computing system ([0012]), the particular size to the user device (1).
Regarding claim 16, Szeli discloses the non-transitory computer-readable medium according to claim 15, wherein the instruction code ([0065]) is executable to cause the computing system ([0012]) to perform operations comprising: training the sizing recommendation engine (“clothing size recommendation model”) with calibrated member training data ([0150]-[0152], and S60) that specifies a plurality of profiles (S60) associated with different subjects (2), and for each subject (2), garment information (Table 1) that specifies one or more garments selected by the subject (2), wherein for each garment, the garment information specifies a category (Table 1; “product category”), a brand (Table 1; “brand”), a style (Table 1; “product fit”), and a size (Table 1; “Size 1,2,3,4…”).
Regarding claim 17, Szeli discloses the computing system according to claim 16, wherein the instruction code ([0065]) is executable to cause the computing system ([0012]) to perform operations comprising: receiving, from the user device (1), an indication (Fig. 6; “RI”) of whether the particular size of garment communicated to the user device fits the subject; and when the particular size of garment fits the subject, updating the calibrated member training data ("LSM") to include the subject profile information, the selected garment, and the particular size of garment (Table 1).
Regarding claim 18, Szeli discloses the non-transitory computer-readable medium according to claim 16, wherein the sizing recommendation engine (“clothing size recommendation model”) includes style-specific recommendation logic ([0138]) for each of a plurality of different styles (Table 1; “product fit”), brand-specific recommendation logic ([0138]) for each of a plurality of different brands (Table 1; “brand”), and category-specific recommendation logic ([0138]) for each of a plurality of different categories (Table 1; “product category”), wherein the instruction code ([0065]) is executable to cause the computing system ([0012]) to perform operations comprising: training the style-specific recommendation logic ([0138]) with a subset of calibrated member training data associated with a corresponding style to determine a particular size of a selected garment of that style associated with the subject profile information ("LSM"); training brand-specific recommendation logic ([0138]) with a subset of calibrated member training data associated with a corresponding brand to determine a particular size of a selected garment of that brand associated with the subject profile information ("LSM"); and training category-specific recommendation logic ([0138]) with a subset of calibrated member training data associated with a corresponding category to determine a particular size of a selected garment of that category associated with the subject profile information ("LSM").
Regarding claim 19, Szeli discloses the non-transitory computer-readable medium according to claim 18, wherein in determining the particular size (“size”) of the garment that is associated with the subject profile information (S60), the instruction code ([0065]) is executable to cause the computing system ([0012]) to perform operations comprising: responsive to determining that the subset of calibrated member training data ([0150]-[0152]) associated with a style of the selected garment includes a threshold amount ([0151]; “τ”) of calibrated member training data for each of the plurality of sizes (Table 1; “Size 1,2,3,4…”) associated with the garment, determining, by the style-specific recommendation logic ([0138]) and based on the plurality of body measurements (S60), a particular size (Table 1; “Size 1,2,3,4…”) of the garment that is associated with the subject profile information (S60); otherwise, responsive to determining that the subset of calibrated member training data ([0150]-[0152]) associated with a brand (Table 1; “brand”) of the selected garment includes a threshold amount ([0151]; “τ”) of calibrated member training data ([0150]-[0152]) for each of a plurality of sizes (Table 1; “Size 1,2,3,4…”) associated with the brand (Table 1; “brand”), determining, by the brand-specific recommendation logic recommendation logic ([0138]) and based on the plurality of body measurements (S60), a particular size (Table 1; “Size 1,2,3,4…”) of the garment that is associated with the subject profile information (S60); and otherwise, determining, by the category-specific recommendation logic ([0138]) and based on the plurality of body measurements (S60), a particular size (Table 1; “Size 1,2,3,4…”) of the garment that is associated with the subject profile information (S60).
Claim 20 has been cancelled.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Applicant-cited Szeli in view of Lee (USPGPUB 20050022708) “Lee”.
Regarding claim 21, Szeli discloses the method of claim 1, wherein the plurality of body measurements (S60) comprise a waist measurement ([0020]; “waist”), and a chest measurement ([0020]; “chest”).
Szeli does not disclose a low hip measurement or a high hip measurement.
Lee teaches in figure 2, a low hip measurement (30) and a high hip measurement (28).
It would have been obvious to one of ordinary skill in the art before the effective filing date to include Lee’s low and high hip measurements into Szeli’s plurality of body measurements to ensure a more complete and accurate hip measurement is provided, thus improving Szeli’s garment size recommendations.
Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANNA JOSEPHINE SAUNDERS whose telephone number is (571)272-6528. The examiner can normally be reached 7:30-5:00 EST.
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/ANNA JOSEPHINE SAUNDERS/Examiner, Art Unit 2855
/PETER J MACCHIAROLO/Supervisory Patent Examiner, Art Unit 2855