Patent Application
20240403614
This application relates generally to a building system of a building. This application relates more particularly to systems for managing and processing data of the building system.
At least one embodiment relates to a method. The method can include providing, by one or more processors, a digital occupant assistant application for occupants of a commercial building. The method can include dynamically generating, by the one or more processors using a generative artificial intelligence (AI) model, data to present to an occupant via the digital occupant assistant application. The generative AI model can dynamically generate the data based on at least one of a prompt from the occupant provided via an input interface of the digital occupant assistant application, or context relating to at least one of the occupant, the commercial building, a space of the commercial building, an event relating to the commercial building, one or more other occupants, or building equipment or other assets of the commercial building.
In some embodiments, the generative AI model can include a pretrained generative transformer model.
In some embodiments, the prompt from the occupant can include unstructured data conforming to a plurality of different predetermined formats and/or not conforming to the plurality of different predetermined formats. The generative AI model can generate the data using the unstructured data.
In some embodiments, the generative AI model can autonomously generate the data from the unstructured data without requiring manual user intervention.
In some embodiments, dynamically generating the data can include dynamically generating a recommendation to the occupant based on the at least one of the prompt or the context. The recommendation can include information aggregated by the generative AI model. The information aggregated by the generative AI model can be separate prior to training of the generative AI model.
In some embodiments, at least a portion of the data can include new data generated by the generative AI model responsive to the at least one of the prompt or the context, the new data not preexisting, at the time of receiving the prompt and/or the context, in a data set upon which the generative AI model has been trained.
In some embodiments, the method can include receiving, by the one or more processors, from one or more data sources of the commercial building, first information associated with a component of the commercial building. The method can include dynamically generating, by the one or more processors by inputting the first information into the generative AI model, second data to present to the occupant via the digital occupant assistant application. The second data can be generated based on a persona of the occupant. The method can include dynamically generating, by the one or more processors by inputting the first information into the generative AI model, third data to present to a second occupant via the digital occupant assistant application. The third data can be generated based on a persona of the second occupant. The persona of the occupant can be different than the persona of the second occupant. The second data can be different than the third data.
In some embodiments, the prompt from the occupant can include an indication of a status of a component of the commercial building. The method can include retrieving, by the one or more processors responsive to receiving the prompt, information associated with the component of the commercial building. The method can include inputting, by the one or more processors, the information associated with the component of the commercial building into the generative AI model to cause the generative AI model to generate a response that addresses the status of the component of the commercial building. The method can include presenting, by the one or more processors via the digital occupant assistant application, the response to the occupant.
In some embodiments, the method can include receiving, by the one or more processors, from the occupant via the digital occupant assistant application, an indication to modify a component of the commercial building. The method can include retrieving, by the one or more processors, information associated with the component of the commercial building. The method can include inputting, by the one or more processors, the information associated with component of the commercial building into the generative AI model to cause the generative AI model to generate one or more recommendations to modify the component of the commercial building. The method can include presenting, by the one or more processors via the digital occupant assistant application, the one or more recommendations to the occupant.
In some embodiments, the method can include receiving, by the one or more processors, a first plurality of unstructured building reports for a first plurality of occupants of a plurality of buildings, the first plurality of unstructured building reports conforming to a plurality of different predetermined formats and/or comprising unstructured data not conforming to the plurality of different predetermined formats. The method can include training, by the one or more processors, the generative AI model using the first plurality of unstructured building reports. The method can include providing, by the one or more processors using the generative AI model, one or more responses with respect to one or more requests from occupants of a building via the digital occupant assistant application.
At least one embodiment relates to one or more non-transitory storage media. The one or more non-transitory storage media can store instructions. The instructions can, when executed by one or more processors, cause the one or more processors to perform operations that include receiving a first plurality of unstructured building reports for a first plurality of occupants of a plurality of buildings, the first plurality of unstructured building reports conforming to a plurality of different predetermined formats and/or comprising unstructured data not conforming to the plurality of predetermined formats. The operations can include training a generative artificial intelligence (AI) model using the first plurality of unstructured building reports. The operations can include providing, using the generative AI model, one or more responses with respect to one or more requests from occupants of a building.
In some embodiments, the operations can include providing a digital occupant assistant application for occupants of the building. The operations can include dynamically generating, using the generative AI model, data to present to an occupant via the digital occupant assistant application.
In some embodiments, the generative AI model can dynamically generate the data based on at least one of a prompt from the occupant provided via an input interface of the digital occupant assistant application, or context relating to at least one of the occupant, the building, a space of the building, an event relating to the building, one or more other occupants, or building equipment or other assets of the building.
In some embodiments, the prompt from the occupant can include an indication of a status of a component of the building. The operations can include retrieving, by the one or more processors responsive to receiving the prompt, information associated with the component of the building. The operations can include inputting, by the one or more processors, the information associated with the component of the building into the generative AI model to cause the generative AI model to generate a response that addresses the status of the component of the building. The operations can include presenting, by the one or more processors via the digital occupant assistant application, the response to the occupant.
In some embodiments, the operations can include receiving from the occupant via the digital occupant assistant application, an indication to modify a component of the building. The operations can include retrieving information associated with the component of the building. The operations can include inputting the information associated with component of the building into the generative AI model to cause the generative AI model to generate one or more recommendations to modify the component of the building. The operations can include presenting via the digital occupant assistant application, the one or more recommendations to the occupant.
At least one embodiment relates to a system. The system can include one or more memory devices. The one or more memory devices can store instructions. The instructions can, when executed by one or more processors, cause the one or more processors to provide a digital occupant assistant application for occupants of a commercial building. The instructions can cause the one or more processors to dynamically generate, using a generative large language model (LLM), data to present to an occupant via the digital occupant assistant application, the generative LLM configured to dynamically generate the data based on a prompt from the occupant provided via an input interface of the digital occupant assistant application. The prompt from the occupant can include an indication of a status of a component of the commercial building. The instructions can cause the one or more processors to retrieve, responsive to receiving the prompt, information associated with the component of the commercial building. The instructions can cause the one or more processors to input the information associated with the component of the commercial building into the generative LLM to cause the generative LLM to generate a response that addresses the status of the component of the commercial building. The instructions can cause the one or more processors to present, via the digital occupant assistant application, the response to the occupant.
In some embodiments, the generative LLM can include a pretrained generative transformer model.
In some embodiments, the prompt from the occupant can include unstructured data conforming to a plurality of different predetermined formats and/or not conforming to the plurality of predetermined formats. The generative LLM can generate the data using the unstructured data.
In some embodiments, the generative LLM can autonomously generate the data from the unstructured data without requiring manual user intervention.
In some embodiments, dynamically generating the data can include dynamically generating a recommendation to the occupant based on the prompt.
Various objects, aspects, features, and advantages of the disclosure will become more apparent and better understood by referring to the detailed description taken in conjunction with the accompanying drawings, in which like reference characters identify corresponding elements throughout. In the drawings, like reference numbers generally indicate identical, functionally similar, and/or structurally similar elements.
Referring generally to the FIGURES, systems, and methods in accordance with the present disclosure can implement various systems to precisely generate data relating to one or more aspects of a building. For example, various systems described herein can be implemented to more precisely generate data for various applications including, for example and without limitation, virtual assistance in tracking and reporting on service requests; generating reports to notify occupants of pending service requests; providing various persona-based experiences for occupants of a building; assisting in space configuration and/or equipment tracking; providing navigational assistance for occupants moving about a building; providing scheduling assistance of concurrently booked areas and/or zones of a building; and/or among various possible combinations and/or variances. Various such applications can facilitate both asynchronous and real-time service operations, including by generating text data for such applications based on data from disparate data sources that may not have predefined database associations amongst the data sources, yet may be relevant at specific steps or points in time during service operations.
In some embodiments, the systems and methods described herein can be executed by and/or implemented using Artificial Intelligence (AI) and/or Machine Learning (ML). For example, an AI model can receive data pertaining to a layout of a building. The data can also include information identifying and/or indicating equipment and/or devices of the building. For example, the data can include a number of chairs and a number of portable and/or collapsible tables. The data provided to the AI model can be and/or include at least one of a Building Information Model, information pertaining to a Building Automation System (BAS), entity graphs, and/or digital twins of various component of a building.
A BAS is, in general, a system of devices configured to control, monitor, and manage equipment in or around a building or building area. A BAS can include, for example, a HVAC system, a security system, a lighting system, a fire alerting system, any other system that is capable of managing building functions or devices, or any combination thereof.
A BIM is a representation of the physical and/or functional characteristics of a building. A BIM may represent structural characteristics of the building (e.g., walls, floors, ceilings, doors, windows, etc.) as well as the systems or components contained within the building (e.g., lighting components, electrical systems, mechanical systems, HVAC components, furniture, plumbing systems or fixtures, etc.). In some embodiments, a BIM is a 3D graphical model of the building. A BIM may be created using computer modeling software or other computer-aided design (CAD) tools and may be used by any of a plurality of entities that provide building-related services.
In some embodiments, a BIM represents building components and/or building equipment as objects (e.g., software objects). For example, a BIM may include a plurality of objects that represent building equipment within the building as well as building spaces. Each object may include a collection of attributes that define the physical geometry of the object, the type of object, and/or other properties of the object. For example, objects representing building spaces may define the size and location of the building space. Objects representing physical components may define the geometry of the physical component, the type of component (e.g., lighting fixture, air handling unit, wall, etc.), the location of the physical component, a material from which the physical component is constructed, and/or other attributes of the physical component.
In some embodiments, the building may be at least one of a commercial building, a medical facility (e.g., a hospital, a clinic, an assisted living facility, a nursing home, etc.), a residential complex (e.g., an apartment complex, condos, etc.), an educational building (e.g., schools, college campus buildings, etc.), mixed use buildings (e.g., stores/shops occupy a portion of the building and residential areas occupy a portion of the building), and/or among other possible combinations. For example, the building may be an office building and the office building may include multiple floors having multiple zones and/or multiple rooms.
As described herein, “one or more aspects of a building” may refer to and/or include at least one of room scheduling management, navigational support, building maintenance scheduling and/or tracking, occupant assistance, health and wellness support, building layout assistance, parking and/or transportation assistance, and/or among various possible combinations. As described herein, “components of a building” may refer to and/or include at least one of office supplies (e.g., paper, pens, pencils, tape, folders, etc.), building supplies (e.g., building hardware, dry wall material, light bulbs, furniture, furnishings), facilities equipment (e.g., toilets, sinks, water dispensers, coffee makers, lights, doors, elevators, stairways), environmental control devices (e.g., thermostats, light switches, shade pull downs), electronic equipment (e.g., mobile devices, phones, tablets, wearables, tags, computers, microphones, cameras, sensors, badge scanners, laptops), and/or among various possible combinations.
In some systems, service operations can be supported by text information, such as predefined text documents such as service, diagnostic, and/or troubleshooting guides. Various such text information may not be useful for specific service requests and/or technicians performing the service. For example, the text information may correspond to different items of equipment or versions of items of equipment to be serviced. The text information, being predefined, may not account for specific technical issues that may be present in the items of equipment to be serviced.
The AI model can track the building subsystems by storing entities (e.g., data representing equipment, buildings, spaces, floors, software services, policies, etc.), relationships (e.g., relationships between equipment and their locations, API calls between software services, etc.), and events (e.g., data that has occurred, measurements, commands, statuses, etc.). The AI model can create graph projections, e.g., a graph with nodes for the entities and events of the building and edges for the relationships between the entities and/or events. The graph projections can be built on particular policies (e.g., what entities, events, and/or relationships should be included within the graph) and/or ontologies (the types of relationships that should be made with different types of entities and/or events). In this regard, particular graph projections can be generated for particular subscribers, users, systems, etc. The AI model can also track the components of the building by storing the components as one or more nodes in graph projections.
AI and/or machine learning (ML) systems, including but not limited to LLMs, can be used to generate text data and data of other modalities in a more responsive manner to real-time conditions, including generating strings of text data that may not be provided in the same manner in existing documents, yet may still meet criteria for useful text information, such as relevance, style, and coherence. For example, LLMs can predict text data based at least on inputted prompts and by being configured (e.g., trained, modified, updated, fine-tuned) according to training data representative of the text data to predict or otherwise generate.
However, various considerations may limit the ability of such systems to precisely generate appropriate data for specific conditions. For example, due to the predictive nature of the generated data, some LLMs may generate text data that is incorrect, imprecise, or not relevant to the specific conditions. Using the LLMs may require a user to manually vary the content and/or syntax of inputs provided to the LLMs (e.g., vary inputted prompts) until the output of the LLMs meets various objective or subjective criteria of the user. The LLMs can have token limits for sizes of inputted text during training and/or runtime/inference operations (and relaxing or increasing such limits may require increased computational processing, API calls to LLM services, and/or memory usage), limiting the ability of the LLMs to be effectively configured or operated using large amounts of raw data or otherwise unstructured data.
Systems and methods in accordance with the present disclosure can use machine learning models, including LLMs and other generative AI systems, to capture data, including but not limited to unstructured knowledge from various data sources, and process the data to accurately generate outputs, such as completions responsive to prompts, including in structured data formats for various applications and use cases. The system can implement various automated and/or expert-based thresholds and data quality management processes to improve the accuracy and quality of generated outputs and update training of the machine learning models accordingly. The system can enable real-time messaging and/or conversational interfaces for users to provide field data regarding equipment to the system (including presenting targeted queries to users that are expected to elicit relevant responses for efficiently receiving useful response information from users) and guide users, such as service technicians, through relevant service, diagnostic, troubleshooting, and/or repair processes.
This can include, for example, receiving data from building reports and/or occupant inputs in various formats, including various modalities and/or multi-modal formats (e.g., text, speech, audio, image, and/or video). The system can facilitate automated, flexible customer report generation, such as by processing information received from service technicians and other users into a standardized format, which can reduce the constraints on how the user submits data while improving resulting reports. The system can couple unstructured building data to other input/output data sources and analytics, such as to relate unstructured data with outputs of timeseries data from equipment (e.g., sensor data; report logs) and/or outputs from models or algorithms of equipment operation, which can facilitate more accurate analytics, prediction services, recommendations, diagnostics, and/or fault detection. The system can perform classification or other pattern recognition or trend detection operations to facilitate more timely assignment of technicians, rearranging of rooms, scheduling of rooms, scheduling of technicians based on expected times for jobs, and provisioning of trucks, tools, and/or parts. The system can perform root cause prediction by being trained using data that includes indications of root causes of faults or errors, where the indications are labels for or otherwise associated with (unstructured or structure) data such as service requests, service reports, service calls, etc. The system can receive, from a service technician in the field evaluating the issue with the equipment, feedback regarding the accuracy of the root cause predictions, as well as feedback regarding how the service technician evaluated information about the equipment (e.g., what data did they evaluate; what did they inspect; did the root cause prediction or instructions for finding the root cause accurately match the type of equipment, etc.), which can be used to update the root cause prediction model.
For example, the system can provide a platform for fault detection and servicing processes in which a machine learning model is configured based on connecting or relating unstructured data and/or semantic data, such as human feedback and written/spoken reports, with time-series product data regarding items of equipment, so that the machine learning model can more accurately detect causes of alarms or other events that may trigger service responses. For instance, responsive to an alarm for a chiller, the system can more accurately detect a cause of the alarm, and generate a prescription (e.g., for a service technician) for responding to the alarm; the system can request feedback from the service technician regarding the prescription, such as whether the prescription correctly identified the cause of the alarm and/or actions to perform to respond to the cause, as well as the information that the service technician used to evaluate the correctness or accuracy of the prescription; the system can use this feedback to modify the machine learning models, which can increase the accuracy of the machine learning models.
In some instances, significant computational resources (or human user resources) can be required to process data relating to equipment operation, such as time-series product data and/or sensor data, to detect or predict faults and/or causes of faults. In addition, it can be resource-intensive to label such data with identifiers of faults or causes of faults, which can make it difficult to generate machine learning training data from such data. Systems and methods in accordance with the present disclosure can leverage the efficiency of language models (e.g., GPT-based models or other pre-trained LLMs) in extracting semantic information (e.g., semantic information identifying faults, causes of faults, and other accurate expert knowledge regarding equipment servicing) from the unstructured data in order to use both the unstructured data and the data relating to equipment operation to generate more accurate outputs regarding equipment servicing. As such, by implementing language models using various operations and processes described herein, building management and equipment servicing systems can take advantage of the causal/semantic associations between the unstructured data and the data relating to equipment operation, and the language models can allow these systems to more efficiently extract these relationships in order to more accurately predict targeted, useful information for servicing applications at inference-time/runtime. While various implementations are described as being implemented using generative AI models such as transformers and/or GANs, in some embodiments, various features described herein can be implemented using non-generative AI models or even without using AI/machine learning, and all such modifications fall within the scope of the present disclosure.
The system can enable a generative AI-based digital assistance wizard interface. For example, the interface can include user interface and/or user experience features configured to provide a question/answer-based input/output format, such as a conversational interface, that directs users through providing targeted information for accurately generating predictions of root cause, presenting solutions, or presenting instructions for repairing or inspecting the equipment to identify information that the system can use to detect root causes or other issues. The system can use the interface to present information regarding parts and/or tools to service the equipment, as well as instructions for how to use the parts and/or tools to service the equipment. The wizard interface can be user and/or persona based user experience. For example, based on the persona of an individual (e.g., tenant, manager, technician, etc.) the AI model can modify, adjust, and/or change the wizard interface to be constructed and/or deployed based on the persona of the individual.
In various implementations, the systems can include a plurality of machine learning models that may be configured using integrated or disparate data sources. This can facilitate more integrated user experiences or more specialized (and/or lower computational usage for) data processing and output generation. Outputs from one or more first systems, such as one or more first algorithms or machine learning models, can be provided at least as part of inputs to one or more second systems, such as one or more second algorithms or machine learning models. For example, a first language model can be configured to process unstructured inputs (e.g., text, speech, images, etc.) into a structure output format compatible for use by a second system, such as a root cause prediction algorithm or equipment configuration model.
The system can be used to automate interventions for equipment operation, servicing, fault detection and diagnostics (FDD), and alerting operations. For example, by being configured to perform operations such as building furniture tracking, the system can monitor data regarding placement and/or movement of building furniture to provide recommendations to user as to where given pieces of furniture may be located within the building. The system can present to a technician or manager of the building a report regarding the recommendation and requesting feedback regarding the accuracy of the recommendation, which can be used to update the machine learning models to more accurately generate recommendations.
For example, the system 100 can be implemented for operations associated with any of a variety of building management systems (BMSs) or equipment or components thereof. A BMS can include a system of devices that can control, monitor, and manage equipment in or around a building or building area. The BMS can include, for example, a HVAC system, a security system, a lighting system, a fire alerting system, any other system that is capable of managing building functions or devices, or any combination thereof. The BMS can include or be coupled with items of equipment, for example and without limitation, such as heaters, chillers, boilers, air handling units, sensors, actuators, refrigeration systems, fans, blowers, heat exchangers, energy storage devices, condensers, valves, or various combinations thereof.
The items of equipment can operate in accordance with various qualitative and quantitative parameters, variables, setpoints, and/or thresholds or other criteria, for example. In some instances, the system 100 and/or the items of equipment can include or be coupled with one or more controllers for controlling parameters of the items of equipment, such as to receive control commands for controlling operation of the items of equipment via one or more wired, wireless, and/or user interfaces of controller.
Various components of the system 100 or portions thereof can be implemented by one or more processors coupled with or more memory devices (memory). The processors can be a general purpose or specific purpose processors, an application specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGAs), a group of processing components, or other suitable processing components. The processors may be configured to execute computer code and/or instructions stored in the memories or received from other computer readable media (e.g., CDROM, network storage, a remote server, etc.). The processors can be configured in various computer architectures, such as graphics processing units (GPUs), distributed computing architectures, cloud server architectures, client-server architectures, or various combinations thereof. One or more first processors can be implemented by a first device, such as an edge device, and one or more second processors can be implemented by a second device, such as a server or other device that is communicatively coupled with the first device and may have greater processor and/or memory resources.
The memories can include one or more devices (e.g., memory units, memory devices, storage devices, etc.) for storing data and/or computer code for completing and/or facilitating the various processes described in the present disclosure. The memories can include random access memory (RAM), read-only memory (ROM), hard drive storage, temporary storage, non-volatile memory, flash memory, optical memory, or any other suitable memory for storing software objects and/or computer instructions. The memories can include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described in the present disclosure. The memories can be communicably connected to the processors and can include computer code for executing (e.g., by the processors) one or more processes described herein.
The system 100 can include or be coupled with one or more first models 104. The first model 104 can include one or more neural networks, including neural networks configured as generative models. For example, the first model 104 can predict or generate new data (e.g., artificial data; synthetic data; data not explicitly represented in data used for configuring the first model 104). The first model 104 can generate any of a variety of modalities of data, such as text, speech, audio, images, and/or video data. The neural network can include a plurality of nodes, which may be arranged in layers for providing outputs of one or more nodes of one layer as inputs to one or more nodes of another layer. The neural network can include one or more input layers, one or more hidden layers, and one or more output layers. Each node can include or be associated with parameters such as weights, biases, and/or thresholds, representing how the node can perform computations to process inputs to generate outputs. The parameters of the nodes can be configured by various learning or training operations, such as unsupervised learning, weakly supervised learning, semi-supervised learning, or supervised learning.
The first model 104 can include, for example and without limitation, one or more language models, LLMs, attention-based neural networks, transformer-based neural networks, generative pretrained transformer (GPT) models, bidirectional encoder representations from transformers (BERT) models, encoder/decoder models, sequence to sequence models, autoencoder models, generative adversarial networks (GANs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), diffusion models (e.g., denoising diffusion probabilistic models (DDPMs)), or various combinations thereof.
For example, the first model 104 can include at least one GPT model. The GPT model can receive an input sequence, and can parse the input sequence to determine a sequence of tokens (e.g., words or other semantic units of the input sequence, such as by using Byte Pair Encoding tokenization). The GPT model can include or be coupled with a vocabulary of tokens, which can be represented as a one-hot encoding vector, where each token of the vocabulary has a corresponding index in the encoding vector; as such, the GPT model can convert the input sequence into a modified input sequence, such as by applying an embedding matrix to the token tokens of the input sequence (e.g., using a neural network embedding function), and/or applying positional encoding (e.g., sin-cosine positional encoding) to the tokens of the input sequence. The GPT model can process the modified input sequence to determine a next token in the sequence (e.g., to append to the end of the sequence), such as by determining probability scores indicating the likelihood of one or more candidate tokens being the next token, and selecting the next token according to the probability scores (e.g., selecting the candidate token having the highest probability scores as the next token). For example, the GPT model can apply various attention and/or transformer based operations or networks to the modified input sequence to identify relationships between tokens for detecting the next token to form the output sequence.
The first model 104 can include at least one diffusion model, which can be used to generate image and/or video data. For example, the diffusional model can include a denoising neural network and/or a denoising diffusion probabilistic model neural network. The denoising neural network can be configured by applying noise to one or more training data elements (e.g., images, video frames) to generate noised data, providing the noised data as input to a candidate denoising neural network, causing the candidate denoising neural network to modify the noised data according to a denoising schedule, evaluating a convergence condition based on comparing the modified noised data with the training data instances, and modifying the candidate denoising neural network according to the convergence condition (e.g., modifying weights and/or biases of one or more layers of the neural network). In some implementations, the first model 104 includes a plurality of generative models, such as GPT and diffusion models, that can be trained separately or jointly to facilitate generating multi-modal outputs, such as technical documents (e.g., service guides) that include both text and image/video information.
In some implementations, the first model 104 can be configured using various unsupervised and/or supervised training operations. The first model 104 can be configured using training data from various domain-agnostic and/or domain-specific data sources, including but not limited to various forms of text, speech, audio, image, and/or video data, or various combinations thereof. The training data can include a plurality of training data elements (e.g., training data instances). Each training data element can be arranged in structured or unstructured formats; for example, the training data element can include an example output mapped to an example input, such as a query representing a service request or one or more portions of a service request, and a response representing data provided responsive to the query. The training data can include data that is not separated into input and output subsets (e.g., for configuring the first model 104 to perform clustering, classification, or other unsupervised ML operations). The training data can include human-labeled information, including but not limited to feedback regarding outputs of the models 104, 116. This can allow the system 100 to generate more human-like outputs.
In some implementations, the training data includes data relating to building management systems. For example, the training data can include examples of HVAC-R data, such as operating manuals, technical data sheets, configuration settings, operating setpoints, diagnostic guides, troubleshooting guides, user reports, technician reports. In some implementations, the training data used to configure the first model 104 includes at least some publicly accessible data, such as data retrievable via the Internet.
In some embodiments, the training data can include data relating to components of buildings. For example, the training data can include examples of floor layouts, such as blueprints, building spec, floorplans, BIMs, and how given components of a building can be orientated and/or positioned throughout the building.
Referring further to
The second model 116 can be similar to the first model 104. For example, the second model 116 can have a similar or identical backbone or neural network architecture as the first model 104. In some implementations, the first model 104 and the second model 116 each include generative AI machine learning models, such as LLMs (e.g., GPT-based LLMs) and/or diffusion models. The second model 116 can be configured using processes analogous to those described for configuring the first model 104.
In some implementations, the model updater 108 can perform operations on at least one of the first model 104 or the second model 116 via one or more interfaces, such as application programming interfaces (APIs). For example, the models 104, 116 can be operated and maintained by one or more systems separate from the system 100. The model updater 108 can provide training data to the first model 104, via the API, to determine the second model 116 based on the first model 104 and the training data. The model updater 108 can control various training parameters or hyperparameters (e.g., learning rates, etc.) by providing instructions via the API to manage configuring the second model 116 using the first model 104.
The model updater 108 can determine the second model 116 using data from one or more data sources 112. For example, the system 100 can determine the second model 116 by modifying the first model 104 using data from the one or more data sources 112. The data sources 112 can include or be coupled with any of a variety of integrated or disparate databases, data warehouses, digital twin data structures (e.g., digital twins of items of equipment or building management systems or portions thereof), data lakes, data repositories, documentation records, or various combinations thereof. In some implementations, the data sources 112 include HVAC-R data in any of text, speech, audio, image, or video data, or various combinations thereof, such as data associated with HVAC-R components and procedures including but not limited to installation, operation, configuration, repair, servicing, diagnostics, and/or troubleshooting of HVAC-R components and systems. Various data described below with reference to data sources 112 may be provided in the same or different data elements, and may be updated at various points. The data sources 112 can include or be coupled with items of equipment (e.g., where the items of equipment output data for the data sources 112, such as sensor data, etc.). The data sources 112 can include various online and/or social media sources, such as blog posts or data submitted to applications maintained by entities that manage the buildings. The system 100 can determine relations between data from different sources, such as by using timeseries information and identifiers of the sites or buildings at which items of equipment are present to detect relationships between various different data relating to the items of equipment (e.g., to train the models 104, 116 using both timeseries data (e.g., sensor data; outputs of algorithms or models, etc.) regarding a given item of equipment and freeform natural language reports regarding the given item of equipment).
The data sources 112 can include unstructured data or structured data (e.g., data that is labeled with or assigned to one or more predetermined fields or identifiers). For example, using the first model 104 and/or the second model 116 to process the data can allow the system 100 to extract useful information from data in a variety of formats, including unstructured/freeform formats, which can allow service technicians to input information in less burdensome formats. The data can be of any of a plurality of formats (e.g., text, speech, audio, image, video, etc.), including multi-modal formats. For example, the data may be received from service technicians in forms such as text (e.g., laptop/desktop or mobile application text entry), audio, and/or video (e.g., dictating findings while capturing video).
The data sources 112 can include engineering data regarding one or more items of equipment. The engineering data can include manuals, such as installation manuals, instruction manuals, or operating procedure guides. The engineering data can include specifications or other information regarding operation of items of equipment. The engineering data can include engineering drawings, process flow diagrams, refrigeration cycle parameters (e.g., temperatures, pressures), or various other information relating to structures and functions of items of equipment.
In some implementations, the data sources 112 can include operational data regarding one or more items of equipment. The operational data can represent detected information regarding items of equipment, such as sensor data, logged data, user reports, or technician reports. The operational data can include, for example, service tickets generated responsive to requests for service, work orders, data from digital twin data structures maintained by an entity of the item of equipment, outputs, or other information from equipment operation models (e.g., chiller vibration models), or various combinations thereof. Logged data, user reports, service tickets, billing records, time sheets, and various other such data can provide temporal information, such as how long service operations may take, or durations of time between service operations, which can allow the system 100 to predict resources to use for performing service as well as when to request service.
The data sources 112 can include, for instance, warranty data. The warranty data can include warranty documents or agreements that indicate conditions under which various entities associated with items of equipment are to provide service, repair, or other actions corresponding to items of equipment, such as actions corresponding to service requests.
The data sources 112 can include service data. The service data can include data from any of various service providers, such as service reports. The service data can indicate service procedures performed, including associated service procedures with initial service requests and/or sensor data related conditions to trigger service and/or sensor data measured during service processes.
In some implementations, the data sources 112 can include parts data, including but not limited to parts usage and sales data. For example, the data sources 112 can indicate various parts associated with installation or repair of items of equipment. The data sources 112 can indicate tools for performing service and/or installing parts.
The data sources 112 can include building data. The building data can include data pertaining to one or more buildings. For example, the building data can include building specs, BIMs, equipment inventory, blueprints, lists of occupants, building schedules, maintenance records, and/or among various other possible combinations.
The data sources 112 can include environmental data. The environment data can include data pertaining to an area proximate to a building and/or an area traveled to reach the building. For example, the environmental data can include weather reports for the city where the building is located, traffic reports for major roadways that can be taken to reach the building, predicted travel times, and/or among various other possible environmental data.
The system 100 can include, with the data of the data sources 112, labels to facilitate cross-reference between items of data that may relate to common items of equipment, sites, service technicians, customers, or various combinations thereof. For example, data from disparate sources may be labeled with time data, which can allow the system 100 (e.g., by configuring the models 104, 116) to increase a likelihood of associating information from the disparate sources due to the information being detected or recorded (e.g., as service reports) at the same time or near in time.
For example, the data sources 112 can include data that can be particular to specific or similar items of equipment, buildings, equipment configurations, environmental states, or various combinations thereof. In some implementations, the data includes labels or identifiers of such information, such as to indicate locations, weather conditions, timing information, uses of the items of equipment or the buildings or sites at which the items of equipment are present, etc. This can enable the models 104, 116 to detect patterns of usage (e.g., spikes; troughs; seasonal or other temporal patterns) or other information that may be useful for determining causes of issues or causes of service requests, or predict future issues, such as to allow the models 104, 116 to be trained using information indicative of causes of issues across multiple items of equipment (which may have the same or similar causes even if the data regarding the items of equipment is not identical). For example, an item of equipment may be at a site that is a museum; by relating site usage or occupancy data with data regarding the item of equipment, such as sensor data and service reports, the system 100 can configure the models 104, 116 to determine a high likelihood of issues occurring before events associated with high usage (e.g., gala, major exhibit opening), and can generate recommendations to perform diagnostics or servicing prior to the events.
Referring further to
For example, the model updater 108 can identify one or more parameters (e.g., weights and/or biases) of one or more layers of the first model 104, and maintain (e.g., freeze, maintain as the identified values while updating) the values of the one or more parameters of the one or more layers. In some implementations, the model updater 108 can modify the one or more layers, such as to add, remove, or change an output layer of the one or more layers, or to not maintain the values of the one or more parameters. The model updater 108 can select at least a subset of the identified one or parameters to maintain according to various criteria, such as user input or other instructions indicative of an extent to which the first model 104 is to be modified to determine the second model 116. In some implementations, the model updater 108 can modify the first model 104 so that an output layer of the first model 104 corresponds to output to be determined for applications 120.
Responsive to selecting the one or more parameters to maintain, the model updater 108 can apply, as input to the second model 116 (e.g., to a candidate second model 116, such as the modified first model 104, such as the first model 104 having the identified parameters maintained as the identified values), training data from the data sources 112. For example, the model updater 108 can apply the training data as input to the second model 116 to cause the second model 116 to generate one or more candidate outputs.
The model updater 108 can evaluate a convergence condition to modify the candidate second model 116 based at least on the one or more candidate outputs and the training data applied as input to the candidate second model 116. For example, the model updater 108 can evaluate an objective function of the convergence condition, such as a loss function (e.g., L1 loss, L2 loss, root mean square error, cross-entropy or log loss, etc.) based on the one or more candidate outputs and the training data; this evaluation can indicate how closely the candidate outputs generated by the candidate second model 116 correspond to the ground truth represented by the training data. The model updater 108 can use any of a variety of optimization algorithms (e.g., gradient descent, stochastic descent, Adam optimization, etc.) to modify one or more parameters (e.g., weights or biases of the layer(s) of the candidate second model 116 that are not frozen) of the candidate second model 116 according to the evaluation of the objective function. In some implementations, the model updater 108 can use various hyperparameters to evaluate the convergence condition and/or perform the configuration of the candidate second model 116 to determine the second model 116, including but not limited to hyperparameters such as learning rates, numbers of iterations or epochs of training, etc.
As described further herein with respect to applications 120, in some implementations, the model updater 108 can select the training data from the data of the data sources 112 to apply as the input based at least on a particular application of the plurality of applications 120 for which the second model 116 is to be used for. For example, the model updater 108 can select data from the parts data source 112 for the product recommendation generator application 120, or select various combinations of data from the data sources 112 (e.g., engineering data, operational data, and service data) for the service recommendation generator application 120. The model updater 108 can apply various combinations of data from various data sources 112 to facilitate configuring the second model 116 for one or more applications 120.
In some implementations, the system 100 can perform at least one of conditioning, classifier-based guidance, or classifier-free guidance to configure the second model 116 using the data from the data sources 112. For example, the system 100 can use classifiers associated with the data, such as identifiers of the item of equipment, a type of the item of equipment, a type of entity operating the item of equipment, a site at which the item of equipment is provided, or a history of issues at the site, to condition the training of the second model 116. For example, the system 100 combine (e.g., concatenate) various such classifiers with the data for inputting to the second model 116 during training, for at least a subset of the data used to configure the second model 116, which can enable the second model 116 to be responsive to analogous information for runtime/inference time operations.
Referring further to
The applications 120 can include any of a variety of desktop, web-based/browser-based, or mobile applications. For example, the applications 120 can be implemented by enterprise management software systems, employee, or other user applications (e.g., applications that relate to BMS functionality such as temperature control, user preferences, conference room scheduling, etc.), equipment portals that provide data regarding items of equipment, or various combinations thereof. The applications 120 can include user interfaces, wizards, checklists, conversational interfaces, chatbots, configuration tools, or various combinations thereof. The applications 120 can receive an input, such as a prompt (e.g., from a user), provide the prompt to the second model 116 to cause the second model 116 to generate an output, such as a completion in response to the prompt, and present an indication of the output. The applications 120 can receive inputs and/or present outputs in any of a variety of presentation modalities, such as text, speech, audio, image, and/or video modalities. For example, the applications 120 can receive unstructured or freeform inputs from a user, such as a service technician, and generate reports in a standardized format, such as a customer-specific format. This can allow, for example, technicians to automatically, and flexibly, generate customer-ready reports after service visits without requiring strict input by the technician or manually sitting down and writing reports; to receive inputs as dictations in order to generate reports; to receive inputs in any form or a variety of forms, and use the second model 116 (which can be trained to cross-reference metadata in different portions of inputs and relate together data elements) to generate output reports (e.g., the second model 116, having been configured with data that includes time information, can use timestamps of input from dictation and timestamps of when an image is taken, and place the image in the report in a target position or label based on time correlation).
In some implementations, the applications 120 include at least one digital occupant assistant application 120. The digital occupant assistant application can provide various services to support occupants of a building, such as presenting information pertaining to building maintenance, receiving queries regarding actions to perform to arrange a room in a given layout, and presenting responses indicating actions to perform to arrange the room. The digital occupant assistant application can receive information regarding a component of the building, such as text descriptions or camera images of the component and process the received information using the second model 116 to generate corresponding responses.
In some embodiments, the digital occupant assistant application 120 may detect a presence of one or more users based on information collected by the cameras. For example, the digital occupant assistant application 120 may implement machine vision to detect users as they enter the building. In some embodiments, the digital occupant assistant application 120 may generate or control given display devices. For example, the digital occupant assistant application 120 may detect the presence of a given visitor to the building. In this example, the digital occupant assistant application 120 may cause a display device located in the lobby of the building to display a message welcoming the given visitor. As another example, the digital occupant assistant application1120 may cause a user device associated with a greeter to display a message to inform the greeter that the visitor has arrived.
In some embodiments, the digital occupant assistant application 120 may control one or more building functions or building actions based on user preferences. For example, the digital occupant assistant application 120 may detect that a given user scheduled a meeting for a given conference. In this example, the digital occupant assistant application 120 may detect, subsequent to the scheduling of the meeting, that the preferred conference room of the given user has become available. In some embodiments, the digital occupant assistant application 120 may inform the given user that the preferred conference room has become available. The digital occupant assistant application 120 may also assist the given user in reserving the preferred conference room.
In some embodiments, the digital occupant assistant application 120 may assist one or more users in navigating throughout the building. For example, the digital occupant assistant application 120 may alert a given user regarding elevator maintenance or floor construction. As another example, the digital occupant assistant application 120 may alert a given user to one or more parking spots responsive to the given user arriving to the parking lot.
In some embodiments, the digital occupant assistant application 120 may assist one or more users to improve their comfort in the building. For example, the digital occupant assistant application 120 may receive a query from a user asking, “why is hot in this room?” and the digital occupant assistant application 120 may retrieve a control schedule for the room. In this example, the digital occupant assistant application 120 may detect differences between the control schedule and a setpoint of the room. For example, the digital occupant assistant application 120 may detect that a current temperature of the room exceeds a temperature setpoint for the room. In some embodiments, the digital occupant assistant application 120 may generate a maintenance request to have a technician look in a potential cause.
The digital occupant assistant application 120 can be implemented in a UI/UX wizard configuration, such as to provide a sequence of requests for information from the user (the sequence may include requests that are at least one of predetermined or dynamically generated responsive to inputs from the user for previous requests). For example, the digital occupant assistant application 120 can provide one or more requests for users such as service technicians, facility managers, or other occupants, and provide the received responses to at least one of the second model 116 or a building component function (e.g., algorithm, model, data structure mapping inputs to candidate recommendations, etc.) to determine recommendations. In some embodiments, the digital occupant assistant application 120 may dynamically generate responses and/or outputs by making predictions about what a user query is asking and produce responses based on the predictions. For example, the digital occupant assistant application 120 may dynamically generate responses vs just retrieving and returning information. As another example, the digital occupant assistant application 120 may dynamically generate responses by combining or otherwise aggregating separate pieces of information to form a response. Stated otherwise the digital occupant assistant application 120 may dynamically generate a recommendation that includes information aggregated by an AI model. The information may be separate prior to the training the AI model (e.g., the information was aggregated after the AI model was trained).
The digital occupant assistant application 120 can use requests for information such as unstructured text by which the user describes characteristics of a room for which the occupant would like assistance; answers expected to correspond to different scenarios indicative of recommendations; and/or image and/or video input (e.g., images of layouts, equipment, spaces, etc. that can provide more context around the requests). For example, responsive to receiving a response via the digital occupant assistant application 120 indicating that the problem is a room is missing chairs, the system 100 can request, via the digital occupant assistant application 120, information regarding where the chairs have been moved, such as pictures of the location of the chairs within the building, a route from the occupant to the chairs, an alternative solution, or various combinations thereof.
In some embodiments, the digital occupant assistant application 120 may provide one or more prompts to a user. For example, the digital occupant assistant application 120 may provide prompts to proactively assist the user. As another example, the digital occupant assistant application 120 may provide prompts to alert the user of a change in a building. The alerts may include displaying banners or flags on a display device. In some embodiments, the digital occupant assistant application 120 may provide different prompts to one or more users based on a given piece of information. For example, a sink in a given restroom may be leaking water. In this example, the digital occupant assistant application 120 may provide a first prompt to inform a visitor of the building to utilize a different restroom. The digital occupant assistant application 120 may provide a second prompt to direct a technician of the building to address the leak.
In some implementations, the applications 120 include at least one virtual assistant (e.g., virtual assistance for technician services) application 120. The virtual assistant application can provide various services to support technician operations, such as presenting information from service requests, receiving queries regarding actions to perform to service items of equipment, and presenting responses indicating actions to perform to service items of equipment. The virtual assistant application can receive information regarding an item of equipment to be serviced, such as sensor data, text descriptions, or camera images, and process the received information using the second model 116 to generate corresponding responses.
For example, the virtual assistant application 120 can be implemented in a UI/UX wizard configuration, such as to provide a sequence of requests for information from the user (the sequence may include requests that are at least one of predetermined or dynamically generated responsive to inputs from the user for previous requests). For example, the virtual assistant application 120 can provide one or more requests for users such as service technicians, facility managers, or other occupants, and provide the received responses to at least one of the second model 116 or a root cause detection function (e.g., algorithm, model, data structure mapping inputs to candidate causes, etc.) to determine a prediction of a cause of the issue of the item of equipment and/or solutions. The virtual assistant application 120 can use requests for information such as for unstructured text by which the user describes characteristics of the item of equipment relating to the issue; answers expected to correspond to different scenarios indicative of the issue; and/or image and/or video input (e.g., images of problems, equipment, spaces, etc. that can provide more context around the issue and/or configurations). For example, responsive to receiving a response via the virtual assistant application 120 indicating that the problem is with temperature in the space, the system 100 can request, via the virtual assistant application 120, information regarding HVAC-R equipment associated with the space, such as pictures of the space, an air handling unit, a chiller, or various combinations thereof.
The applications 120 can include a plurality of applications 120 (e.g., variations of interfaces or customizations of interfaces) for a plurality of respective user types. For example, the digital occupant assistant application 120 can include a first application 120 for a customer user, and a second application 120 for a service technician user. The plurality of respective user types can correspond to and/or be constructed to provide various persona-based user experiences and/or persona-based information. For example, a guest of the building may be able to access information pertaining to where the restrooms are located and a route to reach restrooms while not being able to reserve a conference room. As another example, a building manager may be able to access information pertaining to a power outage in a given zone of the building and schedule a technician to service the given zone.
The digital occupant assistant applications 120 can allow for updating and other communications between the first and second applications 120 as well as the second model 116. Using one or more of the first application 120 and the second application 120, the system 100 can manage continuous/real-time conversations for one or more users, and evaluate the users' engagement with the information provided (e.g., did the user, customer, service technician, etc., follow the provided steps for responding to the issue or performing service, did the user discontinue providing inputs to the digital occupant assistant application 120, etc.), such as to enable the system 100 to update the information generated by the second model 116 for the digital occupant assistant application 120 according to the engagement. In some implementations, the system 100 can use the second model 116 to detect sentiment of the user of the digital occupant assistant application 120, and update the second model 116 according to the detected sentiment, such as to improve the experience provided by the digital occupant assistant application 120.
The applications 120 can include at least one document writer application 120, such as a technical document writer. The document writer application 120 can facilitate preparing structured (e.g. form-based) and/or unstructured documentation, such as documentation associated with service requests. For example, the document writer application 120 can present a user interface corresponding to a template document to be prepared that is associated with at least one of a service request or the item of equipment for which the service request is generated, such as to present one or more predefined form sections or fields. The document writer application 120 can use inputs, such as prompts received from the users and/or technical data provided by the user regarding the item of equipment, such as sensor data, text descriptions, or camera images, to generate information to include in the documentation. For example, the document writer application 120 can provide the inputs to the second model 116 to cause the second model 116 to generate completions for text information to include in the fields of the documentation.
The applications 120 can include, in some implementations, at least one diagnostics and troubleshooting application 120. The diagnostics and troubleshooting application 120 can receive inputs including at least one of a service request or information regarding the item of equipment to be serviced, such as information identified by a service technician. The diagnostics and troubleshooting application 120 can provide the inputs to a corresponding second model 116 to cause the second model 116 to generate outputs such as indications of potential items to be checked regarding the item of equipment, modifications or fixes to make to perform the service, or values or ranges of values of parameters of the item of equipment that may be indicative of specific issues to for the service technician to address or repair.
The applications 120 can at least one service recommendation generator application 120. The service recommendation generator application 120 can receive inputs such as a service request or information regarding the item of equipment to be serviced, and provide the inputs to the second model 116 to cause the second model 116 to generate outputs for presenting service recommendations, such as actions to perform to address the service request.
In some implementations, the applications 120 can include a product recommendation generator application 120. The product recommendation generator application 120 can process inputs such as information regarding the item of equipment or the service request, using one or more second models 116 (e.g., models trained using parts data from the data sources 112), to determine a recommendation of a part or product to replace or otherwise use for repairing the item of equipment.
Referring further to
The feedback repository 124 can include feedback received from users regarding output presented by the applications 120. For example, for at least a subset of outputs presented by the applications 120, the applications 120 can present one or more user input elements for receiving feedback regarding the outputs. The user input elements can include, for example, indications of binary feedback regarding the outputs (e.g., good/bad feedback; feedback indicating the outputs do or do not meet the user's criteria, such as criteria regarding technical accuracy or precision); indications of multiple levels of feedback (e.g., scoring the outputs on a predetermined scale, such as a 1-5 scale or 1-10 scale); freeform feedback (e.g., text or audio feedback); or various combinations thereof.
The system 100 can store and/or maintain feedback in the feedback repository 124. In some implementations, the system 100 stores the feedback with one or more data elements associated with the feedback, including but not limited to the outputs for which the feedback was received, the second model(s) 116 used to generate the outputs, and/or input information used by the second models 116 to generate the outputs (e.g., service request information; information captured by the user regarding the item of equipment).
The feedback trainer 128 can update the one or more second models 116 using the feedback. The feedback trainer 128 can be similar to the model updater 108. In some implementations, the feedback trainer 128 is implemented by the model updater 108; for example, the model updater 108 can include or be coupled with the feedback trainer 128. The feedback trainer 128 can perform various configuration operations (e.g., retraining, fine-tuning, transfer learning, etc.) on the second models 116 using the feedback from the feedback repository 124. In some implementations, the feedback trainer 128 identifies one or more first parameters of the second model 116 to maintain as having predetermined values (e.g., freeze the weights and/or biases of one or more first layers of the second model 116), and performs a training process, such as a fine tuning process, to configure parameters of one or more second parameters of the second model 116 using the feedback (e.g., one or more second layers of the second model 116, such as output layers or output heads of the second model 116).
In some implementations, the system 100 may not include and/or use the model updater 108 (or the feedback trainer 128) to determine the second models 116. For example, the system 100 can include or be coupled with an output processor (e.g., an output processor similar or identical to accuracy checker 316 described with reference to
Referring further to
The system 100 can be used to automate operations for scheduling, provisioning, and deploying service technicians and resources for service technicians to perform service operations. For example, the system 100 can use at least one of the first model 104 or the second model 116 to determine, based on processing information regarding service operations for items of equipment relative to completion criteria for the service operation, particular characteristics of service operations such as experience parameters of scheduled service technicians, identifiers of parts provided for the service operations, geographical data, types of customers, types of problems, or information content provided to the service technicians to facilitate the service operation, where such characteristics correspond to the completion criteria being satisfied (e.g., where such characteristics correspond to an increase in likelihood of the completion criteria being satisfied relative to other characteristics for service technicians, parts, information content, etc.). For example, the system 100 can determine, for a given item of equipment, particular parts to include on a truck to be sent to the site of the item of equipment. As such, the system 100, responsive to processing inputs at runtime such as service requests, can automatically and more accurately identify service technicians and parts to direct to the item of equipment for the service operations. The system 100 can use timing information to perform batch scheduling for multiple service operations and/or multiple technicians for the same or multiple service operations. The system 100 can perform batch scheduling for multiple trucks for multiple items of equipment, such as to schedule a first one or more parts having a greater likelihood for satisfying the completion criteria for a first item of equipment on a first truck, and a second one or more parts having a greater likelihood for satisfying the completion criteria for a second item of equipment on a second truck.
The system 100 can be used to automate operations for coordinating, scheduling, provisioning, and/or managing aspects of the building. For example, the system 100 can assist occupants in finding a supplemental conference responsive to the system 100 receiving a request including an indication that a projection in the previous reserved room is broken. In this example, the system 100 can extract information from the reservation to provide one or more recommendations. For example, the system 100 can determine the number of occupants in the reservation and provide room recommendations that can accommodate the number of occupants. The system 100 can also automatically schedule a service request to fix the broken projection and automatically blocked off the given room while the service request is still pending.
In some embodiments, the system 100 can receive inputs from service technicians that indicate an AHU for a given zone of a building is malfunctioning. The system 100 can use information pertaining to the given zone of the building to identify one or more rooms that may have been originally reserved. The system 100 can automatically detect and/or identify one or more subsequent rooms that can be reserved in place of the one or more rooms. In some embodiments, the system 100 can provide prompts and/or messages to a display device (e.g., a wall panel, a TV, a monitor, a dashboard, etc.) proximate to the one or more rooms to indicate the new room assignments. The system 100 can also provide, via the digital occupant assistant applications 120, messages to user devices of one or more occupants associated with the original reservations.
The system 200 can include at least one data repository 204, which can be similar to the data sources 112 described with reference to
The data repository 204 can include a product database 212, which can be similar or identical to the parts data of the data sources 112. The product database 212 can include, for example, data regarding products available from various vendors, specifications or parameters regarding products, and indications of products used for various service operations. The product database 212 can include data such as events or alarms associated with products; logs of product operation; and/or time series data regarding product operation, such as longitudinal data values of operation of products and/or building equipment.
The data repository 204 can include an operations database 216, which can be similar or identical to the operations data of the data sources 112. For example, the operations database 216 can include data such as manuals regarding parts, products, and/or items of equipment; customer service data; and or reports, such as operation or service logs.
In some implementations, the data repository 204 can include an output database 220, which can include data of outputs that may be generated by various machine learning models and/or algorithms. For example, the output database 220 can include values of pre-calculated predictions and/or insights, such as parameters regarding operation items of equipment, such as setpoints, changes in setpoints, flow rates, control schemes, identifications of error conditions, or various combinations thereof.
As depicted in
In some implementations, the prompt management system 228 includes a pre-processor 232. The pre-processor 232 can perform various operations to prepare the data from the data repository 204 for prompt generation. For example, the pre-processor 232 can perform any of various filtering, compression, tokenizing, or combining (e.g., combining data from various databases of the data repository 204) operations.
The prompt management system 228 can include a prompt generator 236. The prompt generator 236 can generate, from data of the data repository 204, one or more training data elements that include a prompt and a completion corresponding to the prompt. In some implementations, the prompt generator 236 receives user input indicative of prompt and completion portions of data. For example, the user input can indicate template portions representing prompts of structured data, such as predefined fields or forms of documents, and corresponding completions provided for the documents. The user input can assign prompts to unstructured data. In some implementations, the prompt generator 236 automatically determines prompts and completions from data of the data repository 204, such as by using any of various natural language processing algorithms to detect prompts and completions from data. In some implementations, the system 200 does not identify distinct prompts and completions from data of the data repository 204.
Referring further to
The training management system 240 can include a training manager 244. The training manager 244 can incorporate features of at least one of the model updater 108 or the feedback trainer 128 described with reference to
In some implementations, the training management system 240 includes a prompts database 224. For example, the training management system 240 can store one or more training data elements from the prompt management system 228, such as to facilitate asynchronous and/or batched training processes.
The training manager 244 can control the training of machine learning models using information or instructions maintained in a model tuning database 256. For example, the training manager 244 can store, in the model tuning database 256, various parameters or hyperparameters for models and/or model training.
In some implementations, the training manager 244 stores a record of training operations in a jobs database 252. For example, the training manager 244 can maintain data such as a queue of training jobs, parameters or hyperparameters to be used for training jobs, or information regarding performance of training.
Referring further to
The model system 260 can include a model configuration processor 264. The model configuration processor 264 can incorporate features of the model updater 108 and/or the feedback trainer 128 described with reference to
The client device 304 can be a device of a user, such as a technician or building manager. The client device 304 can include any of various wireless or wired communication interfaces to communicate data with the model system 260, such as to provide requests to the model system 260 indicative of data for the machine learning models 268 to generate, and to receive outputs from the model system 260. The client device 304 can include various user input and output devices to facilitate receiving and presenting inputs and outputs.
In some implementations, the system 200 provides data to the client device 304 for the client device 304 to operate the at least one application session 308. The application session 308 can include a session corresponding to any of the applications 120 described with reference to
In some implementations, the model system 260 includes at least one sessions database 312. The sessions database 312 can maintain records of application session 308 implemented by client devices 304. For example, the sessions database 312 can include records of prompts provided to the machine learning models 268 and completions generated by the machine learning models 268. As described further with reference to
In some implementations, the system 200 includes an accuracy checker 316. The accuracy checker 316 can include one or more rules, heuristics, logic, policies, algorithms, functions, machine learning models, neural networks, scripts, or various combinations thereof to perform operations including evaluating performance criteria regarding the completions determined by the model system 260. For example, the accuracy checker 316 can include at least one completion listener 320. The completion listener 320 can receive the completions determined by the model system 260 (e.g., responsive to the completions being generated by the machine learning model 268 and/or by retrieving the completions from the sessions database 312).
The accuracy checker 316 can include at least one completion evaluator 324. The completion evaluator 324 can evaluate the completions (e.g., as received or retrieved by the completion listener 320) according to various criteria. In some implementations, the completion evaluator 324 evaluates the completions by comparing the completions with corresponding data from the data repository 204. For example, the completion evaluator 324 can identify data of the data repository 204 having similar text as the prompts and/or completions (e.g., using any of various natural language processing algorithms), and determine whether the data of the completions is within a range of expected data represented by the data of the data repository 204.
In some implementations, the accuracy checker 316 can store an output from evaluating the completion (e.g., an indication of whether the completion satisfies the criteria) in an evaluation database 328. For example, the accuracy checker 316 can assign the output (which may indicate at least one of a binary indication of whether the completion satisfied the criteria or an indication of a portion of the completion that did not satisfy the criteria) to the completion for storage in the evaluation database 328, which can facilitate further training of the machine learning models 268 using the completions and output.
The feedback system 400 can receive feedback (e.g., from the client device 304) in various formats. For example, the feedback can include any of text, speech, audio, image, and/or video data. The feedback can be associated (e.g., in a data structure generated by the application session 308) with the outputs of the machine learning models 268 for which the feedback is provided. The feedback can be received or extracted from various forms of data, including external data sources such as manuals, service reports, or Wikipedia-type documentation.
In some implementations, the feedback system 400 includes a pre-processor 404. The pre-processor 404 can perform any of various operations to modify the feedback for further processing. For example, the pre-processor 404 can incorporate features of, or be implemented by, the pre-processor 232, such as to perform operations including filtering, compression, tokenizing, or translation operations (e.g., translation into a common language of the data of the data repository 204).
The feedback system 400 can include a bias checker 408. The bias checker 408 can evaluate the feedback using various bias criteria, and control inclusion of the feedback in a feedback database 416 (e.g., a feedback database 416 of the data repository 204 as depicted in
The feedback system 400 can include a feedback encoder 412. The feedback encoder 412 can process the feedback (e.g., responsive to bias checking by the bias checker 408) for inclusion in the feedback database 416. For example, the feedback encoder 412 can encode the feedback as values corresponding to outputs scoring determined by the model system 260 while generating completions (e.g., where the feedback indicates that the completion presented via the application session 308 was acceptable, the feedback encoder 412 can encode the feedback by associating the feedback with the completion and assigning a relatively high score to the completion).
As indicated by the dashed arrows in
For example, the data filters 500 can be used to evaluate data relative to thresholds relating to data including, for example and without limitation, acceptable data ranges, setpoints, temperatures, pressures, flow rates (e.g., mass flow rates), or vibration rates for an item of equipment. The threshold can include any of various thresholds, such as one or more of minimum, maximum, absolute, relative, fixed band, and/or floating band thresholds.
The data filters 500 can enable the system 200 to detect when data, such as prompts, completions, or other inputs and/or outputs of the system 200, collide with thresholds that represent realistic behavior or operation or other limits of items of equipment. For example, the thresholds of the data filters 500 can correspond to values of data that are within feasible or recommended operating ranges. In some implementations, the system 200 determines or receives the thresholds using models or simulations of items of equipment, such as plant or equipment simulators, chiller models, HVAC-R models, refrigeration cycle models, etc. The system 200 can receive the thresholds as user input (e.g., from experts, technicians, or other users). The thresholds of the data filters 500 can be based on information from various data sources. The thresholds can include, for example and without limitation, thresholds based on information such as equipment limitations, safety margins, physics, expert teaching, etc. For example, the data filters 500 can include thresholds determined from various models, functions, or data structures (e.g., tables) representing physical properties and processes, such as physics of psychometrics, thermodynamics, and/or fluid dynamics information.
The system 200 can determine the thresholds using the feedback system 400 and/or the client device 304, such as by providing a request for feedback that includes a request for a corresponding threshold associated with the completion and/or prompt presented by the application session 308. For example, the system 200 can use the feedback to identify realistic thresholds, such as by using feedback regarding data generated by the machine learning models 268 for ranges, setpoints, and/or start-up or operating sequences regarding items of equipment (and which can thus be validated by human experts). In some implementations, the system 200 selectively requests feedback indicative of thresholds based on an identifier of a user of the application session 308, such as to selectively request feedback from users having predetermined levels of expertise and/or assign weights to feedback according to criteria such as levels of expertise.
In some implementations, one or more data filters 500 correspond to a given setup. For example, the setup can represent a configuration of a corresponding item of equipment (e.g., configuration of a chiller, etc.). The data filters 500 can represent various thresholds or conditions with respect to values for the configuration, such as feasible or recommendation operating ranges for the values. In some implementations, one or more data filters 500 correspond to a given situation. For example, the situation can represent at least one of an operating mode or a condition of a corresponding item of equipment.
The system 200 can perform various actions responsive to the processing of data by the data filters 500. In some implementations, the system 200 can pass data to a destination without modifying the data (e.g., retaining a value of the data prior to evaluation by the data filter 500) responsive to the data satisfying the criteria of the respective data filter(s) 500. In some implementations, the system 200 can at least one of (i) modify the data or (ii) output an alert responsive to the data not satisfying the criteria of the respective data filter(s) 500. For example, the system 200 can modify the data by modifying one or more values of the data to be within the criteria of the data filters 500.
In some implementations, the system 200 modifies the data by causing the machine learning models 268 to regenerate the completion corresponding to the data (e.g., for up to a predetermined threshold number of regeneration attempts before triggering the alert). This can enable the data filters 500 and the system 200 selectively trigger alerts responsive to determining that the data (e.g., the collision between the data and the thresholds of the data filters 500) may not be repairable by the machine learning model 268 aspects of the system 200.
The system 200 can output the alert to the client device 304. The system 200 can assign a flag corresponding to the alert to at least one of the prompt (e.g., in prompts database 224) or the completion having the data that triggered the alert.
For example, the validation system 600 can receive data such as data retrieved from the data repository 204, prompts outputted by the prompt management system 228, completions outputted by the model system 260, indications of accuracy outputted by the accuracy checker 316, etc., and provide the received data to at least one of an expert system or a user interface. In some implementations, the validation system 600 receives a given item of data prior to the given item of data being processed by the model system 260, such as to validate inputs to the machine learning models 268 prior to the inputs being processed by the machine learning models 268 to generate outputs, such as completions.
In some implementations, the validation system 600 validates data by at least one of (i) assigning a label (e.g., a flag, etc.) to the data indicating that the data is validated or (ii) passing the data to a destination without modifying the data. For example, responsive to receiving at least one of a user input (e.g., from a human validator/supervisor/expert) that the data is valid or an indication from an expert system that the data is valid, the validation system 600 can assign the label and/or provide the data to the destination.
The validation system 600 can selectively provide data from the system 200 to the validation interface responsive to operation of the data filters 500. This can enable the validation system 600 to trigger validation of the data responsive to collision of the data with the criteria of the data filters 500. For example, responsive to the data filters 500 determining that an item of data does not satisfy a corresponding criteria, the data filters 500 can provide the item of data to the validation system 600. The data filters 500 can assign various labels to the item of data, such as indications of the values of the thresholds that the data filters 500 used to determine that the item of data did not satisfy the thresholds. Responsive to receiving the item of data from the data filters 500, the validation system 600 can provide the item of data to the validation interface (e.g., to a user interface of client device 304 and/or application session 308; for comparison with a model, simulation, algorithm, or other operation of an expert system) for validation. In some implementations, the validation system 600 can receive an indication that the item of data is valid (e.g., even if the item of data did not satisfy the criteria of the data filters 500) and can provide the indication to the data filters 500 to cause the data filters 500 to at least partially modify the respective thresholds according to the indication.
In some implementations, the validation system 600 selectively retrieves data for validation where (i) the data is determined or outputted prior to use by the machine learning models 268, such as data from the data repository 204 or the prompt management system 228, or (ii) the data does not satisfy a respective data filter 500 that processes the data. This can enable the system 200, the data filters 500, and the validation system 600 to update the machine learning models 268 and other machine learning aspects (e.g., generative AI aspects) of the system 200 to more accurately generate data and completions (e.g., enabling the data filters 500 to generate alerts that are received by the human experts/expert systems that may be repairable by adjustments to one or more components of the system 200).
In some implementations, the expert system 700 retrieves data to be provided to the application session 308, such as completions generated by the machine learning models 268. The expert system 700 can present the data via the expert session 708, such as to request feedback regarding the data from the client device 704. For example, the expert system 700 can receive feedback regarding the data for modifying or validating the data (e.g., editing or validating completions). In some implementations, the expert system 700 requests at least one of an identifier or a credential of a user of the client device 704 prior to providing the data to the client device 704 and/or requesting feedback regarding the data from the expert session 708. For example, the expert system 700 can request the feedback responsive to determining that the at least one of the identifier or the credential satisfies a target value for the data. This can allow the expert system 700 to selectively identify experts to use for monitoring and validating the data.
In some implementations, the expert system 700 facilitates a communication session regarding the data, between the application session 308 and the expert session 708. For example, the expert system 700, responsive to detecting presentation of the data via the application session 308, can request feedback regarding the data (e.g., user input via the application session 308 for feedback regarding the data), and provide the feedback to the client device 704 to present via the expert session 708. The expert session 708 can receive expert feedback regarding at least one of the data or the feedback from the user to provide to the application session 308. In some implementations, the expert system 700 can facilitate any of various real-time or asynchronous messaging protocols between the application session 308 and expert session 708 regarding the data, such as any of text, speech, audio, image, and/or video communications or combinations thereof. This can allow the expert system 700 to provide a platform for a user receiving the data (e.g., customer or field technician) to receive expert feedback from a user of the client device 704 (e.g., expert technician). In some implementations, the expert system 700 stores a record of one or more messages or other communications between the application session 308 and the expert session 708 in the data repository 204 to facilitate further configuration of the machine learning models 268 based on the interactions between the users of the application session 308 and the expert session 708.
Referring further to
For example, in some implementations, various data discussed herein may be stored in, retrieved from, or processed in the context of building data platforms and/or digital twins; processed at (e.g., processed using models executed at) a cloud or other off-premises computing system/device or group of systems/devices, an edge or other on-premises system/device or group of systems/devices, or a hybrid thereof in which some processing occurs off-premises and some occurs on-premises; and/or implemented using one or more gateways for communication and data management amongst various such systems/devices. In some such implementations, the building data platforms and/or digital twins may be provided within an infrastructure such as those described in U.S. patent application Ser. No. 17/134,661 filed Dec. 28, 2020, Ser. No. 18/080,360, filed Dec. 13, 2022, Ser. No. 17/537,046 filed Nov. 29, 2021, and Ser. No. 18/096,965, filed Jan. 13, 2023, and Indian Patent Application No. 202341008712, filed Feb. 10, 2023, the disclosures of which are incorporated herein by reference in their entireties.
As described herein, the digital occupant assistant applications 120 can assist one or more occupants of the building. The digital occupant assistant applications 120 can interact with the occupants of the building. The interactions with the occupants can be persona-based interactions. For example, a first occupant may have food allergies and the interaction between the digital occupant assistant applications 120 and the first occupant may be modified, adjusted, and/or constructed to assist the first occupant with their food allergies. The interactions can include the digital occupant assistant applications 120 removing food items that the first occupant is allergic to from a digital menu that is provided to the first occupant.
The digital occupant assistant applications 120 can also receive conversational inputs from the occupants. The conversational inputs can correspond to room reservations, room layouts, service requests, travel assistance, building navigation, productivity assistance, equipment difficulties, building notifications, and/or among various combinations. For example, the digital occupant assistant applications 120 can receive a conversational input of “the coffee maker in zone 3 of the building is not working” and the digital occupant assistant applications 120 can generate one or more responses based on the conversational input. The digital occupant assistant applications 120 can provide responses identifying one or more additional coffee makers located in the building and routes to reach the additional coffee makers.
The digital occupant assistant applications 120 can assist the occupants of the building with their health and wellness. For example, the digital occupant assistant applications 120 can receive data corresponding to a work schedule for at least one occupant of the building and the digital occupant assistant applications 120 can detect, identify and/or generate recommendations indicating points during the day to take breaks and/or indicating types of breaks (e.g., a 5 minute walk, a 10 minute sensory deprivation session, a 30 minute lunch break, etc.). The digital occupant assistant applications 120 can also monitor water intake during the day to assistant the occupants in tracking their water intake.
The digital occupant assistant applications 120 can track meetings and/or the locations of the meetings for the occupants of the building. The digital occupant assistant applications 120 can provide reminders to indicate efficient points in time for the occupants to leave their desk and/or stations in order to reach their scheduled meetings. For example, the digital occupant assistant applications 120 can determine a distance and/or a travel time between the occupant and a room for which the meeting is scheduled to take place in and the digital occupant assistant applications 120 can provide a notification to the occupant when it is time to leave. The notification can also include a route that the occupant can take to reach the meeting room.
The digital occupant assistant applications 120 can provide recommendations to assist in occupant navigation within the building. For example, the digital occupant assistant applications 120 can track elevator outages and provide notifications to the occupants to indicate given elevators that are experiencing outages. The digital occupant assistant applications 120 can also provide routes that an occupant can take to increase and/or decrease a daily number of steps that they take while navigating the building. For example, the digital occupant assistant applications 120 can generate routes that differ from routinely traveled routes of the occupants and the generate routes can include a step increase and/or a step decrease. The digital occupant assistant applications 120 can also generate routes to assist occupants in avoiding potential navigational difficulties. For example, the digital occupant assistant applications 120 can generate routes that avoid stairs for occupants that may have difficulty in using stairs.
The digital occupant assistant applications 120 can receive indications from occupants about damaged, broken, and/or malfunction equipment and/or components of the building. For example, the digital occupant assistant applications 120 can receive an input from an occupant that a door in a given room of the building does not lock. The digital occupant assistant applications 120 can assist the occupant in generating a maintenance request to address the door. For example, the digital occupant assistant applications 120 can generate service requests and the occupant can be prompted to provide and/or enter additional information for the service requests.
The digital occupant assistant applications 120 can receive indications from occupants about room reservations and the digital occupant assistant applications 120 can assist the occupants with their room reservations. For example, an occupant of the building can provide a request, to the digital occupant assistant applications 120, for a different conference room. The digital occupant assistant applications 120 can detect, identify, and/or determine one or more conferences rooms and the digital occupant assistant applications 120 can provide responses including indications of the conference rooms.
The digital occupant assistant applications 120 can also provide prompts to the occupant asking why they requested a different conference room. The occupant can provide, to the digital occupant assistant applications 120, information indicating why they requested a different room. For example, the occupant can provide information that the previous scheduled room is always noisy as it is located near a break room, that the scheduled room is always cold, that the blinds in the scheduled room do not block the sun, and/or that the scheduled room is too far away from their desk. The digital occupant assistant applications 120 can use the responses from the prompts to identify occupant preferences and the identified occupant preferences can be used in future room reservations. For example, the digital occupant assistant applications 120 can learn that a first occupant of the building does not prefer room A of the building and room recommendations generated for the first occupant can have room A omitted and/or not included.
The digital occupant assistant applications 120 can also interact with and/or interface with various display devices located within and/or proximate to the building. For example, conferences located in the building may have a monitor disposed on a wall near the entrance to each room and the monitors can display information, via user interfaces, corresponding to the conference rooms and/or the buildings. The digital occupant assistant applications 120 can adjust, change, and/or modify the information displayed near the conference rooms to provide statuses of the rooms. For example, the monitor can display a current configuration of a modular conference room as well as a current seating capacity for the modular conference room. The monitor can also display that a scheduled meeting has been cancelled and/or rescheduled as well as a contact person if the occupants have any questions.
The digital occupant assistant applications 120 can also be implemented and/or utilized with a healthcare environment. For example, the digital occupant assistant applications 120 can provide prompts to floor supervisors indicating that in 20 minutes a group of patients will be partaking in a walking session on a given floor and that the given floor is currently congested with equipment. The digital occupant assistant applications 120 can assist the floor supervisors in finding and/or location areas in the building that the equipment can be moved to adjust the congestion of the given floor.
The digital occupant assistant applications 120 can assist occupants in identifying and/or locating available parking spots. For example, the digital occupant assistant applications 120 can receive and/or access cameras feeds of the parking lot and the digital occupant assistant applications 120 can identify one or more open parking spots. The digital occupant assistant applications 120 can also identify charging ports for occupants that are traveling by electric vehicles.
It should be understood that, in various implementations, the digital occupant assistant applications 120 may provide one or more of the features described herein, and need not provide all of the features. All such implementations are contemplated within the scope of the present disclosure.
As described above, systems and methods in accordance with the present disclosure can use machine learning models, including LLMs and other generative AI models, to ingest data regarding one or more aspects of a building and/or one more requests from occupants of the building in various unstructured and structured formats, and generate completions and other outputs targeted to provide useful information to users. Various systems and methods described herein can use machine learning models to support applications for presenting data with high accuracy and relevance.
At step 805, a digital occupant assist application can be provided. The digital occupant assistant application can be provided for occupants of a commercial building. For example, the digital occupant assistant application can be provided as the digital occupant assistant application 120 described herein. The digital occupant assistant application be provided by at least one of the system 100 and/or the system 200. The occupants of the building can interact with, interface with, and/or communicate with the digital occupant assistant application.
At step 810, data to present to an occupant via the digital occupant assistant application can be generated. The data can be generated by at least one of the models described herein. For example, the data can be generated by the models 116. The data can be dynamically generated by a generative large language model (LLM). For example, the models 116 can be generative LLMs and the models 116 can dynamically generate the data. The data can be presented by the digital occupant assistant application that was provided in step 805. For example, the data dynamically generated by the generative LLM can be provided to the digital occupant assistant application and the digital occupant assistant application can present the data to an occupant via a user interface.
The generative LLM can dynamically generate the data based on at least one of a prompt from the occupant provided via an input interface of the digital occupant assistant application or context relating to at least one of the occupant, the building, a space of the building, an event relating to the building, one or more other occupants, or building equipment or other assets of the building. For example, the occupant can provide a prompt for assistance in reserving a conference room and the prompt can be provided via the digital occupant assistant application. The generative LLM can dynamically generate the data based on the prompt for assistance in reserving the conference room. For example, the generative LLM can dynamically generate data including conference rooms that a located near the occupant of the building.
The generative LLM can also generate the data based on context relating to the at least one of the occupant, the building, a space of the building, an event relating to the building, one or more other occupants, or building equipment or other assets of the building. For example, the generative LLM retrieve, receive, detect, identify, and/or determine an identification of the occupant (e.g., a name of the occupant, an employee number, an employee ID, etc.) and the generative LLM can generate the data based on the identification of the occupant.
In some embodiments, the generative LLM includes a pretrained generative transformer model. For example, the generative LLM can be trained to convert an input sequence (e.g., a prompt provided by a user via the digital occupant assistant applications 120) into a modified input sequence.
In some embodiments, the prompt from the occupant includes unstructured data conforming to a plurality of different predetermined formats and/or not conforming to a predetermined format. The generative LLM can generate the data in step 810 using the unstructured data included in the prompt. For example, the prompt can be provided as a conversational input and the generative LLM can generate the data in step 810 using the conversational input. In some embodiments, the generative LLM can autonomously generate the data in step 810 from the unstructured data included in the prompt without requiring manual user intervention. For example, the generative LLM can receive the prompt and the automatically generate the data.
In some embodiments, dynamically generating the data can include dynamically generating a recommendation to the occupant based on at least one of the prompt or the context. For example, the generative LLM can provide some of the recommendations described herein. The recommendations generated by the generative LLM can include at least one of route recommendations, room configuration recommendations, automated service request recommendations, and/or among various other possible recommendations.
In some embodiments, at least a portion of the data generated in step 810 includes new data generated by the generative LLM responsive to at least one of the prompt or the context. The new data is not preexisting, at the time of receiving the prompt and/or the context, in a data set upon which the generative LLM has been trained. For example, the data generated by the generative LLM includes at least a portion of data that was not just retrieved from a database and/or previously known by the generative LLM.
At step 905, a plurality of unstructured building reports for a plurality of occupants of a building can be received. The plurality of unstructured building reports can be training data. The plurality of unstructured building reports can be provided as training data to a model (e.g., the training data is inputted into the model. For example, the plurality of unstructured building reports can be inputted into the first model 104. The plurality of unstructured building reports can include prompts, responses, and/or recommendations similar to those described herein. For example, the plurality of unstructured building reports can include a prompt provided by an occupant of a building and/or one or more recommendations and/or data generated based on the prompt. The plurality of unstructured building reports can be received from at least one of the data sources described herein. For example, the plurality of unstructured building reports can be received from the building data 112.
In some embodiments, the plurality of unstructured building reports may conform to a plurality of different predetermined formats. The predetermined formats of the unstructured building reports may include at least one of the multiple data formats described herein. The unstructured building reports may also include unstructured data that does not conform to a predetermined format. For example, the unstructured data may include handwritten notes that include a list of previous recommendations.
At step 910, a model can be trained using the plurality of unstructured building reports. For example, the model can be trained using the plurality of unstructured building reports received in step 905. The model can be and/or include at least one of the models described herein. For example, the model can be the model 104. The model updater 108 can train the model using at least one of the techniques described herein. For example, the model updater 108 can provide occupant preferences and/or data included with the unstructured building reports received in step 905 to cause the model to generate an output (e.g., a recommendation to include in a response to a prompt from an occupant of the building). The model can be a generative AI model and the generative AI model can be trained using the plurality of unstructured building reports.
At step 915, one or more responses with respect to one or more occupants of a building can be provided. The responses can be provided by the model trained in step 910. For example, a generative LLM can provide one or more responses to one or more prompts from occupants of the building subsequent to the training of the generative LLM. In some embodiments, the generative LLM can generate responses including information similar to information provided by the digital occupant assistant applications 120.
The construction and arrangement of the systems and methods as shown in the various exemplary embodiments are illustrative only. Although only a few embodiments have been described in detail in this disclosure, many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes and proportions of the various elements, values of parameters, mounting arrangements, use of materials, colors, orientations, etc.). For example, the position of elements may be reversed or otherwise varied and the nature or number of discrete elements or positions may be altered or varied. Accordingly, all such modifications are intended to be included within the scope of the present disclosure. The order or sequence of any process or method steps may be varied or re-sequenced according to alternative embodiments. Other substitutions, modifications, changes, and omissions may be made in the design, operating conditions and arrangement of the exemplary embodiments without departing from the scope of the present disclosure.
The present disclosure contemplates methods, systems and program products on any machine-readable media for accomplishing various operations. The embodiments of the present disclosure may be implemented using existing computer processors, or by a special purpose computer processor for an appropriate system, incorporated for this or another purpose, or by a hardwired system. Embodiments within the scope of the present disclosure include program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored thereon. Such machine-readable media can be any available media that can be accessed by a general purpose or special purpose computer or other machine with a processor. By way of example, such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code in the form of machine-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer or other machine with a processor. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a machine, the machine properly views the connection as a machine-readable medium. Thus, any such connection is properly termed a machine-readable medium. Combinations of the above are also included within the scope of machine-readable media. Machine-executable instructions include, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing machines to perform a certain function or group of functions.
Although the figures show a specific order of method steps, the order of the steps may differ from what is depicted. Also two or more steps may be performed concurrently or with partial concurrence. Such variation will depend on the software and hardware systems chosen and on designer choice. All such variations are within the scope of the disclosure. Likewise, software implementations could be accomplished with standard programming techniques with rule based logic and other logic to accomplish the various connection steps, processing steps, comparison steps and decision steps.
In various implementations, the steps and operations described herein may be performed on one processor or in a combination of two or more processors. For example, in some implementations, the various operations could be performed in a central server or set of central servers configured to receive data from one or more devices (e.g., edge computing devices/controllers) and perform the operations. In some implementations, the operations may be performed by one or more local controllers or computing devices (e.g., edge devices), such as controllers dedicated to and/or located within a particular building or portion of a building. In some implementations, the operations may be performed by a combination of one or more central or offsite computing devices/servers and one or more local controllers/computing devices. All such implementations are contemplated within the scope of the present disclosure. Further, unless otherwise indicated, when the present disclosure refers to one or more computer-readable storage media and/or one or more controllers, such computer-readable storage media and/or one or more controllers may be implemented as one or more central servers, one or more local controllers or computing devices (e.g., edge devices), any combination thereof, or any other combination of storage media and/or controllers regardless of the location of such devices.
This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63/470,817, filed on Jun. 2, 2023, the entirety of which is incorporated by reference herein.
| Number | Date | Country | |
|---|---|---|---|
| 63470817 | Jun 2023 | US |
