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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter (a judicial exception without significantly more). Claims are eligible for patent protection under § 101 if they are in one of the four statutory categories and not directed to a judicial exception to patentability. Alice Corp. v. CLS Bank Int'l, 573 U.S. 208 (2014). Claims 1-20, each considered as a whole and as an ordered combination, are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1 recites a method. Claim 11 recites a non-transitory computer-readable storage medium. Claim 20 recites a system.
Step 2A, prong 1: Claim 1 recites the abstract idea of managing add-ons for delivery service orders. This idea is described by the following steps:
A method comprising:
obtaining an anchor item that a user interacted with;
mapping the anchor item to an anchor category describing a category the anchor item is assigned to;
generating a request for a set of beverage categories based on the anchor category and for each beverage category, a respective pairing score indicating a degree of relevance between the anchor category and the beverage category and a reasoning for the pairing of the beverage category;
receiving the set of candidate beverage categories, the pairing score for each beverage category, and the reasoning for the beverage category;
obtaining one or more beverage items from a catalog that is assigned to one or more beverage categories in the set of beverage categories;
selecting a subset of the one or more beverage items; and
providing the subset of beverage items to the user.
Claims 11 and 20 recite equivalent limitations.
This idea falls into the certain methods of organizing human activity grouping of abstract ideas as it is directed towards commercial interactions including advertising, marketing or sales activities or behaviors (i.e., suggesting potential alcohol parings that pair well with the food as the user submitting the order).
Step 2A, prong 2: Claims 1, 11 and 20 recite additional elements that fail to integrate the abstract idea into practical application.
Claims 1, 11 and 20 recite a processor, an online system, client device; and a non-transitory, computer-readable media storing instructions that are executable by the one or more processors to cause the computing system to perform operations. However, these elements are generic computing components (see at least paragraph 0102) that are simply used to perform operations that would otherwise be abstract (see MPEP2106.05(f)).
Claims 1, 11 and 20 additionally recite using a machine-learned model. However, the machine-learned models are recited at a high level of generality and are merely used as tools to perform the process (i.e., determining the cost of fulfilling an add-on order offer and determining a conversion rate) (see MPEP 2106.05(f)).
They are not "additional elements" to be analyzed under this part of the framework, and merely serve to add a general link to a technological environment in which the abstract idea/commercial interaction is carried out, and instructions to apply (execute) it. The additional elements do not amount to significantly more for the same reasons they do not integrate the abstract idea into a practical application (i.e., that they merely provide a general link to a particular technological environment and instructions to "apply it").
Step 2B: Claims 1, 11 and 20 fail to recite additional elements that amount to an inventive concept.
For the reasons identified with respect to Step 2A, prong 2, claims 1, 11 and 20 fail to recite additional elements that amount to an inventive concept. For example, use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more (see MPEP 2106.05(g)).
Dependent Claims Step 2A:
The limitations of the dependent claims merely set forth further refinements of the abstract idea identified at step 2A—Prong One, without changing the analysis already presented. Additionally, for the same reasons as above, the limitations fail to integrate the abstract idea into a practical application because they use the same general technological environment and instructions to implement the abstract idea as the independent claims identified at step 2A—Prong Two.
Dependent Claims Step 2B:
The dependent claims merely use the same general technological environment and instructions to implement the abstract idea. These do not amount to significantly more for the same reasons they fail to integrate the abstract idea into a practical application. Moreover, the Specification also indicates this is the routine use of known components for the same reasons presented with respect to the elements in the independent claims above.
Thus, when considering the combination of elements and the claimed invention as a whole, the claims are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 5, 8, 10-13, 15, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Pickelsimer (US 20130339179) in view of Rembert (US 20240029134).
Claim 1. Pickelsimer teaches a computer-implemented of pairing wines and foods, the method comprising:
obtaining an anchor item that a user of an online system interacted with [0020], [0280];
mapping the anchor item to an anchor category describing a category the anchor item is assigned to [0020];
generating a request for a set of beverage categories based on the anchor category and for each beverage category, a respective pairing score indicating a degree of relevance between the anchor category and the beverage category and a reasoning for the pairing of the beverage category (Fig. 15, [0264], [0220]);
receiving, as an output from the machine learning language model, the set of candidate beverage categories, the pairing score for each beverage category, and the reasoning for the beverage category (Fig. 15, [0224]);
obtaining one or more beverage items from a catalog database that is assigned to one or more beverage categories in the set of beverage categories [0225], [0171];
selecting a subset of the one or more beverage items [0035]; and
providing the subset of beverage items to a client device of the user to cause presentation of the subset of beverage items on a page generated on the client device [0035].
Pickelsimer does not teach using a machine learning language model.
Rembert teaches a computer-implemented method for presenting food product information including: applying a machine learning word vector algorithm to identify a category of food products with which the food product description matches, and utilizing a large language model to identify a category of food products with which the food product description matches, wherein the food product catalog service is operable to apply a set of logical filtering elements to the normalized food product data to produce filtered food product data [0022].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pickelsimer to include using a machine learning language model, as disclosed in Rembert, because it would advantageously facilitate the purchase and sale of food products based on the comparative food product data provided, as taught by Rembert [0007].
Claim 2. Pickelsimer teaches said method wherein obtaining the one or more alcohol items further comprises: accessing the catalog database storing a hierarchy of entities; matching the one or more beverage categories to corresponding entities in the catalog database; and identifying the one or more beverage items that are assigned to the matched entities in the catalog database [0131].
Claim 3. Pickelsimer teaches said method, further comprising: obtaining beverage preferences of the user extracted from an order history of the user, wherein the prompt further specifies the beverage preferences of the user and for each beverage category, a request for a respective preference score indicating a degree of preference of the user for the beverage category [0022].
Claim 5. Pickelsimer teaches said method, further comprising: generating a merged list of beverage categories based on the set of beverage categories and the second set of beverage categories; for each beverage category in the merged list, computing a combined score by combining the pairing score and the co-occurrence score for the beverage category; and storing a mapping from the anchor category, the merged list of beverage categories, and the combined scores in a datastore [0114], [0295].
Claim 8. Pickelsimer teaches said method, wherein the page is one or a combination of a description page of the anchor item or an order page of the user [0220].
Claim 10. Pickelsimer teaches said method, further comprising: obtaining feedback from the user indicating whether the user interacted with the subset of beverage items; responsive to receiving indication that the user interacted with the subset of beverage items, creating a training dataset including the anchor category and beverage categories of the subset of beverage items; and fine-tuning parameters of the machine-learning model using the training dataset [0234].
Claims 11-13, 15, 18 are rejected on the same rationale as set forth above in claims 1-3, 5, 8 and 10.
System claim 20 repeats the subject matter of method claim 1, as a set of apparatus elements rather than a series of steps. As the underlying processes of claim 1 have been shown to be fully disclosed by the teachings of Pickelsimer in the above rejections of claim 1, it is readily apparent that the system disclosed by Pickelsimer includes the apparatus to perform these functions. As such, these limitations are rejected for the same reasons given above for method claim 1, and incorporated herein.
Claims 4, 7, 14 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Pickelsimer in view of Brandenberg et al. (US 20170103447).
Claim 4. Pickelsimer teaches all the limitations of claim 4 except accessing engagement history data for previous orders to the online system.
Brandenberg et al. (Brandenberg) teaches a computer-implemented method for recommending multiple product for purchase wherein if the consumer is recurring then using relevant data points from previous actions by the wine buyer, generating a list of potential wines of interest ranked based on a likelihood of purchase [0082].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pickelsimer to include except accessing engagement history data for previous orders to the online system, as disclosed in Brandenberg, because it would advantageously help consumers and buyers to select appropriate products, as taught by Brandenberg [0002].
Claim 7. Pickelsimer teaches all the limitations of claim 7, except generating a likelihood for the beverage item indicating whether the user will interact with the beverage item in association with the anchor item.
Brandenberg teaches a computer-implemented method for recommending multiple product for purchase wherein the system includes an algorithm module for generating a list of potential wines of interest ranked on likelihood of purchase based on the user profile and/or the entry of relevant data points linked to wine characteristics; a module for generating a personalized group of product listings in a personalized communication email or page where the list of potential wines of interest are ranked and displayed according to their likelihood of purchase [0077].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pickelsimer to include generating a likelihood for the beverage item indicating whether the user will interact with the beverage item in association with the anchor item, as disclosed in Brandenberg, because it would advantageously help consumers and buyers to select appropriate products, as taught by Brandenberg [0002].
Claim 14 is rejected on the same rationale as set forth above in claim 4.
Claim 17 is rejected on the same rationale as set forth above in claim 7.
Claims 6, 9, 16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Pickelsimer in view of Saad et al. (US 20240078572).
Claim 6. Pickelsimer teaches all the limitations of claim 6, except identifying the one or more beverage/product categories that are associated with combined scores above a predetermined threshold.
Saad et al. (Saad) teaches a computer-implemented method for prediction and computation of electronic shopping carts wherein the recommender system generates, a compatibility score that quantifies compatibility between that item and the current contents of the e-shopper's electronic shopping cart, and recommender system applies a threshold compatibility score to predict a much smaller subset of catalog items that might be associated with the current contents of the e-shopper's electronic shopping cart. The recommender system generates a compatibility score to quantify compatibility between the embedding of each item in the cart and each other item in the catalog, and recommender system generates a composite representation of all those resulting compatibility scores [0040].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pickelsimer to include identifying the one or more beverage/product categories that are associated with combined scores above a predetermined threshold, as suggested in Saad, because it would advantageously filter out catalog items that have compatibility scores than a designated threshold to identify a subset of predicted catalog items that may be associated with the current cart contents, as taught by Saad [0040].
Claim 9. Pickelsimer teaches all the limitations of claim 9, except the selected subset of beverage items is presented in a carousel user interface (UI) element on the page generated on the client device.
Saad et al. teaches a computer-implemented method for prediction and computation of electronic shopping carts wherein cart prediction score quantifies similarity (e.g. cosine similarity) between embeddings for each pair of the different items in the cart and combines pairwise similarity scores to generate a cart combination score for the entire cart [0019]. A user interface on the e-shopper's device presents a carousel [0021].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Pickelsimer to include the selected subset of beverage items is presented in a carousel user interface (UI) element on the page generated on the client device, as disclosed in Saad, because it would advantageously offer a space-efficient way to showcase multiple items, such as products, images, or promotions, within a single, prominent area; increase visual engagement and interactivity, allowing users to swipe or click through content, which is particularly beneficial for small mobile screens.
Claim 16 is rejected on the same rationale as set forth above in claim 6.
Claim 19 is rejected on the same rationale as set forth above in claim 9.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 10083521 to Dhua et al. discloses a system for content recommendation. In order to determine categories of accessories that are most commonly purchased along with apparel from various categories in at least some embodiments, some analysis of items in an electronic catalog or other data repository is performed in order to determine something about the visual characteristics of the items. In this example, input variables can include a set of categories of apparel items. One of more of these categories can be set for any training or query example. The outputs can be a set of categories of accessory items and should be categories most likely to be purchased along with the input apparel item. The historical action data can be analyzed to generate an index that maps searches to one or more types of products, where each of these mappings is associated with a probability that indicates the likelihood that the search was intended for a particular type of product. Additionally, the historical data can be used to train any machine learning tool such as a decision trees/forests, SVMs, deep networks, etc. After the training phase, the trained system can be used to predict which category (or categories) of accessory items should be recommend for a particular item of apparel.
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/MILA AIRAPETIAN/Primary Examiner, Art Unit 3688