Color selection service having color standards libraries and a color analysis engine

Abstract

A Color Selection Service has a server, Color Standards Libraries, and a Color Analysis Engine. The Color Selection Service further comprises computer-readable instructions that, when executed, cause the server to; access the Color Standards Libraries, the Color Standards Libraries comprising a plurality of Standard Colors; provide access to designers using client computers; provide tools to designers for selecting one or more Standard Colors from the Color Standards Libraries; send selected Standard Colors to the Color Analysis Engine; receive results from the Color Analysis Engine; and provide received results to the designer via the client computer. The Color Analysis Engine generates a ranking of the selected Standard Colors based on at least one of: current trend data for Standard Colors within the Color Selection Service; color forecast data generated by expert human analysis; and response data of social media data segmented into demographic groups.

Description

Color Selection Service Having Color Standards Libraries and a Color Analysis EngineBackground.

[0001] Color systems for assisting product designers or fashion designers are known. For example, color systems having standard colors for designing products are known. For example, the Pantone Matching System, Pantone Fashion, Home + Interiors System, Pantone Skin Tone Guide systems are known. While advantageous in providing designers with inspiration for product design and for communicating color information globally, the sheer volume of colors to be considered is very large. For example, there are more than 15,000 colors in Pantone libraries.

[0002] Color trend forecasting is performed to assist in product design. For example, global color trend experts may be consulted for predicted color trends. This is advantageous because product designers select colors for products months, if not a year or longer, in advance of when the product is offered for sale. Having advance knowledge of what colors are expected to be popular during the product design phase increases the likelihood that a popular color is selected and that the product will be successful in the marketplace. However, this is a manual process requiring years of experience and expertise.

[0003] Image analysis engines are known. In some embodiments, models are trained to recognize objects in an image and apply tags to the objects corresponding to the recognized object. For example, a chair may be detected and a “chair” tag applied with coordinates corresponding to the location of the chair in the image. Other visual features may be detected and tagged. A list of detected objects may also be provided. In some examples, a color scheme is detected.

[0004] In some examples, an Artificial Intelligence (Al) engine is trained to identify images that are most likely to appeal to certain demographic groups. An extractor extracts features from the training images such as object features, scene features, and color features, among others. The training images may also include metadata such as how favorably the image was viewed by persons and their respective demographic information.

[0005] A candidate image then undergoes the same extraction process and its image features are assessed by the trained Al and the image is ranked. The ranking may be by selected demographic criteria, e.g., gender, age, activities, geographical location. This may help a marketer select the highest-ranked image from a plurality of candidate images. Another use case1SUBSTITUTE ^HEET (RULE 26)is to upload an image of a product and have the Al engine predict how popular various colors would be with various demographic groups.

[0006] However, such known image analysis processes appear to be historically based — e.g., the image ranking is based on social media streams, purchase histories, customer engagement, i.e., what certain demographic groups currently like or have liked in the past. This does not provide any color trend forecasting.Object of the Invention

[0007] Improved methods for helping designers select colors is required. In one aspect, it would be advantageous to train an Al engine to forecast color trends segmented into demographic categories. Additionally, the demographically-segmented color trends should be associated with their respective standard color numbers, e.g., Pantone Matching System, Pantone Fashion, Home + Interiors System, Pantone Skin Tone Guide numbers.

[0008] In another aspect, it would be advantageous to improve ranking of images corresponding to products to be launched in the future by integrating Forecast Colors or Trend Colors data into Al engine training.Summary

[0009] In some embodiments, a Color Selection Service has a server, Color Standards Libraries, and a Color Analysis Engine. The Color Selection Service further comprises computer-readable instructions that, when executed, cause the server to: access the Color Standards Libraries, the Color Standards Libraries comprising a plurality of Standard Colors; provide access to designers using client computers; provide tools to designers for selecting one or more Standard Colors from the Color Standards Libraries; send selected Standard Colors to the Color Analysis Engine; receive results from the Color Analysis Engine; and provide received results to the designer via the client computer. The Color Analysis Engine generates a ranking of the selected Standard Colors based on at least one of: current trend data for Standard Colors within the Color Selection Service; color forecast data generated by expert human analysis; and response data of social media data segmented into demographic groups.

[0010] In some embodiments, the Color Analysis Engine is integrated into the Color Selection Service and ranks the selected Standard Colors based on current trend data forStandard Colors within the Color Selection Service. The Color Analysis Engine may be an Artificial Intelligence engine trained on current trend data for Standard Colors within the Color Selection Service and color forecast data generated by expert human analysis. The Color Analysis Engine may be an Artificial Intelligence engine trained on social media data and demographic information related to posters of the social media data. In some embodiments, the Color Selection Service comprises both of these examples of Color Analysis engines.

[0011] In some embodiments, the Color Analysis Engine is an Artificial Intelligence engine trained on social media data and demographic information related to posters of the social media data, the Color Analysis Engine is remote from the server, and the server sends selected Standard Colors to the Color Analysis Engine and received results from the Color Analysis Engine via at least one computer network.

[0012] In some embodiments, the Color Selection Service of claim 1, wherein the Tools for selecting a Standard Color comprise at least one of the group consisting of: providing access to browse one or more Color Standard Libraries and to select one or more colors; providing a continuously-variable hue selector and displaying related Standard Colors; receiving color identification data; receiving input of RGB, LAB, CMYK or other color space values and converting to the closest matching Standard Color; receiving spectral or colorimetric measurements of a sample color and returning a closest matching Standard Color; receiving a selection of one or more pixels or an area of interest in an image and returning a closest matching Standard Color; providing examples of Standard Colors in use on various products; and providing color scientist-curated palettes of Standard Colors. In some embodiments, several or all of the above Tools are employed.

[0013] In some embodiments, the Color Analysis Engine is an Artificial Intelligence engine trained on datasets of images where objects have been identified in the images and colors extracted from the identified objects.

[0014] In some embodiments, the computer-readable instructions further cause the server to: receive designer inputs from the designer comprising at least one selected from the group consisting of: time-frame for product / campaign launch; Target User Demographics; product / graphical design, including product / label / packaging images; advertising headline / copy;and multiple alternatives of input image; and send designer inputs along with the selected Standard Colors to the Color Analysis Engine.Brief Description of the Drawings

[0015] Figure 1 comprises an illustration of a server embodying a first aspect of the present invention in combination with a plurality of client computing devices.

[0016] Figure 2 comprises a block diagram of the server of Figure 1 having an Integrated Color Analysis Engine in combination with a plurality of client computing devices.

[0017] Figure 3 comprises an illustration of a server embodying another aspect of the present invention in combination with a plurality of client computing devices and a Remote Color Analysis Engine.

[0018] Figure 4 comprises a block diagram of the server of Figure 3 in combination with a plurality of client computing devices and a Remote Color Analysis Engine.

[0019] Figure 5 comprises a block diagram of the server of Figure 1 in combination with a plurality of client computing devices and a Remote Color Analysis Engine.

[0020] Figure 6 comprises a flow chart illustrating a process according to another aspect of the present invention.Detailed Description

[0021] In the present disclosure, references in the singular may also include the plural. Specifically, the word "a" or "an" may refer to one, or one or more, unless the context indicates otherwise.

[0022] "Color" generally refers to the property possessed by an object of producing different sensations on the human eye as a result of the way the object reflects or emits light. Each color has its own distinct appearance, based on three elements: hue, chroma (or saturation) and value (lightness). Color values may be expressed in an arbitrary color space, e.g., in a trichromatic color space like RGB or CIEXYZ, or in any other color space like CIELAB (L*a*b*).

[0023] “Forecast Colors” refers to colors identified by color scientists or other experts through research, observation, and the application expertise.

[0024] “Trend Colors” refers to colors identified by determining a frequency with which colors are being selected by designers. This reflects colors currently popular with designers that are going to be included in products 6-12 months (or more) into the future.

[0025] “Color Standards Library” refers to collections of discrete colors and associated data and naming conventions. The discrete colors may comprise spectral information, color space information, or physical samples. Pantone color systems, including Pantone Matching System, Pantone Fashion, Home + Interiors System, Pantone Skin Tone Guide systems, are examples of Color Standards Libraries. Color Standards Libraries also includes libraries of Dependent Color Standards, such as PantoneLIVE.

[0026] The term "database" refers to an organized collection of data that can be accessed electronically by a computer system. In simple embodiments, the database can be a searchable electronic fde in an arbitrary format. Examples include a Microsoft Excel™ spreadsheet or a searchable PDF document. In more sophisticated embodiments, a database can be a relational database that is maintained by a relational database management system using a language like SQL.

[0027] The term "computer" or "computing device" refers to any device that can be instructed to carry out sequences of arithmetic or logical operations automatically via a program. Without limitation, a computer can take the form of a desktop computer, a notebook computer, a tablet computer, a smartphone, a programmable digital signal processor etc. A computer generally includes at least one processor and at least one memory device. A computer may be a subunit of another device, such as an appearance capture device. A computer may be configured to establish a wired or wireless connection to another computer, including a computer for querying a database. A computer can be configured to be coupled to a data input device like a keyboard or a computer mouse or to a data output device like a display or a printer via a wired or wireless connection.

[0028] A "computer system" is to be broadly understood as encompassing one or more computers. If the computer system comprises more than one computer, these computers do not necessarily need to be in the same physical location. The computers within a computer system may communicate with one another via wired or wireless connections.

[0029] A "processor" is an electronic circuit which performs operations on an external data source, in particular, a memory device.

[0030] A "memory device" or briefly "memory" is a device that is used to store information for use by the processor. The memory device may include volatile memory, as for random-access memory (RAM), and nonvolatile memory, as for read-only memory (ROM). In some embodiments, the memory device may include a non-volatile semiconductor memory device such as an (E)EPROM or a flash memory device, which may take the form of, e.g., a memory card or a solid-state disk. In some embodiments, the memory device may include a mass storage device having mechanical components, like a hard disk. The memory device can store a program for execution by the processor. A non-volatile memory device may also be called a non-volatile computer-readable medium.

[0031] A "program" is a collection of instructions that can be executed by processor to perform a specific task.

[0032] An “Color Analysis Engine” may comprise a machine learning agent, such as a reinforced learning agent, a data driven model-free agent, or other Artificial Intelligence engine trained on one or more of the following data sets: Color Standards Libraries, usage information concerning Color Standards Libraries, industry feedback concerning Color Standards Libraries, images from social media streams, images from ecommerce websites, demographic information from persons interacting with the images, and manually ascertained color trend predictions.

[0033] Referring to Figures 1 and 2, in some embodiments, a Color Selection Service 10 is provided. In some embodiments, the Color Selection Service 10 is provided as a Software as a Service Application. One or more physical or virtual servers 12 host the software. Clients 14 access the Color Selection Service via private or public computer networks 16. such as the Internet. In some embodiments, the Color Selection Service is accessed via a mobile application on a smartphone or tablet. In some embodiments, the Color Selection Service is provided as an extensions (plug in) to a product design software application.

[0034] In some embodiments, the Color Selection Service comprises one or more Color Standards Libraries 20, tools for selecting a Standard Color or a palette of Standard Colors 22, and a Color Analysis Engine 24. Tools for selecting Standard Colors include providing access to browse one or more Color Standard Libraries and select one or more colors, providing acontinuously-variable hue selector and displaying related Standard Color hues, receiving color identification data, such as a Pantone number, receiving input of RGB, LAB, CMYK or other color space values and converting to the closest matching standard color, receiving spectral or colorimetric measurements of a sample color and returning a closest matching Standard Color, receiving a selection of one or more pixels or an area of interest in an image and returning a closest matching Standard Color, providing examples of colors in use on various products, providing color scientist-curated palettes, and other color selection methods.

[0035] Referring to figures 3 and 4, in some embodiments, one or more selected Standard Colors are passed to the Color Analysis Engine 24. In some embodiments, the Color Analysis Engine 24 is hosted on a separate server 26 from the server(s) hosting the Color Standards Libraries and color selecting Tools. In these embodiments, an API may be used to transfer information to and from the Color Analysis Engine 24 over a computer network. The computer network may be a local network, a global network (e.g., the Internet), or a combination of local and global networks. The Color Analysis Engine analyzes the selected colors and returns an indication of “likability” of the color or colors.

[0036] In some embodiments, the Color Analysis Engine 24 comprises the computer processor which pulls in color related data from sources such as: Color Trend Data 28 for fashion, interiors, etc.; planned color marketing campaigns; designer data (Trend Colors) (e.g., data associated with actual users of color software such as which colors are picked or popular or desired within a particular software / online system or app, which may be indicative of color trends in the next 6-12 months); seasonal or other time-based color history, trend, or projection data; design purpose data (e.g., designing a color scheme for a specific use, e.g., fashion, home, interior decorating, painting, etc.) advertising or sales data (e.g., click volume data, sales volume data, etc.); other real-time data from product designers / end users; and demographic data (e.g., end user demographics, fashion industry, age, etc.)

[0037] In some embodiments, the Color Analysis Engine 24 is trained to recognize the context in which colors are viewed as favorable or unfavorable. For example, some colors may be viewed more favorably in the context of home furnishings and less favorably in the context of sporting gear. To provide this context, the Color Analysis Engine 24 would be trained on data sets including images where objects have been recognized and tagged and colors extracted fromthe objects. In some embodiments, the Color Analysis Engine 24 is trained on data sets including images where human faces have been recognized and tagged and skin tones extracted from the faces.

[0038] In some embodiments, a designer provides a selected color and category of object to the Color Analysis Engine 24. In some embodiments, a group or category of related objects may be inferred by the Color Analysis Engine 24 from metadata associated with the selected color, such as its Standard Color Library or color identification information. For example, Pantone Color identifiers include text indicating whether they are for graphical printing, textiles, or pigments and coatings. In some embodiments, the designer also selects a demographic group or target audience.

[0039] In some embodiments, the Color Analysis Engine 24 comprises an Al engine that is trained on images having an indication of popularity based on Social Media Streams 30. For example, the Al engine may be trained on images posted on X (formerly Twitter), Instagram, Facebook, etc. and having an indication of a number of “likes” or other positive or negative user feedback.

[0040] In some embodiments, the Al engine may determine expressions on faces in an image and return a confidence value across a set of emotions for each face in the image.

[0041] In some embodiments, the Al Engine is trained on one or more of the following data sets: color forecast empirically derived via Pantone research “Forecast colors”; color trends based on professional usage from 1stparty data (color trend experts) “Trend colors”; forecast colors and Trend colors are codified as Pantone colors; and / or Skin Tone colors can also be codified as Pantone Skin Tone colors

[0042] Referring to Figure 5, in some embodiments, the Color Selection Service 10 comprises more than one Color Analysis Engine 24. For example an engine trained on color trend information may be separate from an Al engine trained on social media inputs. One, the other, or both Color Analysis Engines 24 may be consulted in this example.

[0043] Additional designer-provided inputs to the Al Engine may include: time-frame for product / campaign launch (6 months, 12 months, 24 months); Target User Demographics (e g., designer selects gender, age range, income range, geographical location); product / graphicaldesign, including product / label / packaging images; advertising headline / copy; and multiple alternatives of input images.

[0044] Additional Color Analysis Engine outputs may include color forecast, color trend and skin tone inputs applied to design / actor; colors are identified by Pantone number and color system; trend or forecast recommendations could be selected by designer and / or Skin Tone colors could be selected by the designer. Recommendation of best "scoring" results is based on A. I. Engine logic includes ranking of multiple alternatives based on forecast / trend / demographic information; and ranking of demographic groups most likely to find a design appealing by forecast / trend / demographic information. Colors that are selected by a designer for each element may be recorded, along with the demographics of the "target user".

[0045] The designer may then select on or more Standard Colors. Optionally, the selected Standard Colors may be applied to a virtual prototype generator, which creates an image of the product under development using one or more selected Standard Colors. This image may then be analyzed for favorability within selected demographic groups as is conventionally known.

[0046] An accumulation of recorded color choices by product development professionals based on the service-recommended colors is additional data that can be used to develop a new Al model for recommending color choices in the future.

[0047] Referring to Figure 6, a method 40 of using a Color Selection Service is illustrated. In step 41, a client uses the color selection tools on the Color Selection Service to select one or more candidate colors. In step 42, selected candidate colors are submitted to the color analysis engine. In step 43, the color analysis engine processes the selected candidate colors and ranks the candidate colors. In step 44, the ranked candidate colors are returned to the Color Selection Service. In step 45, the Color Selection Service provides the ranked candidate colors to the client.

[0048] The various embodiments described herein may be implemented in a wide variety of operating environments, which in some cases may include one or more user computers, computing devices, or processing devices which may be utilized to operate any of a number of applications. User or client devices may include any of a number of general purpose personal computers, such as desktop or laptop computers running a standard operating system, as well as cellular, wireless, and handheld devices running mobile software and capable of supporting anumber of networking and messaging protocols. Such a system also may include a number of workstations running any of a variety of commercially-available operating systems and other known applications for purposes such as development and database management. These devices also may include other electronic devices, such as dummy terminals, thin-clients, gaming systems, and other devices capable of communicating via a network.

[0049] Most embodiments utilize at least one network that would be familiar to those skilled in the art for supporting communications using any of a variety of commercially-available protocols, such as TCP / IP stack protocols, FTP, SMB, OSI, HTTP -based protocols, SSL, Bitcoin, Ethereum, blockchain- or smart contracts-supported protocols. Such a network may include, for example, a local area network, a wide-area network, a virtual private network, the Internet, an intranet, an extranet, a public switched telephone network, an infrared network, a wireless network, and any combination thereof. The network may, furthermore, incorporate any suitable network topology. Examples of suitable network topologies include, but are not limited to, simple point-to-point, star topology, self-organizing peer-to-peer topologies, and combinations thereof.

[0050] In embodiments utilizing a Web server, the Web server may run any of a variety of server or mid-tier applications, including HTTP servers, FTP servers, CGI servers, data servers, Java servers, and business application servers. The server(s) also may be capable of executing programs or scripts in response requests from user devices, such as by executing one or more Web applications that may be implemented as one or more scripts or programs written in any programming language, such as Java®, C, C# or C++, or any scripting language, such as Perl, Python, or TCL, as well as combinations thereof. The server(s) may also include database servers, including without limitation those commercially available from Oracle®, Microsoft®, Sybase®, and IBM®.

[0051] The environment may include a variety of data stores and other memory and storage media as discussed above. These may reside in a variety of locations, such as on a storage medium local to (and / or resident in) one or more of the computers or remote from any or all of the computers across the network. In a particular set of embodiments, the information may reside in a storage-area network (“SAN”) familiar to those skilled in the art. Similarly, any necessary files for performing the functions attributed to the computers, servers, or other network devicesmay be stored locally and / or remotely, as appropriate. Where a system includes computerized devices, each such device may include hardware elements that may be electrically or optically coupled via a bus, the elements including, for example, at least one central processing unit (CPU), at least one input device (e.g., a mouse, keyboard, controller, touch screen, or keypad), and at least one output device (e.g., a display device, printer, or speaker). Such a system may also include one or more storage devices, such as disk drives, optical storage devices, and solid- state storage devices such as random access memory (“RAM”) or read-only memory (“ROM”), as well as removable media devices, memory cards, flash cards, etc.

[0052] Such devices also may include a computer-readable storage media reader, a communications device (e.g., a modem, a network card (wireless or wired), an infrared communication device, etc.), and working memory as described above. The computer-readable storage media reader may be connected with, or configured to receive, a computer-readable storage medium, representing remote, local, fixed, and / or removable storage devices as well as storage media for temporarily and / or more permanently containing, storing, transmitting, and retrieving computer-readable information. The system and various devices may also include one or more software applications, modules including program modules, services, or other elements located within at least one working memory device, including an operating system and application programs, such as a client application or Web browser. It should be appreciated that alternate embodiments may have numerous variations from that described above. For example, customized hardware might also be utilized and / or particular elements might be implemented in hardware, software (including portable software, such as applets), or both. Further, connection to other computing devices such as network input / output devices may be employed.

[0053] Storage media and computer readable media for containing code, or portions of code, may include any appropriate media known or used in the art, including storage media and communication media, such as but not limited to volatile and non-volatile, removable and nonremovable media implemented in any method or technology for storage and / or transmission of information such as computer readable instructions, data structures, program modules, or other data, including RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which may be utilized to store the desired information and which may be accessed by one or more of the method, system,or device. Program modules, program components and / or programmatic objects may include computer-readable and / or computer-executable instructions, for example computer-executable instructions comprising a programmatic description of one or more of the methods, of and / or corresponding to any suitable computer programming language. In at least one embodiment, each computer-readable medium may be tangible. In at least one embodiment, each computer- readable medium may be non-transitory in time. Based on the disclosure and teachings provided herein, a person of ordinary skill in the art will appreciate other ways and / or methods to implement the various embodiments.

[0054] Embodiments of the present disclosure, for example, one or more of the computing systems, comprise or utilize a special purpose or general-purpose computer including computer hardware, such as, for example, one or more processors and system memory, as discussed in additional detail below. Embodiments within the scope of the present disclosure also include physical and other computer-readable media for carrying or storing computer-executable instructions and / or data structures. In particular, one or more of the processes or methods described herein may be implemented at least in part as instructions embodied in a non-transitory computer-readable medium and executable by one or more computing devices (e.g., any of the media content access devices described herein). In general, a processor (e.g., a microprocessor) receives instructions, from a non-transitory computer-readable medium, (e.g., a memory, etc.), and executes those instructions, thereby performing one or more processes or methods, including one or more of the processes or methods described herein.

[0055] Computer-readable media can be any available media that can be accessed by a general purpose or special purpose computer system. Computer-readable media that store computer-executable instructions are non-transitory computer-readable storage media (devices). Computer-readable media that carry computer-executable instructions are transmission media. Thus, by way of example, and not limitation, embodiments of the disclosure can comprise at least two distinctly different kinds of computer-readable media: non-transitory computer- readable storage media (devices) and transmission media.

[0056] Non-transitory computer-readable storage media (devices) includes ROM, EEPROM, CD-ROM, solid state drives (“SSDs”) (e.g., based on RAM), Flash memory, phase-change memory (“PCM”), other types of memory, other optical disk storage, magnetic disk storage orother magnetic storage devices, or any other medium which can be used to store desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer.

[0057] A digital communication interface, or network, is defined as one or more data links that enable the transport of electronic data between computer systems and / or modules and / or other electronic devices. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a computer, the computer properly views the connection as a transmission medium. Transmissions media can include a network and / or data links which can be used to carry desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer. Combinations of the above should also be included within the scope of computer-readable media.

[0058] Further, upon reaching various computer system components, program code means in the form of computer-executable instructions or data structures can be transferred automatically from transmission media to non-transitory computer-readable storage media (devices) (or vice versa). For example, computer-executable instructions or data structures received over a network or data link can be buffered in RAM within a network interface module (e.g., a “NIC”), and then eventually transferred to computer system RAM and / or to less volatile computer storage media (devices) at a computer system. Thus, it should be understood that non-transitory computer- readable storage media (devices) can be included in computer system components that also (or even primarily) utilize transmission media.

[0059] Computer-executable instructions comprise, for example, instructions and data which, when executed at a processor, cause a general -purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. In some embodiments, computer-executable instructions are executed on a general-purpose computer to turn the general-purpose computer into a special purpose computer implementing elements of the disclosure. The computer executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, or even source code. Although the subject matter has been described in language specific to structural features and / or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited tothe described features or acts described above. Rather, the described features and acts are disclosed as example forms of implementing the claims.

[0060] Those skilled in the art will appreciate that the disclosure may be practiced in network computing environments with many types of computer system configurations, including, personal computers, desktop computers, laptop computers, message processors, hand-held devices, multi -processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile telephones, PDAs, tablets, pagers, routers, switches, and the like. The disclosure may also be practiced in distributed system environments where local and remote computer systems, which are linked (either by hardwired data links, wireless data links, or by a combination of hardwired and wireless data links) through a network, both perform tasks. In a distributed system environment, program modules may be located in both local and remote memory storage devices.

[0061] Embodiments of the present disclosure can also be implemented in cloud computing environments. In this description, “cloud computing” is defined as a model for enabling on- demand network access to a shared pool of configurable computing resources. For example, cloud computing can be employed in the marketplace to offer ubiquitous and convenient on- demand access to the shared pool of configurable computing resources. The shared pool of configurable computing resources can be rapidly provisioned via virtualization and released with low management effort or service provider interaction, and then scaled accordingly.

[0062] A cloud-computing model can be composed of various characteristics such as, for example, on-demand self-service, broad network access, resource pooling, rapid elasticity, measured service, and so forth. A cloud-computing model can also expose various service models, such as, for example, Software as a Service (“SaaS”), Platform as a Service (“PaaS”), and Infrastructure as a Service (“laaS”). A cloud-computing model can also be deployed using different deployment models such as private cloud, community cloud, public cloud, hybrid cloud, and so forth. In this description and in the claims, a “cloud-computing environment” is an environment in which cloud computing is employed.

[0063] In the foregoing specification, the invention has been described with reference to specific exemplary embodiments thereof. Various embodiments and aspects of the invention(s) are described with reference to details discussed herein, and the accompanying drawingsillustrate the various embodiments. The description above and drawings are illustrative of the invention and are not to be construed as limiting the invention. Numerous specific details are described to provide a thorough understanding of various embodiments of the present invention.

[0064] The present invention may be embodied in other specific forms without departing from its spirit or essential characteristics. The described embodiments are to be considered in all respects only as illustrative and not restrictive. For example, the methods described herein may be performed with less or more steps / acts or the steps / acts may be performed in differing orders. Additionally, the steps / acts described herein may be repeated or performed in parallel with one another or in parallel with different instances of the same or similar steps / acts. The scope of the invention is, therefore, indicated by the appended claims rather than by the foregoing description All changes that come within the meaning and range of equivalency of the claims are to be embraced within their scope.

Claims

What is claimed is:

1. A Color Selection Service having a server, Color Standards Libraries, and a Color Analysis Engine, comprising: computer-readable instructions that, when executed, cause the server to: access the Color Standards Libraries, the Color Standards Libraries comprising a plurality of Standard Colors; provide access to designers using client computers; provide tools to designers for selecting one or more Standard Colors from the Color Standards Libraries; send selected Standard Colors to the Color Analysis Engine; receive results from the Color Analysis Engine; and provide received results to the designer via the client computer; wherein the Color Analysis Engine generates a ranking of the selected Standard Colors based on at least one of: current trend data for Standard Colors within the Color Selection Service; color forecast data generated by expert human analysis; and response data of social media data segmented into demographic groups.

2. The Color Selection Service of claim 1, wherein the Color Analysis Engine is integrated into the Color Selection Service and ranks the selected Standard Colors based on current trend data for Standard Colors within the Color Selection Service.

3. The Color Selection Service of claim 1, wherein the Color Analysis Engine is an Artificial Intelligence engine trained on current trend data for Standard Colors within the Color Selection Service and color forecast data generated by expert human analysis.

4. The Color Selection Service of claim 1, wherein the Color Analysis Engine is an Artificial Intelligence engine trained on social media data and demographic information related to posters of the social media data.

5. The Color Selection Service of claim 1, wherein the Color Analysis Engine is an Artificial Intelligence engine trained on social media data and demographic information related to posters of the social media data, the Color Analysis Engine is remote from the server, and the server sends selected Standard Colors to the Color Analysis Engine and receive results from the Color Analysis Engine via at least one computer network.

6. The Color Selection Service of claim 1, wherein the Tools for selecting a Standard Color comprise at least one of the group consisting of: providing access to browse one or more Color Standard Libraries and to select one or more colors; providing a continuously-variable hue selector and displaying related Standard Colors; receiving color identification data; receiving input of RGB, LAB, CMYK or other color space values and converting to the closest matching Standard Color;receiving spectral or colorimetric measurements of a sample color and returning a closest matching Standard Color; receiving a selection of one or more pixels or an area of interest in an image and returning a closest matching Standard Color; providing examples of Standard Colors in use on various products; and providing color scientist-curated palettes of Standard Colors.

7. The Color Selection Service of claim 1, wherein the Color Analysis Engine is an Artificial Intelligence engine trained on datasets of images where objects have been identified in the images and colors extracted from the identified objects.

8. The Color Selection Service of claim 1, wherein the computer-readable instructions further cause the server to: receive designer inputs from the designer comprising at least one selected from the group consisting of: time-frame for product / campaign launch; Target User Demographics; product / graphical design, including product / label / packaging images; advertising headline / copy; and multiple alternatives of input image; and send designer inputs along with the selected Standard Colors to the Color Analysis Engine.

Images