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 communications filed on 12/5/2025 have been considered.
Claim 3 has been canceled.
Claims 1-2 and 4-20 are currently pending and have been examined.
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 that the claims recite significantly more than the abstract idea because “[claim 1 recites] specific operations performed by the computer… [requiring] the computer system to integrate a specific physical device… with a specific functionality of the computer system” (Remarks Page 11). This argument has been considered but is not persuasive. A claim that recites a judicial exception is not directed to that judicial exception, if the claim as a whole “integrates the recited judicial exception into a practical application of that exception.” The evaluation of Prong Two requires the use of the considerations (e.g. improving technology, effecting a particular treatment or prophylaxis, implementing with a particular machine, etc.) identified by the Supreme Court and the Federal Circuit, to ensure that the claim as a whole “integrates [the] judicial exception into a practical application [that] will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception”. Accordingly, the question is not whether the claims recite specific operations performed by a computer, but whether the claimed operations recite any improvement to the functioning of the computer or other claimed additional elements. In the instant case, the independent claims include additional elements such as 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 comprising: a database; accessing application programming interfaces (APIs); an online system; tuning a language model; input into the language model; causing a device to display a user interface; and a computer program product comprising a non-transitory computer readable storage medium having instructions coded thereon that, when executed by a processor, cause the processor to perform steps. While these elements are recited, they are merely peripherally incorporated in order to implement the abstract idea. Put another way, these additional elements are merely used to apply the abstract idea of providing product recommendations in a technological environment without effectuating any improvement or change to the functioning of the additional elements or other technology. Applicant’s disclosure does not articulate or suggest how these additional elements function, individually or in combination, in any manner other than using generic functionality nor does the disclosure articulate how the elements provide a technical improvement. It is further noted that Applicant’s specification (see at least [0067-0070]) describes the data store of the online concierge system as “[using] computer-readable media to store data”, and “[using] databases to organize the stored data”, however these statements amount to utilizing the claimed additional elements in order to implement the abstract idea, rather than effectuating a change of improvement to the technology itself. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they merely amount to using the claimed technology as a tool to perform the abstract idea.
Applicant’s arguments filed with respect to the rejection of claims under 35 USC 103 have been fully considered but are rendered moot under new grounds of rejection.
Applicant argues that the claims, as amended, overcome the currently cited prior art because “Balasubramanian is silent [with regards to the amended limitations] as recited in amended claim 1” (Remarks Page 12). This argument has been considered but is rendered moot under new grounds of rejection. Claim 1 now stands rejected in view of the combination of previously cited Balasubramanian and newly cited Ogle. Balasubramanian has been further relied upon to teach querying a plurality of catalogs to gather input data stored in the plurality of catalogs, the input data including a set of recipes and user data associated with a user of an online system, at (Balasubramanian, [0069][0071]), disclosing that the online concierge system receives recipe data from multiple sources, including warehouses or third party systems, and receives interaction data regarding user interactions with items or recipes. On the other hand, newly cited Ogle has been cited to teach querying, responsive to the one or more changes in the database and by accessing application programming interfaces (APIs) of the computer system, the database to gather input data, tuning a language model using the input data, generating a prompt for input into the language model, and requesting the language model to generate a list of items, based on the prompt input, at (Ogle, [0036][0039][0042][0045][0103-0105][0108]), as discussed below, such that the rejection has been made in view of the combination of Balasubramanian/Ogle. Accordingly, Ogle has been relied upon to teach the newly amended aspects of querying a database in response to one or more changes in the database. This argument is rendered moot under new grounds of rejection.
With regards to Applicant’s argument that “’input data including a set of recipes and user data’ of claim 1 cannot be equated with ‘inventory information’ of Balasubramanian (Remarks Page 12), this argument has been considered but is not persuasive. In the previous Non-Final Rejection, filed 9/26/2025, the disclosed “inventory information” of Balasubramanian was not relied upon to teach the input data. Rather, Balasubramanian was cited at ([0069][0071]), discussing the online system receiving recipe data from one or more sources, as well as user interaction data, and subsequently applying a label to said data. Balasubramanian has been similarly relied upon in the current 103 rejection, as discussed in the above paragraph. Accordingly, this argument is not persuasive, and the rejection has been maintained.
For the reasons discussed above, the rejections of independent claims 13 and 20, as well as dependent claims 2, 4-12, and 14-19, have been maintained.
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 1-2 and 4-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite an abstract idea. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Under Step 1 of the Subject Matter Eligibility Test for Products and Processes, the claims must be directed to one of the four statutory categories. See MPEP 2106.03. Claims 1 and 2-12 are directed towards a process. Claims 13-19 are directed towards a manufacture. Claim 20 is directed towards a machine Therefore, claims 1-20 are directed to one of the four statutory categories (Step 1: YES, regarding claims 1-2 and 4-20).
Under Step 2A of the MPEP, it is determined whether the claims are directed to a judicially recognized exception. See MPEP 2106.04. Step 2A is a two-prong inquiry.
Under Prong 1, it is determined whether the claim recites a judicial exception. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception.
Taking Claim 20 as representative, claim 20 recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
monitoring a storage for one or more changes in a set of features for a set of items stored in the storage;
querying, responsive to the one or more changes in the storage, a plurality of catalogs of the storage to gather input data stored in the plurality of catalogs, the input data including a set of recipes and user data associated with a user, wherein items in the set of items include ingredients of the set of recipes;
using the gathered input data;
generating a prompt for input, the prompt including the gathered input data and a request for generating a list of recipes for the user;
requesting to generate, based on the prompt input, the list of recipes for the user, wherein each recipe in the list of recipes includes a list of ingredients;
selecting one or more recipes from the list of recipes for presentation to the user; and
causing a display associated with the user to display a suggestion for the user to include, in a cart, a set of ingredients of the selected one or more recipes.
Claims 1 and 13 recite the same limitations believed to be abstract as recited in claim 20.
Claim 20, as exemplary, recites the abstract idea of providing product recommendations. These recited limitations fall within the "Certain Methods of Organizing Human Activities" Grouping of abstract ideas as it relates to commercial interactions of sales activities or behaviors. Accordingly, the claim recites an abstract idea. See MPEP 2106.04.
Accordingly, under Prong One of Step 2A of the Alice/Mayo test, claims 1, 13 and 20 recite an abstract idea (Step 2A, Prong One: YES).
Under Prong 2, it is determined whether the claim recites additional elements that integrate the exception into a practical application of the exception.
Claim 20 recites additional elements beyond the judicial exception(s), 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; a database; accessing application programming interfaces (APIs) of the computer system; tuning a language model; input into the language model; and causing a device to display a user interface. Claim 1 recites the same additional elements as recited in claim 20. Claim 13 recites the same additional elements as recited in claim 20, and additionally recites a computer program product comprising a non-transitory computer readable storage medium having instructions coded thereon that, when executed by a processor, cause the processor to perform steps.
These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. As such, these computer-related limitations are not found to be sufficient to integrate the abstract idea into a practical application. Claims 1, 13 and 20 specifying that the abstract idea of providing product recommendations is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer. As such, under Prong Two of Step 2A of the Alice/Mayo test, when considered both individually and as a whole, the limitations of claims 1, 13 and 20 are not indicative of integration into a practical application (Step 2A, Prong Two: NO).
Since claims 1, 13 and 20 recite an abstract idea and fail to integrate the abstract idea into a practical application, claims 1, 13 and 20 are “directed to” an abstract idea (Step 2A: YES). Accordingly, the judicial exception is not integrated into a practical application.
Next, under Step 2B, the instant claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, the additional elements of 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; a database; accessing application programming interfaces (APIs) of the computer system; tuning a language model; input into the language model; causing a device to display a user interface; and a computer program product comprising a non-transitory computer readable storage medium having instructions coded thereon that, when executed by a processor, cause the processor to perform steps amount to no more than mere instructions to apply the exception using generic computer components. For the same reason these elements are not sufficient to provide an inventive concept. Therefore when considering the additional elements alone, and in combination, there is no inventive concept in the claim, and thus the claim is not patent eligible (Step 2B: NO).
Dependent claims 2, 4-12 and 14-19, 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. As for dependent claims 2, 4, 6, 8-10, 14-15, and 17-18, these claims recite limitations that further define the same abstract idea noted in independent claims 1, 13 and 20, and do not recite any additional elements other than what is disclosed in independent claims 1, 13 and 20. Therefore, claims 2, 4-6, 11-12, 14-15, and 17-18 are considered patent ineligible for the reasons given above.
As for dependent claims 7-10, 16 and 19, these claims recite limitations that further define the abstract idea noted in independent claims 1, 13 and 20. Additionally, they recite the following additional limitations:
generating a second prompt for input into a second language model, the second prompt including the list of recipes, at least a portion of the input data, and feedback data associated with the user; and
requesting the second language model to decide, based on the second prompt input into the second language model, whether to alert the user about the list of recipes
responsive to an alert from the second language model to the user about the list of recipes, triggering selection of the one or more recipes from the list of recipes for presentation to the user; and
responsive to an alert from the second language model to the user about the list of recipes, generating a notification message for the user.
The additional element of a second language model is recited at a high level of generality such that it amounts to no more than instructions to apply the judicial exception in a generic technological environment. Even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. Accordingly, under the Alice/Mayo test, claims 1-2 and 4-20 are 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 5-8, 10-13, 16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over previously cited Balasubramanian (US 2023/0316375 A1), in view of newly cited U.S Patent Application No. 2016/0328409 A1 to Ogle et al., hereinafter Ogle .
Regarding Claim 1, Balasubramanian discloses A method, performed at a computer system comprising a processor and a computer-readable medium, comprising ([0033]):
monitoring a database of a computer system for one or more changes in a set of features for a set of items stored in the database ([0042] the online concierge system 102 includes an inventory management engine 302, which interacts with inventory systems of each warehouse 210… the inventory management engine monitors changes in inventory for each participating warehouse 210. The inventory management engine 302 is also configured to store inventory records in an inventory database 304 … inventory information includes attributes of items that include both quantitative and qualitative information about items);
querying a plurality of catalogs to gather input data stored in the plurality of catalogs, the input data including a set of recipes and user data associated with a user of an online system, wherein items in the set of items include ingredients of the set of recipes ([0069] An online concierge system 102 obtains 505 recipes from one or more sources including a warehouse 210 or a third party system 130… each recipe includes one or more items, or a plurality of items; [0071] To train the machine learning recommendation model, the online concierge system 102 obtains 510 training data from prior interactions by users with items or with recipes… from the prior interactions, the online concierge system 102 generates examples that each include a user and a recipe. A label is applied to each example that indicates whether the user performed a specific interaction with the recipe);
tuning a model using the gathered input data ([0065] The training datasets 320 may be periodically updated with recent previous delivery orders; [0071] To train the machine learning recommendation model, the online concierge system 102 obtains 510 training data from prior interactions by users with items or with recipes);
generating a prompt for input into the model, the prompt including the gathered input data and a request for generating a list of recipes for the user ([0099] the online concierge system 102 identifies 530 users in response to the user requesting a recommendation for one or more recipes; [0100] To generate the user embedding for the identified user, the online concierge system 102 applies the trained machine learning recommendation model to features of the identified user);
requesting the model to generate, based on the prompt input into the model, the list of recipes for the user, wherein each recipe in the list of recipes includes a list of ingredients ([0099] the online concierge system 102 identifies 530 users in response to the user requesting a recommendation for one or more recipes; [0101] From the user embedding for the identified user and the recipe embeddings for the obtained recipes, the online concierge system 102 determines 535 a set of candidate recipes… the online concierge system 102 determines distances between the user embedding for the identified user and recipe embeddings in the latent space and determines 535 the set of candidate recipes as recipes with recipe embeddings within a threshold distance of the user embedding for the identified user in the latent space);
selecting one or more recipes from the list of recipes for presentation to the user ([0102] the online concierge system 102 applies the trained machine learning recommendation model to different combinations of the user embedding for the identified user and recipe embeddings for candidate recipes. Based on the measures of similarity between user embedding for the identifier user and recipe embeddings for candidate recipes, the online concierge system 102 selects 540 one or more recipes of the set of candidate recipes); and
causing a device associated with the user to display a user interface with a suggestion for the user to include, in a cart, a set of ingredients of the selected one or more recipes ([0103] The online concierge system 102 transmits information describing the one or more selected recipes to a client device 110 of the identified user for display. For example, information describing a selected recipe is displayed in an interface of the customer mobile application 206 executing on the client device 110 of the identified user… see [0066] the customer mobile application (CMA) 206 includes an ordering interface 402, which provides an interactive interface with which the user 204 can browse through and select products and place an order; [0067] the running record of items in an order is known as a basket);
But does not explicitly disclose querying, responsive to the one or more changes in the database and by accessing application programming interfaces (APIs) of the computer system, the database to gather input data; tuning a language model using the input data; generating a prompt for input into the language model; and requesting the language model to generate a list of items, based on the prompt input.
Ogle, on the other hand, discloses querying, responsive to the one or more changes in the database and by accessing application programming interfaces (APIs) of the computer system, the database to gather input data ([0105] As users implement the recommendations by selecting the playlist generated based from the recommendations, their feedback is recorded in user feedback database 818. Such feedback can include skips, saves, length of playback, etc. This feedback is fed back into the latent factor models that are combined in block 812… see [0036] Taste profile management and recommendation system 200 includes an application programming interface (API) 214 that is used to communicate with a client device; [0039] user activity database is an open source relational database management system (RDBMS) that runs as a server providing multi-user access to a number of databases named mySQL; [0108] A database server 1002 can store data and receive data download requests from client device 1003);
tuning a language model using the input data ([0104] latent factor models, including natural language processing models 808 are trained and used to determine signals from user activity; [0105] As users implement the recommendations by selecting the playlist generated based from the recommendations, their feedback is recorded in user feedback database 818. Such feedback can include skips, saves, length of playback, etc. This feedback is fed back into the latent factor models that are combined in block 812. The feedback loop thus continuously re-trains the models, allowing for new recommendations to be generated… see [0103] the processes continue to iterate once the recommendation model has been implemented. In other words, the recommendation model continues to obtain feedback based on activity data; [0042] latent factor models provide recommendations);
generating a prompt for input into the language model ([0045] a client device 106 (FIG. 1) collects user activity information and context information. The activity information and context information is stored in allocated memory. The processor of the client device, in turn, executes code which causes to be generated messages containing the user activity information, the context information, or a combination of both and transits the messages (e.g., periodically or in realtime) to taste profile management and recommendation system 200); and
requesting the language model to generate a list of items, based on the prompt input ([0045] The messages are transmitted to taste profile management and recommendation system 200 for processing… Taste profile and recommendation system 200, in turn, processes the activity information and/or context information and generates playlists with recommendations; [0042] latent factor models provide recommendations).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the system, as taught by Balasubramanian, querying, responsive to the one or more changes in the database and by accessing application programming interfaces (APIs) of the computer system, the database to gather input data; tuning a language model using the input data; generating a prompt for input into the language model; and requesting the language model to generate a list of items, based on the prompt input, as taught by Ogle, 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 Balasubramanian, to include the teachings of Ogle, in order to improve future recommendations based on how a user interacts with recommendations (Ogle, [0011-0012]).
Regarding Claim 5, Balasubramanian and Ogle teach the limitations of claim 1.
Balasubramanian further discloses requesting the model to generate, based on the prompt input, a score for each recipe in the list of recipes ([0099] the online concierge system 102 identifies 530 users in response to the user requesting a recommendation for one or more recipes; [0102] a trained purchase model outputting a predicted probability of the user purchasing one or more items in a recipe, to various combinations of the user embedding for the identified user and recipe embeddings for candidate recipes and selects 540 one or more recipes of the set of candidate recipes based on the output of the other model (e.g., selects 540 recipes having at least a threshold position in a ranking based on the output of the other model)); and
selecting, based on the score for each recipe, the one or more recipes for presentation to the user ([0102] a trained purchase model outputting a predicted probability of the user purchasing one or more items in a recipe, to various combinations of the user embedding for the identified user and recipe embeddings for candidate recipes and selects 540 one or more recipes of the set of candidate recipes based on the output of the other model (e.g., selects 540 recipes having at least a threshold position in a ranking based on the output of the other model);
But does not explicitly disclose requesting the language model to generate, based on the prompt input into the language model, information.
Ogle, on the other hand, discloses requesting the language model to generate, based on the prompt input into the language model, information ([0045] a client device 106 (FIG. 1) collects user activity information and context information. The activity information and context information is stored in allocated memory. The processor of the client device, in turn, executes code which causes to be generated messages containing the user activity information, the context information, or a combination of both and transits the messages (e.g., periodically or in realtime) to taste profile management and recommendation system 200. The messages are transmitted to taste profile management and recommendation system 200 for processing… Taste profile and recommendation system 200, in turn, processes the activity information and/or context information and generates playlists with recommendations).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the system, as taught by Balasubramanian, requesting the language model to generate, based on the prompt input into the language model, information, as taught by Ogle, for the same reasons discussed above with respect to claim 1.
Regarding Claim 6, Balasubramanian and Ogle teach the limitations of claim 1.
Balasubramanian further discloses wherein selecting the one or more recipes comprises: scoring, based at least in part on the user data, the list of recipes to generate a score for each recipe in the list of recipes ([0102] outputting a predicted probability of the user purchasing one or more items in a recipe, to various combinations of the user embedding for the identified user and recipe embeddings for candidate recipes and selects 540 one or more recipes of the set of candidate recipes based on the output of the other model (selects 540 recipes having at least a threshold position in a ranking based on the output of the other model)); and
identifying, based on the score for each recipe in the list of recipes, the one or more recipes for presentation to the user ([0102] selects 540 one or more recipes of the set of candidate recipes based on the output of the other model (selects 540 recipes having at least a threshold position in a ranking based on the output of the other model); [0103] The online concierge system 102 transmits information describing the one or more selected recipes to a client device 110 of the identified user for display).
Regarding Claim 7, Balasubramanian and Ogle teach the limitations of claim 1.
Balasubramanian further discloses further comprising: generating a second prompt for input into a second model, the second prompt including the list of recipes, at least a portion of the input data, and feedback data associated with the user ([0102] the online concierge system 102 applies another model, such as a trained purchase model outputting a predicted probability of the user purchasing one or more items in a recipe, to various combinations of the user embedding for the identified user and recipe embeddings for candidate recipes and selects 540 one or more recipes of the set of candidate recipes based on the output of the other model… see [0097] the online concierge system 102 applies the trained machine learning recommendation model 600 to an example of training data including a user and a recipe to which a label was applied indicating the user performed the specific interaction with the recipe); and
requesting the second model to decide, based on the second prompt input into the second model, whether to alert the user about the list of recipes ([0102] the online concierge system 102 applies another model, such as a trained purchase model outputting a predicted probability of the user purchasing one or more items in a recipe, to various combinations of the user embedding for the identified user and recipe embeddings for candidate recipes and selects 540 one or more recipes of the set of candidate recipes based on the output of the other model;
Balasubramanian further discloses wherein items comprise the list of recipes ([0102]), but does not explicitly disclose wherein a model is a language model.
Ogle, on the other hand, discloses wherein a model is a language model ([0104] latent factor models, including natural language processing models 808 are trained and used to determine signals from user activity).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the system, as taught by Balasubramanian, wherein a model is a language model, as taught by Ogle, for the same reasons discussed above with respect to claim 1.
Regarding Claim 8, Balasubramanian and Ogle teach the limitations of claim 7.
Balasubramanian further discloses wherein selecting the one or more recipes for presentation to the user comprises: responsive to an alert from the second model about the list of recipes, triggering selection of the one or more recipes from the list of recipes for presentation to the user ([0102] a trained purchase model outputting a predicted probability of the user purchasing one or more items in a recipe, to various combinations of the user embedding for the identified user and recipe embeddings for candidate recipes and selects 540 one or more recipes of the set of candidate recipes based on the output of the other model (e.g., selects 540 recipes having at least a threshold position in a ranking based on the output of the other model); [0103] For example, information describing a selected recipe is displayed in an interface of the customer mobile application 206 executing on the client device 110 of the identified user;
But does not explicitly disclose wherein the model is the language model.
Ogle, on the other hand, discloses wherein the model is the language model ([0104] latent factor models, including natural language processing models 808 are trained and used to determine signals from user activity).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the system, as taught by Balasubramanian, wherein the model is the language model, as taught by Ogle, for the same reasons discussed above with respect to claim 1.
Regarding Claim 10, Balasubramanian and Ogle teach the limitations of claim 7.
Balasubramanian further discloses wherein generating the second prompt for input into the second model comprises: obtaining, from at least one of the database or the device associated with the user, the feedback data including at least one of
information about conversions by the user in relation to a list of items associated with the list of recipes over a defined time period, or
information about sentiment of the user in relation to list of items ([0050] the order fulfillment engine 306 and/or shopper management engine 310 may access a user database 314 which stores information describing each user. This information could include… favorite items; [0100] The online concierge system 102 obtains a user embedding for the identified user. In some embodiments, the online concierge system 102 generates the user embedding for the identified user in response to identifying 530 the user. To generate the user embedding for the identified user, the online concierge system 102 applies the trained machine learning recommendation model to features of the identified user, as further described above); [0102] the online concierge system 102 applies another model, such as a trained purchase model outputting a predicted probability of the user purchasing one or more items in a recipe, to various combinations of the user embedding for the identified user and recipe embeddings for candidate recipes) (Examiner notes that, according to the limitation reciting “at least one of…”, only one of the subsequent limitations must be present);
But does not explicitly disclose wherein the model is the language model.
Ogle, on the other hand, discloses wherein the model is the language model ([0104] latent factor models, including natural language processing models 808 are trained and used to determine signals from user activity).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the system, as taught by Balasubramanian, wherein the model is the language model, as taught by Ogle, for the same reasons discussed above with respect to claim 1.
Regarding Claim 11, Balasubramanian and Ogle teach the limitations of claim 1.
Balasubramanian further discloses further comprising: gathering tuning data by collecting information about at least one of
the set of items having prices that were adjusted over a defined time period,
a set of one or more promotions in relation to the set of items,
one or more changes in a catalog of items stored at the database ([0042] the inventory management engine monitors changes in inventory for each participating warehouse 210… inventory information includes attributes of items that include both quantitative and qualitative information about items), or
information about responses by the user in relation to the set of items ([0071] the online concierge system 102 retrieves the prior interactions from information in the transaction records database 308… a label is applied to each example that indicates whether the user performed a specific interaction with the recipe, such as whether a user included one or more items in a recipe in an order, or whether a user selected the recipe) (Examiner notes that, according to the limitation reciting “collecting… information about at least one of…”, only one of the subsequent limitations must be present); and
tuning the model using the gathered tuning data ([0071] To train the machine learning recommendation model, the online concierge system 102 obtains 510 training data from prior interactions by users with items or with recipes);
But does not explicitly disclose gathering tuning data from the database; and tuning the language model.
Ogle, on the other hand, discloses gathering tuning data from the database ([0105] As users implement the recommendations by selecting the playlist generated based from the recommendations, their feedback is recorded in user feedback database 818. Such feedback can include skips, saves, length of playback, etc. This feedback is fed back into the latent factor models that are combined in block 812); and
tuning the language model ([0104] latent factor models, including natural language processing models 808 are trained and used to determine signals from user activity; [0105] As users implement the recommendations by selecting the playlist generated based from the recommendations, their feedback is recorded in user feedback database 818. Such feedback can include skips, saves, length of playback, etc. This feedback is fed back into the latent factor models that are combined in block 812. The feedback loop thus continuously re-trains the models, allowing for new recommendations to be generated… see [0103] the processes continue to iterate once the recommendation model has been implemented. In other words, the recommendation model continues to obtain feedback based on activity data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the system, as taught by Balasubramanian, gathering tuning data from the database; and tuning the language model, as taught by Ogle, for the same reasons discussed above with respect to claim 1.
Regarding Claim 12, Balasubramanian and Ogle teach the limitations of claim 1.
Balasubramanian further discloses further comprising: collecting feedback data with information about at least one of a conversion by the user in relation to the set of ingredients of the one or more recipes ([0071] the online concierge system 102 retrieves the prior interactions from information in the transaction records database 308… a label is applied to each example that indicates whether the user performed a specific interaction with the recipe, such as whether a user included one or more items in a recipe in an order, or whether a user selected the recipe; [0071] the online concierge system 102 obtains 510 training data from prior interactions by users with items or with recipes… the online concierge system 102 retrieves the prior interactions from information in the transaction records database 308… a label indicates whether a user included one or more items included in a recipe in an order), or
a sentiment of the user in relation to the one or more recipes (Examiner notes that, according the limitation reciting “at least one of…”, only one of a conversion or a sentiment must be present); and
tuning the model using the collected feedback data ([0088] a user embedding output by the user model of the machine learning recommendation model is also input into a cross-modal recipe set of layers. The cross-modal recipe set of layers receives the user embedding and generates a predicted recipe embedding representing a predicted recipe with which the user corresponding to the user embedding has at least a threshold likelihood of performing the specific interaction used to generate the examples of the training data);
But does not explicitly disclose tuning the language model.
Ogle, on the other hand, discloses tuning the language model ([0104] latent factor models, including natural language processing models 808 are trained and used to determine signals from user activity; [0105] As users implement the recommendations by selecting the playlist generated based from the recommendations, their feedback is recorded in user feedback database 818. Such feedback can include skips, saves, length of playback, etc. This feedback is fed back into the latent factor models that are combined in block 812. The feedback loop thus continuously re-trains the models, allowing for new recommendations to be generated).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the system, as taught by Balasubramanian, tuning the language model, as taught by Ogle, for the same reasons discussed above with respect to claim 1.
Claim 13 is directed to a computer program product. Claim 13 recites limitations that are substantially parallel in nature to those addressed above for claim 1 which is directed towards a method. The system of Balasubramanian/Ogle teaches the limitations of claim 1 as noted above. Balasubramanian further discloses 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 (Balasubramanian: [0107]). Claim 13 is therefore rejected for the reasons set forth above in claim 1 and in this paragraph.
Claim 16 recites a computer program product comprising substantially similar limitations as claim 7. The claim is rejected under substantially similar grounds as claim 7.
Claim 18 recites a computer program product comprising substantially similar limitations as claim 10. The claim is rejected under substantially similar grounds as claim 10.
Claim 19 recites a computer program product comprising substantially similar limitations as claim 11. The claim is rejected under substantially similar grounds as claim 11.
Claim 20 is directed to a computer system. Claim 20 recites limitations that are substantially parallel in nature to those addressed above for claim 1 which is directed towards a method. The system of Balasubramanian/Ogle teaches the limitations of claim 1 as noted above. Balasubramanian further discloses 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 (Balasubramanian: [0036]). Claim 20 is therefore rejected for the reasons set forth above in claim 1 and in this paragraph.
Claims 2, 4, and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Balasubramanian in view of Ogle, and further in view of previously cited Borar (US 2021/0201340 A1).
Regarding Claim 2, Balasubramanian and Ogle teach the limitations of claim 1.
Balasubramanian further discloses wherein gathering the input data comprises: retrieving the set of recipes associated with the set of items having prices ([0046] The online concierge system 102 also includes an order fulfillment engine 306 which is configured to synthesize and display an ordering interface to each user 204 (for example, via the customer mobile application 206). The order fulfillment engine 306 is also configured to access the inventory database 304 in order to determine which products are available at which warehouse 210… The order fulfillment engine 306 determines a sale price for each item ordered by a user 204); and
retrieving the user data including at least one of ([0100] The online concierge system 102 obtains a user embedding for the identified user. In some embodiments, the online concierge system 102 generates the user embedding for the identified user in response to identifying 530 the user. To generate the user embedding for the identified user, the online concierge system 102 applies the trained machine learning recommendation model to features of the identified user, as further described above)
one or more preferences for the user ([0050] information included in the user database includes each user’s shopping preferences; [0072] examples of user characteristics include dietary references stores in associated with the user) (Examiner notes that, per Applicant’s spec (See at least [0067]), multiple databases may be used to organize stored data),
an ordering history of the user over a second time period,
an average size of a cart associated with the user, or
information about quantity of items ordered by the user over a third time period (Examiner notes that, according to the limitation reciting “at least one of…”, only one of the subsequent limitations need be present in order to teach the limitation);
But does not explicitly disclose wherein querying the database comprises: retrieving, from the database, data regarding items having prices that were decreased within a first time period.
Ogle, on the other hand, discloses wherein querying the database comprises retrieving, from the database, data regarding items ([0105] As users implement the recommendations by selecting the playlist generated based from the recommendations, their feedback is recorded in user feedback database 818. Such feedback can include skips, saves, length of playback, etc. This feedback is fed back into the latent factor models that are combined in block 812).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the system, as taught by Balasubramanian, wherein querying the database comprises retrieving, from the database, data regarding items, as taught by Ogle, for the same reasons discussed above with respect to claim 1.
Borar, on the other hand, discloses retrieving data regarding items having prices that were decreased within a first time period ([0026] the data collection module 112 is configured to collect historical data such as pricing data 30… pricing data 30 includes average input discount, trade discount, coupon discount, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the system, as taught by Balasubramanian and Ogle, retrieving data regarding items having prices that were decreased within a first time period, as taught by Borar, 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 Balasubramanian and Ogle, to include the teachings of Borar, in order to maximize revenue in an Internet retail channel by actively learning customers’ price elasticity (Borar, [0003]).
Regarding Claim 4, Balasubramanian and Ogle teach the limitations of claim 1.
Balasubramanian further discloses wherein generating the prompt for input into the model comprises: including the request into the prompt for generating the list of recipes that are associated with the set of items having prices ([0099] the online concierge system 102 identifies 530 users in response to the user requesting a recommendation for one or more recipes; [0101] From the user embedding for the identified user and the recipe embeddings for the obtained recipes, the online concierge system 102 determines 535 a set of candidate recipes… see [0046] The order fulfillment engine 306 determines a sale price for each item ordered by a user 204);
But does not explicitly disclose generating the prompt for input into the language model; and items having prices that were decreased over a defined time period.
Ogle, on the other hand, discloses generating the prompt for input into the language model ([0045] a client device 106 (FIG. 1) collects user activity information and context information. The activity information and context information is stored in allocated memory. The processor of the client device, in turn, executes code which causes to be generated messages containing the user activity information, the context information, or a combination of both and transits the messages (e.g., periodically or in realtime) to taste profile management and recommendation system 200).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the system, as taught by Balasubramanian, generating the prompt for input into the language model, as taught by Ogle, for the same reasons discussed above with respect to claim 1.
Borar, on the other hand, discloses items having prices that were decreased over a defined time period ([0026] the data collection module 112 is configured to collect historical data such as pricing data 30… pricing data 30 includes average input discount, trade discount, coupon discount, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the system, as taught by Balasubramanian and Ogle, items having prices that were decreased over a defined time period, as taught by Borar, for the same reasons discussed above with respect to claim 2.
Claim 14 recites a computer program product comprising substantially similar limitations as claim 2. The claim is rejected under substantially similar grounds as claim 2.
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.
Claims 9 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Balasubramanian in view of Ogle, and further in view of previously cited Verma (US 2023/0139513 A1).
Regarding Claim 9, Balasubramanian and Ogle teach the limitations of claim 7.
Balasubramanian further discloses further comprising: responsive to an alert received from the second model to the user about the list of recipes ([0102] selects 540 recipes having at least a threshold position in a ranking based on the output of the other model; [0103] The online concierge system 102 transmits information describing the one or more selected recipes to a client device 110 of the identified user for display),
causing the device associated with the user to display the selected one or more recipes ([0103] For example, information describing a selected recipe is displayed in an interface of the customer mobile application 206 executing on the client device 110 of the identified user);
Balasubramanian further discloses wherein items comprise the list of recipes ([0102]), but does not explicitly disclose wherein the model is the language model; responsive to information received from the language model about items, generating a notification message; and displaying another user interface with the notification message.
Ogle, on the other hand, discloses wherein the model is the language model ([0104] latent factor models, including natural language processing models 808 are trained and used to determine signals from user activity); and
responsive to an alert received from the language model about items, generating recommendations ([0045] The messages are transmitted to taste profile management and recommendation system 200 for processing… Taste profile and recommendation system 200, in turn, processes the activity information and/or context information and generates playlists with recommendations; [0075] a visual signal indicator can be generated on the client device to provide a visual notification that a personalized playlist has been generated… see [0042] latent factor models provide recommendations; [0043] generating user taste vectors using latent factor models; [0068] latent factor modeling includes natural language processing).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the system, as taught by Balasubramanian, wherein the model is the language model, and responsive to an alert received from the language model about items, generating recommendations, as taught by Ogle, for the same reasons discussed above with respect to claim 1.
Verma, on the other hand, discloses responsive to an alert received from the model, generating a notification message ([0084] one or more machine learning algorithms and/or category filters can be used to generate the list of relevant products; [0088] The relevant product display region 530 can include a carousel of cards, each card can include media associated with a top-ranked relevant product of the one or more top-ranked relevant products… the user can scroll the carousel can reveal a second top-ranked relevant product of the one or more top-ranked relevant products.. see [0087] describing various conditions in which the overlay containing the list of relevant product may be presented, such as presenting the overlay when the user submits a search query, or when a user navigates to a detail page of a product); and
displaying another user interface with the notification message ([0088] The relevant product display region 530 can include a carousel of cards, each card can include media associated with a top-ranked relevant product of the one or more top-ranked relevant products… the user can scroll the carousel can reveal a second top-ranked relevant product of the one or more top-ranked relevant products; [Fig. 5] displays the recommendations within the carousel overlaid over the shopping interface).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the system, as taught by Balasubramanian and Ogle, responsive to an alert received from the model, generating a notification message, and displaying another user interface with the notification message, as taught by Verma, 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 Balasubramanian and Ogle, to include the teachings of Verma, in order to provide personalized content to a user, preventing lower sales and/or site traffic when a user is shown less relevant content (Verma, [0003-0005]).
Claim 17 recites a computer program product comprising substantially similar limitations as claim 9. The claim is rejected under substantially similar grounds as claim 9.
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 ZACHARY R DONAHUE whose telephone number is (571)272-5850. The examiner can normally be reached M-F 8a-5p.
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/ZACHARY RYAN DONAHUE/Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689
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