Description:Complete Specification:
The following specification describes and ascertains the nature of this invention and the manner in which it is to be performed.

Field of the invention:
[0001] The present invention relates to conversational device and method for a vehicle.

Background of the invention:
[0002] According to a patent literature US2020226167, methods and systems for dynamic content provisioning is disclosed. The method, system, and computer program product for dynamic content provisioning, such as for online marketing and product promotion, may include collecting user content from sources based upon collection parameters, and organizing the collected user content for optimization. There may also be the features of receiving rights-management data for the organized user content and tagging the organized user content. Additionally, there may be receiving rights-management data for the organized user content and generating an optimized display representation of tagged user content on an output interface for dynamic content provisioning.

Brief description of the accompanying drawings:
[0003] An embodiment of the disclosure is described with reference to the following accompanying drawings,
[0004] Fig. 1 illustrates a block diagram of a conversational device for a vehicle, according to an embodiment of the present invention, and
[0005] Fig. 2 illustrates a flow diagram of a method performed by the conversational device for the vehicle, according to the present invention.

Detailed description of the embodiments:
[0006] Fig. 1 illustrates a block diagram of a conversational device for a vehicle, according to an embodiment of the present invention. The controller 110 configured to monitor at least one of user parameter comprising a tone of a user, a visual of the user, and a conversation of the user. The user parameters are obtained from respective input means such as at least one microphone 102 within the vehicle, at least one camera 104 within the vehicle. The user parameter also comprises physiological parameters obtained from strategically positioned sensors such as in the seat belt, steering wheel, etc. The controller 110 processes the user parameters using a model 114, trained using Artificial Intelligence (AI) and Machine Learning (ML), to estimate an emotion of the user followed by estimation of an emotion intensity, characterized in that, the controller 110 configured to determine a context of the emotion through text extracted from the conversation when the emotion intensity is greater than a predetermined threshold. The text extraction is performed using known audio-to-text converter and analysis of the extracted text is done through known text analysis module. The controller 110 detects a current location of the vehicle through a satellite positioning unit 106, and while the current location is not a known location, the controller 110 configured to process the current location, a current time and date, the context and the emotion intensity through a classifier 116, and saves a moment in a memory element 112 after classification of the moment as a nostalgic moment by the classifier 116. Similarly, while the current location is a known location, the controller 110 is configured to check for saved moments in the memory element 112, and remind to the user the nostalgic moment linked to the current location through an output means 108. The known location corresponds to those which are already saved in the memory element 112 due to creation of nostalgic memory in that location. The satellite positioning unit 106 corresponds to systems such as Global Positioning System (GPS), Indian Regional Navigation Satellite System (IRNSS), and the like.

[0007] The output means 108 is at least one selected from a group comprising at least one speaker within the vehicle for audio based signals, and at least one display screen within the vehicle for video and image based signals. The display screen at least one of an instrument cluster or an infotainment screen or a dedicated screen for passengers or a head-up display (HUD), windshield integrated screen and the like.

[0008] According to an embodiment of the present invention, the controller 110 is configured to save the moment in the memory element 112 based on a frequency of visit to the current location. Alternatively, the moment is saved in the memory element 112 whenever a nostalgic moment worthy incident is detected by the classifier 116.

[0009] According to an embodiment of the present invention, the classifier 116 is comprises a Natural Language Processing (NLP) model to process and classify whether a moment is a nostalgic moment to be reminded to the user or not. The classifier 116 filters out negative emotions and considers only positive emotions for saving as the nostalgic moment/memory.

[0010] According to an embodiment of the present invention, the current location is anyone of same as current location and proximity to the location. The proximity is defined to be certain distance from the known location which is configurable by the driver of the vehicle.

[0011] According to the present invention, a working of the conversational device 100 is explained and the same must not be understood in limiting manner. Consider the user travelling to specific destination. The controller 110 leverages the important features or the user parameters as travel companion such as location, date and time of the incident, emotion, and user interaction for creating a nostalgic memory to the user. The user’s emotion from different modalities like text, visual, tonal is monitored. The text is extracted from the user conversation and analyzed through known text analysis modules. When the intensity of emotion particularly positive emotion exceeds the threshold, then the companion initiates further conversation with the user. The conversational device 100 with its intelligence tries to understand the specific reason for the emotion of the user and stores/saves it in memory element 112 with location details and time as the moment. The next time when the user visits the place or nearby to that location, the conversational device 100 checks the known location and if the current location matches, the controller 110 reminds the special moment to the user through the output means 108 such as a story or sentence through an audio signal through at least one speaker within the vehicle. Alternatively, if the user is nearby or in proximity to a known location, even then the controller 110 is configured to remind the nostalgic moment/memory to the user.

[0012] According to the present invention, the nostalgic worthy moment classifier 116 takes the decision of whether the incident is nostalgic worthy to remind later. The information should really create that nostalgic moment to the user. The classifier 116 is a Natural Language Processing (NLP) model created to classify where it is a nostalgic moment. The parameters considered are intensity of emotion while user describes the incident, location details, user interaction and so on. In addition, the decision of whether the location is special is based on frequency of visit. For example, consider a student visiting college daily. The student might share a lot of moments with the user. But the location is frequently visited so though the conversational device 100 saves the moments, and will not remind the user at that time. It reminds when the student visits the college as alumni after years.

[0013] In accordance to an embodiment of the present invention, the controller 110 is provided with necessary signal detection, acquisition, and processing circuits. The controller 110 is the one which comprises input interface, output interfaces having pins or ports, the memory element 112 such as Random Access Memory (RAM) and/or Read Only Memory (ROM), Analog-to-Digital Converter (ADC) and a Digital-to-Analog Convertor (DAC), clocks, timers, counters and at least one processor (capable of implementing machine learning) connected with each other and to other components through communication bus channels. The memory element 112 is pre-stored with logics or instructions or programs or applications or modules/models and/or threshold values/ranges, reference values, predefined/predetermined criteria/conditions, known locations, which is/are accessed by the at least one processor as per the defined routines. The internal components of the controller 110 are not explained for being state of the art, and the same must not be understood in a limiting manner. The controller 110 may also comprise communication units such as transceivers to communicate through wireless or wired means such as Global System for Mobile Communications (GSM), 3G, 4G, 5G, Wi-Fi, Bluetooth, Ethernet, serial networks, and the like. The controller 110 is implementable in the form of System-in-Package (SiP) or System-on-Chip (SOC) or any other known types. Examples of controller 110 comprises but not limited to, microcontroller, microprocessor, microcomputer, etc.

[0014] Further, the processor may be implemented as any or a combination of one or more microchips or integrated circuits interconnected using a parent board, hardwired logic, software stored in the memory element 112 and executed by a microprocessor, firmware, an application specific integrated circuit (ASIC), and/or a field programmable gate array (FPGA). The processor is configured to exchange and manage the processing of various Artificial Intelligence (AI) modules.

[0015] In accordance to an embodiment of the present invention, the conversational device 100 is at least one of an internal device (part of the vehicle) and an external device (not a part of the vehicle). The internal device corresponds to devices such as infotainment system, a dedicated hardware on a dashboard which are connected to the input means and the output means as known in the art. At least one of the Engine Control Unit (ECU), a Cluster Control Unit (CCU), a Telecommunication Control Unit (TCU) and any other electronic control unit of the vehicle is usable as controller 110 for the conversational device 100. Similarly, the external device corresponds to at least one of a cloud computer or a remote computer or a portable device which is connected to the vehicle through wired or wireless communications such as through the Telecommunication Control Unit (TCU) or through a dongle connected to On-Board Diagnostics (OBD) port of the vehicle. The dongle and TCU providing the wireless connectivity features (Bluetooth™, Wi-Fi, etc.) as known in the art.

[0016] The external device is provided with the controller 110 which receives the data through the connecting medium such as the TCU or dongle or similar devices, processes and either stores or saves the moment in the memory element 112 as already explained above. In another embodiment, at least one of the internal device and the external device are usable as the conversational device 100. Hence, if needed and if available, both of the internal device and the external device together share the processing of the user parameter till the moment is saved or reminded to the user. The sharing is done based on respective loads.

[0017] Fig. 2 illustrates a flow diagram of a method performed by the conversational device for the vehicle, according to the present invention. The method is performed by the conversational device 100 for the vehicle. The method comprises plurality of steps of which a step 202 comprises monitoring, by the controller 110, at least one of user parameter comprising the tone of the user, the visual of the user, and the conversation of the user. The user parameters are captured through respective input means. A step 204 comprises, processing, by the controller 110, the user parameters using the model 114, trained using Artificial Intelligence (AI) and Machine Learning (ML), and estimate the emotion of the user followed by estimation of the emotion intensity. The method is characterized by a step 206 which comprises determining the context of the emotion through text extracted from the conversation when the emotion intensity is greater than the predetermined threshold. A step 208 comprises detecting the current location of the vehicle through the satellite positioning unit 106. While the current location is not the known location, a step 210 comprises processing the current location, current time and date, the context, and the emotion intensity through the classifier 116, and saving the moment in the memory element 112 after classification as the nostalgic moment by the classifier 116. While the current location is the known location, a step 212 comprises checking for saved moments in the memory element 112, and reminding to the user through the output means 108.

[0018] The output means 108 is at least one selected from a group comprising the at least one speaker within the vehicle for audio based signals, and at least one display screen within the vehicle for the video or image based signals. The method of saving the moment in the memory element 112 is based on a frequency of visit to the current location or independent of the same. The step of processing is performed through a Natural Language Processing (NLP) model to classify whether the moment is the nostalgic moment to be reminded to the user through the output means 108. The current location is anyone of same as the known location and proximity to the known location.

[0019] According to the method, the conversational device 100 is either the internal device or the external device or both as explained before.

[0020] According to the present invention, the conversational device 100 creates nostalgic moments to the user while travelling. In other words, the conversational device 100 creates nostalgic moments to the user as travel companion. The conversational device 100 is a digital travel companion that has empathetic and personalized conversation with the user and provides nostalgic moment/memory of the place that the user visited few years or months back. The normal locations become special one because of the good moments happened in that place specific to the user. An incident becomes a nostalgic moment/memory if the person remembers it when visited again. As the travel companion, the conversational device 100 reminds some of the user’s best memories/moments from the specific places after months or years when the user visit again or pass by that location. The nostalgic memories are brought back and surprises the user. The conversational device 100 is personalized for the user based on their own experiences. The specific incident is saved based on user interaction and other user parameters.

[0021] It should be understood that the embodiments explained in the description above are only illustrative and do not limit the scope of this invention. Many such embodiments and other modifications and changes in the embodiment explained in the description are envisaged. The scope of the invention is only limited by the scope of the claims.
, Claims:We claim:
1. A conversational device (100) for a vehicle, said conversational device (100) comprises a controller (110) configured to:
monitor at least one of user parameter comprising a tone of said user, a visual of said user, and a conversation of said user;
process said user parameters using a model (114), trained using Artificial Intelligence (AI) and Machine Learning (ML), and estimate an emotion of said user followed by estimation of an emotion intensity, characterized in that,
determine a context of said emotion through text extracted from said conversation when said emotion intensity is greater than a predetermined threshold;
detect a current location of said vehicle through a satellite positioning unit (106), and
while said current location is not a known location,
process said current location, a current time and date, said context and said emotion intensity through a classifier (116), and
save a moment in a memory element (112) after classification as a nostalgic moment, and
while said current location is a known location,
check for saved moments in said memory element (112), and remind to said user, said nostalgic moment linked to said current location, through an output means (108).

2. The conversational device (100) as claimed in claim 1, wherein said output means (108) is at least one selected from a group comprising, at least one speaker within said vehicle for audio based signals, and at least one display screen within said vehicle for video or image based signals.

3. The conversational device (100) as claimed in claim 1, wherein said controller (110) configured to save said moment in said memory element (112) based on a frequency of visit to said current location.

4. The conversational device (100) as claimed in claim 1, wherein said classifier (116) comprises a Natural Language Processing (NLP) model to classify whether a moment is a nostalgic moment to be reminded to said user, wherein said classifier (116) filters out negative emotions and saves only positive emotions.

5. The conversational device (100) as claimed in claim 1, wherein said current location is anyone of same as current location and proximity to said location.

6. A method performed by a conversational device (100) for a vehicle, said method comprising the steps of:
monitoring at least one of user parameter comprising a tone of said user, a visual of said user, and a conversation of said user;
processing said user parameters using a model (114), trained using Artificial Intelligence (AI) and Machine Learning (ML), and estimating an emotion of said user followed by estimating an emotion intensity, characterized by,
determining a context of said emotion through text extracted from said conversation when said emotion intensity is greater than a predetermined threshold;
detecting a current location of said vehicle through a satellite positioning unit (106), and
while said current location is not a known location,
processing said current location, a current time and date, said context and said emotion intensity through a classifier (116), and
saving a moment in a memory element (112) after classification as a nostalgic moment,
while said current location is a known location,
checking for saved moments in said memory element (112), and reminding said nostalgic moment linked to said current location through an output means (108).

7. The method as claimed in claim 6, wherein said output means (108) is at least one selected from a group comprising, at least one speaker within said vehicle for audio based signals, and at least one display screen within said vehicle for video or image based signals.

8. The method as claimed in claim 6, wherein saving said moment in said memory element (112) is based on a frequency of visit to said current location.

9. The method as claimed in claim 6, wherein said processing is performed by a classifier (116) comprising a Natural Language Processing (NLP) model, said classifier (116) classifies whether a moment is a nostalgic moment to be reminded to said user through said output means (108).

10. The method as claimed in claim 6, wherein said current location is anyone of same as current location and proximity to said location