Technique for providing user-adapted service to user

Abstract

To disclose a technique for providing a user-adapted service.SOLUTION: A method implementation of the technique is performed by a computing system and comprises: a step (S1002) of acquiring a digital representation of personality data of a user, the personality data of the user being computed on the basis of input regarding the user, the input regarding the user including actual personality information of the user, the actual personality information specifically relating to a user-adapted service and including at least one of a current mood of the user at the time of providing the user-adapted service to the user, one or more preferences of the user specifically relating to the user-adapted service, and one or more goals of the user specifically relating to the user-adapted service; and a step (S1004) of processing the digital representation of the personality data to provide the user-adapted service to the user.SELECTED DRAWING: Figure 10

Description

Technical Field

[0001] The present disclosure generally relates to the field of data retrieval, and more particularly, to techniques for enabling the efficient retrieval of digital representations of a user's personality data from a server by a client device. Further, techniques for providing user-adapted services to a user of a client device are presented. This technology can be implemented as a method, computer program, apparatus, and system.

Background Art

[0002] Personality tests have been used for decades to evaluate human personality traits. Generally, they are conducted based on personality survey data obtained from the test subjects, and the survey data is evaluated by experts such as psychologists to draw conclusions about human personality. The so-called "OCEAN" model is a widely accepted taxonomy of personality traits, also known as the "Big Five" personality traits, and includes openness, conscientiousness, extraversion, agreeableness, and neuroticism as personality dimensions. Widely known personality tests that utilize the OCEAN model include tests based on the so-called International Personality Item Pool (IPIP), HEXACO-60 inventory, and Big-Five-Inventory-10 (BFI-10), and include, for example, a set of questions for testing a person for each of the five personality dimensions. In conventional personality tests, generally, a review by an expert on humans, such as a psychologist, is required to obtain a qualified evaluation of human personality traits, but it is difficult to integrate the execution of the personality test and its results into the processes executed on a technical system. Such integration can be adjusted to make the process more suitable for the user's personality, and can be beneficial, for example, to improve the user experience, such as providing the user with a service adapted to the user. Summary of the Invention

[0003] Therefore, a technical implementation that actually enables the integration of a personality test and its results into the processes executed on a technical system is required.

[0004] According to aspects of the present disclosure, a method, computer program product, and client device for providing user-adapted services to a user of a client device are provided according to the independent claims. Preferred embodiments are described in the dependent claims.

[0005] According to a first exemplary aspect, a method is provided that enables a digital representation of a user's personality data to be efficiently obtained from a server by a client device, the digital representation of the personality data being processed in the client device to provide user-adapted services to the user. The method includes storing a neural network trained by a server to compute the user's personality data based on input received from the user, receiving a request for a digital representation of the user's personality data from the client device, and transmitting the requested digital representation of the user's personality data to the client device, wherein the user's personality data is computed using the neural network based on input received from the user.

[0006] By storing the trained neural network on a server and applying it to the calculation of the user's personality data, (eliminating the need for traditional human review) the acquisition of the digital representation of the user's personality data is automated, making it possible to integrate the acquisition and use of the user's personality data into a (e.g., automated) process executed on a technical system. In particular, the neural network can be said to be an efficient functional data structure, capable of calculating the required personality data in a single computational execution, i.e., by inputting the inputs obtained from the user at the input nodes of the neural network and reading the output value of the result representing the personality data from the output nodes of the neural network. In this way, the neural network enables the efficient provision of personality data in digital representation form to the client device and can be used to provide services tailored to the specific personality of the user, thereby improving the user experience on the client device side. Through the efficient provision of data, the digital representation of the personality data is provided to the client device without significant delay and is immediately processed on the client device, making the integration of the acquisition and use of the personality data particularly practical. This achieves a technically feasible implementation that can actually integrate the acquisition and use of personality data into a process executed on a technical system in general.

[0007] Since the user's personality data can indicate the user's psychological characteristics and / or preferences, personality data generally includes, for example, classical personality data based on personality dimensions such as openness, honesty, extraversion, agreeableness, and neuroticism (known as the Big Five as described above), or psychological data including personality dimensions of conventional "16 personalities", "Big Six", or other established classifications, as well as medical data (e.g., data indicating tendencies such as curiosity, anxiety, depression, etc.). The digital representation of the user's personality data can include, for example, the digital representation of the above-described characteristics such as at least one digital representation of the personality dimensions of openness, honesty, extraversion, agreeableness, and neuroticism calculated by a neural network for the user.

[0008] A client device may be configured to process a digital representation of personality data for the purpose of enabling the provision of user-adapted services to a user. In one variation, the client device itself may be configurable based on the digital representation of personality data. Exemplary devices that may be configurable by the digital representation of personality data can be, for example, a vehicle, in which case the vehicle can be the client device. The vehicle processes the received digital representation of the personality data of a user (e.g., the driver of the vehicle) and adapts the driving settings of the vehicle to the personality of the driver, so that it can set itself (e.g., including its sub-components) to provide a driving service adapted to the user's personality. If the personality data indicates that the driver tends to avoid risks or is anxiety-prone, for example, the driving settings of the vehicle can be set to be more safety-oriented, while in the case of a driver who tends to have a more risk-seeking personality, the vehicle can be set such that the driving settings become more sporty. For this purpose, among other settings, the fuel and brake response behavior of the vehicle can be adapted accordingly. Sub-components of the vehicle that provide vehicle-related services, such as the vehicle's sound system including sound and volume settings, may also be set based on the personality data to further adapt to the user's personality. Optionally, the digital representation of the personality data can be shown to the user and the user can be given the opportunity to modify at least one value of the digital representation of the personality data before providing the user-adapted service to the user, thereby (at least to some extent) varying the user-adapted service according to the user's current preferences.

[0009] In another variant, the client device may configure at least one other device based on the digital representation of the personality data, for example, if it is at least one other device that provides services to the user. In such a variant, the client device can be, for example, a mobile terminal (e.g., a smartphone), and can interact with a vehicle (i.e., in this case, the vehicle corresponds to at least one other device) (e.g., using Bluetooth (registered trademark)). When receiving the digital representation of the personality data from the server, the mobile terminal can configure the vehicle via an interface. Therefore, it can be said that the digital representation of the user's personality data can be processed in the client device to configure at least one device that provides services to the user. Configuring at least one device may include configuring at least one setting of at least one device and / or configuring at least one setting of the services provided by at least one device. The vehicle is merely an example of a device that can be configured based on personality data, and it will be understood that the client device and / or at least one other device can also correspond to other types of devices. Another example of the client device in such a variant may be a server that provides user-adapted services to the user through (at least partially) a web service or a website. In that case, at least one other device may be a (computing) device that ultimately provides user-adapted services to the user using the web service or the website.

[0010] In one embodiment, the method executed by the server can further include receiving feedback characterizing the user, updating a neural network based on the feedback, and transmitting a digital representation of the user's updated personality data to a client device, where the user's updated personality data is calculated using the updated neural network. The digital representation of the user's updated personality data can be processed at the client device to improve the settings of at least one device that provides services to the user (e.g., one of the vehicle settings described above). The feedback can be collected at the client device and / or at least one device that provides services to the user and can indicate the user's personality. The feedback can include, for example, behavioral data reflecting the user's behavior monitored at at least one device when using the services provided by at least one device. In one variation, the behavioral data can be monitored by at least one device that provides services to the user using (e.g., sensor-based) measurements. In the example of a vehicle, the user's behavior to be monitored can be, for example, the user's driving behavior, and the driving behavior is measured by the vehicle's sensors. To measure the driving behavior, the sensors can, for example, detect the user's braking reaction and intensity, and such measurements can indicate the user's personality (e.g., enthusiasm for driving), so this information is transmitted as feedback to the server to update the neural network, thereby improving the function of the neural network that calculates the user's personality data.

[0011] Updating the neural network can include training the neural network based on feedback received from a client device. When the feedback represents new input values that have not yet been input into the neural network, new input nodes can be added to the neural network and the new input values can be assigned to the new input nodes when training the neural network. This makes particularly clear the ability of the neural network as an efficient functional data structure employed in the technical implementation presented herein. That is, the neural network represents an efficiently updatable data structure and is updated based on any feedback regarding the user's personality received from the client device, improving the function of calculating personality data. The information conveyed by the feedback can be directly integrated into the neural network and, once trained, is immediately reflected in subsequent requests sent to the server that requests a digital representation of the personality data. Conventional personality assessment techniques are rather fixed and may not support such updatability at all.

[0012] The digital representation of a user's personality sent from a server to a client device may correspond to a previously calculated digital representation of the user's personality by the server in response to a previous request for calculation of the user's personality (e.g., when the user performs a personality test by answering a set of questions). Thus, the user's personality data can be calculated before receiving a request from the client device, and the request can include an access code previously provided to the user by the server when calculating the user's personality data, and the access code enables the user to access the digital representation of the user's personality data from other client devices. Such an implementation can save computational resources on the server because it is not necessary to newly calculate the digital representation of the user's personality every time the digital representation of the specific user's personality data is requested from the client device, and it can also return based on the pre-calculated personality data. And the user can use the access code to access the digital representation of the personality data from multiple other client devices, such as other vehicles the user can drive, e.g., cars and motorcycles, or other types of devices.

[0013] The input obtained from the user can correspond to a digital score that reflects the answer to a question regarding at least one of the user's personality, goals, and motivations (e.g., obtained in a question - answering scheme in a personality test approach), and optionally, the question may include questions from an intelligence (「IQ」) test. Each digital score can be used as an input to a separate input node of a neural network when calculating the user's personality data using the neural network. The digital score can correspond to, for example, a 5 - level Likert scale with values from 1 to 5. The neural network can correspond to a deep neural network having at least two hidden layers between an input layer including the input nodes of the neural network and an output layer including the output nodes. Questions regarding personality can correspond to (or 「include」) questions from conventional IPIP, HEXACO - 60, and / or BFI - 10 pools, although it will be understood that other questions regarding the user's personality, including questions about the user's psychological characteristics, demographic features, and / or preferences, can be used as well. In particular, questions regarding the user's goals and motivations can define additional dimensions (e.g., in addition to the Big Five) that enhance the accuracy of the calculated personality data compared to conventional IPIP, HEXACO - 60, and BFI - 10 approaches. The network can be trained based on data collected in a baseline survey conducted on a plurality of testers (e.g., 1000 or more), and the baseline survey can be conducted using the questions described above.

[0014] Exemplary questions that go beyond the questions of the conventional IPIP, HEXACO-60, and BFI-10 are shown in the tables presented later. Table 1 provides an exemplary list of questions particularly relevant to the user's motivation, Table 2 provides an exemplary list of questions particularly relevant to the user's goals, and Table 3 provides an exemplary list of questions regarding other personality aspects of the user, including questions regarding the user's demographic aspects (e.g., questions 1-10 in Table 3), questions regarding the user's preferences (e.g., questions 11-15 in Table 3), and questions of an IQ test (e.g., questions 16-18 in Table 3). It will be understood that not all questions described in the later tables necessarily require answers that can be directly mapped to corresponding digital scores, such as on a Likert scale. This is because the expected answers may be free-text answers (e.g., questions 11-22 in Table 2 and the questions in Table 3). Those skilled in the art will also understand that such answers can be easily associated with corresponding digital scores, for example, by associating free-text answers with pre-defined digital scores. Similarly, when a question is described as "corresponding" to a question of the conventional IPIP, HEXACO-60, and / or BFI-10, it will be understood that the question does not necessarily have to use the exact wording of the pre-defined conventional question verbatim, and it may be rephrased as long as the semantic similarity or correspondence with the pre-defined conventional question is maintained. The same applies to the exemplary questions described in the later tables.

[0015] The personality data of a user calculated using a neural network may be obtained as the "raw value" of the user's personality data. In some variations, the raw value of the user's personality data may be associated with the personality data of a group of people to be compared (e.g., the group to be compared includes at least one limited number of people, and the personality data of the group to be compared is calculated as the personality data averaged among the people within the group) in order to obtain the "comparison value" (or "relative value") of the user's personality data. That is, the comparison value of the user's personality data may be obtained by measuring the distance (or difference) between the raw value of the user's personality data and the personality data of the comparison group (e.g., for each personality data dimension). The distance (or difference) can indicate the personality of the user compared to the comparison group. The comparison group may be selected in different ways depending on the use case (exemplary comparison groups can be "males only", "females only", a specific "age group", "professional group", "educational group", etc.), and the comparison value of the user's personality data can vary depending on the use case. As a mere example, a user having a certain raw value in the dimension of extroversion may be considered to have a high comparison value in the dimension of extroversion compared to the user's family, while on the other hand, may be considered to have a low comparison value in the dimension of extroversion compared to the user's work colleagues.

[0016] To reduce the computational complexity when calculating the user's personality data, the neural network can be designed to have a specific network structure. Considering the context of the above questions, the structure of the neural network can generally be designed such that the number of input nodes decreases compared to the number of input nodes available when all of the above questions are used. Therefore, the questions can correspond to questions selected from a set of questions that represent the optimal achievable results for calculating the user's personality data (i.e., when the user answers all the questions in the set of questions), where the selected questions can correspond to the questions in the set of questions determined to be the most influential with respect to the optimal achievable results. As described above, since each answer to a question is input to an individual input node of the neural network, selecting a subset of the set of questions reduces the number of input nodes when calculating the personality data and reduces the computational complexity. Due to the fact that the most influential questions with respect to the achievable results are selected, the accuracy of the results output by the neural network is approximately maintained.

[0017] In fact, tests have shown that the number of questions can be significantly reduced without significantly sacrificing the accuracy of the results. Taking a set of questions that includes the standard IPIP, HEXACO-60, and BFI-10 questions (a total of 370 questions) as the set of questions that represent the optimal achievable results for calculating the personality data, and optionally supplemented by additional questions regarding the user's goals and motivations (resulting in a total number of questions exceeding 370), tests have shown that when only the 30 most influential questions are used, approximately 90% of the accuracy of the optimal achievable results is achieved. Therefore, the number of questions selected can be less than 10% (preferably less than 5%) of the number of questions included in the set of questions that represent the optimal achievable results. In this case, since the number of input nodes of the neural network can be significantly reduced, computational resources can be significantly saved, and the personality data can be calculated more efficiently.

[0018] In one variation, to determine the questions of the most influential set of questions regarding the optimally achievable result, the result achievable by each single question of the set of questions is correlated with the optimally achievable result, and questions are selected from the set of questions based on selecting the questions from the set of questions with the highest correlation with the optimally achievable result. Thus, a fixed subset of the set of questions representing the optimally achievable result can be determined and used to reduce the number of input nodes and train the neural network as described above.

[0019] As described above, the optimally achievable result can correspond to the result achieved when the user answers all questions, optionally including a set of questions including the standard IPIP, HEXACO-60, and BFI-10 questions, supplemented with additional questions regarding the user's goals and motivations as described above. On the one hand, in one variation, the standard IPIP score (obtained by answering all questions of the standard IPIP test), the standard HEXACO-60 score (obtained by answering all questions of the standard HEXACO-60 test), and the score of the standard BFI-10 (obtained by answering all questions of the standard BFI-10 test) can be obtained individually as references for the optimally achievable result. In another variation, an improvement can be achieved by calculating a combined score of these individual scores as a reference for the optimally achievable result, where the combined score is calculated, for example, as the (e.g., weighted) average of the individual scores. The combined score can also be represented as a "super score" representing the "truth" derivable from the individual scores, generally improving the meaning of the determined scores and the reference for the optimally achievable result.

[0020] In another variant, the questions can be repeatedly selected from a set of questions, and in each of the repetitions, the next question can be selected according to the user's answer to the previous question. In each of the repetitions, the next question can be selected as one of the questions in the set of questions that is determined to have the most impact on the achievable results for calculating the user's personality data. This can be regarded as an adaptive selection of questions, and the questions are determined for each user in a step-by-step manner considering the user's answers to previous questions. In one particular variant, the neural network can include a plurality of output nodes representing the probability curve of the results of the user's personality data. Here, determining the most influential question in the set of questions as the next question in each repetition may include determining the degree to which the change in the digital score input to each of the input nodes of the neural network changes the probability curve for each of the input nodes of the neural network. The question associated with the input node determined to have the highest degree of change in the probability curve can be selected as the most influential question in each repetition.

[0021] To further reduce the computational complexity, the above-described iterative and adaptive selection can be performed under at least one constraint such as the maximum number of questions to be selected, the minimum result accuracy to be achieved (the result accuracy improves with the answer to the question in each repetition and the calculation can be stopped when the required minimum result accuracy is reached), and the maximum available time (the test can be stopped when the maximum available time has elapsed, or each question can be associated with the estimated time for the user's answer, and the number of questions to be selected can be determined based on the estimated time). These constraints can be set individually for each calculation of the personality data.

[0022] According to a second exemplary aspect, a method is provided that enables a digital representation of a user's personality data to be efficiently obtained from a server by a client device. The method is executed by the client device and includes sending a request for the digital representation of the user's personality data to the server and receiving the requested digital representation of the user's personality data from the server, where the user's personality data is calculated using a neural network trained to calculate the user's personality data based on input obtained from the user, and may include processing the digital representation of the personality data to provide a user-tailored service to the user.

[0023] The method according to the second aspect defines a method from the perspective of a client device that can complement the method executed by the server according to the first aspect. The server and client device of the second aspect may correspond to the server and client device described above in relation to the first aspect. Accordingly, those aspects described in relation to the method of the first aspect that are applicable to the method of the second aspect are also included by the method of the second aspect, and vice versa. Accordingly, unnecessary repetition is omitted hereinafter.

[0024] Similar to the method of the first aspect, the digital representation of the user's personality data can be processed on a client device to configure at least one device that provides services to the user, where the at least one device can include the client device. The method executed by the client device can further include sending feedback characterizing the user to the server and receiving a digital representation of the updated personality data of the user from the server, where the updated personality data of the user can be calculated using a neural network updated based on the feedback. The digital representation of the updated personality data of the user can be processed on the client device to improve the configuration of at least one device that provides services to the user. The feedback can include behavioral data reflecting the user's behavior monitored on at least one device when using the services provided by the at least one device, where the behavioral data can be monitored using measurements executed on at least one device that provides services to the user. The at least one device can include a vehicle, and the behavioral data can include data reflecting the user's driving behavior. The user's personality data can be calculated before sending a request to the server, and the request can include an access code previously provided to the user by the server when calculating the user's personality data, and the access code enables the user to access the digital representation of the user's personality data from other client devices. The input obtained from the user can correspond to a digital score reflecting the answer to a question regarding at least one of the user's personality, goals, and motivations.

[0025] According to a third exemplary aspect, a computer program product is provided. The computer program product includes a program code portion for performing at least one of the methods of the above-described aspects (including the first aspect and the second aspect) when the computer program product is executed on one or more computing devices (e.g., a processor or a distributed set of processors). The computer program product can be stored on a computer-readable recording medium such as a semiconductor memory, a DVD, a CD-ROM, etc.

[0026] According to a fourth exemplary aspect, a server is provided that enables a client device to efficiently obtain a digital representation of a user's personality data from the server, where the digital representation of the personality data is processed in the client device to provide a user-adapted service to the user. The server includes at least one processor and at least one memory, and the at least one memory includes instructions executable by the at least one processor such that the server is operable to perform any of the method steps presented herein with respect to the first aspect.

[0027] According to a fifth exemplary aspect, a client device is provided for enabling the efficient acquisition of a digital representation of a user's personality data from a server. The client device includes at least one processor and at least one memory, and the at least one memory includes instructions executable by the at least one processor such that the client device is operable to perform any of the method steps presented herein with respect to the second aspect.

[0028] According to a sixth exemplary aspect, a system is provided that includes a server according to the fourth aspect and at least one client device according to the fifth aspect.

[0029] Further details and advantages of the technology presented herein are described with reference to the exemplary implementations shown in the following figures.

Brief Description of the Drawings

[0030]

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Modes for Carrying Out the Invention

[0031] In the following, specific details are set forth for purposes of illustration and not limitation to provide a thorough understanding of the present disclosure. It will be apparent to those skilled in the art that the present disclosure may be practiced with other implementations departing from these specific details.

[0032] Those skilled in the art will further appreciate that the steps, services, and functions described herein below may be implemented using individual hardware circuits, software that functions in conjunction with a programmed microprocessor or a general purpose computer, one or more application specific integrated circuits (ASICs), and / or one or more digital signal processors (DSP). When the present disclosure is described with respect to a method, it will also be appreciated that it is embodied in one or more processors and one or more memories coupled to the one or more processors, where the one or more memories may be encoded with one or more programs that execute the steps, services, and functions presented herein when executed by the one or more processors.

[0033] FIG. 1a schematically shows an exemplary configuration of a server 100 that enables a digital representation of a user's personality data to be efficiently obtained by a client device from the server 100, where the digital representation of the personality data is processed in the client device to provide user-adapted services to the user. The server 100 has at least one processor 102 and at least one memory 104, and the at least one memory 104 includes instructions executable by the at least one processor 102 such that the requesting server 100 is operative to execute the steps of the methods described herein with reference to the "server" herein.

[0034] It will be understood that server 100 can be implemented on a physical computing unit or, for example, on a virtualized computing unit such as a virtual machine. Further, server 100 is not necessarily limited to being implemented on a stand-alone computing unit, and it will also be understood that it can be implemented as a component existing on multiple distributed computing units, such as in a cloud computing environment, etc., which are realized by software and / or hardware.

[0035] FIG. 1b schematically shows an exemplary configuration of client device 110 that enables a digital representation of a user's personality data to be efficiently retrieved from a server by client device 110. Client device 110 has at least one processor 112 and at least one memory 114, and at least one memory 114 includes instructions executable by at least one processor 112 such that requesting client device 110 is operable to perform the steps of the methods described herein with reference to the "client device". The client device may sometimes simply be referred to as the "client". In some variations, for example, client 110 and server 100 may be implemented on the same computing device (or computing system), and client 110 and server 100 may be implemented as components running on the same computing device apparatus / system.

[0036] FIG. 2 shows a method that can be executed by server 100 according to the present disclosure. This method is specialized in enabling the digital representation of the user's personality data to be efficiently obtained from server 100 by a client device (e.g., client device 110). In this method, server 100 can execute the steps described herein with reference to "server". Along the above description, in step S202, server 100 can store a neural network trained to calculate the user's personality data based on the input obtained from the user. In step S204, server 100 can receive a request for the digital representation of the user's personality data from the client device. In step S206, server 100 can send the requested digital representation of the user's personality data to the client device, where the user's personality data is calculated using the neural network based on the input obtained from the user.

[0037] Figure 3 shows a method that can be executed by client device 110 according to the present disclosure. This method is specialized to enable the digital representation of the user's personality data to be efficiently obtained by client device 110 from a server (e.g., server 100). In this method, client device 110 can execute the steps described herein with reference to "client device". Along the lines of the above description, in step S302, client device 110 can send a request for the digital representation of the user's personality data to the server. In step S304, client device 110 can receive the requested digital representation of the user's personality data from the server. Here, the user's personality data is calculated using a neural network trained to calculate the user's personality data based on the input obtained from the user. In step S306, client device 110 can process the digital representation of the personality data to provide a user-adapted service to the user.

[0038] Figure 4 shows an exemplary interaction between a user 402, a server 404 that stores a neural network trained to calculate a user's personality data based on input obtained from the user, and a client device that obtains a digital representation of the user 402's personality data and provides a user-tailored service to the user 402. In the example shown here, the client device can be a vehicle 406 driven by the user 402. As shown in the figure, the user 402 can perform an automated personality test by answering questions using, for example, a web interface or an app on his laptop or smartphone, and provide input to the neural network stored on the server 404. Based thereon, the neural network can calculate the personality data of the user 402. Instead of sending a digital representation of the personality data to the user 402, in the figure shown, the server 404 can provide the user 402 with an access code that can be used by the user 402 to access the personality data using other client devices including the vehicle 406. The user 402 can use the access code to register or log in to the vehicle 406 (more specifically, its board computer), and the vehicle 406 can use the access code to request a digital representation of the user's personality data from the server 404 (in the figure, the user's personality data is denoted as the user's "MindDNA").

[0039] Upon receiving a request from vehicle 406, server 404 can reply with the user's personality data to vehicle 406, and vehicle 406 can set its driving settings (and optionally, sub-components of vehicle 406) according to the personality data of user 402. For example, vehicle 406 can adapt the fuel and brake response behavior of vehicle 406 to provide a driving experience that is particularly adapted to the user's personality (e.g., risk avoidance, risk seeking, etc.). When user 402 drives vehicle 406, vehicle 406 can monitor the user's driving behavior, for example, using sensors that measure the user's brake response and strength. Vehicle 406 can provide this information as feedback to server 404, where the feedback is processed to update (by training) a neural network to improve the ability to calculate the personality data of user 402. In response, server 404 can transmit the correspondingly updated personality data of user 402 to vehicle 406, and vehicle 406 can use the digital representation of the updated personality data to improve the vehicle settings to be more in line with the actual personality of user 402. In summary, a system is provided that integrates the acquisition and use of user personality data into an automated process, adapts the settings of the provided device or service according to the user's preferences obtained from the user's personality data, thereby improving the user experience.

[0040] Figure 5 shows other connection options among the mobile terminal 502 (e.g., smartphone) of user 402, vehicle 406, and server 404 according to the present disclosure. In one variant, vehicle 406 can communicate directly with server 404 via the Internet, and when user 402 is authenticated in vehicle 406 (e.g., using a key, smart card, NFC / RFID, smartphone with NFC, fingerprint, manually entered code, etc.), vehicle 406 can request the user's personality data (again denoted as the user's "MindDNA" in Figure 5) to improve the user's driving experience. In another variant, when user 402 is carrying mobile terminal 502, mobile terminal 502 can communicate with server 404 via the Internet (e.g., using a dedicated app installed thereon) and request the user's personality data. In this variant, vehicle 406 can communicate locally with mobile terminal 502 (e.g., using Bluetooth (registered trademark), Wi-Fi, or a USB cable) and obtain the user's personality data from mobile terminal 502. The direct connection between vehicle 406 and mobile terminal 502 can be further used to utilize sensors installed in mobile terminal 502 (e.g., a gyroscope for movement and acceleration detection, GPS for movement and acceleration detection as well as driving route detection, or a medical sensor for measuring pulse, blood pressure, etc.), supplement the feedback collected by vehicle 406 itself (e.g., related to the user's driving behavior), thereby providing the additional feedback detected by mobile terminal 502 to server 404, and as described above, the neural network can be updated based on the feedback.

[0041] FIG. 6a shows an exemplary structure of a neural network 602 according to the present disclosure. The neural network 602 includes an input layer, an output layer, and two hidden layers. The neural network 602 shown in FIG. 6a merely shows the structure of a general deep neural network, and the actual number of nodes (at least in the input layer and hidden layers) of the neural network 602 stored in the server 404 can be significantly higher than that shown. As described above, the test is performed using the 30 most influential questions out of a total of 370 or more questions (obtained from the questions of the standard IPIP, HEXACO-60, and BFI-10, and optionally supplemented by additional questions regarding goals and motivations), resulting in 30 input nodes in the input layer of the neural network 602. In this case, for example, each hidden layer can be composed of 50 nodes. Further, as shown, the neural network 602 may include a single output node in the output layer. In this case, the resulting value of the output node in the output layer may represent the value of one of the personality dimensions of the (Big Five) for which the neural network 602 was trained. Such a structure of the neural network 602 is merely exemplary, and it will be understood that other structures are generally conceivable.

[0042] The more advanced structure of the neural network 602 has input nodes corresponding to the number of the complete set of available questions, and the questions can be obtained from the standard IPIP, HEXACO-60, and BFI-10 questions, and can further include additional questions regarding the user's goals and motivations, as well as other psychological characteristics and / or preferences of the user that are not covered by the above questions, and potentially hundreds of questions, for example, more than 600 questions can be further added. Thus, such a neural network 602 can have more than 600 input nodes each corresponding to a single question of the complete set of available questions, and the number of nodes in the hidden layer can be selected according to the performance of the neural network 602. For example, the neural network 602 can be composed of two hidden layers each having 100 nodes. Further, in the input layer, the above more than 600 input nodes can be duplicated, and each of the duplicated input nodes can be used as a missing-question-indicator. The missing-question-indicator can be dichotomous, that is, it can have two values (for example, 0 and 1) indicating whether the question of the corresponding (original) input node has been answered. Since the input nodes are duplicated, the input layer can have more than 1200 input nodes in total.

[0043] The output layer of the more advanced neural network 602 can have multiple output nodes that together represent the probability curve of one personality dimension. For example, if the scale used for the output of this personality dimension ranges from 0 to 10 and the number of output nodes is 50, each of the output nodes can represent a scale portion corresponding to a part of the scale, that is, the 0 - 0.2, 0.2 - 0.4, 0.4 - 0.6, ··· 9.8 - 10 parts of the scale. Such an output layer can provide the entire probability curve of the output value of this personality dimension instead of a single output value. FIG. 6b shows an exemplary output layer together with the corresponding probability curve 604. Such a curve enables determination of where the mode of the output value (i.e., indicated by the peak of the curve) is, and also enables determination of the accuracy with which the neural network 602 calculates the result (i.e., indicated by the width of the curve). Using the more advanced neural network 602, by training the neural network 602 separately for each dimension, the personality data of the user can be calculated in the form of several probability curves (e.g., 5 probability curves corresponding to the Big Five) for any number of answered questions. In the initial state where the questions have not yet been answered, all missing question indicators can have a value of "missing" (e.g., 0). Each time a question is answered, an update of the output value is calculated, and as the number of answered questions increases, the width of the probability curve of the output layer decreases, and the accuracy with which the neural network 602 calculates the result steadily improves.

[0044] Such a structure of the neural network 602 is particularly advantageous because it allows the user to repeatedly select the next question to be answered from the complete set of questions, where in each repetition, the next question can be selected according to the user's answer to the previous question, and where in each repetition, the next question can be selected as one of the questions in the complete set of questions that is determined to have the most impact on the achievable results for calculating the user's personality data. For this purpose, for each answered question, several (e.g., five) probability curves can be recalculated, and among the recalculated probability curves, the one with the largest width (i.e., representing the probability curve with the currently lowest accuracy) can be determined. As the next question in the repetition, the question of this dimension can be selected to improve the accuracy of this dimension. To determine the most influential question, the degree to which a change in the digital score input to each of the input nodes changes the probability curve (e.g., the degree to which the width of the curve changes) can be determined for each of the input nodes of the neural network 602. Based on this, the question associated with the input node determined to have the highest degree of change in the probability curve can be selected as the most influential question in each repetition.

[0045] The advanced structure of the neural network 602 can also be advantageous for enabling easy integration of feedback into the neural network. As described above, when the feedback represents new input values that have not yet been input into the neural network 602, when training the neural network 602, new input nodes can simply be added to the neural network 602, and the new input values are assigned to the new input nodes. In this way, any kind of new feedback can be easily integrated into the network, and the neural network 602 can improve its ability to calculate personality data. As an implementation to reduce the computational complexity when adding new input nodes, when the network is trained to correlate new input nodes with other nodes in the network, it is conceivable to incorporate only those nodes that are determined to be the most influential with respect to the optimally achievable results into the calculation, thereby avoiding incorporating all nodes into the calculation. Also, when the network is trained to correlate new input nodes with other nodes in the network, for example, it is conceivable to limit the number of layers to be calculated in advance (e.g., to 2 or 3) so as not to calculate all subsequent combinations of nodes.

[0046] In the above description, the technology for efficiently obtaining the digital representation of the user's personality data was exemplified in the context of adapting the driving settings of a vehicle, such as the fuel and brake reaction behaviors of the vehicle, to the user's personality. In this case, the method described in this specification can also be shown as a method for adapting the driving settings of a vehicle, including efficiently obtaining the digital representation of the user's personality data. Adapting the fuel and brake reaction behaviors of a vehicle is only an example of adapting the driving settings of a vehicle. More generally, it will be understood that adapting the driving settings of a vehicle may include adapting any vehicle settings that affect the driving behavior of the vehicle. Adapting the driving settings of a vehicle may include at least one of adapting the fuel and brake reaction behaviors of the vehicle to the user's personality, adapting the chassis settings of the vehicle, adapting the drive mode of the vehicle, and adapting the settings of adaptive cruise control (ACC) of the vehicle or the like. Adapting the drive mode of the vehicle may include setting the economy, comfort, or sport mode that affects the accelerator pedal and fuel consumption behavior of the vehicle according to the driver's personality. For example, when the personality data indicates that the driver tends to avoid risks, the drive mode can be set to economy or comfort. On the other hand, for a driver who tends to have a personality seeking risks, the drive mode can be set to sport mode. Adapting the drive mode of the vehicle may also include, for example, enabling / disabling the automatic four-wheel drive (4WD) mode of the vehicle. Adapting the settings of ACC may include, for example, setting the distance to the vehicle ahead and / or the target driving speed according to the driver's risk avoidance.For example, in the case of an electric vehicle, adapting the driving settings of the vehicle may include adapting the charging / discharging behavior of the vehicle battery (e.g., slow / fast charging, charging capacity level, slow / fast / uniform / non-uniform discharge of energy) according to the user's personality, or adapting the simulated motor / exhaust sound generated by an external vehicle speaker (e.g., adapting the sound type and / or equalizer settings of the corresponding sound system). Similarly, the charging / discharging behavior of the vehicle battery may be adapted by appropriately adapting the charging / discharging behavior of the charging station.

[0047] The technology presented in this specification can also be used for other purposes in the context of a vehicle, such as adapting the environmental conditions in the passenger compartment of the vehicle (or more generally, for other means of transportation such as airplanes, trains, space shuttles, etc., adapting the environmental conditions in the passenger compartment). In this case, the method presented in this specification can also be shown as a method for adapting the environmental conditions in the passenger compartment of a means of transportation that includes efficiently obtaining a digital representation of the user's personality data. Adapting the environmental conditions in the passenger compartment of a means of transportation can include adapting the temperature of the passenger compartment (e.g., by adapting the air conditioning settings of the passenger compartment), adapting the interior lighting of the passenger compartment, and adjusting at least one of the oxygen levels in the passenger compartment (e.g., related to astronauts on a space shuttle) to match the user's personality. In addition to or instead of adapting the environmental conditions in the passenger compartment, the technology presented in this specification can also be used to adapt user-specific settings related to the passenger compartment. Adapting user-specific settings related to the passenger compartment of a means of transportation can include adapting the seat settings (e.g., seat height, seat position, seat massage settings, seat belt tension, etc.) to the user in the passenger compartment and adapting the equalizer settings of the sound system provided to the user in the passenger compartment (e.g., increasing or decreasing bass or treble) to the user's personality, and can include at least one of these. Regarding means of transportation with multiple seats for a large number of passengers such as vehicles, trains, and airplanes, the technology shown in this specification can be used for seat allocation in the means of transportation. In this case, the method described in this specification can be expressed as a method for adapting seat allocation in the passenger compartment of a means of transportation that includes efficiently obtaining a digital representation of the user's personality data. Adapting seat allocation in the passenger compartment can include allocating seats to users that are particularly adapted to the user's personality (e.g., allocating aisle-side or middle seats next to other passengers to outgoing and highly communicative users, and rather window-side seats to introverted users).When a seat is assigned, a ticket for using the assigned seat (e.g., a printed train or airplane ticket) may be issued and provided to the user.

[0048] At least some of the above-mentioned adaptations, namely, the adaptation of the driving settings of the vehicle, the adaptation of the environmental conditions in the passenger compartment, and the adaptation of the user-specific settings related to the passenger compartment, may be adaptively executed depending on each other. That is, when a certain setting is adapted manually or in consideration of the user's personality data, it will be understood that a series of further adaptations considering the user's personality data may automatically follow. For example, when the accelerator and brake reaction operations of the vehicle are adapted to the user's personality, further adaptations such as adapting the chassis settings and steering wheel settings accordingly may automatically follow. As another example, when the vehicle's headlights are turned on for a cautious driver, the 4WD and differential gears may also be automatically activated. In yet another example, when the user turns on the vehicle's heating device, the steering wheel heating and / or seat heating may also be turned on and set to a heat level suitable for the user.

[0049] Any of the above-mentioned conformances of the vehicle / transportation means settings can be carried out in addition to conforming to the user's personality, taking into account (or "based on" / "according to") the user's sensor data indicating the attention level of the user obtained in the passenger compartment. In other words, the client device can be set to adapt at least one of the driving settings of the vehicle, the environmental conditions in the passenger compartment, and the user-specific settings related to the passenger compartment, considering not only the digital representation of the user's personality data but also the sensor data indicating the attention level of the user. That is, the digital representation of the user's personality data and the sensor data indicating the attention level of the user can be combined before performing the above-mentioned conformance. The sensor data indicating the attention level of the user can include, for example, data related to at least one of the user's heart rate, breathing, sense of fatigue, reaction time, and alcohol / drug level. The sensor data is collected, for example, by at least one sensor installed in the passenger compartment or the user's mobile terminal.

[0050] FIG. 7 shows an exemplary implementation that includes considering a driver's attention level in combination with the driver's personality data to adapt the driving settings of a vehicle, the environmental conditions in the passenger compartment, and / or user-specific settings related to the passenger compartment. The driver's attention level is checked by corresponding sensors, for example, regarding the user's reaction time, drowsiness, heart rate, breathing, alcohol / drug level, or abnormal behavior of the user. In the left portion of the figure, since the collected sensor data indicates the user's normal attention level, vehicle settings, for example, including speed, volume, temperature, seat settings, etc., can remain at the normal level (e.g., be adapted to the driver's personality, i.e., "MindDNA"). In the central portion of the figure, since the sensor data indicates a decrease in the driver's attention level, vehicle settings can be changed, including turning on the seat massage function, decelerating, increasing the volume, lowering the temperature setting, etc., to refresh the driver's attention again. Optionally, an attention test can be performed, for example, by asking the driver to provide a voice-based response in a question / answer scheme, and the result of the attention test can be considered when adapting the above settings. On the other hand, in the right portion of the figure, since the sensor data indicates that the driver's attention level is very low, a warning can be issued to the user and the vehicle settings can be adapted accordingly, for example, driving at a very low speed (and, for example, forcing the vehicle to stop at the next stop opportunity), muting the sound, and / or guiding the user to the next hotel by the navigation system, etc.

[0051] The adaptation of the above vehicle / transport means settings may also be performed in consideration of (or "based on" / "in accordance with") at least one of geographical data, weather data, and time data regarding the planned route on which the vehicle or transport means is to travel. That is, the client device may be configured to adapt at least one of the driving settings of the vehicle, the environmental conditions in the passenger compartment, and the user-specific settings regarding the passenger compartment, taking into account not only the digital representation of the user's personality data but also the geographical data, weather data, and / or time data regarding the planned route. The digital representation of the user's personality data and the additional data regarding the planned route may, in other words, be combined before the adaptation is performed. The geographical data may include data regarding the terrain of the planned route, for example, the uphill / downhill gradient of mountain roads, information on winding or coastal roads, altitude, etc. The weather data may include information on the current weather conditions (e.g., detected by the vehicle or transport means itself using a rain sensor, temperature sensor, etc.), or the predicted weather conditions of the planned route (e.g., rain, cloudiness, sunshine, etc.). The time data may include information regarding the time schedule of the planned route, for example, driving during the day, driving during the light transition periods (dusk or dawn), or driving at night. Depending on such data, the driving settings of the vehicle, the environmental conditions in the passenger compartment, and the user-specific settings regarding the passenger compartment may be adapted according to the user's personality. For example, when difficult terrain / weather / time conditions occur on the planned route, 4WD or the like may be activated to provide a safe drive for a risk-averse driver.

[0052] As described above (e.g., by adapting at least one of the driving settings of the vehicle, the environmental conditions in the passenger compartment, and the user-specific settings related to the passenger compartment), in order to provide the user with a user-adapted service, the client device can further consider body scan data indicating the user's (e.g., physical) characteristics that can be derived by scanning the user's body (e.g., at least a part thereof) before providing the user with the user-adapted service (e.g., before the user drives the vehicle). User characteristics that can be derived by scanning the user's body can include, for example, at least one of the user's size, weight, gender, age, height, posture, and emotional state. Alternatively or additionally, user characteristics that can be derived by body scan can also include, for example, specific movements of the user or items carried by the user. The body scan data is obtained by a radar device, a camera (e.g., including a 360-degree camera, an infrared (IR) camera, etc., of the user's mobile terminal or installed in the vehicle / means of transportation), or a voice recorder by acquiring one or more images or voice signals of the user, where body / face / voice recognition technology can be used to scan the user's body to derive the above-mentioned user characteristics. Therefore, the client device is configured to provide a user-adapted service by considering not only the digital representation of the user's personality data but also the body scan data (or "based on" / "therefore"). That is, the digital representation of the user's personality data and the body scan data can be combined before providing the user with the user-adapted service. FIG. 8 shows an exemplary implementation that includes considering the driver's body scan data (e.g., obtained by the driver's mobile terminal such as a smartphone, smartwatch, fitness tracker, etc. before entering the vehicle) in combination with the driver's personality data in order to appropriately adapt the driving settings of the vehicle, the environmental conditions in the passenger compartment, and / or the user-specific settings related to the passenger compartment. In this figure, the body scan data is denoted as "BodyDNA" and is combined with "MindDNA" to form the so-called "LifeDNA".It will also be understood that the acquired body scan data can also be used to provide feedback that characterizes the user in order to update the neural network, as described above.

[0053] Furthermore, in other embodiments, it will be understood that the client device may be configured to provide user-adapted services considering only body scan data, i.e., without considering the digital representation of the user's personality data. In such an example, the body scan may detect the user (e.g., using face recognition for authentication purposes), and when the movement of the user (determined by the body scan) indicates that the user is approaching the vehicle, the vehicle door may be opened. Similarly, when it is detected that the user is carrying an item (e.g., a bag or a suitcase), for example, the trunk of the vehicle may be automatically opened. Such a method can generally be referred to as a method of providing user-adapted services to the user, which is executed by the client device and includes obtaining body scan data indicating user characteristics derived by scanning at least a part of the user's body, and processing the body scan data to provide user-adapted services. Any of the above-exemplified body scan data may be used for such purposes. For example, if the client device is a vehicle, the body scan data may be used to adapt at least one of the vehicle's driving design, in-cabin environmental conditions, and user-specific settings related to the cabin (i.e., without further considering the user's personality data in the above sense). If at least a part of the body scan data is already available in the user's user profile (e.g., pre-stored), such data may be retrieved from the user profile when authenticating the user, and in that case, it will be understood that it is not necessarily required to perform a body scan in real time to determine the corresponding data.It is similarly generally contemplated that, for all of the other vehicle-related use cases described herein, such as the use case that considers sensor data indicating the user's attention level, the use case that considers at least one of the geographical data, weather data, and time data regarding the planned driving route described above, the use case that considers predefined conditions monitored and potentially indicating the user's suicidal intention, and the use case that considers the goals and / or preferences of a user driving a nearby vehicle to perform a collectively improved driving behavior of a set of vehicle groups described below, the client device may be configured to provide a user-adapted service by considering only the body scan data, i.e., without considering the digital representation of the user's personality data. Similarly, it is generally contemplated that all of these may operate in the same manner without additionally (or "in combination") considering the digital representation of the user's personality data.

[0054] In other vehicle-related use cases, the techniques presented herein can be used to determine a vehicle configuration that is adapted to a user's personality before the vehicle is manufactured, and the vehicle can be manufactured at least partially based on (or "in accordance with") the determined vehicle configuration. The vehicle can be manufactured with different setting options, such as different motor options each having a different motor output, drive technology options (e.g., support for two-wheel drive (2WD) or 4WD technology), chassis options, different drive mode options, support for ACC, etc. (e.g., provided by a vehicle manufacturer). When a new vehicle is manufactured for a user, the vehicle configuration is determined to specifically suit the user's personality. For example, if the personality data indicates that the user has a tendency to avoid risks, the determined vehicle configuration may include the selection of a motor with a lower output compared to the vehicle configuration determined for a user whose personality data indicates a preference for taking risks. Based on the determined vehicle configuration, the vehicle can be manufactured as appropriate. Thus, in accordance with the above description, a method of manufacturing a vehicle can also be envisioned that includes efficiently obtaining, by a client device from a server, a digital representation of the user's personality data, and processing the digital representation of the personality data at the client device to provide a vehicle configuration adapted to the user's personality. This method includes sending, from the client device to the server, a request for the digital representation of the user's personality data, and the client device receiving, from the server, the requested digital representation of the user's personality data, wherein the digital representation of the user's personality data is calculated using a neural network trained to calculate the user's personality data based on inputs obtained from the user, processing the digital representation of the personality data to determine a vehicle configuration adapted to the user's personality, and manufacturing the vehicle at least partially based on the determined vehicle configuration.For example, if the determined vehicle settings are discarded and the vehicle is not ultimately manufactured, etc., the actual manufacturing steps may be optional. While it is conceivable that the vehicle settings are determined based only on the user's personality data, it will be understood that additional factors may be considered for the determination of the vehicle settings. For example, the user may make at least one pre-selection regarding a specific vehicle setting option (e.g., selection of a specific model or a specific vehicle color), and the determination of the vehicle settings may be performed depending on the at least one pre-selection. Additionally or alternatively, recommendations from an online advisor (e.g., a human advisor or a virtual advisor such as a chatbot) may be considered for the determination of the vehicle design. For example, the user may have an online discussion with the online advisor, and the determination of the vehicle design may be performed depending on one or more recommendations made by the online advisor. In the vehicle manufacturing process, it will be understood that the determined vehicle settings may also affect the manufacture of the vehicle parts required for the manufacture of the vehicle. For example, the manufacture of the vehicle can include the manufacture of one or more vehicle parts used in the manufacture of the vehicle, and the vehicle parts are manufactured according to the determined vehicle settings (e.g., using a 3D printer).

[0055] In the generalization of the above use cases, the techniques presented herein may be used to determine the configuration of a product that conforms to a user's personality before the product is manufactured, and the product may be manufactured at least partially based on (or "according to") the determined configuration. Such products may be not only vehicles as described in the previous use case, but also, for example, chemical products or pharmaceuticals (such as cosmetics including creams such as skin creams), textile products, or food products. The product may be manufacturable with different configuration options (such as provided by a manufacturing company). For example, chemical products, pharmaceuticals, or food products may be manufacturable with different ingredient options or ingredient composition options, and textile products may be manufacturable with different fiber materials, clothing styles, or cut options. When a user attempts to order such a product, the configuration of the product may be determined to particularly conform to the user's personality. For example, in the case of cosmetics, at least one of the moisture level (e.g., moist / dry), gloss level (e.g., glossy / matte), flavor type (e.g., flavored / neutral), fragrance type (e.g., fragrant / neutral), and skin effect type (e.g., skin-soothing / tingling) may be determined to conform to the user's personality. And based on the determined configuration, a corresponding product may be manufactured. Thus, a method of manufacturing a product, including efficiently obtaining a digital representation of a user's personality data from a server by a client device in accordance with the above description, may be envisioned, wherein the digital representation of the personality data is processed at the client device to provide a configuration of a product that conforms to the user's personality.This method includes sending a request for a digital representation of a user's personality data from a client device to a server, receiving, by the client device from the server, the requested digital representation of the user's personality data, wherein the user's personality data is calculated based on input obtained from the user using a neural network trained to calculate the user's personality data based on input obtained from the user, processing the digital representation of the personality data to determine a configuration of a product that conforms to the user's personality, and manufacturing the product at least partially based on the determined configuration. For example, the manufacturing step may be optional, such as when the determined configuration is discarded and the product is not ultimately manufactured. While it is contemplated that the configuration of the product may be determined based solely on the user's personality data, it will be understood that additional factors may be considered for the determination of the product. For example, the user may make at least one pre-selection regarding an option of a particular configuration (e.g., selection of a particular component), and the determination of the configuration may be made depending on the at least one pre-selection. Additionally or alternatively, recommendations from an online advisor (e.g., a human advisor or a virtual advisor such as a chatbot) may be considered for the determination of the configuration. For example, the user may have an online discussion with the online advisor, and the determination of the vehicle configuration may be made depending on one or more recommendations made by the online advisor.

[0056] In yet another vehicle-related use case, the provision of user-adapted services to users may be related to security features aimed at preventing harm from users who potentially have a tendency to commit suicide. For this purpose, a client device (e.g., a vehicle) may monitor predefined conditions (e.g., based on sensor measurements) that potentially indicate a user's intention to commit suicide. If it is determined based on such conditions that there is an intention to commit suicide, the client device compares the detected conditions with the user's personality data, and if it reaches the conclusion that the combination of the detected conditions and the user's personality data (e.g., indicating that the user is suffering from severe depression) may actually pose a suicide risk, preventive measures may be taken. Thus, the client device may be configured to provide user-adapted services taking into account not only the digital representation of the user's personality data but also the monitored predefined conditions that potentially indicate the user's intention to commit suicide (or "based on" / "in response to"), and providing user-adapted services to the user may include triggering one or more preventive measures against the user's intention to commit suicide. Exemplary conditions may include detecting that the user remains seated or lies down inside the vehicle while the vehicle's motor is still running but the vehicle has not moved for at least a predetermined time (which potentially indicates that exhaust gas is entering the passenger compartment. Optionally, this may be sensed by sensors in the passenger compartment). Corresponding countermeasures may include triggering an alarm, triggering an emergency call (e.g., a depression hotline, the police, friends, family, etc.), or simply stopping the motor. Other predefined conditions may include detecting that the user has parked the vehicle in a location where there is a risk of suicide, such as near a bridge, a steep cliff, or the side of a river or lake, and similarly, this may trigger an alarm or an emergency call.As yet another condition, it is detected that the user is tailgating the vehicle in front during high-speed driving, and optionally, in combination with detecting a scream inside the vehicle indicating that the user has exploded with anger, at the same time, it is detected that the user is alone in the vehicle (e.g., using seat occupancy detection) to rule out the possibility that the scream is the result of a dispute among multiple passengers. Corresponding countermeasures may include, for example, automatically decelerating / limiting the vehicle's driving speed, automatically maintaining a safe distance, starting an automated conversation or playing music to relax the user, and suggesting other driving routes, among at least one of these. It is understood that these conditions and countermeasures are merely illustrative, and various other use cases are generally conceivable.

[0057] In yet other vehicle-related use cases, the provision of user-adapted services to users may be related not only to the user's vehicle itself but also to the entire group of vehicles. If a group of vehicles (including the user's vehicle) are driving close to each other (e.g., within sight), and the personality data of other vehicle users (e.g., drivers / passengers) is available (e.g., in the same / similar way as described above for the current user himself), the personality data of the current user may be compared (or "matched") with the personality data of each other driver to determine and implement improvements to the collective driving behavior of the group of vehicles, i.e., taking into account (or "respecting") the personality of each individual driver, and optionally further considering the driving goals, preferences, or mood of each additional driver, so that traffic is improved (or "optimized") by the driving behavior (or "configuration") of the group of vehicles. Thus, a vehicle may be one of a plurality of vehicles driving close to each other, and the digital representation of the user's personality data may be compared with one or more digital representations of the personality data of users of other vehicles among the plurality of vehicles in order to implement an improved collective driving behavior of the plurality of vehicles, taking into account the individual personalities of each user, and optionally further considering the driving goals, preferences, or mood of each user. For example, if the group of vehicles is driving in autonomous mode, a vehicle with a stressed driver may be considered to allow another vehicle with a more relaxed driver who can accept such overtaking maneuvers to overtake. The improved collective driving behavior may be directed, for example, towards improving (or "optimizing") the traffic flow or energy consumption among the group of vehicles. Thus, in a vehicle platoon, a vehicle with a more relaxed driver may drive in the slipstream of other vehicles, or an electric vehicle with sufficient electrical energy for a short distance may transfer some of its energy (e.g., using induction) to another vehicle driven by a more conservative driver for a longer distance.To consider a user's specific driving goals, preferences, or mood, the user may, for example, enter the corresponding goals, preferences, or mood at the start or during the movement in the vehicle by statements such as "in a hurry", "relaxed", "under pressure", etc. Such information may also be collected based on answers to questions presented to the user that reflect the user's driving goals, preferences, or mood. Exemplary questions are described in Table 4 later. When there are multiple passengers in the vehicle, the personality data of all passengers in the vehicle may be used to determine collective personality data representative of all passengers in the vehicle and to compare it with the personality data of other vehicles. The determination of the collective personality data may include, for example, averaging or weighting the personality data of individual passengers in the vehicle and their values. The same may apply to the user's driving goals and preferences, which may likewise be combined with collective goals and / or preferences for comparison with other vehicles. To implement collectively improved driving behavior among vehicle groups, the vehicles may communicate with each other using, for example, vehicle-to-vehicle (V2V) communication and adjust themselves accordingly.

[0058] It will be understood that the above-described concept of determining collective personality data is generalized and can be adopted independently of the above-described use cases of the plurality of vehicles. In fact, the collective personality data can be defined for virtually any use case in which multiple users use user-adapted services together. Thus, when multiple users collectively use a user-adapted service, the collective personality data representing all the users who collectively use the service may be determined by combining the personality data of all the users. Determining the collective personality data may include, for example, averaging or weighting the personality data and its values of individual users. Providing a user-adapted service may then be based on the collective personality data, i.e., processing the digital representation of the personality data may then include processing the digital representation of the collective personality data to provide the user with a user-adapted service.

[0059] Furthermore, it will be understood that the above-described concepts defining the driving goals or preferences of a user driving multiple vehicles can be generalized and adopted independently of the above-described use cases of the multiple vehicles. Goals and preferences related to such use cases can be defined for substantially any use case, and thus, goals and preferences related to use cases can generally be adopted when providing user-adapted services to users. The user's goals and preferences related to a use case may be particularly relevant to the user-adapted services provided to the user. Such goals and preferences are particularly relevant to the "actual" user-adapted services currently provided (or to be provided) to the user, and thus, may be referred to herein as the user's "actual personality information". Thus, goals and preferences related to use cases are distinguished from the goals and preferences described above in relation to "input obtained from the user". As described above, the input obtained from the user may correspond to an answer to a question regarding at least one of the user's personality, goals, and motivations (where questions regarding personality may correspond to questions regarding the user's preferences). Goals and preferences related to use cases may similarly be obtained from answers to questions presented to the user, but such questions correspond to questions specifically directed to the "actual" use case (i.e., user-adapted service) and are dependent on a specific use case, whereas the questions described above in relation to "input obtained from the user" correspond to general questions regarding the user's "general" goals and preferences, i.e., questions not specifically directed to the current use case, or in other words, questions independent of the current use case. Goals and preferences need not be the only "actual personality information", and it will be understood that other types of actual personality information are generally contemplated. One such example may be the user's current mood (e.g., understood in the sense of the current "feeling" or "condition") at the time of providing the user-adapted service to the user, which may be considered to specifically adapt the service to the user.Similarly, information regarding the user's current mood may also be obtained from answers to questions presented to the user.

[0060] Exemplary questions related to use case-related goals, preferences, and / or mood are shown in the tables presented later in this specification. Table 4 provides an exemplary list of questions particularly relevant to the use case of riding in a vehicle, Table 5 provides an exemplary list of questions particularly relevant to the use case of manufacturing a vehicle, Table 6 provides an exemplary list of questions particularly relevant to the use case of allocating seats for a means of transportation, and Table 7 provides an exemplary list of questions particularly relevant to the use case of e-commerce (products available for purchase). These sets of questions related to use cases are merely exemplary, and it will be understood that various other types of questions for these and other use cases are generally contemplated as long as the questions are directed towards goals, preferences, and / or mood related to the use case in the above-described sense. It will be readily understood from the exemplary sets of questions presented in Tables 4 through 7 how these types of questions (particularly those directed towards "actual" use cases) are distinguished from the questions regarding the user's "general" goals and preferences presented in Tables 2 and 3 which are use case-independent.

[0061] In some embodiments, "actual personality information" may be used as the "input obtained from the user" in the method described above in connection with FIGS. 2 and 3, or as a standalone "input obtained from the user", and may also be used in combination with the other "inputs obtained from the user" described above. Thus, a method of providing a user-adapted service to the user generally corresponding to the method described above in connection with FIGS. 2 and 3 may be envisioned, the only difference being that "actual personality information" may be used as the (sole or additional) "input obtained from the user", based on which, subsequently, the neural network may calculate the user's personality data in accordance with the above description.

[0062] As described above, the actual personality information of the user may be obtained from the answers to the questions presented to the user. In other variations, the actual personality information such as the user's current mood and the user's preferences specific to the use case may not only be obtained from the user in a question / answer manner. For example, based on the body scan data in the sense of the above description, the actual personality information may be obtained. Thus, at least one of the user's current mood and one or more of the user's preferences may be obtained from body scan data indicating user characteristics derivable by scanning at least a part of the user's body. The body scan data may correspond to the above description regarding the body scan data and may also be obtained in accordance with the above description regarding the body scan data. To evaluate the user's current mood, the user's emotional state may be derived, for example, by using one of the above techniques such as interpreting the user's facial expression, gesture, and / or voice using body / face / voice recognition technology. Some items of the body scan data may be combined to determine the user's mood or preference. For example, in a vehicle, a sensor of the steering wheel may measure hand pressure, blood pressure, and pulse to accurately determine the user's stress level. As another example, it may be detected that the user is hectic from the times of door unlocking, door opening and closing, sitting behind the steering wheel, ignition, shift change, etc. (each operation is detected by different sensors in the vehicle). Therefore, at least two different types of body scan data may be combined to determine at least one of the user's current mood and one or more of the user's preferences. Other variations of the body scan data may be obtained based on eye-tracking, which may be used, for example, to detect the user's preference based on an item on which the user has gazed for more than a threshold time. The eye-tracking data may similarly be combined with other body scan data such as blood pressure / pulse measurement, etc., which may indicate whether the item gazed at has caused an emotional change in the user.It will be understood that, in addition to eye tracking, for example, when a user uses a computer, mouse-tracking may be used as an alternative technique. Thus, at least one of one or more preferences of the user may be obtained by tracking the user's eyes or mouse.

[0063] When a plurality of users use a user-adapted service in a group, it will be understood that the body scan data obtained for all individual users may be combined to determine collective body scan data representing all users who use the service in the group, i.e., the user group. Determining the collective body scan data may include, for example, averaging or weighting the body scan data and its values of individual users. And the current collective mood of the user group and the collective preferences of the user group may be obtained from the collective body scan data. And providing a user-adapted service may be performed based on the collective body scan data, i.e., processing the digital representation of personality data may include processing the digital representation of collective personality data to provide a user-adapted service to the user group, where the collective personality data may be calculated based on the collective body scan data. As a mere example, when it is detected by face recognition that 3 out of 4 passengers in a vehicle are anxious considering the bad weather while driving along a winding coastal route, the current collective mood of the entire group of passengers may be determined to be anxious, and as a result, the driving settings of the vehicle may be adapted to be based on more safety functions.

[0064] The technology presented in this specification is understood to be applicable not only to use cases related to vehicles / transportation means, but also to other use cases such as, for example, adapting the settings of smart home appliances or robots to the user's personality. Thus, in line with the above description, a method of adapting the settings of smart home appliances (e.g., automatic roller shutters, air conditioners, refrigerators, washing machines, televisions, set-top boxes, etc.) including efficiently obtaining a digital representation of the user's personality data can also be envisioned, where the digital representation of the user's personality data is processed in a client device to adapt the settings of the smart home appliance to the user's personality (e.g., for the smart home appliance to adapt the way it performs major tasks such as, for example, shutter (roller shutter), heating / cooling (air conditioner), refrigeration (refrigerator), washing (washing machine), or recording / display (television / set-top box) tasks). Similarly, in line with the above description, a method of adapting the settings of robots (e.g., humanoid robots configured to perform one or more household tasks, domestic robots, robots functioning as virtual drivers for driving vehicles, vendor robots in supermarkets, agricultural robots, robot exoskeletons, etc.) including efficiently obtaining a digital representation of the user's personality data can be envisioned, where the digital representation of the user's personality is processed in a client device to adapt the settings of the robot to the user's personality (e.g., for the robot's movement, such as a humanoid robot adapting the way it mimics facial expressions (e.g., lip or eye movements), or for adapting the robot's actions such as the way it executes work procedures or controls, or for adapting the way a domestic robot performs household chores, or for adapting the way an agricultural robot performs transplanting work, or for adapting the way a robot exoskeleton supports the movement of a user wearing the exoskeleton).

[0065] Various other use cases are commonly considered. Other use cases can include, for example, adapting the settings of virtual robots, adapting the settings of medical devices, or even stimulating the brain. Thus, in line with the above description, it is also possible to envision a method of adapting the settings of virtual robots (such as chatbots, virtual service agents, virtual personal assistants) that includes efficiently obtaining a digital representation of the user's personality data, where the digital representation of the user's personality is processed on a client device in order to adapt the settings of the virtual robot to the user's personality (e.g., to adapt the way the virtual robot performs tasks to support the user). In some variations, the virtual robot may be displayed in the form of a hologram (e.g., displayed in free space or as part of a vehicle's head-up display). The hologram that is displayed may be considered to reflect a person (e.g., an avatar) who converses with the user, but it will also be understood that other images or videos adapted to the user's personality can be employed in the display of the hologram. Furthermore, not only the display of the hologram, but also the way the virtual robot interacts (e.g., talks) with the user can be adapted, such as adapting the voice characteristics (e.g., voice frequency / volume, male / female, etc.) or the way the virtual robot talks. As a mere example, a hologram displayed on a vehicle's head-up display may be displayed as a police officer who speaks in authoritarian terms. Adapting the configuration of the virtual robot may be related to the way of providing notifications, instructions, or warnings to the user. Such messages may be provided to the user in a way adapted to the user's personality, for example, so that the probability of a behavioral disorder is reduced and / or so that the user is more likely to accept the message (e.g., by providing a user-adapted statement that explains / justifies the provision of the message). In the context of a vehicle, for example, if the user has a curious personality, it may be considered to provide a warning message aimed at preventing the user from rubbernecking when an accident has occurred nearby, thereby potentially avoiding further accidents.

[0066] Similarly, in line with the above description, it is possible to envision a method for adapting a patient's treatment plan or a method for adapting the settings of a medical device (e.g., a bedside medical device) that includes efficiently obtaining a digital representation of the user's personality data, where the digital representation of the user's personality is used to change the method of a user-adapted medical treatment, such as a treatment that physically applies force to the user's body and / or a treatment that administers a medical substance (e.g., a drug) to the user, in order to adapt the settings of the medical device to the user's personality (e.g., to adapt the settings of a cardiac pacemaker, to adapt the mechanical configuration of an electromechanically adjustable prosthesis, to adapt the dispensing process of a drug, to adapt the dosing plan such as the dosage of an analgesic, etc.), and may be processed on a client device. Similar to medical devices, a method for adapting the configuration of a sports device (e.g., training devices such as treadmills, fitness bikes, crosstrainers, etc.) that includes efficiently obtaining a digital representation of the user's personality data is envisioned, where the digital representation of the user's personality is processed on a client device and the configuration of the sports device may be adapted to the user's personality (e.g., adapting the resistance of the sports device to increase or decrease the force applied by the user, adapting the position adopted by the user on the sports device, adapting the training program stored in the sports device to the user's personality, etc.). As described above for virtual robots, it will be understood that adapting the configuration of a medical device or a sports device may similarly be related to a method of providing a notification, instruction, or warning to the user (e.g., ensuring that a drug is taken by the user at the appropriate time or motivating the user of a sports device during training in the most suitable way for the user).

[0067] Moreover, more generally in this context, any type of message or information provided to a user, such as an advertising message for example, as part of a user-adapted service (as generally described herein), may be adapted to the user's personality. It will be understood that such messages may, in some variations, be displayed on a remote screen near the user (e.g., in the line of sight), such as on an electronic advertising panel (or "billboards") installed in a public place (e.g., at an airport, on the road, etc.). A client device (e.g., a smartphone or tablet carried by the user) may send personality data to a server providing the user-adapted service (e.g., via a local network in which the client device is registered, such as a Wi-Fi network available in a public place), and the server may adapt the message or information displayed on the remote screen to the user. It will be understood that the personality data may be sent to such a server via other technical channels in addition to via the Wi-Fi network. In one variation, the personality data may be transmitted to the server along with a transaction (e.g., a payment transaction for a purchased product or service) executed using the client device. For example, the personality data may be transmitted to the server along with the transaction. In such a variation, it is conceivable to use the personality data as a kind of "means of payment" (or "currency") for the transaction being processed. That is, the user may, for example, receive a certain (e.g., monetary) value, such as a reduction in the price (or free offer) of the product or service to be purchased, in exchange for providing the user's personality data, in return for permission to access the user's personality data.

[0068] As part of a user-adapted service, providing messages or information to a user, as described above, may be related not only to advertising messages but also to any information provided to the user. For example, a user accessing an e-commerce service (e.g., visiting an e-commerce website or using an e-commerce app) may be presented with content (e.g., purchasable products) that is particularly adapted to the user's personality. As another example, a user using an infotainment system in a means of transportation (e.g., a vehicle, an aircraft, or a train) may be presented with infotainment options (e.g., selectable movies, etc.) that are particularly adapted to the user's personality. It will be understood that various other use cases for presenting user-adapted information to a user are generally conceivable. In this regard, note that not only the content is presented in a user-adapted manner, but also the look-and-feel of the information presented may be presented in a user-adapted manner. In one variant, presenting user-adapted information to a user may be implemented using a filter executed on a client device or at least one other device providing the service to the user (e.g., an end-user device such as a smartphone, a tablet, or a laptop), where the filter is executed locally on such a device and may filter out content based on the user's personality (e.g., preferences, etc.) before the content is presented to the user. As a mere example, if the content is provided to the user in the form of a website, the filter may be executed locally on the end-user device and remove from the website content that the user is likely not interested in before the content is presented to the user on the device.

[0069] Other use cases for providing information to a user as part of a user-adapted service may be related to communication applications. In communication applications such as video telephony or chat applications, important elements of common non-verbal communication that are normally recognizable when the communication partner is physically present (e.g., elements such as physical presence / energy, body posture, etc.) may be lost in digital communication. To mitigate such loss, for example, it may be considered to adapt the display of a communication application (e.g., a chat or video conferencing application) based on information regarding the personality data of the communication partner, whereby the user can better understand the personality of the communication partner, and thus, the communication method can be adapted so that the user can better conform to the personality of the communication partner. In other words, the personality data may be shared with the communication partner so that the user can deal with the communication partner in a more empathetic way, and thus, the lost personal contact may be (at least to some extent) compensated. As a result, the quality and effectiveness of digital communication may be improved.

[0070] While it is understood that the personality data used to adapt the display of a communication application may correspond to the "raw values" of the user's personality data (in the sense described above), in the context of the use case of communication, the personality data used to adapt the display may sometimes correspond to the "comparative value" (or "relative value") of the user's personality data (in the sense described above). More specifically, the personality data used to adapt the display may correspond to the "comparative value" of the user's personality data compared to the personality data of each communication partner. In one variant, adapting the display of the communication application based on information about the personality data of the communication partner may include displaying at least a part of the personality data of the communication partner (for example, the value of the user's personality dimension or the value of the personality characteristic obtained therefrom) to enable the user to better evaluate the personality characteristics of the partner. In another variant, it is conceivable to display words that should not be used considering the personality of the communication partner or words that are actively used and are likely to gain the favor of the communication partner. In yet another variant, adapting the display of the communication application may include adapting the video or background image displayed to the user, where the video or background image is particularly adapted to the user's personality and may, for example, have a positive impact on the user's attitude / feelings towards the communication partner (as a simple example, in a video conference, it is conceivable that the color of the partner's tie in the video image is adapted to the color preferred by the user). Not only visual presentations but also auditory presentations may be adapted to have a positive impact on the user's attitude / feelings towards the communication partner. For example, the audio settings (such as audio frequency / volume, etc.) that the partner can hear may be adapted according to the user's preferences. It will be understood that such visual or auditory adaptations may similarly be applied on the side of the communication partner.

[0071] In a particular context, once the digital representation of a user's personality is calculated according to one of the techniques presented herein using, for example, a neural network based on input obtained from the user, it may be stored in a device that emulates a chip card or a chip card (such as a smartphone that emulates a chip card function using NFC (Near Field Communication)). Here, the user's personality data may be read from the chip card or the device that emulates the chip card before the personality data is processed on the client device, and as described above, user-adapted services may be provided to the user. Accordingly, the above-described transmission and reception steps (e.g., steps S302 and S304) between the client device and the server may be omitted. Instead, a method may be envisioned in which the client device reads the digital representation of the user's personality data from the chip card or the device that emulates the chip card and processes the digital representation of the personality data to provide user-adapted services to the user. Before storing the digital representation of the personality data in the chip card or the device that emulates the chip card, the digital representation of the personality data may be calculated based on the input obtained from the user using a neural network trained to calculate the user's personality data based on the input obtained from the user, as generally described herein. For example, in a medical context, the digital representation of the user's personality may be stored as part of a digital health record (or "digital patient file") so that it can be automatically obtained before treatment of the patient, for example, by reading the personality data from a chip card on which the digital health record is stored. And the personality data obtained from the chip card may be processed as described above to configure a medical device or to adapt any other medical service to the user, such as by assigning the user a hospital room suitable for the user's personality. Various other contexts in which such chip cards can be used are generally conceivable.As a mere example, personality data may be stored in a bank card (e.g., processed to fit payment-related services for the user), an insurance card (e.g., processed to fit insurance products for the user), a payback card (e.g., processed to fit payback offers for the user), etc.

[0072] For purchasable products, it is also conceivable to adapt subsequent steps in the value chain, such as the manufacturing and delivery of the product, to the user's personality. Providing a user-adapted service to the user may, in these cases, include adapting the manufacturing of the product and / or the delivery of the product according to the user's personality. Thus, if the product is manufactured even after purchase, the manufacturing of the product may be particularly adapted to the user's preferences (e.g., a product printed using a 3D printer after purchase may be printed in a way that is particularly adapted to the user's personality / preferences). Similarly, in some variations, providing a user-adapted service to the user may include providing a logistics / delivery service that is particularly adapted to the user's personality. For example, the packaging of the product (e.g., the color or material of the packaging) may be particularly adapted to the user's personality / preferences. Additionally or alternatively, the selected delivery technology (e.g., drone, delivery truck, bicycle courier) may be particularly adapted to the user's personality / preferences (e.g., an elderly person may prefer to receive a delivery from a human, while a young person may prefer to receive a package from a drone). Also, the delivery modalities (e.g., delivery time, delivery location, and / or delivery priority, etc.) may be particularly adapted to the user's personality.

[0073] As described above, the neural network described in this specification can be regarded as an efficient functional data structure that calculates the required personality data and enables the calculated personality data to be provided to the client device in the form of a digital representation. With regard to feedback, it has been explained that the neural network represents an efficient updatable data structure that can be updated based on (any) feedback regarding the user's personality and improve its ability to calculate personality data. Such a neural network is considered to be a data structure that can be enhanced to improve the reflection of the user's personality over time by continuously learning based on various feedback from the user. Thus, with the emergence of continuously increasing computing resources over the next few years to decades, it can be considered that the neural network will evolve as a copy of the user's mind that has the ability to steadily improve the calculation accuracy of the user's personality as the amount of feedback increases. From a long-term perspective, it is also conceivable to construct a copy of the human mind that enables mental inquiries as if directly asking the user himself. Therefore, it can be said that the user's mind is (at least to some extent) "preserved". As described above, the feedback for updating the neural network may be collected in at least one device that provides services to the client device and / or the user. However, it will be understood that additional devices may be used to collect feedback regarding the user's personality. In one such variant, an implantable brain-computer-interface (such as, for example, developed by Neuralink Corporation, http: / / neuralink.com / ) can be used to continuously collect feedback regarding the user's personality data directly from the brain and accordingly update the neural network over time.

[0074] In some variations, the mind copy may then be used to adapt the behavior of a robot or virtual robot (e.g., the robot or virtual robot described above) by configuring the robot or virtual robot according to the mind copy. In other words, the virtual representation of the brain may be supplied to a robot or other form of intelligent system to influence the operation of such a system based on the user's personality. As a mere example, a humanoid robot or virtual robot (e.g., in the form of a virtual personal assistant or hologram) may be configured based on the mind copy to act as a copy of a real human (as realistically as possible). And the copy of the real human may be used to take over tasks that a real human would normally perform. As a mere example, it is conceivable that a copy of a real human may conduct a phone conversation on behalf of the real human so that the conversation partner does not notice that the conversation is not being conducted with a real human. Further, it is possible to envision a method of stimulating a brain (e.g., a biological or virtual representation of a brain) that includes efficiently obtaining a digital representation of the user's personality data, where the digital representation of the personality is processed on the user's client device to adapt the brain stimulation procedure based on the user's personality. The stimulation procedure may include, for example, electrical stimulation of a biological brain or adapting / resetting a virtual representation of the brain. In other use cases, as already shown, it is conceivable to use the mind copy, more specifically all of the personality data, as a kind of "payment means" or "currency", and the user may be able to monetize their own personality data, for example, when conducting payment transactions, etc.

[0075] In all of the above examples and use cases, when referring to "adapting" a configuration or setting "to the user's personality", such adaptation can be implemented using a pre-defined mapping that maps certain characteristics of the user's personality (represented by the digital representation of the user's personality data) to specific configurations or settings of the corresponding device / apparatus (e.g., vehicles, means of transportation, smart home appliances, robots, medical devices, etc. as described above). As described above, for example, if the personality data indicates that the driver has a tendency to avoid risks, the drive mode of the vehicle can be set to economy or comfort, while for a driver who tends to have a risk-seeking personality, the drive mode can be set to sports mode. Such a mapping can be pre-defined for each possible combination of personality characteristics - configuration / settings, and the configuration or settings of the device / apparatus can be appropriately adapted according to the obtained user personality data. The mapping may be pre-defined in the client device. For example, when the client device sets at least one other device that provides services to the user as described above, the client device may provide the pre-defined mapping to at least one other device so that the mapping is implemented on the at least one other device and a user-adapted service is provided to the user. In this way, the computational load on at least one other device is reduced. In other words, this device can function as a "mapping recipient" that receives the mapping from a client device that can function as a "mapping provider".In other variations, the pre - defined mapping may be pre - defined (or "pre - saved") on at least one other device, in which case it will be understood that the at least one other device may receive a given characteristic of the user's personality and map it to a particular configuration or setting of the at least one other device. The user's personality characteristics may correspond, for example, to the values of personality dimensions (e.g., from the Big Five) output by a neural network as described above.

[0076] In the above description, the technology presented in this specification has been described as a technology that enables a client device to efficiently obtain a digital representation of a user's personality data from a server (which can be used in various use cases). However, it will be understood that the calculated digital representation of the user's personality data does not necessarily have to be sent directly from the server to the client device. Rather, once the user's personality data becomes available to the user, it may be manually entered into the client device by the user. Thus, on the side of the client device, a method of providing a user-adapted service to the user of the client device (in this case, since there may not be a direct client-server relationship, this "client device" may not necessarily be understood in the sense of a device in a client-server relationship, and thus the client device may sometimes simply be referred to as a "device") is also envisioned, where this method may be executed by the client device and includes steps of obtaining a digital representation of the user's personality data via manual input by the user, and processing the digital representation of the personality data to provide a user-adapted service to the user. Such a method is illustrated in FIG. 9, where in step S902, the corresponding step of obtaining a digital representation of the user's personality data is shown, and in step S904, the corresponding step of processing the digital representation of the user's personality data is shown. Except for the different method of entering the digital representation of the user's personality data into the client device (i.e., entering it via manual input instead of obtaining it directly from the server), all of the above-described aspects may be applied to the method of FIG. 9, particularly with respect to the client device and the server.Therefore, the digital representation of the user's personality data obtained by the client device via the user's manual input may be calculated by the server based on the input obtained from the user using a neural network trained to calculate the user's personality data based on the input obtained from the user (however, since it is also conceivable that the user's manual input corresponds to the user's personality data determined by other means, it is not necessarily calculated in this way). In one variant, in accordance with the above description, the client device may be a vehicle, and providing the user with a user-adapted service may include adapting the vehicle's driving settings to the user's personality.

[0077] Instead of manually entering the digital representation of the user's personality data, it is also conceivable to use the vehicle identification number to identify selected vehicle configuration options, where the vehicle may be manufactured based on the vehicle configuration options (e.g., as provided by the vehicle manufacturer as described above). And the vehicle configuration thus identified may be used as the "input obtained from the user" in the sense described above, i.e., to request the server to calculate the user's personality data using a neural network based on the input. The user's personality data obtained in this way may then be used in any of the ways described above to provide the vehicle user with a user-adapted service.

[0078] The above embodiments have been mainly described with reference to the interaction between the client device and the server. For example, from the perspective of the client device, it includes the step of sending a request for a digital representation of the user's personality data to the server (S302), and the step of receiving the digital representation of the requested user's personality data from the server (S304). However, the efficient acquisition of the digital representation of personality data presented in this specification does not necessarily have to be implemented in such a client / server scenario. More generally, it is possible to efficiently acquire the digital representation of the user's personality data, and a method of processing the digital representation of personality data to provide the user with a user-adapted service. This method can be expressed as a method that includes acquiring the digital representation of the user's personality data, where the user's personality data is calculated based on the input obtained from the user using a neural network trained to calculate the user's personality data based on the input obtained from the user, and processing the digital representation of personality data to provide the user with a user-adapted service. It will be understood that such a more general formulation may be applied to all of the above embodiments instead of the exact sending and receiving steps between the client device and the server.

[0079] Referring to the above description of "actual personality information", it should be noted that in some implementations, the user's personality data may be calculated based only on the actual personality information without using a neural network. In such an implementation illustrated in FIG. 10, a method of providing a user-adapted service to the user is assumed, and this method is executed by a computing system. In step S1002, it is a step of obtaining a digital representation of the user's personality data, where the user's personality data is calculated based on an input related to the user, the input related to the user includes the user's actual personality information, the user's actual personality information is particularly relevant to the user-adapted service, and includes at least one of the user's current mood at the time of providing the user-adapted service to the user, one or more preferences of the user particularly relevant to the user-adapted service, and one or more goals of the user particularly relevant to the user-adapted service. And in step S1004, a step of processing the digital representation of the personality data to provide the user with a user-adapted service may be included. The computing system may be formed by a client device and a server. Therefore, it will be understood that step S1002 of obtaining may similarly be realized in a client / server scenario along the corresponding steps of transmitting and receiving, S302 and S304. As described above, the user's actual personality information may be obtained from the answers to the questions presented to the user. Such a calculation of the user's personality data may, in one variant, be performed using a proprietary algorithm (e.g., including a mapping from actual personality information to the digital representation of each user's personality data, etc.), but it will be understood that in other variants, it may also be performed using a neural network according to the method described above. Therefore, the user's personality data may be calculated based on an input related to the user using a neural network trained to calculate the user's personality based on an input related to the user.Inputs regarding the user may correspond to digital scores that reflect answers to questions presented to the user, and each digital score may be used as an input to separate input nodes of a neural network when calculating the user's personality data using the neural network. As described above, when combining "actual personality information" with "inputs obtained from the user" as additional inputs, the inputs regarding the user may further correspond to digital scores that reflect answers to questions regarding at least one of the user's personality, goals, and motivations. Similarly, each digital score may be used as an input to separate input nodes of a neural network, for example, when calculating the user's personality data using the neural network.

[0080] The advantages of the technology presented in this specification are fully understood from the above description, and it will be apparent that various changes can be made to the form, structure, and arrangement of its exemplary embodiments without departing from the scope of the present disclosure or sacrificing all of its advantageous effects. Since the technology presented in this specification can be modified in many ways, it will be understood that the present disclosure should be limited only by the following claims.

[0081]

Table 1

[0082]

Table 2

[0083]

Table 3

[0084]

Table 4

[0085]

Table 5

[0086]

Table 6

[0087]

Table 7

[0088]

Table 8

[0089]

Table 9

[0090]

Table 10

[0091]

Table 11

[0092]

Table 12

[0093]

Table 13

[0094]

Table 14

[0095]

Table 15

[0096]

Table 16

[0097] Advantageous examples of the present disclosure can be expressed as follows. [Example 1] A method for enabling a digital representation of a user's (402) personality data to be efficiently obtained by a client device (502, 406) from a server (404), wherein the digital representation of the personality data is processed in the client device (406) to provide a user-adapted service to the user (402), the method being executed by the server (404), Storing (S202) a neural network (602) trained to calculate the user's (402) personality data based on an input obtained from the user (402); Receiving (S204) a request for a digital representation of the user's (402) personality data from the client device (502, 406); Transmitting (S206) the requested digital representation of the user's (402) personality data to the client device (502, 406), wherein the user's (402) personality data is calculated using the neural network (602) based on an input obtained from the user (402), A method having the above. [Example 2] The digital representation of the user's (402) personality data is processed in the client device (502, 406) to configure at least one device (406) that provides a service to the user (402), Optionally, the at least one device (406) includes the client device (406), The method according to Example 1. [Example 3] Receiving feedback characterizing the user (402); Updating the neural network (602) based on the feedback; Transmitting a digital representation of the updated personality data of the user (402) to the client device (502, 406), wherein the updated personality data of the user (402) is calculated using the updated neural network (602); Optionally, the digital representation of the updated personality data of the user (402) is processed at the client device (502, 406) to improve the settings of the at least one device (406) that provides the service to the user (402); The method according to Example 1 or 2. [Example 4] The feedback includes behavioral data reflecting the behavior of the user (402) monitored at the at least one device (406) when using the service provided by the at least one device (406); Optionally, the behavioral data is monitored using measurements performed by the at least one device (406) that provides the service to the user (402); The method according to Example 3. [Example 5] The at least one device (406) includes a vehicle, and the behavioral data includes data reflecting the driving behavior of the user (402); The method according to Example 4. [Example 6] The personality data of the user (402) is calculated before receiving the request from the client device (502, 406), and the request includes an access code that was previously provided to the user (402) by the server (404) when calculating the personality data of the user (402), and the access code enables the user (402) to access the digital representation of the personality data of the user (402) from other client devices (502, 406). The method according to any one of Examples 1 to 5. [Example 7] The input obtained from the user corresponds to a digital score reflecting an answer to a question regarding at least one of the personality, goals, and motivations of the user (402), and each digital score is used as an input to a separate input node of the neural network (602) when calculating the personality data of the user (402) using the neural network (602). The method according to any one of Examples 1 to 6. [Example 8] The question corresponds to a question selected from a set of questions representing results that can optimally achieve the calculation of the personality data of the user (402). The selected question corresponds to a question in the set of questions determined to be the most influential regarding the optimally achievable result. Optionally, the number of the selected questions is less than 10% of the number of questions included in the set of questions. The method according to Example 7. [Example 9] The question is selected from the set of questions based on correlating the results achievable by each of the single questions in the set of questions with the optimally achievable result and selecting questions from the set of questions having the highest correlation with the optimally achievable result, or The question is repeatedly selected from the question set, and in each iteration, the next question is selected according to the user's answer to the previous question. In each iteration, the next question is selected as one of the questions in the question set that is determined to have the most influence on the achievable results for calculating the user's personality data. Optionally, the neural network (602) includes a plurality of output nodes representing a probability curve (604) of the result of the personality data of the user (402). Determining the most influential question in the question set as the next question in each iteration includes, for each input node of the neural network (602), determining the degree to which a change in the digital score input to each input node of the neural network (602) changes the probability curve (604). The method according to Example 8. [Example 10] A method for enabling a digital representation of the personality data of a user (402) to be efficiently obtained from a server (404) by a client device (502, 406), the method being executed by the client device (502, 406). Sending a request for a digital representation of the personality data of the user (402) to the server (404) (S302). Receiving the requested digital representation of the personality data of the user (402) from the server (404) (S304), wherein the personality data of the user (402) is calculated based on the input obtained from the user (402) using a neural network (602) trained to calculate the personality data of the user (402) based on the input obtained from the user (402). Processing (S306) the digital representation of the personality data to provide a user-adapted service to the user (402). A method having the above steps. [Example 11] A computer program product comprising program code portions for performing the method according to any one of Examples 1 to 10 when the computer program product is executed on one or more computing units. [Example 12] A computer program product according to Example 11, stored on one or more computer-readable recording media. [Example 13] The server (100, 404) for enabling the digital representation of the personality data of the user (402) to be efficiently obtained from the server (404) by the client devices (502, 406), wherein the digital representation of the personality data is processed in the client devices (502, 406) for providing user-adapted services to the user (402), the server (404) comprising at least one processor (102) and at least one memory (104), the at least one memory (104) including instructions executable by the at least one processor (102) such that the server (404) is operable to perform the method according to any one of Examples 1 to 9. [Example 14] The client device (110, 502, 406) for enabling the digital representation of the personality data of the user (402) to be efficiently obtained from the server (404), the client device (110, 502, 406) comprising at least one processor (112) and at least one memory (114), the at least one memory (114) including instructions executable by the at least one processor (112) such that the client device (110, 502, 406) is operable to perform the method according to Example 10. [Example 15] A system comprising the server (100, 404) according to Example 13 and at least one client device (110, 502, 406) according to Example 14. [Example 16] A method for providing a user-adapted service to a user (402) of a vehicle (406), the method being executed by the vehicle (406), obtaining, via manual input by the user (402), a digital representation of the personality data of the user (402) (step S902); processing the digital representation of the personality data to provide a user-adapted service to the user (402) (step S904); and providing the user-adapted service to the user (402) includes adapting the driving settings of the vehicle (406) to the personality of the user (402). [Example 17] The method according to Example 16, wherein providing the user-adapted service to the user (402) further includes at least one of adapting environmental conditions in the passenger compartment of the vehicle (406) and adapting user-specific settings related to the passenger compartment of the vehicle (406) to the personality of the user (402). [Example 18] The method according to Example 16 or 17, wherein providing the user-adapted service to the user (402) is further performed in consideration of sensor data indicating the attention level of the user (402) obtained in the passenger compartment of the vehicle (406). [Example 19] The method according to any one of Examples 16 to 18, wherein providing the user-adapted service to the user (402) is further performed in consideration of at least one of geographical data, weather data, and time data related to a planned route for moving using the vehicle. [Example 20] The method according to any one of Examples 16 to 19, wherein providing the user-adapted service to the user (402) is further performed in consideration of body scan data indicating characteristics of the user (402) derivable by scanning at least a part of the body of the user (402). [Example 21] Providing the user-adapted service to the user (402) is monitored and further performed in consideration of predefined conditions that potentially indicate the user's (402) suicidal intent, and providing the user-adapted service to the user (402) further includes triggering one or more preventive measures against the user's (402) suicidal intent, the method according to any one of examples 16 to 20. [Example 22] The vehicle (406) is one of a plurality of vehicles (406) traveling close to each other, and the digital representation of the personality data of the user (402) is optionally compared with one or more digital representations of the personality data of the users (402) of the other vehicles among the plurality of vehicles (406) in consideration of the individual personalities of each user (402) and further in consideration of the driving goals or preferences of each user (402) to implement an improved driving behavior of the plurality of vehicles (406) collectively, the method according to any one of examples 16 to 21. [Example 23] The personality data of the user (402) is calculated by the server (404) based on the input obtained from the user (402) using a neural network (602) trained to calculate the personality data of the user (402) based on the input obtained from the user (402), the method according to any one of examples 16 to 22. [Example 24] The input obtained from the user (402) corresponds to a digital score reflecting an answer to a question regarding at least one of the personality, goals, and motivations of the user (402), and each digital score is used as an input to a separate input node of the neural network (602) when calculating the personality data of the user (402) using the neural network (602), the method according to example 23. [Example 25] The question corresponds to a question selected from a set of questions representing results that can optimally achieve calculating the personality data of the user (402), The selected question corresponds to the question of the question set determined to be the most influential regarding the optimally achievable result, Optionally, the number of the selected questions is less than 10% of the number of questions included in the question set, the method according to Example 24. [Example 26] The question is selected from the question set based on correlating the results achievable by each of the single questions of the question set with the optimally achievable result and selecting questions from the question set having the highest correlation with the optimally achievable result, or, The question is repeatedly selected from the question set, and in each repetition, the next question is selected according to the user's answer to the previous question, and in each repetition, the next question is selected as one of the questions of the question set determined to be the most influential on the result for calculating the user's personality data, Optionally, the neural network (602) includes a plurality of output nodes representing a probability curve (604) of the result of the personality data of the user (402), and determining the most influential question of the question set as the next question in each of the repetitions includes determining, for each of the input nodes of the neural network (602), the degree to which a change in the digital score input to each of the input nodes of the neural network (602) changes the probability curve (604), the method according to Example 25. [Example 27] A computer program product including a program code portion for executing the method according to any one of Examples 16 to 26 when the computer program product is executed on one or more computing units. [Example 28] The computer program product according to Example 27 stored on one or more computer-readable recording media. [Example 29] A vehicle (406) for providing a user-adapted service to a user (402), having at least one processor (112) and at least one memory (114), wherein the at least one memory (114) includes instructions executable by the at least one processor (112) such that the vehicle (406) is operable to execute the method according to any one of items 16 to 26.

Claims

1. A method for providing a user-tailored service to a user (402), the method comprising: a step (S1002) of obtaining a digital representation of personality data of a user (402), the personality data of the user (402) being calculated based on inputs relating to the user (402), the inputs relating to the user (402) including actual personality information of the user (402) resulting from at least one answer to at least one question presented to the user (402), the at least one question being specifically related to a driving service of the vehicle (406) provided to the user (402); (a) one or more questions directed to the current mood of the user (402) at the time of providing the driving service to the user (402); (b) one or more questions directed to one or more preferences of the user (402) specifically related to the driving services to be provided to the user (402); and (c) one or more questions directed to one or more goals of the user (402) that are specifically related to the driving services to be provided to the user (402); and A step (S1004) of processing the digital representation of the personality data to provide a user-tailored service to the user (402), the step of providing the user-tailored service to the user (402) comprising: Adapting driving settings of the vehicle (406) to the user (402), the driving settings of the vehicle (406) corresponding to vehicle settings that influence the driving behavior of the vehicle (406); Adapting environmental conditions in the passenger compartment of the vehicle (406) to the user (402); and adapting user-specific settings for the passenger compartment of the vehicle (406) to the user (402); and having at least one of the steps of:

2. 2. The method of claim 1, wherein at least one of the current mood of the user (402) and the one or more preferences of the user (402) is further obtained from body scan data indicative of characteristics of the user (402) derivable by scanning at least a portion of the body of the user (402).

3. 3. The method of claim 2, further comprising combining at least two different types of body scan data obtained from the user to determine at least one of a current mood of the user and one or more preferences of the user.

4. The method of claim 2 or 3, wherein at least one of the one or more preferences of the user (402) is obtained by eye tracking or mouse tracking of the user (402).

5. 5. The method of claim 2, wherein when a plurality of users (402) collectively use the driving service, body scan data is acquired for all individual users (402) of the plurality of users (402) and combined to determine collective body scan data, and the driving service is provided based on the collective body scan data.

6. 6. The method of claim 1, wherein the personality data of the user (402) is calculated based on inputs relating to the user (402) using a neural network (602) trained to calculate personality data of the user (402) based on inputs relating to the user (402).

7. 7. The method of claim 6, wherein the inputs for the users (402) correspond to digital scores reflecting the answers to the questions posed to the users (402), and each digital score is used as an input to a separate input node of the neural network (602) when using the neural network to calculate the personality data for the users (402).

8. 8. The method of claim 7, wherein the inputs regarding the user (402) further correspond to digital scores reflecting answers to questions regarding at least one of the user's (402) personality, goals, and motivations, each digital score being used as an input to a separate input node of the neural network (602) when using the neural network (602) to calculate the personality data for the user (402).

9. the questions correspond to questions selected from a set of questions representing optimally achievable results for computing personality data of the user (402); the selected questions correspond to questions from the question set that are determined to be most influential with respect to optimally achievable outcomes; 8. The method of claim 6 or 7, optionally wherein the number of selected questions is greater than or equal to 1 and less than 10% of the number of questions included in the question set.

10. the questions are selected from the set of questions based on correlating the results achievable by each single question in the set of questions with optimally achievable results and selecting the question from the set of questions having the highest correlation with the optimally achievable results; or the questions are iteratively selected from the set of questions, and in each iteration a next question is selected depending on the user's answer to a previous question, and in each iteration the next question is selected as the one question from the set of questions determined to be most influential on the achievable results for calculating the personality data of the user; 10. The method of claim 9, wherein optionally, the neural network (602) includes a plurality of output nodes representing a probability curve (604) of outcomes of the personality data of the user (402), and determining a most influential question of the set of questions as the next question for each of the iterations comprises determining, for each input node of the neural network (602), the extent to which a change in a digital score input to each of the input nodes of the neural network (602) changes the probability curve (604).

11. 11. A computer program product comprising program code portions for performing the method according to any one of claims 1 to 10, when said computer program product is executed on one or more computing units.

12. 12. The computer program product of claim 11 stored on one or more computer readable recording media.

13. A computing system for providing user-adapted services to a user (402), the computing system having at least one processor and at least one memory, the at least one memory including instructions executable by the at least one processor such that the computing system is operable to perform a method according to any one of claims 1 to 10.

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