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 .
Priority
Applicant has not claimed priority to another application. Application 18/676,345 was filed 5/28/2024.
Information Disclosure Statement
No IDS has been submitted.
Status of Claims
Applicant’s amended claims, filed 6/22/2026, have been entered. Claims 1, 3-6, 8-10, 12, 13, and 15-20 have been amended. Claims 1-20 are currently pending in this application 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. The claim(s) recite(s) an abstract idea. This judicial exception is not integrated into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Under Step 1 of the Alice/Mayo test the claims are directed to statutory categories. Specifically, the method, as claimed in claims 1-12, are directed to a process, the non-transitory computer readable medium, as claimed in claims 13-19, are directed to an article of manufacture, and the system, as claimed in claim 20, is directed to a machine, (see MPEP 2106.03).
Under Step 2A (prong 1), claim 1, taken as representative, recites at least the following limitations (emphasis added) that recite an abstract idea:
receiving session data related to a current session of the user;
accessing a user type prediction model, wherein the user type prediction model is a classifier-based model trained to predict a type of the user for the current session by classifying the user as one of a browser type or a type that is not the browser type;
applying the user type prediction model to the session data to generate a score for the user indicative of a likelihood that the user is of the browser type for the predicted type of the user for the current session;
comparing the score for the user with a threshold score;
responsive to the score for the user being greater than the threshold score, identifying, using the score for the user, user data associated with the user, and information about the current session, a set of elements arranged in a specific order for presentation to the user, wherein the set of elements includes at least one discovery flow element comprising one or more items or one or more item collections selected based on an order history of the user and not directly responsive to a search query of the user;
generating a [display] associated with the user that includes the set of elements arranged according to the specific order, wherein the [display] includes the at least one discovery flow element that would not be included in a [display] generated for a user who is not classified as the browser type and whose score does not exceed the threshold; and
causing the display with the set of elements arranged according to the specific order.
These limitations recite certain methods of organizing human activity, such as performing commercial interactions (see MPEP 2106.04(a)(2)(II)). Certain methods of organizing human activity are defined by MPEP 2106.04 as including “fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).” In this case, the abstract ideas recited in representative claim 1 are certain methods of organizing human activity because displaying ordered elements based on a predicted type of user for a current session (i.e., a recommendation) is a commercial or legal interaction because it is a advertising, marketing or sales activity, or business relations. Thus, claim 1 recites an abstract idea.
Independent claims 13 and 20 recite the same abstract idea as recited in independent claim 1. As such, the analysis under Step 2A, Prong 1 is the same for independent claims 13 and 20 as described above for independent claim 1.
Under Step 2A (prong 2), if it is determined that the claims recite a judicial exception, it is then necessary to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of that exception (see MPEP 2106.04). As stated in the MPEP, when “an additional element merely recites the words ‘apply it (or an equivalent) with the judicial exception, or merely uses a computer as a tool to perform an abstract idea,” the judicial exception has not been integrated into a practical application. In this case, representative claim 1 includes additional elements such as (additional elements are bolded):
receiving, from a device associated with a user of the computer system and via a network, session data related to a current session of the user with the computer system;
accessing a user type prediction model of the computer system, wherein the user type prediction model is a classifier-based machine-learning model trained to predict a type of the user for the current session by classifying the user as one of a browser type or a type that is not the browser type;
applying the user type prediction model to the session data to generate a score for the user indicative of a likelihood that the user is of the browser type for the predicted type of the user for the current session;
comparing the score for the user with a threshold score;
responsive to the score for the user being greater than the threshold score, identifying, using the score for the user, user data associated with the user, and information about the current session, a set of user interface elements arranged in a specific order for presentation to the user, wherein the set of user interface elements includes at least one discovery flow element comprising one or more items or one or more item collections selected based on an order history of the user and not directly responsive to a search query of the user;
generating a user interface of the device associated with the user that includes the set of user interface elements arranged according to the specific order, wherein the user interface includes the at least one discovery flow element that would not be included in a user interface generated for a user who is not classified as the browser type and whose score does not exceed the threshold; and
causing the device associated with the user to display the user interface with the set of user interface elements arranged according to the specific order.
In addition to the additional elements bolded above, claim 13 additionally recites “A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps” and claim 20 additionally recites “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.”
Although reciting these additional elements, taken alone or in combination these elements are not sufficient to integrate the abstract idea into a practical application. These additional elements merely amount to the general application of the abstract idea to a technical environment (“from a device”, “of an computer system”, “via a network”, a classifier-based “machine-learning” model “is trained”, “a user interface of the device” including “user interface elements”, “a computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps”, “a computer system comprising: a processor; and a non-transitory computer-readable storage medium having instructions” and insignificant pre-and-post solution activity (receiving information, accessing information, displaying information). The specification makes clear the general-purpose nature of the technological environment. This is because the additional elements of claims 1, 13, and 20 are recited at a high level of generality (i.e., as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform the abstract idea) (see Fig. 1; paragraphs [0014], [0019], [0027], [0030], [0052]-[0056], [0073]-[0074], [00104]-[00106]). The specification indicates that while exemplary general-purpose systems may be specific for descriptive purposes, any elements capable of implementing the claimed invention are acceptable. That is, the technology used to implement the invention is not specific or integral to the claim. The description demonstrates that these additional elements are merely generic devices such as a generic computer. Further, the additional elements do no more than generally link the use of a judicial exception to a particular environment or field of use (such as the Internet or computing networks).
Therefore, considered both individually and as an ordered pair, the additional elements do no more than generally link the use of the abstract idea to a particular technological environment or field of use. That is, given the generality with which the additional elements are recited, the limitations do not implement the abstract idea with, or use the abstract idea in conjunction with, a particular machine or manufacture that is integral to the claim. Additionally, the claims do not reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, do not transform or reduction of a particular article to a different state or thing; and do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technology environment, such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea into a practical application, and is therefore “directed to” the abstract idea.
In addition to the above, the recited receiving, accessing, displaying steps (even assuming arguendo they do not form part of the abstract idea, which the Examiner does not acquiesce), are at best little more than extra-solution activity (e.g., data gathering, presentation of data) that contributes nominally or insignificantly to the execution of the claimed system (see MPEP 2106.05(g)).
In view of the above, under Step 2A (prong 2), claims 1, 13, and 20 do not integrate the recited exception into a practical application.
Under Step 2B, examiners should evaluate additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Returning to claims 1, 13, and 20, taken individually or as a whole the additional elements of claims 1, 13, and 20 do not provide an inventive concept (i.e. they do not amount to “significantly more” than the exception itself). As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Furthermore, the additional elements fail to provide significantly more also because the claim simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. For example, the additional elements of claims 1, 13, and 20 utilize operations the courts have held to be well-understood, routine, and conventional (see: MPEP 2106.05(d)(II)), including at least:
receiving or transmitting data over a network,
storing or retrieving information from memory,
presenting offers
Even considered as an ordered combination (as a whole), the additional elements of claims 1, 13, and 20 do not add anything further than when they are considered individually.
In view of the above, claims 1, 13, and 20 do not provide an inventive concept (“significantly more”) under Step 2B, and is therefore ineligible for patenting.
Regarding claims 5
Dependent claim(s) 5, 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 claim(s) 5 merely further define the abstract limitations of claim(s) 1 or provide further embellishments of the limitations recited in independent claim claim(s) 1.
Claim 5 sets forth:
wherein applying the user type prediction model comprises: applying the user type prediction model further to information about a retailer that is related to the current session to generate the score for the user.
Such recitations merely embellish the abstract idea of displaying ordered elements based on a predicted type of user for a current session (i.e., a recommendation). The claims do not set forth any further additional limitations, and therefore such abstract embellishments are applied to the additional limitations recited in claim(s) 1, which do no more than generally link the use of the abstract idea to a particular technological environment, do not integrate the abstract idea into a practical application, and do not provide an inventive concept. Accordingly, the claims do not confer eligibility on the claimed invention and is ineligible for similar reasons to claim(s) 1.
Thus, dependent claim 5 is ineligible.
Regarding claim 2-4, 6-12, and 14-19
Dependent claim(s) 2-4, 6-12, and 14-19 sets forth:
wherein receiving the session data comprises: receiving, via the device associated with the user and via the network, at least one of a search query entered by the user via a search interface of the device associated with the user, a number of predefined collections of items the user engaged with during the current session, a ratio between a number of unique first items the user engaged with during the current session and a total number of unique items the user added to a cart during the current session, a ratio between a number of unique second items added to the cart without the user viewing details associated with the second items and the total number of unique items added to the cart, or a timestamp of each unique item added to the cart.
wherein receiving the session data comprises: gathering, via one or more sensors mounted to a physical receptacle utilized by the user during the current session for shopping at a location of a retailer, data with information about at least one of a duration of the current session at the location of the retailer, a number of items scanned by a computing system associated with the physical receptacle during the current session, an average speed of movement of the physical receptacle during the current session, or an average distance traveled by the physical receptacle during the current session per item added to the physical receptacle; and receiving, from the computing system associated with the physical receptacle and via the network, the gathered data as at least a portion of the session data.
retrieving, from a database of the computer system, data with information about at least one of a ratio between a number of unique items the user converted during a defined time period by directly adding the unique items to shopping carts without further engagement with the unique items and a total number of items the user converted during the defined time period, or an average shopping time per unique item converted by the user during the defined time period; and applying the user type prediction model further to the retrieved data to generate the score for the user.
gathering, over a defined time period, data related to interactions between a collection of users of the computer system and the computer system; assigning, using the gathered data, a label to each user in the collection of users; and training, using the gathered data and the assigned label for each user in the collection of users, the user type prediction model to generate a set of initial values for a set of parameters of the user type prediction model.
collecting feedback data with information about engagement by the user with the set of user interface elements; and re-training the user type prediction model by updating, using the collected feedback data, a set of parameters of the user type prediction model.
wherein identifying the set of user interface elements comprises: ranking, using the score for the user, the user data, and the information about the current session, a plurality of user interface elements retrieved from a database of the computer system to identify a rank of each user interface element of the plurality of user interface elements; selecting, using the rank of each user interface element, a defined number of user interface elements from the plurality of user interface elements as the set of user interface elements for presentation to the user; and arranging, using the rank of each user interface element, the set of user interface elements in the specific order.
wherein ranking the plurality of user interface elements comprises: accessing a content priority model of the computer system, wherein the content priority model is a machine-learning model trained to identify a priority of a user interface element for presentation to the user; and applying the content priority model to the score for the user, the user data, and the information about the current session to generate the rank for each user interface element of the plurality of user interface elements that is indicative of a priority of that user interface element for presentation to the user.
retrieving, from a database of the computer system, the user data comprising information about at least one of a plurality of user interface elements associated with a plurality of items converted by the user during each order of a plurality of orders for a defined time period, or affinities for the defined time period between a plurality of categories of user interface elements.
receiving, from the device associated with the user and via the network, the information about the current session including at least one of content of a cart associated with the current session or a browsing history of the user for the current session.
gathering, via one or more sensors mounted to a physical receptacle utilized by the user during the current session for shopping at a location of a retailer, information about physical locations of the physical receptacle during the current session; and receiving, from a computing system associated with the physical receptacle and via the network, the gathered information as at least a portion of the information about the current session.
Such recitations merely embellish the abstract idea of displaying ordered elements based on a predicted type of user for a current session (i.e., a recommendation). While the claim(s) do set forth the additional elements of “a search interface of the device associated with the user”, “one or more sensors mounted to a physical receptacle”, “wherein the content priority model is a machine-learning model trained”, “a database of the computer system”, “training…the user type prediction model”, “re-training the user type prediction model”, “a computing system associated with the physical receptacle and via the network” these recitations are similar to the additional limitations in claims 1 and 13, as they do no more than generally link the use of the abstract idea to a particular technological environment. That is these additional elements merely amount to the general application of the abstract idea to a technical environment. The specification makes clear the general-purpose nature of the technological environment. Paragraphs [0014], [0019], [0027], [0030], [0032], [0052]-[0056], [0073]-[0074], [0094], [00104]-[00106] indicate that while exemplary general-purpose systems may be specific for descriptive purposes, any elements capable of implementing the claimed invention are acceptable. That is, the technology used to implement the invention is not specific or integral to the claim. Therefore, these additional elements do not integrate the abstract idea into a practical application because they merely amount to using a computer to apply the abstract idea and no more than a general link of the use of the abstract idea to a particular technological environment or field of use and thus do not act to integrate the abstract idea into a practical application of the abstract idea. Further, the “sensors mounted to a physical receptacle” is recited at a high level and amounts to merely applying the abstract idea (see paragraphs [0032], [0094]).
Additionally, the additional elements do not amount to significantly more because they merely amount to using a computer to apply the abstract idea and amount to no more than a general link of the use of the abstract idea to a particular technological environment.
Thus, dependent claims 2-4, 6-12, and 14-19 are also ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, 5-11, 13, 14, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Santos et al. (US 2019/0325069 A1) in view of Ali et al. (US 2024/0256556 A1 [previously recited]).
Regarding claim 1, Santos et al., hereinafter Santos, discloses a method, performed at a computer system comprising a processor and a computer-readable medium (Figs. 1-2; abstract; ¶0017, ¶0019), comprising:
receiving, from a device associated with a user of the computer system and via a network, session data related to a current session of the user with the computer system (Figs. 2 and 5; ¶0059 [A user-input computer-readable query can be received (820), with the query requesting search results from a computerized search engine. The technique can also include receiving an impression including contextual data (830), which can be connected to the query in the computer system, with the contextual data encoding information about a context of the query.] in view of ¶¶0022-0029 [devices communication over network], ¶¶0036-0037 [processing components (250, 252, 254, and 256) discussed here can each receive at least part of an impression (240), which is computer-readable data that provides information about an individual query just received from the client device (210). For example, the impression (240) can include the query (242) itself (which may be in the same form as received from the client device (210) or in some other form, such as a translation of the query, etc.). The impression (240) can also include contextual data or query context data (244). The query context data (244) is data other than the query (242) itself, but which encodes information about the context in which the query was entered or received.], ¶0045 [some examples of query context data (244) that may be processed using the classification model (260)]);
accessing a user type prediction model of the computer system, wherein the user type prediction model is a classifier-based machine-learning model trained to predict a type of the user for the current session by classifying the user as one of a browser type or a type that is not the browser type (Figs. 2, 6, and 8; ¶¶0014-0017 [classify this contextual data into a pre-defined profile. Following are examples of such profiles: Profile 1: a non-scrollable profile, where the computer system has some degree of confidence user input will not scroll down the page of search results; Profile 2: a reader profile, where the computer system has some degree of confidence that more time will be spent “reading” the page, i.e., viewing the page without clicking on links on the page; Profile 3: a competitor profile, where the computer system has some degree of confidence user input will be provided to navigate from the search engine page to another search engine; and Profile 4: a browsing profile, where the computer system has some degree of confidence user input will click on many links on the page, and click back to the page… The model to be used can be any of various different computerized classification models. For example, the classification model can be a machine learning model], ¶0020 [user profile], ¶0037 [a user profile (246) may be logged in at the client device (210) in connection with the submission of the query (242), so that the query (242) is considered to be received from the user profile (246). In this case, the query context data (244) may include at least a portion of the user profile (246), which may include indications of past actions of the user profile (246) in response to receiving search results and/or user preferences explicitly entered into the user profile (246) by user input.], ¶¶0038-0042, ¶0045 [some examples of query context data (244) that may be processed using the classification model (260)]);
applying the user type prediction model to the session data to generate a score for the user indicative of a likelihood that the user is of the browser type for the predicted type of the user for the current session (Figs. 2, 6, and 8; ¶0048 [when responding to a query (242), the query classification engine (250) can select a user interface profile (262) based on the results of processing the impression (240) using the classification model (260). Specifically, the classification engine (250) can determine which, if any, of the user interface profiles (262) applies to the query (242). For example, the query classification engine (250) may determine whether any of the resulting confidence scores exceeds a threshold value. For example, the processing of the classification model (260) may indicate a reading profile with a confidence score of 0.7, and a non-scrolling profile with a confidence score of 0.3. The query classification engine may be programmed to only select a user interface profile (262) if it has a score greater than a threshold value of 0.6. Thus, the query classification engine (250) may determine that the query (242) is classified into the reading profile.] in view of ¶0037 [a user profile (246) may be logged in at the client device (210) in connection with the submission of the query (242), so that the query (242) is considered to be received from the user profile (246). In this case, the query context data (244) may include at least a portion of the user profile (246)], ¶0020 [user profile], ¶0045 [some examples of query context data (244) that may be processed using the classification model (260)], and ¶¶0059-0060);
comparing the score for the user with a threshold score (Figs. 2, 6, and 8; ¶0046 [The system (200) can define features (values, thresholds, labels, etc.) of such output data that can be indicative of a particular user interface profile (262)… The system may define threshold values for such output data, where the system can take exceeding a threshold as indicative of a corresponding user interface profile (262).], ¶0047-¶0048 [when responding to a query (242), the query classification engine (250) can select a user interface profile (262) based on the results of processing the impression (240) using the classification model (260). Specifically, the classification engine (250) can determine which, if any, of the user interface profiles (262) applies to the query (242). For example, the query classification engine (250) may determine whether any of the resulting confidence scores exceeds a threshold value… this selection may compare confidence values for one or more user interface profiles (262) to one or more computer-readable thresholds or and/or rules. For example, a user interface structure generator (272) may be selected if a confidence score for the corresponding user interface profile is above a threshold value.] in view of ¶0037 [a user profile (246) may be logged in at the client device (210) in connection with the submission of the query (242), so that the query (242) is considered to be received from the user profile (246). In this case, the query context data (244) may include at least a portion of the user profile (246)] and ¶¶0059-0060);
responsive to the score for the user being greater than the threshold score, identifying, using the score for the user, user data associated with the user, and information about the current session, a set of user interface elements arranged in a specific order for presentation to the user, wherein the set of user interface elements includes at least one discovery flow element (Figs. 2, 6, and 8; ¶0049 [the user interface structure generators (272) may include user interface libraries that define user interface structural changes to be applied for corresponding user interface profiles (262). For example, these may include changes such as those discussed above for… a browser profile… . The results from the selected user interface structure generator(s) (272) can be provided to a general results page generator (274). The general results page generator (274) can also receive data from the other query processing components (252, 254, and 256), indicating data such as ranked results lists, advertising item lists, and query answers to be included in a search results page (280) produced by the general results page generator (274). The selected user interface structure generator(s) (272) can impose the corresponding user interface structure changes corresponding to one or more of the selected user interface profile(s) (262) on the search results page (280) by indicating such changes to the general results page generator (274), which can be programmed to implement the user interface structure changes indicated by the selected user interface structure generator(s) (272).], ¶0045 [some examples of query context data (244) that may be processed using the classification model (260)], ¶0055 [Referring to FIG. 6, the search results page (280) is illustrated with a browsing visual structure (630). The browsing visual structure (630) can make clicking on links in the search results easier and less error prone. For example, as shown, each listing in the Web page search results can include an increased clickable area for clicking on the link and may also include a box around the text of the title for the listing, to form a displayed button. For example, a clickable button may include the title “RESULT NUMBER 1”, as shown in FIG. 6.] in view of ¶0020 [user profile], ¶¶0036-0037 [The search engine (230) can also include a Web page results ranker (252), which can rank indexed result items that link to Web pages, with the ranking being indicative of a score based on various factors, so that the result items can be ordered in the search results according to the rankings… a user profile (246) may be logged in at the client device (210) in connection with the submission of the query (242), so that the query (242) is considered to be received from the user profile (246). In this case, the query context data (244) may include at least a portion of the user profile (246), which may include indications of past actions of the user profile (246) in response to receiving search results and/or user preferences explicitly entered into the user profile (246) by user input.], ¶0059, ¶0063 [in some scenarios, content of the search results may be changed along with imposing the visual structure on the search results page]);
generating a user interface of the device associated with the user that includes the set of user interface elements arranged according to the specific order, wherein the user interface includes the at least one discovery flow element that would not be included in a user interface generated for a user who is not classified as the browser type and whose score does not exceed the threshold (Figs. 2-8; ¶¶0052-0057 [FIGS. 4-7 illustrate the same search results content (320) in a search results page (280), but with different visual structures imposed on the search results page (280)… Referring to FIG. 6, the search results page (280) is illustrated with a browsing visual structure (630). The browsing visual structure (630) can make clicking on links in the search results easier and less error prone. For example, as shown, each listing in the Web page search results can include an increased clickable area for clicking on the link and may also include a box around the text of the title for the listing, to form a displayed button. For example, a clickable button may include the title “RESULT NUMBER 1”, as shown in FIG. 6.], ¶0059 [responding to the receiving (820) of the query by selecting (850) a visual structure generator out of multiple available visual structure generators, with the selecting (850) using results of the classifying (840) of the query. The available visual structure generators can include different visual structure generators that are each programmed to impose a different visual structure to displayable search results pages. For example, such visual structure generators can include one or more user interface libraries as well as components that are programmed to use the user interface libraries to impose corresponding visual structures on displayable search results pages, such as hypertext markup language pages or other types of search results pages. The technique can also include responding to the receiving (820) of the query by generating (860) a search results page including at least a portion of the requested search results. The generating (860) of the search results page can include using the selected visual structure generator to impose a selected visual structure on the search results page, with the selected visual structure corresponding to the selected visual structure generator.] in view of ¶0017-0020 [user profile], ¶0037 [a user profile (246) may be logged in at the client device (210) in connection with the submission of the query (242), so that the query (242) is considered to be received from the user profile (246). In this case, the query context data (244) may include at least a portion of the user profile (246), which may include indications of past actions of the user profile (246) in response to receiving search results and/or user preferences explicitly entered into the user profile (246) by user input.], ¶0045 [examples of query context data (244) that may be processed using the classification model (260)], ¶0063 [in some scenarios, content of the search results may be changed along with imposing the visual structure on the search results page]); and
causing the device associated with the user to display the user interface with the set of user interface elements arranged according to the specific order (Figs. 2-8; ¶0050 [The search results page (280) that includes the user interface structures imposed by the selected user interface structure generator(s) (272) can be returned to the client device (210) via the computer network (220). The client device (210) can display the search results page (280) on the computer display (212), and can receive user input directed at user interface features of the search results page (280), such as user input selecting links on the search results page, user input scrolling the search results page (280), etc.], ¶0059 [returning (870) the generated search results page in response to the receiving (820) of the query. The technique may further include displaying the returned search results page and receiving user input directed at the search results page]).
While Santos discloses identifying a set of user interface elements arranged in a specific order for presentation to the user, wherein the set of user interface elements includes at least one discovery flow element (Figs. 2-8; ¶¶0052-0057, ¶0045, ¶0055, ¶0020, ¶¶0036-0037, ¶0059, ¶0063), Santos does not explicitly disclose the at least one discovery flow element comprising one or more items or one or more item collections selected based on an order history of the user and not directly responsive to a search query of the user. However, in the field of generating dynamic interfaces (abstract), Ali et al., hereinafter Ali discloses identifying a set of user interface elements including a discovery flow element comprising one or more items or one or more item collections selected based on an order history of the user and not directly responsive to a search query of the user (Figs. 2-6; ¶¶0062-0063, ¶¶0067-0068 [historic purchase data], ¶¶0070-0072). The step of Ali is applicable to the method of Santos as they share characteristics and capabilities, namely, they are directed to displaying customized information on a user interface. It would have been obvious to one of ordinary skill in the art at the time of filing to modify the identified user interface elements and content of the search results as taught by Santos with the content modules selected based on personalization data which includes historic purchase data of a user as taught by Ali. One of ordinary skill in the art at the time of filing would have been motivated to expand the method of Santos in order to recommend content specific to a user (¶¶0002-0003).
Regarding claim 2, Santos in view of Ali teaches the method of claim 1, Santos further discloses wherein receiving the session data comprises:
receiving, via the device associated with the user and via the network, at least one of a search query entered by the user via a search interface of the device associated with the user, a number of predefined collections of items the user engaged with during the current session, a ratio between a number of unique first items the user engaged with during the current session and a total number of unique items the user added to a cart during the current session, a ratio between a number of unique second items added to the cart without the user viewing details associated with the second items and the total number of unique items added to the cart, or a timestamp of each unique item added to the cart (Figs. 2 and 5; ¶0059 [A user-input computer-readable query can be received (820), with the query requesting search results from a computerized search engine. The technique can also include receiving an impression including contextual data (830), which can be connected to the query in the computer system, with the contextual data encoding information about a context of the query.] in view of ¶¶0022-0029 [devices communication over network], ¶¶0036-0037 [processing components (250, 252, 254, and 256) discussed here can each receive at least part of an impression (240), which is computer-readable data that provides information about an individual query just received from the client device (210). For example, the impression (240) can include the query (242) itself (which may be in the same form as received from the client device (210) or in some other form, such as a translation of the query, etc.). The impression (240) can also include contextual data or query context data (244). The query context data (244) is data other than the query (242) itself, but which encodes information about the context in which the query was entered or received.], ¶0045 [some examples of query context data (244) that may be processed using the classification model (260)]).
Regarding claim 5, Santos in view of Ali teaches the method of claim 1. While Santos further discloses, wherein applying the user type prediction model comprises:
applying the user type prediction model further to information to the current session to generate the score for the user (Figs. 2, 6, and 8; ¶0048 in view of ¶0016, ¶0037, ¶0020 [user profile], ¶¶0026-0027, ¶0037, ¶0042-0045, and ¶¶0059-0060), Santos does not explicitly disclose applying the user type prediction model further to information about a retailer that is related to the current session to generate the score for the user. However, Ali further teaches applying the user type prediction model further to information about a retailer that is related to the current session to generate the score for the user (Figs. 2-5, 8-11; ¶0047 [e-commerce interface], ¶0059-0060, ¶0062, ¶¶0064-0065 [In an e-commerce environment, a generic content module can include, for example, seasonal items, high-traffic items, promotional items, etc.… In the context of an e-commerce interface, thematic groupings can include, but are not limited to, departments, sub-departments, seasons, and promotions.] ¶¶0076-0077 [items within a large catalog of items is comparable to information about a retailer], ¶0084 [bestselling or highest traffic items]). It would have been obvious to one of ordinary skill in the art at the time of filing to modify the applied information as taught by Santos with the information about a retailer as taught by Ali. One of ordinary skill in the art at the time of filing would have been motivated to expand the method of Santos in order to include an e-commerce interface and select content for display to include items selected from a catalog (¶0047, ¶0063, ¶0076).
Regarding claim 6, Santos in view of Ali teaches the method of claim 1. Santos further discloses further comprising:
gathering, over a defined time period, data related to interactions between a collection of users of the computer system and the computer system (Figs. 2, 6, and 8; ¶0043 [When training the decision tree, the computer system (200) can utilize search engine logs, such as a month of search engine logs from a major search engine. For each query in the logs, the training technique can process the corresponding inputs from the query context data (244) using the tests defined for the nodes of the decision tree. Upon arriving at a leaf of the decision tree with the processing, the computer system can identify which of the user interface profiles (262) applies for that query and can update the model accordingly.] in view of ¶0037 [a user profile (246) may be logged in at the client device (210) in connection with the submission of the query (242), so that the query (242) is considered to be received from the user profile (246));
assigning, using the gathered data, a label to each user in the collection of users (Figs. 2, 6, and 8; ¶0016, ¶0039, ¶0043, ¶0046 in view of ¶0037); and
training, using the gathered data and the assigned label for each user in the collection of users, the user type prediction model to generate a set of initial values for a set of parameters of the user type prediction model (Figs. 2, 6, and 8; ¶0016, ¶0036, ¶0043, ¶¶0046-0048).
Regarding claim 7, Santos in view of Ali teaches the method of claim 1. While Santos further discloses further comprising: collecting data and re-training the user type prediction model by updating, using the collected data, a set of parameters of the user type prediction model (Figs. 2, 6, and 8; ¶0043 [update the model accordingly. For example, this can include adjusting weights in the model, to reduce differences between the type(s) of user interface profile(s) (262) indicated by the results of applying the model, and what user interface actions are recorded in the search engine logs for that query.] in view of 0016, ¶0036, ¶¶0046-0048), Santos does not explicitly disclose collecting feedback data with information about engagement by the user with the set of user interface elements and re-training the user type prediction model by updating, using the collected feedback data. However, Ali further teaches collecting feedback data with information about engagement by the user with the set of user interface elements (Figs. 2-5, 8-11; ¶¶0022-0023, ¶0050, ¶0074) and re-training the user type prediction model by updating, using the collected feedback data, a set of parameters of the user type prediction model (Figs. 2-5, 8-11; ¶0050, ¶0075, ¶0087, ¶¶0091-0097). It would have been obvious to one of ordinary skill in the art at the time of filing to modify the collected information as taught by Santos with the feedback data as taught by Ali. One of ordinary skill in the art at the time of filing would have been motivated to expand the method of Santos in order to generate updated weights based on the feedback (¶¶0074-0075).
Regarding claim 8, Santos in view of Ali teaches the method of claim 1. While Santos further discloses wherein identifying the set of user interface elements comprises:
ranking using the information about the current session, a plurality of user interface elements of the computer system to identify a rank of each user interface element of the plurality of user interface elements (Figs. 2, 6, and 8; ¶0036 [the search engine (230) can include a query classification engine (250), which is discussed more below. The search engine (230) can also include a Web page results ranker (252), which can rank indexed result items that link to Web pages, with the ranking being indicative of a score based on various factors, so that the result items can be ordered in the search results according to the rankings. The search engine (230) can also include an advertisement ranker (254), which can rank available digital advertisements for inclusion with the main Web page search result items.], ¶0049);
selecting, using the rank of each user interface element, a number of user interface elements from the plurality of user interface elements as the set of user interface elements for presentation to the user (Figs. 5-6; ¶0049, ¶0052); and
arranging, using the rank of each user interface element, the set of user interface elements in the specific order (Figs. 5-6; ¶0049, ¶0052),
Santos does not explicitly disclose ranking using the score for the user, the user data, and the information about the current session, a plurality of user interface elements retrieved from a database of the computer system, and a defined number of user interface elements. However, Ali further teaches ranking using the score for the user, the user data, and the information about the current session, a plurality of user interface elements retrieved from a database of the computer system to identify a rank of each user interface element of the plurality of user interface elements (Figs. 2-11; ¶¶0066-0071), selecting, using the rank of each user interface element, a defined number of user interface elements from the plurality of user interface elements as the set of user interface elements for presentation to the user (Figs. 2-11; ¶¶0066-0071), and arranging, using the rank of each user interface element, the set of user interface elements in the specific order (Figs. 2-11; ¶¶0066-0071). It would have been obvious to one of ordinary skill in the art at the time of filing to modify the ranking as taught by Santos with the ranking as taught by Ali. One of ordinary skill in the art at the time of filing would have been motivated to expand the method of Santos in order to select the N highest ranked candidates based on the ranking (¶0066).
Regarding claim 9, Santos in view of Ali teaches the method of claim 8. While Santos further discloses, wherein ranking the plurality of user interface elements comprises:
accessing a content priority model of the computer system, wherein the content priority model is a machine-learning model trained to identify a priority of a user interface element for presentation to the user (Figs. 1-6; ¶¶0052-0055); and
applying the content priority model to the user data, and the information about the current session to generate the rank for each user interface element of the plurality of user interface elements that is indicative of a priority of that user interface element for presentation to the user (Figs. 1-6; ¶¶0052-0055),
Santos does not explicitly disclose applying the content priority model to the score for the user. However, Ali further teaches accessing a content priority model of the computer system, wherein the content priority model is a machine-learning model trained to identify a priority of a user interface element for presentation to the user (Figs. 2-11; ¶¶0066-0071) and applying the content priority model to the score for the user, the user data, and the information about the current session to generate the rank for each user interface element of the plurality of user interface elements that is indicative of a priority of that user interface element for presentation to the user (Figs. 2-11; ¶¶0066-0071). It would have been obvious to one of ordinary skill in the art at the time of filing to modify the ranking as taught by Santos with the ranking as taught by Ali. One of ordinary skill in the art at the time of filing would have been motivated to expand the method of Santos in order to select the N highest ranked candidates based on the ranking (¶0066).
Regarding claim 10, Santos in view of Ali teaches the method of claim 8. While Santos further discloses,
retrieving, from a database of the computer system, the user data (Figs. 2, 6, and 8; ¶0037 [a user profile (246) may be logged in at the client device (210) in connection with the submission of the query (242), so that the query (242) is considered to be received from the user profile (246). In this case, the query context data (244) may include at least a portion of the user profile (246)], ¶0020 [user profile], ¶0045 [some examples of query context data (244) that may be processed using the classification model (260)], and ¶¶0059-0060), Santos does not explicitly disclose the user data comprising information about at least one of a plurality of user interface elements associated with a plurality of items converted by the user during each order of a plurality of orders for a defined time period, or affinities for the defined time period between a plurality of categories of user interface elements. However, Ali further teaches retrieving from a database the user data comprising information about at least one of a plurality of user interface elements associated with a plurality of items converted by the user during each order of a plurality of orders for a defined time period, or affinities for the defined time period between a plurality of categories of user interface elements (Figs. 2-11; ¶0060, ¶0062, ¶0068 [temporal], ¶0070, ¶¶0082-0083, ¶0086 [a time since deployment metric]). It would have been obvious to one of ordinary skill in the art at the time of filing to modify the ranking as taught by Santos with the ranking as taught by Ali. One of ordinary skill in the art at the time of filing would have been motivated to expand the method of Santos in order to select the highest ranked candidates based on the ranking (¶0066).
Regarding claim 11, Santos in view of Ali teaches the method of claim 8, Santos further discloses further comprising: receiving, from the device associated with the user and via the network, the information about the current session including at least one of content of a cart associated with the current session or a browsing history of the user for the current session (Figs. 2, 6, and 8; ¶0037 [the query context data (244) may include at least a portion of the user profile (246), which may include indications of past actions of the user profile (246) in response to receiving search results and/or user preferences explicitly entered into the user profile (246) by user input]).
Regarding claims 13 and 20, the claims disclose substantially the same limitations, as claim 1, except claim 1 is directed to a process while claim 13 is directed to an article of manufacture and claim 20 is directed to a machine. The added elements of “a computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps” (claim 13) and “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” (claim 20) are also taught by Santos (Figs. 1-2; ¶¶0024-0031). Therefore, claims 13 and 20 are rejected for the same rational over the prior art recited in claim 1.
Regarding claims 14 and 17-19, the claims disclose substantially the same limitations, as claims 2, 6-8, 10, and 11 except claims 2, 6-8, 10, and 11 are directed to processes depending from independent claim 1 while claims 14 and 17-19 are directed to articles of manufacture depending from independent claim 13. All limitations as recited have been analyzed and rejected with respect to claims2, 6-8, 10, and 11, and do not introduce any additional narrowing of the scopes of the claims as analyzed. Therefore, claims 14 and 17-19 are rejected for the same rational over the prior art cited in claims 2, 6-8, 10, and 11.
Claim(s) 3, 12, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Santos in view of Ali and in further view of Mattingly et al. (US 2018/0300788 A1).
Regarding claim 3, Santos in view of Ali teaches the method of claim 1. While Santos further discloses wherein receiving the session data comprises:
gathering during the current session, data with information about at least one of a duration of the current session (Figs. 1-2; ¶0037 [impression data], ¶¶0046-0048 [clicking on a link on the search results page and then returning to the search results page more than a threshold number of times can indicate a browsing user interface profile (262)]), a number of items scanned by a computing system associated with the physical receptacle during the current session, an average speed of movement of the physical receptacle during the current session, or an average distance traveled by the physical receptacle during the current session per item added to the physical receptacle; and
receiving, from the computing system via the network, the gathered data as at least a portion of the session data (Figs. 1-2; ¶0037 [impression data], ¶¶0046-0048, ¶0059 [A user-input computer-readable query can be received (820), with the query requesting search results from a computerized search engine. The technique can also include receiving an impression including contextual data (830), which can be connected to the query in the computer system, with the contextual data encoding information about a context of the query.] in view of ¶¶0022-0029 [devices communication over network]).
Santos in view of Ali does not explicitly teach gathering, via one or more sensors mounted to a physical receptacle utilized by the user during the current session for shopping at a location of a retailer, data with information about at least one of a duration of the current session at the location of the retailer and receiving the gathered data from the computing system associated with the physical receptacle. However, in the field of tracking/monitoring a retail environment (¶0329) Mattingly et al., hereinafter Mattingly, teaches a shopping cart at a location of a retailer that gathers data via one or more sensors mounted to a physical receptacle utilized by a user during a shopping session while shopping at a location of a retailer including dwell times within an aisle and a computing system receiving the gathered data associated with the physical receptacle (Fig. 32; ¶¶0328-0336). The step of Mattingly is applicable to the method of Santos in view of Ali as they share characteristics and capabilities, namely, they are directed to collecting and storing user session information. It would have been obvious to one of ordinary skill in the art at the time of filing to modify the data as taught by Santos in view of Ali with the in-store shopping cart data as taught by Mattingly. One of ordinary skill in the art at the time of filing would have been motivated to sense customer activities or monitor customer behaviors, such as, for example, location, dwell time, pathway through the store etc. (¶0329).
Regarding claim 12, Santos in view of Ali teaches the method of claim 8. While Santos further discloses, further comprising:
gathering information during the current session (Figs. 1-2; ¶0037 [impression data], ¶¶0046-0048 [clicking on a link on the search results page and then returning to the search results page more than a threshold number of times can indicate a browsing user interface profile (262)]); and
receiving, from a computing system via the network, the gathered information as at least a portion of the information about the current session (Figs. 1-2; ¶0037 [impression data], ¶¶0046-0048, ¶0059 [A user-input computer-readable query can be received (820), with the query requesting search results from a computerized search engine. The technique can also include receiving an impression including contextual data (830), which can be connected to the query in the computer system, with the contextual data encoding information about a context of the query.] in view of ¶¶0022-0029 [devices communication over network]).
Santos in view of Ali does not explicitly teach gathering, via one or more sensors mounted to a physical receptacle utilized by the user during the current session for shopping at a location of a retailer, information about physical locations of the physical receptacle during the current session and receiving, from a computing system associated with the physical receptacle and via the network, the gathered information. However, Mattingly, teaches a shopping cart at a location of a retailer that gathers data via one or more sensors mounted to a physical receptacle utilized by a user during a shopping session while shopping at a location of a retailer including dwell times within an aisle and a computing system receiving the gathered data associated with the physical receptacle (Fig. 32; ¶¶0328-0336). The step of Mattingly is applicable to the method of Santos in view of Ali as they share characteristics and capabilities, namely, they are directed to collecting and storing user session information. It would have been obvious to one of ordinary skill in the art at the time of filing to modify the data as taught by Santos in view of Ali with the in-store shopping cart data as taught by Mattingly. One of ordinary skill in the art at the time of filing would have been motivated to sense customer activities or monitor customer behaviors, such as, for example, location, dwell time, pathway through the store etc. (¶0329).
Regarding claim 15, the claims disclose substantially the same limitations, as claim 3, except claim 3 is directed to processes depending from independent claim 1 while claim 15 is directed to an article of manufacture depending from independent claim 13. All limitations as recited have been analyzed and rejected with respect to claim 3, and does not introduce any additional narrowing of the scopes of the claims as analyzed. Therefore, claim 15 is rejected for the same rational over the prior art cited in claim 3.
Claim(s) 4 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Santos in view of Ali and in further view of Maniyar (US 2020/0007453 A1 [previously recited]).
Regarding claim 4, Santos in view of Ali teaches the method of claim 1. While Santos further discloses further comprising:
retrieving, from a database of the computer system, data (Figs. 1-2; ¶0016 [labeled data for training the models comes from search engine log data, which may include contextual data for queries, as well as data indicating user interface actions that followed receipt of search results. For example, the logs may be logs that do not include personally identifiable information for user profiles submitting the queries], ¶¶0042-0043 [what user interface actions are recorded in the search engine logs for that query] in view of ¶0020 [user profile], ¶¶0026-0027, ¶0037, ¶0045); and
applying the user type prediction model further to the retrieved data to generate the score for the user (Figs. 2, 6, and 8; ¶¶0042-0045 [For each query in the logs, the training technique can process the corresponding inputs from the query context data (244) using the tests defined for the nodes of the decision tree. Upon arriving at a leaf of the decision tree with the processing, the computer system can identify which of the user interface profiles (262) applies for that query and can update the model accordingly. For example, this can include adjusting weights in the model, to reduce differences between the type(s) of user interface profile(s) (262) indicated by the results of applying the model, and what user interface actions are recorded in the search engine logs for that query.], ¶0048 in view of ¶0016, ¶0037, ¶0020 [user profile], ¶¶0026-0027, ¶0037, and ¶¶0059-0060),
Santos in view of Ali does not explicitly teach retrieving data with information about at least one of a ratio between a number of unique items the user converted during a defined time period by directly adding the unique items to shopping carts without further engagement with the unique items and a total number of items the user converted during the defined time period, or an average shopping time per unique item converted by the user during the defined time period. However, in the field of optimizing transaction processing (abstract, ¶0021) Maniyar teaches tracking conversion rates including a processing conversion ratio that may be based on an amount for each transaction, number of completed/abandoned transactions, and/or amount of data processing and delivery used for each completed/abandoned transaction and the conversion ratio may correspond to a function of transaction value over time, such as a median or average transaction value over time, number of visits, and/or computing resources consumed. (Figs. 1-5; ¶0021). The step of Maniyar is applicable to the method of Santos in view of Ali as they share characteristics and capabilities, namely, they are directed to browsing information online. It would have been obvious to one of ordinary skill in the art at the time of filing to modify the data as taught by Santos in view of Ali with the conversion information as taught by Maniyar. One of ordinary skill in the art at the time of filing would have been motivated to expand the method of Santos in view of Ali in order to determine if the shopping session will likely lead to a converted transaction (¶0021).
Regarding claim 16, the claims disclose substantially the same limitations, as claim 4, except claim 4 is directed to processes depending from independent claim 1 while claim 16 is directed to an article of manufacture depending from independent claim 13. All limitations as recited have been analyzed and rejected with respect to claim 4, and does not introduce any additional narrowing of the scopes of the claims as analyzed. Therefore, claim 16 is rejected for the same rational over the prior art cited in claim 4.
Response to Arguments
Applicant’s arguments, on pages 15-17 of the Remarks filed 6/22/2026, with respect to the previous 35 USC §101 rejections have been fully considered but they are not persuasive. Applicant argues the amended claims overcomes the 101 rejection because the independent claims integrates the recited judicial exception into a practical application by improving the functioning of the computer system’s user interface technology. Examiner respectfully disagrees.
Specifically, Applicant argues the claimed invention dynamically adapts the interface structure based on real-time machine-learning classification solving the problem of an inefficient user interface that treats different types of users in the same manner. Examiner respectfully disagrees.
If it is asserted that the invention improves upon conventional function of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement (see MPEP 2106.05(a)). Although the specification need not explicitly set forth the improvement, it must describe the invention such that eh improvement would be apparent to one of ordinary sill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology (see MPEP 2106.05(a); MPEP 2106.04(d)(1)).
Applicant’s specification does not provide the requisite detail necessary such that one of ordinary skill in the art could recognize the claimed invention as providing an improvement. Applicant’s specification does not provide sufficient detail with respect to generating an improved user interface, and is specific only in their use in facilitating the abstract idea of displaying ordered elements based on a predicted type of user for a current session (i.e., a recommendation). The alleged improvement by Applicant of a more “efficient” interface is at best a bare assertion of an improvement sans sufficient detail to demonstrate that Applicant has provided the alleged improvement to the technical field.
Contrary to Applicant’s assertion, the improvements manifested by the claimed invention are improvements to the abstract idea itself, not the computer or another technology or technical field. Applicant’s own disclosure reveal the impetus is improving the commercial process, not in technology. For example, the Specification in paragraph [0030]-[0032] describes enabling a user to search or browse for products and generating/presenting a customized interface to a user to by presenting content ranked by the user’s preferences. This content can include presenting additional discovery flows (e.g., upsells, additional ads, etc.) (see Specification paragraph [0072]). Examiner disagrees with the argument that the “discovery flow limitation is not a generic computer component-it is a specific, conditional functional element of the user interface that exists only as a result of the machine-learning classification, and its presence or absence directly determines the structure and content of the interface presented to the user” as argued on page 17. The “discovery flow” limitation, as currently claimed is directed to the abstract idea and is not considered an additional elements. Presenting different content within a generic interface based upon an output of a model does not describe an improvement to interface technology. Customizing and ranking content to display to a user in order to identify items that the user is most likely to order and present those items to the user (see Specification paragraph [0040]) is directed to the abstract idea and is not considered a technological improvement.
The character of the claims as a whole is not directed to improving computer performance and do not recite any such benefit. The claims of the instant application, however, merely represent the use of generic computing technology used as a tool to perform the abstract idea in an online environment. The claims lack any restriction on the manner in which the computing operations are to be performed. The manner in which the currently pending claims are written is much more akin to the myriad of ineligible court decisions that employed generic computer components at a high-level to achieve improvements in commercial processes.
Unlike argued Desjardins, neither the claimed amendments nor the specification describe a technical improvement and how such an improvement was accomplished. The claims do not delineate steps thorough which the machine learning technology achieves an improvement in the functioning of a computer, or an improvement to other technology or a technical field, which includes user interfaces. The only thing the claims disclose about the use of machine learning is that machine learning is used in and/or applied to a (potentially) new environment.
Further, the instant claims are not directed to improving “the existing technological process” requiring the generic components to operate in an unconventional manner to achieve an improvement in computer functionality or requiring the non-conventional and non-generic arrangement of known, conventional pieces to improve a technical process. As currently recited, the instant claims are directed to improving the business task of displaying ordered elements based on a predicted type of user for a current session (i.e., a recommendation).
Accordingly, the Examiner maintains the claims do not recite additional elements that integrate the judicial exception into a practical application of that exception and maintains the rejection Step 2A, Prong Two.
Therefore, the previous 35 USC §101 rejections have been maintained.
Applicant’s arguments, on pages 17-18 of the Remarks filed 6/22/2026, with respect to the 35 USC §102 and 35 USC §103 rejections have been fully considered but are moot in view of the new 35 USC §103 rejections applied to applicant’s amended claims.
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.
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LINDSEY B. SMITH
Examiner
Art Unit 3688
/LINDSEY B SMITH/ Examiner, Art Unit 3688
/MARISSA THEIN/ Supervisory Patent Examiner, Art Unit 3689
Prosecution Insights