Patent Application
20200202138
The field of the invention is data processing, or, more specifically, methods, apparatus, and products for advertisement delivery using audience grouping and image object recognition.
The development of the EDVAC computer system of 1948 is often cited as the beginning of the computer era. Since that time, computer systems have evolved into extremely complicated devices. Today's computers are much more sophisticated than early systems such as the EDVAC. Computer systems typically include a combination of hardware and software components, application programs, operating systems, processors, buses, memory, input/output devices, and so on. As advances in semiconductor processing and computer architecture push the performance of the computer higher and higher, more sophisticated computer software has evolved to take advantage of the higher performance of the hardware, resulting in computer systems today that are much more powerful than just a few years ago.
Advertisers may use public displays such as smart bulletin boards or electronic displays to present advertisements. It may be difficult to target advertisements to a particular audience.
Advertisement delivery using audience grouping and image object recognition may include: receiving image data; identifying, based on the image data, a plurality of image objects associated with a plurality of persons; generating, from the plurality of persons and based on the plurality of image objects, a plurality of clusters, each of the plurality of clusters comprising one or more persons of the plurality of persons; determining a classification for a cluster of the plurality of clusters; determining an advertisement associated with the classification; and sending the advertisement to an advertising platform based on a location of the cluster.
The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular descriptions of exemplary embodiments of the invention as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts of exemplary embodiments of the invention.
Exemplary methods, apparatus, and products for advertisement delivery using audience grouping an image object recognition in accordance with the present invention are described with reference to the accompanying drawings, beginning with
The image data captured by the cameras 102 is received by a server 106 via a network 108. The cameras 102 may each be configured with a direct connection to the network 108 (e.g., a wired or wireless connection) and may accordingly communicate directly with the server 106. The cameras 102 may also be connected to an intermediary server or other computing device that aggregates the image data for sending to the server 106. The network 108 may comprise one or more Local Area Networks (LANs), Wide Area Networks (WANs), Personal Area Networks, mesh networks, cellular networks, internets, intranets, or other networks and combinations thereof. The network 108 may comprise one or more wired connections, wireless connections, or combinations thereof.
After receiving the image data, the server 106 identifies a plurality of image objects associated with the plurality of persons. Identifying the plurality of image objects may comprise processing the image data using a trained classifier, such as a neural network (e.g., a convolutional neural network (CNN) or deep learning neural network), an untrained classifier, or other machine learning model or computer vision algorithm. Identifying the plurality of image objects may include identifying a plurality of person image objects (e.g., image objects corresponding to a particular person 104) and a plurality of component image objects of the plurality of person image objects (e.g., image objects within a particular person image objects). For example, a person image object may correspond to a particular person 104, while a component image object for that person 104 may include individual articles of clothing, detectable logos, a face, held physical objects, etc. Thus, the plurality of image objects may comprise both person image objects and associated component image objects.
The server 106 may then generate a plurality of clusters 110a,b,c from the persons 104 such that each cluster 110a,b,c, indicates one or more persons 104. Generating the plurality of clusters 110a,b,c serves to identify persons 104 likely to be associated with a same group, demographic, or other targetable audience. Generating the plurality of clusters 110a,b,c may comprise applying a clustering algorithm (e.g., k-means clustering or another clustering algorithm) to each person 104 based on a plurality of attributes based on their associated image objects. Such attributes can include a worn color or pattern, worn styles of clothing, a worn logo, a held object, etc. Accordingly, clusters 110a,b,c may be generated such that persons 104 having similar attributes are grouped into a same cluster 110a,b,c. For example, persons 104 wearing a same sports team logo or color scheme are likely to be part of the same group.
Additional attributes may also be derived from the image data to generate the plurality of clusters 110a,b,c. A proximity between persons 104 (e.g., a distance between person image objects) may be used as an attribute to generate clusters 110a,b,c. For example, persons 104 close together are likely to be part of the same group, and therefore part of the same cluster 110a,b,c. As another example, image data captured over time can indicate a movement direction of persons 104 based on a change in position of their respective person image objects. Persons 104 moving in the same direction are more likely to be part of the same group, and therefore more likely to be included in the same cluster 110a,b,c. As a further example, the cameras 102 may also capture audio data. Language or keyword detection may be applied to the audio data to determine that persons 104 are speaking the same language or discussing a same topic, thereby increasing the likelihood that they should be included in the same cluster 110a,b,c.
Moreover, attributes used in generating the clusters 110a,b,c may be derived from metadata associated with particular image objects. For example, image objects for a particular article of clothing or held item may be associated with a particular retail price. The retail prices of worn or held items may be used to calculate an estimated income bracket. The estimated income bracket may then be used as an attribute for generating the clusters 110a,b,c.
After generating the clusters 110a,b,c, a classification of one or more of the clusters 110a,b,c may be determined. A classification may be determined for each of the clusters 110a,b,c, or for one or more clusters 110a,b,c, selected from the plurality of clusters 110a,b,c. Determining a classification for a particular cluster 110a,b,c may comprise applying a classifier to one or more classification attributes of the particular cluster 110a,b,c. The classifier may then determine a classification of a plurality of predefined classifications for the particular cluster 110a,b,c.
The classification attributes used to classify a particular cluster 110a,b,c may comprise those attributes used to generate the clusters 110a,b,c (e.g, attributes associated with the plurality of image objects associated with the particular cluster 110a,b,c). For example, the classification attributes may include visible logos, color schemes, types of clothing, held objects, movement direction, etc. The classification attributes may also include attributes associated with audio data (e.g., spoken languages, topics of discussion, etc.). The classification attributes may also include a number of persons 104 in the particular cluster 110a,b,c or a location of the particular cluster 110a,b,c (e.g., latitude and longitude, or within a predefined boundary or geofence).
A given classification attribute may not be applicable to all persons 104 in the particular cluster 110a,b,c. For example, each person 104 in a particular cluster 110a,b,c may be wearing different colors, but are nonetheless clustered together due to similarities in other attributes (e.g., movement direction, proximity, type of clothing). The classification attributes for a particular cluster 110a,b,c may reflect all values for a particular classification attribute reflected in the particular cluster 110a,b,c. For example, assume a cluster 110a,b,c includes persons wearing suits, dresses, and gym clothing. The cluster 110a,b,c may then be classified using the “clothing type” attribute for each value “suits,” “dresses,” and “gym clothing.”
The classifier may weigh each value of a particular classification attribute according to a degree to which it is reflected in the cluster 110a,b,c. For example, assuming that a majority of persons 104 in a cluster 110a,b,c are wearing suits, the classifier may weigh the “suits” value for the “clothing type” attribute higher than other values. The classifier may also filter values for classification attributes. For example, again assuming that persons 104 in a cluster 110a,b,c are wearing either suits, dresses, or gym clothing, the classifier may only classify the cluster 110a,b,c based on the clothing type value applicable to the majority of persons 104 in the cluster 110a,b,c.
Determining the classification for a cluster 110a,b,c may comprise selecting a data structure entry from a plurality of data structure entries each corresponding to a respective classification. For example, the plurality of data structure entries may each indicate a respective plurality of classification attributes. Each data structure entry may also comprise one or more relevant topics for the particular category. Determining the classification for a cluster 110a,b,c may comprise selecting, from the plurality of data structure entries, a data structure based on a degree of similarity (e.g., cosine similarity) between the classification attributes of the data structure and the classification attributes of the cluster 110a,b,c.
The server 106 may select a cluster 110a,b,c for targeting with an advertisement. Selecting a cluster 110a,b,c may be performed based on a number of persons 104 in the cluster 110a,b,c (e.g, selecting a cluster 110a,b,c with the most persons 104). Selecting a cluster 110a,b,c may also be performed based on a movement speed or detectable facial expression of the persons 104 in the cluster 110a,b,c. For example, a fast-moving cluster 110a,b,c or a cluster 110a,b,c with persons 104 expressing irritation, anger, etc. may be less preferentially selected as the audience may be less receptive to advertisements at that time. Selecting a cluster 110a,b,c may also be performed based on whether the cluster 110a,b,c was classified in a predefined classification. For example, a classification may correspond to a demographic selected for preferential targeting. A cluster 110a,b,c that was classified into this classification may be preferentially selected.
One skilled in the art would understand that a combination of factors may be used and/or weighted in determining a cluster 110a,b,c for targeting. Additionally, one skilled in the art would appreciate that a cluster 110a,b,c may be selected before or after classification. For example, a cluster 110a,b,c may be selected for targeting before classification based on factors independent of the classification (e.g., a number of persons 104 in the cluster 110a,b,c, a movement direction or speed), and then subsequently classified. This approach saves on computational resources that would otherwise be used to classify clusters 110a,b,c that may not be ultimately selected for targeting. As another example, a cluster 110a,b,c may be selected after each of the clusters 110a,b,c have been classified such that the classification may be used as a factor in selecting a cluster 110a,b,c for targeting.
An advertisement associated with the classification may be determined (e.g, an advertisement associated with the classification of a cluster 110a,b,c selected for targeting) by the server 106. The classification may be associated with one or more topics of interest. The advertisement may be determined based at least in part on the associated one or more topics of interest. The advertisement may also be selected based on one or more business rules (e.g., predicted revenue, advertising placement fees, advertisement testing purposes, etc.). The advertisement may comprise one or more still images, audio, video, or other content as can be appreciated.
The determined advertisement may then be sent by the server 106 to an advertising platform 112. The advertising platform 112 may comprise a stationary or immobile advertising platform, such as an electronic billboard, an e-ink advertising display, etc. Sending the advertisement to the advertising platform 112 causes the advertising platform 112 to render and/or display the advertisement. The server 106 may instruct the advertising platform 112 until a predefined event occurs (e.g., as determined by the server 106 or the advertising platform 112).
For example, the server 106 may instruct the advertising platform 112 to display the advertisement for a predefined duration. As another example, the server 106 may instruct the advertising platform 112 to display the advertisement until the advertising platform 112 is out of a line of sight of the cluster 110a,b,c (e.g., as determined based on image data received from cameras 102).
Sending the advertisement to the advertising platform 112 may comprise determining an advertising platform 112 based at least on a location of the cluster 110a,b,c. Determining the advertising platform 112 may comprise determining an advertising platform 112 nearest to the cluster 110a,b,c. For example, predefined locations associated with the advertising platforms 112 can be compared to predefined locations of cameras 102 and/or predicted locations of the cluster 110a,b,c (e.g., based on triangulation using a plurality of cameras 102) to determine a nearest advertising platform 112.
Determining the advertising platform 112 may also be performed based on a direction of movement or line of sight of the cluster 110a,b,c. For example, a pool of advertising platforms 112 may be filtered so as to only include those advertising platforms 112 toward which the cluster 110a,b,c is moving, or those advertising platforms 112 that are within a line of sight of the cluster 110a,b,c (e.g., based on a movement direction and/or direction of facing indicated in the image objects). The advertising platform 112 may then be selected from the filtered pool of advertising platforms 112.
The server 106 may also send the advertisement to one or more mobile devices. For example, the advertisement may be sent to one or more mobile devices proximate to the advertising platform 112 based on a near-field communication link. The advertisement may also be sent to one or more mobile devices connected to a same network as the advertising platform 112, or connected to a same wireless access point as the advertising platform 112. Sending the advertisement to a mobile device may include sending the advertisement as a push notification, text message, email, or other message. Sending the advertisement to the mobile device may include embedding the advertisement as a popup or as embedded content in a web page accessed by the mobile device through a wireless network accessible to the server 106.
The server 106 may receive feedback associated with the presented advertisement. For example, the advertising platform 112 may include a touch screen interface or other user input device that allows a user to indicate whether they found the advertisement to be relevant, pleasing, etc. The server 106 may also receive feedback by determining feedback based on image data from the cameras 102 indicating actions of one or more persons 104 in the cluster 110a,b,c that was targeted. Movement speed may be used to indicate feedback. For example, a person 104 in the cluster 110a,b,c slowing down may indicate positive feedback, as the person 104 may have slowed down to view the advertisement. Conversely, a person 104 speeding up or not changing speed may indicate neutral or negative feedback.
Facial expressions or gestures captured as image objects can also indicate feedback. For example, a facial expression indicating pleasure (e.g., a smile or laugh) may indicate positive feedback, while a facial expression or gesture indicating displeasure (e.g., a scowl or shaking of the head) may indicate negative feedback. Additionally, a duration that a person 104 in the targeted cluster 110a,b,c looks at the advertising platform 112 may also indicate feedback. For example, a look duration satisfying a threshold may indicate positive feedback, while a look duration falling below a threshold may indicate negative feedback. To these ends, the advertising platform 112 may include cameras 102 so as to better capture image data showing the facial expressions, gestures, etc. of persons 104 in the targeted cluster 110a,b,c. This image data may then be sent to the server 106 for processing into image objects in order to determine the particular feedback.
Where the advertisement is sent to a mobile device, the server 106 may receive feedback in the form of an input to a web page or user interface displaying the advertisement. For example, a user may indicate whether an advertisement was relevant, subjectively good or bad, etc. Feedback may also comprise a duration that the advertisement was displayed or rendered by the mobile device. For example, a duration satisfying a threshold may indicate positive feedback, while a duration falling below a threshold may indicate negative feedback.
In response to receiving the feedback, the server 106 may modify one or more models. For example, the server 106 may modify a model used for generating the clusters 110a,b,c (e.g., a reinforced clustering model or a supervised learning clustering model). The server 106 may also modify a model used for classifying the clusters 110a,b,c (e.g., adjusting one or more parameters, weights, etc. of the classification model).
The arrangement of servers and other devices making up the exemplary system illustrated in
Advertisement delivery using audience grouping and image object recognition in accordance with the present invention is generally implemented with computers, that is, with automated computing machinery. In the system of
Stored in RAM 204 is an operating system 210. Operating systems useful in computers configured for advertisement delivery using audience grouping and image object recognition according to embodiments of the present invention include UNIX™, Linux™, Microsoft Windows™, AIX™, IBM's i OS™, and others as will occur to those of skill in the art. The operating system 210 in the example of
The computer 200 of
The example computer 200 of
The exemplary computer 200 of
For further explanation,
The method of
The method of
Generating the plurality of clusters 110a,b,c may also be based on additional attributes derived from the image data and/or image objects. A proximity between persons 104 (e.g., a distance between person image objects) may be used as an attribute to generate clusters 110a,b,c. For example, persons 104 close together (e.g., having proximate corresponding person image objects) are likely to be part of the same group, and therefore part of the same cluster 110a,b,c. As another example, image data captured over time can indicate a movement direction of persons 104 based on a change in position of their respective person image objects. Persons 104 moving in the same direction are more likely to be part of the same group, and therefore more likely to be included in the same cluster 110a,b,c. As a further example, the cameras 102 and/or other dedicated audio sensors may capture audio data. Language or keyword detection may be applied (e.g., by the server 106) to the audio data to determine that persons 104 are speaking the same language or discussing a same topic, thereby increasing the likelihood that they should be included in the same cluster 110a,b,c.
Moreover, attributes used in generating the clusters 110a,b,c may be derived from metadata associated with particular image objects. For example, image objects for a particular article of clothing or held item may be associated with a particular retail price. The retail prices of worn or held items may be used to calculate an estimated income bracket to be associated with a person 104. The estimated income bracket may then be used as an attribute for generating the clusters 110a,b,c.
The method of
The classification attributes used to classify a cluster 110a,b,c may comprise those attributes used to generate the clusters 110a,b,c (e.g, attributes associated with the plurality of image objects associated with the particular cluster 110a,b,c). For example, the classification attributes may include visible logos, color schemes, types of clothing, held objects, movement direction, etc. The classification attributes may also include attributes associated with audio data (e.g., spoken languages, topics of discussion, etc.). The classification attributes may also include a number of persons 104 in the particular cluster 110a,b,c or a location of the particular cluster 110a,b,c (e.g., latitude and longitude, or within a predefined boundary or geofence).
A given classification attribute may not be applicable to all persons 104 in the cluster 110a,b,c. For example, each person 104 in the cluster 110a,b,c may be wearing different colors, but are nonetheless clustered together due to similarities in other attributes (e.g., movement direction, proximity, type of clothing). The classification attributes for the cluster 110a,b,c may reflect all values for a particular classification attribute reflected in the cluster 110a,b,c. For example, assume a cluster 110a,b,c includes persons wearing suits, dresses, and gym clothing. The cluster 110a,b,c may then be classified using each value “suits,” “dresses,” and “gym clothing” for the “clothing type” classification attribute.
Determining the classification for the cluster 110a,b,c may comprise weighing each value of a particular classification attribute according to a degree to which it is reflected in the cluster 110a,b,c. For example, assuming that a majority of persons 104 in a cluster 110a,b,c are wearing suits, the classifier may weigh the “suits” value for the “clothing type” attribute higher than other values. The classifier may also filter values for classification attributes. For example, again assuming that persons 104 in a cluster 110a,b,c are wearing either suits, dresses, or gym clothing, the classifier may only classify the cluster 110a,b,c based on the clothing type value applicable to the majority of persons 104 in the cluster 110a,b,c.
The method of
The method of
Sending the advertisement to the advertising platform 112 may comprise determining an advertising platform 112 based at least on a location of the cluster 110a,b,c. Determining the advertising platform 112 may comprise determining an advertising platform 112 nearest to the cluster 112. For example, predefined locations associated with the advertising platforms 112 can be compared to predefined locations of cameras 102 and/or predicted locations of the cluster 110a,b,c (e.g., based on triangulation using a plurality of cameras 102) to determine a nearest advertising platform 112.
Determining the advertising platform 112 may also be performed based on a direction of movement or line of sight of the cluster 110a,b,c. For example, a pool of advertising platforms 112 may be filtered so as to only include those advertising platforms 112 toward which the cluster 110a,b,c is moving, or those advertising platforms 112 that are within a line of sight of the cluster 110a,b,c (e.g., based on a movement direction and/or direction of facing indicated in the image objects). The advertising platform 112 may then be selected from the filtered pool of advertising platforms 112.
For further explanation,
The server 106 may also receive feedback as facial expressions or gestures captured as image objects. For example, a facial expression indicating pleasure (e.g., a smile or laugh) may indicate positive feedback, while a facial expression or gesture indicating displeasure (e.g., a scowl or shaking of the head) may indicate negative feedback. Additionally, a duration that a person 104 in the targeted cluster 110a,b,c looks at the advertising platform 112 may also indicate feedback. For example, a look duration satisfying a threshold may indicate positive feedback, while a look duration falling below a threshold may indicate negative feedback. To these ends, the advertising platform 112 may include cameras 102 so as to better capture image data showing the facial expressions, gestures, etc. of persons 104 in the targeted cluster 110a,b,c. This image data may then be sent to the server 106 for processing into image objects in order to determine the particular feedback.
The method of
For further explanation,
For further explanation,
For further explanation,
One skilled in the art would understand that a combination of factors may be used and/or weighted in determining a cluster 110a,b,c for targeting. Additionally, although the flowchart of
In view of the explanations set forth above, readers will recognize that the benefits of advertisement delivery using audience grouping an image object recognition according to embodiments of the present invention include:
Exemplary embodiments of the present invention are described largely in the context of a fully functional computer system for advertisement delivery using audience grouping an image object recognition. Readers of skill in the art will recognize, however, that the present invention also may be embodied in a computer program product disposed upon computer readable storage media for use with any suitable data processing system. Such computer readable storage media may be any storage medium for machine-readable information, including magnetic media, optical media, or other suitable media. Examples of such media include magnetic disks in hard drives or diskettes, compact disks for optical drives, magnetic tape, and others as will occur to those of skill in the art. Persons skilled in the art will immediately recognize that any computer system having suitable programming means will be capable of executing the steps of the method of the invention as embodied in a computer program product. Persons skilled in the art will recognize also that, although some of the exemplary embodiments described in this specification are oriented to software installed and executing on computer hardware, nevertheless, alternative embodiments implemented as firmware or as hardware are well within the scope of the present invention.
The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.
The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.
Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.
Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.
These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.
It will be understood from the foregoing description that modifications and changes may be made in various embodiments of the present invention without departing from its true spirit. The descriptions in this specification are for purposes of illustration only and are not to be construed in a limiting sense. The scope of the present invention is limited only by the language of the following claims.
