Invented by Iraklis Pappas, Bartosz Luczynski, Donghui Wu, Dong Hyun Kim, Colgate Palmolive Co
One of the key drivers behind the growing market for systems and methods for producing personal care products is the increasing consumer preference for natural and organic ingredients. Consumers are now more aware of the potential harmful effects of synthetic chemicals and are actively seeking products that are free from such ingredients. This has led to a rise in the demand for systems and methods that enable the production of personal care products using natural and organic ingredients.
Another factor contributing to the market growth is the rising trend of personalized and customized personal care products. Consumers are no longer satisfied with generic products that cater to the masses. They want products that are tailored to their specific needs and preferences. This has led to the development of systems and methods that allow for the customization of personal care products, such as skincare products that target specific skin concerns or haircare products that address individual hair types.
Furthermore, advancements in technology have played a significant role in driving the market for systems and methods for producing personal care products. The use of automation and robotics in the production process has not only increased efficiency but also improved the quality and consistency of the products. These technological advancements have allowed manufacturers to produce personal care products on a larger scale while maintaining high standards.
In addition to the demand from consumers, the personal care industry itself has also been a driving force behind the market growth. With the industry constantly evolving and introducing new products, manufacturers are constantly seeking innovative systems and methods to stay ahead of the competition. This has led to a continuous demand for systems and methods that can produce personal care products efficiently and cost-effectively.
However, despite the positive growth prospects, the market for systems and methods for producing personal care products does face some challenges. One of the main challenges is the stringent regulations and standards imposed by regulatory authorities. Manufacturers need to ensure that their production processes comply with these regulations, which can be time-consuming and costly.
Moreover, the market is highly competitive, with numerous players vying for market share. This has led to intense competition and price pressures, making it essential for manufacturers to constantly innovate and differentiate their products.
In conclusion, the market for systems and methods for producing personal care products is experiencing significant growth due to factors such as increasing consumer preference for natural and organic ingredients, the demand for personalized products, and advancements in technology. However, manufacturers need to navigate through challenges such as regulatory compliance and intense competition to capitalize on the opportunities presented by this growing market.
The Colgate Palmolive Co invention works as follows
A system, apparatus and/or method for the production of a personal care products is disclosed.” A model (e.g. a machine-learning model) can be built using the identity of a chemical composition. Ingredients may be included in the identity of a considered chemical composition. Each ingredient of the considered composition can be assigned a value for a chemoinformatic characteristic of the composition. The model can be used to determine the value of a property in the considered chemical composition. The value can be determined by the identity of the chemical composition. At least two ingredients in the considered composition can interact to affect the property. “A personal care product containing the considered chemical composition can be produced.
Background for Systems and Methods for Producing Personal Care Products
Many products are made from chemical compositions. Chemical compositions usually include a variety of ingredients. Each ingredient has a specific value (such as a chemoinformatic) associated with it. Further, some properties (e.g. a pH or consumer perception) may be unique, depending on the ingredients in the composition. The interaction between the ingredients in the composition can change the value of a property within the chemical composition.
There are conventional methods for predicting the values of one of more properties in a chemistry composition. These methods can be time-consuming and complex. To predict the value of pH in a chemical composition, for example, the conventional approach is to (1) measure the composition experimentally to determine pH and (2) perform a mathematical calculation to calculate pH (e.g. using known acidity constants such as pKa). The time and/or the complexity of these approaches make them ineffective. “Therefore, it is desirable to have a system or method that can be used to determine the value of an attribute of a chemistry composition with less time and/or complexity.
A system, apparatus and/or method for the production of a personal care products is disclosed.” A model (e.g. a machine-learning model) can be built using the identity of a chemical composition. Ingredients may be included in the identity of a considered chemical composition. Each ingredient of the considered composition can be assigned a value for a chemoinformatic characteristic of the composition. The model can be used to determine the value of a property in the considered chemical composition. The value can be determined by the identity of the chemical composition. At least two ingredients in the considered composition can interact to affect the property. “A personal care product containing the considered chemical composition can be produced.
A system, apparatus and/or method for the production of a personal care item is disclosed.” Inputs to a machine-learning model may include at least one of two things: an identity of the chemical composition under consideration or values of its chemoinformatic property. Ingredients may be included in the identity of a considered chemical composition. Each ingredient may be assigned a value for a chemoinformatic attribute of chemoinformatic characteristics of the considered composition. The machine learning model can be used to determine a fitting parameter value for the chemical composition. This is based at least on the identity of the chemical composition. Fitting parameter values can be linked to a value for a property in a chemical composition. At least two ingredients in the considered composition can interact to affect the property. “A personal care product containing the considered chemical composition can be produced.
A system, apparatus and/or method for the production of a personal care item is disclosed. Input into a model at least one value of chemoinformatic property of an ingredient of the chemical composition considered or a number of properties of the chemical composition considered. At least two ingredients in the considered composition can interact to affect the property. Models can be used to determine the identity of a considered chemical formulation based on at least one value of chemoinformatic property of an ingredient of the composition considered or the value for the composition considered. “A personal care product containing the identified considered composition can be produced.
In another aspect, it is disclosed a system, apparatus and/or method for the production of a personal care item. The values of the chemoinformatic characteristics of the ingredients in a sample composition chemical are received. The value of an ingredient of the sample composition that is affected by the interaction of two or more ingredients is received. Input into a computer model are the values of chemoinformatic property of ingredients in the sample composition, and the value for the property. The model determines the identity of the chemical composition considered based at least on (1) the chemoinformatic property values of ingredients in the composition considered or (2) the value of a chemical composition considered. “The personal care product containing the considered chemical composition” is produced.
In another aspect, it is disclosed a system, an apparatus and/or a method for determining the value of a particular property of a chemical composition. A sample chemical composition’s identity may be provided. Ingredients may be included in a sample chemical composition. Each ingredient may be assigned a value for a chemoinformatic or chemoinformatic component of the sample composition. The value of a particular property of the chemical composition sample may be received. A property of the chemical composition sample may be affected due to an interaction between at least two ingredients. Input into a model the value of a property of a sample chemical formulation and either (1) the identity or (2) values of chemoinformatic characteristics of ingredients in the sample composition. Models can be used to determine the value of the property in the considered composition based on either (1) the identity of the composition considered or (2) the values of chemoinformatic characteristics of its ingredients. A property of the chemical composition considered may be affected by the interaction between at least two ingredients.
In another aspect, the value of an attribute of a chemical composition considered may be determined. A sample chemical composition can be identified. Ingredients may be included in the identifier. The value of one or more of the ingredients (e.g. each) may be associated with the chemoinformatic characteristics of the sample composition. You may receive a value for a sample physiochemical component of the sample composition. At least two ingredients in the sample composition can interact to affect the sample physiochemical properties. Input the value of the sample property physiochemical of the chemical composition into a computer model. Input into a model can be the identity of the chemical composition sample and/or values of chemoinformatic property of its ingredients. Models can be used to determine the value of an aforementioned physiochemical property. The value can be determined by the identity of the composition considered and/or the values of chemoinformatic property of ingredients in the composition considered. At least two ingredients in the considered composition can interact to affect the physiochemical property. The sample physiochemical properties may not be the same as the considered physiochemical properties.
In another aspect you can determine the value of an attribute of a chemical composition. You may receive the identity of an important characteristic. You may receive the identities of sample chemical compositions. Each sample chemical composition may contain ingredients that are each associated with a specific value for a chemoinformatic or chemoinformatic component of the composition. A value of a particular property, which is affected by the interaction between at least two ingredients in the sample composition, may be received for each sample composition. Input into a model the value of a property and either (1) the identity or (2) the values chemoinformatic of each ingredient of each sample chemical formulation. Models can be used to determine the value of a property of a chemical composition. The value can be determined by at least one (1) identity of the composition considered or (2) values for chemoinformatic property of the components of the composition considered. “The property of the chemical composition considered may be affected by the interaction of two or more ingredients in the composition.
In another aspect, it is possible to produce a product consisting of a chemical composition with a property of a chemical composition. A sample chemical composition containing ingredients can be identified. Each ingredient may be assigned a value for a chemoinformatic or chemoinformatic component of the sample composition. The sample chemical composition can be given a value for a training chemical. The value of a training chemical can be determined by either an experimental measurement or a mathematical calculation of its value. The values of chemoinformatic property of a sample chemical composition, and the value for the training chemical of that sample composition can be used to construct a learning model. Input the value of the chemical property to the learning model. The learning model may be used to determine the chemoinformatic values of the chemical composition that has the value of this chemical property. “The product containing the considered composition of chemical having the value chemical property can be produced.
In a second aspect, the value of a chemical composition with ingredients can be determined. You may receive the identity of a test chemical composition with a defined value for a chemical property. The sample chemical composition can be made up of different ingredients than the composition under consideration. The training set may include the sample composition’s identity and the value defined for the chemical property. The training set can be used to construct a model that determines the value of a chemical characteristic of the chemical composition under consideration. Models can be used to determine the value of a chemical property in a considered chemical composition, for instance, using the identity of that composition and the training sets. “The value of chemical property can be received.
In another aspect, a sample composition containing ingredients can be identified. Each ingredient may be associated to a value for a chemoinformatic or chemoinformatic component of the sample composition. You may receive a value for a fitting parametre associated with the value of an attribute of the sample composition. At least two ingredients in the sample composition can interact to affect the property of the composition. Input into a model the fitting parameter value that is associated with the value for the property of a sample chemical formulation and/or the values of chemoinformatic characteristics of at least two of its ingredients. The model can be used to determine a fitting parameter value for the chemical composition under consideration. The fitting parameter value can be determined by at least (1) the identity of the chemical composition considered or (2) the values of chemoinformatic property of the ingredients in the chemical composition considered.
In a different aspect, it is possible to identify a chemical composition. You can receive values of the chemoinformatic characteristics of an ingredient in a sample composition. You may receive a value for a particular property in the sample composition. A property can be affected by the interaction between at least two ingredients. Input into a computer model can be the values of chemoinformatic property of ingredients in the sample composition, and the value for the sample composition’s property. The model can be used to determine the identity of the chemical composition under consideration, for instance, by using at least one (1) value of chemoinformatic property of an ingredient of the chemical composition under consideration or (2) a measure of a chemical composition’s property. A property of the chemical composition considered may be affected due to an interaction between at least two ingredients.
In another aspect”, a list of the components of a composition can be received. Each component may have the value of one or more predefined characteristics. It is possible to receive a value for a property in the first composition. A property can be affected by the interaction between at least two components of the composition. The values of predefined characteristics for the components of the composition of the model and the value of its property can be used to train the learning model. The learning model may be given the identities of second components from a second composition. At least one second component may be different from at least one first component. The learning model can be used to determine the value of a second composition property. A property of the composition can be affected by the interaction between at least two components.
In another aspect, the value of an attribute of a chemical composition considered may be determined. The identity of the chemical composition considered may be determined from chemical compositions that are associated with personal care products. Ingredients may be included in the considered chemical composition. You may receive values of chemoinformatic property. Each value can be assigned to a particular ingredient of the chemical composition under consideration. The value of a property of a considered chemical formulation can be determined by at least one (1) of either the identity of a considered chemical component or (2) of values of chemoinformatic characteristics associated with corresponding one of its ingredients. Models can be trained using at least one (1) identity of chemical composition or (2) values for chemoinformatic property of an ingredient of the composition, along with the value of a particular property. “The value of a property of a considered chemical composition can be affected by the interaction of at least 2 ingredients in the composition.
In a second aspect, it is possible to create a model that can be used to calculate the value of an attribute of a chemical composition. It is possible to receive the identity of a chemical composition sample comprising ingredients. Each ingredient may be assigned a value for a chemoinformatic or chemoinformatic component of the sample composition. The sample composition can be given a value. A property can be affected by the interaction between at least two ingredients in the sample composition. The model can be trained to determine a property’s value by combining the value of a property in the sample chemical formulation with at least (1) the identity of that sample composition, or (2) values of chemoinformatic property of its ingredients. The model can be configured to determine value of property of considered chemical composition using at least one (1) identity of considered chemical or (2) values of chemical-informatic properties for ingredients in the considered composition.
The following description is only exemplary and does not limit the invention. The description of illustrative examples is meant to be read with the accompanying illustrations, which should be considered part and parcel of the written description. Any reference to orientation or direction in the description of exemplary embodiments is only for the convenience of description, and not to limit the scope. The discussion described and illustrated herein illustrates some non-limiting combinations that can exist either alone or with other features. As used in this document, the term “or” is also to be interpreted as a logical operator that results in true whenever one or more of its operands are true. The term?or? is to be understood as a logical operation that produces true when one or more operands of the operator are true. As used in this document, the phrase “based on” is also to be interpreted as meaning “based at least in part on,? The phrase ‘based on’ is meant to mean ‘based at least partly on. It is not restricted to the interpretation “based on entirely.
As used throughout, “ranges” are used to describe each and every value within the range. The range’s end can be any value in the range. All references cited in this document are also hereby referenced and incorporated. If there is a conflict between a definition of the disclosure and a reference cited, the disclosure will take precedence.
Features of this invention can be implemented in hardware, software, firmware or combinations thereof.” The computer programs described in this document are not restricted to a particular embodiment and can be implemented as an operating system or application program or foreground, background, or driver processes. The computer programs can be run on multiple computers or servers or a single processor.
The “Processors” described herein can be any central processor (CPU), or microprocessor, microcontroller, computational or programmable circuit or device configured to execute computer program instructions (e.g. code). The processors can be embedded in any type of computer or server hardware (e.g. desktop, laptops, notebooks, tablets, cell phones, etc.). The data processing device may also include other components such as a bus, data storage (such as volatile or non-volatile memories), input/output devices and graphical user interfaces.
The instructions are encoded on the medium and can be retrieved by the respective processor. This allows the processor to perform desired functions by executing them. The device containing a processor that can be configured to execute non-transitory computer programs or instructions may be called a “programmable device” or “device”, and multiple devices communicating with each other may be referred as a “programmable system”. Note that a non-transitory “computer-readable medium” is a non-transitory computer-readable media. The non-transitory?computer-readable medium? The medium may include, without limitation, any suitable volatile or non-volatile memory including random access memory (RAM) and various types thereof, read-only memory (ROM) and various types thereof, USB flash memories, and magnetic or optical data storage devices (e.g., internal/external hard disks, floppy discs, magnetic tape CD?
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