Patent Publication Number: US-7225089-B2

Title: System and method for remote identification of energy consumption systems and components

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
   This application is a continuation of U.S. patent application Ser. No. 10/004,549 filed Dec. 4, 2001, now U.S. Pat. No. 6,937,946 and entitled “System and Method for Remote Identification of Energy Consumption Systems and Components.” 

   TECHNICAL FIELD OF THE INVENTION 
   This invention relates in general to the field of energy systems and, more particularly, to a system and method for remote identification of energy consumption systems and components. 
   BACKGROUND OF THE INVENTION 
   Schools, office buildings, homes, department stores, hospitals, and other types of facilities consume energy in varying amounts using a variety of different types of systems and components. For example, energy consumption systems and components may be used for environmental control, such as heating and cooling, for lighting, for security system applications, for computer usage applications, and for a variety of other energy consumption applications corresponding to the particular type of facility. 
   Because the types of facilities vary to a generally large degree, the energy usage associated with each type of facility also varies to a generally large degree. For example, energy consumption systems and components associated with homes are different than the energy consumption systems and components associated with an office building or hospital, and generally use less energy per unit of conditioned area than the systems and components of the office building or hospital. 
   Accordingly, because energy usage varies among different types of facilities, different energy consumption systems and components are designed to accommodate the various energy usage requirements of a particular facility. Additionally, in order to evaluate the efficiency of particular energy consumption systems or components, information associated with the facility and the energy consumption systems and/or components must be determined, as well as the amount of energy used by the particular energy consumption systems and/or components. Obtaining the required information, however, generally requires access to the facility and/or the energy consumption systems and components. Accordingly, obtaining the required information is generally expensive and time-consuming. 
   SUMMARY OF THE INVENTION 
   Accordingly, a need has arisen for an improved system and method of remotely identifying and analyzing energy consumption systems and components associated with a variety of facilities. The present invention provides a system and method for remote identification of energy consumption systems and components that addresses shortcomings and disadvantages associated with prior energy consumption system and component identification and analysis. 
   According to one embodiment of the present invention, a method for energy consumption system identification of a facility includes receiving aggregated energy consumption data associated with the facility and receiving external variable data for the facility corresponding to the aggregated energy consumption data. The method also includes generating facility data associated with the facility, and generating disaggregated energy consumption data from the aggregated energy consumption data using the facility data and the external variable data. The method further includes identifying an energy consumption system of the facility using the disaggregated energy consumption data. 
   According to another embodiment of the present invention, a system for energy consumption system identification of a facility includes a processor, and a memory coupled to the processor. The system includes an energy consumption database accessible by the processor and including aggregated energy consumption data associated with the facility. The system also includes a facility database accessible by the processor including facility data associated with the facility. The system includes an external variable database also accessible by the processor and including external variable data corresponding to the energy consumption data. The system further includes an analysis engine residing in the memory and executable by the processor. The analysis engine is operable to generate disaggregated energy consumption data using the aggregated energy consumption data, the facility data, and the external variable data. The analysis engine is further operable to identify an energy consumption system of the facility using the disaggregated energy consumption data. 
   The present invention provides several technical advantages. For example, according to one embodiment of the present invention, facility data is generated corresponding to a particular facility, such as the physical characteristics of the facility and/or energy usage characteristics associated with the particular type of facility. External variable data associated with the facility is also retrieved, such as the environmental conditions surrounding the facility during a particular period of time. Aggregated energy consumption data is also generated or retrieved associated with the particular facility for a particular time period. Using the aggregated energy data, the facility data, and the external variable data, identification of energy consumption systems and/or components associated with the facility may be determined remotely from the facility. Therefore, access to the facility and/or the energy consumption systems and components is generally not required, thereby substantially reducing the time and costs associated with system and component identification. 
   Another technical advantage of the present invention includes identifying operating parameters of energy consumption systems and/or components associated with a particular facility and determining whether modifications to the operating parameters should be performed to increase efficiency. For example, after identifying the energy consumption systems and/or components associated with a particular facility, the facility data, the aggregated and/or disaggregated energy consumption data, and/or the external variable data may be used to determine whether a modification to the operating parameters of a particular energy consumption system and/or component would increase efficiency and/or reduce energy usage and costs. For example, in the case of an office building or department store, the external variable data, the facility data, and the energy usage data may indicate that activation and deactivation of the energy consumption systems and/or components may be modified to predetermined times or intervals to maximize efficiency and reduce associated energy usage costs. 
   Other technical advantages are readily apparent to those skilled in the art from the following figures, descriptions, and claims. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     For a more complete understanding of the present invention, and the advantages thereof, reference is now made to the following description, taken in conjunction with the accompanying drawings, wherein like reference numerals represent like parts, in which: 
       FIG. 1  is a block diagram illustrating a system for remote identification of energy consumption systems and components in accordance with an embodiment of the present invention; 
       FIG. 2  is another block diagram illustrating the system for remote identification of energy consumption systems and components in accordance with an embodiment of the present invention; and 
       FIGS. 3A and 3B  are flow charts illustrating a method of remotely identifying energy consumption systems and components in accordance with an embodiment of the present invention. 
   

   DETAILED DESCRIPTION OF THE INVENTION 
     FIG. 1  is a block diagram in which a system  10  for remote identification of energy consumption systems and components in accordance with an embodiment of the present invention is illustrated. In the illustrated embodiment, system  10  is coupled to a target facility  12 , a comparable facility  14 , an energy supplier  16 , and an environmental service  18  via a communications network  20 . The communications network  20  may be different networks, or the same network, and may include any Internet, intranet, extranet, or similar communication network. The communications network  20  provides an electronic medium for transmitting and receiving information between the system  10  and facilities  12  and  14 , the environmental service  18 , and the energy supplier  16 . However, other electronic and non-electronic modes of communication may also be used for transmitting and receiving information between the system  10  and the facilities  12  and  14 , the environmental service  18 , and the energy supplier  16 . 
   The target facility  12  includes one or more energy consumption systems  30  such as, but not limited to, heating and cooling systems, lighting systems, computer systems, medical systems, product manufacturing systems, and/or a variety of other types of energy consumption systems. Accordingly, each energy consumption system  30  may include one or more discrete energy consumption components  32 . For example, a heating/cooling energy consumption system  30  may include energy consumption components  32  such as boilers, heat exchangers, fans, compressors, and other related components. Accordingly, depending on the type of energy consumption system  30 , the energy consumption components  32  relate to the function and operation of the particular energy consumption system  30 . 
   The target facility  12  may also include one or more data collectors  40  each coupled to or disposed proximate to one or more of the energy consumption systems  30  and/or components  32 . Each data collector  40  may also include or be coupled to a sensor  42  for determining energy consumption or usage corresponding to energy consumption systems  30  and the energy consumption components  32 . For example, each sensor  42  may be coupled to or disposed proximate to a corresponding energy consumption component  32  and/or system  30  to acquire energy consumption or other information associated with the operation and efficiency of a particular energy consumption system  30  and/or component  32 , such as, but not limited to, electrical usage, water flow rates, internal and external temperature data, internal and external humidity values, wind speed and direction, precipitation, and cloud conditions. Each sensor  42  may also include processing, memory, communication, and other functional capabilities for collecting, processing, manipulating, storing, and/or transmitting the acquired information associated with a particular energy consumption component  32  and/or system  30 . 
   Each data collector  40  may also include processing, memory, communication, and other functional capabilities for receiving, manipulating, processing, storing and/or transmitting the energy consumption and other information acquired by the sensors  42 . For example, each data collector  40  may receive, process and store energy usage and/or environmental information associated with a particular energy consumption system  30  and/or component  32  as energy consumption data  44 . The energy consumption data  44  may then be shared between one or more other data collectors  40 , transmitted to a central monitoring station, or otherwise stored, transferred and/or manipulated. 
   Facility  14  is generally a structure having similar or comparable energy consumption features as the target facility  12 . For example, facilities  12  and  14  may both be a hospital, an office building, a department store, or other type of structure having similar energy usage characteristics such that the energy usage characteristics of the facility  14  may be used to determine or approximate the energy usage characteristics for the target facility  12 . As described above in connection with the target facility  12 , the comparable facility  14  may also include one or more energy consumption systems  50  each comprising one or more energy consumption components  52 . Also as described above in connection with the target facility  12 , the comparable facility  14  may also include one or more data collectors  54 , each data collector  54  comprising or coupled to one or more sensors  56 . The data collectors  54  and sensors  56  may also be used to process and store energy usage information associated with the facility  14  as energy consumption data  58 . 
   The energy supplier  16  generally includes a utility company or other provider of energy services to businesses, homes, or other energy users. The energy supplier  16  generally includes an energy consumption database  60  containing aggregated energy consumption data  62  associated with each of the facilities  12  and  14 . The aggregated energy consumption data  62  may reflect energy usage as a function of time and expressed in a variety of different formats; however, the aggregated energy consumption data  62  may also include other energy-related information within the scope of the present invention. 
   The environmental service  18  comprises a weather service, meteorological service, or other service containing weather and/or environmental information, such as, but not limited to, the National Weather Service or other regional or local weather services or stations. The environmental service  18  generally includes an environmental database  70  containing environmental data  72  corresponding to particular periods of time and associated with the vicinity of the facilities  12  and/or  14 . The environmental data  72  may include temperature data, humidity measurements, wind speed and direction, precipitation, cloud conditions, and other environmental information that may affect energy usage or consumption during a particular period of time. 
   Briefly, the identification system  10  retrieves energy consumption information associated with the target facility  12  via the communications network  20  from the energy supplier  16  and/or directly from the target facility  12 . The identification system  10  may also retrieve energy consumption information via the communications network  20  associated with the facility  14  from the energy supplier  16  and/or directly from the facility  14 . Additionally, the identification system  10  retrieves environmental data  72  via the communications network  20  from the environmental service  18 . Using the energy consumption information and the environmental data  72 , the identification system  10  is used to remotely identify the energy consumption systems  30  and/or components  32  of the target facility  12  and analyze various operating parameters of the energy consumption systems  30  and/or components  32 . The system  10  is described in greater detail below in connection with  FIGS. 2 ,  3 A and  3 B. 
     FIG. 2  is a block diagram illustrating the system  10  in accordance with an embodiment of the present invention. In this embodiment, system  10  includes a processor  100 , an input device  102 , an output device  104 , and a memory  106 . The present invention also encompasses computer software that may be stored in memory  106  and executed by processor  100 . The computer software may also be stored in a variety of other types of storage media including, but not limited to, floppy disk drives, hard drives, CD-ROM disk drives, or magnetic tape drives. Information, such as environmental data, energy usage data, or other types of information, may be received from a user of system  10  using a keyboard or any other type of input device  102 . Output values or results may be output to a user of system  10  through output device  104 , which may include a display, printer, or any other type of output device. The system  10  may also include an interface  108  for communicating via the communications network  20 . 
   System  10  includes an analysis engine  120  and a validation engine  122 , which are computer software programs. In  FIG. 2 , the analysis engine  120  and validation engine  122  are illustrated as being stored in the memory  106 , where they can be executed by the processor  100 . However, the analysis engine  120  and validation engine  122  may also be stored on other suitable types of storage media. 
   System  10  also includes an external variable database  130 , a facility database  132 , an energy consumption database  134 , and an operating parameter database  136 . In  FIG. 2 , the external variable database  130 , facility database  132 , energy consumption database  134 , and operating parameter database  136  are illustrated as being stored in the memory  106 , where they can be accessed by the processor  100 . However, the databases  130 ,  132 ,  134 , and  136  may also be stored on other suitable types of storage media. 
   The external variable database  130  includes external variable data  150  associated with the target facility  12  and/or comparable facility  14 . For example, the external variable data  150  may include environmental data  152  associated with the facilities  12  and  14 . The environmental data  152  may include information associated with environmental conditions internal and external to the physical location of the facilities  12  and/or  14 , such as temperatures, humidity, wind speed and direction, precipitation, cloud conditions, and other environment-related information. The environmental data  152  may be downloaded to the external variable database  130  from the environmental service  18  via the communications network  20 . The environmental data  152  may also be retrieved directly from the facilities  12  and/or  14  via the communications network  20 . For example, as described above, the energy consumption data  44  and  58  associated with each of the respective facilities  12  and  14  may include information associated with the internal and external environmental conditions proximate to and affecting the operating parameters of the energy consumption systems  30  and  50  and/or components  32  and  52 . It should be understood, however, that the environmental data  152  may be otherwise obtained and/or stored within the scope of the present invention. 
   The facility database  132  includes facility data  160  associated with the target facility  12  and/or comparable facility  14 . For example, the facility data  160  may include facility physical characteristic data  162  and facility usage characteristic data  164 . The facility physical characteristic data  162  may include information corresponding to the physical features of target facility  12  or comparable facility  14 , such as, but not limited to, the quantity of floors or levels, the square footage of each floor or level, whether the facility adjoins another structure, the architectural aspects of the facility, and the type of materials used in the construction of the facility. 
   The facility usage characteristic data  164  may include information associated with energy usage patterns and characteristics corresponding to the type of target facility  12 . For example, the data  164  may include information such as, but not limited to, whether the facility is a hospital, office building, department store, grocery store, home, or other type of facility, and the energy usage cycles and patterns associated with the type of facility, such as, but not limited to, periods of minimal or peak energy usage, the types of energy consumption systems and components generally used in corresponding types of facilities, whether one or more floors or levels of the facility incur greater energy usage than other levels or floors due to the energy usage applications generally found on the particular levels or floors, or other information associated with energy usage characteristics unique to the target facility  12  and/or comparable facility  14 . For example, a hospital may experience a generally consistent energy usage pattern while an office building or department store may experience more cyclic energy usage patterns. Additionally, for example, in an office building application, one or more floors, or a portion of one or more floors, may be dedicated to computer server or network applications for providing computer services to various locations within the building. Accordingly, the floors or portions of floors containing the computer server and network applications may experience greater energy consumption than other floors of the building. 
   The energy consumption database  134  includes aggregated energy consumption data  170  associated with the target facility  12  and/or comparable facility  14 . The aggregated energy consumption data  170  may be downloaded via the communications network  20  from the energy supplier  16 , the target facility  12 , and/or the comparable facility  14 . For example, the aggregated energy consumption data  62 , the energy consumption data  44 , and/or the energy consumption data  58  may be retrieved via the communications network  20  and stored in the energy consumption database  134  as the aggregated energy consumption data  170 . However, the aggregated energy consumption data  170  may be otherwise received and stored within the scope of the present invention. 
   In operation, the system  10  retrieves and stores the aggregated energy consumption data  170  from the energy supplier  16 , the target facility  12 , and/or the comparable facility  14 . As described above, the aggregated energy consumption data  170  may be retrieved via the communications network  20  or other suitable electronic or non-electronic communication modes. The system  10  also retrieves and stores the external variable data  150  and the facility data  160  in a similar manner and as described above. The analysis engine  120  may also be used to generate the facility data  160  using the aggregated energy consumption data  170 . For example, the aggregated energy consumption data  170  may exhibit energy usage patterns generally associated with particular types of facilities and generally associated with particular sizes of facilities. Thus, the physical characteristic data  162  and usage characteristic data  164  may be derived from the aggregated energy consumption data  170 . 
   The validation engine  122  is used to validate the aggregated energy consumption data  170  and ensure that the aggregated energy consumption data  170  is complete and, therefore, not missing energy consumption information. For example, the aggregated energy consumption data  170  may include energy consumption information corresponding to specific time intervals or periods. The validation engine  122  determines whether energy consumption information is missing from the aggregated energy consumption data  170  and reconstructs the missing energy consumption information. For example, energy consumption data  58  from the comparable facility  14  may be retrieved and energy consumption information associated with particular time periods or intervals may be reconstructed from the energy consumption data  58  alone or in combination with the environmental data  152 . 
   The analysis engine  120  is used to generate and store disaggregated energy consumption data  180  associated with the target facility  12  using the facility data  160 , the aggregated energy consumption data  170 , and the external variable data  150 . For example, the aggregated energy consumption data  170  for particular periods or intervals of time and the environmental data  152  may be used to disaggregate the energy consumption information associated with the entire target facility  12  where the energy consumption associated with the energy consumption components  32  may be additively combined. The analysis engine  120  may utilize heuristic and/or semi-empirical calculations to analyze the energy consumption of the energy consumption components  32  and to provide a mechanism for generating the disaggregated energy consumption data  180  from the aggregated energy consumption data  170 . The analysis engine  120  may also use 1 parameter, 2 parameter, 3 parameter, 4 parameter, 5 parameter, change point multiple linear regression, or bin analysis techniques and calculations to analyze the energy consumption associated with the energy consumption components  32  to provide a mechanism for generating the disaggregated energy consumption data  180  from the aggregated energy consumption data  170 . A weather-daytyping method for generating the disaggregated energy consumption data  180  may also be used by the analysis engine  120 . For example, facilities using controlled sequencing of energy loads may be used to identify energy consumption levels of individual energy loads on consumption of the entire target facility  12 , combined with a 24-hour profile for generating the disaggregated energy consumption data  180 . However, other suitable methods may also be used to generate the disaggregated energy consumption data  180 . 
   From the disaggregated energy consumption data  180 , the analysis engine  120  is used again to determine the energy consumption components  32  and/or systems  30  of the target facility  12 . For example, one or more of the environmental data  152 , the facility physical characteristic data  162 , and the facility usage characteristic data  164  may be used in combination with the disaggregated energy consumption data  180  to identify precisely or approximately the energy consumption systems  30  and/or components  32  used by and within the target facility  12 . Thus, the system  10  may be used to remotely identify the systems  30  and/or components  32  of the target facility  12 , thereby substantially reducing or eliminating costly and time-consuming site visits and information gathering operations. 
   The analysis engine  120  may also be used to generate and store operating parameter data  140  in the operating parameter database  136 . The operating parameter data  140  includes information associated with the operating parameters of the energy consumption systems  30  and/or components  32  of the target facility  12  based on the disaggregated energy consumption data  180 . Additionally, the analysis engine  120  may be used to determine the operating efficiency of the systems  30  and/or components  32  of the target facility  12  using the operating parameter data  140 . For example, using the environmental data  152 , the facility data  160 , and/or the disaggregated energy consumption data  180 , the analysis engine  120  may remotely determine the operating efficiency of the systems  30  and/or components  32 . 
     FIGS. 3A and 3B  are flowcharts illustrating a method for remote identification of energy consumption systems  30  and/or components  32  in accordance with an embodiment of the present invention. The method begins at step  300 , where a target facility  12  is identified. At decisional step  302 , a determination is made whether aggregated energy consumption data  170  for the target facility  12  is known. If the aggregated energy consumption data  170  for the target facility  12  is known, the method proceeds from step  302  to step  316 . If the aggregated energy consumption data  170  for the target facility  12  is unknown, the method proceeds from step  302  to decisional step  304 , where a determination is made whether the aggregated energy consumption data  170  is available directly from the target facility  12 . If the aggregated energy consumption data  170  is available directly from the target facility  12 , the method proceeds from step  304  to step  306 , where the energy consumption data  44  may be retrieved from the data collector  40  of the target facility  12  via the communications network  20  and stored as the aggregated energy consumption data  170  in the energy consumption database  134 . 
   If the aggregated energy consumption data  170  is not available directly from the target facility  12 , the method proceeds from step  304  to decisional step  308 , where a determination is made whether the aggregated energy consumption data  170  is available from the energy supplier  16 . If the aggregated energy consumption data  170  is available from the energy supplier  16 , the method proceeds from step  308  to step  310 , where the aggregated energy consumption data  62  associated with the target facility  12  is retrieved via the communications network  20  and stored as the aggregated energy consumption data  170  in the energy consumption database  134 . 
   If the aggregated energy consumption data  170  associated with the target facility  12  is not available from the energy supplier  16 , the method proceeds from step  308  to step  312 , where a comparable facility  14  is identified. At step  314 , aggregated energy consumption data  170  associated with the comparable facility  14  is retrieved. For example, as described above in connection with the target facility  12 , the energy consumption data  58  may be retrieved directly from the comparable facility  14  via the communications network  20 , or the aggregated energy consumption data  62  associated with the comparable facility  14  may be retrieved from the energy supplier  16  via the communications network  20 . However, as described above, aggregated energy consumption data  170  associated with the comparable facility  14  may be otherwise retrieved and stored as the aggregated energy consumption data  170 . 
   At decisional step  316 , a determination is made whether the external variable data  150  associated with either the target facility  12  or the comparable facility  14  is known. If the external variable data  150  is known, the method proceeds from step  316  to step  320 . If the external variable data  150  is unknown, the method proceeds from step  316  to step  318 , where the environmental data  152  is retrieved from the environmental service  18  via the communications network  20  corresponding to the facilities  12  and/or  14 . For example, the environmental data  72  may be retrieved via the communications network  20  and stored as the environmental data  152  within the external variable database  130 . Additionally, as described above, the energy consumption data  44  and  58  may also contain information associated with the internal and external environmental conditions of the respective facilities  12  and  14 . Thus, the environmental information contained within the data  44  and  58  may be extracted and stored as the environmental data  152 . However, as described above, the environmental data  152  may be otherwise retrieved and stored within the scope of the present invention. 
   At decisional step  320 , a determination is made whether the facility data  160  associated with the target facility  12  is known. If the facility data  160  is known, the method proceeds from step  320  to step  326 . If the facility data  160  associated with the target facility  12  is unknown, the method proceeds from step  320  to step  322 , where the facility physical characteristic data  162  is generated for the target facility  12 . For example, facility physical characteristic data  162  associated with the target facility  12  may be stored within the facility database  132 , such as the size of the target facility  12 , the quantity of floors or levels of the target facility  12 , and other information associated with the physical characteristics of the target facility  12 . At step  324 , the facility usage characteristic data  164  associated with the target facility  12  is generated and stored within the facility database  132 . For example, the facility usage characteristic data  164  may include information such as whether the target facility  12  is a hospital, business, home, or other type of facility generally indicating energy consumption patterns associated with a particular type of target facility  12 . 
   At decisional step  326 , a determination is made whether the aggregated energy consumption data  170  is complete. For example, the aggregated energy consumption data  170  may be incomplete such that energy consumption information is missing or is unavailable for various time periods or intervals. If the aggregated energy consumption data  170  is complete, the method proceeds from step  326  to step  330 . If the aggregated energy consumption data  170  is incomplete, the method proceeds from step  326  to step  328 , where the validation engine  122  is used to reconstruct the missing data. For example, as described above, the missing energy consumption information may be reconstructed using energy consumption data  58  associated with a comparable facility  14  in combination with the environmental data  152 . Alternatively, the validation engine  122  may also use the aggregated energy consumption data  170  associated with the target facility  12  corresponding to other similar environmental conditions for other predetermined time periods or intervals to generate the missing energy consumption data. Other suitable methods and/or techniques alone or in combination may be utilized to reconstruct the missing data. Some examples are as follows: statistical analysis, such as linear and non-linear interpolation, calculation methods, such as autoregressive integrated moving average and multiple autoregressive integrated moving average, pattern recognition techniques, and expert systems, such as neural network(s), fuzzy logic and first principles analysis. 
   At step  330 , disaggregated energy consumption data  180  is generated for the target facility  12  using the aggregated energy consumption data  170 , the external variable data  150 , and the facility data  160 . At step  332 , one or more energy consumption systems  30  are identified for the target facility  12  using the disaggregated energy consumption data  180 . At step  334 , one or more energy consumption components  32  are identified for each corresponding energy consumption system  30  using the disaggregated energy consumption data  180 . 
   At step  336 , operating parameter data  140  is generated for each of the energy consumption systems  30  and/or energy consumption components  32  associated with the target facility  12 . For example, portions of the disaggregated energy consumption data  180  may be identified with particular energy consumption systems  30  and/or energy consumption components  32  such that the operating parameters associated with the energy consumption systems  30  and/or components  32  are known. At step  338 , the operating efficiencies of the energy consumption systems  30  and/or components  32  are determined. At decisional step  340 , a determination is made whether modifications to the operating parameters of the energy consumption systems  30  and/or components  32  is required to increase the operating efficiency. If a modification to an operating parameter is desired, the method proceeds from step  340  to step  342 , where the analysis engine  120  may be used to determine the modification to the corresponding operating parameter to increase operating efficiency. If no modification to an operating parameter is desired, the method ends. 
   Thus, the present invention provides a system  10  for remotely identifying and analyzing the energy consumption systems  30  and/or components  32  of the facility  12  absent costly and time-consuming site visits to the facility  12 . Additionally, the operating parameters and efficiencies of the various systems  30  and components  32  of the facility  12  may be determined and analyzed remotely to identify operating efficiency improvements for the facility  12 . 
   Although the present invention is described in detail, various changes and modifications may be suggested to one skilled in the art. The present invention encompasses such changes and modifications as falling within the scope of the appended claims.