Claims:1. A processor-implemented method comprising:
under control of a chatbot coupled to an instant message network, in real-time, wherein the chatbot runs on a computing apparatus connected to the instant message network, and wherein the chatbot communicates with an asset and a device in the instant message network,
receiving information associated with multiple parameters of the asset from sensors associated with the asset at a time period;
comparing the received information associated with the multiple parameters with corresponding predetermined threshold values and multi stream based expressions and rule conditions;
substantially simultaneously comparing the received information associated with the multiple parameters with historical information associated with the multiple parameters, respectively; and
sending a notification regarding the asset, via the chatbot, to an authenticated user of the device based on the comparisons, machine learning based insights and audio visual metadata of the asset, thereby facilitating autonomic monitoring of the asset.

2. The method as claimed in claim 1, wherein sending the notification to the authenticated user of the device based on the comparisons, comprises:
sending the notification to the authenticated user of the device when at least one of the information associated with the multiple parameters is greater than the corresponding predetermined threshold values and satisfying the multi stream based expressions and rule conditions, and the received information associated with the multiple parameters is deviated from the historical information associated with the multiple parameters.

3. The method as claimed in claim 1, wherein the notification comprises a notification regarding at least one of abnormal functionality and probable failure of the asset.

4. The method as claimed in claim 1, further comprising:
obtaining image information associated with the asset at a time period;
performing image analytics based on the obtained image information and historical image information of the asset to determine a physical change in the asset; and
sending a notification, via the chatbot, to the authenticated user of the device upon determining the physical change in the asset.

5. The method as claimed in claim 1, wherein receiving the information associated with multiple parameters of the asset from the sensors associated with the asset at the time period, comprises:
receiving the information regarding the asset from a digital twin of the asset, wherein the digital twin represents real-time view of the asset based on the information from the sensors.

6. The method of claim 1, further comprising:
receiving a request from user of the device, wherein the request is regarding the asset;
authenticating the user upon receiving the request;
converting the request into a machine readable query upon authenticating the user;
analyzing information received from the sensors connected to the asset at the time period based on the machine readable query to obtain requested information; and
sending the requested information, via the chatbot, to the authenticated user of the device.

7. The method as claimed in claim 6, wherein receiving the request from the user of the device, comprises:
receiving the request from the user of the device in the form of at least one of a call, e-mail, text chat and gestures.

8. The method as claimed in claim 6, wherein sending the requested information to the authenticated user of the device, comprises:
converting the requested information into a user understandable format; and
sending, via the chatbot, the requested information in the user understandable format to the authenticated user of the device.

9. A system comprising:
an asset in an instant message network;
a device in the instant message network;
a computing apparatus communicatively coupled to the asset and the device in the instant message network, wherein the computing apparatus comprises:
one or more memories, wherein the one or more memories comprise a chatbot; and
one or more hardware processors, the one or more memories coupled to the one or more hardware processors, wherein the one or more hardware processors are configured to execute programmed instructions stored in the memory to:
receive information associated with multiple parameters of the asset from sensors associated with the asset at a time period;
compare the received information associated with the multiple parameters with corresponding predetermined threshold values and multi stream based expressions and rule conditions;
substantially simultaneously compare the received information associated with the multiple parameters with historical information associated with the multiple parameters, respectively; and
send a notification regarding the asset, via the chatbot, to an authenticated user of the device based on the comparisons, machine learning based insights and audio visual metadata of the asset, thereby facilitating autonomic monitoring of the asset.

10. The system as claimed in claim 9, wherein the one or more hardware processors are configured to execute the programmed instructions to:
send the notification to the authenticated user of the device when at least one of the information associated with the multiple parameters is greater than the corresponding predetermined threshold values and satisfying the multi stream based expressions and rule conditions, and the received information associated with the multiple parameters is deviated from the historical information associated with the multiple parameters.

11. The system as claimed in claim 9, wherein the notification comprises a notification regarding at least one of abnormal functionality and probable failure of the asset.

12. The system as claimed in claim 9, wherein the one or more hardware processors are further configured to execute programmed instructions to:
obtain image information associated with the asset at a time period;
perform image analytics based on the obtained image information and historical image information to determine a physical change in the asset; and
send a notification, via the chatbot, to the authenticated user of the device upon determining the physical change in the asset.

13. The system as claimed in claim 9, wherein the one or more hardware processors are further configured to execute programmed instructions to:
receive a request from user of the device, wherein the request is regarding the asset;
authenticate the user upon receiving the request;
convert the request into a machine readable query upon authenticating the user;
analyze information received from the sensors associated with the asset at the time period based on the machine readable query to obtain requested information; and
send the requested information, via the chatbot, to the authenticated user of the device.

14. The system as claimed in claim 13, wherein the one or more hardware processors are configured to execute the programmed instructions to:
receive the request from the user of the device in the form of at least one of a call, e-mail, text chat and gestures.

15. The system as claimed in claim 13, wherein the one or more hardware processors are configured to execute the programmed instructions to:
convert the requested information into a user understandable format; and
send, via the chatbot, the requested information in the user understandable format to the authenticated user of the device.
, Description:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003

COMPLETE SPECIFICATION
(See section 10 and rule 13)

Title of invention:
SYSTEM AND METHOD FOR AUTONOMIC MONITORING OF ASSETS USING CHATBOTS

Applicant:
Tata Consultancy Services Limited
A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th Floor,
Nariman Point, Mumbai 400021,
Maharashtra, India

The following specification particularly describes the invention and the manner in which it is to be performed.

TECHNICAL FIELD
[001] The embodiments herein generally relate to asset monitoring and, more particularly, to autonomic monitoring of assets using chatbots.
BACKGROUND
[002] Generally, asset (e.g., data server and so on) monitoring includes managing configuration settings (e.g., storage, memory, and logs) and retrieving diagnostic information. For example, remote data server administration describes the administration of data servers remotely via a network connection, for example, a remote login session or via a web browser. Data servers that provide an administration Application Programming Interface (API) may be administered remotely using dedicated standalone programs (e.g., standalone data server administration applications) or web applications. Standalone programs lack the flexibility of World Wide Web (“Web”) applications, which may be executed from a standard browser running on a machine coupled to a network to which the data server is also coupled. However, web applications require the user to run a browser and lack the flexibility and accessibility of a purely text based interface. If a user has a command session on a machine that the data server is installed on, the data server typically supplies a set of command line utilities to administer the data server. Remote standalone data server administration applications sometimes provide an SQL command interface that enables the user to enter SQL commands. However, not all data servers support administration of the server itself via SQL commands. Even in cases in which a standalone data server administration application does connect to a data server that does support data server administration via SQL, the standalone data server administration application lacks the flexibility of switching between data servers with simple commands.
SUMMARY
[003] The following presents a simplified summary of some embodiments of the disclosure in order to provide a basic understanding of the embodiments. This summary is not an extensive overview of the embodiments. It is not intended to identify key/critical elements of the embodiments or to delineate the scope of the embodiments. Its sole purpose is to present some embodiments in a simplified form as a prelude to the more detailed description that is presented below.
[004] In view of the foregoing, an embodiment herein provides methods and systems for autonomic monitoring of assets using chatbots are disclosed. In one aspect, a processor-implemented method includes steps of: under control of a chatbot coupled to an instant message network, in real-time, wherein the chatbot runs on a computing apparatus connected to the instant message network, and wherein the chatbot communicates with an asset and a device in the instant message network, receiving information associated with multiple parameters of the asset from sensors associated with the asset at a time period; comparing the received information associated with the multiple parameters with corresponding predetermined threshold values and multi stream based expressions and rule conditions; substantially simultaneously comparing the received information associated with the multiple parameters with historical information associated with the multiple parameters, respectively; and sending a notification regarding the asset, via the chatbot, to an authenticated user of the device based on the comparisons, machine learning based insights and audio visual metadata of the asset, thereby facilitating autonomic monitoring of the asset.
[005] In another aspect, a system for autonomic monitoring of assets using chatbots is disclosed. In an embodiment, the system comprises an asset in an instant message network; a device in the instant message network; a computing apparatus communicatively coupled to the asset and the device in the instant message network, wherein the computing apparatus comprises: one or more memories, wherein the one or more memories comprise a chatbot; and one or more hardware processors, the one or more memories coupled to the one or more hardware processors, wherein the one or more hardware processors are capable of executing programmed instructions stored in the memory to: receive information associated with multiple parameters of the asset from sensors associated with the asset at a time period; compare the received information associated with the multiple parameters with corresponding predetermined threshold values and multi stream based expressions and rule conditions; substantially simultaneously compare the received information associated with the multiple parameters with historical information associated with the multiple parameters, respectively; and send a notification regarding the asset, via the chatbot, to an authenticated user of the device based on the comparisons, machine learning based insights and audio visual metadata of the asset, thereby facilitating autonomic monitoring of the asset.
[006] In yet another aspect, a non-transitory computer-readable medium having embodied thereon a computer program for executing a method for autonomic monitoring of assets using chatbots. The method includes steps of: under control of a chatbot coupled to an instant message network, in real-time, wherein the chatbot runs on a computing apparatus connected to the instant message network, and wherein the chatbot communicates with an asset and a device in the instant message network, receiving information associated with multiple parameters of the asset from sensors associated with the asset at a time period; comparing the received information associated with the multiple parameters with corresponding predetermined threshold values and multi stream based expressions and rule conditions; substantially simultaneously comparing the received information associated with the multiple parameters with historical information associated with the multiple parameters, respectively; and sending a notification regarding the asset, via the chatbot, to an authenticated user of the device based on the comparisons, machine learning based insights and audio visual meta data of the asset, thereby facilitating autonomic monitoring of the asset.
[007] It should be appreciated by those skilled in the art that any block diagram herein represents conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it is appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computing device or processor, whether or not such computing device or processor is explicitly shown.
BRIEF DESCRIPTION OF THE FIGURES
[008] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to reference like features and modules.
[009] FIG. 1 illustrates a system for autonomic monitoring of assets using chatbots, according to an embodiment of a present subject matter;
[0010] FIG. 2 illustrates a system for autonomic monitoring of an intensive care unit (ICU) bed using chatbots, according to an embodiment of a present subject matter;
[0011] FIG. 3 is a block diagram illustrating a various components of the system shown in FIG. 1, according to an embodiment of a present subject matter; and
[0012] FIG. 4 illustrates a flow diagram of a method for autonomic monitoring of assets using chatbots, in accordance with an example embodiment.
[0013] It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems and devices embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown.
DETAILED DESCRIPTION
[0014] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
[0015] The present technique discloses a smart asset monitoring system which is capable to providing accessibility to other machine and human being to communicate with it. Each chatbot in the system have a unique network identity (e.g., PSTN number) assigned which can be called from any phone. Further, the chatbot can be able to identify the call and authenticate the caller with plurality of authentication and authorization techniques like state of the art speech signature matching along with voice recognition. For a communication over email or text chat, the system need to do pre authentication before responding to any query. This way every user can have their own personalized chatbot with ubiquitous identity via a unique network identifier like a public switched telephone network (PSTN) number or Internet protocol (IP). The chatbot is able to provide response with voice and text based commands. Further, the system performs health checks of the asset and computes predictive parametric failure of the components in the asset. Furthermore, the chatbot sends pictures of the assets to enable remote computer vision. This allows the user to get real-time view of the asset remotely on demand. For example, the chatbot can recite in the asset or can recite in the system communicatively coupled to the asset.
[0016] The methods and systems are not limited to the specific embodiments described herein. In addition, the method and system can be practiced independently and separately from other modules and methods described herein. Each device element/module and method can be used in combination with other elements/modules and other methods.
[0017] The manner, in which the system and method for autonomic monitoring of assets using chatbots, has been explained in details with respect to the FIGS. 1 through 4. While aspects of described methods and systems for autonomic monitoring of assets using chatbots can be implemented in any number of different systems, utility environments, and/or configurations, the embodiments are described in the context of the following exemplary system(s).
[0018] FIG. 1 illustrates a system 100 for autonomic monitoring of assets using chatbots, in accordance with an example embodiment. As shown in FIG. 1, the system 100 includes an asset 102, user devices 104A-C and a computing apparatus 106 communicatively coupled to the asset 102 and devices 104A-C in an instant message network. The devices 104A-C can be used by users A-C, respectively. In FIG. 1, the devices 104A-C communicate with the computing apparatus 106 using PSTN or IP numbers. Further, the computing apparatus 106 and the asset 102 are communicatively coupled to each other via machine-to-machine (M2M) protocol. Example M2M protocol includes constrained application protocol (Coap), Message Queue Telemetry Transport (Mqtt), Transmission Control Protocol (TCP), User Datagram Protocol (UDP), Modbus, Zigbee, continua, Bluetooth, open protocol for communication (OPC), backnet etc., Even though it is shown in FIG. 1 that the computing apparatus 106 is coupled to the asset 102, one can envision that the computing apparatus 106 can recite in the asset 102. As shown in FIG. 1, the asset 102 includes multiple sensors A-C, devices A-C, an external interface, a microcontroller or processor and other subsystems. Exemplary system 200, shown in FIG. 2, shows an intensive care unit (ICU) bed (i.e., an asset), the computing apparatus 106, a hospital RMO using a computing device, a doctor, nurse and OPD supervisor with respective devices. For example, the ICU bed includes sensors to detect pressure, sugar, and pulse, an Electrocardiography (ECG), a ventilator, a dialysis unit, a microcontroller/processor, an external interface and other subsystems.
[0019] In an example embodiment, the computing apparatus 106 may be embodied in, or is in direct communication with a computing device. The apparatus 106 includes or is otherwise in communication with one or more hardware processors such as processor(s) 108, one or more memories such as a memory 110, and a network interface unit such as a network interface unit 112. In an embodiment, the processor 108, memory 110, and the network interface unit 112 may be coupled by a system bus such as a system bus or a similar mechanism. Although FIG. 1 shows example components of the system 100, in other implementations, the system 100 may contain fewer components, additional components, different components, or differently arranged components than depicted in FIG. 1.
[0020] The processor 108 may include circuitry implementing, among others, audio and logic functions associated with the communication. For example, the processor 108 may include, but are not limited to, one or more digital signal processors (DSPs), one or more microprocessor, one or more special-purpose computer chips, one or more field-programmable gate arrays (FPGAs), one or more application-specific integrated circuits (ASICs), one or more computer(s), various analog to digital converters, digital to analog converters, and/or other support circuits. The processor 102 thus may also include the functionality to encode messages and/or data or information. The processor 102 may include, among other things, a clock, an arithmetic logic unit (ALU) and logic gates configured to support operation of the processor 108. Further, the processor 108 may include functionality to execute one or more software programs, which may be stored in the memory 110 or otherwise accessible to the processor 108.
[0021] The functions of the various elements shown in the figure, including any functional blocks labeled as “processor(s)”, may be provided through the use of dedicated hardware as well as hardware capable of executing software in association with appropriate software. When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. Moreover, explicit use of the term “processor” should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation DSP hardware, network processor, application specific integrated circuit (ASIC), FPGA, read only memory (ROM) for storing software, random access memory (RAM), and non-volatile storage. Other hardware, conventional, and/or custom, may also be included.
[0022] The interface(s) 112 may include a variety of software and hardware interfaces, for example, interfaces for peripheral device(s), such as a keyboard, a mouse, an external memory, and a printer. The interface(s) 112 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, local area network (LAN), cable, etc., and wireless networks, such as Wireless LAN (WLAN), cellular, or satellite.
[0023] The one or more memories such as a memory 110, may store any number of pieces of information, and data, used by the apparatus to implement the functions of the apparatus. The memory 110 may include for example, volatile memory and/or non-volatile memory. Examples of volatile memory may include, but are not limited to volatile random access memory. The non-volatile memory may additionally or alternatively comprise an electrically erasable programmable read only memory (EEPROM), flash memory, hard drive, or the like. Some examples of the volatile memory includes, but are not limited to, random access memory, dynamic random access memory, static random access memory, and the like. Some example of the non-volatile memory includes, but are not limited to, hard disks, magnetic tapes, optical disks, programmable read only memory, erasable programmable read only memory, electrically erasable programmable read only memory, flash memory, and the like. The memory 110 may be configured to store information, data, applications, instructions or the like for enabling the apparatus 106 to carry out various functions in accordance with various example embodiments. Additionally or alternatively, the memory 110 may be configured to store instructions which when executed by the processor 108 causes the apparatus 106 to behave in a manner as described in various embodiments. The memory 110 includes a chatbot 114, a database 116, an authentication module 118, a speech and gesture recognizer 120, a natural language processor 122, a machine learning unit 124, an IOT platform connector 126, and an image analytics unit 128. The chatbot 114 includes routines, programs, objects, components, data structures, etc., which perform particular tasks or implement particular abstract data types. Even though, the components 114 to 128 are shown in the computing apparatus 106, one can envision that the components 114 to 128 can recite outside the computing apparatus 106 and communicatively coupled to the device 104 and asset 102. The chatbot 114 may communicate with the asset 102 and the devices 104A-N in the instant message network. For example, the user devices 104A-C communicate with the chatbot 114 via the PSTN or IP numbers. The computing apparatus 106 can connect to the IOT platform using the IOT platform connector 126.
[0024] In operation, the computing apparatus 106 receives information associated with multiple parameters of the asset 102 from the sensors A-C associated with the asset 102 at a time period and stores the information in the database 116. In an example embodiment, the computing apparatus 106 receives the information regarding the asset 102 from a digital twin of the asset 102. The digital twin represents real-time view of the asset based on information from the sensors A-C. In this embodiment, the digital twin model is created and threshold rules for dominant parameters of the asset 102 for normal standard operation process (SOP) are defined. The asset’s normal running parameters are also kept in a machine learning model as base values for normal running of the asset. In an example, the machine learning unit 124 uses different types of machine learning algorithms like Bayesian Network, Hierarchical clustering, k-Nearest Neighbor, Multivariate Adaptive Regression Splines (MARS) etc., to keep updating the base model values for the threshold updated for each dominant property of the digital twin.
[0025] Further, the chatbot 114 compares the received information associated with the multiple parameters with the corresponding predetermined threshold values and multi stream based expressions and rule conditions. For example, x and y are parameters of an asset. Then, the multi stream based expressions and rule conditions may include x should be greater than y at a particular time interval and so on. The chatbot 114 substantially simultaneously compares the received information associated with the multiple parameters with historical information associated with the multiple parameters, respectively, stored in the database 116. In addition, the chatbot 114 sends, via the interface, an immediate notification regarding the asset 102 to an authenticated user (any of users A-C of the devices 104A-C) based on the comparisons, machine learning based insights and audio visual metadata of the asset, thereby facilitating autonomic monitoring of the asset. In an embodiment, the computing apparatus 106 sends the notification to the authenticated user of the device 104 when the information associated with the multiple parameters is greater than the corresponding predetermined threshold values and satisfying multi stream based expressions and rule conditions, and/or the received information associated with the multiple parameters is deviated from the historical information associated with the multiple parameters. For example, the notification includes a notification regarding abnormal functionality, probable failure of the asset and the like. In an example embodiment, if any time the input from different sensors A-C of the asset 102 come beyond the base value and out of the threshold, the asset 102 generates alerts which enables the chatbot 114 to inform the authenticated user for abnormal run. In this example, the chatbot 114 uses the machine learning algorithms, from the machine learning unit 124, in combination with the historical data from the asset 102 and a reliability theorem to predict possible failure of the asset 102. For example, MTBF (Mean Time Between Failure), MTTR (Mean Time To Repair), MTTF (Mean Time To Failure) and FIT (Failure In Time) are reliability terms based on methods and procedures for lifecycle predictions for a product. Customers often must include reliability data when determining what product to buy for their application. MTBF, MTTR, MTTF and FIT are ways of providing a numeric value based on a compilation of data to quantify a failure rate and the resulting time of expected performance. The numeric value can be expressed using any measure of time, but hours is the most common unit in practice. All these information can be derived from the historically stored sensor data.
[0026] Consider an example of an asset (e.g., positive displacement pump). As per the pump’s specification, the revolution per minute (RPM) must be 1200 when the pump is at the normal condition to start and the inlet pressure is 12 Pascal at the same time with outlet pressure 15 Pascal. Now, SOP is defined as 1200 RPM, 12 Pascal inlet and 16 Pascal outlet pressure. Further, a metamodel, including metadata, of the pump with all its properties like RPM, inlet pressure, outlet pressure and many other parameters is created. There are rules defined for the threshold of each of such limiting parameters values to generate alarms. Now, during the running of the pump, if any of these parameters are not met, an event is triggered which keeps an entry of the event as well as send notification based of the rules set. The pump maintenance schedule and history also available to the chatbot 114, then using reliability theorem of MTBF and past history of recovery time, a predictive failure warring can be generated by machine learning algorithms like pattern matching or support vector machines. Now if any user calls the chatbot, the chatbot 114 have all these data available in its database and gives appropriate answer for the running of the pump to the user. In another example, the chatbot 114 compares information about how many times the pump has restarted and the reasons for the restart with the predefined data or historical data or metadata and provides information to the user.
[0027] In some embodiments, the chatbot 114 obtains image information associated with the asset 102 at a time period from a camera coupled to the asset. The chatbot 114 then performs image analytics, by calling the image analytics unit 128, based on the obtained image information and historical image information to determine a physical change in the asset. Further, the chatbot 114 sends a notification to the authenticated user of the device 104 upon determining the physical change in the asset. For example, if smoke is coming out of the pump, if any spark is seen can be determined by the image analytics. In this example, a pump is over heated and the exhaust from the pump is giving black smoke out which is a visual change of the pump. Now, the SOP of the pump also have a normal pump image and the over headed pump image can be compared to detect the change which gives an alert to the user.
[0028] In some embodiments, the chatbot 114 receives a request from the user (e.g., user A) of the device 104A, the request is regarding the asset 102. For example, the request is in the form of a call, e-mail, text chat, gestures and the like. Further, the chatbot 114 authenticates the user A upon receiving the initiation request by calling the authentication module 118. In an embodiment, the speech and gesture recognizer 120 receives the request when a mode of request is a voice call and gestures, performs authentication of the user and then converts the voice call or gestures to text. Furthermore, the chatbot 114 converts the request into a machine readable query upon authenticating the user by calling the natural language processor 122. In addition, the chatbot 114 analyzes or searches information received from the sensors A-C at the time period based on the machine readable query to obtain requested information. This is possible as the sensor data is stored as a time series based big data in IOT data platform 126 which can be processed for any outlier detection and pattern matching. Also, the chatbot 114 sends the requested information to the authenticated user A of the device 104A. For example, the chatbot 114 displays the requested information on the device 104A. In an example, the chatbot 114 converts the requested information into a user understandable format and sends the requested information in the user understandable format to the authenticated user A of the device 104A. The user understandable format may be audio/video/text format. In an example embodiment, the chatbot 114 may send any information about abnormal functionality or failure to the authenticated user along with the requested information. Example chat with the chatbot 114 is shown below:
User - Hit Chatbot ! Can you take a snap of the Pump001 at Siruseri and send me
Chatbot - Sure - Here it is - sends a picture (Picture taken after receiving commands)
Chatbot again - I detect some change. Pump looks different since the last snap I took dated XXX.
User - How did it run yesterday night?
Chatbot - Overall good. Pump STARTED twice yesterday. Average output was 50 m3/hr.
[0029] Referring now to FIG. 3, a block diagram 300 illustrating various components of the system 100, according to an embodiment of a present subject matter. As shown in FIG. 3, the block diagram includes a user device 302 (e.g., device 104 of FIG. 1), an asset 304 (e.g., asset 102 of FIG. 1), an input analyzer and authenticator 306, a speech/gesture to text convertor 308, a natural language processor 310, an artificial intelligence (AI) based system to act as a controlling unit 312, an image analytics unit 314, a device manager 316, and a digital asset manager 318 communicatively coupled to each other. For example, the components 306, 308, 314, 316 and 318 recite in the computing apparatus 106 of FIG. 1.
[0030] In operation, user of the device 302 sends a request regarding the asset 304 to the input analyzer and authenticator 306. The request can be made in any mode like e-mail, chat, voice call, gestures and so on. The input analyzer and authenticator 306 (i.e., the component 118) then analyzes the request for a mode of communication and authenticates the user for a valid connection with multi factor authentication like bio metric figure print and retina matching, face recognition, gesture based patterns with secret coding, combination of hardware and software locks, use of specific cryptographically algorithms like data encryption standard (DES), advanced encryption standard (AES), message-digest algorithm 5 (MD5) etc., For example, this component identifies the voice and authenticate a user with his voice itself when he calls. Further, the speech/gesture to text converter 308 (i.e., the component 120) receives the request from the input analyzer and authenticator 306 when the mode of request is a voice call and gestures. The speech/gesture to text convertor 308 then converts the voice call or gestures to text. Further, the natural language processor 310 (i.e., the component 122) receives the request directly from the user device 302 or input analyzer and authenticator 306 or the speech/gesture to text convertor 308. The natural language processor 310 decodes and encodes the human language commands to machine understandable command. The natural language processor 310 may use state of the art natural language processor, semantic web, machine vision and pattern recognition algorithms for encoding and decoding processes.
[0031] Furthermore, the natural language processor 310 sends the machine readable request to the controlling unit 312. The controlling unit 312 analyzes the request and sends the user requested information to the user device 302 via the device manager 316 and digital asset manager 318. The device manager 316 may include information received from sensors in the asset 304. This module 316 is for controlling the device 302 and also do enable remote command execution and device management. In some embodiments, controlling unit 312 uses the image analytics module 314 (i.e., the component 128) to identify any physical change in the asset 304 and report the same to the user device 302 via the device manager 316 and digital asset manager 318.
[0032] FIG. 4 illustrates a flow diagram of a method 400 for autonomic monitoring of assets using chatbots, in accordance with an example embodiment. The processor-implemented method 400 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method 400 may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communication network. The order in which the method 400 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 400, or an alternative method. Furthermore, the method 400 can be implemented in any suitable hardware, software, firmware, or combination thereof. In an embodiment, the method 400 depicted in the flow chart may be executed by a system, for example, the system 100 of FIG. 1.
[0033] In an example embodiment, under control of a chatbot coupled to an instant message network, in real-time, the method 400 is performed. The chatbot runs on a computing apparatus connected to the instant message network and the chatbot communicates with an asset and a device. At block 402, information associated with multiple parameters of the asset are received from sensors associated with the asset at a time period. In an example, the information regarding the asset is received from a digital twin of the asset, the digital twin represents real-time view of the asset based on information from the sensors.
[0034] At block 404, the received information associated with the multiple parameters is compared with corresponding predetermined threshold values and multi stream based expressions and rule conditions. At block 406, the received information associated with the multiple parameters is substantially simultaneously compared with historical information associated with the multiple parameters, respectively. At block 408, an immediate notification regarding the asset is sent, via the chatbot, to an authenticated user of the device based on the comparisons, machine learning based insights and audio visual metadata of the asset, thereby facilitating autonomic monitoring of the asset. For example, the notification is sent to the user of the device when the information associated with the multiple parameters is greater than the corresponding predetermined threshold values, and/or the received information associated with the multiple parameters is deviated from the historical information associated with the multiple parameters. In this example, the notification includes a notification regarding abnormal functionality and/or probable failure of the asset.
[0035] In some embodiments, image information associated with the asset is obtained at a time period. Further, image analytics is performed based on the obtained image information and historical image information to determine a physical change in the asset. Furthermore, a notification is sent, via the chatbot, to the authenticated user of the device upon determining the physical change in the asset.
[0036] In other embodiments, a request is received from the user of the device, the request is regarding the asset. For example, the request is received from the user of the device in the form of a call, e-mail, text chat and/or gestures. Further, the user is authenticated upon receiving the initiation request. Furthermore, the request is converted into a machine readable query upon authenticating the user. In addition, the information received from the sensors connected to the asset at the time period is analyzed based on the machine readable query to obtain requested information. Also, the requested information is sent, via the chatbot, to the authenticated user of the device. In an embodiment, the requested information is converted into a user understandable format and then the requested information in the user understandable format is sent to the authenticated user of the device.
[0037] In various embodiments described in FIGs. 1-4, system and method for autonomic monitoring of assets using chatbots are disclosed. In other words, a technique for smart remote asset monitoring by natural language processing and voice command analysis is disclosed. The technique ensures ease of use for any type of user including restricted capability also. Moreover, the multi-channel input provider option to a system allows the asset being capable of communicating to human user, machines and other events in autonomous way. This system considers a chatbot for doing natural language processing for asset monitoring. This system also covers on demand and real-time status visual and text based reporting.
[0038] The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
[0039] It is, however to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such non-transitory computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g. hardware means like e.g. an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g. using a plurality of CPUs.
[0040] The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various modules described herein may be implemented in other modules or combinations of other modules. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
[0041] The foregoing description of the specific implementations and embodiments will so fully reveal the general nature of the implementations and embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.
[0042] The preceding description has been presented with reference to various embodiments. Persons having ordinary skill in the art and technology to which this application pertains will appreciate that alterations and changes in the described structures and methods of operation can be practiced without meaningfully departing from the principle, spirit and scope.