WO2003015056A2 - Automated behavioral and cognitive profiling for training and marketing segmentation - Google Patents
Automated behavioral and cognitive profiling for training and marketing segmentation Download PDFInfo
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- WO2003015056A2 WO2003015056A2 PCT/EP2002/008928 EP0208928W WO03015056A2 WO 2003015056 A2 WO2003015056 A2 WO 2003015056A2 EP 0208928 W EP0208928 W EP 0208928W WO 03015056 A2 WO03015056 A2 WO 03015056A2
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
- G09B7/02—Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student
- G09B7/04—Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying a further explanation
Definitions
- US6338628 proposes a personal training and development system.
- a user logs on a computer and is led through some questionnaires (four or five lists of 18 statements each) to fill in manually.
- the application matches the answers to a Kinsel-Hartman Profile to unveil strengths and weaknesses of the user and propose training accordingly.
- the profile test is repeated periodically to benchmark training success and alter the training style. Users are profiled manually with questionnaires and profiles are not updated continuously.
- US5551880 proposes a sophisticated employee success system. Behavioral information is derived from the individuals through questionnaires. The system then analyzes the answers and compares these against standards for behavior and values previously calculated for a specific job. The output is a one-off benchmark report only and does not imply any customized training thereafter.
- US5326270 describes a system and method for assessing an individual's task-processing style. This time the user is put in a case study with a simulated situation and the individual's responses and time for resolving the situation are recorded and statistically analyzed to benchmark the individual against others. Again, the method is mainly used to decide whether an individual is suitable for a job and only manually implies to propose some training to improve the user's ability to handle tasks.
- US 6164975 describes an interactive instructional system using adaptive cognitive profiling.
- the system derives at' a cognitive profile of the user via presenting different multimedia presentations of the same content. Via a utility function the system is testing the comprehension of content by the user for the different representations and establishes a cognitive profile and continuously updates the utility function. The neither claim an adaptive training session nor automatically tracks the user's cognitive behavior.
- US5597312 discloses an intelligent tutoring method and system comprising a computer system for selecting adjustable teaching parameters and a student model which is monitored and updated. Again the system lacks to automatically sense the user's behavior as the performance is watched along manually provided teaching parameters.
- US6298328 discloses an apparatus, method, and system for sizing markets.
- the system utilizes a general purpose computer and simulates market and submarket evolutions by taking weighted coefficients for parameters such as products, geographic area, market segment, provider, time period, regional market data, demographic, psychographic and/or firmagraphic data and profiles. It requires the operator to weight the different coefficient and does not observe the behavior of all individuals to derive at different socio-demographic, behavioral and cognitive profiles for market segmentation as the invention does.
- US6236975 discloses a system and method for profiling customers for targeted marketing.
- the marketing system presents selected questions to an individual, receives responses and stores the data. The data is then analyzed in comparison to a selected peer group to the individual. A report is provided to give immediate graphical feedback of the individual's standing in respect to the peer group.
- the system however requires the user to respond to several questions and does not observe the individual non-intrusively. It also lacks to observe a behavioral profile and concentrates on socio-demographic information.
- US5848396 and a second filing US5991735 of the same inventor disclose a method, apparatus and computer program for determining behavioral profile of a computer user.
- the system presents via a network several visual displays, such as market or stock data, theatre or television schedules, weather and/or travel information and advertisements, to a targeted user group in order to obtain psychographic profiles.
- the user is tracked while processing the information, especially the individual's way of formatting graphically presented information such as color schemes, text sizes and shapes, is recoded. All together with demographic data, a psychographic profile is established and via regression analysis constantly updated. With the profile a customized displaying is possible.
- the system lacks to record non- intrusively human-factor input and behavior such as gesture, mimics, head movements or eye movements on the content and the system only profiles via pre-defined artificial images presented to the individual. The system also foregoes an opportunity to use profile information for marketing segmentation.
- a methodology and system is disclosed to utilize multi-variant analysis techniques or other statistical or heuristic methods to cluster users based on behavior and socio-demographic data. Behavioral and socio-demographic data is retrieved while observing users via sensors and the recording of user inputs.
- One aspect of the disclosed methodology allows automating an objective training process. Trainees are observed during training sessions and user profile is recorded that includes behavioral factors, socio-demographic factors and potential other psychological methods based e.g. on questionnaires.
- the observation system comprises multi-modal user input devices and user sensors that sense the behaviors of the trainees extracted from the trainees head and eye movements, mimics and gestures, voice, physical stress conditions as inputs coming from mouse, joystick or any other haptic device.
- the trainee socio-demographic and behavioral profile is automatically matches to expert profiles solving the same task and thereby to target success strategy for the trainee to solve the task.
- the system is able to monitors training performance in multiple sessions, adapts content, the training process or the user interface to best suit the individual trainee and thereby trains the trainee fully automatically towards the target success strategies for solving tasks on hand.
- Another aspect of the disclosed methodology allows automating an objective segmentation process of users in marketing and advertising.
- the method of market segmentation does not match expert behavior, performance and target success strategies to trainee behavior. It however matches different tasks, stimuli or marketing messages to segmented clusters of users.
- the observation system comprises multi-modal user input devices and user sensors that sense the behaviors of the users extracted from the users head and eye movements, mimics and gestures, voice, physical stress conditions as inputs coming from mouse, joystick or any other haptic device.
- Socio-demographic profiles and the behavior of the users are assessed and clustered for the messages or stimuli exposed to the user.
- the users are therefore objectively and automatically segmented based on socio-demographic and behavioral information while watching marketing messages or content, or other stimuli or performing tasks.
- the process allows also building a strong database of behavioral reference profiles that automatically links the best suited message, content or stimuli to users observed.
- the segmentation process copes with single user observation but also with the observation of selected focus groups and panels.
- the invention allows monitoring and adjusting a training process as well as segmenting users and adjusting content, messages or stimuli in marketing or advertising.
- Outstanding criteria of the invention are:
- ⁇ Behavioral data is captured non-intrusively from user via user sensors and recording of user inputs
- FIG. 1 illustrates the tutoring and training method according to the preferred embodiment of the present invention.
- FIG. 2 illustrates the tutoring and training system according to the preferred embodiment of the tutoring and training method as of FIG. 1.
- FIG. 3 illustrates the method for market segmentation according to the preferred embodiment of the present invention.
- FIG. 4 illustrates the system for market segmentation according to the preferred embodiment of the market segmentation method as of FIG. 3.
- a methodology and system is disclosed to utilize multi-variant analysis techniques or other statistical or heuristic methods to cluster users based on behavior and socio-demographic data. Behavioral and socio-demographic data is retrieved while observing users via sensors and the recording of user inputs.
- FIG. 1 explains the disclosed method for tutoring and training subjects.
- tasks and goals for the training are defined and entered into the system ( ⁇ ).
- Training goals and the tasks are qualified via performance measures, such as time to results, quality of the anticipated results, negative side effects, costs, but not limited to these.
- performance measures such as time to results, quality of the anticipated results, negative side effects, costs, but not limited to these.
- experts are observed while solving the tasks recording a variety of user inputs to the training system and sensing the behaviors of the experts ((D). Inputs could come from mouse, joystick or any other haptic device, keyboard.
- User passive behavior such as eye movements on the task, head movements, mimics, gestures, speech and physical stress could be recorded by variety of user sensors directed on or mounted to the experts. Behavioral aspects include among others e.g.
- trainees are observed in the same way while solving the tasks (®). Again behavior and performance of the trainees are recorded via different user input devices and sensors. Both the trainees and the training sessions are profiles and assessed ( ⁇ ). The profile of the trainee is matched to a corresponding behavioral and/or socio- demographic expert profile with an accompanying target success strategy for the task. Once the matching was successful, training sessions can be modified and adjusted to train target success strategies ( ⁇ ). The user interface of the training program but also the solution process can be modified to improve the trainee's performance.
- the training results can be fed back into the training methodology.
- One feedback loop according to FIG. 1 can adapt training goals and task definition for establishing a modified reference database. Iterative training sessions for the trainee are possible to monitor training improvements. Training sessions can be adjusted to reflect trainees growing expertise in iterative training sessions.
- the automated multi-modal and objective user behavior observation is directed to create a comprehensive and normative database behavioral profiles that allows predicting online the trainee's behavior while acting on new tasks. This enables the trainer to adjust the training to the trainee proactively as the behavior of the trainee is predictive.
- FIG. 2 exhibits the system best suited to tutoring and training methodology.
- Expert user and trainees 1 are recoded via user sensors 3 and multi-modal user input devices 4.
- One possible sensor would be at least one imaging sensor that records eye movements, head movements and gestures of the user.
- a voice recorder can record the user's speech, medical diagnosis system could record stress symptoms of the user.
- different multi-modal user input devices can be observed such as keyboard, mouse, joystick or any other haptic device but not limited to these. While the user sensors and input devices are mainly observing the behavior of the user, additional socio-demographic data can be entered, e.g. via the use of questionnaires.
- a recorder 7 captures the stream of available user data, which could be clicks and moves of mouse and joystick or any other haptic device, eye movement coordinates on the user screen of the training system 5, eye saccades, head movements, gestures and facial marks, voice commands and many more and are synchronized with one single time code.
- all recorded data is stored in the expert profile and success strategy database 8.
- the recorded data can also be filtered to store just relevant data in database 8 according to performance measures previously defined.
- the database differentiates expert behavior and performance for a task and establishes success strategies for resolving the tasks, which a stored in the database as well.
- the interpreter 11 can both work online and offline, i.e. online while the training is performed for instantaneous guidance, user interface adaptation or training process adaptation or offline once a training session is performed to monitor results and later discussion.
- the interpreter 11 thereby also can have access to a database of different training programs 10 best suited for the target success strategies solving the training task. That database 10 could be held separate as drawn or be integrated in the other databases 8 or 9.
- the different training programs are used to modify the training sessions via adapting the process or user interface.
- FIG. 3 exhibits a methodology for market segmentation that differs from the method of tutoring and training.
- the method of market segmentation does not match expert behavior, performance and target success strategies to trainee behavior. It however matches different stimuli to segmented clusters of users.
- goals e.g. messages, tasks, stimuli target user segment, and performance measures are defined ( ⁇ ).
- Different standard profile segmentations can optionally be generated e.g. a standard socio-demographic segmentation using age, income and education variables, or a standard segmentation using innovation adoption types etc. to pre-cluster users according to standard market research profile segments.
- users are observed and tested recording a variety of user inputs to the stimuli presentation system and sensing the behaviors of the users ( ⁇ ). Inputs could come from mouse, joystick or any other haptic device, keyboard.
- User passive behavior such as eye movements on the task, head movements, mimics, gestures, speech and physical stress could be recorded by variety of user sensors directed on or mounted to the users. Behavioral aspects include among others e.g. duration of fixations, speed of eye movements, facial expression, certain activity sequences. Testing can be performed for single users, focus groups but also panels. The user testing is optional and can be skipped if a central database of abstracted reference profiles of user behavior, e.g. per type of message, stimulus, etc., is in place.
- All recorded data from the users are used to assess behavior and performance analytically or manually and thereby profile user behavior and build an abstracted central reference database of user behavioral profiles. Users observed are clustered across segments, e.g. socio-demographic or behavioral segmentation (®), while the segmentation information could either come from profiles defined in the dedicated test or directly from the central reference database, if in place.
- segments e.g. socio-demographic or behavioral segmentation (®)
- Performance e.g. marketing, user interface, stimuli, etc
- Performance is analyzed per user or per segment ( ⁇ ).
- An abstraction of created user behavioral profiles, e.g. per type of marketing message, stimuli, etc. is stored in a central reference database, which builds up over time ( ⁇ ).
- the method also comprises three feedback loops, one to vary user segments and test more users before even clustering the users to find more behavioral profiles that can be stored in the central reference database, on to cluster a varied users segment based on the central reference database.
- the third loop allows adapting messages, stimuli, and goals or tasks globally or per segment before extracting new behavioral profiles or clustering more users based on the central reference database.
- the process results in a central database of user behaviors related to the type of message, task, stimuli or target user segment and allow adapting messages and stimuli preventive to the observed user behavior.
- the methodology can be used in a lab based environment with a small number of pre-selected subjects but also in panel research.
- FIG. 4 exhibits the system adapted to best suit market segmentation.
- goals e.g. messages, tasks, stimuli and target user segment, and performance measures
- users 12 are recoded via user sensors 3 and multi-modal user input devices 4.
- One possible sensor would be at least one imaging sensor that records eye movements, head movements and gestures of the user.
- a voice recorder can record the user's speech, medical diagnosis systems could record stress symptoms of the users.
- different multi-modal user input devices can be observed such as keyboard, mouse, joystick or any other haptic device but not limited to these. While the user sensors and input devices are mainly observing the behavior of the user, additional socio-demographic data can be entered, e.g. via the use of questionnaires.
- a recorder 7 captures the stream of available user data, which could be clicks and moves of mouse and joystick or any other haptic device, eye movement coordinates on the user screen of the training system 5, eye saccades, head movements, gestures and facial marks, voice commands and many more and are synchronized with one single time code.
- All recorded user data is stored in a user database 13.
- the recorded data can also be filtered to store just relevant data in database 13 according to performance measures previously defined.
- the interpreter 11 extracts abstracted user behavioral profiles depending on message, stimuli and task and stores these in the central reference database 14. All related messages and stimuli are logged in a stimuli database 15. This stimuli database 15 could well be part of the cluster profile database 13 but do not have. All databases 13, 14 and 15 could be held separated or could be combined.
- Results are provided through a multi-modal behavior monitor:
- the multi-modal monitor can be used to supervise trainees and alert the trainer to specific deficits in performance that the trainer should watch more carefully.
- training can be automated to close the identified gap by adapting content and the user interface best suited to the trainees' proficiency and the task on hand. Comparisons between different training rounds and stages for the same user and task can be used to analyze individual progress and to indicate the most valuable areas of future training for the individual trainee in order to approach the target performance and target profile with a personal training program.
- the multi-modal behavior monitor is used to present the different user profiles found.
- User segments are presented combining both behavioral and socio-demographic data.
- An additional database matches user segments with content representations that worked and that did not work for the relevant segment using quantitative behavioral feedback and/or qualitative feedback from the users.
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Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
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US31113401P | 2001-08-09 | 2001-08-09 | |
US60/311,134 | 2001-08-09 |
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WO2003015056A2 true WO2003015056A2 (en) | 2003-02-20 |
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PCT/EP2002/008928 WO2003015056A2 (en) | 2001-08-09 | 2002-08-09 | Automated behavioral and cognitive profiling for training and marketing segmentation |
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Cited By (11)
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FR2861197A1 (en) * | 2003-10-16 | 2005-04-22 | France Telecom | Mobile phone/Internet service user reaction having biological reaction information and assembly determining action effected from set provided |
US8418085B2 (en) | 2009-05-29 | 2013-04-09 | Microsoft Corporation | Gesture coach |
US8898687B2 (en) | 2012-04-04 | 2014-11-25 | Microsoft Corporation | Controlling a media program based on a media reaction |
EP2824630A1 (en) * | 2013-07-11 | 2015-01-14 | Samsung Electronics Co., Ltd | Systems and methods for obtaining user feedback to media content |
US8959541B2 (en) | 2012-05-04 | 2015-02-17 | Microsoft Technology Licensing, Llc | Determining a future portion of a currently presented media program |
US9100685B2 (en) | 2011-12-09 | 2015-08-04 | Microsoft Technology Licensing, Llc | Determining audience state or interest using passive sensor data |
US9154837B2 (en) | 2011-12-02 | 2015-10-06 | Microsoft Technology Licensing, Llc | User interface presenting an animated avatar performing a media reaction |
WO2015183397A1 (en) * | 2014-05-30 | 2015-12-03 | Linkedin Corporation | Control and modification of live presentation |
US9372544B2 (en) | 2011-05-31 | 2016-06-21 | Microsoft Technology Licensing, Llc | Gesture recognition techniques |
US10102112B2 (en) | 2015-12-07 | 2018-10-16 | Wipro Limited | Method and system for generating test strategy for a software application |
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- 2002-08-09 WO PCT/EP2002/008928 patent/WO2003015056A2/en not_active Application Discontinuation
Cited By (20)
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FR2861197A1 (en) * | 2003-10-16 | 2005-04-22 | France Telecom | Mobile phone/Internet service user reaction having biological reaction information and assembly determining action effected from set provided |
US8418085B2 (en) | 2009-05-29 | 2013-04-09 | Microsoft Corporation | Gesture coach |
US10331222B2 (en) | 2011-05-31 | 2019-06-25 | Microsoft Technology Licensing, Llc | Gesture recognition techniques |
US9372544B2 (en) | 2011-05-31 | 2016-06-21 | Microsoft Technology Licensing, Llc | Gesture recognition techniques |
US9154837B2 (en) | 2011-12-02 | 2015-10-06 | Microsoft Technology Licensing, Llc | User interface presenting an animated avatar performing a media reaction |
US9100685B2 (en) | 2011-12-09 | 2015-08-04 | Microsoft Technology Licensing, Llc | Determining audience state or interest using passive sensor data |
US9628844B2 (en) | 2011-12-09 | 2017-04-18 | Microsoft Technology Licensing, Llc | Determining audience state or interest using passive sensor data |
US10798438B2 (en) | 2011-12-09 | 2020-10-06 | Microsoft Technology Licensing, Llc | Determining audience state or interest using passive sensor data |
US8898687B2 (en) | 2012-04-04 | 2014-11-25 | Microsoft Corporation | Controlling a media program based on a media reaction |
US9788032B2 (en) | 2012-05-04 | 2017-10-10 | Microsoft Technology Licensing, Llc | Determining a future portion of a currently presented media program |
US8959541B2 (en) | 2012-05-04 | 2015-02-17 | Microsoft Technology Licensing, Llc | Determining a future portion of a currently presented media program |
EP2824630A1 (en) * | 2013-07-11 | 2015-01-14 | Samsung Electronics Co., Ltd | Systems and methods for obtaining user feedback to media content |
CN106537927A (en) * | 2014-05-30 | 2017-03-22 | 邻客音公司 | Control and modification of live presentation |
US9754011B2 (en) | 2014-05-30 | 2017-09-05 | Linkedin Corporation | Storing and analyzing presentation data |
US10073905B2 (en) | 2014-05-30 | 2018-09-11 | Microsoft Technology Licensing, Llc | Remote control and modification of live presentation |
CN106537927B (en) * | 2014-05-30 | 2019-11-15 | 微软技术许可有限责任公司 | The control and modification that fact is presented |
WO2015183397A1 (en) * | 2014-05-30 | 2015-12-03 | Linkedin Corporation | Control and modification of live presentation |
US10102112B2 (en) | 2015-12-07 | 2018-10-16 | Wipro Limited | Method and system for generating test strategy for a software application |
CN111461153A (en) * | 2019-01-22 | 2020-07-28 | 刘宏军 | Crowd characteristic deep learning method |
CN111461153B (en) * | 2019-01-22 | 2023-08-04 | 刘宏军 | Crowd feature deep learning method |
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