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 .
This Office Action has been issued in response to Applicant’s Communication of application S/N 19/215,510 filed on May 22, 2025. Claims 1-20 are pending with the application.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim(s) 1-7, 9-16, and 18-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim(s) 1, 7, and 23 of U.S. Patent No. 17456777.
Instant Application
U.S. Patent No. 17456777
1. A method comprising: receiving, from a computing device, a request for travel data, the request comprising a plurality of parameters; determining that a first parameter of the plurality of parameters is of a first type and a second parameter of the plurality of parameters is of a second type different than the first type; in response to determining that the first parameter is of the first type, transmitting, to a database, a request for historical travel data associated with the plurality of parameters; receiving, from the database, a first data structure comprising the historical travel data that satisfies at least one parameter of the plurality of parameters; generating, by an optimization engine, a second data structure comprising a subset of external computing systems predicted to identify layover candidates that satisfy a criteria; transmitting a request for the travel data to each of the subset of external computing systems in the second data structure; receiving a plurality of search results from the subset of external computing systems; and providing, to the computing device, instructions to cause the computing device to present the plurality of search results. 2. The method of claim 1, wherein determining that the first parameter of the plurality of parameters is of the first type comprises accessing a parameter field of the first parameter in the request for the travel data, the parameter field associated with an identifier indicating a parameter type.
3. The method of claim 1, wherein determining that the second parameter of the plurality of parameters is of the second type comprises determining a location of the second parameter in the request for the travel data.4. The method of claim 1, wherein determining that the second parameter of the plurality of parameters is of the second type comprises determining a location of the second parameter in the request for the travel data.6. The method of claim 1, wherein generating the second data structure comprises inputting the first data structure comprising the historical travel data into the optimization engine.
7. The method of claim 1, wherein the optimization engine comprises at least one of a machine learning model or a neural network.
9. The method of claim 1, wherein generating the second data structure comprising the subset of external computing systems comprises predicting, by the optimization engine, a price of one or more layover candidates based in part on processing a shared embedding for the one or more layover candidates.
1. A method implemented by a data processing system for generating search results, the method comprising: receiving, by one or more computers over a network, a search query submitted by a client device, the search query specifying one or more parameters including a first parameter for a web resource request; determining whether the first parameter from the search query is a predetermined parameter, the predetermined parameter including a condition that the web resource request enable one or more layover candidates between an origin and a destination specified by the search query; in response to determining that the first parameter from the search query is a predetermined parameter: determining, by the one or more computers and using a historical search database, historical search data that was retrieved from a plurality of databases, the historical search data satisfying a threshold similarity to the search query, the historical search data comprising historical travel itineraries including layover candidate data; inputting the historical search data comprising the historical travel itineraries including the layover candidate data into a machine learning model configured to analyze the historical search data and (1) determine prior search results that are responsive to at least a first threshold number of parameters from the one or more parameters and (2) determine prior web resources that are responsive to at least a second threshold number of parameters from the one or more parameters included in the search query; predicting, based at least in part on utilizing the machine learning model by, a subset of databases from the plurality of databases that are predicted to have first layover candidates that are more likely to be associated with lower prices relative to second layover candidates stored on a second, different subset of the plurality of databases that are predicted to have higher prices; and obtaining, from the subset of databases, a dataset that includes a plurality of layover candidates that each satisfy at least one parameter of the search query or at least partially satisfy the first parameter; generating, by the one or more computers and using the plurality of layover candidates included in the dataset, a plurality of search results for the web resource request; and providing, to the client device, instructions to cause the client device to present a user interface that includes data for the plurality of search results.2
5. The method of claim 1, wherein the criteria comprise at least one of a shorter travel leg time, a shorter layover time, or a lower price relative to other layover candidates.
23. The method of claim 1, wherein the first layover candidates that are more likely responsive to the search query relative to the second layover candidates stored on the second, different subset of the plurality of databases further comprise one or more of a shorter travel leg time, a shorter layover time, or lower combined cost relative to the second layover candidates.
10. The method of claim 1, wherein the plurality of search results from the subset of external computing systems comprise a result including a total itinerary having ae; first itinerary leg received from a first external computing system and a second itinerary leg received from a second external computing system.
7. The method of claim 6, wherein generating the plurality of search results comprises generating, using a first web resource that identifies a first itinerary leg and a second web resource that identifies a second itinerary leg, a search result that has a total itinerary that includes the first itinerary leg and the second itinerary leg.
11. A system comprising: one or more processors; and one or more non-transitory computer readable media storing instructions, that when executed by the one or more processors, cause the system to perform operations comprising: receiving, from a computing device, a request for travel data, the request comprising a plurality of parameters; determining that a first parameter of the plurality of parameters is of a first type and a second parameter of the plurality of parameters is of a second type different than the first type; in response to determining that the first parameter is of the first type, transmitting, to a database, a request for historical travel data associated with the plurality of parameters; receiving, from the database, a first data structure comprising the historical travel data that satisfies at least one parameter of the plurality of parameters; generating, by an optimization engine, a second data structure comprising a subset of external computing systems predicted to identify layover candidates that satisfy a criteria; transmitting a request for the travel data to each of the subset of external computing systems in the second data structure; receiving a plurality of search results from the subset of external computing systems; and providing, to the computing device, an instruction to cause the computing device to present the plurality of search results via a user interface.12. The system of claim 11, wherein determining that the first parameter of the plurality of parameters is of the first type comprises accessing a parameter field of the first parameter in the request for the travel data, the parameter field associated with an identifier indicating a parameter type.
13. The system of claim 11, wherein determining that the second parameter of the plurality of parameters is of the second type comprises determining a location of the second parameter in the request for the travel data.
14. The system of claim 11, wherein generating, by the optimization engine, the second data structure comprises: determining prior search results that were previously responsive to at least a first threshold number of parameters from the plurality of parameters included in the request for the travel data; and determining external computing systems that were previously responsive to at least a second threshold number of parameters from the plurality of parameters included in the request for the travel data.16. The system of claim 11, wherein generating the second data structure comprises inputting the first data structure comprising the historical travel data into the optimization engine.
1. A method implemented by a data processing system for generating search results, the method comprising: receiving, by one or more computers over a network, a search query submitted by a client device, the search query specifying one or more parameters including a first parameter for a web resource request; determining whether the first parameter from the search query is a predetermined parameter, the predetermined parameter including a condition that the web resource request enable one or more layover candidates between an origin and a destination specified by the search query; in response to determining that the first parameter from the search query is a predetermined parameter: determining, by the one or more computers and using a historical search database, historical search data that was retrieved from a plurality of databases, the historical search data satisfying a threshold similarity to the search query, the historical search data comprising historical travel itineraries including layover candidate data; inputting the historical search data comprising the historical travel itineraries including the layover candidate data into a machine learning model configured to analyze the historical search data and (1) determine prior search results that are responsive to at least a first threshold number of parameters from the one or more parameters and (2) determine prior web resources that are responsive to at least a second threshold number of parameters from the one or more parameters included in the search query; predicting, based at least in part on utilizing the machine learning model by, a subset of databases from the plurality of databases that are predicted to have first layover candidates that are more likely to be associated with lower prices relative to second layover candidates stored on a second, different subset of the plurality of databases that are predicted to have higher prices; and obtaining, from the subset of databases, a dataset that includes a plurality of layover candidates that each satisfy at least one parameter of the search query or at least partially satisfy the first parameter; generating, by the one or more computers and using the plurality of layover candidates included in the dataset, a plurality of search results for the web resource request; and providing, to the client device, instructions to cause the client device to present a user interface that includes data for the plurality of search results..
15. The system of claim 11, wherein the criteria comprise at least one of a shorter travel leg time, a shorter layover time, or a lower price relative to other layover candidates.
23. The method of claim 1, wherein the first layover candidates that are more likely responsive to the search query relative to the second layover candidates stored on the second, different subset of the plurality of databases further comprise one or more of a shorter travel leg time, a shorter layover time, or lower combined cost relative to the second layover candidates.
18. The system of claim 11, wherein the plurality of search results from the subset of external computing systems comprise a result including a total itinerary having a first itinerary leg received from a first external computing system and a second itinerary leg received from a second external computing system.
7. The method of claim 6, wherein generating the plurality of search results comprises generating, using a first web resource that identifies a first itinerary leg and a second web resource that identifies a second itinerary leg, a search result that has a total itinerary that includes the first itinerary leg and the second itinerary leg.
19. A non-transitory computer storage medium encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising: receiving, from a computing device, a request for travel data, the request comprising a plurality of parameters; determining that a first parameter of the plurality of parameters is of a first type and a second parameter of the plurality of parameters is of a second type different than the first type; in response to determining that the first parameter is of the first type, transmitting, to a database, a request for historical travel data associated with the plurality of parameters; receiving, from the database, a first data structure comprising the historical travel data that satisfies at least one parameter of the plurality of parameters; generating, by an optimization engine, a second data structure comprising a subset of external computing systems predicted to identify layover candidates that satisfy a criteria; transmitting a request for the travel data to each of the subset of external computing systems in the second data structure; receiving a plurality of search results from the subset of external computing systems; and providing, to the computing device, an instruction to cause the computing device to present the plurality of search results.
20. The non-transitory computer storage medium of claim 19, wherein generating the second data structure comprising the subset of external computing systems comprises predicting, by the optimization engine, a price of one or more layover candidates based in part on processing a shared embedding for the one or more layover candidates.
1. A method implemented by a data processing system for generating search results, the method comprising: receiving, by one or more computers over a network, a search query submitted by a client device, the search query specifying one or more parameters including a first parameter for a web resource request; determining whether the first parameter from the search query is a predetermined parameter, the predetermined parameter including a condition that the web resource request enable one or more layover candidates between an origin and a destination specified by the search query; in response to determining that the first parameter from the search query is a predetermined parameter: determining, by the one or more computers and using a historical search database, historical search data that was retrieved from a plurality of databases, the historical search data satisfying a threshold similarity to the search query, the historical search data comprising historical travel itineraries including layover candidate data; inputting the historical search data comprising the historical travel itineraries including the layover candidate data into a machine learning model configured to analyze the historical search data and (1) determine prior search results that are responsive to at least a first threshold number of parameters from the one or more parameters and (2) determine prior web resources that are responsive to at least a second threshold number of parameters from the one or more parameters included in the search query; predicting, based at least in part on utilizing the machine learning model by, a subset of databases from the plurality of databases that are predicted to have first layover candidates that are more likely to be associated with lower prices relative to second layover candidates stored on a second, different subset of the plurality of databases that are predicted to have higher prices; and obtaining, from the subset of databases, a dataset that includes a plurality of layover candidates that each satisfy at least one parameter of the search query or at least partially satisfy the first parameter; generating, by the one or more computers and using the plurality of layover candidates included in the dataset, a plurality of search results for the web resource request; and providing, to the client device, instructions to cause the client device to present a user interface that includes data for the plurality of search results.2
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.
Claim(s) 1-8 and 10-19 and is/are rejected under 35 U.S.C. 103 as being unpatentable over Gonzalez et al. (U.S. Publication No.: US 20180053121 A1) hereinafter Gonzalez, in view of Horovitz et al. (U.S. Publication No.: US 20210287166 A1) hereinafter Horovitz.
As to claim 1:
Gonzalez discloses:
A method comprising: receiving, from a computing device, a request for travel data, the request comprising a plurality of parameters [Paragraph 0003 teaches receiving, by at least one processor, requirements for a trip from a user using a user interface; collecting, by the at least one processor, preference information of the user for the trip. Paragraph 0034 teaches In block 210, the intelligent travel planning system 130 gathers personal preference information of the user and travel requirements… Non-limiting examples of travel requirements may be, destination, price, time of year, month, and day, etc. Non-limiting examples of preference information may be, demographic information of the user preferred travel style such as average days spent in one destination, recorded past travel experience of the user.]; determining that a first parameter of the plurality of parameters is of a first type and a second parameter of the plurality of parameters is of a second type different than the first type [Paragraph 0003 teaches receiving, by at least one processor, requirements for a trip from a user using a user interface; collecting, by the at least one processor, preference information of the user for the trip. Paragraph 0034 teaches In block 210, the intelligent travel planning system 130 gathers personal preference information of the user and travel requirements… Non-limiting examples of travel requirements may be, destination, price, time of year, month, and day, etc. Non-limiting examples of preference information may be, demographic information of the user preferred travel style such as average days spent in one destination, recorded past travel experience of the user. Note: Separating the inputs into two different categories such travel requirements (first type) and preference information (second type).]; in response to determining that the first parameter is of the first type, transmitting, to a database, a request for historical travel data associated with the plurality of parameters [Paragraph 0026 teaches by use of the purchase history, the scoring process 139 may weigh in past travel tendencies of the user more than other sources of preference information. Also, by use of a large scale purchase history of a group of users, the scoring process 139 may weigh in general tendency of travelers for a certain destination. Paragraph 0043 teaches the intelligent travel planning system 130 may adjust the default preferences based on a large scale historical data that had been established by a statistically meaningful number of users over time, to predict personal preferences better for a future user by using historical preferences by other travelers having similar travel requirements. Note: Receiving large scale historical data or purchase history based on input requirements such as finding travelers with similar travel requirements or data for a certain destination reads on the claims.]; receiving, from the database, a first data structure comprising the historical travel data that satisfies at least one parameter of the plurality of parameters [Paragraph 0020 teaches the history tracker 133 may build large-scale purchase history data for a group of users having similar purchase histories… The purchase histories of the intelligent travel planning system 130 may include travel destinations, travel reviews/evaluations, duration of travels, etc. Note: Receiving large-scale purchase history data or purchase histories (first data structure) reads on the claims.]; transmitting a request for the travel data to each of the subset of external computing systems in the second data structure [Paragraph 0022 teaches the itinerary builder 137 of the intelligent travel planning system 130 generates an itinerary by use of respective results from one or more travel booking systems 150 that provide conventional reservation services. Paragraph 0023 teaches the catalog database may be external to the intelligent travel planning system 130 and may be accessed by use of the external source coordinator 141, or directly by the itinerary builder 137. Paragraph 0027 teaches the external source coordinator 141 of the intelligent travel planning system 130 maps and transfers information from the external data sources 160 in real time.]; receiving a plurality of search results from the subset of external computing systems [Paragraph 0022 teaches the itinerary builder 137 of the intelligent travel planning system 130 generates an itinerary by use of respective results from one or more travel booking systems 150 that provide conventional reservation services. Paragraph 0023 teaches the catalog database may be external to the intelligent travel planning system 130 and may be accessed by use of the external source coordinator 141, or directly by the itinerary builder 137. Paragraph 0027 teaches the external source coordinator 141 of the intelligent travel planning system 130 maps and transfers information from the external data sources 160 in real time. Note: Utilizing an external source coordinator by querying external data sources and travel booking system for results from these external system reads on the claims.] and providing, to the computing device, instructions to cause the computing device to present the plurality of search results [Paragraph 0054 teaches the intelligent travel planning system 130 displays candidate locations as selected above by marking in a map… The intelligent travel planning system 130 also explains the map as displayed by, “Highlights are the candidates for Cherry Blossom in March and April. Do you see ones you like or need more information?” The map presented during the travel planning session is similar to the high-level view map 410, having highlights in candidate locations 415. Note: Querying available destinations matching the users’s timeframes and interests, it retrieves multiple valid options, wherein the retrieved multiple valid options involves displaying and presenting these candidate location as highlights on an interactive map reads on the claims.]
Gonzalez discloses some of the limitations as set forth in claim 1 but does not appear to expressly disclose generating, by an optimization engine, a second data structure comprising a subset of external computing systems predicted to identify layover candidates that satisfy a criteria.
Horovitz discloses:
generating, by an optimization engine, a second data structure comprising a subset of external computing systems predicted to identify layover candidates that satisfy a criteria [Paragraph 0020 teaches the Classification algorithm is substituted by a Probabilistic Classification algorithm, that is given a request for a vacation deal, is able to return an ordered list of suppliers, sorted by their anticipated return of the deal goal function. Paragraph 0098 teaches the processor may submit e.g. to a travel service provider such as booking.com or expedia, a request (source: FlightA.source, destination: FlightB.destination) to look for flights with connection/layover of Location FlightA.destination. Note: Generating an ordered list of suppliers (second data structure), wherein the ordered list of suppliers is sorted by their anticipated return (predicted to satisfy a criteria) for flights with connection/layovers from booking.com or Expedia (external computing systems) reads on the claims.];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Gonzalez, by incorporating generating an ordered list of suppliers (second data structure), wherein the ordered list of suppliers is sorted by their anticipated return (predicted to satisfy a criteria) for flights with connection/layovers (see Horovitz Paragraph 0020 and 0098), because both applications are directed to travel queries; incorporating generating an ordered list of suppliers (second data structure), wherein the ordered list of suppliers is sorted by their anticipated return (predicted to satisfy a criteria) for flights with connection/layovers is advantageous when making predictions (see Horovitz Paragraph 0115).
Claims 11 and 19 recite similar limitations as in claim 1. Therefore claims 11 and 19 are rejected for the same reasons as set forth above. See claim 1 for analysis.
As to claim 2:
Gonzalez discloses:
The method of claim 1, wherein determining that the first parameter of the plurality of parameters is of the first type comprises accessing a parameter field of the first parameter in the request for the travel data, the parameter field associated with an identifier indicating a parameter type [Paragraph 0018 teaches as the cognitive process 131 parses inputs from the user 110 into a specific request for other components of the intelligent travel planning system 130. Paragraph 0034 teaches non-limiting examples of travel requirements may be, destination, price, time of year, month, and day, etc. Note: Receiving a travel query with multiple user inputs (includes a first parameter), wherein the query is parsed individually processed such as time of year or destination is accessed reads on the claims.]
Claim 12 recites similar limitations as in claim 2. Therefore claim 12 is rejected for the same reasons as set forth above. See claim 2 for analysis.
As to claim 3:
Gonzalez discloses:
The method of claim 1, wherein determining that the second parameter of the plurality of parameters is of the second type comprises determining a location of the second parameter in the request for the travel data [Paragraph 0018 teaches as the cognitive process 131 parses inputs from the user 110 into a specific request for other components of the intelligent travel planning system 130. Paragraph 0034 teaches non-limiting examples of travel requirements may be, destination, price, time of year, month, and day, etc. Note: Receiving a travel query with multiple user inputs (includes a second parameter), wherein the query is parsed individually (location of the second parameter) processed such as time of year or destination is accessed reads on the claims.]
Claim 13 recites similar limitations as in claim 3. Therefore claim 13 is rejected for the same reasons as set forth above. See claim 3 for analysis.
As to claim 4:
Gonzalez discloses:
The method of claim 1, wherein generating, by the optimization engine, the second data structure comprises: determining prior search results that were previously responsive to at least a first threshold number of parameters from the plurality of parameters included in the request for the travel data [Paragraph 0043 teaches predict personal preferences better for a future user by using historical preferences by other travelers having similar travel requirements. Note: Identifying similar travel requirements from past travelers that must include evaluating prior search against a baseline threshold of matching parameters reads on the claims.]; and
Gonzalez and Horovitz discloses all of the limitations as set forth in claim 1.
Horovitz also discloses:
determining external computing systems that were previously responsive to at least a second threshold number of parameters from the plurality of parameters included in the request for the travel data [Paragraph 0151 teaches the learning classifier algorithm (for example: SVM, J48) is provided with a training set of historical data where per each query the supplier with the best price is set. Upon each query, the classifier will select the optimal supplier according to the trained model. Paragraph 0154 teaches the number of classes is typically the product of the number of suppliers and the number of splits. it is appreciated that a “regular” or deterministic classifier typically comprises a processor configured to operate a rule, or function, that assigns to a sample x a class label 5/, where the samples come from some set X (with features such as DayOfWeek, month, number of requested nights, requested room type etc.) and the class labels form a finite set Y defined prior to training. Note: Determining suppliers (external computing systems) by evaluating the current query features such as DayOfWeek, month, number of requested nights against historical training for each query reads on the claims because the supplier that was previously responsive to a parameter condition is selected to handle the current query.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Gonzalez, by incorporating determining suppliers (external computing systems) by evaluating the current query features such as DayOfWeek, month, number of requested nights against historical training for each query (see Horovitz Paragraph 0151 and 0154), because both applications are directed to travel queries; incorporating determining suppliers (external computing systems) by evaluating the current query features such as DayOfWeek, month, number of requested nights against historical training for each query is advantageous when making predictions (see Horovitz Paragraph 0115).
Claim 14 recites similar limitations as in claim 4. Therefore claim 14 is rejected for the same reasons as set forth above. See claim 4 for analysis.
As to claim 5:
Gonzalez discloses:
The method of claim 1, wherein the criteria comprise at least one of a shorter travel leg time, a shorter layover time, or a lower price relative to other layover candidates [Paragraph 0040 teaches the intelligent travel planning system 130 investigates weather patterns of the regions/countries, flight routes, etc., compares costs for various options, and makes recommendations. Paragraph 0043 teaches the default preference is a general preference as employed in conventional travel booking systems, such as cost, departure and arrival schedules, duration including flight time and layovers, etc. Note: Comparing the costs of different mult-leg flight routes to satisfy a minimum cost requirement (lower price relative to other candidates) reads on the claims.]
Claim 15 recites similar limitations as in claim 5. Therefore claim 15 is rejected for the same reasons as set forth above. See claim 5 for analysis.
As to claim 6:
Gonzalez and Horovitz discloses all of the limitations as set forth in claim 1.
Horovitz also discloses:
The method of claim 1, wherein generating the second data structure comprises inputting the first data structure comprising the historical travel data into the optimization engine [Paragraph 0020 teaches the Classification algorithm is substituted by a Probabilistic Classification algorithm, that is given a request for a vacation deal, is able to return an ordered list of suppliers, sorted by their anticipated return of the deal goal function. Paragraph 0098 teaches the processor may submit e.g. to a travel service provider such as booking.com or expedia, a request (source: FlightA.source, destination: FlightB.destination) to look for flights with connection/layover of Location FlightA.destination. Paragraph 0151 teaches the learning classifier algorithm (for example: SVM, J48) is provided with a training set of historical data where per each query the supplier with the best price is set. Upon each query, the classifier will select the optimal supplier according to the trained model. Note: Generating an ordered list of suppliers (second data structure) based on input historical data (first data structure), wherein the ordered list of suppliers is sorted by their anticipated return (predicted to satisfy a criteria) for flights with connection/layovers from booking.com or Expedia (external computing systems) reads on the claims.];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Gonzalez, by incorporating generating an ordered list of suppliers (second data structure), wherein the ordered list of suppliers is sorted by their anticipated return (predicted to satisfy a criteria) for flights with connection/layovers (see Horovitz Paragraph 0020 and 0098), because both applications are directed to travel queries; incorporating generating an ordered list of suppliers (second data structure), wherein the ordered list of suppliers is sorted by their anticipated return (predicted to satisfy a criteria) for flights with connection/layovers is advantageous when making predictions (see Horovitz Paragraph 0115).
Claim 16 recites similar limitations as in claim 6. Therefore claim 16 is rejected for the same reasons as set forth above. See claim 6 for analysis.
As to claim 7:
Gonzalez and Horovitz discloses all of the limitations as set forth in claim 1.
Horovitz also discloses:
The method of claim 1, wherein the optimization engine comprises at least one of a machine learning model or a neural network Paragraph 0020 teaches the Classification algorithm is substituted by a Probabilistic Classification algorithm, that is given a request for a vacation deal, is able to return an ordered list of suppliers, sorted by their anticipated return of the deal goal function. Paragraph 0098 teaches the processor may submit e.g. to a travel service provider such as booking.com or expedia, a request (source: FlightA.source, destination: FlightB.destination) to look for flights with connection/layover of Location FlightA.destination. Paragraph 0151 teaches the learning classifier algorithm (for example: SVM, J48) is provided with a training set of historical data where per each query the supplier with the best price is set. Upon each query, the classifier will select the optimal supplier according to the trained model. Note: Utlizing a learning classifier algorithm to generate an ordered list of suppliers (second data structure) based on input historical data (first data structure), wherein the ordered list of suppliers is sorted by their anticipated return (predicted to satisfy a criteria) for flights with connection/layovers from booking.com or Expedia (external computing systems) reads on the claims.];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Gonzalez, by incorporating generating an ordered list of suppliers (second data structure), wherein the ordered list of suppliers is sorted by their anticipated return (predicted to satisfy a criteria) for flights with connection/layovers (see Horovitz Paragraph 0020 and 0098), because both applications are directed to travel queries; incorporating generating an ordered list of suppliers (second data structure), wherein the ordered list of suppliers is sorted by their anticipated return (predicted to satisfy a criteria) for flights with connection/layovers is advantageous when making predictions (see Horovitz Paragraph 0115).
Claim 18 recites similar limitations as in claim 10. Therefore claim 18 is rejected for the same reasons as set forth above. See claim 10 for analysis.
As to claim 8:
Gonzalez and Horovitz discloses all of the limitations as set forth in claim 1.
Horovitz also discloses:
The method of claim 1, wherein the request for the travel data is received at a first time and the historical travel data is associated with a predetermined period of time prior to the first time [Paragraph 0151 teaches the learning classifier algorithm (for example: SVM, J48) is provided with a training set of historical data where per each query the supplier with the best price is set. Upon each query, the classifier will select the optimal supplier according to the trained model. Paragraph 0178 teaches the CR may be either computed based on historical data (such as last 24 hours, last week, or some other window) or predicted based on historical data… the algorithm predicts the next day number of bookings and clicks. Paragraph 0197 teaches the Bid CPC may be set for live requests on a daily basis or other periods (depends on the meta-search partner). Note: Relying on historical data such as a past window 24 hours or last week that are different from live requests (travel data request received at a first time) reads on the claims.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Gonzalez, by incorporating relying on historical data such as a past window 24 hours or last week that are different from live requests (travel data request received at a first time) (see Horovitz Paragraph 0151, 0178, and 0197), because both applications are directed to travel queries; incorporating relying on historical data such as a past window 24 hours or last week that are different from live requests (travel data request received at a first time is advantageous when making predictions (see Horovitz Paragraph 0115).
Claim 17 recites similar limitations as in claim 8. Therefore claim 17 is rejected for the same reasons as set forth above. See claim 8 for analysis.
As to claim 10:
Gonzalez and Horovitz discloses all of the limitations as set forth in claim 1.
Horovitz also discloses:
The method of claim 1, wherein the plurality of search results from the subset of external computing systems comprise a result including a total itinerary having a first itinerary leg received from a first external computing system and a second itinerary leg received from a second external computing system [Paragraph 0078 teaches the usage of nested returns allows also to mix flight providers, while optimizing each return separately and save more. For example, the return trip TLV>ZRH>TLV may be replaced—instead of $461 of SwissAir as shown in FIG. 2h, $287 of El-Al, as shown in FIG. 2d, may be chosen, increasing the total saving, e.g. the saving instead of $51, $225=The difference between 872$ (TLV>ZRH+ZRH>MUC+MUC>ZRH +ZRH>TLV) and the sum of the following two return flights: $287 (TLV>ZRH>TLV using ElAl+$360 (ZRH>MLTC>ZRH using SwissAir). Note: Querying separate different flight providers (a first external computer system and a second external computer system) and combining a first leg operated by EI-AI with a second leg operated by Swiss Air into a single results reads on the claims.]
Claim 18 recites similar limitations as in claim 10. Therefore claim 18 is rejected for the same reasons as set forth above. See claim 10 for analysis.
Claim(s) 9 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gonzalez et al. (U.S. Publication No.: US 20180053121 A1) hereinafter Gonzalez, in view of Horovitz et al. (U.S. Publication No.: US 20210287166 A1) hereinafter Horovitz, and further in view of Beynel et al. (U.S. Publication No.: US 20220138875 A1) hereinafter Beynel.
As to claim 9:
Gonzalez and Horovitz discloses all of the limitations as set forth in claim 1.
Horovitz also discloses:
The method of claim 1, wherein generating the second data structure comprising the subset of external computing systems comprises predicting, by the optimization engine, a price of one or more layover candidates [Paragraph 0098 teaches the processor may submit e.g. to a travel service provider such as booking.com or expedia, a request (source: FlightA.source, destination: FlightB.destination) to look for flights with connection/layover. Paragraph 0151 teaches the learning classifier algorithm (for example: SVM, J48) is provided with a training set of historical data where per each query the supplier with the best price is set. Upon each query, the classifier will select the optimal supplier according to the trained model. Paragraph 0152 teaches an OTA or other system may use a Probabilistic Classifier… and then the resulted probabilities may be sorted, launching the requests to said suppliers in that order. Note: Utilizing a shared ]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Gonzalez, by incorporating relying on historical data such as a past window 24 hours or last week that are different from live requests (travel data request received at a first time) (see Horovitz Paragraph 0151, 0178, and 0197), because both applications are directed to travel queries; incorporating relying on historical data such as a past window 24 hours or last week that are different from live requests (travel data request received at a first time is advantageous when making predictions (see Horovitz Paragraph 0115).
Gonzalez and Horovitz discloses all of the limitations as set forth in claim 1 and some of 9 but does not appear to expressly disclose based in part on processing a shared embedding for the one or more layover candidates.
Horovitz discloses:
based in part on processing a shared embedding for the one or more layover candidates [Paragraph 0128 teaches an example of an experience features 712 may include data regarding an amount of time spent by a user during any segment or portion of an event (e.g., flight time, delays, layover time, time spent in an airline lounge, etc.). Paragraph 0144 teaches the machine-learned experience predictive model is configured to generate a reusable experience embedding module for each of the one or more user experiences associated with the event. Additionally, each reusable experience embedding module may be configured to be used for a second event for a different type of industry. Note: Integrating the reusable (shared embedding) for candidate layover features to produce output that is fed into probabilistic classifier of Horovitz to predict the minimal price reads on the claims.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Gonzalez and Horovitz, by incorporating integrating the reusable (shared embedding) for candidate layover features to produce output that is fed into probabilistic classifier of Horovitz to predict the minimal price (see Beynel Paragraph 0128 and 0144), because the three applications are directed to travel queries; incorporating integrating the reusable (shared embedding) for candidate layover features to produce output that is fed into probabilistic classifier of Horovitz to predict the minimal price provides for a reduced disruption metric that quantifies a relative impact of disruption events on the impacted segment (see Beynel Paragraph 0091).
Claim 20 recite similar limitations as in claim 9. Therefore claim 20 is rejected for the same reasons as set forth above. See claim 9 for analysis.
Conclusion
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/EARL LEVI ELIAS/Examiner, Art Unit 2169
/SHERIEF BADAWI/Supervisory Patent Examiner, Art Unit 2169
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