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 submission filed on 8/15/25 has been entered. Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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 1-7, 11-17 are rejected under 35 U.S.C. 103 as being unpatentable over Salehian et al. (2019/0355465 A1), in view of Owens (2008/0235096 A1), in view of Durazo et al. (US 11257049 B1),
in further view of Haghighat Kashani et al. (US 20210264493 A1)
Re-claim 1, Salehian et al. teach a method comprising:
-obtaining, at an online concierge system, a plurality of recipes, each recipe including one or more generic item descriptions and instructions for combining the generic item descriptions included in the recipe;
(see e.g. paragraph [0036] The server 200 comprises a computerized device or data processing system configured to run one or more software applications on a processor thereof (e.g. the network-side health tracking program 218). The server 200 of the present embodiment is further configured to receive a plurality of consumable records which include item descriptions, as well as caloric and nutritional contents of a respective plurality of consumable items which are entered at the health tracking devices 110, other consumer devices, and/or provided from one or more manufacturing or distributing entities. The consumable records are stored at a storage apparatus or memory of the server 200 (e.g., consumable records 224.
[0040] In some embodiments, “dish” and “recipe” correspond to different types of consumable records in the database 220.
[00092] In the case that the received consumable record is a recipe, the consumable record may further include one or more ingredient description strings.)
-extracting the one or more generic item descriptions from a recipe; -determining a category from a set of categories maintained by the online concierge system for a generic item description extracted from the recipe;
(see e.g. paragraphs [0078] The ingredient sub-graph 408 includes a plurality of generic consumable name labels (indicated by a rectangle) and a plurality of categories of consumables (indicated by an oval). The ingredient sub-graph 408 is essentially similar to the ingredient sub-graph 400 of FIG. 4A and not described in complete detail. [0097] Accordingly, the dish labeler 606 further matches the “pizza” generic consumable name label to the item description string of consumable record 614. Similarly, the dish labeler 606 identifies that “cheese pizza” and “pizza” are in the “pie” category of consumables based on the ontological relationships defined by the food knowledge graph 230. Accordingly, the dish labeler 606 further matches the “pie” category of consumables label to the item description string of consumable record 614.)
-- wherein determining the category for the generic item description comprises:
-obtaining the hierarchical taxonomy including a plurality of categories and a hierarchy of levels where lower levels in the hierarchy corresponding to more specific categories, wherein an item in the item catalog is categorized based on the hierarchical taxonomy,
(see e.g. fig. 4A, paragraphs 0068 -0074, 0094 ).
[0068] FIG. 4A shows an exemplary partial food knowledge graph illustrating ontological relationships that interconnect consumables names of an ingredient sub-graph 400. Particularly, one exemplary relationship that might be defined in a food knowledge graph is an ontological relationship (“is a” and/or “has the category”) indicating that a first consumable name is a subclass of a second consumable name, or is within a particular category of consumables.
[0071] Furthermore, the ingredient sub-graph 400 includes a plurality of ontological relationships which indicate that a consumable name is within a particular category of consumable names (indicated by a dashed arrow).
[0074] The dish/recipe sub-graph 402 and ingredient sub-graph 404 each include a plurality of generic consumable name labels (indicated by a rectangle) and a plurality of categories of consumables (indicated by an oval).
-storing an association between the determined category and the generic item description extracted from the recipe; (see e.g. paragraph [0098] Accordingly, the dish labeler 606 further matches the “mozzarella cheese,” “pizza sauce,” “pizza dough,” “tomato,” “flour,” “sugar,” “yeast,” “olive oil,” and “salt” generic consumable name labels to the item description string of consumable record 614.)
[0102] FIG. 6C shows an exemplary supplemented consumable record 614′ which has been updated based on the labels to which the consumable record 614 of FIG. 6B was matched. Particularly, a generic_name field 622 of the supplemented consumable record 614′ is updated to associate the record with the “cheese pizza” and “pizza” generic consumable name labels. Similarly, a consumable category 624 field of the supplemented consumable record 614′ is updated to associate the record with the “pie” category of consumables label. A brand_name field 626 of the supplemented consumable record 614′ is updated to associate the record with the “Pizza Hut®” brand name label. A brand_category field 628 of the supplemented consumable record 614′ is updated to associate the record with the “restaurant” category of brand names label. An ingredients field 630 of the supplemented consumable record 614′ is updated to associate the record with the “mozzarella cheese,” “pizza sauce,” “pizza dough,” “tomato,” “flour,” “sugar,” “yeast,” “olive oil,” and “salt” generic consumable name labels.
-storing an association between the generic item description extracted from the recipe, the determined category, an identifier of the specific warehouse, and the selected specific item at the online concierge system.
(see e.g. paragraphs [0090] The method 500 improves the functioning of the system server 200 by enabling the processor circuitry/logic 204 to utilize the structured knowledge defined by the food knowledge graph 230 to provided additional information and/or metadata which is stored in or referenced by consumable records, in addition to the user-generated information of the consumable records. ----The method 500 supplements this basic user-generated information by labeling the consumable records 224 with additional information such as a standardized generic consumable name, a consumable category, a standardized brand name, a brand name category, dietary substitutes, included ingredients, included allergens, flavors, a type of cuisine, dietary restriction compliance, and other miscellaneous useful descriptive labels.
[0107] Each consumable record comprises a plurality of data fields that relate to a particular consumable item, including at least one description field have a descriptive string.
[0039] Each consumable record comprises a plurality of data fields that relate to a particular consumable item. In some embodiments, each consumable record includes a description field that includes data, such as a text string, that identifies or describes the particular consumable. In some embodiments, each consumable record includes a brand field that includes data, such as a text string, that identifies or describes the brand of the particular consumable. In some embodiments, each consumable record includes an ingredients field that includes data, such as one or more text strings, that list ingredients for a particular consumable.)
--determining the category for the generic item description based on the measure of similarity
[0093] In one embodiment, the food labeler 600 is configured to, when executed by the processor circuitry/logic 204, compare the text of a respective descriptive string with the labels and match the respective descriptive string to the most similar label of the food knowledge graph 230 or labels of the food knowledge graph 230 having a threshold level of similarity.)
[0094] Particularly, in one embodiment, the dish labeler 606 is configured to match the item description string of the received consumable record with a most similar generic consumable name label of the food knowledge graph 230).
Salehian et al. do not explicitly teach the following limitations.
However, Owens teaches
--obtaining, at the online concierge system, an item catalog stored in a database for one or more warehouses, the catalog arranged in the database according to a hierarchical taxonomy, the item catalog for a warehouse identifying specific items offered by the warehouse;
(see e.g. [0028] Arranging receiving grocery store information from a plurality of grocery stores,
[0031] In accordance with a preferred embodiment thereof, this invention provides Internet web site shopping method and system comprising: database means for storing at least one organized listing of a plurality of grocery item general descriptions; database means for storing information describing at least two unrelated grocery stores; computer processor means for relating at least one particular grocery item to at least one grocery item general description from such at least one organized listing of a plurality of grocery item general descriptions;
[0218] The preferred primary screen for building a new shopping list is shown in FIG. 56A. Each user will preferably use the three tier menu of grocery categories and sub-categories to select grocery item general descriptions which they desire to purchase.
[0244] Selecting the “Add Product Category” button preferably provide the user the opportunity to use the previously described three tier menu structure to select grocery item general descriptions to be included in the meal. )
---retrieving an item catalog for a specific warehouse; --selecting, using the retrieved item catalog, a specific item from the items offered by the specific warehouse, comprising determining that the specific item from the item catalog is associated with the determined category and is available from the specific warehouse;
(see e.g. [0028] at least one particular grocery item selected for purchase)
[0029] at least one first computer processor to relate at least one generic grocery-item description to at least one particular grocery item description; at least one third database to store information about at least one plurality of grocery stores; and at least one second interface to permit selecting at least one particular grocery item to be purchased at a selected grocery store.)
Therefore it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Salehian et al. and include the steps cited above, as taught by Owens, in order to identify specific products that meet selected dietary preferences, provide the user the ability to include any required ingredients of a meal or menu plan in their current shopping list. (see e.g. paragraphs [0018], [0019]).
Salehian et al., in view of Owens, do not explicitly teach the following limitations.
However, Durazo et al. teach
--generating an embedding of the generic item description and an embedding of category description for each of the plurality of categories, --determining a measure of similarity between the embedding of the generic item description and at least one embedding of category description, and
(see e.g. (63) The mapping component 220 determines keywords for receipt items in the extracted text. The mapping component 220 may use the keywords to identify item identifiers in the catalog and may identify catalog items that match or approximate the keywords. ---In some embodiments, a “keyword” may comprise an object representing a particular product, such as a picture or other metadata indicative of the product, a type of product, a query that can be used to query a catalog for the same or a similar item, or a vector representative of one or more attributes of the receipt items that enables the same or similar items to be located in different catalogs.
Durazo et al. also teach --determining the category for the generic item description based on the measure of similarity
(see e.g. (63) In some instances, a keyword for a catalog item may serve to describe or categorize the catalog item.
51) In examples, the text analysis component 218 receives extracted text from the text extraction component 216 and performs operations on the text. In some embodiments, the text analysis component 218 preprocesses the text data. In some embodiments, the text analysis component 218 assigns the words of the extracted text to categories (e.g., a description of a receipt item, a merchant, a date, a time, a total cost, etc.). The text analysis component 218 may base the identification of words of the categories on patterns typically found in text of that category that is unlikely to be found in other words in the text (such as word order, presence of numbers/symbols, adjacent line or words, etc.).
--wherein selecting the specific item comprises:
Therefore it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Salehian et al., in view of Owens, and include the similarity feature of Durazo et al. in order to enable the same or similar items to be located in different catalogs. (see e.g. (63)).
Salehian et al., in view of Owens, in view of Durazo et al., do not explicitly teach the following limitations.
However, Haghighat Kashani et al. teach
--wherein selecting the specific item comprises:
--identifying a plurality of candidate items of the specific items in the item catalog for the specific warehouse, identifying a category in the taxonomy in which each of the candidate items is categorized,
(see e.g. Abstract --When a user selects a particular ingredient-based lens, the system can determine a subset of available dish items from the plurality of merchants by removing dish items from all available dish items. The resulting dish items that correspond with the selected ingredient-based lens can be provided to a graphical user interface (GUI) at a user device of the user.)
--disregarding one or more items offered by the specific warehouse based on a determination that the one or more items are categorized in one or more categories differing from the category having the stored association with the generic item description, and selecting the specific item from the candidate items that have not been disregarded; and
(see e.g. [0025] As a sample illustration, a merchant provides a plurality of dish items that the merchant offers for ordering. The plurality of dish items includes at least a “bowl of fried rice” as an available dish item, as well as a list of ingredients as including white rice, scrambled eggs, and soy sauce. The system can provide the dish name and list of ingredients to the ML model to determine a proper binary classification for a first ingredient-based lens (e.g., vegetarian, not vegetarian). For example, “white rice” may correspond with a positive indicator for the “vegetarian” ingredient-based lens, “scrambled eggs” may correspond with a positive indicator for the “vegetarian” ingredient-based lens, and “soy sauce” may correspond with a positive indicator for the “vegetarian” ingredient-based lens. However, when the system provides the dish name and list of ingredients to a second ML model to determine a proper binary classification for a second ingredient-based lens (e.g., vegan, not vegan), the “scrambled eggs” may correspond with a negative indicator for the “vegan” ingredient-based lens. As such, the bowl of fried rice may correspond with the first ingredient-based lens (e.g., vegetarian) and not the second ingredient-based lens (e.g., vegan). When these ingredient-based lenses are provided for selection, activation of the first ingredient-based lens may include the “bowl of fried rice” as an available dish item and activation of the second ingredient-based lens may remove the “bowl of fried rice” as an available dish item.
[0060] Lens engine 318 may be configured to create a subset of a plurality of dish items that correspond with an ingredient-based lens. For example, an ingredient-based lens may correspond with positive indicators for “vegetarian.” Any dish items that correspond with terms that are associated with a negative indicator of this “vegetarian” ingredient-based lens may be removed. The removal of the dish items may generate a subset of a plurality of dish items that correspond with this ingredient-based lens. Available ingredient-based lenses may be stored with the lens database 336.
[0066] Lens engine 318 may also be configured to compare and match a received ingredient-based lens from a consumer user device with a determined ingredient-based lens stored with the lens database 336. For example, the received ingredient-based lens may be identified in a drop-down user interface tool and selected by the consumer user at the consumer user device. In some examples, the selected ingredient-based lens may be transmitted by the user interface to lens engine 318 and compared with one or more ingredient-based lenses stored with lens database 336. In other examples, the selected ingredient-based lens may be compared with one or more ingredient-based lenses stored locally with the consumer user device. Upon determining a match between the received ingredient-based lens and the stored ingredient-based lens, a subset of the plurality of dishes may be provided to user interface of the consumer user device by removing dish items from the plurality of dish items available, based on the comparing and matching.
Abstract - The resulting dish items that correspond with the selected ingredient-based lens can be provided to a graphical user interface (GUI) at a user device of the user.)
Haghighat Kashani et al. also teach --determining the category for the generic item description based on the measure of similarity
(see e.g. [0071] In other examples, the ML model may correspond with a Deep Learning Neural Network consisting of more than one layer of processing elements between an input layer and an output layer, or an unsupervised learning method such as K-nearest neighbors to classify inputs based on observed similarities among multivariate distribution densities of independent variables.)
Therefore it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Salehian et al., in view of Owens, in view of Durazo et al., and include the disregarding items based on categories, as taught by Haghighat Kashani et al., because such classification of the dish item will increase the likelihood that the dish item corresponds with the ingredient-based lens (see e.g. [0068]).
Re-claim 2, Salehian et al. teach the method of claim 1, wherein selecting, using the retrieved item catalog, a specific item comprises:
-determining measures of similarity between specific items in the item catalog for the specific warehouse and the generic item description; -selecting a specific item in the item catalog for the specific warehouse associated with the determined category having a maximum measure of similarity.(see e.g. paragraph [0125] Particularly, a user selected a consumable record with the item description “Fiber One® Oats & Chocolate Bar” and requested recommendation of consumable records from the databases 220 that are similar to the selected consumable record by pressing a similar option 1012. The graphical user interface shows a title 1010 indicating the nature of the recommendations (e.g., ‘Similar to “Fiber One® Oats & Chocolate Bar”’) and a list 1020 of recommended consumable records. In the example, the processor circuitry/logic 204 identified that the selected consumable was associated with, for example, the generic consumable name “granola bar,” the category of consumables “on-the-go snacks,” and the ingredient names “oats,” “granola,” and “chocolate.” As can be seen, the list 1020 includes consumable records for various other on-the-go snacks that are similar to the “Fiber One® Oats & Chocolate Bar” consumable record.)
Re-claim 3, Salehian et al. teach the method of claim 1, wherein selecting, using the retrieved item catalog, a specific item comprises:
- identifying candidate items of the specific items in the item catalog for the specific warehouse, each candidate item associated with the determined category; ---determining measures of similarity between the candidate items in the item catalog and the generic item description; (see e. g. paragraphs [0093, 0126]
-selecting a candidate item having a maximum measure of similarity.
(see e. g. paragraphs [0127] Depending on how the food knowledge graph 230 defines substitutes, the generated list of recommended substitute consumable records may require much more narrow similarity, as opposed to the recommendation of merely “similar” consumable records.
[0126] In one embodiment, the processor circuitry/logic 204 is configured to included consumable records in the list only if they are associated with a minimum threshold amount of the same descriptive labels as the particular consumable record (e.g., at least 75% overlap in the associated labels). In one embodiment, the processor circuitry/logic 204 is configured to included consumable records in the list only if they are associated with a same category of consumables label as the particular consumable record. In one embodiment, the processor circuitry/logic 204 is configured to exclude consumable records that are associated with exactly the same set of labels as the selected consumable record (i.e., 100% overlap in the associated labels), to avoid recommending duplicative records or minor variants of the selected consumable record.)
Re-claim 4, Salehian et al. teach the method of claim 3, wherein a measure of similarity between a candidate item and the generic item description is determined by matching text of the generic item description to text describing the candidate item (see e.g. paragraph [0093] The food labeler 600 is configured to, when executed by the processor circuitry/logic 204, match at least one descriptive string 602 with one or more descriptive labels of the food knowledge graph 230 and output a set of matched labels 604. In one embodiment, the food labeler 600 is configured to, when executed by the processor circuitry/logic 204, compare the text of a respective descriptive string with the labels and match the respective descriptive string to the most similar label of the food knowledge graph 230 or labels of the food knowledge graph 230 having a threshold level of similarity.)
Re-claim 5, Salehian et al. teach the method of claim 4, wherein the text describing the item comprises a name of the item. (see e.g. paragraph [0094] Particularly, in one embodiment, the brand labeler 608 is configured to match the brand description string of the received consumable record with a most similar brand name label of the food knowledge graph 230.)
Re-claim 6, Salehian et al. teach the method of claim 1, wherein determining the category from a set of categories maintained by the online concierge system for the generic item description extracted from the recipe comprises: -determining a confidence value for each category in the taxonomy being associated with the generic item description; see e.g. paragraphs [0093] compare the text of a respective descriptive string with the labels and match the respective descriptive string to the most similar label of the food knowledge graph 230 or labels of the food knowledge graph 230 having a threshold level of similarity.
[0127] Depending on how the food knowledge graph 230 defines substitutes, the generated list of recommended substitute consumable records may require much more narrow similarity, as opposed to the recommendation of merely “similar” consumable records.)
-selecting a category in the taxonomy having a maximum confidence value.
see e.g. paragraphs [0096] Similarly, the brand labeler 608 of the food labeler 600 has directly matched the brand description string (i.e., “the Pizza hut on 11.sup.th st.”) to the label “Pizza Hut®,” which is the most similar brand name label. The matched generic consumable name label (i.e., “cheese pizza”) and matched brand name label (i.e., “Pizza Hut®”) can be considered the direct matches of the descriptive strings of received consumable record 614.
[0126] In one embodiment, the processor circuitry/logic 204 is configured to included consumable records in the list only if they are associated with a minimum threshold amount of the same descriptive labels as the particular consumable record (e.g., at least 75% overlap in the associated labels). In one embodiment, the processor circuitry/logic 204 is configured to included consumable records in the list only if they are associated with a same category of consumables label as the particular consumable record. In one embodiment, the processor circuitry/logic 204 is configured to exclude consumable records that are associated with exactly the same set of labels as the selected consumable record (i.e., 100% overlap in the associated labels), to avoid recommending duplicative records or minor variants of the selected consumable record.
[0134] In one embodiment, the processor circuitry/logic 204 is configured to include consumable records in the list only if they are associated with a minimum threshold amount of the identified subset of the most frequent labels (e.g., at least 10% overlap with the most frequent labels).
Re-claim 7, Salehian et al. teach the method of claim 6, wherein determining the confidence value for each category in the taxonomy being associated with the generic item description comprises:
-applying one or more models to a textual description of the generic item description and to a textual description of the category in the taxonomy. (see e.g. paragraph [0093] In some embodiments, rather than using a rules-based text comparison, the food labeler 600 is configured to, when executed by the processor circuitry/logic 204, instead use a machine learning model to match the descriptive strings of the received consumable record to labels of the food knowledge graph 230.)
Claim 11 recites similar limitations as claim 1 and is rejected under the same arts and rationale.
Claim 12 recites similar limitations as claim 2 and is rejected under the same arts and rationale.
Claim 13 recites similar limitations as claim 3 and is rejected under the same arts and rationale.
Claim 14 recites similar limitations as claim 4 and is rejected under the same arts and rationale.
Claim 15 recites similar limitations as claim 5 and is rejected under the same arts and rationale.
Claim 16 recites similar limitations as claim 6 and is rejected under the same arts and rationale.
Claim 17 recites similar limitations as claim 7 and is rejected under the same arts and rationale.
Claims 8-10, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Salehian et al. (2019/0355465 A1), in view of Owens (2008/0235096 A1), in view of Durazo et al. (US 11257049 B1), in view of Haghighat Kashani et al. (US 20210264493 A1), in further view of Iannone (US20200051125).
Re-claims 8-10 Salehian et al.do not teach the following limitations.
Salehian et al., in view of Owens, in view of Durazo et al., in view of Haghighat Kashani et al., do not explicitly teach the following limitations.
However, Iannone explicitly teaches the method of claim 1, further comprising:
-determining a frequency with which the recipe has been displayed to users from whom the online concierge system received requests for orders;
(see e.g. paragraph [0031] The consumer would take the downloaded recipe representation with them to a retail establishment, such as a grocery store or convenience store, where they may shop for the ingredients contained within the recipe. The consumer may present the recipe to the Point-of-Sale system for scanning, where the consumer may receive an incentive such as a coupon or other incentive for scanning the recipe. Upon scanning, the consumer would receive the incentive and the retail establishment would have information from the universal identifier on what recipe the consumer is preparing. The universal identifier information would then be analyzed and placed within the proper aggregated dataset in which the scanned recipe is a member. In this fashion, the retail establishment would receive a more accurate count of the number of particular recipes are being presented for each category of dishes being prepared.
[0045] The shopping list section 120 would have the shopping list 124. The shopping list 124 provides the specific items required to prepare the menu or recipe. An additional items section 140 provides the shopper with a convenient place to add other needed items. Finally, a section 130 includes the barcode 134 that identifies the specific menu-shopping list. This barcode 134 may be scanned to link an individual transaction as the check-out of the store to a specific menu-shopping list.
-determining a frequency with which users included one or more specific items associated with one or more generic item descriptions in the recipe in an order after the recipe was displayed to the one or more users;
(see e.g. paragraph [0049] At the store, the action 21 is that the customer purchases at least some of the items on the list, possibly all of the items on the list, or some other combination such as some of the items plus other items. Tracking the use of the list and what the customer buys is potentially useful to allow the grocery store owner to determine which menu-shopping lists result in the greatest number of total sales, establish promotions, etc.)
-determining a metric for the recipe from the frequency with which the recipe has been displayed to users from whom the online concierge system received requests for orders and the frequency with which users included one or more specific items associated with one or more generic item descriptions in the recipe in the order after the recipe was displayed to the one or more users; and - in response to determining that the metric satisfies one or more criteria, reviewing the stored association between the generic item description extracted from the recipe, the determined category, the identifier of the specific warehouse, and the selected specific item.
(see e.g. paragraph [0035] Analysis of data collected from the scanned identifiers provides statistics that may drive marketing, advertising and merchandising strategy. For instance, if a grocer finds that certain stores have a large percentage of Italian recipes scanned, larger Italian ingredient displays may be implemented. If analysis indicates that a specific customer cooks specific recipes, incentives for matching ingredients may be offered by email.)
(see e.g. paragraph [0050] At action block 23, the customer then checks out. The point of sale system collects a list of what items are purchased during this trip to the store and collects that barcode that is a unique identifier for this specific menu or recipe card. Thus, the information of which items sold is associated with specific menus is collected through scanning and stored on the store's network to create a database.)
The method of claim 8, wherein reviewing the stored association between the generic item description extracted from the recipe, the determined category, the identifier of the specific warehouse, and the selected specific item comprises:
-determining a predicted availability of the selected specific item at the specific warehouse; responsive to the predicted availability being less than a threshold availability, maintaining the stored association between the generic item description extracted from the recipe, the determined category, the identifier of the specific warehouse, and the selected specific item; and
-responsive to the predicted availability being greater than the threshold availability, storing a modified association between the generic item description extracted from the recipe, the determined category, the identifier of the specific warehouse, and an alternative specific item from the item catalog for the specific warehouse associated with the determined category. (see e.g. paragraphs [0054] Menus for vegan meals may be made available for one person if the process notes that the typical vegan menu has a number of purchases that are scaled down from the base level of two adult diners.
[0055] Menus for certain items may be rescaled for additional diners if it becomes apparent that these items are usually made for special events where there are six, eight, or more diners.)
The method of claim 8, wherein the metric comprises a ratio of a number of orders received from one or more users including one or more specific items associated with one or more generic item descriptions in the recipe in the order after the recipe was displayed to the one or more to a number of time the recipe has been displayed to users from whom the online concierge system received requests for orders. (see e.g. paragraph [0062] The retailer information management system may be operative to aggregate all instance of the scanned universal identifiers and scan the aggregated recipe information to determine how many instances of each recipe occur in the accumulated data. The recipe instances may also include ancillary data on the consumer presenting the recipe, as well as additional information such as nutritional information, source of each recipe, specific author of each recipe, and specific source for each recipe, among other data that may be associated with the recipe. The data may be analyzed by the retailer information management system to determine what recipes occur most frequently, from what sources recipes are most frequently culled, and additional analyses of the accumulated data that will assist in making marketing and advertising decisions both more targeted and more granular.)
Therefore it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Salehian et al., in view of Owens, in view of Durazo et al., in view of Haghighat Kashani et al., and include the steps cited above, as taught by Iannone in order to make inform decisions regarding marketing, advertising, production strategies, and other outreach to consumers (see e.g. paragraphs [0061]).
Claim 18 recites similar limitations as claim 8 and is rejected under the same arts and rationale.
Claim 19 recites similar limitations as claim 9 and is rejected under the same arts and rationale.
Claim 20 recites similar limitations as claim 10 and is rejected under the same arts and rationale.
Response to Arguments
Applicant’s arguments, dated 8/1/24, with respect to claims 1-20 have been considered but are moot due to the new rejection.
Applicant’s remark:
In rejecting the claims, the Office Action cited various combinations of the following references. However, none of these references is directed to the core problem of mapping a generic ingredient from a recipe to an actual product in a catalog using a hierarchical taxonomy, specifically by excluding items in clearly irrelevant categories of that taxonomy. Etc..
Examiner’s response:
The Examiner disagrees with Applicant’s characterization of the references.
--Salehian et al. teach relationships between branded consumable names of a dish/recipe sub-graph and brand names of the brand name sub-graph; and matching item description strings received from a user with the brand descriptions. (see e.g. [0017] [0095])
--Salehian et al. is modified by Owens et al. which teach an interactive internet shopping and data integration system including classifying received grocery store information and selecting specific items. [0028] Arranging receiving grocery store information from a plurality of grocery stores,
[0029] at least one first computer processor to relate at least one generic grocery-item description to at least one particular grocery item description; at least one third database to store information about at least one plurality of grocery stores; and at least one second interface to permit selecting at least one particular grocery item to be purchased at a selected grocery store.)
-- Durazo et al. further modify the systems of Salehian et al. and Owens et al. with an item identification system including comparing vector representative of one or more attributes of the receipt items that enables the same or similar items to be located in different catalogs.
--- The new reference Haghighat Kashani et al. further details the method for selecting a specific item from candidate items by disregarding irrelevant items.
Please see the motivation statements in the rejection above.
According to KSR ( Rational C), in this case, one of ordinary skill in the art would have been capable of applying the known methods in the prior arts and the results would have been predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
DENG et al. (CN 111858694 A) – Identification Method and Device of Dish Information.
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/LUNA CHAMPAGNE/Primary Examiner, Art Unit 3627
September 15, 2025