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
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 9/10/2025 has been entered.
Accordingly, claims 1, 4-10 and 12 are pending in this application. Claim 1 is currently amended.
Response to Arguments
Applicant’s arguments with respect to amended pending claims filed on 9/10/2025 have been fully considered. In view of the claim amendment filed, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made.
Further, regarding the new limitations of “embed the electronic message into primary content at a publisher server, the primary content accessed from the publisher server by a client device, the embedded electronic message configured to divert the client device to a target server” recited in claim 1, it is submitted that they are properly addressed by the new ground of rejection.
Furthermore, it is also submitted that all limitations in pending claims, including those not specifically argued, are properly addressed. The reason is set forth in the rejections. See claim analysis below for detail.
Claim Rejections - 35 USC § 103
4. 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 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.
5. 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.
6. Claims 1, 4-10, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Green et al. (US Patent Number US 9639842 B1) in view of Valecha et al. (US 20190139165 A1) and Mysen (US 20170191839 A1).
7. Regarding Claim 1, Green discloses an optimization server for matching and delivery of contextual data to a client device (Fig. 5; optimization server 516); comprising:
a processor coupled to a memory for storing programming instructions that when executed on said processor cause said processor to (Fig. 5; [Col. 8, lines 65-67]: In an exemplary embodiment, business tier 504 may represent the processing components of system 500):
receive a travel itinerary dataset associated with a traveler account (Fig. 1; [Col. 2, lines 46-50]: In an exemplary embodiment, the original travel itinerary may be the itinerary reflecting the travel scheduled when it was first booked or a modified itinerary; Figs. 5, 8; [Col. 10, lines 45-50]: Server 514 may parse the PNR data to extract relevant travel information… such as, for example the origin and destination information. The information may be stored in database 520);
However, Green does not explicitly teach “receive a knowledge graph that assigns possible activities in a destination to a trip; receive a destination activities dataset wherein a portion of said knowledge graph satisfies said destination activities dataset; receive an indirect dataset associated with said traveler account; through primary criteria filters, select a subset of said destination activities dataset based on said indirect dataset; assign weights to links in said subset of said destination activities dataset; select, according to said weights assigned to said links, at least one destination activity within said subset; generate an electronic message for said traveler account based on said at least one destination activity for delivery to a client device associated with said traveler account, and embed the electronic message into primary content at a publisher server, the primary content accessed from the publisher server by a client device, the embedded electronic message configured to divert the client device to a target server.”
On the other hand, in the same field of endeavor, Valecha teaches
receive a knowledge graph that assigns possible activities in a destination to a trip (Fig. 3; [0046]: In various examples, POI data 304 is stored as a knowledge graph (e.g., a graph database) for places, attractions, events, and the like, that a user may wish to visit or experience on a vacation);
receive a destination activities dataset wherein a portion of said knowledge graph satisfies said destination activities dataset (Fig. 3; [0045]-[0047]: POI data 304 may include data on transit modes 314, interest types 316, activity level 318, budget 320, hours of operation 322, popularity 324, and recommended times to visit 326… Some of the values data in POI data 304 may be the selected from the same data ranges/categories as the values used in preference types 120);
receive an indirect dataset associated with said traveler account (Fig. 4; [0054]-[0055]: At element 420, a list of top attractions/things to do for the determined location destination in search query 302 may be retrieved… In some examples, the top (e.g., three) interest types in the user's stored preferences are used);
through primary criteria filters, select a subset of said destination activities dataset based on said indirect dataset (Fig. 4; [0055]: The list may further be filtered based on the duration of the trip (e.g., only so many attractions may be seen in a given time period). Other POI may also be used to filter the list (e.g., the days the attraction is open compared to suggested Monday through Friday trip); [Abstract]: based on the plurality of characteristics and the plurality of travel-related preferences, identifying a subset of the plurality of POIs to include in a trip itinerary; Fig. 5; [0066]: At operation 510… a subset of the plurality of POIs to include in a trip itinerary may be identified);
assign weights to links in said subset of said destination activities dataset (Fig. 4; [0056]: Accordingly, weights may be assigned to the various attributes of the user's preferences and types of POI data to reduce the number of attractions; Fig. 5; [0065]: In some examples, the characteristics have assigned weights. The weights may be assigned based on the travel-related preferences);
select, according to said weights assigned to said links, at least one destination activity within said subset ([0057]: A Monte Carlo simulation (e.g., 10,000 randomized trials) may be run using a different number of attractions and different selected attractions to determine the total highest score given the assigned weights. Thus, the attractions that are in the highest randomized trial that meet the duration limits may be selected for inclusion in the user-specific itinerary);
generate an electronic message for said traveler account based on said at least one destination activity for delivery to a client device associated with said traveler account (Fig. 4; [0056]-[0059]: At element 424, a user-specific itinerary is generated. The itinerary may be based on an optimized route (e.g., using route optimizer 310) between the selected (e.g., filtered) attractions and intended recommended stay duration… The itinerary may be stored in a standardized format such that different output modes may be used (e.g., e-mail, mobile phone presentation, desktop browser, etc.)… At element 426, the user-specific itinerary (e.g., custom itinerary 328) is presented to user 102A in a graphical user interface (GUI)).
Additionally, Mysen teaches embed the electronic message into primary content at a publisher server (Figs. 1, 4; [0061]: computing server(s) 402 can receive feature selection and/or bid information 408 from the entities 406… For example, the entity 406a may provide an advertisement (e.g., “Save 20% on a flight to Hawaii.”) including parameters or embedded promotion codes enabling the entity 406a to identify users that interact with the advertisement; [0044]: entities can publish information (e.g., content, recommendations, advertisements) to users matching their criteria entities can publish information (e.g., content, recommendations, advertisements) to users matching their criteria),
the primary content accessed from the publisher server by a client device (Figs. 1, 4; [0061]-[0063]: the entities 406 may also provide information associated with content (e.g., advertisements) to be provided to users… In the present example, the activity-related information selector 116 can access a travel-related profile associated with the user 104a to determine that the user has visited Hawaii in the past),
the embedded electronic message configured to divert the client device to a target server (Figs 1, 4-5; [0061]-[0067]: Thus, the entity 406a in the present example can manage a targeted advertising campaign… the information selector 116 can determine that the user 104a is currently traveling. Thus, information that may be relevant to the user 104a while vacationing in Hawaii, such as local travel activity options (e.g., helicopter rides, snorkeling, luaus, etc.), for example, may be selected… [0067] Selected information can be provided (514) to the individual; [0012]: Advertisers can bid on specific characteristics of an event, and can receive improved return on investment from improved data/content targeting).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Green to incorporate the teachings of Valecha and Mysen to receive a knowledge graph, receive a destination activities dataset, receive an indirect dataset, select a subset of destination activities dataset based on the indirect dataset, assign weights, select at least one destination activity, and generate an electronic message for the traveler account for delivery to a client device.
The motivation for doing so would be to identify points of interest to include in a trip itinerary and present the trip itinerary to the user, as recognized by Valecha ([Abstract] of Valecha: A method may include… accessing a plurality of points of interests (POIs) associated with the location, wherein respective point of interests are associated with a plurality of respective characteristics; based on the plurality of characteristics and the plurality of travel-related preferences, identifying a subset of the plurality of POIs to include in a trip itinerary; and presenting the trip itinerary to the user) and to identify, from a network presence of an individual, data that is relevant to the preferences of the individual, as recognized by Mysen ([Abstract] of Mysen: One example method includes identifying, from a network presence of an individual, historical data associated with the individual including determining data that is relevant to an activity, identifying, from the network presence of the individual, data associated with preferences of the individual including determining data that is relevant to the activity).
Regarding Claim 4, the combined teachings of Green, Valecha, and Mysen disclose the optimization server of claim 1.
Green further teaches wherein said weights are based on or more of a duration of time and a distance travelled within said travel itinerary dataset ([Col. 4, lines 10-19]: A score may be based on any predetermined criteria, such as, for example, any or all of the following: the new departure time; new arrival time; cabin downgrade; cabin upgrade; new number of stops; new elapsed time; and/or new departure or arrival airport. Each criterion may be weighted based on the impact to the traveler… Each of the variables may be defined as set forth in the table below).
Regarding Claim 5, the combined teachings of Green, Valecha, and Mysen disclose the optimization server of claim 1.
Valecha further teaches wherein said weights are based on one or more of season, weather, temperature associated with a location and travel dates within said travel itinerary dataset (Fig. 1; [0013]: Also illustrated are user preference types 120 that may be determined by preference identification 118. The types may include, but are not limited to… seasonal preferences 130, and places to visit 132… [0030]: Seasonal preferences 130 may identify both the changes in user preferences based on season, as well as preferred times to travel… Fig. 4; [0051]-[0056]: At element 414, the seasonal preferences, attractions, interest types, etc., may be retrieved from user data 114… Accordingly, weights may be assigned to the various attributes of the user's preferences and types of POI data).
Regarding Claim 6, the combined teachings of Green, Valecha, and Mysen disclose the optimization server of claim 1.
Valecha further teaches wherein said weights are based on a proximity to a hotel identified within said travel itinerary dataset (Fig. 4; [0056]: Accordingly, weights may be assigned to the various attributes of the user's preferences and types of POI data to reduce the number of attractions. For example, weights may be assigned to cost, travel time (e.g., between two attractions based on a POIs address/GPS information and transmit modes), popularity, etc.; [0027]: Financial preferences 124 may identity the quantitative or qualitative preferences for a user as they relate to an interest type or aspects of travel (e.g., hotels, flights, etc.)).
Regarding Claim 7, the combined teachings of Green, Valecha, and Mysen disclose the optimization server of claim 1.
Valecha further teaches wherein said weights are based on demographic data associated with said traveler account (Fig. 1; [0056]: Accordingly, weights may be assigned to the various attributes of the user's preferences and types of POI data to reduce the number of attractions. For example, weights may be assigned to cost, travel time (e.g., between two attractions based on a POIs address/GPS information and transmit modes), popularity, etc.; [0013]: Also illustrated are user preference types 120 that may be determined by preference identification 118. The types may include, but are not limited to, interest types 122, financial preferences 124, activity level 126, travel duration 128, seasonal preferences 130, and places to visit 132).
Regarding Claim 8, the combined teachings of Green, Valecha, and Mysen disclose the optimization server of claim 1.
Valecha further teaches wherein said destination activities dataset is pre-filtered according to a range of dates that correspond with said travel itinerary data (Figs. 3-4; [0001]: For example, the user may navigate to a travel website and enter in a date range; [0021]: The results may be furthered narrowed by adding a time period to the input criteria; [0055]-[0056]: At element 422, the list is filtered based on the common attributes determined at element 418... The itinerary may be based on an optimized route (e.g., using route optimizer 310) between the selected (e.g., filtered) attractions and intended recommended stay duration).
Regarding Claim 9, the combined teachings of Green, Valecha, and Mysen disclose the optimization server of claim 1.
Valecha further teaches wherein said indirect dataset includes a list of top previously searched or booked activities during travel dates within said travel itinerary dataset (Figs. 3-4; [0054]-[0055]: At element 420, a list of top attractions/things to do for the determined location destination in search query 302 may be retrieved. The attractions may be retrieved by querying POI data 304 for the top (e.g., 100) attractions based on popularity in the determined location destination. In some examples, the top X attractions for each interest type may be retrieved for the location destination… In some examples, the top (e.g., three) interest types in the user's stored preferences are used; [0021]: The results may be furthered narrowed by adding a time period to the input criteria).
Regarding Claim 10, the combined teachings of Green, Valecha, and Mysen disclose the optimization server of claim 1.
Valecha further teaches wherein said programming instructions further cause said processor to receive a rating of or a confirmation of attendance at a destination activity and accordingly update said destination activities dataset ([0045]: As illustrated in FIG. 3, trip itinerary system 106 includes user data 114… and user feedback 312… Fig. 4; Feedback Analysis 406; [0060]: In some examples, the GUI includes controls (e.g., drag-and-drop, numbered lists, etc.) to edit the list of determined attractions in their itinerary… At element 428, the changes made to the user-specific itinerary are tracked and logged).
Regarding Claim 12, the combined teachings of Green, Valecha, and Mysen disclose the optimization server of claim 1.
Valecha further teaches wherein said delivery is responsive to a future travel inquiry (Fig. 1; [0022]: traveler identifier 108 may identify a subset of users that have shown a propensity to be interested in travel based on using travel keywords 110 or visiting travel websites 112. The process of travel identification is discussed further with reference to FIG. 2; [0045]: As illustrated in FIG. 3, trip itinerary system 106 includes user data 114… and recommended times to visit 326; Fig. 4; [0056]: At element 424, a user-specific itinerary is generated. The itinerary may be based on an… intended recommended stay duration; [0067]: The trip itinerary may include a day-by-day breakdown of which POI to visit, when to visit, how to get to the POI, etc.).
Conclusion
16. Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHIRLEY D. HICKS whose telephone number is (571)272-3304. The examiner can normally be reached Mon - Fri 7:30 - 4:00.
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/S.D.H./Examiner, Art Unit 2168
/CHARLES RONES/Supervisory Patent Examiner, Art Unit 2168