System and method for monitoring by using artificial intelligence

Abstract

Aspects of the present application relate to a method of monitoring performance of equipment, the method comprising: acquiring, at a mobile device, sensed data from a noise sensor, where the noise sensor is configured to monitor the equipment; receiving the sensed data at a machine learning model; receiving a prediction of equipment performance from the machine learning model; and displaying the prediction on a graphical user interface. The method may further include determining that the prediction includes a poor performance prediction, where "displaying the prediction on a graphical user interface" further includes generating an alert for the prediction in response to the determination.

Description

[0001] Cross-references to related applications This application claims priority to U.S. Patent Application No. 19 / 029167, filed January 17, 2025, entitled "System and Method for Monitoring Using Artificial Intelligence," which itself claims priority to U.S. Provisional Patent Application No. 63 / 667639, filed July 3, 2024, entitled "Architectural Framework, Design and Development Supporting Unified Applications of Narrow Artificial Intelligence, General Artificial Intelligence and Super Artificial Intelligence," the entire contents of which are incorporated herein by reference. This application also claims priority to U.S. Provisional Patent Application No. 63 / 667639, filed July 3, 2024, entitled "Architectural Framework, Design and Development Supporting Unified Applications of Narrow Artificial Intelligence, General Artificial Intelligence and Super Artificial Intelligence," the entire contents of which are incorporated herein by reference. This application is also a continuation of U.S. Patent Application No. 19 / 029167, filed January 17, 2025, entitled "System and Method for Monitoring Using Artificial Intelligence," which itself claims priority to U.S. Provisional Patent Application No. 63 / 667639, filed July 3, 2024, entitled "Architectural Framework, Design and Development Supporting Unified Applications of Narrow Artificial Intelligence, General Artificial Intelligence and Super Artificial Intelligence," the entire contents of which are incorporated herein by reference. Technical Field

[0002] This invention relates to an artificial intelligence architecture, and more particularly to applications for analysis, planning, and monitoring. Background Technology

[0003] Existing artificial intelligence (AI) solutions may include, for example, demand forecasting and image classification. However, these AI solutions may be limited to specific, narrow applications. In particular, they may be limited to solving problems within a single domain, and solving problems in another domain may require different AI solutions. Furthermore, the development of these AI solutions is often customized, cumbersome, and time-consuming. Additionally, while generative AI solutions do exist, they are not integrated with other AI solutions into a unified system. Summary of the Invention

[0004] The following embodiments can provide methods and systems for analysis, planning, and monitoring using artificial intelligence.

[0005] According to at least one embodiment, a method for monitoring equipment performance is disclosed, the method comprising: acquiring sensing data from a noise sensor at a mobile device, wherein the noise sensor is configured to monitor the equipment; receiving the sensing data at a machine learning model; receiving an equipment performance prediction from the machine learning model; and displaying the prediction on a graphical user interface.

[0006] In some implementations, the method may further include: determining that the prediction includes a poor performance prediction; and "displaying the prediction on a graphical user interface" further includes generating an alert for the prediction in response to the determination.

[0007] In some implementations, "displaying the prediction on the graphical user interface" may further include generating a customized visual representation based on the prediction.

[0008] In some embodiments, the method may further include: acquiring sensing data at a mobile device from at least one of the following: a noise sensor, a vibration sensor, a temperature sensor, a relative humidity sensor, a gyroscope, a magnetometer, a Global Positioning System (GPS) device, a microphone, a vision sensor, a light sensor, a vibration sensor, a severity sensor, a pressure sensor, a current sensor, a carbon dioxide sensor, a water leakage sensor, a passive infrared (PIR) sensor, a magnetic gate sensor, a soil sensor, an air quality sensor, a volatile organic compound sensor, or a particulate matter sensor.

[0009] In some implementations, the sensing data from the noise sensor may further include noise data in the range that is inaudible to humans.

[0010] In some implementations, the method may further include: preprocessing the sensed data and receiving the preprocessed sensed data at a machine learning model.

[0011] In some implementations, the machine learning model can be retrained based on at least one of sensing data, performance predictions, or new sensing data acquired after the machine learning model receives a prediction.

[0012] In some embodiments, the method may further include: performing maintenance or automatically adjusting the equipment in response to a poor performance prediction.

[0013] In some implementations, poor performance prediction can be at least one of equipment failure prediction, equipment mean time between failures (MTBF) prediction, equipment maintenance required prediction, prediction for automatically adjusting equipment operating parameters, or equipment health status prediction.

[0014] In some embodiments, the method may further include mounting a noise sensor on the equipment and connecting the noise sensor to the mobile device.

[0015] In some implementations, the noise sensor may be integrated into the mobile device.

[0016] In some implementations, the noise sensor may be the microphone of a mobile device.

[0017] In some implementations, the sensing data may be continuously acquired by a noise sensor.

[0018] In some implementations, displaying the prediction on the graphical user interface may further include generating a report summarizing the equipment performance over a period of time based on continuously acquired sensor data.

[0019] In some embodiments, the equipment may be located in at least one of a heating, ventilation, and air conditioning (HVAC) unit, a manufacturing plant, a cement plant, a transportation vehicle, a retail environment, a telecommunications facility, a mine, agricultural equipment, a residential facility, or a warehouse.

[0020] In some implementations, the machine learning model can be hosted in a cloud computing environment, and sensed data can be transmitted from a mobile device to the cloud computing environment via a network for inference.

[0021] In some implementations, the machine learning model can run on a mobile device, and inference can be performed on the device without transmitting sensed data to an external server.

[0022] In some implementations, the first part of the machine learning model can run on a device, and the second part can run in a cloud computing environment, which allows some inference to occur on the mobile device while the final inference occurs in the cloud computing environment.

[0023] According to at least one embodiment, a system for monitoring equipment performance is disclosed, the system including a memory and at least one processor, the processor being configured to: acquire sensing data from a noise sensor at a mobile device, wherein the noise sensor is configured to monitor the equipment; receive the sensing data at a machine learning model; receive equipment performance predictions from the machine learning model; and display the predictions on a graphical user interface.

[0024] In some implementations, the at least one processor may be further configured to: determine that the prediction includes a poor performance prediction, wherein "displaying the prediction on a graphical user interface" may further include generating an alert for the prediction in response to the determination.

[0025] In some implementations, "displaying the prediction on the graphical user interface" may further include generating a customized visual representation based on the prediction.

[0026] In some embodiments, the at least one processor may be further configured to: acquire sensing data at a mobile device from at least one of the following: a noise sensor, a vibration sensor, a temperature sensor, a relative humidity sensor, a gyroscope, a magnetometer, a Global Positioning System (GPS) device, a microphone, a vision sensor, a light sensor, a vibration sensor, a severity sensor, a pressure sensor, a current sensor, a carbon dioxide sensor, a water leakage sensor, a passive infrared (PIR) sensor, a magnetic gate sensor, a soil sensor, an air quality sensor, a volatile organic compound sensor, or a particulate matter sensor.

[0027] In some implementations, the sensing data from the noise sensor may further include noise data in the range that is inaudible to humans.

[0028] In some implementations, the at least one processor may be further configured to preprocess the sensed data and receive the preprocessed sensed data at a machine learning model.

[0029] In some implementations, the machine learning model can be retrained based on at least one of sensing data, performance predictions, or new sensing data acquired after the machine learning model receives a prediction.

[0030] In some embodiments, the at least one processor may be further configured to perform maintenance or automatically adjust the equipment in response to a poor performance prediction.

[0031] In some implementations, poor performance prediction can be at least one of equipment failure prediction, equipment mean time between failures (MTBF) prediction, equipment maintenance requirements prediction, prediction for automatically adjusting equipment operating parameters, or equipment health status prediction.

[0032] In some implementations, the noise sensor may be mounted on the equipment and connected to the mobile device.

[0033] In some implementations, the noise sensor may be integrated into the mobile device.

[0034] In some implementations, the noise sensor may be the microphone of a mobile device.

[0035] In some implementations, the sensing data may be continuously acquired by a noise sensor.

[0036] In some implementations, displaying the prediction on the graphical user interface may further include generating a report summarizing the equipment performance over a period of time based on continuously acquired sensor data.

[0037] In some embodiments, the equipment may be located in at least one of a heating, ventilation, and air conditioning (HVAC) unit, a manufacturing plant, a cement plant, a transportation vehicle, a retail environment, a telecommunications facility, a mine, agricultural equipment, a residential facility, or a warehouse.

[0038] In some implementations, the at least one processor may be configured to perform inference in a cloud computing environment, wherein the mobile device transmits sensed data to the cloud computing environment for analysis.

[0039] In some implementations, the at least one processor may be configured to perform inference on a mobile device, such that the machine learning model resides on the mobile device.

[0040] In some implementations, the at least one processor may be distributed between the edge device and the cloud computing server, and instructions may cause partial preprocessing to be performed on the edge device before the preprocessed data is transmitted to the cloud computing server to generate a prediction.

[0041] According to at least one embodiment, one or more non-volatile computer-readable media are disclosed having computer-executable instructions stored thereon, which, when executed by at least one computer, cause the at least one computer to perform a method comprising: acquiring sensing data from a noise sensor at a mobile device, wherein the noise sensor is configured to monitor the equipment; receiving the sensing data at a machine learning model; receiving an equipment performance prediction from the machine learning model; and displaying the prediction on a graphical user interface. Attached Figure Description

[0042] The embodiments will now be described with reference to the accompanying drawings, in which: Figure 1 This is a block diagram of a system integrating an analytics engine according to some embodiments; Figure 2 A block diagram of an exemplary artificial intelligence (AI) agent; Figure 3 It is based on some implementation methods Figure 1 A schematic diagram of the analysis engine is shown; Figures 4A to 4B It is based on other implementation methods. Figure 1 Another schematic diagram of the analysis engine is shown; Figure 5 yes Figure 1 A block diagram of the system environment shown; Figure 6It is based on some embodiments of the invention. Figure 1 The flowchart shown is a decision tree executed by the analysis engine. Figure 7 According to other embodiments Figure 1 The diagram shows another flowchart of another decision tree executed by the analysis engine; Figure 8 This is a block diagram of another system integrating an analytics engine, according to some embodiments; Figure 9 yes Figure 8 The input block diagram within the system shown is as follows; Figure 10 According to some embodiments Figure 8 The diagram shows the analysis engine's block diagram; Figure 11 yes Figure 10 The diagram shows the block diagram of the AI ​​agent in the analysis engine. Figure 12 Through Figure 10 The diagram shows an example business overview generated by the analytics engine. Figure 13 Through Figure 10 The diagram shows an example business plan generated by the analysis engine. Figures 14 to 18 This is based on the utilization of some embodiments. Figure 8 The system shown represents a method for generating business plans. Figure 19 This is a block diagram of yet another system integrating an analytics engine, based on some embodiments; Figure 20 yes Figure 19 Block diagram of an exemplary sensor group within the system shown; Figure 21 According to some embodiments Figure 19 The diagram shows the analysis engine's block diagram; Figures 21 to 23 This is based on the utilization of some embodiments. Figure 19 The method for predicting equipment performance in the system shown; Figure 24 This is a block diagram of a computing device according to one embodiment. Detailed Implementation

[0043] Analysis Engine Figure 1 The system 100 is shown, which includes user equipment 102, analysis engine 104, and environment 106.

[0044] User equipment 102 may be a computing device, such as a mobile device, personal computer, server, embedded system, or other device with computing capabilities. User equipment 102 may receive input from a user (e.g., a human) or other computing devices (e.g., one or more sensors, instruments, and / or information systems).

[0045] User device 102 communicates with analytics engine 104, for example, via a network (not shown). User device 102 and analytics engine 104 can exchange information with each other, allowing user device 102 to both send and receive information from analytics engine 104. The network may include the Internet, intranet, WiFi network, Bluetooth network, iBeacon network, or other communication protocols that allow user device 102 and analytics engine 104 to exchange information.

[0046] In some implementations, the analytics engine 104 may run, be hosted, and / or stored on one or more servers or other computing devices. In these implementations, cloud computing may be used to allow user device 102 to communicate with the analytics engine 104.

[0047] In some implementations, analytics engine 104, or a portion thereof, may run, be hosted, and / or stored on user device 102. In these implementations, edge computing or cloud-edge collaborative computing may be used to allow user device 102 to communicate with analytics engine 104. In implementations where only a portion of analytics engine 104 runs, is hosted, and / or stored on user device 102, the analytics engine 104 contained on user device 102 may communicate with that portion of analytics engine 104 that runs, is hosted, and / or stored on a server or other external computing device.

[0048] The analytics engine 104 can also communicate with the environment 106, for example, via a network (not shown). Examples of networks may include the Internet, an intranet, a WiFi network, a Bluetooth network, an iBeacon network, or other communication protocols. The analytics engine 104 can query the environment 106, send data to the environment 106, retrieve data from the environment 106, and respond to queries from the environment 106. The analytics engine 104 and the environment 106 can communicate bidirectionally, similar to the relationship between the user device 102 and the analytics engine 104. As will be further detailed below, the environment 106 may include databases, the Internet (such as websites), application-specific interfaces (APIs), computing devices, sensors, intranets, internal systems, etc.

[0049] The analytics engine 104 can be used to process data, generate analytical insights based on the data, respond to queries received from the input device 102, automate tasks and processes, and / or solve problems.

[0050] In some implementations, analytics engine 104 can provide a unified application that embodies artificial intelligence in the narrow sense (ANI), artificial intelligence general (AGI), and artificial intelligence superintelligence (ASI). Analytics engine 104 enables organizations or users of user devices 102 to design artificial intelligence (AI) solutions and automate tasks.

[0051] ANI can also be called "weak AI." ANI can refer to AI that focuses on a specific task or a narrow range of tasks. The key characteristics of ANI are that it is task-specific, limited in scope, and lacks true understanding. However, in some examples, ANI can be more efficient than humans.

[0052] AGI can understand, learn, and perform any intelligent task that humans can accomplish.

[0053] ASI surpasses human capabilities across various domains, thus going a step further than AGI. ASI can create self-driven purposes and / or goals.

[0054] AGI and ASI are capable of solving a wide variety of problems across multiple domains, demonstrating versatility and adaptability. Some of the problems that AGI and ASI can solve include complex decision-making, cross-domain multitasking, autonomous innovation, improving efficiency and productivity, enhancing personalization, and addressing global challenges.

[0055] Currently, AGI and ASI may still be theoretical concepts that are difficult to achieve. While ANI exists, its development is often customized, cumbersome, and time-consuming. System 100 and Analytics Engine 104 offer a streamlined solution that can efficiently address and resolve any problem across the entire AI capability spectrum.

[0056] For example, existing technologies can include very different ANI solutions, such as demand prediction models, image classification models, etc. These solutions may often be limited to specific, narrow applications. In addition, generative AI solutions (such as GPT-4, Gemini 1.5, and Llama 3) are available, but cannot be integrated into a unified system that encompasses ANI, AGI, and ASI.

[0057] In some implementations, the analysis engine 104 may also include a group of AI agents. In this document, an AI agent may include at least one AI model, such as a large language model (LLM) or a multimodal large language model (MLLM). An AI agent may also include one or more other models or tools to allow the AI ​​agent to perform one or more tasks. Additionally, the terms "AI agent" and "agent" may be used herein to refer to a single AI agent or to one or more AI agents, such as a group of AI agents that can collaboratively solve a problem.

[0058] Figure 2 An exemplary AI agent is illustrated, which may include LLM or MLLM, knowledge and memory, and tools. The AI ​​agent can receive input. In some examples, the AI ​​agent may also include system prompts. The AI ​​agent can generate output actions based on the input, LLM / MLLM, knowledge and memory, and / or system prompts.

[0059] In some examples, an AI agent can be a computational entity designed to perform tasks by perceiving input, processing information, and executing actions to achieve a specific purpose. At its core, the AI ​​agent may include an LLM or MLLM, which acts as the brain of the AI ​​agent, enabling understanding of input data and its processing, contextual reasoning, and decision-making. The AI ​​agent can be equipped with tools, such as task-specific APIs, plugins, or computational modules, that extend its capabilities beyond language processing to include data retrieval, numerical analysis, and / or automated workflows. The AI ​​agent can receive input through various connections, including natural language commands, structured data (such as tables or databases), sensed data (such as audio, video, or environmental metrics), and external APIs for real-time information. These inputs can be preprocessed in the processing layer to ensure context-aware decision-making. The AI ​​agent can produce output actions ranging from generating natural language responses to performing tasks via APIs, controlling physical devices, and / or providing data insights and visualizations. To achieve continuous improvement, the AI ​​agent can integrate feedback loops and learning mechanisms, leveraging user feedback, recorded interactions, and / or reinforcement learning to optimize its performance over time.

[0060] As briefly described above, the implementation of system 100 can include cloud computing, edge computing, and cloud-edge collaborative computing. For example, in some implementations of cloud computing, edge computing, and cloud-edge collaborative computing, predictive AI, generative AI, and agent frameworks consisting of a group of AI agents can be employed. These implementations can be used to implement ANI / AGI / ASI.

[0061] Figure 3 A schematic diagram of an analysis engine 104 according to some implementations is shown.

[0062] The analysis engine 104 includes input 110, which can be received from user device 102 and / or environment 106. Input 110 may include information, data, queries, prompts, unresolved problems, and / or other inputs. Input 110 can exist in the form of text, images, video, streams, documents, and / or other data formats.

[0063] In some implementations, input 110 (which may include data and / or one or more prompts) may be passed to each step of the analytics engine 104, as discussed below. Passing input 110 to each step allows each step in the analytics engine 104 to determine whether a portion of input 110 is relevant to the current step, and this can reduce the response time of the analytics engine 104 to input 110. The steps in the analytics engine 104 may include AI agents and / or LLMs within the analytics engine 104, which will be discussed in further detail below.

[0064] The analysis engine 104 may also include an additional information loop 112. At the additional information loop 112, the analysis engine 104 can determine whether the input 110 is sufficient for the analysis engine 104 to provide a result, solution, or answer, such as a response to a query or a solution to a problem. If the input 110 is insufficient, for example, if more information or data is needed, the additional information loop 112 can request more information, data, and / or other input at the input 110.

[0065] The analytics engine 104 may also include an AI agent 114. In some implementations, the AI ​​agent 114 can distinguish between the organization's, enterprise's, or other entity's internal pressing needs (i.e., hunger) and long-term goals (i.e., vision). For example, the AI ​​agent 114 can identify attributes of senior management within the organization / company and attributes of the company itself.

[0066] The analysis engine 104 may include a decision point 116 that determines whether the input 110 involves future predictions or historical queries. If the input 110 involves future predictions, the decision point 116 may connect to the future prediction module 118. Conversely, if the input 110 involves historical queries, the decision point 116 may connect to the historical query module 120. In some examples, such as when the input 110 involves both future predictions and historical queries, the decision point 116 may connect to both the future prediction module 118 and the historical query module 120.

[0067] The future prediction module 118 can evaluate whether the problem explicitly specified in input 110 is a new problem or a known problem. Based on the problem type determination, a pre-trained model can be employed, which can be one or more generative AI models. Specifically, if the problem is a known problem, the problem type can be identified and one or more historical models, pre-trained models, or custom-trained models can be employed. If the problem is a new problem, predictive and / or generative AI models can be used to generate or select one or more pre-trained models or historical models. In some examples, input 110 may contain multiple problems, and / or a single problem may require multiple models; therefore, the outputs of multiple models can be integrated into a solution list at the future prediction module 118.

[0068] The historical query module 120 may include a Retrieval Enhanced Generation (RAG) model and / or a data vault. The historical query module 120 may also include resources for Enterprise Resource Planning (ERP), including Customer Relationship Management (CRM), Material Requirements Planning (MRP), and financial resources.

[0069] The analysis engine 104 may also include a comparator 122. At comparator 122, one or more output solutions from the future prediction module 118 and / or the past history query module 120 can be evaluated to determine whether this or these solutions provide an acceptable or complete answer to the question or query contained in input 110. If this or these solutions are unacceptable or incomplete, comparator 122 can loop back to an earlier stage within the analysis engine 104 to repeat or optimize the solution generation process, for example by requesting more data or information at input 110. If this or these solutions are acceptable or complete, comparator 122 can continue.

[0070] As a prerequisite for execution, the analysis engine 104 may also include a planner 124, which may include a solution planner or a project manager. The planner 124 may break down the solution into multiple smaller steps as needed before execution. The planner 124 may include solutions from the comparator 122.

[0071] The analysis engine 104 may also include an executor 126, which can perform actions based on the planner 124. Actions may include computer actions, such as sending emails, performing or coordinating sales, robotic process automation (RPA), etc.

[0072] As described above, analytics engine 104 can communicate with environment 106. In some examples, environment 106 may include company infrastructure, systems, computing devices, resellers, websites, etc. Actuator 126 can perform actions on environment 106. Analytics engine 104 can also receive feedback from external feedback mechanism 128, which may be connected to company or organizational infrastructure, such as within environment 106. Feedback may include response feedback, new requests from customers (e.g., customers of the company or organization or the organization itself), or other forms of feedback. This feedback can be fed back to input 110, which can be used in another process loop of analytics engine 104 or considered for further processing.

[0073] In addition, motion feedback 130 can be generated by actuator 126 for analysis engine 104 so that it can be fed into input 110 in subsequent process loops or considered for further processing.

[0074] It should be understood that other implementations and examples of the analysis engine 104 are also possible. Some or all of the modules or stages discussed above in the analysis engine 104 may be rearranged, removed or replaced, and new modules or other modules not yet discussed may also be incorporated into the analysis engine 104.

[0075] The analytics engine 104 can integrate predictive AI, generative AI, and agent-based AI workflows. As discussed further below, the analytics engine 104 can employ a group of AI agents in conjunction with a telephone application (e.g., user device 102) for data input. The computations can then be performed in the cloud, at edge devices, and / or a combination of both.

[0076] Data Integration and Sources: Analytics engine 104 can connect AI agents to various data sources, such as ERP, CRM, and financial systems (e.g., within environment 106). Analytics engine 104 facilitates seamless data flow and AI configuration. Data can also be collected from noise, vibration, harshness (NVH), GPS, voice, and visual sensors embedded in telephones (e.g., providing input to analytics engine 104, such as user device 102). This data can be used to train custom models or for real-time inference to predict future outcomes.

[0077] Overall application functionality: The analytics engine 104 can perform comprehensive analysis by examining historical data to answer questions about past events. Predictions can be generated using pre-trained models and / or custom-trained models, which can be deployed in the cloud or on edge devices.

[0078] Feedback loops and continuous improvement: Feedback loops can be integrated into the analytics engine 104 to address unresolved or partially resolved issues. Even fully resolved issues can remain open until the corresponding response or result is recorded, ensuring continuous improvement and accuracy.

[0079] Figures 4A-4B Analysis engine 104' is shown according to some other embodiments of analysis engine 104. It should be understood that analysis engine 104 and analysis engine 104' are interchangeable within system 100, and all references to analysis engine 104 herein may also refer to analysis engine 104'.

[0080] The analysis engine 104' can receive external input. In some examples, external input may include sensor information and new information. Sensor information may include noise / sound, vibration, severity, and visual (NVH-V) information.

[0081] In a further example, external input may supplementally or alternatively include information describing the company, such as name and industry information, company revenue, number of company employees, company competitors, company customers, and company suppliers.

[0082] As a supplement or alternative, external input may include computational data and / or cue data. Cue data can be parsed by a large language model, such as via an API like the ChatGPT™ API.

[0083] The analytics engine 104' may include external inputs to the decryptor. The decryptor may generate business overviews, internal analyses, external landscapes, AI suggestions, and / or AI opportunities identified by the analytics engine 104'. The decryptor may also store its inputs and outputs in the memory of the analytics engine 104'.

[0084] It should be understood that memory and cognition can be important determinants in decision-making. Memory can be analogous to weights and biases in a pre-trained AI model. Cognition can be analogous to a processor. Memory and cognition can be derived from Human Feedback Reinforcement Learning (RLHF), which can incorporate human cognition and benefit from well-trained models.

[0085] The analytics engine 104' may also include a business creator / generator, which can generate AI workflows, AI value, and / or AI roadmaps. The business creator / generator may also store its inputs and outputs in the memory of the analytics engine 104'.

[0086] The analytics engine 104' may also include an analytical answer generator, which can receive and / or generate CRM, ERP, and documents. The analytical answer generator may also store its inputs and outputs in the memory of the analytics engine 104'.

[0087] The analytics engine 104' may also include an AI / machine learning (ML) predictor, which can receive and / or generate data, models, and software applications. The AI / ML predictor may also store its inputs and outputs in the memory of the analytics engine 104'. In this document, the term "artificial intelligence (AI)" also includes machine learning.

[0088] The analytics engine 104' can also generate AI insights, which may include predictions, analyses, and / or recommendations. These AI insights can also be stored in the memory of the analytics engine 104'.

[0089] The analysis engine 104' may also include an AI trainer and / or be able to perform actions on the output.

[0090] The effector and external output can be passed in a feedback loop, acting in conjunction with human actions on the output. The feedback loop may include a comparator that compares the output with past memories (such as the memories of analysis engine 104'). The feedback loop can return to the input and be fed into analysis engine 104' as external input. In some examples, the output may be discarded by analysis engine 104'.

[0091] It should be understood that other implementations and examples of the analysis engine 104' are also possible. Some or all of the modules or stages discussed above in the analysis engine 104' can be rearranged, removed or replaced, and new modules or other modules not yet discussed can also be incorporated into the analysis engine 104'.

[0092] Figure 5 An environment 106 is shown according to some implementations. Environment 106 may include a company website 142, an internal company system 144, a third-party system 146, a sensor 148, social media 150, and a communication system 152.

[0093] Company website 142 may include the website of a company or organization that uses analytics engine 104, such as a company related to user device 102. Company website 142 may also include the websites of suppliers and / or competitors.

[0094] The company’s internal systems 144 may include financial systems, records, company databases, employee information, email and messaging systems and / or company software, as well as other internal systems.

[0095] Third-party systems 146 may include APIs, mechanical / robotic software and hardware interfaces, systems of other organizations, financial institutions, news websites and / or software, and other systems.

[0096] Sensor 148 may include discrete hardware sensors, sensors integrated into mobile devices or equipment, software sensors, and sensors accessible through a third-party interface (such as third-party system 146). Examples of sensors may include microphones, gyroscopes, temperature sensors, cameras, etc.

[0097] Social media 150 can include X™ (formerly Twitter™), Facebook™, Instagram™, Reddit™, news websites, and other social media platforms that allow organizations to promote products, initiatives, or services.

[0098] Communication system 152 may include email, text messaging, messaging, telephone, video conferencing, and other systems that allow the company to communicate with external organizations.

[0099] Environment 106 may include, to a greater or lesser extent, the systems, networks and modules described above, and may also include other systems, networks and modules not discussed or shown.

[0100] Environment 106 can also be broadly defined to include the Internet, intranets, networked devices, Internet of Things (IoT) devices and systems, WiFi networked systems, Bluetooth networked systems, iBeacon networked systems, and other networked systems and services.

[0101] Figure 6 A decision tree 160 is shown according to some implementations, which can be executed by the analysis engine 104 to determine how to solve a problem or answer a query.

[0102] For example, analytics engine 104 can receive queries or questions. Analytics engine 104 can first determine whether the query is an urgent external threat or an opportunity. If the threat is urgent, analytics engine 104 can respond to the environment (such as environment 106) using AGI. If the threat is not urgent, analytics engine 104 can respond through a self-driven solution, such as using ASI.

[0103] The analysis engine 104 can then determine whether the problem is a past problem or a future prediction problem. If the problem is a past problem, historical data can be used, and the analysis engine 104 can utilize historical data RAG. The answer can be based on the past.

[0104] If the problem is about predicting the future, then future prediction can be used. In some examples, future prediction can include learning from observations and creating new models, similar to how humans build mental models.

[0105] Analysis engine 104 can determine whether it knows how to answer the question / future prediction, or whether it has a model to answer the question. If analysis engine 104 does not have a model, it can generate one and use the generated model to solve / answer the question. However, if analysis engine 104 already has a model, it will use the existing model to answer / solve the problem. The answer may be based on predictions from these new models.

[0106] The analytics engine 104 can also create new objectives, such as self-driven objectives. These new objectives can be based on past data (such as historical data). As mentioned above, these new objectives can be supplemented or alternatively based on future predictions.

[0107] Figure 7 Another decision tree 170 according to other implementations is shown, which can be executed by the analysis engine 104 to determine how to resolve the problem or answer the query.

[0108] In previous workflows, the problem may have already been solved by existing workflows (i.e., manual workflows). However, the decision tree 170 depicts a process that can consider the costs of predicting errors or failures before determining whether a fully automated solution (e.g., without human intervention), a partially automated solution (e.g., with intermediate human involvement), or an existing workflow (e.g., manual, human-guided, and / or non-automated) is suitable for solving the problem or completing the task. The decision tree 170 can also determine whether a problem or task can be solved / completed using AI.

[0109] One challenge with autonomous solutions for ANI, AGI, and ASI may lie in determining when to stop multiple iterations aimed at solving a problem or completing a task. One possible solution to this challenge could involve calculating the AI ​​value generated by automating each task. In some examples, analytics systems 104 and / or 104' can be applied to business applications. In these examples, basic inputs about the organization, such as business name, website, revenue, and employees, can be recorded. Tasks can be identified, and approximate values ​​can be determined for annual working hours and costs (hourly rates), inventory turnover, machine downtime achieved using AI, etc. If possible, tasks can be validated, such as actual time and costs, inventory turnover, and machine downtime. The value (e.g., revenue) generated by automating with AI and analytics can also be calculated. Iterations of automating different tasks can be repeated until all feasible automations are completed. Industry benchmarks can also be used as a reference.

[0110] Business applications Figure 8 System 200 is shown, which can be used to determine a business plan. System 200 includes inputs 202, an analysis engine 204, and an environment 106. System 200 may also include outputs 208. System 200 can be used by a company or enterprise. In this document, the terms "company," "enterprise," and "organization" are used interchangeably.

[0111] Input 202 may be provided to the analysis engine 204 by the user device 102. In other embodiments, different computing devices or systems may provide input 202. Similarly, output 208 may also be provided to the user device 102 by the analysis engine 204, or in other embodiments to different computing devices or systems.

[0112] Input 202 may include detailed information describing the enterprise, such as an enterprise related to user equipment 102, etc. Figure 9As shown. For example, input 202 may include business name 212, industry related to the business 214, company website 216, business-related revenue 218, number of employees who work for or have worked for the company 220, the company's suppliers 222, the company's competitors 224, and / or the company's customers (not shown). Input 202 may contain some or all of the detailed information described above about the business. Supplementally or alternatively, input 202 may include other information about the business.

[0113] Analysis engine 204 may be the same as or substantially similar to analysis engine 104 and / or analysis engine 104'. For example, Figure 10 An analysis engine 204 according to some embodiments is shown. Analysis engine 204 includes an AI agent 232. Analysis engine 204 may also include a business overview 234 and a business plan 236. It should be understood that analysis engine 204 may include more or fewer modules or components compared to analysis engine 104 and / or analysis engine 104'. Analysis engine 204 may also include or instead include other modules or components as a supplement to or replacement of the existing modules and components in analysis engine 104 and / or analysis engine 104'.

[0114] AI agent 232 can interact with Figure 2 and / or Figure 3 The AI ​​agent 114 shown is the same or similar. AI agent 232 may include multiple agents, such as a first AI agent 232a, a second AI agent 232b, and a third AI agent 232c, as shown below. Figure 11 As shown. AI agent 114 may also include a fourth AI agent 232d and a fifth AI agent 232e. In some embodiments, AI agent 232 may include up to N AI agents, including the Nth AI agent 232N.

[0115] In this document, an AI agent can refer to a single AI agent, which may include an LLM or MLLM, equipped with a model or tool for performing one or more tasks of the analytics engine 204. An AI agent can also refer to a group of AI agents that can be used to perform one or more tasks of the analytics engine 204. Therefore, the terms "AI agent" and "a group of AI agents" are used interchangeably herein.

[0116] The analytics engine 204 can generate a business overview 234 and / or a business plan 236, for example, by using an AI agent 232. The business overview 232 can be generated based on input 202. The business plan 236 can be generated based on the business overview 234 and / or input 202.

[0117] Figure 12A business overview 234 is shown based on some examples. In some examples, the business overview 234 may summarize or include part or all of the content of input 202. In other examples, the business overview 234 may be generated based on input 202 and using AI agent 232.

[0118] The business overview 234 may include a business description 240, which may describe the enterprise provided by input 202. For example, the business description 240 may include a business summary generated by the AI ​​agent 232, including a summary of other elements contained in the business overview 234. The business description 240 may also include at least one of the following: business name 212, industry 214, company website 216, revenue 218, number of employees 220, suppliers 222, and / or competitors 224. The business description 240 may also include the enterprise's customers.

[0119] The business overview 234 may also include internal analysis 242, which can summarize analyses of the company's internal operations, finances, manufacturing, inventory, etc. Internal analysis 242 can be generated by an AI agent 242.

[0120] The business overview 234 may also include the external landscape 244, which can summarize the company's competitors, technological developments in related industries, historical and / or future customer needs, growth areas, etc. The external landscape 244 may also be generated by the AI ​​agent 242.

[0121] Business Overview 234 may further include Recommendations 246. In some examples, Recommendations 246 may include suggestions generated by AI Agent 222 for improving the business, such as recommendations to incorporate AI and / or automation into the business. Recommendations 246 may also include sales forecasts, revenue growth, cost efficiency, or other improvements resulting from incorporating AI and / or automation into the business, such as cost savings for the business resulting from compliance with the recommendations.

[0122] Furthermore, the business overview 234 may also include opportunities 248, which may include opportunities for growth, investment, R&D, expansion, improvement, and / or automation that the company may wish to explore. Opportunities 248 may include other unlisted opportunities that could achieve cost savings, growth, synergies, or other advantages for the company. It is conceivable that opportunities 248 may overlap with recommendations 246, which may include suggestions for pursuing certain opportunities 248. For example, opportunities 248 may identify aspects of the company in which improvements can be made through the use of AI and / or automation. Opportunities 248 may be generated by AI agent 232.

[0123] Similarly, the business overview 234 may also include a sales forecast 250, which may overlap with recommendations 246 and / or opportunities 248. For example, recommendations 246 and / or opportunities 248 may respectively indicate a forecast of increased sales revenue in pursuit of the recommendations or opportunities, and this sales revenue growth forecast may be included in the sales forecast 250. The sales forecast 250 may also include a sales forecast assuming no pursuit of any recommendations 246 and / or opportunities 248 (e.g., the business maintains the status quo). The AI ​​agent 232 can generate the sales forecast 250.

[0124] Sales forecasts 250 can be based on economic statistics, such as those accessible in environment 106 by analytics engine 204.

[0125] It should be understood that the Business Overview 234 may include fewer or more aspects than those described above, and may include other aspects as supplements or alternatives to it.

[0126] The business overview 234 may be presented as one or more documents that can be shared with user device 102, for example, in output 208. The business overview 234 may also be presented on a website accessible to user device 102, or in other formats accessible via email or to users associated with the business and / or user device 102.

[0127] Figure 13 A business plan 236 is shown based on some examples. In some examples, the business plan 236 may summarize or include part or all of the content of input 202 and / or part or all of the content of business overview 234. In other examples, the business plan 236 may be generated by AI agent 232 based on input 202 and / or business overview 234.

[0128] It should be understood that business plan 236 may overlap to some extent with recommendation 246. However, as detailed below, business plan 236 can provide more detailed information than recommendation 246 for improving the business. In particular, business plan 236 may specify concrete steps for implementing some or all of recommendation 246, as well as potential other recommendations. In other examples, business plan 236 may include steps that can be fully or partially automated by AI agent 232 or another AI or software tool (which may be performed by the AI ​​agent, for example), and therefore business plan 236 may include detailed information sufficient to enable the AI ​​agent to perform these steps.

[0129] Business plan 236 may include strategic plan 260, which may specify high-level opportunities and recommendations that the company can pursue to improve profitability, growth, and achieve other desired corporate goals. For example, strategic plan 260 may include long-term corporate strategies or directions for improving sales or profitability, such as increasing automation, reducing overhead costs, and expanding into overseas markets. Strategic plan 260 may also summarize other plans within business plan 236, which will be described in detail below. Strategic plan 260 may be generated by AI agent 232.

[0130] Business plan 236 may also include R&D plan 262, which may indicate the industries or technologies the company should invest in for R&D. For example, R&D plan 262 may instruct the company to focus on improving the technology of a particular product, improving the manufacturing process of said product, or designing new products in industries the company has not previously entered. Other examples of R&D plan 262 are also possible. R&D plan 262 may also be based on strategic plan 260, which may indicate a particular product, industry, or technology that the company should focus on or invest more resources in. R&D plan 262 may be generated by AI agent 232.

[0131] Business plan 236 may further include marketing plan 264, which may specify marketing strategies the company should explore, different types of marketing suitable for the company, suggestions for specific marketing activities, suggestions for outsourcing marketing to specific distributors or establishing a marketing team within the company, etc. For example, marketing plan 264 may generate and include a complete marketing campaign, such as a social media campaign with a specific style, to showcase a product that the company sells. Marketing plan 264 may also be based on strategic plan 260, which may indicate a product that the company should focus on or invest more resources in; therefore, marketing plan 264 can provide suggestions and / or specific plans for marketing said product. Marketing plan 264 may be generated by AI agent 232.

[0132] Furthermore, the business plan 236 may include an operations plan 266, which may specify improvements to one or more operational inefficiencies within the enterprise. For example, operational inefficiencies may already be identified in opportunity 248 within the business overview 234. The operations plan 266 may include steps to improve the enterprise's operations, such as introducing AI and / or automation within the enterprise. The operations plan 266 may also be based on the strategic plan 260, which may specify certain high-level initiatives or directions that the enterprise should pursue in the long term. The operations plan 266 may be generated by the AI ​​agent 232.

[0133] Business plan 236 may also include supply chain plan 268, which may specify potential improvements to the company's supply chain. Improvements may include changing or diversifying suppliers, adjusting shipping frequency, outsourcing some manufacturing, relocating some manufacturing in-house, adjusting product distribution or transportation, and logistical changes. Supply chain plan 268 may also be based on strategic plan 260, which may specify certain high-level initiatives or directions that the company should pursue in the long term, such as expanding into foreign markets. For example, supply chain plan 268 may specify recommendations and steps for establishing a supply chain to penetrate new or foreign markets. In another example, supply chain plan 268 may specify robotic equipment that can be integrated into logistics tasks to improve profitability. In yet another example, supply chain plan 268 may suggest AI tools for handling incoming and outgoing packages, such as a tool that can use image processing to read packaging labels and input this data into the logistics system. Supply chain plan 268 may be generated by AI agent 232.

[0134] Business plan 236 may further include financial plan 270, which may specify steps for improving the company's financial condition, financial management methods, etc. For example, financial plan 270 may specify software that can improve the company's financial condition, such as AI or automation for certain bookkeeping, payroll, invoicing, etc. Financial plan 270 may also be based on strategic plan 260, and therefore may specify certain improvements or changes to the company's finances after compliance with strategic plan 260, including R&D plan 262, marketing plan 264, operations plan 266, supply chain plan 268, and / or other plans. Financial plan 270 may be generated by AI agent 232.

[0135] It should be understood that a business plan 236 may include the above-mentioned plans to a greater or lesser extent, and may include other aspects as supplements or alternatives to them.

[0136] The business plan 236 may also be presented as one or more documents that can be shared with user device 102, for example, in output 208. The business plan 236 may also be presented on a website accessible to user device 102, or in other formats accessible by email or to users associated with the business and / or user device 102.

[0137] Additionally, business plan 236 may include aspects that can be automated and executed by one or more software tools (such as AI tools or AI agents, robotic devices, etc.). Business plan 236 can be stored in a format that facilitates execution by AI tools or other automated software.

[0138] Figure 14A method 300 is shown for generating a company's business description and / or business plan, such as business description 240 and / or business plan 236. Method 300 can be executed by system 200, specifically using analytics engine 204.

[0139] In step S302, obtain the company's relevant business name.

[0140] For example, business name 212 can be received in input 202 from user device 102. Business name 212 can be received by analysis engine 204.

[0141] In other implementations, the business name 212 may be received by a first AI agent (e.g., first AI agent 232a).

[0142] In other examples, as a supplement or alternative, analytics engine 204 may receive additional or other information, such as industry 214, company website 216, revenue 218, number of employees 220, suppliers 222, and / or competitors 224. Alternatively or supplementally, analytics engine 204 may receive information related to enterprise customers.

[0143] In step S304, the first AI agent obtains the company's business description based on the business name.

[0144] For example, business description 240 can be obtained by the first AI agent 232a. Business description 240 can be based on business name 212 received in step S302.

[0145] The first AI agent 232a can generate a business description 240 based on the business name 212. For example, the first AI agent 232a can query environment 106 based on the business name 212 to retrieve relevant enterprise information. In some examples, the first AI agent 232a can query the internet and / or intranet to identify the industry 214, obtain the company website 216, estimate or obtain revenue 218, and estimate or obtain the number of employees 220. The first AI agent 232a can also query the internet and / or intranet to obtain suppliers 222 (which may include one or more suppliers of the enterprise) and competitors 224 (which may include one or more competitors of the enterprise). The first AI agent 232a can also query the internet and / or intranet to obtain one or more customers of the enterprise. For example, the first AI agent 232a can query company websites 144 (e.g., websites related to the enterprise itself, suppliers, and / or competitors) and internal company systems 144 (including internal systems of the enterprise, suppliers, distributors, customers, etc., which are accessible to the analysis engine 104). The first AI agent 232a has the ability to crawl or extract information from websites identified during the query environment 106.

[0146] The first AI agent 232a can use enterprise-related information obtained from environment 106 to generate a business description 240 as a supplement or replacement to input 202. For example, the first AI agent 232a can use enterprise-related information obtained from environment 106 to generate a business description 240 as a supplement or replacement to input 202. In some examples, the first AI agent 232a can summarize the query results it retrieves from environment 106, while in other examples, the first AI agent 232a can obtain the business description 240 from environment 106 (e.g., a news website or company website).

[0147] In other examples, the first AI agent 232a may have already received some or all of the enterprise-related information from input 202, so AI agent 232a can summarize input 202 to generate business description 240. In other examples, AI agent 232a may still obtain some enterprise-related information from environment 106.

[0148] The business description 240 may include at least one of the following: business name 212, industry 214, company website 216, revenue 218, number of employees 220, suppliers 222, and / or competitors 224. As a supplement or alternative, the business description 240 may include corporate customers.

[0149] In some implementations, the business description 240 may also include a summary of at least one of internal analysis 242, external landscape 244, recommendations 246, opportunities 248, and / or sales forecast 250. The first AI agent 232a may also utilize information obtained from the environment 106 to generate at least one of internal analysis 242, external landscape 244, recommendations 246, opportunities 248, and / or sales forecast 250.

[0150] It should be understood that in some implementations, the analytics engine 204 may obtain a business overview 234 instead of obtaining a business description 240 only in step S302. As described above, the business overview 234 may include at least one of the following: business description 240, internal analysis 242, external landscape 244, recommendations 246, opportunities 248, and / or sales forecasts 250.

[0151] In step S306, the second AGI agent generates a business plan for the company based on the business description.

[0152] For example, the second AI agent 232b can generate a business plan 236 for the company based on the business description (such as business description 240) obtained in step S306. The business plan 236 may include at least one of a strategic plan 260, a research and development plan 262, a marketing plan 264, an operations plan 266, a supply chain plan 268, and / or a financial plan 270.

[0153] In some examples, business plan 236 may include recommendations to integrate AI or analytics into the company to replace inefficient processes within the company, for example, by using AI-assisted automation. Business plan 236 may also include projections of cost savings resulting from complying with recommendations to integrate AI or analytics into the company.

[0154] The second AI agent 232b can generate a business plan 236 based on one or more business aspects summarized in the business description 240 (e.g., business name 212, industry 214, company website 216, revenue 218, number of employees 220, suppliers 222, competitors 224, and / or enterprise customers). In other embodiments, the business description 240 may also include a summary of internal analysis 242, external landscape 244, recommendations 246, opportunities 248, and / or sales forecasts 250. The second AI agent 232b can also generate a business plan 236 based on input 202. Furthermore, the second AI agent 232b can query environment 106 to obtain information or more details about the enterprise, other industries, markets, technologies, company website 144, company internal systems 144, etc. The second AI agent 232b can generate a business plan 236 based on the query results from environment 106.

[0155] In a further implementation, if a business overview 234 is obtained in step S302, the second AI agent 232b can generate a business plan 236 based on the business overview 234.

[0156] Method 300 can be executed iteratively. For example, the analytics engine 204 can receive feedback or human input via input 202, such as from user device 102. Feedback or human input can be used in step S304 to modify the business description. Step S306 can be repeated to generate a modified business plan based on the modified business description. Human input can specify modifications, corrections, fine-tuning, additions, or other changes to the business description, which may be helpful for the analytics engine 204 in determining an effective business plan in step S306. Feedback can also include performance results of the business plan, customer feedback, sensor data, computer alerts, competitor market changes, etc. Even after the modified business plan is generated or executed, feedback or human input can be received again to iteratively improve the business plan.

[0157] In some implementations, industry benchmarks may also be used to determine when to stop iterating method 300. Industry benchmarks may be obtained from environment 106.

[0158] Method 300 may include additional steps for executing a business plan (such as business plan 236). Business plan 236 may be executed by an AI agent, as will be discussed in further detail below. The discussion below regarding the execution of business plan 236 also applies to method 300.

[0159] It is conceivable that, in addition to using the second AI agent 232b to generate the business plan 236, other aspects / modules within the analysis engine 204 could also be utilized (e.g., Figure 3 and Figures 4A-4B (The aspects / modules shown in the analysis engine 104 and analysis engine 104′ respectively) are used to generate business plans 236.

[0160] Additionally, any step of method 300 can utilize other aspects / modules within analysis engine 204 (e.g., in...). Figure 3 and Figures 4A-4B The analysis engine 204 (which is shown in the diagram with respect to those aspects / modules of analysis engine 104 and analysis engine 104') is used to perform this. As described above, in some embodiments, analysis engine 204 may be substantially similar to or the same as analysis engine 104 and / or analysis engine 104'.

[0161] The steps performed by the AI ​​agent in method 300 can be performed by one or more AI agents. Furthermore, steps performed by the same AI agent in method 300 can be performed by different AI agents or different combinations of AI agents in other embodiments. Similarly, steps performed by different AI agents in method 300 can be performed by the same AI agent or by a group of AI agents including a shared AI agent in other embodiments. It is also conceivable that "AI agent" can refer to a group of AI agents, or to a shared AI agent within a group of AI agents.

[0162] In addition to the steps described above, method 300 may add or remove steps as appropriate. For example, method 300 may include an additional step of incorporating business description 240 and / or business plan 236 into output 206, which may be provided to user device 102. Method 300 may include the step of outputting output 206 to user device 102.

[0163] Method 300 may also omit step S306, performing only steps S302 and S304. Other steps may also be performed after step S304, such as outputting the business description 240 to user equipment 102.

[0164] In some implementations, some steps of method 300 may also include additional steps not yet discussed. For example, Figure 15 A method 400 according to some embodiments is illustrated for performing step S304 of method 300 to obtain a business description 240, particularly obtaining a website summary related to a company or enterprise. Method 400 may be performed by an analytics engine 204.

[0165] In step 402, identify the company's relevant websites.

[0166] For example, a website can be a company website 216. A website can be hosted by a company, a branch office of a company, a news website, a compliance agency or government agency, or other entity that already describes the company on its own website. A website can also be identified through a search environment 106, which includes company websites 142, internal company systems 144, third-party systems 146, etc. It should be understood that while business details can be obtained from a company's own website, they can also exist on websites not directly managed or hosted by the company.

[0167] Business details may include a business description (which can be used to generate a business description 240), business name 212, industry 214, company website 216, revenue 218, number of employees 220, suppliers 222, competitors 224, and / or corporate clients.

[0168] The first intelligent agent 232a can determine whether a website is related to a company or enterprise based on the business name 212, which is obtained by the analysis engine 204 in step 302. The business name 212 may correspond to the business name listed on the website, the website's Uniform Resource Locator (URL), the topic of news articles on the website, etc.

[0169] It is also feasible to use other methods to determine whether a website is relevant to a business. For example, the first agent 232a can be configured to predict whether a website is relevant to a particular business after viewing the website (and possibly after scraping some data from the website).

[0170] In step S404, business details are extracted from the website, wherein the business description includes the business details.

[0171] For example, the first intelligent agent 232a can extract or crawl business details from a website (such as company website 216 or other websites describing the enterprise, such as any company website 142 in environment 106). The first intelligent agent 232a can extract or crawl the industry 214 in which the enterprise operates, company website 216, revenue 218, number of employees 220, suppliers 222, competitors 224 and / or enterprise customers.

[0172] Step S404 can be repeated and / or performed in combination with step S402. As described above, business details can be scraped from the website before identifying the website's relevance to the company in step S402.

[0173] Method 400 may also reduce the number of steps or perform additional steps (not shown).

[0174] The steps performed by the AI ​​agent in method 400 can be performed by one or more AI agents. Furthermore, steps performed by the same AI agent in method 400 can be performed by different AI agents or different combinations of AI agents in other embodiments. Similarly, steps performed by different AI agents in method 400 can be performed by the same AI agent or by a group of AI agents including a shared AI agent in other embodiments. It is also conceivable that "AI agent" can refer to a group of AI agents, or to a shared AI agent within a group of AI agents.

[0175] Figure 16 A method 500 according to some embodiments is illustrated for performing step S304 of method 300 to obtain a business description 240, particularly obtaining a summary of websites related to competitors. Method 500 may be performed by an analytics engine 204.

[0176] In step S502, competitors related to the company can be identified.

[0177] For example, competitor 224 can be obtained from input 202.

[0178] In other examples, competitor 224 can be obtained from websites such as company website 142, news websites, or other websites related to the enterprise (such as company website 142). The first agent 232a can browse one or more websites and determine the names of the enterprise's competitors (such as competitor 224).

[0179] In other examples, competitor 224 may use other resources within environment 106 for identification, such as internal company systems 144, third-party systems 146, social media 150, etc.

[0180] In step S504, websites related to competitors are identified.

[0181] For example, the first agent 232a may search for websites related to competitor 224 within the Internet and / or the entire environment 106. In other examples, the first agent 232a may also have identified websites related to competitor 224, for example, by locating competitor websites or other resources that link to competitor websites.

[0182] In step S506, competitor details are extracted from websites related to the competitor.

[0183] For example, detailed information describing competitor 224 can be extracted or scraped from the competitor's website using a first intelligent agent 232a.

[0184] Method 500 may also reduce the number of steps or perform additional steps (not shown).

[0185] The steps performed by the AI ​​agent in method 500 can be performed by one or more AI agents. Furthermore, steps performed by the same AI agent in method 500 can be performed by different AI agents or different combinations of AI agents in other embodiments. Similarly, steps performed by different AI agents in method 500 can be performed by the same AI agent or by a group of AI agents including a shared AI agent in other embodiments. It is also conceivable that "AI agent" can refer to a group of AI agents, or to a shared AI agent within a group of AI agents.

[0186] In other embodiments, method 500 can be used to obtain a summary of a website related to at least one of a competitor, supplier, or customer. For example, in step S502, at least one of a competitor, supplier, or customer related to the company can be obtained. In step S504, a website related to at least one of a competitor, supplier, or customer can be identified. In step S506, detailed information can be extracted from the website of at least one of the competitors, suppliers, or customers. Other embodiments of method 500 are also possible.

[0187] Figure 17 A method 600 for executing a company's business plan, based on some examples, is shown. It is conceivable that method 600 may include steps overlapping with those of methods 300, 400, and / or 500 described above. Method 600 may be executed by analysis engine 204.

[0188] In step S602, the first AI agent receives input describing the company.

[0189] For example, input 202 can be received by a first AI agent 232a. Input 202 may include at least one of the following: business name 212, industry 214, company website 216, revenue 218, number of employees 220, supplier 222, competitor 224, and / or enterprise customer.

[0190] The first AI agent 232a may include an LLM or an MLLM. The first AI agent 232a may also include one or more models or tools to assist the first AI agent 232a in completing one or more tasks, such as receiving input and determining a business overview 234.

[0191] In step S604, the first AI agent determines the company's business overview based on the input.

[0192] For example, the business overview 234 can be determined by the first AI agent 232a based on the input (e.g., input 202) received in step S602. The business overview 234 may include at least one of the following: business description 240, internal analysis 242, external landscape 244, recommendations 246, opportunities 248, and / or sales forecast 250.

[0193] Business description 240 can be generated as discussed above regarding step S304 in method 300. Furthermore, description 240, internal analysis 242, external landscape 244, recommendations 246, opportunities 248, and / or sales forecasts 250 can also be determined using input 202 and environment 106. Input 202 can be used to query environment 106, such as company website 142, internal company systems 144, third-party systems 146, sensors 148, social media 150, etc. First AI agent 232a can retrieve part or all of the business description 240 from the query results from environment 106, and / or first AI agent 232a can generate part or all of the business description 240 based on the query results from environment 106.

[0194] For example, sales forecast 250 can be based on economic statistics. The first AI agent 232a can retrieve economic statistics from environment 106, or determine economic statistics from other data retrieved from environment 106. Sales forecast 250 can also be based on other data describing the enterprise, such as other aspects of business overview 234 and / or input 202, as well as other information in environment 106.

[0195] In step S606, the second AI agent determines a business plan for the company based on the business overview.

[0196] For example, business plan 236 can be determined by a second AI agent 232b based on a business overview (such as business overview 234) determined in step S604.

[0197] In some implementations, the second AI agent 232b may determine a business plan 236 based on the business overview 234 and input 202. The second AI agent 232b may also query the environment 106 to generate the business plan 236. For example, the second AI agent 232b may generate a query based on the business overview 234 and / or input 202. Based on this query, the second AI agent 232b may receive detailed information from the environment 106, which may include economic statistics, details from the company website 142, internal company systems 144, third-party systems 146, sensors 148, social media 150, and communication systems 152. The business plan 236 may be generated based on the availability of various systems in the environment 106. In an example where the business plan 236 includes a marketing plan 264, the marketing plan 264 may be generated based on resources available in social media 150.

[0198] In an alternative implementation, the second AI agent 232b may generate the business overview 234 using only input 202.

[0199] The business plan 236 may include at least one of the following: strategic plan 260, research and development plan 262, marketing plan 264, operations plan 266, supply chain plan 268, and / or financial plan 270.

[0200] In some implementations, business plan 236 may be generated as discussed above with respect to step S306 in method 300.

[0201] The second AI agent 232b may include an LLM. The second AI agent 232b may also include one or more models or tools to assist the second AI agent 232b in completing one or more tasks, such as determining a business plan 236.

[0202] In step S608, the business plan is executed by the third AI agent.

[0203] For example, the third AI agent 232c can execute business plan 236.

[0204] In the example where business plan 236 includes strategic plan 260, a third AI agent 232c can communicate with company employees via internal system 144 and / or communication system 152 to initiate strategic changes regarding new research directions specified in R&D plan 262. The third AI agent 232c can generate strategic documents for company executives and management autonomously or with human assistance after reviewing internal documents. The third AI agent 232c can also coordinate the execution of other plans included in business plan 236 autonomously or with human assistance.

[0205] In the example where business plan 236 includes R&D plan 262, the third AI agent 232c can communicate with company employees via internal system 144 and / or communication system 152 to initiate R&D by outlining new research directions specified in R&D plan 262. In some implementations, the third AI agent 232c can partially autonomously conduct R&D, for example, by simulating multiple solutions to existing problems specified in R&D plan 262. The third AI agent 232c can also access real-world test data from sensor 148, allowing it to test solutions to existing problems specified in R&D plan 262 autonomously or with human assistance. In other implementations, the third AI agent 232c can contact suppliers or external facilities regarding R&D initiatives via third-party system 146 and / or communication system 152. In other examples, the third AI agent 232c can contact suppliers based on business plan 236 to, for example, obtain additional materials required for the R&D initiative.

[0206] In the example where business plan 236 includes marketing plan 264, the third AI agent 232c can implement the marketing campaigns specified in marketing plan 264 in environment 106 (particularly in social media 106). Other marketing platforms in environment 106 are also available, such as communication system 152 (e.g., email marketing campaigns). The third AI agent 232c can implement the marketing campaigns autonomously or with human assistance.

[0207] In the example where business plan 236 includes operational plan 266, the third AI agent 232c can contact company employees via internal system 144 and / or communication system 152 regarding new operational initiatives specified in operational plan 266. The third AI agent 232c can also implement new operational initiatives autonomously or with human assistance, such as developing or co-developing new applications (such as software applications) specified in operational plan 266.

[0208] In the example where business plan 236 includes supply chain plan 268, the third AI agent 232c can contact company employees regarding new supply chain initiatives specified in supply chain plan 268 through internal company system 144 and / or communication system 152. The third AI agent 232c can also implement new supply chain initiatives autonomously or with human assistance, such as developing or co-developing new applications (e.g., software applications) specified in supply chain plan 266, contacting suppliers through third-party system 146 and / or communication system, and utilizing environment 106 to research new suppliers and / or foreign markets. In other examples, the third AI agent 232c can contact suppliers based on business plan 236 to, for example, increase supply, obtain new products, or achieve other business objectives.

[0209] In the example where business plan 236 includes financial plan 270, the third AI agent 232c can contact company employees regarding new financial initiatives specified in financial plan 270 through internal company system 144 and / or communication system 152. The third AI agent 232c can also implement new financial initiatives autonomously or with human assistance, such as developing or co-developing new applications (e.g., software applications) specified in financial plan 270, communicating with financial institutions, suppliers, and other third parties using third-party system 146 and / or communication system 152, and viewing financial documents using internal company system 144.

[0210] Method 600 can be executed iteratively. For example, the analysis engine 204 can receive feedback or human input via input 202, such as from user equipment 102. Feedback or human input can be used to modify the input in step S602, and then a modified business overview and business plan can be generated in steps S604 and S606. The modified business plan can be executed in step S608. Alternatively or additionally, feedback or human input can be used to modify the business overview in step S604, and then a modified business plan can be generated and executed in steps S606 and S608. Alternatively or additionally, feedback or human input can be used to modify the business plan in step S606, and then the modified business plan can be executed in step S608. Human input can specify modifications, corrections, fine-tuning, additions, or other changes to the business description, which may be beneficial for the analysis engine 204 to determine and execute an effective business plan in steps S606 and S608. Feedback can also include performance results of the business plan, customer feedback, sensor data, computer alarms, competitor market changes, etc. Even after a revised business plan is generated or executed, feedback or human input can still be received to iteratively improve the business plan.

[0211] In some implementations, industry benchmarks may also be used to determine when to stop iterating method 600. Industry benchmarks may be obtained from environment 106.

[0212] Any step of method 600 can utilize other aspects / modules within analysis engine 204 (e.g., those in...). Figure 3 and Figures 4A-4B The aspects / modules shown for analysis engine 104 and analysis engine 104′, respectively, are used to perform this. As described above, in some embodiments, analysis engine 204 may be substantially similar to or the same as analysis engine 104 and / or analysis engine 104′.

[0213] The steps performed by the AI ​​agent in method 600 can be performed by one or more AI agents. Furthermore, steps performed by the same AI agent in method 600 can be performed by different AI agents or different combinations of AI agents in other embodiments. Similarly, steps performed by different AI agents in method 600 can be performed by the same AI agent or by a group of AI agents including a shared AI agent in other embodiments. It is also conceivable that "AI agent" can refer to a group of AI agents, or to a shared AI agent within a group of AI agents.

[0214] In addition to the steps described above, method 600 may add or remove steps as appropriate. For example, method 600 may include additional steps to incorporate business description 240 and / or business plan 236 into output 206, which may be provided to user device 102. Method 600 may include the step of outputting output 206 to user device 102.

[0215] Figure 18 Another method 700 for executing a company's business plan is shown, based on other examples. It is conceivable that method 700 may include steps overlapping with those of methods 300, 400, 500, and / or 600 described above. Method 600 may be executed by analysis engine 204.

[0216] In addition, method 700 can be substantially the same as method 600. For example, method steps S702, S704, S706 and S708 in method 700 are the same as or substantially the same as method steps S602, S604, S606 and S608 in method 600.

[0217] In method step S710, the fourth AI agent receives feedback on the business plan, including at least one of customer feedback and company performance results.

[0218] For example, feedback on business plan 236 can be received by a fourth AI agent 232d. In this step, the term "receive" can also refer to generating, acquiring, retrieving, estimating, evaluating, or deriving.

[0219] Feedback can be provided by the user of user device 102, for example, by providing additional input to the analytics engine 204. Feedback can also be received from another user device or an external server.

[0220] As described above, feedback may include customer feedback and / or company performance results. In the example where feedback includes customer feedback, the customer may be a user of user device 102 and may provide feedback using user device 102, for example, by providing feedback to analytics engine 204 within input 202.

[0221] Customer feedback can also be obtained from environment 106, such as from company website 142 (e.g., comments or reviews), company internal systems 144 (e.g., customer feedback recorded in the company's database), third-party systems 146 (e.g., feedback from customer systems), sensors 148 (e.g., microphones and / or cameras installed at customer locations), social media 150 (e.g., social media posts or comments), and / or communication systems 152 (e.g., email).

[0222] In examples where feedback includes company performance results, these results can be acquired, evaluated, and / or determined through analytics engine 204 and / or a fourth AI agent 232d. The fourth AI agent 232d can retrieve performance results or information needed to determine them from environment 106, such as from internal company systems 144 (e.g., company performance reports generated by company executives, employees, or third parties), company website 142, sensors 148 (e.g., measuring the performance of R&D initiatives or supply chain metrics), and so on. For example, sensor 148 can record shipment volumes, which can be used to determine the performance results of supply chain plan 268 executed in step S708. Social media engagement can also be measured from social media 150 to evaluate the performance results of marketing plan 264. Other examples are conceivable.

[0223] The fourth AI agent 232d can be configured to obtain information about performance results from the environment 106 and evaluate or determine performance results based on the information it retrieves.

[0224] It is conceivable that in some implementations, feedback may be provided prior to any of steps S702, S704, S706, and S708, for example, in the case where output 208 is provided to user equipment 102 or other devices at the beginning or end of each step of method 700.

[0225] In step S712, the fourth AI agent may determine the revised business plan based on the business plan and feedback.

[0226] For example, business plan 236 can be modified by the fourth AI agent 232d based on the feedback received in step S712.

[0227] As described above, feedback may include customer feedback and / or company performance results. In the example where feedback includes customer feedback, the customer feedback may also specify that a certain aspect of business plan 236 should be modified or canceled. For example, one or more of the strategic plan 260, R&D plan 262, marketing plan 264, operations plan 266, supply chain plan 268, financial plan 270, or other aspects of business plan 236 may be modified or deleted.

[0228] Customer feedback can directly indicate which aspect of business plan 236 should be modified. For example, customer feedback could specify that marketing campaigns related to marketing plan 264 should be removed.

[0229] In other implementations, customer feedback can be interpreted by a fourth AI agent 232d to determine appropriate modifications to business plan 236. For example, customer feedback may indicate that the marketing campaign related to marketing plan 264 is confusing, so marketing plan 264 can be modified to make the campaign less confusing. Alternatively, customer feedback may indicate that the marketing campaign related to marketing plan 264 is successful (e.g., positive customer feedback on the campaign), so marketing plan 264 can be modified to expand the marketing campaign to more social media platforms and / or other websites or media.

[0230] In an example where feedback includes company performance results, these results may indicate that a particular aspect of business plan 236 should be modified or canceled. The performance results can be interpreted by the fourth AI agent 232d to determine appropriate modifications to business plan 236. For example, the performance results may indicate that a marketing campaign related to marketing plan 264 was unsuccessful (e.g., low sales), so marketing plan 264 can be modified to make the campaign more attractive. The fourth AI agent 232d can create a new marketing plan 264 or modify the existing marketing plan based on queries about environment 106 regarding possible reasons for the poor performance results. Furthermore, customer feedback can also assist the fourth AI agent 232d in creating a new marketing plan 264. Alternatively, the performance results may indicate that a marketing campaign related to marketing plan 264 was successful (e.g., sales increased after the introduction of the campaign), so marketing plan 264 can be modified to expand the campaign to more social media platforms and / or other websites or media.

[0231] Other modifications to business plan 236 are also possible. For example, supply chain plan 268 may lead to poor logistical performance results (such as lost packages, supply chain delays) or customer feedback regarding delivery delays. Supply chain plan 268 can be modified based on this feedback.

[0232] Other examples that can be thought of for making modifications to business plan 236 based on feedback are also feasible.

[0233] Steps S708, S710, and S712 can be iterated repeatedly. The modified business plan 236 can be executed in step S708 (e.g., by a third AI agent 232c). In step S710, feedback can be received again after the modified business plan 236 has been executed in step S708. In step S712, the business plan 236 can be modified again after receiving this new feedback. The business plan 236, after being modified twice, can then be executed again in step S708, and so on. It should be understood that in this way, the business plan 236 can continuously adapt to customer feedback and performance results, which may be affected both by the business plan 236 itself and by constantly changing conditions such as environment 106, market, economy, regulatory situation, and other factors.

[0234] In addition, feedback can include other forms of feedback besides customer feedback and performance results. Feedback can be received from the company itself, suppliers, competitors, government agencies, and any other entities that interact with the company. Feedback can be obtained, received, or derived from the environment, such as from news websites or government agencies, and can also be delivered via email, social media, and other media.

[0235] Feedback and / or inputs to the analysis engine 204 (e.g., input 202) may also include data collected by sensor 148. For example, at a transportation facility or other location related to the enterprise, the fifth AI agent may receive a label image associated with goods sent or received by the company. The label may contain product details. For example, the fifth AI agent 232e may receive a label image associated with goods sent or received by the company. The label may be detected by sensor 148, obtained from internal company system 144 and / or third-party system 146. Product details may be determined by the fifth AI agent 232e, for example, using machine vision or another technology capable of extracting product details from the label. As described above, product details are included in input 202 in step S702 (or step S602 in method 600) and / or in the feedback in step S710.

[0236] In some implementations, industry benchmarks may also be used to determine when to stop iterating method 700. Industry benchmarks may be obtained from environment 106.

[0237] Any step of method 700 can utilize other aspects / modules within analysis engine 204 (e.g.) Figure 3 and Figures 4A-4B The analysis engine 204 (which is shown in the diagram with respect to those aspects / modules of analysis engine 104 and analysis engine 104') is used to perform this. As mentioned above, in some embodiments, analysis engine 204 may be substantially similar to or the same as analysis engine 104 and / or analysis engine 104'.

[0238] The steps performed by the AI ​​agent in method 700 can be performed by one or more AI agents. Furthermore, steps performed by the same AI agent in method 700 can be performed by different AI agents or different combinations of AI agents in other embodiments. Similarly, steps performed by different AI agents in method 700 can be performed by the same AI agent or by a group of AI agents including a shared AI agent in other embodiments. It is also conceivable that "AI agent" can refer to a group of AI agents, or to a shared AI agent within a group of AI agents.

[0239] In addition to the steps described above, method 700 may add or remove steps as appropriate. For example, method 700 may include an additional step of incorporating business description 240 and / or business plan 236 into output 206, which may be provided to user device 102. Method 700 may include a step of outputting said output 206 to user device 102.

[0240] Sensing applications Figure 19 A system 800 for monitoring equipment performance is shown. System 800 can be used by businesses or companies, individuals, households, building managers, system administrators, and / or any other entity interested in monitoring equipment or the physical environment.

[0241] System 800 includes equipment 802, sensor group 804, mobile device 806, user equipment 808, and analysis engine 810.

[0242] Equipment 802 may include machinery such as furnaces, assembly lines, vehicles, computer servers, etc. Equipment 802 may be located in heating, ventilation, and air conditioning (HVAC) units, manufacturing plants, cement plants, transportation vehicles, retail environments, telecommunications facilities, mines, agricultural equipment, residential facilities, and / or warehouses. In this document, equipment 802 may also include physical environments such as rooms within a warehouse, freight containers, basements, pipes, etc. In a broader sense, equipment 802 may include any machinery or physical environment that physically interacts with its surroundings, such as generating heat, sound, motion, light, electromagnetic radiation, etc. Other physical interactions between equipment 802 and its surroundings are also possible.

[0243] Sensor group 804 may include one or more sensors configured to locate, measure, or monitor equipment 802, such as a mechanical or physical environment. For example, sensor group 804 may measure position, heat, sound, motion, light, and / or electromagnetic radiation generated by the mechanical or physical environment. Other physical interactions are also possible.

[0244] Figure 20A sensor group 804 is shown according to some examples. Optionally, the sensor group 804 may include a noise sensor 820, a vibration sensor 822, a severity sensor 824 (e.g., for measuring temperature), a pressure sensor 826, a vision sensor 828, and / or a position sensor 830 (such as GPS). For example, the noise sensor 820 may measure noise data in the range inaudible to humans. The noise sensor 820 may be a microphone.

[0245] Other sensors may also be present in sensor group 804. Although not exhaustive and only optional, sensor group 804 may include at least one of the following: noise sensor, vibration sensor, temperature sensor, relative humidity sensor, gyroscope, magnetometer, global positioning system (GPS) device, microphone, vision sensor, light sensor, vibration sensor, severity sensor, pressure sensor, current sensor, carbon dioxide sensor, water leakage sensor, passive infrared (PIR) sensor, magnetic gate sensor, soil sensor, air quality sensor, volatile organic compound sensor, or particulate matter sensor.

[0246] Sensor group 804 may include one or more of the sensors described above, and sensor group 804 may include redundant configurations of each sensor and / or other sensors not discussed above.

[0247] Sensor group 804 may be mounted or positioned near equipment 802. The mounting or positioning of sensor group 804 enables it to monitor equipment 802. In some examples, sensor group 804, or portions thereof, may be mounted inside and / or integrated with equipment 802. For example, one of the sensors in sensor group 804 may be a sensor built into equipment 802, such as a temperature sensor.

[0248] Since sensor group 804 may include more than one sensor, the different sensors in sensor group 804 may be located or mounted in different locations. The sensors in sensor group 804 may be configured to communicate with one or more computing devices, as discussed further below. These sensors may or may not be configured to communicate with each other.

[0249] It should be understood that environment 106 may include equipment 802 and sensor group 804. For example, sensor 148 in environment 106 may include sensor group 804. Third-party system 146, internal corporate system 144, and / or communication system 152 may also include equipment 802. Sensor 148 may also include equipment 802, for example, when equipment 802 includes integrated sensors.

[0250] System 800 also includes a mobile device 806 that can communicate with sensor group 804. Sensors within sensor group 804 can be configured to transmit measurement data from device 802 to mobile device 806. Sensor group 804 can communicate with mobile device 806 using one or more communication protocols, including Bluetooth™, WiFi, iBeacon, and / or other communication protocols. Sensor group 804 can employ multiple communication protocols, for example, when different sensors within sensor group 804 transmit sensing data using different communication protocols.

[0251] In other embodiments, sensor group 804 may be directly connected to mobile device 806, allowing some or all of the sensors in sensor group 804 to be directly connected to mobile device 806. This may be suitable in the following example: one or more sensors do not have networking capabilities, i.e., they do not have the ability to wirelessly transmit data.

[0252] Mobile device 806 can be a computing device, such as a smartphone, mobile phone, and / or any other device containing a transceiver. For example, mobile device 806 can be a data relay disk (puck) capable of receiving sensed data from sensor group 804 and transmitting the received sensed data to analytics engine 810, as discussed further below. Mobile device 806 can transmit sensed data to analytics engine 810 wirelessly or via wired means, for example using one or more communication protocols, including Ethernet, Bluetooth™, WiFi, iBeacon, cellular networks, and / or other communication protocols.

[0253] In some embodiments, mobile device 806 may also include a processor and memory, and mobile device 806 may be configured to process and / or preprocess sensing data received from sensor group 804 and acquired from equipment 802. As discussed below, in other embodiments, mobile device 806 may be configured to perform some or all of the processing of analysis engine 810.

[0254] Some or all of the sensors in sensor group 804 can be mounted on mobile device 806. For example, if sensor group 804 includes noise sensor 820, then noise sensor 820 can be a microphone. Noise sensor 820 can be integrated into mobile device 806, for example, as the default microphone of mobile device 806. In this example, mobile device 806 can be a smartphone, and sensor group 804 can include the microphone in the smartphone as noise sensor 820. Additionally, in this example, the microphone / noise sensor 820 and mobile device 806 can therefore be located near device 802, allowing the microphone within sensor group 804 and mobile device 806 to monitor device 802.

[0255] It should be understood that environment 106 may also include mobile device 806. For example, internal system 144, third-party system 146, sensor 148 (e.g., when the microphone of mobile device 806 is used to acquire sensing data), and / or communication system 152 may all include mobile device 806. For example, communication system 152 may include mobile device 806 for acquiring or receiving sensing data from sensor group 804 (such as sensor 148) and transmitting the sensing data to analysis engine 810.

[0256] In some implementations, the mobile device 806 may also communicate with the user equipment 808. The mobile device 808 may transmit raw sensed data acquired from the sensor group 806 to the user equipment 808, and / or the user equipment 808 may send instructions to the mobile device 806 or configure the mobile device 806.

[0257] It is conceivable that User Equipment 808 can be related to the above. Figure 1 The user equipment 102 described herein is the same as or substantially similar to that described herein.

[0258] In other embodiments, user equipment 808 may include, or is specifically, mobile device 806, allowing a user to connect to sensor group 804 using their user equipment 808 when within the coverage area of ​​the communication network or protocol used by sensor group 804. However, in these embodiments, mobile device 806 can only acquire sensing data from sensor group 804 in real time when it is within the coverage area of ​​sensor group 804 or the communication protocol of sensor group 804, which may be suitable for specific use cases. In other use cases, sensing data may be continuously acquired by sensor group 804 (e.g., noise sensor 820).

[0259] As described above, the mobile device 806 can transmit the collected sensed data from the sensor group 804 to the analysis engine 810. The analysis engine 810 may be the same as or substantially similar to the analysis engine 104, analysis engine 104', and / or analysis engine 204. For example, Figure 21 An analysis engine 810 according to some embodiments is shown. Analysis engine 810 includes storage 840, processing 842, AI model 844, and custom visualization 846. Analysis engine 810 may also include visualization representation 848 and / or alerts 850. It should be understood that analysis engine 810 may include fewer or more modules or components than analysis engine 104, analysis engine 104', and / or analysis engine 204. Analysis engine 810 may also include or instead include other modules or components as a supplement to or replacement of existing modules or components in analysis engine 104, analysis engine 104', and / or analysis engine 204.

[0260] The analytics engine 810 can be hosted on a server and communicate with mobile device 806 and / or user device 808 via the Internet or a local area network (such as an intranet, Bluetooth, iBeacon, etc.). This could be an example of cloud computing. In other implementations, the analytics engine 810 can be hosted on mobile device 806 or user device 808. For example, mobile device 806 can transmit sensed data from sensor group 804 to user device 808, which can host a local copy of analytics engine 810. This could be an example of edge computing. Alternatively, user device 808 can host a portion of the analytics engine 810 and transmit processed sensed data or computation results to a server hosting the remaining modules of analytics engine 810. This could be an example of cloud-edge collaborative computing. Other examples are conceivable.

[0261] As described below, the analysis engine 810 can also communicate with the user equipment 808. The analysis engine 810 can provide output to the user equipment 808, such as a performance prediction of the equipment 802 determined by the analysis engine 810. The output from the analysis engine 810 can be displayed on a graphical user interface (GUI), which can be viewed by the user equipment 808. The user equipment 808 can also request information from the analysis engine 810, such as sensing data or predictions for a specific time period. The user equipment 808 can also configure the analysis engine 810 to perform certain calculations, analyses, and / or generate certain outputs.

[0262] The analysis engine 810 includes storage 840. Storage 840 may include one or more memories that can be configured to store sensing data received from the mobile device 806 and / or the sensor group 804. Herein, sensing data may include measurement data acquired by the sensor group 804 for the equipment 802.

[0263] Processing 842 can preprocess the sensing data acquired by sensor group 804. Processing 842 may include filtering, transforming, smoothing, cleaning, purifying, filling, and / or other operations to prepare the sensing data for further analysis by analysis engine 810. In some embodiments, processing 842 may be optional and may depend on the quality of the sensing data acquired by sensor group 804 for device 802.

[0264] In some examples, processing 842 may also include: distinguishing sensing data acquired by different sensors in sensor group 804, and / or identifying erroneous or unreliable sensing data that may not be suitable for further analysis by analysis engine 810.

[0265] AI model 844 may include one or more AI models. In this document, the term "AI model" may include neural networks, classifiers, machine learning models, regression models, and any other predictive models, including but not limited to other machine learning algorithms.

[0266] The AI ​​model 844 may have been pre-trained before the sensor group 804 collects sensing data from the equipment 802. In other examples, the AI ​​model 844 may have been pre-trained or fine-tuned based on historical sensing data collected by the sensor group 804 from the equipment 802.

[0267] AI model 844 can be repeatedly trained, retrained, or fine-tuned over time based on sensed data, performance predictions of equipment 802, and / or new sensed data collected after AI model 844 generates performance predictions of equipment 802 (which may correspond to the actual performance of equipment 802) to improve the predictive ability of AI model 844. For example, the predicted performance of equipment 802 and its corresponding actual performance can be used to retrain or fine-tune AI model 844. Some examples may include determining the error between the actual performance and the predicted performance of equipment 802, and retraining or fine-tuning AI model 844 based on that error. It is conceivable that the longer AI model 844 is used to monitor equipment 802, the more accurate the AI ​​model 844's predictions of equipment 802's performance may be.

[0268] Other training data may also be used, such as training data related to different equipment and / or environments, for example, training data obtained from environment 106. The training data may also be preprocessed by processing 842 before the AI ​​model 844 is trained, retrained, or fine-tuned based on the training data.

[0269] AI model 844 can be configured to receive sensing data collected from equipment 802 by sensor group 804 and generate performance predictions based on this sensing data. For example, AI model 844 can generate one or more of the following: equipment 802 failure prediction, equipment 802 mean time between failures (MTBF) prediction, equipment 802 maintenance requirement prediction, prediction for automatically adjusting equipment 802 operating parameters, and / or equipment 802 health status prediction. AI model 844 can also predict when equipment 802 may need maintenance, possible causes of equipment 802 failure, and the expected lifespan of equipment 802 before replacement may be necessary.

[0270] AI model 844 can generate performance predictions based on continuously acquired sensing data from sensor group 804 (such as noise sensor 820, vibration sensor 822, severity sensor 824, pressure sensor 826, vision sensor 828, position sensor 830, and / or other or different sensors). In other embodiments, AI model 844 can generate performance predictions based on historical, batch-acquired, and / or uploaded to analysis engine 810 from different time periods.

[0271] As described above, AI model 844 may include more than one AI model. In these implementations, multiple AI models may be used to generate one or more performance predictions, which may be integrated into a single result or viewed as separate metrics.

[0272] Custom visualization 846 can receive performance predictions from AI model 844 and generate customized visualizations of these predictions, which may include one or more metrics related to the performance predictions of equipment 802. Custom visualization 846 can be configured by user equipment 808 and may include graphs, tables, spreadsheets, figures, pie charts, and / or reports. For example, custom visualization 846 may include an LLM configured to interpret the performance predictions from AI model 844 and summarize those predictions in a report.

[0273] In other examples, the custom visualization 846 may include a visualization representation 848 and / or an alarm 850. The custom visualization 846 can generate the visualization representation 848 and / or the alarm 850. For example, if the performance prediction of equipment 802 includes a poor performance prediction, the custom visualization 846 may generate an alarm 850 about that prediction if it is determined that the prediction includes a poor performance prediction. The alarm 850 may be transmitted to user equipment 808 to warn the user that equipment 802 may experience poor performance. In this document, a poor performance prediction may include a failure prediction of equipment 802, a prediction that equipment 802 requires maintenance, a prediction that equipment 802 is not operating within specifications, a prediction for automatically adjusting the operating parameters of equipment 802, and / or a prediction about the cause and / or timing of the poor performance. The user can schedule maintenance for equipment 802 based on the alarm 850.

[0274] In other examples, alarm 850 may include a prediction of the correct or expected performance of equipment 802.

[0275] In another example, if sensor group 804 continuously acquires sensed data from equipment 802, customized visualization 846 can generate a visualization representation 840 containing a report summarizing the performance of equipment 802 over a period of time. Customized visualization 846 can generate visualization representation 840 based on continuously acquired sensed data, including a report summarizing changes in equipment 802's performance over time. The report can be a graph or a written report. Visualization representation 840 can be transmitted to user equipment 808. The user can schedule maintenance of equipment 802 based on visualization representation 840.

[0276] It is conceivable that visualization representation 840 can summarize the poor performance and / or expected performance of equipment 802 over time.

[0277] In some implementations, the customized visualization 846 can be executed on the user equipment 808, such that the visualization representation 848 and / or alarm 850 can be generated on the user equipment 808. For example, the user equipment 808 can receive a performance prediction from the AI ​​model 844 and generate the visualization representation 848 and / or alarm 850 upon receiving a performance prediction from the equipment 802.

[0278] Figure 22 A method 900 for monitoring equipment performance is shown. Method 900 can be executed by system 800.

[0279] In step S902, sensing data from the noise sensor is acquired at the mobile device. The noise sensor is configured to monitor the equipment.

[0280] For example, sensing data from noise sensor 820 can be collected at mobile device 808. Noise sensor 820 can be configured to monitor equipment 802.

[0281] Equipment 802 may include HVAC units, boilers, water pipes, switchboards, water heaters, computer servers, etc. Furthermore, equipment 802 may be located in HVAC units, manufacturing plants, cement plants, transportation vehicles, retail environments, telecommunications facilities, mines, agricultural equipment, residential facilities, or warehouses.

[0282] The noise sensor 820 may be mounted or positioned near the equipment 802. The noise sensor 820 may record noise data from the equipment 802, including noise data within the human audible range and / or the inaudible range.

[0283] In some examples, the noise sensor 820 may be mounted on the device 802. The noise sensor 820 may be connected to the mobile device 808, for example, wirelessly or via a wired connection. For example, the noise sensor 820 may be wirelessly connected to the mobile device 808 using Bluetooth, iBeacon, WiFi, and / or other wireless networks.

[0284] In other examples where the noise sensor 820 does not have wireless capabilities or where wireless connectivity is not preferred, the noise sensor 820 may be physically connected to the mobile device 820, for example using any type of Universal Serial Bus (USB) connector (such as a USB Type-C connector), Ethernet connector, FireWire™ connector, and / or any type of wired connector.

[0285] In other examples, the noise sensor 820 may be integrated within the mobile device 820. For instance, the noise sensor 820 may be a microphone of the mobile device 820.

[0286] In some examples, the noise sensor 820 can continuously acquire sensing data. In other examples, the noise sensor 820 can acquire sensing data in batches.

[0287] Noise sensor 820 may belong to sensor group 804. Sensor group 804 may include one or more other sensors besides noise sensor 820. For example, sensor group 804 may include one or more of the following: noise sensor, vibration sensor, temperature sensor, relative humidity sensor, gyroscope, magnetometer, GPS device, microphone, vision sensor, light sensor, vibration sensor, severity sensor, pressure sensor, current sensor, carbon dioxide sensor, water leakage sensor, PIR sensor, magnetic gate sensor, soil sensor, air quality sensor, volatile organic compound sensor, or particulate matter sensor.

[0288] In step S904, the machine learning model receives the sensing data.

[0289] For example, the sensed data can be received by AI model 844. The sensed data can be received from mobile device 806 by analysis engine 810, for example, via the Internet or intranet, WiFi, Bluetooth and / or other communication means. Sensor group 804 (which may include the noise sensor 820 discussed in step S902) can transmit the sensed data to mobile device 806.

[0290] In some implementations, the analytics engine 810 may be partially or fully hosted on the mobile device 806, so the sensed data can be received directly from the sensor group 804 by the analytics engine 810.

[0291] In other embodiments, the sensed data may be received by the analysis engine 810 and processed by storage 840 and / or processing 842 before the AI ​​model 844 receives the sensed data. Storage 840 may store the sensed data in one or more memories. Processing 842 may perform preprocessing on the sensed data, such as filtering, removing erroneous data, transformation, and / or other preprocessing operations.

[0292] In other embodiments, the sensing data may also be transmitted directly from the mobile device 806 and / or the analytics engine 810 to the user device 808.

[0293] In step S906, equipment performance predictions are received from the machine learning model.

[0294] For example, a performance prediction of the equipment 802 can be received from a machine learning model (such as AI model 844). AI model 844 can generate a performance prediction of the equipment 802 based on the sensing data received by AI model 844 in step S904.

[0295] Performance predictions may include predictions of poor performance of equipment 802 and / or predictions of satisfactory or acceptable performance. Performance predictions may also include: equipment 802 failure predictions, equipment 802 MTBF predictions, equipment 802 maintenance requirements predictions, predictions for automatically adjusting equipment 802 operating parameters, and / or equipment 802 health status predictions. Performance predictions may further include the time when equipment 802 may require maintenance, possible causes of predicted equipment 802 failures, and / or predictions of equipment 802's expected lifespan before replacement may be necessary.

[0296] Performance predictions can be received from the AI ​​model 844 by another module or component of the custom visualization 846 and / or analysis engine 810.

[0297] In some examples, performance predictions can be transmitted to user equipment 808 without any additional processing. In other examples, performance predictions can be transmitted to user equipment 808 after minimal additional processing.

[0298] In step S908, the prediction is displayed on the GUI.

[0299] In some examples, performance predictions can be received by the user device 808 and displayed on the GUI.

[0300] In other examples, user device 808 can access a website, application programming interface (API), or other portal and view performance predictions. In the website example, performance predictions can be displayed on a website GUI that user device 808 can browse.

[0301] User device 808 can also use local software (such as mobile applications, computer applications, or other alternatives) to present or display performance predictions.

[0302] In some examples, performance predictions may be received from AI model 844 by a custom visualization 846. The custom visualization 846 may be executed on a server or user device 808. The custom visualization 846 may generate custom visualization results of the performance predictions, which may include one or more metrics related to the performance predictions of equipment 802. The custom visualization 846 may be configured by user device 808 and may include graphs, tables, spreadsheets, figures, pie charts, and / or reports. For example, the custom visualization 846 may include an LLM configured to interpret the performance predictions from AI model 844 and summarize the predictions in a report.

[0303] Custom visualization 846 can also include visualization representation 848 and / or alarm 850. Custom visualization 846 can generate visualization representation 848 and / or alarm 850.

[0304] Performance prediction may include a prediction of poor performance of equipment 802. In some embodiments, method 900 may include additional steps of maintaining or automatically adjusting equipment 802 in response to a poor performance prediction by analysis engine 810. Maintenance of equipment 802 may resolve the predicted poor performance problem of equipment 802, allowing the equipment to operate as expected. Automatic adjustment of equipment 802 (which may include modifying the operating parameters of equipment 802) may also resolve the predicted poor performance problem of equipment 802. In some embodiments, analysis engine 810 may automatically schedule maintenance for equipment 802 in response to a prediction of poor performance.

[0305] Any step of method 900 can utilize other aspects / modules in the analysis engine 810 (e.g.) Figure 3 , Figures 4A-4B and Figure 10 The analysis engine 810 (as shown in the diagrams regarding analysis engines 104, 104', and 204) is used to perform these functions. As described above, in some embodiments, analysis engine 810 may be substantially similar to or identical to analysis engines 104, 104', and / or 204.

[0306] In addition to the steps described above, method 900 may add or remove steps as appropriate. For example, method 900 may include additional steps of outputting sensing data and / or predicting the performance of device 802, which may be provided to user equipment 808.

[0307] In some implementations, the machine learning model (such as AI model 844) is hosted in a cloud computing environment, and sensed data is transmitted from the mobile device to the cloud computing environment via a network for inference.

[0308] In other implementations, the machine learning module (such as AI model 844) runs on a mobile device (such as mobile device 806), and inference is performed on the device without transmitting sensed data to an external server.

[0309] In other implementations, a first part of the machine learning model (such as AI model 844) runs on a mobile device (such as mobile device 806), and a second part runs in a cloud computing environment, which makes some inference happen on the device while the final inference happens in the cloud computing environment.

[0310] Figure 23 A method 1000 is shown for performing step S908 of method 900, particularly for displaying performance predictions on a GUI. Method 1000 can be executed by system 800.

[0311] In step S1002, it is determined whether the performance prediction of the equipment 802 includes poor performance prediction.

[0312] In some examples, poor performance prediction may include equipment 802 failure prediction, prediction of equipment 802 maintenance requirements, prediction of equipment 802 operating below standard, prediction of automatic adjustment of equipment 802 operating parameters, and / or prediction of the cause and / or duration of poor performance.

[0313] If the prediction includes a poor performance prediction, an alarm can be generated for the poor performance prediction in step S1004a. For example, alarm 850 can be generated and transmitted to user equipment 808. In other examples where custom visualization 846 is performed on user equipment 808, alarm 850 can be generated by user equipment 808 and displayed on user equipment 808. In other examples, alarm 850 can be transmitted to other devices to, for example, notify maintenance personnel that equipment 802 needs maintenance.

[0314] If the prediction does not include poor performance prediction, method 900 can proceed to step S1004b. In step S1004b, it can be determined whether the sensing data is continuously acquired by the sensors. For example, noise sensor 820 and / or other sensors in sensor group 804 can be configured to continuously acquire sensing data about device 802. The sensing data can be continuously transmitted from noise sensor 820 and / or other sensors in sensor group 804 to mobile device 806 and analysis engine 810. In this document, the term "continuous" can refer to any of the following: periodic, real-time, at fixed intervals, with consistent delay between sensing data acquisition and / or transmission, and / or small batch processing. The term "continuous" can also include scenarios where sensing data is acquired and / or transmitted relatively regularly, but with some data sample loss, batch processing, irregular periodicity, or irregular sampling intervals.

[0315] If it is determined in step S1004b that the sensing data is continuously acquired by the sensor, then method 900 can proceed to step S1006a. In step S1006a, a report summarizing the changes in equipment performance over time can be generated. For example, a customized visualization 846 can generate a visualization representation 848. The visualization representation 848 may include a report summarizing the changes in equipment 802 performance over time. The report may include written text, graphs, figures, and / or other methods for summarizing changes in equipment performance over time. The customized visualization 846 may include an LLM or other model or tool.

[0316] In other embodiments, step S1006a can be performed even if the sensing data is not continuously acquired. For example, the customized visualization 846 can still generate a visualization representation 848 even without continuous sensing data. In these embodiments, method 900 may skip step S1004b and is not based on whether the sensing data is continuously acquired by a sensor (e.g., noise sensor 820 and / or any other sensor in sensor group 804).

[0317] If it is determined in step S1004b that the sensing data is continuously acquired by the sensor and / or it is determined in step S1004b that the sensing data is not continuously acquired by the sensor, then method 900 may execute step S1006b. In embodiments where method 900 skips step S1004b, method step S1006b may also be executed, regardless of the type of sensing data.

[0318] In step S1006b, a customized visualization can be generated based on the prediction. For example, the customized visualization 846 can generate a visualization representation 848 based on the predicted performance of the equipment 802. The visualization representation 848 may include graphs, reports, figures, and any other means of displaying the predicted performance on a GUI (such as the GUI of user equipment 808).

[0319] It should be understood that in some embodiments, either or both of method steps S1006a and S1006b may be performed, and therefore, both method steps S1006a and S1006b may be performed simultaneously. In other embodiments, any one of method steps S1004a, S1006a, and / or S1006b may be performed individually or simultaneously, such that alarm 850 and visual representation 848 can be generated and displayed on a GUI (e.g., the GUI of user equipment 808).

[0320] Any step of method 1000 can utilize other aspects / modules in the analysis engine 810 (e.g. Figure 3 , Figures 4A-4B and Figure 10The analysis engine 810 is executed by those aspects / modules shown in the description of analysis engines 104, 104', and 204, respectively. As described above, in some embodiments, analysis engine 810 may be substantially similar to or the same as analysis engine 104, 104', and / or analysis engine 204.

[0321] In addition to the steps described above, Method 1000 may also include or omit steps as appropriate.

[0322] computing devices Components of system 100, system 200, and / or system 800 can be implemented on a computing device, such as analysis engine 104, analysis engine 104', analysis engine 204, and / or analysis engine 810. The same applies to user device 102, user device 808, and / or mobile device 806. Other components of system 100, system 200, and / or system 800 can also be implemented on a computing device.

[0323] Figure 24 This is a schematic diagram of a computing device 1100 according to some embodiments, the computing device being configured to implement components of system 100, system 200, and / or system 800. The computing device 1100 includes a memory 1102, a processor 1104, and a bus 1106. The computing device 1100 may also include a network interface 1108. The memory 1102, processor 1104, and network interface 1108 are communicatively connected via the bus 1106.

[0324] Processor 1106 and network interface 1108 are configured to execute the steps of methods 300, 400, 500, 600, 700, 900, and / or 1000 when a program or computer-executable instructions stored in memory 1102 are executed by processor 1104. Processor 1104 and network interface 1108 may also be configured to execute any other processes or modules discussed with respect to system 100, system 200, system 800, analysis engine 104, analysis engine 104', analysis engine 204, and / or analysis engine 810 (including decision trees 160 and 170) when a program or computer-executable instructions stored in memory 1102 are executed by processor 1104.

[0325] Memory 1102 may be a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). Memory 1102 may store programs or computer-executable instructions. Memory 1102 may be non-volatile memory.

[0326] The processor 1104 may be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), a graphics processing unit (GPU), or one or more integrated circuits.

[0327] Furthermore, processor 1104 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of methods 300, 400, 500, 600, 700, 900, and / or 1000 may be executed by integrated logic circuitry in hardware form or by instructions in software form within processor 1104. Furthermore, processor 1102 may be a general-purpose processor, digital signal processor (DSP), ASIC, field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component. Processor 1102 may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor or similar device. The method steps described herein may be executed directly by a hardware decoding processor, or may be executed by combining the hardware of the decoding processor with software modules. The software modules may reside in industry-standard storage media, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. The storage media may reside within memory 1102. Processor 1104 can read information from memory 1104 and use the hardware of processor 1104 to complete the steps of method 300, method 400, method 500, method 600, method 700, method 900 and / or method 1000.

[0328] Network interface 1108 enables communication between computing device 1100 and one or more other devices and / or computing devices via a communication network, such as using transceiver devices (e.g., including but not limited to transceivers). For example, components of system 100, system 200, and / or system 800 may be configured to communicate with each other via a communication network. In a particular example, user device 102 and analytics engine 104 may communicate with each other using their respective network interfaces.

[0329] Bus 1106 may include a path for transmitting information between all components of computing device 1100.

[0330] It should be noted that, although Figure 24The computing devices shown in the diagram only include memory, processor, and communication interface. However, in practice, those skilled in the art will understand that systems 100, 200, and / or 800, as well as analysis engines 104, 104', 204, and / or 810, may further include other components required for implementation, such as one or more additional computing devices, servers, networks, memory, processors, etc. Furthermore, based on specific needs, those skilled in the art will also understand that the components of these systems may further include hardware components for implementing other additional functions. Moreover, those skilled in the art will understand that systems 100, 200, and / or 800 may only include the components required for implementing embodiments of the present invention, and may not necessarily include... Figure 24 All the components shown.

[0331] The systems and methods described in this paper provide a framework that integrates ANI, AGI, and ASI. This framework can be used to solve systemic problems across multiple domains and offers streamlined solutions that can efficiently address and resolve a wide range of problems across the entire AI capability spectrum. Compared to existing ANI solutions (which often involve customized, cumbersome, and time-consuming development), the solutions provided by the systems and methods described in this paper reduce the development time and computational resources required to build customized problem solutions. Furthermore, a single solution deployment may be sufficient. Although AGI and ASI are currently difficult concepts to implement, the systems and methods described in this paper can also support the future integration of these AI solutions into a unified system encompassing ANI, AGI, and ASI. This framework can solve a wide range of problems across multiple domains, demonstrating versatility and adaptability compared to existing AI solutions such as ANI. Some key problems that the systems and methods described in this paper can address include complex decision-making, cross-domain multitasking, autonomous innovation, improving efficiency and productivity, enhancing personalization, and / or solving global challenges.

[0332] The systems and methods described herein can also improve business planning. For example, system 200 and methods 300, 400, 500, 600, and 700 can allow users to provide limited information describing the business (e.g., business name or other descriptive information) and automatically receive a business overview and / or business plan. AI agent 232 can retrieve further information about the business from environment 106 and generate a business overview and / or business plan with no or minimal user input. Additionally, users can provide feedback after the business plan is generated. Feedback can also come from customer feedback and / or one or more business performance results after the business plan has been adopted. Feedback can be re-entered into system 200 to refine the business overview and / or business plan. In other embodiments, the systems and methods described herein may also include automating the execution of the business plan.

[0333] In addition to streamlining business plan generation, the system and method described in this paper can provide business planners and implementers with more types of information compared to existing business plan generation methods. Business plan generation can automatically access information related to competitors, suppliers, economic factors, and other factors that may not be readily available without AI assistance.

[0334] Furthermore, the systems and methods described in this paper can also identify improvements that enterprises can achieve by adopting AI and automation at different stages within their operations. These improvements may not be immediately apparent to the enterprise, but they can lead to cost reductions, profit increases, improved logistics, enhanced customer satisfaction and engagement, and other quantifiable business performance outcomes.

[0335] The systems and methods described herein can also improve equipment monitoring and maintenance. For example, faulty equipment may consume unnecessary energy; equipment requiring maintenance may be scrapped and replaced, wasting resources and harming the environment. Equipment requiring specific maintenance may require significant maintenance work to diagnose poor performance and identify faulty components. During this period, the equipment may be downtime and / or underperform. However, the systems and methods described herein can monitor the equipment over time and learn to identify baseline and / or acceptable performance. If acceptable performance is not achieved, it can be automatically identified by the systems and methods described herein, and the user will be notified or alerted immediately upon detection of an equipment problem. By identifying and diagnosing equipment failures or malfunctions early, the systems and methods described herein can reduce resource waste caused by faulty equipment. Furthermore, the systems or methods described herein can diagnose or identify specific causes or predicted failure times, thereby reducing maintenance time spent diagnosing problems. In addition, the systems and methods described herein can provide maintenance personnel with diagnostic tools, enabling them to review the historical performance of equipment detected and / or analyzed by the systems and methods described herein. Moreover, compared to replacing equipment or maintaining irreparable equipment, the systems and methods described herein can help determine whether maintenance or replacement is worthwhile.

[0336] The systems and methods described herein may provide additional improvements that have not been discussed to date but can be understood by those skilled in the art.

[0337] In the embodiments described herein, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system implementations described are merely examples. For example, the unit division is only a logical functional division, and other division methods may be used in actual implementation. For example, multiple units or components may be combined or integrated into another system, or certain features may be ignored or not performed. Furthermore, the mutual coupling, direct coupling, or communication connections shown or discussed may be implemented using certain interfaces. Indirect coupling or communication connections between devices or units can be implemented in electronic, mechanical, or other forms.

[0338] Units that are independent components may or may not be physically separated; while components in the form of units may or may not be physical units, and may be located in a single location or distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of implementing the solution.

[0339] Furthermore, the functional units in these embodiments may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0340] When these functions are implemented as software functional units and sold or used as independent products, the functions can be stored in a non-volatile computer-readable storage medium. Based on this understanding, the essential part of the technical solution of the embodiments, the part that contributes to the prior art, or part of the technical solution therein can be implemented in the form of a software product. The software product is stored in a storage medium and contains several instructions for instructing a computer device (which may be a personal computer, server, or network device) to perform all or part of the steps of the method described in the embodiments. The aforementioned storage medium includes any medium capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0341] In the method described above, boxes can represent events, steps, functions, processes, modules, state-based operations, etc. Although the above examples are described as occurring in a specific order, those skilled in the art will understand that some steps or processes can be performed in different orders, as long as a change in the order of steps does not prevent or hinder the occurrence of subsequent steps. Furthermore, some of the above messages or steps can be deleted or merged in other embodiments, and some messages or steps can be divided into several sub-messages or sub-steps in other embodiments. Further, some or all steps can be repeated as needed. Elements described as methods or steps also apply to systems or sub-components, and vice versa. Terms such as "send" or "receive" can be used interchangeably depending on the perspective of a specific device, module, or logic element.

[0342] While some embodiments are described at least in part as methods, those skilled in the art will understand that some embodiments also refer to various components for performing at least some aspects and features of the described processes, whether implemented as hardware components, software, a combination of both, or in any other manner. Additionally, some embodiments also refer to pre-recorded storage devices or other similar computer-readable media having program instructions stored thereon for performing the processes described herein. Computer-readable media include any non-volatile storage media, such as RAM, ROM, flash memory, optical discs, USB drives, DVDs, HD-DVDs, or any other such computer-readable storage devices.

[0343] It should be understood that the device described herein includes one or more processors and associated memory. The memory may include one or more application programs, modules, or other programming structures containing computer-executable instructions, which, when executed by one or more processors, are used to implement the methods or processes described herein.

[0344] The various embodiments described above are merely examples and are not intended to limit the scope of the embodiments. Various variations of the innovations described herein will be apparent to those skilled in the art and fall within the intended scope of the embodiments. In particular, features can be selected from one or more of the above embodiments to form alternative embodiments that include feature sub-combinations that may not be explicitly described above. Furthermore, features can also be selected and combined from one or more of the above embodiments to form alternative embodiments that include feature combinations that may not be explicitly described above. Features applicable to these combinations and sub-combinations will become apparent to those skilled in the art upon a comprehensive review of the described embodiments. The subject matter described herein is intended to cover all suitable technical variations.

[0345] Certain adaptive adjustments and modifications can be made to the described embodiments. Therefore, the above embodiments should be considered exemplary rather than restrictive.

Claims

1. A method of monitoring equipment performance, the method comprising: collecting, at a mobile device, sensing data from a noise sensor, wherein the noise sensor is configured to monitor the equipment; receiving, at a machine learning model, the sensing data; receiving, from the machine learning model, a prediction of equipment performance; and displaying the prediction on a graphical user interface.

2. The method of claim 1, further comprising: determining that the prediction comprises a performance poor prediction, wherein "displaying the prediction on a graphical user interface" further comprises generating an alert for the prediction in response to the determination.

3. The method of claim 1, wherein, "Displaying the prediction on a graphical user interface" further comprises generating a customized visual representation based on the prediction.

4. The method of claim 1, further comprising: collecting, at the mobile device, the sensing data from at least one of: a noise sensor, a vibration sensor, a temperature sensor, a relative humidity sensor, a gyroscope, a magnetometer, a global positioning system (GPS) device, a microphone, a vision sensor, a light sensor, a vibration sensor, a harshness sensor, a pressure sensor, a current sensor, a carbon dioxide sensor, a water leak sensor, a passive infrared (PIR) sensor, a magnetic door sensor, a soil sensor, an air quality sensor, a volatile organic compound sensor, or a particulate matter sensor.

5. The method of claim 1, wherein, the sensing data from the noise sensor further comprises noise data in a human inaudible range.

6. The method of claim 1, further comprising: preprocessing the sensing data and receiving, at the machine learning model, the preprocessed sensing data.

7. The method of claim 1, wherein, retraining the machine learning model based on at least one of the sensing data, the performance prediction, or new sensing data collected after the machine learning model receives the prediction.

8. The method of claim 2, further comprising: performing maintenance on or automatically adjusting the equipment in response to a performance poor prediction.

9. The method of claim 2, wherein, the performance poor prediction is at least one of an equipment failure prediction, a prediction of equipment mean time between failures (MTBF), a prediction of equipment required maintenance, a prediction for automatically adjusting an equipment operating parameter, or an equipment health prediction.

10. The method of claim 1, further comprising: installing the noise sensor on the equipment and connecting the noise sensor to the mobile device.

11. The method of claim 1, wherein, the noise sensor is integrated within the mobile device.

12. The method of claim 11, wherein, the noise sensor is a microphone of the mobile device.

13. The method of claim 1, wherein, the sensing data is continuously collected by the noise sensor.

14. The method of claim 13, wherein, "Displaying the prediction on a graphical user interface" further comprises generating a report summarizing equipment performance over a period of time based on the continuously collected sensing data.

15. The method of claim 1, wherein, the equipment is located within at least one of a heating, ventilation, and air conditioning (HVAC) unit, a manufacturing plant, a cement plant, a transportation vehicle, a retail environment, a telecommunications facility, a mine, agricultural equipment, a residential facility, or a warehouse.

16. The method of claim 1, wherein, the machine learning model is hosted in a cloud computing environment and the sensing data is transmitted from the mobile device to the cloud computing environment for inference via a network.

17. The method of claim 1, wherein, the machine learning model is run on the mobile device and the inference is performed on-device without transmitting the sensing data to an external server.

18. The method of claim 1, wherein, A first portion of the machine learning model runs on the mobile device and a second portion runs in a cloud computing environment, which causes partial inference to occur on the device and final inference to occur in the cloud computing environment.

19. A system to monitor equipment performance, the system comprising a memory and at least one processor, the processor to: acquiring, at the mobile device, sensed data from a noise sensor, wherein configure the noise sensor to monitor the equipment; receive the sensing data at a machine learning model; receive a prediction of equipment performance from the machine learning model; and display the prediction on a graphical user interface.

20. The system of claim 19, the at least one processor is further configured to determine that the prediction comprises a poor performance prediction, wherein, "Display the prediction on a graphical user interface" further comprises generating an alert for the prediction in response to the determination.

21. The system of claim 19, wherein, "Display the prediction on a graphical user interface" further comprises generating a customized visual representation based on the prediction.

22. The system of claim 19, the at least one processor further configured to, at the mobile device, collect the sensing data from at least one of a noise sensor, a vibration sensor, a temperature sensor, a relative humidity sensor, a gyroscope, a magnetometer, a global positioning system (GPS) device, a microphone, a visual sensor, a light sensor, a vibration sensor, a harshness sensor, a pressure sensor, a current sensor, a carbon dioxide sensor, a water leak sensor, a passive infrared (PIR) sensor, a magnetic door sensor, a soil sensor, an air quality sensor, a volatile organic compound sensor, or a particulate matter sensor.

23. The system of claim 19, wherein, The sensing data from the noise sensor further comprises noise data in a human inaudible range.

24. The system of claim 19, the at least one processor further configured to pre-process the sensing data and receive the pre-processed sensing data at a machine learning model.

25. The system of claim 19, wherein, The machine learning model is retrained based on at least one of the sensing data, the performance prediction, or new sensing data collected after the prediction is received at the machine learning model.

26. The system of claim 20, the at least one processor further configured to, in response to the poor performance prediction, perform maintenance on the equipment or automatically adjust the equipment.

27. The system of claim 20, wherein, The poor performance prediction is at least one of an equipment failure prediction, an equipment mean time between failures (MTBF) prediction, a prediction of required maintenance for the equipment, a prediction to automatically adjust an equipment operating parameter, or an equipment health prediction.

28. The system of claim 19, wherein, The noise sensor is mounted on the equipment and connected to the mobile device.

29. The system of claim 19, wherein, The noise sensor is integrated within the mobile device.

30. The system of claim 29, wherein, The noise sensor is a microphone of the mobile device.

31. The system of claim 19, wherein, The sensing data is continuously collected by the noise sensor.

32. The system of claim 31, wherein, "Display the prediction on a graphical user interface" further comprises generating a report summarizing equipment performance over a period of time based on the continuously collected sensing data.

33. The system of claim 19, wherein, The equipment is located in at least one of a heating, ventilation, and air conditioning (HVAC) unit, a manufacturing plant, a cement plant, a transportation vehicle, a retail environment, a telecommunications facility, a mine, agricultural equipment, a residential facility, or a warehouse.

34. The system of claim 19, wherein, The at least one processor is configured to perform inference in a cloud computing environment, and wherein the mobile device transmits the sensed data to the cloud computing environment for analysis.

35. The system of claim 19, wherein, The at least one processor is configured to perform inference on the mobile device such that the machine learning model is located on the mobile device.

36. The system of claim 19, wherein, The at least one processor is distributed between an edge device and a cloud computing server, and the instructions cause partial pre-processing on the edge device prior to transmitting pre-processed data to the cloud computing server for generating the prediction.

37. One or more non-transitory computer-readable media having stored thereon computer-executable instructions that, when executed by at least one computer, cause the at least one computer to perform a method comprising: collecting, at a mobile device, sensed data from a noise sensor, wherein the noise sensor is configured to monitor the equipment; receiving, at a machine learning model, the sensed data; receiving, from the machine learning model, a prediction of equipment performance; and displaying, on a graphical user interface, the prediction.

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