Abstract:
An integrated wireless location and facial/speaker-recognition system that provides distinct advantages over facial-recognition systems and speaker-recognition systems of the prior art is disclosed. The integrated system is capable of using information from a wireless location system to improve the performance of the facial recognition and speaker recognition. The system is capable of processing photographs and/or audio samples captured by a camera/microphone at a fixed location (e.g., a digital pan-zoom-tilt (PZT) surveillance camera, etc.) as well as those captured by a mobile camera/microphone (e.g., a digital camera and microphone in a smartphone, etc.). The system also features a feedback mechanism by which the location-informed results can be used to improve the system&#39;s recognition abilities.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Patent Application No. 61/363,777, filed Jul. 13, 2011, entitled “Wireless Location System Control of Surveillance Cameras,” which is also incorporated by reference. 
    
    
     FIELD OF THE INVENTION 
     The present invention relates to data-processing systems in general, and, more particularly, to an integrated wireless location and facial/speaker recognition system. 
     BACKGROUND OF THE INVENTION 
     A facial-recognition system is a data-processing system that employs one or more algorithms for identifying a person from a digital image or video frame. Many facial -recognition algorithms are based on identifying and quantifying distinguishing features of a person&#39;s face, such as the relative position, size, and shape of the eyes, the nose, the cheekbones, the jaw, and so forth. 
     A speaker-recognition system is a data-processing system that employs one or more algorithms for identifying a person from an audio voice sample. Many speaker-recognition systems employ a library of voice prints (also known as templates or models) that are obtained by extracting features from a recording of a person&#39;s voice. In such systems, a speaker-recognition algorithm attempts to identify an unknown speaker by determining a best match between an audio sample of his or her voice and a voice print in the library. Speaker-recognition algorithms employ a wide variety of techniques for generating and processing voice prints, including frequency estimation, hidden Markov models, Gaussian mixture models, and decision trees. 
     SUMMARY OF THE INVENTION 
     The present invention comprises an integrated wireless location and facial/speaker-recognition system that provides distinct advantages over facial-recognition systems and speaker-recognition systems of the prior art. In particular, the integrated system is capable of using information from a wireless location system to improve the performance of the facial recognition and speaker recognition. For example, suppose that the facial-recognition system identifies a photograph of a gentleman as either John Smith or Bob Walker, and the photograph is captured by a real-time surveillance camera in New York&#39;s Times Square. Suppose further that the estimated location of John Smith&#39;s smartphone (obtained, perhaps, via the Global Positioning System [GPS] or some other technique) is in Manhattan at 43 rd  Street and Seventh Avenue, while the estimated location of Bob Walker&#39;s smartphone is in Marshall, Tex. It is overwhelmingly more likely, then, that the person in the photograph is John Smith. 
     In accordance with the illustrative embodiment of the present invention, the integrated wireless location and facial/speaker-recognition system is capable of processing photographs and/or audio samples captured by a camera/microphone at a fixed location (e.g., a digital pan-zoom-tilt (PZT) surveillance camera, etc.) as well as those captured by a mobile camera/microphone (e.g., a digital camera and microphone in a smartphone, etc.). The illustrative embodiment also provides a feedback mechanism by which the facial-recognition and speaker-recognition systems can “learn” from the location-informed results and improve their recognition abilities. 
     The illustrative embodiment comprises: receiving, by a data-processing system from a facial-recognition system, an assessment A of a photograph taken by a camera, wherein the assessment A comprises a non-empty set of candidate user identifiers, and wherein the photograph is of one or more locations; transmitting, by the data-processing system, one or more of the non-empty set of candidate user identifiers to a wireless location system; receiving, by the data-processing system from the wireless location system, a non-empty set of estimated locations, wherein each of the non-empty set of estimated locations is for a respective wireless terminal that is associated with a respective one of the non-empty set of candidate user identifiers; and generating, by the data-processing system, a new assessment A′ that is based on: (i) the assessment A, (ii) the non-empty set of estimated locations, and (iii) the one or more locations photographed by the camera. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  depicts a block diagram of the salient components of integrated wireless location and facial/speaker-recognition system  100 , in accordance with the illustrative embodiment of the present invention. 
         FIG. 2  depicts a block diagram of the salient components of master data-processing system  104 , as shown in  FIG. 1 , in accordance with the illustrative embodiment of the present invention. 
         FIG. 3  depicts a flowchart of the salient tasks of master data-processing system  104 , in accordance with the illustrative embodiment of the present invention. 
         FIG. 4  depicts a detailed flowchart of task  325 , as shown in  FIG. 3 , in accordance with the illustrative embodiment of the present invention. 
         FIG. 5  depicts a detailed flowchart of task  340 , as shown in  FIG. 3 , in accordance with the illustrative embodiment of the present invention. 
         FIG. 6  depicts a detailed flowchart of task  370 , as shown in  FIG. 3 , in accordance with the illustrative embodiment of the present invention. 
         FIG. 7  depicts a detailed flowchart of task  390 , as shown in  FIG. 3 , in accordance with the illustrative embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     For the purposes of this specification, the following terms and their inflected forms are defined as follows:
         The term “location” is defined as a zero-dimensional point, a one-dimensional line, a two-dimensional area, or a three-dimensional volume.       

       FIG. 1  depicts a block diagram of the salient components of integrated wireless location and facial/speaker-recognition system  100 , in accordance with the illustrative embodiments of the present invention. As shown in  FIG. 1 , integrated wireless location and facial/speaker-recognition system  100  comprises wireless location system  101 , facial-recognition system  102 , speaker-recognition system  103 , and master data-processing system  104 , interconnected as shown. 
     Wireless location system  101  is a system that comprises hardware and software and that is capable of estimating the location of a plurality of wireless terminals (not shown in  FIG. 1 ), of receiving location queries from master data-processing system  104 , and of reporting location estimates to master data-processing system  104 . As is well-known in the art, wireless location system  101  might be based on any one of a variety of technologies, such as radio frequency (RF) fingerprinting, Global Positioning System (GPS), triangulation, and so forth. 
     Facial-recognition system  102  is a data-processing system that comprises hardware and software that is capable of receiving a digital image of a person&#39;s face and of estimating the identity of the person, in well-known fashion. In accordance with the illustrative embodiment of the present invention, facial-recognition system  102  estimates the identity of the person by generating an assessment that comprises one or more ordered pairs (person-ID, p), where person-ID is an identifier of a person (e.g., a social security number, an email address, etc.) and p is a probability. For example, an assessment by facial-recognition system  102  of an image might be:
 
{(111-11-1111, 0.6), (222-22-2222, 0.3), (333-33-3333, 0.1) }
 
     As will be appreciated by those skilled in the art, in some embodiments of the present invention facial-recognition system  102  might generate another type of assessment (e.g., a single identifier of a person without any probability, a ranked plurality of person identifiers without any probabilities, an unranked plurality of person identifiers without any probabilities, etc.), and it will be clear to those skilled in the art, after reading this disclosure, how to make and use such alternative embodiments. 
     In accordance with the illustrative embodiment, facial-recognition system  102  employs a pattern classifier (e.g., a decision tree, a neural network, etc.) that has been trained on a set of photographs. As will be appreciated by those skilled in the art, in some other embodiments of the present invention facial-recognition system  102  might employ some other type of algorithm or technology to generate assessments. 
     Speaker-recognition system  103  is a data-processing system that comprises hardware and software that is capable of receiving a digital audio sample of a person speaking and of estimating the identity of the person, in well-known fashion. In accordance with the illustrative embodiment of the present invention, speaker-recognition system  103  estimates the identity of the person by generating an assessment similar in form to that of facial-recognition system  102  (i.e., a set of one or more ordered pairs (person-ID, p)). 
     As will be appreciated by those skilled in the art, in some embodiments of the present invention speaker-recognition system  103  might generate another type of assessment (e.g., a single identifier of a person without any probability, a ranked plurality of person identifiers without any probabilities, an unranked plurality of person identifiers without any probabilities, etc.), and it will be clear to those skilled in the art, after reading this disclosure, how to make and use such alternative embodiments. 
     In accordance with the illustrative embodiment, speaker-recognition system  103  employs a pattern classifier (e.g., a decision tree, a neural network, etc.) that has been trained on a set of audio samples. As will be appreciated by those skilled in the art, in some other embodiments of the present invention speaker-recognition system  103  might employ some other type of algorithm or technology to generate assessments. 
     Master data-processing system  104  is a data-processing system that is capable of receiving photographic data from one or more cameras (e.g., illustrative cameras  105 - 1  and  105 - 2  that are depicted in  FIG. 2 , etc.), of receiving audio data from one or more microphones (e.g., illustrative microphones  106 - 1  and  106 - 2  that are depicted in  FIG. 2 , etc.), of transmitting location queries to wireless location system  101 , of receiving location estimates of wireless terminals from wireless location system  101 , of transmitting digital images to facial-recognition system  102 , of transmitting digital audio samples to speaker-recognition system  103 , of receiving assessments from facial-recognition system  102  and speaker-recognition system  103 , and of executing the tasks described below and with respect to  FIGS. 3 through 7 . 
     As will be appreciated by those skilled in the art, although in the illustrative embodiment each of wireless location system  101 , facial-recognition system  102 , speaker-recognition system  103 , and master data-processing system  104  are distinct data-processing systems, in some other embodiments two or more of these systems might be part of a common data-processing system (e.g., might be software applications executing on a common hardware platform, etc.), and it will be clear to those skilled in the art, after reading this disclosure, how to make and use such alternative embodiments. 
     As will further be appreciated by those skilled in the art, the depiction of two cameras and two microphones in  FIG. 2  is merely illustrative (i.e., master data-processing system  104  is capable of receiving photographic data from any number of cameras and audio data from any number of microphones). As will yet further be appreciated by those skilled in the art, each camera (e.g., camera  105 - 1 , camera  105 - 2 , etc.) might be at a fixed location (e.g., a digital pan-zoom-tilt (PZT) surveillance camera, etc.) or might be a mobile camera (e.g., a camera in a smartphone, etc.); similarly, each microphone (e.g., microphone  106 - 1 , microphone  106 - 2 , etc.) might be at a fixed location (e.g., a surveillance microphone that is capable of being rotated around one or more axes, etc.) or might be a mobile microphone (e.g., a microphone in a smartphone, etc.). 
       FIG. 2  depicts a block diagram of the salient components of master data-processing system  104 , in accordance with the illustrative embodiment of the present invention. As shown in  FIG. 2 , master data-processing system  104  comprises processor  201 , memory  202 , and transceiver  203 , interconnected as shown. 
     Processor  201  is a general-purpose processor that is capable of performing the functionality described below and in the accompanying figures. For example, processor  201  is capable of receiving information from transceiver  203 , of reading data from and writing data into memory  202 , of executing instructions stored in memory  202 , and of forwarding information to transceiver  203 , in well-known fashion. As will be appreciated by those skilled in the art, in some alternative embodiments of the present invention processor  201  might be a special-purpose processor, rather than a general-purpose processor. 
     Memory  202  is capable of storing data and executable instructions, in well-known fashion, and might be any combination of random-access memory (RAM), flash memory, disk drive, etc. In accordance with the illustrative embodiment, memory  202  stores executable instructions corresponding to the pertinent tasks of the flowcharts of  FIGS. 3 through 7  below. 
     Transceiver  203  is capable of receiving signals from wireless location system  101 , facial-recognition system  102 , and speaker-recognition system  103 , and forwarding information encoded in these signals to processor  201 ; of receiving photographic and/or audio data and forwarding these data to processor  201 ; and of receiving information from processor  201  and transmitting signals that encode this information to wireless location system  101 , facial-recognition system  102 , and speaker-recognition system  103 , in well-known fashion. 
       FIG. 3  depicts a flowchart of the salient tasks of master data-processing system  104 , in accordance with the illustrative embodiment of the present invention. It will be clear to those skilled in the art, after reading this disclosure, which tasks depicted in  FIG. 3  can be performed simultaneously or in a different order than that depicted. 
     At task  310 , photographic and/or audio data is received by master data-processing system  104 . In accordance with the illustrative embodiment of the present invention, the photographic data comprises one or more photographs taken by a camera, as well as information about the camera (e.g., the location of the camera, the orientation of the camera, the field of view of the camera lens, etc.). As described in detail below and with respect to task  330 , the information about the camera enables master data-processing system  104  to determine what location(s) are photographed. As will be appreciated by those skilled in the art, in some other embodiments of the present invention the photographic data might explicitly specify the photographed location(s), either instead of, or in addition to, the camera information. 
     The audio data comprises one or more audio samples captured by a microphone, as well as information about the microphone (e.g., the location of the microphone, the orientation of the microphone, the type of microphone, etc.). As described in detail below and with respect to task  345 , the information about the microphone enables master data-processing system  104  to determine what location(s) are covered by the microphone. As will be appreciated by those skilled in the art, in some other embodiments of the present invention the audio data might explicitly specify these location(s), either instead of, or in addition to, the microphone information. 
     As will further be appreciated by those skilled in the art, in some embodiments of the present invention the photographic and/or audio data might be obtained from a remote data-processing system not depicted in the figures), while in some other embodiments of the present invention the camera/microphone apparatus might transmit these data directly to master data-processing system  104 . 
     At task  315 , Boolean variables flag 1  and flag 2  are set based on whether photographic data and audio data, respectively, were received at task  310 . 
     At task  320 , master data-processing system  104  tests variable flag 1 : if true, execution proceeds to task  325 , otherwise execution continues at task  335 . 
     At task  325 , master data-processing system  104  obtains an assessment A 1  of the photograph(s) received at task  310 . Task  325  is described in detail below and with respect to  FIG. 4 . 
     At task  330 , master data-processing system  104  determines what location(s) were photographed based on the camera information received at task  310 , in well-known fashion. As will be appreciated by those skilled in the art, in some other embodiments of the present invention the photographed location(s) might be specified in the photographic data, in which case this determination merely involves extracting this information from the photographic data, rather than solving any requisite equations. 
     At task  335 , master data-processing system  104  tests variable flag 2 : if true, execution proceeds to task  340 , otherwise execution continues at task  350 . 
     At task  340 , master data-processing system  104  obtains an assessment A 2  of the audio sample(s) received at task  310 . Task  340  is described in detail below and with respect to  FIG. 5 . 
     At task  345 , master data-processing system  104  determines what location(s) were covered by the microphone based on the microphone information received at task  310 , in well-known fashion. As will be appreciated by those skilled in the art, in some other embodiments of the present invention the covered location(s) might be specified in the audio data, in which case this determination merely involves extracting this information from the audio data, rather than solving any requisite equations. 
     At task  350 , master data-processing system  104  sets the value of variable A. If flag 1  is true and flag 2  is false, then assessment A 1  is copied to A; if flag 1  is false and flag 2  is true, then assessment A 2  is copied to A. If both flag 1  and flag 2  are true, then assessments A 1  and A 2  are combined in some fashion into a composite assessment A. As will be appreciated by those skilled in the art, there are a variety of ways in which assessments A 1  and A 2  might be combined (e.g., averaging two probabilities for a person and then re-normalizing across all candidates; using the maximum of two probabilities for a person and then re-normalizing across all candidates; using the minimum of two probabilities for a person and then re-normalizing across all candidates, etc.), and it will be clear to those skilled in the art, after reading this disclosure, how to make and use embodiments of master data-processing system  104  that perform task  350 . 
     At task  360 , variable S is initialized to the set of all candidates of assessment A, in well-known fashion. 
     At task  370 , master data-processing system  104  obtains estimated locations of the candidates of set S. Task  370  is described in detail below and with respect to  FIG. 6 . 
     At task  380 , master data-processing system  104  generates a new assessment A′ based on:
         (i) assessment A,   (ii) the estimated locations of candidates of set S, received at task  370 , and   (iii) the location(s) that were photographed and/or covered by the microphone.       

     As will be appreciated by those skilled in the art, the precise manner in which master data-processing system  104  generates a new assessment A′ at task  380  can vary considerably among implementations. For example, in some embodiments of the present invention, assessment A′ might be generated simply by removing any candidate from assessment A (i.e., “zeroing out” the candidate&#39;s probability and re-normalizing) whose estimated location is conclusively inconsistent with the location(s) that were photographed and/or covered by the microphone. Some other embodiments of the present invention, meanwhile, might employ a more sophisticated technique that is also capable of fine-tuning the probabilities of assessment A based on differences in location that are not as “flagrant.” 
     At task  390 , the assessment A′ generated at tasks  380  is fed back to facial-recognition system  102  and/or speaker-recognition system  103 . Task  390  is described in detail below and with respect to  FIG. 7 . After task  390 , execution continues back at task  310 . 
       FIG. 4  depicts a detailed flowchart of task  325 , in accordance with the illustrative embodiment of the present invention. 
     At subtask  410 , master data-processing system  104  transmits the photograph(s) received at task  310  to facial-recognition system  102 , in well-known fashion. 
     At subtask  420 , master data-processing system  104  receives an assessment A 1  from facial-recognition system  102 , in well-known fashion. After subtask  420 , execution continues at task  330  of  FIG. 3 . 
       FIG. 5  depicts a detailed flowchart of task  340 , in accordance with the illustrative embodiment of the present invention. 
     At subtask  510 , master data-processing system  104  transmits the audio sample(s) received at task  310  to speaker-recognition system  103 , in well-known fashion. 
     At subtask  520 , master data-processing system  104  receives an assessment A 2  from speaker-recognition system  103 , in well-known fashion. After subtask  520 , execution continues at task  345  of  FIG. 3 . 
       FIG. 6  depicts a detailed flowchart of task  370 , in accordance with the illustrative embodiment of the present invention. It will be clear to those skilled in the art, after reading this disclosure, which subtasks depicted in  FIG. 6  can be performed simultaneously or in a different order than that depicted. 
     At subtask  610 , variable T is initialized to the set of wireless terminal identifiers for the candidates of set S. In accordance with the illustrative embodiment, master data-processing system  104  maintains a hash table in memory  102  that associates candidate identifiers with wireless terminal identifiers. As will be appreciated by those skilled in the art, in some other embodiments the wireless terminal identifiers might be obtained in some other fashion (e.g., via queries to an external database, etc.), and it will be clear to those skilled in the art, after reading this disclosure, how to make and use such embodiments of the present invention. 
     At subtask  620 , variable L is initialized to the empty set, in well-known fashion. At the conclusion of task  370 , set L will contain the estimated locations of the candidates of set S. 
     At subtask  630 , master data-processing system  104  selects a wireless terminal identifier from set T and copies the identifier into variable t, in well-known fashion. 
     At subtask  640 , master data-processing system  104  transmits wireless terminal identifier t to wireless location system  101 , in well-known fashion. 
     At subtask  650 , master data-processing system  104  receives an estimated location y of wireless terminal identifier t from wireless location system  101 , in well-known fashion. 
     At subtask  660 , an ordered pair comprising:
         (i) the candidate identifier associated with wireless terminal identifier t, and   (ii) estimated location y is added to set L, in well-known fashion.       

     At subtask  670 , wireless terminal identifier t is removed from set T, in well-known fashion. 
     At subtask  680 , master data-processing system  104  tests whether set T is empty; if so, execution continues at task  380  of  FIG. 3 , otherwise execution continues back at subtask  630 . 
       FIG. 7  depicts a detailed flowchart of task  390 , in accordance with the illustrative embodiment of the present invention. It will be clear to those skilled in the art, after reading this disclosure, which subtasks depicted in  FIG. 7  can be performed simultaneously or in a different order than that depicted. 
     At subtask  710 , master data-processing system  104  tests variable flag 1 : if true, execution proceeds to subtask  720 , otherwise execution continues at subtask  740 . 
     At subtask  720 , master data-processing system  104  transmits assessment A′ to facial-recognition system  102 , in well-known fashion. 
     At subtask  730 , facial-recognition system  102  modifies one or more of its parameters and/or algorithms in view of new assessment A′ versus original assessment A 1 . In accordance with the illustrative embodiment, facial-recognition system  102  performs subtask  730  by re-training its pattern classifier based on the supplied photograph(s) and assessment A′. As will be appreciated by those skilled in the art, some other embodiments of the present invention might perform subtask  730  in some other fashion, while in still some other embodiments subtask  730  might not even be performed at all. 
     At subtask  710 , master data-processing system  104  tests variable flag 1 : if true, execution proceeds to subtask  720 , otherwise execution continues at subtask  740 . 
     At subtask  720 , master data-processing system  104  transmits assessment A′ to facial-recognition system  102 , in well-known fashion. 
     At subtask  730 , facial-recognition system  102  modifies one or more of its parameters and/or algorithms in view of new assessment A′ versus original assessment A 1 . In accordance with the illustrative embodiment, facial-recognition system  102  performs subtask  730  by re-training its pattern classifier based on the supplied photograph(s) and assessment A′. As will be appreciated by those skilled in the art, some other embodiments of the present invention might perform subtask  730  in some other fashion, while in still other embodiments subtask  730  might not even be performed at all. 
     At subtask  740 , master data-processing system  104  tests variable flag 2 : if true, execution proceeds to subtask  750 , otherwise execution continues back at task  310  of  FIG. 3 . 
     At subtask  750 , master data-processing system  104  transmits assessment A′ to speaker-recognition system  103 , in well-known fashion. 
     At subtask  760 , speaker-recognition system  103  modifies one or more of its parameters and/or algorithms in view of new assessment A′ versus original assessment A 2 . In accordance with the illustrative embodiment, speaker-recognition system  103  performs subtask  730  by re-training its pattern classifier based on the supplied audio sample(s) and assessment A′. As will be appreciated by those skilled in the art, some other embodiments of the present invention might perform subtask  760  in some other fashion, while in still other embodiments subtask  760  might not even be performed at all. 
     After subtask  760  has been completed, execution continues back at task  310  of  FIG. 3 . 
     As will be appreciated by those skilled in the art, after reading this disclosure, there are a variety of additional capabilities and features that can be integrated into some embodiments of the present invention. For example, in some embodiments facial-recognition system  102  might be part of a general imaged-based “person-recognition system” that is capable of recognizing bodily characteristics such as the size of a person&#39;s body, the shape of a person&#39;s body, the relative sizes of two or more parts of a person&#39;s body, hair color, hairstyle, and so forth, as well as more temporally-sensitive characteristics such as the type and color of a person&#39;s clothing, the presence (or absence) and type of accessories (e.g., hats, purses, briefcases, backpacks, wireless handsets, etc.), and so forth. As will be appreciated by those skilled in the art, many of these bodily characteristics can be more easily recognized than facial features at wider zooms, further distances, lower camera resolutions, poorer look angles or lighting, and so forth. 
     As another example, in some embodiments of the present invention, one or more of facial-recognition system  102 , speaker-recognition system  103 , and the aforementioned person-recognition system might make use of a historical sequence of locations from wireless location system  101 . For example, consider the case in which a particular person of interest (or “target”) is captured by a plurality of cameras (e.g., for example, by a plurality of surveillance cameras, etc.), but cannot be reliably identified due to the presence of extraneous persons in the images. Over time, however, the system might be able to recognize (e.g., via pattern classification, etc.) that a single person persists within the various images in common and therefore must be the target. The analysis of a sequence of target locations associated with various camera images or audio samples, therefore, could provide such embodiments with improved recognition capabilities. 
     As another example, in some embodiments of the present invention, one or more of facial-recognition system  102 , speaker-recognition system  103 , and the aforementioned person-recognition system might be presented with a set of images or audio samples in which a target whose appearance or voice is unknown is believed to be present among a plurality of persons (e.g., photographs of meetings, audio feeds from meetings, photographs of public spaces, etc.). In such embodiments, facial-recognition system  102  (or speaker-recognition system  103  or the aforementioned person-recognition system) might be capable of inferring which person is most likely to be the target based on their repeated appearance in the training photographs or audio samples. 
     As yet another example, in some embodiments of the present invention, integrated wireless location and facial/speaker-recognition system  100  might also have the ability to identify a person of interest (or “target”) in an image or audio sample that contains a plurality of persons. For example, if it is known that a target is present in a public square and a surveillance camera in the public square captures a photograph of a plurality of persons, such embodiments could ascertain which, if any, of the persons in the photograph is likely to be the target. 
     As still another example, in some embodiments of the present invention, wireless location system  101  might be capable of autonomously (i.e., without explicit queries from master data-processing system  104 ) tracking one or more persons of interest (or “targets”) and of issuing alerts to master data-processing system  104  when a target enters or exits a particular geographical area. In such embodiments, these alerts might prompt master data-processing system  104  to accordingly initiate capture and processing of video and audio associated with the geographical area (e.g., video feeds from surveillance cameras in the geographical area, etc.) 
     It is to be understood that the disclosure teaches just one example of the illustrative embodiment and that many variations of the invention can easily be devised by those skilled in the art after reading this disclosure and that the scope of the present invention is to be determined by the following claims.