FIELD OF INVENTION
[0001] The present subject matter relates 5 lates to recommendation generation and, particularly,
but not exclusively, to generating dynamic recommendations in a digital television environment.
BACKGROUND
[0002] With the advent of digital television (DTV), users have access to unprecedentedly
diversified multimedia content. The users may access the multimedia content by using different
10 electronic devices that are configured to store, organize, and playback songs, videos, games, and
other forms of digital media. However, with the availability of huge amount of information, the
users may face challenges in finding and acquiring new multimedia content. For example, the
users have an ever-increasing selection of multimedia content to choose from, such as television
programs, movies, videos, and music, which is available for selection and viewing. In this
15 respect, various content recommendation systems are available that can recommend content to
the users to facilitate in selecting content of interest with ease.
SUMMARY
[0003] This summary is provided to introduce concepts related to generating dynamic
recommendations in a digital television (DTV) environment. This summary is not intended to
20 identify essential features of the claimed subject matter nor is it directed to use in determining or
limiting the scope of the claimed subject matter.
[0004] In an embodiment of the present subject matter, a method for generating dynamic
recommendations in a digital television (DTV) environment is disclosed. The method includes,
determining, by a processor, occurrence of an activity from a plurality of activities in the DTV
25 environment. The DTV environment includes a content viewing device. Further, the method
includes, generating, based on the determination, by the processor, a recommendation prompt for
the content viewing device. The recommendation prompt is generated on the basis of a current
activity on the content viewing device. The method further includes, performing, by the
3
processor, an action in the DTV environment based on a response to the recommendation
prompt.
[0005] In accordance with an embodiment, a method for generating dynamic
recommendations in a digital television (DTV) environment is disclosed. The method includes,
determining, by a processor, occurrence of an activity from a plurality of 5 activities in the DTV
environment. The DTV environment includes a main content viewing device and at least one
linked content viewing device. Further, the method includes, generating, based on the
determination, by the processor, a recommendation prompt for the main content viewing device.
The recommendation prompt is generated on the basis of a current activity associated with at
10 least one of the main content viewing device and the linked content viewing device. The method
further includes, performing, by the processor, an action in the DTV environment based on a
response to the recommendation prompt.
[0006] In accordance with another embodiment of the present subject matter, a
recommendation system is disclosed. The recommendation system includes a processor, an
15 analysis module coupled to the processor, and a recommendation module coupled to the
processor. The analysis module may determine occurrence of an activity from a plurality of
activities in a digital television (DTV) environment. Further, the recommendation module may
generate a recommendation prompt for the main content viewing device. The recommendation
prompt may be generated on the basis of a current activity associated with at least one of the
20 main content viewing device and the linked content viewing device. The recommendation
module also performs an action in the DTV environment, based on a response to the
recommendation prompt.
[0007] In accordance with another embodiment of the present subject matter, a nontransitory
computer readable medium comprising instructions to implement a method for
25 generating dynamic recommendations in a digital television (DTV) environment is disclosed.
The method includes, determining, by a processor, occurrence of an activity from a plurality of
activities in the DTV environment. The DTV environment includes a content viewing device.
Further, the method includes, generating, based on the determination, by the processor, a
recommendation prompt for the content viewing device. The recommendation prompt is
30 generated on the basis of a current activity on the content viewing device. The method further
4
includes, performing, by the processor, an action in the DTV environment based on a response to
the recommendation prompt.
BRIEF DESCRIPTION OF THE FIGURES
[0008] The detailed description is described with reference to the accompanying figures. In
the figures, the left-most digit(s) of a reference 5 nce number identifies the figure in which the
reference number first appears. The same numbers are used throughout the figures to reference
like features and components. Some embodiments of system and/or methods in accordance with
embodiments of the present subject matter are now described, by way of example only, and with
reference to the accompanying figures, in which:
10 [0009] Figure 1 schematically illustrates a digital television environment comprising a
recommendation system, in accordance with an embodiment of the present subject matter.
[0010] Figure 2 shows a flowchart illustrating an exemplary method for generating dynamic
recommendations in a digital television environment, in accordance with an embodiment of the
present subject matter.
15 [0011] It should be appreciated by those skilled in the art that any block diagrams herein
represent conceptual views of illustrative systems embodying the principles of the present
subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state
transition diagrams, pseudo code, and the like represent various processes which may be
substantially represented in computer readable medium and so executed by a computer or
20 processor, whether or not such computer or processor is explicitly shown.
DESCRIPTION OF EMBODIMENTS
[0012] Systems and methods for generating dynamic recommendations in a digital
television (DTV) environment are described. The systems and the methods can be implemented
in a variety of content viewing devices used in the DTV environment. The DTV environment
25 may be understood as a network environment in which digital TV services are provided by a
digital TV content provider to various content viewing devices, and includes, for example,
Internet Protocol (IP)TV, mobile TV, direct-to-home (DTH) TV, satellite TV, cable TV, and the
like. Further, the DTV environment includes content broadcast in standard definition or high
definition or any other format as will be appreciated by a person skilled in the art. The content
5
viewing devices that can implement the described method(s) include, but are not limited to,
devices such as a television (TV), smart TV, a mobile phone, a tablet, and the like. Further, the
systems and methods can be implemented in a variety of transmission environments. Although
the description herein is with reference to IP networks, the systems and the methods may be
implemented in other transmission modes and networks, albeit with a few variations, 5 , as will be
understood by a person skilled in the art.
[0013] Nowadays, a large amount of multimedia content is available to users through
different sources, such as web, Video on Demand (VOD), and social media. Further, content
providers may deliver the multimedia content to multiple users through multiple content viewing
10 devices, generally referred to as multi-screen platform. Typically, the multi-screen platform may
include a main content viewing device and one or more linked content viewing devices. The
main content viewing device may control the content being delivered to the one or more linked
content viewing devices.
[0014] Typically, the multimedia content that is sent across to various users is encoded
15 by an encoder at a content provider’s end. The encoded multimedia content is then transmitted to
users’ ends, where the encoded multimedia content is decoded by a decoder. In case of direct-tohome
transmission and digital cable, the decoder is generally referred to as a set top box
("STB"). For example, in case of television ("TV") users, the STB allows the users to access the
multimedia content offered by the content provider. In another example, a TV tuner card may be
20 used with a laptop or a digital TV receiver may be used with a smart phone and the laptop or
smart phone may have the decoder in built in them. The user may choose between broadcast TV
programs, pay-per-view programs, on-demand programs, interactive games, or music, all
through the decoder. However, the large amount of multimedia content offered by the content
providers may make it difficult for the user to find and select desired content in a timely manner.
25 [0015] To this end, on-screen program guides, such as an electronic program guide
(EPG) may help users in selecting content of choice. The EPG typically provides users a list of
current and scheduled programs that are or will be available on each channel. In addition, the
EPG may provide a short summary for each program. A typical EPG may include options to
search for programs based on a category, view account details, and the like. As the amount of the
30 multimedia content continues to expand, the EPGs may become inadequate.
6
[0016] Given the large volume of the various types of content to choose from, content
recommendation techniques are provided to users, to facilitate the users in deciding upon the
multimedia content, such as movies and television programs to be provided to them.
Conventional content recommendation techniques provide recommendations to users based on
the multimedia content viewed by a user in the past. There may be situations where a user 5 may
not want to watch the same content as viewed earlier. Also, there may be situations where users
may not want their kids to watch certain channels they had watched in the past. For example,
content of those channels may have changed or become irrelevant for the kids. In such situations,
the conventional content recommendation techniques may not be of help.
10 [0017] Further, in case of the multi-screen platform, content providers attempt to provide
services that enhance user experience. Examples of such services may include providing
multiplayer games, media sharing between devices of the multi-screen platform, and the like. In
this respect, the conventional content recommendation techniques provide recommendations to
the content viewing devices based on past viewing behavior of the users. Also, the conventional
15 recommendation techniques provide recommendations on the basis of current activities of the
main content viewing device and do not take into consideration the lined content viewing
devices.
[0018] According to an embodiment of the present subject matter, a method and a system
for generating dynamic recommendations in a digital television (DTV) environment are
20 described. In an implementation, the DTV environment may include a content viewing device. In
another implementation, the DTV environment may be a multi-screen environment that includes
a main content viewing device and at least one linked content viewing device. In a typical multiscreen
environment, the main content viewing device may be associated with the linked content
viewing devices such that the main content viewing device can control the multimedia content
25 being provided to the one or more linked content viewing devices. In case of the multi-screen
environment, the multimedia content is transformed into multiple formats, bit rates and
resolutions for display on devices, such as television, mobile phone, tablet computer, and
computer.
[0019] In an implementation, a content viewing device may be associated with a user
30 profile. The user profile may include information related to a subscription of a user, such as type
of the content viewing device associated with the user and an Internet Protocol (IP) address or
7
other unique identifier of the content viewing device. In the multi-screen environment, the
content viewing devices may be associated with separate user profiles. The user profile
associated with each of the content viewing devices may be stored in a backend server of a
content provider. Further, the content viewing device may include a mobile phone, a tablet, a
television (TV), and the like. In case of content viewing 5 devices, such as TVs, the content
viewing devices may receive the content through STBs, which decode the encoded content for
the content viewing device. On the other hand, in case of content viewing devices such as mobile
phone and tablets, which are capable of decoding the encoded content, the content viewing
devices may not need the set top box for decoding of the content.
10 [0020] To facilitate a user in selecting content of interest, the present subject matter
provides a recommendation system. The recommendation system may determine occurrence of
an activity from a plurality of activities in the DTV environment. The activity may be understood
as a viewing behavior of a user of the content viewing device. Based on the determination, the
recommendation system may generate a recommendation prompt for the content viewing device,
15 such as the main content viewing device, on the basis of a current activity of the content viewing
devices in the DTV environment. Further, based on a response to the recommendation prompt,
the recommendation system may perform an action in the DTV environment. In an
implementation, in case of the multi-screen environment, the recommendation prompts are
generated for the main content viewing device.
20 [0021] In an example, the activity may be considered as viewing of content on a content
viewing device by a user. The recommendation system may determine a current activity of the
user, in this case watching a program, such as cartoons, on a particular channel. The
recommendation system may communicate with the backend server of the content server to
access a user profile stored therein. Based on the user profile, the recommendation system may
25 identify any preferences that may be set by the user. The recommendation system may further
communicate with the content viewing device to identify similar content and prompt the user
about similar programs that are or will be available on the same or different channels. It will be
appreciated by a person skilled in the art that in case where the content viewing device is a TV,
the processing device may provide details of the content being watched by the user to the
30 recommendation system. In addition, the recommendation prompt may include timing of the
program along with the channel number. In case of the multi-screen environment, the
8
recommendation prompt may indicate if any of the linked content viewing devices are also
watching the same program. In an example, the recommendation prompt may be provided as an
interactive prompt to the user. The user may accept or ignore the recommendation prompt. In
another example, the recommendation prompt may be provided to the user in the form of
messages, such as a text message that may 5 y pop-up on a screen of the content viewing device and
the user may read the same. In case where the user is watching the multimedia content through a
mobile phone, the recommendation prompt may be provided to the user as a short service
message (SMS).
[0022] In another example, the activity may be zapping channels for a pre-defined time.
10 In this case, the recommendation system may, through the content viewing device, may
determine that the user is not able to decide upon the content to be watched and is facing
difficulty in selecting any specific content on the content viewing device. Accordingly, the
recommendation system may generate recommendation prompts for the user suggesting the user
to view content that may be based on ratings provided to different programs and channels. For
15 example, the recommendation prompt may be based on most watched programs, number of hits,
user preferences, and the like. To provide such recommendation prompts, the recommendation
system may communicate with the backend server of the content provider of the user. In case of
the multi-screen environment, the recommendation system may generate personalized messages
for the main content viewing device and provide the recommendations.
20 [0023] In the above-example, the recommendation system may also recommend the
content that is rated high by members of social networking communities of which the user may
be a member. For example, the user profile may be linked with the user profiles of the
community members and based on the preferences defined by the community members, the
recommendation system may recommend the content to the user.
25 [0024] Further, in the multi-screen environment, the recommendation system may
monitor the activities of the one or more linked content viewing devices. In the multi-screen
environment, the activity may be considered as a linked content viewing device being active for
more than a pre-defined amount of time. In such case, the recommendation system may generate
a recommendation prompt for the main content viewing device. The recommendation prompt
30 may be intended to inform the user of the main content viewing device that the linked content
viewing device is functional for more than a pre-defined amount of time. The recommendation
9
system may prompt the user of the main content viewing device to take an action that may be
associated with recommendation prompt. Further, the recommendation prompt may provide the
user of the main content viewing device with an option of sending personalized message to the
user of the linked content viewing device. The recommendation system thus also facilitates in
monitoring the activities of other users in the multi-5 lti-screen environment.
[0025] In an implementation, in case the user of the main content viewing device has not
seen the recommendation prompt due to any reason, the recommendation system may take
actions as may be defined by the user of the main content viewing device in the user profile.
Referring to the above example, if the linked content viewing device is active for a long time and
10 the user of main content viewing device is not taking any action in response to the
recommendation prompt, the recommendation system may be re-configured to switch off the
linked content viewing device or may take any other action as may be defined by the user of the
main content viewing device. In an example, the user may pre-define the actions that the user of
the main content viewing device may take in case the above-mentioned activity. The
15 recommendation system may then provide recommendation prompts which are associated with
the actions defined by the user.
[0026] The recommendation system further enables users of different content viewing
devices to communicate amongst themselves. For example, when the recommendation system
has recognized the activity as "viewing of the content", the recommendation system may
20 generate recommendation prompts for the user of the main content viewing device to interact
with the users of the linked content viewing devices, during telecast of the content. During any
activity, the recommendation system may identify different instances in the activity. Once
identified, the recommendation system may prompt the users to initiate interaction amongst other
users. For example, in the multi-screen environment, during live streaming of a game, such as a
25 football game, the recommendation system may generate a recommendation prompt for the user
of the main content viewing device to initiate a chat with other users. In an implementation, the
recommendation prompt may be generated at pre-defined instances, such as when a goal is
scored by a player in the football game. The recommendation system may identify the content
being viewed by the user and dynamically generate the recommendations of the user. The user of
30 the main content viewing device may accept or reject the recommendation. Further, if the user
10
decides to initiate the chat with other users, the chat may get stored in a storage medium along
with the content during telecast of which the chat has started.
[0027] In an implementation, when the user of the main content viewing device selects
content to be watched, the recommendation system may generate recommendation prompts
indicating about availability of superior 5 content quality of the same content on a linked content
viewing device. To provide such recommendation prompts, the recommendation system may
compare the attributes, such as bit rates, pixel density, and key frames, of a currently viewed
content with the attributes of the content of similar title in a central server of the content
provider.
10 [0028] Accordingly, the present subject matter provides a dynamic recommendation
system that provides users with recommendation prompts based on a current activity of the user.
The recommendation system, while generating recommendation prompts, may take into
consideration any change in user's viewing behavior. The recommendation system may
determine the current activity associated with a content viewing device in the DTV environment
15 and accordingly provide recommendation prompts to the users. In the multi-screen environment,
the recommendation system facilitates in keeping a check on the content being watched by the
linked content viewing devices. Further, the recommendation system provides recommendation
prompts based on the profiles of the community members of social networking sites.
[0029] The above methods and system are further described in conjunction with the
20 following figures. It should be noted that the description and figures merely illustrate the
principles of the present subject matter. It will thus be appreciated that those skilled in the art
will be able to devise various arrangements that, although not explicitly described or shown
herein, embody the principles of the present subject matter and are included within its spirit and
scope. Furthermore, all examples recited herein are principally intended expressly to be only for
25 pedagogical purposes to aid the reader in understanding the principles of the present subject
matter and the concepts contributed by the inventor(s) to furthering the art, and are to be
construed as being without limitation to such specifically recited examples and conditions.
Moreover, all statements herein reciting principles, aspects, and embodiments of the present
subject matter, as well as specific examples thereof, are intended to encompass equivalents
30 thereof.
11
[0030] It will also be appreciated by those skilled in the art that the words during, while,
and when as used herein are not exact terms that mean an action takes place instantly upon an
initiating action but that there may be some small but reasonable delay, such as a propagation
delay, between the initial action, and the reaction that is initiated by the initial action.
5 Additionally, the words “connected” and “coupled” are used throughout, for clarity of the
description and can include either a direct connection or an indirect connection.
[0031] The manner in which the systems and methods for dynamic recommendations in a
digital television (DTV) environment shall be explained in details with respect to the Figures 1-
2. While aspects of described systems and methods for dynamic recommendations in a the DTV
10 environment can be implemented in any number of different computing systems, transmission
environments, and/or configurations, the embodiments are described in the context of the
following exemplary system(s).
[0032] Figure 1 illustrates a DTV environment 100 comprising a recommendation system
102, in accordance with an embodiment of the present subject matter. The recommendation
15 system 102 may be communicatively coupled over a network 104 with one or more content
viewing devices 106-1, 106-2, …, 106-N, hereinafter collectively referred to as the content
viewing devices 106 and individually referred to as the content viewing device 106. In one
implementation, the content viewing devices 106 may receive content from a content server 108
of a content service provider through the network 104. In an implementation, the DTV
20 environment 100 may be a single screen environment that includes one content viewing device
106. In another implementation, the DTV environment 100 may be a multi-screen environment
that includes a main content viewing device 106 and at least one linked content viewing device
106. The main and the linked content viewing devices 106 may be associated with each other
such that the main content viewing device 106 may control the content being displayed on the
25 linked content viewing devices 106.
[0033] For the purpose of explanation and clarity, one content server 108 has been
shown, however, one or more content servers 108 pertaining to different service providers may
exist in the DTV environment 100. Although the recommendation system 102 is shown external
to the content viewing devices 106 and the content server 108, it may be understood that the
30 recommendation system 102 can reside in one or more of the content viewing devices 106 and/or
the content server 108.
12
[0034] Further, the content server 108 may also be implemented on one or more discrete
servers, mainframe computers, super-computers, and the like, located across different geographic
locations and coupled to each other. The network 104 may be a combination of wired and
wireless networks. The network 104 may be implemented by the content provider systems
through satellite communication, terrestrial communication, or may be implemented 5 ented through the
use of routers and access points connected to various Digital Subscriber Line Access
Multiplexers (DSLAMs) of wired networks. The network 104 can be implemented as one of the
different types of networks, such as intranet, local area network (LAN), wide area network
(WAN), the Internet, and such. Further, the network 104 may be an Internet Protocol (IP) TV
10 network, a mobile communication network, and digital broadcasting TV network.
[0035] In one implementation, the content viewing devices 106 may or may not have an
ability to decode the encoded content received from the content server 108. In such cases, the
processing device 110 may be used to decode the content for the content viewing devices 106.
As shown in Figure 1, in case of the content viewing device 106-2, a processing device 110, such
15 as a set top box, is used to decode the content. Once the content is decoded by the processing
device 110, the decoded content may be provided to the content viewing device 106. For
example, a TV may not have decoding capabilities to decode the content encoded by the content
server 108. In such cases, the encoded content is first decoded by the processing device 110 and
then provided to the content viewing device 106. Thereafter, the content viewing device 106 may
20 display the content on a screen of the content viewing device 106.
[0036] Although the processing device 110 is shown to be connected directly to the
content viewing device 106-2, it would be appreciated by those skilled in the art that the
processing device 110 may be distributed locally or across a different geographic location. In one
implementation, in case of the content viewing devices 106 in which the decoding capabilities
25 are integrated within the content viewing devices 106, such as a laptop, a personal computer, a
mobile phone, and a tablet, the processing device 110 may not be needed as shown in Figure 1.
Further, the processing devices 110 can be implemented with any of a variety of display devices
known in the art, such as an electro luminescent display (ELD), a plasma display panel (PDP), an
organic light emitting diode (OLED), a light emitting diode (LED) display, a liquid crystal
30 display (LCD), and a thin-film transistor LCD (TFT-LCD), and a projector coupled to a projector
13
screen. Further, these content viewing devices 106 may perform functions of a television set and
a monitor of a computing device.
[0037] According to an embodiment of the present subject matter, a user subscribed to
the services of a content service provider may view content broadcasted by the content service
provider through a content viewing device, 5 say, the content viewing device 106-1 coupled to the
processing device 110. For the purpose, the recommendation system 102 includes one or more
processor(s) 112, I/O interface(s) 114, and a memory 116 coupled to the processor(s) 112. The
processor(s) 112 may be implemented as one or more microprocessors, microcomputers,
microcontrollers, digital signal processors, central processing units, state machines, logic
10 circuitries, and/or any devices that manipulate signals based on operational instructions. Among
other capabilities, the processor(s) 112 are configured to fetch and execute computer-readable
instructions stored in the memory 116.
[0038] The functions of the various elements shown in the figures, including any
functional blocks labeled as “processor(s)”, may be provided through the use of dedicated
15 hardware as well as hardware capable of executing software in association with appropriate
software. When provided by a processor, the functions may be provided by a single dedicated
processor, by a single shared processor, or by a plurality of individual processors, some of which
may be shared. Moreover, explicit use of the term “processor” should not be construed to refer
exclusively to hardware capable of executing software, and may implicitly include, without
20 limitation, digital signal processor (DSP) hardware, network processor, application specific
integrated circuit (ASIC), field programmable gate array (FPGA), read only memory (ROM) for
storing software, random access memory (RAM), and non volatile storage. Other hardware,
conventional and/or custom, may also be included.
[0039] The I/O interface(s) 114 may include a variety of software and hardware
25 interfaces, for example, interfaces for peripheral device(s), such as data input output devices,
referred to as I/O devices, storage devices, network devices, etc. The I/O device(s) may include
Universal Serial Bus (USB) ports, Ethernet ports, host bus adaptors, etc., and their corresponding
device drivers. The I/O interface(s) 114 facilitate the communication of the recommendation
system 102 with various networks, such as the network 104 and various communication and
30 computing devices, such as the content viewing devices 106.
14
[0040] The memory 116 may include any computer-readable medium known in the art
including, for example, volatile memory, such as Static Random Access Memory (SRAM) and
Dynamic Random Access Memory (DRAM), and/or non-volatile memory, such as read only
memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and
magnetic 5 gnetic tapes.
[0041] The recommendation system 102 may also include various modules 118 and data
120. The modules 118, amongst other things, include routines, programs, objects, components,
data structures, etc., which perform particular tasks or implement particular abstract data types.
The modules 118 may also be implemented as, signal processor(s), state machine(s), logic
10 circuitries, and/or any other device or component that manipulate signals based on operational
instructions.
[0042] Further, the modules 118 can be implemented in hardware, instructions executed
by a processing unit, or by a combination thereof. The processing unit can comprise a computer,
a processor, such as the processor 112, a state machine, a logic array or any other suitable
15 devices capable of processing instructions. The processing unit can be a general-purpose
processor which executes instructions to cause the general-purpose processor to perform the
required tasks or, the processing unit can be dedicated to perform the required functions.
[0043] In another aspect of the present subject matter, the modules 118 may be machinereadable
instructions (software) which, when executed by a processor/processing unit, perform
20 any of the described functionalities. The machine-readable instructions may be stored on an
electronic memory device, hard disk, optical disk or other machine-readable storage medium or
non-transitory medium. In one implementation, the machine-readable instructions can be also be
downloaded to the storage medium via a network connection.
[0044] The module(s) 118 further include an analysis module 122, a recommendation
25 module 124, and other module(s) 126. The other module(s) 126 may include programs or coded
instructions that supplement applications and functions of the recommendation system 102. The
data 120 amongst other things, serves as a repository for storing data processed, received,
associated, and generated by one or more of the module(s) 118. The data 120 includes, for
example, user profile 128, parameters 130, and other data 132. The other data 132 includes data
30 generated as a result of the execution of one or more modules in the other module(s) 126.
Although the data 120 is shown internal to the recommendation system 102, it may be
15
understood that the data 120 can reside in the memory 116, which is coupled to the processor
112.
[0045] During initial setup, to receive the content from a content provider, a user of the
content viewing device 106 may subscribe to different types of content, such as programs,
movies, and games, from the content provider. The content provider may 5 y provide the content to
the user through the content server 108 over the network 104. When the user subscribes some
content from the content provider, the content provider may create a user profile for each user.
The user profile may include details pertaining to a content viewing device which the user may
use for viewing the subscribed content, preferences about type of content that the user would like
10 to receive, activities on which the recommendation prompts may be generated, actions to be
specified in the recommendation prompts, and the like. Based on the user profile, the content
provider may provide the content to the user. Further, the analysis module 122 may store
information related to users as the user profiles 128. The user profile may be stored in the
backend server of the content provider. The recommendation system 102 may contact the content
15 provider, which in turn communicate with the backend server to retrieve the user profile.
[0046] In an implementation, the analysis module 122 may determine occurrence of an
activity in the DTV environment 100. For example, the processing device 110 may communicate
with the recommendation system 102 to provide details about the occurrence of the activity. As
mentioned above, in cases where the content viewing device 106 include the capabilities of the
20 processing device 110, the recommendation system 102 may receive details about the occurrence
of the activity from such content viewing device 106. In case the DTV environment 100 includes
one content viewing device 106, the occurrence of the activity may include selection of content
and zapping of channels for a pre-defined time by the user of the content viewing device 106. In
case the DTV environment 100 includes a main content viewing device 106-1 and a linked
25 content viewing device 106-2, the occurrence of the activity may include content selection by the
main content viewing device 106-1, content viewing time of the linked content viewing device
106-2 exceeding a pre-defined time, channel zapping by the user of main content viewing device
106-2 for a pre-defined time period, and initiation of a program on one of the main content
viewing device 106-1 and the linked content viewing device 106-2.
30 [0047] Once the analysis module 122 determines occurrence of any of the pre-defined
activities, the recommendation module 124 may identify preferences that may be defined by the
16
user of the content viewing device 106. In an implementation, the recommendation module 124
may, based on the occurrence of activities, generate recommendation prompts for the user. The
recommendation module 124 may send a request to the processing device 110 to retrieve
information about the content being currently watched by the user. Based on the retrieved
content, the recommendation module 124 may generate 5 rate recommendation prompts for the user. In
an implementation, the recommendation prompts may be based on the user profiles of the users.
For example, the recommendation module 124 may communicate with the backend server of the
content provider to access the user profile. In an implementation, the recommendation module
124 may link the user profiles of users with social networking portals and may access social
10 profiles of the user for generating the recommendation prompts. In addition, the recommendation
module 124 may generate the recommendation prompts based on a plurality of parameters.
Examples of the parameters include, but are not limited to, title of the content, names of
characters of the content, and theme of the content.
[0048] For example, upon determination of the activity "viewing of content" by the
15 analysis module 122, the recommendation module 124 may retrieve details about the content
being viewed by the user. As may be understood, the recommendation module 124 may retrieve
the parameters associated with the content from the content server 108. Once retrieved, the
recommendation module 124 may parse the user profile stored in the backend server of the
content provider for identifying new content that may be recommended to the user. To do so, the
20 recommendation module 124 may compare, for example, the title of the content with the titles of
the content that may be subscribed by the user. Accordingly, the recommendation may generate
recommendation prompts for the user indicating the content that may be of interest to the user.
The recommendation module 124 may store the plurality of parameters as parameters 130.
[0049] Further, the user profile may include actions pre-defined by the user. These pre25
defined actions may be associated with the recommendation prompts. In an example, upon
receiving the recommendation prompts, the users may respond to the recommendation prompts
or may ignore the recommendation prompts. In order to respond to the recommendation prompts,
the users may select the action that may be proposed by the recommendation prompt. As may be
understood, the users may navigate between one or more actions that may be suggested by the
30 recommendation prompt by using a controller, such as a keyboard, a remote controller, and the
like. In an implementation, in absence of any response from the user, the recommendation
17
module 124 may take pre-defined actions on the content viewing device 106. In another
example, the recommendation prompts may be provided as linear messages, such as text
messages, subtitles, short service messages (SMS), and the like. In such cases, a user, while
watching a movie on a TV, may press an “information” button of a remote of a set top box (STB)
and may see the action that may 5 y be suggested by the recommendation prompt.
[0050] In a scenario, the analysis module 122 may determine that the user of a content
viewing device 106 has selected content for watching. For example, the processing device 110
may inform the analysis module 122, of the recommendation system 102, about the current
activity of the content viewing device 106. The analysis module 122 may identify occurrence of
10 an event, in this case, selection of content, from a plurality of events. Based on the
determination, the recommendation module 124 may determine similar content from the content
server 108 as per the user's subscription. For example, if the user is watching a mythological
program, the recommendation module 124 may generate recommendations for the user
indicating which other channels are also providing such programs. The recommendations may
15 include timings at which the other channels will show similar programs. Further, the
recommendation may be associated with actions, such as set a reminder for repeat telecast of the
same program, switch to the channel showing similar program, and the like. The user may select
any of the recommendations and accordingly, the recommendation module 124 may take action.
It will be understood that the user may select amongst the recommendations by means of a
20 controller, such as a remote control of a TV.
[0051] Further, in another scenario, the processing device 110 may inform the analysis
module 122 about the activity as "zapping of channels by a user for more than a pre-defined
time". For example, if the user is zapping channels for more than 3 minutes, the recommendation
module 124 may generate recommendation prompts for the user. The recommendation prompts
25 may provide suggestions to the user about the content that the user may watch. For example, the
recommendation module 124 may access content from the Internet, such as content that is most
liked by social networking communities of which the user is a member. Users of member social
networking communities may provide ratings to the content and based on the ratings the content
is ranked as most liked content.
30 [0052] In case of a multi-screen environment, the recommendation module 124 may also
recommend the main content viewing device 106-1 about the content that may be viewed by a
18
linked content viewing system 106-2. As the recommendation prompts may include actions
associated therewith, the user may select any action to be performed. For example, the action
may include "switch off the content viewing device", "shift to the content as viewed by the
linked content viewing device", "play the content most liked by the social networking
5 community", and the like.
[0053] As mentioned above, the DTV environment 100 may include a multi-screen
platform having the main content viewing device 106-1 and one or more linked content viewing
devices 106-2. In the multi-screen platform, the recommendation system 102 may generate the
recommendation prompts for the main content viewing device 106-1. The recommendations may
10 be based on a current activity of the main content viewing device 106-1 and linked content
viewing device 106-2. For example, if the processing device 110 may indicate the analysis
module 122 that the linked content viewing device 106-2 is operational for more than the predefined
time, the analysis module 122 may trigger the recommendation module 124 to generate a
recommendation prompt for the main content viewing device 106-1. For example, if a child is
15 watching some content on the linked content viewing device 106-2 for more than 2 hours, the
recommendation system 102 may generate a recommendation prompt for the parent on the main
content viewing device 106-1 indicating that the linked content viewing device 106-2 is
operational for more than the pre-defined time.
[0054] In an implementation, the recommendation module 124 may facilitate the user of
20 the main content viewing device 106-1 to send a personalized message to the user of the linked
content viewing device 106-2, in response to the recommendation prompt. The recommendation
prompt may include actions, such as "send a personalized message" and "remind after another
hour". The user of the main content viewing device 106-1 may select any of the actions as may
be provided by the recommendation prompt. In case, the user of the main content viewing device
25 106-1 does not respond to the recommendation prompt, i.e., does not take any action specified by
the recommendation prompt, due to some reason, the recommendation module 124 may
automatically take an action, as may be defined in the user profile, on the linked content viewing
device 106-2. For example, the action may include stopping streaming of the content to the
linked content viewing device 106-2 for a pre-defined time or until the main content viewing
30 device 106-1 takes further action.
19
[0055] In another example, in the multi-screen platform, the recommendation system 102
may generate recommendation prompts for the users to interact amongst themselves while
watching some content, such as a live game. For example, if one of the users of the multi-screen
platform is watching a live football game, the recommendation module 124 may generate
recommendation prompts at specific instances of the football game. The specific instances 5 may
be identified by the recommendation module 124 based on the type of the content being
displayed. The content server 108 may notify the recommendation system 102 about the
instances that may be occurring on any channel in the DTV environment 100. In an
implementation, the content server 108 may share details of the channel on which the content is
10 being telecast and a specific instance that is identified in the content, with the recommendation
system 102. The recommendation module 124 may compare the channel details received from
the content server 108 with a current channel being watched by the user. Once identified that the
user is also watching the same channel, the recommendation module 124 may generate a
recommendation prompt for the user. For example, in case of the live football game, the
15 instances may include beginning of the match, toss, goal scored by a player, and the like. Once
identified, the recommendation module 124 may prompt the user of the main content viewing
device 106-1 to initiate a chat with one or more linked content viewing devices 106-2. Once the
user of the main content viewing device 106-1 initiates chat with the user of the linked content
viewing device 106-2, the recommendation module 124 may send a request to the content server
20 108 to store the communication between the two users along with the football match. In an
implementation, the content server 108 may store the communication and the football match in a
storage media, such as operator network storage, Universal Serial Bus (USB) memory, cloud
storage memory, the processing device 110, memory, Network Attached Storage (NAS), and
internal storage of the content viewing device 106.
25 [0056] Further, the recommendation module 124 may generate recommendation prompts
for the user of the main content viewing device 106-1 based on the quality of the content being
viewed by the user of the main content viewing device 106-1. In an implementation, the
recommendation module 124 may receive details about the content being currently viewed by
the main content viewing device 106-1. For example, the details about the content may include
30 bit rates, pixel density, and key frames, and the like. The recommendation module 124 may
analyze the details of the content with respect to specifications of the content viewing devices
20
106 of the multi-screen platform. If the quality of the content, being currently viewed by the user
of the main content viewing device 106-1, is superior on a linked content viewing device 106-2,
the recommendation module 124 may generate recommendation prompts for the user suggesting
the user of the main content viewing device 106-1 to switch to the linked content viewing device
5 106-2 for better quality.
[0057] Accordingly, the recommendation system 102 may provide recommendations to
users of the content viewing devices 106 based on a current activity of the users. The
recommendations may be shared as interactive prompts or non-interactive messages. Further, the
recommendations may be associated with an action to be taken on the content viewing devices
10 106. In addition, the recommendation system 102 of the present subject matter may generate the
recommendations by accessing social profiles of the users from various social networking
portals. The recommendations may be based on a current viewing behavior of the user therefore
provides recommendations on the fly.
[0058] Figure. 2 illustrates a method 200 for generating recommendations in a DTV
15 environment 100, according to an embodiment of the present subject matter. The order in which
the method is described is not intended to be construed as a limitation, and any number of the
described method blocks can be combined in any order to implement the method 200 or any
alternative method. Additionally, individual blocks may be deleted from the method without
departing from the spirit and scope of the subject matter described herein. Furthermore, the
20 method can be implemented in any suitable hardware, software, firmware, or combination
thereof.
[0059] The method(s) may be described in the general context of computer executable
instructions. Generally, computer executable instructions can include routines, programs, objects,
components, data structures, procedures, modules, functions, etc., that perform particular
25 functions or implement particular abstract data types. The methods may also be practiced in a
distributed computing environment where functions are performed by remote processing devices
that are linked through a communications network. In a distributed computing environment,
computer executable instructions may be located in both local and remote computer storage
media, including memory storage devices.
30 [0060] A person skilled in the art will readily recognize that steps of the method(s) 200
can be performed by programmed computers. Herein, some embodiments are also intended to
21
cover program storage devices or computer readable medium, for example, digital data storage
media, which are machine or computer readable and encode machine-executable or computerexecutable
programs of instructions, where said instructions perform some or all of the steps of
the described method. The program storage devices may be, for example, digital memories,
magnetic storage media, such as a magnetic disks and 5 magnetic tapes, hard drives, or optically
readable digital data storage media. The embodiments are also intended to cover both
communication network and communication devices to perform said steps of the method(s).
[0061] At block 202, the method 200 may include associating a user profile with each of
a plurality of content viewing devices 106 in a DTV environment 100. In an implementation, the
10 analysis module 122 may associate the user profiles with the content viewing devices 106. The
user profile may includes details pertaining to a content viewing device which the user may use
for viewing the subscribed content, preferences about type of content that the user would like to
receive, activities on which the recommendation prompts may be generated, actions to be
specified in the recommendation prompts, and the like. The user profiles may be stored in a
15 backend server of the content provider.
[0062] At block 204, the method 200 may include determining occurrence of an activity
from a plurality of activities in the DTV environment 100. The activity may be understood as a
viewing behavior of the user of the content viewing device 106. The DTV environment 100 may
include at least one of a main content viewing device 106-1 and a linked content viewing device
20 106-2. In an implementation, the analysis module 122 may determine the activity associated with
the content viewing device 106. For example, the processing device 110 may inform the analysis
module 122 about occurrence of the activity in the content viewing device 106. The plurality of
activities may include, but are not limited to, content selection by the main content viewing
device 106-1, content viewing time of the linked content viewing device 106-2 exceeding a pre25
defined time, channel surfing by the user of main content viewing device 106-2 for a pre-defined
time period, and initiation of a program on one of the main content viewing device 106-1 and the
linked content viewing device 106-2.
[0063] At block 206, the method 200 may include generating a recommendation prompt
for the main content viewing device 106-1, based on the determination of occurrence of an
30 activity. The recommendation prompt may be generated on the basis of a current activity of the
main content viewing device 106-1. In an implementation, the recommendation module 124 may
22
generate the recommendation prompts for the users. The recommendation prompts may be
rendered in the form of a text message that may pop-up on a screen of the content viewing device
and the user may read the same. Further, the recommendation prompts may be associated with a
pre-defined action as indicated in the user profile.
[0064] At block 208, the method 200 may include 5 e performing an action in the DTV
environment 100, in response to the recommendation prompt. In an implementation, the
recommendation module 124 may perform the action as may be selected by the user. The user
may select to perform the action as per the recommendation prompt or may ignore the
recommendation prompt.
10 [0065] Although embodiments for generating recommendations in a DTV environment
have been described in a language specific to structural features or method(s), it is to be
understood that the invention is not necessarily limited to the specific features or method(s)
described. Rather, the specific features and methods are disclosed as embodiments for generating
recommendations in a DTV environment.
15
23
I/We claim:
1. A method for generating recommendations in a digital television (DTV) environment
(100), the method comprising:
determining, by a processor (112), occurrence 5 ce of an activity from a plurality of
activities in the DTV environment (100), wherein the DTV environment (100) includes a
content viewing device (106);
based on the determination, generating, by the processor (112), a recommendation
prompt for the content viewing device (106), wherein the recommendation prompt is
10 generated on the basis of a current activity on the content viewing device (106); and
performing, by the processor (112), an action in the DTV environment (100),
based on a response to the recommendation prompt.
2. The method as claimed in claim 1, wherein the activity includes one of a content
15 selection and content surfing by the content viewing device (106).
3. The method as claimed in claim 1, wherein the recommendation prompt is based on at
least one parameter associated with the activity, and wherein the at least one parameter
includes title of a program, name of a character of the content, a theme of the content,
20 quality of content, and a user preference.
4. A method for generating recommendations in a DTV environment (100), the method
comprising:
determining, by a processor (112), occurrence of an activity from a plurality of
25 activities in the DTV environment (100), wherein the DTV environment (100) includes a
main content viewing device (106-1) and at least one linked content viewing device (106-
2);
based on the determination, generating, by the processor (112), a recommendation
prompt for the main content viewing device (106-1), wherein the recommendation
30 prompt is generated on the basis of a current activity associated with at least one of the
24
main content viewing device and the at least one linked content viewing device (106-2);
and
based on a response to the recommendation prompt, performing, by the processor
(112), an action in the DTV environment (100).
5
5. The method as claimed in claim 4, wherein the activity includes one of a content
selection, content viewing time exceeding a pre-defined time, channel surfing for a predefined
time period, and initiation of a program in the DTV environment (100).
10 6. The method as claimed in claim 4, wherein the recommendation prompt is based on at
least one parameter associated with the activity, and wherein the at least one parameter
includes title of a program, name of a character of the content, a theme of the content,
quality of content, and a user preference.
15 7. The method as claimed in claim 4 further comprising associating a user profile with each
of a plurality of content viewing devices (106) in the DTV environment (100).
8. The method as claimed in claim 7, wherein the user profile includes information related
to subscription of users and details about capabilities of each of the plurality of content
20 viewing devices (106) in the DTV environment (100).
9. A recommendation system (102) for generating recommendations in a DTV environment
(100), the recommendation system (102) comprising:
a processor (112);
25 an analysis module (122), coupled to the processor (112), to determine occurrence
of an activity from a plurality of activities in the DTV environment (100); and
a recommendation module (124), coupled to the processor (112), to,
generate a recommendation prompt for the main content viewing device
(106-1), wherein the recommendation prompt is generated on the basis of a
30 current activity associated with at least one of the main content viewing device
(106-1) and a linked content viewing device (106-2); and
25
based on a response to the recommendation prompt, perform an action in
the DTV environment (100).
10. The recommendation system (102) as claimed in claim 9, wherein the activity includes
one of a content selection, content viewing time exceeding 5 a pre-defined time, channel
surfing for a pre-defined time period, and initiation of a program in the DTV environment
(100).
11. The recommendation system (102) as claimed in claim 9, wherein the recommendation
10 prompt is based on at least one parameter associated with the activity, and wherein the at
least one parameter includes title of a program, name of a character of the content, a
theme of the content, quality of content, and a user preference.
12. The recommendation system (102) as claimed in claim 9 further comprising associating a
15 user profile with each of a plurality of content viewing devices (106) in the DTV
environment (100).
13. The recommendation system (102) as claimed in claim 12, wherein the user profile
includes information related to subscription of users and details about capabilities of each
20 of the plurality of content viewing devices (106) in the DTV environment (100).
14. A non-transitory computer-readable medium having embodied thereon a computer
program for executing a method for generating recommendations in a DTV environment
(100), the method comprising:
25 determining, by a processor (112), occurrence of an activity from a plurality of
activities in the DTV environment (100), wherein the DTV environment (100) includes a
content viewing device (106);
based on the determination, generating, by the processor (112), a recommendation
prompt for the content viewing device (106), wherein the recommendation prompt is
30 generated on the basis of a current activity of the content viewing device (106);
26
performing, by the processor (112), an action in the DTV environment (100),
based on a response to the recommendation prompt.