DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Application
This is a Non-Final Action in response to the claims/remarks submitted on 01/14/2026.
Claims 6, and 10 are amended.
Claims 5, 7-9 and 11-20 are canceled.
Claims 21-34 are new.
Claims 1-4, 6, 10 and 21-34 are pending and examined herein.
Election/Restrictions
Applicant's election with traverse of Group I (Claims 1-3) in the reply filed on 01/14/2026 is acknowledged.
The traversal is on the ground(s) that:
“at least claims 1-14 share the technical feature of an event recommendation based on a user profile as a percent match and additional search burden on the Office is minimal.” Although Group I (claims 1-3), generically require to make an event recommendation to a user based on user data and data related to an event focusing on structural elements and does not require the specifics of Group 2 (claims 4-14), and after considering applicant’s remarks and the application overall, it has been determined by the examiner to withdraw the restriction requirement.
Claim Objections
Claim 6 is objected to because of the following informalities: recitation of a typographical error in the following limitation “wherein the user can invites friends”, wherein it should read “wherein the user can invite friends”. Appropriate correction is required.
Claim 10 is objected to because of the following informalities: recitation of a typographical error in the following limitation “wherein ate least one of the notification”, wherein it should read “wherein at least one of the notification”. Additionally, the claim has two periods at the end of the claim. Appropriate correction is required.
Claim 33 is objected to because of the following informalities: recitation of a typographical error in the following limitation “available venues for a event…”, wherein it should read “available venues for an event…”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 28-29 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 28 recites the limitations "the user profile data" and “the user profile” in lines 1 and 4. There is insufficient antecedent basis for these limitations in the claim. The lack of antecedent basis makes the claim unclear because the terms were not introduced properly. For examiner purposes the limitation is interpreted as reciting “further comprising, user profile data including…” and “a user profile”.
Claim 29 recites the limitation “the user profile” in line 2. There is insufficient antecedent basis for this limitation in the claim. The lack of antecedent basis makes the claim unclear because the term was not introduced properly. For examiner purposes the limitation is interpreted as reciting “a user profile”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 6, 10 and 21-34 are rejected under 35 U.S.C. 101 because the claims are directed to an abstract idea without significantly more.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that claims 1 and 4 are directed to at least one potentially eligible category of subject matter (i.e., process and machine, respectively). Thus, Step 1 of the Subject Matter Eligibility test for claims 1-4, 6, 10 and 21-34 is satisfied.
With respect to Step 2A Prong One, it is next noted that the claims recite an abstract idea that falls under the “Mental Processes” and “Certain Methods Of Organizing Human Activity” group within the enumerated groupings of abstract ideas set forth in the MPEP 2106 since the claims set forth steps that recite concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Claims 1 and 4 recites the abstract idea of providing an event recommendation to a user. This idea is described by the following claim steps:
For claim 1, the abstract idea is described by:
use data from a user and data about an event to make an event recommendation to a user based on a percent match.
This idea falls within the mental processes and certain methods of organizing human activity grouping of abstract ideas because it is directed towards an evaluation of the data received in order to develop an opinion (i.e. recommendation). The noted abstract idea is also directed to managing interactions between people such as that required during communications when providing or transmitting the recommendation to a user.
With respect to independent claim 4, the limitations reciting the abstract idea are indicated in bold below:
receiving user preferences regarding events;
developing a user profile based on the received user preferences;
comparing the user preferences against event data for an event;
calculating a percent match between the user profile and the event;
presenting the percent match to the user.
As noted, this idea falls within the mental processes and certain methods of organizing human activity grouping of abstract ideas because it is directed towards an evaluation of the data received in order to develop an opinion (i.e. recommendation based on the percent match). The noted abstract idea is also directed to managing interactions between people such as that required during communications when providing or transmitting the match to the user.
Because the above-noted limitations recite steps falling within the Mental Processes and Certain Methods Of Organizing Human Activity abstract idea groupings of the MPEP 2106, they have been determined to recite at least one abstract idea when evaluated under Step 2A Prong One of the eligibility inquiry.
Therefore, because the limitations above set forth activities falling within the Mental Processes Certain Methods Of Organizing Human Activity abstract idea groupings described in the MPEP 2106, the additional elements recited in the claims are further evaluated, individually and in combination, under Step 2A Prong Two and Step 2B below.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements that fail to integrate the abstract idea into a practical application are:
a user API server and a user interface associated with the user API server;
an admin server;
an event organizer API server and an event organizer interface associated with the event organizer API server;
a cloud server linked to each of the user API server, the admin server, and the event organizer API; and
at least one task algorithm associated with the cloud server, wherein the at least one task algorithm includes an event recommender task algorithm.
However, using a computer environment such as a APIs, servers and other recited computer elements amounts to no more than generally linking the use of the abstract idea to a particular technological environment. Providing an event recommendation to a user based on user preferences and event information can reasonably be performed by pencil and paper until limited to a computerized environment by requiring the use of APIs and servers. For example, specifying that the abstract idea of providing an event recommendation to a user based on a user preferences relates to a process that is executed in a computer environment through the recited computing elements merely limits the claims to the computer field, similar to how specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment in FairWarning v. Iatric Sys., 839 F.3d 1089, 1094-95, 120 USPQ2d 1293, 1295 (Fed. Cir. 2016) was insufficient. This concept is also similar to, buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1354, 112 USPQ2d 1093, 1095-96 (Fed. Cir. 2014) wherein it was determined that requiring the abstract idea of creating a contractual relationship guarantees performance of a transaction (a) be performed using a computer that receives and sends information over a network, or (b) be limited to guaranteeing online transactions simply attempted to limit the use of the abstract idea to computer environments. See MPEP 2106.05(h).
These additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or computer-executable instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and alternatively serve to link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(f) and 2106.05(h).
In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As noted above, the claims as a whole merely describes a method, computer system, and computer program product that generally “apply” the concepts discussed in prong 1 above. (See MPEP 2106.05 f (II)) In particular applicant has recited the computing components at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. As the court stated in TLI Communications v. LLC v. AV Automotive LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) merely invoking generic computing components or machinery that perform their functions in their ordinary capacity to facilitate the abstract idea are mere instructions to implement the abstract idea within a computing environment and does not add significantly more to the abstract idea. Accordingly, these additional computer components do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, even when viewed as a whole, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea and as a result the claim is not patent eligible.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
For the reasons identified with respect to Step 2A, prong 2, claims 1 and 4 fail to recite additional elements that amount to an inventive concept. For example, use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a commercial or legal interaction or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more (see MPEP 2106.05(g)). In addition, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (see MPEP 2106.05(h)).
Dependent claims 2-3, 6, 10 and 21-34 recite the same abstract idea as recited in the independent claims, and when evaluated under Step 2A Prong One are found to merely recite details that serve to narrow the same abstract idea recited in the independent claims accompanied by the same generic computing elements or software as those addressed above in the discussion of the independent claims, which is not sufficient to amount to a practical application or add significantly more, or other additional elements that fail to amount to a practical application or add significantly more, as noted above.
Dependent claim 2 further elaborates the abstract idea by providing an event hosting recommendation based on a likelihood of success which is a process that can be performed manually until limited by an event recommender task algorithm. Further embellishing that the invention is capable of processing information in a generic computing environment does not integrate the abstract idea into a practical application or adds significantly more to the abstract idea. Therefore the claims are also non-statutory subject matter.
Dependent claim 3 further limits the abstract idea by linking the judicial exception to a particular technological environment by introducing the limitation wherein the at least one task algorithm further comprises an event archiver schedule task algorithm and a web scraper schedule task algorithm. Further embellishing that the invention is capable of processing information in a generic computing environment does not integrate the abstract idea into a practical application or adds significantly more to the abstract idea. Therefore the claims are also non-statutory subject matter.
Dependent claim 6 further elaborates the abstract idea by providing an calculating percent matches between friend user profiles which is a process that can be performed manually until limited by a technological environment. Further embellishing that the invention is capable of processing information in a generic computing environment does not integrate the abstract idea into a practical application or adds significantly more to the abstract idea. Therefore the claims are also non-statutory subject matter.
Dependent claims 10, 21 further limits the abstract idea by embellishing the abstract idea and linking the judicial exception to a particular technological environment by introducing the limitation sending a notification to members of an event group, wherein ate least one of the notification is sent when a member purchases a ticket to the event or the notification is sent when a member arrives at the event and wherein the user interface comprises a map, and wherein the event recommendation is displayed on the map. Further embellishing that the invention is capable of transmitting and displaying information in a generic computing environment does not integrate the abstract idea into a practical application or adds significantly more to the abstract idea. Therefore the claims are also non-statutory subject matter.
Dependent claims 22-24 further limits the abstract idea by embellishing the abstract idea and linking the judicial exception to a particular field of use by introducing the limitation wherein the map depicts locations for a plurality of events, and wherein the event recommender task algorithm makes an event recommendation to the user for each event of the plurality of events; wherein the event recommendation is visually associated with each event at or proximate its location on the map; wherein the visual association includes at least one of a color according to the percent match and a symbol. Further embellishing that the invention is capable of displaying information in a generic computing environment does not integrate the abstract idea into a practical application or adds significantly more to the abstract idea. The claimed limitation merely appears to link the use of the judicial exception to a particular technological environment. Therefore the claims are also non-statutory subject matter.
Dependent claims 25-27 further limits the abstract idea by embellishing the abstract idea and links the judicial exception to a particular field of use by introducing the limitation wherein the event recommendation is presented in an event card; wherein the event card includes enticing information about the event, wherein the enticing information includes at least one of event date, event start time, event cost, and associates of the user attending the event; and wherein the event card includes a link to purchase tickets or merchandise related to the event. Further embellishing that the invention is capable of processing and displaying information in a generic computing environment does not integrate the abstract idea into a practical application or adds significantly more to the abstract idea. The claimed limitation merely appears to link the use of the judicial exception to a particular technological environment. Therefore the claims are also non-statutory subject matter.
Dependent claim 28 further limits the abstract idea by embellishing the data, introducing the limitation wherein the user profile data includes event type preferences, past events attended, associates of the user, and user geo-location, and wherein an update task algorithm of the at least one task algorithm is configured to update the user profile after the user attends the event. Further embellishing that the invention is capable of processing and displaying information in a generic computing environment does not integrate the abstract idea into a practical application or adds significantly more to the abstract idea. The claimed limitation merely appears to link the use of the judicial exception to a particular technological environment. Therefore the claims are also non-statutory subject matter.
Dependent claims 29-30 further limits the abstract idea by embellishing the data, introducing the limitation wherein the event recommender task algorithm is further configured to identify upcoming events, compare the user profile against event data for the upcoming events, and send notifications to the user with recommendations for certain upcoming events, wherein the certain upcoming events are identified based on calculating a percent match between the user profile and the event and choosing the certain upcoming events for recommendation based on events that exceed a threshold percent match; wherein the at least one task algorithm further comprises an event organizer recommendation task algorithm, wherein the event organizer task algorithm is configured to; compare proposed event details against existing event details of scheduled events; calculate a percent likelihood of success of the proposed event; and present the percent likelihood of success to an event organizer through the event organizer interface. Further embellishing that the invention is capable of processing and displaying information in a generic computing environment does not integrate the abstract idea into a practical application or adds significantly more to the abstract idea. The claimed limitation merely appears to link the use of the judicial exception to a particular technological environment. Therefore the claims are also non-statutory subject matter.
Dependent claim 31-32 further limits the abstract idea by embellishing the data, introducing the limitation wherein the event organizer task algorithm is configured to evaluate alternative event details for the proposed event, calculate a percent likelihood of success based on the alternative event details, and presenting at least one alternative recommendation for the event to the event organizer; wherein the at least one alternative recommendation is made based on exceeding a threshold percent likelihood of success. Further embellishing that the invention is capable of processing information in a generic computing environment does not integrate the abstract idea into a practical application or adds significantly more to the abstract idea. The claimed limitation merely appears to link the use of the judicial exception to a particular technological environment. Therefore the claims are also non-statutory subject matter.
Dependent claim 33-34 further limits the abstract idea by embellishing the data, introducing the limitation wherein the event organizer interface includes a selection of available venues for a event based on at least one of space, date, and price; wherein the user can select friends through the user interface, wherein the event recommender task algorithm is further configured to calculate percent matches between friend user profiles and the event and present the percent matches to the user, wherein the user can invite friends to the event based on the percent match of the friend user profile and the event, and wherein friends who accept an invitation from the user are grouped into an event group. Further embellishing that the invention is capable of processing and information in a generic computing environment does not integrate the abstract idea into a practical application or adds significantly more to the abstract idea. The claimed limitation merely appears to link the use of the judicial exception to a particular technological environment. Therefore the claims are also non-statutory subject matter.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and the collective functions merely provide high level of generality computer implementation. Therefore, whether taken individually or as an order combination, the claims are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
For more information see MPEP 2106.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 3-4, 25-29 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Howard (US Patent Publication 2018/0013861) .
Regarding Claim 1, Howard discloses a system for event organizing and attending (abstract and [0004] An events ecosystem in an online social network may be used to organize and promote real-world events such as concerts, theater performances, sports games, or other social events.), the system comprising:
a user API server and a user interface associated with the user API server (See Figure 1 and [0035] In particular embodiments, social-networking system 160 may be capable of linking a variety of entities. As an example and not by way of limitation, social-networking system 160 may enable users to interact with each other as well as receive content from third-party systems 170 or other entities, or to allow users to interact with these entities through an application programming interfaces (API));
an admin server (Fig. 1 and [032] In particular embodiments, social-networking system 160 may include one or more servers 162. );
an event organizer API server and an event organizer interface associated with the event organizer API server ([0035] In particular embodiments, social-networking system 160 may be capable of linking a variety of entities. As an example and not by way of limitation, social-networking system 160 may enable users to interact with each other as well as receive content from third-party systems 170 or other entities, or to allow users to interact with these entities through an application programming interfaces (API). [0063] An events ecosystem in an online social network may be used to organize and promote real-world events such as concerts, theater performances, sports games, or other social events. [0079] FIGS. 4A-4B illustrate example events discovery user interfaces 402. A control element 403, which may select filter criteria for restricting the displayed events… “Hosting” to view events the user is hosting” These paragraphs in combination disclose the system comprising API servers and interfaces to allow a user to organize and host events.);
a cloud server linked to each of the user API server, the admin server, and the event organizer API (See Fig. 1 “network 110” and related paragraphs including [0027] FIG. 1 illustrates an example network environment 100 associated with a social-networking system. Network environment 100 includes a client system 130, a social-networking system 160, and a third-party system 170 connected to each other by a network 110. Although FIG. 1 illustrates a particular arrangement of client system 130, social-networking system 160, third-party system 170, and network 110, this disclosure contemplates any suitable arrangement of client system 130, social-networking system 160, third-party system 170, and network 110. As an example and not by way of limitation, two or more of client system 130, social-networking system 160, and third-party system 170 may be connected to each other directly, bypassing network 110. As another example, two or more of client system 130, social-networking system 160, and third-party system 170 may be physically or logically co-located with each other in whole or in part. Moreover, although FIG. 1 illustrates a particular number of client systems 130, social-networking systems 160, third-party systems 170, and networks 110, this disclosure contemplates any suitable number of client systems 130, social-networking systems 160, third-party systems 170, and networks 110. As an example and not by way of limitation, network environment 100 may include multiple client system 130, social-networking systems 160, third-party systems 170, and networks 110. );
and at least one task algorithm associated with the cloud server, wherein the at least one task algorithm includes an event recommender task algorithm, wherein the event recommender task algorithm uses data from a user and data about an event to make an event recommendation to a user based on a percent match (See Fig.8 and [099] disclosing an example method for presenting recommendations to a user based on user preferences and event information such as geographical information or categories of the events. [0099] FIG. 8 illustrates an example method 800 for presenting event categories and events in an events discovery user interface. The method may begin at step 810, where the social-networking system 160 may identify one or more event categories of interest to a user of a social-networking system, wherein each category comprises one or more social-networking system events.).
Regarding Claim 3, Howard discloses:
wherein the at least one task algorithm further comprises an event archiver schedule task algorithm ([0065] In particular embodiments, one or more servers 162 may be events discovery servers that implement the events discovery service 180. The events discovery service 180 may perform tasks as appropriate to respond to the requests, such as identifying, in a database such as the data store 164, events 182 that may interest the user 190) and a web scraper schedule task algorithm (032] As an example and not by way of limitation, client system 130 may access social-networking system 160 using a web browser 132. [0036] In particular embodiments, a third-party system 170 may include one or more web services. [039] A web server may be used for linking social-networking system 160 to one or more client systems 130 or one or more third-party system 170 via network 110.).
Regarding Claim 4, Howard discloses a method for making recommendations to a user, wherein the user is an event goer (abstract):
receiving user preferences regarding events ([041] Users and user nodes 202 described herein may, where appropriate, refer to registered users and user nodes 202 associated with registered users. In addition or as an alternative, users and user nodes 202 described herein may, where appropriate, refer to users that have not registered with social-networking system 160. In particular embodiments, a user node 202 may be associated with information provided by a user or information gathered by various systems, including social-networking system 160. As an example and not by way of limitation, a user may provide his or her name, profile picture, contact information, birth date, sex, marital status, family status, employment, education background, preferences, interests, or other demographic information. In particular embodiments, a user node 202 may be associated with one or more data objects corresponding to information associated with a user. See also paragraph [039].);
developing a user profile based on the received user preferences ([0032] In particular embodiments, social-networking system 160 may be a network-addressable computing system that can host an online social network. Social-networking system 160 may generate, store, receive, and send social-networking data, such as, for example, user-profile data. See also paragraph [039] “A user profile may include, for example, biographic information, demographic information, behavioral information, social information, or other types of descriptive information, such as work experience, educational history, hobbies or preferences, interests, affinities, or location. Interest information may include interests related to one or more categories. Categories may be general or specific. As an example and not by way of limitation, if a user “like” an article about a brand of shoes the category may be the brand, or the general category of “shoes” or “clothing.” A connection store may be used for storing connection information about users.” See also [041]);
comparing the user preferences against event data for an event (FIGS. 9A-9F illustrate examples of connections between users and events that may be identified in social graphs and used to determine scores for events for use in for ranking and filtering the events. Further see [0121-0126]. Paragraphs [0135-0137] provide further explanation regarding the comparison between the user preferences and event data for the events.);
calculating a percent match between the user profile and the event ([0135] FIG. 11 illustrates example event signal data and corresponding scores based on the example social graph 1000 of FIG. 10. In particular embodiments, the event ranking method of FIG. 13 determines an event score for each event that satisfies a query condition.);
presenting the percent match to the user (Figure 3 and paragraph [066] discloses the events discovery user interface wherein the user is presented with the events that match user preferences including the score of each event. “The category content 333 may be based on at least one of the events 308, 310, 312 in the corresponding event category 302. For example, the category content 333 may be based on a highest-ranking event 308 in the event category 302, and may be an image based on the highest-ranking event 308, such as an Image A′ based on the Image A.” [0143] At step 1330, the social-networking system 160 may present one or more of the identified events to the target user in an order based on the event score of each identified event.).
Regarding Claim 25, Howard discloses
wherein the event recommendation is presented in an event card (See Figures 5A-5B [0070] In particular embodiments, if the category user interface element 332 is selected by the user 190, then the first event 308 in the ranked list of events 308, 310, 312 that is displayed in response to the user selection is the top-ranking event 308. [085] In particular embodiments, when a category UI element is selected by the user, the events discovery user interface 134 may present one or more event UI elements that represent events in the selected category as shown in FIGS. 5A and 5B.).
Regarding Claim 26, Howard discloses
wherein the event card includes enticing information about the event, wherein the enticing information includes at least one of event date, event start time, event cost, and associates of the user attending the event (See Figures 5A-5B [0070] In particular embodiments, if the category user interface element 332 is selected by the user 190, then the first event 308 in the ranked list of events 308, 310, 312 that is displayed in response to the user selection is the top-ranking event 308. [085] In particular embodiments, when a category UI element is selected by the user, the events discovery user interface 134 may present one or more event UI elements that represent events in the selected category as shown in FIGS. 5A and 5B.).
Regarding Claim 27, Howard disclose:
wherein the event card includes a link to purchase tickets or merchandise related to the event ([0050] A user may interact with an advertisement in any suitable manner. The user may click or otherwise select the advertisement. By selecting the advertisement, the user may be directed to (or a browser or other application being used by the user) a page associated with the advertisement. At the page associated with the advertisement, the user may take additional actions, such as purchasing a product or service associated with the advertisement, receiving information associated with the advertisement, or subscribing to a newsletter associated with the advertisement. An advertisement with audio or video may be played by selecting a component of the advertisement (like a “play button”). Alternatively, by selecting the advertisement, social-networking system 160 may execute or modify a particular action of the user.).
Regarding Claim 28, Howard discloses:
wherein the user profile data includes event type preferences, past events attended, associates of the user, and user geo-location ([039] A user profile may include, for example, biographic information, demographic information, behavioral information, social information, or other types of descriptive information, such as work experience, educational history, hobbies or preferences, interests, affinities, or location. Interest information may include interests related to one or more categories. [041] In particular embodiments, a user node 202 may be associated with information provided by a user or information gathered by various systems, including social-networking system 160. As an example and not by way of limitation, a user may provide his or her name, profile picture, contact information, birth date, sex, marital status, family status, employment, education background, preferences, interests, or other demographic information. In particular embodiments, a user node 202 may be associated with one or more data objects corresponding to information associated with a user.), and wherein an update task algorithm of the at least one task algorithm is configured to update the user profile after the user attends the event ([079] [0079] FIGS. 4A-4B illustrate example events discovery user interfaces 402. A control element 403, which may select filter criteria for restricting the displayed events, displays the state “Upcoming” to indicate that upcoming events are being displayed in the user interface 402… and “Past” to view events that occurred in the past. [0124] . In this graph, the user node “A” may be associated with the event node “E2” by a “goes to” link 206 that indicates one or more of past, present or future attendance at the event by the user “A”).
Regarding Claim 29, Howard discloses
wherein the event recommender task algorithm is further configured to identify upcoming events, compare the user profile against event data for the upcoming events, and send notifications to the user with recommendations for certain upcoming events, wherein the certain upcoming events are identified based on calculating a percent match between the user profile and the event and choosing the certain upcoming events for recommendation based on events that exceed a threshold percent match ([004] For example, a relevance score may be determined for each event based on characteristics of the user and of the event, and a threshold number of the events having the highest scores may be sorted by score and displayed to the user. [0012] FIG. 4A illustrates example upcoming events, categories of nearby events occurring on particular dates, and events that are popular with friends in an events discovery user interface. [0063] An events ecosystem in an online social network may be used to organize and promote real-world events such as concerts, theater performances, sports games, or other social events. The real-world events may be represented as online events in the online social network, which may be presented to users in a user interface as, for example, an event name and associated details such as date and location, and an optional associated graphical image. Users may interact with the online events, e.g., to register to attend, view a list of friends who are registered to attend, manage, or otherwise view or modify information associated with the online events. Because of the large number of such online events, it may be time-consuming or otherwise difficult for users to discover online events of interest. In particular embodiments, to assist users in finding events of interest, an events discovery system may identify and present categories of online events, e.g., music, food, sports, and so on. A user may select a particular category, e.g., music, and the events discovery system may present events from the selected category. The events may be presented in an order based on their relevance to the user. For example, a relevance score may be determined for each event based on characteristics of the user and of the event, and a threshold number of the events having the highest scores may be sorted by score and displayed to the user. ).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 2, 30-33 is/are rejected under 35 U.S.C. 103 as being unpatentable over Howard (US Patent Publication 2018/0013861) in view of Bhatia (US Patent Publication 2019/0180297).
Regarding Claim 2, Howard discloses:
hosting and organizing events ([0004] An events ecosystem in an online social network may be used to organize and promote real-world events such as concerts, theater performances, sports games, or other social events.)
Howard does not explicitly disclose:
wherein the event recommender task algorithm uses event data from an event organizer and regional data to make event hosting recommendations based on a likelihood of success.
However Bhatia which is similarly directed to organizing events and provides further details such as determine relationships that are indicative of interest in a live event using one or more types of data, including live event data, ticket data, user data, and/or affinities of individual users further teaches:
wherein the event recommender task algorithm uses event data from an event organizer and regional data to make event hosting recommendations based on a likelihood of success (See Figs 5-6 disclosing providing the user event hosting recommendations based on likelihood of success of the events. [0080] FIG. 5 shows interface 502 as including multiple elements for presenting and editing characteristics associated with a live event 104. For example, interface element 504 presents the time, date, and venue of the live event 104, and presents functionality for editing these characteristics. FIG. 5 also shows elements 506 and 508 as presenting characteristics relating to the acts and the type of seating for the live event 104, respectively. The interface 502 is further shown as including an element for selecting the prices of tickets for the live event 104. Element 510 is shown as including a slider that allows a user to adjust the pricing of tickets for the live event 104 in relation to the base prices for the venue. The interface 512 may also include an element 512 that presents the estimated level of interest in a live event 104 having the characteristics presented in elements 504-510. In some embodiments, element 512 may also display other estimated information such as a number of tickets expected to be sold, a chance of selling out all tickets to the live event 104, a date that all tickets for the live event 104 are expected to be sold, an estimated gross ticket sales for the live event 104, etc. In response to one or more characteristics presented in 504-510 being changed, element 512 may be configured to update the information it displays so that it reflects a new expected interest in a live event 104 having the updated characteristics. [0081] FIG. 5 also illustrates an element 514 for optimizing live event characteristics to meet one or more goals. For example, the element 514 may include options to optimize characteristics of an upcoming live event 104 so as to maximize profits, maximize ticket sales, maximize merchandise sales (e.g., parking, albums, food, drinks, clothing, etc.), to maximize social media interest (e.g., to generate the most interest from users that have a large social media presence), or a combination thereof. In this way, the interface 502 uses estimated levels of interest to select characteristics of the live event 104 that are likely to cause an upcoming live event 104 to meet the goals of the venue 108/act 106/other party. [0085] FIG. 6 also shows a recommendation 608 of a live event 104. The recommendation 606 may include recommended characteristics for the recommended live event 104 such as acts, venues, dates, ticket prices, etc. The recommendation 608 may also include estimated information about the recommended live event 104, such as an estimate profit, number of tickets sold, chance of the recommended live event 104 selling out, etc. In some embodiments, the recommended live event 104 and/or one or more characteristics of the recommended live event 104 may be determined by the live event service 102 using and interest model and/or relationships indicative of interest in live events 104. The recommendation 608 may also be generated based on other information, such as a schedule of an act. For example, where an act currently has a first live event 104 in Eugene, Oreg. on November 22.sup.nd, and a second live event 104 in Spokane, Wash. on November 25.sup.th, the interface may recommend that the act add a show in Portland, Oreg. on one of November 23.sup.rd or 24.sup.th (since one would likely travel through Portland, Oreg. from Eugene, Oreg. to Spokane, Wash.). The recommendation 608 may also include a recommendation of one or more acts that live event service 102 estimates would increase interest in the live event 104.).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to use event data from an event organizer and regional data to make event hosting recommendations based on a likelihood of success since such modification in the system of Howard is a combination of elements well-known in the art that provides known benefits such as provide the user additional information before hosting an event such as an estimate profit, chance of the recommended live event 104 selling out chances of sellout as disclosed by Bhatia in paragraphs [080], [085] and Figures 5-6.
Regarding Claim 30, Bhatia further teaches:
wherein the at least one task algorithm further comprises an event organizer recommendation task algorithm (See Figs 5-6 disclosing providing the user event hosting recommendations based on likelihood of success of the events. [0080] FIG. 5 shows interface 502 as including multiple elements for presenting and editing characteristics associated with a live event 104. For example, interface element 504 presents the time, date, and venue of the live event 104, and presents functionality for editing these characteristics. FIG. 5 also shows elements 506 and 508 as presenting characteristics relating to the acts and the type of seating for the live event 104, respectively. The interface 502 is further shown as including an element for selecting the prices of tickets for the live event 104. Element 510 is shown as including a slider that allows a user to adjust the pricing of tickets for the live event 104 in relation to the base prices for the venue. The interface 512 may also include an element 512 that presents the estimated level of interest in a live event 104 having the characteristics presented in elements 504-510. In some embodiments, element 512 may also display other estimated information such as a number of tickets expected to be sold, a chance of selling out all tickets to the live event 104, a date that all tickets for the live event 104 are expected to be sold, an estimated gross ticket sales for the live event 104, etc. In response to one or more characteristics presented in 504-510 being changed, element 512 may be configured to update the information it displays so that it reflects a new expected interest in a live event 104 having the updated characteristics., wherein the event organizer task algorithm is configured to;
compare proposed event details against existing event details of scheduled events (See Fig. 5 and [0081] FIG. 5 also illustrates an element 514 for optimizing live event characteristics to meet one or more goals. For example, the element 514 may include options to optimize characteristics of an upcoming live event 104 so as to maximize profits, maximize ticket sales, maximize merchandise sales (e.g., parking, albums, food, drinks, clothing, etc.), to maximize social media interest (e.g., to generate the most interest from users that have a large social media presence), or a combination thereof. In this way, the interface 502 uses estimated levels of interest to select characteristics of the live event 104 that are likely to cause an upcoming live event 104 to meet the goals of the venue 108/act 106/other party.);
calculate a percent likelihood of success of the proposed event (See Fig. 5 and section 512 disclosing the calculated chance of sellout. [0080] FIG. 5 shows interface 502 as including multiple elements for presenting and editing characteristics associated with a live event 104. For example, interface element 504 presents the time, date, and venue of the live event 104, and presents functionality for editing these characteristics. FIG. 5 also shows elements 506 and 508 as presenting characteristics relating to the acts and the type of seating for the live event 104, respectively. The interface 502 is further shown as including an element for selecting the prices of tickets for the live event 104. Element 510 is shown as including a slider that allows a user to adjust the pricing of tickets for the live event 104 in relation to the base prices for the venue. The interface 512 may also include an element 512 that presents the estimated level of interest in a live event 104 having the characteristics presented in elements 504-510. In some embodiments, element 512 may also display other estimated information such as a number of tickets expected to be sold, a chance of selling out all tickets to the live event 104, a date that all tickets for the live event 104 are expected to be sold, an estimated gross ticket sales for the live event 104, etc. In response to one or more characteristics presented in 504-510 being changed, element 512 may be configured to update the information it displays so that it reflects a new expected interest in a live event 104 having the updated characteristics.); and
present the percent likelihood of success to an event organizer through the event organizer interface (See Fig. 5 and[0080] FIG. 5 shows interface 502 as including multiple elements for presenting and editing characteristics associated with a live event 104. For example, interface element 504 presents the time, date, and venue of the live event 104, and presents functionality for editing these characteristics. FIG. 5 also shows elements 506 and 508 as presenting characteristics relating to the acts and the type of seating for the live event 104, respectively. The interface 502 is further shown as including an element for selecting the prices of tickets for the live event 104. Element 510 is shown as including a slider that allows a user to adjust the pricing of tickets for the live event 104 in relation to the base prices for the venue. The interface 512 may also include an element 512 that presents the estimated level of interest in a live event 104 having the characteristics presented in elements 504-510. In some embodiments, element 512 may also display other estimated information such as a number of tickets expected to be sold, a chance of selling out all tickets to the live event 104, a date that all tickets for the live event 104 are expected to be sold, an estimated gross ticket sales for the live event 104, etc. In response to one or more characteristics presented in 504-510 being changed, element 512 may be configured to update the information it displays so that it reflects a new expected interest in a live event 104 having the updated characteristics. ).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to use event data from an event organizer and regional data to make event hosting recommendations based on a likelihood of success since such modification in the system of Howard is a combination of elements well-known in the art that provides known benefits such as provide the user additional information before hosting an event such as an estimate profit, chance of the recommended live event 104 selling out chances of sellout as disclosed by Bhatia in paragraphs [080], [085] and Figures 5-6.
Regarding Claim 31, Bhatia further teaches:
wherein the event organizer task algorithm is configured to evaluate alternative event details for the proposed event, calculate a percent likelihood of success based on the alternative event details, and presenting at least one alternative recommendation for the event to the event organizer (See Fig. 5 and[0080] FIG. 5 shows interface 502 as including multiple elements for presenting and editing characteristics associated with a live event 104. For example, interface element 504 presents the time, date, and venue of the live event 104, and presents functionality for editing these characteristics. FIG. 5 also shows elements 506 and 508 as presenting characteristics relating to the acts and the type of seating for the live event 104, respectively. The interface 502 is further shown as including an element for selecting the prices of tickets for the live event 104. Element 510 is shown as including a slider that allows a user to adjust the pricing of tickets for the live event 104 in relation to the base prices for the venue. The interface 512 may also include an element 512 that presents the estimated level of interest in a live event 104 having the characteristics presented in elements 504-510. In some embodiments, element 512 may also display other estimated information such as a number of tickets expected to be sold, a chance of selling out all tickets to the live event 104, a date that all tickets for the live event 104 are expected to be sold, an estimated gross ticket sales for the live event 104, etc. In response to one or more characteristics presented in 504-510 being changed, element 512 may be configured to update the information it displays so that it reflects a new expected interest in a live event 104 having the updated characteristics. ).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to use event data from an event organizer and regional data to make event hosting recommendations based on a likelihood of success since such modification in the system of Howard is a combination of elements well-known in the art that provides known benefits such as provide the user additional information before hosting an event such as an estimate profit, chance of the recommended live event 104 selling out chances of sellout as disclosed by Bhatia in paragraphs [080], [085] and Figures 5-6.
Regarding Claim 32, Bhatia further teaches:
wherein the at least one alternative recommendation is made based on exceeding a threshold percent likelihood of success ([0081] FIG. 5 also illustrates an element 514 for optimizing live event characteristics to meet one or more goals. For example, the element 514 may include options to optimize characteristics of an upcoming live event 104 so as to maximize profits, maximize ticket sales, maximize merchandise sales (e.g., parking, albums, food, drinks, clothing, etc.), to maximize social media interest (e.g., to generate the most interest from users that have a large social media presence), or a combination thereof. In this way, the interface 502 uses estimated levels of interest to select characteristics of the live event 104 that are likely to cause an upcoming live event 104 to meet the goals of the venue 108/act 106/other party.).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to use event data from an event organizer and regional data to make event hosting recommendations based on a likelihood of success since such modification in the system of Howard is a combination of elements well-known in the art that provides known benefits such as provide the user additional information before hosting an event such as an estimate profit, chance of the recommended live event 104 selling out chances of sellout as disclosed by Bhatia in paragraphs [080], [085] and Figures 5-6.
Regarding Claim 33, Bhatia further teaches:
wherein the event organizer interface includes a selection of available venues for a event based on at least one of space, date, and price (See Fig. 5 and [0080] FIG. 5 shows interface 502 as including multiple elements for presenting and editing characteristics associated with a live event 104. For example, interface element 504 presents the time, date, and venue of the live event 104, and presents functionality for editing these characteristics. FIG. 5 also shows elements 506 and 508 as presenting characteristics relating to the acts and the type of seating for the live event 104, respectively. The interface 502 is further shown as including an element for selecting the prices of tickets for the live event 104. Element 510 is shown as including a slider that allows a user to adjust the pricing of tickets for the live event 104 in relation to the base prices for the venue. The interface 512 may also include an element 512 that presents the estimated level of interest in a live event 104 having the characteristics presented in elements 504-510. In some embodiments, element 512 may also display other estimated information such as a number of tickets expected to be sold, a chance of selling out all tickets to the live event 104, a date that all tickets for the live event 104 are expected to be sold, an estimated gross ticket sales for the live event 104, etc. In response to one or more characteristics presented in 504-510 being changed, element 512 may be configured to update the information it displays so that it reflects a new expected interest in a live event 104 having the updated characteristics. ).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to use event data from an event organizer and regional data to make event hosting recommendations based on a likelihood of success since such modification in the system of Howard is a combination of elements well-known in the art that provides known benefits such as provide the user additional information before hosting an event such as an estimate profit, chance of the recommended live event 104 selling out chances of sellout as disclosed by Bhatia in paragraphs [080], [085] and Figures 5-6.
Claim(s) 6, 10, 21-24, 34 is/are rejected under 35 U.S.C. 103 as being unpatentable over Howard (US Patent Publication 2018/0013861) in view of Freeman (US Patent Publication 2014/0365484).
Regarding Claim 6, Howard discloses
wherein the user can invites friends to an event ([079] Other choices for the filter criteria that may be selected via the selection element 403 include “Invites” to display invitations to events. [091] “A “Share” 421 button element may also appear below the control row 418. The user may select the “Share” button 421 to share the event on the social-networking system, e.g., with other users”) and wherein friends who accept an invitation form the user are grouped into an event group ([0120] Examples of such signals generated for a user may be based on the user liking a genre of the event, the user's friend being registered to go to an event, or the user being in a group associated with or attending the event.).
Howard does not explicitly disclose:
calculating percent matches between friend user profiles and the event and presenting the percent matches to the user, wherein the user can invites friends to the event based on the percent match of the friend user profile and the event.
However Freeman which similarly is directed to recommend events to a user based on a match on user’s preferences, further teaches:
calculating percent matches between friend user profiles and the event and presenting the percent matches to the user, wherein the user can invites friends to the event based on the percent match of the friend user profile and the event ([043] “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.” [0050] “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.” Figure 3 and [0060] 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. [0061] “Exemplary screen shot 303 depicts additional details about the specific event recommendation, including the names of relevant friend recommendations 307. [0074] Included are the elements from FIG. 3 in a different visual arrangement: specials/relevant advertisements 508, relevant person/friend recommendations 507. See also Figure 6” [0076] 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”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to include the teachings as presented in Freeman since such modification is a combination of well-known prior art elements that yield the predictable result of allowing a user to identify friends that share the same interest and 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 as disclosed by Freeman in [0050].
Regarding Claim 10, Howard discloses
sending a notification to members of an event group, wherein ate least one of the notification is sent when a member purchases a ticket to the event or the notification is sent when a member arrives at the event ([016] Before the event or activity or at the event or activity, a member may be notified of one or more other attending members with similar interests or background. [0072] In some implementations, notification by the discovery sub-module 151 may be based on a privacy setting of one or more members. For example, based on a member's privacy setting, discovery sub-module 151 may exclude that member from being shown to other members of other groups, or may show the member to other members with a similar interest or background, but exclude the member's identification information, location information, or any other private information. In some implementations, discovery sub-module may show a member's location information, if allowed by the member's privacy setting, so that other members having a similar interest or background (e.g., college friends) can locate him or her at the event or activity.)..
Regarding Claim 21, Freeman further teaches:
wherein the user interface comprises a map, and wherein the event recommendation is displayed on the map (Fig. 7 and [0079] Embodiment depicts a map overlay with recommendations 209 displayed on the map, as the user indicates interest preferences 202.).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to include the teachings as presented in Freeman since such modification is a combination of well-known prior art elements that yield the predictable result of allowing a user to identify recommendations as disclosed by Freeman in [0079].
Regarding Claim 22, Howard discloses:
wherein the event recommender task algorithm makes an event recommendation to the user for each event of the plurality of events (See Fig. 3 and [066] disclosing recommendations for a plurality of events. See also Figs 4A-5B.).
Freeman further teaches:
wherein the map depicts locations for a plurality of events (See Fig. 7 disclosing the legend indicating “nearby you” and [0079] Embodiment depicts a map overlay with recommendations 209 displayed on the map, as the user indicates interest preferences 202.).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to include the teachings as presented in Freeman since such modification is a combination of well-known prior art elements that yield the predictable result of allowing a user to identify recommendations as disclosed by Freeman in [0079].
Regarding Claim 23, Freeman further teaches:
wherein the event recommendation is visually associated with each event at or proximate its location on the map (See Fig. 3 and [066] disclosing recommendations for a plurality of events. See also Figs 4A-5B.).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to include the teachings as presented in Freeman since such modification is a combination of well-known prior art elements that yield the predictable result of allowing a user to identify recommendations as disclosed by Freeman in [0079].
Regarding Claim 24, Freeman further teaches:
wherein the visual association includes at least one of a color according to the percent match and a symbol (See Fig. 3 and [066] disclosing the symbols associated with the events.).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to include the teachings as presented in Freeman since such modification is a combination of well-known prior art elements that yield the predictable result of allowing a user to identify recommendations as disclosed by Freeman in [0079].
Regarding Claim 34, Howard discloses:
wherein the user can select friends through the user interface ([004] Users may interact with the online events, e.g., to register to attend, view a list of friends who are registered to attend. [079] Other choices for the filter criteria that may be selected via the selection element 403 include “Invites” to display invitations to events. [091] A “Share” 421 button element may also appear below the control row 418. The user may select the “Share” button 421 to share the event on the social-networking system, e.g., with other users ); wherein the user can invite friends to the event ([079] Other choices for the filter criteria that may be selected via the selection element 403 include “Invites” to display invitations to events. [091] “A “Share” 421 button element may also appear below the control row 418. The user may select the “Share” button 421 to share the event on the social-networking system, e.g., with other users”); and wherein friends who accept an invitation form the user are grouped into an event group ([0120] Examples of such signals generated for a user may be based on the user liking a genre of the event, the user's friend being registered to go to an event, or the user being in a group associated with or attending the event.).
Howard does not explicitly disclose:
wherein the event recommender task algorithm is further configured to calculate percent matches between friend user profiles and the event and present the percent matches to the user, wherein the user can invite friends to the event based on the percent match of the friend user profile and the event.
However Freeman further teaches:
calculate percent matches between friend user profiles and the event and present the percent matches to the user, wherein the user can invite friends to the event based on the percent match of the friend user profile and the event ([043] “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.” [0050] “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.” Figure 3 and [0060] 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. [0061] “Exemplary screen shot 303 depicts additional details about the specific event recommendation, including the names of relevant friend recommendations 307. [0074] Included are the elements from FIG. 3 in a different visual arrangement: specials/relevant advertisements 508, relevant person/friend recommendations 507. See also Figure 6” [0076] 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”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filled to include the teachings as presented in Freeman since such modification is a combination of well-known prior art elements that yield the predictable result of allowing a user to identify friends that share the same interest and 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 as disclosed by Freeman in [0050].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wang, US 2015/0019642, CALENDAR-EVENT RECOMMENDATION SYSTEM. An event-recommendation system may assist a user in discovering events that the user may be interested in attending based on one or more factors. For example, the event-recommendation system may consider the user's current location, available time slots, interests, past events, events attended or hosted by the user's contacts, or other factors. The event-recommendation system may be implemented in a calendar application that runs on the user's smartphone, tablet, computer, or other device. The event recommendation system may also be implemented as a desktop application, a web page, on a server, or in another manner known to one of skill in the art.
Wiseman, US 2018/0300821 GROUP EVENT OR ACTIVITY RECOMMENDATIONS VIA SOCIAL-RELATIONSHIP-RELATED OVERRIDE CONDITIONS. The invention relates to systems and methods for facilitating event or activity recommendations, including, for example, determining a group event or activity recommendation via social-relationship-related override conditions, providing event-attendance-responsive notifications via a hybrid architecture, etc.
I. Boutsis, S. Karanikolaou and V. Kalogeraki, "Personalized Event Recommendations Using Social Networks," 2015 16th IEEE International Conference on Mobile Data Management, Pittsburgh, PA, USA, 2015, pp. 84-93, doi: 10.1109/MDM.2015.62.
G. Nkuna and M. Coetzee, "Social Event Invitation and Recommendation for Event based Social Networks," 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), Durban, South Africa, 2020, pp. 1-5, doi: 10.1109/icABCD49160.2020.9183860.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIA C SANTOS-DIAZ whose telephone number is (571)272-6532. The examiner can normally be reached Monday-Friday 8:00AM-5:00PM.
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/MARIA C SANTOS-DIAZ/ Primary Examiner, Art Unit 3629