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 10/28/2025. Claims 1-6, 8, 10-16, 18, and 20-22 are pending. Claims 7, 9, 17, and 19 stand cancelled. Claims 1-2, 11-12, and 20 have been amended. The 112(a) rejection and claim objections have been overcome.
Information Disclosure Statement
The IDS filed 8/12/2025 was received and has been considered.
The IDS filed 11/20/2025 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 1-6, 8, 10-16, 18, and 20-22 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. See MPEP 2106.03(II). In the instant case, claims 1-6, 8, 10, and 21-22 are directed to a process, claims 11-17 and 18 are directed to an article of manufacture, and claim 20 is directed to a machine. Therefore, the claims are directed to statutory subject matter under Step 1 of the Alice/Mayo test (Step 1: YES).
The claims are then analyzed to determine if the claims are directed to a judicial exception. See MPEP 2106.04. 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 1 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 2 of Step 2A). See MPEP 2106.
Taking Claim 1 as representative, independent claim 1 recites at least the following limitations that are believed to recite an abstract idea:
establishing a session for a user with a shopping cart, wherein the session associates actions taken with respect to the shopping cart with the user;
responsive to establishing the session for the user, causing display of a recipe suggestion for a recipe at a first timestamp within the session, wherein the recipe comprises a set of recipe items that are used in the recipe;
identifying an item added to a storage area of the shopping cart at a second timestamp within the session;
detecting a target action associated with the shopping cart, wherein the target action is performed with regards to the identified item at a third timestamp that is later in the session than the second timestamp;
transmitting data describing the target action, the identified item, the first timestamp, and the second timestamp;
matching the identified item to a recipe item of the set of recipe items associated with the recipe;
attributing the target action to the recipe suggestion by applying an attribution model to the identified item, the recipe suggestion, the first timestamp, and the second timestamp, wherein the attribution model comprises a set of attribution rules for determining whether to attribute the target action to a recipe suggestion, and applying the attribution model comprises:
comparing the first timestamp to the second timestamp,
determining that the first timestamp is earlier within the session than the second timestamp, and
responsive to the first timestamp being earlier within the session than the second timestamp, attributing the target action to the recipe suggestion based on the first timestamp being earlier within the session than the second timestamp; and
storing an indication of the attribution of the target action to the recipe suggestion.
The above limitations recite the concept of recommendations and advertising analytics. 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 and behaviors, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions. Independent claims 11 and 20 recite similar limitations as claim 1 and, as such, fall within the same identified groupings of abstract ideas. Accordingly, under Prong One of Step 2A of the Alice/Mayo test, claims 1, 11, and 20 recite an abstract idea (Step 2A, Prong One: YES).
Step 2A prong 2 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 use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
In this instance, independent claims 1, 11, and 20 recite the additional elements such as:
A computer system comprising a processor and a non-transitory memory
A processor of a shopping cart
The shopping cart displaying data
The item being identified based on sensor data from one or more sensors coupled to the shopping cart
Action being detected based on sensor data captured by a sensor coupled to the shopping cart
A computer server communicatively coupled to the processor
A database
A non-transitory computer-readable medium storing instructions that, when executed by a processor, causes the processor to perform steps
A system comprising: a processor; and a non-transitory computer-readable medium storing instructions that, when executed by the processor, causes 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 designed to monopolize the exception.
In addition, the recitation of the 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 designed 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, 4, 8, 10, 12, 14, 18, and 22 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, 5-6, 13, 15-16, and 21, these claims are similar to the independent claims except that they recite the further additional elements of an automated checkout system, a display of the shopping cart, sensor data, and a trained machine-learning model. 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 computer system comprising a processor and a non-transitory memory
A processor of a shopping cart
The shopping cart displaying data
The item being identified based on sensor data from one or more sensors coupled to the shopping cart
Action being detected based on sensor data captured by a sensor coupled to the shopping cart
A computer server communicatively coupled to the processor
A database
A non-transitory computer-readable medium storing instructions that, when executed by a processor, causes the processor to perform steps
A system comprising: a processor; and a non-transitory computer-readable medium storing instructions that, when executed by the processor, causes 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:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1-6, 8, 10-16, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kawada et al (US 20080215427 A1), hereinafter Kawada, in view of McHugh et al (US 20160078470 A1), hereinafter McHugh, and further in view of Battisti (US 9911130 B1), hereinafter Battisti.
Regarding Claim 1, Kawada discloses a method comprising: at a computer system comprising a processor and a non-transitory memory (Kawada: [0038-0039]):
establishing, by a processor of a shopping cart, a session for a user with the shopping cart, wherein the session associates actions taken with respect to the shopping cart with the user (Kawada: “When a customer passes the gate 6, the gate 6 reading the IC tag 12 of the product 11 carried around by the customer tells what kind of product the customer is intended to buy. ” [0089] – “a customer uses the cart 8, the system may handle both an in-basket event and a gate-pass event” [0153] – “the event detection section 94a of the cart terminal 9 acknowledges that the product 11 under this ID has put in the basket of the cart 8, i.e., an in-basket event has occurred.” [0145] – It is recognized that the gate-pass event, which first identifies the user’s activity with the cart, establishes/initiates a session. – “ this cart terminal 9 is configured by a small-sized computer …the cart terminal 9 is configured to include … a computation section …the computation section 92 applies any predetermined processing to information” [0142-0143]);
responsive to establishing the session for the user, causing, by the processor of the shopping cart, the shopping cart to display a recipe suggestion for a recipe at a first date-stamp [day/target period] within the session, wherein the recipe comprises a set of recipe items [ingredients] that are used in the recipe (Kawada: “when advertisement information comes in return from the product information management server 3, the information is displayed on the display of the cart terminal 9.” [0142] – “the advertisement information displayed to a customer is recipe information about beef stew … and the ingredients for the beef stew include potato, carrot, onion, and beef.” [0121] – “the related product information is about a combination of a plurality of products, which are recommended for purchasing all at once” [0043] – See also Figure 1, where the cart terminal is mounted to the cart. – “perform this advertisement information evaluation processing … every after business hours. … searches the event information database 39 for a purchase event in a target period, i.e., events occurred during a day today” [0083-0084]);
identifying, by the processor of the shopping cart, an item added to a storage area of the shopping cart at a second timestamp within the session, wherein the item is identified based on sensor data from one or more sensors [reader] coupled to the shopping cart (Kawada: “the reader control section 91 of the cart terminal 9 drives the cart reader 84 at regular intervals, and also at regular intervals, makes the cart reader 84 read a product unique ID from the IC tag 12 in the basket of the cart 8 …By the product unique ID being read as such, the event detection section 94a of the cart terminal 9 acknowledges that the product 11 under this ID has put in the basket of the cart 8, i.e., an in-basket event has occurred.” [0145] – “this event information database 39 is con figured to include an event number field 391, a product unique ID field 392, an event type field 393, a date and time field 394” [0057]);
detecting, by the processor of the shopping cart, a target action associated with the shopping cart based on sensor data captured by a sensor coupled to the shopping cart, wherein the target action is performed with regards to the identified item at a third timestamp that is later in the session than the second timestamp (Kawada: “the cash register terminal 4 reads the product unique ID of the product 11 from the IC tag 12 attached to the purchased product 11. …forwards the result of reading as such to the product information management server 3. Such reading of the IC tag 12 of the product 11 by the cash register terminal 4 is hereinafter referred to as a purchase event” [0044] – “the product information management server 3 is in the state of waiting for event information (S100), if event information comes from any of the components, i.e., the shelf terminal 2, the cash register terminal 4, and the cart terminal 9, the event information is accordingly received (S200).” [0149] – “The event type field 393 is stored with event type, i.e., … purchase, and the date and time field 394 is stored with a date and time when the event is detected.” [0057] – See also Figures 1 and 14.);
transmitting, by the processor of the shopping cart, data describing the target action, the identified item, the first date-stamp, and the second timestamp to a computer server communicatively coupled to the processor of the shopping cart (Kawada: “The cart terminal 9 receives a result of reading by the cart reader 84. The cart terminal 9 then processes the reading result, and transmits the result to the product information management server 3 as in-basket event information.” [0142] – “when the product information management server 3 is in the state of waiting for event information (S100), if event information comes from any of the components, i.e., the shelf terminal 2, the cash register terminal 4, and the cart terminal 9, the event information is accordingly received” [0149] – See Figure 6.);
matching, by the computer server, the identified item to a recipe item of the set of recipe items associated with the recipe (Kawada: “By referring to the flowchart of FIG. 8, described next is processing of evaluating advertisement information by the advertisement evaluation section 32 d of the product information management server 3.” [0083] – “a comparison is made between one or more product types purchased by a customer and a plurality of product types found in the advertisement information displayed to the customer when he or she takes out a product, thereby finding out how many of the product types found in the advertisement information are purchased by the customer.” [0087] – See Step 1006 in Figure 8.);
attributing, by the computer server, the target action to the recipe suggestion by applying an attribution model to the identified item, the recipe suggestion, the first date-stamp, and the second timestamp, wherein the attribution model comprises a set of attribution rules [policy] for determining whether to attribute the target action to a recipe suggestion (Kawada: “advertisement display is accordingly made for a plurality of products …to encourage the customer to make purchase. …the advertisement information is evaluated based on which of a plurality of product types displayed to the customer as the advertisement information contributes to the purchasing so that the effects of the advertisement information can be evaluated quantitatively.” [0094] – “The advertisement evaluation section 32d searches the event information database 39 for a purchase event in a target period, i.e., events occurred during a day today” [0084] – “As shown in FIG. 9, the evaluation point provision policy includes a box for one-time purchasing range …segmented depending on how many of the information-provided products are purchased. The evaluation-made point field 332 is stored with the value of the evaluation-made point for every item …includes a plurality of items of “purchasing all of information-provided products … and "no purchasing of information provided products”. The advertisement evaluation section 33d determines to which item of the evaluation point provision policy the purchasing pattern with the purchase event fits, and acquires the value of the evaluation-made point predetermined for the item” [0089] – With reference to Figure 5, it is noted that the points correspond to product attributed to the advertisement. It is understood that the advertisement evaluation section, which executes the evaluation policy [0089], constitutes a model for performing the evaluation.); and
storing, by the computer server, an indication of the attribution of the target action to the recipe suggestion in a database (Kawada: “After finding the value of the evaluation-made point as such, the advertisement evaluation section 33d then sets a log number for storage into a log number field 4001 in the advertisement evaluation log database 40 of FIG. 5.” [0090]).
While Kawada teaches that the a timestamp is recorded for a “gate-pass” event that triggers an advertisement to be returned & displayed [0098], it does not specifically teach that the suggestion is at a first timestamp; that the first timestamp is earlier in the session than the second timestamp; or that applying the attribution model comprises: comparing the first timestamp to the second timestamp, determining that the first timestamp is earlier within the session than the second timestamp, and responsive to the first timestamp being earlier within the session than the second timestamp, attributing the target action to the recipe suggestion based on the first timestamp being earlier within the session than the second timestamp.
However, McHugh teaches systems for attributing advertisements for conversion events (McHugh: [0001]), including that the suggestion is at a first timestamp (McHugh: “ events stored in event store 255 may represent impressions of advertisements that are linked to advertising campaigns. Similarly, if a user clicks on an advertisement, the corresponding event stored in event store” [0041] – “The event store 255 stores information describing these various events …each event is represented as a tuple comprising an event identifier, a timestamp associated with the event, a type of the event, and other data relevant to the event.” [0038] – See also [0084]).
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, Kawada would continue to teach responsive to establishing the session for the user, causing, by the processor of the shopping cart, the shopping cart to display a recipe suggestion for a recipe at a first date-stamp within the session, except that now it would also teach that the suggestion is at a first timestamp, according to the teachings of McHugh. 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 for advertisers to evaluate their campaigns (McHugh: [0003]).
While Kawada/McHugh teach attributing, by the computer server, the target action to the recipe suggestion, they do not specifically teach that the first date-stamp is earlier in the session than the second timestamp; and that applying the attribution model comprises: comparing the first timestamp to the second timestamp, determining that the first timestamp is earlier within the session than the second timestamp, and responsive to the first timestamp being earlier within the session than the second timestamp, attributing the target action to the recipe suggestion based on the first timestamp being earlier within the session than the second timestamp.
However, Battisti teaches models for advertisement attribution (Battisti: Abstract), including
that the first date-stamp is earlier in the session than the second timestamp (Battisti: “customer 302 may … add an item offered for sale to their cart. The customer 302 may purchase the item added to their cart for one hundred dollars, the dollar value may be considered the success event's total attributed value … This formation may be captured by the stream processing service 308 as well as all the eligible hits prior to the cart-add. ” Col. 6, lines 15-35 – “an eligible hit may correspond to the customer clicking on an advertisement ” Col. 5, lines 35-45); and
that applying the attribution model comprises:
comparing the first timestamp to the second timestamp, determining that the first timestamp is earlier within the session than the second timestamp (Battisti: “customer 302 may … add an item offered for sale to their cart. The customer 302 may purchase the item added to their cart for one hundred dollars, the dollar value may be considered the success event's total attributed value … This formation may be captured by the stream processing service 308 as well as all the eligible hits prior to the cart-add. ” Col. 6, lines 15-35– “an eligible hit may correspond to the customer clicking on an advertisement ” Col. 5, lines 35-45), and
responsive to the first timestamp being earlier within the session than the second timestamp, attributing the target action to the recipe suggestion based on the first timestamp being earlier within the session than the second timestamp (Battisti: “The customer 302 may purchase the item added to their cart for one hundred dollars, the dollar value may be considered the success event's total attributed value … This formation may be captured by the stream processing service 308 as well as all the eligible hits prior to the cart-add. The attribution service 312 may assign a percentage value to each eligible hit based at least in part on … the success event's total attributed value (one hundred dollars in this example).” Col. 6, lines 15-35 – “an eligible hit may correspond to the customer clicking on an advertisement ” Col. 5, lines 35-45).
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, Kawada /McHugh would continue to teach attributing, by the computer server, the target action to the recipe suggestion, except that now it would also teach that the first date-stamp is earlier in the session than the second timestamp; and that applying the attribution model comprises: comparing the first timestamp to the second timestamp, determining that the first timestamp is earlier within the session than the second timestamp, and responsive to the first timestamp being earlier within the session than the second timestamp, attributing the target action to the recipe suggestion based on the first timestamp being earlier within the session than the second timestamp, according to the teachings of Battisti. 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 calculation of attributed value of eligible hits (Battisti: Col. 11, lines 35-45).
Regarding Claim 2, Kawada/McHugh/Battisti teach the method of claim 1, wherein causing the shopping cart to display the recipe suggestion comprises: accessing a set of candidate recipe suggestions from a plurality of candidate recipe suggestions; generating a suggestion score for each candidate recipe suggestion of the set of candidate recipe suggestions based on each candidate recipe suggestion and user data describing characteristics [addition of products by the customer] of the user; selecting a recipe suggestion to apply to the user from the set of candidate recipe suggestions based on the generated suggestion scores for the set of candidate recipe suggestions; and causing the shopping cart to display the selected recipe suggestion (Kawada: “step 601 show an addition of minced pork compared with the products in the comparison use event information. In step 604, it is thus determined that there is a new addition of product, and the procedure goes to step 606. In step 606, a determination is made whether the product being the addition is included in the related product information corresponding to the advertisement information displayed for the comparison target event. …a case where the advertisement information displayed to a customer is recipe information about beef stew …and the ingredients for the beef stew include potato, carrot, onion, and beef. … because the addition of product (minced pork) is not included in the related product information corresponding to the advertisement information provided for the comparison target event, the determination made in step 606 is No, and the procedure thus goes to step 607.” [0128] – “the search processing is then performed in step 610 for the related product information. Exemplified here is a case where any related product information including ingredients needed for coquette is found, and thus found related product information includes potato, egg, onion, and minced pork.” [0130]).
Regarding Claim 3, Kawada/McHugh/Battisti teach the method of claim 1, wherein causing the shopping cart to display the recipe comprises: transmitting instructions from an automated checkout system [register terminal] to the shopping cart to display the recipe (Kawada: “the cash register terminal 4 is connected with a cash register reader 54 equipped with an antenna 53. This cash register terminal 4 is configured to include a reader control section 41, a computation section 42, an input/output section 43, a communications section 44, and a program storage section 45. … The computation section 42 is configured to include an event detection section 42a, an event information creation section 42b, an input/output control section 42c, and a communications control section 42d. The event detection section 42a detects whether a purchase event has occurred based on the reading result derived by the cash register reader 54” [0048]).
Regarding Claim 4, Kawada/McHugh/Battisti teach the method of claim 1, wherein displaying the recipe comprises: displaying one of an image, a title, or a description of the recipe on a display of the shopping cart (Kawada: “With advertisement information 110 of FIG. 16, for example, the advertisement information creation section 32c acquires… text data of …the product category names …and the quantity for use of the products under the product category names. … image data is written into a dish image box 113 in the advertisement screen template” [0113] – See Figure 16.).
Regarding Claim 5, Kawada/McHugh/Battisti teach the method of claim 1, wherein Kawada teaches displaying the recipe comprises: displaying, on a display of the shopping cart (Kawada: “information is displayed on the display of the cart terminal” [0142]), one or more elements for viewing the recipe (Kawada: “With advertisement information 110 of FIG. 16, for example, the advertisement information creation section 32c acquires… text data of …the product category names …and the quantity for use of the products under the product category names. … image data is written into a dish image box 113 in the advertisement screen template” [0113] – See Figure 16.),
while McHugh further teaches displaying one or more user elements for interacting with the recommendation (McHugh: “he user viewed a few advertisements and clicked on one of them” [0054] – “a user of the client device 205 to interact … interactions may correspond to various actions performed by users including …receiving impressions of advertisements, clicking on advertisements,” [0028]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine McHugh with Kawada/Battisti for the reasons identified above with respect to claim 1.
Regarding Claim 6, Kawada/McHugh/Battisti teach the method of claim 1, further comprising identifying the user that is using the shopping cart based on sensor data (Kawada: “a membership card may be read at the gate 6 together with the products 11. …purchasing history in the member information database 38 may be searched to find any recently-purchased products” [0139] – “When an IC-chip-attached membership card or others is issued for any customers registered as members, and when the customer presents his or her membership card … performs reading of the membership card together with reading of the IC tag 12 of the product 11.” [0044]).
Regarding Claim 8, Kawada/McHugh/Battisti teach the method of claim 6, further comprising: associating the target action with the identified user based on the session between the user and the shopping cart (Kawada: “The advertisement evaluation section 32d searches the event information database 39 for a purchase event in a target period, i.e., events occurred during a day today (S1001). … when there is any applicable purchase event, the advertisement evaluation section 32d extracts event information for every applicable purchase event from the event information database 39 (S1002).” [0084] – “the reader control …drives the cart reader 84 at regular intervals, and …makes the cart reader 84 read a product unique ID from the IC tag 12 in the basket of the cart 8, and in some cases, read a membership number or others from a membership card (S10). By the product unique ID being read as such, the event detection section 94a of the cart terminal 9 acknowledges that the product 11 under this ID has put in the basket of the cart 8, i.e., an in-basket event has occurred.” [0145] – “After an event occurrence is detected as such, the event information creation section 92b of the cart terminal 9 creates event information (S30). This event information includes an event number, a product unique ID being a reading result, an event type (in-basket and out-basket), date and time when the event is detected, and the location of the event occurrence.” [0146]– See also Figure 13. It is understood that the period of user activity from first detection until payment constitutes a session.).
Regarding Claim 10, Kawada/McHugh/Battisti teach the method of claim 1, wherein applying the set of attribution rules comprises: determining whether more than one item from the set of recipe items has been added to the storage area of the shopping cart (Kawada: “the evaluation point provision policy includes a box for one-time purchasing range 331… includes a plurality of items of “purchasing all of information-provided products being two or more', 'purchasing all of information provided products being one', 'purchasing a part of information-provided products', and "no purchasing of information provided products”.” [0089] – See Figures 5 & 9.).
Regarding Claims 11-16 and 18, the limitations of Claims 11-16 and 18 are closely parallel to the limitations of 1-6 and 8, with the additional limitation of a non-transitory computer-readable medium storing instructions that, when executed by a processor (Kawada: [0038-0039]), and are rejected on the same basis.
Regarding Claim 20, the limitations of Claim 20 are closely parallel to the limitations of claim 1, with the additional limitation of a system comprising: a processor; and a non-transitory computer-readable medium storing instructions that, when executed by the processor, causes the processor to perform steps (Kawada: [0038-0039]), and are rejected on the same basis.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Kawada/McHugh/Battisti, and further in view of Collin et al (US 10169778 B1), hereinafter Collin.
Regarding Claim 21, Kawada/McHugh/Battisti teach the method of claim 1, but do not specifically teach that applying the attribution model further comprises: applying a machine-learning model to the identified item, the recipe suggestion, the first timestamp, and the second timestamp, wherein the machine-learning model is trained to predict a likelihood that the recipe suggestion caused the target action with regards to the identified item.
However, Collin teaches methods for crediting shopping events to an advertisement (Collin: Abstract), including the step of applying a machine-learning model to the identified item, the recipe suggestion, the first timestamp, and the second timestamp, wherein the machine-learning model is trained to predict a likelihood that the recipe suggestion caused the target action with regards to the identified item (Collin: “ applying one or more machine learning algorithms to the data to train the models to generate more accurate attribution of conversion events to advertisements” Col. 15, lines 10-16 – “The ad attribution engine 248 may identify a date associated with the conversion event and may search data … for an advertisement … served to the user 102 … within an attribution window. …a period of time prior to the conversion event used to identify an advertisement that may receive credit for the conversion event.” Col. 21, lines 10-25).
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, Kawada/McHugh/Battisti would continue to teach applying the attribution model, except that now it would also teach applying a machine-learning model to the identified item, the recipe suggestion, the first timestamp, and the second timestamp, wherein the machine-learning model is trained to predict a likelihood that the recipe suggestion caused the target action with regards to the identified item, according to the teachings of Collin. 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 attribute conversion events to advertisements (Collin: Col. 15, lines 15-16).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Kawada/McHugh/Battisti, and further in view of Sim et al (US 20140358672 A1), hereinafter Sim.
Regarding Claim 22, Kawada/McHugh/Battisti teach the method of claim 1, but do not specifically teach that applying the attribution model further comprises: comparing the identified item to a list of staple items; and responsive to the identified item not being included in the list of staple items, attributing the target action to the recipe suggestion.
However, Sim teaches methods for crediting conversion events to advertisements (Sim: [0029]), including comparing the identified item to a list of staple items; and responsive to the identified item not being included in the list of staple items, attributing the target action to the recipe suggestion (Sim: “To determine whether to credit an impression of an advertisement to a conversion event, the ad credit module 340 compares the received content ID with content IDs stored in the retrieved ad history. If the content ID matches a content ID of an advertisement stored in the ad history, the ad credit module 340 credits the conversion event to the advertisement if a time between the conversion event and providing the advertisement to the client device 104A is less than a threshold time period.” [0059] – Without further clarification, it is understood that the ad history constitutes a list of staple items, such that the attribution will not be given to an advertisement if the conversion matches a different advertisement.).
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, Kawada/McHugh/Battisti would continue to teach applying the attribution model, except that now it would also teach comparing the identified item to a list of staple items; and responsive to the identified item not being included in the list of staple items, attributing the target action to the recipe suggestion, according to the teachings of Sim. 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 measure an advertisement’s effectiveness (Sim: [0003]).
Response to Arguments
Applicant's arguments filed 10/28/2025 have been fully considered but they are not persuasive.
Claim Rejection – 35 USC §101
Applicant argues that “by combining real-time sensor data from physical interactions with server-side attribution modeling, the method enhances the operation of shopping cart systems in a physical retail environment.” Applicant argues that this “creates a technological capability for linking recipe content to purchasing actions with precision and without requiring manual input from the user. This results in improved tracking of the relationship between content displayed on the shopping cart interface and customer purchasing behavior, producing a more effective and responsive integrated physical-digital shopping experience.”
Examiner respectfully disagrees. With reference to the rejection above, the claims recite steps which fall within the concept of recommendations and advertising analytics that falls 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 and behaviors, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions; except for the recitation of computer-related additional elements. These elements, rather than integrating the abstract idea into a practical application or amounting to significantly more than the abstract idea, are recited as mere instructions to apply the abstract idea to a technological environment [MPEP 2106.05(f)]. The argued improvement is at best a business improvement stemming solely from the abstract idea, with the alleged efficiency/responsiveness coming only from the inherent speed or efficiency of a general purpose computer, which does not amount to significantly more than the abstract idea [MPEP 2106.05(a)].
Claim Rejection – 35 USC §103
Applicant’s arguments with respect to the prior art 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, the determination of the first timestamp being prior to the second; and the attribution of the target event to the recipe suggestion based on this determination, are taught by newly-relied upon reference Battisti in the rejection above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Achan et al (US 20190236676 A1) teaches a recommendation engine that recommends recipes.
Rizvi et al (US 20200236157 A1) teaches a system for conversion attribution from electronic recommendations, wherein recommendations and conversions are both timestamped, with conversions occurring after a recommendation.
Matayoshi (US 20200286135 A1) teaches in-store systems for suggesting recipes to a user based on assessed interest.
Thomas et al (US 20150006319 A1) teaches a shopping cart terminal device for detecting items being placed in a cart and suggesting items by a recipe.
Reference U (NPL – see attached) discusses a recipe recommendation engine for grocery stores.
Reference V (NPL – see attached) discusses a recipe recommendation engine for grocery stores based on cart context. Examiner notes that this NPL does not appear to constitute prior art.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/T.J.S./Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689