COMPUTERIZED SYSTEMS AND METHODS FOR PROVIDING PROTECTIVE SAFEGUARDS WITHIN A LOCATION FOR ITEMS LOCATED THEREIN

Info

Publication number: 20240288192
Type: Application
Filed: Feb 7, 2024
Publication Date: Aug 29, 2024
Applicant: Ademco Inc. (Golden Valley, MN)
Inventors: Julio Alberto Delgado Trevizo (Chihuahua), Mario Ballesteros (Chihuahua), Miguel Diaz (Chihuahua), Abraham Gonzalez Romero (Chihuahua), Jose Luis Garcia (Chihuahua), Cesar Rodriguez (Chihuahua), Rodolfo Piña (Chihuahua)
Application Number: 18/435,535

Abstract

Disclosed are systems and methods that provide a novel framework that automatically and dynamically adjusts and controls the real-world conditions of an environment within a location. Such adjustment can be based on characteristics of items and/or persons within a location, such that the integrity of the items are prevented from being compromised. The framework can function by determining and enforcing safe thresholds for environmental conditions (e.g., temperature and humidity, for example) using artificial intelligence and/or machine learning (AI/ML) techniques. Climate and environmental condition information within the location, as well as attributes/characteristics of the location and/or items therein can be accounted for, and leveraged in determining environmentally safe thresholds that can be utilized for monitoring the location. When real-world conditions in the environment approach and/or exceed the safe thresholds, a climate system (e.g., a HVAC, for example) can be triggered, which can effectuate remediation of the unsafe conditions.

Description

Description

RELATED APPLICATION(S)

This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63/486,853, filed Feb. 24, 2023, the contents of which are incorporated herein by reference in its entirety.

FIELD OF THE DISCLOSURE

The present disclosure is generally related to a thermostat control system, and more particularly, to a decision intelligence (DI)-based computerized framework for a dynamically controlled and adjusted climate environment based on real-world conditions of the environment and materials existing therein.

BACKGROUND

Conventional climate control systems, such as heating, ventilation and cooling (HVAC) systems, can be programmed to control the temperature according to zones within a location. For example, the upstairs of a home can be programmed to setpoint temperature X while the downstairs can be programmed to setpoint temperature Y. Some systems may be equipped with capabilities for controlling air quality, or enabling air purification in-line with the controlled temperature.

SUMMARY OF THE DISCLOSURE

However, there does not currently exist a climate control system, and specifically configured thermostat device, that enables a dynamically controlled climate respective to a plurality of climate variables to ensure the safety and integrity of the materials and objects within a location.

According to some embodiments, as discussed herein, disclosed are systems and methods for a safety management framework that automatically and dynamically adjusts and controls the real-world conditions of the environment within a location. Such adjustment can be based on characteristics of items and/or persons within a location, such that the integrity of the items are prevented from being compromised.

Accordingly, in some embodiments, the disclosed framework can function by determining and enforcing safe thresholds for environmental conditions (e.g., temperature and humidity, for example) using artificial intelligence and/or machine learning (AI/ML) techniques. That is, according to some embodiments, an initial configuration of the disclosed framework's operation can involve an initial interview that involves inputs/interactions where, using a provided user interface (UI), selections relevant to aspects of a location and the items therein can be performed. For example, the selected items can include, but are not limited to, vulnerable people, pets or plants with special needs, specific construction materials, furniture, and the like. Moreover, in some embodiments, selections may further be related to, but not limited to, specific areas within a location (e.g., rooms within a home, for example), types of rooms, types of construction (e.g., rooms with wooden floors, for example), and the like.

As such, in some embodiments, the disclosed framework can account for the climate and environmental condition information within the location, as well as the attributes/characteristics of the location and/or items therein, and determine environmentally safe thresholds that can be leveraged for monitoring the location. When real-world conditions in the environment of the location approach and/or exceed the safe thresholds, a climate system (e.g., a HVAC, for example) can be triggered, which can effectuate remediation of the unsafe conditions.

By way of a non-limiting example, if a small dog is in a user's house, then the disclosed framework may prevent the indoor temperature from reaching levels which are dangerous for the small dog (while ensuring that the indoor temperature also remains safe for any other house occupants/items also located in the house).

According to some embodiments, a location, as discussed herein, can refer to, but is not limited to, any type of building, structure, whether enclosed or open-air, that has a definable geographical/physical area. For example, a structure can be a building with a set of rooms (e.g., a house or office, for example).

According to some embodiments, a method is disclosed for a dynamically controlled and adjusted climate environment based on real-world conditions of the environment and materials existing therein. In accordance with some embodiments, the present disclosure provides a non-transitory computer-readable storage medium for carrying out the above-mentioned technical steps of the framework's functionality. The non-transitory computer-readable storage medium has tangibly stored thereon, or tangibly encoded thereon, computer readable instructions that when executed by a device cause at least one processor to perform a method for a dynamically controlled and adjusted climate environment.

In accordance with some embodiments, a system is provided that includes one or more processors and/or computing devices configured to provide functionality in accordance with such embodiments. In accordance with one or more embodiments, functionality is embodied in steps of a method performed by at least one computing device. In accordance with one or more embodiments, program code (or program logic) executed by a processor(s) of a computing device to implement functionality in accordance with one or more such embodiments is embodied in, by and/or on a non-transitory computer-readable medium.

DESCRIPTIONS OF THE DRAWINGS

The features, and advantages of the disclosure will be apparent from the following description of embodiments as illustrated in the accompanying drawings, in which reference characters refer to the same parts throughout the various views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating principles of the disclosure:

FIG. 1 is a block diagram of an example configuration within which the systems and methods disclosed herein could be implemented according to some embodiments of the present disclosure;

FIG. 2 is a block diagram illustrating components of an exemplary system according to some embodiments of the present disclosure;

FIG. 3A and FIG. 3B illustrate exemplary workflows according to some embodiments of the present disclosure;

FIG. 4 depicts an exemplary implementation of an architecture according to some embodiments of the present disclosure;

FIG. 5 depicts an exemplary implementation of an architecture according to some embodiments of the present disclosure; and

FIG. 6 is a block diagram illustrating a computing device showing an example of a client or server device used in various embodiments of the present disclosure.

DETAILED DESCRIPTION

The present disclosure will now be described more fully hereinafter with reference to the accompanying drawings, which form a part hereof, and which show, by way of non-limiting illustration, certain example embodiments. Subject matter may, however, be embodied in a variety of different forms and, therefore, covered or claimed subject matter is intended to be construed as not being limited to any example embodiments set forth herein; example embodiments are provided merely to be illustrative. Likewise, a reasonably broad scope for claimed or covered subject matter is intended. Among other things, for example, subject matter may be embodied as methods, devices, components, or systems. Accordingly, embodiments may, for example, take the form of hardware, software, firmware or any combination thereof (other than software per se). The following detailed description is, therefore, not intended to be taken in a limiting sense.

Throughout the specification and claims, terms may have nuanced meanings suggested or implied in context beyond an explicitly stated meaning. Likewise, the phrase “in one embodiment” as used herein does not necessarily refer to the same embodiment and the phrase “in another embodiment” as used herein does not necessarily refer to a different embodiment. It is intended, for example, that claimed subject matter include combinations of example embodiments in whole or in part.

In general, terminology may be understood at least in part from usage in context. For example, terms, such as “and”, “or”, or “and/or,” as used herein may include a variety of meanings that may depend at least in part upon the context in which such terms are used. Typically, “or” if used to associate a list, such as A, B or C, is intended to mean A, B, and C, here used in the inclusive sense, as well as A, B or C, here used in the exclusive sense. In addition, the term “one or more” as used herein, depending at least in part upon context, may be used to describe any feature, structure, or characteristic in a singular sense or may be used to describe combinations of features, structures or characteristics in a plural sense. Similarly, terms, such as “a,” “an,” or “the,” again, may be understood to convey a singular usage or to convey a plural usage, depending at least in part upon context. In addition, the term “based on” may be understood as not necessarily intended to convey an exclusive set of factors and may, instead, allow for existence of additional factors not necessarily expressly described, again, depending at least in part on context.

The present disclosure is described below with reference to block diagrams and operational illustrations of methods and devices. It is understood that each block of the block diagrams or operational illustrations, and combinations of blocks in the block diagrams or operational illustrations, can be implemented by means of analog or digital hardware and computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer to alter its function as detailed herein, a special purpose computer, ASIC, or other programmable data processing apparatus, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, implement the functions/acts specified in the block diagrams or operational block or blocks. In some alternate implementations, the functions/acts noted in the blocks can occur out of the order noted in the operational illustrations. For example, two blocks shown in succession can in fact be executed substantially concurrently or the blocks can sometimes be executed in the reverse order, depending upon the functionality/acts involved.

For the purposes of this disclosure a non-transitory computer readable medium (or computer-readable storage medium/media) stores computer data, which data can include computer program code (or computer-executable instructions) that is executable by a computer, in machine readable form. By way of example, and not limitation, a computer readable medium may include computer readable storage media, for tangible or fixed storage of data, or communication media for transient interpretation of code-containing signals. Computer readable storage media, as used herein, refers to physical or tangible storage (as opposed to signals) and includes without limitation volatile and non-volatile, removable and non-removable media implemented in any method or technology for the tangible storage of information such as computer-readable instructions, data structures, program modules or other data. Computer readable storage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, optical storage, cloud storage, magnetic storage devices, or any other physical or material medium which can be used to tangibly store the desired information or data or instructions and which can be accessed by a computer or processor.

For the purposes of this disclosure the term “server” should be understood to refer to a service point which provides processing, database, and communication facilities. By way of example, and not limitation, the term “server” can refer to a single, physical processor with associated communications and data storage and database facilities, or it can refer to a networked or clustered complex of processors and associated network and storage devices, as well as operating software and one or more database systems and application software that support the services provided by the server. Cloud servers are examples.

For the purposes of this disclosure a “network” should be understood to refer to a network that may couple devices so that communications may be exchanged, such as between a server and a client device or other types of devices, including between wireless devices coupled via a wireless network, for example. A network may also include mass storage, such as network attached storage (NAS), a storage area network (SAN), a content delivery network (CDN) or other forms of computer or machine-readable media, for example. A network may include the Internet, one or more local area networks (LANs), one or more wide area networks (WANs), wire-line type connections, wireless type connections, cellular or any combination thereof. Likewise, sub-networks, which may employ differing architectures or may be compliant or compatible with differing protocols, may interoperate within a larger network.

For purposes of this disclosure, a “wireless network” should be understood to couple client devices with a network. A wireless network may employ stand-alone ad-hoc networks, mesh networks, Wireless LAN (WLAN) networks, cellular networks, or the like. A wireless network may further employ a plurality of network access technologies, including Wi-Fi, Long Term Evolution (LTE), WLAN, Wireless Router mesh, or 2nd, 3rd, 4^thor 5^thgeneration (2G, 3G, 4G or 5G) cellular technology, mobile edge computing (MEC), Bluetooth, 802.11b/g/n, or the like. Network access technologies may enable wide area coverage for devices, such as client devices with varying degrees of mobility, for example.

In short, a wireless network may include virtually any type of wireless communication mechanism by which signals may be communicated between devices, such as a client device or a computing device, between or within a network, or the like.

A computing device may be capable of sending or receiving signals, such as via a wired or wireless network, or may be capable of processing or storing signals, such as in memory as physical memory states, and may, therefore, operate as a server. Thus, devices capable of operating as a server may include, as examples, dedicated rack-mounted servers, desktop computers, laptop computers, set top boxes, integrated devices combining various features, such as two or more features of the foregoing devices, or the like.

For purposes of this disclosure, a client (or user, entity, subscriber or customer) device may include a computing device capable of sending or receiving signals, such as via a wired or a wireless network. A client device may, for example, include a desktop computer or a portable device, such as a cellular telephone, a smart phone, a display pager, a radio frequency (RF) device, an infrared (IR) device a Near Field Communication (NFC) device, a Personal Digital Assistant (PDA), a handheld computer, a tablet computer, a phablet, a laptop computer, a set top box, a wearable computer, smart watch, an integrated or distributed device combining various features, such as features of the forgoing devices, or the like.

A client device may vary in terms of capabilities or features. Claimed subject matter is intended to cover a wide range of potential variations, such as a web-enabled client device or previously mentioned devices may include a high-resolution screen (HD or 4K for example), one or more physical or virtual keyboards, mass storage, one or more accelerometers, one or more gyroscopes, global positioning system (GPS) or other location-identifying type capability, or a display with a high degree of functionality, such as a touch-sensitive color 2D or 3D display, for example.

Certain embodiments and principles will be discussed in more detail with reference to the figures. With reference to FIG. 1, system 100 is depicted which includes user equipment (UE) 102 (e.g., a client device, as mentioned above and discussed below in relation to FIG. 6), sensors 110, network 104, cloud system 106, database 108, safety engine 200 and peripheral device 112. It should be understood that while system 100 is depicted as including such components, it should not be construed as limiting, as one of ordinary skill in the art would readily understand that varying numbers of UEs, peripheral devices, sensors, cloud systems, databases and networks can be utilized; however, for purposes of explanation, system 100 is discussed in relation to the example depiction in FIG. 1.

According to some embodiments, UE 102 can be any type of device, such as, but not limited to, a mobile phone, tablet, laptop, sensor, Internet of Things (IoT) device, autonomous machine, and any other device equipped with a cellular or wireless or wired transceiver. In some embodiments, UE 102 can be a device associated with an individual (or set of individuals) for which disclosed services are being provided. In some embodiments, UE 102 may correspond to a device of a HVAC or climate-control related entity (e.g., a HVAC provider, whereby the device can be and/or can have corresponding sensors 110, as discussed herein)—for example, a thermostat.

In some embodiments, peripheral device 112 can be connected to UE 102, and can be any type of peripheral device, such as, but not limited to, a wearable device (e.g., smart watch), printer, speaker, sensor, and the like. In some embodiments, peripheral device 112 can be any type of device that is connectable to UE 102 via any type of known or to be known pairing mechanism, including, but not limited to, Bluetooth™, Bluetooth Low Energy (BLE), NFC, and the like.

According to some embodiments, a sensors 110 can correspond to sensors associated with a location of system 100. In some embodiments, the sensors 110 can be associated with security sensors, such as, for example, cameras, glass break detectors, motion detectors, door and window contacts, heat and smoke detectors, carbon monoxide (CO2) detectors, passive infrared (PIR), time-of-flight (ToF) sensors, and the like. In some embodiments, the sensors can be associated with devices associated with the location of system 100, such as, for example, lights, smart locks, garage doors, smart appliances (e.g., thermostat, refrigerator, television, personal assistants (e.g., Alexa®, Nest®, for example)), smart phones, smart watches or other wearables, tablets, personal computers, and the like, and some combination thereof. Thus, the sensors 110 can be, wholly or in part, part of an IoT sensor network. For example, the sensors 110 can include the sensors on UE 102 (e.g., smart phone) and/or peripheral device 112 (e.g., a paired smart watch).

In some embodiments, network 104 can be any type of network, such as, but not limited to, a wireless network, cellular network, the Internet, and the like (as discussed above). Network 104 facilitates connectivity of the components of system 100, as illustrated in FIG. 1.

According to some embodiments, cloud system 106 may be any type of cloud operating platform and/or network based system upon which applications, operations, and/or other forms of network resources may be located. For example, system 106 may be a service provider and/or network provider from where services and/or applications may be accessed, sourced or executed from. For example, system 106 can represent the cloud-based architecture associated with a location monitoring and/or control system provider (e.g., Resideo®, for example), which has associated network resources hosted on the internet or private network (e.g., network 104), which enables (via engine 200) the temperature management discussed herein.

In some embodiments, cloud system 106 may include a server(s) and/or a database of information which is accessible over network 104. In some embodiments, a database 108 of cloud system 106 may store a dataset of data and metadata associated with local and/or network information related to a user(s) of UE 102/device 112 and the UE 102/device 112, sensors 110, and the services and applications provided by cloud system 106 and/or safety engine 200.

In some embodiments, for example, cloud system 106 can provide a private/proprietary climate management platform, whereby engine 200, discussed infra, corresponds to the novel functionality system 106 enables, hosts and provides to a network 104 and other devices/sensors/platforms operating thereon.

Turning to FIG. 4 and FIG. 5, in some embodiments, the exemplary computer-based systems/platforms, the exemplary computer-based devices, and/or the exemplary computer-based components of the present disclosure may be specifically configured to operate in a cloud computing/architecture 106 such as, but not limiting to: infrastructure a service (IaaS) 510, platform as a service (PaaS) 508, and/or software as a service (SaaS) 506 using a web browser, mobile app, thin client, terminal emulator or other endpoint 504. FIG. 4 and FIG. 5 illustrate schematics of non-limiting implementations of the cloud computing/architecture(s) in which the exemplary computer-based systems for administrative customizations and control of network-hosted application program interfaces (APIs) of the present disclosure may be specifically configured to operate.

Turning back to FIG. 1, according to some embodiments, database 108 may correspond to a data storage for a platform (e.g., a network hosted platform, such as cloud system 106, as discussed supra), a plurality of platforms, and/or UE 102 and/or sensors 110. Database 108 may receive storage instructions/requests from, for example, engine 200 (and associated microservices), which may be in any type of known or to be known format, such as, for example, standard query language (SQL). According to some embodiments, database 108 may correspond to any type of known or to be known storage, for example, a memory or memory stack of a device, a distributed ledger of a distributed network (e.g., blockchain, for example), a look-up table (LUT), and/or any other type of secure data repository.

Safety engine 200, as discussed above and further below in more detail, can include components for the disclosed functionality. According to some embodiments, safety engine 200 may be a special purpose machine or processor, and can be hosted by a device on network 104, within cloud system 106 and/or on UE 102 (and/or peripheral device 112). In some embodiments, engine 200 may be hosted by a server and/or set of servers associated with cloud system 106.

According to some embodiments, as discussed in more detail below, safety engine 200 may be configured to implement and/or control a plurality of services and/or microservices, where each of the plurality of services/microservices are configured to execute a plurality of workflows associated with performing the disclosed temperature management. Non-limiting embodiments of such workflows are provided below in relation to at least FIG. 3A and FIG. 3B.

According to some embodiments, as discussed above, safety engine 200 may function as an application provided by cloud system 106. In some embodiments, engine 200 may function as an application installed on a server(s), network location and/or other type of network resource associated with system 106. In some embodiments, engine 200 may function as application installed and/or executing on UE 102. In some embodiments, such application may be a web-based application accessed by UE 102 and/or devices associated with sensors 110 over network 104 from cloud system 106. In some embodiments, engine 200 may be configured and/or installed as an augmenting script, program or application (e.g., a plug-in or extension) to another application or program provided by cloud system 106 and/or executing on UE 102 and/or sensors 110.

As illustrated in FIG. 2, according to some embodiments, safety engine 200 includes identification module 202, analysis module 204, determination module 206 and control module 208. It should be understood that the engine(s) and modules discussed herein are non-exhaustive, as additional or fewer engines and/or modules (or sub-modules) may be applicable to the embodiments of the systems and methods discussed. More detail of the operations, configurations and functionalities of engine 200 and each of its modules, and their role within embodiments of the present disclosure will be discussed below.

Turning to FIG. 3A and FIG. 3B, Process 300 provides non-limiting example embodiments for the disclosed safety management framework. In some embodiments, as discussed herein, the disclosed framework provides functionality for automatically and dynamically managing and controlling the real-world conditions of an environment within a location. Such control and management can be based on characteristics of items within a location, such that the integrity of the items are prevented from being compromised. The framework can function by determining and enforcing safe thresholds for environmental conditions (e.g., temperature, humidity, light, air quality, ultra-violet (UV) light, and the like, for example) using AI/ML techniques, as discussed herein.

Accordingly, according to some embodiments, climate and/or environmental condition information within the location, as well as attributes/characteristics of the location and/or items located therein can be accounted for and leveraged in determining environmentally safe thresholds that can be utilized for monitoring the location. Thus, when real-world conditions in the environment approach and/or exceed the safe thresholds, a climate system (e.g., a HVAC, for example) can be triggered, which can effectuate remediation of the unsafe conditions.

According to some embodiments, the instant disclosure herein relative to FIG. 3A and FIG. 3B will be discussed with reference to a single, undivided location. However, it should be understood that the functionality of Process 300 can be utilized for multiple locations and/or sub-divisions of a location. Thus, for example, Process 300 can provide a thermostat feature via engine 200's execution that enables the climate monitoring and control of the environment associated with and controlled by the thermostat.

For example, as discussed in reference below respective to the steps of Process 300, a single location will be discussed as being climate-monitored and controlled; however, it should be understood that a climate system for a location (e.g., a home) can have many sub-locations (e.g., rooms or zones, for example), whereby for each sub-location Process 300 can be executed so as to maintain the safety and integrity of the environment and items located therein.

According to some embodiments, Step 302 of Process 300 can be performed by identification module 202 of safety engine 200; Steps 304, 306, 310, 312 and 320 can be performed by determination module 206; Steps 308 and 318 can be performed by analysis module 204; and Steps 314, 316, 322 and 324 can be performed by control module 208.

According to some embodiments, Process 300 begins with Step 302 where engine 200 can identify a set of items within a location. As discussed above, the set of items can correspond to the items that are physically located in the location. For example, the items can be a musical instrument, the floor, paintings and the like that are located in a person's home.

In some embodiments, Step 302 can involve a user entering into a user interface (UI) information indicating the items. In some embodiments, the information related to the set of items can correspond to, but not be limited to, a type of item, category of item, constitution of the item, material of the item, age of the item, color of the item, and the like, or some combination thereof. In some embodiments, such information can be selected from a selectable list of information via the UI; in some embodiments, such information can be input by a user; and in some embodiments, some combination thereof.

In some embodiments, Step 302 can involve engine 200 executing a camera device (e.g., UE 102 and/or sensor 110, as discussed above), which can capture a picture of the location. Then in Step 302, engine 200 can execute any type of known or to be known computational analysis, AI/ML technique, to segment and identify the depicted items from the captured imagery. For example, engine 200 can execute a computer vision algorithm to detect the category, type and/or identity of specific items within the location. In some embodiments, this information can be provided within the UI, for the user to filter and/or select therefrom.

Accordingly, by way of a non-limiting example, Step 302 can involve the identifying of items within a location that can correspond to, but not be limited to, walls, floors, ceilings, decorations (e.g., drapes, blinds, for example), windows, paintings, televisions, appliances, people (e.g., adults, children, babies, elderly, for example), clothes, books, collectibles (e.g., records, tapes, tapestry, for example), pets, plants, and the like, or some combination thereof.

In Step 304, engine 200 can analyze the item information, and determine the characteristics of each of items. In some embodiments, the characteristics information can be provided via the UI in a similar manner as discussed above in Step 302. According to some embodiments, similar to the above discussion, the characteristics can further be based on a computational analysis of depicted imagery of the items. For example, computer vision can enable the deciphering of the color, texture, shape, dimensions and the like, of the items within the location.

In Step 306, engine 200 can determine a set of real-world parameters for the location. According to some embodiments, Step 304 can involve each of (or at least a portion of) the sensors at a the location (e.g., sensors 110 from FIG. 1, discussed supra) collecting data about the location for a predetermined period of time. Upon completion, and/or after the predetermined period of time, the sensors can provide engine 200 with information related to the collected sensor data. Such sensor data can correspond to the real-world attributes of the room, which can involve, but are not limited to, temperature, humidity, light, UV, air quality, ventilation, and the like, or some combination thereof. Accordingly, engine 200 can analyze such collected data and determine the current values for each parameter in the location.

For example, engine 200 can determine that the air temperature is 72 degrees Fahrenheit, the setpoint temperature is 70 degrees Fahrenheit, the relative humidity (RH) level is 50%, the dew point is 23 percent, the barometric reading is 30.11 inHg and rising, and the like.

According to some embodiments, the data/information collected and determined in Steps 302-306 can be stored in a data store (e.g., database 108), which can be used for further training the AI/ML models discussed herein, and/or leveraging as reference points for future modifications for how engine 200 monitors the location and triggers climate adjustments.

In Step 308, engine 200 can analyze the set of characteristics of the items (and the information associated with the items) and the real-world parameters. According to some embodiments, such information can be compiled into a query that can be input into a AI/ML model for the performance of a computational analysis of the input information.

According to some embodiments, the computational analysis of Step 308 can implementing any type of known or to be known computational analysis technique, algorithm, mechanism or technology to analyze the collected sensor data. In some embodiments, engine 200 may include a specific trained AI/ML model, a particular machine learning model architecture, a particular machine learning model type (e.g., convolutional neural network (CNN), recurrent neural network (RNN), autoencoder, support vector machine (SVM), and the like), or any other suitable definition of a machine learning model or any suitable combination thereof.

In some embodiments, engine 200 may be configured to utilize one or more AI/ML techniques chosen from, but not limited to, computer vision, feature vector analysis, decision trees, boosting, support-vector machines, neural networks, nearest neighbor algorithms, Naïve Bayes, bagging, random forests, logistic regression, and the like. By way of a non-limiting example, engine 200 can implement an XGBoost algorithm for regression and/or classification to analyze the sensor data, as discussed herein.

According to some embodiments and, optionally, in combination of any embodiment described above or below, a neural network technique may be one of, without limitation, feedforward neural network, radial basis function network, recurrent neural network, convolutional network (e.g., U-net) or other suitable network. In some embodiments and, optionally, in combination of any embodiment described above or below, an implementation of Neural Network may be executed as follows:

- a. define Neural Network architecture/model,
- b. transfer the input data to the neural network model,
- c. train the model incrementally,
- d. determine the accuracy for a specific number of timesteps,
- e. apply the trained model to process the newly-received input data,
- f. optionally and in parallel, continue to train the trained model with a predetermined periodicity.

In some embodiments and, optionally, in combination of any embodiment described above or below, the trained neural network model may specify a neural network by at least a neural network topology, a series of activation functions, and connection weights. For example, the topology of a neural network may include a configuration of nodes of the neural network and connections between such nodes. In some embodiments and, optionally, in combination of any embodiment described above or below, the trained neural network model may also be specified to include other parameters, including but not limited to, bias values/functions and/or aggregation functions. For example, an activation function of a node may be a step function, sine function, continuous or piecewise linear function, sigmoid function, hyperbolic tangent function, or other type of mathematical function that represents a threshold at which the node is activated. In some embodiments and, optionally, in combination of any embodiment described above or below, the aggregation function may be a mathematical function that combines (e.g., sum, product, and the like) input signals to the node. In some embodiments and, optionally, in combination of any embodiment described above or below, an output of the aggregation function may be used as input to the activation function. In some embodiments and, optionally, in combination of any embodiment described above or below, the bias may be a constant value or function that may be used by the aggregation function and/or the activation function to make the node more or less likely to be activated.

In some embodiments, as a result of the computational analysis in Step 308 (based on the input from Steps 302-306, discussed supra), engine 200 can determine a set of safe thresholds for each of the items in the set of items, as in Step 310. Thus, the safe thresholds can correspond to a range, limit (e.g., maximum and/or minimum value), and the like that corresponds to a safe value for the item to exist within the location respective to a particular real-world parameter

For example, if the item is a hardwood floor, the safe temperature threshold can be between 60-80 degrees Fahrenheit within the location, with a safe RH threshold of 35-55%.

In another non-limiting example, wood furniture can have a safe humidity threshold of 40-60%, as determined based on the conditions in the location and the characteristics of the furniture.

In yet another non-limiting example, a painting can have a safe temperature threshold of 70-75 degrees Fahrenheit and a safe RH threshold of 40-50%, whereby the safe RH threshold involves a limiter that monitors an increase of RH within that range (e.g., less than 5% RH changes per hour).

And, in another non-limiting example, a painting may have a safe UV threshold of 7.5 mW/m², since the surface of the painting is illuminated at 100 lux (e g., item characteristics) with a relative UV measurement of 75 μW/lumen (real-world parameter).

In some embodiments, Step 310 can involve aggregating the safe thresholds for the items, which can involve generating a single set of safe thresholds for each real-world parameter for each item. Thus, in some embodiments, rather than having a set of safe thresholds for each item, the location can have a set of safe thresholds that accounts for the characteristic-based safe thresholds for each item located therein. In some embodiments, such aggregation can include compiling the single set of safe thresholds for the location via any of the AI/ML techniques discussed above, inter alia.

In Step 312, engine 200 can store the determine safe thresholds in a data store, a similar manner as discussed above.

Continuing with Process 300, in Step 314, engine 200 can perform monitoring of the location and/or environment of the location. Such monitoring can be performed in a similar manner as discussed above respective to the collection of sensor data from Step 306. In some embodiments, such monitoring can be performed according to a predetermined time period, detection of an event (e.g., sunrise/sunset, a person entering the room, a drop/increase of a real-world attribute at a rate above a threshold, and the like, or some combination thereof), upon a user request, when the system is armed or activated, based on a particular operation mode (e.g., cooling, heating, for example), and the like, or some combination thereof.

Thus, based on the interval (e.g., periodic or continuous) monitoring in Step 314, sensor data from at least a portion of the sensors at the location is collected, as in Step 316. In Step 316, therefore, engine 200 can determine the current real-world values of the parameters/conditions in the location, which as discussed above, can correspond to, but are not limited to, temperature, humidity, light, UV, air quality, ventilation, and the like, or some combination thereof. Such values can be determined in a similar manner as discussed above at least in relation to Step 306, discussed supra.

In Step 318, engine 200 analyzes the set of safe thresholds (e.g., for the location and/or the items located therein) based on the collected values from step 318. In some embodiments, Step 318 determines whether the values related to the real-world parameters/conditions have exceeded the safe thresholds. For example, if the current RH is 50%, yet the safe RH threshold for a piano is 51%, then this safe RH threshold will be determined as being exceeded.

In some embodiments, Step 318 can perform an analysis as to whether the values of the real-world parameters/conditions are increasing, decreasing and/or adjusting at a rate that indicates surpassing a safe threshold is imminent (e.g., a prediction). For example, if the current indoor temperature is 78 degrees Fahrenheit and the safe temperature threshold for a piece of wood furniture is 84 degrees Fahrenheit, but the analysis indicates that the indoor temperature is increasing at a rate of 2 degrees per five minutes, the analysis may indicate that the safe temperature threshold for the furniture item is going to be exceeded within a threshold period of time. Accordingly, in some embodiments, such analysis may be based on and/or in view of a threshold time period.

As such, in some embodiments, the analysis of Step 318 may be performed via the AI/ML computational analysis techniques discussed above at least in relation to Step 308.

In some embodiments, such AI/ML analysis may enable a comparison of the safe thresholds between items so as to enable monitoring of the environment respective to an item's safe thresholds in view of each other item's safe thresholds.

For example, if the safe threshold temperature for a painting is 78 degrees, and the safe threshold temperature for an elderly person is 82 degrees, as discussed below, the analysis-based operation of engine 200 may involve ensuring that the regulated indoor temperature stays below 78 degrees Fahrenheit so as to ensure a safe temperature threshold is satisfied for each item in the location.

Accordingly, based on the analysis in Step 318, in Step 320, engine 200 can determine whether safe thresholds are satisfied, as discussed above. In some embodiments, when a safe threshold is determined to be surpassed (e.g., a real-world parameter/condition in the location is either at, below or above a respective safe threshold value), engine 200 can proceed to Step 324.

In Step 324, engine 200 can operate to trigger the execution of the climate system so as to adjust the real-world conditions so that the real-world condition(s) that exceeded/surpassed (or failed) a respective safe threshold is automatically adjusted. As above, this adjustment is performed in accordance with other items' safe thresholds so as to ensure that the adjustment does not cause another item's safe threshold to be surpassed.

For example, if the indoor temperature is 81 degrees Fahrenheit, and the safe temperature threshold for a wood floor in the location is between 60-80 degrees, engine 200 can execute (e.g., via the thermostat, for example) a cooling mode so as to lower the temperature into the safe temperature threshold range. In some embodiments, a duration of the climate system's execution may be based on a proportional value of how far the current parameter value exceeds the respective threshold (e.g., if 1 degree surpassed, the climate system can operate for 10 minutes, for example). In some embodiments, rather than executing the climate system, engine 200 may trigger a ceiling fan to operate, which can be based on a type of remedial climate action required and/or a value of remediation (e.g., if less than 3 degrees, the ceiling fan may be more economical, for example).

In some embodiments, when the safe thresholds are not determined to be surpassed for the items within a location, engine 200 proceed from Step 320 to Step 322, where engine 200 can continuing monitoring the environment/location in a similar manner as discussed above respective to Step 314. Thus, further and/or continued collection of the sensor data can be performed so that the system can recursively operate so as to continuously protect the items within the location.

In some embodiments, after execution of Step 324, engine 200 can also recursively proceeds to continued monitoring, which can enable further remediation and/or turning off the remediation, in that upon further monitoring, a safe threshold previously determined as being surpassed may now be determined to be satisfied, as per the recursive steps of Process 300, as depicted in FIG. 3B.

FIG. 6 is a schematic diagram illustrating a client device showing an example embodiment of a client device that may be used within the present disclosure. Client device 600 may include many more or less components than those shown in FIG. 6. However, the components shown are sufficient to disclose an illustrative embodiment for implementing the present disclosure. Client device 600 may represent, for example, UE 102 discussed above at least in relation to FIG. 1.

As shown in the figure, in some embodiments, Client device 600 includes a processing unit (CPU) 622 in communication with a mass memory 630 via a bus 624. Client device 600 also includes a power supply 626, one or more network interfaces 650, an audio interface 652, a display 654, a keypad 656, an illuminator 658, an input/output interface 660, a haptic interface 662, an optional global positioning systems (GPS) receiver 664 and a camera(s) or other optical, thermal or electromagnetic sensors 666. Device 600 can include one camera/sensor 666, or a plurality of cameras/sensors 666, as understood by those of skill in the art. Power supply 626 provides power to Client device 600.

Client device 600 may optionally communicate with a base station (not shown), or directly with another computing device. In some embodiments, network interface 650 is sometimes known as a transceiver, transceiving device, or network interface card (NIC).

Audio interface 652 is arranged to produce and receive audio signals such as the sound of a human voice in some embodiments. Display 654 may be a liquid crystal display (LCD), gas plasma, light emitting diode (LED), or any other type of display used with a computing device. Display 654 may also include a touch sensitive screen arranged to receive input from an object such as a stylus or a digit from a human hand.

Keypad 656 may include any input device arranged to receive input from a user. Illuminator 658 may provide a status indication and/or provide light.

Client device 600 also includes input/output interface 660 for communicating with external. Input/output interface 660 can utilize one or more communication technologies, such as USB, infrared, Bluetooth™, or the like in some embodiments. Haptic interface 662 is arranged to provide tactile feedback to a user of the client device.

Optional GPS transceiver 664 can determine the physical coordinates of Client device 600 on the surface of the Earth, which typically outputs a location as latitude and longitude values. GPS transceiver 664 can also employ other geo-positioning mechanisms, including, but not limited to, triangulation, assisted GPS (AGPS), E-OTD, CI, SAI, ETA, BSS or the like, to further determine the physical location of client device 600 on the surface of the Earth. In one embodiment, however, Client device may through other components, provide other information that may be employed to determine a physical location of the device, including for example, a MAC address, Internet Protocol (IP) address, or the like.

Mass memory 630 includes a RAM 632, a ROM 634, and other storage means. Mass memory 630 illustrates another example of computer storage media for storage of information such as computer readable instructions, data structures, program modules or other data. Mass memory 630 stores a basic input/output system (“BIOS”) 640 for controlling low-level operation of Client device 600. The mass memory also stores an operating system 641 for controlling the operation of Client device 600.

Memory 630 further includes one or more data stores, which can be utilized by Client device 600 to store, among other things, applications 642 and/or other information or data. For example, data stores may be employed to store information that describes various capabilities of Client device 600. The information may then be provided to another device based on any of a variety of events, including being sent as part of a header (e.g., index file of the HLS stream) during a communication, sent upon request, or the like. At least a portion of the capability information may also be stored on a disk drive or other storage medium (not shown) within Client device 600.

Applications 642 may include computer executable instructions which, when executed by Client device 600, transmit, receive, and/or otherwise process audio, video, images, and enable telecommunication with a server and/or another user of another client device. Applications 642 may further include a client that is configured to send, to receive, and/or to otherwise process gaming, goods/services and/or other forms of data, messages and content hosted and provided by the platform associated with engine 200 and its affiliates.

As used herein, the terms “computer engine” and “engine” identify at least one software component and/or a combination of at least one software component and at least one hardware component which are designed/programmed/configured to manage/control other software and/or hardware components (such as the libraries, software development kits (SDKs), objects, and the like).

Examples of hardware elements may include processors, microprocessors, circuits, circuit elements (e.g., transistors, resistors, capacitors, inductors, and so forth), integrated circuits, application specific integrated circuits (ASIC), programmable logic devices (PLD), digital signal processors (DSP), field programmable gate array (FPGA), logic gates, registers, semiconductor device, chips, microchips, chip sets, and so forth. In some embodiments, the one or more processors may be implemented as a Complex Instruction Set Computer (CISC) or Reduced Instruction Set Computer (RISC) processors; x86 instruction set compatible processors, multi-core, or any other microprocessor or central processing unit (CPU). In various implementations, the one or more processors may be dual-core processor(s), dual-core mobile processor(s), and so forth.

Computer-related systems, computer systems, and systems, as used herein, include any combination of hardware and software. Examples of software may include software components, programs, applications, operating system software, middleware, firmware, software modules, routines, subroutines, functions, methods, procedures, software interfaces, API, instruction sets, computer code, computer code segments, words, values, symbols, or any combination thereof. Determining whether an embodiment is implemented using hardware elements and/or software elements may vary in accordance with any number of factors, such as desired computational rate, power levels, heat tolerances, processing cycle budget, input data rates, output data rates, memory resources, data bus speeds and other design or performance constraints.

For the purposes of this disclosure a module is a software, hardware, or firmware (or combinations thereof) system, process or functionality, or component thereof, that performs or facilitates the processes, features, and/or functions described herein (with or without human interaction or augmentation). A module can include sub-modules. Software components of a module may be stored on a computer readable medium for execution by a processor. Modules may be integral to one or more servers, or be loaded and executed by one or more servers. One or more modules may be grouped into an engine or an application.

One or more aspects of at least one embodiment may be implemented by representative instructions stored on a machine-readable medium which represents various logic within the processor, which when read by a machine causes the machine to fabricate logic to perform the techniques described herein. Such representations, known as “IP cores,” may be stored on a tangible, machine readable medium and supplied to various customers or manufacturing facilities to load into the fabrication machines that make the logic or processor. Of note, various embodiments described herein may, of course, be implemented using any appropriate hardware and/or computing software languages (e.g., C++, Objective-C, Swift, Java, JavaScript, Python, Perl, QT, and the like).

For example, exemplary software specifically programmed in accordance with one or more principles of the present disclosure may be downloadable from a network, for example, a website, as a stand-alone product or as an add-in package for installation in an existing software application. For example, exemplary software specifically programmed in accordance with one or more principles of the present disclosure may also be available as a client-server software application, or as a web-enabled software application. For example, exemplary software specifically programmed in accordance with one or more principles of the present disclosure may also be embodied as a software package installed on a hardware device.

For the purposes of this disclosure the term “user”, “subscriber” “consumer” or “customer” should be understood to refer to a user of an application or applications as described herein and/or a consumer of data supplied by a data provider. By way of example, and not limitation, the term “user” or “subscriber” can refer to a person who receives data provided by the data or service provider over the Internet in a browser session, or can refer to an automated software application which receives the data and stores or processes the data. Those skilled in the art will recognize that the methods and systems of the present disclosure may be implemented in many manners and as such are not to be limited by the foregoing exemplary embodiments and examples. In other words, functional elements being performed by single or multiple components, in various combinations of hardware and software or firmware, and individual functions, may be distributed among software applications at either the client level or server level or both. In this regard, any number of the features of the different embodiments described herein may be combined into single or multiple embodiments, and alternate embodiments having fewer than, or more than, all of the features described herein are possible.

Functionality may also be, in whole or in part, distributed among multiple components, in manners now known or to become known. Thus, myriad software/hardware/firmware combinations are possible in achieving the functions, features, interfaces and preferences described herein. Moreover, the scope of the present disclosure covers conventionally known manners for carrying out the described features and functions and interfaces, as well as those variations and modifications that may be made to the hardware or software or firmware components described herein as would be understood by those skilled in the art now and hereafter.

Furthermore, the embodiments of methods presented and described as flowcharts in this disclosure are provided by way of example in order to provide a more complete understanding of the technology. The disclosed methods are not limited to the operations and logical flow presented herein. Alternative embodiments are contemplated in which the order of the various operations is altered and in which sub-operations described as being part of a larger operation are performed independently.

While various embodiments have been described for purposes of this disclosure, such embodiments should not be deemed to limit the teaching of this disclosure to those embodiments. Various changes and modifications may be made to the elements and operations described above to obtain a result that remains within the scope of the systems and processes described in this disclosure.

Claims

1. A method comprising:

identifying, by a device, information related to an item within a location, the item being a physical object currently located within the location, the item information indicating at least one of a material of the item and a type of the item;

identifying, by the device, location parameters, the location parameters comprising information indicating real-world attributes of an environment within the location;

analyzing, by the device, the item information based on the location parameters;

determining, by the device, a set of safe thresholds for the item, each safe threshold in the set corresponding to a real-world attribute of the environment within the location;

monitoring, by the device, over a time period, the environment within the location, the monitoring comprising collecting information related to the environment within the location;

determining, by the device, based on the collected information, that at least one safe threshold is predicted as being surpassed; and

executing, by the device, a climate-control system at the location to modify the environment such that real-world attributes associated with the at least one safe threshold are adjusted to a value within the at least one safe threshold.

2. The method of claim 1, wherein the location parameters comprise at least one of temperature, humidity, light, air quality and ultraviolet (UV) light.

3. The method of claim 1, wherein the item information comprises information indicating a category of the item, the category corresponding to an associated set of location parameters.

4. The method of claim 1, wherein the location is a building structure, wherein the building structure comprises a set of rooms, wherein at least a portion of the rooms comprise at least one item.

5. The method of claim 1, further comprising:

analyzing each of the safe thresholds for each item;

determining an aggregation of the safe thresholds; and

compiling a single set of safe thresholds for the location, the single set comprising the aggregation of the safe thresholds for each item.

6. The method of claim 1, further comprising:

determining that the at least one safe threshold is actually exceeded, wherein the execution of the climate-control system is based on the determination of the actual exceeding of the at least one safe threshold.

7. The method of claim 1, further comprising:

determining, based on the predicted surpassing of the at least one safe threshold, a value corresponding to a manner the at least one safe threshold is exceeded;

determining, based on the value, a type of climate-control system; and

executing the type of climate-control system.

8. The method of claim 7, further comprising:

determining, based on the determined value, a duration for executing the climate-control system.

9. The method of claim, 1, further comprising:

determining a manner in which the at least one safe threshold is surpassed; and

determining a type of operation mode of the climate-control system, wherein the execution of the climate-control system is based on the type of operation mode.

10. The method of claim 1, wherein the climate-control system comprises a heating, ventilation and air conditioning (HVAC) system.

11. The method of claim 1, wherein the climate-control system comprises a ceiling fan operation at the location.

12. The method of claim 1, wherein the device is a thermostat.

13. A device comprising:

a processor configured to: identify information related to an item within a location, the item being a physical object currently located within the location, the item information indicating at least one of a material of the item and a type of the item; identify location parameters, the location parameters comprising information indicating real-world attributes of an environment within the location; analyze the item information based on the location parameters; determine a set of safe thresholds for the item, each safe threshold in the set corresponding to a real-world attribute of the environment within the location; monitor, over a time period, the environment within the location, the monitoring comprising collecting information related to the environment within the location; determine, based on the collected information, that at least one safe threshold is predicted as being surpassed; and execute a climate-control system at the location to modify the environment such that real-world attributes associated with the at least one safe threshold are adjusted to a value within the at least one safe threshold.

14. The device of claim 13, wherein the processor is further configured to:

analyze each of the safe thresholds for each item;

determine an aggregation of the safe thresholds; and

compile a single set of safe thresholds for the location, the single set comprising the aggregation of the safe thresholds for each item.

15. The device of claim 13, wherein the processor is further configured to:

determine, based on the predicted surpassing of the at least one safe threshold, a value corresponding to a manner the at least one safe threshold is exceeded;

determine, based on the value, a type of climate-control system; and

execute the type of climate-control system.

16. The device of claim, 13, wherein the processor is further configured to:

determine a manner in which the at least one safe threshold is surpassed; and

determine a type of operation mode of the climate-control system, wherein the execution of the climate-control system is based on the type of operation mode.

17. A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions that when executed by a device, perform a method comprising:

identifying, by the device, information related to an item within a location, the item being a physical object currently located within the location, the item information indicating at least one of a material of the item and a type of the item;