Patent Application
20140156654
The present application claims priority from Indian Application Number 5070/CHE/2012, filed on 5 Dec. 2012, the disclosure of which is hereby incorporated by reference herein.
The embodiments herein relate to web searches and, more particularly, to a gaze controlled approach to automate web search.
Internet has established itself as a highly favored knowledge sharing media. Plenty of websites are available in the internet which provides detailed explanation on various subjects/topics. A user who is searching for details related to specific topic may perform a search in any search engine which in turn searches in associated databases and displays matching results, in any specific order as set by the user.
Normally, each webpage shows information regarding multiple topics; say for example a cricket website may display information such as player profiles, team profiles, status of live matches, results of recently ended matches and so on. A user who opens that particular page may be interested in reading specific content. In the above example the user may be interested in viewing profile of a particular player. In order to view that particular player profile, the user may have to click on corresponding link, which may be a hyperlink. Similarly, the user has to manually navigate to view contents of his/her choice.
Similarly, if the user has to fetch more information regarding that particular subject (i.e. the player in this example), he/she has to continue searching using any of the available search engines. A disadvantage of these existing systems is that time consumed for manually searching for similar contents each time is more. Further, the search result accuracy may vary based on the search inputs used by the user.
A method and system for automating content search on web, the method further comprises of identifying subject of interest for a user based on gaze of the user; fetching results matching the identified subject of interest from at least one associated database; and displaying the fetched results to the user.
The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:
The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
The embodiments herein disclose a contextual web search by monitoring user gaze and identifying user preference. Referring now to the drawings, and more particularly to
The gaze controlled search engine 103 further accepts input from the display engine 102. By processing the inputs from the display unit 102 and the gaze capture unit 101, the gaze controlled search engine 102 identifies to which semantic zone (s) the user is gazing at. Further, the gaze controlled search engine 103 at least one subject of interest from a plurality of subjects in the webpage being viewed by the user. Further, the gaze controlled search engine 103 searches in the database unit 104 for all matching contents corresponding to the identified subject of interest and the results of the search are displayed to the user using the display unit 102. In various embodiments, the gaze controlled contextual web search system may be a dedicated system or may be implemented with any computing unit with inbuilt or interfaced gaze capture unit and a human readable display unit.
The gaze capture engine 201 processes input received from the gaze capturing unit 101 and forms a gaze vector. The gaze vector may comprise information on coordinates on the device display unit 102 towards the user is gazing at, at each instance of time. The gaze vector information is further fed to the correlation engine 203.
The semantics engine 202 fetches input from the display unit 102 regarding displayed content, preferably a webpage. Further, the received information is processed and the contents being displayed on the webpage is grouped to different semantic zones. The semantic zone information is further fed to the correlation engine 203.
The correlation engine 203 processes the received semantic zone information and the gaze vector information and identifies to which semantic zone, the gaze vector is pointing at i.e. the semantic zone the user is gazing at. Once the semantic zone is identified, then the correlation engine 203 identifies the contents/subjects listed in that particular semantic zone. From the identified subjects, the correlation engine 203 identifies at least one subject of user's interest. Further, information regarding the identified subject of interest information is fed to the database resource handler 204.
The database resource handler 204 is connected to multiple databases 206 across various enterprises and web servers in the internet through the database engine 205. The database resource handler 204 transfers information regarding the identified subject of interest to the database engine 205. The database engine 205 searches in the associated databases 206 and fetches information related to the subject of interest.
Further, the fetched information is sent to the contextual processing engine 207. The contextual processing engine 207 categorizes data received from the database engine 205 based on types of data or in any such manner specified by a user. Further, the data is sent to the display unit 102, which is then displayed to the user.
Further, the recorded data is fed to the gaze capturing engine 201. The gaze capturing engine 201 processes the received information and forms (302) a gaze vector. The gaze capture engine 201 analyzes data such as head position, eye details and so on and measures parameters such as pixel information of eyes, distance between user head and display unit 102 and so on. The gaze capture engine 102 also fetches information regarding display dimensions of the display unit 102. By comparing the display dimensions, pixel information of the eye, distance between the user head and the display unit 102, angle at which the user is gazing at the display unit 102 and so on, the gaze capturing engine 102 identifies coordinates of the display unit 102 towards the user is gazing at, at each instance of time. This information is further embedded in the gaze vector and is then fed to the correlation engine 203.
The semantic engine 202 fetches information about content, preferably a webpage being viewed by the user at that instance of time, from the display unit 102. The semantic engine 202 then groups (303) the content being displayed on the screen/display module 102 to different semantic zones of equal size. A semantic zone may refer to a particular area of the whole screen in a specific shape; say rectangular shape. Each semantic zone may comprise information or link related to at least one subject/content. For example, when the user is browsing through a cricket related website, the webpage may display information related to various player and country profiles and statistics. Each of this player profiles and country profiles form separate subjects. The semantic engine 202 feeds the semantic zone information to the correlation engine 203.
The correlation engine 203 processes the gaze vector information and the semantic zone information and identifies to which semantic zone the gaze vector is pointing at. For example, if the gaze vector is identified to be pointing towards semantic zone “A”, then the gaze controlled contextual web search system assumes that the user is reading content/subject displayed/listed under that particular semantic zone. From the identified semantic zone, the correlation engine 203 identifies (304) at least one subject of interest for that user. Considering the above example, if user is gazing at semantic zone “A” and if the semantic zone “A” has information regarding a particular player profile, then that player/profile is considered to be subject of interest of that user.
Further, the correlation engine 203 provides information regarding the identified subject of interest to the database resource handler 204. The database resource handler 204 passes information regarding the subject of interest to the database engine 205. The database engine 205 is connected to a plurality of databases 206 across various enterprises and web servers and searches for contents related to the identified subject of interest in the associated databases 206.
Further, the matching results obtained from the database 206 are fed to the contextual processing engine 207. The contextual processing engine may categorize the received data based on various attributes such as social media trends, social sentiments, chronological, technological and so on and sends the data to the display unit 102 and is displayed to the user. The various actions in method 300 may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some actions listed in
Further, the correlation engine 203 identifies the contents/subject (s) in the identified semantic zones. In an embodiment, the information regarding subjects present in each semantic zone may be provided to the correlation engine 203 by the semantic engine 202. In various other embodiments, each semantic zone may comprise one or more subjects. If the identified semantic zone (s) comprises information or link related to only one subject, then that particular subject is set (405) as the user's subject of interest.
If the identified semantic zones comprise more than one subject, then the correlation engine 203 identifies (404) most common subject among the identified subjects. For example, consider that the user is gazing at two semantic zones namely “Zone A” and “Zone B”. The correlation engine 203 identifies that Zone A comprises information related to subjects “A”, “B” and “C”, whereas Zone B comprises information related to subjects “C” and “D”. Now, in order to identify user's subject of interest, the correlation engine 203 checks for any common member among the identified subjects i.e. “C” in this example. So the correlation engine 203 considers “C” as the user's subject of interest. Further, the identified common subject is set (405) as the user's subject of interest. The various actions in method 400 may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some actions listed in
The embodiments disclosed herein can be implemented through at least one software program running on at least one hardware device and performing network management functions to control the network elements. The network elements shown in
The embodiment disclosed herein specifies a system for automated web searches. The mechanism allows a gaze controlled web search, providing a system thereof. Therefore, it is understood that the scope of the protection is extended to such a program and in addition to a computer readable means having a message therein, such computer readable storage means contain program code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The method is implemented in a preferred embodiment through or together with a software program written in e.g. Very high speed integrated circuit Hardware Description Language (VHDL) another programming language, or implemented by one or more VHDL or several software modules being executed on at least one hardware device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof, e.g. one processor and two FPGAs. The device may also include means which could be e.g. hardware means like e.g. an ASIC, or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. Thus, the means are at least one hardware means and/or at least one software means. The method embodiments described herein could be implemented in pure hardware or partly in hardware and partly in software. The device may also include only software means. Alternatively, the embodiment may be implemented on different hardware devices, e.g. using a plurality of CPUs.
The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the claims as described herein.
| Number | Date | Country | Kind |
|---|---|---|---|
| 5070/CHE/2012 | Dec 2012 | IN | national |
