METHOD AND SYSTEM FOR PROVIDING INFORMATION ABOUT LOCATIONS RELEVANT TO USERS DURING THE JOURNEY

Abstract

The present disclosure relates to a method for providing information about locations relevant to a user along a driving route. The method comprises generating one or more segments corresponding to a driving route based on route information received from one or more sources; identifying one or more locations relevant to a user associated with the vehicle within each of the one or more segments using geographic information associated with the respective segment, based on at least one user preference, a first-level filter condition, and second-level filter conditions. One or more prompts are generated for each of the one or more locations based on predefined prompt parameters.The process involves obtaining descriptive information corresponding to each of one or more locations, based on respective prompts from a processing model. Subsequently, audiovisual information corresponding to each of the one or more locations is provided to the user, based on this descriptive information.

Description

[0001] The present disclosure relates generally to the automotive industry. In particular, but not exclusively, the present disclosure relates to a method and a system for providing information about locations relevant to a user while driving.

[0002] While traveling from one place to another, a user can typically pass many interesting locations along a route, such as cultural, historical, culinary, and similar sites. The user can be the driver or a passenger in a vehicle. The vehicle can include, but is not limited to, electric vehicles, hybrid vehicles, non-electric vehicles, and similar types. In some cases, the user may be unfamiliar with the places along the route, or they may be traveling on a new or unfamiliar route. To learn more about these places, a guide or someone knowledgeable about the area might be needed to recommend them. However, the guide or someone may not always accompany the user. Therefore, the user might miss these places due to a lack of information about them while traveling.

[0003] Patent application US2021341294A1 discloses an approach for training an artificial intelligence-based system to understand user preferences, learning at least some of these preferences from social media data relevant to the user. The approach identifies points of interest (POs) between a starting location and a planned destination. This identification occurs while the user is driving to the planned destination, which is included in a trip plan. The approach calculates an affinity score between the POs and the user's preferences and recommends a set of POs based on the calculated affinity scores. The user selects one of the POs from the recommended set. The trip plan is updated to include the trip to the selected PO, and the user receives a set of driving directions according to the updated trip plan.

[0004] Patent application WO2020140899A1 discloses an onboard personalized route selection and planning system for a vehicle mapping application. The mapping application (407) can enable route selection and planning, taking into account routing by autonomous driving (AD), and / or identifying points of interest specific to a particular user along the route, while simultaneously reducing travel time and distance. The points of interest can be recommended using machine learning techniques such as content-based recommendations or collaborative filtering. The AD can maximize a proportion of the AD travel time or distance on roads that are suitable or advantageous for AD driving. Each user of a vehicle can have a personalized route guidance service where points of interest can be identified on a map between different locations, e.g.,Restaurants, bars, hotels, theaters, shopping centers and the like.

[0005] Patent application US2021063182A1 discloses systems and methods for suggesting points of interest along a vehicle's route. Some methods include determining a vehicle's route; determining the vehicle's current location on the route; predicting the vehicle's future location based on the route and current location; selecting one or more points of interest according to the vehicle's future location without human intervention; and displaying a corresponding photograph of each selected scenic location on a vehicle display.

[0006] Patent application CN116821492A discloses an intelligent navigation method and device, equipment, and storage medium, and belongs to the technical field of the Internet. The method comprises the following steps: acquiring interest data of a navigation object and data on scenic locations in an area to be visited; generating recommended navigation information for the navigation object based on the acquired interest data and data on scenic locations; during the visit to the navigation object, determining the language type used by the language data based on the recommended navigation information in response to the acquired language data of the navigation object; and performing language recognition on the language data based on a language recognition model matched with the language type.wherein a response feedback is generated that is matched with the recognition text, and the speech output type of the response feedback is determined; and output of the response feedback by applying a speech variant that is matched with the determined speech output type under the language type. Depending on the procedure, personalized navigation information can be recommended for different navigation objects, and navigation services of different language types can be provided in the intelligent voice navigation process.

[0007] Traditionally, when the user specifies a starting location and a destination location, an application programming interface (API), such as a location map and the like, can display locations near the starting and destination locations, as in Fig. As shown in Figure 1, the map may only recommend places and provide information about them at the starting point (Bangalore) and the destination (Mysore). For example, if a user travels from Bangalore to Mysore, the map can only suggest places and provide information about them at the starting point (Bangalore) and the destination (Mysore). Furthermore, the AI-powered travel guide may only provide generic information about the places at either the starting point or the destination, and the user will not receive specific information about the places. In addition, specific recommendations for places based on user preference are generally not provided. Therefore, the user may not be able to decide which places to visit along a route during a trip.

[0008] The information disclosed in this background section of the “Disclosure” section is provided only to facilitate a better understanding of the general background of the invention and should not be construed as an acknowledgment or any form of suggestion that this information represents prior art already known to the person skilled in the art.

[0009] In one embodiment, the present disclosure relates to a method for providing information about locations relevant to a user along a route. The method comprises generating one or more segments corresponding to a route based on route information received from one or more sources. Furthermore, the method comprises identifying one or more locations relevant to a user associated with the vehicle within each of the one or more segments using geographic information associated with the respective segment, based on at least one user preference, a first-level filter condition, and second-level filter conditions. The method also comprises generating one or more prompts for each of the one or more locations based on predefined prompt parameters.The process then involves obtaining descriptive information corresponding to each of the one or more locations, based on the respective prompts from a processing model. Audiovisual information corresponding to each of the one or more locations is then provided to the user based on this descriptive information.

[0010] In one embodiment, the present disclosure discloses a control system for providing information about locations relevant to a user along a route. The control system comprises one or more processors and a memory. The one or more processors are configured to generate one or more segments corresponding to a route based on route information received from one or more sources. Furthermore, the one or more processors are configured to identify one or more locations relevant to a user associated with the vehicle within each of the one or more segments using geographic information associated with the respective segment, based on at least one user preference, a first-level filter condition, and second-level filter conditions.Furthermore, the one or more processors are configured to generate one or more prompts for each of the one or more locations based on predefined prompt parameters. Subsequently, the one or more processors are configured to receive descriptive information corresponding to each of the one or more locations, based on the respective prompts from a processing model. Here, audiovisual information corresponding to each of the one or more locations is provided to the user based on the respective descriptive information.

[0011] The preceding summary serves only for illustration and is in no way intended to be limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become clear with reference to the drawings and the following detailed description.

[0012] The accompanying drawings, which are incorporated into and form part of this disclosure, illustrate exemplary embodiments and, together with the description, explain the disclosed principles. In the figures, the one or more digits of a reference numeral on the far left identify the figure in which the reference numeral first appears. The same numbers are used throughout the figures to indicate similar features and components. Some embodiments of the system and / or methods according to embodiments of the present subject matter are now described only by way of example and with reference to the accompanying figures, which show: Fig. 1. An existing illustration of a map to identify locations for the user during the journey; Fig. 2 an exemplary environment for providing information relating to locations relevant to a user, according to some embodiments of the present disclosure; Fig. 3 a detailed block diagram of a control system for a vehicle according to some embodiments of the present disclosure; Fig. 4 a flowchart showing a method for providing information about locations relevant to a user, according to some embodiments of the present disclosure; Fig. 5a an illustration of the provision of an audiovisual description of a place according to some embodiments of the present disclosure; Fig. 5b an illustration of the generation of segments corresponding to a route according to some embodiments of the present disclosure; Fig. 6. A flowchart illustrating a method for providing information about locations relevant to a user, according to some embodiments of the present disclosure; and Fig. 7 A block diagram of an exemplary computer system for providing information about locations relevant to a user, according to some embodiments of the present disclosure.

[0013] The person skilled in the art will understand that all block diagrams presented here represent conceptual views of illustrative systems that establish the principles of the subject matter at hand. Likewise, it is understood that all flowcharts, flow diagrams, state transition diagrams, pseudocode, and the like represent various processes that are essentially represented in a computer-readable medium and can be executed by a computer or processor, regardless of whether that computer or processor is explicitly depicted.

[0014] In this document, the word "exemplary" is used to mean "serving as an example or illustration". Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as being preferable or advantageous over other embodiments.

[0015] Although various modifications can be made to the disclosure and it can be developed into alternative forms, specific embodiments are shown by way of example in the drawings and are described in detail below. It should be understood, however, that the disclosure is not intended to be limited to the specific forms disclosed; on the contrary, it is intended to cover all modifications, equivalents, and alternatives that fall within the scope of the disclosure.

[0016] The terms “includes,” “comprehensive,” or any other variant thereof are intended to cover non-exclusive inclusion, so that a setup, device, or process that includes a list of components or steps may include not only those components or steps but also other components or steps not expressly listed or inherent in that setup, device, or process. In other words, one or more elements in a system or device followed by “includes…” do not, without further limitations, preclude the presence of other elements or additional elements in the system or device.

[0017] While traveling from one location to another, a user typically passes many points of interest along a route. Traditional application programming interfaces (APIs) and travel guides recommend places near the starting and ending points of the route and provide generic information about locations at both points. However, they do not provide specific recommendations for places based on the user's preferences. Therefore, the user may not be able to decide which places to visit during a trip. It is thus necessary to identify relevant places along the route and provide the user with information about them.

[0018] Accordingly, the present disclosure provides a method and a system for providing information about locations relevant to a user along a journey route. In the present disclosure, information about a journey route or the current location of the vehicle can be received to generate one or more segments for the journey route. Upon generation of the one or more segments, user-relevant locations can then be identified for each segment based on its geographic information when the vehicle is within that segment. Upon identification of the user-relevant locations, one or more prompts can be generated using a processing model to obtain descriptive information about the user-relevant locations. This descriptive information can be provided to the user in the vehicle as audiovisual information.Thus, the present disclosure identifies relevant locations that are preferred by the user and provides information about these locations. Therefore, the user can decide which places to visit based on the audiovisual information. The present disclosure thus provides audiovisual information about locations that are important to the user along their travel route. Therefore, the user cannot miss any locations when traveling along unfamiliar or new routes.

[0019] Fig. Figure 2A illustrates an exemplary environment 200 for providing information relating to locations relevant to a user along a journey route, according to some embodiments of the present disclosure. The exemplary environment 200 comprises a vehicle 202 and one or more sources 204. The present disclosure relates to the provision of information about the locations relevant to the user. In the present disclosure, the vehicle 202 can include any vehicle, including, but not limited to, two-wheeled vehicles (i.e., bicycles), three-wheeled vehicles (i.e., passenger cars), four-wheeled vehicles (i.e., automobiles), electric vehicles, hybrid vehicles, automated vehicles, and the like.

[0020] As in Fig. As shown in Figure 2A, the vehicle 202 comprises a control system 206, a loudspeaker 208, and a display 210. The vehicle 202 may include other devices / components not explicitly shown here. In one embodiment, the control system 206 may include an I / O interface 216, a processor 218, and a memory 220. In another embodiment, the control system 206 may be an electronic control unit (ECU) associated with the vehicle 202. The ECU is an embedded system that controls electrical systems or subsystems in the vehicle 202. The ECU is configured to control an accelerator pedal, signal transmission, lighting in the vehicle 202, instruments, and the like. According to embodiments of this disclosure, the ECU may be configured to provide information relating to one or more locations relevant to the user.In another embodiment, the control system 206 can be a unit assigned to the ECU. In one embodiment, the control system 206 can be assigned to various components of the vehicle 202, for example, the loudspeaker 208, the display 210, the receiver unit, and the like, to provide information about locations relevant to a user 214 of the vehicle 202.

[0021] In the present disclosure, the control system 206 can be configured to provide information about the locations relevant to the user along the route. Here, the control system 206 generates one or more segments corresponding to the route based on route information received from one or more sources 204. In one embodiment, the one or more sources 204 can include, but are not limited to, a server 212, a user 214, and the like. In one embodiment, the server 212 can refer to a tracker with a Global Positioning System (GPS tracker) to track the current location of the vehicle 202. In another embodiment, the control system 206 can receive a starting point and a destination point corresponding to the route from the user 214.In another embodiment, the control system 206 can receive the current location of the vehicle 202 based on its geographic location. The control system 206 can then generate one or more segments corresponding to the route. In one embodiment, the control system 206 can generate multiple segments along the route by performing a function of a total distance between the starting point and the destination point and a constant distance. The constant distance can refer to a constant value that can be preconfigured as required, e.g., 10 km, 15 km, 20 km, and the like. The control system 206 can generate the one or more segments (as shown in...). Fig. (5b shown), if the user 214 provides the starting point and the destination point according to the route. Here, to generate one or more segments, if the starting point and the destination point are provided by the user, the total distance can be divided by the constant distance. In another embodiment, the control system 206 can generate a segment by locating the constant distance on the route based on the current location of the vehicle 202. The control system 206 can generate the segment if the current location of the vehicle 202 is taken into account without providing the starting point and destination point of the route. Thus, the segment can be generated based on the current location of the vehicle 202 while the vehicle 202 is moving along the route.

[0022] When generating the one or more segments, the control system 206 can be configured to identify one or more locations relevant to the user 214 in each of the one or more segments using geographic information associated with that segment. In one embodiment, the geographic information can refer to geocodes associated with each of the one or more segments. Each of the one or more segments can have a unique geocode. The control system 206 can identify geocodes for each of the one or more segments to identify the one or more locations. The control system 206 can identify the one or more locations based on at least one user preference, a first-level filter condition, and second-level filter conditions.The one or more locations may include historical sites, museums, tourist attractions, hotels, resorts, and the like. The one or more locations identified by the control system 206 are based on user preference. In one embodiment, the user preferences may be linked to types of locations preferred by the user 214. In this embodiment, the user preferences may be provided to the control system 206 during the route information setup or during the initial setup in the vehicle 202. The control system 206 can then identify the one or more locations relevant to the user 214 in each of the one or more segments as the vehicle 202 approaches the respective segment.The one or more locations relevant to user 214 can be identified by filtering locations present in each of the one or more segments based on the first-level filter condition and the second-level filter conditions. The first-level filter condition can correspond to removing a duplicate of the one or more locations in a junction of the one or more segments. The one or more generated segments can overlap (see...). Fig. 5b) The control system 206 thus removes the locations that might be considered in a previous segment, while identifying the one or more locations in a subsequent segment. The second-level filter conditions correspond to a predefined number of reviews required for the one or more locations and a predefined rating required for the one or more locations. In one embodiment, the control system 206 can identify the one or more locations present in each of the one or more segments based on the number of reviews associated with the respective location and the rating given for the respective location.In one embodiment, the control system 206 can identify one or more locations when the number of reviews exceeds a review constant and the rating corresponding to the one or more locations potentially exceeds a rating constant. The review constant and the rating constant can be predefined and configurable.

[0023] In one embodiment, the control system 206, upon identifying the one or more locations relevant to the user 214, can generate one or more prompts for each of the one or more locations based on predefined prompt parameters. The predefined prompt parameters include, among other things, engaging information, fact-based information, holistic information, and action-oriented information. In one embodiment, the one or more prompts for a processing model can be generated by extracting one or more relevant keywords corresponding to each of the one or more locations from accessible information associated with that location, using the Term Frequency Inverse Document Frequency (TF-IDF) technique.Here, the present disclosure may use any other method for extracting the keywords and is not limited to the TF-IDF technique. In one embodiment, the accessible information may include online reviews for the respective locations, available information relating to the respective locations, and the like. In one embodiment, the one or more prompts may be uniquely generated for each of the one or more locations.

[0024] Furthermore, the control system 206 can be configured to receive descriptive information corresponding to each of the one or more locations, based on the respective prompts from the processing model. In particular, the control system 206 can provide the one or more prompts associated with a location to the processing model for the purpose of generating the descriptive information for that location.

[0025] In one embodiment, the control system 206, after receiving the descriptive information from the processing model, can perform a sentiment analysis on the descriptive information to identify negative sentiments associated with words in the descriptive information. The sentiment analysis can be performed by identifying a sentiment value associated with each word in the descriptive information. The sentiment value is then analyzed to identify at least one word indicating a negative sentiment, based on a comparison of each word's sentiment value with a sentiment threshold. In one embodiment, the sentiment threshold can be predefined based on the expected sentiment level for the descriptive information. For example, a low-level sentiment, a medium-level sentiment, and a high-level sentiment.If negative sentiment is identified for at least one word, the control system 206 can remove the identified at least one word indicating negative sentiment.

[0026] Furthermore, after identifying negative sentiment, control system 206 can generate new prompts for the processing model to obtain updated descriptive information. That is, control system 206 can perform sentiment analysis to remove words with negative sentiment and continue the process of generating descriptive information.

[0027] Subsequently, the control system 206 can generate audiovisual information based on the respective descriptive information and make it available to the user 214, corresponding to each of the one or more locations. In one embodiment, the audiovisual information can be generated by converting the descriptive information into speech and displaying an image of the location in the vehicle 202. The audio information can be provided via the loudspeaker 208, and the visual information is displayed via the display 210 of the vehicle 202. Since the present disclosure contains information about locations relevant to the user 214 along the route, the user 214 can no longer miss any locations when traveling along the new or unfamiliar route. Furthermore, since the present disclosure provides descriptive information about the one or more locations that correspond to the user 214's preferences, the user 214 can decide whether to visit the locations. Thus, the present disclosure assists in decision-making regarding the locations to be visited along the route.

[0028] Fig. Figure 3 shows a detailed block diagram of a control system for providing information about locations along the route that are relevant to the user, according to some embodiments of the present disclosure. The control system 206 can include the input / output interface 216 (I / O interface), the one or more processors 218, and the memory 220. In some embodiments, the memory 220 can be communicatively coupled to the one or more processors 218. The memory 220 stores instructions that can be executed by the one or more processors 218. The one or more processors 218 can include at least one data processor for executing program components for fulfilling user- or system-generated requests. The memory 220 can be communicatively coupled to the one or more processors 218.Memory 220 stores instructions that can be executed by the one or more processors 218, which, when executed, can cause the one or more processors 218 to provide information about locations on the route that are relevant to the user 214.

[0029] In one embodiment, the memory 220 can contain data 302 and one or more modules 304. The one or more modules 304 can be configured to perform the steps of this disclosure using the data 302 to provide information about user-relevant locations 214 along the route. In one embodiment, each of the one or more modules 304 can be a hardware unit located outside the memory 220 and coupled to the control system 206. As used herein, the term module 304 refers to an application-specific integrated circuit (ASIC), an electronic circuit, a field-programmable gate array (FPGA), a programmable system-on-chip (PSoC), a combinational logic circuit, and / or other suitable components that provide described functionality.The one or more modules 304, when configured with the described functionality defined in this disclosure, result in novel hardware. Furthermore, the I / O interface 216 is coupled to the one or more processors 218, through which an input and / or output signal is communicated. For example, the control system 206 can receive route information via the I / O interface 216.

[0030] In an implementation, the modules 304 might, for example, include a segment generation module 316, a location identification module 318, a prompt generation module 320, a location description generation module 322, and other modules 324. It is understood that the aforementioned modules 304 can be represented as a single module or as a combination of different modules. In an implementation, the data 302 might, for example, include segment data 306, relevant location data 308, prompt data 310, location description data 312, and other data 314.

[0031] In one embodiment, the segment generation module 316 can be configured to generate the one or more segments corresponding to the route based on route information received from one or more sources 204. The one or more sources 204 may include, but are not limited to, the user 214, the server 212, and the like. The server 212 may be associated with the vehicle 202. In one embodiment, the route may refer to the route on which the user 214 is currently located. In one embodiment, the route information may be the starting point and destination point information provided by the user 214 of the vehicle 202. In another embodiment, the route information may include the current location of the vehicle 202, received from the server 212.

[0032] In one embodiment, the segment generation module 316 can generate one or more segments based on a function of the total distance between the starting point and the target point and the constant distance, as represented by the following equation (1). Each segment = total distance / constant distance

[0033] In another embodiment, the segment generation module 316 can generate the one or more segments by locating the constant distance on the route from the current location of the vehicle 202, if the route information includes the current location.

[0034] In one embodiment, one or more segments can have the same radius (as in Fig. (5b shown). The radius can be considered based on the constant distance. In one embodiment, the constant distance can be predefined.

[0035] According to Fig. In step 402, user 214 can receive route information, including details of the starting point and destination. In step 404, the current location of vehicle 202 can be received based on its geographic location. The route information or the current location can be used to generate one or more segments. Regarding Fig. 5B can generate one or more segments along the route. The one or more segments can have the same length. For example, if the user travels from Bangalore to Mysore, the total distance between Bangalore and Mysore is 200 km and the constant distance is 10 km. In this case, according to equation (1) above, 200 km / 10 km = 20 km. Thus, each of the one or more segments can be 20 km long. The one or more segments generated by the segment generation module 316 can be stored as segment data 306.

[0036] In relation to Fig. 2. After generating one or more segments, the location identification module 318 can, using the geographic information associated with each segment, identify the one or more locations relevant to the user 214 associated with the vehicle. The one or more locations can be identified based on at least one user preference, the first-level filter condition, and the second-level filter conditions. In one embodiment, the location identification module 318 can identify the one or more locations in each segment based on the geographic information when the vehicle is in that segment. The first-level filter condition can relate to removing duplicates of the one or more locations in the connection of the one or more segments.In one embodiment, the one or more locations in the connection of the one or more segments can be removed by removing the one or more locations present in the previous segment. In one embodiment, the HashMap technique can be used to remove duplicate locations. The one or more locations present in the previous segment can be removed when vehicle 202 reaches the subsequent segment. In one embodiment, the one or more locations can be removed using a quantity difference method.

[0037] With renewed reference to Fig. 5b refers to the one or more segments S1 ... SN that lie between a starting point A and a destination point B. For the current segment Si, the next segment can be Si + 1. If the locations sampled in segment Si are denoted by Pi, then locations sampled in Si + 1 can likewise be Pi + 1. To remove the one or more locations from the previous segment Si from the list, the set difference method is used, as shown in equation (2) below.

[0038] (M) = P i+1 - (P i+1 ∩ P i ), where ∩ represents the intersection point of the set, i.e., common locations between the one or more segments.

[0039] For example, location XX might exist in the connection between the first and second segments. In this case, when user 214 starts driving, location XX can be identified in the first segment. When vehicle 202 enters the second segment, location XX can then be removed.

[0040] The second-level filter condition can refer to the predefined number of reviews required for the one or more locations and the predefined ratings required for the one or more locations. For example, the predefined number of reviews could be 1500 and the predefined rating 4.5. The one or more identified locations can be stored as relevant location data 308.

[0041] With renewed reference to Fig. 4. The one or more locations in step 410 can be identified based on the geographic location received in step 408 and the user preference received in step 406. In one embodiment, the user preferences can indicate the types of locations preferred by user 214. Furthermore, in one embodiment, user 214 can also change the user preference based on an interest of user 214.

[0042] With renewed reference to Fig. 5b. One or more locations can be identified for each of the one or more segments. For example, if the user starts in Bangalore, a location "XX" can be identified in the first segment when vehicle 202 is traveling in that segment. When vehicle 202 reaches a second segment, a location "YY" can also be identified. Similarly, when vehicle 202 reaches a fifth segment, a location "MM" can be identified. The locations "XX, YY, and MM" can be identified based on user preference and first-level filter conditions, as well as second-level filter conditions, when vehicle 202 reaches the corresponding segments. For example, user 214 might prefer historical temples, and location XX could be the Panchmukhi Ganapathi Temple in the first segment.In one example, the number of reviews for the Panchmukhi Ganapathi Temple might be set to 1600, and the predefined rating to be 4.7. Since the number of reviews and the rating for the Panchmukhi Temple exceed the predefined number of reviews and the predefined rating, the Panchmukhi Ganapathi Temple can be identified as the place to recommend to User 214. Thus, in this revelation, one or more places relevant to User 214 along their travel route are identified. Therefore, User 214 cannot miss the places along the new or unknown routes.

[0043] With renewed reference to Fig. 3. After identifying one or more locations, the prompt generation module 320 can generate one or more prompts for each of those locations based on predefined prompt parameters. These prompt parameters can include, but are not limited to, appealing information, fact-based information, holistic information, and action-relevant information. In one embodiment, appealing information can refer to interesting and historical information related to the respective locations. Fact-based information can refer to actual information about the respective locations, rather than imaginary information. Holistic information refers to information covering general aspects of the respective locations.The action-relevant information relates to rules and regulations associated with the respective locations. The Prompt Generation Module 320 can generate one or more prompts by extracting the one or more relevant keywords corresponding to each of the one or more locations from the accessible information associated with that location, using the Term Frequency Inverse Document Frequency (TF-IDF) technique. The TF-IDF technique can be implemented based on the fact that "the more frequently a word appears in a body of text, the less significant its overall meaning"—there is an inverse relationship between the distribution frequency of the word and its inverse frequency in the document.In one embodiment, the TF-IDF rating of each keyword is calculated by considering the keyword's occurrence within the text body, and infrequent occurrences of the keyword can be considered important. Furthermore, one or more prompts can be generated using the extracted keywords based on predefined prompt parameters. The generated prompt(s) can be stored as prompt data (310).

[0044] With renewed reference to Fig. In step 412, one or more prompts can be generated after the locations have been identified in step 410. The prompt(s) can be generated based on keywords extracted from the available information and predefined prompt parameters.

[0045] With reference to Fig. 5a can be identified after the Panchmukhi Ganapathi Temple is located in the first segment (see Fig. 5b) A prompt is generated. The prompt indicates that "the user is passing near location XX (Panchamukhi Ganesha Temple). Describe this location for the user as a tour guide. Your description must cover these aspects of the location: divine, cool, meditation, peace, enjoyment." In the prompt, the words divine, cool, meditation, peace, and enjoyment are extracted from available reviews or information about the Panchmukhi Ganesha Temple. The words "divine, cool, meditation, peace, and enjoyment" can be identified using the predefined prompt parameters. In other cases, the keywords may also include, but are not limited to, the opening hours of the Panchmukhi Ganesha Temple, the dress code for visiting the Panchmukhi Ganesha Temple, and the historical background of the Panchmukhi Ganesha Temple.Therefore, one or more prompts can be generated based on the extracted keywords to obtain descriptive information about the one or more locations.

[0046] With renewed reference to Fig. 3. After generating one or more prompts, the location description generation module 322 can obtain descriptive information corresponding to each of the one or more locations, based on the respective prompts from the processing model. In one embodiment, the processing model can include, but is not limited to, Large Language Models (LLMs). In another embodiment, the processing model can provide the descriptive information for the one or more locations. In one embodiment, the one or more prompts are generated to obtain the descriptive information for the respective locations from the processing model. The descriptive information can be generated taking into account the predefined prompt parameters.In one embodiment, the descriptive information can be generated from available sources by retrieving information based on one or more keywords present in the respective prompt.

[0047] After receiving the descriptive information, the location description generation module 322 can perform sentiment analysis on the descriptive information to remove negative sentiments associated with at least one word in the descriptive information. Sentiment analysis can be performed by identifying the sentiment value associated with each word in the descriptive information.

[0048] The sentiment scores are then analyzed to identify the one or more words indicating negative sentiment. This word can be identified by comparing each word's sentiment score to a predefined sentiment threshold. For example, the predefined sentiment threshold might be 0.2. The predefined sentiment threshold can be set based on the expected sentiment level for the descriptive information. The word indicating negative sentiment is then removed. This removal can be achieved by generating a new prompt. In one embodiment, the new prompt can be generated by modifying the previous prompt by assigning words with positive sentiments.Subsequently, the location description generation module 322 can generate and provide to the user the audiovisual information corresponding to each of the one or more locations, based on the respective descriptive information.

[0049] With renewed reference to Fig. In step 414, descriptive information for one or more locations can be retrieved. In step 416, a sentiment analysis can be performed on this descriptive information. If a negative sentiment is identified in relation to at least one word in the descriptive information, a feedback loop is provided in step 420 to generate a new prompt for retrieving the descriptive information. If the descriptive information does not contain any words indicating a negative sentiment, the audiovisual information for the respective location can be provided to user 214 in step 418, based on the descriptive information from step 414.

[0050] Referring again to 5a, the processing model, after providing the one or more prompts in step 502, can supply descriptive information about the respective location to the vehicle's control system 206. The control system 206 can then provide the audiovisual information about the respective locations based on this descriptive information. In one embodiment, a display 504 can show the image of the respective location to the user 214. A loudspeaker 506 can provide the user 214 with an audio description of the respective location. For example, the display shows and describes the Panchmukhi Ganapathi Temple. The present disclosure thus provides the information about the one or more locations relevant to the user. Furthermore, the user cannot miss a location while driving on new or unfamiliar routes.

[0051] With renewed reference to Fig. 3. After generating one or more prompts, the location description generation module 322 can obtain descriptive information corresponding to each of the one or more locations, based on the respective prompts from the processing model. In one embodiment, the processing model can include, but is not limited to, Large Language Models (LLMs). In one embodiment, the one or more prompts are generated to obtain the descriptive information for the respective locations from the processing model. The descriptive information can be generated taking into account the predefined prompt parameters. After obtaining the descriptive information, the location description generation module 322 can perform a sentiment analysis on the descriptive information to remove negative sentiments associated with the one or more words in the descriptive information.Sentiment analysis can be performed by identifying the sentiment score. Subsequently, based on the respective descriptive information, the location description generation module 322 can provide the user 214 with the audiovisual information corresponding to each of the one or more locations.

[0052] The other data 314 can contain data, including temporary data and temporary files, generated by the one or more modules 304 to provide information about locations relevant to the user 214. The one or more modules 304 can also include the other modules 324 to perform various other functionalities of the control system 206. The other data 314 can be stored in memory 220. It is understood that the one or more modules 304 mentioned above can be represented as a single module or as a combination of different modules.

[0053] Fig. Figure 6 shows an exemplary flowchart of process steps for providing information about locations along the route that are relevant to the user, according to some embodiments of the present disclosure. As in Fig. As illustrated in Figure 6, Procedure 600 can comprise one or more steps. Procedure 600 can be described in the general context of computer-executable instructions. In general, computer-executable instructions can include routines, programs, objects, components, data structures, operations, modules, and functions that perform specific functions or implement specific abstract data types.

[0054] The order in which Method 600 is described is not to be understood as restrictive, and any number of the described method blocks can be combined in any order to implement the method. Furthermore, individual blocks can be removed from the method without altering the scope of the subject matter described herein. In addition, the method can be implemented in any suitable hardware, software, firmware, or combination thereof.

[0055] In step 602, the one or more segments corresponding to a route can be generated by the control system 206 assigned to the vehicle 202, based on route information received from the one or more sources 204.

[0056] In step 604, the one or more locations relevant to the user 214 associated with the vehicle can be identified in each of the one or more segments by the control system 206, using the geographical information associated with the respective segment, based on at least one of the user preference, the first level filter condition and the second level filter conditions.

[0057] In step 606, the one or more prompts for each of the one or more locations can be generated by the control system 206 based on predefined prompt parameters. These predefined prompt parameters can include, but are not limited to, appealing information, fact-based information, holistic information, and action-oriented information.

[0058] In step 608, the descriptive information corresponding to each of the one or more locations can be obtained from the control system 206 based on the respective prompts from the processing model. Audiovisual information corresponding to each of the one or more locations is then provided to the user 214 based on the respective descriptive information. COMPUTER SYSTEM

[0059] Fig. Figure 7 shows a block diagram of an exemplary computer system 702 for implementing embodiments according to the present disclosure. In one embodiment, the computer system 702 can be used to implement the control system 206. Therefore, the computer system 702 can be used to provide information about locations relevant to the user along the route. The computer system 702 can communicate with the source 204 via a communication network 716. The computer system 702 can include a central processing unit 712 (also referred to as a "CPU," Central Processing Unit, or "processor"). The processor 712 can include at least one data processor. The processor 712 can include special processing units such as integrated system controllers (bus controllers), memory management control units, floating-point units, and the like.

[0060] The 712 processor can be used with one or more input / output devices (I / O devices) (not shown) via the 708 I / O interface. The 708 I / O interface can use, but is not limited to, communication protocols / methods such as audio, analog, digital, etc.

[0061] The computer system 702 can communicate with one or more I / O devices via the I / O interface 708. For example, the input device 704 can be an antenna, keyboard, mouse, joystick, (infrared) remote control, camera, card reader, and the like. The output device 706 can be a printer, fax machine, video display, and the like.

[0062] The processor 712 can communicate with the communication network 716 via a network interface 714. The network interface 714 can communicate with the communication network 716. The communication network 716 can include, but is not limited to, a direct connection, a local area network (LAN), a wide area network (WAN), a wireless network, etc. The network interface 714 can use connection protocols such as direct connection, Ethernet (e.g., twisted pair 10 / 100 / 1000 Base T), and the like.

[0063] The communication network 716 includes, but is not limited to, a direct connection, an e-commerce network, a wide area network (WAN), a wireless network, and the like. In some embodiments, the processor 712 can communicate with a memory 724 (e.g., RAM, ROM, etc.) via a memory interface 718. Fig. (not shown in section 7). The operating system 736 can facilitate resource management and the operation of the computer system 702. Examples of operating systems include APPLE MACINTOSH R , without, however, being limited to that.

[0064] In some embodiments, the computer system 702 can implement a stored program component of the web browser 732. The web browser 732 can be an application for displaying hypertext, e.g., Microsoft. R INTERNET EXPLORER TM , GOOGLE R CHROME TM0 and the like.

[0065] Furthermore, one or more computer-readable storage media can be used in the implementation of embodiments according to the present disclosure. A computer-readable storage medium refers to any type of physical storage on which information or data that can be read by a processor can be stored.

[0066] The terms “an embodiment”, “elaboration”, “elaborations”, “the embodiment”, “the embodiments”, “one or more embodiments”, and “some embodiments” mean “one or more (but not all) embodiments of the invention(s)”, unless expressly stated otherwise. The terms “comprising”, “having”, “encompassing”, and variations thereof mean “including, but not exclusive”, unless expressly stated otherwise. The enumerated list of elements does not imply that all such elements are mutually exclusive unless expressly stated otherwise. The terms “a”, “an”, “a”, “one”, and “the”, “a”, “one” mean “one or more”, unless expressly stated otherwise. A description of an embodiment with multiple components exchanging data does not imply that all such components are required.On the contrary, a large number of optional components are described to illustrate the wide range of possible embodiments of the invention.

[0067] Where a single unit or product is described herein, it is readily apparent that more than one unit / product (regardless of whether they interact) may be used instead of a single unit / product. Likewise, it is readily apparent that where more than one device or product is described herein (whether they interact or not), a single device / product may be used instead of the more than one device or product, or a different number of devices / products may be used instead of the number of devices or programs shown. The in Fig.The seven processes depicted show specific events that occur in a particular sequence. In alternative embodiments, certain processes can be performed in a different order, modified, or removed. Furthermore, steps can be added to the logic described above while still conforming to the described embodiments. Additionally, the processes described here can occur sequentially, or certain processes can be performed in parallel. Finally, processes can be performed by a single processing unit or by distributed processing units.

[0068] Finally, the language used in the patent specification was chosen primarily for reasons of readability and clarity, and may not have been chosen to delimit or describe the inventive subject matter. Therefore, the scope of the invention is not intended to be limited by this detailed description, but rather by the claims granted on an application based thereon. Accordingly, the disclosure of embodiments of the invention is intended to illustrate, but not limit, the scope of the invention as set forth in the following claims. While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be obvious to the person skilled in the art. The various aspects and embodiments disclosed herein serve only for illustration and are not intended to be limiting, the true scope being specified by the following claims. QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

[0000] US 2021341294A1

[0003] WO 2020140899A1

[0004] US 2021063182A1

[0005] CN 116821492A

[0006]

Claims

[1] Method for providing information about locations relevant to a user (214) along a journey route, the method comprising: Generating one or more segments corresponding to a route by a control system (206) belonging to a vehicle (202) on the basis of route information received from one or more sources; Identifying one or more locations by the control system (206) that are relevant to a user (214) linked to the vehicle (202) in each of the one or more segments using geographical information, which are linked to the respective segment, based on at least one user preference, a first-level filter condition, and second-level filter conditions; Generating one or more prompts for each of the one or more locations based on predefined prompt parameters by the control system (206); and Receiving descriptive information corresponding to each of the one or more locations by the control system (206) based on the respective prompts from a processing model, wherein audiovisual information corresponding to each of the one or more locations is made available to the user (214) based on the respective descriptive information. [2] The method of claim 1, wherein the production of one or more segments comprises: Performing a function of a total distance between a starting point and a destination point corresponding to the route and a constant distance, if the route information includes the starting point and the destination point; and Locating the constant distance on the route using the current location of the vehicle (202) when the route information includes the current location. [3] Method according to claim 1, wherein the first level filter condition corresponds to the removal of duplicates of the one or more locations in a connection of the one or more of the segments and the second level filter conditions correspond to a predefined number of reviews required with respect to the one or more locations and to a predefined rating required for the one or more locations. [4] The method of claim 1, comprising generating the one or more prompts for the one or more locations: Extracting one or more relevant keywords that are relevant to each of the one or which correspond to multiple locations, from accessible information linked to the respective location, using the technique "Term Frequency Inverse Document Frequency" (TF-IDF). [5] The method of claim 1, wherein obtaining the descriptive information further comprises: Identifying a sentiment value that is associated with each word in the descriptive information; Analyzing the sentiment score associated with each word to identify at least one word indicating a negative sentiment, based on a comparison of each word's sentiment score with the predefined sentiment threshold; and Removing at least one identified word that indicates a negative sentiment. [6] Control system (206) for providing information about locations relevant to a user (214) on a journey route, wherein the control system (206) comprises: one or more processors; and a memory, wherein the memory stores processor-executable instructions which, when executed, cause one or more processors to: Generating one or more segments that correspond to a route, based on route information received from one or more sources: Identifying one or more locations relevant to a user (214) associated with the vehicle (202) in each of the one or more segments using geographic information associated with the respective segment, based on at least one of user preference, a first-level filter condition and second-level filter conditions; Generating one or more prompts for each of the one or more locations based on predefined prompt parameters; and Obtaining descriptive information corresponding to each of the one or more locations, based on the respective prompts from a processing model, wherein audiovisual information corresponding to each of the one or more locations is made available to the user (214) on the basis of the respective descriptive information. [7] Control system (206) according to claim 6, wherein the one or more processors are configured to generate the one or more segments by one of: Performing a function of a total distance between a starting point and a destination point corresponding to the route and a constant distance, if the route information includes a starting point and a destination point; and Locating the constant distance on the route using the current location of the vehicle (202) when the route information includes the current location. [8] Control system (206) according to claim 6, wherein the first level filter condition corresponds to the removal of duplicates of the one or more locations in a connection of the one or more of the segments and the second level filter conditions correspond to a predefined number of reviews required with respect to the one or more locations and to a predefined rating required for the one or more locations. [9] Control system (206) according to claim 6, wherein the one or more processors are configured to generate the one or more prompts for the one or more locations by: Extracting one or more relevant keywords that are relevant to each of the one or which correspond to multiple locations, from accessible information linked to the respective location, using the technique "Term Frequency Inverse Document Frequency" (TF-IDF). [10] Control system (206) according to claim 6, wherein the one or more processors are configured to receive the descriptive information by: Identifying a sentiment value that is associated with each word in the descriptive information; Analyzing the sentiment score associated with each word to identify at least one word indicating a negative sentiment, based on a comparison of each word's sentiment score with the predefined sentiment threshold; and Removing at least one identified word that indicates a negative sentiment.

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