This application claims priority to India Provisional Patent Application No. 202211032277, filed Jun. 6, 2022, the entire content of which is incorporated by reference herein.
The subject matter described herein relates generally to vehicle systems, and more particularly, embodiments of the subject matter relate to avionics systems and methods for intelligent, contextual management of messages such as notices to airmen (NOTAMs).
Air traffic control typically involves voice communications between air traffic control and a pilot or crewmember onboard the various aircrafts within a controlled airspace. For example, an air traffic controller (ATC) may communicate an instruction or a request for pilot action by a particular aircraft using a call sign assigned to that aircraft. In addition to audio communications with ATC, aircraft may also receive alerts, advisories, notices, instructions, such as notices to airmen (NOTAMs), pilot reports (PIREPs) or the like, or receive clearance communications or other messages from various other sources, such as, for example, a controller-pilot datalink (CPDLC) system, an automatic terminal information service (ATIS), an aircraft communications addressing and reporting system (ACARS), and the like. Often, the content of such messages includes unstructured text or data that can be difficult to quickly review and comprehend what information is relevant. Moreover, the volume of such messages and the diversity of data or information contained therein further increases the burden on the pilot or other user to ascertain the relevant information. For example, a typical briefing package for a pilot may include tens (and in some cases more than one hundred) of pages of NOTAMs related to a planned flight.
Accordingly, it is desirable to provide aircraft systems and methods that reduce head-down time (HDT) and facilitate a pilot maintaining situational awareness while improving comprehension and adherence to information contained in messages or other communications to improve safety and efficiency of operation. Other desirable features and characteristics of the methods and systems will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the preceding background.
Methods and systems are provided for assisting operation of a vehicle, such as an aircraft. One method of assisting operation of a vehicle involves obtaining a message relevant to a route for the vehicle, analyzing textual content of the message to automatically identify values for a plurality of fields of information and obtaining current values for one or more fields of the plurality of fields from one or more data sources associated with the vehicle. In response to identifying a difference between at least one of the values for the one or more fields of the plurality of fields and at least one of the current values for the one or more fields of the plurality of fields, the method continues by automatically assigning a priority level to the message based at least in part on the difference and providing graphical indicia of the priority level assigned to the message and the at least one of the values for the plurality of fields.
In another embodiment, a computer-readable medium having computer-executable instructions stored thereon is provided. The computer-executable instructions, when executed by a processing system, cause the processing system to obtain a message relevant to a route for a vehicle, analyze textual content of the message to automatically identify values for a plurality of fields of information, obtain current values for one or more fields of the plurality of fields from one or more data sources associated with the vehicle, and in response to a difference between at least one of the values for the one or more fields of the plurality of fields and at least one of the current values for the one or more fields of the plurality of fields, automatically assign a priority level to the message based at least in part on the difference and provide graphical indicia of the priority level assigned to the message and the at least one of the values for the plurality of fields.
In another embodiment, a system is provided that includes a display device, an onboard system to provide a current value for a field of information relating to a route for a vehicle, a data storage element to maintain a prioritization model, and a processing system coupled to the display device, the onboard system and the data storage element to obtain a message relevant to the route for the vehicle, analyze textual content of the message to automatically identify a specified value for the field of information, and in response to a difference between the current value and the specified value, automatically assign a priority level to the message based at least in part on the difference using the prioritization model and provide graphical indicia of the priority level assigned to the message and the specified value on the display device.
This summary is provided to describe select concepts in a simplified form that are further described in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
Embodiments of the subject matter will hereinafter be described in conjunction with the following drawing figures, wherein like numerals denote like elements, and:
The following detailed description is merely exemplary in nature and is not intended to limit the subject matter of the application and uses thereof. Furthermore, there is no intention to be bound by any theory presented in the preceding background, brief summary, or the following detailed description.
Embodiments of the subject matter described herein generally relate to systems and methods for intelligently analyzing and prioritizing messages related to vehicle operation in a context-sensitive manner to improve comprehension and actionability. For purposes of explanation, the subject matter is primarily described herein in the context of analyzing messages relevant to a flight plan (or planned flight path) for an aircraft; however, the subject matter described herein is not necessarily limited to aircraft or avionic environments, and in alternative embodiments, may be implemented in an equivalent manner for ground operations, marine operations, or otherwise in the context of other types of vehicles with respect to a planned route of travel.
As described in greater detail below primarily in the context of
In one or more exemplary embodiments, the subject matter described herein is implemented in the context of notice to airmen (NOTAM) messages and utilizes artificial intelligence (AI) to determine the intent of a particular NOTAM and prioritize the respective NOTAM based on the intent of the NOTAM, the discrepancy between a specified value for a particular field contained within the NOTAM and the current value for that field, and the current operational context or status of the aircraft (e.g., the current flight plan, the current flight phase, the current altitude, the current aircraft configuration, the current meteorological conditions and/or the like). In this regard, the subject matter described herein is capable of intelligently classifying or sorting the NOTAMs into different priority levels or classifications based on the relationship between the textual content and intent of the respective NOTAM and the current flight plan and current operational context of the aircraft. Thus, higher priority NOTAMs may be preferentially displayed or identified, thereby reducing head-down time (HDT) and alleviating the pilot of the manual burden of reviewing a comprehensive set of NOTAMs to manually ascertain what NOTAMs are most operationally significant. In exemplary embodiments, AI techniques are also utilized to analyze the intent and content of the NOTAM in relation to the current flight plan and current operational context of the aircraft to intelligently recommend one or more actions that may be initiated or otherwise performed by a pilot or other user in response to a discrepancy between a specified value in a NOTAM and a current value associated with the current flight plan and/or current state of the aircraft. Thus, in addition to alleviating the pilot of the manual burden of identifying what NOTAMs are relevant or operationally significant, the subject matter described herein may also alleviate the cognitive burden of determining how to best respond to an operationally significant NOTAM, thereby improving safety and efficiency of operation.
In exemplary embodiments, the display device 102 is realized as an electronic display capable of graphically displaying flight information or other data associated with operation of the aircraft 120 under control of the display system 108 and/or processing system 106. In this regard, the display device 102 is coupled to the display system 108 and the processing system 106, and the processing system 106 and the display system 108 are cooperatively configured to display, render, or otherwise convey one or more graphical representations or images associated with operation of the aircraft 120 on the display device 102. The user input device 104 is coupled to the processing system 106, and the user input device 104 and the processing system 106 are cooperatively configured to allow a user (e.g., a pilot, co-pilot, or crew member) to interact with the display device 102 and/or other elements of the system 100, as described in greater detail below. Depending on the embodiment, the user input device(s) 104 may be realized as a keypad, touchpad, keyboard, mouse, touch panel (or touchscreen), joystick, knob, line select key or another suitable device adapted to receive input from a user. In some exemplary embodiments, the user input device 104 includes or is realized as an audio input device, such as a microphone, audio transducer, audio sensor, or the like, that is adapted to allow a user to provide audio input to the system 100 in a “hands free” manner using speech recognition.
The processing system 106 generally represents the hardware, software, and/or firmware components configured to facilitate communications and/or interaction between the elements of the system 100 and perform additional tasks and/or functions to support operation of the system 100, as described in greater detail below. Depending on the embodiment, the processing system 106 may be implemented or realized with a general purpose processor, a content addressable memory, a digital signal processor, an application specific integrated circuit, a field programmable gate array, any suitable programmable logic device, discrete gate or transistor logic, processing core, discrete hardware components, or any combination thereof, designed to perform the functions described herein. The processing system 106 may also be implemented as a combination of computing devices, e.g., a plurality of processing cores, a combination of a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other such configuration. In practice, the processing system 106 includes processing logic that may be configured to carry out the functions, techniques, and processing tasks associated with the operation of the system 100, as described in greater detail below. Furthermore, the steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in firmware, in a software module executed by the processing system 106, or in any practical combination thereof. For example, in one or more embodiments, the processing system 106 includes or otherwise accesses a data storage element (or memory), which may be realized as any sort of non-transitory short or long term storage media capable of storing programming instructions for execution by the processing system 106. The code or other computer-executable programming instructions, when read and executed by the processing system 106, cause the processing system 106 to support or otherwise perform certain tasks, operations, functions, and/or processes described herein.
The display system 108 generally represents the hardware, software, and/or firmware components configured to control the display and/or rendering of one or more navigational maps and/or other displays pertaining to operation of the aircraft 120 and/or onboard systems 110, 112, 114, 116 on the display device 102. In this regard, the display system 108 may access or include one or more databases suitably configured to support operations of the display system 108, such as, for example, a terrain database, an obstacle database, a navigational database, a geopolitical database, an airport database, a terminal airspace database, a special use airspace database, or other information for rendering and/or displaying navigational maps and/or other content on the display device 102.
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In exemplary embodiments, the processing system 106 is also coupled to the FMS 114, which is coupled to the navigation system 112, the communications system 110, and one or more additional avionics systems 116 to support navigation, flight planning, and other aircraft control functions in a conventional manner, as well as to provide real-time data and/or information regarding the operational status of the aircraft 120 to the processing system 106. Although
In the illustrated embodiment, the aircraft system 100 includes a data storage element 118, which is capable of storing, maintaining or otherwise implementing one or more of the databases that support operations of the aircraft system 100 described herein. In some embodiments, the data storage element 118 contains aircraft procedure information (or instrument procedure information) for a plurality of airports and maintains association between the aircraft procedure information and the corresponding airports. Depending on the embodiment, the data storage element 118 may be physically realized using RAM memory, ROM memory, flash memory, registers, a hard disk, or another suitable data storage medium known in the art or any suitable combination thereof. As used herein, aircraft procedure information should be understood as a set of operating parameters, constraints, or instructions associated with a particular aircraft action (e.g., approach, departure, arrival, climbing, and the like) that may be undertaken by the aircraft 120 at or in the vicinity of a particular airport. An airport should be understood as referring to any sort of location suitable for landing (or arrival) and/or takeoff (or departure) of an aircraft, such as, for example, airports, runways, landing strips, and other suitable landing and/or departure locations, and an aircraft action should be understood as referring to an approach (or landing), an arrival, a departure (or takeoff), an ascent, taxiing, or another aircraft action having associated aircraft procedure information. An airport may have one or more predefined aircraft procedures associated therewith, wherein the aircraft procedure information for each aircraft procedure at each respective airport are maintained by the data storage element 118 in association with one another.
Depending on the embodiment, the aircraft procedure information may be provided by or otherwise obtained from a governmental or regulatory organization, such as, for example, the Federal Aviation Administration (FAA) in the United States. In an exemplary embodiment, the aircraft procedure information comprises instrument procedure information, such as instrument approach procedures, standard terminal arrival routes, instrument departure procedures, standard instrument departure routes, obstacle departure procedures, or the like, traditionally displayed on a published charts, such as Instrument Approach Procedure (IAP) charts, Standard Terminal Arrival (STAR) charts or Terminal Arrival Area (TAA) charts, Standard Instrument Departure (SID) routes, Departure Procedures (DP), terminal procedures, approach plates, and the like. In exemplary embodiments, the data storage element 118 maintains associations between prescribed operating parameters, constraints, and the like and respective navigational reference points (e.g., waypoints, positional fixes, radio ground stations (VORs, VORTACs, TACANs, and the like), distance measuring equipment, non-directional beacons, or the like) defining the aircraft procedure, such as, for example, altitude minima or maxima, minimum and/or maximum speed constraints, RTA constraints, and the like. In this regard, although the subject matter may be described in the context of a particular procedure for purpose of explanation, the subject matter is not intended to be limited to use with any particular type of aircraft procedure and may be implemented for other aircraft procedures in an equivalent manner.
Additionally, in some embodiments, the data storage element 118 may be realized as a remote database external to the aircraft 120 that is communicatively coupled to the processing system 106 over a communications network (e.g., via the communications system 110). For example, in some embodiments, the data storage element 118 may be realized as a NOTAM database, a PIREP database or another remote database or data source from which the processing system 106 may obtain notices, reports or any other sort of communiques or messages that include data or information relevant to operation of the aircraft 120. In this regard, in some embodiments, such a message database may be maintained or otherwise provided by a governmental or regulatory organization. For example, in one embodiment, the data storage element 118 may be realized as a NOTAM database maintained by the FAA in the United States. In other embodiments, the data storage element 118 may be realized as a database or other data storage associated with a third-party data service.
It should be understood that
In the illustrated embodiment, the NOTAM prioritization system 200 includes a processing system 208 (e.g., processing system 106) that is configurable to support a NOTAM analysis service 210 that retrieves and analyzes NOTAMs 202 from a remote system 204 using one or more AI models 214 maintained in a data storage element 212 (e.g., data storage element 118) coupled to the processing system 208. In this regard, the NOTAM analysis service 210 utilizes the AI models 214 in connection with NLP, parts of speech tagging and other language processing techniques to analyze the textual content of the NOTAMs 202 that are relevant to the planned flight path (or route) using NLP) to determine the intent, objective or semantic significance of a respective NOTAM 202 and identify specified values for different fields of information contained within a respective NOTAM 202. The processing system 208 is also communicatively coupled to one or more onboard systems 206 (e.g., one or more of the systems 108, 110, 112, 114, 116) to obtain the current values or currently planned values for different fields of information, which, in turn, are utilized by the NOTAM analysis service 210 to identify discrepancies or differences between the content of a particular NOTAM 202 and the current flight plan or current aircraft state.
As described in greater detail below in the context of
In one or more embodiments, the AI models 214 utilized by the NOTAM analysis service 210 are dynamically updated over time to adapt to pilot behaviors to improve the prioritization in a manner that better reflects the pilot(s) subjective prioritization of different NOTAMs 202, while also adapting the automated recommendations to better comport with pilot actions. For example, the GUI display 218 generated by the NOTAM analysis service 210 may include one or more GUI elements that are manipulable by a pilot or other user using the user input device 220 to perform actions with respect to different NOTAMs 202, such as, for example, reassigning a different priority to a NOTAM 202, deprioritizing a NOTAM 202, initiating (or declining to initiate) a recommended action responsive to a NOTAM 202, manually initiating an action different from a recommended action responsive to a NOTAM 202, and/or the like. In this regard, the NOTAM analysis service 210 may utilized self-learning AI techniques to dynamically update and adapt the AI models 214 to better reflect observed pilot behaviors.
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The obtained subset of NOTAMs relevant to the current flight plan are input or otherwise provided to an intent recognition service 304 that is configured to utilize NLP, parts of speech tagging or other semantic or syntactic AI techniques to analyze the textual content of a respective NOTAM to identify the different fields of information contained within the respective NOTAM, the specified values for those fields of information, and the intent, objective or semantic significance of a respective NOTAM. In this regard, for each NOTAM analyzed by the NOTAM analysis service 300, the intent recognition service 304 outputs a structured data record or entry that includes the textual content of the respective NOTAM along with the metadata tags identifying the intent assigned to the respective NOTAM and the extracted values for the different fields of information contained within the respective NOTAM. The intent recognition service 304 parses or otherwise analyzes the textual content of a NOTAM using an information identifier model 314 to extract or otherwise identify, for example, the operational subject of the NOTAM (e.g., a runway, a taxiway, a waypoint, or the like), an operational parameter value associated with the operational subject in the NOTAM (e.g., the runway identifier, taxiway identifier, waypoint identifier or the like), a status associated with the operational subject, an action associated with the NOTAM and/or other restrictions, instructions or conditions associated with the NOTAM. For example, for a NOTAM with the textual content “RWY 09/27 Closed Except 24 HR Prior Permission Required,” the intent recognition service 304 may parse and analyze the text to identify a runway as the operational subject of the NOTAM, identify runway identifier 09/27 as the specified value for the runway field, identify the runway status as closed, identify permission required within 24 hours of using the runway as a condition associated with the runway, and determine the intent of the NOTAM to notify a pilot of the requirement to obtain clearance from air traffic control (ATC) within 24 hours prior to using the runway. In one or more embodiments, the intent recognition service 304 is implemented or realized using language generation or a generative model. In this regard, in one implementation, the information identifier model 314 is trained using an offline or previously-collected training data set before being deployed using language generation or a generative model, and then after deployment, meta learning or few-shot learning is utilized to adaptively update the intent recognition service 304 and/or the information identifier model 314 in response to user input actions to provide user-specific performance.
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In one or more embodiments, the contextual prioritization service 306 automatically classifies and assigns each NOTAM received from the intent recognition service 304 to one of a high priority level (e.g., “Attention”), an intermediate priority level (e.g., “Caution”) and a low priority level (e.g., “Notice”) based on the current operational context, the intent of the NOTAM, the particular field(s) of information where the discrepancy or difference between the specified value(s) in the NOTAM and the current value(s), the magnitude or nature of the discrepancy or difference and the current flight plan using the NOTAM prioritization model 316. For example, the contextual prioritization service 306 may be implemented or realized using a neural network, such as a transformer neural network, that utilizes the NOTAM prioritization model 316 to classify and assign an input NOTAM to a particular priority level based on the structured metadata associated with the NOTAM and the current operational context. In this regard, similar to the intent recognition service 304 and/or the information identifier model 314, the contextual prioritization service 306 and/or the NOTAM prioritization model 316 is initially trained using language generation or a generative model and then adaptively updated using meta learning or few-shot learning responsive to subsequent user input actions.
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For a message relevant to the planned route of travel, the message prioritization process 700 analyzes the textual content of the respective message to extract or otherwise identify the intent of the message and specified values for fields of information relating to vehicle operation along the route and generates or otherwise creates a structured representation of the message that maintains an association between the textual content of the message, the intent associated with the message, and the specified field values extracted from the message (tasks 704, 706). In this regard, as described above, the intent recognition service 304 associated with the NOTAM analysis service 210, 300 may parse or otherwise analyze the textual content of a NOTAM 202 using NLP techniques and/or AI techniques using the information identifier model 314 to identify what the intent or objective associated with the NOTAM 202 is while also identifying the specified values for various operational subjects or parameters referenced by the textual content of the NOTAM 202. For example, for a NOTAM 202 with the textual content of “RWY 09/27 Closed Except 24 HR Prior Permission Required,” the intent recognition service 304 may determine the intent of the NOTAM to notify a pilot of the requirement to obtain clearance from air traffic control (ATC) within 24 hours prior to attempting to use a runway, identify runway identifier 09/27 as the specified value for the runway field and identify the runway status as closed. Thereafter, the intent recognition service 304 may create a data structure that provides a structured representation of the NOTAM 202 that maintains extracted specified field values and the determined intent associated with the NOTAM 202 as fields of metadata associated with the textual content and other identifying information associated with the NOTAM 202 (e.g., the source of the NOTAM, the timestamp of the NOTAM, and/or the like). For example, if a NOTAM 202 includes a condition, a condition field of the data structure that provides a structured representation may be populated with a specified value that was identified within and extracted from the NOTAM 202 by the intent recognition service 304.
After analyzing the content of a message to identify its intent and extract specified field values, the message prioritization process 700 continues by receiving, retrieving or otherwise obtaining current values for the fields of information relating to vehicle operation along the route of travel from one or more data sources associated with the vehicle and automatically assigns a priority level to the message based on the relationship between one or more of the current values and the specified values associated with the message (tasks 708, 710). For example, as described above, the contextual prioritization service 306 of the NOTAM analysis service 210, 300 may retrieve current values that characterize the current aircraft status and the current flight plan from the FMS 114 or other onboard systems 108, 110, 112, 116, 206 and analyze the current values to identify any differences or discrepancies between a current value for a parameter or field related to the current (or currently planned) operation of the aircraft 120 and a specified value for that parameter or field associated with the NOTAM 202. When the contextual prioritization service 306 identifies a discrepancy between one or more of the specified value(s) for an operational subject or an operational parameter derived from the NOTAM 202 and the corresponding current value(s) for that operational subject or operational parameter maintained at one or more onboard systems 108, 110, 112, 114, 116, 206, the contextual prioritization service 306 automatically assigns a priority level to the NOTAM 202 that is influenced by the discrepancy and the intent of the NOTAM 202.
In one or more exemplary embodiments, the contextual prioritization service 306 utilizes the prioritization AI model 316 to determine the priority level to be assigned to the NOTAM 202 as a function of the intent associated with the NOTAM 202, the discrepancy or difference between specified field value(s) from the NOTAM 202 and the current field value(s) at the onboard systems 108, 110, 112, 114, 116, 206 and/or other current values characterizing the current operational context associated with the aircraft 120. For example, the current values characterizing the current operational context associated with the aircraft 120 and the intent associated with the NOTAM 202 may be provided as input variables to the prioritization AI model 316 along with indicia of the discrepancy or difference between specified field value(s) from the NOTAM 202 and the current field value(s), which, in turn results in the contextual prioritization service 306 classifying the NOTAM 202 into a particular priority level using the prioritization AI model 316. As described above, the prioritization AI model 316 may be trained or developed in a manner that reflects historical pilot behaviors for different combinations of NOTAMs and corresponding aircraft operational contexts. Thus, NOTAMs that are consistent with or otherwise conform to the current state of operation or current flight plan may be assigned to a relatively lower priority level, while NOTAMs that are divergent or inconsistent with the current state of operation or current flight plan may be assigned to a relatively higher priority level based on historical pilot behaviors.
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After automatically assigning a priority level and automatically determining a recommended action, the message prioritization process 700 generates or otherwise provides graphical indicia of the automatically assigned priority level and autogenerated recommended action responsive to the message (task 714). For example, as described above in the context of
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For the sake of brevity, conventional techniques related to user interfaces, avionics systems, NOTAMs, and other functional aspects of the systems (and the individual operating components of the systems) may not be described in detail herein. Furthermore, the connecting lines shown in the various figures contained herein are intended to represent exemplary functional relationships and/or physical couplings between the various elements. It should be noted that many alternative or additional functional relationships or physical connections may be present in an embodiment of the subject matter.
The subject matter may be described herein in terms of functional and/or logical block components, and with reference to symbolic representations of operations, processing tasks, and functions that may be performed by various computing components or devices. It should be appreciated that the various block components shown in the figures may be realized by any number of hardware components configured to perform the specified functions. For example, an embodiment of a system or a component may employ various integrated circuit components, e.g., memory elements, digital signal processing elements, logic elements, look-up tables, or the like, which may carry out a variety of functions under the control of one or more microprocessors or other control devices. Furthermore, embodiments of the subject matter described herein can be stored on, encoded on, or otherwise embodied by any suitable non-transitory computer-readable medium as computer-executable instructions or data stored thereon that, when executed (e.g., by a processing system), facilitate the processes described above.
The foregoing description refers to elements or nodes or features being “coupled” together. As used herein, unless expressly stated otherwise, “coupled” means that one element/node/feature is directly or indirectly joined to (or directly or indirectly communicates with) another element/node/feature, and not necessarily mechanically. Thus, although the drawings may depict one exemplary arrangement of elements directly connected to one another, additional intervening elements, devices, features, or components may be present in an embodiment of the depicted subject matter. In addition, certain terminology may also be used herein for the purpose of reference only, and thus are not intended to be limiting.
The foregoing detailed description is merely exemplary in nature and is not intended to limit the subject matter of the application and uses thereof. Furthermore, there is no intention to be bound by any theory presented in the preceding background, brief summary, or the detailed description.
While at least one exemplary embodiment has been presented in the foregoing detailed description, it should be appreciated that a vast number of variations exist. It should also be appreciated that the exemplary embodiment or exemplary embodiments are only examples, and are not intended to limit the scope, applicability, or configuration of the subject matter in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient road map for implementing an exemplary embodiment of the subject matter. It should be understood that various changes may be made in the function and arrangement of elements described in an exemplary embodiment without departing from the scope of the subject matter as set forth in the appended claims. Accordingly, details of the exemplary embodiments or other limitations described above should not be read into the claims absent a clear intention to the contrary.
Number | Date | Country | Kind |
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202211032277 | Jun 2022 | IN | national |