Patent Publication Number: US-2018032621-A1

Title: Location and social aware, multi-dimensional, dynamic, positive recommendations

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present invention claims priority to U.S. Provisional Patent Application No. 62/369,595, filed Aug. 1, 2016, which is hereby incorporated in its entirety including all tables, figures, and claims. 
    
    
     BACKGROUND 
     Field of the Invention 
     The present invention is intended to utilize location and socially aware, multi-dimensional, dynamic, recommendations in a mobile app on a social media network platform. 
     Description of the Related Art 
     Five figures have been provided that relate to the items found in Claims. 
     SUMMARY 
     The present invention comprises a method and system in which a group of friends can share, get and ask for recommendations with and from each other in an organized, trusting and information rich way. 
     In one embodiment of the present invention, the system allows people to come together for one purpose: To exchange recommendations that matter to them, based on location and interest, and with a multitude of dimensions. 
     In a second embodiment of the present invention, the system allows a person to give a recommendation to their peers covering different aspects of the recommendation, such as how much they like it, why they like it, giving their own experience of the recommendation and providing a multitude of dimensions related to this recommendation with the intention to help other users make smart decisions. 
     In a third embodiment of the present invention, the system allows the user to search and browse through recommendations provided by their friends or other people. 
     In a fourth embodiment of the present invention, the system dynamically generates recommendation to the user based on their interests and recommendations they have made or liked previously. 
     In a fifth embodiment of the present invention, the system allows a person to ask for recommendations from their friends and get dynamically generated recommendations based on similarities with what they are asking for. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1 : Illustrates people, using the mobile app and website of the current invention, connecting through their devices (Mobile or desktop) to the cloud of this current invention, to share, get and ask for multi-dimensional and dynamic recommendations. 
         FIG. 2 : Illustrates a person (User of the current invention) sharing a multi-dimensional and dynamic recommendation 
         FIG. 3 : Illustrates a person (User of the current invention) getting a multi-dimensional and dynamic recommendation 
         FIG. 4 : Illustrates a person (User of the current invention) asking for a multi-dimensional and dynamic recommendation 
         FIG. 5 : Illustrates the multi dimensions of a recommendation of this current invention. 
     
    
    
     DETAILED DESCRIPTION 
     Embodiments of the present invention comprise a method and a system that allows people to share, get and ask for multi-dimensional and dynamic recommendations. 
     Referring to drawing in  FIG. 1 , the network cloud  100  of the current invention, which is comprised of backend servers and database servers, connects people together, through their mobile devices or desktop computers, in order to share, get and ask for recommendations. User  110  uses his mobile phone  120  to connect to the cloud  100  and find other users and recommendations. User  110  starts by adding other users that they may know and start performing one of the following actions: Sharing, getting or asking for recommendations. 
     In  FIG. 2 , the flow diagram shows a person  200  entering the system. First, they choose an action  201 . If they decide to share a recommendation, they search  202  if it&#39;s in the system. If the recommendation exists  203 , they add it  204 , if it doesn&#39;t exist, they create it  210 . In both cases, the user starts adding the multi-dimensional factors  204  of the recommendation, then share  200  the recommendation. The multi-dimensional factors will be explained further in this detailed description. 
     The user  200  also can decide  207  to get a recommendation  209  (Explained in the  FIG. 3 ), or ask for a recommendation  208  (Explained in  FIG. 4 ). 
       FIG. 3 , is also a flow chart that shows that the user  300  has chosen to get a recommendation. They first search for the recommendation they are looking for  301 . If the recommendation exists  302 , the user  300  adds it  303 , then, if the user decides  304  to share it, they add the multi-dimensional parameters  305  and share it  306 . Otherwise, the user to not share it, it will be bookmarked and end of process  307 . However, if the user search for the recommendation  301  and didn&#39;t find it, the process ends  307  or they can start a new search  301 . 
       FIG. 4  is also a flow chart that shows the user  400  entering the system to ask for a recommendation. First, they select the type of recommendation  401  they need. The type could be geo located such as a place or business, or not, such as products, media content such as movies, TV shows, music videos or videos, or it could be books, articles, websites apps and so on. Then, the user enters a question  402  on what they are exactly looking for and also select an audience to whom the question is directed to  403 . The audience could be friends, or public. Upon submission, the system proposes dynamic recommendations  404  that it thinks the criteria matches with what they are asking for. The user has a choice to accept  405  the recommendation and add it  406 . In this case, the process  407  ends. Or, they don&#39;t accept the dynamically generated recommendation and in this case, the user proceeds with posting the question  408  then the process  407  ends. 
       FIG. 5  illustrates the multi-dimensional aspect of a recommendation  500  of the present invention. When a user shares a recommendation, they bring more life to it by adding many parameters that help other people decide whether this recommendation is good for them or not. Therefore, the user starts by giving a type  501  to this recommendation. This could be place, product, movie, music or anything else. Then the user gives a name  502  to help easily identify this recommendation, then a description  503  helps give more information in a textual form for this recommendation. The user then proceeds by selecting how much they like this recommendation using a proprietary scale  504  or points called Yups. One Yup means “Good” and weighs 1, two Yups mean “Great” and weighs 2 and three Yups mean “Awesome” and weighs 3. The recommendation points or Yups breakdown  505  gives a visual indication on how much a recommendation is Great or Awesome or both, or Good. A recommendation that weighs 19 Yups could be broken down to 2 people thinking it&#39;s Awesome (+6 Yups), 5 people thinking it&#39;s Great (+10 Yups) and 3 people thinking it&#39;s Good (+3 Yups), therefore the number 19=(2×3)+(5×2)+(3×1). 
     The user can then select a set of pre-defined reasons  506  why they like this recommendation. These reasons are different based on the type  501  they have selected. Reasons for a business could be “Clean”, “Helpful”, “Professional” and so on. Reasons for a movie could be “Suspenseful”, “Dramatic”, and so on. This will give other people a quick idea about a recommendation and also, serve as links to other recommendations that have the same commonalities. The user can also attach a picture  507  describing their own experience of this recommendation in a visual manner. Another parameter that helps other people would be links  508  that the user can add that facilitate either purchasing the product or viewing a video about it or listening to a sample music of it etc. . . . The user can also add tags  509  that help link other recommendations related by a certain aspect. For example, in the case of an event, for example the Olympics 2016, a user can add #olympic2016, therefore any other related recommendations can be linked together. The users could share restaurants, articles, music videos all related to the Olympics. 
     A recommendation can also have a location  510  or a map attached to it. When applicable, other users can easily locate the venue with just a click. The user, can add more contact info  511  if applicable, such as a phone number, an email or so. People can then just call or email to get more info about this recommendation. 
     Finally, a recommendation will have two sets of people  512  attached to it. In network and out of network. This will help other prospective users make a smart decision based on who else in their network has recommended the same recommendation and what they thought of it based on their multi-dimensional parameters. This help get non-biased positive opinion. Or, should the user choose, they can see why everyone else likes this recommendation. 
     In one embodiment, a perfect scenario on how the present invention works, would be, a user searches for and adds a restaurant that they would like to recommend to their community. The user then writes a recommendation summary of this restaurant for other community users to view (one of the multi-dimensional factors). The user then adds how much they like this restaurant by adding the Yups proprietary scale. Let&#39;s say in this case they choose “Awesome”, which is three Yups (or points) and is based on their satisfaction level (another multi-dimensional factor). The user then adds the reasons that are predefined in the system to quickly tell their impression to other users. In this case, they could probably select “Clean”, “Fast” and “Reasonable” (another multi-dimensional factor). They can then add photos of their experience (another multi-dimensional factor). They could add an address for this restaurant which helps view the map on where it is located (another multi-dimensional factor). The user could add a phone number (another multi-dimensional factor) for others to quickly call this restaurant. Once some or all these multi-dimensional factors are added, a social network page of this restaurant is added to and is associated with this user. It is displayed in the feed and profile page for other connected users to view. Other users can then either click on the restaurant in the feed or can view the profile of the person that created this restaurant review and add a multi-dimensional recommendation themselves. 
     If others users feel the same or at least like this restaurant, the person who recommended it see their reputation increase by the same number of Yups this restaurant received. For example, a user thinks that this restaurant is great, this restaurant will receive +2 Yups (1: Good, 2: Great, 3: Awesome) and the user who recommended it gets also +2 Yups. That way, both the restaurant and the user who recommended it view their reputation increase. 
     In another embodiment, a user searches for and adds a pair of running shoes they would like to recommend to their community. The user then writes a recommendation summary of these running shoes for other community users to view (one multi-dimensional factor). The user then tells how much they like the running shoes based on their satisfaction level (another multi-dimensional factor). The user can add set of pre-defined reasons, such as “Light”, “Sturdy” (another multi-dimensional factor), The user then adds a photo of the running shoes (another multi-dimensional factor). The user can also supply a link from where they bought them, to help other users purchase them (another multi-dimensional factor). In the end, a social network page of these running shoes is added to and is associated with this user, and is displayed in the feed and profile page for other connected users to view. 
     Another embodiment of the present invention, is a user needing a recommendation. They enter the system to “Ask” for a recommendation. They first select the type of recommendation they need. This could be a place, business, product, recipe or any other type that can be recommended. Then they add a description of what exactly they are looking for and proceed. The system then will look for any similarities with the users request and proposes dynamically a set of recommendations. This is another aspect of the present invention. If the user feels one of the recommendations is what they are looking for. They can select it, view it and add. In this case, they won&#39;t have to post their question for their friends to answer. If none of the proposed dynamic recommendations fits the user&#39;s requirement, they can then proceed by posting their question. All other users in network will receive a notification, based on their preferences: In app notification, push notification and email notification. If any of the users would like to answer, they can directly push one of their recommendations to the asking user, or they can just comment on the question and give some ideas.