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 .
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
“A method comprising: maintaining a database storing information about hair coloring products from a plurality of brands; receiving input data from a client device, the input data comprising (1) an image of current hair and (2) a desired hair condition after applying one or more color products of at least one of the plurality of brands; accessing a machine-learning model trained to output values of one or more attributes that characterize hair conditions based on images of hair, the one or more attributes comprising at least a shade of hair and a tone of hair; applying the machine-learning model to the image of hair to output values of the one or more attributes that characterize the current hair condition; applying a decision tree based model to the values of the one or more attributes that characterize the current hair condition and the desired hair condition to output a hair color treatment that is to be applied to the current hair, the hair color treatment causing the current hair to transition from the current hair condition to the desired hair condition; identifying a hair coloring formulation based on the hair color treatment, wherein the hair coloring formulation includes one or more hair coloring products from at least one of the plurality of brands, along with a ratio between the one or more hair coloring products, and a time period for which a mixture of the one or more hair coloring products is to be applied to the hair to cause the hair to transition from the current condition to the desired hair condition; and causing the hair coloring formulation to be presented on the client device”
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the groupings of subject matter when recited as such in a claim limitation that falls into the grouping of subject matter when recited as such in a claim limitation, that covers mental processes — concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, the steps of “applying a decision tree based model to the values of the one or more attributes that characterize the current hair condition and the desired hair condition to output a hair color treatment that is to be applied to the current hair, the hair color treatment causing the current hair to transition from the current hair condition to the desired hair condition; identifying a hair coloring formulation based on the hair color treatment” are treated as belonging to mental process grouping because a human has the ability to assess, make predictions, determine, and make recommendations from the data.
With regards to the steps of “applying a decision… to the values of the one or more attributes that characterize the current hair condition and the desired hair condition to output a hair color treatment that is to be applied to the current hair, the hair color treatment causing the current hair to transition from the current hair condition to the desired hair condition”, these mental steps represents a processes that, under its broadest reasonable interpretation, cover performance of the limitations in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind. In the context of this claim, it encompasses the user making mental decisions (evaluation/judgement) with regards to identifying a hair coloring formulation based on the hair color treatment.
Additionally, the limitations listed above under mental processes are very close to Example 47 in AI-related SME examples 47-49 issued in 2024 due to “receives continuous training data at a computer, uses the computer to discretize the continuous training data to generate input data, trains the ANN using the input data 6 and a selected backpropagation algorithm and gradient descent algorithm, detects and analyzes anomalies in a data set using the trained ANN, and outputs anomaly data from the trained ANN. The claimed discretizing, detecting, and analyzing steps encompass mental choices or evaluations, and the claimed discretizing and training using a backpropagation algorithm and gradient descent algorithm encompasses performing mathematical calculations ”, in this case the CNN instead of an ANN.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that
integrate the exception into a practical application of that exception.
The above claims comprise the following additional elements:
Claim 1: A method comprising: maintaining a database storing information about hair coloring products from a plurality of brands; receiving input data from a client device, the input data comprising (1) an image of current hair and (2) a desired hair condition after applying one or more color products of at least one of the plurality of brands; accessing a machine-learning model trained to output values of one or more attributes that characterize hair conditions based on images of hair, the one or more attributes comprising at least a shade of hair and a tone of hair; applying the machine-learning model to the image of hair to output values of the one or more attributes that characterize the current hair condition, wherein the hair coloring formulation includes one or more hair coloring products from at least one of the plurality of brands, along with a ratio between the one or more hair coloring products, and a time period for which a mixture of the one or more hair coloring products is to be applied to the hair to cause the hair to transition from the current condition to the desired hair condition; and causing the hair coloring formulation to be presented on the client device
Claim 9: A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to: maintain a database storing information about hair coloring products from a plurality of brands; receive input data from a client device, the input data comprising (1) an image of current hair and (2) a desired hair condition after applying one or more color products of at least one of the plurality of brands; access a machine-learning model trained to output values of one or more attributes that characterize hair conditions based on images of hair, the one or more attributes comprising at least a shade of hair and a tone of hair; apply the machine-learning model to the image of hair to output values of the one or more attributes that characterize the current hair condition, wherein the hair coloring formulation includes one or more hair coloring products from at least one of the plurality of brands, along with a ratio between the one or more hair coloring products, and a time period for which a mixture of the one or more hair coloring products is to be applied to the hair to cause the hair to transition from the current condition to the desired hair condition; and cause the hair coloring formulation to be presented on the client device
Claim 17: A computer system, comprising: a processor; and a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by the processor, cause the processor to: maintain a database storing information about hair coloring products from a plurality of brands; receive input data from a client device, the input data comprising (1) an image of current hair and (2) a desired hair condition after applying one or more color products of at least one of the plurality of brands; access a machine-learning model trained to output values of one or more attributes that characterize hair conditions based on images of hair, the one or more attributes comprising at least a shade of hair and a tone of hair; apply the machine-learning model to the image of hair to output values of the one or more attributes that characterize the current hair condition wherein the hair coloring formulation includes one or more hair coloring products from at least one of the plurality of brands, along with a ratio between the one or more hair coloring products, and a time period for which a mixture of the one or more hair coloring products is to be applied to the hair to cause the hair to transition from the current condition to the desired hair condition; and cause the hair coloring formulation to be presented on the client device
The above additional elements in Claim 1 such as a method comprising: maintaining a database storing information about hair coloring products from a plurality of brands; receiving input data from a client device, the input data comprising (1) an image of current hair and (2) a desired hair condition after applying one or more color products of at least one of the plurality of brands are examples of data gathering and are generically recited and are not meaningful.
The additional elements in Claims 9 and 17 such a processor, and a non-transitory computer-readable medium comprising instructions that are executable by a processor for causing the processor is an example of generic computer equipment (components) that is generally recited and, therefore, is not qualified as a particular machine.
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis) because these additional elements/steps are well-understood and conventional in the relevant art based on the prior art of record including references (Mourad and Nash).
The independent claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-8, 10-16, and 18-20 provide additional features/steps which are either part of an expanded abstract idea of the independent claims (additionally comprising mental processes (Claims 2-8, 10-16, and 18-20) or adding additional elements/steps that are not meaningful as they are recited in generality and/or not qualified as particular machine/ and/or eligible transformation and, therefore, do not reflect a practical application as well as not qualified for “significantly more” based on prior art of record.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mourad et al. (US 20110313885), hereinafter referred to as ‘Mourad’ and in further view of Nash et al. (US 20210106122), hereinafter referred to as ‘Nash’, Bonnin et al. (US 20210337955), hereinafter referred to a ‘Bonnin’, and Schmenger et al. (US 20130160222), hereinafter referred to as ‘Schmenger’.
Regarding Claim 1, Mourad discloses a method comprising: receiving input data from a client device (…The database server 122 may also maintain a database of user information previously input into the system and stored for use in reordering or reformulation of hair colorant[0020]), the input data comprising (2) a desired hair condition (Once the user has selected the desired hair color, the suitable hair colorant is identified by the system at 330. This formulation and selection process is shown below with reference to FIG. 4 [0041]); one or more attributes that characterize hair conditions based the one or more attributes comprising at least a shade of hair and a tone of hair (Numeric values such as those in Tables 1 and 2 may be used in conjunction with a series of algorithms and functions to derive those colorants that are suitable for a given combination of hair characteristics and to, upon selection of a desired and available target color (i.e. shade), provide the user with a hair colorant, tone, treatment and instruction combination that is suitable for most users with similar characteristics to arrive at that hair color after treatment [0051]); … the one or more attributes that characterize the current hair condition (Numeric values such as those in Tables 1 and 2 may be used in conjunction with a series of algorithms and functions to derive those colorants that are suitable for a given combination of hair characteristics and to, upon selection of a desired and available target color, provide the user with a hair colorant, tone, treatment and instruction combination that is suitable for most users with similar characteristics to arrive at that hair color after treatment [0051]); the one or more attributes that characterize the current hair condition and the desired hair condition to output a hair color treatment that is to be applied to the current hair (Numeric values such as those in Tables 1 and 2 may be used in conjunction with a series of algorithms and functions to derive those colorants that are suitable for a given combination of hair characteristics and to, upon selection of a desired and available target color, provide the user with a hair colorant, tone, treatment and instruction combination that is suitable for most users with similar characteristics to arrive at that hair color after treatment [0051); identifying a hair coloring formulation based on the hair color treatment (The available treatment type is function of the current color, the target color, the hair texture, hair length, hair tone, root level and whether the colorant process is the first colorant application using this system or a repeat application. Additional factors may also be relevant and applied by the algorithm. The algorithm then utilizes these variables to derive a suitable treatment type [0056]), wherein the hair coloring formulation includes a mixture of the one or more hair coloring products is to be applied to the hair to cause the hair to transition from the current condition to the desired hair condition; and causing the hair coloring formulation to be presented on the client device (Numeric values such as those in Tables 1 and 2 may be used in conjunction with a series of algorithms and functions to derive those colorants that are suitable for a given combination of hair characteristics and to, upon selection of a desired and available target color, provide the user with a hair colorant, tone, treatment and instruction combination that is suitable for most users with similar characteristics to arrive at that hair color after treatment [0051]; If the user has long hair and requires color to be added to their current dyed hair, then two bottles are required at 428, 434. If the user has chosen a color substantially different from their current dyed color, then two bottles are required because of the necessity to formulate differently for the natural color and their current dyed color at 428, 434. Otherwise, in most other cases one bottle of hair color is sufficient for the user to achieve their desired color goal at 430, 436 [0054]).
However, Mourad does not explicitly disclose a method comprising: maintaining a database storing information about hair coloring products from a plurality of brands (data); receiving input data from a client device, the input data comprising (1) an image of current hair and (2) a desired hair condition after applying one or more color products of at least one of the plurality of brands, accessing a machine-learning model trained to output values of one or more attributes that characterize hair conditions based on images of hair, the one or more attributes comprising at least a shade of hair and a tone of hair, applying the machine-learning model to the image of hair to output values of the one or more attributes that characterize the current hair condition, applying a decision tree based model to the values of the one or more attributes that characterize the current hair condition and the desired hair condition to output a hair color treatment that is to be applied to the current hair, the hair color treatment causing the current hair to transition from the current hair condition to the desired hair condition; identifying a hair coloring formulation based on the hair color treatment, and wherein the hair coloring formulation includes one or more hair coloring products from at least one of the plurality of brands, along with a ratio between the one or more hair coloring products, and a time period for which a mixture of the one or more hair coloring products is to be applied to the hair to cause the hair to transition from the current condition to the desired hair condition; and causing the hair coloring formulation to be presented on the client device.
Nevertheless, Nash discloses maintaining a database storing information about hair coloring products from a plurality of brands (data); receiving input data from a client device (A method for hair dye color conversion in which a hair dye color mixture of one manufacture is matched and reproduced using a hair dye color mixture of another manufacture, may be summarized as including accessing a control system having at least a processor, a memory, and user input controls, the memory configured to store ingredients of a hair dye color mixture, each ingredient of the hair dye color mixture being from a first manufacturer color line, i.e., database [0014]), the input data comprising (2) a desired hair condition after applying one or more color products of at least one of the plurality of brands (FIG. 4, depicts a screenshot of a client history screen 400 associated with the “Client History” GUI module according to one embodiment [0100]; As shown, the client history screen 400 has the “Created” tab 406 selected by default. Other embodiments may have a different tab selected as the default tab upon the user reaching the client history screen 400. The “Created” tab 406 may generally include a client's service history such as the dates on which the client came to the salon for one or more services, the number of services (e.g., formulas) created by the user on each service date, the products purchased by the client on each service date, the length of the service(s), whether or not the service was especially liked by the client (e.g., favorite), and the like….[0101]); applying the machine-learning model (In another aspect of the system and method for hair dye color conversion, the system employs machines learning and linear regression to improve the accuracy of the prediction modelling [0043]); applying a decision tree based model (This product color estimate adjustment embodiment of the system and method for hair dye color conversion estimates new values for the Base Color Matrix in view of the observed data… Other sub-optimal techniques include decision tree based methods, neural networks, nearest neighbor based methods, support vector machines, Gaussian processes, and other types of linear regression [0068]), wherein the hair coloring formulation includes one or more hair coloring products from at least one of the plurality of brands (The hair dye color conversion system and method 100 enables an operator to recreate the hair dye mixture using different branded products. These may be different branded products from the same manufacture or different branded products from the different manufactures [0041]; The hair color data further comprises measurements corresponding to the color of hair resulting from application of the hair coloring agent on hair [0059]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Mourad with the teachings of Nash to predict a color of a mixture of a first and second formula product lines to match and improve color prediction accuracy.
However, Mourad and Nash do not explicitly disclose the input data comprising (1) an image of current hair and (2) a desired hair condition after applying one or more color products of at least one of the plurality of brands; accessing a machine-learning model trained to output values of one or more attributes that characterize hair conditions based on images of hair, the one or more attributes comprising at least a shade of hair and a tone of hair; applying the machine-learning model to the image of hair to output values of the one or more attributes that characterize the current hair condition; applying a decision tree based model to the values of the one or more attributes that characterize the current hair condition and the desired hair condition to output a hair color treatment that is to be applied to the current hair, the hair color treatment causing the current hair to transition from the current hair condition to the desired hair condition and wherein the hair coloring formulation includes one or more hair coloring products from at least one of the plurality of brands, along with a ratio between the one or more hair coloring products, and a time period for which a mixture of the one or more hair coloring products is to be applied to the hair to cause the hair to transition from the current condition to the desired hair condition; and causing the hair coloring formulation to be presented on the client device.
Nevertheless, Bonnin discloses the input data comprising (1) an image of current hair (In various exemplary embodiments, an initial hair color can be selected from a plurality of initial hair colors, for example by a hair styling professional or by the person to whom the hair belongs. The initial hair color can for example be chosen as being the one that makes the greatest color impression on a person observing the hair. The determination of the initial hair color can also be done using a device or a data processing of a picture of the hair of a user [0080]); accessing a machine-learning model trained to output values of one or more attributes that characterize hair conditions based on images of hair (In various exemplary embodiments, an initial hair color can be selected from a plurality of initial hair colors, for example by a hair styling professional or by the person to whom the hair belongs. The initial hair color can for example be chosen as being the one that makes the greatest color impression on a person observing the hair. The determination of the initial hair color can also be done using a device or a data processing of a picture of the hair of a user [0080]; To determine expected hair colors for a (e.g. arbitrary) initial state and for a large number of combinations of relevant concentrations of a plurality of dye precursors, methods from the field of predictive analytics (also known as “big data”, “data mining” or “machine learning) can be applied, using the hair color data including the added artificial hair color data [0072]); applying the machine-learning model to the image of hair to output values of the one or more attributes that characterize the current hair condition (In various exemplary embodiments, an initial hair color can be selected from a plurality of initial hair colors, for example by a hair styling professional or by the person to whom the hair belongs. The initial hair color can for example be chosen as being the one that makes the greatest color impression on a person observing the hair. The determination of the initial hair color can also be done using a device or a data processing of a picture of the hair of a user [0080]; To determine expected hair colors for a (e.g. arbitrary) initial state and for a large number of combinations of relevant concentrations of a plurality of dye precursors, methods from the field of predictive analytics (also known as “big data”, “data mining” or “machine learning) can be applied, using the hair color data including the added artificial hair color data [0072]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Mourad and Nash with the teachings of Bonnin to obtain the initial hair color to determine the greatest color impression and improve color prediction accuracy.
However, Mourad, Nash and Bonnin does not explicitly disclose wherein the hair coloring formulation includes one or more hair coloring products from at least one of the plurality of brands, along with a ratio between the one or more hair coloring products, and a time period for which a mixture of the one or more hair coloring products is to be applied to the hair to cause the hair to transition from the current condition to the desired hair condition; and causing the hair coloring formulation to be presented on the client device.
Nevertheless, Schmenger discloses the hair coloring formulation includes one or more hair coloring products, along with a ratio (For example, a ColorTouch.RTM. hair coloring product from Wella will usually be mixed in 1:2 ratio (by weight) with a 4% or 1.9% H.sub.2O.sub.2 emulsion thus resulting in a 2.7% H.sub.2O.sub.2, respectively 1.3% concentration on-head [0046]), and a time period for which a mixture of the one or more hair coloring products is to be applied to the hair to cause the hair to transition from the current condition to the desired hair condition (…The hair coloring composition remains on the treated hair surface while the end hair color develops for a time period of 5 to 45 minutes to form oxidatively colored hair. The consumer then rinses his/her oxidatively colored hair thoroughly with tap water and allows it to dry and/or styles the oxidatively colored hair [0041]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Mourad, Nash, and Bonnin with the teachings of Schmenger to treat hair surface completely while ensuring uniform application and improve coloring hair treatment.
Regarding Claim 2, Mourad, Nash, Bonnin, and Schmenger disclose the claimed invention discussed in claim 1.
Mourad discloses the hair color treatment includes a level of shade indicating how much a shade of the current hair needs to be darkened or lightened ( If the user indicated that their hair was previously colored or treated 318, the user may indicate the time when the last treatments were applied to the hair 320. For example, the user may enter a date. The user may be presented with a series of checkboxes indicating various time-frames. Alternatively a slider bar representing a timeline may be used. At this stage, the direction of coloring (i.e. shade) (lighter or darker) may also be indicated by the user [0035]).
Regarding Claim 3, Nash, Bonnin, and Schmenger disclose the claimed invention discussed in claim 1.
Mourad discloses the hair color treatment includes a level of tone indicating how much a tone of the current hair needs to be changed (For example, if a user desires to move to a lighter shade relative to their natural color, then a permanent dye coupled with a developing agent are required. Another treatment example is where the user has highlights, and where they opted to cover the highlights. The user is required to use a two process color fill treatment where they first pre-pigment their highlights with the natural underlying pigment present in that shade level, and then apply the desired final color. To do otherwise would result in an unnaturally flat-looking hair color [0057]).
Regarding Claim 4, Mourad, Nash, Bonnin, and Schmenger disclose the claimed invention discussed in claim 1.
However, Mourad does not explicitly disclose the input data further includes selecting a brand from the plurality of brands, and the one or more coloring products are identified from the selected brand.
Nevertheless, Nash discloses the input data further includes selecting a brand from the plurality of brands, and the one or more coloring products are identified from the selected brand (In other aspects, the hair dye color conversion system and method 100 converts a particular formula from one brand of product to another brand of product. For example, if a particular mixture of colors in one brand needs to be duplicated for a different brand, the particular ingredients will be different. The hair dye color conversion system and method 100 enables an operator to recreate the hair dye mixture using different branded products. These may be different branded products from the same manufacture or different branded products from the different manufactures [0041]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Mourad, Nash, and Bonnin with the teachings of Schmenger to predict a color of a mixture of a first and second formula product lines to match and improve color prediction accuracy.
Regarding Claim 5, Mourad, Nash, Bonnin, and Schmenger disclose the claimed invention discussed in claim 1.
However, Mourad does not explicitly disclose translating one or more hair coloring products of a first brand to one or more hair coloring products of a second brand that provide a same level of coloring treatment.
Nevertheless, Nash discloses translating one or more hair coloring products of a first brand to one or more hair coloring products of a second brand that provide a same level of coloring treatment (In other aspects, the hair dye color conversion system and method 100 converts a particular formula from one brand of product to another brand of product. For example, if a particular mixture of colors in one brand needs to be duplicated for a different brand, the particular ingredients will be different. The hair dye color conversion system and method 100 enables an operator to recreate the hair dye mixture using different branded products. These may be different branded products from the same manufacture or different branded products from the different manufactures [0041]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Mourad, Nash, and Bonnin with the teachings of Schmenger to predict a color of a mixture of a first and second formula product lines to match and improve color prediction accuracy.
Regarding Claim 6, Mourad, Nash, Bonnin, and Schmenger disclose the claimed invention discussed in claim 1.
Mourad discloses receiving a user feedback indicating whether the output of the machine-learning model is accurate, and using the output of the machine-learning model as an additional positive or negative example to retrain the machine-learning model (Numeric values such as those in Tables 1 and 2 may be used in conjunction with a series of algorithms and functions to derive those colorants that are suitable for a given combination of hair characteristics and to, upon selection of a desired and available target color, provide the user with a hair colorant, tone, treatment and instruction combination that is suitable for most users with similar characteristics to arrive at that hair color after treatment. Through the use of this process, unattainable colors are eliminated from a user's available selections and colorant, tone, treatment and instruction combinations that would not be suitable for a given user are also eliminated. As a result, a desired target color may be reached by a user of this system [0051]).
Regarding Claim 7, Mourad, Nash, Bonnin, and Schmenger disclose the claimed invention discussed in claim 1.
Mourad discloses before and after treatment hair condition pairs labeled with levels of hair color treatment (The user is required to use a two process color fill treatment where they first pre-pigment their highlights with the natural underlying pigment present in that shade level, and then apply the desired final color. To do otherwise would result in an unnaturally flat-looking hair color [0057]).
However, Mourad does not explicitly disclose the decision tree based model is trained using before and after treatment hair condition pairs labeled with levels of hair color treatment.
Nevertheless, Nash discloses the decision tree based model (This product color estimate adjustment embodiment of the system and method for hair dye color conversion estimates new values for the Base Color Matrix in view of the observed data… Other sub-optimal techniques include decision tree based methods, neural networks, nearest neighbor based methods, support vector machines, Gaussian processes, and other types of linear regression [0068])It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Mourad, Nash and Bonnin with the teachings of Schmenger to predict hair coloring likelihood and tailor advice and improve hair color treatment accuracy.
Regarding Claim 8, Nash, Bonnin, and Schmenger disclose the claimed invention discussed in claim 7.
Mourad discloses receiving a user feedback… additional positive or negative (If the user characteristics indicate negative responses at 418 and 420, then the user may be presented with a choice of whether to receive a permanent dye or a demi-permanent dye at 424. Next, at 426 and 432, a determination is made on whether the user requires 1 or 2 bottles of color. The two factors, which impact this, are the length of the user's hair and the color difference between their dyed color and desired color [0054]).
However, Mourad does not explicitly disclose receiving a user feedback indicating whether the output of the decision tree based model is accurate, and using the output of the decision tree based model as an additional positive or negative example to retrain the decision tree based model.
Nevertheless, Bonnin discloses receiving a user feedback indicating whether the output of the decision tree based model is accurate, and using the output of the decision tree based model as an additional positive or negative example to retrain the decision tree based model (Predictive analytics can be generally described as a method for extracting information from large amounts of data and generating a model from said data which make it possible to also make predictions for values that are not part of the data set. Using a predictive analytics method, part of the data set can be typically used as a training data set (also referred to as a training set or training data). Based on this training data set, one or multiple models can be generated, which can be tested on the basis of data which is not part of the training data set, on the basis of the overall data, or on the basis of a specially selected part of the data [0102]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Mourad, Nash, and Bonnin with the teachings of Schmenger to predict hair coloring likelihood and tailor advice and improve hair color treatment accuracy.
Regarding Claim 9, Mourad discloses a computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to: maintain a database storing information about hair coloring products from a plurality of brands (The server 112, however physically configured, is responsible for accessing the database server 122 to thereby provide the web server 124 with information to fill web pages served to the client 114. The database server 122 may contain information related to the hair products to be used, the various hair styles, colors and other characteristics that may have an effect on the user 116 selection [0014]), receive input data from a client device, the input data comprising (1) an image of current hair and (2) a desired hair condition after applying one or more color products of at least one of the plurality of brands (Once the user has selected the desired hair color, the suitable hair colorant is identified by the system at 330. This formulation and selection process is shown below with reference to FIG. 4 [0041]); one or more attributes that characterize hair conditions based on images of hair, the one or more attributes comprising at least a shade of hair and a tone of hair (Numeric values such as those in Tables 1 and 2 may be used in conjunction with a series of algorithms and functions to derive those colorants that are suitable for a given combination of hair characteristics and to, upon selection of a desired and available target color, provide the user with a hair colorant, tone, treatment and instruction combination that is suitable for most users with similar characteristics to arrive at that hair color after treatment [0051]); the one or more attributes that characterize the current hair condition and the desired hair condition to output a hair color treatment that is to be applied to the current hair, the hair color treatment causing the current hair to transition from the current hair condition to the desired hair condition (The available treatment type is function of the current color, the target color, the hair texture, hair length, hair tone, root level and whether the colorant process is the first colorant application using this system or a repeat application. Additional factors may also be relevant and applied by the algorithm. The algorithm then utilizes these variables to derive a suitable treatment type [0056]); identifying a hair coloring formulation based on the hair color treatment (The available treatment type is function of the current color, the target color, the hair texture, hair length, hair tone, root level and whether the colorant process is the first colorant application using this system or a repeat application. Additional factors may also be relevant and applied by the algorithm. The algorithm then utilizes these variables to derive a suitable treatment type [0056]), wherein the hair coloring formulation includes one or more hair coloring products from at least one of the plurality of brands, along with a ratio between the one or more hair coloring products, and a time period for which a mixture of the one or more hair coloring products is to be applied to the hair to cause the hair to transition from the current condition to the desired hair condition; and cause the hair coloring formulation to be presented on the client device (Numeric values such as those in Tables 1 and 2 may be used in conjunction with a series of algorithms and functions to derive those colorants that are suitable for a given combination of hair characteristics and to, upon selection of a desired and available target color, provide the user with a hair colorant, tone, treatment and instruction combination that is suitable for most users with similar characteristics to arrive at that hair color after treatment [0051]; If the user has long hair and requires color to be added to their current dyed hair, then two bottles are required at 428, 434. If the user has chosen a color substantially different from their current dyed color, then two bottles are required because of the necessity to formulate differently for the natural color and their current dyed color at 428, 434. Otherwise, in most other cases one bottle of hair color is sufficient for the user to achieve their desired color goal at 430, 436 [0054]).
However, Mourad does not explicitly disclose maintain a database storing information about hair coloring products from a plurality of brands; receive input data from a client device, the input data comprising (1) an image of current hair and (2) a desired hair condition after applying one or more color products of at least one of the plurality of brands; access a machine-learning model trained to output values of one or more attributes that characterize hair conditions based on images of hair, the one or more attributes comprising at least a shade of hair and a tone of hair; apply the machine-learning model to the image of hair to output values of the one or more attributes that characterize the current hair condition; apply a decision tree based model to the values of the one or more attributes that characterize the current hair condition and the desired hair condition to output a hair color treatment that is to be applied to the current hair, the hair color treatment causing the current hair to transition from the current hair condition to the desired hair condition; identify a hair coloring formulation based on the hair color treatment, wherein the hair coloring formulation includes one or more hair coloring products from at least one of the plurality of brands, along with a ratio between the one or more hair coloring products, and a time period for which a mixture of the one or more hair coloring products is to be applied to the hair to cause the hair to transition from the current condition to the desired hair condition; and cause the hair coloring formulation to be presented on the client device.
Nevertheless, Nash discloses a method comprising: maintaining a database storing information about hair coloring products from a plurality of brands (data); receiving input data from a client device (A method for hair dye color conversion in which a hair dye color mixture of one manufacture is matched and reproduced using a hair dye color mixture of another manufacture, may be summarized as including accessing a control system having at least a processor, a memory, and user input controls, the memory configured to store ingredients of a hair dye color mixture (i.e. a database), each ingredient of the hair dye color mixture being from a first manufacturer color line [0014]), the input data comprising (2) a desired hair condition after applying one or more color products of at least one of the plurality of brands (FIG. 4, depicts a screenshot of a client history screen 400 associated with the “Client History” GUI module according to one embodiment [0100]; As shown, the client history screen 400 has the “Created” tab 406 selected by default. Other embodiments may have a different tab selected as the default tab upon the user reaching the client history screen 400. The “Created” tab 406 may generally include a client's service history such as the dates on which the client came to the salon for one or more services, the number of services (e.g., formulas) created by the user on each service date, the products purchased by the client on each service date, the length of the service(s), whether or not the service was especially liked by the client (e.g., favorite), and the like….[0101]); applying the machine-learning model (In another aspect of the system and method for hair dye color conversion, the system employs machines learning and linear regression to improve the accuracy of the prediction modelling [0043]); applying a decision tree based model (This product color estimate adjustment embodiment of the system and method for hair dye color conversion estimates new values for the Base Color Matrix in view of the observed data… Other sub-optimal techniques include decision tree based methods, neural networks, nearest neighbor based methods, support vector machines, Gaussian processes, and other types of linear regression [0068]), wherein the hair coloring formulation includes one or more hair coloring products from at least one of the plurality of brands (The hair dye color conversion system and method 100 enables an operator to recreate the hair dye mixture using different branded products. These may be different branded products from the same manufacture or different branded products from the different manufactures [0041]; The hair color data further comprises measurements corresponding to the color of hair resulting from application of the hair coloring agent on hair [0059]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Mourad with the teachings of Nash to predict a color of a mixture of a first and second formula product lines to match and improve color prediction accuracy.
However, Mourad and Nash do not explicitly disclose receive input data from a client device, the input data comprising (1) an image of current hair and (2) a desired hair condition after applying one or more color products of at least one of the plurality of brands; access a machine-learning model trained to output values of one or more attributes that characterize hair conditions based on images of hair, the one or more attributes comprising at least a shade of hair and a tone of hair; apply the machine-learning model to the image of hair to output values of the one or more attributes that characterize the current hair condition; apply a decision tree based model to the values of the one or more attributes that characterize the current hair condition and the desired hair condition to output a hair color treatment that is to be applied to the current hair, the hair color treatment causing the current hair to transition from the current hair condition to the desired hair condition; identify a hair coloring formulation based on the hair color treatment, wherein the hair coloring formulation includes one or more hair coloring products from at least one of the plurality of brands, along with a ratio between the one or more hair coloring products, and a time period for which a mixture of the one or more hair coloring products is to be applied to the hair to cause the hair to transition from the current condition to the desired hair condition; and cause the hair coloring formulation to be presented on the client device.
Nevertheless, Bonnin discloses the input data comprising (1) an image of current hair (In various exemplary embodiments, an initial hair color can be selected from a plurality of initial hair colors, for example by a hair styling professional or by the person to whom the hair belongs. The initial hair color can for example be chosen as being the one that makes the greatest color impression on a person observing the hair. The determination of the initial hair color can also be done using a device or a data processing of a picture of the hair of a user [0080]); accessing a machine-learning model trained to output values of one or more attributes that characterize hair conditions based on images of hair (In various exemplary embodiments, an initial hair color can be selected from a plurality of initial hair colors, for example by a hair styling professional or by the person to whom the hair belongs. The initial hair color can for example be chosen as being the one that makes the greatest color impression on a person observing the hair. The determination of the initial hair color can also be done using a device or a data processing of a picture of the hair of a user [0080]; To determine expected hair colors for a (e.g. arbitrary) initial state and for a large number of combinations of relevant concentrations of a plurality of dye precursors, methods from the field of predictive analytics (also known as “big data”, “data mining” or “machine learning) can be applied, using the hair color data including the added artificial hair color data [0072]); applying the machine-learning model to the image of hair to output values of the one or more attributes that characterize the current hair condition (In various exemplary embodiments, an initial hair color can be selected from a plurality of initial hair colors, for example by a hair styling professional or by the person to whom the hair belongs. The initial hair color can for example be chosen as being the one that makes the greatest color impression on a person observing the hair. The determination of the initial hair color can also be done using a device or a data processing of a picture of the hair of a user [0080]; To determine expected hair colors for a (e.g. arbitrary) initial state and for a large number of combinations of relevant concentrations of a plurality of dye precursors, methods from the field of predictive analytics (also known as “big data”, “data mining” or “machine learning) can be applied, using the hair color data including the added artificial hair color data [0072]); and wherein the hair coloring formulation includes one or more hair coloring products from at least one of the plurality of brands, along with a ratio between the one or more hair coloring products, and a time period for which a mixture of the one or more hair coloring products is to be applied to the hair to cause the hair to transition from the current condition to the desired hair condition; and causing the hair coloring formulation to be presented on the client device (The difference between the color properties of the recommended hair coloration product and the color properties of the hair coloring agent determined from the model can be calculated as a distance in a color space, such as the L*a*b color space [0043]; This parameter can be adjusted as the relative proportion of available hair coloring agents changes over time, for example when some hair colors become more popular and offer more diversity in the choice of hair coloring agents to dye hairs in such color shades [0071]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Mourad and Nash with the teachings of Bonnin to obtain the initial hair color to determine the greatest color impression and improve color prediction accuracy.
However, Mourad, Nash and Bonnin does not explicitly disclose the hair coloring formulation includes one or more hair coloring products from at least one of the plurality of brands, along with a ratio between the one or more hair coloring products, and a time period for which a mixture of the one or more hair coloring products is to be applied to the hair to cause the hair to transition from the current condition to the desired hair condition; and cause the hair coloring formulation to be presented on the client device.
Nevertheless, Schmenger discloses the hair coloring formulation includes one or more hair coloring products, along with a ratio, and a time period for which a mixture of the one or more hair coloring products is to be applied to the hair to cause the hair to transition from the current condition to the desired hair condition (…The hair coloring composition remains on the treated hair surface while the end hair color develops for a time period of 5 to 45 minutes to form oxidatively colored hair. The consumer then rinses his/her oxidatively colored hair thoroughly with tap water and allows it to dry and/or styles the oxidatively colored hair [0041]; For example, a ColorTouch.RTM. hair coloring product from Wella will usually be mixed in 1:2 ratio (by weight) with a 4% or 1.9% H.sub.2O.sub.2 emulsion thus resulting in a 2.7% H.sub.2O.sub.2, respectively 1.3% concentration on-head [0046]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Mourad, Nash, and Bonnin with the teachings of Schmenger to treat hair surface completely while ensuring uniform application and improve coloring hair treatment.
Regarding Claim 10, Mourad, Nash, Bonnin, and Schmenger disclose the claimed invention discussed in claim 9.
Mourad discloses the hair color treatment includes a level of shade indicating how much a shade of the current hair needs to be darkened or lightened ( If the user indicated that their hair was previously colored or treated 318, the user may indicate the time when the last treatments were applied to the hair 320. For example, the user may enter a date. The user may be presented with a series of checkboxes indicating various time-frames. Alternatively a slider bar representing a timeline may be used. At this stage, the direction of coloring (lighter or darker) may also be indicated by the user [0035]).
Regarding Claim 11, Mourad, Nash, Bonnin, and Schmenger disclose the claimed invention discussed in claim 9.
Mourad discloses the hair color treatment includes a level of tone indicating how much a tone of the current hair needs to be changed (For example, if a user desires to move to a lighter shade relative to their natural color, then a permanent dye coupled with a developing agent are required. Another treatment example is where the user has highlights, and where they opted to cover the highlights. The user is required to use a two process color fill treatment where they first pre-pigment their highlights with the natural underlying pigment present in that shade level, and then apply the desired final color. To do otherwise would result in an unnaturally flat-looking hair color [0057]).
Regarding Claim 12, Mourad, Nash, Bonnin, and Schmenger disclose the claimed invention discussed in claim 9.
However, Mourad does not explicitly disclose the input data further includes selecting a brand from the plurality of brands, and the one or more coloring products are identified from the selected brand.
Nevertheless, Nash discloses the input data further includes selecting a brand from the plurality of brands, and the one or more coloring products are identified from the selected brand (In other aspects, the hair dye color conversion system and method 100 converts a particular formula from one brand of product to another brand of product. For example, if a particular mixture of colors in one brand needs to be duplicated for a different brand, the particular ingredients will be different. The hair dye color conversion system and method 100 enables an operator to recreate the hair dye mixture using different branded products. These may be different branded products from the same manufacture or different branded products from the different manufactures [0041]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Mourad, Nash, and Bonnin with the teachings of Schmenger to predict a color of a mixture of a first and second formula product lines to match and improve color prediction accuracy.
Regarding Claim 13, Mourad, Nash, Bonnin, and Schmenger disclose the claimed invention discussed in claim 9.
However, Mourad does not explicitly disclose translating one or more hair coloring products of a first brand to one or more hair coloring products of a second brand that provide a same level of coloring treatment.
Nevertheless, Nash discloses translating one or more hair coloring products of a first brand to one or more hair coloring products of a second brand that provide a same level of coloring treatment (In other aspects, the hair dye color conversion system and method 100 converts a particular formula from one brand of product to another brand of product. For example, if a particular mixture of colors in one brand needs to be duplicated for a different brand, the particular ingredients will be different. The hair dye color conversion system and method 100 enables an operator to recreate the hair dye mixture using different branded products. These may be different branded products from the same manufacture or different branded products from the different manufactures [0041]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Mourad, Nash, and Bonnin with the teachings of Schmenger to predict a color of a mixture of a first and second formula product lines to match and improve color prediction accuracy.
Regarding Claim 14, Mourad, Nash, Bonnin, and Schmenger disclose the claimed invention discussed in claim 9.
Mourad discloses receiving a user feedback indicating whether the output of the machine-learning model is accurate, and using the output of the machine-learning model as an additional positive or negative example to retrain the machine-learning model (Numeric values such as those in Tables 1 and 2 may be used in conjunction with a series of algorithms and functions to derive those colorants that are suitable for a given combination of hair characteristics and to, upon selection of a desired and available target color, provide the user with a hair colorant, tone, treatment and instruction combination that is suitable for most users with similar characteristics to arrive at that hair color after treatment. Through the use of this process, unattainable colors are eliminated from a user's available selections and colorant, tone, treatment and instruction combinations that would not be suitable for a given user are also eliminated. As a result, a desired target color may be reached by a user of this system [0051]).
Regarding Claim 15, Mourad, Nash, Bonnin, and Schmenger disclose the claimed invention discussed in claim 9.
Mourad discloses before and after treatment hair condition pairs labeled with levels of hair color treatment (The user is required to use a two process color fill treatment where they first pre-pigment their highlights with the natural underlying pigment present in that shade level, and then apply the desired final color. To do otherwise would result in an unnaturally flat-looking hair color [0057]).
However, Mourad does not explicitly disclose the decision tree based model is trained using before and after treatment hair condition pairs labeled with levels of hair color treatment.
Nevertheless, Nash discloses the decision tree based model (as discussed above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Mourad, Nash and Bonnin with the teachings of Schmenger to predict hair coloring likelihood and tailor advice and improve hair color treatment accuracy.
Regarding Claim 16, Mourad, Nash, Bonnin, and Schmenger disclose the claimed invention discussed in claim 15.
Mourad discloses receiving a user feedback… additional positive or negative (If the user characteristics indicate negative responses at 418 and 420, then the user may be presented with a choice of whether to receive a permanent dye or a demi-permanent dye at 424. Next, at 426 and 432, a determination is made on whether the user requires 1 or 2 bottles of color. The two factors, which impact this, are the length of the user's hair and the color difference between their dyed color and desired color [0054]).
However, Mourad does not explicitly disclose receiving a user feedback indicating whether the output of the decision tree based model is accurate, and using the output of the decision tree based model as an additional positive or negative example to retrain the decision tree based model.
Nevertheless, Bonnin discloses receiving a user feedback indicating whether the output of the decision tree based model is accurate, and using the output of the decision tree based model as an additional positive or negative example to retrain the decision tree based model (Predictive analytics can be generally described as a method for extracting information from large amounts of data and generating a model from said data which make it possible to also make predictions for values that are not part of the data set. Using a predictive analytics method, part of the data set can be typically used as a training data set (also referred to as a training set or training data). Based on this training data set, one or multiple models can be generated, which can be tested on the basis of data which is not part of the training data set, on the basis of the overall data, or on the basis of a specially selected part of the data [0102]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Mourad, Nash, and Bonnin with the teachings of Schmenger to predict hair coloring likelihood and tailor advice and improve hair color treatment accuracy.
Regarding Claim 17, Mourad discloses a computer system, comprising: a processor; and a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by the processor, cause the processor to: maintain a database storing information (The server 112, however physically configured, is responsible for accessing the database server 122 to thereby provide the web server 124 with information to fill web pages served to the client 114. The database server 122 may contain information related to the hair products to be used, the various hair styles, colors and other characteristics that may have an effect on the user 116 selection [0014]), receive input data from a client device, the input data comprising (Once the user has selected the desired hair color, the suitable hair colorant is identified by the system at 330. This formulation and selection process is shown below with reference to FIG. 4 [0041]); access a machine-learning model trained to output values (Numeric values such as those in Tables 1 and 2 may be used in conjunction with a series of algorithms and functions to derive those colorants that are suitable for a given combination of hair characteristics and to, upon selection of a desired and available target color, provide the user with a hair colorant, tone, treatment and instruction combination that is suitable for most users with similar characteristics to arrive at that hair color after treatment [0051]); the one or more attributes that characterize the current hair condition and the desired hair condition to output a hair color treatment that is to be applied to the current hair, the hair color treatment causing the current hair to transition from the current hair condition to the desired hair condition (The available treatment type is function of the current color, the target color, the hair texture, hair length, hair tone, root level and whether the colorant process is the first colorant application using this system or a repeat application. Additional factors may also be relevant and applied by the algorithm. The algorithm then utilizes these variables to derive a suitable treatment type [0056]); identify a hair coloring formulation based on the hair color treatment, (The available treatment type is function of the current color, the target color, the hair texture, hair length, hair tone, root level and whether the colorant process is the first colorant application using this system or a repeat application. Additional factors may also be relevant and applied by the algorithm. The algorithm then utilizes these variables to derive a suitable treatment type [0056]), wherein the hair coloring formulation includes one or more hair coloring products from at least one of the plurality of brands (Numeric values such as those in Tables 1 and 2 may be used in conjunction with a series of algorithms and functions to derive those colorants that are suitable for a given combination of hair characteristics and to, upon selection of a desired and available target color, provide the user with a hair colorant, tone, treatment and instruction combination that is suitable for most users with similar characteristics to arrive at that hair color after treatment [0051]; If the user has long hair and requires color to be added to their current dyed hair, then two bottles are required at 428, 434. If the user has chosen a color substantially different from their current dyed color, then two bottles are required because of the necessity to formulate differently for the natural color and their current dyed color at 428, 434. Otherwise, in most other cases one bottle of hair color is sufficient for the user to achieve their desired color goal at 430, 436 [0054]).
However, Mourad does not explicitly disclose maintain a database storing information about hair coloring products from a plurality of brands; receive input data from a client device, the input data comprising (1) an image of current hair and (2) a desired hair condition after applying one or more color products of at least one of the plurality of brands; access a machine-learning model trained to output values of one or more attributes that characterize hair conditions based on images of hair, the one or more attributes comprising at least a shade of hair and a tone of hair; apply the machine-learning model to the image of hair to output values of the one or more attributes that characterize the current hair condition; apply a decision tree based model to the values of the one or more attributes that characterize the current hair condition and the desired hair condition to output a hair color treatment that is to be applied to the current hair, the hair color treatment causing the current hair to transition from the current hair condition to the desired hair condition; identify a hair coloring formulation based on the hair color treatment, wherein the hair coloring formulation includes one or more hair coloring products from at least one of the plurality of brands, along with a ratio between the one or more hair coloring products, and a time period for which a mixture of the one or more hair coloring products is to be applied to the hair to cause the hair to transition from the current condition to the desired hair condition; and cause the hair coloring formulation to be presented on the client device.
Nevertheless, Nash discloses maintain a database storing information about hair coloring products from a plurality of brands; receive input data from a client device (A method for hair dye color conversion in which a hair dye color mixture of one manufacture is matched and reproduced using a hair dye color mixture of another manufacture, may be summarized as including accessing a control system having at least a processor, a memory, and user input controls, the memory configured to store ingredients of a hair dye color mixture, each ingredient of the hair dye color mixture being from a first manufacturer color line [0014]), the input data comprising (1) an image of current hair and (2) a desired hair condition after applying one or more color products of at least one of the plurality of brands (FIG. 4, depicts a screenshot of a client history screen 400 associated with the “Client History” GUI module according to one embodiment [0100]; As shown, the client history screen 400 has the “Created” tab 406 selected by default. Other embodiments may have a different tab selected as the default tab upon the user reaching the client history screen 400. The “Created” tab 406 may generally include a client's service history such as the dates on which the client came to the salon for one or more services, the number of services (e.g., formulas) created by the user on each service date, the products purchased by the client on each service date, the length of the service(s), whether or not the service was especially liked by the client (e.g., favorite), and the like….[0101]); apply the machine-learning model to the image of hair to output values of the one or more attributes that characterize the current hair condition (In another aspect of the system and method for hair dye color conversion, the system employs machines learning and linear regression to improve the accuracy of the prediction modelling [0043]); apply a decision tree based model to the values of the one or more attributes that characterize the current hair condition and the desired hair condition to output a hair color treatment that is to be applied to the current hair, the hair color treatment causing the current hair to transition from the current hair condition to the desired hair condition; identify a hair coloring formulation based on the hair color treatment (This product color estimate adjustment embodiment of the system and method for hair dye color conversion estimates new values for the Base Color Matrix in view of the observed data… Other sub-optimal techniques include decision tree based methods, neural networks, nearest neighbor based methods, support vector machines, Gaussian processes, and other types of linear regression [0068]), wherein the hair coloring formulation includes one or more hair coloring products from at least one of the plurality of brands, and a period for which a mixture of the one or more hair coloring products is to be applied to the hair to cause the hair to transition from the current condition to the desired hair condition (The hair dye color conversion system and method 100 enables an operator to recreate the hair dye mixture using different branded products. These may be different branded products from the same manufacture or different branded products from the different manufactures [0041]; The hair color data further comprises measurements corresponding to the color of hair resulting from application of the hair coloring agent on hair [0059]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Mourad with the teachings of Nash to predict a color of a mixture of a first and second formula product lines to match and improve color prediction accuracy.
However, Mourad and Nash do not explicitly disclose receive input data from a client device, the input data comprising (1) an image of current hair and (2) a desired hair condition after applying one or more color products of at least one of the plurality of brands; access a machine-learning model trained to output values of one or more attributes that characterize hair conditions based on images of hair, the one or more attributes comprising at least a shade of hair and a tone of hair; apply the machine-learning model to the image of hair to output values of the one or more attributes that characterize the current hair condition; wherein the hair coloring formulation includes one or more hair coloring products from at least one of the plurality of brands, along with a ratio between the one or more hair coloring products, and a time period for which a mixture of the one or more hair coloring products is to be applied to the hair to cause the hair to transition from the current condition to the desired hair condition; and cause the hair coloring formulation to be presented on the client device.
Nevertheless, Bonnin discloses receive input data from a client device, the input data comprising (1) an image of current hair (In various exemplary embodiments, an initial hair color can be selected from a plurality of initial hair colors, for example by a hair styling professional or by the person to whom the hair belongs. The initial hair color can for example be chosen as being the one that makes the greatest color impression on a person observing the hair. The determination of the initial hair color can also be done using a device or a data processing of a picture of the hair of a user [0080]); access a machine-learning model trained to output values of one or more attributes that characterize hair conditions based on images of hair, the one or more attributes comprising at least a shade of hair and a tone of hair (In various exemplary embodiments, an initial hair color can be selected from a plurality of initial hair colors, for example by a hair styling professional or by the person to whom the hair belongs. The initial hair color can for example be chosen as being the one that makes the greatest color impression on a person observing the hair. The determination of the initial hair color can also be done using a device or a data processing of a picture of the hair of a user [0080]; To determine expected hair colors for a (e.g. arbitrary) initial state and for a large number of combinations of relevant concentrations of a plurality of dye precursors, methods from the field of predictive analytics (also known as “big data”, “data mining” or “machine learning) can be applied, using the hair color data including the added artificial hair color data [0072]); apply the machine-learning model to the image of hair to output values of the one or more attributes that characterize the current hair condition (In various exemplary embodiments, an initial hair color can be selected from a plurality of initial hair colors, for example by a hair styling professional or by the person to whom the hair belongs. The initial hair color can for example be chosen as being the one that makes the greatest color impression on a person observing the hair. The determination of the initial hair color can also be done using a device or a data processing of a picture of the hair of a user [0080]; To determine expected hair colors for a (e.g. arbitrary) initial state and for a large number of combinations of relevant concentrations of a plurality of dye precursors, methods from the field of predictive analytics (also known as “big data”, “data mining” or “machine learning) can be applied, using the hair color data including the added artificial hair color data [0072]); wherein the hair coloring formulation includes one or more hair coloring products…, along with a ratio between the one or more hair coloring products, and a mixture of the one or more hair coloring products (The difference between the color properties of the recommended hair coloration product and the color properties of the hair coloring agent determined from the model can be calculated as a distance in a color space, such as the L*a*b color space [0043]; This parameter can be adjusted as the relative proportion of available hair coloring agents changes over time, for example when some hair colors become more popular and offer more diversity in the choice of hair coloring agents to dye hairs in such color shades [0071]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Mourad and Nash with the teachings of Bonnin to obtain the initial hair color to determine the greatest color impression and improve color prediction accuracy.
However, Mourad, Nash and Bonnin does not explicitly disclose the hair coloring formulation includes one or more hair coloring products from at least one of the plurality of brands, along with a ratio between the one or more hair coloring products, and a time period for which a mixture of the one or more hair coloring products is to be applied to the hair to cause the hair to transition from the current condition to the desired hair condition; and cause the hair coloring formulation to be presented on the client device
Nevertheless, Schmenger discloses the hair coloring formulation includes one or more hair coloring products, along with a ratio, and a time period for which a mixture of the one or more hair coloring products is to be applied to the hair to cause the hair to transition from the current condition to the desired hair condition (…The hair coloring composition remains on the treated hair surface while the end hair color develops for a time period of 5 to 45 minutes to form oxidatively colored hair. The consumer then rinses his/her oxidatively colored hair thoroughly with tap water and allows it to dry and/or styles the oxidatively colored hair [0041]; For example, a ColorTouch.RTM. hair coloring product from Wella will usually be mixed in 1:2 ratio (by weight) with a 4% or 1.9% H.sub.2O.sub.2 emulsion thus resulting in a 2.7% H.sub.2O.sub.2, respectively 1.3% concentration on-head [0046]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Mourad, Nash, and Bonnin with the teachings of Schmenger to treat hair surface completely while ensuring uniform application and improve coloring hair treatment.
Regarding Claim 18, Mourad, Nash, Bonnin, and Schmenger disclose the claimed invention discussed in claim 17.
Mourad discloses the hair color treatment includes a level of shade indicating how much a shade of the current hair needs to be darkened or lightened ( If the user indicated that their hair was previously colored or treated 318, the user may indicate the time when the last treatments were applied to the hair 320. For example, the user may enter a date. The user may be presented with a series of checkboxes indicating various time-frames. Alternatively a slider bar representing a timeline may be used. At this stage, the direction of coloring (lighter or darker) may also be indicated by the user [0035]).
Regarding Claim 19, Mourad, Nash, Bonnin, and Schmenger disclose the claimed invention discussed in claim 17.
Mourad discloses the hair color treatment includes a level of tone indicating how much a tone of the current hair needs to be changed (For example, if a user desires to move to a lighter shade relative to their natural color, then a permanent dye coupled with a developing agent are required. Another treatment example is where the user has highlights, and where they opted to cover the highlights. The user is required to use a two process color fill treatment where they first pre-pigment their highlights with the natural underlying pigment present in that shade level, and then apply the desired final color. To do otherwise would result in an unnaturally flat-looking hair color [0057]).
Regarding Claim 20, Mourad, Nash, Bonnin, and Schmenger disclose the claimed invention discussed in claim 17.
However, Mourad does not explicitly disclose the input data further includes selecting a brand from the plurality of brands, and the one or more coloring products are identified from the selected brand.
Nevertheless, Nash discloses the input data further includes selecting a brand from the plurality of brands, and the one or more coloring products are identified from the selected brand (In other aspects, the hair dye color conversion system and method 100 converts a particular formula from one brand of product to another brand of product. For example, if a particular mixture of colors in one brand needs to be duplicated for a different brand, the particular ingredients will be different. The hair dye color conversion system and method 100 enables an operator to recreate the hair dye mixture using different branded products. These may be different branded products from the same manufacture or different branded products from the different manufactures [0041]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Mourad, Nash, and Bonnin with the teachings of Schmenger to predict a color of a mixture of a first and second formula product lines to match and improve color prediction accuracy.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tamim Mourad (US20110313879) discloses accepting user input of new user characteristics including natural hair color, current hair coloring, hair treatments, hair coloring characteristics and hair treatment characteristics.
Georg Knuebel (US20180310692) discloses preparing hair color data, wherein the hair color data has values for a multitude of color pre-condition parameters and for at least one coloring result parameter for a multitude of coloring processes.
Esther Kumpan-Bahrami (US20200113313) discloses the method can have a detecting of at least one sensor value on hair of a user by employing at least one portable sensor, a determining of a hair condition of the user by employing the detected at least one sensor value.
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/SHARAH ZAAB/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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