Patent Application
20130339156
Embodiments of the invention relate to a system for selecting, or targeting, when advertising is to be displayed on a digital display device based on an approaching object and associated viewer.
Digital signage is the term that is often used to describe the use of an electronic display device, such as a Liquid Crystal Display (LCD), Light Emitting Diode (LED) display, plasma display, or a projected display to show news, advertisements, local announcements, and other multimedia content in public venues such as public billboards, restaurants or shopping malls. In recent years, the digital signage industry has experienced tremendous growth, and it is now only second to the Internet advertising industry in terms of annual revenue growth.
Targeted advertising involves selecting the time and location for an advertisement (“ad”) to be displayed to a potential audience member or viewer based on various factors such as demographics, purchase history, or observed viewing behavior. Targeted advertising helps to identify a potential viewer, and improves advertisers' Return on Investment (ROI) by providing timely and relevant advertisement to the potential viewer. Targeted advertising in the digital signage industry involves digital signs that have the capability to dynamically select and play advertisements according to the traits or actions of the potential viewer in front of the digital signs.
What is needed is a way to identify patterns in viewing behavior or location so that ad content can be targeted and adapted to the specific demographics of the people viewing the ad content.
Embodiments of the present invention will be understood more fully from the detailed description given below and from the accompanying drawings of various embodiments of the invention, which, however, should not be taken to limit the invention to the specific embodiments, but are for explanation and understanding only.
Video Analytics (VA) is a passive and automated audience or viewer measurement technology designed for digital signage networks that can be used to provide digital signage operators with quantitative viewership information and return on investment (ROI) data. Embodiments of the present invention use VA data and data mining techniques to achieve targeted advertising, which can be used to measure and improve the advertising ROI of a digital sign.
Embodiments of the present invention make use of video analytics (VA) in displaying advertising on a digital sign comprising a digital display screen or device. By providing digital signs access to a sensor, such as one or more front-facing cameras proximate the digital display device, and VA software coupled with processors, such as Intel Core I5 and Intel Core I7 processors, digital signs according to an embodiment of the invention have the intelligence to detect the number of viewers, their gender, their age bracket, and object associated with the viewers, and then adapt ad content based on one or more pieces of that information. For example, if a viewer is a teenage girl, then an embodiment of the invention may change the content to highlight a back to school shoe promotion a few stores down from where the digital display screen is presently located. If the viewer is a senior male, then an embodiment may cause the digital display screen to display an advertisement about a golf club sale at a nearby sporting goods store. If the viewer is wearing a pair of shoes, a baseball cap, or a shirt, with a logo, then an embodiment may cause a digital display screen to display an advertisement at a nearby store that is perhaps of interest to the viewer. As used herein, the term logo refers to a graphic mark or emblem commonly used by commercial enterprises, organizations, or individuals to aid and promote instant public recognition. Logos may be either graphic (symbols/icons) or are composed of the name of the organization.
Embodiments of the invention involve targeted advertising in which future viewers or customers belonging to the same or similar demographic as previous viewers are targeted based on the viewing behavior or patterns of the previous viewers. Other embodiments detect objects associated with viewers or on their persons at a particular location and time, and then target advertising to the viewers at the same or a different location and time. By analyzing VA or objects associated with viewers, or collected from viewers positioned in front of a digital display device, embodiments can discover patterns, such as viewing patterns, and use this information to train advertising models that can be deployed to the digital sign. These advertising models can then be used to choose specific advertisements from the inventory of available advertising content to intelligently target viewers with relevant advertisements.
The advertising models utilize data mining techniques and can be built using tools such as Microsoft's SQL Server Analysis System (MS SSAS). The advertising models are created using a well-known data mining algorithm such as Naïve Bayes, Decision Trees, Logistic Regression analysis, and Association Rules, and may also use large scale clustering, all of which are available in MS SSAS.
The playback of multimedia content on a digital sign is accomplished through a content management system (CMS). A description follows of the architecture of a digital sign advertising system in accordance with an embodiment of the invention, in which advertising models are deployed in real time on a digital sign through the CMS, even when the CMS is located “in the cloud”. The CMS can then be used to generate a customized advertising list based on at least two parameters: a trained advertising model, and advertising data. According to an embodiment of the invention, the advertising data is combined with the trained advertising model to enable real-time content triggering.
Embodiments of the invention analyze the type of viewer information, such as age, in particular, an age range or age bracket, and gender, as well as contextual information, such as location, weather and time information, to select the most appropriate advertisement to be played on the digital sign display device. In one embodiment, the invention analyzes an object associated with a viewer, for example, a vehicle in which the viewer is traveling, or a pair of shoes the viewer is wearing, and determines the type the object, such as a sedan, or a pair of running shoes. Further, an embodiment attempts to determine features or characteristics of the object, such as the make and model of a vehicle, or a logo on the pair of shoes. Further references herein to “age” shall be understood to include an age range, category or bracket.
Real time video analytics data is collected and analyzed to predict the type of viewers for a future time slot, for example, the next time slot. In one embodiment, the next time slot is 30 seconds. However, the time slot could be 60 seconds, 30 minutes, one hour, or an even greater length of time. Depending on the prediction, appropriate ads are played on a display device. The CMS generates a default play list by using advertising information and advertiser preference. If viewership information is not available or the prediction is for some reason not made or not reasonably accurate or if for some reason the accuracy of the prediction is considered suspect, then an offline (default) play list generated by CMS may be played on the display device.
The data capture functionality may be embodied in software executed by the digital sign module, and in one embodiment of the invention, captures real time video analytic data that may be used by data mining module 110 to make real time predictions and schedule a digital advertisement for display, and/or may be used as historical data for generating rules (training advertising models) in the data mining module at 220.
In the data mining module, the advertising models are generated and trained (that is, refined) at 220 using the video analytic data based on well-known data mining algorithms, such as the Naive Bayes algorithm, the Decision Trees algorithm, Logistic Regression analysis, and the Association Rules algorithm. In addition to using the video analytic data, the data mining module may also consider contextual information such as the weather conditions corresponding at the time the video analytic data was captured. Weather conditions data, or simply, weather data 135, may be maintained in a permanent store that can be accessed by data mining module 110. In one embodiment, the same permanent store may be used to store the video analytic data captured by the digital sign module 105 as well. Further, data mining module 110 receives as input a list of digital advertisements 125 available for display on the digital sign, and metadata associated the list of advertisements, such as the demographic characteristics of viewers to which advertisers wish to target their advertisements. Digital sign module 105 also supplies to the data mining module “proof-of-play” data, that is, advertising data indicating what ads were displayed by the digital sign, when those ads where displayed, and where those ads were displayed (e.g., by providing a device identifier (ID) for the digital sign that can be used as a basis for determining the location of the digital sign). In one embodiment of the invention, sales data 130, for example, from a Point-of-Sale terminal, may be input to data mining module 110. The sales data may be correlated with the VA data to gauge the effectiveness of an advertisement on a certain demographic group in terms of the sale of products or services featured in the advertisement.
The data mining module 110 generates at 220 trained advertising models which according to an embodiment of the invention are used to predict suitable advertising categories as well as future viewer types based on previous viewer types (“passer pattern types”). Once a trained advertising model 115 is generated it is transmitted by the data mining module and received and stored by the content management system (CMS) 120 where along with advertising data, a customized advertising list is generated and stored at 225. In one embodiment, the CMS stores all trained advertising models, advertisement lists, advertiser preferences, and advertising data. CMS 120 transmits the customized advertising list through communication link 140 to digital sign module 105 for display. In one embodiment of the invention, digital sign module 105 comprises a digital signage media player module (digital player module) 145, which may be used to generate the advertising lists in real time. Module 145 operates as a condensed repository for information stored in the CMS, according to one embodiment of the invention.
The CMS obtains trained advertising models from the data mining module. In one embodiment, multiple digital sign modules 105, or multiple digital signage media players 145, or multiple digital display devices are installed. The CMS therefore will segregate the advertising models by digital sign module, or digital player, etc., as the case may be. The CMS generates segregated customized ad lists based on the advertising models and obtained advertising data. The CMS also generates offline ad lists, that is, default ad lists, based on advertiser preferences obtained from advertisers 125. These segregated models, customized ad lists, and default ad lists are sent to each digital sign module or digital player at 230 for display on the digital sign.
Another embodiment of the invention is now described with reference to flow chart 600 in
In one embodiment, object detection or recognition functionality is provided by an object detection algorithm that incorporates deformable parts modeling, such as the latent, i.e., hidden, Support Vector Machine (SVM) algorithm, operating in conjunction with a processor and the camera. Various types of objects may also be detected and differentiated, for example, a bicycle, a motorcycle, an automobile, or a tractor-trailer. In one embodiment of the invention, this object information, whether identification of an object, or the type of object identified, or both, may be used to train advertising models at 625.
Further, in one embodiment, features or characteristics of an object may be detected at 615. For example, a manufacturer of an automobile, or the identity or origin of a graphic symbol or trademark on a baseball cap may be detected. In one embodiment, an algorithm executed by a processor operating in conjunction with the camera provides feature recognition functionality. For example, key point recognition using a Ferns algorithm identifies a set of key points of an image and compares the set to a set of key points of a test image. In this manner, a logo, for example, on a car, or on a hat, can be identified. In one embodiment of the invention, this feature information relating to an object may also be used to train advertising models at 625.
Finally, an object may be tracked at 620 to determine a direction and speed of motion. For example, an object, such as a car, may be tracked over a series of consecutive images captured over fixed intervals of a period of time, to determine the direction and speed of direction of the object. In one embodiment, an algorithm executed by a processor operating in conjunction with the camera provides the object tracking function. For example, a Lucas Kanade algorithm may be used to track the object among the images. The algorithm can be used to determine the speed of each object in the series of images, as well as the average speed of objects appearing in the images, such as the average speed of vehicles appearing in the images. This average speed information may be used to estimate the approximate time that objects, e.g., cars, are going to come into a viewing range of an approaching digital sign. In one embodiment, this tracking information, whether direction of motion, or speed of motion, or both, may also be used to train the advertising models, and an appropriate advertisement from an advertisement playlist is selected at 630 by, and displayed on, the approaching the digital sign.
The data capture functionality may be embodied in software executed by the digital sign module, and in one embodiment of the invention, captures real time video analytic data that may be used by data mining module 110 to make real time predictions and schedule a digital advertisement for display, and/or may be used as historical data for generating rules (training advertising models) in the data mining module at 625.
In the data mining module, the advertising models are generated and trained (that is, refined) at 625 using the video analytic data based on well-known data mining algorithms, such as the Naïve Bayes algorithm, the Decision Trees algorithm, Logistic Regression analysis, and the Association Rules algorithm. In addition to using the video analytic data, the data mining module may also consider contextual information such as the weather conditions corresponding at the time the video analytic data was captured. Weather conditions data, or simply, weather data 135, may be maintained in a permanent store that can be accessed by data mining module 110. In one embodiment, the same permanent store may be used to store the video analytic data captured by the digital sign module 105 as well. Further, data mining module 110 receives as input a list of digital advertisements 125 available for display on the digital sign, and metadata associated the list of advertisements, such as the demographic characteristics of viewers to which advertisers wish to target their advertisements. Digital sign module 105 also supplies to the data mining module “proof-of-play” data, that is, advertising data indicating what ads were displayed by the digital sign, when those ads where displayed, and where those ads were displayed (e.g., by providing a device identifier (ID) for the digital sign that can be used as a basis for determining the location of the digital sign). In one embodiment of the invention, sales data 130, for example, from a Point-of-Sale terminal, may be input to data mining module 110. The sales data may be correlated with the VA data to gauge the effectiveness of an advertisement on a certain demographic group in terms of the sale of products or services featured in the advertisement.
The data mining module 110 generates at 625 trained advertising models which according to an embodiment of the invention are used to predict suitable advertising categories. Once a trained advertising model 115 is generated it is transmitted by the data mining module and received and stored by the content management system (CMS) 120 where along with advertising data, a customized advertising list is generated and stored at 630. In one embodiment, the CMS stores all trained advertising models, advertisement lists, advertiser preferences, and advertising data. CMS 120 then transmits the customized advertising list to digital sign module 105 for display. In one embodiment of the invention, digital sign module 105 comprises a digital signage media player module (digital player module) 145, which may be used to generate the advertising lists in real time. Module 145 operates as a condensed repository for information stored in the CMS, according to one embodiment of the invention.
The CMS obtains trained advertising models from the data mining module. In one embodiment, multiple digital sign modules 105, or multiple digital signage media players 145, or multiple digital display devices are installed. The CMS therefore will segregate the advertising models by digital sign module, or digital player, etc., as the case may be. The CMS generates segregated customized ad lists based on the advertising models and obtained advertising data. The CMS also generates offline ad lists, that is, default ad lists, based on advertiser preferences obtained from advertisers 125. These segregated models, customized ad lists, and default ad lists are sent to each digital sign module or digital player for display on the digital sign.
The point of targeted advertising is to show a future audience certain advertisements that have, or likely have, in the past been viewed for a reasonable amount of time by a previous audience having the same or similar demographics as the future audience. The process of targeted advertising according to an embodiment of the invention can be characterized in three phases and corresponding components of the digital advertising system according to an embodiment of the invention: learning, or training, advertising models in the data mining module 110, creating customized ad lists, or playlists, in the CMS 120, and playing the playlists with a digital sign module 105.
Data mining technology involves exploring large amounts of data to find hidden patterns and relationships between different variables in the dataset. These findings can be validated against a new dataset. A typical usage of data mining is to use the discovered pattern in the historical data to make a prediction regarding new data. In embodiments of the invention, the data mining module 110 is responsible for training and querying advertising models. In particular, two types of advertising models are generated, an advertising category (ad category) model, and a passer pattern model. In the ad category model, a set of rules is correlated with the most appropriate ad category for a particular audience or context (e.g., time, location, weather).
Video analytic data 305, according to one embodiment of the invention, comprises the date and time a particular digital advertisement was displayed on the digital sign, as well the day the ad was displayed, a device ID or alternatively a display ID that indicates a location at which the ad was displayed. Sensor input may also provide the amount of time that the digital ad was viewed while being displayed on the digital display device, in one embodiment. Finally, an indication of the potential target viewership based on characteristics such as age and gender is included.
Advertising data 310, received by data mining module 110 from the advertisements repository 125, includes the date and time a particular digital advertisement was scheduled for display on the digital sign, as well a device ID or alternatively a display ID that indicates a location at which the ad was scheduled to be displayed, and a duration or length of the digital advertisement, in seconds. Weather data 315 includes the date, temperature, and conditions on or around the date and time the digital advertising was displayed on the digital sign.
After the advertising models are generated by data mining module 110, the models are transferred to the Content Management System (CMS) 120. The CMS then extracts the ad categories from the ad category models and creates an ad category list. The advertising data corresponding to these ad categories are then retrieved from a permanent store, such as a database, accessible to CMS 120. Based on the ad category list, CMS 120 also creates advertisement lists. In one embodiment of the invention, a generated ad list may be modified based on advertiser input at 125. In one embodiment, each advertiser is assigned a certain priority that can be used as a basis for rearranging the ad list.
Referring again to
The ad list generator 420 fetches ads for the categories that are scheduled for a particular day, along with the advertising data. The tentative play list generator module 435 analyzes the ad list and generates a tentative play list that is sent to the advertiser input scheduler 430. Generator 420 compiles a play list based on arranged advertising categories, and an advertising list. The selection of advertisements is based on the roulette-wheel selection, according to one embodiment, where each advertisement is randomly picked based on a probability. The advertiser input scheduler module 420 fetches advertiser input and incorporates advertiser preferences in the tentative play list to generate the default play list which is sent to the digital sign module.
The ad refresh module 405 checks for new advertisements by comparing the versions maintained in a permanent store, e.g., a database, accessible to the CMS against versions obtained from the advertisements repository. If a new version of an advertisement is found then the actual advertisements (video files) are transferred to the digital sign module. If new ads (ads which were not present earlier in the ad repository) are present then module 405 fetches advertising data from SQL server DB 440 and sends such to the digital sign module 105. In one embodiment, proof-of-play analyzer 410 keeps track of which advertisements were played, at what time, at what location and who were the audience for those advertisements.
CMS 120 transfers the ad list through communication link 140 to the digital sign module 105. In one embodiment, digital sign module generates a default playlist by extracting file directory path information from the ad list and then retrieving the corresponding advertisements from an advertisements repository 125 that holds the advertisement files. The digital sign module operates in both an online and an offline mode. In the offline mode, the default playlist is played to the digital sign. The playlist for the online mode is generated using the real time VA data described below with reference to
The video analytic (VA) analyzer (predictor) module 510 fetches real time VA data 505 and retrieves passer pattern models from CMS 120 to predict VA data 510. The predicted VA data 510 is sent to model analyzer module 515. The model analyzer module 515 receives the predicted VA data as input and retrieves ad category models from CMS 120 and extracts an advertising category based on the predicted VA data. In one embodiment, confidence values of the passer pattern model and the ad category model are multiplied to generate a multiplied confidence value. If the multiplied confidence value is greater than a threshold, then an advertisement for the extracted advertising category is sent to the tentative play list generator 520, otherwise the digital sign module continues in an offline mode. The tentative play list generator module 520 retrieves an advertising list from CMS 120 and generates the tentative play list by considering the advertising category from the model analyzer and sends the tentative play list to online mode.
Scheduler module 525 contains the three sub-modules: an online sub-module 530 that selects an advertisement based on a probability distribution and associates it with an actual advertisement that is then scheduled and sent to display at 545; an offline sub-module 535 that selects an advertisement from a default play list based on the scheduling time and associates it with an actual advertisement that is then scheduled and sent to display at 545; and a preference sub-module 540 that checks for an advertiser preference and schedules an advertiser preferred advertisement for display at 545.
According to an embodiment of the invention, viewers are targeted in real time. The real time processing takes place at the digital sign module. Each digital sign module receives both an advertising category as well as passer pattern models from the CMS. Broadly speaking, according to one embodiment, a plurality of viewers is detected, the demographics of those viewers are analyzed, and viewing patterns for those viewers is collected. Based thereon, advertisements are targeted to the digital sign module. In one embodiment, the passer pattern model has a parameter referred to as the confidence value that indicates whether to play digital advertisements in online mode or offline mode. Thus, when the AVA data is analyzed in real time mode, the rules from the passer pattern model are chosen and the confidence value attached to these rules is compared with a threshold value. If the confidence value falls short of the threshold, then the default playlist is played, but if the value is the same or greater than the threshold, then the advertisements list is modified and advertisements targeting current viewers are played. After the current advertisement is played, either the digital sign module can return to playing the default playlist or could continue playing targeted advertisements.
Data mining technology involves exploring large amounts of data to find hidden patterns and relationship between different variables in the dataset. Embodiments of the invention use data mining algorithms to discover the patterns on viewing behaviors of the audience. The basic idea is to show a future audience certain ads that have in the past been viewed for a reasonable amount of time by the audience belonging to the same demographics.
For the purpose of capturing the patterns contained in the viewership data, two embodiments are used to retrain the advertising models: regular retraining and on demand retraining. Regular retraining is triggered regularly, such as weekly or monthly. On-demand retraining is triggered when the performance of the advertising models is lower than a predefined threshold or a retaining request is received from users or operators. In one embodiment, to fully take use of the advantages of different data mining algorithms, multiple data mining algorithms, including Decision Tree, Association Rule and Naive Bayes, and Logistic Regression analysis are used to train advertising models in parallel. The best advertising model or multiple advertising models is used for ad selection.
Seeing based targeting refers to targeting an audience based on the digital sign “seeing” the audience. Demographic information is obtained from the digital sign's sensor, such as one or more front-facing cameras proximate the digital display device. The sensor, and AVA software coupled with processors provide embodiments to anonymously detect the number of viewers, their gender, and their age bracket, and then adapt ad content based on that information. For example, if three young females and one senior male are seen passing by the digital sign, then the advertising models are queried using this information as input, and the most appropriate ad is selected to play.
Prediction based targeting first predicts the viewers, or passers, arriving at the digital sign in a future period of time and then targets them. For example, if it is predicted that three young females and one senior male will pass by the digital sign within the next 20 seconds, then an appropriate ad, for example, the most appropriate ad, is selected per the advertising models and prepared to play.
Context based targeting targets ads depending on the context, such as date/time, digital sign location, weather information, etc. For example, on a clear Wednesday morning between 9 AM and 11 AM during November and December, an ad for senior males may be selected to play on a particular digital sign according to the advertising models. This embodiment is useful when the passer type prediction based targeting is not reliable or no passer patterns are, or can be, discovered from the viewership data.
The following examples pertain to further embodiments of the invention.
One embodiment involves a method for selecting when to display one of a plurality of advertisements on a digital sign, comprising: gathering video analytics data from a plurality of objects that pass by a sensor; analyzing the gathered video analytics data to determine a type for each of the objects; training advertising models based on the determined types; and selecting an advertisement from a plurality of advertisements for display on the digital sign based on the trained advertising models. According to the embodiment, one of a plurality of viewers is associated with a respective one of the plurality of objects that pass by the sensor, and wherein selecting the advertisement for display on the digital sign comprises selecting the advertisement for display on the digital sign for viewing by a viewer associated with the object. One embodiment further comprises analyzing the gathered video analytics data for the purpose of determining a feature for each of the objects, and further training advertising models based on the determined features. One embodiment further comprises analyzing the gathered video analytics data to determine a direction of motion for each of the objects, and further training the advertising models based on the direction of motion of the objects.
One embodiment of the invention comprises analyzing the gathered video analytics data to determine a speed for each of the objects, and further training the advertising models based on the speed of the objects.
One embodiment of the invention further comprises receiving advertiser preferences as to which advertisement to display on the digital sign, and wherein selecting the advertisement for display on the digital sign based on the trained advertising models comprises selecting the advertisement for display based on the trained advertising models and the advertiser preferences.
One embodiment of the invention further comprises receiving advertising data corresponding to the advertisements displayed on the digital sign; and wherein training advertising models based on the determined types comprises training advertising models based on the determined types and the advertising data. The advertising data comprises a date and time, a display location, an ad category, and a duration or length or time for each advertisement displayed on the digital sign.
According to one embodiment, the digital advertising system comprises an input to receive a plurality of digital advertisements; an output via which to transmit the digital advertisements for display on a digital sign module; a plurality of objects that pass by a sensor and generate trained advertising models based on the video analytics data according to a data mining algorithm; and a content management system module coupled to the data mining module to receive the trained advertising models, and to the input to receive the plurality of digital advertisements, the content management system to generate and transmit to the digital sign module a subset of the plurality of advertisements for display based on the trained advertising models and the plurality of digital advertisements.
One embodiment further comprises an advertisements module coupled to the input to provide the plurality of digital advertisements. One embodiment further comprises the digital sign module coupled to the output to receive the digital advertisements, the digital sign module to display the digital advertisements and to capture and transmit to a permanent store the video analytics data.
One embodiment further comprises the data mining module coupled to the permanent store to retrieve the video analytics data. The data mining module generates trained advertising models based on the video analytics according to one of a number of well-known data mining algorithms including a Naïve Bayes, a Decision Trees, and an Association Rules, data mining algorithm.
In one embodiment, the digital sign module comprises a digital sign player module in which to store, and from which to transmit to a digital display screen, the subset of the plurality of advertisements for display.
In one embodiment the input further receives advertiser preferences as to which advertisement to transmit to the digital sign, and the content management system generates and transmits to the digital sign module a subset of the plurality of advertisements for display based on the trained advertising models, the plurality of digital advertisements, and the advertiser preferences.
According to one embodiment, the data mining module couples to the digital sign module to retrieve video analytics data and advertising data corresponding to display of the advertisements transmitted for display to the digital sign, and generates trained advertising models based on the video analytics data and the advertising data according to the data mining algorithm.
In one embodiment, the video analytics data comprises one or more object types and features. The one or more object types and features comprises a type of vehicle, a make of a vehicle, a model of a vehicle, a direction of motion of the vehicle, and a speed at which the vehicle moves in the direction of motion.
In one embodiment, the video analytics data further comprises one or more of a date and time, a day-of-the-week, a timeslot, and a location.
In this description, numerous details have been set forth to provide a more thorough explanation of embodiments of the present invention. It should be apparent, however, to one skilled in the art, that embodiments of the present invention may be practiced without these specific details. In other instances, well-known structures and devices have been shown in block diagram form, rather than in detail, in order to avoid obscuring embodiments of the present invention.
Some portions of this detailed description are presented in terms of algorithms and symbolic representations of operations on data within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.
It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise, as apparent from this discussion, it is appreciated that throughout the description, discussions utilizing terms such as “processing” or “computing” or “calculating” or “determining” or “displaying” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such
Embodiments of present invention also relate to apparatuses for performing the operations herein. Some apparatuses may be specially constructed for the required purposes, or it may comprise a general purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, DVD-ROMs, and magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, NVRAMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus.
The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform the required method steps. The required structure for a variety of these systems appear from the description herein. In addition, embodiments of the present invention are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the invention as described herein.
A machine-readable medium includes any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer). For example, a machine-readable medium includes read only memory (“ROM”); random access memory (“RAM”); magnetic disk storage media; optical storage media; flash memory devices; etc.
Whereas many alterations and modifications of the embodiment of the present invention will no doubt become apparent to a person of ordinary skill in the art after having read the foregoing description, it is to be understood that any particular embodiment shown and described by way of illustration is in no way intended to be considered limiting. Therefore, references to details of various embodiments are not intended to limit the scope of the claims that recite only those features regarded as essential to the invention.
| Filing Document | Filing Date | Country | Kind | 371c Date |
|---|---|---|---|---|
| PCT/US12/32417 | 4/5/2012 | WO | 00 | 9/4/2013 |
