This application is related to:
U.S. application Ser. No. 15/076,701 filed on Mar. 22, 2016, entitled “Method and system for surveillance camera arbitration of uplink consumption,” now U.S. Patent Publication No. US2017/0278368A1;
U.S. application Ser. No. 15/076,703 filed on Mar. 22, 2016, entitled “Method and system for pooled local storage by surveillance cameras,” now U.S. Patent Publication No. US2017/0280102A1;
U.S. application Ser. No. 15/076,704 filed on Mar. 22, 2016, entitled “System and method for designating surveillance camera regions of interest,” now U.S. Patent Publication No. US2017/0277967A1;
U.S. application Ser. No. 15/076,705 filed on Mar. 22, 2016, entitled “System and method for deadzone detection in surveillance camera network,” now U.S. Patent Publication No. US2017/0278366A1;
U.S. application Ser. No. 15/076,706 filed on Mar. 22, 2016, entitled “System and method for overlap detection in surveillance camera network,” now U.S. Patent Publication No. US2017/0278367A1;
U.S. application Ser. No. 15/076,708 filed on Mar. 22, 2016, entitled “System and method for retail customer tracking in surveillance camera network,” now U.S. Patent Publication No. US2017/0278137A1;
U.S. application Ser. No. 15/076,709 filed on Mar. 22, 2016, entitled “Method and system for modeling image of interest to users,” now U,S. Patent Publication No. US2017/0277785A1;
U.S. application Ser. No. 15/076,710 filed on Mar. 22, 2016, entitled “System and method for using mobile device of zone and correlated motion detection,” now U.S. Patent Publication No. US2017/0280103A1;
U.S. application Ser. No. 15/076,712 filed on Mar. 22, 2016, entitled “Method and system for conveying data from monitored scene via surveillance cameras,” now U.S. Patent Publication No. US2017/0277947A1;
and
U.S. application Ser. No. 15/076,717 filed on Mar. 22, 2016, entitled “System and method for controlling surveillance cameras,” now U.S. Patent Publication No. US2017/0280043A1.
All of the afore-mentioned applications are incorporated herein by this reference in their entirety.
Video surveillance systems are often deployed in schools, government buildings, small businesses, retail stores and corporate offices, and even many residences. These surveillance systems are typically comprised of surveillance cameras that capture image data, image data storage systems that store the image data along with possibly metadata, and increasingly analytics systems that analyze the image data and possibly generate the metadata.
The installation of surveillance systems is often complex and time consuming. First, an installer has to identify and select locations throughout the building, for example, to install the surveillance cameras. Next, the installer has to physically mount the surveillance cameras in the building and supply them with power. Data connections between the surveillance cameras and the image data storage systems and possibly the analytics systems must then be established. In many cases, this requires running data cables from data transmission devices (e.g., routers, switches, and hubs) to all of the devices, although wireless systems are becoming increasingly common.
After the physical installation, the installer must then configure the systems. Generally, configuration of the surveillance camera systems is tedious, requiring the installer to repeatedly enter configuration information such as device names, Internet Protocol (IP) addresses, media access control (MAC) addresses, device locations, and/or port settings for devices. In many cases, the installer has to travel between different locations throughout the building to configure the various components, the network, and any monitoring station.
Recently, the surveillance camera systems have begun using open standards. Among other advantages, this enables users to more easily access the image data from the surveillance cameras. On user devices such as computer workstations and mobile computing devices such as tablets, smart phones and laptop computers, users can access and select image data from specific surveillance cameras for real-time viewing upon and downloading to the user devices. In addition, the users on the user devices can also access previously recorded image data stored on the image data storage systems.
Another trend concerns the analytics systems, which are becoming increasingly powerful. Often, the analytics systems will track moving objects against fixed background models. More sophisticated functions include object detection to determine the presence of an object or classify the type of object or event. The analytics systems generate video primitives or metadata for the detected objects and events, which the analytics systems can further process or send over the data networks to other systems for storage and incorporation into the image data as metadata, for example.
While analytics systems have historically been separate systems apart from the surveillance cameras, the surveillance cameras themselves are increasingly providing this functionality. Integrating the analytics functionality within the cameras themselves has advantages. It eliminates the cost and maintenance associated with deploying a separate analytics system to accomplish the same objective, and enables more efficient analysis by eliminating the messaging overhead associated with sending the image data over the data network for analysis by the separate analytics systems, in examples.
Similar trends have emerged in the case of image data storage systems. Surveillance cameras are being offered that include image data storage on the camera itself. Such surveillance cameras are especially attractive to smaller organizations such as stores, small companies, and local offices that want to reduce installation and maintenance expenses. Each camera can function as a stand-alone unit, and as a result there is no need to have a specialized image data storage system. With the advent of improved image compression on one hand, and the decreasing costs of data storage on the other hand, each surveillance camera is often able to store substantially larger amounts of image data than it generates.
At the same time, remote cloud image data storage systems are also available. These systems can offer a number of advantages over local image data storage systems. The organizations have fewer components to buy and install, which lowers both purchase and depreciation cost. Organizations can also pay on a per usage basis for infrequently used value-added services. Finally, the service providers of the cloud storage systems bear the responsibility of maintaining and upgrading the storage systems and their capabilities, the cost of which the service providers can share across their managed clients.
The present invention concerns a method and system for configuring surveillance cameras. It can be used to leverage many of the previously discussed trends, while also enabling the configuration of cameras to operate in such systems.
In general, according to one aspect, the invention features a method for configuring surveillance cameras. The method comprises displaying configuration images on user devices, deriving the configuration images from image data from the surveillance cameras, and pairing configuration information from the user devices with corresponding surveillance cameras by reference to the configuration images.
The step of displaying the configuration images could comprise displaying QR codes, for example.
Typically, image data from the surveillance cameras is sent to a registration server that derives the configuration images from the image data and pairs the configuration information from the user devices with the surveillance cameras. In examples, the user device configuration information includes location information for the surveillance cameras or user account information for owners of the surveillance cameras. The configuration images are derived from image data from the surveillance cameras using a remote analytics system or an analytics system integrated within the surveillance cameras.
In another example, the camera configuration information includes camera type information. Moreover, unregistered surveillance cameras can periodically sending image data to a registration server, which is used to pair the configuration information from the user device with the corresponding surveillance cameras. The method can also additionally store image data from the surveillance cameras to a cloud storage system after registration of the surveillance cameras.
In general, according to another aspect, the invention features a surveillance camera system. The surveillance camera system includes user devices on which configuration images are displayed, surveillance cameras, and a registration server. The surveillance cameras generate image data of the user devices on which the configuration images are displayed, and the registration server pairs configuration information from the user devices with corresponding surveillance cameras by reference to the configuration images. In one example, the configuration images are test patterns.
The above and other features of the invention including various novel details of construction and combinations of parts, and other advantages, will now be more particularly described with reference to the accompanying drawings and pointed out in the claims. It will be understood that the particular method and device embodying the invention are shown by way of illustration and not as a limitation of the invention. The principles and features of this invention may be employed in various and numerous embodiments without departing from the scope of the invention.
In the accompanying drawings, reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale; emphasis has instead been placed upon illustrating the principles of the invention. Of the drawings:
The invention now will be described more fully hereinafter with reference to the accompanying drawings, in which illustrative embodiments of the invention are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items. Further, the singular forms including the articles “a”, “an” and “the” are intended to include the plural forms as well, unless expressly stated otherwise. It will be further understood that the terms: includes, comprises, including and/or comprising, when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Further, it will be understood that when an element, including component or subsystem, is referred to and/or shown as being connected or coupled to another element, it can be directly connected or coupled to the other element or intervening elements may be present.
The system 10 includes surveillance cameras 103 along with possibly other security devices installed at a premises 52 of an organization. The surveillance cameras 103 generate image data 250 and communicate with each other and with other security devices over a local (enterprise) network 210 which may be wired, wireless, or a hybrid of wired and wireless links.
A number of approaches may be employed in the alternative or in a hybrid fashion to store the image data 250 generated by the surveillance cameras 103-1, 103-2, 103-3. A local image data storage system 212 is shown, deployed on the local network 210. In other examples, each or some of the cameras 103 includes a camera image data storage system 174. Further, streams of image data 250 can be transferred over a network cloud 50 to a cloud or remote image data storage system 310.
The image data 250 can then be accessed over the network cloud 50 by user mobile computing devices 400 such as smartphones, tablet computing devices, laptop computer running operating systems such as Windows, Android, Linux, or IOS, in examples. User mobile computing devices 400 are also referred to as user devices. Each user device 400 includes a display screen or touch screen 402 and one or more applications 412, or “apps.” The apps 412 execute upon the operating systems of the user devices 400.
A specific example showing how the cameras might be deployed is illustrated. Within the premises 52, cameral 103-1 focuses upon person 60 located within an aisle. Camera2103-2 detects motion of individuals 60 near a door 62 providing entry to or exit from the premises 52. Finally, camera3103-3 detects motion near a safe 64.
Other components of the system 10 are the video analytics systems. A cloud video analytics system is shown 312 that receives the image data from the surveillance cameras 103 via the network cloud 50. Here, the cloud video analytics system 312 might be managed by a third party hosting company and is presented to the enterprise local network 210 as a single virtual entity, in one example. In other examples, the analytics system is installed on the local network 210 and may be owned by the same business entity as the surveillance camera system 10. Further, an optional camera analytics system 176 integrated within one or more of the surveillance cameras 103 is another option.
Also shown is a registration server 314 and a registration database 316 on the network 318 of the cloud system. In one example, the registration server 314 is used to maintain user accounts for organizations that own surveillance camera systems 10. As a result, this registration server 314 is used, in one example, as part of a service that enables access to dedicated storage in the cloud image data storage system 310 for various organizations that subscribe to the service. In one embodiment, the registration server 314 includes a web server.
The registration server 314 could further be used to provide access to other services. It could provide subscribing organizations with remote analytics of the image data generated by the surveillance cameras 103 in the cloud video analytics system 312. In still other examples, the registration server 314 would use the cloud system to distribute image data 250 from the surveillance cameras 103 such as to user devices 400,
As part of a registration process, as the surveillance cameras 103 are installed by an installer 60, for example, it is necessary to associate those surveillance cameras 103 with configuration information such as the account for the organization owning the premises 52 and/or subscribing to services available on the cloud network 318. Further, configuration information such as where are the surveillance cameras are installed, names for each of the surveillance cameras, the type and model of surveillance cameras 103 should also be stored to the registration database 316 in various examples. Still further, device data such as Internet Protocol (IP) addresses, media access control (MAC) addresses is also typically stored in connection with each of the surveillance cameras 103 and in connection with the account of the organization that owns or manages the surveillance cameras 103.
In the illustrated example, the pairing between the surveillance cameras 103 and the configuration information is achieved by using a user device 400 typically operated by the installer 60.
In more detail, the installer 60 places the user device 400 such that the surveillance camera is able to capture an image of the display 402 of the user device. That is, the display 402 of the user device 400 is placed within the field of view 105 of the surveillance camera 103-1 by the installer 60.
Displayed on display 402 is a configuration image 410. In one example, this configuration image is a Quick Response (QR) code. Moreover, this configuration image 410 is preferably supplied by the registration server 314. In one example, the registration server 314 downloads the image when the installer 60 invokes a camera registration web page supplied by the registration server 314. In other examples, the configuration image 410 is included in or accessed by or generated by an app 412 that is installed on the user device 400.
The image data 250 collected by the surveillance camera 103-1 is then processed on an analytics system. This could be the integrated analytic system 176, a local analytics system 212 installed on the local network 210 or the cloud video analytics system 312.
Wherever the image data is processed, the configuration image 410 is extracted and passed to the registration server 314 or otherwise compared to the configuration image 410 provided to the user device. This allows the registration server 314 to pair configuration information entered, for example, at the user device 400 with the specific surveillance camera 103-1 that captured the configuration image displayed by the user device 400.
In more detail, upon startup, the surveillance camera 103-1 sends a request to the registration server 314 for an IP address in step 608. In one example, the request references a preconfigured Universal Resource Locator (URL) of the registration server 314 for this purpose. After receiving an IP address 264 (in the example, “10.10.10.1”) from the registration server 314, the process transitions to step 610.
During normal operation, the surveillance camera 103-1 stores image data to image data storage in step 610. In one example, a discrete local image data storage system 212 is used. In other examples, integrated image data storage 174 located on the surveillance camera. receives the image data 250. In still other examples, the image data could be stored in the cloud image data storage system 310.
The surveillance camera 103-1 begins sending image data 250 to the registration server 314 in step 612. In one example, the surveillance camera 103-1 sends the image data periodically, such as once a minute or when the surveillance camera is triggered to enter into a configuration mode. The surveillance camera can be triggered to enter the configuration mode in response to an installer pressing an associated button on the surveillance camera, in response to the installer selecting a configuration mode setting within the app 412 executing on the user device 400, or when the surveillance camera 103-1 is first powered-on and has never been configured before, in examples.
At the same time or in a generally contemporaneous fashion, the app 412 installed on the user device 400 is controlled by the installer 60 to send a “request to register” message to the registration server 314 in step 614.
In one example, a configuration image 410 is then sent by the registration server 314 to the app 412 executing on the user device 400 in step 616. The app could also generate this image and send it to the server 314. In one example, the configuration image 410 is a QR code. The image type, however, is not critical. The configuration image might be any random or predefined image or pattern, such as a test pattern.
The app 412 displays the configuration image 410 on the display 402 of the user device 400. In this way, in step 618, the configuration image is presented to the surveillance camera 103-1 so that the surveillance camera 103-1 can include the configuration image 410 within image data 250-1 of the scene that the surveillance camera 103-1 captures.
In this process, the user device 400 and specifically the app 412 executing on the user device sends configuration information to the registration server 314 in step 620. As discussed previously, this configuration information includes possibly a user account for the owner of the surveillance camera 103. Additional information, such as where the surveillance camera 103-1 is installed is also sometimes entered at the user device 400 and sent to the registration server 314. Other information such as a name for the surveillance camera, the type and model of surveillance camera 103 should also be passed to the registration server 314 and stored to the registration database 316, in various examples.
In step 622, the configuration image is sent to the registration server 314 by the surveillance camera 103-1 in conjunction with surveillance camera information obtained from the surveillance camera 103-1. In one example, the configuration image 410 is included in image data 250-1 captured by the surveillance camera 103-1.
Configuration information can include information such as the assigned Internet Protocol (IP) address 264, media access control (MAC) address, camera type and serial number, in examples. The configuration information can then be sent to the registration server 314 and stored in connection with the surveillance camera 103-1 and in connection with the account of the organization that owns or manages the surveillance cameras 103 in the registration database 316.
In step 624, the registration server 314 extracts the configuration image 410 (e.g. QR code) sent by the surveillance camera 103 in step 622. In this case, the registration server 314 derives the configuration image 410 from the image data 250-1 sent by the surveillance camera 103-1. To accomplish this, the registration server 314 sends the image data 250-1 to the cloud video analytics system 312, which then extracts the configuration image from the image data 250-1. In other examples, the configuration image 410 is extracted from the image data 250-1 using the local analytics system or an integrated analytic system 176 of the surveillance camera. In any event, the registration server 314 extracts and matches the configuration image 410 (e.g. QR code) with the user device 400 and probably with configuration information such as the user account that was typically entered at the user device 400.
In step 626, the registration server 314 associates the surveillance camera 103-1 with the user account and any other configuration information added at the user device 400 by the installer 60, for example, and/or provided by the surveillance camera 103-1. This information is stored in the registration database 316, in one example.
In one specific example, the registration database 316 or the registration server 314 allocates cloud storage in step 628 for the surveillance camera 103-1. Then, the registration server 314 sends path information for the cloud image data storage system to the local image data storage 212 in step 630. As a result, in step 632, image data 250-2 can now be stored to the cloud image data storage system 310 either directly by the surveillance camera 103-1 or by the local image data storage 212. In other examples, other services could be made available to the surveillance camera 103-1 such as image analytics provided by the cloud video analytics system 312.
While this invention has been particularly shown and described with references to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention encompassed by the appended claims.
Number | Name | Date | Kind |
---|---|---|---|
3217098 | Oswald | Nov 1965 | A |
4940925 | Wand et al. | Jul 1990 | A |
5164827 | Paff | Nov 1992 | A |
5204536 | Vardi | Apr 1993 | A |
5317394 | Hale et al. | May 1994 | A |
5729471 | Jain et al. | Mar 1998 | A |
5850352 | Moezzi et al. | Dec 1998 | A |
5940538 | Spiegel et al. | Aug 1999 | A |
5951695 | Kolovson | Sep 1999 | A |
5969755 | Courtney | Oct 1999 | A |
6341183 | Goldberg | Jan 2002 | B1 |
6359647 | Sengupta et al. | Mar 2002 | B1 |
6581000 | Hills et al. | Jun 2003 | B2 |
6643795 | Sicola et al. | Nov 2003 | B1 |
6724421 | Glatt | Apr 2004 | B1 |
6812835 | Ito et al. | Nov 2004 | B2 |
6970083 | Venetianer et al. | Nov 2005 | B2 |
7091949 | Hansen | Aug 2006 | B2 |
7242423 | Lin | Jul 2007 | B2 |
7286157 | Buehler | Oct 2007 | B2 |
7342489 | Milinusic et al. | Mar 2008 | B1 |
7382244 | Donovan et al. | Jun 2008 | B1 |
7409076 | Brown et al. | Aug 2008 | B2 |
7428002 | Monroe | Sep 2008 | B2 |
7450735 | Shah et al. | Nov 2008 | B1 |
7456596 | Goodall et al. | Nov 2008 | B2 |
7460149 | Donovan et al. | Dec 2008 | B1 |
7529388 | Brown et al. | May 2009 | B2 |
7623152 | Kaplinsky | Nov 2009 | B1 |
7623676 | Zhao et al. | Nov 2009 | B2 |
7733375 | Mahowald | Jun 2010 | B2 |
7996718 | Ou et al. | Aug 2011 | B1 |
8249301 | Brown et al. | Aug 2012 | B2 |
8300102 | Nam et al. | Oct 2012 | B2 |
8325979 | Taborowski et al. | Dec 2012 | B2 |
8482609 | Mishra et al. | Jul 2013 | B1 |
8483490 | Brown et al. | Jul 2013 | B2 |
8502868 | Buehler et al. | Aug 2013 | B2 |
8594482 | Fan et al. | Nov 2013 | B2 |
8675074 | Salgar et al. | Mar 2014 | B2 |
8723952 | Rozenboim | May 2014 | B1 |
8849764 | Long et al. | Sep 2014 | B1 |
8995712 | Huang et al. | Mar 2015 | B2 |
9015167 | Ballou et al. | Apr 2015 | B1 |
9058520 | Xie et al. | Jun 2015 | B2 |
9094615 | Aman et al. | Jul 2015 | B2 |
9129179 | Wong | Sep 2015 | B1 |
9158975 | Lipton et al. | Oct 2015 | B2 |
9168882 | Mirza et al. | Oct 2015 | B1 |
9197861 | Saptharishi et al. | Nov 2015 | B2 |
9280833 | Brown et al. | Mar 2016 | B2 |
9412269 | Saptharishi et al. | Aug 2016 | B2 |
9495614 | Boman et al. | Nov 2016 | B1 |
9594963 | Bobbitt et al. | Mar 2017 | B2 |
9641763 | Bernal et al. | May 2017 | B2 |
9674458 | Teich et al. | Jun 2017 | B2 |
9785898 | Hofman et al. | Oct 2017 | B2 |
9860554 | Samuelsson et al. | Jan 2018 | B2 |
9965680 | Burke et al. | May 2018 | B2 |
9967446 | Park | May 2018 | B2 |
20020104098 | Sustak et al. | Aug 2002 | A1 |
20030107649 | Flickner et al. | Jun 2003 | A1 |
20030169337 | Wilson et al. | Sep 2003 | A1 |
20050012817 | Hampapur et al. | Jan 2005 | A1 |
20050057653 | Maruya | Mar 2005 | A1 |
20060001742 | Park | Jan 2006 | A1 |
20060173856 | Jackson et al. | Aug 2006 | A1 |
20060181612 | Lee et al. | Aug 2006 | A1 |
20060239645 | Curtner et al. | Oct 2006 | A1 |
20060243798 | Kundu et al. | Nov 2006 | A1 |
20070178823 | Aronstam et al. | Aug 2007 | A1 |
20070182818 | Buehler | Aug 2007 | A1 |
20070279494 | Aman et al. | Dec 2007 | A1 |
20070294207 | Brown et al. | Dec 2007 | A1 |
20080004036 | Bhuta et al. | Jan 2008 | A1 |
20080101789 | Sharma | May 2008 | A1 |
20080114477 | Wu | May 2008 | A1 |
20080158336 | Benson et al. | Jul 2008 | A1 |
20090237508 | Arpa et al. | Sep 2009 | A1 |
20090268033 | Ukita | Oct 2009 | A1 |
20090273663 | Yoshida | Nov 2009 | A1 |
20090284601 | Eledath et al. | Nov 2009 | A1 |
20100013917 | Hanna et al. | Jan 2010 | A1 |
20100110212 | Kuwahara et al. | May 2010 | A1 |
20100153182 | Quinn et al. | Jul 2010 | A1 |
20100232288 | Coatney et al. | Sep 2010 | A1 |
20110043631 | Marman et al. | Feb 2011 | A1 |
20110128384 | Tiscareno et al. | Jun 2011 | A1 |
20110246626 | Peterson et al. | Oct 2011 | A1 |
20110289119 | Hu et al. | Nov 2011 | A1 |
20110289417 | Schaefer et al. | Nov 2011 | A1 |
20110320861 | Bayer et al. | Dec 2011 | A1 |
20120072420 | Moganti et al. | Mar 2012 | A1 |
20120098969 | Wengrovitz et al. | Apr 2012 | A1 |
20120206605 | Buehler et al. | Aug 2012 | A1 |
20120226526 | Donovan et al. | Sep 2012 | A1 |
20130166711 | Wang et al. | Jun 2013 | A1 |
20130169801 | Martin et al. | Jul 2013 | A1 |
20130223625 | de Waal et al. | Aug 2013 | A1 |
20130278780 | Cazier et al. | Oct 2013 | A1 |
20130343731 | Pashkevich et al. | Dec 2013 | A1 |
20140085480 | Saptharishi | Mar 2014 | A1 |
20140172627 | Levy et al. | Jun 2014 | A1 |
20140211018 | de Lima et al. | Jul 2014 | A1 |
20140218520 | Teich et al. | Aug 2014 | A1 |
20140282991 | Watanabe | Sep 2014 | A1 |
20140330729 | Colangelo | Nov 2014 | A1 |
20150039458 | Reid | Feb 2015 | A1 |
20150092052 | Shin et al. | Apr 2015 | A1 |
20150121470 | Rongo et al. | Apr 2015 | A1 |
20150208040 | Chen et al. | Jul 2015 | A1 |
20150215583 | Chang | Jul 2015 | A1 |
20150244992 | Buehler | Aug 2015 | A1 |
20150249496 | Muijs et al. | Sep 2015 | A1 |
20150294119 | Gundam et al. | Oct 2015 | A1 |
20150358576 | Hirose et al. | Dec 2015 | A1 |
20150379729 | Datta et al. | Dec 2015 | A1 |
20150381946 | Renkis | Dec 2015 | A1 |
20160065615 | Scanzano et al. | Mar 2016 | A1 |
20160224430 | Long et al. | Aug 2016 | A1 |
20160225121 | Gupta et al. | Aug 2016 | A1 |
20160269631 | Jiang et al. | Sep 2016 | A1 |
20160357648 | Keremane et al. | Dec 2016 | A1 |
20160379074 | Nielsen et al. | Dec 2016 | A1 |
20170193673 | Heidemann et al. | Jul 2017 | A1 |
20170277785 | Burke | Sep 2017 | A1 |
20170277947 | Burke et al. | Sep 2017 | A1 |
20170277967 | Burke et al. | Sep 2017 | A1 |
20170278137 | Burke | Sep 2017 | A1 |
20170278366 | Burke et al. | Sep 2017 | A1 |
20170278367 | Burke et al. | Sep 2017 | A1 |
20170278368 | Burke | Sep 2017 | A1 |
20170280043 | Burke et al. | Sep 2017 | A1 |
20170280102 | Burke | Sep 2017 | A1 |
20170280103 | Burke et al. | Sep 2017 | A1 |
20180218209 | Burke et al. | Aug 2018 | A1 |
Number | Date | Country |
---|---|---|
2 164 003 | Mar 2010 | EP |
2 538 672 | Dec 2012 | EP |
2003151048 | May 2003 | JP |
2010074382 | Apr 2010 | JP |
2007030168 | Mar 2007 | WO |
2013141742 | Sep 2013 | WO |
2014114754 | Jul 2014 | WO |
Entry |
---|
International Search Report and the Written Opinion of the International Searching Authority, dated May 31, 2017, from International Application No. PCT/US2017/023430, filed Mar. 21, 2017. Fourteen pages. |
International Search Report and the Written Opinion of the International Searching Authority, dated Jun. 12, 2017, from International Application No. PCT/US2017/023440, filed on Mar. 21, 2017. Fourteen pages. |
International Search Report and the Written Opinion of the International Searching Authority, dated Jun. 19, 2017, from International Application No. PCT/US2017/023436, filed on Mar. 21, 2017. Fourteen pages. |
International Search Report and the Written Opinion of the International Searching Authority, dated Jun. 21, 2017, from International Application No. PCT/US2017/023444, filed on Mar. 21, 2017. Thirteen pages. |
International Search Report and the Written Opinion of the International Searching Authority, dated Jun. 28, 2017, from International Application No. PCT/US2017/023434, filed on Mar. 21, 2017. Thirteen pages. |
International Preliminary Report on Patentability, dated Oct. 4, 2018, from International Application No. PCT/US2017/023440, filed on Mar. 21, 2017. Eight pages. |
International Preliminary Report on Patentability, dated Oct. 4, 2018, from International Application No. PCT/US2017/023434, filed on Mar. 21, 2017. Eight pages. |
International Preliminary Report on Patentability, dated Oct. 4, 2018, from International Application No. PCT/US2017/023430, filed Mar. 21, 2017. Eight pages. |
International Preliminary Report on Patentability, dated Oct. 4, 2018, from International Application No. PCT/US2017/023436, filed on Mar. 21, 2017. Eight pages. |
International Preliminary Report on Patentability, dated Oct. 4, 2018, from International Application No. PCT/US2017/023444, filed on Mar. 21, 2017. Seven pages. |
Weilin, L., et al., “Personalizaation of Trending Tweets Using Like-Dislike Caegory Model,” Procedia Computer Science, 60: 236-245 (2015). |
Number | Date | Country | |
---|---|---|---|
20170278365 A1 | Sep 2017 | US |