DETAILED ACTION
Claims 1-24 are presented for examination.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
The drawings received on 28 September 2022 are accepted.
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)(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, 8-10, 12-20, and 22-24 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 11,305,463 B2 Wright, et al. [herein “Wright”].
Claim 1 recites “1. A method of generating information associated with a customized nipple for use in a baby or nursing or nipple-related product.” Wright column 2 lines 37-43 disclose “integrate the process of 3D scanning lactating women's breasts to generate an AutoCAD model of the maternal nipple. The 3D scanning and generation of a plurality of maternal nipple shapes (in an embodiment, e.g. four) for creation of breastfeeding accessories and molds is intended to closely mimic a specific mother's unique nipple shape.”
Wright column 5 lines 57-59 disclose “These products can include but are not limited to bottle nipples yes, as discussed, but also, pacifiers, breast-feeding pump flanges, sippy cups, and nipple shields.” Bottles, pacifiers, breast-feeding pump flanges, sippy cups, and nipple shields are baby or nipple related products.
Claim 1 further recites “the method comprising: scanning a woman's nipple to generate data indicative of characteristics of the woman's nipple.” Wright column 4 lines 17-34 disclose:
STEP 1 The embodiments herein integrate the process of 3D scanning lactating women's breasts to generate an AutoCAD model of the maternal nipple (where that mother is currently lactating).
STEP 2 To 3D scan a lactating breast, it is important that the nipple be reproduced during a "usable-lactating" state. Accordingly, the mother must be first engaged in breastfeeding for one full minute. After the one-minute wait, a person using a handheld 3D scanner selects 'small object' specification for scanning objects less than 16 inches in size. As shown in FIG. 2, Holding the 3D scanner 12-15 inches away from the profile view of the entire exposed breast and with the nipple in clear view, the holder of the scanning device slowly moves the 3D scanner closer and encircling 360 degrees around the entire nipple. In an embodiment, an Afinia 3D scanner is used to 3D-scan the mothers lactating breasts, although a variety of other 3D scanner/phone apps could be used.
Scanning the breast to reproduce a usable-lactating state corresponds with scanning a woman’s nipple to generate data of the woman’s nipple characteristics.
Wright column 4 lines 50-52 disclose “to measure a slope and nipple length from areola base 404 to nipple tip in each of the nipple 3D scans.” Wright column 4 lines 65-66 disclose “Calculating the average nipple height 420 from the nipple tip 408 to the areola edge in each of the 3D scans.” The slope, length, and height are characteristics of the woman’s nipple. See further Wright column 5 lines 19-27.
Claim 1 further recites “processing the data to compare the characteristics of the woman's nipple to a plurality of other nipples' characteristics.” Wright column 4 lines 39-43 disclose “create a database of 3D scans of a large number (e.g. >270) of unique maternal breasts. This database is used to design a plurality of broad nipple categories representative of a much larger sample of sub-categories.” The database of a plurality of nipple categories corresponds with a plurality of other nipples’ characteristics.
Wright column 9 lines 61-64 disclose “3D scanning her nipple ahead of time using a proprietary mobile application (not shown), and letting the proprietary mobile application steer her toward the most appropriate of the e.g. four representative nipple types.” Wright column 10 lines 2-3 disclose “picking a relative object that is most similar to her nipple dimensions.” Picking a most similar nipple type corresponds with comparing characteristics to determine the similarity.
Claim 1 further recites “and generating information for a customized nipple for the woman based on the comparison of the characteristics of the woman's nipple with the other nipples' characteristics such that the customized nipple mimics the characteristics of the woman's nipple.” Wright column 9 lines 61-64 disclose “3D scanning her nipple ahead of time using a proprietary mobile application (not shown), and letting the proprietary mobile application steer her toward the most appropriate of the e.g. four representative nipple types.” Wright column 10 lines 2-3 disclose “picking a relative object that is most similar to her nipple dimensions.” Picking a most similar nipple type corresponds with generating information (a selection) for a customized nipple that mimics the characteristics of the woman’s nipple.
Claim 2 further recites “2. The method of claim 1, wherein the characteristics of the woman's nipple comprise at least one of: diameter of the nipple, diameter of an areola, depth/length of the nipple, depth/length of the areola, depth/length of the nipple plus the areola, color of the nipple, color of the areola, slope of the nipple, slope of the areola, texture of the nipple, or texture of the areola, or any combination thereof.” From the above list of alternatives the Examiner is selecting “slope of the nipple.”
Wright column 4 lines 50-52 disclose “to measure a slope and nipple length from areola base 404 to nipple tip in each of the nipple 3D scans.” Wright column 4 lines 65-66 disclose “Calculating the average nipple height 420 from the nipple tip 408 to the areola edge in each of the 3D scans.” The slope, length, and height are characteristics of the woman’s nipple. See further Wright column 5 lines 19-27.
Claim 8 further recites “8. The method of claim 1, wherein processing the data comprising classifying characteristics of the woman's nipple.” Wright column 10 lines 2-5 disclose “picking a relative object that is most similar to her nipple dimensions, and then the receiving a recommendation of which of the e.g. four representative nipple types is best.” Picking a object of a respective nipple type which is most similar corresponds to classifying the characteristics of her nipples as properly categorized with the representative nipple type classification.
Claim 9 further recites “9. The method of claim 8, wherein the characteristics are identified by machine classification.” Wright column 9 lines 61-64 disclose “3D scanning her nipple ahead of time using a proprietary mobile application (not shown), and letting the proprietary mobile application steer her toward the most appropriate of the e.g. four representative nipple types.” Wright column 5 line 42 discloses “the mobile phone.” A mobile phone is a machine. A mobile application steering towards an appropriate nipple type corresponds with identifying the classification/selection by the mobile phone machine.
Claim 10 further recites “10. The method of claim 8, wherein the characteristics are predetermined based on a classified dataset of the plurality of other nipples' characteristics.” Wright column 4 lines 39-43 disclose “create a database of 3D scans of a large number (e.g. >270) of unique maternal breasts. This database is used to design a plurality of broad nipple categories representative of a much larger sample of sub-categories.” The database of a plurality of nipple categories corresponds with a plurality of other nipples’ characteristics. The designed categories of representative nipples correspond with set(s) of predetermined characteristics based on a dataset of other nipples. The database corresponds with a dataset.
Claim 12 further recites “12. The method of claim 1, wherein the method is performed using an app downloaded to a computing device.” Wright column 9 lines 61-64 disclose “3D scanning her nipple ahead of time using a proprietary mobile application (not shown), and letting the proprietary mobile application steer her toward the most appropriate of the e.g. four representative nipple types.” A mobile application is an app for a computing device.
Claim 13 further recites “13. The method of claim 1, further comprising sending the data to a remote computing system, and wherein processing the data and generating the information occur at the remote computing system.” Wright column 9 lines 61-64 disclose “3D scanning her nipple ahead of time using a proprietary mobile application (not shown), and letting the proprietary mobile application steer her toward the most appropriate of the e.g. four representative nipple types.” Without loss of generality, the mobile app is considered remote. For example, the mobile app is remote from at least the database (300). Furthermore, having the proprietary mobile application steer towards the most appropriate nipple type is the generation of the information (i.e. the selection information) by the remote mobile application.
Claim 14 further recites “14. The method of claim 1, further comprising sending at least one product incorporating the customized nipple to the woman.” Wright column 9 lines 57-59 disclose “the lactating mother who is making a purchasing decision, and trying to decide which of the e.g. four representative nipple types is closest to her.” A person of ordinary skill in the art would understand a purchase to correspond with a delivery of the purchased product. Here, the purchase is of a respective nipple type and thus corresponds with sending at least one of the respective product nipples to the mother making the purchase.
Claim 15 further recites “15. The method of claim 14, wherein the at least one product comprises at least one baby bottle nipple, pacifier, prosthetic nipple, nipple shield, attachment for supplemental nursing system, or breast pump flange, or any combination thereof.” From the above list of alternatives the Examiner is selecting “baby bottle nipple.”
Wright column 5 lines 57-59 disclose “These products can include but are not limited to bottle nipples yes, as discussed, but also, pacifiers, breast-feeding pump flanges, sippy cups, and nipple shields.”
Claim 16 further recites “16. The method of claim 1, wherein generating the information comprises selecting the customized nipple for the woman from a plurality of customized nipples with predetermined characteristics, each of the plurality of customized nipples having at least one different predetermined characteristic from another.” Wright column 4 lines 39-43 disclose “create a database of 3D scans of a large number (e.g. >270) of unique maternal breasts. This database is used to design a plurality of broad nipple categories representative of a much larger sample of sub-categories.” The database of a plurality of nipple categories corresponds with a plurality of other nipples’ characteristics. The designed categories of representative nipples correspond with set(s) of predetermined characteristics based on a dataset of other nipples.
Wright column 9 lines 61-64 disclose “3D scanning her nipple ahead of time using a proprietary mobile application (not shown), and letting the proprietary mobile application steer her toward the most appropriate of the e.g. four representative nipple types.” The representative nipple types correspond with predetermined characteristics different for each representative type. Selecting or recommending an appropriate nipple type of the representative nipple types corresponds with selecting the customized nipple from a plurality of customized nipples with predetermined characteristics.
Claim 17 further recites “17. The method of claim 16, wherein selecting the customized nipple comprises executing a matching algorithm to match the woman's nipple to the customized nipple.” Wright column 5 lines 32-34 disclose “A key principle of the embodiments herein is to give an end-user and purchaser a choice that best matches her own nipple.”
Wright column 9 lines 61-64 disclose “3D scanning her nipple ahead of time using a proprietary mobile application (not shown), and letting the proprietary mobile application steer her toward the most appropriate of the e.g. four representative nipple types.” A proprietary mobile application steering the selection towards a most appropriate nipple type corresponds with the execution of a matching algorithm.
Claim 18 further recites “18. The method of claim 1, further comprising forming the customized nipple.” Wright column 6 lines 8-14 disclose “STEP 10 The next step is casting the PDMS mold. Specifically, the nipple type models will then be used to produce a silicone nipple by 3D printing a mold from PDMS (polydimethylsiloxane, AKA silicone). This in turn results in a bottle nipple prototype that accurately represent mother's unique nipple type.” Producing a silicone nipple corresponds with forming the customized nipple.
Claim 19 further recites “19. The method of claim 18, wherein forming the customized nipple comprises printing the customized nipple.” Wright column 6 lines 4-5 disclose “3D printers become able to directly print food grade food-usable packaging materials.” Directly printing corresponds with printing the respective customized nipple.
Claim 20 further recites “20. The method of claim 1, wherein the customized nipple is formed of a material selected to mimic feel of the woman's nipple.” Wright column 6 lines 20-22 disclose “e.g. silicone polymer. Instead of silicone polymer, urethane rubber, plastics, and foams could be used for the molding material 1008.” At least silicone, rubber, and plastics (i.e. PVC) are materials which mimic the feel of a nipple. Compare with Specification [0034].
Claim 22 further recites “22. A product bottle comprising the customized nipple of claim 1, the product selected from a bottle, a pacifier, a nipple shield, a prosthetic nipple, a breast pump flange, or a component for a supplemental nursing system.” From the above list of alternatives the Examiner is selecting “a bottle.”
Wright column 5 lines 57-59 disclose “These products can include but are not limited to bottle nipples yes, as discussed, but also, pacifiers, breast-feeding pump flanges, sippy cups, and nipple shields.” Bottles, pacifiers, breast-feeding pump flanges, sippy cups, and nipple shields are baby or nipple related products.
Claim 23 recites “23. A non-transitory computer readable medium comprising instructions that, when executed by at least one processor.” Wright column 10 lines 28-29 disclose “generating a plurality of computer-generated models.” A computer includes a processor and memory.
Claim 23 further recites “cause the at least one processor to: receive data indicative of characteristics of a woman's nipple based on a scan of the woman's nipple.” Wright column 4 lines 17-34 disclose:
STEP 1 The embodiments herein integrate the process of 3D scanning lactating women's breasts to generate an AutoCAD model of the maternal nipple (where that mother is currently lactating).
STEP 2 To 3D scan a lactating breast, it is important that the nipple be reproduced during a "usable-lactating" state. Accordingly, the mother must be first engaged in breastfeeding for one full minute. After the one-minute wait, a person using a handheld 3D scanner selects 'small object' specification for scanning objects less than 16 inches in size. As shown in FIG. 2, Holding the 3D scanner 12-15 inches away from the profile view of the entire exposed breast and with the nipple in clear view, the holder of the scanning device slowly moves the 3D scanner closer and encircling 360 degrees around the entire nipple. In an embodiment, an Afinia 3D scanner is used to 3D-scan the mothers lactating breasts, although a variety of other 3D scanner/phone apps could be used.
Scanning the breast to reproduce a usable-lactating state corresponds with scanning a woman’s nipple to generate data of the woman’s nipple characteristics.
Wright column 4 lines 50-52 disclose “to measure a slope and nipple length from areola base 404 to nipple tip in each of the nipple 3D scans.” Wright column 4 lines 65-66 disclose “Calculating the average nipple height 420 from the nipple tip 408 to the areola edge in each of the 3D scans.” The slope, length, and height are characteristics of the woman’s nipple. See further Wright column 5 lines 19-27.
Claim 23 further recites “process the data to compare the characteristics of the woman's nipple to a plurality of other nipples' characteristics.” Wright column 4 lines 39-43 disclose “create a database of 3D scans of a large number (e.g. >270) of unique maternal breasts. This database is used to design a plurality of broad nipple categories representative of a much larger sample of sub-categories.” The database of a plurality of nipple categories corresponds with a plurality of other nipples’ characteristics.
Wright column 9 lines 61-64 disclose “3D scanning her nipple ahead of time using a proprietary mobile application (not shown), and letting the proprietary mobile application steer her toward the most appropriate of the e.g. four representative nipple types.” Wright column 10 lines 2-3 disclose “picking a relative object that is most similar to her nipple dimensions.” Picking a most similar nipple type corresponds with comparing characteristics to determine the similarity.
Claim 23 further recites “and generate information for a customized nipple for the woman based on the comparison of the characteristics of the woman's nipple with the other nipples' characteristics such that the customized nipple mimics the characteristics of the woman's nipple.” Wright column 9 lines 61-64 disclose “3D scanning her nipple ahead of time using a proprietary mobile application (not shown), and letting the proprietary mobile application steer her toward the most appropriate of the e.g. four representative nipple types.” Wright column 10 lines 2-3 disclose “picking a relative object that is most similar to her nipple dimensions.” Picking a most similar nipple type corresponds with generating information (a selection) for a customized nipple that mimics the characteristics of the woman’s nipple.
Claim 24 recites “24. A system for generating information associated with a customized nipple for use in a baby or nursing or nipple-related product.” Wright column 2 lines 37-43 disclose “integrate the process of 3D scanning lactating women's breasts to generate an AutoCAD model of the maternal nipple. The 3D scanning and generation of a plurality of maternal nipple shapes (in an embodiment, e.g. four) for creation of breastfeeding accessories and molds is intended to closely mimic a specific mother's unique nipple shape.”
Wright column 5 lines 57-59 disclose “These products can include but are not limited to bottle nipples yes, as discussed, but also, pacifiers, breast-feeding pump flanges, sippy cups, and nipple shields.” Bottles, pacifiers, breast-feeding pump flanges, sippy cups, and nipple shields are baby or nipple related products.
Claim 24 further recites “the system comprising at least one processor configured to execute instructions.” Wright column 10 lines 28-29 disclose “generating a plurality of computer-generated models.” A computer includes a processor and memory.
Claim 24 further recites “to cause the at least one processor to: receive data indicative of characteristics of a woman's nipple based on a scan of the woman's nipple.” Wright column 4 lines 17-34 disclose:
STEP 1 The embodiments herein integrate the process of 3D scanning lactating women's breasts to generate an AutoCAD model of the maternal nipple (where that mother is currently lactating).
STEP 2 To 3D scan a lactating breast, it is important that the nipple be reproduced during a "usable-lactating" state. Accordingly, the mother must be first engaged in breastfeeding for one full minute. After the one-minute wait, a person using a handheld 3D scanner selects 'small object' specification for scanning objects less than 16 inches in size. As shown in FIG. 2, Holding the 3D scanner 12-15 inches away from the profile view of the entire exposed breast and with the nipple in clear view, the holder of the scanning device slowly moves the 3D scanner closer and encircling 360 degrees around the entire nipple. In an embodiment, an Afinia 3D scanner is used to 3D-scan the mothers lactating breasts, although a variety of other 3D scanner/phone apps could be used.
Scanning the breast to reproduce a usable-lactating state corresponds with scanning a woman’s nipple to generate data of the woman’s nipple characteristics.
Wright column 4 lines 50-52 disclose “to measure a slope and nipple length from areola base 404 to nipple tip in each of the nipple 3D scans.” Wright column 4 lines 65-66 disclose “Calculating the average nipple height 420 from the nipple tip 408 to the areola edge in each of the 3D scans.” The slope, length, and height are characteristics of the woman’s nipple. See further Wright column 5 lines 19-27.
Claim 24 further recites “process the data to compare the characteristics of the woman's nipple to a plurality of other nipples' characteristics.” Wright column 4 lines 39-43 disclose “create a database of 3D scans of a large number (e.g. >270) of unique maternal breasts. This database is used to design a plurality of broad nipple categories representative of a much larger sample of sub-categories.” The database of a plurality of nipple categories corresponds with a plurality of other nipples’ characteristics.
Wright column 9 lines 61-64 disclose “3D scanning her nipple ahead of time using a proprietary mobile application (not shown), and letting the proprietary mobile application steer her toward the most appropriate of the e.g. four representative nipple types.” Wright column 10 lines 2-3 disclose “picking a relative object that is most similar to her nipple dimensions.” Picking a most similar nipple type corresponds with comparing characteristics to determine the similarity.
Claim 24 further recites “and generate information for a customized nipple for the woman based on the comparison of the characteristics of the woman's nipple with the other nipples' characteristics such that the customized nipple mimics the characteristics of the woman's nipple.” Wright column 9 lines 61-64 disclose “3D scanning her nipple ahead of time using a proprietary mobile application (not shown), and letting the proprietary mobile application steer her toward the most appropriate of the e.g. four representative nipple types.” Wright column 10 lines 2-3 disclose “picking a relative object that is most similar to her nipple dimensions.” Picking a most similar nipple type corresponds with generating information (a selection) for a customized nipple that mimics the characteristics of the woman’s nipple.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 3-7, 11, and 21
Claims 3-7, 11, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Wright as applied to claims 1 and 8 above, and further in view of US 9,251,591 B2 Song, et al. [herein “Song”].
Claim 3 further recites “3. The method of claim 1, wherein processing the data comprising generating a score for each of the characteristics of the woman's nipple to compare to corresponding scores stored for the plurality of other nipples' characteristics.” Wright column 9 lines 61-64 disclose “3D scanning her nipple ahead of time using a proprietary mobile application (not shown), and letting the proprietary mobile application steer her toward the most appropriate of the e.g. four representative nipple types.”
But Wright does not explicitly disclose generating scores for the characteristics; however, in analogous art of body shape analysis, Song column 14 lines 63-64 teach “A new person's group membership can therefore be predicted by calculating her discriminant function score(s).” The discriminant function scores are generated scores on characteristics from the person’s body. The group membership corresponds to a plurality of other body characteristics.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Wright and Song. One having ordinary skill in the art would have found motivation to use cluster analysis of body shape into the system of generating nipple prototypes for customized breastfeeding accessories to mimic a specific mother's unique nipple shape for the advantageous purpose of “identify different body shapes and create body shape categories.” See Song column 11 lines 57-59.
Claim 4 further recites “4. The method of claim 3, wherein the scores are weighted by characteristic such that scores of a more significant characteristic receive a higher weighted value than less significant characteristics.” Song column 14 lines 45-50 teaches “Discriminant analysis can be used to classify body shapes identified from the cluster analysis. The dataset preferably includes body shape group memberships coded by the cluster analysis as a 'grouping variable', and the measurements corresponding to principal components identified as 'independent variables'.” See also Song column 13 lines 22-25. Song column 13 lines 35-39 teach “The decision on the number of components to be retained can be made with consideration of three aspects: (a) eigenvalues that correspond to the sum of the squared loadings for a principal component.” The eigenvalue of a PCA component corresponds with a weight proportional to the significance of the corresponding characteristics.
Song column 14 lines 52-53 teach “Once the discriminant functions are calculated, their significance.” The significance of the discriminant functions which correspond with PCA components are weights which correspond with the identified discriminant scores on the body characteristics.
Claim 5 further recites “5. The method of claim 3, further comprising representing the scores for each of the characteristics as at least one vector.” Song column 33 lines 48-50 teaches “their corresponding [Principal Component (PC)] scores derived through transposing the measurements with the eigenvectors.” The principal component eigenvectors correspond with vector representations of the characteristics.
Claim 6 further recites “6. The method of claim 5, wherein the at least one vector is inputted into at least one neural network to generate at least one matrix for the customized nipple for the woman.” Song column 13 lines 33-35 teach “a representative method of Orthogonal coordinate system, can be used, since it provides independence among principal components.” The orthogonal basis of the orthogonal coordinate system corresponds to a generated matrix. See also Song column 7 lines 29-30 regarding “rotated component matrix” of PCA.
Song column 18 lines 18-23 teaches:
As is well known in the art, a starting point for any machine learning method is a documented dataset containing multiple instances of system inputs and correct outcomes. This data set can be used, using methods known in the art, including but not limited to standardized machine learning methods such as parametric classification methods non-parametric methods, decision tree learning, neural networks, methods combining both inductive and analytic learning, and modeling approaches such as regression models, to train the machine learning system and to evaluate and optimize the performance of the trained system.
Training a neural network machine learning system corresponds to inputting respective dataset(s) into at least one neural network. Song column 18 lines 31-35 teach “The outcome (output) will be a pattern specification that is known to be a good-fit corresponding to each subject's body measurements. The body measurements can be automatically determined from a 3D body scan, which is also included in the documented data set.” The set of outputs corresponds with a matrix of data values for the customized good-fit.
Claim 7 further recites “7. The method of claim 6, wherein the at least one neural network utilizes machine learning technology for the customized nipple of the woman.” Wright column 9 lines 61-64 disclose “3D scanning her nipple ahead of time using a proprietary mobile application (not shown), and letting the proprietary mobile application steer her toward the most appropriate of the e.g. four representative nipple types.”
But Wright does not explicitly disclose a neural network; however, in analogous art of body shape analysis, Song column 18 lines 18-23 and 31-35 teaches:
As is well known in the art, a starting point for any machine learning method is a documented dataset containing multiple instances of system inputs and correct outcomes. This data set can be used, using methods known in the art, including but not limited to standardized machine learning methods such as parametric classification methods non-parametric methods, decision tree learning, neural networks, methods combining both inductive and analytic learning, and modeling approaches such as regression models, to train the machine learning system and to evaluate and optimize the performance of the trained system.
…
The outcome (output) will be a pattern specification that is known to be a good-fit corresponding to each subject's body measurements. The body measurements can be automatically determined from a 3D body scan, which is also included in the documented data set.
The output being a good-fit corresponds with the neural network being used for the respective customized body customized product.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Wright and Song. One having ordinary skill in the art would have found motivation to use machine learning of good-fit into the system of generating nipple prototypes for customized breastfeeding accessories to mimic a specific mother's unique nipple shape for the advantageous purpose of “identify different body shapes and create body shape categories.” See Song column 11 lines 57-59.
Claim 11 further recites “11. The method of claim 8, further comprising training a trained model of classified datasets associated with characteristics of the plurality of other nipples using the classified characteristics of the woman's nipple.” Wright does not explicitly disclose a trained model; however, in analogous art of body shape analysis, Song column 18 lines 18-23 teaches:
As is well known in the art, a starting point for any machine learning method is a documented dataset containing multiple instances of system inputs and correct outcomes. This data set can be used, using methods known in the art, including but not limited to standardized machine learning methods such as parametric classification methods non-parametric methods, decision tree learning, neural networks, methods combining both inductive and analytic learning, and modeling approaches such as regression models, to train the machine learning system and to evaluate and optimize the performance of the trained system.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Wright and Song. One having ordinary skill in the art would have found motivation to use machine learning of good-fit into the system of generating nipple prototypes for customized breastfeeding accessories to mimic a specific mother's unique nipple shape for the advantageous purpose of “identify different body shapes and create body shape categories.” See Song column 11 lines 57-59.
Claim 21 further recites “21. The method of claim 1, further comprising scaling the customized nipple based on user input associated with information on the woman's baby.” Wright column 9 lines 61-64 disclose “3D scanning her nipple ahead of time using a proprietary mobile application (not shown), and letting the proprietary mobile application steer her toward the most appropriate of the e.g. four representative nipple types.”
But Wright does not explicitly disclose a neural network; however, in analogous art of body shape analysis, Song column 18 lines 36-44 teaches:
The template pattern sets the foundation for the customized pattern. Although the initial template may be specified in a number of different ways known in the art (for example, a basic pattern that is re-sized or graded on an XY coordinate measurement system with values derived from simple arc and/or circumferential body measurements of the individual). The initial template can be re-encoded into a parametric model wherein the parameters specify the customization to a subject's measurements.
A parametric modeling and a re-sizing are a scaling of the product based on the user-input information.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Wright and Song. One having ordinary skill in the art would have found motivation to use machine learning of good-fit into the system of generating nipple prototypes for customized breastfeeding accessories to mimic a specific mother's unique nipple shape for the advantageous purpose of “identify different body shapes and create body shape categories.” See Song column 11 lines 57-59.
Conclusion
Prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 12254562 B2 Zeev; Shilo Ben et al.
teaches
Evaluated but not prior art for most purposes. Only provisional 63/238569 is before the effective filing date of the instant application and the disclosure in 63/238569 is limited in comparison to the published patent US 12,254,562 B2. Accordingly, significant portions of Zeev have an effective filing date after that of the instant application. See MPEP §2136.03(III) (Requiring written description support for any subject matter relied upon in a prior art rejection).
US 9044380 B2 Sabree; Luvina et al.
Custom molded nipple replicating a human nipple for use in both pacifiers and baby bottles;
Column 4 lines 49-51 “a custom molded nipple for a pacifier which mimics the contour, size, texture, color, and firmness of a mother's natural nipple and areola area of the breast
US 10603247 B2 Lofaro; Maura J. et al.
Custom-made artificial nipple
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jay B Hann whose telephone number is (571)272-3330. The examiner can normally be reached M-F 10am-7pm EDT.
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/Jay Hann/Primary Examiner, Art Unit 2186 19 December 2025