U.S. patent application number 15/583857 was filed with the patent office on 2017-10-26 for comprehensive user/event matching or recommendations based on awareness of entities, activities, interests, desires, location.
The applicant listed for this patent is Daniel Freeman. Invention is credited to Daniel Freeman.
Application Number | 20170308608 15/583857 |
Document ID | / |
Family ID | 52006364 |
Filed Date | 2017-10-26 |
United States Patent
Application |
20170308608 |
Kind Code |
A1 |
Freeman; Daniel |
October 26, 2017 |
COMPREHENSIVE USER/EVENT MATCHING OR RECOMMENDATIONS BASED ON
AWARENESS OF ENTITIES, ACTIVITIES, INTERESTS, DESIRES, LOCATION
Abstract
A method for comprehensive user/event matching or
recommendations is described. The method includes a network
environment which receives one or more pieces of user data, event
data, or social data from users or third party data sources,
determining the relevance of the data for users, and displaying the
identified data to user in the form of recommendations.
Inventors: |
Freeman; Daniel; (Tampa,
FL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Freeman; Daniel |
Tampa |
FL |
US |
|
|
Family ID: |
52006364 |
Appl. No.: |
15/583857 |
Filed: |
May 1, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14211867 |
Mar 14, 2014 |
9639608 |
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15583857 |
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61794283 |
Mar 15, 2013 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04W 4/21 20180201; G06F
16/95 20190101; G06Q 50/01 20130101 |
International
Class: |
G06F 17/30 20060101
G06F017/30; H04W 4/20 20090101 H04W004/20; G06Q 50/00 20120101
G06Q050/00 |
Claims
1: A computer processor implemented method to match recommendations
to a user for an event, said method comprising: receiving or
collecting, by a network environment, one or more portions of user
data, event data, or social data from users or third party data
sources via said network; whereby the network environment comprises
one or more servers connected to one or more user devices via a
network; identifying, by said network environment, one or more
portions of user data, event data, or social data from users or
third party data sources via said network; determining
recommendations by calculating a relevancy score of said user data,
said social data, or said event data for users when one or more of
said data points of user, social, or event data exceeds one or more
thresholds; wherein said determining is based upon a significance
analysis, by a computer processor implemented method, which ranks
said user data, said event data, or said social data to be of
interest to one or more users based on awareness of entities,
activities, interests, desires, or location; wherein said
calculated relevancy score is greater than a threshold and includes
said user data, said social data or said event data; categorizing
said recommendations by applying a relevancy popularity analysis
thereby narrowing down most relevant data based upon interests,
activities, experiences, people, deals, specials, desired matchups,
advertisements, or recommendations which have previously elicited
engagement; matching said identified said user data, social data,
or said event data based at least on said relevancy score for each
categorized recommendation; determining one or more recommendation
results based at least on said relevancy score for each categorized
recommendation; transmitting to said user the one or more
recommendation results based at least on said relevancy score for
each categorized recommendation by displaying matched identified
said user data, social data, or said event data based on calculated
recommendations.
2: The method of claim 1, wherein said user data includes
information about a user's real world explicitly or implicitly
stated interests, disinterests, or desires.
3: The method of claim 2 wherein said social data may include
information about users and ghost users' real world interests or
disinterests.
4: The method of claim 3 wherein said user data further comprises a
current, recent, last known, or estimated physical location of one
more of said users.
5: The method of claim 4 wherein said user data further comprises a
current, recent, last known, or estimated physical location of one
or more ghost users.
6: The method of claim 5 wherein at least one of said thresholds is
said user's physical location in relation to the event described by
said event data.
7: The method of claim 5 wherein at least one of said thresholds is
said user's physical location in relation to attributes of said
social data.
8: The method of claim 1, wherein said event data comprises events,
activities, experiences, people, deals, specials, desire matchups,
or advertisements.
9: The method of claim 1, wherein the network environment receives
and prioritizes said event data using said user data.
10: The method from claim 1, wherein the recommendations are
further narrowed based on significance/relevance, or
attendance/engagement.
11: The method from claim 1, wherein recommendations are presented
to the user in a feedback mechanism, such as over the
internet/intranet, via a dedicated or within a separate
mobile/web/tablet application, via augmented reality layers, email,
bluetooth, in-ear audio/video/display systems, mobile tethering,
mobile hotspots, near-field communication, or any transmission that
offers relevant real-world content.
12: A system for a network environment to match recommendations to
a user, said method comprising: one or more processors; and a
memory coupled to the processor comprising instructions executable
by the processors, the processors operable executing the
instructions to: receiving or collecting, by a network environment,
one or more portions of user data, event data, or social data from
users or third party data sources via said network; whereby the
network environment comprises one or more servers connected to one
or more user devices via a network; identifying, by said network
environment, one or more portions of user data, event data, or
social data from users or third party data sources via said
network; determining recommendations by calculating a relevancy
score of said user data, said social data, or said event data for
users when one or more of said data points of user, social, or
event data exceeds one or more thresholds; wherein said determining
is based upon a significance analysis, by a computer processor
implemented method, which ranks said user data, said event data, or
said social data to be of interest to one or more users based on
awareness of entities, activities, interests desires, or location;
wherein said calculated relevancy score is greater than a threshold
and includes said user data, said social data or said event data;
categorizing said recommendations by applying a relevancy
popularity analysis thereby narrowing down most relevant data based
upon interests, activities, experiences, people, deals, specials,
desired matchups, advertisements, or recommendations which have
previously elicited engagement; matching said identified said user
data, social data, or said event data based at least on said
relevancy score for each categorized recommendation; determining
one or more recommendation results based at least on said relevancy
score for each categorized recommendation; transmitting to said
user the one or more recommendation results based at least on said
relevancy score for each categorized recommendation by displaying
matched identified said user data, social data, or said event data
based on calculated recommendations. Although the invention has
been described in specific detail with reference to the disclosed
embodiments, it will be understood to an individual with expertise
in the prior art that many natural variations and modifications may
be effected within the spirit and scope of the invention as
described the submission hereto.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present application incorporates by reference U.S.
Provisional Patent Application Ser. No. 61/794,283 filed on Mar.
15, 2013 for "Comprehensive User/Event Matching or Recommendations
Based on Awareness of Entities, Activities, Interests, Desires,
Location."
BACKGROUND
Field of the Invention
[0002] The invention relates generally to social networking and, in
particular, to systems and methods for serving meaningful
"real-world" recommendations.
Description of the Related Art
[0003] Social networking services include social utilities that
track and enable connections between users (including people,
businesses, and other entities), which have become prevalent in
recent years. In particular, social networking services allow users
to communicate more efficiently information that is relevant to
their friends or other connections in the social networking
service. Social networking services typically incorporate a system
for connecting users to content that is likely to be relevant to
each user. For example, users may be grouped according to one or
more common attributes in their profiles, such as geographic
location, employer, job type, age, music preferences, page likes,
followers, or other attributes. Users of the social networking
service and/or external parties can then use these groups to
customize or target information delivery so that information that
might be of particular interest to a group can be communicated to
that group.
[0004] Furthermore, advertisers wishing to use members' affinities,
or common attributes, as targeting criteria for advertisements have
difficulty placing their ads in contextually relevant areas, a
problem called "ad blindness." As a result, members are inundated
with advertisements for products unrelated to the context of what
the members are currently viewing. Thus, these ads are largely
ignored by members of a social network.
[0005] Despite prior art designed to increase the relevance of this
information, the systems and methods of the prior art do not
achieve the desired outcome, and continue to overwhelm the user
with large amounts of non-relevant information. Additionally,
methods for indicating user interests including page likes and
followers on social networks today are largely comprised of
non-useful and non-meaningful data for real-world (offline)
interactions, thus the power for directing content,
recommendations, and advertising that promotes real world
interactions is greatly limited.
SUMMARY
Brief Summary of Invention
[0006] A recommendation engine that connects users with the things
that they find important. This creates a real-world social network
promoting the connection of people in meaningful interactions based
on the context of their interests and behavior. [0007] Useful
matches or recommendations of other users (1 or more) and events
(one-time, repeating, deal-based, or general), including any
combinations (users with users, users with events, events with
users, events with events). [0008] Based on interest (as indicated
by a user or friend, inferred, or profiled/guessed) or intent
(indicated or guessed), location (current place, or a remote place,
where one plans to be or would like to be), compatibility. [0009]
Towards the promotion of "real world"/offline activities or
connections. [0010] Connecting brand/businesses/advertisers' events
with the users who, based on a set of algorithms, may find the
recommendation of interest [0011] Data received or gathered from
external systems, public information, or from the invention itself.
[0012] In one embodiment, these matches or recommendations are
displayed in a feed on the web, a mobile device, by text message,
or in an email. [0013] In another embodiment in an augmented
reality application, such as a pair of glasses or contact lenses
projecting a data layer on top of real objects in the field of
vision. [0014] Recommendations can be conveyed in any means whereby
such data can be relayed, such as over internet/intranet, via a
dedicated or within a separate mobile/web/tablet application, via
augmented reality layers, email, bluetooth, in-car
audio/video/display systems, mobile tethering, mobile hotspots,
near-field communication, or any transmission that offers relevant
real-world content with regard to any of the items indicated
hereto.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] FIG. 1 illustrates an exemplary environment for generating
relevant recommendations in a social network environment;
[0016] FIG. 2 is a block diagram of an exemplary recommendation
engine;
[0017] FIG. 3 is an exemplary screen shot of one embodiment of a
feed of recommendations on a mobile device;
[0018] FIG. 4 is a flow diagram of an exemplary process, for the
importing/classification of data, display of relevant
recommendations, and an optional feedback loop to improve future
recommendations; and
[0019] FIG. 5-FIG. 9 are additional embodiments of the present
invention as described in U.S. Provisional Patent Application Ser.
No. 61/794,283 filed on Mar. 15, 2013 and noted as FIGS. 1-2, and
6-8 respectively therein.
[0020] FIG. 5 is an exemplary screen shot of one embodiment of a
feed of recommendations on a mobile device
[0021] FIG. 6 is an exemplary screen shot of one embodiment of a
details view for one recommendation, on a mobile device
[0022] FIG. 7 is an early mockup screen shot of one embodiment of a
view allowing users to specify interest preferences
[0023] FIG. 8 is an early mockup screen shot of one embodiment of a
feed of recommendations on a mobile device
[0024] FIG. 9 is an early mockup screen shot of one embodiment of a
settings menu allowing users to specify general preferences for
recommendations.
[0025] The figures depict various embodiments of the present
invention for purposes of illustration only. One skilled in the art
will readily recognize from the following discussion that
alternative embodiments of the structures and methods illustrated
herein may be employed without departing from the principles of the
invention described herein.
DETAILED DESCRIPTION
Detailed Description of Invention
[0026] A comprehensive matching system for matching or recommending
users and events, to create a "real-world" social network--the
content delivered from this system could be outputted through any
means whereby electronic data is delivered, for example in a feed
on the web, a mobile device, by text message, in an email, through
augmented reality in the field of vision, or in any means whereby
such data can be relayed, such as over internet/intranet, via a
dedicated--or within a separate--mobile/web/tablet application, via
augmented reality layers; email, bluetooth, in-car
audio/video/display systems, mobile tethering, mobile hotspots,
near-field communication, other outputs as known in the art, or any
transmission that offers relevant real-world content with regard to
any of the items indicated hereto. [0027] To create a novel and
meaningful experience, in one embodiment of the system, multiple
steps of information gathering and contextualization occur
including: [0028] User interest or intent data through explicit
input by self or friend, inferred by interactions within the
system, and/or profiled/guessed based on similarities to other
users or other available data. [0029] Current, future, or desired
location, based on user provided input, inferred using data
provided by a front-end system/application, or guessed based on
other information, such as IP address. [0030] Entity and
event/activity/experience awareness to understand possibilities of
things to do, and the entities associated with those activities to
understand which user(s) should receive the specific
recommendation. [0031] Involves an ontology/graph/model of several
layers of possible interests and interest relations, in one
embodiment using multi-step natural language contextualization
system to take diverse inputs from disparate sources online and
offline (external systems, public information, or from the
invention itself) to understand and "tag" accordingly. [0032] This
information is used to recommend or match, in one embodiment of the
system using matching algorithms, statistical models, or other data
analysis techniques the following combinations: users with users,
users with events, events with users, events with events toward the
promotion of "real world"/offline activities or connections. [0033]
Users could be a single or group of users, potentially with
compatibility considerations from user data provided or
inferred/guessed. [0034] Events could be one-time events, such as a
concert, repeating such as a weekly special, a deal-based event
such as an expiring coupon/special promotion, or a general event
such as "hike in the park." [0035] Certain embodiments include
(each user-provided, inferred, or profiled/guessed): varying
importance of weighting for recommendations to receive more/less
and nearer/farther recommendations, user importance ratings as
positives (likes) and as negatives (dislikes). [0036] Automatic
awareness and changes to system parameters based on new habits,
user located in a new place (one they do not typically go to),
actively moving, and others. [0037] Different modes (either user or
system induced) to receive different numbers/types of
recommendations for events and users, as well as times and places
when the system will be on/off automatically. [0038]
Subscription-like recommendations or matches such that "tastemaker"
or popular users curate and can be followed for their
feeds/playlists/activity. [0039] Connecting
brands'/businesses'/advertisers' events with the users who, based
on a set of algorithms, may find the recommendation of interest.
[0040] Could occur on a one-time basis, similar to a notification,
or a recurring/continual basis, similar to an advertising campaign.
[0041] FIG. 1 illustrates an exemplary system for generating
relevant recommendations in a network environment 104. One or more
users, such as users 101 at user devices 102, are coupled to a
network environment 104. The network environment 104 can query data
from social data provider 105 and/or event data provider 106. All
data received by the network environment 104 is then processed by
recommendation engine 107, which may filter the information
according to thresholds set on the inputs to recommendation engine
107 (as more fully described in FIG. 2), and generally the guidance
of self-stated and/or discovered/inferred interests. It sends
recommendations believed that user 101 will find relevant given
aforementioned interests, to user device 102. Recommendations
provided may include events, activities, experiences, people,
deals, specials, desire matchups, and/or advertisements for user
101 alone, or include friends/people in said recommendations who
may have a corresponding interest. This mutual interest could be
realized directly by users 101 and 103, not necessarily friends,
because of their direct involvement on the same network environment
104, or explicitly between users 103 and "ghost" user 103a--a
friend who has not subscribed or directly provided information to
network environment 104, but whose data was obtained because of
user 103's engagement with the network environment 104 or social
data provider 105. [0042] One or more users such as user 101 and
user 103, connect to network environment 104 through user device
102. User device 102 may comprise of a web, mobile/tablet
application, augmented reality layers, email, bluetooth, in-car
audio/video/display systems, mobile tethering, mobile hotspots,
near-field communication, other outputs as known in the art, or any
transmission that offers relevant real-world content with regard to
any of the items indicated hereto. [0043] Network environment 104
consists of one or more servers, with one or more hard drives, an
amount of memory, and other hardware specifications known in the
art necessary to receive and transmit information appropriately to
user device 102. Network environment 104 may be accessed via the
internet, a wireless or wired network such as a mobile device
carrier network or any other network that can be used for
communication between a server and client. [0044] Social data
provider 105 may comprise any user or entity that provides social
relationship or interest data, communication services, dating
services, company intranets, and so forth. Event data provider 106
may comprise any user or entity that provides structured or
unstructured data about events, activities, experiences, people,
deals, specials, desire matchups, and/or advertisements for
recommendation engine 107 to recommend. This may occur via
Application Programming Interfaces (API's), data scraping, user
additions, or inferred by the network environment using methods
such as locations/interests of users, and so forth. [0045]
Referring now to FIG. 2, a block diagram of an exemplary
recommendation engine 107, such as the recommendation engine 107
shown in FIG. 1. A user-interests database 202 is provided for
storing interest and other user data associated with each of the
users, such as the user 101 associated with user device 102. When a
user 101 subscribes to services provided by the network environment
104, a user profile may be generated for user 101. For example,
user 101 may specify interests explicitly, and through engagement
with the service, implicitly specify other interests. For example,
the user 101 may select interests `electronic music`, `yoga`, and
`sushi`. Furthermore by frequently viewing and attending
events/experiences associated with `yoga` and `electronic music`,
the user also provides meaningful data about true interests, and
provides meaningful data about `sushi` a potentially less relevant
interest. For each input to recommendation engine 107, thresholds
may exist to define and refine recommendations 209. For example, if
event data 201 contained an event `Sushi and Yoga Night`, this may
trigger a threshold for user to receive that event. However, one or
more user or system-defined criterion may limit or further refine
the threshold. For example, a certain number of ghost users may be
necessary to show the event, or the event may have to be within a
certain distance of the user's location. [0046] According to some
embodiments, the user profile is created, outside of the network
environment 104 and provided to or accessed by the network
environment 104. Alternatively, the user interests 202 may be
located remotely and accessed by the network environment 104.
[0047] Optionally, if user 103 engages network environment 104 in
such a manner where social relationship data is available, ghost
users 103a may be created. These ghost users 103a may contain
information about user 103's relationships with these ghost users
and their interests, known as ghosts' interests 204, so that
recommendation engine 107 may recommend relevant ghosts using
social thresholds, along with events/activities/experiences
recommended by recommendation engine 107 through network
environment 104. [0048] Significance analysis 206 may be performed
to determine which people or friends are most relevant to the
potential recommendation, and to rank this relevancy in order to
show most relevant people/friends before others. This analysis may
consider factors such as user location, significance to each of the
users of the overlapping interests for the particular
recommendation, or any other thresholds regarding the inputs
considered by the recommendation engine 107 to arrive at 209
recommendation. [0049] Engagement data with the service--clicks,
views, etc may also factor into recommendations 209 provided by
recommendation engine 107 to network environment 104. [0050]
Categorized event data 201 can be considered by recommendation
engine 107, narrowing down for the most relevant events,
activities, experiences, people, deals, specials, desire matchups,
and/or advertisements using relevancy popularity analysis 203. This
relevancy analysis may include known or guessed information of the
attendance/engagement numbers of a particular recommendation, the
distance of the potential recommendation to the user's current,
estimated, and/or anticipated location, the significance to the
user of the interests belonging to the potential recommendation, or
any other thresholds regarding the inputs considered by the
recommendation engine 107 to arrive at 209 recommendation. [0051]
Relevant context-aware specials/advertisements 205 may be shown--to
users nearby, potentially interested in the entity hosting a
particular special/advertisement, and optionally including social
recommendations with a particular special/advertisement. [0052]
Finally, previous attendance data 207 may be used to further
determine user-interest relevancy for whether a specific
recommendation should appear through network environment 104 to
user 101/103 via user device 102. [0053] The output of the
recommendations engine 209 is then relayed to the network
environment 104, where it exists for any amount of time before
being sent to user 101 and/or 103 via user device 102.
Recommendations 209 offered may be one or more, are not final, and
can be further modified/refined at any time as a result of
additional input received by the network environment 104 and thus
recommendation engine 107. Recommendations 209 may include events,
activities, experiences, people, deals, specials, desire matchups,
and/or advertisements with/without corresponding people/friend
recommendations, nearby the users current and/or future
known/anticipated location. [0054] FIG. 3 is an exemplary screen
shot of one embodiment of a `feed` 302 of recommendations 209 from
recommendation engine 107, displaying through the network
environment 104 on a user device 102 to the viewing user 103. The
user device 102 in this instance is a mobile phone. The exemplary
screen shot 302 represents a display page showing four
recommendations 209 to user 103, and including both ghost users
103a and network environment users 101. However, more or fewer than
four items with or without ghost/network environment users may be
displayed. The total number displayed may be limited by the
significance analysis 206. [0055] Interest indication 301 is one
embodiment of a method whereby user 101 and/or 103 may provide
user-interests 202 explicitly to network environment 104. [0056]
Various, types of recommendations--events, activities, experiences,
people, deals, specials, desire matchups, advertisements and/or
other content may be displayed in the feed 302. In the exemplary
screen shot shown in feed 302, nearby events for the weekend are
displayed. [0057] Interests relevant for the events displayed may
be specified explicitly along subcategories of those shown in
interests 301. For example `dogs` and `yoga` may be explicitly
indicated by the user, which results in 2 of the events shown in
feed 302--a Pup Crawl' and `Free Sunset Yoga in the Park`. [0058]
In this exemplary screen shot 302, relevant interests to the user
are filtered and shown 305 as part of the recommendation.
Additionally, optional relevant person/friend recommendations may
be displayed 306 if the users data allows for such recommendations
to be made by recommendation engine 107. Finally, specials/relevant
advertisements 304 are shown in this exemplary screen shot in-line
with the event recommendation. They may also appear as separate
notifications, separate recommendations, or using any of the
methods of data transmission known in the art or described hereto.
[0059] Exemplary screen shot 303 depicts additional details about
the specific event recommendation, including the names of relevant
friend recommendations 307, non-truncated special/advertisement
details 308, and full list of interests for the event 309. These
details and the others depicted in the exemplary screen shot 303
represent one embodiment, however many modifications and variations
are also in the spirit of the invention as defined hereto. [0060]
Referring now to FIG. 4, a flow diagram of an exemplary process for
the importing/classification of data, display of relevant
recommendations, and an optional feedback loop to improve future
recommendations. [0061] Multiple layers: events, users, and ghosts,
can be processed in this exemplary process. [0062] At step 406,
event data can be imported from event data provider 106. This may
occur via Application Programming Interfaces (API's), data
scraping, user additions, or inferred by the network environment
using methods such as locations/interests of users. [0063] In this
exemplary diagram, a user 103 explicitly provides user-interest
info 202 using a mechanism similar to 301. Additional data may be
optionally gathered, if permitted, from external social data
provider(s) 105. Similarly, data from social data provider 105
generates ghost profiles 103a as relevant to user 103. [0064]
Matching/classification of events to real-world interests on the
network environment 403 can occur via natural language processing,
text classification, and/or be manually assisted. One skilled in
the art will be aware of many software implementations to reach
this goal.
[0065] Refinement and exclusion of events 404 also takes: place,
using thresholds--which can be user or system defined, as described
herein, to ensure the most meaningful events can be displayed to
users 101 and 103. Factors considered by the exclusion analysis may
include event size, guessed popularity, distance, and what the user
101 or 103 has attended or engaged with previously. [0066] At
optional step 407, network environment 104 creates relationships
between user-interests on the network environment 104, and the
ghost's guessed or explicitly stated interests via natural language
processing, text classification, and or manual assistance. Once
these associations are made, recommendation 107 can adequately map
single outputs from event data provider 106, and their associated
user-interests 202, with relevant ghosts who may be interested in
also attending a particular event; [0067] For example user 103
provides an interest in `sushi` explicitly to network environment
104. When she engages with the service, ghost user 103a is also
created, and it is explicitly determined this user has an interest
in `Japanese food`. When the event `Sushi and Wine on the Beach` is
upcoming, user 103 will see a recommendation 209 that may include
ghost friend 103a based on the association of Japanese food with
sushi, which occurred in process 407. [0068] Other items depicted
in FIG. 4 prior to step 401 can be handled by recommendation engine
107. Once recommendations 209 are synthesized, they can be
displayed for the user or otherwise sent to the user according to
methods of data transmission known in the art. [0069] Optionally,
other implicit or explicit feedback is gathered (click, attendance
data, changing/updating user-interests, or other data points) to
refine and improve recommendations 209 in the future. [0070]
Referring now to FIG. 5, similar to FIG. 3 herein, and as noted as
FIG. 1 in U.S. Provisional Patent Application Ser. No. 61/794,283
filed on Mar. 15, 2013, illustrates one embodiment of a `feed` 502
of recommendations 209 from recommendation engine 107, displaying
through the network environment 104 on a user'device 102 to the
viewing user 103. The user device 102 in this instance is also a
mobile phone. The total number displayed may be limited by the
significance analysis 206. For readability, the same descriptions
and numbering as FIG. 3 are used. [0071] This exemplary embodiment
shows an optional expanded `details` view 503 within the feed 502.
[0072] Included are the elements from FIG. 3 in a different visual
arrangement: specials/relevant advertisements 508, relevant
person/friend recommendations 507, and event relevancy filtering
509. [0073] FIG. 6, as noted as FIG. 2 in U.S. Provisional Patent
Application Ser. No. 61/794,283 filed on Mar. 15, 2013, illustrates
another embodiment of an expanded `details` view 603 of one
recommendation 209. For readability, the same descriptions and
numbering as FIG. 3 are used. [0074] Included are the elements from
FIG. 3 in a different visual arrangement: specials/relevant
advertisements 608, relevant person/friend recommendations 607, and
event relevancy filtering 609. [0075] Interest slider 610
highlights one embodiment of user importance ratings, an ingredient
of event relevancy/popularity 203 as described herein. [0076]
Referring now to FIG. 7, as noted as FIG. 6 in U.S. Provisional
Patent Application Ser. No. 61/794,283 filed on Mar. 15, 2013,
illustrates an early mockup screen shot of an alternative
embodiment of a view allowing users to specify interest preferences
202. [0077] Embodiment depicts a map overlay with recommendations
209 displayed on the map, as the user indicates interest
preferences 202. [0078] Embodiment depicts interest slider feature
610 to specify importance of a particular interest. [0079] FIG. 8,
as noted as FIG. 7 in U.S. Provisional Patent Application Ser. No.
61/794,283 filed on Mar. 15, 2013, illustrates an early mockup
screen shot of an alternative embodiment of a `feed` 302 of
recommendations 209 from recommendation engine 107, displaying
through the network environment 104 on a user device 102 to the
viewing user 103. The user device 102 in this instance is also a
mobile phone. The total number displayed may be' limited by the
significance analysis 206. [0080] Included are each of the elements
from FIG. 3 in a different visual arrangement. [0081] FIG. 9, as
noted as FIG. 8 in U.S. Provisional Patent Application Ser. No.
61/794,283 filed on Mar. 15, 2013, illustrates an early mockup
screen shot of a settings menu intended to be inputs for user
interest relevancy 202 and engagement data 208 for recommendation
engine 107 to arrive at recommendations 209. [0082] Invention uses
an improvement in the quality of data and the manner in which it'is
contextualized and joined, to offer a new, useful, and non-obvious
improvement over the currently available user & event matching,
recommendations, and/or real-life interactions fostered in current
social networking solutions.
[0083] The foregoing description of the embodiments of the
invention has been presented for the purpose of illustration; it is
not intended to be exhaustive or to limit the invention to the
precise forms disclosed. Persons skilled in the relevant art can
appreciate that many modifications and variations are possible in
light of the above disclosure.
[0084] Some portions of this description describe the embodiments
of the invention in terms of algorithms and symbolic
representations of operations on information. These algorithmic
descriptions and representations are commonly used by those skilled
in the data processing arts to convey the substance of their work
effectively to others skilled in the art: These operations, while
described functionally, computationally, or logically, are
understood to be implemented by computer programs or equivalent
electrical circuits, microcode, or the like. Furthermore, it has
also proven convenient at times, to refer to these arrangements of
operations as modules, without loss of generality. The described
operations and their associated modules may be embodied in
software, firmware, hardware, or any combinations thereof.
[0085] Any of the steps, operations, or processes described herein
may be performed or implemented with one or more hardware or
software modules, alone or in combination with other devices. In
one embodiment, a software module is implemented with a computer
program product comprising a computer-readable medium containing
computer program code, which can be executed by a computer
processor for performing any or all of the steps, operations, or
processes described.
[0086] Embodiments of the invention may also relate to an apparatus
for performing the operations herein. This apparatus may be
specially constructed for the required purposes, and/or it may
comprise a general-purpose computing device selectively activated
or reconfigured by a computer program stored in the computer. Such
a computer program may be stored in a tangible computer readable
storage medium or any type of media suitable for storing electronic
instructions, and coupled to a computer system bus. Furthermore,
any computing systems referred to in the specification may include
a single processor or may be architectures employing multiple
processor designs for increased computing capability.
[0087] Embodiments of the invention may also relate to a computer
data signal embodied in a carrier wave, where the computer data
signal includes any embodiment of a computer program product or
other data combination described herein. The computer data signal
is a product that is presented in a tangible medium or carrier wave
and modulated or otherwise encoded in the carrier wave, which is
tangible, and transmitted according to any suitable transmission
method.
[0088] Finally, the language used in the specification has been
principally selected for readability and instructional purposes,
and it may not have been selected to delineate or circumscribe the
inventive subject matter. It is therefore intended that the scope
of the invention be limited not by this detailed description; but
rather by any claims that issue on an application based hereon.
Accordingly, the disclosure of the embodiments of the invention is
intended to be illustrative, but not limiting, of the scope of the
invention, which is set forth in the following claims.
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