Technique for efficient acquisition of data

Abstract

To enable efficient acquisition of digital representation of personality data of a user by a client device from a server.SOLUTION: Digital representation of the personality data is processed at a client device (406) to provide a user-adapted service to a user (402). A method implementation of the technique is performed by a server (404), and comprises storing a neural network being trained to compute personality data of the user (402) based on input obtained from the user (402), receiving, from the client device (406), a request for digital representation of personality data for the user (402), and sending, to the client device (406), the requested digital representation of the personality data of the user (402), wherein the personality data of the user (402) is computed using a neural network (602) based on the input obtained from the user.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present disclosure relates generally to the field of data retrieval, and in particular, presents techniques for enabling efficient retrieval of a digital representation of a user's personality data from a server by a client device, which may be embodied as a method, a computer program, an apparatus, and a system. [Background technology]

[0002] Personality tests have been used for decades to assess human personality traits and are typically based on personality survey data obtained from test subjects, which are then evaluated by experts such as psychologists to draw conclusions about a person's personality. The so-called "OCEAN" model is a widely accepted taxonomy of personality traits, also known as the "Big Five" personality traits, and includes personality dimensions such as openness, conscientiousness, extraversion, agreeableness, and neuroticism. Popular personality tests that utilize the OCEAN model include tests based on the so-called International Personality Item Pool (IPIP), the HEXACO-60 inventory, and the Big-Five-Inventory-10 (BFI-10), which, for example, include sets of questions to test a person on each of the five personality dimensions. While traditional personality tests typically require review by a human expert, such as a psychologist, to obtain a qualified assessment of a person's personality traits, it is difficult to administer the personality test and integrate its results into processes executed on technology systems. Such integration can be beneficial because it allows processes to be tailored to better suit a user's personality, thereby improving the user experience, for example, by providing tailored services to the user. Summary of the Invention

[0003] Therefore, there is a need for a technical implementation that makes it practical to integrate personality tests and their results into processes that run on technical systems.

[0004] According to a first aspect, there is provided a method for enabling a digital representation of a user's personality data to be efficiently retrieved from a server by a client device, the digital representation of the personality data being processed at the client device to provide user-tailored services to the user. The method is performed by a server and comprises storing a neural network trained to calculate the user's personality data based on input obtained from the user, receiving a request for the 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 calculated using the neural network based on the input obtained from the user.

[0005] Storing a trained neural network on a server and applying it to the calculation of a user's personality data automates the process of obtaining a digital representation of the user's personality data (removing the need for traditional human review), enabling the integration of the acquisition and use of the user's personality data into (e.g., automated) processes performed on technical systems. In particular, neural networks are efficient functional data structures that can calculate requested personality data in a single computational run, i.e., by inputting user inputs at the neural network's input nodes and reading resulting output values representing the personality data from the neural network's output nodes. In this way, neural networks enable the efficient provision of personality data in the form of digital representations to client devices, which can be used to provide services tailored to the user's specific personality, thereby enhancing the user experience at the client device. This efficient data provision makes the integration of the acquisition and use of personality data particularly practical, as the digital representation of the personality data is provided to the client device without significant delay and is processed immediately at the client device. This achieves a practically feasible technical implementation that generally integrates the acquisition and use of personality data into processes executed on technical systems.

[0006] A user's personality data may be indicative of the user's psychological characteristics and / or preferences, and therefore may generally include psychological data, including, for example, classic personality data based on the personality dimensions of openness, conscientiousness, extraversion, agreeableness, and neuroticism (known as the Big Five, as discussed above), as well as medical data (e.g., data indicative of tendencies toward curiosity, anxiety, depression, etc.). The digital representation of the user's personality data may include digital representations of the above-mentioned traits, such as, for example, digital representations of at least one of the personality dimensions of openness, conscientiousness, extraversion, agreeableness, and neuroticism computed for the user by a neural network.

[0007] The client device may be configured to process the digital representation of personality data for the purpose of enabling the provision of user-tailored services to the user. In one variation, the client device itself may be configurable based on the digital representation of personality data. An exemplary device that may be configurable by the digital representation of personality data may be, for example, a vehicle, in which case the vehicle may be the client device. The vehicle may process the received digital representation of the personality data of a user (e.g., the vehicle's driver) and configure itself (e.g., including its subcomponents) to adapt the vehicle's driving settings to the driver's personality, thereby providing a driving service tailored to the user's personality. For example, if the personality data indicates that the driver is risk-averse or anxious, the vehicle's driving settings may be configured to be more safety-oriented, while for a driver who tends to have a more risk-seeking personality, the vehicle may be configured to have sportier driving settings. To this end, the vehicle's fuel and brake response behavior, among other settings, may be adapted accordingly. Subcomponents of the vehicle that provide vehicle-related services, such as the vehicle's sound system including sound and volume settings, may be configured based on the personality data to further match the user's personality.

[0008] In other variations, 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 a service to the user. In such variations, the client device may be, for example, a mobile terminal (e.g., a smartphone) that can interact (e.g., using Bluetooth) with a vehicle (i.e., in this case, the vehicle corresponds to the at least one other device), and upon receiving the digital representation of the personality data from the server, the mobile terminal can configure the vehicle via the interface. Thus, it can be said that the digital representation of the user's personality data can be processed at the client device to configure at least one device that provides a service to the user. Configuring the at least one device may include configuring at least one setting of the at least one device and / or configuring at least one setting of a service provided by the at least one device. It will be understood that a vehicle is merely an example of a device that can be configured based on personality data, and that the client device and / or the at least one other device may correspond to other types of devices.

[0009] In one embodiment, the method performed by the server may further include receiving feedback characterizing the user, updating the neural network based on the feedback, and transmitting a digital representation of the user's updated personality data to the 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 may be processed at the client device to improve settings of at least one device providing services to the user (e.g., one of the vehicle settings described above). The feedback may be collected at the client device and / or at least one device providing services to the user and may be indicative of the user's personality. The feedback may include, for example, behavioral data reflecting user behavior monitored at the at least one device when using services provided by the at least one device; in one variation, the behavioral data may be monitored by the at least one device providing services to the user using (e.g., sensor-based) measurements. In the vehicle example, the monitored user behavior may be, for example, the user's driving behavior, where the driving behavior is measured by a sensor in the vehicle. To measure driving behavior, sensors can detect, for example, the user's braking response and strength; such measurements can indicate the user's personality (e.g., aggressiveness in driving), and this information can be sent as feedback to the server to update the neural network, thereby improving the neural network's ability to calculate the user's personality data.

[0010] Updating the neural network can include training the neural network based on feedback received from the client device, and if the feedback represents new input values that have not yet been input to 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 particularly highlights the power of the neural network as an efficient functional data structure employed in the technical implementations presented herein. That is, the neural network represents an efficiently updatable data structure, updated based on any feedback regarding the user's personality received from the client device to improve its ability to calculate personality data. The information conveyed by the feedback can be directly integrated into the neural network, and once trained, it is immediately reflected in subsequent requests sent to the server requesting a digital representation of personality data. Traditional personality assessment techniques are fairly fixed and may not support such updatable capabilities at all.

[0011] The digital representation of the user's personality transmitted from the server to the client device may correspond to a digital representation of the user's personality previously calculated by the server in response to a previous request for the 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 may be calculated before receiving a request from the client device, and the request may include an access code previously provided to the user by the server when calculating the user's personality data, the access code allowing the user to access the digital representation of the user's personality data from other client devices. Such an implementation may conserve computational resources at the server because the digital representation of the user's personality does not need to be calculated anew each time a digital representation of that particular user's personality data is requested from a client device, and may also return based on pre-calculated personality data. The user may then use the access code to access the digital representation of the personality data from multiple other client devices, such as other vehicles the user may drive, e.g., cars and motorcycles, or other types of devices.

[0012] The input obtained from the user may correspond to digital scores (e.g., obtained in a question-answering scheme in a personality testing methodology) reflecting answers to questions related to at least one of the user's personality, goals, and motivations, and each digital score may be used as an input to a separate input node of the neural network when using the neural network to calculate the user's personality data. The digital scores may correspond, for example, to a five-level Likert scale with values ranging from 1 to 5. The neural network may correspond to a deep neural network having at least two hidden layers between an input layer including input nodes of the neural network and an output layer including output nodes. The personality questions may correspond, for example, to questions from traditional IPIP, HEXACO-60, and / or BFI-10 pools, although it will be understood that other questions related to the user's personality, including questions about the user's psychological characteristics and / or preferences, may be used as well. In particular, questions about the user's goals and motivations can define additional dimensions (e.g., in addition to the Big Five) that increase the accuracy of the calculated personality data over traditional IPIP, HEXACO-60, and BFI-10 methods. The network can be trained based on data collected in a baseline survey administered to multiple test subjects (e.g., 1,000 or more individuals), which can be conducted using the questions described above.

[0013] In order to reduce the computational complexity when calculating the user's personality data, the neural network can be designed to have a specific network structure. Taking into account the context of the above-mentioned questions, the structure of the neural network can generally be designed to reduce the number of input nodes compared to the number of input nodes available when all of the above-mentioned questions are used. Thus, the questions can correspond to questions selected from a question set that represent optimally achievable results for calculating the user's personality data (i.e., when the user answers all questions in the question set), where the selected questions can correspond to questions in the question set that are determined to be most influential with respect to the optimally achievable results. As described above, each answer to a question is input into a separate input node of the neural network, so selecting a subset of the question set reduces the number of input nodes and reduces the computational complexity when calculating the personality data. 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 largely maintained.

[0014] In fact, tests have shown that the number of questions can be significantly reduced without significantly sacrificing the accuracy of the results. Taking a question set that represents optimally achievable results for calculating personality data as including standard IPIP, HEXACO-60, and BFI-10 questions (a total of 370 questions), optionally supplemented with additional questions about the user's goals and motivations (a total of more than 370 questions), tests have shown that using only the 30 most influential questions achieves approximately 90% of the accuracy of the optimally achievable results. Therefore, the number of questions selected can be less than 10% (preferably less than 5%) of the number of questions included in the question set that represents optimally achievable results. In this case, the number of input nodes of the neural network can be significantly reduced, thereby significantly saving computational resources and allowing personality data to be calculated more efficiently.

[0015] To determine the questions in the question set that are most influential with respect to the optimally achievable results, in one variation, questions can be selected from the question set based on correlating the results achievable by each single question in the question set with the optimally achievable results and selecting the questions from the question set that have the highest correlation with the optimally achievable results. Thus, a fixed subset of the question set that represents the optimally achievable results can be determined and used to train a neural network with a reduced number of input nodes, as described above.

[0016] As described above, the optimally achievable result may correspond to the result that would be achieved if the user were to answer all questions in a question set, such as those including standard IPIP, HEXACO-60, and BFI-10 questions, optionally supplemented with additional questions regarding the user's goals and motivations, as described above. Meanwhile, in one variation, the standard IPIP score (obtained by answering all questions in the standard IPIP test), the standard HEXACO-60 score (obtained by answering all questions in the standard HEXACO-60 test), and the standard BFI-10 score (obtained by answering all questions in the standard BFI-10 test) may be taken individually as a reference for the optimally achievable result, and in another variation, improvement may be achieved by calculating a combined score of these individual scores as a reference for the optimally achievable result, where the combined score is, for example, calculated as a (e.g., weighted) average of the individual scores. The combined score may also be expressed as a "superscore" that represents the "truth" derivable from the individual scores, generally improving the meaning of the determined scores and providing a better reference to optimally achievable results.

[0017] In another variation, questions can be selected iteratively from the question set, and in each iteration, the next question can be selected depending on the user's answer to the previous question, and in each iteration, the next question can be selected as one of the questions from the question set determined to have the greatest influence on the achievable results for calculating the user's personality data. This can be considered adaptive selection of questions, and questions are determined for each user in a stepwise manner, taking into account the user's answers to previous questions. In one particular variation, the neural network can include multiple output nodes representing a probability curve for the user's personality data outcomes, and determining the most influential question of the question set as the next question in each iteration can include, for each input node of the neural network, determining the degree to which a change in the digital score input to each input node of the neural network changes the probability curve. The question associated with the input node determined to have the greatest degree of change in the probability curve can be selected as the most influential question in each iteration.

[0018] To further reduce the computational complexity, the above-mentioned iterative and adaptive selection may be performed under at least one constraint, such as a maximum number of questions to be selected, a minimum result accuracy to be achieved (the result accuracy improves with each iteration as questions are answered, and the computation may stop when the required minimum result accuracy is reached), and a maximum available time (the test may stop when the maximum available time has elapsed, or each question may be associated with an estimated time for the user to answer, and the number of questions to be selected may be determined based on the estimated time), etc. These constraints may be set individually for each computation of personality data.

[0019] According to a second aspect, there is provided a method for enabling a digital representation of a user's personality data to be efficiently retrieved by a client device from a server, the method being executed by the client device and may include sending a request for the digital representation of the user's personality data to the server, receiving the requested digital representation of the user's personality data from the server, where 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, and processing the digital representation of the personality data to provide user-tailored services to the user.

[0020] The method according to the second aspect defines a method from the perspective of a client device that can complement the method performed 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. Therefore, 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. Therefore, unnecessary repetition will be omitted below.

[0021] Similar to the method of the first aspect, the digital representation of the user's personality data can be processed at a client device to configure at least one device that provides services to the user, where the at least one device may include the client device. The method performed by the client device may further include sending feedback characterizing the user to a server and receiving a digital representation of the user's updated personality data from the server, where the updated personality data for the user may be calculated using an updated neural network based on the feedback. The digital representation of the user's updated personality data can be processed at the client device to improve the configuration of the at least one device that provides services to the user. The feedback may include behavioral data reflecting user behavior monitored at the at least one device when using a service provided by the at least one device, where the behavioral data may be monitored using measurements performed on the at least one device that provides services to the user. The at least one device may include a vehicle, and the behavioral data may include data reflecting the user's driving behavior. The user's personality data can be calculated prior to sending the request to the server, and the request can include an access code previously provided to the user by the server in calculating the user's personality data, the access code allowing the user to access the digital representation of the user's personality data from other client devices. Input obtained from the user can correspond to a digital score reflecting answers to questions regarding at least one of the user's personality, goals, and motivations.

[0022] According to a third aspect, there is provided a computer program product, the computer program product including program code portions for performing at least one of the methods of the first and second aspects 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 may be stored on a computer-readable recording medium such as a semiconductor memory, a DVD, a CD-ROM, etc.

[0023] According to a fourth aspect, there is provided a server that enables a digital representation of a user's personality data to be efficiently retrieved by a client device from the server, wherein the digital representation of the personality data is processed at the client device to provide user-tailored services to the user, the server including 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 server is operable to perform any of the method steps presented herein with respect to the first aspect.

[0024] According to a fifth aspect, there is provided a client device for enabling efficient retrieval of a digital representation of a user's personality data from a server, the client device including 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 client device is operable to perform any of the method steps presented herein with respect to the second aspect.

[0025] According to a sixth aspect, there is provided a system comprising a server according to the fourth aspect and at least one client device according to the fifth aspect.

[0026] Further details and advantages of the techniques presented herein are explained with reference to the exemplary implementations shown in the following figures. [Brief explanation of the drawings]

[0027] [Figure 1a] FIG. 1a illustrates an exemplary configuration of a server according to the present disclosure. [Figure 1b] FIG. 1b illustrates an exemplary configuration of a client device according to the present disclosure. [Figure 2] FIG. 2 illustrates a method performed by a server according to the present disclosure. [Figure 3] FIG. 3 illustrates a method performed by a client device according to the present disclosure. [Figure 4] FIG. 4 illustrates an exemplary interaction between a user, a server, and a client device (exemplified by a vehicle) according to the present disclosure. [Figure 5] FIG. 5 illustrates another connectivity option between a user's mobile terminal, a vehicle, and a server according to the present disclosure. [Figure 6a] FIG. 6a illustrates an exemplary structure of a neural network according to the present disclosure. [Figure 6b] FIG. 6b illustrates an exemplary structure of a neural network according to the present disclosure. [Figure 7] FIG. 7 illustrates an example implementation that includes considering a driver's attention level to adapt vehicle settings according to the present disclosure. [Figure 8] FIG. 8 illustrates an example implementation that includes considering a user's body scan data to provide user-tailored services according to the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0028] In the following, for purposes of explanation and not limitation, specific details are set forth in order 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 in other implementations that depart from these specific details.

[0029] Those skilled in the art will further appreciate that the steps, services, and functions described herein below may be implemented using discrete hardware circuits, software working in combination with a programmed microprocessor or general-purpose computer, one or more application-specific integrated circuits (ASICs), and / or one or more digital signal processors (DSPs). Where the present disclosure is described in terms of a method, it will be understood that it may also be 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 coded with one or more programs that, when executed by the one or more processors, perform the steps, services, and functions presented herein.

[0030] 1a illustrates a schematic diagram of an exemplary configuration of a server 100 that enables digital representations of user personality data to be efficiently retrieved from the server 100 by client devices, where the digital representations of the personality data are processed at the client devices to provide user-tailored services to the users. The server 100 has 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 requesting server 100 is operable to perform steps of the methods described herein with reference to "server."

[0031] It will be appreciated that server 100 may be implemented on a physical computing unit or a virtualized computing unit, such as a virtual machine. It will also be appreciated that server 100 is not necessarily limited to being implemented on a standalone computing unit, but may also be implemented as a component residing on multiple distributed computing units, such as in a cloud computing environment, implemented in software and / or hardware.

[0032] 1b illustrates schematically an exemplary configuration of a client device 110 that enables a digital representation of a user's personality data to be efficiently retrieved by the client device 110 from a server. The client device 110 has 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 requesting client device 110 is operable to perform the steps of the methods described herein with reference to a "client device."

[0033] 2 illustrates a method that may be performed by server 100 according to the present disclosure. The method is focused on enabling a digital representation of a user's personality data to be efficiently retrieved from server 100 by a client device (e.g., client device 110). In this method, server 100 may perform the steps described herein with reference to "server," and consistent with the above description, in step S202, server 100 may store a neural network trained to calculate the user's personality data based on input obtained from the user; in step S204, server 100 may receive a request for the digital representation of the user's personality data from the client device; and in step S206, server 100 may transmit 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 input obtained from the user.

[0034] 3 illustrates a method that may be performed by a client device 110 according to the present disclosure. The method is focused on enabling the client device 110 to efficiently retrieve a digital representation of a user's personality data from a server (e.g., server 100). In this method, the client device 110 may perform the steps described herein with reference to a "client device," and in line with the above description, in step S302, the client device 110 may send a request for the digital representation of the user's personality data to the server; in step S304, the client device 110 may receive the requested digital representation of the user's personality data from the server, where the user's personality data is calculated based on inputs obtained from the user using a neural network trained to calculate the user's personality data based on inputs obtained from the user; and in step S306, the client device 110 may process the digital representation of the personality data to provide user-tailored services to the user.

[0035] 4 illustrates an exemplary interaction between a user 402, a server 404 that stores a neural network trained to calculate the user's personality data based on inputs obtained from the user, and a client device that retrieves a digital representation of the user's 402's personality data and provides user-tailored services to the user 402; in the illustrated example, the client device may be a vehicle 406 driven by the user 402. As shown in the figure, the user 402 can take an automated personality test by answering questions using, for example, a web interface or app on his laptop or smartphone, and provide inputs to the neural network stored on the server 404, based on which the neural network can calculate the user's 402's personality data. Instead of sending a digital representation of the personality data to the user 402, in the illustrated illustration, the server 404 can provide the user 402 with an access code that the user 402 can use to access the personality data using other client devices, including the vehicle 406. A user 402 can use an access code to register or log into a vehicle 406 (more specifically, its on-board computer), and the vehicle 406 can use the access code to request a digital representation of the user's personality data from a server 404 (represented in the figure as the user's "MindDNA").

[0036] Upon receiving a request from vehicle 406, server 404 can transmit the user's personality data back to vehicle 406, and vehicle 406 can configure its driving settings (and, optionally, subcomponents of vehicle 406) according to user 402's personality data, for example, adapting vehicle 406's fuel and braking response behavior to provide a driving experience specifically tailored to the user's personality (e.g., risk averse, risk seeking, etc.). And, as user 402 drives vehicle 406, vehicle 406 can monitor the user's driving behavior, for example, using sensors that measure the user's braking response and strength, and vehicle 406 can provide this information as feedback to server 404, where the feedback is processed to update (by training) the neural network to improve its ability to compute user 402's personality data. In response, server 404 can transmit correspondingly updated personality data of user 402 to vehicle 406, which can use the digital representation of the updated personality data to improve vehicle settings to better match 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 and is capable of adapting settings of offered devices or services according to user preferences derived from the user's personality data, thereby improving the user experience.

[0037] 5 illustrates other connectivity options between a mobile terminal 502 (e.g., a smartphone) of a user 402, a vehicle 406, and a server 404 according to the present disclosure. In one variation, the vehicle 406 can communicate directly with the server 404 over the internet, and once the user 402 is authenticated with the vehicle 406 (e.g., using a key, a smart card, NFC / RFID, a smartphone with NFC, a fingerprint, etc.), the vehicle 406 can request the user's personality data (again labeled in FIG. 5 as the user's "MindDNA") to improve the user's 402 driving experience. In another variation, when the user 402 is carrying the mobile terminal 502, the mobile terminal 502 can communicate with the server 404 over the internet (e.g., using a dedicated app installed thereon) to request the user's 402 personality data. In this variation, the vehicle 406 can communicate locally with the mobile terminal 502 (e.g., using Bluetooth, Wi-Fi, or a USB cable) and obtain the user's personality data from the mobile terminal 502. The direct connection between the vehicle 406 and the mobile terminal 502 can further be used to utilize sensors installed on the mobile terminal 502 (e.g., a gyroscope for detecting movement and acceleration, a GPS for detecting movement and acceleration and detecting a driving path, or medical sensors for measuring pulse, blood pressure, etc.) to supplement feedback collected by the vehicle 406 itself (e.g., related to the user's driving behavior), thereby providing additional feedback sensed by the mobile terminal 502 to the server 404, which can update the neural network based on the feedback, as described above.

[0038] FIG. 6a illustrates 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 illustrated in FIG. 6a merely illustrates the structure of a typical deep neural network; the actual number of nodes (at least in the input and hidden layers) of the neural network 602 stored on the server 404 may be significantly higher than illustrated. As described above, testing was performed using the 30 most influential questions of a total of over 370 questions (taken from standard IPIP, HEXACO-60, and BFI-10 questions, optionally supplemented with 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 may consist of 50 nodes. Furthermore, as illustrated, the neural network 602 may include a single output node in the output layer. In this case, the resulting value of the output node of the output layer may represent the value of one personality dimension (of the Big Five) on which the neural network 602 was trained. It will be understood that such a structure of the neural network 602 is merely exemplary and that other structures are generally possible.

[0039] A more advanced structure of the neural network 602 may have input nodes corresponding to the complete set of available questions, which may be derived from standard IPIP, HEXACO-60, and BFI-10 questions, and may further include additional questions regarding the user's goals and motivations, as well as questions regarding other psychological characteristics and / or preferences of the user not covered by the above questions, potentially adding hundreds of questions, e.g., 600 or more questions. Thus, such a neural network 602 may have 600 or more input nodes, each corresponding to a single question in the complete set of available questions, and the number of nodes in the hidden layer may be selected depending on the performance of the neural network 602. For example, the neural network 602 may be configured with two hidden layers, each with 100 nodes. Furthermore, the input layer may duplicate the 600 or more input nodes, each of which may be used as a missing-question indicator. The missing question indicators are dichotomous, i.e., they can have two values (e.g., 0 and 1) that indicate whether the question of the corresponding (original) input node was answered. Because input nodes are duplicated, the input layer can have more than 1200 input nodes in total.

[0040] The output layer of a more advanced neural network 602 can have multiple output nodes that together represent a probability curve for one personality dimension. For example, if the scale used to output this personality dimension ranges from 0 to 10 and there are 50 output nodes, each of the output nodes can represent a portion of the scale, i.e., a portion corresponding to the 0-0.2, 0.2-0.4, 0.4-0.6, ..., 9.8-10 portions of the scale. Such an output layer can provide the entire probability curve for output values for this personality dimension instead of a single output value. Figure 6b shows an exemplary output layer along with the corresponding probability curve 604. Such a curve allows one to determine where the most common output value (i.e., indicated by the peak of the curve) lies, as well as the precision with which the neural network 602 calculates the result (i.e., indicated by the width of the curve). An advanced neural network 602 can be used to calculate a user's personality data in the form of several probability curves (e.g., five probability curves corresponding to the Big Five) for any number of answered questions, with the neural network 602 being trained separately for each dimension. Initially, when no questions have yet been answered, all missing question indicators may have a value of "missing" (e.g., 0). Each time a question is answered, an update to the output value is calculated; as the number of answered questions increases, the width of the output layer's probability curves decreases, steadily improving the accuracy with which the neural network 602 calculates results.

[0041] Such a structure of the neural network 602 is particularly advantageous because it allows iterative selection of the next question to be answered by the user from the complete set of questions, where in each iteration, the next question can be selected depending on the user's answer to the previous question, and where in each iteration, the next question can be selected as one question from the complete set of questions determined to have the greatest influence on the achievable results for calculating the user's personality data. To this end, 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 currently with the lowest accuracy) can be determined. As the next question in the iteration, a question in this dimension can be selected to improve the accuracy of this dimension. To determine the most influential question, the extent to which a change in the digital score input to each input node changes the probability curve (e.g., the extent to which the width of the curve changes) can be determined for each input node of the neural network 602. Based on this, the question associated with the input node determined to have the greatest degree of change in the probability curve can be selected as the most influential question in each iteration.

[0042] The advanced structure of the neural network 602 can also be advantageous because it allows feedback to be easily integrated into the neural network. As described above, if the feedback represents a new input value that has not yet been input to the neural network 602, when training the neural network 602, the new input node can simply be added to the neural network 602, and the new input value is assigned to the new input node. In this way, any type of new feedback can be easily integrated into the network, and the neural network 602 can improve its ability to calculate personality data. To reduce the computational complexity when adding a new input node, when the network is trained to correlate the new input node with other nodes in the network, it is possible to incorporate only those nodes determined to be most influential with respect to optimally achievable results into the calculation, thereby avoiding incorporating all nodes into the calculation. Also, when the network is trained to correlate the new input node with other nodes in the network, it is possible, for example, to limit the number of layers pre-computed (e.g., to two or three) to avoid calculating all subsequent combinations of nodes.

[0043] In the above description, the techniques for efficiently obtaining a digital representation of a user's personality data have been illustrated in the context of adapting a vehicle's driving settings, such as the vehicle's fuel and braking response behavior, to a user's personality. In this case, the methods described herein may also be viewed as methods for adapting a vehicle's driving settings, including efficiently obtaining a digital representation of a user's personality data. It will be understood that adapting a vehicle's fuel and braking response behavior is merely one example of adapting a vehicle's driving settings, and more generally, adapting a vehicle's driving settings may include adapting any vehicle setting that affects the vehicle's driving behavior. Adapting a vehicle's driving settings may include at least one of adapting a vehicle's fuel and braking response behavior, adapting a vehicle's chassis settings, adapting a vehicle's drive mode, and adapting adaptive cruise control (ACC) settings to a user's personality. Adapting the vehicle's drive mode may include setting an economy, comfort, or sport mode depending on the driver's personality, which affects the vehicle's accelerator pedal and fuel consumption behavior. For example, if the personality data indicates that the driver tends to be risk-averse, the drive mode may be set to economy or comfort, while for a driver who tends to have a risk-seeking personality, the drive mode may be set to sport. Adapting the vehicle's drive mode may also include, for example, enabling / disabling the vehicle's automatic four-wheel drive (4WD) mode. Adapting the ACC setting may include, for example, setting the distance to the vehicle ahead and / or the target driving speed depending on the driver's risk aversion.

[0044] It will be appreciated that the techniques presented herein may also be used for other purposes in the vehicle context, such as adapting environmental conditions within the cabin of a vehicle (or, more generally, may be similarly applied to other means of transportation, such as airplanes, trains, etc., such as adapting environmental conditions within the cabin of a vehicle). In this case, the methods presented herein may also be described as methods for adapting environmental conditions within the cabin of a vehicle, including efficiently obtaining a digital representation of a user's personality data. Adapting environmental conditions within the cabin of a vehicle may include adapting at least one of the following to the user's personality: adapting the cabin temperature (e.g., by adapting cabin air conditioning settings), adapting interior cabin lighting, adjusting the oxygen level within the cabin, etc. In addition to or instead of adapting environmental conditions within the cabin, the techniques presented herein may also be used to adapt user-specific settings for the cabin. Adapting user-specific settings for the cabin of the vehicle may include at least one of adapting seat settings (e.g., seat height, seat position, seat massage settings, seat belt tension, etc.) to the user in the cabin and adapting equalizer settings (e.g., increasing or decreasing bass or treble) of a sound system provided to the user in the cabin to the user's personality.

[0045] Any of the above-described adaptations of the vehicle / vehicle settings can be performed taking into account (or "based on" / "according to") the user's sensor data indicative of the user's attention level obtained in the cabin, in addition to adapting to the user's personality. In other words, the client device can be configured to adapt at least one of the vehicle's driving settings, the environmental conditions in the cabin, and the user-specific settings for the cabin not only taking into account the digital representation of the user's personality data, but also the sensor data indicative of the user's attention level. That is, the digital representation of the user's personality data and the sensor data indicative of the user's attention level can be combined before performing the above-described adaptations. The sensor data indicative of the user's attention level can include, for example, data related to at least one of the user's heart rate, breathing, fatigue, reaction time, and alcohol / drug levels. The sensor data can be collected, for example, by at least one sensor installed in the cabin or on the user's mobile terminal.

[0046] FIG. 7 illustrates an exemplary implementation that considers a driver's attention level in combination with the driver's personality data to adapt the vehicle's driving settings, the environmental conditions in the cabin, and / or user-specific settings for the cabin. The driver's attention level is checked by corresponding sensors, for example, for the user's reaction time, fatigue, heart rate, breathing, alcohol / drug levels, or abnormal user behavior. In the left portion of the figure, collected sensor data indicates the user's normal attention level, so vehicle settings, including speed, volume, temperature, seat settings, etc., can remain at normal levels (e.g., adapted to the driver's personality, i.e., "MindDNA"). In the center portion of the figure, sensor data indicates a decrease in the driver's attention level, so vehicle settings can be modified, such as slowing down, increasing volume, or lowering temperature settings, including turning on a seat massage function, to refresh the driver's attention. Optionally, an attention test can be performed, such as requiring the driver to provide a voice-based response in a question / answer scheme, and the results of the attention test can be taken into account when adapting the settings. On the other hand, in the right part of the diagram, the sensor data indicates that the driver's attention level is very low, so the user can be warned and the vehicle settings can be adapted accordingly, such as a very slow speed (and, for example, forcing the vehicle to stop at the next stopping opportunity), muting the audio, and / or directing the navigation system to the next hotel.

[0047] In order to provide the user with a user-tailored service as described above (e.g., by adapting at least one of the vehicle's driving settings, the environmental conditions in the cabin, and the user-specific settings related to the cabin), the client device may further consider body scan data indicating a user's (e.g., physical) characteristics derivable by scanning (e.g., at least a portion of) the user's body before providing the user-tailored service to the user (e.g., before the user drives the vehicle). The user characteristics derivable by scanning the user's body may include, for example, at least one of the user's size, weight, gender, age, height, posture, and emotional state. The body scan data is obtained by capturing one or more images or audio signals of the user by a camera or voice recorder (e.g., installed on the user's mobile terminal or in the vehicle / transportation), where body / face / voice recognition technology may be utilized to scan the user's body and derive the above-mentioned user characteristics. Thus, the client device is configured to provide user-tailored services not only by taking into account the digital representation of the user's personality data, but also by taking into account (or "based on" / "according to") the body scan data. That is, the digital representation of the user's personality data and the body scan data can be combined before providing the user with user-tailored services. FIG. 8 illustrates an exemplary implementation that includes taking into account the driver's body scan data (e.g., acquired by the driver's mobile device, such as a smartphone, smartwatch, or fitness tracker, before entering the vehicle) in combination with the driver's personality data to appropriately adapt the vehicle's driving settings, the environmental conditions within the cabin, and / or user-specific settings related to the cabin. In this illustration, the body scan data is labeled "BodyDNA," which, when combined with "MindDNA," forms so-called "LifeDNA." It will be appreciated that the acquired body scan data can also be used to provide user-characterizing feedback for updating the neural network, as described above.

[0048] In other vehicle-related use cases, the technology presented herein can also be used to determine vehicle settings that match a user's personality before manufacturing the vehicle, and the vehicle can be manufactured based on (or "according to") the determined vehicle settings. Vehicles can be manufactured with different configuration options (e.g., provided by the vehicle manufacturer), such as different motor options, each with a different motor output, drive technology options (e.g., support for two-wheel drive (2WD) or 4WD technology), chassis options, different drive mode options, ACC support, etc., and when a new vehicle is manufactured for a user, the vehicle settings are determined to specifically match the user's personality. For example, if personality data indicates that the user tends to be risk-averse, the determined vehicle settings may include selecting a motor with a lower output compared to vehicle settings determined for a user whose personality data indicates that they are risk-seeking. Based on the determined vehicle settings, the vehicle can be manufactured accordingly. Thus, in line with the above description, a method of vehicle manufacturing can also be envisioned that includes efficiently retrieving a digital representation of a user's personality data from a server by a client device, where the digital representation of the personality data is processed at the client device to provide a vehicle configuration that is tailored to the user's personality. The method can include sending a request for the digital representation of the user's personality data from the client device to the server, receiving the requested digital representation of the user's personality data from the server by the client device, where the digital representation of the user's personality data is calculated based on inputs obtained from the user 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 that is tailored to the user's personality, and manufacturing the vehicle based on the determined vehicle configuration. It will be appreciated that in the vehicle manufacturing process, the determined vehicle configuration can also affect the production of vehicle parts necessary for manufacturing the vehicle.For example, manufacturing the vehicle may include manufacturing one or more vehicle parts used in manufacturing the vehicle, where the vehicle parts are manufactured (e.g., using a 3D printer) according to the determined vehicle configuration.

[0049] It will be appreciated that the techniques presented herein can be used not only in vehicle / transportation related use cases but also in other use cases, such as, for example, adapting the settings of a smart appliance or robot to a user's personality. Thus, in line with the above description, a method for adapting the settings of a smart appliance (e.g., automatic roller shutters, air conditioning, refrigerator, washing machine, television, set-top box, etc.) can also be envisioned that involves efficiently obtaining a digital representation of a user's personality data, where the digital representation of the user's personality data is processed at a client device in order to adapt the settings of the smart appliance to the user's personality (e.g., adapting the way the smart appliance performs a primary task, such as, for example, shutter (roller shutters), heating / cooling (air conditioning), refrigeration (refrigerator), laundry (washing machine), or recording / display (television / set-top box) tasks). Similarly, in line with the above description, one can envisage a method of adapting the settings of a robot (e.g., a humanoid robot or a domestic robot configured to perform one or more domestic tasks) that involves efficiently obtaining a digital representation of a user's personality data, where the digital representation of the user's personality is processed at a client device in order to adapt the settings of the robot to the user's personality (e.g., to adapt the way in which the domestic robot performs household tasks).

[0050] Various other use cases are generally contemplated. Other use cases may include, for example, adapting the settings of a virtual robot, adapting the settings of a medical device, or even brain stimulation. Thus, in line with the above description, a method for adapting the settings of a virtual robot (e.g., a chatbot, a virtual service representative, a virtual personal assistant) can also be envisioned, which includes efficiently obtaining a digital representation of a user's personality data, where the digital representation of the user's personality is processed at a client device to adapt the settings of the virtual robot to the user's personality (e.g., to adapt the way in which the virtual robot performs a task to support the user). Similarly, in line with the above description, a method for adapting the settings of a medical device (e.g., a bedside medical device) can also be envisioned, which includes efficiently obtaining a digital representation of a user's personality data, where the digital representation of the user's personality is processed at a client device to adapt the settings of the medical device to the user's personality (e.g., to adapt a dosing regimen, such as a pain medication dosage). Further, methods of stimulating a brain (e.g., a living organism or a virtual representation of a brain) can be envisioned that involve efficiently obtaining a digital representation of a user's personality data, where the digital representation of the personality is processed at the user's client device to adapt a brain stimulation procedure based on the user's personality. The stimulation procedure may include, for example, electrical stimulation of the living organism's brain, or adapting / reconfiguring the virtual representation of the brain. The virtual representation of the brain is provided to, for example, a robot or other form of intelligent system to affect the behavior of such system based on the user's personality.

[0051] In all of the above examples and use cases, when reference is made to "adapting" a configuration or setting to a "user's personality," it will be understood that such adaptation may be implemented using a predefined mapping that maps certain characteristics of a user's personality (as indicated by a digital representation of the user's personality data) to a particular configuration or setting of a corresponding device / apparatus (e.g., a vehicle, transportation means, smart appliance, robot, medical device, etc., as described above). As described above, for example, if the personality data indicates that a driver tends to be risk-averse, the vehicle's drive mode may be set to economy or comfort, while for a driver who tends to have a risk-seeking personality, the drive mode may be set to sport mode. Such mappings may be predefined for each possible personality characteristic-configuration / setting combination, allowing the device / apparatus configuration or setting to be adapted accordingly depending on the obtained user's personality data. The user's personality traits may correspond, for example, to values of personality dimensions (eg, from the Big Five) output by a neural network, as described above.

[0052] The advantages of the technology presented herein will be fully appreciated from the foregoing description, and it will be apparent that various changes can be made in the form, construction, and arrangement of exemplary embodiments thereof without departing from the scope of the disclosure or sacrificing all of its advantageous effects. Because the technology presented herein can be varied in many ways, it will be understood that the disclosure should be limited only by the scope of the following claims. Some or all of the above-described embodiments can be described as, but are not limited to, the following supplementary notes. (Appendix 1) 1. A method for enabling efficient retrieval of a digital representation of personality data of a user (402) by a client device (502, 406) from a server (404), wherein the digital representation of the personality data is processed at the client device (406) to provide user-tailored services to the user (402), the method being performed by the server (404); storing (S202) a neural network (602) trained to calculate personality data of the user (402) based on inputs obtained from the user (402); receiving (S204) from the client device (502, 406) a request for a digital representation of personality data of a user (402); transmitting (S206) the requested digital representation of the personality data of the user (402) to the client device (502, 406), wherein the personality data of the user (402) is calculated using the neural network (602) based on input obtained from the user (402); A method having the following. (Appendix 2) the digital representation of the personality data of the user (402) is processed at the client device (502, 406) to configure at least one device (406) that provides services to the user (402); Optionally, said at least one device (406) comprises said client device (406); The method described in Appendix 1. (Appendix 3) receiving feedback characterizing the user (402); updating the neural network (602) based on the feedback; and transmitting a digital representation of updated personality data of the user (402) to the client device (502, 406), the updated personality data of the user (402) being 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 configuration of the at least one device (406) providing the service to the user (402). 3. The method according to claim 1 or 2. (Appendix 4) the feedback includes behavioral data reflecting the user's (402) behavior monitored at the at least one device (406) when using the service provided by the at least one device (406); Optionally, said behavioral data is monitored using measurements performed by said at least one device (406) providing said service to said user (402). The method described in Appendix 3. (Appendix 5) the at least one device (406) includes a vehicle, and the behavioral data includes data reflecting a driving behavior of the user (402); The method described in Appendix 4. (Appendix 6) the personality data of the user (402) is calculated prior to receiving the request from the client device (502, 406), the request including an access code previously provided to the user (402) by the server (404) when calculating the personality data of the user (402), the access code enabling the user (402) to access the digital representation of the personality data of the user (402) from another client device (502, 406); 6. The method of any one of appendices 1 to 5. (Appendix 7) The inputs obtained from the user correspond to digital scores reflecting answers to questions regarding at least one of the user's (402) personality, goals, and motivations, each of which is 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). 7. The method of any one of appendices 1 to 6. (Appendix 8) the questions correspond to questions selected from a set of questions that represent 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 the optimally achievable outcome; Optionally, the number of selected questions is less than 10% of the number of questions in the question set. The method described in Appendix 7. (Appendix 9) 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 the optimally achievable results and selecting the question from the set of questions that has the highest correlation with the optimally achievable results; or the questions are selected iteratively 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 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 results of the personality data of the user (402), and determining a most influential question of the question set as the next question in each of the iterations includes determining, for each input node of the neural network (602), an extent 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 described in Appendix 8. (Appendix 10) 1. A method for enabling efficient retrieval of a digital representation of personality data of a user (402) by a client device (502, 406) from a server (404), said method being performed by said client device (502, 406), said method comprising: Sending (S302) a request for a digital representation of personality data of a user (402) to the server (404); receiving (S304) from the server (404) the requested digital representation of the personality data of the user (402), wherein the personality data of the user (402) is calculated based on input obtained from the user (402) using a neural network (602) trained to calculate the personality data of the user (402) based on input obtained from the user (402); processing (S306) the digital representation of the personality data to provide user-tailored services to the user (402); A method having the following. (Appendix 11) 11. A computer program product comprising program code portions for performing the method of any one of claims 1 to 10, when the computer program product is run on one or more computing units. (Appendix 12) 12. The computer program product of claim 11, stored on one or more computer-readable recording media. (Appendix 13) A server (100, 404) that enables a client device (502, 406) to efficiently retrieve a digital representation of a user's (402) personality data from the server (404), the digital representation of the personality data being processed at the client device (502, 406) to provide user-tailored 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 described in any one of appendices 1 to 9. (Appendix 14) A client device (110, 502, 406) that enables efficient retrieval of a digital representation of personality data of a user (402) from a 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 described in Appendix 10. (Appendix 15) A system comprising a server (100, 404) according to claim 13 and at least one client device (110, 502, 406) according to claim 14.

Claims

1. 1. A method comprising: obtaining, by a client device (110) from a server (100) a digital representation of a user's personality data, the digital representation of the personality data being processed at the client device (110) to provide a vehicle configuration that is adapted to the personality of the user; storing (S202) a neural network (602) trained to calculate personality data of a user based on inputs obtained from the user; receiving (S204) from the client device (110) a request for a digital representation of a user's personality data; transmitting (S206) the requested digital representation of the user's personality data to the client device (110), wherein the user's personality data is calculated using the neural network (602) based on input obtained from the user; the digital representation of the user's personality data is processed at the client device (110) prior to manufacturing a vehicle to determine a vehicle configuration for the manufactured vehicle, the vehicle being capable of being manufactured with different configuration options, and the determined vehicle configuration being adapted to the personality of the user; The method comprises: receiving feedback characterizing the user; updating the neural network (602) based on the feedback; transmitting a digital representation of the user's updated personality data to the client device (110), wherein the updated personality data of the user is calculated using the updated neural network (602).

2. The vehicle is manufactured based on the determined vehicle settings. The method of claim 1.

3. 3. The method of claim 2, wherein manufacturing the vehicle includes manufacturing one or more vehicle parts used to manufacture the vehicle, the vehicle parts being manufactured according to the determined vehicle configuration.

4. the digital representation of the user's updated personality data is processed at the client device (110) to improve the vehicle configuration.

4. The method according to any one of claims 1 to 3.

5. The feedback is collected at the client device (110).

5. The method according to any one of claims 1 to 4.

6. the feedback being indicative of the personality of the user; 6. The method according to any one of claims 1 to 5.

7. The personality data of the user is psychological characteristics of the user; the user's preferences; Indicating at least one of 7. The method according to any one of claims 1 to 6.

8. the inputs obtained from the user correspond to digital scores reflecting answers to questions regarding at least one of the user's personality, goals, and motivations, each of the digital scores 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; 8. The method according to any one of claims 1 to 7.

9. The questions about the personality of the user include: International Personality Item Pool (IPIP), HEXACO-60 pool, Big-Five-Inventory-10 (BFI-10) pool, questions about the user's psychological characteristics; Questions about the user's preferences; Responding to at least one of the questions The method of claim 8.

10. the questions correspond to questions selected from a set of questions that represent optimally achievable results for calculating the user's personality data; the selected questions correspond to questions from the question set that are determined to be most influential with respect to the optimally achievable outcome; 10. The method according to claim 8 or 9.

11. the number of selected questions is less than 10% of the number of questions included in the question set; The method of claim 10.

12. 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 the optimally achievable results, and selecting the question from the set of questions that has the highest correlation with the optimally achievable results; 12. The method according to claim 10 or 11.

13. the questions are selected iteratively 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 results achievable for calculating the user's personality data.

12. The method according to claim 10 or 11.

14. the neural network (602) includes a plurality of output nodes representing a probability curve (604) of results of the personality data of the user, and determining a most influential question of the question set as the next question for each of the iterations includes determining, for each input node of the neural network (602), the extent 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 of claim 13.

15. The personality data of the user is calculated prior to receiving the request from the client device (110), the request including an access code previously provided to the user by the server (100) when calculating the personality data of the user, the access code enabling the user to access the digital representation of the personality data of the user from another client device (110).

15. The method of any one of claims 1 to 14.

16. 1. A method comprising: obtaining, by a client device (110), a digital representation of a user's personality data from a server (100), said method being executed by said client device (110); Sending (S302) to the server (100) a request for a digital representation of the user's personality data; receiving (S304) from the server (100) the requested digital representation of the user's personality data, wherein the user's personality data is calculated based on inputs obtained from the user using a neural network (602) trained to calculate the user's personality data based on inputs obtained from the user; processing (S306) the digital representation of the personality data to determine a vehicle configuration for the vehicle to be manufactured prior to manufacturing the vehicle, the vehicle being capable of being manufactured with different configuration options, the determined vehicle configuration being adapted to the personality of the user; The method comprises: sending feedback characterizing the user to the server (100); receiving from the server (100) a digital representation of the user's updated personality data, the updated personality data of the user being calculated using the neural network (602) that is updated based on the feedback.

17. The vehicle is manufactured based on the determined vehicle settings.

17. The method of claim 16.

18. 20. The method of claim 17, wherein manufacturing the vehicle includes manufacturing one or more vehicle parts used to manufacture the vehicle, the vehicle parts being manufactured according to the determined vehicle configuration.

19. the digital representation of the user's updated personality data is processed at the client device (110) to improve the vehicle configuration.

19. The method of any one of claims 16 to 18.

20. The feedback is collected at the client device (110).

20. The method of any one of claims 16 to 19.

21. the feedback being indicative of the personality of the user; 21. The method of any one of claims 16 to 20.

22. The personality data of the user is psychological characteristics of the user; the user's preferences; Indicating at least one of 22. The method of any one of claims 16 to 21.

23. the inputs obtained from the user correspond to digital scores reflecting answers to questions regarding at least one of the user's personality, goals, and motivations, each of the digital scores 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; 23. The method of any one of claims 16 to 22.

24. The questions about the personality of the user include: International Personality Item Pool (IPIP), HEXACO-60 pool, Big-Five-Inventory-10 (BFI-10) pool, questions about the user's psychological characteristics; Questions about the user's preferences; Responding to at least one of the questions 24. The method of claim 23.

25. the questions correspond to questions selected from a set of questions that represent optimally achievable results for calculating the user's personality data; the selected questions correspond to questions from the question set that are determined to be most influential with respect to the optimally achievable outcome; 25. The method of claim 23 or 24.

26. the number of selected questions is less than 10% of the number of questions included in the question set; 26. The method of claim 25.

27. 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 the optimally achievable results, and selecting the question from the set of questions that has the highest correlation with the optimally achievable results; 27. The method of claim 25 or 26.

28. the questions are selected iteratively 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 results achievable for calculating the user's personality data.

27. The method of claim 25 or 26.

29. the neural network (602) includes a plurality of output nodes representing a probability curve (604) of results of the personality data of the user, and determining a most influential question of the question set as the next question for each of the iterations includes 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); 29. The method of claim 28.

30. The personality data of the user is calculated prior to sending the request to the server (100), the request including an access code previously provided to the user by the server (100) when calculating the personality data of the user, the access code enabling the user to access the digital representation of the personality data of the user from another client device (110).

30. The method of any one of claims 16 to 29.

31. 31. A computer program comprising program code portions which, when executed on one or more computing units, cause said one or more computing units to carry out a method according to any one of claims 1 to 30.

32. 32. The computer program of claim 31 stored on one or more computer readable recording media.

33. A server (100) that enables a client device (110) to obtain a digital representation of a user's personality data from the server (100), the digital representation of the personality data being processed at the client device (110) to provide a vehicle setting that is adapted to the personality of the user, the server (100) 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 (100) is operable to perform the method of any one of claims 1 to 15.

34. A client device (110) that enables retrieval of a digital representation of a user's personality data from a server (100), the client device (110) comprising at least one processor (112) and at least one memory (114), the at least one memory (114) containing instructions executable by the at least one processor (112) such that the client device (110) is operable to perform the method of any one of claims 16 to 30.

35. A system comprising a server (100) according to claim 33 and at least one client device (110) according to claim 34.

36. 1. A method comprising: obtaining a digital representation of personality data of a user, the digital representation of the personality data being processed to provide a vehicle configuration that is consistent with the personality of the user, the method being performed by one or more computing devices; obtaining a digital representation of a user's personality data, the personality data of the user being calculated based on inputs obtained from the user using a neural network (602) 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 for the vehicle to be manufactured prior to manufacturing the vehicle, the vehicle being capable of being manufactured with different configuration options, the determined vehicle configuration being adapted to the personality of the user; The method comprises: obtaining feedback characterizing the user; obtaining a digital representation of updated personality data of the user, the updated personality data of the user being calculated using the neural network (602) that is updated based on the feedback.

37. 37. The method of claim 36, wherein the vehicle is manufactured based on the determined vehicle settings.

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