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 .
This office action is in response to applicant’s amendments filed on January 21, 2026. Claims 1-5,7-11 and 13-21 are pending. Claims 1 and 8 have been amended. Claim 15 is withdrawn from further consideration as being drawn to a nonelected invention. Claims 6,12 have been cancelled.
All prior rejections are maintained as set forth below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5,7-11,13,14 and 16-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The claims recite producing a recipe of a hair coloring agent by selecting an initial hair color, selecting a desired hair color, obtaining hair color data and separating the data into specific color groups and sorting, adjusting data groups and analyzing the data through an analysis model to produce a predictive result for a dye recipe. These steps which can all be accomplished through mental processes under their broadest reasonable interpretation cover performance of the limitation in the mind but for the recitation of generic computer processor. That is, other than the “processor” nothing in the claim limitations preclude the invention practically being performed in the mind, including the producing the recipe, wherein “producing” could be a mere printing of the recipe or recitation of the recipe. The dependent claims are also, in their broadest reasonable interpretation mental processes or mathematical calculations which are able to be mentally performed. See MPEP 2106.04(a)(2).
This judicial exception is not integrated into a practical application because the claim only recites one additional element of using a processor. The processor is generally recited ( i.e. as a generic processor performing a generic computer function of sorting and analyzing hair color data) such that it amounts to no more than mere instructions to apply the exception during a generic computer component. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional element of using a processor to perform the data sorting and analysis are no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are nor patent eligible.
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 1-5,7-10,13 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Knubel (WO 2017/103050) in view of Inzinna (US 7,877,294) and Chawla (SMOTE: Synthetic Minority over-sampling Technique). US 11,140,967 is being used for citation purposes as it is the English equivalent document of WO 2017/103050 which is not in English.
Knubel teaches using prediction of properties of hair colors in creation of an individual “customized” hair coloring agent using recipe optimization (column 1, lines 3-44), wherein the optimization of the recipe and creation of the custom coloring agent meets the claimed limitation of determining the recipe of the hair coloring agent associated with the desired coloring result and producing the recipe. Knubel teaches selecting the initial hair color and desired hair coloring result, which meets the claimed limitation of selecting an initial hair color from a plurality of initial hair colors and selecting the desired coloring result (Figure 11, 1110; column 11, 13-19; column 5, lines 1-33). Knubel teaches displaying images of the hair color and desired coloring result on computer screens or printed on hair dye packages at a point of sale (claims 13, 14, 16 and 17) and doing the predictive analysis and processing at the point of sale of the hair dye (column 16, line 45 to column 17, line 11). Knubel teaches computer supported determination of properties of hair colors wherein hair color data are prepared comprising pre-color data and coloring results for the data (abstract). Knubel teaches the methods use data processing devices (column 1, lines 19-21). Knubel teaches the coloring results has a measured specification with a property of hair colors and determining a relationship between the multitude of coloring pre-condition parameters and the at least one coloring result by employing predictive analytics such as linear or multi-linear regression, multiple polynomic regression, neuronal network methods, support vector machine methods and decision tree (abstract; column 6, lines 53-63). Knubel teaches the pre-condition parameters contain data on the hair color agents such as concentrations, base hair color, additional ingredients, which can be parameterized in a color space, preliminary damage of the hair, degree of graying and other color pre-condition parameters (column 3, lines 40-49). Table 1 contains hair coloring agent recipes for 53 products used on natural hair and measured colormetrically. (column 8-9). Knubel teaches data from Table 1 can form coloring result parameters column 10). Knubel teaches determining the desired color result parameter from the relationship between the pre-condition color parameters and the coloring result using predictive analytics (column 11, lines 13-19). Knubel teaches recipe optimization and calculation of the optimal colors, e.g. of parameters of a hair color parameterized in color space and used on packaging, online, in apps to create individual custom hair color (column 1, lines 33-67). Knubel teaches the analysis provides a predicted color result from the training data (claims 1-20).It is noted that Knubel contains data sets of brown and black color as natural colors and red, orange and purple fashion colors (such as N&E 568 intense red, N&E 588 glossy acaiberry, Nectra 499 and Syoss Oleo 5-92, Cashmere Red variant 2, Syoss Color 2014 5-22, Syoss Oleo 4-29 , Ingora Royal 5-88), Nectra 777, Ingora Royal 4-88, Ingora Royal 7-887, Syoss Color 2012 4-2, Syoss Color 2012 5-22, Syoss Color 2D12 5-29 (Table 1) wherein the fashion color (red orange and purple dyes: 15 data points) have less data than the natural colors (brown and black: 34 data points).
Knubel does not teach the first color group comprises more data than the second color group and adding artificial data generated using the obtained hair color data.
Inzinna teaches computer implemented methods for determining the formulas of haircoloring agents by receiving input on the current color, state, desired color of hair and using a databases of hair colors to determine coloring agents to be used, quantities of the hair colors and determining the correct hair color (abstract). Inzinna teaches data contains information related to the state of the hair and desired or target color is entered into a color formulating software algorithm and uses a processor (column 5, lines 37-35; column 6, lines 1-5). Inzinna teaches the database contain hair color formulas for changing an initial hair col and state of hair to a target color of hair and a process by which an accurate target hair color is achieved through the use of the hair color formulating software algorithm and a database, wherein the system provides optimal hair color formulas (column 5, lines 27-67). Inzinna teaches using semantic data such as information entered from another stylist or haircoloring professional and data such as hair texture, tenacity, porosity, form and length can be entered and analyzed to achieve an expected color outcome (column 7, lines 55; column 9, lines 1-20). Inzinna teaches current hair color and condition data are entered and target hair color is correlated to color provided by commercial color lines (column 9, lines 49-65). Inzinna teaches applying the hair color and comparing the color achieved versus target color and reanalyzing data if the target hair color isn’t achieved (column 10, lines 39-50). Inzinna teaches taking digital images of the client and modifying the digital images to the desired hair color and processing the data to determine how to change the initial color to the desired color (claims 1 and 15, column 6, lines 55 to column 7, line 7; column 4, lines 19-48).
Chawla teaches a decision tree method for predictive analytics in which a minority data set is over-sampled (page 326-327, section 4.1; Figure 3) by creating artificial or “synthetic” data by performing operations on real data (page 328). This is called SMOTE or Single Minority Over-sampling Techniques (title, page 328). The addition of the synthetic or artificial data to the minority group creates larger and less specific decision regions and results in decision trees generalizing better (page 328).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the methods of Knubel by using data sets of hair coloring agents and coloring results, splitting the hair color data into a first majority and second minority hair color group and adding artificial data to the minority group to a level where the groups have a substantially identical number of data points and performing SMOTE predictive analysis to output an optimal hair dye formulation recipe to achieve the target hair color as Inzinna teaches determining hair coloring agent recipes from hair coloring agent and coloring result data are effective in using predictive analytics to determine what hair color would best produce a desired result. IN the data analysis and designation of groups, Knubel teaches data for natural colors (black and brown) and underrepresented fashion colors (red, orange and purple). Since the fashion colors have less data they become the underrepresented second hair color group.
It would have been further obvious to use the SMOTE Predictive analytics of Chawla as Chawla teaches this analysis of over-sampling a minority data set, in the case of Knubel the fashion colors data set, by adding artificial data to the fashion colors set, to compensate for the inequal distribution and so the set produces larger and less specific decision regions and results in decision trees generalizing better. It would have been further obvious to select an initial hair color by data processing of a picture of the hair of the user as Knubel teaches inputting data on the initial condition of the hair and Inzinna teaches this initial information can be in the form of the hair of the client before modifying the picture to the desired end result hair color. Processing information from the initial hair color photo to the end result desired hair color photo is obvious as the predictive analysis analyzes data on initial hair color to produce a desired hair color.
It would have been obvious to select the claimed type of hair color data to produce the data sets and use effective SMOTE predictive analytic software algorithms to arrive at an optimal dye formulation to achieve a desired target hair color. Knubel invites the inclusion of decision tree predictive analytics into the method.
Claims 14 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Knubel (WO 2017/103050) in view of Inzinna (US 7,877,294) and Chawla (SMOTE: Synthetic Minority over-sampling Technique) and further in view of Saranow (WO 2012/112497).
Knubel, Inzinna and Chawla are relied upon as set forth above.
Knubel. Inzinna and Chawla do not teach ordering the recommended hair color product.
Saranow teaches blending control systems can create purchase incentive programs and provide suggested recommendation of retail products to clients and based on needs online order needed hair dyes (paragraphs 115,121). Saranow teaches inventory of the needed dyes for a client and restocking when inventories are low by automatic ordering online (140-142). Saranow teaches requesting authorization for ordering the sample, as any hair product amount meets the limitation of sample, ordering the hair dye product and indication of the location of the hair dye product as the vendor identity would indicate location where the recommended hair product is available (paragraph 0115, 0142-0143, claim 33, 61).
It would have been obvious to one of ordinary skill in the art to modify the methods of Knubel, Inzinna and Chawla by ordering the recommended hair coloration product as Saranow teaches databased for customized hair colors for clients in a salon monitor the inventory based on the recommended hair dye formulation for a client and automatically reorder the needed colorants when inventory is low or a particular color is needed.
Claims 11,20 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Knubel (WO 2017/103050) in view of Inzinna (US 7,877,294) and Chawla (SMOTE: Synthetic Minority over-sampling Technique) and further in view of Landa (CN 103635176).
Knubel, Inzinna and Chawla are relied upon as set forth above.
Knubel. Inzinna and Chawla do not specify pyrazole hair dye components.
Landa teaches that pyrazole components are conventional dye precursors in hair dye compositions used in custom hair coloring products which are recommended from predictive analytics (abstract; paragraph 0378,0827).
It would have been obvious to one of ordinary skill in the art to modify the methods of Knubel, Inzinna and Chawla by using hair coloring agents comprising pyrazole compounds as they are conventional and effective dye precursors used in custom hair dye products. Using a known dye recommended from a predictive analysis for hair coloration would be obvious as Landa teaches these are exemplary dye precursors (paragraph 0828).
Regarding claim 21, Knubel teaches displaying images of the hair color and desired coloring result on computer screens or printed on hair dye packages at a point of sale and doing the predictive analysis and processing at the point of sale of the hair dye. If the predictive analysis processing is done at the point of sale, it means the item is also sold there, so it would be obvious that the recipe would be produced at the point of sale.. Further Inzinna teaches determining the optimal hair dye formulation to achieve a desired hair color in a hair salon, using the salon computer, wherein the client would then get their hair dyed to the desired color by a stylist in the salon, meeting the limitation of producing the recipe at a point of sale.
Response to Arguments
Applicant's arguments filed regarding the prior art have been fully considered but they are not persuasive. The examiner argues Knubel teaches computer supported determination of properties of hair colors wherein hair color data are prepared comprising pre-color data and coloring results for the data using data processing devices and determining a relationship between the multitude of coloring pre-condition parameters and the at least one coloring result by employing predictive analytics such as linear or multi-linear regression, multiple polynomic regression, neuronal network methods, support vector machine methods and decision tree. The data can be sorted based on color group and sorting based on color group shows an imbalance in data points for less common hair coloring vs. more common natural hair groups. Chawla teaches a decision tree method for predictive analytics in which a minority data set is over-sampled by creating artificial or “synthetic” data by performing operations on real data. Since Knubel teaches sorting hair data by colors and analyzing using computer processor and decision tree methods using a known effective method which involves decision tree analysis methods that recognized an imbalance in data between comparative groups would be obvious. It is not necessary for Chawla to teach hair color analysis which is already taught by Knubel. Knubel invites the inclusion of any type of decision tree analysis and the data presented in the table shows color groups with differing numbers of data points based on the hair color chosen. Chawla teaches the SMOTE method which addresses such imbalanced comparative data sets and can effectively analyze them. Knubel teaches data on initial hair color and desired hair color, recognizing hair color groups as integral to the analysis. Selecting a decision tree method of data analysis for imbalanced data sets is obvious based on the data present in Knubel and the advantages taught by Chawla. Applicant’s allegations of unexpected results are conclusory and not supported by factual evidence. Accordingly, the rejections are maintained.
Conclusion
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/AMINA S KHAN/Primary Examiner, Art Unit 1761
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