TECHNIQUE FOR EFFICIENT RETRIEVAL OF PERSONALITY DATA

Abstract

A technique for enabling efficient retrieval of a digital representation of personality data of a user (402) by a client device (406) from a server (404) is disclosed, wherein the digital representation of the personality data is processed at the client device (406) to provide a user-adapted service to the user (402). A method implementation of the technique is performed by the server (404) and comprises storing a neural network being trained to compute personality data of a user based on input obtained from the user (402), receiving, from the client device (406), a request for a digital representation of personality data for a 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 is computed using the neural network based on input obtained from the user (402).

Claims

1. A method including a retrieval of a digital representation of personality data of a user by a client device from a server, the digital representation of the personality data being processed at the client device to provide a user-adapted service to the user, the method being performed by the server and comprising: storing a neural network trained to compute personality data of a user based on input obtained from the user; receiving, from the client device, a request for a digital representation of personality data for a user; and sending, to the client device, the requested digital representation of the personality data of the user, wherein the digital representation of the personality data of the user is processed at the client device to adapt a configuration of at least one device providing a service to the user, wherein adapting the configuration of the at least one device includes one of: adapting a vehicle's driving configuration, wherein the at least one device comprises a vehicle and wherein the digital representation of the personality data of the user is processed at the client device to adapt a driving configuration of the vehicle to a personality of the user, adapting an environmental condition in a passenger cabin of a transport means, wherein the at least one device comprises the transport means and wherein the digital representation of the personality data of the user is processed at the client device to adapt an environmental condition in a passenger cabin of the transport means to a personality of the user, and adapting a user-specific setting regarding a passenger cabin of the transport means, wherein the at least one device comprises the transport means and wherein the digital representation of the personality data of the user is processed at the client device to adapt a user-specific setting regarding a passenger cabin of the transport means to a personality of the user, wherein the personality data of the user is computed using the neural network based on input obtained from the user, wherein the input obtained from the user corresponds to one or more digital scores, each digital score reflecting at least one answer to at least one question regarding at least one of personality, goals and motivations of the user, wherein each digital score is used as input to a separate input node of the neural network when computing the personality data of the user using the neural network.

2. The method of claim 1, wherein adapting the configuration of the at least one device to the personality of the user is implemented using mappings that map characteristics of the personality of the user as indicated by the digital representation of the personality data of the user to particular configurations of the at least one device.

3. The method of claim 1, wherein the questions regarding the personality of the user correspond to questions of at least one of: an International Personality Item Pool, IPIP, a HEXACO-60 pool, a Big-Five-Inventory-10, BFI-10, pool, questions on psychological characteristics of the user, and questions on preferences of the user.

4. The method of claim 1, wherein the personality data of the user is indicative of at least one of: psychological characteristics of the user, and preferences of the user.

5. The method of claim 1, wherein the at least one device comprises the client device.

6. The method of claim 1, further comprising: receiving feedback characterizing the user; updating the neural network based on the feedback; and sending, to the client device, a digital representation of updated personality data of the user, wherein the updated personality data of the user is computed using the updated neural network, and, optionally: wherein the digital representation of the updated personality data of the user is processed at the client device to refine a configuration of the at least one device providing the service to the user.

7. The method of claim 6, wherein the feedback is indicative of the personality of the user.

8. The method of claim 6, wherein the feedback includes behavioral data reflecting behavior of the user monitored at the at least one device when using the service provided by the at least one device, and, optionally: wherein the behavioral data is monitored using measurements performed by the at least one device providing the service to the user.

9. The method of claim 8, wherein the at least one device comprises a vehicle and wherein the behavioral data comprises data reflecting a driving behavior of the user.

10. The method of claim 1, wherein the personality data of the user is computed prior to receiving the request from the client device and wherein the request includes an access code previously provided by the server to the user upon computing the personality data of the user, the access code allowing the user to access the digital representation of the personality data of the user from different client devices.

11. The method of claim 1, wherein the questions correspond to questions selected from a set of questions representative of an optimally achievable result of computing personality data of a user, wherein the selected questions correspond to questions of the set of questions which are determined to be most influential with respect to the optimally achievable result, and, optionally: wherein the number of the selected questions is less than 10% of the number of questions included in the set of questions.

12. The method of claim 11, wherein the questions are selected from the set of questions based on correlating results achievable by each single question of the set of questions with the optimally achievable result and selecting questions from the set of questions which have a highest correlation with the optimally achievable result, or wherein the questions are selected iteratively from the set of questions, wherein, in each iteration, a next question is selected depending on an answer of the user to a previous question, wherein, in each iteration, the next question is selected as a question of the set of questions which is determined to be most influential on an achievable result for computing personality data of the user, and, optionally: wherein the neural network comprises a plurality of output nodes representative of a probability curve of a result of the personality data of the user, wherein determining the most influential question of the set of questions as the next question of the respective iteration includes determining, for each input node of the neural network, a degree according to which a change in the digital score input to the respective input node of the neural network changes the probability curve.

13. The method of claim 1, wherein: (a) when the at least one device comprises the transport means, providing the user-adapted service to the user is further performed in consideration of sensor data indicative of an attention level of the user obtained in a passenger cabin of the transport means; (b) providing the user-adapted service to the user is further performed in consideration of body scan data indicative of characteristics of the user derivable by scanning at least a portion of the body of the user; or (a) and (b).

14. The method of claim 1, wherein the transport means is a vehicle, an aircraft, or a train.

15. A method including a retrieval of a digital representation of personality data of a user, the digital representation of the personality data being processed to provide a user-adapted service to the user, the method comprising: obtaining a digital representation of personality data of a user, the personality data of the user being computed, based on input obtained from the user, using a neural network trained to compute personality data for a user based on input obtained from the user, wherein the input obtained from the user corresponds to one or more digital scores, each digital score reflecting at least one answer to at least one question regarding at least one of personality, goals and motivations of the user, wherein each digital score is used as input to a separate input node of the neural network when computing the personality data of the user using the neural network; and processing the digital representation of the personality data to provide a user-adapted service to the user, wherein the digital representation of the personality data of the user is processed to adapt a configuration of at least one device providing a service to the user, wherein adapting the configuration of the at least one device includes one of: adapting a vehicle's driving configuration, wherein the at least one device comprises a vehicle and wherein the digital representation of the personality data of the user is processed to adapt a driving configuration of the vehicle to a personality of the user, adapting an environmental condition in a passenger cabin of a transport means, wherein the at least one device comprises the transport means and wherein the digital representation of the personality data of the user is processed to adapt an environmental condition in a passenger cabin of the transport means to a personality of the user, and adapting a user-specific setting regarding a passenger cabin of the transport means, wherein the at least one device comprises the transport means and wherein the digital representation of the personality data of the user is processed to adapt a user-specific setting regarding a passenger cabin of the transport means to a personality of the user.

16. A method including a retrieval of a digital representation of personality data of a user by a client device from a server, the digital representation of the personality data being processed at the client device to provide a user-adapted service to the user, the method being performed by the server and comprising: storing a neural network trained to compute personality data of a user based on input obtained from the user; receiving, from the client device, a request for a digital representation of personality data for a user; and sending, to the client device, the requested digital representation of the personality data of the user, wherein the digital representation of the personality data of the user is processed at the client device to adapt a configuration of at least one device providing a service to the user, wherein adapting the configuration of the at least one device includes one of: adapting a vehicle's driving configuration, wherein the at least one device comprises a vehicle and wherein the digital representation of the personality data of the user is processed at the client device to adapt a driving configuration of the vehicle to a personality of the user, adapting an environmental condition in a passenger cabin of a transport means, wherein the at least one device comprises the transport means and wherein the digital representation of the personality data of the user is processed at the client device to adapt an environmental condition in a passenger cabin of the transport means to a personality of the user, and adapting a user-specific setting regarding a passenger cabin of the transport means, wherein the at least one device comprises the transport means and wherein the digital representation of the personality data of the user is processed at the client device to adapt a user-specific setting regarding a passenger cabin of the transport means to a personality of the user, wherein the personality data of the user is computed using the neural network based on input obtained from the user, and wherein the method comprises: receiving feedback characterizing the user; updating the neural network based on the feedback, wherein updating the neural network includes training the neural network based on the feedback; and sending, to the client device, a digital representation of updated personality data of the user, wherein the updated personality data of the user is computed using the updated neural network, wherein the digital representation of the updated personality data of the user is processed at the client device to refine the configuration of the at least one device providing the service to the user.

17. The method of claim 16, wherein adapting the configuration of the at least one device to the personality of the user is implemented using mappings that map characteristics of the personality of the user as indicated by the digital representation of the personality data of the user to particular configurations of the at least one device.

18. The method of claim 16, wherein the feedback is indicative of the personality of the user.

19. The method of claim 18, wherein the feedback is gathered at the client device.

20. The method of claim 16, wherein the personality data of the user is indicative of at least one of: psychological characteristics of the user, and preferences of the user.

21. The method of claim 16, wherein the at least one device comprises the client device.

22. The method of claim 16, wherein the feedback includes behavioral data reflecting behavior of the user monitored at the at least one device when using the service provided by the at least one device, and, optionally: wherein the behavioral data is monitored using measurements performed by the at least one device providing the service to the user.

23. The method of claim 22, wherein the at least one device comprises a vehicle and wherein the behavioral data comprises data reflecting a driving behavior of the user.

24. The method of claim 16, wherein the input obtained from the user corresponds to one or more digital scores, each digital score reflecting at least one answer to at least one question regarding at least one of personality, goals and motivations of the user and wherein each digital score is used as input to a separate input node of the neural network when computing the personality data of the user using the neural network.

25. The method of claim 24, wherein the questions regarding the personality of the user correspond to questions of at least one of: an International Personality Item Pool, IPIP, a HEXACO-60 pool, a Big-Five-Inventory-10, BFI-10, pool, questions on psychological characteristics of the user, and questions on preferences of the user.

26. The method of claim 24, wherein the questions correspond to questions selected from a set of questions representative of an optimally achievable result of computing personality data of a user, wherein the selected questions correspond to questions of the set of questions which are determined to be most influential with respect to the optimally achievable result, and, optionally: wherein the number of the selected questions is less than 10% of the number of questions included in the set of questions.

27. The method of claim 26, wherein the questions are selected from the set of questions based on correlating results achievable by each single question of the set of questions with the optimally achievable result and selecting questions from the set of questions which have a highest correlation with the optimally achievable result, or wherein the questions are selected iteratively from the set of questions, wherein, in each iteration, a next question is selected depending on an answer of the user to a previous question, wherein, in each iteration, the next question is selected as a question of the set of questions which is determined to be most influential on an achievable result for computing personality data of the user, and, optionally: wherein the neural network comprises a plurality of output nodes representative of a probability curve of a result of the personality data of the user, wherein determining the most influential question of the set of questions as the next question of the respective iteration includes determining, for each input node of the neural network, a degree according to which a change in the digital score input to the respective input node of the neural network changes the probability curve.

28. The method of claim 16, wherein: (a) the personality data of the user is computed prior to receiving the request from the client device and wherein the request includes an access code previously provided by the server to the user upon computing the personality data of the user, the access code allowing the user to access the digital representation of the personality data of the user from different client devices; (b) when the at least one device comprises the transport means, providing the user-adapted service to the user is further performed in consideration of sensor data indicative of an attention level of the user obtained in a passenger cabin of the transport means; (c) providing the user-adapted service to the user is further performed in consideration of body scan data indicative of characteristics of the user derivable by scanning at least a portion of the body of the user; (a) and (b); (a) and (c); (b) and (c); or (a), (b), and (c).

29. The method of claim 16, wherein the transport means is a vehicle, an aircraft, or a train.

30. A method including a retrieval of a digital representation of personality data of a user, the digital representation of the personality data being processed to provide a user-adapted service to the user, the method comprising: obtaining a digital representation of personality data of a user, the personality data of the user being computed, based on input obtained from the user, using a neural network trained to compute personality data for a user based on input obtained from the user; and processing the digital representation of the personality data to provide a user-adapted service to the user, wherein the digital representation of the personality data of the user is processed to adapt a configuration of at least one device providing a service to the user, wherein adapting the configuration of the at least one device includes one of: adapting a vehicle's driving configuration, wherein the at least one device comprises a vehicle and wherein the digital representation of the personality data of the user is processed to adapt a driving configuration of the vehicle to a personality of the user, adapting an environmental condition in a passenger cabin of a transport means, wherein the at least one device comprises the transport means and wherein the digital representation of the personality data of the user is processed to adapt an environmental condition in a passenger cabin of the transport means to a personality of the user, and adapting a user-specific setting regarding a passenger cabin of the transport means, wherein the at least one device comprises the transport means and wherein the digital representation of the personality data of the user is processed to adapt a user-specific setting regarding a passenger cabin of the transport means to a personality of the user, wherein the method further comprises: obtaining feedback characterizing the user; and obtaining a digital representation of updated personality data of the user, wherein the updated personality data of the user is computed using the neural network being updated based on the feedback, wherein updating the neural network includes training the neural network based on the feedback, wherein the digital representation of the updated personality data of the user is processed to refine the configuration of the at least one device providing the service to the user.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Further details and advantages of the technique presented herein will be described with reference to exemplary implementations illustrated in the figures, in which:

[0028] FIGS. 1a and 1b illustrate exemplary compositions of a server and a client device according to the present disclosure;

[0029] FIG. 2 illustrates a method which may be performed by the server according to the present disclosure;

[0030] FIG. 3 illustrates a method which may be performed by the client device according to the present disclosure;

[0031] FIG. 4 illustrates an exemplary interaction between a user, the server and a client device (exemplified by a car) according to the present disclosure;

[0032] FIG. 5 illustrates different connectivity options between a mobile terminal of the user, the car and the server according to the present disclosure;

[0033] FIGS. 6a and 6b illustrate exemplary structures of the neural network according to the present disclosure;

[0034] FIG. 7 illustrates an exemplary implementation which involves considering the driver's attention level to adapt settings of a vehicle according to the present disclosure; and

[0035] FIG. 8 illustrates an exemplary implementation which involves considering body scan data of a user to provide a user-adapted service to the user according to the present disclosure.

DETAILED DESCRIPTION

[0036] In the following description, 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 one skilled in the art that the present disclosure may be practiced in other implementations that depart from these specific details.

[0037] Those skilled in the art will further appreciate that the steps, services and functions explained herein below may be implemented using individual hardware circuitry, using software functioning in conjunction with a programmed micro-processor or general purpose computer, using one or more Application Specific Integrated Circuits (ASICs) and/or using one or more Digital Signal Processors (DSPs). It will also be appreciated that when the present disclosure is described in terms of a method, it may also be embodied in one or more processors and one or more memories coupled to the one or more processors, wherein the one or more memories are encoded with one or more programs that perform the steps, services and functions disclosed herein when executed by the one or more processors.

[0038] FIG. 1a schematically illustrates an exemplary composition of a server 100 for enabling efficient retrieval of a digital representation of personality data of a user by a client device from the server 100, wherein the digital representation of the personality data is to be processed at the client device to provide a user-adapted service to the user. The server 100 comprises at least one processor 102 and at least one memory 104, wherein the at least one memory 104 contains instructions executable by the at least one processor 102 such that the request server 100 is operable to carry out the method steps described herein with reference to the “server”.

[0039] It will be understood that the server 100 may be implemented on a physical computing unit or a virtualized computing unit, such as a virtual machine, for example. It will further be appreciated that the server 100 may not necessarily be implemented on a standalone computing unit, but may be implemented as components—realized in software and/or hardware—residing on multiple distributed computing units as well, such as in a cloud computing environment, for example.

[0040] FIG. 1b schematically illustrates an exemplary composition of a client device 110 for enabling efficient retrieval of a digital representation of personality data of a user by the client device 110 from a server. The client device 110 comprises at least one processor 112 and at least one memory 114, wherein the at least one memory 114 contains instructions executable by the at least one processor 112 such that the request client device 110 is operable to carry out the method steps described herein with reference to the “client device”.

[0041] FIG. 2 illustrates a method which may be performed by the server 100 according to the present disclosure. The method is dedicated to enabling efficient retrieval of a digital representation of personality data of a user by a client device (e.g., the client device 110) from the server 100. In the method, the server 100 may perform the steps described herein with reference to the “server” and, in line with the above description, in step S202, the server 100 may store a neural network being trained to compute personality data of a user based on input obtained from the user, in step S204, the server 100 may receive, from the client device, a request for a digital representation of personality data for a user and, in step S206, the server 100 may send, to the client device, the requested digital representation of the personality data of the user, wherein the personality data of the user is computed using the neural network based on input obtained from the user.

[0042] FIG. 3 illustrates a method which may be performed by the client device 110 according to the present disclosure. The method is dedicated to enabling efficient retrieval of a digital representation of personality data of a user by the client device 110 from a server (e.g., the server 100). In the method, the client device 110 may perform the steps described herein with reference to the “client device” and, in line with the above description, in step S302, the client device 110 may send, to the server, a request for a digital representation of personality data for a user, in step S304, the client device 110 may receive, from the server, the requested digital representation of the personality data of the user, the personality data of the user being computed, based on input obtained from the user, using a neural network trained to compute personality data for a user based on input obtained from the user and, in step S306, the client device 110 may process the digital representation of the personality data to provide a user adapted service to the user.

[0043] FIG. 4 illustrates an exemplary interaction between a user 402, a server 404 storing a neural network being trained to compute personality data of users based on input obtained from the users, and a client device for retrieving a digital representation of personality data of the user 402 to provide a user-adapted service to the user 402, wherein, in the shown example, the client device is a car 406 which may be driven by the user 402. As shown in the figure, the user 402 may perform an automated personality test by answering questions, e.g., using a web interface or an app on his laptop or smartphone, to thereby provide input to the neural network stored at the server 404 based on which the neural network may compute personality data for the user 402. Instead of sending a digital representation of the personality data to the user 402, in the shown example, the server 404 provides an access code to the user 402 which can be used by the user 402 to access the personality data using different client devices, including the car 406. The user 402 may register or login at the car 406 (more specifically, at its board computer) with the access code and the car 406 may then request, using the access code, the digital representation of the user's personality data from the server 404 (in the figure, the personality data of the user is denoted as the user's “MindDNA”).

[0044] Upon receiving the request from the car 406, the server 404 may return the user's personality data to the car 406, which may then configure its driving configuration (and, optionally, subcomponents of the car 406) in accordance with the personality data of the user 402, e.g., adapting the gas and brake reaction behavior of the car 406, to thereby provide a driving experience that is specifically adapted to the user's personality (e.g., risk-averse, risk-seeking, etc.). When the user 402 then drives the car 406, the car 406 may monitor the user's driving behavior, e.g., using sensors measuring the user's break reaction and intensity, and the car 406 may provide this information as feedback to the server 404, where the feedback may be processed to update (by training) the neural network to refine its capability of computing the personality data of the user 402. In response, the server 404 may send correspondingly updated personality data of the user 402 to the car 406 which may then use the digital representation of the updated personality data to refine the car configuration for a better alignment with the actual personality of the user 402. In sum, a system is therefore provided which may allow integrating retrieval and use of the user's personality data into an automated process to adapt the configuration of devices or services provided thereon in accordance with the preferences of the user derived his personality data, to thereby improve user experience.

[0045] FIG. 5 illustrates different connectivity options between a mobile terminal 502 (e.g., a smartphone) of the user 402, the car 406 and the server 404 in accordance with the present disclosure. In one variant, the car 406 may communicate with the server 404 directly via the Internet and, upon authentication of the user 402 with the car 406 (e.g., using a key, smartcard, NFC/RFID, a smartphone with NFC, fingerprint, or the like), the car 406 may request the personality data of the user (in FIG. 5 again denoted as the user's “MindDNA”) to improve the driving experience of the user 402. In another variant, when the user 402 carries the mobile terminal 502, the mobile terminal 502 may (e.g., using a dedicated app installed thereon) communicate with the server 404 via the Internet and request the personality data of the user 402. In this variant, the car 406 may communicate locally with the mobile terminal 502 (e.g., using Bluetooth, Wi-Fi or USB cable) and retrieve the personality data of the user from the mobile terminal 502. A direct connection between the car 406 and the mobile terminal 502 may additionally be used to exploit sensors installed at the mobile terminal 502 (e.g., gyroscope for movement and acceleration detection, GPS for movement and acceleration detection as well as detection of driving routes, or medical sensors measuring pulse, blood pressure, or the like) to supplement the feedback gathered by the car 406 itself (e.g., in relation to the user's driving behavior) to thereby provide additional feedback sensed by the mobile terminal 502 to the server 404 for updating the neural network based on the feedback, as described above.

[0046] FIG. 6a illustrates an exemplary structure of a neural network 602 in accordance with the present disclosure. The neural network 602 comprises an input layer, an output layer and two hidden layers. It will be understood that the neural network 602 shown in FIG. 6a merely illustrates the structure of deep neural networks in general and that the actual number of nodes (at least in the input layer and the hidden layers) of the neural network 602 stored in the server 404 may be significantly higher than shown in the figure. As mentioned above, a test has been performed using the 30 most influential questions among a total number of 370 questions or more (taken from standard IPIP, HEXACO-60 and BFI-10 questions and, optionally, supplemented by further questions regarding goals and motivations of the user), resulting in 30 input nodes in the input layer of the neural network 602. In such a case, each of the hidden layers could be configured with 50 nodes, for example. Further, as shown in the figure, the neural network 602 may comprise a single output node in the output layer. In this case, the result value at the output node of the output layer may be representative of the value of one personality dimension (out of the Big Five) on which the neural network 602 has been trained. It will be understood that such structure of the neural network 602 is merely exemplary and that other structures are generally conceivable.

[0047] A more advanced structure of the neural network 602 comprises input nodes in accordance with the number of a full set of questions available, which may be taken from standard IPIP, HEXACO-60 and BFI-10 questions including further questions regarding goals and motivations of the user as well as still further questions on other psychological characteristics and/or preferences of the user not covered by the above questions, potentially adding up to several hundreds of questions, e.g., more than 600 questions. Such neural network 602 may thus have more than 600 input nodes, each corresponding to one of the questions of the full set of available questions, and the number of nodes of the hidden layers may be selected depending on the performance of the neural network 602. For example, the neural network 602 may comprise two hidden layers with 100 nodes each. Further, in the input layer, the above-mentioned more than 600 input nodes may be duplicated, wherein each duplicated input node may be used as a missing-question-indicator. The missing-question-indicators may be dichotomous, i.e., they may only have two values (e.g., 0 and 1) indicating whether the question of the corresponding (original) input node has been answered or not. Due to the duplicated input nodes, the input layer may comprise a total of more than 1200 input nodes.

[0048] The output layer of the more advanced neural network 602 may have a plurality of output nodes that together represent a probability curve for one personality dimension. If the scale used for the output in this personality dimension ranges from 0 to 10 and the number of output nodes is 50, for example, then each output node may be representative for a portion of the scale, i.e., corresponding to the portions 0-0.2, 0.2-0.4, 0.4-0.6, . . . 9.8.10 of the scale. Instead of a single output value, such output layer may deliver a whole probability curve for the output value on this personality dimension. FIG. 6b illustrates an exemplary output layer together with a corresponding probability curve 604. Such curve may allow determining where the output value most probably is (i.e., indicated by the peak of the curve) as well as determining the accuracy with which the neural network 602 calculates the result (i.e., indicated by the width of the curve). Using the advanced neural network 602, it may be possible to calculate the personality data of the user in the form of several probability curves (e.g., five probability curves corresponding to the Big Five) for an arbitrary number of answered questions, provided that the neural network 602 is trained separately for each dimension. In the initial state, in which no question has been answered yet, all missing-question-indicators may have the value of “missing” (e.g., 0). With every question which is then answered, an update of the output values may be calculated so that the width of the probability curves on the output layer becomes less with an increasing number of answered questions, so that the accuracy with which the neural network 602 calculates the result steadily increases.

[0049] Such structure of the neural network 602 may be particularly advantageous because it may allow iteratively selecting questions to be answered by the user next from the full set of questions, wherein, in each iteration, a next question may be selected depending on an answer of the user to the previous question, wherein, in each iteration, a next question may be selected as a question of the full set of questions which is determined to be most influential on an achievable result for computing personality data of the user. To this end, upon each answered question, the several (e.g., five) probability curves may be recalculated and, among the recalculated probability curves, the one which has the largest width (i.e., representing the probability curve currently having the at least accuracy) may be determined. As next question for the iteration, a question on this dimension may be selected to improve the accuracy on this dimension. In order to determine the most influential question, a degree according to which a change in the digital score input to the respective input node changes the probability curve (e.g., a degree in which the width of the curve changes) may be determined for each input node of the neural network 602. Based on this, the question associated with the input node for which the degree of change in the probability curve is determined to be highest may be selected as the most influential question for the respective iteration.

[0050] The advanced structure of the neural network 602 may also be advantageous because it may allow integrating feedback easily into the neural network. As described above, if the feedback represents a new input value which has not yet been input to the neural network 602, a new input node may simply be added to the neural network 602 and the new input value may be assigned to the new input node when training the neural network 602. In this way, any kind of new feedback may easily integrated into the network so that the neural network 602 may be refine its capability to compute personality data. As an implementation which reduces the computational complexity when adding a new input node, it may be conceivable that, when the network is trained to correlate the new input node with the other nodes of the network, only those nodes may be incorporated into the calculation which are determined to be most influential with respect to the optimally achievable result, to thereby avoid incorporating all nodes into the calculation. Also, it may be conceivable that, when the network is trained to correlate the new input node with the other nodes of the network, the number of layers being precalculated is limited (e.g., to 2 or 3) to avoid calculating all subsequent combinations of nodes, for example.

[0051] In the above description, the presented technique for efficient retrieval for a digital representation of personality data of a user has been exemplified in the context of adapting a vehicle's driving configuration, such as adapting the gas and brake reaction behavior of the vehicle to the personality of the user. In this case, the method described herein may also be denoted as a method for adapting a vehicle's driving configuration including an efficient retrieval of a digital representation of personality data of a user. It will be understood that adapting the gas and brake reaction behavior of the vehicle is just one example of adapting a vehicle's driving configuration and that, more generally, adapting the vehicle's driving configuration may comprise adapting any vehicle configuration that influences the driving behavior of the vehicle. Adapting the vehicle's driving configuration may as such comprise at least one of adapting a gas and brake reaction behavior of the vehicle, adapting chassis settings of the vehicle, adapting a driving mode of the vehicle, and adapting settings of an adaptive cruise control (ACC) of the vehicle, or the like, to the personality of the user. Adapting a driving mode of the vehicle may comprise setting an economy, comfort or sport mode to influence gas pedal and fuel consumption behavior of the vehicle depending on the driver's personality. If the personality data indicates that the driver tends to be risk-averse, for example, the driving mode may be set to economy or comfort, whereas for drivers that tend to have a risk-seeking personality, the driving mode may be set to sport mode. Adapting a drive mode of the vehicle may also comprise enabling/disabling an automatic four-wheel-drive (4WD) mode of the vehicle, for example. Adapting the settings of the ACC may comprise setting the distance to the vehicle ahead and/or the target driving speed, e.g., depending on the risk-averseness of the driver.

[0052] It will be understood that the technique presented herein may also be employed for other purposes in a vehicle context, such as to adapt the environmental conditions in the passenger cabin of the vehicle (or, more generally, of a transport means, as an adaptation of the environmental conditions in the passenger cabin may similarly apply to other means of transport, such as aircrafts, trains, etc.). In this case, the method described herein may also be denoted as a method for adapting an environmental condition in a passenger cabin of a transport means including an efficient retrieval of a digital representation of personality data of a user. Adapting an environmental condition in a passenger cabin of a transport means may comprise adapting at least one of adapting a temperature of the passenger cabin (e.g., by adapting the air condition settings for the passenger cabin), adapting an internal lighting of the passenger cabin, and adapting an oxygen level in the passenger cabin, or the like, to the personality of the user. Additionally or alternatively to adapting an environmental condition in the passenger cabin, the technique presented herein may also be employed to adapt user-specific settings regarding the passenger cabin. Adapting a user-specific setting regarding a passenger cabin of a transport means may comprise adapting at least one of adapting a seat configuration (e.g., seat height, seat position, seat massage settings, seat belt tensioning, etc.) for the user in the passenger cabin, and adapting equalizer settings of a sound system (e.g., increasing/decreasing basses or heights) provided to the user in the passenger cabin, or the like, to the personality of the user.

[0053] Any of the above adaptations of vehicle/transport means settings may—in addition to the adaptation to the personality of the user—also be performed in consideration of (or “based on”/“in accordance with”) sensor data indicative of a user's attention level obtained in the passenger cabin. In other words, the client device may be configured to adapt at least one of the vehicle's driving configuration, the environmental conditions in the passenger cabin, and the user-specific settings regarding the passenger cabin not only in consideration of the digital representation of the personality data of the user, but also in consideration of sensor data indicative of a user's attention level. The digital representation of the personality data of the user and the sensor data indicative of the user's attention level may in other words be combined prior to performing the above-mentioned adaptations. The sensor data indicative of the user's attention level may comprise data regarding at least one of the user's heartbeat, breath, tiredness, reaction time, and alcohol/drug level, for example. The sensor data may be collected by at least one sensor installed in the passenger cabin or in the mobile terminal of the user, for example.

[0054] FIG. 7 illustrates an exemplary implementation which involves considering the driver's attention level in combination with the driver's personality data in order to adapt the vehicle's driving configuration, the environmental conditions in the passenger cabin and/or the user-specific settings regarding the passenger cabin. The driver's attention level may be checked by corresponding sensors in terms of the user's reaction time, tiredness, heartbeat, breath, alcohol/drug level, or unusual behavior of the user, for example. In the left portion of the figure, the collected sensor data is indicative of a normal attention level of the user and, hence, the vehicle settings may remain at the normal levels (e.g., as adapted to the driver's personality or “MindDNA”), including the speed, audio volume, temperature and seat settings, for example. In the middle portion of the figure, the sensor data is indicative of a reduced attention level of the driver and the vehicle settings may thus be changed to reduced speed, higher audio volume, lower temperature settings, including turning on seat massage features, in order to refresh the driver's attention again. Optionally, attention tests may be performed, such as requesting the driver to provide a voice-based response in a question/answer scheme, for example, and the results of the attention tests may be considered in adapting the above-mentioned settings. In the right portion of the figure, on the other hand, the sensor data is indicative of a very low driver's attention level and, therefore, a user warning may be provided and the vehicle settings may be adapted accordingly, e.g., to a very slow speed (and forcing to stop the vehicle at the next stopping opportunity, for example), to muted audio and/or to provide directions to the next hotel by a navigation system, for example.

[0055] In order to provide a user-adapted service to the user, as described above (e.g., by adapting at least one of the vehicle's driving configuration, the environmental conditions in the passenger cabin, and the user-specific settings regarding the passenger cabin), the client device may further consider body scan data indicative of (e.g., physical) characteristics of the user derivable by scanning (e.g., at least a portion of) the user's body prior to providing the user-adapted service to the user (e.g., prior to the user driving the vehicle). The user characteristics which are derivable by scanning the user's body may include at least one of the user's size, weight, sex, age, stature, posture, and emotional state, for example. The body scan data may be obtained by a camera or voice recorder (e.g., of the mobile terminal of the user, or installed at the vehicle/transport means) acquiring one or more images or speech signals of the user, wherein body/face/voice recognition techniques may be employed to scan the user's body and derive the user characteristics mentioned above. The client device may thus be configured to provide a user-adapted service not only in consideration of the digital representation of the personality data of the user, but also in consideration of (or “based on”/“in accordance with”) the body scan data. The digital representation of the personality data of the user and the body scan data may in other words be combined prior to providing the user-adapted service to the user. FIG. 8 illustrates an exemplary implementation which involves considering a driver's body scan data (e.g., obtained by the driver's mobile terminal, such as the driver's smartphone, smartwatch or fitness tracker, prior to entering the vehicle) in combination with the personality data of the driver in order to adapt the vehicle's driving configuration, the environmental conditions in the passenger cabin and/or the user-specific settings regarding the passenger cabin accordingly. In the figure, the body scan data is denoted as “BodyDNA”, which in combination with the “MindDNA”, forms the so-called “LifeDNA”. It will be understood that the obtained body scan data may also be used to provide feedback characterizing the user to update the neural network, as described above.

[0056] In another vehicle-related use case, the technique presented herein may also be used to determine a vehicle configuration that is adapted to the personality of the user prior to manufacturing the vehicle, wherein the vehicle may then be manufactured based on (or “in accordance with”) the determined vehicle configuration. The vehicle may be manufacturable in different configuration options (e.g., as offered by a vehicle manufacturer), such as with different motor options each having a different motor power, drive technology options (e.g., support of two-wheel-drive (2WD) or 4WD technology), chassis options, different drive mode options, support of ACC, etc., and when a new vehicle is to be manufactured for the user, the vehicle configuration may be determined to be specifically adapted to the personality of the user. For example, if the personality data indicates that the user tends to be risk-averse, the determined vehicle configuration may comprise a selection of a motor having a lower power as compared to a vehicle configuration determined for a user whose personality data indicates a risk-seeking personality. Based on the determined vehicle configuration, the vehicle may then be manufactured accordingly. As such, in line with the above description, it may also be envisaged a method for vehicle manufacturing including an efficient retrieval of a digital representation of personality data of a user by a client device from a server, the digital representation of the personality data being processed at the client device to provide a vehicle configuration adapted to the personality of the user. The method may comprise sending, from the client device to the server, a request for a digital representation of personality data for a user, receiving, by the client device from the server, the requested digital representation of the personality data of the user, the personality data of the user being computed, based on input obtained from the user, using a neural network trained to compute personality data for a user based on input obtained from the user, processing the digital representation of the personality data to determine a vehicle configuration which is adapted to the personality of the user, and manufacturing the vehicle based on the determined vehicle configuration. In the manufacturing process of the vehicle, it will be understood that the determined vehicle configuration may also affect the manufacturing of vehicle parts needed for the manufacturing of the vehicle. For example, manufacturing the vehicle may comprise manufacturing one or more vehicle parts to be used for manufacturing the vehicle, wherein the vehicle parts are manufactured (e.g., using a 3D printer) in accordance with the determined vehicle configuration.

[0057] It will be understood that the technique presented herein may not only be employed in vehicle/transport means related use cases, but also in other use cases, such as to adapt the configuration of smart home appliances or robots to the personality of a user, for example. As such, in line with the above description, it may also be envisaged a method for adapting a configuration of a smart home appliance (e.g., automatic roller shutters, air conditions, refrigerators, washing machines, televisions, set-top boxes, etc.) including an efficient retrieval of a digital representation of personality data of a user, wherein the digital representation of the personality of the user may be processed at the client device to adapt a configuration of the smart home appliance to the personality of the user (e.g., to adapt the way in which the smart home appliance carries out its primary task, such as its shutting (roller shutters), heating/cooling (air conditions), refrigerating (refrigerators), washing (washing machines) or recording/display (televisions/set-top boxes) tasks). Similarly, in line with the above description, it may be envisaged a method for adapting a configuration of a robot (e.g., a humanoid robot or domestic robot configured to carry out one or more household tasks) including an efficient retrieval of a digital representation of personality data of a user, wherein the digital representation of the personality of the user may be processed at the client device to adapt a configuration of the robot to the personality of the user (e.g., to adapt the way in which household tasks are carried out by the domestic robot).

[0058] Various other use cases are generally conceivable. Other use cases may comprise the adaptation of the configuration of virtual robots, the adaptation of the configuration of medical devices, or even the stimulation of a brain, for example. As such, in line with the above description, it may also be envisaged a method for adapting a configuration of a virtual robot (e.g., a chatbot, virtual service personnel or virtual personal assistant) including an efficient retrieval of a digital representation of personality data of a user, wherein the digital representation of the personality of the user may be processed at the client device to adapt a configuration of the virtual robot to the personality of the user (e.g., to adapt the way in which the virtual robot carries out its task of supporting the user). Similarly, in line with the above description, it may be envisaged a method for adapting a configuration of a medical device (e.g., a bedside medical device) including an efficient retrieval of a digital representation of personality data of a user, wherein the digital representation of the personality of the user may be processed at the client device to adapt a configuration of the medical device to the personality of the user (e.g., to adapt a dosage regime, such as the dosage of an analgesic, or the like). Even further, it may be envisaged a method for stimulating a brain (e.g., of a living being or a virtual representation of a brain) including an efficient retrieval of a digital representation of personality data of a user, wherein the digital representation of the personality of the user may be processed at the client device to adapt a stimulation procedure for the brain based on the personality of the user. The stimulation procedure may comprise an electrical stimulation of a living being's brain or an adaptation/reconfiguration of a virtual representation of a brain, for example. A virtual representation of a brain may be fed into a robot or other form of intelligent system in order to influence the behavior of such system based on the personality of the user, for example.

[0059] In all of the above-described examples and use cases, when it is referred to “adapting” a configuration or setting “to the personality of a user”, it will be understood that such adaptation may be implemented using predefined mappings that map a given characteristic of the user's personality (as indicated by the digital representation of the personality data of the user) to a particular configuration or setting of the corresponding device/apparatus (e.g., vehicle, transport means, smart home appliance, robot, medical device, etc., as described above). As said, for example, if the personality data indicates that a driver tends to be risk-averse, the driving mode of a vehicle may be set to economy or comfort, whereas for drivers that tend to have a risk-seeking personality, the driving mode may be set to sport mode. Such mappings may be predefined for each possible personality characteristic-configuration/setting combination and, depending on the obtained personality data of the user, the configuration or setting of the device/apparatus may be adapted accordingly. The personality characteristic of the user may correspond to a value of a personality dimension (e.g., out of the Big Five) output by the neural network, as described above, for example.

[0060] The following numbered statements describe some various embodiments of the present invention.

[0061] Statement #1: A method may be provided 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), the digital representation of the personality data being processed at the client device (406) to provide a user-adapted service to the user (402), the method being performed by the server (404) and comprising: [0062] storing (S202) a neural network (602) being trained to compute personality data of a user (402) based on input obtained from the user (402); [0063] receiving (S204), from the client device (502; 406), a request for a digital representation of personality data for a user (402); and [0064] sending (S206), to the client device (502; 406), the requested digital representation of the personality data of the user (402), wherein the personality data of the user (402) is computed using the neural network (602) based on input obtained from the user (402).

[0065] Statement #2: The method according to Statement #1 may be provided, wherein 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) providing a service to the user (402), and, optionally: [0066] wherein the at least one device (406) comprises the client device (406).

[0067] Statement #3: The method according to Statement #1 or Statement #2 may be provided, further comprising: [0068] receiving feedback characterizing the user (402); [0069] updating the neural network (602) based on the feedback; and [0070] sending, to the client device (502; 406), a digital representation of updated personality data of the user (402), wherein the updated personality data of the user (402) is computed using the updated neural network (602), and, optionally: [0071] wherein the digital representation of the updated personality data of the user (402) is processed at the client device (502; 406) to refine a configuration of the at least one device (406) providing the service to the user (402).

[0072] Statement #4: The method according to Statement #3 may be provided, wherein the feedback includes behavioral data reflecting 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), and, optionally: [0073] wherein the behavioral data is monitored using measurements performed by the at least one device (406) providing the service to the user (402).

[0074] Statement #5: The method according to Statement #4 may be provided, wherein the at least one device (406) comprises a vehicle and wherein the behavioral data comprises data reflecting a driving behavior of the user (402).

[0075] Statement #6: The method according to any one of Statements #1 to #5 may be provided, wherein the personality data of the user (402) is computed prior to receiving the request from the client device (502; 406) and wherein the request includes an access code previously provided by the server (404) to the user (402) upon computing the personality data of the user (402), the access code allowing the user (402) to access the digital representation of the personality data of the user (402) from different client devices (502; 406).

[0076] Statement #7: The method according to any one of Statements #1 to #6 may be provided, wherein the input obtained from the user corresponds to digital scores reflecting answers to questions regarding at least one of personality, goals and motivations of the user (402) and wherein each digital score is used as input to a separate input node of the neural network (602) when computing the personality data of the user (402) using the neural network (602).

[0077] Statement #8: The method according to Statement #7 may be provided, wherein the questions correspond to questions selected from a set of questions representative of an optimally achievable result of computing personality data of a user (402), wherein the selected questions correspond to questions of the set of questions which are determined to be most influential with respect to the optimally achievable result, and, optionally: [0078] wherein the number of the selected questions is less than 10% of the number of questions included in the set of questions.

[0079] Statement #9: The method according to Statement #8 may be provided, wherein the questions are selected from the set of questions based on correlating results achievable by each single question of the set of questions with the optimally achievable result and selecting questions from the set of questions which have a highest correlation with the optimally achievable result, or [0080] wherein the questions are selected iteratively from the set of questions, wherein, in each iteration, a next question is selected depending on an answer of the user to a previous question, wherein, in each iteration, the next question is selected as a question of the set of questions which is determined to be most influential on an achievable result for computing personality data of the user, and, optionally: [0081] wherein the neural network (602) comprises a plurality of output nodes representative of a probability curve (604) of a result of the personality data of the user (402), wherein determining the most influential question of the set of questions as the next question of the respective iteration includes determining, for each input node of the neural network (602), a degree according to which a change in the digital score input to the respective input node of the neural network (602) changes the probability curve (604).

[0082] Statement #10: A method may be provided 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), the method being performed by the client device (502; 406) and comprising: [0083] sending (S302), to the server (404), a request for a digital representation of personality data for a user (402); [0084] receiving (S304), from the server (404), the requested digital representation of the personality data of the user (402), the personality data of the user (402) being computed, based on input obtained from the user (402), using a neural network (602) trained to compute personality data for a user (402) based on input obtained from the user (402); and [0085] processing (S306) the digital representation of the personality data to provide a user-adapted service to the user (402).

[0086] Statement #11: A computer program product may be provided comprising program code portions for performing the method according to any one of Statements #1 to #10 when the computer program product is executed on one or more computing units.

[0087] Statement #12: The computer program product of Statement #11 may be provided, stored on one or more computer readable recording media.

[0088] Statement #13: A server (100; 404) may be provided for enabling efficient retrieval of a digital representation of personality data of a user (402) by a client device (502; 406) from the server (404), the digital representation of the personality data being processed at the client device (502; 406) to provide a user-adapted service 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) containing 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 Statements #1 to #9.

[0089] Statement #14: A client device (110; 502; 406) may be provided for enabling 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) containing 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 Statement #10.

[0090] Statement #15: A system may be provided comprising a server (100; 404) according to Statement #13 and at least one client device (110; 502; 406) according to Statement #14.

[0091] It is believed that the advantages of the technique presented herein will be fully understood from the foregoing description, and it will be apparent that various changes may be made in the form, constructions and arrangement of the exemplary aspects thereof without departing from the scope of the disclosure or without sacrificing all of its advantageous effects. Because the technique presented herein can be varied in many ways, it will be recognized that the disclosure should be limited only by the scope of the claims that follow.