DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Applicant's “Amendment” filed on 1/23/2026 has been considered.
Rejection to Claims 1-20 under 35 USC 101 have not been overcome.
Claims 1-20 are amended.
Claims 1-20 are currently pending and have been examined.
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 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. All the claims are directed to one of the four statutory categories (YES).
Under Step 2A of the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG), it is determined whether the claims are directed to a judicially recognized exception. Step 2A is a two-prong inquiry.
Under Prong 1, it is determined whether the claim recites a judicial exception (YES). Taking Claim 12 as representative, the claim recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
• a method, performed at a computer system comprising a processor and a computer-readable medium, comprising:
• collecting, at a device associated with a user of an online system, online data by integrating one or more widgets with a first set of one or more online sites and gathering the online data via the one or more widgets:
• receiving, via a network and from the device associated with the user in communication with the computer system using an application programming interface (API), the online data gathered via the one or more widgets:
• collecting, at the device associated with the user, activity data including information about one or more activities of the user at a second set of one or more online sites:
• receiving, via the network and from the device associated with the user in communication with the computer system using the API, the activity data:
• responsive to the user engaging with the online system,
o accessing a prediction model of the computer system,
o wherein the prediction model is a machine-learning model trained to output a likelihood of the user conducting an action within a future time period;
• applying the prediction model to user data associated with the user including the online data and the activity data to output the likelihood of the user conducting the action within the future time period;
• responsive to the likelihood of the user conducting the action being above a threshold value, generating, using information about conversion by the user of a set of items during one or more past time periods, a list of one or more items for recommendation to the user related to the action of the user within the future time period; and
• causing a device associated with the user to display a user interface with the list of one or more items for inclusion into a cart of the user.
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 (deemphasized), are a process that, under its broadest reasonable interpretation, covers a commercial interaction. That is, other than reciting that a user interface is generated from the list and products are displayed on the user interface, nothing in the claim element precludes the step from practically being performed by people. For example, “accessing, applying, generating, and causing” in the context of this claim encompasses advertising, and marketing or sales activities.
If a claim limitation, under its broadest reasonable interpretation, covers a commercial interaction but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
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).
The claim recites additional elements beyond the judicial exception(s), including:
A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor of a computer system, cause the processor to perform steps comprising:
• collecting, at a device associated with a user of an online system, online data by integrating one or more widgets with a first set of one or more online sites and gathering the online data via the one or more widgets:
• receiving, via a network and from the device associated with the user in communication with the computer system using an application programming interface (API), the online data gathered via the one or more widgets:
• collecting, at the device associated with the user, activity data including information about one or more activities of the user at a second set of one or more online sites:
• receiving, via the network and from the device associated with the user in communication with the computer system using the API, the activity data:
• responsive to the user engaging with the online system,
o accessing a prediction model of the computer system,
o wherein the prediction model is a machine-learning model trained to output a likelihood of the user conducting an action within a future time period;
• applying the prediction model to user data associated with the user including the online data and the activity data to output the likelihood of the user conducting the action within the future time period;
• responsive to the likelihood of the user conducting the action being above a threshold value, generating, using information about conversion by the user of a set of items during one or more past time periods, a list of one or more items for recommendation to the user related to the action of the user within the future time period; and
• causing a device associated with the user to display a user interface with the list of one or more items for inclusion into a cart of the user.
These limitations (emphasized) 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.) Specifically, the additional element of a device, an online system, online data, one or more widgets, one or more online sites, a network, , an API, a trained machine learning model, and a user interface is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of connecting to a platform on a network) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Further, the additional elements to 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). For example, stating that the steps are responsive to a user engaging with an online system, only generally links the commercial interactions and management of relationships or interactions between people to a computer environment. 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 12, 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.
Therefore, claim 12 does not provide an inventive concept and does not qualify as eligible subject matter.
Claim 1 is a method reciting similar functions as claim 12, and does not qualify as eligible subject matter for similar reasons.
Claim 20 is a system comprising a computer readable storage medium reciting similar functions as claim 12, and does not qualify as eligible subject matter for similar reasons. Claim 20 further recites additional elements including A computer system comprising: a processor; and a non-transitory computer-readable storage medium having instructions that, when executed by the processor, cause the computer system to perform steps which is recited at a high level of generality and only generally links the abstract idea to a particular technological environment.
Claims 2-11 and 13-19 are dependencies of claims 1, and 12. The dependent claims do not add “significantly more” to the abstract idea. They recite additional functions that describe the abstract idea and only generally link the abstract idea to a particular technological environment, including:
collecting feedback data with information about a conversion by the user of each item from the list of one or more items; and re-training the prediction model by updating, based at least in part on the collected feedback data, a set of parameters of the prediction model. (no details are provided regarding how re-training is performed, what inputs, processes and outputs are involved)
Accordingly, the Examiner concludes that there are no meaningful limitations in the claim that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself. The analysis above applies to all statutory categories of invention.
Claim Rejections - 35 USC § 103
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 of this title, 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.
Claims 1-9, 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20210295382 A1 to BUCHANAN in view of U.S. Patent Application No. 2024/0265432 A1 to Gill.
Regarding Claim 1, BUCHANAN discloses a method, performed at a computer system comprising a processor and a computer-readable medium, comprising:
collecting, at a device associated with a user of an online system, online data by integrating one or more widgets with a first set of one or more online sites and gathering the online data via the one or more widgets: ([0038] A widget is an element of a graphical user interface (GUI) that displays information or provides a predefined manner by which a user is able to interact with an application or website. Widgets may take a number of forms that include, but are not limited to, buttons, pull-down menus, selection boxes, progress indicators, on-off checkmarks, scroll bars, windows, window resizing tools, toggle buttons, and the like for displaying information and for inviting, accepting, and responding to user inputs. [0039] The content provisioning platform derives available inventory from one or more inventory providers during an electronic transaction being performed on an e-commerce website and selects contextually relevant content for display on the e-commerce website.)
receiving, via a network and from the device associated with the user in communication with the computer system using an application programming interface (API), the online data gathered via the one or more widgets: ([0062] The user 190 interacts with the available placements displayed in the widget that appears in the contextual content region 350 in order to select one or more of the inventory items. The widget returns inventory selection information back to the e-commerce website 120, which then informs the e-commerce server 110 of the cart items selected by the user 190 based on the presented inventory items. [0013] webpage is encoded in accordance with a content provisioning application programming interface (API) associated with said content provisioning platform, said webpage including a contextual content display region for displaying said selected inventory and said content provisioning API being adapted to deliver said selected content to said contextual content display region. [0050] The transaction data includes one or more contextual attributes associated with the electronic transaction. Depending on the implementation, the received transaction data and/or consumer data are used by either one or both of the prediction services module 154 and the targeting rules module 156.)
collecting, at the device associated with the user, activity data including information about one or more activities of the user at a second set of one or more online sites: ([0049] The CDP 152 stores customer attributes gathered from customer registrations, third-party websites, and transactions made using the e-commerce server 110 and e-commerce website 120.)
receiving, via the network and from the device associated with the user in communication with the computer system using the API, the activity data: ([0050] The prediction services module 154 and targeting rules module 156 together form a selection engine 153 that determines what content is to be presented during an electronic transaction. In some embodiments, the selection engine 153 receives from the e-commerce server 110 transaction data, consumer data, or a combination thereof,)
responsive to the user engaging with the online system, accessing a prediction model of the computer system, wherein the prediction model is a machine-learning model trained to output a likelihood of the user conducting an action within a future time period; ([0077] The prediction services module 154 selects content based on the likelihood of the consumer to engage along with the value of the engagement, when comparing alternatives, with the objective of maximising value of the transaction. The value may not be directly related to the revenue or profit associated with the transaction itself, but may relate to a longer term value of the consumer resulting from the transaction.)
applying the prediction model to user data associated with the user including the online data and the activity data to output the likelihood of the user conducting the action within the future time period; ([0052] The prediction services module 154 acts to select contextually relevant content based on the likelihood of the consumer to engage, along with the value of the engagement. In some embodiments, the prediction services module 154 receives transaction data and/or consumer data from the e-commerce server 110 in order to improve selection of contextually relevant content from available inventory provided by the local inventory database 155 and the inventory provider server 170. In some arrangements, the prediction services module 154 is implemented using machine learning. )
responsive to the likelihood of the user conducting the action being above a threshold value, generating, using information about conversion by the user of a set of items during one or more past time periods, a list of one or more items for recommendation to the user related to the action of the user within the future time period; and ([0073] The contextually relevant content relates to inventory that the content provisioning platform 150 has determined is available from the inventory provider 170, then filtered based on a set of selection rules (likelihood above a threshold), and applied the prediction services module 154 to select a particular creative and content relating to the inventory. In this example, the contextually relevant content (list of items for recommendation) is inventory presented as a widget in the form of an offer of two different parking options, Parking A and Parking B, that is displayed in a region of a display 425 of a computing device accessed by the consumer. [0048] The inventory provider server 170 provides inventory for the content provisioning platform 150 to provide to the e-commerce server 110. Inventory provided by the inventory provider server 170 are additional goods that may be offered for sale to a user 190 making an electronic transaction via a user computing device 195, such as parking for a concert venue, ticket insurance, merchandise, and the like. [0075] Such consumer and transaction data may include, for example, but is not limited to, interaction history (conversion of items during past time periods) for that particular customer, and contextual and consumer demographic attributes to identify micro segments of similar customers which can be used to inform selection. [0057] When the user 190 decides to complete the purchase of the selected concert tickets, the user 190 activates a “Checkout” button and the e-commerce website 120 notifies the e-commerce server 110 that the user 190 wants to proceed to the checkout and complete the purchase (conversion) of the selected concert tickets.)
causing a device associated with the user to display a user interface with the list of one or more items for inclusion into a cart of the user. ([0057] the goods offered for sale by the e-commerce server 110 are concert tickets. The user 190 browses a selection of concert tickets offered for sale on the e-commerce website 120, selects a set of concert tickets to purchase, and adds the selected concert tickets to a virtual shopping cart.)
But does not explicitly disclose a likelihood of the user conducting an action within a future time period; the likelihood of the user conducting the action being above a threshold value.
GILL, on the other hand, teaches a likelihood of the user conducting an action within a future time period; the likelihood of the user conducting the action being above a threshold value. ([0029] the system may be configured to provide one or more customized recommendations based on a prediction that the user is likely planning an upcoming trip, (conducting an action within a future time period) as discussed above with respect to block 102. For example, the user may have made a flight reservation for an upcoming date and time, an excursion reservation in a location outside of the user's home city and/or state, etc. Based on this transaction data, the system may be configured to provide the user with customized recommendations for clothing, items, etc., that may be of help or use to the user on this particular upcoming trip based on, for example, the type of activities or excursions, the time of year (e.g., the season), the formality of various reservations (e.g., an upper scale restaurant), and the like. [0076] track her incoming transactions and provide a system that may use Susan's incoming transaction data to determine a likelihood that Susan is preparing for an upcoming trip. Based on making such determination, the system may send Susan a notification through a mobile application, the notification being a push-notification that says: “We see you might be planning a trip.) Examiner notes that the broadest reasonable interpretation of the claim language is being applied.
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by BUCHANAN, the features as taught by GILL, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify BUCHANAN, to include the teachings of GILL, in order to provide customized recommendations via data analysis (GILL, [0003]).
Regarding Claim 2, BUCHANAN in view of GILL teaches the method of claim 1.
GILL teaches generating the user data for input into the prediction model, the user data comprising at least one of the information about conversion by the user of the set of items during the one or more past time periods, information about integration of the user with one or more payment card entities, data associated with the user setting a temporary delivery address using the online system, or one or more geographical locations of the user shared via the device. ([0024] what the user has historically purchased ahead of and/or during a trip,)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by BUCHANAN, the features as taught by GILL, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify BUCHANAN, to include the teachings of GILL, in order to provide customized recommendations via data analysis (GILL, [0003]).
Regarding Claim 3, BUCHANAN in view of GILL teaches the method of claim 1.
GILL teaches collecting feedback data with information about a conversion by the user of each item from the list of one or more items; and re-training the prediction model by updating, based at least in part on the collected feedback data, a set of parameters of the prediction model. ([0027] system may be configured to continuously train and update its model to provide future travel item recommendations that are more tailored or customized to a given user, as further discussed below.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by BUCHANAN, the features as taught by GILL, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify BUCHANAN, to include the teachings of GILL, in order to provide customized recommendations via data analysis (GILL, [0003]).
Regarding Claim 4, BUCHANAN in view of GILL teaches the method of claim 1.
GILL teaches accessing a classification model of the computer system, wherein the classification model is a machine-learning model trained to predict a type of the action; and applying the classification model to the user data to predict the type of action. ([0014] utilize, in some instances, MLMs, which are necessarily rooted in computers and technology, to evaluate transaction data, travel data (e.g., travel advisories), and/or image data (e.g., provided via computer vision technology). This, in some examples, may involve using transaction, travel, and/or image input data and an MLM, applied to generate a travel item recommendation. Using an MLM and a computer system configured in this way may allow the system to provide accurate and efficient travel item recommendations to users before and/or during their travels. [0029] Based on this transaction data, the system may be configured to provide the user with customized recommendations for clothing, items, etc., that may be of help or use to the user on this particular upcoming trip based on, for example, the type of activities or excursions, the time of year (e.g., the season), the formality of various reservations (e.g., an upper scale restaurant), and the like.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by BUCHANAN, the features as taught by GILL, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify BUCHANAN, to include the teachings of GILL, in order to provide customized recommendations via data analysis (GILL, [0003]).
Regarding Claim 5, BUCHANAN in view of GILL teaches the method of claim 1.
GILL teaches wherein displaying the user interface further comprising: causing the device associated with the user to display the user interface further with a message prompting the user to provide feedback in relation to the type of action predicted by the classification model; collecting the feedback provided by the user; and re-training the classification model by updating, based at least in part on the collected feedback, a set of parameters of the classification model. ([0029] Based on this transaction data, the system may be configured to provide the user with customized recommendations for clothing, items, etc., that may be of help or use to the user on this particular upcoming trip based on, for example, the type of activities or excursions, the time of year (e.g., the season), the formality of various reservations (e.g., an upper scale restaurant), and the like. ([0027] system may be configured to continuously train and update its model to provide future travel item recommendations that are more tailored or customized to a given user, as further discussed below.) [0023] Providing this type of customer feedback loop may provide a benefit of ensuring the system can accurately identify objects to provide the user with more customized travel item recommendations, as further discussed below.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by BUCHANAN, the features as taught by GILL, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify BUCHANAN, to include the teachings of GILL, in order to provide customized recommendations via data analysis (GILL, [0003]).
Regarding Claim 6, BUCHANAN in view of GILL teaches the method of claim 4.
GILL teaches wherein generating the list of one or more items comprises: applying the classification model to the user data and information about the type of action to identify the list of one or more items for recommendation to the user. ([0029] Based on this transaction data, the system may be configured to provide the user with customized recommendations for clothing, items, etc., that may be of help or use to the user on this particular upcoming trip based on, for example, the type of activities or excursions, the time of year (e.g., the season), the formality of various reservations (e.g., an upper scale restaurant), and the like. ([0027] system may be configured to continuously train and update its model to provide future travel item recommendations that are more tailored or customized to a given user, as further discussed below.) [0023] Providing this type of customer feedback loop may provide a benefit of ensuring the system can accurately identify objects to provide the user with more customized travel item recommendations, as further discussed below.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by BUCHANAN, the features as taught by GILL, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify BUCHANAN, to include the teachings of GILL, in order to provide customized recommendations via data analysis (GILL, [0003]).
Regarding Claim 7, BUCHANAN in view of GILL teaches the method of claim 4.
GILL teaches wherein applying the classification model comprises: applying the classification model to the user data to identify one or more geographical locations associated with the action as part of the predicted type of action. ([0013] examples of the present disclosure may provide for generating travel item recommendations prior to and/or during individuals' travels, and facilitating the ordering and transmitting of various items to individuals at their respective travel destinations. Specifically, examples of the present disclosure may provide for receiving transaction data, travel data, and/or image data associated with a user, generating travel item recommendations based on the transaction, travel, and/or image data, and transmitting requests to merchant systems to provide the user with certain items, based on the recommendations, at a predefined location (e.g., the user's ultimate travel destination).)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by BUCHANAN, the features as taught by GILL, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify BUCHANAN, to include the teachings of GILL, in order to provide customized recommendations via data analysis (GILL, [0003]).
Regarding Claim 8, BUCHANAN in view of GILL teaches the method of claim 7.
GILL teaches wherein generating the list of one or more items comprises: applying the classification model to information about one or more items that the user converted during one or more past time periods when the user was located within a threshold vicinity from the one or more geographical locations to identify the list of one or more items for recommendation to the user. (([0024] what the user has historically purchased ahead of and/or during a trip,) [0013] examples of the present disclosure may provide for generating travel item recommendations prior to and/or during individuals' travels, and facilitating the ordering and transmitting of various items to individuals at their respective travel destinations. Specifically, examples of the present disclosure may provide for receiving transaction data, travel data, and/or image data associated with a user, generating travel item recommendations based on the transaction, travel, and/or image data, and transmitting requests to merchant systems to provide the user with certain items, based on the recommendations, at a predefined location (e.g., the user's ultimate travel destination).)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by BUCHANAN, the features as taught by GILL, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify BUCHANAN, to include the teachings of GILL, in order to provide customized recommendations via data analysis (GILL, [0003]).
Regarding Claim 9, BUCHANAN in view of GILL teaches the method of claim 7.
GILL teaches wherein generating the list of one or more items comprises: applying the classification model to information about one or more items that one or more other users of the online system converted during one or more past time periods when the one or more other users were located within a threshold vicinity from the one or more geographical locations to identify the list of one or more items for recommendation to the user. (([0024] what the user has historically purchased ahead of and/or during a trip,) [0013] examples of the present disclosure may provide for generating travel item recommendations prior to and/or during individuals' travels, and facilitating the ordering and transmitting of various items to individuals at their respective travel destinations. Specifically, examples of the present disclosure may provide for receiving transaction data, travel data, and/or image data associated with a user, generating travel item recommendations based on the transaction, travel, and/or image data, and transmitting requests to merchant systems to provide the user with certain items, based on the recommendations, at a predefined location (e.g., the user's ultimate travel destination).[0024] the system may be configured to compare the objects identified in the first (and/or additional) image data with a predefined database of items users typically carry when traveling to various destinations around the world.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by BUCHANAN, the features as taught by GILL, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify BUCHANAN, to include the teachings of GILL, in order to provide customized recommendations via data analysis (GILL, [0003]).
Claim 12 recites a computer program product comprising substantially similar limitations as claim 1. The claim is rejected under substantially similar grounds as claim 1.
Claim 13 recites a computer program product comprising substantially similar limitations as claim 2. The claim is rejected under substantially similar grounds as claim 2.
Claim 14 recites a computer program product comprising substantially similar limitations as claim 3. The claim is rejected under substantially similar grounds as claim 3.
Claim 15 recites a computer program product comprising substantially similar limitations as claim 4. The claim is rejected under substantially similar grounds as claim 4.
Claim 16 recites a computer program product comprising substantially similar limitations as claim 5. The claim is rejected under substantially similar grounds as claim 5.
Claim 17 recites a computer program product comprising substantially similar limitations as claim 6. The claim is rejected under substantially similar grounds as claim 6.
Claim 18 recites a computer program product comprising substantially similar limitations as claim 7. The claim is rejected under substantially similar grounds as claim 7.
Claim 19 recites a computer program product comprising substantially similar limitations as claim 8. The claim is rejected under substantially similar grounds as claim 8.
Claim 20 recites a computer system comprising substantially similar limitations as claim 1. The claim is rejected under substantially similar grounds as claim 1.
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over US 20210295382 A1 to BUCHANAN in view of U.S. Patent Application No. 2024/0265432 A1 to Gill in view of US 20240144079 A1 to EDWARDS.
Regarding Claim 10, BUCHANAN in view of GILL teaches the method of claim 7.
EDWARDS teaches wherein generating the list of one or more items comprises: accessing an item recommendation ranking model of the computer system, wherein the item recommendation ranking model is a machine-learning model trained to generate a score for each item of a plurality of items; applying the item recommendation ranking model to first data associated with the one or more geographical locations, second data with further information about the action, or third data including a portion of the user data to generate the score for each item of the plurality of items; and applying the item recommendation ranking model to the score for each item of the plurality of items to identify the list of one or more items for recommendation to the user. (([0032] the recommendation generation system 220 may determine, via the trained MLM, whether at least a first trip recommendation of the one or more trip recommendations exceeds a predetermined threshold indicating a likelihood the user will be interested in the first trip recommendation. In some embodiments, the predetermined threshold may be a fraction on a scale of 0 to 1, or may be a score on a scale of 1 to 100. For example, the system may be configured to predict whether a user is at least 75% likely (e.g., satisfying a score of at least 75 on a 1-100 scale) to be interested in a trip recommendation by evaluating user data (e.g., image data, transaction data, etc.), as discussed herein, to analyze general and specific trips the user has historically taken.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by the combination, the features as taught by EDWARDS, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination, to include the teachings of EDWARDS, in order to provide recommendations to the user (EDWARDS, [0079]).
Regarding Claim 11, BUCHANAN in view of GILL teaches the method of claim 10.
GILL teaches further comprising at least one of: generating the first data for input into the item recommendation ranking model, the first data comprising at least one of information about past conversions associated with the one or more geographical locations, information about a season during which the user travels to the one or more geographical locations, information about one or more events occurring during the future time period at the one or more geographical locations, information about one or more cultural norms associated with the one or more geographical locations, culinary information for the one or more geographical locations, or health information for the one or more geographical locations; generating the second data for input into the item recommendation ranking model, the second data comprising at least one of information about the future time period of the action, information about a time duration of the action, or information about one or more accommodations associated with the user during the action; or generating the third data for input into the item recommendation ranking model, the third data comprising information about conversion by the user of one or more items during one or more past time periods. (([0024] what the user has historically purchased ahead of and/or during a trip,) [0013] examples of the present disclosure may provide for generating travel item recommendations prior to and/or during individuals' travels, and facilitating the ordering and transmitting of various items to individuals at their respective travel destinations. Specifically, examples of the present disclosure may provide for receiving transaction data, travel data, and/or image data associated with a user, generating travel item recommendations based on the transaction, travel, and/or image data, and transmitting requests to merchant systems to provide the user with certain items, based on the recommendations, at a predefined location (e.g., the user's ultimate travel destination).[0024] the system may be configured to compare the objects identified in the first (and/or additional) image data with a predefined database of items users typically carry when traveling to various destinations around the world. .)
Response to Arguments
Applicant’s arguments filed with respect to the rejection of claims under 35 USC 101 have been fully considered but they are not persuasive.
Applicant argues the limitations of amended claim1 impose meaningful limits on practicing the judicial exception because they require a computer system to integrate specific devices and entities (i.e., device, widgets, online sites) to collect and receive specific online data and activity data for processing at the computer system.
Examiner disagrees. Using specific technology to collect and receive specific kinds of data only applies the abstract idea of collecting and receiving data to a particular technology (computers and networks). The additional limitations in the amended claims only generally link the recited abstract ideas to a particular technological environment and to not integrate them into a practical application.
Applicant further argues that integrating the one or more widgets with the first set of one or more online sites and gathering the online data via the one or more widgets, by communicating the gathered online data from the device associated with the user to the computer system using an API, and by communicating the activity data from the device to the computer system using the API recites specific technical details about how the online data and activity data are collected and received.
However, a very specific abstract idea is still an abstract idea. The claims collect and receive data in a particular way, but the use of technologies to perform collecting and receiving does not integrate them into a practical application.
Applicant’s arguments with respect to rejection of the claim under 35 USC 103 have been considered but are moot in view of new grounds of rejection, necessitated by Applicant’s amendment.
Applicant argues that the amendment in independent claim 1 overcomes the current prior art of record.
However, a new combination of references is relied upon to teach amended claim 1. Examiner directs Applicant’s attention to the office action, above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michelle T. Kringen whose telephone number is (571)270-0159. The examiner can normally be reached M-F: 11am-7pm.
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/MICHELLE T KRINGEN/Primary Examiner, Art Unit 3689
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