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 .
Priority
Applicant has not claimed priority to another application. Application 18/676,345 was filed 5/28/2024.
Information Disclosure Statement
No IDS has been submitted.
Status of Claims
Applicant’s claims, filed 5/28/2024, have been entered. Claims 1-20 are currently pending in this application and have been examined.
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 an abstract idea without significantly more. The claim(s) recite(s) an abstract idea. This judicial exception is not integrated into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Under Step 1 of the Alice/Mayo test the claims are directed to statutory categories. Specifically, the method, as claimed in claims 1-12, are directed to a process, the non-transitory computer readable medium, as claimed in claims 13-19, are directed to an article of manufacture, and the system, as claimed in claim 20, is directed to a machine, (see MPEP 2106.03).
Under Step 2A (prong 1), claim 1, taken as representative, recites at least the following limitations (emphasis added) that recite an abstract idea:
receiving from a user session data related to a current session of the user;
accessing a user type prediction model, wherein the user type prediction model is trained to predict a type of the user for the current session;
applying the user type prediction model to output, based at least in part on the session data, a score for the user indicative of the predicted type of the user for the current session;
comparing the score for the user with a threshold score;
responsive to the score for the user being greater than the threshold score, identifying, based at least in part on the score for the user, user data associated with the user, and information about the current session, a set of elements arranged in a specific order for presentation to the user;
generating a interface associated with the user that includes the set of elements arranged according to the specific order; and
display the interface with the set of elements arranged according to the specific order.
These limitations recite certain methods of organizing human activity, such as performing commercial interactions (see MPEP 2106.04(a)(2)(II)). Certain methods of organizing human activity are defined by MPEP 2106.04 as including “fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).” In this case, the abstract ideas recited in representative claim 1 are certain methods of organizing human activity because displaying ordered elements based on a predicted type of user for a current session (i.e., a recommendation) is a commercial or legal interaction because it is a advertising, marketing or sales activity, or business relations. Thus, claim 1 recites an abstract idea.
Independent claims 13 and 20 recite the same abstract idea as recited in independent claim 1. As such, the analysis under Step 2A, Prong 1 is the same for independent claims 13 and 20 as described above for independent claim 1.
Under Step 2A (prong 2), if it is determined that the claims recite a judicial exception, it is then necessary to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of that exception (see MPEP 2106.04). As stated in the MPEP, when “an additional element merely recites the words ‘apply it (or an equivalent) with the judicial exception, or merely uses a computer as a tool to perform an abstract idea,” the judicial exception has not been integrated into a practical application. In this case, representative claim 1 includes additional elements such as (additional elements are bolded):
receiving, from a device associated with a user of an online system and via a network, session data related to a current session of the user with the online system;
accessing a user type prediction model of the online system, wherein the user type prediction model is trained to predict a type of the user for the current session;
applying the user type prediction model to output, based at least in part on the session data, a score for the user indicative of the predicted type of the user for the current session;
comparing the score for the user with a threshold score;
responsive to the score for the user being greater than the threshold score, identifying, based at least in part on the score for the user, user data associated with the user, and information about the current session, a set of user interface elements arranged in a specific order for presentation to the user;
generating a user interface of the device associated with the user that includes the set of user interface elements arranged according to the specific order; and
causing the device associated with the user to display the user interface with the set of user interface elements arranged according to the specific order.
In addition to the additional elements bolded above, claim 13 additionally recites “A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps” and claim 20 additionally recites “A computer system comprising: a processor; and a non-transitory computer-readable storage medium having instructions that, when executed by the processor, cause the computer system to perform steps.”
Although reciting these additional elements, taken alone or in combination these elements are not sufficient to integrate the abstract idea into a practical application. These additional elements merely amount to the general application of the abstract idea to a technical environment (“from a device”, “of an online system”, “via a network”, a model “is trained”, “a user interface of the device” including “user interface elements”, “a computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps”, “a computer system comprising: a processor; and a non-transitory computer-readable storage medium having instructions” and insignificant pre-and-post solution activity (receiving information, accessing information, displaying information). The specification makes clear the general-purpose nature of the technological environment. This is because the additional elements of claims 1, 13, and 20 are recited at a high level of generality (i.e., as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform the abstract idea) (see Fig. 1; paragraphs [0014], [0019], [0027], [0030], [0052]-[0056], [0073]-[0074], [00104]-[00106]). The specification indicates that while exemplary general-purpose systems may be specific for descriptive purposes, any elements capable of implementing the claimed invention are acceptable. That is, the technology used to implement the invention is not specific or integral to the claim. The description demonstrates that these additional elements are merely generic devices such as a generic computer. Further, the additional elements do no more than generally link the use of a judicial exception to a particular environment or field of use (such as the Internet or computing networks).
Therefore, considered both individually and as an ordered pair, the additional elements do no more than generally link the use of the abstract idea to a particular technological environment or field of use. That is, given the generality with which the additional elements are recited, the limitations do not implement the abstract idea with, or use the abstract idea in conjunction with, a particular machine or manufacture that is integral to the claim. Additionally, the claims do not reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, do not transform or reduction of a particular article to a different state or thing; and do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technology environment, such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea into a practical application, and is therefore “directed to” the abstract idea.
In addition to the above, the recited receiving, accessing, displaying steps (even assuming arguendo they do not form part of the abstract idea, which the Examiner does not acquiesce), are at best little more than extra-solution activity (e.g., data gathering, presentation of data) that contributes nominally or insignificantly to the execution of the claimed system (see MPEP 2106.05(g)).
In view of the above, under Step 2A (prong 2), claims 1, 13, and 20 do not integrate the recited exception into a practical application.
Under Step 2B, examiners should evaluate additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Returning to claims 1, 13, and 20, taken individually or as a whole the additional elements of claims 1, 13, and 20 do not provide an inventive concept (i.e. they do not amount to “significantly more” than the exception itself). As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Furthermore, the additional elements fail to provide significantly more also because the claim simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. For example, the additional elements of claims 1, 13, and 20 utilize operations the courts have held to be well-understood, routine, and conventional (see: MPEP 2106.05(d)(II)), including at least:
receiving or transmitting data over a network,
storing or retrieving information from memory,
presenting offers
Even considered as an ordered combination (as a whole), the additional elements of claims 1, 13, and 20 do not add anything further than when they are considered individually.
In view of the above, claims 1, 13, and 20 do not provide an inventive concept (“significantly more”) under Step 2B, and is therefore ineligible for patenting.
Regarding claims 5
Dependent claim(s) 5, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they do not add “significantly more” to the abstract idea. More specifically, dependent claim(s) 5 merely further define the abstract limitations of claim(s) 1 or provide further embellishments of the limitations recited in independent claim claim(s) 1.
Claim 5 sets forth:
wherein applying the user type prediction model comprises: applying the user type prediction model to output, further based on information about a retailer associated with the online system that is related to the current session, the score for the user indicative of the predicted type of the user for the current session and for the retailer.
Such recitations merely embellish the abstract idea of displaying ordered elements based on a predicted type of user for a current session (i.e., a recommendation). The claims do not set forth any further additional limitations, and therefore such abstract embellishments are applied to the additional limitations recited in claim(s) 1, which do no more than generally link the use of the abstract idea to a particular technological environment, do not integrate the abstract idea into a practical application, and do not provide an inventive concept. Accordingly, the claims do not confer eligibility on the claimed invention and is ineligible for similar reasons to claim(s) 1.
Thus, dependent claim 5 is ineligible.
Regarding claim 2-4, 6-12, and 14-19
Dependent claim(s) 2-4, 6-12, and 14-19 sets forth:
wherein receiving the session data comprises: receiving, via the device associated with the user and via the network, at least one of a search query entered by the user via a search interface of the device associated with the user, a number of predefined collections of items the user engaged with during the current session, a ratio between a number of unique first items the user engaged with during the current session and a total number of unique items the user added to a cart during the current session, a ratio between a number of unique second items added to the cart without the user viewing details associated with the second items and the total number of unique items added to the cart, or a timestamp of each unique item added to the cart.
wherein receiving the session data comprises: gathering, via one or more sensors mounted to a physical receptacle utilized by the user during the current session for shopping at a location of a retailer associated with the online system, data with information about at least one of a duration of the current session at the location of the retailer, a number of items scanned by a computing system associated with the physical receptacle during the current session, an average speed of movement of the physical receptacle during the current session, or an average distance traveled by the physical receptacle during the current session per item added to the physical receptacle; and receiving, from the computing system associated with the physical receptacle and via the network, the gathered data as at least a portion of the session data.
retrieving, from a database of the online system, data with information about at least one of a ratio between a number of unique items the user converted during a defined time period by directly adding the unique items to shopping carts without further engagement with the unique items and a total number of items the user converted during the defined time period, or an average shopping time per unique item converted by the user during the defined time period; and applying the user type prediction model to output, further based on the retrieved data, the score for the user.
gathering, over a defined time period, data related to interactions between a collection of users of the online system and the online system; assigning, based on the gathered data, a label to each user in the collection of users; and training, using the gathered data and the assigned label for each user in the collection of users, the user type prediction model to generate a set of initial values for a set of parameters of the user type prediction model.
collecting feedback data with information about engagement by the user with the set of user interface elements; and re-training the user type prediction model by updating, using the collected feedback data, a set of parameters of the user type prediction model.
wherein identifying the set of user interface elements comprises: ranking, based at least in part on the score for the user, the user data, and the information about the current session, a plurality of user interface elements retrieved from a database of the online system to identify a rank of each user interface element of the plurality of user interface elements; selecting, based on the rank of each user interface element, a defined number of user interface elements from the plurality of user interface elements as the set of user interface elements for presentation to the user; and arranging, based on the rank of each user interface element, the set of user interface elements in the specific order.
wherein ranking the plurality of user interface elements comprises: accessing a content priority model of the online system, wherein the content priority model is trained to identify a priority of a user interface element for presentation to the user; and applying the content priority model to output, based at least in part on the score for the user, the user data, and the information about the current session, the rank for each user interface element of the plurality of user interface elements that is indicative of a priority of that user interface element for presentation to the user.
retrieving, from a database of the online system, the user data comprising information about at least one of a plurality of user interface elements associated with a plurality of items converted by the user during each order of a plurality of orders for a defined time period, or affinities for the defined time period between a plurality of categories of user interface elements.
receiving, from the device associated with the user and via the network, the information about the current session including at least one of content of a cart associated with the current session or a browsing history of the user for the current session.
gathering, via one or more sensors mounted to a physical receptacle utilized by the user during the current session for shopping at a location of a retailer associated with the online system, information about physical locations of the physical receptacle during the current session; and receiving, from a computing system associated with the physical receptacle and via the network, the gathered information as at least a portion of the information about the current session.
Such recitations merely embellish the abstract idea of displaying ordered elements based on a predicted type of user for a current session (i.e., a recommendation). While the claim(s) do set forth the additional elements of “a search interface of the device associated with the user”, “one or more sensors mounted to a physical receptacle”, “a database of the online system”, “training…the user type prediction model”, “re-training the user type prediction model”, “a computing system associated with the physical receptacle and via the network” these recitations are similar to the additional limitations in claims 1 and 13, as they do no more than generally link the use of the abstract idea to a particular technological environment. That is these additional elements merely amount to the general application of the abstract idea to a technical environment. The specification makes clear the general-purpose nature of the technological environment. Paragraphs [0014], [0019], [0027], [0030], [0032], [0052]-[0056], [0073]-[0074], [0094], [00104]-[00106] indicate that while exemplary general-purpose systems may be specific for descriptive purposes, any elements capable of implementing the claimed invention are acceptable. That is, the technology used to implement the invention is not specific or integral to the claim. Therefore, these additional elements do not integrate the abstract idea into a practical application because they merely amount to using a computer to apply the abstract idea and no more than a general link of the use of the abstract idea to a particular technological environment or field of use and thus do not act to integrate the abstract idea into a practical application of the abstract idea. Further, the “sensors mounted to a physical receptacle” is recited at a high level and amounts to merely applying the abstract idea (see paragraphs [0032], [0094]).
Additionally, the additional elements do not amount to significantly more because they merely amount to using a computer to apply the abstract idea and amount to no more than a general link of the use of the abstract idea to a particular technological environment.
Thus, dependent claims 2-4, 6-12, and 14-19 are also ineligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 2, 5-11, 13, 14, and 17-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ali et al. (US 2024/0256556 A1).
Regarding claim 1, Ali et al., hereinafter Ali, discloses a method, performed at a computer system comprising a processor and a computer-readable medium (Fig. 1; abstract; ¶¶0028-0044), comprising:
receiving, from a device associated with a user of an online system and via a network, session data related to a current session of the user with the online system (Figs. 2-5; ¶¶0061-0062 in view of ¶0048);
accessing a user type prediction model of the online system, wherein the user type prediction model is trained to predict a type of the user for the current session (Figs. 2-5, 8-11; ¶0049 [contextual explore/exploit model is configured to generate weights for a set of content modules based on both positive and negative feedback. For example, in some embodiments, the contextual explore/exploit model includes a contextual bandit model utilizing user intent features, user sub-category affinities, and incorporating click and no-click feedback.], ¶0060 [the contextual engine 266 includes a contextual explore/exploit module 268… The contextual explore/exploit module 268 is configured to generate ranking weights for a plurality of content modules based on a provided context… contexts can include various life-stage categories, such as, for example, new parent, college-age, etc. The context can similarly include various categories related to events or interests, such as new parent/baby, gamer/gaming, hiker/outdoor gear, etc.] in view of ¶¶0091-0097 [trained machine learning models]);
applying the user type prediction model to output, based at least in part on the session data, a score for the user indicative of the predicted type of the user for the current session (Figs. 2-5, 8-11; ¶0059-0060 [ranking weights are comparable to a score], ¶0062);
comparing the score for the user with a threshold score (Figs. 2-5, 8-11; ¶0066 [ranked based on weight and selecting the N highest where N is a positive integer is comparable to a threshold score]);
responsive to the score for the user being greater than the threshold score, identifying, based at least in part on the score for the user, user data associated with the user, and information about the current session, a set of user interface elements arranged in a specific order for presentation to the user (Figs. 2-11; ¶¶0066-0071);
generating a user interface of the device associated with the user that includes the set of user interface elements arranged according to the specific order (Figs. 2-11; ¶¶0071-0073); and
causing the device associated with the user to display the user interface with the set of user interface elements arranged according to the specific order (Figs. 2-11; ¶¶0071-0073).
Regarding claim 2, Ali discloses the method of claim 1, wherein receiving the session data comprises:
receiving, via the device associated with the user and via the network, at least one of a search query entered by the user via a search interface of the device associated with the user, a number of predefined collections of items the user engaged with during the current session, a ratio between a number of unique first items the user engaged with during the current session and a total number of unique items the user added to a cart during the current session, a ratio between a number of unique second items added to the cart without the user viewing details associated with the second items and the total number of unique items added to the cart, or a timestamp of each unique item added to the cart (Fig. 4, element 204; ¶0022 [“active feedback” includes click rate, add-to-cart, purchase, or other actions taken with respect to an interface ], ¶0061, ¶0068, and ¶0075 [initiating a search]).
Regarding claim 5, Ali discloses the method of claim 1, wherein applying the user type prediction model comprises:
applying the user type prediction model to output, further based on information about a retailer associated with the online system that is related to the current session, the score for the user indicative of the predicted type of the user for the current session and for the retailer (Figs. 2-5, 8-11; ¶0047 [e-commerce interface], ¶0059-0060, ¶0062, ¶¶0064-0065 [In an e-commerce environment, a generic content module can include, for example, seasonal items, high-traffic items, promotional items, etc.… In the context of an e-commerce interface, thematic groupings can include, but are not limited to, departments, sub-departments, seasons, and promotions.] ¶¶0076-0077 [items within a large catalog of items is comparable to information about a retailer], ¶0084 [bestselling or highest traffic items]).
Regarding claim 6, Ali discloses the method of claim 1. While Ali further discloses further comprising:
gathering, over a defined time period, data related to interactions between a collection of users of the online system and the online system (Figs. 2-5, 8-11; ¶0050 [a model training engine is configured to receive positive and negative feedback data and generate one or more trained contextual explore/exploit models. The positive and negative feedback data can be stored, for example, in interaction database 30.], ¶0068 [The use of personalization data 262 provides for personalization of the contextual content modules 272 and/or layout of the contextual content modules 272 based on temporal, geographic, and/or user-specific data], ¶¶0082-0083, ¶¶0092-0093);
assigning, based on the gathered data, a label to each user in the collection of users (Figs. 2-5, 8-11; ¶¶0060-0061, ¶0063, ¶0067, ¶¶0082-0083, ¶¶0092-0097); and
training, using the gathered data and the assigned label for each user in the collection of users, the user type prediction model to generate a set of initial values for a set of parameters of the user type prediction model (Figs. 2-5, 8-11; ¶¶0023-0027, ¶0050, ¶0067, ¶¶0092-0097),
Regarding claim 7, Ali discloses the method of claim 1, further comprising:
collecting feedback data with information about engagement by the user with the set of user interface elements (Figs. 2-5, 8-11; ¶¶0022-0023, ¶0050, ¶0074); and
re-training the user type prediction model by updating, using the collected feedback data, a set of parameters of the user type prediction model (Figs. 2-5, 8-11; ¶0050, ¶0075, ¶0087, ¶¶0091-0097).
Regarding claim 8, Ali discloses the method of claim 1, wherein identifying the set of user interface elements comprises:
ranking, based at least in part on the score for the user, the user data, and the information about the current session, a plurality of user interface elements retrieved from a database of the online system to identify a rank of each user interface element of the plurality of user interface elements (Figs. 2-11; ¶¶0066-0071 );
selecting, based on the rank of each user interface element, a defined number of user interface elements from the plurality of user interface elements as the set of user interface elements for presentation to the user (Figs. 2-11; ¶¶0066-0071); and
arranging, based on the rank of each user interface element, the set of user interface elements in the specific order (Figs. 2-11; ¶¶0066-0071).
Regarding claim 9, Ali discloses the method of claim 8, wherein ranking the plurality of user interface elements comprises:
accessing a content priority model of the online system, wherein the content priority model is trained to identify a priority of a user interface element for presentation to the user (Figs. 2-11; ¶¶0066-0071); and
applying the content priority model to output, based at least in part on the score for the user, the user data, and the information about the current session, the rank for each user interface element of the plurality of user interface elements that is indicative of a priority of that user interface element for presentation to the user (Figs. 2-11; ¶¶0066-0071).
Regarding claim 10, Ali discloses the method of claim 8, further comprising:
retrieving, from a database of the online system, the user data comprising information about at least one of a plurality of user interface elements associated with a plurality of items converted by the user during each order of a plurality of orders for a defined time period, or affinities for the defined time period between a plurality of categories of user interface elements (Figs. 2-11; ¶0060, ¶0062, ¶0068 [temporal], ¶0070, ¶¶0082-0083, ¶0086 [a time since deployment metric]).
Regarding claim 11, Ali discloses the method of claim 8, further comprising:
receiving, from the device associated with the user and via the network, the information about the current session including at least one of content of a cart associated with the current session or a browsing history of the user for the current session (Fig. 4, element 204; ¶0022 [“active feedback” includes click rate, add-to-cart, purchase, or other actions taken with respect to an interface ], ¶0045, ¶0048, ¶¶0061-0062, ¶0068 [temporal], and ¶0075 [initiating a search]).
Regarding claims 13 and 20, the claims disclose substantially the same limitations, as claim 1, except claim 1 is directed to a process while claim 13 is directed to an article of manufacture and claim 20 is directed to a machine. The added elements of “a computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps” (claim 13) and “a computer system comprising: a processor; and a non-transitory computer-readable storage medium having instructions that, when executed by the processor, cause the computer system to perform steps” (claim 20) are also taught by Ali (¶¶0028-0044). Therefore, claims 13 and 20 are rejected for the same rational over the prior art recited in claim 1.
Regarding claims 14 and 17-19, the claims disclose substantially the same limitations, as claims 2, 6-8, 10, and 11 except claims 2, 6-8, 10, and 11 are directed to processes depending from independent claim 1 while claims 14 and 17-19 are directed to articles of manufacture depending from independent claim 13. All limitations as recited have been analyzed and rejected with respect to claims2, 6-8, 10, and 11, and do not introduce any additional narrowing of the scopes of the claims as analyzed. Therefore, claims 14 and 17-19 are rejected for the same rational over the prior art cited in claims 2, 6-8, 10, and 11.
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.
Claim(s) 3, 12, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ali in view of Yang et al. (US 2023/0087587 A1).
Regarding claim 3, Ali discloses the method of claim 1. While Ali further discloses wherein receiving the session data comprises:
gathering during the current session with the online system, data with information about at least one of a duration of the current session at the location of the retailer, a number of items scanned by a computing system during the current session, an average speed of movement of the physical receptacle during the current session, or an average distance traveled by the physical receptacle during the current session per item added to the physical receptacle (Figs. 2-11; ¶0022 [click rate, add-to-cart, purchase, or other actions taken with respect to an interface is comparable to a number of items scanned by a computing system], ¶0068); and
receiving, from the computing system via the network, the gathered data as at least a portion of the session data (Figs. 2-5; ¶¶0061-0062 in view of ¶0048),
Ali does not explicitly disclose gathering data via one or more sensors mounted to a physical receptacle utilized by the user during the current session for shopping at a location of a retailer associated with the online system including a number of items scanned by a computing system associated with the physical receptacle during the current session, and receiving the gathered data from the computing system associated with the physical receptacle. However, in the field of self-checkout in a retail store (abstract) Yang et al., hereinafter Yang, teaches a smart shopping cart that gathers data via one or more sensors mounted to a physical receptacle utilized by a user during a shopping session while shopping at a location of a retailer, tracking the number of items scanned by a computing system associated with the physical receptacle, and a computing system receiving the gathered data associated with the physical receptacle (Figs. 1-4 and 26; ¶0003, ¶0005, ¶¶0045-0059, ¶0087). The step of Yang is applicable to the method of Ali as they share characteristics and capabilities, namely, they are directed to shopping for products. It would have been obvious to one of ordinary skill in the art at the time of filing to modify the data as taught by Ali with the in-store shopping cart data as taught by Yang. One of ordinary skill in the art at the time of filing would have been motivated to expand the method of Ali in order to automatically identify selected merchandise when a user is within a store (¶0005).
Regarding claim 12, Ali discloses the method of claim 8, further comprising:
gathering information during the current session (Figs. 2-11; ¶0022, ¶0068); and
receiving, from a computing system via the network, the gathered information as at least a portion of the information about the current session (Figs. 2-5; ¶¶0061-0062 in view of ¶0048).
Ali does not explicitly disclose gathering, via one or more sensors mounted to a physical receptacle utilized by the user during the current session for shopping at a location of a retailer associated with the online system, information about physical locations of the physical receptacle during the current session and receiving, from a computing system associated with the physical receptacle and via the network, the gathered information. However, Yang teaches a smart shopping cart that gathers data via one or more sensors mounted to a physical receptacle utilized by a user during a shopping session while shopping at a location of a retailer, tracking the number of items scanned by a computing system associated with the physical receptacle, and a computing system receiving the gathered data associated with the physical receptacle (Figs. 1-4 and 26; ¶0003, ¶0005, ¶¶0045-0059, ¶0087). The step of Yang is applicable to the method of Ali as they share characteristics and capabilities, namely, they are directed to shopping for products. It would have been obvious to one of ordinary skill in the art at the time of filing to modify the data as taught by Ali with the in-store shopping cart data as taught by Yang. One of ordinary skill in the art at the time of filing would have been motivated to expand the method of Ali in order to automatically identify selected merchandise when a user is within a store (¶0005).
Regarding claim 15, the claims disclose substantially the same limitations, as claim 3, except claim 3 is directed to processes depending from independent claim 1 while claim 15 is directed to an article of manufacture depending from independent claim 13. All limitations as recited have been analyzed and rejected with respect to claim 3, and does not introduce any additional narrowing of the scopes of the claims as analyzed. Therefore, claim 15 is rejected for the same rational over the prior art cited in claim 3.
Claim(s) 4 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ali in view of Maniyar (US 2020/0007453 A1).
Regarding claim 4, Ali discloses the method of claim 1. While Ali further discloses further comprising:
retrieving, from a database of the online system, data (¶0022 [active feedback includes purchase], ¶¶0045-0046 [interaction database], ¶0068); and
applying the user type prediction model to output, further based on the retrieved data, the score for the user (Figs. 2-5, 8-11; ¶0059-0060 [ranking weights are comparable to a score], ¶0062),
Ali does not explicitly disclose retrieving data with information about at least one of a ratio between a number of unique items the user converted during a defined time period by directly adding the unique items to shopping carts without further engagement with the unique items and a total number of items the user converted during the defined time period, or an average shopping time per unique item converted by the user during the defined time period. However, in the field of optimizing transaction processing (abstract, ¶0021) Maniyar teaches tracking conversion rates including a processing conversion ratio that may be based on an amount for each transaction, number of completed/abandoned transactions, and/or amount of data processing and delivery used for each completed/abandoned transaction and the conversion ratio may correspond to a function of transaction value over time, such as a median or average transaction value over time, number of visits, and/or computing resources consumed. (Figs. 1-5; ¶0021). The step of Maniyar is applicable to the method of Ali as they share characteristics and capabilities, namely, they are directed to shopping for products. It would have been obvious to one of ordinary skill in the art at the time of filing to modify the data as taught by Ali with the conversion information as taught by Maniyar. One of ordinary skill in the art at the time of filing would have been motivated to expand the method of Ali in order to determine if the shopping session will likely lead to a converted transaction (¶0021).
Regarding claim 16, the claims disclose substantially the same limitations, as claim 4, except claim 4 is directed to processes depending from independent claim 1 while claim 16 is directed to an article of manufacture depending from independent claim 13. All limitations as recited have been analyzed and rejected with respect to claim 4, and does not introduce any additional narrowing of the scopes of the claims as analyzed. Therefore, claim 16 is rejected for the same rational over the prior art cited in claim 4.
Examiner’s Comment
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Reference U of the Notice of References Cited Non Patent Literature “Amazon to test Dash Cart, a smart grocery shopping cart that sees what you buy” discloses a smart shopping cart that identifies the items in a cart and allows a user to track the shopping session via a display on the cart.
Conclusion
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LINDSEY B. SMITH
Examiner
Art Unit 3688
/LINDSEY B SMITH/Examiner, Art Unit 3688
/Jeffrey A. Smith/Supervisory Patent Examiner, Art Unit 3688
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