DETAILED ACTION
This action is in reply to the original application filed on 04/29/2025.
Claims 1-20 are rejected.
Claims 1-20 are currently pending and have been examined.
Information Disclosure Statement
Information Disclosure Statements received 10/29/2025 and 05/22/2026 have been reviewed and considered.
Priority
This patent Application claims priority from Provisional Application 63/640,752. This benefit has been received and acknowledged and therefore, the instant claims receive the effective filing date of 04/30/2024.
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 .
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under Step 1 of the Subject Matter Eligibility Test for Products and Processes, the claims must be directed to one of the four statutory categories (see MPEP 2106.03). All the claims are directed to one of the four statutory categories (YES).
Under Step 2A of the Subject Matter Eligibility Test, it is determined whether the claims are directed to a judicially recognized exception (see MPEP 2106.04). Step 2A is a two-prong inquiry.
Under Prong 1, it is determined whether the claim recites a judicial exception (YES). Taking Claim 1 as representative, the claim recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
-obtaining, by one or more processing circuits, interaction data indicating a set of interactions of a first user across a plurality of platforms;
-generating, by the one or more processing circuits, a dynamic user profile indicating a plurality of travel preferences for the first user using the interaction data;
-providing, by the one or more processing circuits and to the first user, a first set of travel search results based on the dynamic user profile; and
-generating, by the one or more processing circuits, a comparison among two or more travel items included in the first set of travel search results, wherein the comparison relates to one or more features of the two or more travel items, and wherein generating the comparison comprises selecting the one or more features based at least in part on one or more characteristics of the dynamic user profile
The above limitations recite the concept of generating a user profile of travel preferences determined from user interactions and providing travel items to a user based on features of the travel items selected based characteristics of the user profile. The above limitations fall within the “Certain Methods of Organizing Human Activity” groupings of abstract ideas, enumerated in MPEP 2106.04(a).
Certain methods of organizing human activity include:
fundamental economic principles or practices (including hedging, insurance, and mitigating risk)
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; and business relations)
managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)
The limitations of obtaining, by one or more processing circuits, interaction data indicating a set of interactions of a first user across a plurality of platforms; generating, by the one or more processing circuits, a dynamic user profile indicating a plurality of travel preferences for the first user using the interaction data; providing, by the one or more processing circuits and to the first user, a first set of travel search results based on the dynamic user profile; and generating, by the one or more processing circuits, a comparison among two or more travel items included in the first set of travel search results, wherein the comparison relates to one or more features of the two or more travel items, and wherein generating the comparison comprises selecting the one or more features based at least in part on one or more characteristics of the dynamic user profile are processes that, under their broadest reasonable interpretation, cover a commercial interaction. That is, other than reciting that the obtaining is by one or more processing circuits, that the set of interactions are a set of interactions across a plurality of platforms, that the generating is by the one or more processing circuits, that the providing is by the one or more processing circuits, and that the generating is by the one or more processing circuits, nothing in the claim element precludes the step from practically being performed by people. For example, but for the “one or more processing circuits” language, “obtaining,” “generating,” “providing,” and “generating” in the context of this claim encompasses advertising, and marketing or sales activities.
Under Prong 2, it is determined whether the claim recites additional elements that integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application (NO).
-obtaining, by one or more processing circuits, interaction data indicating a set of interactions of a first user across a plurality of platforms;
-generating, by the one or more processing circuits, a dynamic user profile indicating a plurality of travel preferences for the first user using the interaction data;
-providing, by the one or more processing circuits and to the first user, a first set of travel search results based on the dynamic user profile; and
-generating, by the one or more processing circuits, a comparison among two or more travel items included in the first set of travel search results, wherein the comparison relates to one or more features of the two or more travel items, and wherein generating the comparison comprises selecting the one or more features based at least in part on one or more characteristics of the dynamic user profile
These limitations are not indicative of integration into a practical application because:
The additional elements of claim 1 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea) as supported by paragraph [0189] of Applicant’s specification – “An exemplary system for implementing the overall system or portions of the implementations might include general-purpose computing devices in the form of computers, including a processing unit, a system memory, and a system bus that couples various system components including the system memory to the processing unit.” Specifically, the additional elements of the one or more processing circuits and a plurality of platforms are recited at a high level of generality (i.e. as a generic processor performing the generic computer functions of obtaining data, generating data, and providing data) such that they amount do no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Further, the additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use (such as computers or computing networks). Employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application.
Additionally, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to i) reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, ii) apply the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, iii) effect a transformation or reduction of a particular article to a different state or thing, or iv) apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, the judicial exception is not integrated into a practical application.
Under Step 2B, it is determined whether the claims recite additional elements that amount to significantly more than the judicial exception. The claims of the present application do not include additional elements that are sufficient to amount to significantly more than the judicial exception (NO).
In the case of claim 1, taken individually or as a whole, the additional elements of claim 9 do not provide an inventive concept. As discussed above under step 2A (prong 2) with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed functions amount to no more than a general link to a technological environment.
Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually.
Claim 16 is a computing system reciting similar functions as claim 1. Examiner notes that claim 16 recites the additional elements of a computing system, at least one processing circuit, at least one processor, at least one memory storing instructions therein, and a plurality of platforms, however, claim 16 does not qualify as eligible subject matter for similar reasons as claim 1 indicated above.
Claim 19 is a non-transitory computer-readable medium reciting similar functions as claim 1. Examiner notes that claim 19 recites the additional elements of non-transitory computer-readable medium, computer-executable instructions, at least one processor, a computing system, and a plurality of platforms, however, claim 19 does not qualify as eligible subject matter for similar reasons as claim 1 indicated above.
Therefore, claims 1, 16, and 19 do not provide an inventive concept and do not qualify as eligible subject matter.
Dependent claims 2-15, 17-18, and 20, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. § 101 because they do not add “significantly more” to the abstract idea. More specifically, dependent claims 2-15, 17-18, and 20 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas in that they recite commercial interactions. Dependent claims 5-11 do not recite any farther additional elements, and as such are not indicative of integration into a practical application for at least similar reasons discussed above. Dependent claims 2-4, 12-15, 17-18, and 20 recite the additional elements of the one or more processing circuits, the plurality of platforms, an artificial intelligence (Al) model, a generative AI model, and the instructions being executed by the at least one processor, but similar to the analysis under prong two of Step 2A these additional elements are used as a tool to perform the abstract idea. As such, under prong two of Step 2A, claims 2-15, 17-18, and 20 are not indicative of integration into a practical application for at least similar reasons as discussed above. Thus, dependent claims 2-15, 17-18, and 20 are “directed to” an abstract idea. Next, under Step 2B, similar to the analysis of claims 1, 16, and 19, dependent claims 2-15, 17-18, and 20 when analyzed individually and as an ordered combination, merely further define the commonplace business method (i.e. generating a user profile of travel preferences determined from user interactions and providing travel items to a user based on features of the travel items selected based characteristics of the user profile) being applied on a general-purpose computer and, therefore, do not amount to significantly more than the abstract idea itself. Accordingly, the Examiner concludes that there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself. The analysis above applies to all statutory categories of invention.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5, 7, 12, 14-16, and 18-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jafri et al. (US 2023/0027411 A1), hereinafter Jafri.
Regarding claim 1, Jafri discloses a method for travel comparison, comprising:
-obtaining, by one or more processing circuits, interaction data indicating a set of interactions of a first user across a plurality of platforms (Jafri, see at least: “the profiles can be generated by collecting data and information from the customer [i.e. obtaining interaction data], which can be used by the programs to select travel plans” [0128] and “the information can be collected from public or private databases, such as credit history and professional association of the customers [i.e. across a plurality of platforms]. The information can be collected from the customer activities, such as from correspondence of the customers, which can indicate a traveling preference of the customers [i.e. obtaining interaction data indicating a set of interactions of a first user]. For example, the customers can send emails, discussing traveling, and indicating that early check-in or boarding can be an important consideration in air traveling. This information can be used to choose travel plans, with priority for airlines offering early checking or boarding” [0129] and “the computer processor can contain program code to implement the methods [i.e. by one or more processing circuits]” [0158]);
-generating, by the one or more processing circuits, a dynamic user profile indicating a plurality of travel preferences for the first user using the interaction data (Jafri, see at least: “Operation 1100 forms a profile for a customer [i.e. generating a dynamic user profile], wherein the profile is used for determining a travel schedule … Operation 1120 updates profile using data from social media and the World Wide Web, for example, the customer can post personal data and preferences on professional network or on social media. The preferences can be used to update the customer profiles [i.e. dynamic]” [0148] and “The profiles and inputs can be collected before, during or after finding the available travel plans. For example, preference travel profiles from the customer can be provided to the programs at a beginning, which can allow the programs to choose appropriate travel plans. For example, the customer can prefer flight schedules convenience over price, and thus long overlay time or uncomfortable travel itineraries can be removed from consideration, even though these travel itineraries might have lower fares [i.e. indicating a plurality of travel preferences for the first user using the interaction data]. Different preference travel profiles can be used during the search for the travel plans, such as traveling for business (e.g., business profile) or pleasure (personal profile), with or without family members (single or family profile)” [0119] and “the computer processor can contain program code to implement the methods [i.e. by one or more processing circuits]” [0158]);
-providing, by the one or more processing circuits and to the first user, a first set of travel search results based on the dynamic user profile (Jafri, see at least: “Operation 820 determines optimal itineraries for the customer based on a stored customer profile. Operation 830 presents the optimal itineraries to the customer [i.e. to the first user] in order of a priority, e.g., based on the customer profile [i.e. providing a first set of travel search results based on the dynamic user profile]” [0116] and “the computer processor can contain program code to implement the methods [i.e. by the one or more processing circuits]” [0158]); and
-generating, by the one or more processing circuits, a comparison among two or more travel items included in the first set of travel search results, wherein the comparison relates to one or more features of the two or more travel items, and wherein generating the comparison comprises selecting the one or more features based at least in part on one or more characteristics of the dynamic user profile (Jafri, see at least: “the methods can include machine intelligence, which can contain algorithms to select travel plans, among the travel plans having the desired destination, that are most suitable for the customers [i.e. generating a comparison among two or more travel items included in the first set of travel search results]. The algorithm can be based on customer travel profiles, such as travel preference profiles and behavioral profiles [i.e. based at least in part on one or more characteristics of the dynamic user profile]” [0117] and “a price ranked 8 can be better than an online check in ranked 7 for less than about $100 to $200. If a flight does not have online check in and costs about $100 less than a flight with online check in, [i.e. wherein the comparison relates to one or more features of the two or more travel items] then the flight with online check in can be placed before the flight with lower price [i.e. generating a comparison among two or more travel items] … If a flight does not have online check in and costs about $500 less than a flight with online check in, then the flight with lower price can be placed before the flight with online check in [i.e. wherein generating the comparison comprises selecting the one or more features based at least in part on one or more characteristics of the dynamic user profile]” [0141] and “The profiles can be used to rank the different available travel plans, and the travel plans having high ranks can be selected for the customer review [i.e. generating a comparison among two or more travel items included in the first set of travel search results]” [0144] and “the machine intelligence can make decision, e.g., selecting travel plans, based on profiles and inputs from the customer … For example, the customer can prefer flight schedules convenience over price, and thus long overlay time or uncomfortable travel itineraries can be removed from consideration, even though these travel itineraries might have lower fares [i.e. wherein generating the comparison comprises selecting the one or more features based at least in part on one or more characteristics of the dynamic user profile]” [0119] and “the computer processor can contain program code to implement the methods [i.e. by the one or more processing circuits]” [0158]).
Regarding claim 2, Jafri discloses the method of claim 1. Jafri further discloses:
-wherein the interaction data is first interaction data indicating a first set of interactions of the first user (Jafri, see at least: “the profiles can be generated by collecting data and information from the customer [i.e. wherein the interaction data is first interaction data], which can be used by the programs to select travel plans” [0128] and “the information can be collected from public or private databases, such as credit history and professional association of the customers. The information can be collected from the customer activities, such as from correspondence of the customers, which can indicate a traveling preference of the customers [i.e. indicating a first set of interactions of the first user]. For example, the customers can send emails, discussing traveling, and indicating that early check-in or boarding can be an important consideration in air traveling. This information can be used to choose travel plans, with priority for airlines offering early checking or boarding” [0129]), the method further comprising:
-obtaining, by the one or more processing circuits, second interaction data indicating a second set of interactions of the first user across the plurality of platforms (Jafri, see at least: “Operation 1110 updates the profile using actions from the customer. For example, the customer can select a flight schedule among a number of flight schedules. The preferences of the selected flight schedule can be used to update the customer profiles. Operation 1120 updates profile using data from social media and the World Wide Web, for example, the customer can post personal data and preferences on professional network or on social media [i.e. obtaining second interaction data indicating a second set of interactions of the first user across the plurality of platforms]. The preferences can be used to update the customer profiles” [0148] and “the computer processor can contain program code to implement the methods [i.e. by the one or more processing circuits]” [0158]);
-processing, by the one or more processing circuits, the second interaction data to infer changes to the plurality of travel preferences indicated by the dynamic user profile (Jafri, see at least: “The profiles can also be dynamically enhanced with data collected on the client from social media and the World Wide Web. The dynamic enhancement can help refine the behavioral predictability [i.e. processing the second interaction data to infer changes to the plurality of travel preferences indicated by the dynamic user profile] and help the programs making decisions that mirror the client's own behavior” [0147] and “the computer processor can contain program code to implement the methods [i.e. by the one or more processing circuits]” [0158]); and
-updating, by the one or more processing circuits, the dynamic user profile to include the changes to the plurality of travel preferences (Jafri, see at least: “Operation 1110 updates the profile using actions from the customer. For example, the customer can select a flight schedule among a number of flight schedules. The preferences of the selected flight schedule can be used to update the customer profiles. Operation 1120 updates profile using data from social media and the World Wide Web, for example, the customer can post personal data and preferences on professional network or on social media [i.e. updating the dynamic user profile to include the changes to the plurality of travel preferences]. The preferences can be used to update the customer profiles” [0148] and “the computer processor can contain program code to implement the methods [i.e. by the one or more processing circuits]” [0158]).
Regarding claim 3, Jafri discloses the method of claim 2. Jafri further discloses:
-wherein the comparison is a first comparison provided to the first user at a first time, the one or more features are one or more first features, and the two or more travel items are a first set of two or more travel items (Jafri, see at least: “a price ranked 8 can be better than an online check in ranked 7 for less than about $100 to $200. If a flight does not have online check in and costs about $100 less than a flight with online check in, then the flight with online check in can be placed before the flight with lower price [i.e. wherein the comparison is a first comparison] … If a flight does not have online check in and costs about $500 less than a flight with online check in, then the flight with lower price can be placed before the flight with online check in [i.e. the one or more features are one or more first features, and the two or more travel items are a first set of two or more travel items]” [0141] and “Operation 570 presents the optimal itineraries to the customer in order of a priority [i.e. a first comparison provided to the first user at a first time]” [0069]), the method further comprising:
-generating, by the one or more processing circuits and to the first user at a second time, a second comparison among a second set of two or more travel items, wherein the second time is later than the first time, and wherein the second comparison relates to one or more second features of the second set of two or more travel items, wherein at least one of the one or more second features differs from at least one of the one or more first features based at least in part on the updated dynamic user profile (Jafri, see at least: “a price ranked 8 can be better than an online check in ranked 7 for less than about $100 to $200. If a flight does not have online check in and costs about $100 less than a flight with online check in, then the flight with online check in can be placed before the flight with lower price [i.e. generating a second comparison among a second set of two or more travel items] … If a flight does not have online check in and costs about $500 less than a flight with online check in, then the flight with lower price can be placed before the flight with online check in [i.e. wherein the second comparison relates to one or more second features of the second set of two or more travel items]” [0141] and “Operation 1120 updates profile using data from social media and the World Wide Web, for example, the customer can post personal data and preferences on professional network or on social media. The preferences can be used to update the customer profiles [i.e. wherein at least one of the one or more second features differs from at least one of the one or more first features based at least in part on the updated dynamic user profile]” [0148] and “Operation 570 presents the optimal itineraries to the customer in order of a priority [i.e. a second comparison among a second set of two or more travel items]” [0069] and “the computer processor can contain program code to implement the methods [i.e. by the one or more processing circuits]” [0158] Examiner notes that the time after the profile is updated is a different time than when the profile is originally used/generated [i.e. to the first user at a second time, wherein the second time is later than the first time] and that the updated profile includes updated preferences for future rankings [i.e. wherein at least one of the one or more second features differs from at least one of the one or more first features based at least in part on the updated dynamic user profile]).
Regarding claim 4, Jafri discloses the method of claim 2. Jafri further discloses:
-wherein the first set of travel search results is provided to the first user at a first time, the comparison is a first comparison, the one or more features are one or more first features, and the two or more travel items are a first set of two or more travel items (Jafri, see at least: “a price ranked 8 can be better than an online check in ranked 7 for less than about $100 to $200. If a flight does not have online check in and costs about $100 less than a flight with online check in, then the flight with online check in can be placed before the flight with lower price [i.e. the comparison is a first comparison] … If a flight does not have online check in and costs about $500 less than a flight with online check in, then the flight with lower price can be placed before the flight with online check in [i.e. the one or more features are one or more first features, and the two or more travel items are a first set of two or more travel items]” [0141] and “Operation 1120 updates profile using data from social media and the World Wide Web, for example, the customer can post personal data and preferences on professional network or on social media. The preferences can be used to update the customer profiles” [0148] and “Operation 570 presents the optimal itineraries to the customer in order of a priority [i.e. the first set of travel search results, the comparison is a first comparison]” [0069] Examiner notes that the time after the profile is updated is a different time than when the profile is originally used/generated and that the updated profile includes updated preferences for future result rankings [i.e. wherein the first set of travel search results is provided to the first user at a first time]), the method further comprising:
-providing, by the one or more processing circuits and to the first user at a second time, a second set of travel search results comprising a second set of two or more travel items based on the updated dynamic user profile, wherein the second time is later than the first time, and wherein at least one travel item included in the second set of two or more travel items differs from at least one travel item included the first set of two or more travel items based on the updated dynamic user profile (Jafri, see at least: “Operation 1120 updates profile using data from social media and the World Wide Web, for example, the customer can post personal data and preferences on professional network or on social media. The preferences can be used to update the customer profiles” [0148] and “the profile can be updated based on an action of the customer. For example, the profile can be updated [i.e. based on the updated dynamic user profile] when the customer unselects a flight or selects a flight, e.g., the characteristics of the selected or removed flights can be translated into the profile features, so that future selection of flights can be more accurate [i.e. providing a second set of travel search results comprising a second set of two or more travel items based on the updated dynamic user profile, and wherein at least one travel item included in the second set of two or more travel items differs from at least one travel item included the first set of two or more travel items]” [0149] and “Operation 570 presents the optimal itineraries to the customer in order of a priority [i.e. a second set of travel search results]” [0069] and “the computer processor can contain program code to implement the methods [i.e. by the one or more processing circuits]” [0158] Examiner notes that the time after the profile is updated is a different time than when the profile is originally used/generated and that the updated profile includes updated preferences for future result rankings [i.e. to the first user at a second time, wherein the second time is later than the first time]); and
-generating, by the one or more processing circuits, a second comparison among two or more travel items included in the second set of travel items, wherein the second comparison relates to one or more second features of the two or more travel items included in the second set of travel items, the one or more second features based at least in part on one or more characteristics of the updated dynamic user profile (Jafri, see at least: “a price ranked 8 can be better than an online check in ranked 7 for less than about $100 to $200. If a flight does not have online check in and costs about $100 less than a flight with online check in, then the flight with online check in can be placed before the flight with lower price [i.e. generating a second comparison among two or more travel items included in the second set of travel items] … If a flight does not have online check in and costs about $500 less than a flight with online check in, then the flight with lower price can be placed before the flight with online check in [i.e. wherein the second comparison relates to one or more second features of the two or more travel items included in the second set of travel items]” [0141] and “Operation 1120 updates profile using data from social media and the World Wide Web, for example, the customer can post personal data and preferences on professional network or on social media. The preferences can be used to update the customer profiles [i.e. the one or more second features based at least in part on one or more characteristics of the updated dynamic user profile]” [0148] and “Operation 570 presents the optimal itineraries to the customer in order of a priority [i.e. a second comparison among two or more travel items included in the second set of travel items]” [0069] and “the computer processor can contain program code to implement the methods [i.e. by the one or more processing circuits]” [0158] Examiner notes that the time after the profile is updated is a different time than when the profile is originally used/generated and that the updated profile includes updated preferences for future rankings [i.e. the one or more second features based at least in part on one or more characteristics of the updated dynamic user profile]).
Regarding claim 5, Jafri discloses the method of claim 4. Jafri further discloses:
-wherein at least one of the one or more second features differs from at least one of the one or more first features based on the one or more characteristics of the updated dynamic user profile (Jafri, see at least: “the profile can be updated based on an action of the customer. For example, the profile can be updated when the customer unselects a flight or selects a flight, e.g., the characteristics of the selected or removed flights can be translated into the profile features, so that future selection of flights can be more accurate [i.e. wherein at least one of the one or more second features differs from at least one of the one or more first features based on the one or more characteristics of the updated dynamic user profile]” [0149] and “Operation 1120 updates profile using data from social media and the World Wide Web, for example, the customer can post personal data and preferences on professional network or on social media. The preferences can be used to update the customer profiles [i.e. based on the one or more characteristics of the updated dynamic user profile]” [0148]).
Regarding claim 7, Jafri discloses the method of claim 1. Jafri further discloses:
-wherein the one or more features of the two or more travel items comprises a list of amenities, and wherein a first list of amenities provided in a first comparison between the two or more travel items differs from a second list of amenities provided in a second comparison between the two or more travel items (Jafri, see at least: “the programs can search for flight itineraries meeting the customer specification. The programs can then collect other data, such as fare, amenity and comforts [i.e. wherein the one or more features of the two or more travel items comprises a list of amenities]. Based on the customer profiles, the programs can choose suitable travel plans for the customers. The suitable plans can be presented to the customers for further selection or approval” [0127] and “The suitable flights should meet the customer preferences, including amenities such as flight convenience and comfort, using algorithms to compare the flight characteristics and amenities with a customer profile. The profile can also be updated, such as based on actions of the customer [i.e. wherein a first list of amenities provided in a first comparison between the two or more travel items differs from a second list of amenities provided in a second comparison between the two or more travel items]” [0026]) by at least one of:
-a type of amenity included in the list of amenities (Jafri, see at least: “the programs can search for flight itineraries meeting the customer specification. The programs can then collect other data, such as fare, amenity and comforts [i.e. by at least one of: a type of amenity included in the list of amenities]. Based on the customer profiles, the programs can choose suitable travel plans for the customers” [0127] and “The information can be collected from the customer activities, such as from correspondence of the customers, which can indicate a traveling preference of the customers. For example, the customers can send emails, discussing traveling, and indicating that early check-in or boarding can be an important consideration in air traveling [i.e. by at least one of: a type of amenity included in the list of amenities]” [0129] and “The preferences can be used to update the customer profiles [i.e. wherein a first list of amenities provided in a first comparison between the two or more travel items differs from a second list of amenities provided in a second comparison between the two or more travel items]” [0148]); or
-an order in which one or more amenities are presented in the list of amenities.
Regarding claim 12, Jafri discloses the method of claim 1. Jafri further discloses:
-wherein the comparison is generated using an artificial intelligence (AI) model (Jafri, see at least: “the methods can include machine intelligence, which can contain algorithms to select travel plans, among the travel plans having the desired destination, that are most suitable for the customers [i.e. wherein the comparison is generated using an artificial intelligence (AI) model]. The algorithm can be based on customer travel profiles, such as travel preference profiles and behavioral profiles. For example, the methods can remove travel plans with layover time longer than 5 hours, or travel plans with excessive fares, e.g., fares higher than certain limits” [0117]).
Regarding claim 14, Jafri discloses the method of claim 1. Jafri further discloses:
-wherein the dynamic user profile is a first dynamic user profile, and wherein the comparison is a first comparison (Jafri, see at least: “Operation 1100 forms a profile for a customer [i.e. the dynamic user profile is a first dynamic user profile], wherein the profile is used for determining a travel schedule … Operation 1120 updates profile using data from social media and the World Wide Web, for example, the customer can post personal data and preferences on professional network or on social media. The preferences can be used to update the customer profiles [i.e. the dynamic user profile]” [0148] and “a price ranked 8 can be better than an online check in ranked 7 for less than about $100 to $200. If a flight does not have online check in and costs about $100 less than a flight with online check in, then the flight with online check in can be placed before the flight with lower price [i.e. wherein the comparison is a first comparison] … If a flight does not have online check in and costs about $500 less than a flight with online check in, then the flight with lower price can be placed before the flight with online check in” [0141]), the method further comprising:
-receiving, by the one or more processing circuits, a second dynamic user profile indicating a plurality of travel preferences for a second user (Jafri, see at least: “employing the methods, to provide a travel distribution system to meet the needs of a customer. The travel distribution system can search through multiple airlines to find flights most suitable to the customer. The suitable flights should meet the customer preferences, including amenities such as flight convenience and comfort, using algorithms to compare the flight characteristics and amenities with a customer profile [i.e. receiving a second dynamic user profile indicating a plurality of travel preferences for a second user]. The profile can also be updated, such as based on actions of the customer” [0026] and “The profiles and inputs can be collected before, during or after finding the available travel plans. For example, preference travel profiles from the customer [i.e. receiving a second dynamic user profile indicating a plurality of travel preferences for a second user] can be provided to the programs at a beginning, which can allow the programs to choose appropriate travel plans” [0119] and “the computer processor can contain program code to implement the methods [i.e. by the one or more processing circuits]” [0158]); and
-generating, by the one or more processing circuits, a second comparison among the two or more travel items included in the first set of travel search results, wherein the second comparison differs from the first comparison based on the second dynamic user profile being different than the first dynamic user profile (Jafri, see at least: “a price ranked 8 can be better than an online check in ranked 7 for less than about $100 to $200. If a flight does not have online check in and costs about $100 less than a flight with online check in, then the flight with online check in can be placed before the flight with lower price [i.e. generating a second comparison among the two or more travel items included in the first set of travel search results] … If a flight does not have online check in and costs about $500 less than a flight with online check in, then the flight with lower price can be placed before the flight with online check in” [0141] and “The first group can include flights itineraries that are likely to be selected by the customer, such as flights best matched the customer profile [i.e. wherein the second comparison differs from the first comparison based on the second dynamic user profile being different than the first dynamic user profile]” [0142] and “the methods can include machine intelligence, which can contain algorithms to select travel plans, among the travel plans having the desired destination, that are most suitable for the customers. The algorithm can be based on customer travel profiles, such as travel preference profiles and behavioral profiles [i.e. wherein the second comparison differs from the first comparison based on the second dynamic user profile being different than the first dynamic user profile]. For example, the methods can remove travel plans with layover time longer than 5 hours, or travel plans with excessive fares, e.g., fares higher than certain limits” [0117] and “the computer processor can contain program code to implement the methods [i.e. by the one or more processing circuits]” [0158]).
Regarding claim 15, Jafri discloses the method of claim 14. Jafri further discloses:
-providing, by the one or more processing circuits and to the second user, a second set of travel search results based on the second dynamic user profile (Jafri, see at least: “The first group can include flights itineraries that are likely to be selected by the customer, such as flights best matched the customer profile [i.e. providing, to the second user, a second set of travel search results based on the second dynamic user profile]” [0142] and “the computer processor can contain program code to implement the methods [i.e. by the one or more processing circuits]” [0158]).
Regarding claim 16, Jafri discloses a computing system, comprising:
-at least one processing circuit comprising at least one processor and at least one memory, the at least one memory storing instructions therein that, when executed by the at least one processor (Jafri, see at least: “Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described” [0169]), cause the at least one processor to:
-obtain first interaction data indicating a first set of interactions of a first user across a plurality of platforms at a first time (Jafri, see at least: “the profiles can be generated by collecting data and information from the customer [i.e. obtain first interaction data], which can be used by the programs to select travel plans” [0128] and “the information can be collected from public or private databases, such as credit history and professional association of the customers [i.e. across a plurality of platforms]. The information can be collected from the customer activities, such as from correspondence of the customers, which can indicate a traveling preference of the customers [i.e. obtain first interaction data indicating a first set of interactions of a first]. For example, the customers can send emails, discussing traveling, and indicating that early check-in or boarding can be an important consideration in air traveling. This information can be used to choose travel plans, with priority for airlines offering early checking or boarding” [0129] and “Operation 1120 updates profile using data from social media and the World Wide Web, for example, the customer can post personal data and preferences on professional network or on social media. The preferences can be used to update the customer profiles” [0148] Examiner notes that the time after the profile is updated is a different time than when the profile is originally used/generated [i.e. at a first time]);
-generate a dynamic user profile indicating a plurality of travel preferences for the first user using the first interaction data (Jafri, see at least: “Operation 1100 forms a profile for a customer [i.e. generate a dynamic user profile], wherein the profile is used for determining a travel schedule … Operation 1120 updates profile using data from social media and the World Wide Web, for example, the customer can post personal data and preferences on professional network or on social media. The preferences can be used to update the customer profiles [i.e. dynamic]” [0148] and “The profiles and inputs can be collected before, during or after finding the available travel plans. For example, preference travel profiles from the customer can be provided to the programs at a beginning, which can allow the programs to choose appropriate travel plans. For example, the customer can prefer flight schedules convenience over price, and thus long overlay time or uncomfortable travel itineraries can be removed from consideration, even though these travel itineraries might have lower fares [i.e. indicating a plurality of travel preferences for the first user using the first interaction data]. Different preference travel profiles can be used during the search for the travel plans, such as traveling for business (e.g., business profile) or pleasure (personal profile), with or without family members (single or family profile)” [0119]);
-provide, to the first user, a first set of travel search results based on the dynamic user profile (Jafri, see at least: “Operation 820 determines optimal itineraries for the customer based on a stored customer profile. Operation 830 presents the optimal itineraries to the customer [i.e. to the first user] in order of a priority, e.g., based on the customer profile [i.e. provide a first set of travel search results based on the dynamic user profile]” [0116] and “the computer processor can contain program code to implement the methods [i.e. by the one or more processing circuits]” [0158]); and
-generate a first comparison among two or more travel items included in the first set of travel search results, wherein the first comparison relates to one or more features of the two or more travel items, and wherein generating the comparison comprises selecting the one or more features based at least in part on one or more characteristics of the dynamic user profile (Jafri, see at least: “the methods can include machine intelligence, which can contain algorithms to select travel plans, among the travel plans having the desired destination, that are most suitable for the customers [i.e. generate a first comparison among two or more travel items included in the first set of travel search results]. The algorithm can be based on customer travel profiles, such as travel preference profiles and behavioral profiles [i.e. based at least in part on one or more characteristics of the dynamic user profile]” [0117] and “a price ranked 8 can be better than an online check in ranked 7 for less than about $100 to $200. If a flight does not have online check in and costs about $100 less than a flight with online check in, [i.e. wherein the first comparison relates to one or more features of the two or more travel items] then the flight with online check in can be placed before the flight with lower price [i.e. generate a first comparison among two or more travel items] … If a flight does not have online check in and costs about $500 less than a flight with online check in, then the flight with lower price can be placed before the flight with online check in [i.e. wherein generating the comparison comprises selecting the one or more features based at least in part on one or more characteristics of the dynamic user profile]” [0141] and “The profiles can be used to rank the different available travel plans, and the travel plans having high ranks can be selected for the customer review [i.e. generate a first comparison among two or more travel items included in the first set of travel search results]” [0144] and “the machine intelligence can make decision, e.g., selecting travel plans, based on profiles and inputs from the customer … For example, the customer can prefer flight schedules convenience over price, and thus long overlay time or uncomfortable travel itineraries can be removed from consideration, even though these travel itineraries might have lower fares [i.e. wherein generating the comparison comprises selecting the one or more features based at least in part on one or more characteristics of the dynamic user profile]” [0119]).
Regarding claim 18, Jafri discloses the computing system of claim 16. Jafri further discloses:
-wherein the dynamic user profile is a first dynamic user profile (Jafri, see at least: “Operation 1100 forms a profile for a customer [i.e. the dynamic user profile is a first dynamic user profile], wherein the profile is used for determining a travel schedule … Operation 1120 updates profile using data from social media and the World Wide Web, for example, the customer can post personal data and preferences on professional network or on social media. The preferences can be used to update the customer profiles [i.e. the dynamic user profile]” [0148]), and wherein the instructions, when executed by the at least one processor, further cause the at least one processor to:
-receive a second dynamic user profile indicating a plurality of travel preferences for a second user (Jafri, see at least: “employing the methods, to provide a travel distribution system to meet the needs of a customer. The travel distribution system can search through multiple airlines to find flights most suitable to the customer. The suitable flights should meet the customer preferences, including amenities such as flight convenience and comfort, using algorithms to compare the flight characteristics and amenities with a customer profile [i.e. receive a second dynamic user profile indicating a plurality of travel preferences for a second user]. The profile can also be updated, such as based on actions of the customer” [0026] and “The profiles and inputs can be collected before, during or after finding the available travel plans. For example, preference travel profiles from the customer [i.e. receive a second dynamic user profile indicating a plurality of travel preferences for a second user] can be provided to the programs at a beginning, which can allow the programs to choose appropriate travel plans” [0119] and “the computer processor can contain program code to implement the methods” [0158]); and
-generate a second comparison among the two or more travel items included in the first set of travel search results based on the second dynamic user profile, wherein the second comparison differs from the first comparison based on the second dynamic user profile being different than the first dynamic user profile (Jafri, see at least: “a price ranked 8 can be better than an online check in ranked 7 for less than about $100 to $200. If a flight does not have online check in and costs about $100 less than a flight with online check in, then the flight with online check in can be placed before the flight with lower price [i.e. generate a second comparison among the two or more travel items included in the first set of travel search results] … If a flight does not have online check in and costs about $500 less than a flight with online check in, then the flight with lower price can be placed before the flight with online check in” [0141] and “The first group can include flights itineraries that are likely to be selected by the customer, such as flights best matched the customer profile [i.e. wherein the second comparison differs from the first comparison based on the second dynamic user profile being different than the first dynamic user profile]” [0142] and “the methods can include machine intelligence, which can contain algorithms to select travel plans, among the travel plans having the desired destination, that are most suitable for the customers. The algorithm can be based on customer travel profiles [i.e. based on the second dynamic user profile], such as travel preference profiles and behavioral profiles [i.e. wherein the second comparison differs from the first comparison based on the second dynamic user profile being different than the first dynamic user profile]” [0117] and “the computer processor can contain program code to implement the methods” [0158]).
Claim 19 recite limitations directed towards a non-transitory computer-readable medium. The limitations recited in claim 19 are parallel in nature to those addressed above for claim 1, and are therefore rejected for those same reasons set forth above in claims 1.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 6 is rejected under 35 U.S.C. 103 as being unpatentable over Jafri in view of Will et al. (US 2015/0242927 A1), hereinafter Will.
Regarding claim 6, Jafri discloses the method of claim 1.
Jafri does not explicitly disclose the one or more features of the two or more travel items comprising a preference match score.
Will, however, teaches an online travel website (i.e. abstract) including the known technique of the one or more features of the two or more travel items comprising a preference match score (Will, see at least: “A user can provide a query for a travel service (e.g. a flight query). In step 1306, a list of travel results related to a query provided by the user can be obtained. Each travel result can be parsed to identify the various attributes and options associated with the particular travel result. Attributes and options associated with elements of the user user-specified travel preferences list can be identified. In step 1308, each travel result can be scored based on the weight of each user-specified travel preference it includes [i.e. wherein the one or more features of the two or more travel items comprises a preference match score]. For example, the flight with the window seat can be scored higher than the flight with in-flight Wi-Fi. In step 1310, the list of travel results can be sorted based on each travel result's score. The sorted list of travel results can then be displayed on a web page and/or mobile device client application” [0045]). This known technique is applicable to the method of Jafri as they both share characteristics and capabilities, namely, they are directed to an online travel website.
It would have been recognized that applying the known technique of the one or more features of the two or more travel items comprising a preference match score, as taught by Will, to the teachings of Jafri would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, adding the modification of the one or more features of the two or more travel items comprising a preference match score, as taught by Will, into the method of Jafri would have been recognized by those of ordinary skill in the art as resulting in an improved method that would provide relevant results based on user criteria (Will, [0042]).
Claims 8, 10, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Jafri in view of Bhandari et al. (US 2025/0291628 A1), hereinafter Bhandari.
Regarding claim 8, Jafri discloses the method of claim 1.
Jafri does not explicitly disclose providing the first set of travel search results comprising providing a set of filters associated with the first set of travel search results based on the dynamic user profile, and wherein a first set of filters differs from a second set of filters by at least one of: a type of filter included in the set of filters; or an order in which the set of filters are provided.
Bhandari, however, teaches presenting travel items to a user (i.e. [0038]), including the known technique of providing the first set of travel search results comprising providing a set of filters associated with the first set of travel search results based on the dynamic user profile, and wherein a first set of filters differs from a second set of filters by at least one of: a type of filter included in the set of filters; or an order in which the set of filters are provided (Bhandari, see at least: “the decision task assistant presents the user with comparative parameters and/or filter options that differ in form depending upon relevant attributes of the options that the user is considering and/or based on the user's own preferences and inclinations” [0012] and ““Beachfront” may be selected as one of the filter options 126 in response to a browser-performed determination that multiple accommodations being considered for the decision task “Stay in Krabi” [i.e. wherein providing the first set of travel search results] are characterized by this attribute … one or more of the filter options 126 [i.e. comprises providing a set of filters associated with the first set of travel search results] or other types of comparative parameters are selected based on user-specific preferences, such as preferences determined from the user profile data 111, [i.e. based on the dynamic user profile] the user browser history 112, or the stored website cookies 115. For example, it may be that “pet friendly” is selected as one of the filter options 126 in response to a browser-performed determination that the user has a history of booking pet friendly rentals. In some implementations, the filter options can also dynamically change during the course of the users' decision-making journey based on the user action [i.e. wherein a first set of filters differs from a second set of filters by at least one of: a type of filter included in the set of filters; or an order in which the set of filters are provided] and also on the specific decision task” [0036] and “the decision task assistant intelligently selects user-personalized filter options to provide to the user within the decision task summary. These user-personalized filter options are, in various implementations, provided based on the user's previous web interactions and/or based on stored profile data for the user [i.e. based on the dynamic user profile]” [0013]). This known technique is applicable to the method of Jafri as they both share characteristics and capabilities, namely, they are directed to presenting travel items to a user.
It would have been recognized that applying the known technique of providing the first set of travel search results comprising providing a set of filters associated with the first set of travel search results based on the dynamic user profile, and wherein a first set of filters differs from a second set of filters by at least one of: a type of filter included in the set of filters; or an order in which the set of filters are provided, as taught by Bhandari, to the teachings of Jafri would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, adding the modification of providing the first set of travel search results comprising providing a set of filters associated with the first set of travel search results based on the dynamic user profile, and wherein a first set of filters differs from a second set of filters by at least one of: a type of filter included in the set of filters; or an order in which the set of filters are provided, as taught by Bhandari, into the method of Jafri would have been recognized by those of ordinary skill in the art as resulting in an improved method that would display results in an easy-to-interpret and interact-with format (Bhandari, [0015]).
Regarding claim 10, Jafri discloses the method of claim 1.
Jafri does not explicitly disclose the comparison comprising a summary description of each of the two or more travel items included in the first set of travel search results based on the dynamic user profile.
Bhandari, however, teaches presenting travel items to a user (i.e. [0038]), including the known technique of the comparison comprising a summary description of each of the two or more travel items included in the first set of travel search results based on the dynamic user profile (Bhandari, see at least: “this summary information 124 can, in other implementations, include task-specific comparative parameters that are discovered and elected for selective presentation in the same manner as that described above with respect to the filter options 126. For example, the content summary cards 108, 110 [i.e. wherein the comparison comprises a summary description of each of the two or more travel items included in the first set of travel search results] may display attribute(s) of the corresponding selection option (e.g., the accommodation option) that have been identified as having specific relevance to the corresponding decision task based on the same or similar criteria as that described above with respect to the filter options 126” [0037] and “the decision task assistant intelligently selects user-personalized filter options to provide to the user within the decision task summary. These user-personalized filter options are, in various implementations, provided based on the user's previous web interactions and/or based on stored profile data for the user [i.e. based on the dynamic user profile]” [0013] and Fig. 1 displays content summary cards of properties that match ‘stay in Krabi’ [i.e. of the two or more travel items included in the first set of travel search results]). This known technique is applicable to the method of Jafri as they both share characteristics and capabilities, namely, they are directed to presenting travel items to a user.
It would have been recognized that applying the known technique of the comparison comprising a summary description of each of the two or more travel items included in the first set of travel search results based on the dynamic user profile, as taught by Bhandari, to the teachings of Jafri would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, adding the modification of the comparison comprising a summary description of each of the two or more travel items included in the first set of travel search results based on the dynamic user profile, as taught by Bhandari, into the method of Jafri would have been recognized by those of ordinary skill in the art as resulting in an improved method that would display results in an easy-to-interpret and interact-with format (Bhandari, [0015]).
Regarding claim 13, Jafri discloses the method of claim 12.
Jafri does not explicitly disclose wherein the AI model is a generative AI model, and wherein generating the comparison using the generative AI model comprises: extracting, by the one or more processing circuits, the one or more characteristics from the dynamic user profile; and generating, by the one or more processing circuits, a prompt for the generative AI model including the one or more characteristics.
Bhandari, however, teaches presenting travel items to a user (i.e. [0038]), including the known techniques of wherein the AI model is a generative AI model (Bhandari, see at least: “the attribute discoverer 219 identifies attributes based on data source(s) external to the webpages loaded in the browser tabs of the decision task tab group. For example, the attribute discoverer 219 parses content of other websites that reference the same primary subject as a given website or utilizes a large language model (LLM) 224 to infer applicable attributes for the primary subject of the website … Examples of LLMs include transformer-based models (e.g., a generative pre-trained transformer (GPT) model [i.e. wherein the AI model is a generative AI model]” [0048]), and wherein generating the comparison using the generative AI model comprises:
extracting, by the one or more processing circuits, the one or more characteristics from the dynamic user profile (Bhandari, see at least: “identifying the most “relevant” of the discovered attributes 226 entails an analysis of web-based user history data 228 to identify a selection of the discovered attributes 226 that are most likely to be of interest (and therefore provide a meaningful comparative basis) [i.e. extracting, by the one or more processing circuits, the one or more characteristics from the dynamic user profile] to the user conducting the decision task” [0053] and “The task-specific comparative parameter selector 208 constructs an LLM prompt that includes the web-based user history data 228 for the user [i.e. extracting, by the one or more processing circuits, the one or more characteristics from the dynamic user profile], the webpage subjects (e.g., the subject identifier 222) associated with each of the browser tabs in the decision task tab group, and/or the web content 218. The LLM prompt additionally includes an instruction asking the LLM 224 to identify attributes of the webpage subjects that are most similar (aka relevant) to the web-based user history data 228” [0058] and “task-specific comparative parameters” because the selection of such information for presentation in the decision task summary UI 102 depends upon an analysis of attributes of the options the user is considering (e.g., attributes pertaining to browser tab web content 128 for the decision task tab group 116) and/or attributes of the user conducting the associated browsing session including, for example, attributes identified from user profile data 119, [i.e. from the dynamic user profile] user browser history 112 such as search history and click history, and website cookies 115 stored by the web browser 100 that include user-specific information” [0033]); and
generating, by the one or more processing circuits, a prompt for the generative AI model including the one or more characteristics (Bhandari, see at least: “The task-specific comparative parameter selector 208 constructs an LLM prompt that includes the web-based user history data 228 for the user, the webpage subjects (e.g., the subject identifier 222) associated with each of the browser tabs in the decision task tab group, and/or the web content 218 [i.e. generating, by the one or more processing circuits, a prompt for the generative AI model including the one or more characteristics]. The LLM prompt additionally includes an instruction asking the LLM 224 to identify attributes of the webpage subjects that are most similar (aka relevant) to the web-based user history data 228” [0058]). These known techniques are applicable to the method of Jafri as they both share characteristics and capabilities, namely, they are directed to presenting travel items to a user.
It would have been recognized that applying the known techniques of wherein the AI model is a generative AI model, and wherein generating the comparison using the generative AI model comprises: extracting, by the one or more processing circuits, the one or more characteristics from the dynamic user profile; and generating, by the one or more processing circuits, a prompt for the generative AI model including the one or more characteristics, as taught by Bhandari, to the teachings of Jafri would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, adding the modifications of wherein the AI model is a generative AI model, and wherein generating the comparison using the generative AI model comprises: extracting, by the one or more processing circuits, the one or more characteristics from the dynamic user profile; and generating, by the one or more processing circuits, a prompt for the generative AI model including the one or more characteristics, as taught by Bhandari, into the method of Jafri would have been recognized by those of ordinary skill in the art as resulting in an improved method that would display results in an easy-to-interpret and interact-with format (Bhandari, [0015]).
Claims 9, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Jafri in view of Jafri et al. (US 2017/0316523 A1), hereinafter Jafri523.
Regarding claim 9, Jafri discloses the method of claim 1.
Jafri does not explicitly disclose wherein generating the comparison comprises: providing a plurality of categories of types of comparisons based on the dynamic user profile, providing the plurality of categories comprising determining at least one of the following using the dynamic user profile: the categories to be included in the plurality of categories; or an order in which the plurality of categories are presented; receiving a selection of one of the plurality of categories; and generating the comparison among the two or more travel items based on the selected category.
Jafri523, however, teaches presenting flights to a user based on a use’s travel preference profile (i.e. abstract) including the known technique of generating the comparison comprising: providing a plurality of categories of types of comparisons based on the dynamic user profile, providing the plurality of categories comprising determining at least one of the following using the dynamic user profile: the categories to be included in the plurality of categories; or an order in which the plurality of categories are presented (Jafri523, see at least: “Operation 1750 displays search results in a matrix format [i.e. wherein generating the comparison comprises:], wherein a first dimension comprises different categories of flight itineraries [i.e. providing a plurality of categories of types of comparisons] resulted from the search, wherein a second dimension comprises different data for the categories, wherein the categories and data are arranged according to a preference of a customer [i.e. providing the plurality of categories comprising determining an order in which the plurality of categories are presented]” [0183] and “Further the categories of the flight itineraries can be arranged so that the important features of the flight itineraries are shown on the left portion, e.g., on the visible screen 1410 … Any amenity features that are considered to be important to a particular customer, such as based on a saved customer preference, can be re-arranged to be on [i.e. providing the plurality of categories comprising determining at least one of the following using the dynamic user profile: the categories to be included in the plurality of categories; or an order in which the plurality of categories are presented] the visible screen 1410 [i.e. providing a plurality of categories of types of comparisons based on the dynamic user profile]” [0149] and “the present invention discloses methods to form a travel preference profile for a customer. The travel preference profile can be used for prioritizing the searched flight itineraries [i.e. using the dynamic user profile], and for selecting suitable flight itineraries, e.g., flight itineraries that meet the customer desires and expectation” [0187])
the known technique of receiving a selection of one of the plurality of categories (Jafri523, see at least: “Operation 1750 displays search results in a matrix format, wherein a first dimension comprises different categories of flight itineraries resulted from the search, wherein a second dimension comprises different data for the categories, wherein the categories and data are arranged according to a preference of a customer. Operation 1760 updates the preference based on at least one of a selection [i.e. receiving a selection of one of the plurality of categories] and a final purchase of the customer” [0183]); and
the known technique of generating the comparison among the two or more travel items based on the selected category (Jafri523, see at least: “Operation 1820 updates the preference based on at least one of a selection and a final purchase of the customer. Operation 1830 arranges search results based on the updated preference. For example, after a selection from the customer, in which the customer selects preferred aspects of flights on a flight matrix, or in which the customer selects final flight itinerary for purchase, the flight matrix can be reconfigured [i.e. generating the comparison among the two or more travel items based on the selected category], and the customer preference updated” [0186] and “Operation 1750 displays search results in a matrix format, wherein a first dimension comprises different categories of flight itineraries resulted from the search, wherein a second dimension comprises different data for the categories, wherein the categories [i.e. based on the selected category] and data are arranged according to a preference of a customer. Operation 1760 updates the preference based on at least one of a selection and a final purchase of the customer” [0183]). These known techniques are applicable to the method of Jafri as they both share characteristics and capabilities, namely, they are directed to presenting flights to a user based on a use’s travel preference profile.
It would have been recognized that applying the known techniques of generating the comparison comprising: providing a plurality of categories of types of comparisons based on the dynamic user profile, providing the plurality of categories comprising determining at least one of the following using the dynamic user profile: the categories to be included in the plurality of categories; or an order in which the plurality of categories are presented; receiving a selection of one of the plurality of categories; and generating the comparison among the two or more travel items based on the selected category, as taught by Jafri523, to the teachings of Jafri would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, adding the modifications of generating the comparison comprising: providing a plurality of categories of types of comparisons based on the dynamic user profile, providing the plurality of categories comprising determining at least one of the following using the dynamic user profile: the categories to be included in the plurality of categories; or an order in which the plurality of categories are presented; receiving a selection of one of the plurality of categories; and generating the comparison among the two or more travel items based on the selected category, as taught by Jafri523, into the method of Jafri would have been recognized by those of ordinary skill in the art as resulting in an improved method that would narrow the search to appropriate flight itineraries (Jafri523, [0158]).
Regarding claim 17, Jafri discloses the computing system of claim 16. Jafri further discloses:
-wherein the instructions, when executed by the at least one processor (Jafri, see at least: “the computer processor can contain program code to implement the methods [i.e. by the one or more processing circuits]” [0158]), further cause the at least one processor to:
-obtain second interaction data indicating a set of second interactions of the first user across the plurality of platforms at a second time (Jafri, see at least: “Operation 1110 updates the profile using actions from the customer. For example, the customer can select a flight schedule among a number of flight schedules. The preferences of the selected flight schedule can be used to update the customer profiles. Operation 1120 updates profile using data from social media and the World Wide Web, for example, the customer can post personal data and preferences on professional network or on social media [i.e. obtain second interaction data indicating a set of second interactions of the first user across the plurality of platforms at a second time]. The preferences can be used to update the customer profiles” [0148]); and
-update the dynamic user profile to include changes to the plurality of travel preferences indicated by the second interaction data (Jafri, see at least: “Operation 1110 updates the profile using actions from the customer. For example, the customer can select a flight schedule among a number of flight schedules. The preferences of the selected flight schedule can be used to update the customer profiles. Operation 1120 updates profile using data from social media and the World Wide Web, for example, the customer can post personal data and preferences on professional network or on social media [i.e. update the dynamic user profile to include changes to the plurality of travel preferences indicated by the second interaction data]. The preferences can be used to update the customer profiles” [0148]).
Jafri does not explicitly disclose generating a second comparison among the two or more travel items included in the first set of travel search results based on the updated dynamic user profile, wherein the second comparison differs from the first comparison.
Jafri523, however, teaches presenting flights to a user based on a use’s travel preference profile (i.e. abstract) including the known technique of generating a second comparison among the two or more travel items included in the first set of travel search results based on the updated dynamic user profile, wherein the second comparison differs from the first comparison (Jafri523, see at least: “Periodically, the search can be re-performed, to update the flight information against any changes [i.e. generate a second comparison among the two or more travel items included in the first set of travel search results] … The search can be performed in the background, thus for the customer, the performance of the travel agency services can be instantaneous. In contrast to a search performed immediately after a selection of the customer (which would make the customer waiting for the search results), a background search can be performed before the customer indicating the selection (which would make the customer seeing an instantaneous display right after the selection). The background search can be based on a prediction algorithm, such as it is highly likely that the customer would select a “NEXT” choice, and thus a search based on the NEXT criterion can be performed while the customer browses through the current screen [i.e. wherein the second comparison differs from the first comparison]. The background search can be based on a profile or preferences of the customer [i.e. based on the updated dynamic user profile], to predict the possible selections of the customer” [0061] and “the machine intelligence can make decision, e.g., selecting flight itineraries, based on profiles and inputs from the customer. The profiles and inputs can be collected before, during [i.e. the updated dynamic user profile] or after finding the available flight itineraries” [0157] and “Operation 1750 displays search results in a matrix format [i.e. generate a second comparison], wherein a first dimension comprises different categories of flight itineraries resulted from the search, wherein a second dimension comprises different data for the categories, wherein the categories and data are arranged according to a preference of a customer” [0183] and “operation 1720 displays search results of a travel plan so that flight itineraries are ordered based on a saved customer preference, wherein the flight itineraries that the customer prefers are listed first” [0178]). This known technique is applicable to the system of Jafri as they both share characteristics and capabilities, namely, they are directed to presenting flights to a user based on a use’s travel preference profile.
It would have been recognized that applying the known technique of generating a second comparison among the two or more travel items included in the first set of travel search results based on the updated dynamic user profile, wherein the second comparison differs from the first comparison, as taught by Jafri523, to the teachings of Jafri would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar systems. Further, adding the modification of generating a second comparison among the two or more travel items included in the first set of travel search results based on the updated dynamic user profile, wherein the second comparison differs from the first comparison, as taught by Jafri523, into the system of Jafri would have been recognized by those of ordinary skill in the art as resulting in an improved system that would narrow the search to appropriate flight itineraries (Jafri523, [0158]).
Regarding claim 20, Jafri discloses the non-transitory computer-readable medium of claim 19. Jafri further discloses:
-wherein the interaction data is first interaction data indicating a first set of interactions of the first user, wherein the comparison is a first comparison provided to the first user at a first time (Jafri, see at least: “the profiles can be generated by collecting data and information from the customer [i.e. wherein the interaction data is first interaction data], which can be used by the programs to select travel plans” [0128] and “the information can be collected from public or private databases, such as credit history and professional association of the customers. The information can be collected from the customer activities, such as from correspondence of the customers, which can indicate a traveling preference of the customers [i.e. indicating a first set of interactions of the first user]. For example, the customers can send emails, discussing traveling, and indicating that early check-in or boarding can be an important consideration in air traveling. This information can be used to choose travel plans, with priority for airlines offering early checking or boarding” [0129] and “a price ranked 8 can be better than an online check in ranked 7 for less than about $100 to $200. If a flight does not have online check in and costs about $100 less than a flight with online check in, then the flight with online check in can be placed before the flight with lower price [i.e. wherein the comparison is a first comparison provided to the first user at a first time] … If a flight does not have online check in and costs about $500 less than a flight with online check in, then the flight with lower price can be placed before the flight with online check in” [0141]), and wherein the operations further comprise:
-obtaining second interaction data indicating a second set of interactions of the first user across the plurality of platforms (Jafri, see at least: “Operation 1110 updates the profile using actions from the customer. For example, the customer can select a flight schedule among a number of flight schedules. The preferences of the selected flight schedule can be used to update the customer profiles. Operation 1120 updates profile using data from social media and the World Wide Web, for example, the customer can post personal data and preferences on professional network or on social media [i.e. obtaining second interaction data indicating a second set of interactions of the first user across the plurality of platforms]. The preferences can be used to update the customer profiles” [0148]);
-processing the second interaction data to infer changes to the plurality of travel preferences indicated by the dynamic user profile (Jafri, see at least: “The profiles can also be dynamically enhanced with data collected on the client from social media and the World Wide Web. The dynamic enhancement can help refine the behavioral predictability [i.e. processing the second interaction data to infer changes to the plurality of travel preferences indicated by the dynamic user profile] and help the programs making decisions that mirror the client's own behavior” [0147]);
-updating the dynamic user profile to include the changes to the plurality of travel preferences (Jafri, see at least: “Operation 1110 updates the profile using actions from the customer. For example, the customer can select a flight schedule among a number of flight schedules. The preferences of the selected flight schedule can be used to update the customer profiles. Operation 1120 updates profile using data from social media and the World Wide Web, for example, the customer can post personal data and preferences on professional network or on social media [i.e. updating the dynamic user profile to include the changes to the plurality of travel preferences]. The preferences can be used to update the customer profiles” [0148]).
Jafri does not explicitly disclose providing a second comparison among the two or more travel items included in the first set of travel search results to the first user at a second time, wherein the second time is later than the first time, and wherein the second comparison is based on the updated dynamic user profile including the changes.
Jafri523, however, teaches presenting flights to a user based on a use’s travel preference profile (i.e. abstract) including the known technique of providing a second comparison among the two or more travel items included in the first set of travel search results to the first user at a second time, wherein the second time is later than the first time, and wherein the second comparison is based on the updated dynamic user profile including the changes (Jafri523, see at least: “Periodically, the search can be re-performed, to update the flight information against any changes [i.e. providing a second comparison among the two or more travel items included in the first set of travel search results to the first user at a second time, wherein the second time is later than the first time] … The search can be performed in the background, thus for the customer, the performance of the travel agency services can be instantaneous. In contrast to a search performed immediately after a selection of the customer (which would make the customer waiting for the search results), a background search can be performed before the customer indicating the selection (which would make the customer seeing an instantaneous display right after the selection). The background search can be based on a prediction algorithm, such as it is highly likely that the customer would select a “NEXT” choice, and thus a search based on the NEXT criterion can be performed while the customer browses through the current screen. The background search can be based on a profile or preferences of the customer [i.e. wherein the second comparison is based on the updated dynamic user profile], to predict the possible selections of the customer” [0061] and “the machine intelligence can make decision, e.g., selecting flight itineraries, based on profiles and inputs from the customer. The profiles and inputs can be collected before, during [i.e. wherein the second comparison is based on the updated dynamic user profile including the changes] or after finding the available flight itineraries” [0157] and “Operation 1750 displays search results in a matrix format [i.e. providing a second comparison], wherein a first dimension comprises different categories of flight itineraries resulted from the search, wherein a second dimension comprises different data for the categories, wherein the categories and data are arranged according to a preference of a customer” [0183] and “operation 1720 displays search results of a travel plan so that flight itineraries are ordered based on a saved customer preference, wherein the flight itineraries that the customer prefers are listed first” [0178]). This known technique is applicable to the non-transitory computer-readable medium of Jafri as they both share characteristics and capabilities, namely, they are directed to presenting flights to a user based on a use’s travel preference profile.
It would have been recognized that applying the known technique of providing a second comparison among the two or more travel items included in the first set of travel search results to the first user at a second time, wherein the second time is later than the first time, and wherein the second comparison is based on the updated dynamic user profile including the changes, as taught by Jafri523, to the teachings of Jafri would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar non-transitory computer-readable mediums. Further, adding the modification of providing a second comparison among the two or more travel items included in the first set of travel search results to the first user at a second time, wherein the second time is later than the first time, and wherein the second comparison is based on the updated dynamic user profile including the changes, as taught by Jafri523, into the non-transitory computer-readable medium of Jafri would have been recognized by those of ordinary skill in the art as resulting in an improved non-transitory computer-readable medium that would narrow the search to appropriate flight itineraries (Jafri523, [0158]).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Jafri in view of Wright et al. (US 2017/0103442 A1), hereinafter Wright.
Regarding claim 11, Jafri discloses the method of claim 1.
Jafri does not explicitly disclose the comparison comprises an explanation for recommending each of the two or more travel items included in the first set of travel search results based on the dynamic user profile.
Wright, however, teaches presenting one or more interfaces for selection and purchase of one or more travel packages (i.e. abstract), including the known technique of the comparison comprises an explanation for recommending each of the two or more travel items included in the first set of travel search results based on the dynamic user profile (Wright, see at least: “presentation server 110 may additionally present to the user an indication of the reason (s) for presenting particular TERs, components, or travel packages [i.e. wherein the comparison comprises an explanation for recommending each of the two or more travel items included in the first set of travel search results]” [0088] and “For example, if five out of ten selected TERs are associated with the High Adventure category, then presentation server 110 may present an interface indicating that presented TERs, travel package, and/or destinations were selected for presentation, at least in part, based on the categories associated with the user's selected TER selections [i.e. based on the dynamic user profile]. For example, the interface may display, “Why Trepic's system came up with this destination [or option, or travel package] for you,” [i.e. wherein the comparison comprises an explanation for recommending each of the two or more travel items included in the first set of travel search results] and may indicate that the user's previous selections were” [0089] and “presentation server 110 may use both the user input (e.g., user profile) [i.e. based on the dynamic user profile] and selections of TERs received at step 220 to refine additional TERs presented to the user” [0070]). This known technique is applicable to the method of Jafri as they both share characteristics and capabilities, namely, they are directed to presenting one or more interfaces for selection and purchase of one or more travel packages.
It would have been recognized that applying the known technique of the comparison comprises an explanation for recommending each of the two or more travel items included in the first set of travel search results based on the dynamic user profile, as taught by Wright, to the teachings of Jafri would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, adding the modification of the comparison comprises an explanation for recommending each of the two or more travel items included in the first set of travel search results based on the dynamic user profile, as taught by Wright, into the method of Jafri would have been recognized by those of ordinary skill in the art as resulting in an improved method that would allow a user to select a unique travel package tailored to a user's interests and inspirations (Wright, [0005]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
-Pfeil et al. (US 2019/0347583 A1) teaches creating a customized travel itinerary for a user based on travel and non-travel patterns of behavior.
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/ARIELLE E WEINER/ Primary Examiner, Art Unit 3689
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