Space utilization patterns for building optimization

Information

  • Patent Grant
  • 12210986
  • Patent Number
    12,210,986
  • Date Filed
    Friday, March 17, 2023
    a year ago
  • Date Issued
    Tuesday, January 28, 2025
    10 days ago
Abstract
Occupancy data over time is received for each of several spaces within a building from occupancy sensors that are disposed within each of the spaces. An occupancy value is determined for each of at least some of the several spaces based on the received occupancy data, each occupancy value representative of a percent of time that the respective space was occupied over an identified period of time. The space that had a highest occupancy value over the identified period of time is identified. A utilization value is determined for each of the spaces, wherein the utilization value is representative of a ratio of the occupancy value of the respective space and the highest occupancy value. An operation of the building is changed based at least in part on the utilization value of at least one of the plurality of spaces.
Description
TECHNICAL FIELD

The present disclosure relates generally to buildings. More particularly, the present disclosure relates to identifying space utilization within buildings.


BACKGROUND

Buildings are used for a variety of different purposes, such as but not limited to office space, manufacturing and the like. Buildings typically include a number of different spaces such as rooms, offices, hallways, conference rooms, lunch rooms, and restrooms, among others. It will be appreciated that the relative usage, or occupancy, of the different spaces of the building may vary over time, and may vary depending on the specific building spaces. Some building spaces may be heavily used while other building spaces may be more sparsely used. It will be appreciated that in some cases, spaces that are heavily used may require more maintenance such as cleaning while spaces that are not heavily used may not require as much maintenance. Many buildings have equipment installed within them. As an example, assume a building has a large copier/printer. If a majority of the people utilizing that copier/printer have offices or work spaces that are on an opposite side of the building from the copier/printer, it may make sense to move the copier/printer closer to those people. This is just an example. What would be desirable is an improved way to identify and then manage space utilization within buildings.


SUMMARY

The present disclosure relates generally to managing buildings by tracking their relative usage and occupancy. In one example, a method of operating a building that has a plurality of spaces includes receiving space information from a building information model of the building, the space information defining a plurality of spaces within a building. Occupancy data over time is received for each of two or more of the plurality of spaces within a building from occupancy sensors that are disposed within each of the two or more of the plurality of spaces within the building. An occupancy value is determined for each of at least some of the two or more of the plurality of spaces within the building based on the received occupancy data, each occupancy value representative of a percent of time that the respective space was occupied over an identified period of time. The one of the plurality of spaces that had a highest occupancy value over the identified period of time is identified. A utilization value is determined for each of at least some of the two or more of the plurality of spaces within the building, wherein the utilization value is representative of a ratio of the occupancy value of the respective space relative to the highest occupancy value. An operation of the building is changed based at least in part on the utilization value of at least one of the plurality of spaces.


In another example, a method of improving space utilization within a building having a plurality of spaces includes receiving sensor signals from a plurality of occupancy sensors distributed throughout the plurality of spaces over time. The sensor signals are processed to determine an occupancy value for at least some of the plurality of spaces within the building. A utilization value is calculated for at least some of the plurality of spaces within the building based at least in part upon the occupancy values over an identified period of time. An operation of the building is changed to improve utilization of spaces having a utilization value that is below (or above) a utilization value threshold.


In another example, a method of improving maintenance within a building having a plurality of spaces includes receiving sensor signals from a plurality of occupancy sensors distributed throughout the plurality of spaces, where sensor signals are received from each of the plurality of occupancy sensors over time. The sensor signals are processed to determine an occupancy value for at least some of the plurality of spaces within the building. A utilization value is calculated for at least some of the plurality of spaces within the building based at least in part upon the occupancy values over an identified period of time. The scheduled maintenance frequency is increased for spaces having a utilization value that is at or above a high utilization value threshold and is decreased for spaces having a utilization value that is below a low utilization value threshold.


The preceding summary is provided to facilitate an understanding of some of the innovative features unique to the present disclosure and is not intended to be a full description. A full appreciation of the disclosure can be gained by taking the entire specification, claims, figures, and abstract as a whole.





BRIEF DESCRIPTION OF THE FIGURES

The disclosure may be more completely understood in consideration of the following description of various examples in connection with the accompanying drawings, in which:



FIG. 1 is a schematic block diagram of an illustrative building system;



FIG. 2 is a flow diagram showing an illustrative method of operating a building;



FIG. 3 is a flow diagram showing an illustrative method of improving space utilization within a building;



FIG. 4 is a flow diagram showing an illustrative method of improving maintenance within a building; and



FIG. 5 is a heat map showing an example of occupancy values tracked over time.





While the disclosure is amenable to various modifications and alternative forms, specifics thereof have been shown by way of example in the drawings and will be described in detail. It should be understood, however, that the intention is not to limit the disclosure to the particular examples described. On the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the disclosure.


DESCRIPTION

The following description should be read with reference to the drawings, in which like elements in different drawings are numbered in like fashion. The drawings, which are not necessarily to scale, depict examples that are not intended to limit the scope of the disclosure. Although examples are illustrated for the various elements, those skilled in the art will recognize that many of the examples provided have suitable alternatives that may be utilized.


All numbers are herein assumed to be modified by the term “about”, unless the content clearly dictates otherwise. The recitation of numerical ranges by endpoints includes all numbers subsumed within that range (e.g., 1 to 5 includes 1, 1.5, 2, 2.75, 3, 3.80, 4, and 5).


As used in this specification and the appended claims, the singular forms “a”, “an”, and “the” include the plural referents unless the content clearly dictates otherwise. As used in this specification and the appended claims, the term “or” is generally employed in its sense including “and/or” unless the content clearly dictates otherwise.


It is noted that references in the specification to “an embodiment”, “some embodiments”, “other embodiments”, etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is contemplated that the feature, structure, or characteristic is described in connection with an embodiment, it is contemplated that the feature, structure, or characteristic may be applied to other embodiments whether or not explicitly described unless clearly stated to the contrary.



FIG. 1 is a schematic block diagram of an illustrative building system 10. In its broadest terms, the illustrative building system 10 includes a building 12 and a computing device 14. The building 12 includes a number of building spaces 16, individually labeled as 16a, 16b, 16c. While a total of three building spaces 16 are illustrated, it will be appreciated that this is merely illustrative, as the building 12 may have any number of distinct building spaces. In many cases, the building 12 may have a large number of building spaces, sometimes distributed over multiple floors or levels of the building 12. Each of the building spaces 16 may represent offices, hallways, conference rooms, lunch rooms, break rooms, manufacturing areas and the like.


In some instances, as shown, each of the building spaces 16 includes a sensor 18 individually labeled as 18a, 18b, 18c. While each of the building spaces 16 is shown as having one sensor 18, in some cases at least some of the building spaces 16 may include two or more sensors 18. The sensors 18 may take any of a variety of different forms. In some cases, at least some of the sensors 18 are occupancy sensors that are configured to determine whether a particular space is currently occupied or not. At least some of the sensors 18 may be motion sensors. If motion is detected, the space is presumed to be occupied. If no motion is detected for a period of time, the space is presumed to not be occupied. Some of the sensors 18 may be microphones, listening for noises that may indicate occupancy. Some noises, such as air moving through an HVAC duct, or a PA system, may be detected but do not indicate occupancy. Other noises, such as a person heard talking within a space, or noises made while the person walks, may be detected and do indicate occupancy. In some cases, occupancy detection can be carried out as described in co-pending U.S. patent application Ser. No. 16/224,670 and U.S. patent application Ser. No. 16/224,675, both filed Dec. 18, 2018 and both incorporated hereby by reference.


In some cases, at least some of the sensors 18 may be considered as being part of what is known as a digital ceiling. In some instances, at least some of the sensors 18 may represent smart flooring. Smart flooring is a flooring material such as an entrance mat that includes sensors that can detect a person walking on the entrance mat. Such an entrance mat placed at the entrance of each building space can provide an indication of occupancy. If a person is detected entering a building space, but is not detected leaving that building space, an assumption may be appropriately made that the person is still in that building space, and thus that building space is considered to be occupied.


The sensors 18 are operably coupled with the computing device 14. While shown as being outside of the building 12, this is not required in all cases. Rather, the computing device 14 may be disposed within the building 12. In some cases, the computing device 14 may be remote from the building 12, and may even represent a cloud-based server. The computing device 14 may include a display 20 that may be configured to display information received from at least some of the sensors 18. The computing device 14 may receive information from at least some of the sensors 18 pertaining to occupancy and may generate a heat map that shows relative usage of various building spaces 16 over time.


In some instances, the computing device 14 may utilize the received occupancy data to determine which of the building spaces 16 has been used the most, and may calculate a relative usage for each of the other building spaces 16. In some cases, the computing device 14 may generate and display on the display 20 a heat map that provides a graphical representation of the relative occupancy data. In some instances, the computing device 14 may also display on the heat map an indication of which building spaces 16 are currently occupied. In some cases, the computing device 14 may make recommendations regarding relative maintenance schedules, or even space allocation suggestions, based on the relative occupancy over time.



FIG. 2 is a flow diagram showing an illustrative method 30 of operating a building (such as the building 12) that has a plurality of spaces (such as the building spaces 16). In some cases, at least some of the plurality of spaces include offices, hallways and conference rooms. Space information is received from a building information model (BIM) of the building, the space information defining a plurality of spaces within a building, as indicated at block 32. Occupancy data over time is received for each of two or more of the plurality of spaces within a building from occupancy sensors (such as the sensors 18) that are disposed within each of the two or more of the plurality of spaces within the building, as indicated at block 34. In some cases, the period of time may be selected by a user. The user may, for example, be allowed to select one or more of a start time, an end time and a duration of the identified period of time.


An occupancy value is determined for each of at least some of the two or more of the plurality of spaces within the building based on the received occupancy data, each occupancy value representative of a percent of time that the respective space was occupied over the identified period of time, as indicated at block 36. In some cases, when a space of the plurality of spaces has two or more occupancy sensors providing the occupancy data for that space, the occupancy data for that space is scaled according to the number of occupancy sensors in that space providing the occupancy data. For example, if a space has three occupancy sensors, the occupancy data from the three sensors may be divided by three for that space so as to be properly comparable to spaces that only have one occupancy sensor.


The one of the plurality of spaces that had a highest occupancy value over the identified period of time is identified, as indicated at block 38. An utilization value for each of at least some of the two or more of the plurality of spaces within the building is determined, wherein the utilization value is representative of a ratio of the occupancy value of the respective space with respect to the highest occupancy value, as indicated at block 40. In some cases, an operation of the building is changed based at least in part on the utilization value of at least one of the plurality of spaces, as indicated at block 42.


Changing the operation of the building may include, for example, changing an operation of an HVAC system, an access control system and/or a surveillance system that services the building. Changing the operation of the building may include increasing a maintenance schedule for spaces of the building for which the respective utilization value, for example exceeds a threshold. Maintenance may include janitorial cleaning services and/or equipment maintenance for equipment. In some cases, changing the operation of the building may include redirecting occupants of the building toward spaces of the building that have lower utilization values. Changing the operation of the building may include making physical changes in a structure of the building or contents of the building based at least in part on the utilization value of at least one of the plurality of spaces. This may include adding/removing/moving walls and/or adding/removing/moving equipment within the building.


In some cases, the method 30 may further include displaying a heat map showing the determined utilization values for at least some of the plurality of spaces within the building, as optionally indicated at block 44. A current indication of occupancy may be displayed for at least some of the plurality of spaces within the building on the heat map based on current received occupancy data, as optionally indicated at block 46. In some cases, space labels may be temporarily superimposed over a particular portion of the heat map when a user hovers a pointer icon over the particular portion of the heat map, as optionally indicated at block 48. The space labels may provide additional information about the particular space, such as a space name, utilization value, access to raw occupancy data and/or any other suitable information.



FIG. 3 is a flow diagram showing an illustrative method 50 of improving space utilization within a building (such as the building 12) having a plurality of spaces (such as the building spaces 16). Sensor signals are received from a plurality of occupancy sensors (such as the sensors 18) that are distributed throughout the plurality of spaces over time, as indicated at block 52. The sensor signals are processed to determine an occupancy value for at least some of the plurality of spaces within the building, as indicated at block 54. A utilization value is calculated for at least some of the plurality of spaces within the building based at least in part upon the occupancy values over an identified period of time, as indicated at block 56. An operation of the building is changed to improve utilization of spaces having a utilization value that is below a utilization value threshold.


Changing the operation of the building, as indicated at block 58, may include redirecting occupants of the building toward spaces of the building that have lower utilization values. In some cases, changing the operation of the building may include moving one or more pieces of equipment from a location that is spaced apart from a number of users of that equipment to a new location in the building that is closer to the number of users of that equipment. In some instances, the user may be allowed to specify the identified period of time, as optionally indicated at block 60. The user may be allowed to specify one or more of a start time, an end time and a duration of the identified period of time, for example, as indicated at block 62.



FIG. 4 is a flow diagram showing an illustrative method 70 of improving maintenance within a building (such as the building 12) having a plurality of spaces (such as the building spaces 16). Sensor signals are received from a plurality of occupancy sensors distributed throughout the plurality of spaces, where sensor signals are received from each of the plurality of occupancy sensors over time, as indicated at block 72. The sensor signals are processed to determine an occupancy value for at least some of the plurality of spaces within the building, as indicated at block 74. A utilization value is calculated for at least some of the plurality of spaces within the building based at least in part upon the occupancy values over an identified period of time, as indicated at block 76. The scheduled maintenance frequency is increased for spaces having a utilization value that is at or above a high utilization value threshold, as indicated at block 78 and is decreased for spaces having a utilization value that is below a low utilization value threshold, as indicated at block 80.


In some cases, periods of time during which spaces of the plurality of spaces tend to be occupied and periods of time during which the spaces of the plurality of spaces tend to not be occupied may be determined, as optionally indicated at block 82. A scheduled maintenance time may be adjusted based on the determined periods of time during which spaces of the plurality of spaces tend to be occupied and/or periods of time during which the spaces of the plurality of spaces tend to not be occupied, as optionally indicated at block 84. Scheduled maintenance may include janitorial cleaning services and/or equipment maintenance for equipment disposed within at least some of the spaces of the plurality of spaces.



FIG. 5 shows a heat map 90 that may be considered as a graphical representation of relative occupancy data for a building space. While the heat map 90 may be considered as showing a number of building spaces 16 that are arranged together, such as on the same floor or level, it will be appreciated that heat maps may be generated for only a few building spaces 16, or may be generated for a substantial portion of a large building, for example. The heat map 90 includes a title 92, that indicates a particular period. The heat map 90 includes a pull-down history menu 94 by which a user may select how long of a time period is of interest. It can be seen that the user has selected one week of history, as shown on the pull-down history menu 94 as well as by the length of the period of time reflected in the title 92. In some cases, the user is allowed to select one or more of a start time, an end time and a duration of the time period is of interest (e.g. identified period of time).


The illustrative heat map 90 includes a legend 96 that shows a particular shading for any relative occupancy value ranging from 100 percent to 0 percent. It will be appreciated that any building space 16 that reflects a relative occupancy value of 100 percent means that that particular building space 16 has been occupied more frequently than any other building space 16 that is not shown as being at 100 percent. It does not necessarily mean that a particular building space 16 has been continuously occupied for the prescribed period of time (one week, from November 9 to November 16, weekdays at 11:25 am). As an example, assume there are two building spaces. One space is occupied twice, the other space is occupied once. The space occupied twice would have an occupancy value of 100 percent while the space occupied only once would have an occupancy value of 50 percent.


In some cases, allowing the cursor to hover over a particular building space 16 may cause additional information to be displayed on the heat map 90. In some instances, the additional information may be superimposed over the heat map 90 as a label 98. The example label 98 identifies the particular building space (space 1-2380, meaning room 2380 on the first floor) and its relative occupancy value of 94.7 percent. This means that space 1.2380 is one of the more heavily occupied spaces, at least for the period of time in question. In some cases, the label 98 may provide the name of the particular space, such as “kitchen”, or “north conference room”. These are just examples. It is contemplated that the heat map 90 may also include a number of icons 100 are shown on the heat map 90. The icons 100, which as shown are lower case “o's”, indicate that a particular space is currently occupied


Having thus described several illustrative embodiments of the present disclosure, those of skill in the art will readily appreciate that yet other embodiments may be made and used within the scope of the claims hereto attached. It will be understood, however, that this disclosure is, in many respects, only illustrative. Changes may be made in details, particularly in matters of shape, size, arrangement of parts, and exclusion and order of steps, without exceeding the scope of the disclosure. The disclosure's scope is, of course, defined in the language in which the appended claims are expressed.

Claims
  • 1. A method of using a computing device to operate a building system within a building that has a plurality of spaces and a plurality of occupancy sensors disposed within at least two or more of the plurality of spaces, the method comprising: the computing device receiving occupancy data over time for each of two or more of the plurality of spaces within a building from the occupancy sensors that are disposed within each of the two or more of the plurality of spaces within the building;the computing device determining an occupied time for each the two or more of the plurality of spaces based on the received occupancy data;the computing device determining a mostoccupied space of the two or more of the plurality of spaces based on the occupied times determined for each the two or more of the plurality of spaces, wherein the most occupied space is the space of the two or more of the plurality of spaces that has the highest occupied time of the two or more of the plurality of spaces;the computing device determining an utilization value for each of the two or more of the plurality of spaces, wherein the utilization value represent a proportion of the occupied time of the respective space relative to the occupied time of the most occupied space; andthe computing device changing an operation of the building system based at least in part on the utilization value of at least one of the plurality of spaces, wherein the building system comprises one or more of an HVAC system, an access controls system and a surveillance system, and changing the operation of the building system comprises changing an operation of the HVAC system, the access controls system and/or the surveillance system that services the building.
  • 2. The method of claim 1, further comprising the computing device outputting one or more recommendations based at least in part on the utilization value of at least one of the plurality of spaces, wherein the one or more recommendations include one or more of a recommendation to change a maintenance schedule for a space and/or a maintenance schedule for equipment in the building, a recommendation to change a current space allocation of the building, and a recommendation to make a physical change in a structure of the building and/or a physical change in a placement of one or more pieces of equipment in the building.
  • 3. The method of claim 1, wherein changing the operation of the building system comprises redirecting occupants of the building toward spaces of the building that have lower utilization values.
  • 4. The method of claim 1, wherein when a space of the plurality of spaces has two or more occupancy sensors providing the occupancy data for that space, the computing device scaling the occupancy data for that space according to the number of occupancy sensors in that space providing the occupancy data.
  • 5. The method of claim 1, further comprising the computing device displaying a heat map showing the determined utilization values for at least some of the plurality of spaces within the building.
  • 6. The method of claim 5, further comprising the computing device displaying a current indication of occupancy for at least some of the plurality of spaces within the building on the heat map based on current received occupancy data.
  • 7. The method of claim 5, further comprising the computing device temporarily superimposing space labels over a particular portion of the heat map when a user hovers a pointer icon over the particular portion of the heat map.
  • 8. The method of claim 1, wherein the most occupied space of the two or more of the plurality of spaces is the space of the two or more of the plurality of spaces that has the highest occupied time of the two or more of the plurality of spaces during an identified period of time, and the utilization value for each of the two or more of the plurality of spaces represents a proportion of the occupied time of the respective space during the identified period of time relative to the occupied time of the most occupied space during the identified period of time.
  • 9. The method of claim 8, further comprising the computing device allowing a user to specify the identified period of time.
  • 10. The method of claim 9, further comprising the computing device allowing the user to specify one or more of a start time, an end time and a duration of the identified period of time.
  • 11. The method of claim 1, wherein at least some of the plurality of spaces within the building comprise one or more of a room, an office, a hallway, a conference room, a restroom and a lunch room.
  • 12. A method of using a computing device to operate a building system within a building that has a plurality of spaces and a plurality of occupancy sensors disposed within at least two or more of the plurality of spaces, the method comprising: the computing device receiving occupancy data over time for each of two or more of the plurality of spaces within a building from the occupancy sensors that are disposed within each of the two or more of the plurality of spaces within the building;the computing device determining an occupied time for each the two or more of the plurality of spaces based on the received occupancy data;the computing device determining a mostoccupied space of the two or more of the plurality of spaces based on the occupied times determined for each the two or more of the plurality of spaces, wherein the most occupied space is the space of the two or more of the plurality of spaces that has the highest occupied time of the two or more of the plurality of spaces;the computing device determining an utilization value for each of the two or more of the plurality of spaces, wherein the utilization value represent a proportion of the occupied time of the respective space relative to the occupied time of the most occupied space;the computing device displaying a heat map on a display showing the determined utilization values for at least some of the plurality of spaces within the building; andthe computing device changing an operation of the building system based at least in part on the utilization value of at least one of the plurality of spaces, wherein the building system includes one or more of an HVAC system, an access controls system and a surveillance system, and wherein changing the operation of the building system comprises changing an operation of one or more of the HVAC system, the access controls system and/or the surveillance system that services the building.
  • 13. The method of claim 12, further comprising the computing device displaying a current indication of occupancy for at least some of the plurality of spaces within the building on the heat map based on current received occupancy data.
  • 14. The method of claim 12, further comprising the computing device outputting one or more recommendations based at least in part on the utilization value of at least one of the plurality of spaces, wherein the one or more recommendations include one or more of a recommendation to change a maintenance schedule for a space and/or a maintenance schedule for equipment in the building, a recommendation to change a current space allocation of the building, and a recommendation to make a physical change in a structure of the building and/or a physical change in a placement of one or more pieces of equipment in the building.
  • 15. The method of claim 12, further comprising the computing device changing an operation of the building system based at least in part on the utilization value of at least one of the plurality of spaces, wherein changing an operation of the building system comprises redirecting occupants of the building toward spaces of the building that have lower utilization values.
  • 16. The method of claim 12, wherein the most occupied space of the two or more of the plurality of spaces is the space of the two or more of the plurality of spaces that has the highest occupied time of the two or more of the plurality of spaces during an identified period of time, and the utilization value for each of the two or more of the plurality of spaces represents a proportion of the occupied time of the respective space during the identified period of time relative to the occupied time of the most occupied space during the identified period of time.
  • 17. The method of claim 8, further comprising the computing device allowing a user to specify the identified period of time.
  • 18. A non-transitory computer readable medium storing instructions thereon that when executed by one or more processors causes the one or more processors to: receive occupancy data over time for each of two or more of the plurality of spaces within a building serviced by a building system from one or more occupancy sensors that monitor each of the two or more of the plurality of spaces within the building;determine an occupied time for each the two or more of the plurality of spaces based on the received occupancy data;determine a most occupied space of the two or more of the plurality of spaces based on the occupied times determined for each the two or more of the plurality of spaces, wherein the most occupied space is the space of the two or more of the plurality of spaces that has the highest occupied time of the two or more of the plurality of spaces;determine an utilization value for each of the two or more of the plurality of spaces, wherein the utilization value represent a proportion of the occupied time of the respective space relative to the occupied time of the most occupied space;output one or more recommendations based at least in part on the utilization value of at least one of the plurality of spaces, wherein the one or more recommendations include oneor more of a recommendation to change a maintenance schedule for a space and/or a maintenance schedule for equipment in the building, a recommendation to change a current space allocation of the building, and a recommendation to make a physical change in a structure of the building and/or a physical change in a placement of one or more pieces of equipment in the building; andchange an operation of the building system based at least in part on the utilization value of at least one of the plurality of spaces, wherein the building system comprises one or more of an HVAC system, an access controls system and a surveillance system, and changing the operation of the building system comprises changing an operation of one or more of the HVAC system, the access controls system and/or the surveillance system that services the building.
Parent Case Info

This is a continuation of co-pending U.S. patent application Ser. No. 16/900,394, filed Jun. 12, 2020, and entitled “SPACE UTILIZATION PATTERNS FOR BUILDING OPTIMIZATION”, which is incorporated herein by reference.

US Referenced Citations (402)
Number Name Date Kind
191512 Bennett et al. Jun 1877 A
4009647 Howorth Mar 1977 A
4375637 Desjardins Mar 1983 A
4918615 Suzuki et al. Apr 1990 A
4939922 Smalley et al. Jul 1990 A
5566084 Cmar Oct 1996 A
5727579 Chardack Mar 1998 A
5745126 Jain et al. Apr 1998 A
5751916 Kon et al. May 1998 A
5777598 Gowda et al. Jul 1998 A
5973662 Singers et al. Oct 1999 A
6065842 Fink May 2000 A
6139177 Venkatraman et al. Oct 2000 A
6144993 Fukunaga et al. Nov 2000 A
6157943 Meyer Dec 2000 A
6229429 Horon May 2001 B1
6238337 Kambhatla et al. May 2001 B1
6334211 Kojima et al. Dec 2001 B1
6353853 Gravlin Mar 2002 B1
6369695 Horon Apr 2002 B2
6375038 Daansen et al. Apr 2002 B1
6429868 Dehner, Jr. et al. Aug 2002 B1
6473084 Phillips et al. Oct 2002 B1
6487457 Hull et al. Nov 2002 B1
6580950 Johnson et al. Jun 2003 B1
6598056 Hull et al. Jul 2003 B1
6619555 Rosen Sep 2003 B2
6704012 Lefave Mar 2004 B1
6720874 Fufido et al. Apr 2004 B2
6741915 Poth May 2004 B2
6796896 Laiti Sep 2004 B2
6801199 Wallman Oct 2004 B1
6816878 Zimmers et al. Nov 2004 B1
6876951 Skidmore et al. Apr 2005 B2
6882278 Winings et al. Apr 2005 B2
6904385 Budike, Jr. Jun 2005 B1
6907387 Reardon Jun 2005 B1
6911177 Deal Jun 2005 B2
6993403 Dadebo et al. Jan 2006 B1
6993417 Osann, Jr. Jan 2006 B2
7023440 Havekost et al. Apr 2006 B1
7031880 Seem et al. Apr 2006 B1
7062722 Carlin et al. Jun 2006 B1
7110843 Pagnano et al. Sep 2006 B2
7139685 Bascle et al. Nov 2006 B2
7164972 Imhof et al. Jan 2007 B2
7183899 Behnke Feb 2007 B2
7200639 Yoshida Apr 2007 B1
7222111 Budike, Jr. May 2007 B1
7222800 Wruck May 2007 B2
7257397 Shamoon et al. Aug 2007 B2
7280030 Monaco Oct 2007 B1
7292908 Borne et al. Nov 2007 B2
7295116 Kumar et al. Nov 2007 B2
7302313 Sharp et al. Nov 2007 B2
7308323 Kruk et al. Dec 2007 B2
7308388 Beverina et al. Dec 2007 B2
7313447 Hsiung et al. Dec 2007 B2
7346433 Budike, Jr. Mar 2008 B2
7356548 Culp et al. Apr 2008 B1
7379782 Cocco May 2008 B1
7383148 Ahmed Jun 2008 B2
7434742 Mueller et al. Oct 2008 B2
7447333 Masticola et al. Nov 2008 B1
7466224 Ward et al. Dec 2008 B2
7496472 Seem Feb 2009 B2
7512450 Ahmed Mar 2009 B2
7516490 Riordan et al. Apr 2009 B2
7548833 Ahmed Jun 2009 B2
7551092 Henry Jun 2009 B1
7557729 Hubbard et al. Jul 2009 B2
7567844 Thomas et al. Jul 2009 B2
7596473 Hansen et al. Sep 2009 B2
7610910 Ahmed Nov 2009 B2
7626507 LaCasse Dec 2009 B2
7664574 Imhof et al. Feb 2010 B2
7682464 Glenn et al. Mar 2010 B2
7702421 Sullivan et al. Apr 2010 B2
7729882 Seem Jun 2010 B2
7755494 Melker et al. Jul 2010 B2
7761310 Rodgers Jul 2010 B2
7774227 Srivastava Aug 2010 B2
7797188 Srivastava Sep 2010 B2
7819136 Eddy Oct 2010 B1
7822806 Frank et al. Oct 2010 B2
7856370 Katta et al. Dec 2010 B2
7978083 Melker et al. Jul 2011 B2
7984384 Chaudhri et al. Jul 2011 B2
7986323 Kobayashi et al. Jul 2011 B2
8024666 Thompson Sep 2011 B2
8086047 Penke et al. Dec 2011 B2
8099178 Mairs et al. Jan 2012 B2
8151280 Sather et al. Apr 2012 B2
8176095 Murray et al. May 2012 B2
8218871 Angell et al. Jul 2012 B2
8219660 McCoy et al. Jul 2012 B2
8271941 Zhang et al. Sep 2012 B2
8294585 Barnhill Oct 2012 B2
8302020 Louch et al. Oct 2012 B2
8320634 Deutsch Nov 2012 B2
8334422 Gutsol et al. Dec 2012 B2
8344893 Drammeh Jan 2013 B1
8375118 Hao et al. Feb 2013 B2
8476590 Stratmann et al. Jul 2013 B2
8516016 Park et al. Aug 2013 B2
8558660 Nix et al. Oct 2013 B2
8639527 Rensvold et al. Jan 2014 B2
8698637 Raichman Apr 2014 B2
8816860 Ophardt et al. Aug 2014 B2
8869027 Louch et al. Oct 2014 B2
8904497 Hsieh Dec 2014 B2
8936944 Peltz et al. Jan 2015 B2
8947437 Garr et al. Feb 2015 B2
8950019 Loberger et al. Feb 2015 B2
9000926 Hollock et al. Apr 2015 B2
9030325 Taneff May 2015 B2
9098738 Bilet et al. Aug 2015 B2
9105071 Fletcher et al. Aug 2015 B2
9175356 Peltz et al. Nov 2015 B2
9240111 Scott et al. Jan 2016 B2
9280884 Schultz et al. Mar 2016 B1
9292972 Hailemariam et al. Mar 2016 B2
9320662 Hayes et al. Apr 2016 B2
9370600 DuPuis et al. Jun 2016 B1
9373242 Conrad et al. Jun 2016 B1
9396638 Wildman et al. Jul 2016 B2
9311807 Schultz et al. Aug 2016 B2
9406212 De Luca et al. Aug 2016 B2
9418535 Felch et al. Aug 2016 B1
9418536 Felch et al. Aug 2016 B1
9449219 Bilet et al. Sep 2016 B2
9477543 Henley et al. Oct 2016 B2
9497832 Verberkt et al. Nov 2016 B2
9513364 Hall et al. Dec 2016 B2
9526380 Hamilton et al. Dec 2016 B2
9526806 Park et al. Dec 2016 B2
9536415 De Luca et al. Jan 2017 B2
9558648 Douglas Jan 2017 B2
9591267 Lipton et al. Mar 2017 B2
9613518 Dunn et al. Apr 2017 B2
9618224 Emmons et al. Apr 2017 B2
9640059 Hyland May 2017 B2
9672360 Barkan Jun 2017 B2
9710700 Bilet et al. Jul 2017 B2
9715242 Pillai et al. Jul 2017 B2
9721452 Felch et al. Aug 2017 B2
9729945 Schultz et al. Aug 2017 B2
9784464 Yamamoto et al. Oct 2017 B2
9842129 Hatami-Hanza Dec 2017 B2
9843743 Lewis et al. Dec 2017 B2
9856634 Rodenbeck et al. Jan 2018 B2
9872088 Fadell et al. Jan 2018 B2
9875639 Bone et al. Jan 2018 B2
9911312 Wildman et al. Mar 2018 B2
9940819 Ferniany Apr 2018 B2
9956306 Brais et al. May 2018 B2
9986175 Frank et al. May 2018 B2
10031494 Holaso Jul 2018 B2
10087608 Dobizl et al. Oct 2018 B2
10120397 Zakhor et al. Nov 2018 B1
10222767 Holaso et al. Mar 2019 B2
10223894 Raichman Mar 2019 B2
10228837 Hua et al. Mar 2019 B2
10235865 Thyroff Mar 2019 B2
10251610 Parthasarathy et al. Apr 2019 B2
10303843 Bitran et al. May 2019 B2
10332043 Nair et al. Jun 2019 B2
10332382 Thyroff Jun 2019 B2
10514817 Hua et al. Dec 2019 B2
10565844 Pourmohammad et al. Feb 2020 B2
10602474 Goldstein Mar 2020 B2
10607147 Raykov et al. Mar 2020 B2
10845082 Endel et al. Nov 2020 B2
10871300 Endel et al. Dec 2020 B2
20020111698 Graziano et al. Aug 2002 A1
20020130868 Smith Sep 2002 A1
20030028269 Spriggs et al. Feb 2003 A1
20030030637 Grinstein et al. Feb 2003 A1
20030046862 Wolf et al. Mar 2003 A1
20030071814 Jou et al. Apr 2003 A1
20030078677 Hull et al. Apr 2003 A1
20030083957 Olefson May 2003 A1
20030103075 Rosselot Jun 2003 A1
20030171851 Brickfield et al. Sep 2003 A1
20030214400 Mizutani et al. Nov 2003 A1
20030233432 Davis et al. Dec 2003 A1
20040001009 Winings et al. Jan 2004 A1
20040064260 Padmanabhan et al. Apr 2004 A1
20040143474 Haeberle et al. Jul 2004 A1
20040153437 Buchan Aug 2004 A1
20040168115 Bauernschmidt et al. Aug 2004 A1
20040233192 Hopper Nov 2004 A1
20040260411 Cannon Dec 2004 A1
20050010460 Mizoguchi et al. Jan 2005 A1
20050119767 Kiwimagi et al. Jun 2005 A1
20050143863 Ruane et al. Jun 2005 A1
20050267900 Ahmed et al. Dec 2005 A1
20060004841 Jeikkonen et al. Jan 2006 A1
20060009862 Imhof et al. Jan 2006 A1
20060017547 Buckingham et al. Jan 2006 A1
20060020177 Seo et al. Jan 2006 A1
20060028471 Kincaid et al. Feb 2006 A1
20060029256 Miyoshi et al. Feb 2006 A1
20060058900 Johanson et al. Mar 2006 A1
20060067545 Lewis et al. Mar 2006 A1
20060067546 Lewis et al. Mar 2006 A1
20060077255 Cheng Apr 2006 A1
20060184326 McNally et al. Aug 2006 A1
20060231568 Lynn et al. Oct 2006 A1
20060265664 Simons et al. Nov 2006 A1
20060279630 Aggarwal et al. Dec 2006 A1
20070016955 Goldberg et al. Jan 2007 A1
20070055757 Mairs et al. Mar 2007 A1
20070055760 McCoy et al. Mar 2007 A1
20070061046 Mairs et al. Mar 2007 A1
20070067062 Mairs et al. Mar 2007 A1
20070088534 MacArthur et al. Apr 2007 A1
20070090951 Chan et al. Apr 2007 A1
20070091091 Gardiner et al. Apr 2007 A1
20070101433 Louch et al. May 2007 A1
20070114295 Jenkins May 2007 A1
20070120652 Behnke May 2007 A1
20070139208 Kates Jun 2007 A1
20070216682 Navratil et al. Sep 2007 A1
20070219645 Thomas et al. Sep 2007 A1
20070239484 Arond et al. Oct 2007 A1
20070268122 Kow et al. Nov 2007 A1
20080001763 Raja et al. Jan 2008 A1
20080027885 Van Putten et al. Jan 2008 A1
20080036593 Rose-Pehrsson et al. Feb 2008 A1
20080062167 Boggs et al. Mar 2008 A1
20080099045 Glenn et al. May 2008 A1
20080103798 Domenikos et al. May 2008 A1
20080120396 Jayaram et al. May 2008 A1
20080144885 Zucherman et al. Jun 2008 A1
20080183424 Seem Jul 2008 A1
20080194009 Marentis Aug 2008 A1
20080198231 Ozdemir et al. Aug 2008 A1
20080209342 Taylor et al. Aug 2008 A1
20080222565 Taylor et al. Sep 2008 A1
20080224862 Cirker Sep 2008 A1
20080242945 Gugliotti et al. Oct 2008 A1
20080250800 Wetzel Oct 2008 A1
20080279420 Masticola et al. Nov 2008 A1
20080280275 Collopy Nov 2008 A1
20080303658 Melker et al. Dec 2008 A1
20080306985 Murray et al. Dec 2008 A1
20080320552 Kumar et al. Dec 2008 A1
20090001181 Siddaramanna et al. Jan 2009 A1
20090024944 Louch et al. Jan 2009 A1
20090065596 Seem et al. Mar 2009 A1
20090083120 Strichman et al. Mar 2009 A1
20090096791 Abshear et al. Apr 2009 A1
20090125337 Abri May 2009 A1
20090125825 Rye et al. May 2009 A1
20090144023 Seem Jun 2009 A1
20090157744 McConnell Jun 2009 A1
20090160673 Cirker Jun 2009 A1
20090193217 Korecki Jul 2009 A1
20090322782 Kimchi et al. Dec 2009 A1
20100048167 Chow et al. Feb 2010 A1
20100058248 Park Mar 2010 A1
20100064001 Daily Mar 2010 A1
20100070089 Harrod et al. Mar 2010 A1
20100073162 Johnson et al. Mar 2010 A1
20100123560 Nix et al. May 2010 A1
20100134296 Hwang Jun 2010 A1
20100156628 Ainsbury et al. Jun 2010 A1
20100156630 Ainsbury Jun 2010 A1
20100188228 Hyland Jul 2010 A1
20100223198 Noureldin et al. Sep 2010 A1
20100235004 Thind Sep 2010 A1
20100249955 Sitton Sep 2010 A1
20100286937 Hedley et al. Nov 2010 A1
20100318200 Foslien et al. Dec 2010 A1
20100324962 Nesler et al. Dec 2010 A1
20110010654 Raymond et al. Jan 2011 A1
20110057799 Taneff Mar 2011 A1
20110077779 Fuller et al. Mar 2011 A1
20110083094 Laycock et al. Apr 2011 A1
20110087988 Ray et al. Apr 2011 A1
20110112854 Koch et al. May 2011 A1
20110126111 Gill et al. May 2011 A1
20110154426 Doser et al. Jun 2011 A1
20110161124 Lappinga et al. Jun 2011 A1
20110169646 Raichman Jul 2011 A1
20110184563 Foslien et al. Jul 2011 A1
20110202467 Hilber et al. Aug 2011 A1
20110273298 Snodgrass et al. Nov 2011 A1
20110291841 Hollock et al. Dec 2011 A1
20110298301 Wong et al. Dec 2011 A1
20110316703 Butler et al. Dec 2011 A1
20110320054 Brzezowski Dec 2011 A1
20120022700 Drees et al. Jan 2012 A1
20120039503 Chen et al. Feb 2012 A1
20120062382 Taneff Mar 2012 A1
20120075464 Derenne et al. Mar 2012 A1
20120109988 Li et al. May 2012 A1
20120112883 Wallace et al. May 2012 A1
20120131217 Delorme et al. May 2012 A1
20120158185 El-Mankabady et al. Jun 2012 A1
20120216243 Gill et al. Aug 2012 A1
20120224057 Gill et al. Sep 2012 A1
20120259466 Ray et al. Oct 2012 A1
20120262472 Garr et al. Oct 2012 A1
20120272146 D'souza et al. Oct 2012 A1
20120291068 Khushoo et al. Nov 2012 A1
20120303652 Tseng Nov 2012 A1
20120310418 Harrod et al. Dec 2012 A1
20120323376 Honda et al. Dec 2012 A1
20130055132 Foslien Feb 2013 A1
20130060794 Puttabasappa et al. Mar 2013 A1
20130082842 Balazs et al. Apr 2013 A1
20130086152 Hersche et al. Apr 2013 A1
20130091631 Hayes et al. Apr 2013 A1
20130110295 Zheng et al. May 2013 A1
20130158728 Lee et al. Jun 2013 A1
20130169681 Rasane et al. Jul 2013 A1
20130184880 McMahon Jul 2013 A1
20130187775 Marsden et al. Jul 2013 A1
20130204570 Mendelson et al. Aug 2013 A1
20130229276 Hunter Sep 2013 A1
20130268293 Knudson et al. Oct 2013 A1
20130289774 Day et al. Oct 2013 A1
20140032157 Khiani Jan 2014 A1
20140040998 Hsieh Feb 2014 A1
20140046490 Foslien et al. Feb 2014 A1
20140046722 Rosenbloom et al. Feb 2014 A1
20140058539 Park Feb 2014 A1
20140167917 Wallace et al. Jun 2014 A2
20140207291 Golden et al. Jul 2014 A1
20140292518 Wildman et al. Oct 2014 A1
20140307076 Deutsch Oct 2014 A1
20140309757 Le Sant et al. Oct 2014 A1
20140316582 Berg-Sonne et al. Oct 2014 A1
20140320289 Raichman Oct 2014 A1
20140342724 Hill et al. Nov 2014 A1
20150025329 Amarasingham et al. Jan 2015 A1
20150032264 Emmons et al. Jan 2015 A1
20150056909 Chien Feb 2015 A1
20150070174 Douglas Mar 2015 A1
20150077258 Nelson et al. Mar 2015 A1
20150113462 Chen et al. Apr 2015 A1
20150153918 Chen et al. Jun 2015 A1
20150161874 Thyroff et al. Jun 2015 A1
20150167995 Fadell et al. Jun 2015 A1
20150168949 Hua et al. Jun 2015 A1
20150194043 Dunn et al. Jul 2015 A1
20150198707 Al-Alusi Jul 2015 A1
20150212717 Nair et al. Jul 2015 A1
20150213222 Amarasingham et al. Jul 2015 A1
20150213379 Nair et al. Jul 2015 A1
20150216369 Hamilton et al. Aug 2015 A1
20150253748 Brun et al. Sep 2015 A1
20150281287 Gill et al. Oct 2015 A1
20160012340 Georgescu et al. Jan 2016 A1
20160061476 Schultz et al. Mar 2016 A1
20160061477 Schultz et al. Mar 2016 A1
20160061794 Schultz et al. Mar 2016 A1
20160061795 Schultz et al. Mar 2016 A1
20160063833 Schultz et al. Mar 2016 A1
20160066067 Schultz et al. Mar 2016 A1
20160094445 Sella et al. Mar 2016 A1
20160116181 Aultman et al. Apr 2016 A1
20160139067 Grace May 2016 A1
20160139576 Aiken et al. May 2016 A1
20160253897 Wildman et al. Sep 2016 A1
20160255516 Hill et al. Sep 2016 A1
20160298864 Ekolind et al. Oct 2016 A1
20160306934 Sperry et al. Oct 2016 A1
20160314683 Felch et al. Oct 2016 A1
20160328948 Ferniany Nov 2016 A1
20160335731 Hall Nov 2016 A1
20160367925 Blackley Dec 2016 A1
20170024986 Austin Jan 2017 A1
20170074539 Bentz et al. Mar 2017 A1
20170105129 Teplin et al. Apr 2017 A1
20170153799 Hoyer Jun 2017 A1
20170193792 Bermudez Rodriguez et al. Jul 2017 A1
20170256155 Sengstaken, Jr. Sep 2017 A1
20170280949 Wildman et al. Oct 2017 A1
20170294106 Thyroff Oct 2017 A1
20170365024 Koch et al. Dec 2017 A1
20180016773 Chandler et al. Jan 2018 A1
20180151054 Pi May 2018 A1
20180218591 Easter Aug 2018 A1
20180293038 Meruva et al. Oct 2018 A1
20180299845 Ajax et al. Oct 2018 A1
20180301014 Worral et al. Oct 2018 A1
20180313695 Shim et al. Nov 2018 A1
20180365957 Wright et al. Dec 2018 A1
20190051138 Easter Feb 2019 A1
20190139395 Rogachev et al. May 2019 A1
20190171171 Verteletskyi et al. Jun 2019 A1
20190209719 Andersen et al. Jul 2019 A1
20200009280 Kupa et al. Jan 2020 A1
20200074836 Kolavennu et al. Mar 2020 A1
20200090089 Aston et al. Mar 2020 A1
20200146557 Cheung et al. May 2020 A1
20200191425 Endel et al. Jun 2020 A1
20200191428 Endel et al. Jun 2020 A1
20200200420 Nayak et al. Jun 2020 A1
Foreign Referenced Citations (42)
Number Date Country
2387100 Nov 2003 CA
2538139 Mar 2005 CA
103110410 May 2013 CN
103970977 Aug 2014 CN
105116848 Dec 2015 CN
108961714 Dec 2018 CN
110009245 Jul 2019 CN
110084928 Aug 2019 CN
110827457 Feb 2020 CN
1669912 Jun 2006 EP
2310981 Apr 2011 EP
7085166 Mar 1995 JP
11024735 Jan 1999 JP
11317936 Nov 1999 JP
2001356813 Dec 2001 JP
2005242531 Sep 2005 JP
2005311563 Nov 2005 JP
1172747 Aug 2012 KR
101445367 Oct 2014 KR
1499081 Mar 2015 KR
9621264 Nov 1996 WO
2004029518 Apr 2004 WO
2005045715 May 2005 WO
2008152433 Dec 2008 WO
2008157755 Dec 2008 WO
2009012319 Jan 2009 WO
2009079648 Jun 2009 WO
2010106474 Sep 2010 WO
2011025085 Mar 2011 WO
2011043732 Apr 2011 WO
2011057173 May 2011 WO
2011123743 Oct 2011 WO
2013062725 May 2013 WO
2013178819 Dec 2013 WO
2014009291 Jan 2014 WO
2014098861 Jun 2014 WO
2014135517 Sep 2014 WO
2016094445 Jun 2016 WO
2016123536 Aug 2016 WO
2017057274 Apr 2017 WO
2019046580 Mar 2019 WO
2020024553 Feb 2020 WO
Non-Patent Literature Citations (131)
Entry
“Energy Manager User Guide,” Release 3.2, Honeywell, 180 pages, 2008.
“Fuzzy Logic Toolbox 2.1, Design and Stimulate Fuzzy Logic Systems,” The MathWorks, 2 pages, May 2004.
“Junk Charts, Recycling Chartjunk as junk art,” 3 pages, Oct. 2, 2006.
“Model Predictive Control Toolbox 2, Develop Internal Model-Based Controllers for Constrained Multivariable Processes,” The MathWorks, 4 pages, Mar. 2005.
Honeywell, “Product Guide 2004,” XP-002472407, 127 pages, 2004.
“Statistics Toolbox, for Use with Matlab,” User's Guide Version2, The MathWorks, 408 pages, Jan. 1999.
“Vykon Energy Suite Student Guide,” Tridium Inc., 307 pages, Mar. 3, 2006.
“Web Based Energy Information Systems for Energy Management and Demand Response in Commercial Buildings,” California Energy Commission, 80 pages, Oct. 2003.
Andover Controls, Network News, vol. 2, No. 2, 8 pages, 1997.
Andover Controls World, 4 pages, Spring 1997.
Bell, Michael B. et al., “Early Event Detection-Results from A Prototype Implementation,” AICHE Spring National Meeting, 15 pages, Apr. 2005.
Cadgraphics, “The CADGRAPHICS User's Guide,” 198 pages, 2003.
Carrier Comfort Network CCN Web, “Web Browser User Interface to the Carrier Comfort Network,” 2 pages, 2002.
Carrier Comfort Network CCN Web, Overview and Configuration Manual, 134 pages, Apr. 2006.
Carrier Comfort Network CCN Web, Product Data, 2 pages, Apr. 2006.
Carrier, “i-Vu Powerful and Intuitive Front End for Building Control,” 2 pages, Aug. 2005.
Carrier, “i-Vu Web-Based Integrated Control System,” 3 pages, 2005.
Carrier, Demo Screen Shots, 15 pages, prior to Aug. 27, 2007.
Carrier, i-Vu CCN 4.0, Owner's Guide, 20 pages, Jul. 2007.
Carrier, i-Vu CCN, 7 pages, 2007.
Chen, Tony. F., “Rank Revealing QR Factorizations,” Linear Algebra and It's Applications, vol. 88-89, p. 67-82, Apr. 1987.
Circon, “i-Browse Web-Based Monitoring and Control for Facility Management,” 2 pages, prior to Aug. 27, 2007.
Published Australian Application 2009904740, 28 pages, Application Filed on Sep. 29, 2009.
Echelon, “Energy Control Solutions with the i.Lon SmartServer,” 4 pages, 2007.
Echelon, “i.Lon 100e3 Internet Server Models 72101R-300, 72101R-308, 72102R-300, 72103-R300 . . . ” 5 pages, copyright 2002-2007.
Echelon, “i.Lon 100e3 Internet Server New Features,” 15 pages, Sep. 2006.
Echelon, “i.Lon SmartServer,” 5 pages, 2007.
Honeywell News Release, “Honeywell's New Sysnet Facilities Integration System for Boiler Plant and Combustion Safety Processes,” 4 pages, Dec. 15, 1995.
Honeywell, “Excel Building Supervisor-Integrated R7044 and FS90 Ver. 2.0,” Operator Manual, 70 pages, Apr. 1995.
Honeywell, “Introduction of the S7350A Honeywell WebPAD Information Appliance,” Home and Building Control Bulletin, 2 pages, Aug. 29, 2000; Picture of WebPad Device with touch screen, 1 Page; and screen shots of WebPad Device, 4 pages.
Honeywell, Excel 15B W7760B Building Manager Release 2.02.00, Installation Instructions, 28 pages, Dec. 2004.
Honeywell, The RapidZone Solution, Excel 5000 Open System, Application Guide, 52 pages, Jan. 2004.
http://pueblo.lbl.gov/˜olken . . . , “Remote Building Monitoring and Operations Home Page,” 5 pages, prior to Aug. 27, 2007.
http://www.commercial.carrier.com/commercial/hvac/productdescription . . . , “Carrier: i-Vu CCN,” 1 page, printed Mar. 11, 2008.
http://www.commercial.carrier.com/commercial/hvac/productdescription . . . , “Carrier: 33CSCCNWEB-01 CCN Web Internet Connection to the Carrier Comfort Network,” 1 page, printed Mar. 11, 2008.
http://www.docs.hvacpartners.com/idc/groups/public/documents/techlit/gs-controls-ivuccn.rtf, “Products,” 5 pages, printed Jul. 3, 2007.
http://www.lightstat.com/products/istat.asp, Lightstat Incorporated, “Internet Programmable Communicating Thermostats,” 1 page, printed Mar. 13, 2007.
http://www.sharpsystems.com/products/pc_notebooks/actius/rd/3d/, “Actius RD3D Desktop Replacement Notebook with Industry-Breakthrough 3D Screen,” Sharp, 1 page, printed Jun. 16, 2005.
http://www2.sims.berkeley.edu/courses/is213/s06/projects/lightson;final.html, “Lights on o Wireless Lighting Control System,” 11 pages, printed Mar. 22, 2007.
I.Lon 100e3 Internet Server, 1 page, prior to Aug. 27, 2007.
I.Lon, SmartServer, 2 pages, prior to Aug. 27, 2007.
I-stat, Demo Screen Shots, 9 pages, printed Mar. 13, 2007.
I-stat, The Internet Programmable Thermostat, 2 pages, prior to Aug. 27, 2007.
Jeffrey Ball, “Green Goal of ‘Carbon Neutrality’ Hits Limit,” TheWall Street Journal, 7 pages, Dec. 30, 2008.
Johnson Controls, Network Integration Engine (NIE) 3 pages, Nov. 9, 2007.
Johnson Controls, Network Integration Engine (NIE), Product Bulletin, pp. 1-11, Jan. 30, 2008.
Kourti, Theodora, “Process Analysis and Abnormal Situation Detection: From Theory to Practice,” IEEE Control Systems Magazine, p. 10-25, Oct. 2002.
Mathew, Paul A., “Action-Oriented Benchmarking, Using CEUS Date to Identify and Prioritize Efficiency Opportunities in California Commercial Buildings,” 26 pages, Jun. 2007.
Morrison, Don et al., “The Early Event Detection Toolkit,” Honeywell Process Solutions, 14 pages, Jan. 2006.
Narang, “WEBARC: Control and Monitoring of Building Systems Over the Web,” 53 pages, May 1999.
Olken et al., “Object Lessons Learned from a Distributed System for Remote Building Monitoring and Operation,” ACM SIGPLAN Notices, vol. 33, No. 10, pp. 284-295, Oct. 1998.
Proliphix, Inc., “Proliphix IP Devices: HTTP API,” 28 pages, Jan. 23, 2006.
Proliphix, Inc., Remote Management User Guide, 12 pages, prior to Aug. 27, 2007.
Richard Rogan et al., “Smart and Final Food Stores: A Case Study in Web Based Energy Information and Collection,” Web Based Energy Information and Control Systems: Case Studies and Application, Chapter 6, p. 59-64, 2005.
Sharp, “Actius AL3DU 3D LC Display High Performance 3D Visualization,” 2 pages, prior to Mar. 17, 2006.
So et al., “Building Automation on the Information Superhighway,” ASHRAE (American Society of Heating Refrigerating, and Air Conditioning) Transactions, vol. 104, Part 2, pp. 176-191, 1998.
So et al., “Building Automation Systems on the Internet,” Facilities vol. 15, No. 5/6, pp. 125-133, May/Jun. 1997.
Talon, “Raptor Controller,” 6 pages, Oct. 2003.
Talon, “Workstation Software,” 4 pages, Nov. 2002.
Trane, “System Programming, Tracer Summit Version 14, BMTW-SVP01D-EN,” 623 pages, 2002.
Lucid Design Group, Inc., “Building Dashboard,” 2 pages, Printed May 30, 2013.
“America's Largest Managed Security Services Provider Launches Comprehensive, Integrated Covid-19 Safety Program for Office Buildings and Suites,” KastleSafeSpaces, 5 pages, May 11, 2020.
“Biometric Door Reader With Body Temperature Detection,” Kintronics, 9 pages, accessed May 21, 2020.
“Body Surface Temperature Screening with Alarm Function TVS-200IS/TVS-500IS,” Nippon Avionics Co., 3 pages, accessed May 21, 2020.
“BriefCam announces video analytics innovation for contact tracing, physical distancing, occupancy management and face mask detection,” BriefCam LTD, 11 pages, Jun. 5, 2020.
“Thermal Imaging SmartPhone Can Be used For Temperature Screening of People,” CAT, 3 pages, accessed Jul. 13, 2020.
“Contact Tracing Now Available on Identiv's Hirsch Velocity Access Control Platform,” IDENTIV, 5 pages, May 21, 2020.
Silva et al., “Cough localization for the detection of respiratory diseases in pig houses,” ScienceDirect, 7 pages, May 28, 2008.
Oey et al., “Evaluation of Isolation Compliance Using Real Time Video in Critical Care,” North Shore University Hospital, 1 page, Oct. 9, 2015.
“Facial Attendace System With Temperature Screening Now in India,” IANS, 5 pages, Mar. 19, 2020.
“Plan to Re-Open,” EHIGH, 16 pages, accessed Jun. 13, 2020.
“How Smarter AI-Powered Cameras Can Mitigate the Spread of Wuhan Novel,” AnyConnect, 22 pages, 2020.
“How to fight COVID-19 with machine learning,” DataRevenue, 20 pages, accessed May 25, 2020.
“INNCONTROL 5,” Honeywell, 2 pages, Aug. 8, 2018.
“IP Door Access Control,” KINTRONICS, 21 pages, 2014.
“Kogniz AI Health Response Platform,” KOGNIZ, 9 pages, accessed May 21, 2020.
“Machine Learning Could Check if You're Social Distancing Properly at Work,” MIT Technology Review, 7 pages, Apr. 17, 2020.
Punn et al., “Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques,” 10 pages, May 6, 2020.
“NEC launches dual face biometric and fever detection system for access control,” Biometric Update, 4 pages, May 8, 2020.
“Remote temperature monitoring,” AXIS Communication, 10 pages, 2014.
“FebriEye-AI Based Thermal Temperature Screening System,” vehant, 1 pages, 2020.
Juliana Bocicor et al. “Wireless Sensor Network based System for the Prevention of Hospital Acquired Infections”, arxiv.org, Cornell University Ithaca, NY 14853, May 2, 2017, XP080947042, (Abstract).
Shhedi Zaid Ali et al., “Traditional and ICT Solutions for Preventing the Hospital Acquired Infection”, 2015 20th International Conference on Control Systems and Computer Science, IEEE, May 27, 2015, pp. 867-873, XP033188038.
Extended European Search Report, EP application No. 20151295.1, pp. 13, May 26, 2020.
EP Application No. 19216744, Extended European Search Report, pp. 7, dated Apr. 28, 2020.
www.geappliances.com/home-energy-manager/about-energy-monitors.htm, “Energy Monitor, Home Energy Monitors, GE Nucleus,” 2 pages, printed Jan. 15, 2013.
www.luciddesigngroup.com/network/apps.php#homepage, “Lucid Design Group—Building Dashboard Network—Apps,” 7 pages, Jan. 15, 2013.
Preuveneers et al., “Intelligent Widgets for Intuitive Interaction and Coordination in Smart Home Environments,” IEEE Eighth International Conference on Intelligent Environments, pp. 157-164, 2012.
Wu et al., “A Web 2.0 Based Scientific Application Framework,” 7 pages, prior to Jul. 24, 2014.
“4.0 Today's Activities, The Home Dashboard,” CRBM info@hand website, 46 pages, prior to Apr. 25, 2013.
“Free Facilities Dashboards,” eSight Energy Website, 2 pages, prior to Apr. 25, 2013.
Alerton Building Controls, Gallery Prints, 7 pages, Dec. 19, 2013.
Carter, “Industrial Energy Management Dashboards Require a Toolkit,” Cross Automation, 11 pages, Nov. 4, 2013.
See The World in a New Way Hikvision Thermal Cameras, HIKVISION, 12 pages, 2017.
Allain, “Trying out the iPhone Infrared Camera: The FLIR One,” WIRED, 15 pages, 2014.
Dasgupta, “Your voice may be able to tell you if you have Covid,” Hindustan Times, 4 pages, Apr. 16, 2020.
Ganguty, “Gurugram-based startup Staqu has modified AI-powered JARVIS to battle coronavirus,” YOURSTORY, 7 pages, Mar. 31, 2020.
e-homecontrols.com, “e-Home Controls Website,” link to actual website No. longer works, 1 page, prior to Dec. 19, 2013.
http://www.ccbac.com, “C&C (/)—Omniboard,” 5 pages, Dec. 19, 2013.
http://www.domcontroller.com/en/, “DomController Home Automation Software—Control Anything from Anywhere,” 11 pages, printed Jan. 6, 2015.
http://www.novar.com/ems-bas/opus-building-automation-system, “Novar OPUS BAS,” 1 page, prior to Feb. 13, 2013.
Instituto Superior Tecnico, “A 3D Interactive Environment for Automated Building Control,” Master's Dissertation, 120 pages, Nov. 2012.
Panduit Corp., “Enable a Building Automation with Panduit Enterprise Solutions,” 4 pages, Nov. 2012.
“WEBs-AX Web-Enabled Building Solutions,” sales brochure, Honeywell International Inc., Mar. 2009.
“Attune Advisory Services,” press release, Honeywell International Inc., Mar. 20, 2012.
EnteliWEB product from Delta Controls, web pages retrieved on May 9, 2013 from http://deltacontrols.com/products/facilities-management/supervisory-software et seq. by the Internet Archive at web.archive.org.
“BACnet Protocol Implementation Conformance Statement” for enteliWEB, Delta Controls, Jul. 17, 2013.
Castle, “7 Software Platforms that Make Building Energy Management Easy,” http://greentechadvocates.com/2012/11/28/7-software-platforms-that-make-building-energy-managment-easy/, Nov. 28, 2012.
EnteliWEB catalog sheet, Delta Controls, Inc., 2012.
EnteliWEB catalog sheet, Delta Controls., 2010.
“Intelligent Building Management Systems in Miami,” Advanced Control Corp., Mar. 7, 2013.
“The Ohio State University,” BACnet International Journal, vol. 5, p. 4, Jan. 2013.
Bobker et al., “Operational Effectiveness in Use of BAS,” Proceedings of the 13th International Conference for Enhanced Building Operations, Oct. 8, 2013.
Castelo, “A 3D Interactive Environment for Automated Building Control,” Elsevier, Nov. 8, 2012.
“Creston Special Report: How Intelligent building management solutions are reducing operational costs,” Creston, 2012.
“Building Automation Software Solutions,” Iconics, 2013.
Lacey, “The Top 10 Software Vendors Connecting Smart Buildings to the Smart Grid,” http://www.greentechmedia.com/articles/read/the-top-10-companies-in-enterprise-smart-grid, Jul. 18, 2013.
“Data analytics and smart buildings increase comfort and energy efficiency”, https://www.microsoft.com/itshowcase/Article/Content/845/Data-analytics-and-smart-buildings-increase-comfort-and-energy-efficiency, Dec. 19, 2016, 8 pages.
Donnelly, “Building Energy Management: Using Data as a Tool”, http://www.buildingefficiencyinitiative.org/sites/default/files/legacy/InstituteBE/media/Library/Resources/Existing-Building-Retrofits/Using-Building-Data-as-a-Tool.pdf, Oct. 2012, 9 pages.
“ASHRAE Dashboard Research Project,” 29 pages, Aug. 28, 2008.
“NiagraAX Product Model Overview,” Tridium, Inc., 2005.
“An Overview of NiagraAX: A comprehensive software platform designed to create smart device applications,” Tridium, Inc., 2005.
“Phoenix Controls Portal,” Phoenix Controls, Inc., 2013.
Quirk, “A Brief History of BIM,” Arch Daily, Dec. 7, 2012.
Samad et al., “Leveraging the Web: A Universal Framework for Building Automation,” Proceedings of the 2007 American Control Conference, Jul. 11, 2007.
Sinha et al., “9 Key attributes of energy dashboards and analytics tools,” https://www.greenbiz.com/blog/2013/08/28/9-key-attributes-energy-dashboards-and=analytics-tools, Aug. 28, 2013.
Sinopoli, “Dashboards For Buildings,” http://www/automatedbuildings.com/news/dec10/articles/sinopoli/101119034404sinopoli.html, Dec. 2010.
Sinopoli, “Modeling Building Automation and Control Systems,” http://www.automatedbuildings.com/news/jun13/articles/sinopoli/130521122303sinopoli.html, Jun. 2013.
Zito, “What is Tridium Part 1,” http://blog.buildingautomationmonthly.com/what-is-tridium/, May 12, 2013.
Zito, “What is Tridium Part 2,” http://blog.buildingautomationmonthly.com/tridium-part-2/, Sep. 10, 2013.
Search Report and Written Opinion from related International PCT Application No. PCT/US2018/025189 dated Jul. 17, 2018 (12 pages).
Related Publications (1)
Number Date Country
20230222402 A1 Jul 2023 US
Continuations (1)
Number Date Country
Parent 16900394 Jun 2020 US
Child 18122984 US