Detailed Action
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Action is in reply to the Amendment filed on 3/16/2026. Claims 1-25 are currently pending and have been examined. Claims 1, 4, 10-11, 13-14, 21-25 have been amended.
Request for Continued Examination
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/23/2026 has been entered.
Priority
Applicant’s claim of priority to provisional US application 63/265,760 is acknowledged. The claims are therefore afforded an effective filing date of 12/20/2021.
Information Disclosure Statement
The IDS filed 3/23/2026 was received and has been considered.
Claim Rejection - 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 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
First, it is determined whether the claims are directed to a statutory category of invention. In the instant case, claims 1-21 and 24-25 are directed to a machine, claim 22 is directed to a process, and claim 23 is directed to an article of manufacture. Therefore, claims 1-25 are directed to statutory subject matter under Step 1 as described in MPEP 2106 (Step 1: YES).
The claims are then analyzed to determine whether the claims are directed to 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 (Prong One of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong Two of Step 2A).
Claims 1, 22, and 23 recite at least the following limitations that are believed to recite an abstract idea:
generate account data associated with the user based on monitoring a purchase history of the user, the account data identifying one or more characteristics and the purchase history of the user, wherein the purchase history does not contain a sustainable type of item;
train a procedure based on a plurality of account data;
access the procedure for generating a sustainability score of the user, wherein the sustainability score of the user is indicative of a probability of the user to purchase the sustainable type of item in response to a recommendation,
wherein to generate the sustainability score of the user, the procedure is configured to
determine a propensity for the user to convert to sustainability based on the account data, and
generate the sustainability score of the user based on the propensity for the user to convert to sustainability;
access the procedure for generating a plurality of recommendations for the user, the plurality of recommendations comprising recommendations to purchase non-sustainable items and at least one sustainable item based on the sustainability score of the user and the purchase history, wherein a ratio of recommendations to purchase sustainable items to recommendations to purchase non-sustainable items within the plurality of recommendations is based on the sustainability score of the user such that recommendations for users with a first sustainability score include less recommendations to purchase sustainable items than recommendations for users with a second sustainability score that exceeds the first sustainability score;
cause display, via at least one display, of the plurality of recommendations in a customized manner based on the account data;
in response to obtaining a response by the user to the plurality of recommendations, determine a particular recommendation that caused the response; and
retrain the procedure based on the response and the particular recommendation that cause the response.
The above limitations recite the concept of personalized recommendations. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106, in that they recite commercial interactions, e.g. sales activities/behaviors, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions. Accordingly, under Prong One of Step 2A, claims 1-25 recite an abstract idea (Step 2A, Prong One: YES).
Prong Two of Step 2A is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or user 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 exception.
In this instance, the claims recite the additional elements of:
a system comprising a data store configured to store computer-executable instructions; and a processor in communication with the data store, wherein the computer-executable instructions, when executed by the processor, cause the processor to perform steps
a machine learning model
a first computing device
a computer-implemented method
A non-transitory computer-readable medium storing computer-executable instructions that, when executed by a processor, configure the processor to perform steps
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception.
In addition, the recitations are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception.
The dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception. For example, claims 2, 7-9, 11-16, 18-21, and 24-25 are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above. As for claims 3-6, 10, 17 these claims are similar to the independent claims except that they recite the further additional elements of metadata, additional computing devices, machine-readable instructions, reinforcement learning. These additional elements are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception. Therefore, the dependent claims do not create an integration for the same reasons.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same.
In Step 2A, several additional elements were identified as additional limitations:
a system comprising a data store configured to store computer-executable instructions; and a processor in communication with the data store, wherein the computer-executable instructions, when executed by the processor, cause the processor to perform steps
a machine learning model
a first computing device
a computer-implemented method
A non-transitory computer-readable medium storing computer-executable instructions that, when executed by a processor, configure the processor to perform steps
These additional limitations, including the limitations in the dependent claims, do not amount to an inventive concept because they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea. Therefore, the claims lack one or more limitations which amount to an inventive concept in the claims.
For these reasons, the claims are rejected under 35 U.S.C. 101.
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 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.
Claim Rejection – 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-
obviousness.
Claims 1-25 are rejected under 35 U.S.C. 103 as being unpatentable over Iyer et al (US 20220245701 A1), hereinafter Iyer, in view of Riley et al (US 20210134434 A1), hereinafter Riley, and further in view of Lu et al (US 20240265309 A1), hereinafter Lu.
Regarding Claim 1, Iyer discloses a system to generate recommendations for a user, the system comprising: a data store configured to store computer-executable instructions; and a processor in communication with the data store, wherein the computer-executable instructions, when executed by the processor (Iyer: [0037-0041]), cause the processor to:
generate account data associated with the user based on monitoring a purchase history of the user, the account data identifying one or more characteristics and the purchase history of the user, wherein the purchase history does not contain a new-category type [new/discovery category] of item (Iyer: “obtain the customer information that can include historical or past information regarding the customer's interactions with the website.” [0085] – “various information regarding the customer, the customer's historical purchase data, and other data points …include customer interpurchase intervals, the number or total items in a customer's order, a total dollar amount in a customer's order, a number of unique items in a customer's order, the customer segment of the customer, number of recent purchases, repurchase history of the customer and the like.” [0069] – “a new category that the user has not purchased from before” [0061]);
train a machine learning model based on a plurality of account data (Iyer: “partitions of the underlying training data of the customer data to train the discovery category model 414. Any suitable open source or proprietary machine learning applications, packages or libraries can be used to perform such training… After the discovery category model 414 is trained … the trained discovery category model 414 can be used to determine the discovery category rankings for customers.” [0064-0065] – “model can be trained using supervised learning in which training data can be compiled that consists of customers that have made repeat purchases in a category of items during a particular time period (or otherwise historically). Each of these transactions can be labelled to indicate whether the customer purchased a new item in the category or did not purchase a new item in the category during the time period. ” [0072] – See Figure 4);
access the machine learning model for generating a new-category score [discovery category ranking] of the user, wherein the new-category score of the user is indicative of a probability [likelihood] of the user to purchase the new-category type of item in response to a recommendation (Iyer: “the discovery category engine 404 of the recommender computing device 400 can then determine a discovery category ranking for each category of goods for each customer (or user). The discovery category ranking can indicate a likelihood that the user will purchase an item from a new category that the user has not purchased from before. To make this determination, the discovery category engine 404 can include a discovery category model 414. Any suitable method or algorithm can be used.” [0061] – “a discovery category model that can use machine learning or other suitable methods or techniques to determine the discovery category rankings.” [0049]),
wherein to generate the new-category score of the user, the machine learning model is configured to determine a propensity [user-category interaction] for the user to convert to new-category items based on the account data, and generate the new-category score of the user based on the propensity for the user to convert to new-category items (Iyer: “the customer information can be used to build a User Factor matrix U and a Category Factor Matrix C. The predicted User-Category Interaction can be described by Equation 1 ” [0062] – “As shown in FIG. 5, the User-Category Matrix 502 can be determined as the product of the User Factor Matrix 504 and the Category Factor Matrix 506. …the discovery category model 414 can attempt to predict accurate scores to fully populate the User-Category Matrix 502” [0063] – “the trained discovery category model 414 can be used to determine the discovery category rankings for customers. Once the User-Category Matrix is built, the User-Category Matrix can be factorized into 2 sub-matrices … The discovery category rankings can then be determined by ranking the discovery categories using cosine similarity” [0065]);
access the machine learning model for generating a plurality of recommendations for the user, the plurality of recommendations comprising recommendations to purchase non- new-category [repeat category] items and at least one new-category item based on the new-category score of the user and the purchase history(Iyer: “ The number of discovery categories and the number of repeat categories can differ based on the number of categories that can be presented to the customer on the ecommerce marketplace. For an ecommerce marketplace that is presented on a typical website, … the number of categories that are presented to a customer is limited to four categories. … nursery users can be presented with three discovery categories and with one repeat category. The top three categories as indicated by the discovery category rankings and the top one category from the repeat category rankings can be presented to the user. Within each category, the items with the highest item recommendation rankings (from the item recommendation engine) can be displayed to the customer.” [0076]),
wherein a ratio of recommendations to purchase new-category items to recommendations to purchase non- new-category items within the plurality of recommendations is based on the new-category score of the user such that recommendations for users with a first new-category score include less recommendations to purchase new-category items than recommendations for users with a second new-category score that exceeds the first new-category score (Iyer: “nursery users can be presented with three discovery categories and with one repeat category.” [0076] - “more repeat categories are displayed to an engaged customer than new categories” [0078] – “use one or more rules and/or one or more characteristics of a customer or of a customer segment to determine the blend of repeat and discovery categories” [0052] – “Based on the customer segment (that may have different purchasing and shopping tendencies), the mixture of repeat items and new items can be personalized to increase the likelihood that the customer will continue to purchase those items that it repeatedly purchases while also discovering and/or purchasing new items.”[0028]); and
cause display, via at least one display, of the plurality of recommendations in a customized manner based on the account data (Iyer: “the website 700 can include a display of three rows of items. Each row 702, 704, 706 can correspond to a category of items. …The website 700 can include two discovery categories and one repeat category. For example, the category of “canned goods” of row 702 can be the top ranked category of the discovery category rankings and the category of “cheese” of row 704 can be the second ranked category of the discovery category rankings. The category of “easy frozen dinners” of row 706 can be the top ranked category of the repeat category rankings.” [0077] – See Figure 7.).
While Iyer teaches that the new-category items may be a category of items for sale on a marketplace [0027] and training the model [0065], it does not specifically teach that the new-category items comprise sustainable items; in response to obtaining, from a first computing device, a response by the user to the plurality of recommendations, determine a particular recommendation that caused the response; and retraining the machine learning model based on the response and the particular recommendation that caused the response.
However, Riley teaches systems for item suggestions (Riley: Abstract), including that the new-category items comprise sustainable items (Riley: “one or more suggestions for alternate food items that are equal to or more healthful for the user compared to the user's original selections are provided to the user.” [0006] – “user's food preferences ( which may include factors regarding food sustainability , environmental friendliness … are used as criteria to rank a user's food selections” [0005] – See also [0013] & Claim 11.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Iyer would continue to teach access the machine learning model for generating a new-category score of the user, wherein the new-category score of the user is indicative of a probability of the user to purchase the new-category type of item in response to a recommendation, except that now it would also teach the new-category items comprise sustainable items, and that the category is sustainable items, according to the teachings of Riley. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved ability to more accurately assess the item selections of a user (Riley: [0008]).
Iyer/Riley do not specifically teach in response to obtaining, from a first computing device, a response by the user to the plurality of recommendations, determine a particular recommendation that caused the response; and retraining the machine learning model based on the response and the particular recommendation that caused the response,
However, Lu teaches item recommendation methods [Abstract], including:
in response to obtaining, from a first computing device, a response by the user to the plurality of recommendations, determine a particular recommendation that caused the response (Lu: “ The first application icon list 1010 includes a plurality of application icons, … recommended based on historical interaction data of the user A” [0218] - “After viewing the first application icon list 1010 …the user A selects an interaction operation, such as browsing, tapping, or downloading, based on personal interests. … in FIG. 10A, the user A taps the application icon A, and the mobile phone records interaction data of the operation” [0219]); and
retraining the machine learning model based on the response and the particular recommendation that caused the response (Lu: “uses the historical interaction data in the log as a training sample set to retrain the target recommendation model. … the user A taps the application icon A, and the mobile phone records interaction data of the operation. … the mobile phone retrains the target recommendation model based on the new historical interaction data, re-evaluates a latest point of interest of the user A to obtain a plurality of application icons to be re-recommended” [0219]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Iyer/Riley would continue to teach cause display, via at least one display, of the plurality of recommendations in a customized manner based on the account data, except that now it would also teach in response to obtaining, from a first computing device, a response by the user to the plurality of recommendations, determine a particular recommendation that caused the response; and retraining the machine learning model based on the response and the particular recommendation that caused the response, according to the teachings of Lu. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved accuracy to the recommendation model (Lu: [0028]).
Regarding Claim 2, Iyer/Riley/Lu teach the system of claim 1,
wherein accessing the machine learning model for generating the new-category score of the user comprises accessing a first machine learning model for generating the new-category score of the user (Iyer: “the discovery category engine 404 of the recommender computing device 400 can then determine a discovery category ranking for each category of goods for each customer … To make this determination, the discovery category engine 404 can include a discovery category model 414. Any suitable method or algorithm can be used.” [0061] – “a discovery category model that can use machine learning or other suitable methods or techniques to determine the discovery category rankings.” [0049]), and
wherein accessing the machine learning model for generating the plurality of recommendations comprises accessing a second machine learning model for generating the plurality of recommendations (Iyer: “The item recommendation engine 308 can include an item recommendation model that can use machine learning or other suitable methods or techniques to determine the item rankings.” [0051]).
While Iyer does not teach that the new-category items are sustainable items, Riley teaches that the new-category items are sustainable items (Riley: “one or more suggestions for alternate food items that are equal to or more healthful for the user compared to the user's original selections are provided to the user.” [0006] – “user's food preferences ( which may include factors regarding food sustainability , environmental friendliness … are used as criteria to rank a user's food selections” [0005]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Riley with Iyer/Lu for the reasons identified above with respect to claim 1.
Regarding Claim 3, Iyer/Riley/Lu teach the system of claim 1,
wherein the new-category score of the user is a first new-category score of the user (Iyer: “determining discovery category rankings for each category of items available on the ecommerce marketplace for the customer based on the customer information” Claim 17),
wherein the data store is further configured to store further machine-readable instructions that, when executed by the processor, cause the processor to: identify metadata associated with the user, the metadata identifying one or more clusters of users, each of the one or more clusters of users comprising the user, wherein the machine learning model is further configured to parse the metadata to generate a second new-category score, wherein of repeat items and new items can be personalized to increase the likelihood that the customer will continue to purchase those items that it repeatedly purchases while also discovering and/or purchasing new items.” [0028]).
While Iyer does not teach that the new-category items are sustainable items, Riley teaches that the new-category items are sustainable items (Riley: “one or more suggestions for alternate food items that are equal to or more healthful for the user compared to the user's original selections are provided to the user.” [0006] – “user's food preferences ( which may include factors regarding food sustainability , environmental friendliness … are used as criteria to rank a user's food selections” [0005]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Riley with Iyer/Lu for the reasons identified above with respect to claim 1.
Regarding Claim 4, Iyer/Riley/Lu teach the system of claim 3, but Iyer does not specifically teach that the one or more clusters of users are based on a geographical location of a second user computing device associated with the user wherein the data store is further configured to store further machine-readable instructions that, when executed by the processor, cause the processor to: determine the geographic location of the second computing device, wherein causing display of the plurality of recommendations is in response to determining the geographical location of the second computing device. However, Riley teaches that the one or more clusters of users are based on a geographical location of a second user computing device associated with the user wherein the data store is further configured to store further machine-readable instructions that, when executed by the processor, cause the processor to: determine the geographic location of the second computing device, wherein causing display of the plurality of recommendations is in response to determining the geographical location of the second computing device (Riley: “the first user and the other users in the comparison are grouped by one or more common attributes, such as age range, location (such as city or zip code)” [0010] – “User data stored/pulled from survey database (profile information including user demographics, personal medical history, medications, user food survey responses, prior cart purchases (if available), genomics and other -omic data, biometric, microbiome, and activity tracker data) to deliver optimized food recommendations” [0013] – “ Personal survey data 78 preferably comprises prompting the user to enter basic information 80, such as name, age, sex, race, and optionally address information. Basic address or location information, such as zip code or city and state, may also be obtained through global positioning system (GPS) or other location applications on the user's computing device. ” [0030]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Riley with Iyer/Lu for the reasons identified above with respect to claim 1.
Regarding Claim 5, Iyer/Riley/ Lu teach the system of claim 3, wherein the second new-category score of the user is based on a probability of another user of the one or more clusters of users to purchase the at least one new-category item (Iyer: “segmenting customers into one or more identified customer segments. … the four customer segments include…(2) nursery customers that have two to four transactions on the ecommerce marketplace, (3) …the customer segments can have other characteristics or other rules that can define whether a customer belongs to a particular customer segment.” [0058] – “The results and/or rankings from these engines can merged together using a merging engine that can present a blend of repeat items and new or discovery items to prepare a final set of recommendations that can be presented to the customer …balance mix of repeat items with new items by considering one or more customer segments that the customer may fall into. Based on the customer segment (that may have different purchasing and shopping tendencies), the mixture of repeat items and new items can be personalized to increase the likelihood that the customer will continue to purchase those items that it repeatedly purchases while also discovering and/or purchasing new items.” [0028]).
While Iyer does not teach that the new-category items are sustainable items, Riley teaches that the new-category items are sustainable items (Riley: “one or more suggestions for alternate food items that are equal to or more healthful for the user compared to the user's original selections are provided to the user.” [0006] – “user's food preferences ( which may include factors regarding food sustainability , environmental friendliness … are used as criteria to rank a user's food selections” [0005]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Riley with Iyer/ Lu for the reasons identified above with respect to claim 1.
Regarding Claim 6, Iyer/Riley/ Lu teach the system of claim 3, wherein the data store is further configured to store further machine-readable instructions that, when executed by the processor, cause the processor to:
identify the one or more clusters of users based on the account data; add the user to the one or more clusters of users (Iyer: “segmenting customers into one or more identified customer segments. … the four customer segments include…(2) nursery customers that have two to four transactions on the ecommerce marketplace, (3) …the customer segments can have other characteristics or other rules that can define whether a customer belongs to a particular customer segment.” [0058]); and
generate the metadata based on adding the user to the one or more clusters of users (Iyer: “The results and/or rankings from these engines can merged together using a merging engine that can present a blend of repeat items and new or discovery items to prepare a final set of recommendations that can be presented to the customer …balance mix of repeat items with new items by considering one or more customer segments that the customer may fall into. Based on the customer segment (that may have different purchasing and shopping tendencies), the mixture of repeat items and new items can be personalized to increase the likelihood that the customer will continue to purchase those items that it repeatedly purchases while also discovering and/or purchasing new items.” [0028]).
Regarding Claim 7, Iyer/Riley/ Lu teach the system of claim 1, wherein the non-sustainable items comprise a first food item or a first drink item and the at least one sustainable item is a second food item or a second drink item (Riley: “ food recommendations to close the nutrient gap … utilization of secondary food attributes or preferences for each food item/serving comprising one or more of: taste, texture, engineered food (e.g. Impossible meat), Non-Genetically Modified Organism (GMO), Certified Organic, food additives, artificial ingredients, no preservatives, no pesticides, sustainably harvested, carbon dioxide footprint, ethically produced” [0013] – “suggesting alternative food items 32 (preferably of the same or a higher food score as the user's originally selected item),” [0029]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Riley with Iyer/ Lu for the reasons identified above with respect to claim 1.
Regarding Claim 8, Iyer/Riley/ Lu teach the system of claim 1, wherein the non-sustainable items comprise a carbon positive item and the at least one sustainable item is a carbon neutral item or a carbon negative item (Riley: “ food recommendations to close the nutrient gap … utilization of secondary food attributes or preferences for each food item/serving comprising one or more of: taste, texture, engineered food (e.g. Impossible meat), Non-Genetically Modified Organism (GMO), Certified Organic, food additives, artificial ingredients, no preservatives, no pesticides, sustainably harvested, carbon dioxide footprint, ethically produced” [0013]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Riley with Iyer/ Lu for the reasons identified above with respect to claim 1.
Regarding Claim 9, Iyer/Riley/ Lu teach the system of claim 1, wherein the one or more characteristics of the user comprise a response by the user to a prior recommendation to purchase a particular new-category item of the at least one new-category item (Iyer: “The repeat category model can be trained using supervised learning in which training data can be compiled that consists of customers that have made repeat purchases in a category of items during a particular time period (or otherwise historically). Each of these transactions can be labelled to indicate whether the customer purchased a new item in the category or did not purchase a new item in the category during the time period.” [0072] – “Based on the customers' engagement, the customers are more or less likely to explore new items and to purchase new items on the ecommerce marketplace. … new customers should be shown existing or traditional recommended categories that may include popular categories or top-rated items/categories. More experienced customers (i.e., customers that have used and purchased items on the ecommerce marketplace before) may purchase items from new categories because they are familiar with the platform but still may not know the extent of the catalog of items available on the marketplace.” [0057] – “For returning users (i.e., the nursery users, the engaged users and the super-engaged users), the merging engine can blend the discovery category rankings, the repeat category rankings and the item rankings.” [0075]).
While Iyer does not teach that the new-category items are sustainable items, Riley teaches that the new-category items are sustainable items (Riley: “one or more suggestions for alternate food items that are equal to or more healthful for the user compared to the user's original selections are provided to the user.” [0006] – “user's food preferences ( which may include factors regarding food sustainability , environmental friendliness … are used as criteria to rank a user's food selections” [0005]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Riley with Iyer/ Lu for the reasons identified above with respect to claim 1.
Regarding Claim 10, Iyer/Riley/ Lu teach the system of claim 1, wherein the at least one display comprises at least one display of a second computing device (Iyer: “customer computing devices 104, 106 can be a cellular phone, a smart phone,” [0030] – “deliver recommendations to the customer computing devices 104” [0032]).
Regarding Claim 11, Iyer/Riley/ Lu teach the system of claim 1, wherein to cause display of the plurality of recommendations, the computer-executable instructions, when executed by the processor, further cause the processor to:
cause display, via the at least one display, of a first recommendation of the plurality of recommendations during a first time period, the first recommendation comprising a recommendation to purchase a non-new-category item (Iyer: “ For new users, there is no historical data available, so three months of transaction data across all customers can be used to determine popular categories of items.” [0060] – “The top three categories as indicated by the discovery category rankings and the top one category from the repeat category rankings can be presented to the user.” [0076]); and
cause display, via the at least one display, of a second recommendation of the plurality of recommendations during a second time period, the second recommendation comprising a recommendation to purchase a new-category item (Iyer: “For returning users (i.e., the nursery users, the engaged users and the super-engaged users), the merging engine can blend the discovery category rankings, the repeat category rankings and the item rankings.” [0075] – “the nursery users can be presented with three discovery categories and with one repeat category. The top three categories as indicated by the discovery category rankings and the top one category from the repeat category rankings can be presented to the user.” [0076]).
While Iyer does not teach that the new-category items are sustainable items, Riley teaches that the new-category items are sustainable items (Riley: “one or more suggestions for alternate food items that are equal to or more healthful for the user compared to the user's original selections are provided to the user.” [0006] – “user's food preferences ( which may include factors regarding food sustainability , environmental friendliness … are used as criteria to rank a user's food selections” [0005]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Riley with Iyer/ Lu for the reasons identified above with respect to claim 1.
Regarding Claim 12, Iyer/Riley/ Lu teach the system of claim 1, wherein the one or more characteristics of the user comprise one or more carbon neutral characteristics of the user or one or more carbon negative characteristics of the user (Riley: “ food recommendations to close the nutrient gap … utilization of secondary food attributes or preferences for each food item/serving comprising one or more of: taste, texture, engineered food (e.g. Impossible meat), Non-Genetically Modified Organism (GMO), Certified Organic, food additives, artificial ingredients, no preservatives, no pesticides, sustainably harvested, carbon dioxide footprint, ethically produced” [0013]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Riley with Iyer/ Lu for the reasons identified above with respect to claim 1.
Regarding Claim 13, Iyer/Riley/ Lu teach the system of claim 1, wherein the data store is further configured to store further machine-readable instructions that, when executed by the processor, cause the processor to:
identify the response by the user to the plurality of recommendations (Lu: “ The first application icon list 1010 includes a plurality of application icons, … recommended based on historical interaction data of the user A” [0218] - “After viewing the first application icon list 1010 …the user A selects an interaction operation, such as browsing, tapping, or downloading, based on personal interests. … in FIG. 10A, the user A taps the application icon A, and the mobile phone records interaction data of the operation” [0219]); and
adjust the new-category score of the user based on the response by the second user to the second plurality of recommendations (Lu: “uses the historical interaction data in the log as a training sample set to retrain the target recommendation model. … the user A taps the application icon A, and the mobile phone records interaction data of the operation. … the mobile phone retrains the target recommendation model based on the new historical interaction data, re-evaluates a latest point of interest of the user A to obtain a plurality of application icons to be re-recommended” [0219] - “These application icons are application icons recommended based on historical interaction data of the user A on the mobile phone in a previous period of time, and are ranked in descending order of relevance between the user A and items, so that an application icon that is most likely to be downloaded ranks at the most front location.” [0218]).
While Lu does not teach that the new-category items are sustainable items, Riley teaches that the new-category items are sustainable items (Riley: “one or more suggestions for alternate food items that are equal to or more healthful for the user compared to the user's original selections are provided to the user.” [0006] – “user's food preferences ( which may include factors regarding food sustainability , environmental friendliness … are used as criteria to rank a user's food selections” [0005]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Riley with Iyer/ Lu for the reasons identified above with respect to claim 1.
Regarding Claim 14, Iyer/Riley/ Lu teach the system of claim 1,
wherein the user is a first user and the plurality of recommendations is a first plurality of recommendations (Iyer: “segmenting customers into one or more identified customer segments. … the four customer segments include (1) new customers that have no transactions on the ecommerce marketplace, (2) nursery customers that have two to four transactions on the ecommerce marketplace,.” [0058]),
wherein the data store is further configured to store further machine-readable instructions that, when executed by the processor, cause the processor to:
determine a second user is not associated with particular account data (Iyer: “For new users, there is no historical data available, so three months of transaction data across all customers can be used to determine popular categories of items.” [0060]);
generate a base new-category score of the second user based on determining the second user is not associated with the particular account data (Iyer: “the discovery category engine 404 of the recommender computing device 400 can then determine a discovery category ranking for each category of goods for each customer (or user). The discovery category ranking can indicate a likelihood that the user will purchase an item from a new category that the user has not purchased from before. To make this determination, the discovery category engine 404 can include a discovery category model 414. Any suitable method or algorithm can be used.” [0061] – “a discovery category model that can use machine learning or other suitable methods or techniques to determine the discovery category rankings.” [0049]);
generate a second plurality of recommendations for the second user based on the base new-category score of the second user (Iyer: “ The number of discovery categories and the number of repeat categories can differ based on the number of categories that can be presented to the customer on the ecommerce marketplace. For an ecommerce marketplace that is presented on a typical website, … the number of categories that are presented to a customer is limited to four categories. … users can be presented with three discovery categories and with one repeat category. The top three categories as indicated by the discovery category rankings and the top one category from the repeat category rankings can be presented to the user. Within each category, the items with the highest item recommendation rankings (from the item recommendation engine) can be displayed to the customer.” [0076]);
identify a response by the second user to the second plurality of recommendations (Iyer: “The repeat category model can be trained using supervised learning in which training data can be compiled that consists of customers that have made repeat purchases in a category of items during a particular time period (or otherwise historically). Each of these transactions can be labelled to indicate whether the customer purchased a new item in the category or did not purchase a new item in the category during the time period.” [0072] – “Based on the customers' engagement, the customers are more or less likely to explore new items and to purchase new items on the ecommerce marketplace. … new customers should be shown existing or traditional recommended categories that may include popular categories or top-rated items/categories. More experienced customers (i.e., customers that have used and purchased items on the ecommerce marketplace before) may purchase items from new categories because they are familiar with the platform but still may not know the extent of the catalog of items available on the marketplace.” [0057] – “For returning users (i.e., the nursery users, the engaged users and the super-engaged users), the merging engine can blend the discovery category rankings, the repeat category rankings and the item rankings.” [0075]); and
adjust the base new-category score of the second user based on the response by the second user (Iyer: “Based on the customers' engagement, the customers are more or less likely to explore new items and to purchase new items on the ecommerce marketplace. … new customers should be shown existing or traditional recommended categories that may include popular categories or top-rated items/categories. More experienced customers (i.e., customers that have used and purchased items on the ecommerce marketplace before) may purchase items from new categories because they are familiar with the platform but still may not know the extent of the catalog of items available on the marketplace.” [0057] – “For returning users (i.e., the nursery users, the engaged users and the super-engaged users), the merging engine can blend the discovery category rankings, the repeat category rankings and the item rankings.” [0075] - “For new users, there is no historical data available, so three months of transaction data across all customers can be used to determine popular categories of items.” [0060]).
While Iyer does not teach that the new-category items are sustainable items, Riley teaches that the new-category items are sustainable items (Riley: “one or more suggestions for alternate food items that are equal to or more healthful for the user compared to the user's original selections are provided to the user.” [0006] – “user's food preferences ( which may include factors regarding food sustainability , environmental friendliness … are used as criteria to rank a user's food selections” [0005]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Riley with Iyer/ Lu for the reasons identified above with respect to claim 1.
Regarding Claim 15, Iyer/Riley/ Lu teach the system of claim 1, wherein the data store is further configured to store further machine-readable instructions that, when executed by the processor, cause the processor to: determine the new-category score of the user exceeds a threshold, wherein the plurality of recommendations is based on determining the new-category score of the user exceeds the threshold (Iyer: “the website 700 can include a display of three rows of items. Each row 702, 704, 706 can correspond to a category of items. …The website 700 can include two discovery categories and one repeat category. For example, the category of “canned goods” of row 702 can be the top ranked category of the discovery category rankings and the category of “cheese” of row 704 can be the second ranked category of the discovery category rankings. The category of “easy frozen dinners” of row 706 can be the top ranked category of the repeat category rankings.” [0077]).
While Iyer does not teach that the new-category items are sustainable items, Riley teaches that the new-category items are sustainable items (Riley: “one or more suggestions for alternate food items that are equal to or more healthful for the user compared to the user's original selections are provided to the user.” [0006] – “user's food preferences ( which may include factors regarding food sustainability , environmental friendliness … are used as criteria to rank a user's food selections” [0005]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Riley with Iyer/ Lu for the reasons identified above with respect to claim 1.
Regarding Claim 16, Iyer/Riley/ Lu teach the system of claim 1, wherein the data store is further configured to store further machine-readable instructions that, when executed by the processor, cause the processor to:
monitor a dynamic threshold (Iyer: “Based on the customers' engagement, the customers are more or less likely to explore new items and to purchase new items on the ecommerce marketplace. … new customers should be shown existing or traditional recommended categories that may include popular categories or top-rated items/categories. More experienced customers (i.e., customers that have used and purchased items on the ecommerce marketplace before) may purchase items from new categories because they are familiar with the platform but still may not know the extent of the catalog of items available on the marketplace.” [0057] – “the nursery users can be presented with three discovery categories and with one repeat category. The top three categories as indicated by the discovery category rankings and the top one category from the repeat category rankings can be presented to the user.” [0076] – “the merging engine 408 can determine the final recommendations by selecting the top four repeat categories from the repeat category rankings. If the super-engaged customer only purchases items from a single category or from two categories, the merging engine 408 can determine to include discovery category rankings based on the discovery category rankings.” [0079]); and
determine the new-category score of the user exceeds the dynamic threshold, wherein the plurality of recommendations is based on determining the new-category score of the user exceeds the dynamic threshold (Iyer: “the nursery users can be presented with three discovery categories and with one repeat category. The top three categories as indicated by the discovery category rankings and the top one category from the repeat category rankings can be presented to the user.” [0076] – “the merging engine 408 can determine the final recommendations by selecting the top four repeat categories from the repeat category rankings. If the super-engaged customer only purchases items from a single category or from two categories, the merging engine 408 can determine to include discovery category rankings based on the discovery category rankings.” [0079] – “more repeat categories are displayed to an engaged customer than new categories.” [0078]).
While Iyer does not teach that the new-category items are sustainable items, Riley teaches that the new-category items are sustainable items (Riley: “one or more suggestions for alternate food items that are equal to or more healthful for the user compared to the user's original selections are provided to the user.” [0006] – “user's food preferences ( which may include factors regarding food sustainability , environmental friendliness … are used as criteria to rank a user's food selections” [0005]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Riley with Iyer/ Lu for the reasons identified above with respect to claim 1.
Regarding Claim 17, Iyer/Riley/Lu teach the system of claim 1. While Iyer/Riley teach that the machine learning is iterative (Riley: [0075]), and that the score is the sustainability score (Riley: [0005-0006]), they do not specifically teach that the data store is further configured to store further machine-readable instructions that, when executed by the processor, cause the processor to: perform reinforcement learning to dynamically adjust the score of the user.
However, Lu teaches that the data store is further configured to store further machine-readable instructions that, when executed by the processor, cause the processor to: perform reinforcement learning to dynamically adjust the score of the user (Lu: “Machine learning and deep learning usually include technologies such as … reinforcement learning,” [0082] – “obtaining a pre-trained target recommendation model, where the target recommendation model includes a graph neural network model with one convolutional layer” [0106] - “uses the historical interaction data in the log as a training sample set to retrain the target recommendation model. … the user A taps the application icon A, and the mobile phone records interaction data of the operation. … the mobile phone retrains the target recommendation model based on the new historical interaction data, re-evaluates a latest point of interest of the user A to obtain a plurality of application icons to be re-recommended” [0219] - “These application icons are application icons recommended based on historical interaction data of the user A on the mobile phone in a previous period of time, and are ranked in descending order of relevance between the user A and items, so that an application icon that is most likely to be downloaded ranks at the most front location.” [0218]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Lu with Iyer/Riley for the reasons identified above with respect to claim 1.
Regarding Claim 18, Iyer/Riley/Lu teach the system of claim 1, wherein to generate the new-category score of the user, the machine learning model is further configured to determine a mutability of the user based on a plurality of previous item purchases of the user, wherein the mutability of the user identifies a probability of the user to purchase a different item, wherein the new-category score of the user is further based on the mutability of the user (Iyer: “the discovery category engine 404 of the recommender computing device 400 can then determine a discovery category ranking for each category of goods for each customer (or user). The discovery category ranking can indicate a likelihood that the user will purchase an item from a new category that the user has not purchased from before. To make this determination, the discovery category engine 404 can include a discovery category model 414. Any suitable method or algorithm can be used.” [0061] – “a discovery category model that can use machine learning or other suitable methods or techniques to determine the discovery category rankings.” [0049]).
While Iyer does not teach that the new-category items are sustainable items, Riley teaches that the new-category items are sustainable items (Riley: “one or more suggestions for alternate food items that are equal to or more healthful for the user compared to the user's original selections are provided to the user.” [0006] – “user's food preferences ( which may include factors regarding food sustainability , environmental friendliness … are used as criteria to rank a user's food selections” [0005]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Riley with Iyer/Lu for the reasons identified above with respect to claim 1.
Regarding Claim 19, Iyer/Riley/ Lu teach the system of claim 1, wherein the account data associated with the user comprises a plurality of recency, frequency, and monetary values associated with a plurality of previous item purchases of the user (Iyer: “various information regarding the customer, the customer's historical purchase data, and other data points …include customer interpurchase intervals, the number or total items in a customer's order, a total dollar amount in a customer's order, a number of unique items in a customer's order, the customer segment of the customer, number of recent purchases, repurchase history of the customer and the like.” [0069]).
Regarding Claim 20, Iyer/Riley/ Lu teach the system of claim 19, wherein the data store is further configured to store further machine-readable instructions that, when executed by the processor, cause the processor to:
generate a plurality of recency, frequency, monetary, and new-category score values of the user based on the sustainability score of the user (Iyer: “various information regarding the customer, the customer's historical purchase data, and other data points …include customer interpurchase intervals, the number or total items in a customer's order, a total dollar amount in a customer's order, a number of unique items in a customer's order, the customer segment of the customer, number of recent purchases, repurchase history of the customer and the like.” [0069] – “the discovery category engine 404 of the recommender computing device 400 can then determine a discovery category ranking for each category of goods for each customer (or user). The discovery category ranking can indicate a likelihood that the user will purchase an item from a new category that the user has not purchased from before. To make this determination, the discovery category engine 404 can include a discovery category model 414. Any suitable method or algorithm can be used.” [0061]); and
update the account data associated with the user based on the plurality of recency, frequency, monetary, and new-category score values (Iyer: “For returning users (i.e., the nursery users, the engaged users and the super-engaged users), the merging engine can blend the discovery category rankings, the repeat category rankings and the item rankings.” [0075] – “the discovery category engine 404 of the recommender computing device 400 can then determine a discovery category ranking for each category of goods for each customer (or user). The discovery category ranking can indicate a likelihood that the user will purchase an item from a new category that the user has not purchased from before. To make this determination, the discovery category engine 404 can include a discovery category model 414. Any suitable method or algorithm can be used.” [0061]).
While Iyer does not teach that the new-category items are sustainable items, Riley teaches that the new-category items are sustainable items (Riley: “one or more suggestions for alternate food items that are equal to or more healthful for the user compared to the user's original selections are provided to the user.” [0006] – “user's food preferences ( which may include factors regarding food sustainability , environmental friendliness … are used as criteria to rank a user's food selections” [0005]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Riley with Iyer/ Lu for the reasons identified above with respect to claim 1.
Regarding Claim 21, Iyer/Riley/ Lu teach the system of claim 1, wherein identifying the account data associated with the user is based on obtaining a prompt via a second user computing device associated with the user, wherein the prompt comprises a request to purchase a particular item, an indication that the user is browsing the particular item, or a purchase of the particular item (Iyer: “provide recommendations to customers that may be shopping, browsing or otherwise interacting with the marketplace.” [0002] – “the central ordering computing device 114 can allow a customer 118, 120, via the customer computing devices 104, 106, to browse, search and/or select products for purchase. As will be further explained, the central ordering computing device 114 can also personalize the websites through the display of category or item recommendations” [0035]).
Regarding Claim 22, the limitations of claim 22 are closely parallel to the limitations of claim 1, with the additional limitation of a computer-implemented method (Iyer: [0013-0014]), and are rejected on the same basis.
Regarding Claim 23, the limitations of claim 23 are closely parallel to the limitations of claim 1, with the additional limitation of a non-transitory computer-readable medium storing computer-executable instructions that, when executed by a processor, configure the processor to (Iyer: [0014]), and are rejected on the same basis.
Regarding Claim 24, Iyer/Riley/ Lu teach the system of claim 1, wherein the data store is further configured to store further machine-readable instructions that, when executed by the processor, cause the processor to: determine that the user is interacting with the at least one display, wherein causing display of the plurality of recommendations is in response to determining that the user is interacting with the at least one display (Iyer: “the website 700 can include a display of three rows of items. Each row 702, 704, 706 can correspond to a category of items. …The website 700 can include two discovery categories and one repeat category. For example, the category of “canned goods” of row 702 can be the top ranked category of the discovery category rankings and the category of “cheese” of row 704 can be the second ranked category of the discovery category rankings. The category of “easy frozen dinners” of row 706 can be the top ranked category of the repeat category rankings.” [0077] – “These recommendations can be presented or displayed to the customers by showing such recommended items on the ecommerce marketplace website. ” [0027]).
Regarding Claim 25, Iyer/Riley/Lu teach the system of claim 1, wherein Iyer/Riley teach that the score is a sustainability score (Riley: [0005-0006]), but do not specifically teach that to retrain the machine learning model, the data store is further configured to store further machine-readable instructions that, when executed by the processor, cause the processor to: retrain the machine learning model to generate an updated sustainability score of the user based on the response by the user to the plurality of recommendations; and retrain the machine learning model to generate an updated plurality of recommendations for the user based on the response by the user to the plurality of recommendations.
However, Lu teaches that to retrain the machine learning model, the data store is further configured to store further machine-readable instructions that, when executed by the processor, cause the processor to:
retrain the machine learning model to generate an updated score of the user based on the response by the user to the plurality of recommendations (Lu: “ The first application icon list 1010 includes a plurality of application icons, … recommended based on historical interaction data of the user A” [0218] - “uses the historical interaction data in the log as a training sample set to retrain the target recommendation model. … the user A taps the application icon A, and the mobile phone records interaction data of the operation. … the mobile phone retrains the target recommendation model based on the new historical interaction data, re-evaluates a latest point of interest of the user A to obtain a plurality of application icons to be re-recommended” [0219]); and
retrain the machine learning model to generate an updated plurality of recommendations for the user based on the response by the user to the plurality of recommendations (“uses the historical interaction data in the log as a training sample set to retrain the target recommendation model. … the user A taps the application icon A, and the mobile phone records interaction data of the operation. … the mobile phone retrains the target recommendation model based on the new historical interaction data, re-evaluates a latest point of interest of the user A to obtain a plurality of application icons to be re-recommended” [0219]) .
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Lu with Iyer/Riley for the reasons identified above with respect to claim 1.
Response to Arguments
Applicant's arguments filed 9/15/2025 have been fully considered but they are not persuasive.
Claim Rejection – 35 USC §101
Applicant argues that the claims “do not recite a judicial exception,” arguing that the claimed elements “cannot reasonably be categorized as a method of organizing human activity,” and that “Claim 1 is decidedly different from the provided examples” for commercial interactions and managing personal behavior sub-groupings. Applicant describes the claims instead as “a technical stateful per-user transition architecture focused on sustainability.”
Examiner respectfully disagrees. With reference to the rejection above, the claims recite steps which amount to a concept for providing personalized recommendations. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106, in that they recite commercial interactions, e.g. sales activities/behaviors, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions. The abstract process for recommending sustainable items to a user is not rooted in computer technology, and includes the argued ability to predict a user’s likelihood of converting to a new product category, customizing a ratio of old and new-category recommendations, and updating the procedure based on user engagement with the recommendations.
Applicant further argues that the claims integrate the abstract idea into a practical application, quoting the body of the claim in its entirety and arguing that “at least these recitations represent a practical application,” as the claim “relates to a processor configured to” perform the steps of the claim. Applicant argues that the claims allow the system to make sustainable recommendations “without the user interacting with the same or similar items and this system can enable users to increase sustainability.” Applicant further argues that the claim “enables an improvement in the manner in which recommendations are generated,” including “a user’s persistent, hyper-personalization record is used by a system to identify a sustainability score and a user mutability/switch likelihood, perform controlled exposure testing based on a ratio of recommendations, and use a recommendation and a response to the recommendation as feedback events.”
Examiner disagrees – the argued limitations and capabilities are rooted solely in the abstract idea, which cannot form the sole basis for a technological improvement to itself. The additional elements, rather than integrating this abstract idea and the argued abstract steps into a practical application, are invoked as mere instructions to apply the abstract idea to a technological environment [MPEP 2106.05(f)]. For instance, the recitation that the updating of the procedure based on user feedback constitutes retraining a black-box machine learning model, and that data inputs and outputs occur through a device’s display, provide only a general linking to computer technology. At best, these elements offer only the improved speed or efficiency inherent to a general purpose computer, which does not integrate the abstract idea into a practical application [MPEP 2106.05(a)].
Applicant further argues that the claims amount to significantly more than the abstract idea, arguing that traditional systems “may be unable to generate recommendations for a sustainable type of item for a user where the purchase of [sic] history of the user does not contain the sustainable type of item.”
Examiner disagrees. Similar to the discussion above, the capabilities argued are part of the abstract idea: the method for recommending sustainable items to a user who has not purchased them before, including determining a ratio of sustainable & non-sustainable items to recommend based on user’s sustainably score, are part of the identified abstract idea and are not rooted in computer technology. The additional elements are invoked as mere instructions to apply the abstract idea to a technological environment [MPEP 2106.05(f)].
Claim Rejection – 35 USC §103
Applicant argues that the claims do not teach “a ratio of recommendations to purchase sustainable items to recommendations to purchase non-sustainable items within the plurality of recommendations is based on the sustainability score of the user such that recommendations for users with a first sustainability score include less recommendations to purchase sustainable items than recommendations for users with a second sustainability score that exceeds the first sustainability score” as newly amended.
Examiner disagrees. With reference to the rejection above, Iyer teaches a machine learning model that determines items to recommend to a customer [0051], in a number of different categories. The number of repeat and discovery categories presented to the user is based on the customer segment, with those with a higher likelihood of interacting with new items [e.g. nursery/new customers] receiving more discovery categories [0076] while a more experienced/engaged customer will receive more repeat categories [0078]. This ratio of the amount of each category is done for each user to increase the likelihood that the user will purchase from the displayed categories [0028].In other words, customers that have a higher likelihood to make purchases from the same categories are provided with more of those categories, while those more likely to consider new/discovery categories are presented with a higher ratio of those categories [0081]. Riley further teaches that a new category can be sustainable items, and that a repeated category present in user purchase history can be non-sustainable items.
Applicant’s arguments with respect to the newly amended steps of obtaining a response and retraining the machine learning model have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument; specifically, these limitations are taught by newly-relied-upon reference Lu in the rejection above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
(US 11551096 B1) teaches product recommendation systems using reinforcement learning ML, which is retrained based on user feedback to recommendations, and uses scores to represent user sentiment when determining recommendations.
US 20190188591 A1 teaches recommendation systems that are dynamically re-trained when the user interacts with particular recommendations from a list of recommendations presented together.
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/T.J.S./
Examiner, Art Unit 3689
/Jeffrey A. Smith/Supervisory Patent Examiner, Art Unit 3688
Prosecution Insights