Traveling Comfort Information

Abstract

Personal comfort information for individuals (such as characteristics such as height and weight, and preferences such as preferred temperature, etc) can be gathered and stored in a controller that controls a controlled space. The location of these individuals can be tracked as they move around the controlled space. The personal comfort information of the individuals can be used to modify the state of the current space the individual is in.

Description

RELATED APPLICATIONS

The present application hereby incorporates by reference the entirety of, and claims priority to, U.S. provisional patent application Ser. No. 62/794,976 filed Jun. 6, 2020. The present application hereby incorporates by reference the entirety of, and claims priority to, U.S. provisional patent application Ser. No. 63/070,460 filed Aug. 26, 2020.

FIELD

The present disclosure relates to defining the state zones in building. More specifically, the present disclosure relates to creating moving comfort goals related to individuals.

BACKGROUND

Buildings comprise a varied and complex set of systems for managing and maintaining the building environment. Building automation systems, comprising centralized control of separate systems such as for heating, cooling, ventilation, lighting, climate, security, entertainment, etc., can be used to perform the complex operations required by the building and its occupants and equipment and to optimize those operations for efficiency, cost, energy, priority, and so on. HVAC control systems typically comprise four basic elements: at least one sensor, at least one controller, at least one controlled device, and at least one source of energy. 1) A sensor measures the value of at least one variable such as temperature, humidity, and/or flow and provides its value or values to at least one controller. 2) A controller may receive input from at least one sensor, processes the input, and produces an output signal for at least one controlled device. 3) A controlled device acts to modify at least one variable as directed by a controller. 4) A source of energy provides power to the control system.

An HVAC control system typically comprises one or more sensors that measure the building climate state (e.g., temperature). The measured building climate state is compared with some defined target state (e.g., the desired temperature). The compared difference between the measured state and the target state is used by the controller to determine what actions are to be taken to bring the measured state value closer to the target state value (e.g., start a fan). Advanced controllers today are programmable, allowing one or more users to configure parameters such as set-points, timers, alarms, and/or control logic. These HVAC controllers can allow control of a wide range of environmental conditions beyond temperature, humidity, and air flow, taking into account, for example, changes in occupancy. Fundamentally, building automation systems and HVAC control systems have a purpose of improving the comfort of building occupants. Building occupants are individuals or groups of individuals, living or non-living, present in, near, and/or around the building for any period of time.

Research targeted at building automation systems to improve comfort have shown that the target state of an HVAC control system is generally determined by occupant input through a simple, though often inconvenient to use, interface device, such as by a thermostat. A dependency on an a priori or arbitrary input element such as the thermostat has the undesirable effect of skewed comfort preferences of a typical occupant vis-à-vis other occupants who had last control over the input. This dependency leads to a sub-optimal building environment due to continual adjustments by one or more occupants struggling to ensure their respective comfort. Typically, HVAC control systems are managed by centralized control of temperature set-points, whereby thermostats are accessible to a restricted set of occupants, or in some cases exclusively to facilities management personnel who may or may not be building occupants. In cases where occupants do not have access to set-points, HVAC control systems may involve standardized settings based on building type and use and/or assumptions about the occupants' comfort. These HVAC control systems have limited ability to respond to occupants' preferences, thus providing inadequate level of comfort. Thus, a basic purpose of a HVAC control system, that of providing comfort for building occupants, remained unaddressed.

SUMMARY

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description section. This summary does not identify required or essential features of the claimed subject matter.

These, and other, aspects of the invention will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. The following description, while indicating various embodiments of the embodiments and numerous specific details thereof, is given by way of illustration and not of limitation. Many substitutions, modifications, additions or rearrangements may be made within the scope of the embodiments, and the embodiments includes all such substitutions, modifications, additions or rearrangements.

BRIEF DESCRIPTION OF THE FIGURES

Non-limiting and non-exhaustive embodiments of the present embodiments are described with reference to the following FIGURES, wherein like reference numerals refer to like parts throughout the various views unless otherwise specified.

FIG. 1 depicts a computing system in conjunction with which described embodiments can be implemented.

FIG. 2 depicts a distributed computing system 200 with which embodiments disclosed herein may be implemented.

FIG. 3 depicts an exemplary system 300 for creating and using traveling comfort information.

FIG. 4 depicts a method configured to implement traveling comfort information in accordance with one or more embodiments.

FIG. 5 depicts individuals in zones in accordance with one or more embodiments.

FIG. 6 depicts individuals in zones in accordance with one or more embodiments.

FIG. 7 discloses some ways that a controller may notice that an individual has entered or left a controlled space.

FIG. 8 discloses some actions that may trigger an aggregate comfort goal determination.

FIG. 9 discloses some individual comfort goals.

Corresponding reference characters indicate corresponding components throughout the several views of the drawings. Skilled artisans will appreciate that elements in the FIGURES are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of various embodiments. Also, common but well-understood elements that are useful or necessary in a commercially feasible embodiment are often not depicted in order to facilitate a less obstructed view of these various embodiments.

DETAILED DESCRIPTION

Disclosed below are representative embodiments of methods, computer-readable media, and systems having particular applicability to heterogenous neural networks.

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present embodiments. It will be apparent, however, to one having ordinary skill in the art that the specific detail need not be employed to practice the present embodiments. In other instances, well-known materials or methods have not been described in detail in order to avoid obscuring the present embodiments.

Reference throughout this specification to “one embodiment”, “an embodiment”, “one example” or “an example” means that a particular feature, structure or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present embodiments. Thus, appearances of the phrases “in one embodiment”, “in an embodiment”, “one example” or “an example” in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures or characteristics may be combined in any suitable combinations and/or sub-combinations in one or more embodiments or examples.

Embodiments in accordance with the present embodiments may be implemented as an apparatus, method, or computer program product. Accordingly, the present embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.), or an embodiment combining software and hardware aspects. Furthermore, the present embodiments may take the form of a computer program product embodied in any tangible medium of expression having computer-usable program code embodied in the medium.

Any combination of one or more computer-usable or computer-readable media may be utilized. For example, a computer-readable medium may include one or more of a portable computer diskette, a hard disk, a random access memory (RAM) device, a read-only memory (ROM) device, an erasable programmable read-only memory (EPROM or Flash memory) device, a portable compact disc read-only memory (CDROM), an optical storage device, and a magnetic storage device. Computer program code for carrying out operations of the present embodiments may be written in any combination of one or more programming languages.

Embodiments may be implemented in edge computing environments where the computing is done within a network which, in some implementations, may not be connected to an outside internet, although the edge computing environment may be connected with an internal internet. This internet may be wired, wireless, or a combination of both. Embodiments may also be implemented in cloud computing environments. A cloud model can be composed of various characteristics (e.g., on-demand self-service, broad network access, resource pooling, rapid elasticity, measured service, etc.), service models (e.g., Software as a Service (“SaaS”), Platform as a Service (“PaaS”), Infrastructure as a Service (“IaaS”), and deployment models (e.g., private cloud, community cloud, public cloud, hybrid cloud, etc.).

The flowchart and block diagrams in the flow diagrams illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present embodiments. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It will also be noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, may be implemented by general or special purpose hardware-based systems that perform the specified functions or acts, or combinations of general and special purpose hardware and computer instructions. These computer program instructions may also be stored in a computer-readable medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.

As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having,” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a process, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or inherent to such process, article, or apparatus.

Further, unless expressly stated to the contrary, “or” refers to an inclusive or and not to an exclusive or. For example, a condition A or B is satisfied by any one of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present).

Additionally, any examples or illustrations given herein are not to be regarded in any way as restrictions on, limits to, or express definitions of any term or terms with which they are utilized. Instead, these examples or illustrations are to be regarded as being described with respect to one particular embodiment and as being illustrative only. Those of ordinary skill in the art will appreciate that any term or terms with which these examples or illustrations are utilized will encompass other embodiments which may or may not be given therewith or elsewhere in the specification and all such embodiments are intended to be included within the scope of that term or terms. Language designating such non-limiting examples and illustrations includes, but is not limited to: “for example,” “for instance,” “e.g.,” and “in one embodiment.”

A “cost function”, generally, compares the output of a simulation model with the ground truth—a time curve that represents the answer the model is attempting to match. This gives us the cost—the difference between the simulated truth curve values and the expected values (the ground truth). The cost function may use a least squares function, a Mean Error (ME), Mean Squared Error (MSE), Mean Absolute Error (MAE), a Categorical Cross Entropy Cost Function, a Binary Cross Entropy Cost Function, and so on, to arrive at the answer. In some implementations, the cost function is a loss function. In some implementations, the cost function is a threshold, which may be a single number that indicates the simulated truth curve is close enough to the ground truth. In other implementations, the cost function may be a slope. The slope may also indicate that the simulated truth curve and the ground truth are of sufficient closeness. When a cost function is used, it may be time variant. It also may be linked to factors such as user preference, or changes in the physical model. The cost function applied to the simulation engine may comprise models of any one or more of the following: energy use, primary energy use, energy monetary cost, human comfort, the safety of building or building contents, the durability of building or building contents, microorganism growth potential, system equipment durability, system equipment longevity, environmental impact, and/or energy use CO2 potential. The cost function may utilize a discount function based on discounted future value of a cost. In some embodiments, the discount function may devalue future energy as compared to current energy such that future uncertainty is accounted for, to ensure optimized operation over time. The discount function may devalue the future cost function of the control regimes, based on the accuracy or probability of the predicted weather data and/or on the value of the energy source on a utility pricing schedule, or the like.

A “machine learning algorithm” or “optimization method” is used to determine the next set of inputs after running a simulation model. These machine learning algorithms or optimization methods may include Gradient Descent, methods based on Newton's method, and inversions of the Hessian using conjugate gradient techniques, Evolutionary computation such as Swarm Intelligence, Bee Colony optimization; SOMA, Particle Swarm, Non-linear optimization techniques, and other methods known by those of skill in the art.

A “state” as used herein may be Air Temperature, Radiant Temperature, Atmospheric Pressure, Sound Pressure, Occupancy Amount, Indoor Air Quality, CO2 concentration, Light Intensity, or another state that can be measured and controlled.

I. Overview

People have preferences about how they like their environment. Some like cold and dark, some like hot and brightly lit, with others falling somewhere in-between. Comfort goals are the preferred state of a space. These goals may include heat, humidity, airflow, sound, lighting, etc. Some of these goals are interdependent. For example, how warm a person feels is a combination of temperature, humidity, and air flow, such that changing one variable (such as temperature) will change the allowable values in another variable (such as humidity). As such, in some instances, many of the state variables must be looked at together to determine desired comfort goals. Different zones in a building may have different comfort goals, and so require different comfort paths. A controller associated with a controlled space (such as a building) may know when a user is in the building because their badge was read, they logged on to their computer account, a signature of a wireless device of theirs was noticed, etc. The building control system (e.g., a controller) may have a database of comfort information about specific users. These may include personal information, such as height, weight, sex, and insulation value of clothing.

For example, a user may typically wear shorts, another user may typically wear a wool suit. Their insulation value of clothing would be quite a bit different. The controller may also include information from personal activity devices indicating current activity level (i.e., a fitness device may indicate that someone just came back from exercising). The preference information may also comprise preferences for location, temperature, humidity, lighting, security, entertainment, personal services, or grounds control. Weather, time, building angle (e.g., where the sun is hitting the building), longitude and latitude, etc., may be used (some or all) to determine a comfort goal by the computing environment 100, as well as being used by individual comfort goals.

Here we disclose moveable comfort goals that move with an individual they are associated with. A controller associated with a controlled space (such as a building) may know when a user is in the building because their badge was read, they logged on to their computer account, a signature of a wireless device of theirs was noticed, etc. The controller may then be able to modify the state of the building based on the user's noted preferences. Further, the person's location may be able to be pinpointed to a specific zone in the building using bluetooth sensors, direct communication between a computing environment 100 and a personal electronic device 190, by a computer login at a specific machine, by a location tag that a user is wearing, etc. The comfort information of a person may then be able to follow that person around the building, changing the state of the building as he or she travels. The comfort state of a zone in the building may be dependent on some or all the known people in a specific zone, so if only one person is in a zone (such as an office) then that single person may determine the comfort goals for the entire zone. In the case where many people are in a zone, such as a meeting room, then each person there whose comfort goals are known may contribute a portion of the comfort goals. This contributes to efficiency of HVAC systems and other state change systems, as they can anticipate changes to state that will be requested and adjust accordingly, which allows for greater efficiency of equipment. These disclosures also simplify the amount of physical adjustment of controls needed, which may lead to more accurate workplace environments. This may lead to more contented people in the building and also may lead to energy savings. These also may lead to more efficient computations, which leads to computer systems and controllers running quicker and with more efficiency.

II. Exemplary Computing Environment

FIG. 1 illustrates a generalized example of a suitable computing environment 100 in which described embodiments may be implemented. The computing environment 100 is not intended to suggest any limitation as to scope of use or functionality of the disclosure, as the present disclosure may be implemented in diverse general-purpose or special-purpose computing environments.

With reference to FIG. 1, the core processing is indicated by the core processing 130 box. The computing environment 100 includes at least one central processing unit 110 and memory 120. The central processing unit 110 executes computer-executable instructions and may be a real or a virtual processor. It may also comprise a vector processor 112, which allows same-length neuron strings to be processed rapidly. In a multi-processing system, multiple processing units execute computer-executable instructions to increase processing power and as such the vector processor 112, GPU 115, and CPU can be running simultaneously. The memory 120 may be volatile memory (e.g., registers, cache, RAM), non-volatile memory (e.g., ROM, EEPROM, flash memory, etc.), or some combination of the two. The memory 120 stores software 185 implementing the described methods and systems of creating and utilizing traveling comfort information.

A computing environment may have additional features. For example, the computing environment 100 includes storage 140, one or more input devices 150, one or more output devices 155, one or more network connections (e.g., wired, wireless, etc.) 160 as well as other communication connections 170. An interconnection mechanism (not shown) such as a bus, controller, or network interconnects the components of the computing environment 100. Typically, operating system software (not shown) provides an operating environment for other software executing in the computing environment 100, and coordinates activities of the components of the computing environment 100. The computing system may also be distributed; running portions of the software 185 on different CPUs.

The storage 140 may be removable or non-removable, and includes magnetic disks, magnetic tapes or cassettes, CD-ROMs, CD-RWs, DVDs, flash drives, or any other medium which can be used to store information and which can be accessed within the computing environment 100. The storage 140 stores instructions for the software, such as software 185 to implement systems and methods of creating and utilizing traveling comfort information.

The input device(s) 150 may be a device that allows a user or another device to communicate with the computing environment 100, such as a touch input device such as a keyboard, video camera, a microphone, mouse, pen, or trackball, a digital camera, a scanning device such as a digital camera with a scanner, touchscreen, joystick controller, a wii remote, or another device that provides input to the computing environment 100. For audio, the input device(s) 150 may be a sound card or similar device that accepts audio input in analog or digital form, or a CD-ROM reader that provides audio samples to the computing environment. The output device(s) 155 may be a display, a hardcopy producing output device such as a printer or plotter, a text-to speech voice-reader, speaker, CD-writer, or another device that provides output from the computing environment 100.

The communication connection(s) 170 enable communication over a communication medium to another computing entity. The communication medium conveys information such as computer-executable instructions, compressed graphics information, or other data in a modulated data signal. Communication connections 170 may comprise input devices 150, output devices 155, and input/output devices that allows a client device to communicate with another device over network 160. A communication device may include one or more wireless transceivers for performing wireless communication and/or one or more communication ports for performing wired communication. These connections may include network connections, which may be a wired or wireless network such as the Internet, an intranet, a LAN, a WAN, a cellular network or another type of network. It will be understood that network 160 may be a combination of multiple different kinds of wired or wireless networks. The network 160 may be a distributed network, with multiple computers, which might be building controllers, acting in tandem. A computing connection 170 may be a portable communications device such as a wireless handheld device, a personal electronic device 190, etc. The personal electronic device 190 may be a cell phone, a personal computer, a tablet, or any other sort of device that has a wireless signal. The wireless signal may comprise strength and directionality. The wireless signal may also comprise an identifier that identifies the user of the personal electronic device 190 to the system. This personal device may not be controlled by the computing environment 100. The computing environment may be able to determine when a personal device is within a controlled space 220.

Computer-readable media are any available non-transient tangible media that can be accessed within a computing environment. By way of example, and not limitation, with the computing environment 100, computer-readable media include memory 120, storage 140, communication media, and combinations of any of the above. Computer readable storage media 165 which may be used to store computer readable media comprises instructions 175 and data 180. Data Sources may be computing devices, such as general hardware platform servers configured to receive and transmit information over the communications connections 170. The computing environment 100 may be an electrical controller that is directly connected to various resources, such as HVAC resources, and which has CPU 110, a GPU 115, Memory, 120, input devices 150, communication connections 170, and/or other features shown in the computing environment 100. The computing environment 100 may be a series of distributed computers. These distributed computers may comprise a series of connected electrical controllers.

Although the operations of some of the disclosed methods are described in a particular, sequential order for convenient presentation, it should be understood that this manner of description encompasses rearrangement, unless a particular ordering is required by specific language set forth below. For example, operations described sequentially can be rearranged or performed concurrently. Moreover, for the sake of simplicity, the attached figures may not show the various ways in which the disclosed methods, apparatus, and systems can be used in conjunction with other methods, apparatus, and systems. Additionally, the description sometimes uses terms like “determine,” “build,” and “identify” to describe the disclosed technology. These terms are high-level abstractions of the actual operations that are performed. The actual operations that correspond to these terms will vary depending on the particular implementation and are readily discernible by one of ordinary skill in the art.

Further, data produced from any of the disclosed methods can be created, updated, or stored on tangible computer-readable media (e.g., tangible computer-readable media, such as one or more CDs, volatile memory components (such as DRAM or SRAM), or nonvolatile memory components (such as hard drives) using a variety of different data structures or formats. Such data can be created or updated at a local computer or over a network (e.g., by a server computer), or stored and accessed in a cloud computing environment.

FIG. 2 depicts a distributed computing system 200 with which embodiments disclosed herein may be implemented. Two or more computerized controllers 205 may incorporate all or part of a computing environment 100, 210. These computerized controllers 205 may be connected 215 to each other using wired or wireless connections. The controllers may be within a controlled space 220. A controlled space 220 may be a space that has a resource, sensor, or other equipment that can modify or determine one or more states state of the space, such as a sensor (to determine space state), a heater, an air conditioner (to modify temperature); a speaker (to modify noise), locks, lights, etc. A controlled space may be divided into zones, which might have separate constraint state curves. Controlled spaces might be, e.g., an automated building, a process control system, an HVAC system, an energy system, an irrigation system, a building—irrigation system, etc. These computerized controllers 205 may comprise a distributed system that can run without using connections (such as internet connections) outside of the computing system 200 itself. This allows the system to run with low latency, and with other benefits of edge computing systems. The computerized controllers may run using an internal network with no outside network connection. This allows for a much more secure system much more invulnerable to outside attacks, such as viruses, ransomware, and just generally, computer security being reached in many other ways.

III. Exemplary System Embodiments

FIG. 3 depicts an exemplary system 300 for creating and using traveling comfort information. The system may include at least one controller 307 with at least one processor 309, which may comprise a computing environment 100, and/or may be part of a computerized controller system 200, 307. Memory 310 may also be part of a computing environment 100 and/or may be part of a computerized controller system 200. The memory 310 may comprise a comfort module 315, a location model 320, an association module 325, a grouping module 330, an aggregation module 335, a state change module 340, and/or a comfort input module 345, at least partially stored in memory 310.

The comfort module 315 may be operationally able to record comfort goals associated with individuals, aggregate comfort goals, etc. Individual comfort goals may comprise personal information such as height, or weight; preferences for location, temperature, humidity, security, entertainment, personal services, grounds control behavior, noise level, such as air flow noise level, entertainment noise level, crowd noise level, CO2 levels, lighting level, allergen level, etc. The individual comfort goals may be recorded by an individual inputting them on a computerized controller input device 150, using a browser on a personal electronic device 190, etc. The occupant state contains potentially dynamic information about the current state of the occupant, such as information about current activity levels. The occupant profile may also be related to a non-human item, such as a piece of furniture or musical instrument that requires specific humidity and temperature requirements. In some embodiments, there may be non-human occupant profiles and human occupant profiles.

Additionally, in an embodiment, occupant profile/preference state information may include active or passive occupant feedback on current comfort. In one embodiment, this state information is gathered through a user interface using a mobile, wearable, handheld, and/or other electronic device. The preference/occupant profile creation involves aggregation of profile and state information relating to the comfort states of occupants (human and non-human) into a suitable data structure—the occupant user interface.

The occupant user interface provides a user abstraction of one or more variables such as metabolic rate, body weight, body mass-index, gender, age, occupancy, ethnicity, locality, clothing insulation value, allergen level allowed, and so on. The electronic device may comprise at least one sensor that measures occupant movement, motion, and/or other activity. The sensor or sensors of the above mentioned electronic device, wherein the said movement, motion, and/or other activity is gathered, provide sensor data that may be used to calculate, for example, the metabolic rate, which can be further used in an occupant comfort measure. In addition to the occupant state, current environmental variables such as temperature, wind speed, humidity, noise level, air flow noise level, entertainment noise level, lighting level, allergen level, etc., which together comprise the environmental variables, can be provided to the comfort model.

In some embodiments, these environmental variables (e.g. ambient temperature, humidity, CO2, VOC, allergen levels, etc.) are provided by sensing devices as described above. The comfort model accepts both the environmental variables and occupant state inputs and determines the comfort level of the occupant(s), where the model comprises a mathematical equation of human comfort which outputs the comfort state of the occupant(s). The mathematical equation may comprise one or more of the variables like air temperature, radiant temperature, air velocity, humidity, metabolic rate, skin temperature, skin wetness, total evaporative heat loss from skin, skin surface area, sweat rate, body weight, body mass-index, gender, age, occupancy, ethnicity, locality, and/or clothing insulation value.

The mathematical equation of human comfort may be a derivative of any of the following: Fanger Model, KSU Two-Node Model, Pierce Two-Node Model, Standard Effective Temperature Model, Adaptive Comfort Model, and/or any human comfort model described in the various non-patent literatures. In some instances, an occupant comfort mean function may be used. In such a case, an occupant comfort mean function aggregates the comfort states of occupants. An occupant comfort mean function, is attained by any of the following techniques: averaging methods, such as arithmetic mean, geometric mean, harmonic mean, tri-mean, median, mode, mid-range, quadratic mean (RMS), cubic mean, generalized mean, weighted mean; machine learning and statistical techniques, such as linear regression, logistic regression, polynomial regression, k-means clustering, k-nearest neighbors, decision trees, perceptron, multi-layer perceptron, kernel methods, support vector machines, ensemble methods, boosting, bagging, naïve Bayes, expectation maximization, Gaussian mixture models, Gaussian processes, principal component analysis, singular value decomposition, reinforcement learning, Voronoi decomposition; and social theory voting techniques and concepts, such as social welfare functions, social choice functions, single transferrable vote, Bucklin's rule, social decision schemes, collective utility functions, and/or Condorcet method and extensions such as Copeland's rule, maximin, Dodgson's rule, Young's rule, and/or ranked pairs.

In some embodiments, the comfort model may also comprise comfort levels for non-human assets that allows for comfort models of equipment, building envelope components, animals, plants, collections, systems, and/or other items in/around/near a defined space. These may be used to provide more optimal management comprising the quality, comfort, value, or longevity of these assets. The comfort model for the non-human asset comprises a mathematical equation of a defined space asset comfort which might comprise a mathematical equation of building asset comfort which itself may comprise one or more of an equipment environmental operation model, a metallic rust model, a building material moisture capacity model, a building material mold potential model, an animal comfort model, a plant health model, and a water freeze model. These models and the math underlying them are known to those of skill in the art.

A location module 320 may be operationally able to determine when an individual is in a zone in the controlled space. The location module may also be operationally able to associate the individual with an individual personal computing device. The location module 320 may be operationally able to determine that an individual has entered the controlled space. A location module may use a signal from a personal electronic device 190 to determine a person's location. The location determiner, in some instances noticed signal strength and directionality to determine a location of the personal electronic device within the defined space. In other instances, the location determiner comprises a bluetooth beacon that broadcasts a signal which contains e.g., a universally unique identifier that can be used to determine the phone's location. In some embodiments, an indoor positioning system is used which allows Bluetooth beacons to pinpoint a personal electronic device within a building.

In some embodiments, at least two bluetooth beacons are used to determine the location of the personal electronic device 190. This location determiner may be, e.g., a program that runs on the computerized controller 205. This location determiner may be, e.g., programs or portions of programs that uses sensor data and runs on the controller 205 or multiple controllers. In some embodiments, the sensor may process some of the sensor data prior to passing it onto the controller 205. In some embodiments, at least one signal associated with a personal electronic device is saved; e.g., in the memory 310 and the location determiner can determine that the stored signal has disappeared.

An association module 325 may be operationally able to associate an individual with their comfort goals. This may be achieved using a database or another method known by those of skill in the art. A grouping module 330 may be operationally able to group individuals into the zones creating a zone group. An aggregation module 335 may be operationally able to determine an aggregated comfort goal of the zone group. A state change module 340 may be operationally able to run a state change system associated with the zone group to meet comfort goals of the zone group.

A comfort input module 345 may be operationally able to accept comfort information from an individual. The comfort input module 345 may be operationally able to determine that an individual has entered the controlled space in a number of ways, including, but not limited to, being operationally able to determine that an individual badge was read, being operationally able to determine that an individual computer account has been logged onto, or being operationally able to determine that a signature of the individual personal computing device has been noticed. An individuals current metabolic rate may be determined by the comfort model accessing an individuals wearable fitness device information. The individual may have previously given information to the computerized controller 205 to allow the computerized controller or another designated device to access the information. The comfort input module may be operationally able to use the individual personal computing device to allow an individual to declare current clothing being worn. The comfort module may modify the comfort goals of an individual, may be updated by the individual, such as preferred temperature, current clothing being worn, current metabolic rate, etc.

A scheduler module may be able to look at the number of individuals expected to be in the controlled space at a future time and may be able to instruct the state change module to run the state change system to meet a future time comfort goal based on the number of individuals, an aggregated comfort goal, or a different goal.

IV. Exemplary Method Embodiments

FIG. 4 depicts a method configured to implement traveling comfort information in accordance with one or more embodiments. The operations of method 400 and other methods presented below are intended to be illustrative. In some embodiments, method 400 may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of method 400 are illustrated in FIG. 4 and described below is not intended to be limiting. In some embodiments, method 400 may be implemented in one or more processing devices (e.g., a distributed system, a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices executing some or all of the operations of method 400 in response to instructions stored electronically on an electronic storage medium. The one or more processing devices may include one or more devices configured through hardware, firmware, and/or software to be specifically designed for execution of one or more of the operations of method 400.

Occupancy comfort can be atomized to the comfort of specific humans. Parameters that can be determined on a person-specific level comprise: heat of person, convection, sweat, activity levels, metabolic rate, location, CLO (the insulation value of the clothing a person is wearing). In one embodiment, a controller 205 may have a file of a person (height, weight, etc.). The controller may also be able to monitor activity level by, for example, accessing a wearable fitness device or a phone associated with an individual. Through this information automation processes in the building may be able to infer metabolic rate (met). Using the metabolic rate the automation system may be able to make a good guess as to what temperature (or other states) the person with the wearable fitness device would prefer. A person could also have temperature and other building state preferences on file that the controller automation system then attempts to meet (within the other competing needs within the building.)

The comfort goal may be used to determine permissible comfort values. It is a number value (such as a value between 0 and 3) that defines how close to absolute comfort we hope the model to get. The comfort goal, in some implementations, gives an allowable error range for the final resource control state curves; a low value may indicate that the comfort curve must be closely matched, while a higher number may indicate that there is more leeway allowed. The values may be reversed, such that a low value indicates a higher tolerance for error, etc.

At operation 405, comfort goals associated with individuals are recorded. These goals may be recorded using a personal input device 190 that the computerized controller associates with the user. This may be done using e.g., IMEI & GPS call trackers, or other methods known to those of skill in the art. This personal input device 190 may have a wireless signal that comprises strength and directionality that allows the computing environment to determine where a user associated with the personal electronic device is within the building. A user may also be identified by using a personal electronic device to log on to a wireless network.

At operation 410 it is determined when an individual with associated comfort goals is in the controlled space. This determination may be based on the number of noticed signals from a sensor that can determine location based on bluetooth signals from personal electronic devices 190. The sensor may be a passive infrared sensor, an imaging IR sensor, etc. A value may be updated that indicates number of people in the space, a different method may be used to indicate number of people in the zone. The number of individuals expected to be in the controlled space at a future time may be determined 435. This may be determined by a schedule that the computerized controller has access to, the number may be input into a control panel associated with the computerized controller, etc. The state change system may then be instructed to meet a future time comfort goal based on the number of individuals. If the individuals are known, then the comfort goals associated with the known individuals may be used to determine the associated comfort goals. At a minimum, each person gives off a certain amount of heat (˜350,000 J of energy per hour). It can then be determined how to adjust resources within the controlled space to account for the extra heat. This may involve, e.g., the controller turning up the air conditioner (or turning down the heat) before the extra heat from the people is recorded on temperature thermometers, for example.

At operation 415, an individual is associated with their comfort goals. This may be performed by a method known by those of skill in the art, using keys associated databases stored within memory 120, 310. The comfort goals may be kept in a database associated with the associated individuals. The database may also have information allowing a personal electronic device 190 to be identified with a specific individual. There may also be information allowing a personal electronic device to be recognized.

At operation 420, individuals in a controlled space zone are grouped, creating a zone group. The entire controlled space 220 may be a single zone, the controlled space may be divided into multiple zones, etc. The computerized controller may discover people in a zone using a sensor, using a counter at an opening, using a sign-in mechanism, by noticing an individual's personal electronic device 190, by using triangulation to determine where personal electronic devices are, etc.

At operation 425, an aggregated comfort goal of the zone group is determined based on comfort goals of individuals within the zone group. The controlled space may determine an aggregate comfort goal by a time schedule; e.g., at a certain time check who is in various zones, and then adjust the aggregated comfort goal based on the individual comfort goals of the people in the zones. One or more zones may be checked and adjusted by different or the same time schedules. The controlled space may determine an aggregate comfort goal by zone occupancy. In such a case, when one or more people enter or leave a zone, the aggregate comfort goal may be changed. When the aggregate comfort goal is changed it may trigger the state change system to meet the new aggregate comfort goal. Individual comfort goals may comprise height, weight, preferred clothing, preferred temperature, preferred lighting, preferred security, preferred location, preferred entertainment, preferred grounds control behavior, other preferences, or some combination of the above. An individual comfort goal may take into account an individual activity level derived from a wearable device. Similarly, an individual can modify their individual comfort goals using their wearable device. A desired target path is a possible name for a state curve that models chosen comfort qualities such as temperature.

At operation 430, running a state change system associated with the controlled space zone is run to meet the aggregated comfort goal. This state change system may be an HVAC system, a security system, an entertainment system, a lighting system, an irrigation system, some other type of state change system, or a combination of two or more state change systems. As the controller controls resources in the controlled space, once the controller understands what needs to be modified (e.g., lower the temperature, reduce allergens), the controller can turn on the appropriate resources (e.g., air conditioner, dampers, vents, air purifiers, etc.) to ensure that the temperature and allergen level are lowered, for example, in an individual's office.

In some embodiments, a neural network system may be used to find an optimal or near-optimal resource use to achieve the aggregate comfort goal. The neural network system may comprise a neural network that models the controlled space 305 structure, and a neural network that models the modulatable state-change resources that control the controlled space 305. These resources may model equipment, just general state (eg. heat) moving into the structure, weather, changes to state caused by occupancy; changes to state caused by lighting, and so on. As an example, if a formerly empty 70 degree conference room fills up with 50 bodies in a period of minutes, this extra heat may be accounted for here.

As an example, to determine the aggregated comfort goal, what state values the controlled space 305 should have are determined. A person (or an object) may have an ideal temperature at 70 degrees, for a specific example. However, how people experience temperature is dependent on more than just the straight temperature. It also depends on, e.g., humidity, air flow, radiant heat, and so on. Different state curves with different values may match the desired aggregated comfort goal. For example, higher humidity and lower temperature may be equivalent with state curves modeling lower humidity and higher temperature. We combine this information to determine time-series comfort curves for the different zones.

Generally, the model runs by iteratively taking what we want as output, for this example, heat, as input into the simulation model, run for a specified period of time, and outputs a simulated version of what we are using as input. Neural networks may be run iteratively to optimize values by using the high level output as input and the input as output. So, for example, the structure model may iteratively run by choosing a set of values for the amount of state that needs to be present in various zones to meet the comfort goals (simulated load curves), running them through the structure model, which outputs the curves which represent the simulated comfort achieved in the structure (simulated source curves). These simulated source are then checked to see how closely they match the requested comfort goals (source curves). The difference between the simulated source curves and the aggregated comfort goal are checked using a cost function, described elsewhere. Then, using machine learning techniques, a new set of load curves are chosen that should produce output closer to the given source curves.

An equipment model may behave similarly. The high-level output, information that can be used to program resources, i.e., “control sequences” for the resources, are used as input, the equipment model is run and outputs simulated load curves. These load curves are checked against the initial input control sequences using a cost function, then a new set of control sequence inputs are chosen using machine learning techniques. This process continues until the initial load curves are close enough to the simulated load curves, at which time the last control sequence simulated is used as the answer, and is used to control the state change system to maximize traveling human comfort.

In some implementations, a gradient descent machine learning technique is used for the machine learning technique. The gradient descent algorithm may be used in calculating a cost function. The cost function may evaluate the derivative values between the simulation model and the historical data to determine the next set of inputs. It may also use backpropagation through the model to determine the next set of inputs. Gradient descent is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. To find a local minimum of a function using gradient descent, we take steps proportional to the negative of the gradient (or approximate gradient) of the function at the current point. In some instances, gradient ascent may be used. Gradient ascent comprises taking steps proportional to the positive of the gradient.

In some embodiments, the derivative values between the simulation model and the historical data are used to determine the next set of inputs. In some embodiments, automatic differentiation (also called algorithmic differentiation or computational differentiation)—a set of techniques to evaluate the derivative matrix of a function—is used. In other embodiments symbolic differentiation, numerical differentiation or some combination is used. Autodifferentiation, is a set of techniques used to evaluate the derivative of a function. This can be generalized to multiple variables as a matrix product of Jacobians. Specifically, when automatic differentiation is used, a first-order or second-order differential is taken. In some instances, a Vector Jacobian Product, is used. In some embodiments a genetic algorithm or a similar algorithm is used for backpropagation and for cost function determination.

FIG. 5 at 500 depicts individuals in zones in accordance with one or more embodiments. The sample controlled space has four zones: zone 1 505, zone 2 510, zone 3 515, and zone 4 520. Individual 1 525 and individual 2 530 are in zone 1. In operation 410, determine (or detect) when individuals are in a controlled space, individual 1 525 and individual 2 530 would be determined to be in zone 1, individual 3 535 would be determined to be in zone 3 515, individual 4 540 in zone 4 520, and nobody would be identified in zone 2 510. The individual would be identified in some way, as discussed further with relation to FIG. 7 and the surrounding text. Once an individual is identified, that identification may be used to key into a database or other structure to determine an individual's comfort goals. Individual comfort goals are discussed further with relation to FIG. 9. Individual 1 525 and individual 2 530 will be grouped into a zone 1 group. The zone 2 group contains nobody, the zone 3 group contains individual 3 535, and the zone 4 group contains individual 4 540. Aggregate comfort goals are determined based on the individual comfort goals of each person in each zone. For Zone 1 505, the goals of individual 1 525 and individual 2 530 will be aggregated. For zone 2 510, as there are no people in the zone, there might be comfort goals associated with empty spaces, which would be used here. The same calculations would take place in zone 3 515 and zone 4 520. The state system, such as an HVAC system, may then adjust the state of the zones to meet the aggregate comfort goals in the zones.

FIG. 6 at 600 depicts individuals in zones in accordance with one or more embodiments. Individual 1 525, 605 has moved from zone 1 505 to zone 3 515. Individual 1's individual comfort goals 905 have, in essence, moved with them. When an aggregate comfort goal determination happens (see FIG. 8) then the state change system may run systems (such as heating systems and cooling systems) in the zones whose aggregate comfort goals have changed., or in other zones, the entire controlled space, etc.

FIG. 7 at 700 discloses some ways that a controller 205 may notice that an individual has entered or left a controlled space 705. A badge 710 being worn by an individual may be read by a badge-reading device. A device, such as a personal electronic device, may have a cell identification signature that allows the device to be noticed 715 when it enters a controlled space and as it moves through zones. This signature may be a signal, an idle signal that is emitted when an active call is not being made, a cell identifier, etc. that can be discerned. The Device 715 signal may be noted when an individual logs onto a network, there may be a distinctive number associated with the device that can be noticed. A tag 720 worn by an individual, implanted into an individual, or on a piece of equipment associated with an individual, may be read by a controller 205 keeping track of the label, and thus, the individual as he or she moves through a controlled space 305. When an individual logs into a specific computer account, he or she may be marked as being at the location of the computer 725. Individuals may be able to sign in to a location in a controlled space using a different method. Individuals may have a default location (such as an office) that may be used as a default when other information is not known, or at other times.

FIG. 8 at 800 discloses some actions that may trigger an aggregate comfort goal determination 805. Aggregate comfort goal determination may occur according to a time schedule 810 (such as aggregate comfort goals are determined just after shift changes). Such a determination may be made when a person enters or exits a zone or both 815, when a certain number of people enter or exit a zone, or both, 820, when there is an outside change 825, such as a weather event that causes a big change in the building, a fire alarm, etc.

FIG. 9 at 900 discloses some individual comfort goals 905 that can be moved with the individual. The comfort goals may be kept as a structure in a database associated with a controlled space controller or controller system 205; the information or part of the information may be kept in several structures, or across several controllers; the information may be kept in all or part on a personal device 190, etc. These individual comfort goals may be entered on a personal electronic device 190 using a website, using an input device 150 associated with a computing environment 100 associated with the controlled space, a combination of the above, and using other operations as known by those of skill in the art. Individual comfort goals 905 may comprise personal facts 910 and personal comfort goals 935. These distinctions are just used for clarity, and any appropriate, or no such distinction, may be used. Personal facts that may be used to determine comfort goals include height 915, weight 920, sex 925, clothing preference 930, current clothing, etc. Personal comfort goals may comprise ideal temperature 940, preferred security 945; e.g., what doors should be locked or unlocked when the individual is in the controlled space; personal lighting 950, e.g., what lights should be on or off when the individual is in the building, such as office lighting, lighting for the floor (or section of the floor the individual's office is on, etc.). Location 955 may also be stored; this may be the office location, preferred location often visited, etc.

Individual identification methods 960 may be stored with individual comfort goals 905, or may be stored elsewhere. These identification methods may include ways that an individual may be located within a controlled space 220. Thus may include an identification for a tag 965 that a person could wear (such as an office badge) that can mark a person's location, such as by what the last door entered was. Such a tag 965 may need to be swiped, or may automatically register location. A personal device id 970 may also be stored that can recognize a personal device such as a tablet or a phone when it enters and moves through a controlled space. This might be used by a device that uses gps or wifi pinpoint a specific phone with the given id. A computer at a specific location with a distinctive id, or other distinguishing feature 975, may be used to identify an individual that has logged on to it with a personal account. Facial recognition methods 980 that can track a person may also be used to determine a person's location. A person's individual current metabolic rate 985 may also be taken into account by being stored in individual comfort goals, or stored or reviewed elsewhere. The metabolism may be stored as a value (e.g., low, medium, high), or may be recorded from an individual wearable fitness device with an activity tracker 990 such as a Fitbit or a cell phone. Such a recording may happen when a person enters a controlled space 220, whenever an aggregate comfort goal is determined, etc.

V. Exemplary Computer-Readable Medium that Comprises Traveling Comfort Information

With reference to FIGS. 1, 2 and 3, some embodiments include a configured computer-readable storage medium 165. Medium 165 may include disks (magnetic, optical, or otherwise), RAM, EEPROMS or other ROMs, and/or other configurable memory, including computer-readable media (not directed to a manufactured transient phenomenon, such as an electrical, optical, or acoustical signal). The storage medium which is configured may be a removable storage medium 165 such as a CD, DVD, or flash memory. A general-purpose memory (which may be primary, such as RAM, ROM, CMOS, or flash; or may be secondary, such as a CD, a hard drive, an optical disk, or a removable flash drive), can be configured into an embodiment using the computing environment 100, the computerized controller 205, the controller 307, or any combination of the above, in the form of data 180 and instructions 175, read from a source, such as a removable medium output device 155, to form a configured medium with data and instructions which upon execution by a processor perform a method for computing traveling comfort information. The configured medium 165 is capable of causing a computer system to perform actions as related herein.

Some embodiments provide or utilize a computer-readable storage medium 165 configured with software 185 which upon execution by at least a central processing unit 110 performs methods and systems described herein.

In view of the many possible embodiments to which the principles of the disclosed invention may be applied, it should be recognized that the illustrated embodiments are only preferred examples of the invention and should not be taken as limiting the scope of the invention. Rather, the scope of the invention is defined by the following claims. We therefore claim as our invention all that comes within the scope and spirit of these claims.

Claims

1. A computer-implemented state control system, in a controlled space comprising: a processor, a memory in operational communication with the processor;

a comfort module at least partially stored in memory, operationally able to record comfort goals associated with individuals;

a location module operationally able to determine when an individual is in a zone in the controlled space;

an association module operationally able to associate an individual with individual comfort goals;

a grouping module operationally able to group individuals into the zone creating a zone group;

an aggregation module operationally able to determine an aggregated comfort goal of the zone group; and

a state change module operationally able to run a state change system associated with the zone group to meet comfort goals of the zone group.

2. The computer-implemented state control system of claim 1, further comprising a comfort input module operationally able to accept comfort information from an individual.

3. The computer-implemented state control system of claim 2, wherein the location module is operationally able to associate the individual with an individual personal electronic device.

4. The computer-implemented state control system of claim 3, wherein the location module is operationally able to determine that an individual has entered the controlled space.

5. The computer-implemented state control system of claim 4, wherein the comfort input module is operationally able to determine that an individual has entered the controlled space comprises being operationally able to determine that an individual badge was read, being operationally able to determine that an individual computer account has been logged onto, or being operationally able to determine that a signature of the individual personal computing device has been noticed.

6. The computer-implemented state control system of claim 3, wherein the comfort input module is operationally able to determine individual current metabolic rate by accessing information from an individual wearable fitness device.

7. The computer-implemented state control system of claim 3, wherein the comfort input module is operationally able to use the individual personal computing device to allow an individual to declare current clothing being worn.

8. The computer-implemented state control system of claim 1, further comprising a scheduler module that determines number of individuals expected to be in the controlled space at a future time and instructs the state change module to run the state change system to meet a future time comfort goal based on the number of individuals or an aggregated comfort goal.

9. The computer-implemented state control system of claim 1, wherein individual comfort goals comprise personal height, personal weight, preferences for location, preferences for temperature, preferences for humidity, preferences for lighting, preferences for security, preferences for entertainment, preferences for personal services, preferences for grounds control behavior, personal height, or personal weight.

10. The computer-implemented state control system of claim 1, wherein the state change system is an HVAC system, a security system, an entertainment system, a lighting system, or an irrigation system.

11. A computer-implemented method for creating and using individual moving comfort goals in a controlled space, comprising:

recording individual comfort goals;

determining when an individual is in the controlled space;

associating an individual with their individual comfort goals;

detecting when at least one individuals is in a zone creating a zone group; and

determining an aggregated comfort goal of the zone group based on comfort goals of individuals within the zone group.

12. The computer-implemented method of claim 11, further comprising running a state change system associated with the controlled space to meet the aggregated comfort goal.

13. The computer-implemented method of claim 12, further comprising the controlled space determining an aggregate comfort goal by a time schedule, by zone occupancy, outside change, or by change in number of people in a zone.

14. The computer-implemented method of claim 12, wherein individual comfort goals comprise: height, weight, preferred clothing, preferred temperature, preferred lighting, preferred security, preferred location, preferred entertainment, or preferred grounds control behavior.

15. The computer-implemented method of claim 14, wherein individual comfort goals further comprise activity level derived from a wearable device.

16. The computer-implemented method of claim 15, wherein an individual can modify their individual comfort goals using the wearable device.

17. The computer-implemented method of claim 11, further comprising determining number of individuals expected to be in the controlled space at a future time and instructing the state change system to meet a future time comfort goal based on the number of individuals or the comfort goals associated with individuals expected to be in the controlled space.

18. A computer-readable storage medium configured with data and instructions which upon execution by a processor perform a method for using moveable comfort goals in a controlled space, the method comprising:

recording individual comfort goals;

determining when an individual is in the controlled space;

associating an individual with their individual comfort goals;

detecting when at least one individuals is in a zone creating a zone group;

determining an aggregated comfort goal of the zone group based on comfort goal of individuals within the zone group; and

running a state change system associated with the controlled space to meet the aggregated comfort goal.

19. The computer-readable storage medium of claim 18, wherein a heterogenous neural network is used to determine control sequences using the aggregated comfort goal to run the state change system.

20. The computer-readable storage medium of claim 19, wherein the heterogenous neural network uses automatic differentiation for backpropagation.